shmem experiments

.clang-tidy

@@ -17,10 +17,8 @@ Checks: >
     -clang-analyzer-security.insecureAPI.DeprecatedOrUnsafeBufferHandling,
     performance-*,
     portability-*,
-    -portability-simd-intrinsics,
     misc-*,
     -misc-const-correctness,
     -misc-non-private-member-variables-in-classes,
     -misc-no-recursion,
-    -misc-use-anonymous-namespace,
 FormatStyle: none

.devops/cpu.Dockerfile

@@ -1,92 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-
-FROM ubuntu:$UBUNTU_VERSION AS build
-
-ARG TARGETARCH
-
-ARG GGML_CPU_ARM_ARCH=armv8-a
-
-RUN apt-get update && \
-    apt-get install -y build-essential git cmake libcurl4-openssl-dev
-
-WORKDIR /app
-
-COPY . .
-
-RUN if [ "$TARGETARCH" = "amd64" ]; then \
-        cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON -DGGML_NATIVE=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON; \
-    elif [ "$TARGETARCH" = "arm64" ]; then \
-        cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=${GGML_CPU_ARM_ARCH}; \
-    else \
-        echo "Unsupported architecture"; \
-        exit 1; \
-    fi && \
-    cmake --build build -j $(nproc)
-
-RUN mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
-
-RUN mkdir -p /app/full \
-    && cp build/bin/* /app/full \
-    && cp *.py /app/full \
-    && cp -r gguf-py /app/full \
-    && cp -r requirements /app/full \
-    && cp requirements.txt /app/full \
-    && cp .devops/tools.sh /app/full/tools.sh
-
-## Base image
-FROM ubuntu:$UBUNTU_VERSION AS base
-
-RUN apt-get update \
-    && apt-get install -y libgomp1 curl\
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-COPY --from=build /app/lib/ /app
-
-### Full
-FROM base AS full
-
-COPY --from=build /app/full /app
-
-WORKDIR /app
-
-RUN apt-get update \
-    && apt-get install -y \
-    git \
-    python3 \
-    python3-pip \
-    && pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt \
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-ENTRYPOINT ["/app/tools.sh"]
-
-### Light, CLI only
-FROM base AS light
-
-COPY --from=build /app/full/llama-cli /app
-
-WORKDIR /app
-
-ENTRYPOINT [ "/app/llama-cli" ]
-
-### Server, Server only
-FROM base AS server
-
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-COPY --from=build /app/full/llama-server /app
-
-WORKDIR /app
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/app/llama-server" ]

.devops/cuda.Dockerfile

@@ -1,94 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-# This needs to generally match the container host's environment.
-ARG CUDA_VERSION=12.6.0
-# Target the CUDA build image
-ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
-
-ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
-
-FROM ${BASE_CUDA_DEV_CONTAINER} AS build
-
-# CUDA architecture to build for (defaults to all supported archs)
-ARG CUDA_DOCKER_ARCH=default
-
-RUN apt-get update && \
-    apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1
-
-WORKDIR /app
-
-COPY . .
-
-RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
-    export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
-    fi && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
-    cmake --build build --config Release -j$(nproc)
-
-RUN mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
-
-RUN mkdir -p /app/full \
-    && cp build/bin/* /app/full \
-    && cp *.py /app/full \
-    && cp -r gguf-py /app/full \
-    && cp -r requirements /app/full \
-    && cp requirements.txt /app/full \
-    && cp .devops/tools.sh /app/full/tools.sh
-
-## Base image
-FROM ${BASE_CUDA_RUN_CONTAINER} AS base
-
-RUN apt-get update \
-    && apt-get install -y libgomp1 curl\
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-COPY --from=build /app/lib/ /app
-
-### Full
-FROM base AS full
-
-COPY --from=build /app/full /app
-
-WORKDIR /app
-
-RUN apt-get update \
-    && apt-get install -y \
-    git \
-    python3 \
-    python3-pip \
-    && pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt \
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-
-ENTRYPOINT ["/app/tools.sh"]
-
-### Light, CLI only
-FROM base AS light
-
-COPY --from=build /app/full/llama-cli /app
-
-WORKDIR /app
-
-ENTRYPOINT [ "/app/llama-cli" ]
-
-### Server, Server only
-FROM base AS server
-
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-COPY --from=build /app/full/llama-server /app
-
-WORKDIR /app
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/app/llama-server" ]

.devops/full-cuda.Dockerfile

@@ -0,0 +1,33 @@
+ARG UBUNTU_VERSION=22.04
+# This needs to generally match the container host's environment.
+ARG CUDA_VERSION=12.6.0
+# Target the CUDA build image
+ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
+
+FROM ${BASE_CUDA_DEV_CONTAINER} AS build
+
+# CUDA architecture to build for (defaults to all supported archs)
+ARG CUDA_DOCKER_ARCH=default
+
+RUN apt-get update && \
+    apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1
+
+COPY requirements.txt   requirements.txt
+COPY requirements       requirements
+
+RUN pip install --upgrade pip setuptools wheel \
+    && pip install -r requirements.txt
+
+WORKDIR /app
+
+COPY . .
+
+# Use the default CUDA archs if not specified
+RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
+        export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
+    fi && \
+    cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
+    cmake --build build --config Release -j$(nproc) && \
+    cp build/bin/* .
+
+ENTRYPOINT ["/app/.devops/tools.sh"]

.devops/full-musa.Dockerfile

@@ -0,0 +1,26 @@
+ARG UBUNTU_VERSION=22.04
+# This needs to generally match the container host's environment.
+ARG MUSA_VERSION=rc3.1.0
+# Target the MUSA build image
+ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
+
+FROM ${BASE_MUSA_DEV_CONTAINER} AS build
+
+RUN apt-get update && \
+    apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1
+
+COPY requirements.txt   requirements.txt
+COPY requirements       requirements
+
+RUN pip install --upgrade pip setuptools wheel \
+    && pip install -r requirements.txt
+
+WORKDIR /app
+
+COPY . .
+
+RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
+    cmake --build build --config Release -j$(nproc) && \
+    cp build/bin/* .
+
+ENTRYPOINT ["/app/.devops/tools.sh"]

.devops/full-rocm.Dockerfile

@@ -0,0 +1,50 @@
+ARG UBUNTU_VERSION=22.04
+
+# This needs to generally match the container host's environment.
+ARG ROCM_VERSION=5.6
+
+# Target the CUDA build image
+ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
+
+FROM ${BASE_ROCM_DEV_CONTAINER} AS build
+
+# Unless otherwise specified, we make a fat build.
+# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
+# This is mostly tied to rocBLAS supported archs.
+ARG ROCM_DOCKER_ARCH="\
+    gfx803 \
+    gfx900 \
+    gfx906 \
+    gfx908 \
+    gfx90a \
+    gfx1010 \
+    gfx1030 \
+    gfx1100 \
+    gfx1101 \
+    gfx1102"
+
+COPY requirements.txt   requirements.txt
+COPY requirements       requirements
+
+RUN pip install --upgrade pip setuptools wheel \
+    && pip install -r requirements.txt
+
+WORKDIR /app
+
+COPY . .
+
+# Set nvcc architecture
+ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH}
+# Enable ROCm
+ENV GGML_HIPBLAS=1
+ENV CC=/opt/rocm/llvm/bin/clang
+ENV CXX=/opt/rocm/llvm/bin/clang++
+
+# Enable cURL
+ENV LLAMA_CURL=1
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev
+
+RUN make -j$(nproc)
+
+ENTRYPOINT ["/app/.devops/tools.sh"]

.devops/full.Dockerfile

@@ -0,0 +1,25 @@
+ARG UBUNTU_VERSION=22.04
+
+FROM ubuntu:$UBUNTU_VERSION AS build
+
+RUN apt-get update && \
+    apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev libgomp1
+
+COPY requirements.txt   requirements.txt
+COPY requirements       requirements
+
+RUN pip install --upgrade pip setuptools wheel \
+    && pip install -r requirements.txt
+
+WORKDIR /app
+
+COPY . .
+
+ENV LLAMA_CURL=1
+
+
+RUN make -j$(nproc)
+
+ENV LC_ALL=C.utf8
+
+ENTRYPOINT ["/app/.devops/tools.sh"]

.devops/intel.Dockerfile

@@ -1,91 +0,0 @@
-ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04
-
-## Build Image
-
-FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build
-
-ARG GGML_SYCL_F16=OFF
-RUN apt-get update && \
-    apt-get install -y git libcurl4-openssl-dev
-
-WORKDIR /app
-
-COPY . .
-
-RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
-        echo "GGML_SYCL_F16 is set" \
-        && export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
-    fi && \
-    echo "Building with dynamic libs" && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
-    cmake --build build --config Release -j$(nproc)
-
-RUN mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
-
-RUN mkdir -p /app/full \
-    && cp build/bin/* /app/full \
-    && cp *.py /app/full \
-    && cp -r gguf-py /app/full \
-    && cp -r requirements /app/full \
-    && cp requirements.txt /app/full \
-    && cp .devops/tools.sh /app/full/tools.sh
-
-FROM intel/oneapi-basekit:$ONEAPI_VERSION AS base
-
-RUN apt-get update \
-    && apt-get install -y libgomp1 curl\
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-### Full
-FROM base AS full
-
-COPY --from=build /app/lib/ /app
-COPY --from=build /app/full /app
-
-WORKDIR /app
-
-RUN apt-get update \
-    && apt-get install -y \
-    git \
-    python3 \
-    python3-pip \
-    && pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt \
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-
-ENTRYPOINT ["/app/tools.sh"]
-
-### Light, CLI only
-FROM base AS light
-
-COPY --from=build /app/lib/ /app
-COPY --from=build /app/full/llama-cli /app
-
-WORKDIR /app
-
-ENTRYPOINT [ "/app/llama-cli" ]
-
-### Server, Server only
-FROM base AS server
-
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-COPY --from=build /app/lib/ /app
-COPY --from=build /app/full/llama-server /app
-
-WORKDIR /app
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/app/llama-server" ]
-

.devops/llama-cli-cuda.Dockerfile

@@ -0,0 +1,38 @@
+ARG UBUNTU_VERSION=22.04
+# This needs to generally match the container host's environment.
+ARG CUDA_VERSION=12.6.0
+# Target the CUDA build image
+ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
+# Target the CUDA runtime image
+ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
+
+FROM ${BASE_CUDA_DEV_CONTAINER} AS build
+
+# CUDA architecture to build for (defaults to all supported archs)
+ARG CUDA_DOCKER_ARCH=default
+
+RUN apt-get update && \
+    apt-get install -y build-essential git cmake
+
+WORKDIR /app
+
+COPY . .
+
+# Use the default CUDA archs if not specified
+RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
+        export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
+    fi && \
+    cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
+    cmake --build build --config Release --target llama-cli -j$(nproc) && \
+    mkdir -p /app/lib && \
+    find build -name "*.so" -exec cp {} /app/lib \;
+
+FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime
+
+RUN apt-get update && \
+    apt-get install -y libgomp1
+
+COPY --from=build /app/lib/ /
+COPY --from=build /app/build/bin/llama-cli /
+
+ENTRYPOINT [ "/llama-cli" ]

.devops/llama-cli-intel.Dockerfile

@@ -0,0 +1,28 @@
+ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04
+
+FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build
+
+ARG GGML_SYCL_F16=OFF
+RUN apt-get update && \
+    apt-get install -y git
+
+WORKDIR /app
+
+COPY . .
+
+RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
+        echo "GGML_SYCL_F16 is set" && \
+        export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
+    fi && \
+    echo "Building with static libs" && \
+    cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx \
+    ${OPT_SYCL_F16} -DBUILD_SHARED_LIBS=OFF && \
+    cmake --build build --config Release --target llama-cli
+
+FROM intel/oneapi-basekit:$ONEAPI_VERSION AS runtime
+
+COPY --from=build /app/build/bin/llama-cli /llama-cli
+
+ENV LC_ALL=C.utf8
+
+ENTRYPOINT [ "/llama-cli" ]

.devops/llama-cli-musa.Dockerfile

@@ -0,0 +1,31 @@
+ARG UBUNTU_VERSION=22.04
+# This needs to generally match the container host's environment.
+ARG MUSA_VERSION=rc3.1.0
+# Target the MUSA build image
+ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
+# Target the MUSA runtime image
+ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
+
+FROM ${BASE_MUSA_DEV_CONTAINER} AS build
+
+RUN apt-get update && \
+    apt-get install -y build-essential git cmake
+
+WORKDIR /app
+
+COPY . .
+
+RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
+    cmake --build build --config Release --target llama-cli -j$(nproc) && \
+    mkdir -p /app/lib && \
+    find build -name "*.so" -exec cp {} /app/lib \;
+
+FROM ${BASE_MUSA_RUN_CONTAINER} AS runtime
+
+RUN apt-get update && \
+    apt-get install -y libgomp1
+
+COPY --from=build /app/lib/ /
+COPY --from=build /app/build/bin/llama-cli /llama-cli
+
+ENTRYPOINT [ "/llama-cli" ]

.devops/llama-cli-rocm.Dockerfile

@@ -0,0 +1,45 @@
+ARG UBUNTU_VERSION=22.04
+
+# This needs to generally match the container host's environment.
+ARG ROCM_VERSION=5.6
+
+# Target the CUDA build image
+ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
+
+FROM ${BASE_ROCM_DEV_CONTAINER} AS build
+
+# Unless otherwise specified, we make a fat build.
+# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
+# This is mostly tied to rocBLAS supported archs.
+ARG ROCM_DOCKER_ARCH="\
+    gfx803 \
+    gfx900 \
+    gfx906 \
+    gfx908 \
+    gfx90a \
+    gfx1010 \
+    gfx1030 \
+    gfx1100 \
+    gfx1101 \
+    gfx1102"
+
+COPY requirements.txt   requirements.txt
+COPY requirements       requirements
+
+RUN pip install --upgrade pip setuptools wheel \
+    && pip install -r requirements.txt
+
+WORKDIR /app
+
+COPY . .
+
+# Set nvcc architecture
+ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH}
+# Enable ROCm
+ENV GGML_HIPBLAS=1
+ENV CC=/opt/rocm/llvm/bin/clang
+ENV CXX=/opt/rocm/llvm/bin/clang++
+
+RUN make -j$(nproc) llama-cli
+
+ENTRYPOINT [ "/app/llama-cli" ]

.devops/llama-cli-vulkan.Dockerfile

@@ -0,0 +1,27 @@
+ARG UBUNTU_VERSION=jammy
+
+FROM ubuntu:$UBUNTU_VERSION AS build
+
+# Install build tools
+RUN apt update && apt install -y git build-essential cmake wget libgomp1
+
+# Install Vulkan SDK
+RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
+    wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
+    apt update -y && \
+    apt-get install -y vulkan-sdk
+
+# Build it
+WORKDIR /app
+COPY . .
+RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 && \
+    cmake --build build --config Release --target llama-cli
+
+# Clean up
+WORKDIR /
+RUN cp /app/build/bin/llama-cli /llama-cli && \
+    rm -rf /app
+
+ENV LC_ALL=C.utf8
+
+ENTRYPOINT [ "/llama-cli" ]

.devops/llama-cli.Dockerfile

@@ -0,0 +1,23 @@
+ARG UBUNTU_VERSION=22.04
+
+FROM ubuntu:$UBUNTU_VERSION AS build
+
+RUN apt-get update && \
+    apt-get install -y build-essential git
+
+WORKDIR /app
+
+COPY . .
+
+RUN make -j$(nproc) llama-cli
+
+FROM ubuntu:$UBUNTU_VERSION AS runtime
+
+RUN apt-get update && \
+    apt-get install -y libgomp1
+
+COPY --from=build /app/llama-cli /llama-cli
+
+ENV LC_ALL=C.utf8
+
+ENTRYPOINT [ "/llama-cli" ]

.devops/llama-server-cuda.Dockerfile

@@ -0,0 +1,43 @@
+ARG UBUNTU_VERSION=22.04
+# This needs to generally match the container host's environment.
+ARG CUDA_VERSION=12.6.0
+# Target the CUDA build image
+ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
+# Target the CUDA runtime image
+ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
+
+FROM ${BASE_CUDA_DEV_CONTAINER} AS build
+
+# CUDA architecture to build for (defaults to all supported archs)
+ARG CUDA_DOCKER_ARCH=default
+
+RUN apt-get update && \
+    apt-get install -y build-essential git cmake libcurl4-openssl-dev
+
+WORKDIR /app
+
+COPY . .
+
+# Use the default CUDA archs if not specified
+RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
+        export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
+    fi && \
+    cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
+    cmake --build build --config Release --target llama-server -j$(nproc) && \
+    mkdir -p /app/lib && \
+    find build -name "*.so" -exec cp {} /app/lib \;
+
+FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime
+
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev libgomp1 curl
+
+COPY --from=build /app/lib/ /
+COPY --from=build /app/build/bin/llama-server /llama-server
+
+# Must be set to 0.0.0.0 so it can listen to requests from host machine
+ENV LLAMA_ARG_HOST=0.0.0.0
+
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/llama-server" ]

.devops/llama-server-intel.Dockerfile

@@ -0,0 +1,34 @@
+ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04
+
+FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build
+
+ARG GGML_SYCL_F16=OFF
+RUN apt-get update && \
+    apt-get install -y git libcurl4-openssl-dev
+
+WORKDIR /app
+
+COPY . .
+
+RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
+        echo "GGML_SYCL_F16 is set" && \
+        export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
+    fi && \
+    echo "Building with dynamic libs" && \
+    cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
+    cmake --build build --config Release --target llama-server
+
+FROM intel/oneapi-basekit:$ONEAPI_VERSION AS runtime
+
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev curl
+
+COPY --from=build /app/build/bin/llama-server /llama-server
+
+ENV LC_ALL=C.utf8
+# Must be set to 0.0.0.0 so it can listen to requests from host machine
+ENV LLAMA_ARG_HOST=0.0.0.0
+
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/llama-server" ]

.devops/llama-server-musa.Dockerfile

@@ -0,0 +1,36 @@
+ARG UBUNTU_VERSION=22.04
+# This needs to generally match the container host's environment.
+ARG MUSA_VERSION=rc3.1.0
+# Target the MUSA build image
+ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
+# Target the MUSA runtime image
+ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
+
+FROM ${BASE_MUSA_DEV_CONTAINER} AS build
+
+RUN apt-get update && \
+    apt-get install -y build-essential git cmake libcurl4-openssl-dev
+
+WORKDIR /app
+
+COPY . .
+
+RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
+    cmake --build build --config Release --target llama-server -j$(nproc) && \
+    mkdir -p /app/lib && \
+    find build -name "*.so" -exec cp {} /app/lib \;
+
+FROM ${BASE_MUSA_RUN_CONTAINER} AS runtime
+
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev libgomp1 curl
+
+COPY --from=build /app/lib/ /
+COPY --from=build /app/build/bin/llama-server /llama-server
+
+# Must be set to 0.0.0.0 so it can listen to requests from host machine
+ENV LLAMA_ARG_HOST=0.0.0.0
+
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/llama-server" ]

.devops/llama-server-rocm.Dockerfile

@@ -0,0 +1,54 @@
+ARG UBUNTU_VERSION=22.04
+
+# This needs to generally match the container host's environment.
+ARG ROCM_VERSION=5.6
+
+# Target the CUDA build image
+ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
+
+FROM ${BASE_ROCM_DEV_CONTAINER} AS build
+
+# Unless otherwise specified, we make a fat build.
+# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
+# This is mostly tied to rocBLAS supported archs.
+ARG ROCM_DOCKER_ARCH="\
+    gfx803 \
+    gfx900 \
+    gfx906 \
+    gfx908 \
+    gfx90a \
+    gfx1010 \
+    gfx1030 \
+    gfx1100 \
+    gfx1101 \
+    gfx1102"
+
+COPY requirements.txt   requirements.txt
+COPY requirements       requirements
+
+RUN pip install --upgrade pip setuptools wheel \
+    && pip install -r requirements.txt
+
+WORKDIR /app
+
+COPY . .
+
+# Set nvcc architecture
+ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH}
+# Enable ROCm
+ENV GGML_HIPBLAS=1
+ENV CC=/opt/rocm/llvm/bin/clang
+ENV CXX=/opt/rocm/llvm/bin/clang++
+# Must be set to 0.0.0.0 so it can listen to requests from host machine
+ENV LLAMA_ARG_HOST=0.0.0.0
+
+# Enable cURL
+ENV LLAMA_CURL=1
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev curl
+
+RUN make -j$(nproc) llama-server
+
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/app/llama-server" ]

.devops/llama-server-vulkan.Dockerfile

@@ -0,0 +1,31 @@
+ARG UBUNTU_VERSION=jammy
+
+FROM ubuntu:$UBUNTU_VERSION AS build
+
+# Install build tools
+RUN apt update && apt install -y git build-essential cmake wget
+
+# Install Vulkan SDK and cURL
+RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
+    wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
+    apt update -y && \
+    apt-get install -y vulkan-sdk libcurl4-openssl-dev curl
+
+# Build it
+WORKDIR /app
+COPY . .
+RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \
+    cmake --build build --config Release --target llama-server
+
+# Clean up
+WORKDIR /
+RUN cp /app/build/bin/llama-server /llama-server && \
+    rm -rf /app
+
+ENV LC_ALL=C.utf8
+# Must be set to 0.0.0.0 so it can listen to requests from host machine
+ENV LLAMA_ARG_HOST=0.0.0.0
+
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/llama-server" ]

.devops/llama-server.Dockerfile

@@ -0,0 +1,29 @@
+ARG UBUNTU_VERSION=22.04
+
+FROM ubuntu:$UBUNTU_VERSION AS build
+
+RUN apt-get update && \
+    apt-get install -y build-essential git libcurl4-openssl-dev
+
+WORKDIR /app
+
+COPY . .
+
+ENV LLAMA_CURL=1
+
+RUN make -j$(nproc) llama-server
+
+FROM ubuntu:$UBUNTU_VERSION AS runtime
+
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev libgomp1 curl
+
+COPY --from=build /app/llama-server /llama-server
+
+ENV LC_ALL=C.utf8
+# Must be set to 0.0.0.0 so it can listen to requests from host machine
+ENV LLAMA_ARG_HOST=0.0.0.0
+
+HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
+
+ENTRYPOINT [ "/llama-server" ]

.devops/musa.Dockerfile

@@ -1,108 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-# This needs to generally match the container host's environment.
-ARG MUSA_VERSION=rc3.1.0
-# Target the MUSA build image
-ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
-
-ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
-
-FROM ${BASE_MUSA_DEV_CONTAINER} AS build
-
-# MUSA architecture to build for (defaults to all supported archs)
-ARG MUSA_DOCKER_ARCH=default
-
-RUN apt-get update && \
-    apt-get install -y \
-    build-essential \
-    cmake \
-    python3 \
-    python3-pip \
-    git \
-    libcurl4-openssl-dev \
-    libgomp1
-
-COPY requirements.txt   requirements.txt
-COPY requirements       requirements
-
-RUN pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt
-
-WORKDIR /app
-
-COPY . .
-
-# Use the default MUSA archs if not specified
-RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \
-        export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \
-    fi && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
-    cmake --build build --config Release -j$(nproc)
-
-RUN mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
-
-RUN mkdir -p /app/full \
-    && cp build/bin/* /app/full \
-    && cp *.py /app/full \
-    && cp -r gguf-py /app/full \
-    && cp -r requirements /app/full \
-    && cp requirements.txt /app/full \
-    && cp .devops/tools.sh /app/full/tools.sh
-
-## Base image
-FROM ${BASE_MUSA_RUN_CONTAINER} AS base
-
-RUN apt-get update \
-    && apt-get install -y libgomp1 curl\
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-COPY --from=build /app/lib/ /app
-
-### Full
-FROM base AS full
-
-COPY --from=build /app/full /app
-
-WORKDIR /app
-
-RUN apt-get update \
-    && apt-get install -y \
-    git \
-    python3 \
-    python3-pip \
-    && pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt \
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-
-ENTRYPOINT ["/app/tools.sh"]
-
-### Light, CLI only
-FROM base AS light
-
-COPY --from=build /app/full/llama-cli /app
-
-WORKDIR /app
-
-ENTRYPOINT [ "/app/llama-cli" ]
-
-### Server, Server only
-FROM base AS server
-
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-COPY --from=build /app/full/llama-server /app
-
-WORKDIR /app
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/app/llama-server" ]

.devops/nix/package.nix

@@ -31,7 +31,6 @@
   # Increases the runtime closure size by ~700M
   useMpi ? false,
   useRocm ? config.rocmSupport,
-  rocmGpuTargets ? builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets,
   enableCurl ? true,
   useVulkan ? false,
   llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake
@@ -189,7 +188,7 @@ effectiveStdenv.mkDerivation (finalAttrs: {
     ]
     ++ optionals useRocm [
       (cmakeFeature "CMAKE_HIP_COMPILER" "${rocmPackages.llvm.clang}/bin/clang")
-      (cmakeFeature "CMAKE_HIP_ARCHITECTURES" rocmGpuTargets)
+      (cmakeFeature "CMAKE_HIP_ARCHITECTURES" (builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets))
     ]
     ++ optionals useMetalKit [
       (lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1")

.devops/nix/python-scripts.nix

@@ -34,7 +34,7 @@ let
 
     # server tests
     openai
-    pytest
+    behave
     prometheus-client
   ];
 in

.devops/rocm.Dockerfile

@@ -1,113 +0,0 @@
-ARG UBUNTU_VERSION=24.04
-
-# This needs to generally match the container host's environment.
-ARG ROCM_VERSION=6.3
-ARG AMDGPU_VERSION=6.3
-
-# Target the CUDA build image
-ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
-
-### Build image
-FROM ${BASE_ROCM_DEV_CONTAINER} AS build
-
-# Unless otherwise specified, we make a fat build.
-# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
-# This is mostly tied to rocBLAS supported archs.
-# gfx803, gfx900, gfx1032, gfx1101, gfx1102,not officialy supported
-# gfx906 is deprecated
-#check https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.2.4/reference/system-requirements.html
-
-#ARG ROCM_DOCKER_ARCH='gfx803,gfx900,gfx906,gfx908,gfx90a,gfx942,gfx1010,gfx1030,gfx1032,gfx1100,gfx1101,gfx1102'
-ARG ROCM_DOCKER_ARCH=gfx1100
-
-# Set nvcc architectured
-ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH}
-# Enable ROCm
-# ENV CC=/opt/rocm/llvm/bin/clang
-# ENV CXX=/opt/rocm/llvm/bin/clang++
-
-RUN apt-get update \
-    && apt-get install -y \
-    build-essential \
-    cmake \
-    git \
-    libcurl4-openssl-dev \
-    curl \
-    libgomp1
-
-WORKDIR /app
-
-COPY . .
-
-RUN HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
-    cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=$ROCM_DOCKER_ARCH -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON \
-    && cmake --build build --config Release -j$(nproc)
-
-RUN mkdir -p /app/lib \
-    && find build -name "*.so" -exec cp {} /app/lib \;
-
-RUN mkdir -p /app/full \
-    && cp build/bin/* /app/full \
-    && cp *.py /app/full \
-    && cp -r gguf-py /app/full \
-    && cp -r requirements /app/full \
-    && cp requirements.txt /app/full \
-    && cp .devops/tools.sh /app/full/tools.sh
-
-## Base image
-FROM ${BASE_ROCM_DEV_CONTAINER} AS base
-
-RUN apt-get update \
-    && apt-get install -y libgomp1 curl\
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-COPY --from=build /app/lib/ /app
-
-### Full
-FROM base AS full
-
-COPY --from=build /app/full /app
-
-WORKDIR /app
-
-RUN apt-get update \
-    && apt-get install -y \
-    git \
-    python3-pip \
-    python3 \
-    python3-wheel\
-    && pip install --break-system-packages --upgrade setuptools \
-    && pip install --break-system-packages -r requirements.txt \
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-ENTRYPOINT ["/app/tools.sh"]
-
-### Light, CLI only
-FROM base AS light
-
-COPY --from=build /app/full/llama-cli /app
-
-WORKDIR /app
-
-ENTRYPOINT [ "/app/llama-cli" ]
-
-### Server, Server only
-FROM base AS server
-
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-COPY --from=build /app/full/llama-server /app
-
-WORKDIR /app
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/app/llama-server" ]

.devops/tools.sh

@@ -8,36 +8,28 @@ arg1="$1"
 shift
 
 if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then
-    exec python3 ./convert_hf_to_gguf.py "$@"
+    python3 ./convert_hf_to_gguf.py "$@"
 elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
-    exec ./llama-quantize "$@"
+    ./llama-quantize "$@"
 elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then
-    exec ./llama-cli "$@"
-elif [[ "$arg1" == '--bench' || "$arg1" == '-b' ]]; then
-    exec ./llama-bench "$@"
-elif [[ "$arg1" == '--perplexity' || "$arg1" == '-p' ]]; then
-    exec ./llama-perplexity "$@"
+    ./llama-cli "$@"
 elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
     echo "Converting PTH to GGML..."
-    for i in $(ls $1/$2/ggml-model-f16.bin*); do
+    for i in `ls $1/$2/ggml-model-f16.bin*`; do
         if [ -f "${i/f16/q4_0}" ]; then
             echo "Skip model quantization, it already exists: ${i/f16/q4_0}"
         else
             echo "Converting PTH to GGML: $i into ${i/f16/q4_0}..."
-            exec ./llama-quantize "$i" "${i/f16/q4_0}" q4_0
+            ./llama-quantize "$i" "${i/f16/q4_0}" q4_0
         fi
     done
 elif [[ "$arg1" == '--server' || "$arg1" == '-s' ]]; then
-    exec ./llama-server "$@"
+    ./llama-server "$@"
 else
     echo "Unknown command: $arg1"
     echo "Available commands: "
     echo "  --run (-r): Run a model previously converted into ggml"
     echo "              ex: -m /models/7B/ggml-model-q4_0.bin -p \"Building a website can be done in 10 simple steps:\" -n 512"
-    echo "  --bench (-b): Benchmark the performance of the inference for various parameters."
-    echo "              ex: -m model.gguf"
-    echo "  --perplexity (-p): Measure the perplexity of a model over a given text."
-    echo "              ex: -m model.gguf -f file.txt"
     echo "  --convert (-c): Convert a llama model into ggml"
     echo "              ex: --outtype f16 \"/models/7B/\" "
     echo "  --quantize (-q): Optimize with quantization process ggml"

.devops/vulkan.Dockerfile

@@ -1,89 +0,0 @@
-ARG UBUNTU_VERSION=24.04
-
-FROM ubuntu:$UBUNTU_VERSION AS build
-
-# Install build tools
-RUN apt update && apt install -y git build-essential cmake wget
-
-# Install Vulkan SDK and cURL
-RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
-    wget -qO /etc/apt/sources.list.d/lunarg-vulkan-noble.list https://packages.lunarg.com/vulkan/lunarg-vulkan-noble.list && \
-    apt update -y && \
-    apt-get install -y vulkan-sdk libcurl4-openssl-dev curl
-
-# Build it
-WORKDIR /app
-
-COPY . .
-
-RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \
-    cmake --build build --config Release -j$(nproc)
-
-RUN mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
-
-RUN mkdir -p /app/full \
-    && cp build/bin/* /app/full \
-    && cp *.py /app/full \
-    && cp -r gguf-py /app/full \
-    && cp -r requirements /app/full \
-    && cp requirements.txt /app/full \
-    && cp .devops/tools.sh /app/full/tools.sh
-
-## Base image
-FROM ubuntu:$UBUNTU_VERSION AS base
-
-RUN apt-get update \
-    && apt-get install -y libgomp1 curl libvulkan-dev \
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-COPY --from=build /app/lib/ /app
-
-### Full
-FROM base AS full
-
-COPY --from=build /app/full /app
-
-WORKDIR /app
-
-RUN apt-get update \
-    && apt-get install -y \
-    git \
-    python3 \
-    python3-pip \
-    python3-wheel \
-    && pip install --break-system-packages --upgrade setuptools \
-    && pip install --break-system-packages -r requirements.txt \
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-ENTRYPOINT ["/app/tools.sh"]
-
-### Light, CLI only
-FROM base AS light
-
-COPY --from=build /app/full/llama-cli /app
-
-WORKDIR /app
-
-ENTRYPOINT [ "/app/llama-cli" ]
-
-### Server, Server only
-FROM base AS server
-
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-COPY --from=build /app/full/llama-server /app
-
-WORKDIR /app
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/app/llama-server" ]

.editorconfig

@@ -40,11 +40,3 @@ indent_style = tab
 [examples/cvector-generator/*.txt]
 trim_trailing_whitespace = unset
 insert_final_newline = unset
-
-[models/templates/*.jinja]
-indent_style = unset
-indent_size = unset
-end_of_line = unset
-charset = unset
-trim_trailing_whitespace = unset
-insert_final_newline = unset

.github/ISSUE_TEMPLATE/010-bug-compilation.yml

@@ -65,22 +65,12 @@ body:
         If possible, please do a git bisect and identify the exact commit that introduced the bug.
     validations:
       required: false
-  - type: textarea
-    id: command
-    attributes:
-      label: Compile command
-      description: >
-        Please provide the exact command you used to compile llama.cpp. For example: `cmake -B ...`.
-        This will be automatically formatted into code, so no need for backticks.
-      render: shell
-    validations:
-      required: true
   - type: textarea
     id: logs
     attributes:
       label: Relevant log output
       description: >
-          Please copy and paste any relevant log output, including any generated text.
+          Please copy and paste any relevant log output, including the command that you entered and any generated text.
           This will be automatically formatted into code, so no need for backticks.
       render: shell
     validations:

.github/ISSUE_TEMPLATE/019-bug-misc.yml

@@ -52,16 +52,6 @@ body:
         - Other (Please specify in the next section)
     validations:
       required: false
-  - type: textarea
-    id: command
-    attributes:
-      label: Command line
-      description: >
-        Please provide the exact commands you entered, if applicable. For example: `llama-server -m ... -c ...`, `llama-cli -m ...`, etc.
-        This will be automatically formatted into code, so no need for backticks.
-      render: shell
-    validations:
-      required: false
   - type: textarea
     id: info
     attributes:
@@ -84,7 +74,7 @@ body:
     attributes:
       label: Relevant log output
       description: >
-          If applicable, please copy and paste any relevant log output, including any generated text.
+          If applicable, please copy and paste any relevant log output, including the command that you entered and any generated text.
           This will be automatically formatted into code, so no need for backticks.
       render: shell
     validations:

.github/labeler.yml

@@ -3,18 +3,19 @@ Kompute:
     - changed-files:
         - any-glob-to-any-file:
             - ggml/include/ggml-kompute.h
-            - ggml/src/ggml-kompute/**
+            - ggml/src/ggml-kompute.cpp
             - README-kompute.md
 Apple Metal:
     - changed-files:
         - any-glob-to-any-file:
             - ggml/include/ggml-metal.h
-            - ggml/src/ggml-metal/**
+            - ggml/src/ggml-metal.cpp
             - README-metal.md
 SYCL:
     - changed-files:
         - any-glob-to-any-file:
             - ggml/include/ggml-sycl.h
+            - ggml/src/ggml-sycl.cpp
             - ggml/src/ggml-sycl/**
             - docs/backend/SYCL.md
             - examples/sycl/**
@@ -26,8 +27,8 @@ Nvidia GPU:
 Vulkan:
     - changed-files:
         - any-glob-to-any-file:
-            - ggml/include/ggml-vulkan.h
-            - ggml/src/ggml-vulkan/**
+            - ggml/ggml_vk_generate_shaders.py
+            - ggml/src/ggml-vulkan*
 documentation:
     - changed-files:
         - any-glob-to-any-file:
@@ -74,7 +75,11 @@ server:
 ggml:
     - changed-files:
         - any-glob-to-any-file:
-            - ggml/**
+            - ggml/include/ggml*.h
+            - ggml/src/ggml*.c
+            - ggml/src/ggml*.cpp
+            - ggml/src/ggml*.h
+            - ggml-cuda/**
 nix:
     - changed-files:
         - any-glob-to-any-file:

.github/pull_request_template.md

@@ -1 +1,7 @@
-*Make sure to read the [contributing guidelines](https://github.com/ggerganov/llama.cpp/blob/master/CONTRIBUTING.md) before submitting a PR*
+
+
+- [x] I have read the [contributing guidelines](https://github.com/ggerganov/llama.cpp/blob/master/CONTRIBUTING.md)
+- Self-reported review complexity:
+  - [ ] Low
+  - [ ] Medium
+  - [ ] High

.github/workflows/close-issue.yml

@@ -17,7 +17,7 @@ jobs:
     steps:
       - uses: actions/stale@v5
         with:
-          exempt-issue-labels: "refactor,help wanted,good first issue,research,bug,roadmap"
+          exempt-issue-labels: "refactor,help wanted,good first issue,research,bug"
           days-before-issue-stale: 30
           days-before-issue-close: 14
           stale-issue-label: "stale"

.github/workflows/docker.yml

@@ -28,21 +28,27 @@ jobs:
   push_to_registry:
     name: Push Docker image to Docker Hub
 
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     env:
       COMMIT_SHA: ${{ github.sha }}
     strategy:
-      fail-fast: false
       matrix:
         config:
-          # Multi-stage build
-          - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, freediskspace: false}
-          - { tag: "cuda", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
-          - { tag: "musa", dockerfile: ".devops/musa.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
-          - { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
-          - { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
+          - { tag: "light", dockerfile: ".devops/llama-cli.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          - { tag: "server", dockerfile: ".devops/llama-server.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          - { tag: "full", dockerfile: ".devops/full.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          - { tag: "light-cuda", dockerfile: ".devops/llama-cli-cuda.Dockerfile", platforms: "linux/amd64" }
+          - { tag: "server-cuda", dockerfile: ".devops/llama-server-cuda.Dockerfile", platforms: "linux/amd64" }
+          - { tag: "full-cuda", dockerfile: ".devops/full-cuda.Dockerfile", platforms: "linux/amd64" }
+          - { tag: "light-musa", dockerfile: ".devops/llama-cli-musa.Dockerfile", platforms: "linux/amd64" }
+          - { tag: "server-musa", dockerfile: ".devops/llama-server-musa.Dockerfile", platforms: "linux/amd64" }
+          - { tag: "full-musa", dockerfile: ".devops/full-musa.Dockerfile", platforms: "linux/amd64" }
           # Note: the rocm images are failing due to a compiler error and are disabled until this is fixed to allow the workflow to complete
-          #- {tag: "rocm", dockerfile: ".devops/rocm.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, freediskspace: true }
+          #- { tag: "light-rocm", dockerfile: ".devops/llama-cli-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          #- { tag: "server-rocm", dockerfile: ".devops/llama-server-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          #- { tag: "full-rocm", dockerfile: ".devops/full-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          - { tag: "light-intel", dockerfile: ".devops/llama-cli-intel.Dockerfile", platforms: "linux/amd64" }
+          - { tag: "server-intel", dockerfile: ".devops/llama-server-intel.Dockerfile", platforms: "linux/amd64" }
     steps:
       - name: Check out the repo
         uses: actions/checkout@v4
@@ -50,10 +56,10 @@ jobs:
           fetch-depth: 0 # preserve git history, so we can determine the build number
 
       - name: Set up QEMU
-        uses: docker/setup-qemu-action@v3
+        uses: docker/setup-qemu-action@v2
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@v2
 
       - name: Log in to Docker Hub
         uses: docker/login-action@v2
@@ -73,34 +79,26 @@ jobs:
 
           # determine tag name postfix (build number, commit hash)
           if [[ "${{ env.GITHUB_BRANCH_NAME }}" == "master" ]]; then
-            TAG_POSTFIX="-b${BUILD_NUMBER}"
+            TAG_POSTFIX="b${BUILD_NUMBER}"
           else
             SAFE_NAME=$(echo "${{ env.GITHUB_BRANCH_NAME }}" | tr '/' '-')
-            TAG_POSTFIX="-${SAFE_NAME}-${SHORT_HASH}"
+            TAG_POSTFIX="${SAFE_NAME}-${SHORT_HASH}"
           fi
+
           # list all tags possible
-          if [[ "${{ matrix.config.tag }}" == "cpu" ]]; then
-              TYPE=""
-          else
-              TYPE="-${{ matrix.config.tag }}"
-          fi
-          PREFIX="ghcr.io/${REPO_OWNER}/${REPO_NAME}:"
-          FULLTAGS="${PREFIX}full${TYPE},${PREFIX}full${TYPE}${TAG_POSTFIX}"
-          LIGHTTAGS="${PREFIX}light${TYPE},${PREFIX}light${TYPE}${TAG_POSTFIX}"
-          SERVERTAGS="${PREFIX}server${TYPE},${PREFIX}server${TYPE}${TAG_POSTFIX}"
-          echo "full_output_tags=$FULLTAGS" >> $GITHUB_OUTPUT
-          echo "light_output_tags=$LIGHTTAGS" >> $GITHUB_OUTPUT
-          echo "server_output_tags=$SERVERTAGS" >> $GITHUB_OUTPUT
-          echo "full_output_tags=$FULLTAGS"  # print out for debugging
-          echo "light_output_tags=$LIGHTTAGS"  # print out for debugging
-          echo "server_output_tags=$SERVERTAGS"  # print out for debugging
+          TAGS=""
+          TAGS="${TAGS}ghcr.io/${REPO_OWNER}/${REPO_NAME}:${{ matrix.config.tag }},"
+          TAGS="${TAGS}ghcr.io/${REPO_OWNER}/${REPO_NAME}:${{ matrix.config.tag }}-${TAG_POSTFIX}"
+
+          echo "output_tags=$TAGS" >> $GITHUB_OUTPUT
+          echo "output_tags=$TAGS"  # print out for debugging
         env:
           GITHUB_BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
           GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}'
 
+      # https://github.com/jlumbroso/free-disk-space/tree/54081f138730dfa15788a46383842cd2f914a1be#example
       - name: Free Disk Space (Ubuntu)
-        if: ${{ matrix.config.free_disk_space == true }}
-        uses: ggml-org/free-disk-space@v1.3.1
+        uses: jlumbroso/free-disk-space@main
         with:
           # this might remove tools that are actually needed,
           # if set to "true" but frees about 6 GB
@@ -115,59 +113,13 @@ jobs:
           docker-images: true
           swap-storage: true
 
-      - name: Build and push Full Docker image (tagged + versioned)
-        if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.full == true }}
+      - name: Build and push Docker image (tagged + versioned)
+        if: github.event_name == 'push'
         uses: docker/build-push-action@v6
         with:
           context: .
           push: true
           platforms: ${{ matrix.config.platforms }}
           # tag list is generated from step above
-          tags: ${{ steps.tag.outputs.full_output_tags }}
+          tags: ${{ steps.tag.outputs.output_tags }}
           file: ${{ matrix.config.dockerfile }}
-          target: full
-          provenance: false
-          # using github experimental cache
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
-          # return to this if the experimental github cache is having issues
-          #cache-to: type=local,dest=/tmp/.buildx-cache
-          #cache-from: type=local,src=/tmp/.buildx-cache
-
-      - name: Build and push Light Docker image (tagged + versioned)
-        if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.light == true }}
-        uses: docker/build-push-action@v6
-        with:
-          context: .
-          push: true
-          platforms: ${{ matrix.config.platforms }}
-          # tag list is generated from step above
-          tags: ${{ steps.tag.outputs.light_output_tags }}
-          file: ${{ matrix.config.dockerfile }}
-          target: light
-          provenance: false
-          # using github experimental cache
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
-          # return to this if the experimental github cache is having issues
-          #cache-to: type=local,dest=/tmp/.buildx-cache
-          #cache-from: type=local,src=/tmp/.buildx-cache
-
-      - name: Build and push Server Docker image (tagged + versioned)
-        if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.server == true }}
-        uses: docker/build-push-action@v6
-        with:
-          context: .
-          push: true
-          platforms: ${{ matrix.config.platforms }}
-          # tag list is generated from step above
-          tags: ${{ steps.tag.outputs.server_output_tags }}
-          file: ${{ matrix.config.dockerfile }}
-          target: server
-          provenance: false
-          # using github experimental cache
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
-          # return to this if the experimental github cache is having issues
-          #cache-to: type=local,dest=/tmp/.buildx-cache
-          #cache-from: type=local,src=/tmp/.buildx-cache

.github/workflows/editorconfig.yml

@@ -23,7 +23,5 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-      - uses: editorconfig-checker/action-editorconfig-checker@v2
-        with:
-          version: v3.0.3
+      - uses: editorconfig-checker/action-editorconfig-checker@main
       - run: editorconfig-checker

.github/workflows/nix-ci-aarch64.yml

@@ -0,0 +1,72 @@
+name: Nix aarch64 builds
+
+on:
+  workflow_dispatch: # allows manual triggering
+  schedule:
+    # Rebuild daily rather than on every push because QEMU is expensive (e.g.
+    # 1.5h instead of minutes with the cold cache).
+    #
+    # randint(0, 59), randint(0, 23)
+    - cron: '26 12 * * *'
+  # But also rebuild if we touched any of the Nix expressions:
+  push:
+    branches:
+      - master
+    paths: ['**/*.nix', 'flake.lock']
+  pull_request:
+    types: [opened, synchronize, reopened]
+    paths: ['**/*.nix', 'flake.lock']
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
+# Fine-grant permission
+# https://docs.github.com/en/actions/security-for-github-actions/security-guides/automatic-token-authentication#modifying-the-permissions-for-the-github_token
+permissions:
+  # https://github.com/DeterminateSystems/nix-installer-action?tab=readme-ov-file#with-flakehub
+  id-token: write
+  contents: read
+
+jobs:
+  nix-build-aarch64:
+    runs-on: ubuntu-latest
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+    - name: Install QEMU
+      # Copy-paste from https://github.com/orgs/community/discussions/8305#discussioncomment-5888654
+      run: |
+        sudo apt-get update
+        sudo apt-get install -y qemu-user-static qemu-system-aarch64
+        sudo usermod -a -G kvm $USER
+    - name: Install Nix
+      uses: DeterminateSystems/nix-installer-action@v9
+      with:
+        github-token: ${{ secrets.GITHUB_TOKEN }}
+        extra-conf: |
+          extra-platforms = aarch64-linux
+          extra-system-features = nixos-test kvm
+          extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org
+          extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
+    - uses: DeterminateSystems/magic-nix-cache-action@v2
+      with:
+        upstream-cache: https://${{ matrix.cachixName }}.cachix.org
+    - name: Set-up cachix to push the results to
+      uses: cachix/cachix-action@v13
+      with:
+        authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}'
+        name: llama-cpp
+    - name: Show all output paths
+      run: >
+          nix run github:nix-community/nix-eval-jobs
+          -- --gc-roots-dir gcroot
+          --flake
+          ".#packages.aarch64-linux"
+    - name: Build
+      run: >
+          nix run github:Mic92/nix-fast-build
+          -- --skip-cached --no-nom
+          --systems aarch64-linux
+          --flake
+          ".#checks.aarch64-linux"

.github/workflows/nix-ci.yml

@@ -0,0 +1,79 @@
+name: Nix CI
+
+on:
+  workflow_dispatch: # allows manual triggering
+  push:
+    branches:
+      - master
+  pull_request:
+    types: [opened, synchronize, reopened]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
+# Fine-grant permission
+# https://docs.github.com/en/actions/security-for-github-actions/security-guides/automatic-token-authentication#modifying-the-permissions-for-the-github_token
+permissions:
+  # https://github.com/DeterminateSystems/nix-installer-action?tab=readme-ov-file#with-flakehub
+  id-token: write
+  contents: read
+
+jobs:
+  nix-eval:
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ ubuntu-latest, macos-latest ]
+    runs-on: ${{ matrix.os }}
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+    - name: Install Nix
+      uses: DeterminateSystems/nix-installer-action@v9
+      with:
+        github-token: ${{ secrets.GITHUB_TOKEN }}
+        extra-conf: |
+          extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org
+          extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
+    - uses: DeterminateSystems/magic-nix-cache-action@v2
+      with:
+        upstream-cache: https://${{ matrix.cachixName }}.cachix.org
+    - name: List all flake outputs
+      run: nix flake show --all-systems
+    - name: Show all output paths
+      run: >
+          nix run github:nix-community/nix-eval-jobs
+          -- --gc-roots-dir gcroot
+          --flake
+          ".#packages.$(nix eval --raw --impure --expr builtins.currentSystem)"
+  nix-build:
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ ubuntu-latest, macos-latest ]
+    runs-on: ${{ matrix.os }}
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+    - name: Install Nix
+      uses: DeterminateSystems/nix-installer-action@v9
+      with:
+        github-token: ${{ secrets.GITHUB_TOKEN }}
+        extra-conf: |
+          extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org
+          extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
+    - uses: DeterminateSystems/magic-nix-cache-action@v2
+      with:
+        upstream-cache: https://${{ matrix.cachixName }}.cachix.org
+    - name: Set-up cachix to push the results to
+      uses: cachix/cachix-action@v13
+      with:
+        authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}'
+        name: llama-cpp
+    - name: Build
+      run: >
+          nix run github:Mic92/nix-fast-build
+          -- --skip-cached --no-nom
+          --flake
+          ".#checks.$(nix eval --raw --impure --expr builtins.currentSystem)"

.github/workflows/nix-flake-update.yml

@@ -0,0 +1,22 @@
+name: update-flake-lock
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: '0 0 * * 0' # runs weekly on Sunday at 00:00
+
+jobs:
+  lockfile:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+      - name: Install Nix
+        uses: DeterminateSystems/nix-installer-action@main
+      - name: Update flake.lock
+        uses: DeterminateSystems/update-flake-lock@main
+        with:
+          pr-title: "nix: update flake.lock"
+          pr-labels: |
+            nix
+          pr-reviewers: philiptaron,SomeoneSerge
+          token: ${{ secrets.FLAKE_TOKEN }}

.github/workflows/nix-publish-flake.yml

@@ -0,0 +1,36 @@
+# Make the flake discoverable on https://flakestry.dev and https://flakehub.com/flakes
+name: "Publish a flake to flakestry & flakehub"
+on:
+    push:
+        tags:
+        - "*"
+    workflow_dispatch:
+        inputs:
+            tag:
+                description: "The existing tag to publish"
+                type: "string"
+                required: true
+jobs:
+    flakestry-publish:
+        runs-on: ubuntu-latest
+        permissions:
+            id-token: "write"
+            contents: "read"
+        steps:
+            - uses: flakestry/flakestry-publish@main
+              with:
+                version: "${{ inputs.tag || github.ref_name }}"
+    flakehub-publish:
+      runs-on: "ubuntu-latest"
+      permissions:
+        id-token: "write"
+        contents: "read"
+      steps:
+        - uses: "actions/checkout@v4"
+          with:
+            ref: "${{ (inputs.tag != null) && format('refs/tags/{0}', inputs.tag) || '' }}"
+        - uses: "DeterminateSystems/nix-installer-action@main"
+        - uses: "DeterminateSystems/flakehub-push@main"
+          with:
+            visibility: "public"
+            tag: "${{ inputs.tag }}"

.github/workflows/python-lint.yml

@@ -1,13 +1,6 @@
 name: flake8 Lint
 
-on:
-  push:
-    branches:
-      - master
-    paths: ['.github/workflows/python-lint.yml', '**/*.py']
-  pull_request:
-    types: [opened, synchronize, reopened]
-    paths: ['.github/workflows/python-lint.yml', '**/*.py']
+on: [push, pull_request]
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}

.github/workflows/server.yml

@@ -76,49 +76,20 @@ jobs:
         run: |
           pip install -r examples/server/tests/requirements.txt
 
-      # Setup nodejs (to be used for verifying bundled index.html)
-      - uses: actions/setup-node@v4
-        with:
-          node-version: '22.11.0'
-
-      - name: WebUI - Install dependencies
-        id: webui_lint
-        run: |
-          cd examples/server/webui
-          npm ci
-
-      - name: WebUI - Check code format
-        id: webui_format
+      - name: Verify server deps
+        id: verify_server_deps
         run: |
           git config --global --add safe.directory $(realpath .)
-          cd examples/server/webui
+          cd examples/server
+          git ls-files --others --modified
           git status
-
-          npm run format
+          ./deps.sh
           git status
-          modified_files="$(git status -s)"
-          echo "Modified files: ${modified_files}"
-          if [ -n "${modified_files}" ]; then
-            echo "Files do not follow coding style. To fix: npm run format"
-            echo "${modified_files}"
-            exit 1
-          fi
-
-      - name: Verify bundled index.html
-        id: verify_server_index_html
-        run: |
-          git config --global --add safe.directory $(realpath .)
-          cd examples/server/webui
-          git status
-
-          npm run build
-          git status
-          modified_files="$(git status -s)"
-          echo "Modified files: ${modified_files}"
-          if [ -n "${modified_files}" ]; then
-            echo "Repository is dirty or server/webui is not built as expected"
-            echo "Hint: You may need to follow Web UI build guide in server/README.md"
-            echo "${modified_files}"
+          not_ignored_files="$(git ls-files --others --modified)"
+          echo "Modified files: ${not_ignored_files}"
+          if [ -n "${not_ignored_files}" ]; then
+            echo "Repository is dirty or server deps are not built as expected"
+            echo "${not_ignored_files}"
             exit 1
           fi
 
@@ -135,9 +106,9 @@ jobs:
               -DGGML_OPENMP=OFF ;
           cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
 
-      - name: Build (sanitizers)
-        id: cmake_build_sanitizers
-        if: ${{ matrix.sanitizer != '' && matrix.sanitizer != 'THREAD' }}
+      - name: Build
+        id: cmake_build
+        if: ${{ matrix.sanitizer != 'THREAD' }}
         run: |
           cmake -B build \
               -DGGML_NATIVE=OFF \
@@ -147,37 +118,18 @@ jobs:
               -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
           cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
 
-      - name: Build (sanitizers)
-        id: cmake_build
-        if: ${{ matrix.sanitizer == '' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DLLAMA_CURL=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
       - name: Tests
         id: server_integration_tests
-        if: ${{ matrix.sanitizer == '' }}
         run: |
           cd examples/server/tests
-          ./tests.sh
-
-      - name: Tests (sanitizers)
-        id: server_integration_tests_sanitizers
-        if: ${{ matrix.sanitizer != '' }}
-        run: |
-          cd examples/server/tests
-          LLAMA_SANITIZE=1 ./tests.sh
+          PORT=8888 ./tests.sh
 
       - name: Slow tests
         id: server_integration_tests_slow
         if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
         run: |
           cd examples/server/tests
-          SLOW_TESTS=1 ./tests.sh
+          PORT=8888 ./tests.sh --stop --no-skipped --no-capture --tags slow
 
 
   server-windows:
@@ -228,12 +180,11 @@ jobs:
         run: |
           cd examples/server/tests
           $env:PYTHONIOENCODING = ":replace"
-          pytest -v -x -m "not slow"
+          behave.exe --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp
 
       - name: Slow tests
         id: server_integration_tests_slow
         if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
         run: |
           cd examples/server/tests
-          $env:SLOW_TESTS = "1"
-          pytest -v -x
+          behave.exe --stop --no-skipped --no-capture --tags slow

.gitignore

@@ -18,7 +18,6 @@
 *.metallib
 *.o
 *.so
-*.swp
 *.tmp
 
 # IDE / OS
@@ -105,10 +104,6 @@ examples/server/*.mjs.hpp
 !examples/sycl/*.bat
 !examples/sycl/*.sh
 
-# Server Web UI temporary files
-node_modules
-examples/server/webui/dist
-
 # Python
 
 /.venv

AUTHORS

@@ -1,4 +1,4 @@
-# date: Tue Feb  4 13:04:05 EET 2025
+# date: Wed Jun 26 19:36:34 EEST 2024
 # this file is auto-generated by scripts/gen-authors.sh
 
 0cc4m <picard12@live.de>
@@ -7,7 +7,6 @@
 2f38b454 <dxf@protonmail.com>
 3ooabkhxtn <31479382+3ooabkhxtn@users.noreply.github.com>
 44670 <44670@users.noreply.github.com>
-65a <10104049+65a@users.noreply.github.com>
 AN Long <aisk@users.noreply.github.com>
 AT <manyoso@users.noreply.github.com>
 Aarni Koskela <akx@iki.fi>
@@ -20,30 +19,20 @@ Adithya Balaji <adithya.b94@gmail.com>
 AdithyanI <adithyan.i4internet@gmail.com>
 Adrian <smith.adriane@gmail.com>
 Adrian Hesketh <a-h@users.noreply.github.com>
-Adrien Gallouët <adrien@gallouet.fr>
-Adrien Gallouët <angt@huggingface.co>
-Ahmad Tameem <113388789+Tameem-10xE@users.noreply.github.com>
 Ahmet Zeer <ahmed.zeer@std.yildiz.edu.tr>
 AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com>
-AidanBeltonS <aidan.belton@codeplay.com>
 Aisuko <urakiny@gmail.com>
-Akarshan Biswas <akarshan.biswas@gmail.com>
 Akarshan Biswas <akarshanbiswas@fedoraproject.org>
-Al Mochkin <14274697+amochkin@users.noreply.github.com>
 Albert Jin <albert.jin@gmail.com>
 Alberto <57916483+albbus-stack@users.noreply.github.com>
-Alberto Cabrera Pérez <alberto.cabrera@codeplay.com>
-Alberto Cabrera Pérez <alberto.cabrera@intel.com>
 Alex <awhill19@icloud.com>
 Alex Azarov <alex@azarov.by>
 Alex Azarov <alexander.azarov@mapbox.com>
 Alex Klinkhamer <from.github.com.917@grencez.dev>
 Alex Klinkhamer <git@grencez.dev>
 Alex Nguyen <tiendung@users.noreply.github.com>
-Alex O'Connell <35843486+acon96@users.noreply.github.com>
 Alex Petenchea <alex.petenchea@gmail.com>
 Alex Renda <alexrenda@users.noreply.github.com>
-Alex Tuddenham <61622354+AlexsCode@users.noreply.github.com>
 Alex von Gluck IV <kallisti5@unixzen.com>
 Alexey Parfenov <zxed@alkatrazstudio.net>
 Ali Chraghi <63465728+alichraghi@users.noreply.github.com>
@@ -56,26 +45,18 @@ AmirAli Mirian <37371367+amiralimi@users.noreply.github.com>
 Ananta Bastola <anantarajbastola@gmail.com>
 Anas Ahouzi <112881240+aahouzi@users.noreply.github.com>
 András Salamon <ott2@users.noreply.github.com>
-Andreas (Andi) Kunar <andreask@msn.com>
-Andreas Kieslinger <47689530+aendk@users.noreply.github.com>
 Andrei <abetlen@gmail.com>
 Andrew Canis <andrew.canis@gmail.com>
 Andrew Downing <andrew2085@gmail.com>
 Andrew Duffy <a10y@users.noreply.github.com>
 Andrew Godfrey <AndrewGodfrey@users.noreply.github.com>
-Andrew Minh Nguyen <40281306+amqdn@users.noreply.github.com>
-Andy Salerno <andysalerno@gmail.com>
 Andy Tai <andy-tai@users.noreply.github.com>
-Anthony Van de Gejuchte <anthonyvdgent@gmail.com>
-Antonis Makropoulos <benuix@gmail.com>
 Arik Poznanski <arikpoz@users.noreply.github.com>
-Armen Kaleshian <kriation@users.noreply.github.com>
 Artem <guinmoon@gmail.com>
 Artem Zinnatullin <ceo@abstractny.gay>
 Artyom Lebedev <vagran.ast@gmail.com>
 Asbjørn Olling <asbjornolling@gmail.com>
 Ásgeir Bjarni Ingvarsson <asgeir@fundinn.org>
-Asghar Ghorbani <a-ghorbani@users.noreply.github.com>
 Ashish <1856117+ashishdatta@users.noreply.github.com>
 Ashok Gelal <401055+ashokgelal@users.noreply.github.com>
 Ashraful Islam <ashraful.meche@gmail.com>
@@ -94,21 +75,13 @@ Ben Siraphob <bensiraphob@gmail.com>
 Ben Williams <ben@719ben.com>
 Benjamin Findley <39356821+Kartoffelsaft@users.noreply.github.com>
 Benjamin Lecaillon <84293038+blecaillon@users.noreply.github.com>
-Benson Wong <mostlygeek@gmail.com>
 Bernat Vadell <hounter.caza@gmail.com>
-Bernhard M. Wiedemann <githubbmwprimary@lsmod.de>
-Bert Wagner <github@bertwagner.com>
-Billel Mokeddem <billel.mokeddem.ml@gmail.com>
 Bingan <70050083+binganao@users.noreply.github.com>
-Bjarke Viksøe <164612031+bviksoe@users.noreply.github.com>
 Bodo Graumann <mail@bodograumann.de>
 Bono Lv <lvscar@users.noreply.github.com>
 Borislav Stanimirov <b.stanimirov@abv.bg>
-Borislav Stanimirov <b@ibob.bg>
 Branden Butler <bwtbutler@hotmail.com>
-Brandon Squizzato <35474886+bsquizz@users.noreply.github.com>
 Brian <mofosyne@gmail.com>
-Brian Cunnie <brian.cunnie@gmail.com>
 Bruce MacDonald <brucewmacdonald@gmail.com>
 Bryan Honof <bryanhonof@gmail.com>
 CJ Pais <cj@cjpais.com>
@@ -117,51 +90,32 @@ Calvin Laurenson <calvin@laurenson.dev>
 Cameron <csteele@steelecameron.com>
 Cameron Kaiser <classilla@users.noreply.github.com>
 Carolinabanana <140120812+Carolinabanana@users.noreply.github.com>
-CarryFun <76023481+CarryFun@users.noreply.github.com>
-Carsten Kragelund Jørgensen <carsten@kragelund.me>
-CarterLi999 <664681047@qq.com>
 Casey Primozic <casey@cprimozic.net>
 Casey Primozic <me@ameo.link>
 CausalLM <148736309+CausalLM@users.noreply.github.com>
 Cebtenzzre <cebtenzzre@gmail.com>
-CentricStorm <CentricStorm@users.noreply.github.com>
 Chad Brewbaker <crb002@gmail.com>
-Changyeon Kim <cyzero.kim@samsung.com>
 Chao Jiang <jc19chaoj@zoho.com>
-Charles Xu <63788048+chaxu01@users.noreply.github.com>
-Charles Xu <charles.xu@arm.com>
-Chen Xi <xi2.chen@intel.com>
-Chen Xi <xixichen08@foxmail.com>
 Cheng Shao <terrorjack@type.dance>
-Chenguang Li <87689256+noemotiovon@users.noreply.github.com>
 Chris Elrod <elrodc@gmail.com>
 Chris Kuehl <ckuehl@ckuehl.me>
 Christian Demsar <christian@github.email.demsar.us>
 Christian Demsar <crasm@git.vczf.us>
 Christian Falch <875252+chrfalch@users.noreply.github.com>
-Christian Kastner <ckk@kvr.at>
 Christian Kögler <ck3d@gmx.de>
-Christian Köhnenkamp <cvk5@me.com>
 Christian Zhou-Zheng <59622928+christianazinn@users.noreply.github.com>
-Christopher Nielsen <62156882+mascguy@users.noreply.github.com>
 Clark Saben <76020733+csaben@users.noreply.github.com>
 Clint Herron <hanclinto@gmail.com>
-Conrad Kramer <conrad@conradkramer.com>
-Corentin REGAL <corentin.regal@gmail.com>
 CrispStrobe <154636388+CrispStrobe@users.noreply.github.com>
-Csaba Kecskemeti <csaba.kecskemeti@gmail.com>
 Cuong Trinh Manh <nguoithichkhampha@gmail.com>
 DAN™ <dranger003@gmail.com>
 Damian Stewart <d@damianstewart.com>
-Dan Johansson <164997844+eddnjjn@users.noreply.github.com>
-Dan Johansson <dan.johansson@arm.com>
 Dane Madsen <dane_madsen@hotmail.com>
 DaniAndTheWeb <57776841+DaniAndTheWeb@users.noreply.github.com>
 Daniel Bevenius <daniel.bevenius@gmail.com>
 Daniel Drake <drake@endlessos.org>
 Daniel Hiltgen <dhiltgen@users.noreply.github.com>
 Daniel Illescas Romero <illescas.daniel@protonmail.com>
-Daniel Kleine <53251018+d-kleine@users.noreply.github.com>
 Daniele <57776841+daniandtheweb@users.noreply.github.com>
 DannyDaemonic <DannyDaemonic@gmail.com>
 Dat Quoc Nguyen <2412555+datquocnguyen@users.noreply.github.com>
@@ -175,29 +129,19 @@ David Pflug <david@pflug.email>
 David Renshaw <dwrenshaw@gmail.com>
 David Sommers <12738+databyte@users.noreply.github.com>
 David Yang <davidyang6us@gmail.com>
-DavidKorczynski <david@adalogics.com>
 Dawid Potocki <github@dawidpotocki.com>
 Dawid Wysocki <62249621+TortillaZHawaii@users.noreply.github.com>
 Dean <Dean.Sinaean@gmail.com>
 Deins <deinsegle@gmail.com>
-Denis Spasyuk <34203011+dspasyuk@users.noreply.github.com>
-Derrick T. Woolworth <dwoolworth@gmail.com>
 Deven Mistry <31466137+deven367@users.noreply.github.com>
-Dibakar Gope <dibakar.gope@arm.com>
 Didzis Gosko <didzis@users.noreply.github.com>
-Diego Devesa <slarengh@gmail.com>
-Diogo Teles Sant'Anna <diogoteles@google.com>
-Djip007 <3705339+Djip007@users.noreply.github.com>
 Djip007 <djip.perois@free.fr>
 Don Mahurin <dmahurin@users.noreply.github.com>
 DooWoong Lee (David) <manics99@naver.com>
 Doomsdayrs <38189170+Doomsdayrs@users.noreply.github.com>
-Dou Xinpeng <15529241576@163.com>
-Dou Xinpeng <81913537+Dou-Git@users.noreply.github.com>
 Douglas Hanley <thesecretaryofwar@gmail.com>
 Dr. Tom Murphy VII Ph.D <499244+tom7@users.noreply.github.com>
 Ebey Abraham <ebey97@gmail.com>
-Echo Nolan <echo@echonolan.net>
 Ed Lee <edilee@mozilla.com>
 Ed Lepedus <ed.lepedus@googlemail.com>
 Eddie-Wang <wangjinheng1120@163.com>
@@ -205,16 +149,12 @@ Edward Taylor <edeetee@gmail.com>
 Elaine <elaine.zosa@gmail.com>
 Elbios <141279586+Elbios@users.noreply.github.com>
 Elton Kola <eltonkola@gmail.com>
-Emreerdog <34742675+Emreerdog@users.noreply.github.com>
 Engininja2 <139037756+Engininja2@users.noreply.github.com>
 Equim <sayaka@ekyu.moe>
-Eric Curtin <ecurtin@redhat.com>
-Eric Curtin <ericcurtin17@gmail.com>
 Eric Sommerlade <es0m@users.noreply.github.com>
 Eric Zhang <34133756+EZForever@users.noreply.github.com>
 Erik Garrison <erik.garrison@gmail.com>
 Erik Scholz <Green-Sky@users.noreply.github.com>
-Esko Toivonen <eskot98@gmail.com>
 Ettore Di Giacinto <mudler@users.noreply.github.com>
 Evan Jones <evan.q.jones@gmail.com>
 Evan Miller <emmiller@gmail.com>
@@ -226,27 +166,19 @@ FK <sozforex@gmail.com>
 Fabian <cmdrf@users.noreply.github.com>
 Fabio R. Sluzala <Fabio3rs@users.noreply.github.com>
 Faez Shakil <faez.shakil@gmail.com>
-Faisal Zaghloul <faisal.zaghloul@gmail.com>
-Faisal Zaghloul <quic_fzaghlou@quicinc.com>
-Fan Shupei <dymarkfan@outlook.com>
 FantasyGmm <16450052+FantasyGmm@users.noreply.github.com>
-Farbod Bijary <110523279+farbodbj@users.noreply.github.com>
 Fattire <528174+fat-tire@users.noreply.github.com>
 Felix <stenbackfelix@gmail.com>
 Finn Voorhees <finnvoorhees@gmail.com>
 Firat <firatkiral@gmail.com>
-FirstTimeEZ <179362031+FirstTimeEZ@users.noreply.github.com>
 Folko-Ven <71110216+Folko-Ven@users.noreply.github.com>
 Foul-Tarnished <107711110+Foul-Tarnished@users.noreply.github.com>
 Francisco Melo <43780565+francis2tm@users.noreply.github.com>
 Frank Mai <thxcode0824@gmail.com>
 FrankHB <frankhb1989@gmail.com>
-Frankie Robertson <frankier@users.noreply.github.com>
 Fred Douglas <43351173+fredlas@users.noreply.github.com>
 Frederik Vogel <Schaltfehler@users.noreply.github.com>
 Gabe Goodhart <gabe.l.hart@gmail.com>
-Gabe Goodhart <ghart@us.ibm.com>
-Gaetan Bisson <gaetan@fenua.org>
 GainLee <perfecter.gen@gmail.com>
 Galunid <karolek1231456@gmail.com>
 Gary Linscott <glinscott@gmail.com>
@@ -255,15 +187,12 @@ Gavin Zhao <gavinzhaojw@protonmail.com>
 Genkagaku.GPT <hlhr202@163.com>
 Georgi Gerganov <ggerganov@gmail.com>
 Gilad S <giladgd@users.noreply.github.com>
-Gilad S. <7817232+giladgd@users.noreply.github.com>
 Giuseppe Scrivano <giuseppe@scrivano.org>
 GiviMAD <GiviMAD@users.noreply.github.com>
 Govlzkoy <gotope@users.noreply.github.com>
 Guillaume "Vermeille" Sanchez <Guillaume.V.Sanchez@gmail.com>
 Guillaume Wenzek <gwenzek@users.noreply.github.com>
-Guoliang Hua <32868157+nbcsm@users.noreply.github.com>
 Guoteng <32697156+SolenoidWGT@users.noreply.github.com>
-Guspan Tanadi <36249910+guspan-tanadi@users.noreply.github.com>
 Gustavo Rocha Dias <91472747+gustrd@users.noreply.github.com>
 Haggai Nuchi <h.nuchi@gmail.com>
 Halalaluyafail3 <55773281+Halalaluyafail3@users.noreply.github.com>
@@ -274,47 +203,35 @@ Haoxiang Fei <tonyfettes@tonyfettes.com>
 Harald Fernengel <harald.fernengel@here.com>
 Hatsune Miku <129688334+at8u@users.noreply.github.com>
 HatsuneMikuUwU33 <173229399+HatsuneMikuUwU33@users.noreply.github.com>
-Haus1 <haus.xda@gmail.com>
 Henk Poley <HenkPoley@gmail.com>
 Henri Vasserman <henv@hot.ee>
 Henrik Forstén <henrik.forsten@gmail.com>
 Herman Semenov <GermanAizek@yandex.ru>
 Hesen Peng <hesen.peng@gmail.com>
-HimariO <dsfhe49854@gmail.com>
 Hoang Nguyen <hugo53@users.noreply.github.com>
 Hong Bo PENG <penghb@cn.ibm.com>
 Hongyu Ouyang <96765450+casavaca@users.noreply.github.com>
 Howard Su <howard0su@gmail.com>
 Hua Jiang <allenhjiang@outlook.com>
-Huang Qi <huangqi3@xiaomi.com>
 Huawei Lin <huaweilin.cs@gmail.com>
 Hugo Roussel <hugo.rous@gmail.com>
-Huifeng Ou <79071290+ho2103@users.noreply.github.com>
 Ian Bull <irbull@eclipsesource.com>
 Ian Bull <irbull@gmail.com>
 Ian Scrivener <github@zilogy.asia>
-Icecream95 <the.real.icecream95@gmail.com>
 Ido S <ido.pluto@gmail.com>
 IgnacioFDM <ignaciofdm@gmail.com>
 Igor Okulist <okigan@gmail.com>
-Ihar Hrachyshka <ihrachys@redhat.com>
 Ikko Eltociear Ashimine <eltociear@gmail.com>
 Ilya Kurdyukov <59548320+ilyakurdyukov@users.noreply.github.com>
 Ionoclast Laboratories <brigham@ionoclast.com>
 Isaac McFadyen <isaac@imcf.me>
 IsaacDynamo <61521674+IsaacDynamo@users.noreply.github.com>
-Ivan <nekotekina@gmail.com>
-Ivan Filipov <159561759+vanaka11@users.noreply.github.com>
 Ivan Komarov <Ivan.Komarov@dfyz.info>
 Ivan Stepanov <ivanstepanovftw@gmail.com>
-JFLFY2255 <JFLFY2255@163.com>
 JH23X <165871467+JH23X@users.noreply.github.com>
-Jack Mousseau <jack@software.inc>
 Jack Mousseau <jmousseau@users.noreply.github.com>
 JackJollimore <130917767+JackJollimore@users.noreply.github.com>
-Jaeden Amero <jaeden@patater.com>
 Jaemin Son <woalsdnd@gmail.com>
-Jafar Uruç <jafar.uruc@gmail.com>
 Jag Chadha <jagtesh@gmail.com>
 Jakub N <jakubniemczyk97@gmail.com>
 James A Capozzoli <157492257+jac-jim@users.noreply.github.com>
@@ -326,16 +243,11 @@ Jannis Schönleber <joennlae@gmail.com>
 Jared Van Bortel <cebtenzzre@gmail.com>
 Jared Van Bortel <jared@nomic.ai>
 Jason McCartney <jmac@theroot.org>
-Jason Stillerman <jason.t.stillerman@gmail.com>
 Jean-Christophe Hoelt <hoelt@fovea.cc>
 Jean-Michaël Celerier <jeanmichael.celerier+github@gmail.com>
 Jed Fox <git@jedfox.com>
-Jeff Bolz <jbolz@nvidia.com>
-Jeffrey Morgan <jmorganca@gmail.com>
 Jeffrey Quesnelle <emozilla@nousresearch.com>
-Jeroen Mostert <jeroen.mostert@cm.com>
 Jesse Jojo Johnson <williamsaintgeorge@gmail.com>
-Jett Janiak <jettjaniak@gmail.com>
 Jeximo <jeximo@gmail.com>
 Jhen-Jie Hong <iainst0409@gmail.com>
 Jiahao Li <liplus17@163.com>
@@ -346,9 +258,6 @@ Jiří Podivín <66251151+jpodivin@users.noreply.github.com>
 Jiří Sejkora <Sejseloid@gmail.com>
 Joan Fontanals <jfontanalsmartinez@gmail.com>
 Joan Fontanals <joan.fontanals.martinez@jina.ai>
-João Dinis Ferreira <hello@joaof.eu>
-Joe Eli McIlvain <joe.eli.mac@gmail.com>
-Joe Todd <joe.todd@codeplay.com>
 Johan <JohanAR@users.noreply.github.com>
 Johannes Gäßler <johannesg@5d6.de>
 Johannes Rudolph <johannes.rudolph@gmail.com>
@@ -364,11 +273,8 @@ Josh Ramer <josh.ramer@icloud.com>
 Joyce <joycebrum@google.com>
 Juan Calderon-Perez <835733+gaby@users.noreply.github.com>
 Judd <foldl@users.noreply.github.com>
-Juk Armstrong <69222624+jukofyork@users.noreply.github.com>
 Julius Arkenberg <arki05@users.noreply.github.com>
-Jun Hee Yoo <contact.jhyoo@gmail.com>
 Jun Jie <71215065+junnjiee16@users.noreply.github.com>
-Junil Kim <logyourself@gmail.com>
 Junyang Lin <justinlin930319@hotmail.com>
 Juraj Bednar <juraj@bednar.io>
 Justin Parker <jparkerweb@gmail.com>
@@ -379,7 +285,6 @@ Justine Tunney <jtunney@mozilla.com>
 Juuso Alasuutari <juuso.alasuutari@gmail.com>
 KASR <karim.asrih@gmail.com>
 Kamil Tomšík <info@tomsik.cz>
-Karol Kontny <82021046+kkontny@users.noreply.github.com>
 Karsten Weiss <knweiss@gmail.com>
 Karthick <j.karthic2004@gmail.com>
 Karthik Kumar Viswanathan <195178+guilt@users.noreply.github.com>
@@ -387,19 +292,16 @@ Karthik Sethuraman <k.seth1993@gmail.com>
 Kasumi <90275229+kasumi-1@users.noreply.github.com>
 Kawrakow <48489457+ikawrakow@users.noreply.github.com>
 Keiichi Tabata <keiichi.tabata@outlook.com>
-Keke Han <hankeke303@163.com>
 Kenvix ⭐ <kenvixzure@live.com>
 Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
 Kevin Gibbons <bakkot@gmail.com>
 Kevin Ji <1146876+kevinji@users.noreply.github.com>
 Kevin Kwok <antimatter15@gmail.com>
 Kevin Lo <kevlo@kevlo.org>
-Kevin Wang <kevmo314@gmail.com>
 Kolen Cheung <ickc@users.noreply.github.com>
 Konstantin Herud <konstantin.herud@denkbares.com>
 Konstantin Zhuravlyov <konstantin.zhuravlyov@amd.com>
 Kunshang Ji <kunshang.ji@intel.com>
-Kyle Bruene <KyleBruene@users.noreply.github.com>
 Kyle Liang <liangmanlai@gmail.com>
 Kyle Mistele <kyle@mistele.com>
 Kylin <56434533+KyL0N@users.noreply.github.com>
@@ -413,30 +315,22 @@ LeonEricsson <70749762+LeonEricsson@users.noreply.github.com>
 Leonardo Neumann <leonardo@neumann.dev.br>
 Li Tan <tanliboy@gmail.com>
 Linwei Wang <wanix1988@gmail.com>
-Liu Jia <109258120+Septa2112@users.noreply.github.com>
-Liu Jia <jia3.liu@intel.com>
 LoganDark <github@logandark.mozmail.com>
-Loïc Carrère <loic.carrere@gmail.com>
 LostRuins <39025047+LostRuins@users.noreply.github.com>
-LostRuins Concedo <39025047+LostRuins@users.noreply.github.com>
 Luciano <lucianostrika44@gmail.com>
 Luo Tian <lt@basecity.com>
 Lyle Dean <dean@lyle.dev>
-M-A <maruel@gmail.com>
 M. Yusuf Sarıgöz <yusufsarigoz@gmail.com>
-Ma Mingfei <mingfei.ma@intel.com>
 Maarten ter Huurne <maarten@treewalker.org>
 Mack Straight <eiz@users.noreply.github.com>
 Maël Kerbiriou <m431.kerbiriou@gmail.com>
 MaggotHATE <clay1326@gmail.com>
-Mahesh Madhav <67384846+heshpdx@users.noreply.github.com>
 Manuel <44313466+makuche@users.noreply.github.com>
 Marc Köhlbrugge <subscriptions@marckohlbrugge.com>
 Marco Matthies <71844+marcom@users.noreply.github.com>
 Marcus Dunn <51931484+MarcusDunn@users.noreply.github.com>
 Marian Cepok <marian.cepok@gmail.com>
 Mark Fairbairn <thebaron88@gmail.com>
-Mark Zhuang <zhuangqiubin@gmail.com>
 Marko Tasic <mtasic85@gmail.com>
 Markus Tavenrath <mtavenrath@users.noreply.github.com>
 Martin Delille <martin@delille.org>
@@ -448,16 +342,11 @@ MasterYi1024 <39848311+MasterYi1024@users.noreply.github.com>
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 Matt Pulver <matt.pulver@heavy.ai>
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 Mattheus Chediak <shammcity00@gmail.com>
 Matthew Tejo <matthew.tejo@gmail.com>
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@@ -467,11 +356,8 @@ Maxime <672982+maximegmd@users.noreply.github.com>
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 Michael Hueschen <m@mhueschen.dev>
 Michael Kesper <mkesper@schokokeks.org>
 Michael Klimenko <mklimenko29@gmail.com>
@@ -479,81 +365,52 @@ Michael Podvitskiy <podvitskiymichael@gmail.com>
 Michael Potter <NanoTekGuy@Gmail.com>
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 Miwa / Ensan <63481257+ensan-hcl@users.noreply.github.com>
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 Mohammadreza Hendiani <mohammad.r.hendiani@gmail.com>
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 Niall Coates <1349685+Niall-@users.noreply.github.com>
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@@ -562,16 +419,11 @@ Raj Hammeer Singh Hada <hammeerraj@gmail.com>
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 Richard Kiss <him@richardkiss.com>
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 Rick G <26732651+TheFlipbook@users.noreply.github.com>
@@ -582,39 +434,26 @@ Riley Stewart <ristew@users.noreply.github.com>
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 Shijie <821898965@qq.com>
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 compilade <113953597+compilade@users.noreply.github.com>
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 crasm <crasm@git.vczf.us>
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 hopkins385 <98618192+hopkins385@users.noreply.github.com>
@@ -872,16 +649,12 @@ hxer7963 <hxer7963@gmail.com>
 hydai <z54981220@gmail.com>
 iSma <ismail.senhaji@gmail.com>
 iacore <74560659+iacore@users.noreply.github.com>
-icppWorld <124377669+icppWorld@users.noreply.github.com>
 igarnier <igarnier@protonmail.com>
 intelmatt <61025942+intelmatt@users.noreply.github.com>
 iohub <rickyang.pro@gmail.com>
-issixx <46835150+issixx@users.noreply.github.com>
 jacobi petrucciani <8117202+jpetrucciani@users.noreply.github.com>
 jaime-m-p <167997752+jaime-m-p@users.noreply.github.com>
 jameswu2014 <545426914@qq.com>
-jdomke <28772296+jdomke@users.noreply.github.com>
-jiahao su <damow890@gmail.com>
 jiez <373447296@qq.com>
 jneem <joeneeman@gmail.com>
 joecryptotoo <80373433+joecryptotoo@users.noreply.github.com>
@@ -894,7 +667,6 @@ junchao-loongson <68935141+junchao-loongson@users.noreply.github.com>
 jwj7140 <32943891+jwj7140@users.noreply.github.com>
 k.h.lai <adrian.k.h.lai@outlook.com>
 kaizau <kaizau@users.noreply.github.com>
-kallewoof <kalle.alm@gmail.com>
 kalomaze <66376113+kalomaze@users.noreply.github.com>
 kang <tpdns9032100@gmail.com>
 katsu560 <118887472+katsu560@users.noreply.github.com>
@@ -902,46 +674,32 @@ kchro3 <62481661+kchro3@users.noreply.github.com>
 khimaros <me@khimaros.com>
 kiltyj <kiltyj@gmail.com>
 klosax <131523366+klosax@users.noreply.github.com>
-krystiancha <krystian@krystianch.com>
 kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com>
 kunnis <kunnis@users.noreply.github.com>
 kuronekosaiko <EvanChanJ@163.com>
-kustaaya <58045274+kustaaya@users.noreply.github.com>
 kuvaus <22169537+kuvaus@users.noreply.github.com>
 kwin1412 <42286931+kwin1412@users.noreply.github.com>
 l3utterfly <gc.pthzfoldr@gmail.com>
-laik <laik.lj@me.com>
 ldwang <ftgreat@163.com>
 le.chang <cljs118@126.com>
 leejet <leejet714@gmail.com>
-leo-pony <nengjunma@outlook.com>
-lexasub <lexakopp2212@gmail.com>
-lhez <quic_lih@quicinc.com>
 limitedAtonement <limitedAtonement@users.noreply.github.com>
 liuwei-git <14815172+liuwei-git@users.noreply.github.com>
 lon <114724657+longregen@users.noreply.github.com>
 loonerin <132926317+loonerin@users.noreply.github.com>
-ltoniazzi <61414566+ltoniazzi@users.noreply.github.com>
 luoyu-intel <yu.luo@intel.com>
 m3ndax <adrian.goessl@outlook.com>
 maddes8cht <55592906+maddes8cht@users.noreply.github.com>
-mahorozte <41834471+mahorozte@users.noreply.github.com>
 makomk <makosoft@googlemail.com>
 manikbhandari <mbbhandarimanik2@gmail.com>
 maor-ps <154728172+maor-ps@users.noreply.github.com>
-mashdragon <122402293+mashdragon@users.noreply.github.com>
-matiaslin <45382001+matiaslin@users.noreply.github.com>
-matt23654 <matthew.webber@protonmail.com>
-matteo <matteogeniaccio@yahoo.it>
 mdrokz <mohammadmunshi@gmail.com>
 mgroeber9110 <45620825+mgroeber9110@users.noreply.github.com>
 minarchist <minarchist@users.noreply.github.com>
 mj-shifu <77107165+mj-shifu@users.noreply.github.com>
 mmyjona <jonathan.gonse@gmail.com>
 momonga <115213907+mmnga@users.noreply.github.com>
-momonga <146910567+mmngays@users.noreply.github.com>
 moritzbrantner <31051084+moritzbrantner@users.noreply.github.com>
-musoles <135031143+musoles@users.noreply.github.com>
 mzcu <milos.cubrilo@gmail.com>
 nanahi <130121847+na-na-hi@users.noreply.github.com>
 ngc92 <7938269+ngc92@users.noreply.github.com>
@@ -958,21 +716,16 @@ omahs <73983677+omahs@users.noreply.github.com>
 oobabooga <112222186+oobabooga@users.noreply.github.com>
 opparco <parco.opaai@gmail.com>
 ostix360 <55257054+ostix360@users.noreply.github.com>
-pculliton <phillipculliton@gmail.com>
-peidaqi <peidaqi@gmail.com>
 pengxin99 <pengxin.yuan@intel.com>
 perserk <perserk@gmail.com>
-piDack <104877312+piDack@users.noreply.github.com>
 pmysl <piotr.myslinski@outlook.com>
 postmasters <namnguyen@google.com>
 pudepiedj <pudepiedj@gmail.com>
 qingfengfenga <41416092+qingfengfenga@users.noreply.github.com>
-qingy1337 <qxli2@students.everettcc.edu>
 qouoq <qouoq@fastmail.com>
 qunash <anzoria@gmail.com>
 rabidcopy <rabidcopy@yahoo.com>
 rankaiyx <rankaiyx@rankaiyx.com>
-redbeard <bharrington@alticon.net>
 rhjdvsgsgks <26178113+rhjdvsgsgks@users.noreply.github.com>
 rhuddleston <ryan.huddleston@percona.com>
 rimoliga <53384203+rimoliga@users.noreply.github.com>
@@ -980,7 +733,6 @@ runfuture <runfuture@users.noreply.github.com>
 sandyiscool <sandyiscool@gmail.com>
 sasha0552 <admin@sasha0552.org>
 semidark <me@semidark.net>
-serhii-nakon <57632032+serhii-nakon@users.noreply.github.com>
 sharpHL <132747147+sharpHL@users.noreply.github.com>
 shibe2 <shibe@tuta.io>
 singularity <12184989+singularity-s0@users.noreply.github.com>
@@ -989,59 +741,42 @@ sjxx <63994076+ylsdamxssjxxdd@users.noreply.github.com>
 slaren <2141330+slaren@users.noreply.github.com>
 slaren <slarengh@gmail.com>
 snadampal <87143774+snadampal@users.noreply.github.com>
-someone13574 <81528246+someone13574@users.noreply.github.com>
-standby24x7 <standby24x7@gmail.com>
 staviq <staviq@gmail.com>
 stduhpf <stephduh@live.fr>
 strawberrymelonpanda <152940198+strawberrymelonpanda@users.noreply.github.com>
 swittk <switt1995@gmail.com>
 takov751 <40316768+takov751@users.noreply.github.com>
 tarcey <cey.tarik@gmail.com>
-tc-mb <157115220+tc-mb@users.noreply.github.com>
 texmex76 <40733439+texmex76@users.noreply.github.com>
 thement <40525767+thement@users.noreply.github.com>
-thewh1teagle <61390950+thewh1teagle@users.noreply.github.com>
 tjohnman <tjohnman@users.noreply.github.com>
-toyer <2042519524@qq.com>
 tslmy <tslmy@users.noreply.github.com>
 ubik2 <ubik2@users.noreply.github.com>
 uint256_t <konndennsa@gmail.com>
 uint256_t <maekawatoshiki1017@gmail.com>
 unbounded <haakon@likedan.net>
-uvos <devnull@uvos.xyz>
-uvos <philipp@uvos.xyz>
 valiray <133289098+valiray@users.noreply.github.com>
-vb <vaibhavs10@gmail.com>
 vik <vikhyatk@gmail.com>
 viric <viric@viric.name>
 vodkaslime <646329483@qq.com>
 vvhg1 <94630311+vvhg1@users.noreply.github.com>
 vxiiduu <73044267+vxiiduu@users.noreply.github.com>
-wangshuai09 <391746016@qq.com>
 wbpxre150 <100937007+wbpxre150@users.noreply.github.com>
 whoreson <139810751+whoreson@users.noreply.github.com>
 woachk <24752637+woachk@users.noreply.github.com>
 wonjun Jang <strutive07@gmail.com>
 woodx <124784234+woodx9@users.noreply.github.com>
-wwoodsTM <104587230+wwoodsTM@users.noreply.github.com>
 wzy <32936898+Freed-Wu@users.noreply.github.com>
 xaedes <xaedes@gmail.com>
 xaedes <xaedes@googlemail.com>
-xctan <axunlei@gmail.com>
 xloem <0xloem@gmail.com>
 yangli2 <yangli2@gmail.com>
-ymcki <84055651+ymcki@users.noreply.github.com>
 yuiseki <yuiseki@gmail.com>
-yuri@FreeBSD <yurivict@users.noreply.github.com>
 zakkor <edward.partenie@gmail.com>
 zhangkaihuo <zhangkaihuo@gmail.com>
-zhentaoyu <zhentao.yu@intel.com>
 zhouwg <6889919+zhouwg@users.noreply.github.com>
 zhouwg <zhouwg2000@gmail.com>
 zrm <trustiosity.zrm@gmail.com>
 Ștefan-Gabriel Muscalu <legraphista@users.noreply.github.com>
-杨朱 · Kiki <baofa.fan@daocloud.io>
 源文雨 <41315874+fumiama@users.noreply.github.com>
-蕭澧邦 <45505768+shou692199@users.noreply.github.com>
-谢乃闻 <sienaiwun@users.noreply.github.com>
 Нияз Гарифзянов <112617865+garrnizon@users.noreply.github.com>

CMakeLists.txt

@@ -16,7 +16,6 @@ endif()
 list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")
 
 set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
-set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
 
 if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
     set(LLAMA_STANDALONE ON)
@@ -47,11 +46,11 @@ if (WIN32)
     add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
 endif()
 
-if (MSVC)
-    add_compile_options("$<$<COMPILE_LANGUAGE:C>:/utf-8>")
-    add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/utf-8>")
-    add_compile_options("$<$<COMPILE_LANGUAGE:C>:/bigobj>")
-    add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/bigobj>")
+if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "MSVC")
+    add_compile_options("$<$<COMPILE_LANGUAGE:C>:/source-charset:utf-8>")
+    add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/source-charset:utf-8>")
+    add_compile_options("$<$<COMPILE_LANGUAGE:C>:/execution-charset:utf-8>")
+    add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/execution-charset:utf-8>")
 endif()
 
 #
@@ -80,13 +79,14 @@ option(LLAMA_BUILD_SERVER   "llama: build server example" ${LLAMA_STANDALONE})
 
 # 3rd party libs
 option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF)
-option(LLAMA_LLGUIDANCE "llama-common: include LLGuidance library for structured output in common utils" OFF)
 
 # Required for relocatable CMake package
 include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake)
-include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/common.cmake)
 
 # override ggml options
+set(GGML_SANITIZE_THREAD    ${LLAMA_SANITIZE_THREAD})
+set(GGML_SANITIZE_ADDRESS   ${LLAMA_SANITIZE_ADDRESS})
+set(GGML_SANITIZE_UNDEFINED ${LLAMA_SANITIZE_UNDEFINED})
 set(GGML_ALL_WARNINGS       ${LLAMA_ALL_WARNINGS})
 set(GGML_FATAL_WARNINGS     ${LLAMA_FATAL_WARNINGS})
 
@@ -95,6 +95,10 @@ if (NOT DEFINED GGML_LLAMAFILE)
     set(GGML_LLAMAFILE_DEFAULT ON)
 endif()
 
+if (NOT DEFINED GGML_AMX)
+    set(GGML_AMX ON)
+endif()
+
 if (NOT DEFINED GGML_CUDA_GRAPHS)
     set(GGML_CUDA_GRAPHS_DEFAULT ON)
 endif()
@@ -118,62 +122,16 @@ llama_option_depr(WARNING     LLAMA_SYCL                GGML_SYCL)
 llama_option_depr(WARNING     LLAMA_SYCL_F16            GGML_SYCL_F16)
 llama_option_depr(WARNING     LLAMA_CANN                GGML_CANN)
 
-if (NOT MSVC)
-    if (LLAMA_SANITIZE_THREAD)
-        message(STATUS "Using -fsanitize=thread")
-
-        add_compile_options(-fsanitize=thread)
-        link_libraries     (-fsanitize=thread)
-    endif()
-
-    if (LLAMA_SANITIZE_ADDRESS)
-        message(STATUS "Using -fsanitize=address")
-
-        add_compile_options(-fsanitize=address -fno-omit-frame-pointer)
-        link_libraries     (-fsanitize=address)
-    endif()
-
-    if (LLAMA_SANITIZE_UNDEFINED)
-        message(STATUS "Using -fsanitize=undefined")
-
-        add_compile_options(-fsanitize=undefined)
-        link_libraries     (-fsanitize=undefined)
-    endif()
-endif()
-
 #
-# 3rd-party
+# build the library
 #
 
 if (NOT TARGET ggml)
     add_subdirectory(ggml)
     # ... otherwise assume ggml is added by a parent CMakeLists.txt
 endif()
-
-#
-# build the library
-#
-
 add_subdirectory(src)
 
-#
-# utils, programs, examples and tests
-#
-
-if (LLAMA_BUILD_COMMON)
-    add_subdirectory(common)
-endif()
-
-if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION)
-    include(CTest)
-    add_subdirectory(tests)
-endif()
-
-if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_EXAMPLES)
-    add_subdirectory(examples)
-    add_subdirectory(pocs)
-endif()
-
 #
 # install
 #
@@ -189,14 +147,27 @@ set(LLAMA_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} CACHE PATH "Location o
 set(LLAMA_LIB_INSTALL_DIR     ${CMAKE_INSTALL_LIBDIR}     CACHE PATH "Location of library files")
 set(LLAMA_BIN_INSTALL_DIR     ${CMAKE_INSTALL_BINDIR}     CACHE PATH "Location of binary  files")
 
+# At the moment some compile definitions are placed within the ggml/src
+# directory but not exported on the `ggml` target. This could be improved by
+# determining _precisely_ which defines are necessary for the llama-config
+# package.
+#
+set(GGML_TRANSIENT_DEFINES)
+get_target_property(GGML_DIRECTORY ggml SOURCE_DIR)
+get_directory_property(GGML_DIR_DEFINES DIRECTORY ${GGML_DIRECTORY} COMPILE_DEFINITIONS)
+if (GGML_DIR_DEFINES)
+    list(APPEND GGML_TRANSIENT_DEFINES ${GGML_DIR_DEFINES})
+endif()
+get_target_property(GGML_TARGET_DEFINES ggml COMPILE_DEFINITIONS)
+if (GGML_TARGET_DEFINES)
+    list(APPEND GGML_TRANSIENT_DEFINES ${GGML_TARGET_DEFINES})
+endif()
+get_target_property(GGML_LINK_LIBRARIES ggml LINK_LIBRARIES)
+# all public headers
 set(LLAMA_PUBLIC_HEADERS
     ${CMAKE_CURRENT_SOURCE_DIR}/include/llama.h
     ${CMAKE_CURRENT_SOURCE_DIR}/include/llama-cpp.h)
-
-set_target_properties(llama
-    PROPERTIES
-        PUBLIC_HEADER "${LLAMA_PUBLIC_HEADERS}")
-
+set_target_properties(llama PROPERTIES PUBLIC_HEADER "${LLAMA_PUBLIC_HEADERS}")
 install(TARGETS llama LIBRARY PUBLIC_HEADER)
 
 configure_package_config_file(
@@ -233,4 +204,22 @@ configure_file(cmake/llama.pc.in
         @ONLY)
 
 install(FILES "${CMAKE_CURRENT_BINARY_DIR}/llama.pc"
-        DESTINATION ${CMAKE_INSTALL_LIBDIR}/pkgconfig)
+        DESTINATION lib/pkgconfig)
+
+#
+# utils, programs, examples and tests
+#
+
+if (LLAMA_BUILD_COMMON)
+    add_subdirectory(common)
+endif()
+
+if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION)
+    include(CTest)
+    add_subdirectory(tests)
+endif()
+
+if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_EXAMPLES)
+    add_subdirectory(examples)
+    add_subdirectory(pocs)
+endif()

CMakePresets.json

@@ -31,13 +31,6 @@
     { "name": "sycl_f16", "hidden": true, "cacheVariables": { "GGML_SYCL_F16":    "ON" } },
     { "name": "vulkan",   "hidden": true, "cacheVariables": { "GGML_VULKAN":      "ON" } },
 
-    {
-        "name": "x64-windows-llvm", "hidden": true,
-        "cacheVariables": {
-            "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/x64-windows-llvm.cmake"
-        }
-    },
-
     {
         "name": "arm64-windows-msvc", "hidden": true,
         "architecture": { "value": "arm64",    "strategy": "external" },
@@ -77,11 +70,6 @@
     { "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc",  "reldbg" ] },
     { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc",  "reldbg", "static" ] },
 
-    { "name": "x64-windows-llvm-debug", "inherits": [ "base", "x64-windows-llvm", "debug" ] },
-    { "name": "x64-windows-llvm-release", "inherits": [ "base", "x64-windows-llvm", "release" ] },
-    { "name": "x64-windows-llvm-reldbg", "inherits": [ "base", "x64-windows-llvm", "reldbg" ] },
-    { "name": "x64-windows-llvm+static-release", "inherits": [ "base", "x64-windows-llvm", "reldbg", "static" ] },
-
     { "name": "x64-windows-msvc-debug", "inherits": [ "base", "debug" ] },
     { "name": "x64-windows-msvc-release", "inherits": [ "base", "reldbg" ] },
     { "name": "x64-windows-msvc+static-release", "inherits": [ "base", "reldbg", "static" ] },

CODEOWNERS

@@ -1,11 +0,0 @@
-# collaborators can optionally add themselves here to indicate their availability for reviewing related PRs
-
-/ci/ @ggerganov
-/.devops/*.Dockerfile @ngxson
-/examples/server/ @ngxson
-/ggml/src/ggml-cuda/fattn* @JohannesGaessler
-/ggml/src/ggml-cuda/mmq.* @JohannesGaessler
-/ggml/src/ggml-cuda/mmv.* @JohannesGaessler
-/ggml/src/ggml-cuda/mmvq.* @JohannesGaessler
-/ggml/src/ggml-opt.cpp @JohannesGaessler
-/ggml/src/gguf.cpp @JohannesGaessler

CONTRIBUTING.md

@@ -1,10 +1,9 @@
 # Pull requests (for contributors)
 
 - Test your changes:
+  - Using the commands in the [`tests`](tests) folder. For instance, running the `./tests/test-backend-ops` command tests different backend implementations of the `ggml` library
   - Execute [the full CI locally on your machine](ci/README.md) before publishing
-    - Verify that the perplexity and the performance are not affected negatively by your changes (use `llama-perplexity` and `llama-bench`)
-    - If you modified the `ggml` source, run the `test-backend-ops` tool to check whether different backend implementations of the `ggml` operators produce consistent results (this requires access to at least two different `ggml` backends)
-    - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops`
+- Optionally rate the complexity of your PR (i.e. `Review Complexity : Low`, `Review Complexity : Medium`, `Review Complexity : High`). This makes it easier for maintainers to triage the PRs
 - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly
 - If your PR becomes stale, don't hesitate to ping the maintainers in the comments
 
@@ -13,111 +12,20 @@
 - Squash-merge PRs
 - Use the following format for the squashed commit title: `<module> : <commit title> (#<issue_number>)`. For example: `utils : fix typo in utils.py (#1234)`
 - Optionally pick a `<module>` from here: https://github.com/ggerganov/llama.cpp/wiki/Modules
-- Consider adding yourself to [CODEOWNERS](CODEOWNERS)
 
 # Coding guidelines
 
 - Avoid adding third-party dependencies, extra files, extra headers, etc.
 - Always consider cross-compatibility with other operating systems and architectures
 - Avoid fancy-looking modern STL constructs, use basic `for` loops, avoid templates, keep it simple
-- Vertical alignment makes things more readable and easier to batch edit
+- There are no strict rules for the code style, but try to follow the patterns in the code (indentation, spaces, etc.). Vertical alignment makes things more readable and easier to batch edit
 - Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a`
-- Use sized integer types such as `int32_t` in the public API, e.g. `size_t` may also be appropriate for allocation sizes or byte offsets
-- Declare structs with `struct foo {}` instead of `typedef struct foo {} foo`
-    - In C++ code omit optional `struct` and `enum` keyword whenever they are not necessary
-    ```cpp
-    // OK
-    llama_context * ctx;
-    const llama_rope_type rope_type;
-
-    // not OK
-    struct llama_context * ctx;
-    const enum llama_rope_type rope_type;
-    ```
-
-    _(NOTE: this guideline is yet to be applied to the `llama.cpp` codebase. New code should follow this guideline.)_
-
-- Try to follow the existing patterns in the code (indentation, spaces, etc.). In case of doubt use `clang-format` to format the added code
-- For anything not covered in the current guidelines, refer to the [C++ Core Guidelines](https://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines)
+- Naming usually optimizes for common prefix (see https://github.com/ggerganov/ggml/pull/302#discussion_r1243240963)
 - Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices
 - Matrix multiplication is unconventional: [`C = ggml_mul_mat(ctx, A, B)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means $C^T = A B^T \Leftrightarrow C = B A^T.$
 
 ![matmul](media/matmul.png)
 
-# Naming guidelines
-
-- Use `snake_case` for function, variable and type names
-- Naming usually optimizes for longest common prefix (see https://github.com/ggerganov/ggml/pull/302#discussion_r1243240963)
-
-    ```cpp
-    // not OK
-    int small_number;
-    int big_number;
-
-    // OK
-    int number_small;
-    int number_big;
-    ```
-
-- Enum values are always in upper case and prefixed with the enum name
-
-    ```cpp
-    enum llama_vocab_type {
-        LLAMA_VOCAB_TYPE_NONE = 0,
-        LLAMA_VOCAB_TYPE_SPM  = 1,
-        LLAMA_VOCAB_TYPE_BPE  = 2,
-        LLAMA_VOCAB_TYPE_WPM  = 3,
-        LLAMA_VOCAB_TYPE_UGM  = 4,
-        LLAMA_VOCAB_TYPE_RWKV = 5,
-    };
-    ```
-
-- The general naming pattern is `<class>_<method>`, with `<method>` being `<action>_<noun>`
-
-    ```cpp
-    llama_model_init();           // class: "llama_model",         method: "init"
-    llama_sampler_chain_remove(); // class: "llama_sampler_chain", method: "remove"
-    llama_sampler_get_seed();     // class: "llama_sampler",       method: "get_seed"
-    llama_set_embeddings();       // class: "llama_context",       method: "set_embeddings"
-    llama_n_threads();            // class: "llama_context",       method: "n_threads"
-    llama_adapter_lora_free();    // class: "llama_adapter_lora",  method: "free"
-    ```
-
-    - The `get` `<action>` can be omitted
-    - The `<noun>` can be omitted if not necessary
-    - The `_context` suffix of the `<class>` is optional. Use it to disambiguate symbols when needed
-    - Use `init`/`free` for constructor/destructor `<action>`
-
-- Use the `_t` suffix when a type is supposed to be opaque to the user - it's not relevant to them if it is a struct or anything else
-
-    ```cpp
-    typedef struct llama_context * llama_context_t;
-
-    enum llama_pooling_type llama_pooling_type(const llama_context_t ctx);
-    ```
-
-    _(NOTE: this guideline is yet to be applied to the `llama.cpp` codebase. New code should follow this guideline)_
-
-- C/C++ filenames are all lowercase with dashes. Headers use the `.h` extension. Source files use the `.c` or `.cpp` extension
-- Python filenames are all lowercase with underscores
-
-- _(TODO: abbreviations usage)_
-
-# Preprocessor directives
-
-- _(TODO: add guidelines with examples and apply them to the codebase)_
-
-    ```cpp
-    #ifdef FOO
-    #endif // FOO
-    ```
-
-# Documentation
-
-- Documentation is a community effort
-- When you need to look into the source code to figure out how to use an API consider adding a short summary to the header file for future reference
-- When you notice incorrect or outdated documentation, please update it
-
 # Resources
 
 The Github issues, PRs and discussions contain a lot of information that can be useful to get familiar with the codebase. For convenience, some of the more important information is referenced from Github projects:

Makefile

@@ -1,7 +1,3 @@
-ifndef LLAMA_MAKEFILE
-$(error The Makefile build is deprecated. Use the CMake build instead. For more details, see https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
-endif
-
 # Define the default target now so that it is always the first target
 BUILD_TARGETS = \
 	libllava.a \
@@ -22,7 +18,6 @@ BUILD_TARGETS = \
 	llama-infill \
 	llama-llava-cli \
 	llama-minicpmv-cli\
-	llama-qwen2vl-cli\
 	llama-lookahead \
 	llama-lookup \
 	llama-lookup-create \
@@ -52,7 +47,6 @@ TEST_TARGETS = \
 	tests/test-arg-parser \
 	tests/test-autorelease \
 	tests/test-backend-ops \
-	tests/test-chat \
 	tests/test-chat-template \
 	tests/test-double-float \
 	tests/test-grammar-integration \
@@ -257,11 +251,11 @@ endif
 # Compile flags
 #
 
-# keep standard at C11 and C++17
+# keep standard at C11 and C++11
 MK_CPPFLAGS  = -Iggml/include -Iggml/src -Iinclude -Isrc -Icommon -DGGML_USE_CPU
 MK_CFLAGS    = -std=c11   -fPIC
-MK_CXXFLAGS  = -std=c++17 -fPIC
-MK_NVCCFLAGS = -std=c++17
+MK_CXXFLAGS  = -std=c++11 -fPIC
+MK_NVCCFLAGS = -std=c++11
 
 ifdef LLAMA_NO_CCACHE
 GGML_NO_CCACHE := 1
@@ -447,10 +441,6 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64))
 	MK_CFLAGS     += -march=native -mtune=native
 	HOST_CXXFLAGS += -march=native -mtune=native
 
-	# Usage AMX build test
-	#MK_CFLAGS     += -march=graniterapids -mtune=graniterapids
-	#HOST_CXXFLAGS += -march=graniterapids -mtune=graniterapids
-
 	# Usage AVX-only
 	#MK_CFLAGS   += -mfma -mf16c -mavx
 	#MK_CXXFLAGS += -mfma -mf16c -mavx
@@ -585,18 +575,15 @@ endif
 
 ifndef GGML_NO_AMX
 	MK_CPPFLAGS += -DGGML_USE_AMX
-	OBJ_GGML_EXT += ggml/src/ggml-cpu/amx/amx.o ggml/src/ggml-cpu/amx/mmq.o
+	OBJ_GGML_EXT += ggml/src/ggml-amx/ggml-amx.o ggml/src/ggml-amx/mmq.o
 endif
 
-# only necessary for the CPU backend files
-MK_CPPFLAGS += -Iggml/src/ggml-cpu
-
 ifdef GGML_RPC
 	MK_CPPFLAGS  += -DGGML_USE_RPC
 	OBJ_GGML_EXT += ggml/src/ggml-rpc.o
 endif # GGML_RPC
 
-OBJ_CUDA_TMPL      = $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-mma*.cu))
+OBJ_CUDA_TMPL      = $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-wmma*.cu))
 OBJ_CUDA_TMPL     += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/mmq*.cu))
 
 ifdef GGML_CUDA_FA_ALL_QUANTS
@@ -765,7 +752,7 @@ vulkan-shaders-gen: ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
 
 endif # GGML_VULKAN
 
-ifdef GGML_HIP
+ifdef GGML_HIPBLAS
 	ifeq ($(wildcard /opt/rocm),)
 		ROCM_PATH      ?= /usr
 		AMDGPU_TARGETS ?= $(shell $(shell which amdgpu-arch))
@@ -820,7 +807,7 @@ ggml/src/ggml-cuda/%.o: \
 	ggml/src/ggml-common.h \
 	ggml/src/ggml-cuda/common.cuh
 	$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
-endif # GGML_HIP
+endif # GGML_HIPBLAS
 
 ifdef GGML_MUSA
 	ifeq ($(wildcard /opt/musa),)
@@ -828,7 +815,7 @@ ifdef GGML_MUSA
 	else
 		MUSA_PATH ?= /opt/musa
 	endif
-	MUSA_ARCHITECTURES ?= 21;22
+	MTGPU_TARGETS ?= mp_21 mp_22
 
 	MK_CPPFLAGS += -DGGML_USE_MUSA -DGGML_USE_CUDA
 	MK_LDFLAGS += -L$(MUSA_PATH)/lib -Wl,-rpath=$(MUSA_PATH)/lib
@@ -847,8 +834,7 @@ ifdef GGML_MUSA
 	CXX := $(MUSA_PATH)/bin/clang++
 	MCC := $(CCACHE) $(MUSA_PATH)/bin/mcc
 
-	MUSAFLAGS  = -x musa -mtgpu
-	MUSAFLAGS += $(foreach arch,$(subst ;, ,$(MUSA_ARCHITECTURES)),--cuda-gpu-arch=mp_$(arch))
+	MUSAFLAGS += $(addprefix --cuda-gpu-arch=, $(MTGPU_TARGETS))
 
 ifdef GGML_CUDA_FORCE_MMQ
 	MUSAFLAGS += -DGGML_CUDA_FORCE_MMQ
@@ -892,14 +878,14 @@ ggml/src/ggml-cuda/ggml-cuda.o: \
 	ggml/src/ggml-backend-impl.h \
 	ggml/src/ggml-common.h \
 	$(wildcard ggml/src/ggml-cuda/*.cuh)
-	$(MCC) $(CXXFLAGS) $(MUSAFLAGS) -c -o $@ $<
+	$(MCC) $(CXXFLAGS) $(MUSAFLAGS) -x musa -mtgpu -c -o $@ $<
 
 ggml/src/ggml-cuda/%.o: \
 	ggml/src/ggml-cuda/%.cu \
 	ggml/include/ggml.h \
 	ggml/src/ggml-common.h \
 	ggml/src/ggml-cuda/common.cuh
-	$(MCC) $(CXXFLAGS) $(MUSAFLAGS) -c -o $@ $<
+	$(MCC) $(CXXFLAGS) $(MUSAFLAGS) -x musa -mtgpu -c -o $@ $<
 endif # GGML_MUSA
 
 ifdef GGML_METAL
@@ -954,6 +940,7 @@ DIR_COMMON = common
 
 OBJ_GGML = \
 	$(DIR_GGML)/src/ggml.o \
+	$(DIR_GGML)/src/ggml-aarch64.o \
 	$(DIR_GGML)/src/ggml-alloc.o \
 	$(DIR_GGML)/src/ggml-backend.o \
 	$(DIR_GGML)/src/ggml-backend-reg.o \
@@ -961,11 +948,9 @@ OBJ_GGML = \
 	$(DIR_GGML)/src/ggml-quants.o \
 	$(DIR_GGML)/src/ggml-threading.o \
 	$(DIR_GGML)/src/ggml-cpu/ggml-cpu.o \
-	$(DIR_GGML)/src/ggml-cpu/ggml-cpu_cpp.o \
+	$(DIR_GGML)/src/ggml-cpu/ggml-cpu-cpp.o \
 	$(DIR_GGML)/src/ggml-cpu/ggml-cpu-aarch64.o \
-	$(DIR_GGML)/src/ggml-cpu/ggml-cpu-hbm.o \
 	$(DIR_GGML)/src/ggml-cpu/ggml-cpu-quants.o \
-	$(DIR_GGML)/src/ggml-cpu/ggml-cpu-traits.o \
 	$(OBJ_GGML_EXT)
 
 OBJ_LLAMA = \
@@ -984,7 +969,6 @@ OBJ_COMMON = \
 	$(DIR_COMMON)/ngram-cache.o \
 	$(DIR_COMMON)/sampling.o \
 	$(DIR_COMMON)/speculative.o \
-	$(DIR_COMMON)/chat.o \
 	$(DIR_COMMON)/build-info.o \
 	$(DIR_COMMON)/json-schema-to-grammar.o
 
@@ -1106,10 +1090,17 @@ DEP_FILES = $(OBJ_GGML:.o=.d) $(OBJ_LLAMA:.o=.d) $(OBJ_COMMON:.o=.d)
 # Default target
 all: $(BUILD_TARGETS)
 
-# force c++ build for source file that have same name as c file
 # Note: need this exception because `ggml-cpu.c` and `ggml-cpu.cpp` both produce the same obj/dep files
-$(DIR_GGML)/%_cpp.o: $(DIR_GGML)/%.cpp
-	$(CXX) $(CXXFLAGS) -MMD -c $< -o $@
+#       g++ -M -I ./ggml/include/ -I ./ggml/src ggml/src/ggml-cpu/ggml-cpu.cpp | grep ggml
+$(DIR_GGML)/src/ggml-cpu/ggml-cpu-cpp.o: \
+	ggml/src/ggml-cpu/ggml-cpu.cpp \
+	ggml/include/ggml-backend.h \
+	ggml/include/ggml.h \
+	ggml/include/ggml-alloc.h \
+	ggml/src/ggml-backend-impl.h \
+	ggml/include/ggml-cpu.h \
+	ggml/src/ggml-impl.h
+	$(CXX) $(CXXFLAGS)   -c $< -o $@
 
 # Rules for building object files
 $(DIR_GGML)/%.o: $(DIR_GGML)/%.c
@@ -1146,15 +1137,8 @@ $(LIB_COMMON_S): $(OBJ_COMMON)
 # Include dependency files
 -include $(DEP_FILES)
 
-# Clean generated server assets
-clean-server-assets:
-	find examples/server -type f -name "*.js.hpp"   -delete
-	find examples/server -type f -name "*.mjs.hpp"  -delete
-	find examples/server -type f -name "*.css.hpp"  -delete
-	find examples/server -type f -name "*.html.hpp" -delete
-
 # Clean rule
-clean: clean-server-assets
+clean:
 	rm -vrf $(BUILD_TARGETS) $(TEST_TARGETS)
 	rm -rvf *.a *.dll *.so *.dot
 	find ggml src common tests examples pocs -type f -name "*.o" -delete
@@ -1362,18 +1346,20 @@ llama-server: \
 	examples/server/utils.hpp \
 	examples/server/httplib.h \
 	examples/server/index.html.hpp \
+	examples/server/completion.js.hpp \
 	examples/server/loading.html.hpp \
-	common/chat.cpp \
-	common/chat.hpp \
-	common/chat-template.hpp \
+	examples/server/deps_daisyui.min.css.hpp \
+	examples/server/deps_markdown-it.js.hpp \
+	examples/server/deps_tailwindcss.js.hpp \
+	examples/server/deps_vue.esm-browser.js.hpp \
 	common/json.hpp \
-	common/minja.hpp \
+	common/stb_image.h \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
 
 # Portable equivalent of `cd examples/server/public && xxd -i $(notdir $<) ../$(notdir $<).hpp`:
-examples/server/%.hpp: examples/server/public/% FORCE Makefile
+examples/server/%.hpp: examples/server/public/% Makefile
 	@( export NAME=$(subst .,_,$(subst -,_,$(notdir $<))) && \
 		echo "unsigned char $${NAME}[] = {" && \
 		cat $< | od -v -t x1 -An | sed -E 's/([0-9a-fA-F]+)/0x\1, /g' && \
@@ -1411,14 +1397,6 @@ llama-minicpmv-cli: examples/llava/minicpmv-cli.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
 
-llama-qwen2vl-cli: examples/llava/qwen2vl-cli.cpp \
-	examples/llava/llava.cpp \
-	examples/llava/llava.h \
-	examples/llava/clip.cpp \
-	examples/llava/clip.h \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
-
 ifeq ($(UNAME_S),Darwin)
 swift: examples/batched.swift
 	(cd examples/batched.swift; make build)
@@ -1475,11 +1453,6 @@ tests/test-json-schema-to-grammar: tests/test-json-schema-to-grammar.cpp \
 	$(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-tests/test-chat: tests/test-chat.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
 tests/test-opt: tests/test-opt.cpp \
 	$(OBJ_GGML)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
@@ -1561,7 +1534,7 @@ llama-q8dot: pocs/vdot/q8dot.cpp ggml/src/ggml.o \
 # Deprecated binaries that we want to keep around long enough for people to migrate to the new filenames, then these can be removed.
 #
 # Mark legacy binary targets as .PHONY so that they are always checked.
-.PHONY: FORCE main quantize perplexity embedding server
+.PHONY: main quantize perplexity embedding server
 
 # Define the object file target
 examples/deprecation-warning/deprecation-warning.o: examples/deprecation-warning/deprecation-warning.cpp

Package.swift

@@ -2,6 +2,57 @@
 
 import PackageDescription
 
+var sources = [
+    "src/llama.cpp",
+    "src/llama-vocab.cpp",
+    "src/llama-grammar.cpp",
+    "src/llama-sampling.cpp",
+    "src/unicode.cpp",
+    "src/unicode-data.cpp",
+    "ggml/src/ggml.c",
+    "ggml/src/ggml-aarch64.c",
+    "ggml/src/ggml-alloc.c",
+    "ggml/src/ggml-backend.cpp",
+    "ggml/src/ggml-backend-reg.cpp",
+    "ggml/src/ggml-cpu/ggml-cpu.c",
+    "ggml/src/ggml-cpu/ggml-cpu.cpp",
+    "ggml/src/ggml-cpu/ggml-cpu-aarch64.c",
+    "ggml/src/ggml-cpu/ggml-cpu-quants.c",
+    "ggml/src/ggml-threading.cpp",
+    "ggml/src/ggml-quants.c",
+]
+
+var resources: [Resource] = []
+var linkerSettings: [LinkerSetting] = []
+var cSettings: [CSetting] =  [
+    .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]),
+    .unsafeFlags(["-fno-objc-arc"]),
+    .headerSearchPath("ggml/src"),
+    // NOTE: NEW_LAPACK will required iOS version 16.4+
+    // We should consider add this in the future when we drop support for iOS 14
+    // (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc)
+    // .define("ACCELERATE_NEW_LAPACK"),
+    // .define("ACCELERATE_LAPACK_ILP64")
+]
+
+#if canImport(Darwin)
+sources.append("ggml/src/ggml-common.h")
+sources.append("ggml/src/ggml-metal/ggml-metal.m")
+resources.append(.process("ggml/src/ggml-metal/ggml-metal.metal"))
+linkerSettings.append(.linkedFramework("Accelerate"))
+cSettings.append(
+    contentsOf: [
+        .define("GGML_USE_ACCELERATE"),
+        .define("GGML_USE_METAL"),
+        .define("GGML_USE_CPU")
+    ]
+)
+#endif
+
+#if os(Linux)
+    cSettings.append(.define("_GNU_SOURCE"))
+#endif
+
 let package = Package(
     name: "llama",
     platforms: [
@@ -14,6 +65,26 @@ let package = Package(
         .library(name: "llama", targets: ["llama"]),
     ],
     targets: [
-        .systemLibrary(name: "llama", pkgConfig: "llama"),
-    ]
+        .target(
+            name: "llama",
+            path: ".",
+            exclude: [
+               "build",
+               "cmake",
+               "examples",
+               "scripts",
+               "models",
+               "tests",
+               "CMakeLists.txt",
+               "Makefile",
+               "ggml/src/ggml-metal-embed.metal"
+            ],
+            sources: sources,
+            resources: resources,
+            publicHeadersPath: "spm-headers",
+            cSettings: cSettings,
+            linkerSettings: linkerSettings
+        )
+    ],
+    cxxLanguageStandard: .cxx11
 )

README.md

@@ -4,6 +4,7 @@
 
 [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
 [![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml)
+[![Conan Center](https://shields.io/conan/v/llama-cpp)](https://conan.io/center/llama-cpp)
 
 [Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml)
 
@@ -16,11 +17,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
 
 ## Hot topics
 
-- **How to use [MTLResidencySet](https://developer.apple.com/documentation/metal/mtlresidencyset?language=objc) to keep the GPU memory active?** https://github.com/ggerganov/llama.cpp/pull/11427
-- **VS Code extension for FIM completions:** https://github.com/ggml-org/llama.vscode
-- Universal tool call support in `llama-server`: https://github.com/ggerganov/llama.cpp/pull/9639
-- Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim
-- Introducing GGUF-my-LoRA https://github.com/ggerganov/llama.cpp/discussions/10123
+- **Introducing GGUF-my-LoRA** https://github.com/ggerganov/llama.cpp/discussions/10123
 - Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggerganov/llama.cpp/discussions/9669
 - Hugging Face GGUF editor: [discussion](https://github.com/ggerganov/llama.cpp/discussions/9268) | [tool](https://huggingface.co/spaces/CISCai/gguf-editor)
 
@@ -29,7 +26,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
 ## Description
 
 The main goal of `llama.cpp` is to enable LLM inference with minimal setup and state-of-the-art performance on a wide
-range of hardware - locally and in the cloud.
+variety of hardware - locally and in the cloud.
 
 - Plain C/C++ implementation without any dependencies
 - Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
@@ -39,17 +36,14 @@ range of hardware - locally and in the cloud.
 - Vulkan and SYCL backend support
 - CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity
 
-The `llama.cpp` project is the main playground for developing new features for the [ggml](https://github.com/ggerganov/ggml) library.
+Since its [inception](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022), the project has
+improved significantly thanks to many contributions. It is the main playground for developing new features for the
+[ggml](https://github.com/ggerganov/ggml) library.
 
-<details>
-<summary>Models</summary>
+**Supported models:**
 
 Typically finetunes of the base models below are supported as well.
 
-Instructions for adding support for new models: [HOWTO-add-model.md](docs/development/HOWTO-add-model.md)
-
-#### Text-only
-
 - [X] LLaMA 🦙
 - [x] LLaMA 2 🦙🦙
 - [x] LLaMA 3 🦙🦙🦙
@@ -73,7 +67,6 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
 - [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557)
 - [x] [Phi models](https://huggingface.co/models?search=microsoft/phi)
-- [x] [PhiMoE](https://github.com/ggerganov/llama.cpp/pull/11003)
 - [x] [GPT-2](https://huggingface.co/gpt2)
 - [x] [Orion 14B](https://github.com/ggerganov/llama.cpp/pull/5118)
 - [x] [InternLM2](https://huggingface.co/models?search=internlm2)
@@ -86,7 +79,6 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [SEA-LION](https://huggingface.co/models?search=sea-lion)
 - [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B)
 - [x] [OLMo](https://allenai.org/olmo)
-- [x] [OLMo 2](https://allenai.org/olmo)
 - [x] [OLMoE](https://huggingface.co/allenai/OLMoE-1B-7B-0924)
 - [x] [Granite models](https://huggingface.co/collections/ibm-granite/granite-code-models-6624c5cec322e4c148c8b330)
 - [x] [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) + [Pythia](https://github.com/EleutherAI/pythia)
@@ -96,17 +88,17 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [Bitnet b1.58 models](https://huggingface.co/1bitLLM)
 - [x] [Flan T5](https://huggingface.co/models?search=flan-t5)
 - [x] [Open Elm models](https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca)
-- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b) + [GLMEdge-1.5b](https://huggingface.co/THUDM/glm-edge-1.5b-chat) + [GLMEdge-4b](https://huggingface.co/THUDM/glm-edge-4b-chat)
+- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b)
 - [x] [SmolLM](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966)
 - [x] [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
 - [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a)
 - [x] [Jais](https://huggingface.co/inceptionai/jais-13b-chat)
 - [x] [Bielik-11B-v2.3](https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a)
 - [x] [RWKV-6](https://github.com/BlinkDL/RWKV-LM)
-- [x] [QRWKV-6](https://huggingface.co/recursal/QRWKV6-32B-Instruct-Preview-v0.1)
-- [x] [GigaChat-20B-A3B](https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct)
 
-#### Multimodal
+(instructions for supporting more models: [HOWTO-add-model.md](./docs/development/HOWTO-add-model.md))
+
+**Multimodal models:**
 
 - [x] [LLaVA 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e), [LLaVA 1.6 models](https://huggingface.co/collections/liuhaotian/llava-16-65b9e40155f60fd046a5ccf2)
 - [x] [BakLLaVA](https://huggingface.co/models?search=SkunkworksAI/Bakllava)
@@ -117,13 +109,8 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM)
 - [x] [Moondream](https://huggingface.co/vikhyatk/moondream2)
 - [x] [Bunny](https://github.com/BAAI-DCAI/Bunny)
-- [x] [GLM-EDGE](https://huggingface.co/models?search=glm-edge)
-- [x] [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d)
 
-</details>
-
-<details>
-<summary>Bindings</summary>
+**Bindings:**
 
 - Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
 - Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp)
@@ -136,7 +123,6 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - Rust (more features): [edgenai/llama_cpp-rs](https://github.com/edgenai/llama_cpp-rs)
 - Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
 - Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs)
-- Rust (automated build from crates.io): [ShelbyJenkins/llm_client](https://github.com/ShelbyJenkins/llm_client)
 - C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
 - C#/VB.NET (more features - community license): [LM-Kit.NET](https://docs.lm-kit.com/lm-kit-net/index.html)
 - Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s)
@@ -151,335 +137,316 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - Swift [srgtuszy/llama-cpp-swift](https://github.com/srgtuszy/llama-cpp-swift)
 - Swift [ShenghaiWang/SwiftLlama](https://github.com/ShenghaiWang/SwiftLlama)
 
-</details>
+**UI:**
 
-<details>
-<summary>UIs</summary>
+Unless otherwise noted these projects are open-source with permissive licensing:
+
+- [MindWorkAI/AI-Studio](https://github.com/MindWorkAI/AI-Studio) (FSL-1.1-MIT)
+- [iohub/collama](https://github.com/iohub/coLLaMA)
+- [janhq/jan](https://github.com/janhq/jan) (AGPL)
+- [nat/openplayground](https://github.com/nat/openplayground)
+- [Faraday](https://faraday.dev/) (proprietary)
+- [LMStudio](https://lmstudio.ai/) (proprietary)
+- [Layla](https://play.google.com/store/apps/details?id=com.laylalite) (proprietary)
+- [ramalama](https://github.com/containers/ramalama) (MIT)
+- [LocalAI](https://github.com/mudler/LocalAI) (MIT)
+- [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL)
+- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile)
+- [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all)
+- [ollama/ollama](https://github.com/ollama/ollama)
+- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL)
+- [psugihara/FreeChat](https://github.com/psugihara/FreeChat)
+- [cztomsik/ava](https://github.com/cztomsik/ava) (MIT)
+- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal)
+- [pythops/tenere](https://github.com/pythops/tenere) (AGPL)
+- [RAGNA Desktop](https://ragna.app/) (proprietary)
+- [RecurseChat](https://recurse.chat/) (proprietary)
+- [semperai/amica](https://github.com/semperai/amica)
+- [withcatai/catai](https://github.com/withcatai/catai)
+- [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT)
+- [Msty](https://msty.app) (proprietary)
+- [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT)
+- [KanTV](https://github.com/zhouwg/kantv?tab=readme-ov-file)(Apachev2.0 or later)
+- [Dot](https://github.com/alexpinel/Dot) (GPL)
+- [MindMac](https://mindmac.app) (proprietary)
+- [KodiBot](https://github.com/firatkiral/kodibot) (GPL)
+- [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT)
+- [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT)
+- [AIKit](https://github.com/sozercan/aikit) (MIT)
+- [LARS - The LLM & Advanced Referencing Solution](https://github.com/abgulati/LARS) (AGPL)
+- [LLMUnity](https://github.com/undreamai/LLMUnity) (MIT)
+- [Llama Assistant](https://github.com/vietanhdev/llama-assistant) (GPL)
+- [PocketPal AI - An iOS and Android App](https://github.com/a-ghorbani/pocketpal-ai) (MIT)
 
 *(to have a project listed here, it should clearly state that it depends on `llama.cpp`)*
 
-- [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT)
-- [cztomsik/ava](https://github.com/cztomsik/ava) (MIT)
-- [Dot](https://github.com/alexpinel/Dot) (GPL)
-- [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT)
-- [iohub/collama](https://github.com/iohub/coLLaMA) (Apache-2.0)
-- [janhq/jan](https://github.com/janhq/jan) (AGPL)
-- [KanTV](https://github.com/zhouwg/kantv?tab=readme-ov-file) (Apache-2.0)
-- [KodiBot](https://github.com/firatkiral/kodibot) (GPL)
-- [llama.vim](https://github.com/ggml-org/llama.vim) (MIT)
-- [LARS](https://github.com/abgulati/LARS) (AGPL)
-- [Llama Assistant](https://github.com/vietanhdev/llama-assistant) (GPL)
-- [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT)
-- [LLMUnity](https://github.com/undreamai/LLMUnity) (MIT)
-- [LMStudio](https://lmstudio.ai/) (proprietary)
-- [LocalAI](https://github.com/mudler/LocalAI) (MIT)
-- [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL)
-- [MindMac](https://mindmac.app) (proprietary)
-- [MindWorkAI/AI-Studio](https://github.com/MindWorkAI/AI-Studio) (FSL-1.1-MIT)
-- [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT)
-- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile) (Apache-2.0)
-- [nat/openplayground](https://github.com/nat/openplayground) (MIT)
-- [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) (MIT)
-- [ollama/ollama](https://github.com/ollama/ollama) (MIT)
-- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL)
-- [PocketPal AI](https://github.com/a-ghorbani/pocketpal-ai) (MIT)
-- [psugihara/FreeChat](https://github.com/psugihara/FreeChat) (MIT)
-- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) (MIT)
-- [pythops/tenere](https://github.com/pythops/tenere) (AGPL)
-- [ramalama](https://github.com/containers/ramalama) (MIT)
-- [semperai/amica](https://github.com/semperai/amica) (MIT)
-- [withcatai/catai](https://github.com/withcatai/catai) (MIT)
-- [Autopen](https://github.com/blackhole89/autopen) (GPL)
-
-</details>
-
-<details>
-<summary>Tools</summary>
+**Tools:**
 
 - [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from HuggingFace Hub and convert them to GGML
 - [akx/ollama-dl](https://github.com/akx/ollama-dl) – download models from the Ollama library to be used directly with llama.cpp
 - [crashr/gppm](https://github.com/crashr/gppm) – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption
 - [gpustack/gguf-parser](https://github.com/gpustack/gguf-parser-go/tree/main/cmd/gguf-parser) - review/check the GGUF file and estimate the memory usage
-- [Styled Lines](https://marketplace.unity.com/packages/tools/generative-ai/styled-lines-llama-cpp-model-292902) (proprietary licensed, async wrapper of inference part for game development in Unity3d with pre-built Mobile and Web platform wrappers and a model example)
+- [Styled Lines](https://marketplace.unity.com/packages/tools/generative-ai/styled-lines-llama-cpp-model-292902) (proprietary licensed, async wrapper of inference part for game development in Unity3d with prebuild Mobile and Web platform wrappers and a model example)
 
-</details>
-
-<details>
-<summary>Infrastructure</summary>
+**Infrastructure:**
 
 - [Paddler](https://github.com/distantmagic/paddler) - Stateful load balancer custom-tailored for llama.cpp
 - [GPUStack](https://github.com/gpustack/gpustack) - Manage GPU clusters for running LLMs
 - [llama_cpp_canister](https://github.com/onicai/llama_cpp_canister) - llama.cpp as a smart contract on the Internet Computer, using WebAssembly
-- [llama-swap](https://github.com/mostlygeek/llama-swap) - transparent proxy that adds automatic model switching with llama-server
-- [Kalavai](https://github.com/kalavai-net/kalavai-client) - Crowdsource end to end LLM deployment at any scale
+
+**Games:**
+- [Lucy's Labyrinth](https://github.com/MorganRO8/Lucys_Labyrinth) - A simple maze game where agents controlled by an AI model will try to trick you.
+
+## Demo
+
+<details>
+<summary>Typical run using LLaMA v2 13B on M2 Ultra</summary>
+
+```
+$ make -j && ./llama-cli -m models/llama-13b-v2/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e
+I llama.cpp build info:
+I UNAME_S:  Darwin
+I UNAME_P:  arm
+I UNAME_M:  arm64
+I CFLAGS:   -I.            -O3 -std=c11   -fPIC -DNDEBUG -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -pthread -DGGML_USE_K_QUANTS -DGGML_USE_ACCELERATE
+I CXXFLAGS: -I. -I./common -O3 -std=c++11 -fPIC -DNDEBUG -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar -pthread -DGGML_USE_K_QUANTS
+I LDFLAGS:   -framework Accelerate
+I CC:       Apple clang version 14.0.3 (clang-1403.0.22.14.1)
+I CXX:      Apple clang version 14.0.3 (clang-1403.0.22.14.1)
+
+make: Nothing to be done for `default'.
+main: build = 1041 (cf658ad)
+main: seed  = 1692823051
+llama_model_loader: loaded meta data with 16 key-value pairs and 363 tensors from models/llama-13b-v2/ggml-model-q4_0.gguf (version GGUF V1 (latest))
+llama_model_loader: - type  f32:   81 tensors
+llama_model_loader: - type q4_0:  281 tensors
+llama_model_loader: - type q6_K:    1 tensors
+llm_load_print_meta: format         = GGUF V1 (latest)
+llm_load_print_meta: arch           = llama
+llm_load_print_meta: vocab type     = SPM
+llm_load_print_meta: n_vocab        = 32000
+llm_load_print_meta: n_merges       = 0
+llm_load_print_meta: n_ctx_train    = 4096
+llm_load_print_meta: n_ctx          = 512
+llm_load_print_meta: n_embd         = 5120
+llm_load_print_meta: n_head         = 40
+llm_load_print_meta: n_head_kv      = 40
+llm_load_print_meta: n_layer        = 40
+llm_load_print_meta: n_rot          = 128
+llm_load_print_meta: n_gqa          = 1
+llm_load_print_meta: f_norm_eps     = 1.0e-05
+llm_load_print_meta: f_norm_rms_eps = 1.0e-05
+llm_load_print_meta: n_ff           = 13824
+llm_load_print_meta: freq_base      = 10000.0
+llm_load_print_meta: freq_scale     = 1
+llm_load_print_meta: model type     = 13B
+llm_load_print_meta: model ftype    = mostly Q4_0
+llm_load_print_meta: model size     = 13.02 B
+llm_load_print_meta: general.name   = LLaMA v2
+llm_load_print_meta: BOS token = 1 '<s>'
+llm_load_print_meta: EOS token = 2 '</s>'
+llm_load_print_meta: UNK token = 0 '<unk>'
+llm_load_print_meta: LF token  = 13 '<0x0A>'
+llm_load_tensors: ggml ctx size =    0.11 MB
+llm_load_tensors: mem required  = 7024.01 MB (+  400.00 MB per state)
+...................................................................................................
+llama_new_context_with_model: kv self size  =  400.00 MB
+llama_new_context_with_model: compute buffer total size =   75.41 MB
+
+system_info: n_threads = 16 / 24 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 |
+sampling: repeat_last_n = 64, repeat_penalty = 1.100000, presence_penalty = 0.000000, frequency_penalty = 0.000000, top_k = 40, tfs_z = 1.000000, top_p = 0.950000, typical_p = 1.000000, temp = 0.800000, mirostat = 0, mirostat_lr = 0.100000, mirostat_ent = 5.000000
+generate: n_ctx = 512, n_batch = 512, n_predict = 400, n_keep = 0
+
+
+ Building a website can be done in 10 simple steps:
+Step 1: Find the right website platform.
+Step 2: Choose your domain name and hosting plan.
+Step 3: Design your website layout.
+Step 4: Write your website content and add images.
+Step 5: Install security features to protect your site from hackers or spammers
+Step 6: Test your website on multiple browsers, mobile devices, operating systems etc…
+Step 7: Test it again with people who are not related to you personally – friends or family members will work just fine!
+Step 8: Start marketing and promoting the website via social media channels or paid ads
+Step 9: Analyze how many visitors have come to your site so far, what type of people visit more often than others (e.g., men vs women) etc…
+Step 10: Continue to improve upon all aspects mentioned above by following trends in web design and staying up-to-date on new technologies that can enhance user experience even further!
+How does a Website Work?
+A website works by having pages, which are made of HTML code. This code tells your computer how to display the content on each page you visit – whether it’s an image or text file (like PDFs). In order for someone else’s browser not only be able but also want those same results when accessing any given URL; some additional steps need taken by way of programming scripts that will add functionality such as making links clickable!
+The most common type is called static HTML pages because they remain unchanged over time unless modified manually (either through editing files directly or using an interface such as WordPress). They are usually served up via HTTP protocols – this means anyone can access them without having any special privileges like being part of a group who is allowed into restricted areas online; however, there may still exist some limitations depending upon where one lives geographically speaking.
+How to
+llama_print_timings:        load time =   576.45 ms
+llama_print_timings:      sample time =   283.10 ms /   400 runs   (    0.71 ms per token,  1412.91 tokens per second)
+llama_print_timings: prompt eval time =   599.83 ms /    19 tokens (   31.57 ms per token,    31.68 tokens per second)
+llama_print_timings:        eval time = 24513.59 ms /   399 runs   (   61.44 ms per token,    16.28 tokens per second)
+llama_print_timings:       total time = 25431.49 ms
+```
 
 </details>
 
 <details>
-<summary>Games</summary>
+<summary>Demo of running both LLaMA-7B and whisper.cpp on a single M1 Pro MacBook</summary>
 
-- [Lucy's Labyrinth](https://github.com/MorganRO8/Lucys_Labyrinth) - A simple maze game where agents controlled by an AI model will try to trick you.
+And here is another demo of running both LLaMA-7B and [whisper.cpp](https://github.com/ggerganov/whisper.cpp) on a single M1 Pro MacBook:
+
+https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8b4f-add84093ffff.mp4
 
 </details>
 
+## Usage
+
+Here are the end-to-end binary build and model conversion steps for most supported models.
+
+### Basic usage
+
+Firstly, you need to get the binary. There are different methods that you can follow:
+- Method 1: Clone this repository and build locally, see [how to build](./docs/build.md)
+- Method 2: If you are using MacOS or Linux, you can install llama.cpp via [brew, flox or nix](./docs/install.md)
+- Method 3: Use a Docker image, see [documentation for Docker](./docs/docker.md)
+- Method 4: Download pre-built binary from [releases](https://github.com/ggerganov/llama.cpp/releases)
+
+You can run a basic completion using this command:
+
+```bash
+llama-cli -m your_model.gguf -p "I believe the meaning of life is" -n 128
+
+# Output:
+# I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey.
+```
+
+See [this page](./examples/main/README.md) for a full list of parameters.
+
+### Conversation mode
+
+If you want a more ChatGPT-like experience, you can run in conversation mode by passing `-cnv` as a parameter:
+
+```bash
+llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv
+
+# Output:
+# > hi, who are you?
+# Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today?
+#
+# > what is 1+1?
+# Easy peasy! The answer to 1+1 is... 2!
+```
+
+By default, the chat template will be taken from the input model. If you want to use another chat template, pass `--chat-template NAME` as a parameter. See the list of [supported templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
+
+```bash
+./llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv --chat-template chatml
+```
+
+You can also use your own template via in-prefix, in-suffix and reverse-prompt parameters:
+
+```bash
+./llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv --in-prefix 'User: ' --reverse-prompt 'User:'
+```
+
+### Web server
+
+[llama.cpp web server](./examples/server/README.md) is a lightweight [OpenAI API](https://github.com/openai/openai-openapi) compatible HTTP server that can be used to serve local models and easily connect them to existing clients.
+
+Example usage:
+
+```bash
+./llama-server -m your_model.gguf --port 8080
+
+# Basic web UI can be accessed via browser: http://localhost:8080
+# Chat completion endpoint: http://localhost:8080/v1/chat/completions
+```
+
+### Interactive mode
+
+> [!NOTE]
+> If you prefer basic usage, please consider using conversation mode instead of interactive mode
+
+In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMA emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`.
+
+Here is an example of a few-shot interaction, invoked with the command
+
+```bash
+# default arguments using a 7B model
+./examples/chat.sh
+
+# advanced chat with a 13B model
+./examples/chat-13B.sh
+
+# custom arguments using a 13B model
+./llama-cli -m ./models/13B/ggml-model-q4_0.gguf -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt
+```
+
+Note the use of `--color` to distinguish between user input and generated text. Other parameters are explained in more detail in the [README](examples/main/README.md) for the `llama-cli` example program.
+
+![image](https://user-images.githubusercontent.com/1991296/224575029-2af3c7dc-5a65-4f64-a6bb-517a532aea38.png)
+
+### Persistent Interaction
+
+The prompt, user inputs, and model generations can be saved and resumed across calls to `./llama-cli` by leveraging `--prompt-cache` and `--prompt-cache-all`. The `./examples/chat-persistent.sh` script demonstrates this with support for long-running, resumable chat sessions. To use this example, you must provide a file to cache the initial chat prompt and a directory to save the chat session, and may optionally provide the same variables as `chat-13B.sh`. The same prompt cache can be reused for new chat sessions. Note that both prompt cache and chat directory are tied to the initial prompt (`PROMPT_TEMPLATE`) and the model file.
+
+```bash
+# Start a new chat
+PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/default ./examples/chat-persistent.sh
+
+# Resume that chat
+PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/default ./examples/chat-persistent.sh
+
+# Start a different chat with the same prompt/model
+PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/another ./examples/chat-persistent.sh
+
+# Different prompt cache for different prompt/model
+PROMPT_TEMPLATE=./prompts/chat-with-bob.txt PROMPT_CACHE_FILE=bob.prompt.bin \
+    CHAT_SAVE_DIR=./chat/bob ./examples/chat-persistent.sh
+```
+
+### Constrained output with grammars
+
+`llama.cpp` supports grammars to constrain model output. For example, you can force the model to output JSON only:
+
+```bash
+./llama-cli -m ./models/13B/ggml-model-q4_0.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:'
+```
+
+The `grammars/` folder contains a handful of sample grammars. To write your own, check out the [GBNF Guide](./grammars/README.md).
+
+For authoring more complex JSON grammars, you can also check out https://grammar.intrinsiclabs.ai/, a browser app that lets you write TypeScript interfaces which it compiles to GBNF grammars that you can save for local use. Note that the app is built and maintained by members of the community, please file any issues or FRs on [its repo](http://github.com/intrinsiclabsai/gbnfgen) and not this one.
+
+## Build
+
+Please refer to [Build llama.cpp locally](./docs/build.md)
+
 ## Supported backends
 
 | Backend | Target devices |
 | --- | --- |
-| [Metal](docs/build.md#metal-build) | Apple Silicon |
-| [BLAS](docs/build.md#blas-build) | All |
-| [BLIS](docs/backend/BLIS.md) | All |
-| [SYCL](docs/backend/SYCL.md) | Intel and Nvidia GPU |
-| [MUSA](docs/build.md#musa) | Moore Threads MTT GPU |
-| [CUDA](docs/build.md#cuda) | Nvidia GPU |
-| [HIP](docs/build.md#hip) | AMD GPU |
-| [Vulkan](docs/build.md#vulkan) | GPU |
-| [CANN](docs/build.md#cann) | Ascend NPU |
+| [Metal](./docs/build.md#metal-build) | Apple Silicon |
+| [BLAS](./docs/build.md#blas-build) | All |
+| [BLIS](./docs/backend/BLIS.md) | All |
+| [SYCL](./docs/backend/SYCL.md) | Intel and Nvidia GPU |
+| [MUSA](./docs/build.md#musa) | Moore Threads MTT GPU |
+| [CUDA](./docs/build.md#cuda) | Nvidia GPU |
+| [hipBLAS](./docs/build.md#hipblas) | AMD GPU |
+| [Vulkan](./docs/build.md#vulkan) | GPU |
+| [CANN](./docs/build.md#cann) | Ascend NPU |
 
-## Building the project
+## Tools
 
-The main product of this project is the `llama` library. Its C-style interface can be found in [include/llama.h](include/llama.h).
-The project also includes many example programs and tools using the `llama` library. The examples range from simple, minimal code snippets to sophisticated sub-projects such as an OpenAI-compatible HTTP server. Possible methods for obtaining the binaries:
+### Prepare and Quantize
 
-- Clone this repository and build locally, see [how to build](docs/build.md)
-- On MacOS or Linux, install `llama.cpp` via [brew, flox or nix](docs/install.md)
-- Use a Docker image, see [documentation for Docker](docs/docker.md)
-- Download pre-built binaries from [releases](https://github.com/ggerganov/llama.cpp/releases)
+> [!NOTE]
+> You can use the [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space on Hugging Face to quantise your model weights without any setup too. It is synced from `llama.cpp` main every 6 hours.
 
-## Obtaining and quantizing models
+To obtain the official LLaMA 2 weights please see the <a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a> section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face.
 
-The [Hugging Face](https://huggingface.co) platform hosts a [number of LLMs](https://huggingface.co/models?library=gguf&sort=trending) compatible with `llama.cpp`:
+Note: `convert.py` has been moved to `examples/convert_legacy_llama.py` and shouldn't be used for anything other than `Llama/Llama2/Mistral` models and their derivatives.
+It does not support LLaMA 3, you can use `convert_hf_to_gguf.py` with LLaMA 3 downloaded from Hugging Face.
 
-- [Trending](https://huggingface.co/models?library=gguf&sort=trending)
-- [LLaMA](https://huggingface.co/models?sort=trending&search=llama+gguf)
+To learn more about quantizing model, [read this documentation](./examples/quantize/README.md)
 
-You can either manually download the GGUF file or directly use any `llama.cpp`-compatible models from Hugging Face by using this CLI argument: `-hf <user>/<model>[:quant]`
+### Perplexity (measuring model quality)
 
-After downloading a model, use the CLI tools to run it locally - see below.
-
-`llama.cpp` requires the model to be stored in the [GGUF](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md) file format. Models in other data formats can be converted to GGUF using the `convert_*.py` Python scripts in this repo.
-
-The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with `llama.cpp`:
-
-- Use the [GGUF-my-repo space](https://huggingface.co/spaces/ggml-org/gguf-my-repo) to convert to GGUF format and quantize model weights to smaller sizes
-- Use the [GGUF-my-LoRA space](https://huggingface.co/spaces/ggml-org/gguf-my-lora) to convert LoRA adapters to GGUF format (more info: https://github.com/ggerganov/llama.cpp/discussions/10123)
-- Use the [GGUF-editor space](https://huggingface.co/spaces/CISCai/gguf-editor) to edit GGUF meta data in the browser (more info: https://github.com/ggerganov/llama.cpp/discussions/9268)
-- Use the [Inference Endpoints](https://ui.endpoints.huggingface.co/) to directly host `llama.cpp` in the cloud (more info: https://github.com/ggerganov/llama.cpp/discussions/9669)
-
-To learn more about model quantization, [read this documentation](examples/quantize/README.md)
-
-## [`llama-cli`](examples/main)
-
-#### A CLI tool for accessing and experimenting with most of `llama.cpp`'s functionality.
-
-- <details open>
-    <summary>Run in conversation mode</summary>
-
-    Models with a built-in chat template will automatically activate conversation mode. If this doesn't occur, you can manually enable it by adding `-cnv` and specifying a suitable chat template with `--chat-template NAME`
-
-    ```bash
-    llama-cli -m model.gguf
-
-    # > hi, who are you?
-    # Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today?
-    #
-    # > what is 1+1?
-    # Easy peasy! The answer to 1+1 is... 2!
-    ```
-
-    </details>
-
-- <details>
-    <summary>Run in conversation mode with custom chat template</summary>
-
-    ```bash
-    # use the "chatml" template (use -h to see the list of supported templates)
-    llama-cli -m model.gguf -cnv --chat-template chatml
-
-    # use a custom template
-    llama-cli -m model.gguf -cnv --in-prefix 'User: ' --reverse-prompt 'User:'
-    ```
-
-    </details>
-
-- <details>
-    <summary>Run simple text completion</summary>
-
-    To disable conversation mode explicitly, use `-no-cnv`
-
-    ```bash
-    llama-cli -m model.gguf -p "I believe the meaning of life is" -n 128 -no-cnv
-
-    # I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey.
-    ```
-
-    </details>
-
-- <details>
-    <summary>Constrain the output with a custom grammar</summary>
-
-    ```bash
-    llama-cli -m model.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:'
-
-    # {"appointmentTime": "8pm", "appointmentDetails": "schedule a a call"}
-    ```
-
-    The [grammars/](grammars/) folder contains a handful of sample grammars. To write your own, check out the [GBNF Guide](grammars/README.md).
-
-    For authoring more complex JSON grammars, check out https://grammar.intrinsiclabs.ai/
-
-    </details>
-
-
-## [`llama-server`](examples/server)
-
-#### A lightweight, [OpenAI API](https://github.com/openai/openai-openapi) compatible, HTTP server for serving LLMs.
-
-- <details open>
-    <summary>Start a local HTTP server with default configuration on port 8080</summary>
-
-    ```bash
-    llama-server -m model.gguf --port 8080
-
-    # Basic web UI can be accessed via browser: http://localhost:8080
-    # Chat completion endpoint: http://localhost:8080/v1/chat/completions
-    ```
-
-    </details>
-
-- <details>
-    <summary>Support multiple-users and parallel decoding</summary>
-
-    ```bash
-    # up to 4 concurrent requests, each with 4096 max context
-    llama-server -m model.gguf -c 16384 -np 4
-    ```
-
-    </details>
-
-- <details>
-    <summary>Enable speculative decoding</summary>
-
-    ```bash
-    # the draft.gguf model should be a small variant of the target model.gguf
-    llama-server -m model.gguf -md draft.gguf
-    ```
-
-    </details>
-
-- <details>
-    <summary>Serve an embedding model</summary>
-
-    ```bash
-    # use the /embedding endpoint
-    llama-server -m model.gguf --embedding --pooling cls -ub 8192
-    ```
-
-    </details>
-
-- <details>
-    <summary>Serve a reranking model</summary>
-
-    ```bash
-    # use the /reranking endpoint
-    llama-server -m model.gguf --reranking
-    ```
-
-    </details>
-
-- <details>
-    <summary>Constrain all outputs with a grammar</summary>
-
-    ```bash
-    # custom grammar
-    llama-server -m model.gguf --grammar-file grammar.gbnf
-
-    # JSON
-    llama-server -m model.gguf --grammar-file grammars/json.gbnf
-    ```
-
-    </details>
-
-
-## [`llama-perplexity`](examples/perplexity)
-
-#### A tool for measuring the perplexity [^1][^2] (and other quality metrics) of a model over a given text.
-
-- <details open>
-    <summary>Measure the perplexity over a text file</summary>
-
-    ```bash
-    llama-perplexity -m model.gguf -f file.txt
-
-    # [1]15.2701,[2]5.4007,[3]5.3073,[4]6.2965,[5]5.8940,[6]5.6096,[7]5.7942,[8]4.9297, ...
-    # Final estimate: PPL = 5.4007 +/- 0.67339
-    ```
-
-    </details>
-
-- <details>
-    <summary>Measure KL divergence</summary>
-
-    ```bash
-    # TODO
-    ```
-
-    </details>
-
-[^1]: [examples/perplexity/README.md](./examples/perplexity/README.md)
-[^2]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity)
-
-## [`llama-bench`](examples/llama-bench)
-
-#### Benchmark the performance of the inference for various parameters.
-
-- <details open>
-    <summary>Run default benchmark</summary>
-
-    ```bash
-    llama-bench -m model.gguf
-
-    # Output:
-    # | model               |       size |     params | backend    | threads |          test |                  t/s |
-    # | ------------------- | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |
-    # | qwen2 1.5B Q4_0     | 885.97 MiB |     1.54 B | Metal,BLAS |      16 |         pp512 |      5765.41 ± 20.55 |
-    # | qwen2 1.5B Q4_0     | 885.97 MiB |     1.54 B | Metal,BLAS |      16 |         tg128 |        197.71 ± 0.81 |
-    #
-    # build: 3e0ba0e60 (4229)
-    ```
-
-    </details>
-
-## [`llama-run`](examples/run)
-
-#### A comprehensive example for running `llama.cpp` models. Useful for inferencing. Used with RamaLama [^3].
-
-- <details>
-    <summary>Run a model with a specific prompt (by default it's pulled from Ollama registry)</summary>
-
-    ```bash
-    llama-run granite-code
-    ```
-
-    </details>
-
-[^3]: [RamaLama](https://github.com/containers/ramalama)
-
-## [`llama-simple`](examples/simple)
-
-#### A minimal example for implementing apps with `llama.cpp`. Useful for developers.
-
-- <details>
-    <summary>Basic text completion</summary>
-
-    ```bash
-    llama-simple -m model.gguf
-
-    # Hello my name is Kaitlyn and I am a 16 year old girl. I am a junior in high school and I am currently taking a class called "The Art of
-    ```
-
-    </details>
+You can use the `perplexity` example to measure perplexity over a given prompt (lower perplexity is better).
+For more information, see [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity).
 
+To learn more how to measure perplexity using llama.cpp, [read this documentation](./examples/perplexity/README.md)
 
 ## Contributing
 
@@ -494,19 +461,20 @@ To learn more about model quantization, [read this documentation](examples/quant
 
 ## Other documentation
 
-- [main (cli)](examples/main/README.md)
-- [server](examples/server/README.md)
-- [GBNF grammars](grammars/README.md)
+- [main (cli)](./examples/main/README.md)
+- [server](./examples/server/README.md)
+- [jeopardy](./examples/jeopardy/README.md)
+- [GBNF grammars](./grammars/README.md)
 
-#### Development documentation
+**Development documentation**
 
-- [How to build](docs/build.md)
-- [Running on Docker](docs/docker.md)
-- [Build on Android](docs/android.md)
-- [Performance troubleshooting](docs/development/token_generation_performance_tips.md)
+- [How to build](./docs/build.md)
+- [Running on Docker](./docs/docker.md)
+- [Build on Android](./docs/android.md)
+- [Performance troubleshooting](./docs/development/token_generation_performance_tips.md)
 - [GGML tips & tricks](https://github.com/ggerganov/llama.cpp/wiki/GGML-Tips-&-Tricks)
 
-#### Seminal papers and background on the models
+**Seminal papers and background on the models**
 
 If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:
 - LLaMA:
@@ -517,6 +485,3 @@ If your issue is with model generation quality, then please at least scan the fo
 - GPT-3.5 / InstructGPT / ChatGPT:
     - [Aligning language models to follow instructions](https://openai.com/research/instruction-following)
     - [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155)
-
-#### References
-

Sources/llama/llama.h

@@ -1,4 +0,0 @@
-#pragma once
-
-#include <llama.h>
-

Sources/llama/module.modulemap

@@ -1,5 +0,0 @@
-module llama [system] {
-    header "llama.h"
-    link "llama"
-    export *
-}

ci/run.sh

@@ -326,17 +326,17 @@ function gg_run_open_llama_7b_v2 {
     ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
     ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
 
-    (time ./bin/llama-cli -no-cnv --model ${model_f16}  -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+    (time ./bin/llama-cli --model ${model_f16}  -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
     (time ./bin/llama-perplexity --model ${model_f16}  -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
     (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
@@ -460,17 +460,17 @@ function gg_run_pythia_1_4b {
     ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
     ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
 
-    (time ./bin/llama-cli -no-cnv --model ${model_f16}  -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q8_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q2_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q3_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q6_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+    (time ./bin/llama-cli --model ${model_f16}  -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/llama-cli --model ${model_q8_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/llama-cli --model ${model_q4_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/llama-cli --model ${model_q4_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/llama-cli --model ${model_q5_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/llama-cli --model ${model_q5_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/llama-cli --model ${model_q2_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/llama-cli --model ${model_q3_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/llama-cli --model ${model_q4_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/llama-cli --model ${model_q5_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/llama-cli --model ${model_q6_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
     (time ./bin/llama-perplexity --model ${model_f16}  -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
     (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
@@ -591,17 +591,17 @@ function gg_run_pythia_2_8b {
     ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
     ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
 
-    (time ./bin/llama-cli -no-cnv --model ${model_f16}  -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+    (time ./bin/llama-cli --model ${model_f16}  -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
     (time ./bin/llama-perplexity --model ${model_f16}  -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
     (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
@@ -815,10 +815,7 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then
     ln -sfn ${mnt_models} ${SRC}/models-mnt
 
     # Create a fresh python3 venv and enter it
-    if ! python3 -m venv "$MNT/venv"; then
-        echo "Error: Failed to create Python virtual environment at $MNT/venv."
-        exit 1
-    fi
+    python3 -m venv "$MNT/venv"
     source "$MNT/venv/bin/activate"
 
     pip install -r ${SRC}/requirements.txt --disable-pip-version-check

cmake/build-info.cmake

@@ -44,7 +44,7 @@ if(MSVC)
     set(BUILD_TARGET ${CMAKE_VS_PLATFORM_NAME})
 else()
     execute_process(
-        COMMAND sh -c "\"$@\" --version | head -1" _ ${CMAKE_C_COMPILER}
+        COMMAND sh -c "$@ --version | head -1" _ ${CMAKE_C_COMPILER}
         OUTPUT_VARIABLE OUT
         OUTPUT_STRIP_TRAILING_WHITESPACE
     )

cmake/common.cmake

@@ -1,33 +0,0 @@
-function(llama_add_compile_flags)
-    if (LLAMA_FATAL_WARNINGS)
-        if (CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
-            list(APPEND C_FLAGS   -Werror)
-            list(APPEND CXX_FLAGS -Werror)
-        elseif (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
-            add_compile_options(/WX)
-        endif()
-    endif()
-
-    if (LLAMA_ALL_WARNINGS)
-        if (NOT MSVC)
-            list(APPEND C_FLAGS -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes
-                                -Werror=implicit-int -Werror=implicit-function-declaration)
-
-            list(APPEND CXX_FLAGS -Wmissing-declarations -Wmissing-noreturn)
-
-            list(APPEND WARNING_FLAGS -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function)
-
-            list(APPEND C_FLAGS   ${WARNING_FLAGS})
-            list(APPEND CXX_FLAGS ${WARNING_FLAGS})
-
-            ggml_get_flags(${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION})
-
-            add_compile_options("$<$<COMPILE_LANGUAGE:C>:${C_FLAGS};${GF_C_FLAGS}>"
-                                "$<$<COMPILE_LANGUAGE:CXX>:${CXX_FLAGS};${GF_CXX_FLAGS}>")
-        else()
-            # todo : msvc
-            set(C_FLAGS   "" PARENT_SCOPE)
-            set(CXX_FLAGS "" PARENT_SCOPE)
-        endif()
-    endif()
-endfunction()

cmake/llama-config.cmake.in

@@ -3,13 +3,159 @@ set(LLAMA_BUILD_COMMIT @LLAMA_BUILD_COMMIT@)
 set(LLAMA_BUILD_NUMBER @LLAMA_BUILD_NUMBER@)
 set(LLAMA_SHARED_LIB   @BUILD_SHARED_LIBS@)
 
+set(GGML_STATIC @GGML_STATIC@)
+set(GGML_NATIVE @GGML_NATIVE@)
+set(GGML_LTO    @GGML_LTO@)
+set(GGML_CCACHE @GGML_CCACHE@)
+set(GGML_AVX    @GGML_AVX@)
+set(GGML_AVX2   @GGML_AVX2@)
+set(GGML_AVX512 @GGML_AVX512@)
+set(GGML_AVX512_VBMI @GGML_AVX512_VBMI@)
+set(GGML_AVX512_VNNI @GGML_AVX512_VNNI@)
+set(GGML_AVX512_BF16 @GGML_AVX512_BF16@)
+set(GGML_AMX_TILE @GGML_AMX_TILE@)
+set(GGML_AMX_INT8 @GGML_AMX_INT8@)
+set(GGML_AMX_BF16 @GGML_AMX_BF16@)
+set(GGML_FMA  @GGML_FMA@)
+set(GGML_LASX @GGML_LASX@)
+set(GGML_LSX  @GGML_LSX@)
+set(GGML_RVV  @GGML_RVV@)
+set(GGML_SVE  @GGML_SVE@)
+
+set(GGML_ACCELERATE @GGML_ACCELERATE@)
+set(GGML_OPENMP  @GGML_OPENMP@)
+set(GGML_CPU_HBM @GGML_CPU_HBM@)
+set(GGML_BLAS_VENDOR @GGML_BLAS_VENDOR@)
+
+set(GGML_CUDA_FORCE_MMQ    @GGML_CUDA_FORCE_MMQ@)
+set(GGML_CUDA_FORCE_CUBLAS @GGML_CUDA_FORCE_CUBLAS@)
+set(GGML_CUDA_F16          @GGML_CUDA_F16@)
+set(GGML_CUDA_PEER_MAX_BATCH_SIZE @GGML_CUDA_PEER_MAX_BATCH_SIZE@)
+set(GGML_CUDA_NO_PEER_COPY  @GGML_CUDA_NO_PEER_COPY@)
+set(GGML_CUDA_NO_VMM        @GGML_CUDA_NO_VMM@)
+set(GGML_CUDA_FA_ALL_QUANTS @GGML_CUDA_FA_ALL_QUANTS@)
+set(GGML_CUDA_GRAPHS        @GGML_CUDA_GRAPHS@)
+
+set(GGML_HIP_UMA @GGML_HIP_UMA@)
+
+set(GGML_VULKAN_CHECK_RESULTS @GGML_VULKAN_CHECK_RESULTS@)
+set(GGML_VULKAN_DEBUG         @GGML_VULKAN_DEBUG@)
+set(GGML_VULKAN_MEMORY_DEBUG  @GGML_VULKAN_MEMORY_DEBUG@)
+set(GGML_VULKAN_SHADER_DEBUG_INFO @GGML_VULKAN_SHADER_DEBUG_INFO@)
+set(GGML_VULKAN_PERF      @GGML_VULKAN_PERF@)
+set(GGML_VULKAN_VALIDATE  @GGML_VULKAN_VALIDATE@)
+set(GGML_VULKAN_RUN_TESTS @GGML_VULKAN_RUN_TESTS@)
+
+set(GGML_METAL_USE_BF16 @GGML_METAL_USE_BF16@)
+set(GGML_METAL_NDEBUG   @GGML_METAL_NDEBUG@)
+set(GGML_METAL_SHADER_DEBUG  @GGML_METAL_SHADER_DEBUG@)
+set(GGML_METAL_EMBED_LIBRARY @GGML_METAL_EMBED_LIBRARY@)
+set(GGML_METAL_MACOSX_VERSION_MIN @GGML_METAL_MACOSX_VERSION_MIN@)
+set(GGML_METAL_STD @GGML_METAL_STD@)
+
+set(GGML_SYCL_F16    @GGML_SYCL_F16@)
+set(GGML_SYCL_TARGET @GGML_SYCL_TARGET@)
+set(GGML_SYCL_DEVICE_ARCH @GGML_SYCL_DEVICE_ARCH@)
+
+
 @PACKAGE_INIT@
 
 set_and_check(LLAMA_INCLUDE_DIR "@PACKAGE_LLAMA_INCLUDE_INSTALL_DIR@")
 set_and_check(LLAMA_LIB_DIR     "@PACKAGE_LLAMA_LIB_INSTALL_DIR@")
 set_and_check(LLAMA_BIN_DIR     "@PACKAGE_LLAMA_BIN_INSTALL_DIR@")
 
-find_package(ggml REQUIRED HINTS ${LLAMA_LIB_DIR}/cmake)
+find_package(Threads REQUIRED)
+
+set(_llama_transient_defines "@GGML_TRANSIENT_DEFINES@")
+set(_llama_link_deps "")
+set(_llama_link_opts "")
+foreach(_ggml_lib ggml ggml-base)
+    string(REPLACE "-" "_" _ggml_lib_var "${_ggml_lib}_LIBRARY")
+    find_library(${_ggml_lib_var} ${_ggml_lib}
+        REQUIRED
+        HINTS ${LLAMA_LIB_DIR}
+        NO_CMAKE_FIND_ROOT_PATH
+    )
+    list(APPEND _llama_link_deps "${${_ggml_lib_var}}")
+    message(STATUS "Found ${${_ggml_lib_var}}")
+endforeach()
+
+foreach(backend amx blas cann cpu cuda hip kompute metal musa rpc sycl vulkan)
+    string(TOUPPER "GGML_${backend}" backend_id)
+    set(_ggml_lib "ggml-${backend}")
+    string(REPLACE "-" "_" _ggml_lib_var "${_ggml_lib}_LIBRARY")
+
+    find_library(${_ggml_lib_var} ${_ggml_lib}
+        HINTS ${LLAMA_LIB_DIR}
+        NO_CMAKE_FIND_ROOT_PATH
+    )
+    if(${_ggml_lib_var})
+        list(APPEND _llama_link_deps "${${_ggml_lib_var}}")
+        set(${backend_id} ON)
+        message(STATUS "Found backend ${${_ggml_lib_var}}")
+    else()
+        set(${backend_id} OFF)
+    endif()
+endforeach()
+
+if (NOT LLAMA_SHARED_LIB)
+    if (APPLE AND GGML_ACCELERATE)
+        find_library(ACCELERATE_FRAMEWORK Accelerate REQUIRED)
+        list(APPEND _llama_link_deps ${ACCELERATE_FRAMEWORK})
+    endif()
+
+    if (GGML_OPENMP)
+        find_package(OpenMP REQUIRED)
+        list(APPEND _llama_link_deps OpenMP::OpenMP_C OpenMP::OpenMP_CXX)
+    endif()
+
+    if (GGML_CPU_HBM)
+        find_library(memkind memkind REQUIRED)
+        list(APPEND _llama_link_deps memkind)
+    endif()
+
+    if (GGML_BLAS)
+        find_package(BLAS REQUIRED)
+        list(APPEND _llama_link_deps ${BLAS_LIBRARIES})
+        list(APPEND _llama_link_opts ${BLAS_LINKER_FLAGS})
+    endif()
+
+    if (GGML_CUDA)
+        find_package(CUDAToolkit REQUIRED)
+    endif()
+
+    if (GGML_METAL)
+        find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
+        find_library(METAL_FRAMEWORK    Metal REQUIRED)
+        find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
+        list(APPEND _llama_link_deps ${FOUNDATION_LIBRARY}
+                                     ${METAL_FRAMEWORK} ${METALKIT_FRAMEWORK})
+    endif()
+
+    if (GGML_VULKAN)
+        find_package(Vulkan REQUIRED)
+        list(APPEND _llama_link_deps Vulkan::Vulkan)
+    endif()
+
+    if (GGML_HIP)
+        find_package(hip     REQUIRED)
+        find_package(hipblas REQUIRED)
+        find_package(rocblas REQUIRED)
+        list(APPEND _llama_link_deps hip::host roc::rocblas roc::hipblas)
+    endif()
+
+    if (GGML_SYCL)
+        find_package(DNNL)
+        if (${DNNL_FOUND} AND GGML_SYCL_TARGET STREQUAL "INTEL")
+            list(APPEND _llama_link_deps DNNL::dnnl)
+        endif()
+        if (WIN32)
+            find_package(IntelSYCL REQUIRED)
+            find_package(MKL       REQUIRED)
+            list(APPEND _llama_link_deps IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL)
+        endif()
+    endif()
+endif()
 
 find_library(llama_LIBRARY llama
     REQUIRED
@@ -21,10 +167,12 @@ add_library(llama UNKNOWN IMPORTED)
 set_target_properties(llama
     PROPERTIES
         INTERFACE_INCLUDE_DIRECTORIES "${LLAMA_INCLUDE_DIR}"
-        INTERFACE_LINK_LIBRARIES "ggml::ggml;ggml::ggml-base;"
+        INTERFACE_LINK_LIBRARIES "${_llama_link_deps}"
+        INTERFACE_LINK_OPTIONS   "${_llama_link_opts}"
+        INTERFACE_COMPILE_DEFINITIONS "${_llama_transient_defines}"
         IMPORTED_LINK_INTERFACE_LANGUAGES "CXX"
         IMPORTED_LOCATION "${llama_LIBRARY}"
-        INTERFACE_COMPILE_FEATURES c_std_90
-        POSITION_INDEPENDENT_CODE ON)
+        INTERFACE_COMPILE_FEATURES cxx_std_11
+        POSITION_INDEPENDENT_CODE ON )
 
 check_required_components(Llama)

cmake/llama.pc.in

@@ -1,10 +1,10 @@
 prefix=@CMAKE_INSTALL_PREFIX@
-exec_prefix=@CMAKE_INSTALL_PREFIX@
-libdir=@CMAKE_INSTALL_FULL_LIBDIR@
-includedir=@CMAKE_INSTALL_FULL_INCLUDEDIR@
+exec_prefix=${prefix}
+libdir=${exec_prefix}/lib
+includedir=${prefix}/include
 
 Name: llama
 Description: Port of Facebook's LLaMA model in C/C++
-Version: @LLAMA_INSTALL_VERSION@
-Libs: -L${libdir} -lggml -lggml-base -lllama
+Version: @PROJECT_VERSION@
+Libs: -L${libdir} -lllama
 Cflags: -I${includedir}

cmake/x64-windows-llvm.cmake

@@ -1,11 +0,0 @@
-set( CMAKE_SYSTEM_NAME Windows )
-set( CMAKE_SYSTEM_PROCESSOR x86_64 )
-
-set( CMAKE_C_COMPILER    clang )
-set( CMAKE_CXX_COMPILER  clang++ )
-
-set( arch_c_flags "-march=native" )
-
-set( CMAKE_C_FLAGS_INIT   "${arch_c_flags}" )
-set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags}" )
-

common/CMakeLists.txt

@@ -2,8 +2,6 @@
 
 find_package(Threads REQUIRED)
 
-llama_add_compile_flags()
-
 # Build info header
 #
 
@@ -56,19 +54,14 @@ add_library(${TARGET} STATIC
     arg.cpp
     arg.h
     base64.hpp
-    chat.cpp
-    chat.hpp
-    chat-template.hpp
     common.cpp
     common.h
     console.cpp
     console.h
     json-schema-to-grammar.cpp
     json.hpp
-    llguidance.cpp
     log.cpp
     log.h
-    minja.hpp
     ngram-cache.cpp
     ngram-cache.h
     sampling.cpp
@@ -86,39 +79,12 @@ set(LLAMA_COMMON_EXTRA_LIBS build_info)
 # Use curl to download model url
 if (LLAMA_CURL)
     find_package(CURL REQUIRED)
-    target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_CURL)
+    add_definitions(-DLLAMA_USE_CURL)
     include_directories(${CURL_INCLUDE_DIRS})
     find_library(CURL_LIBRARY curl REQUIRED)
     set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARY})
 endif ()
 
-if (LLAMA_LLGUIDANCE)
-    include(ExternalProject)
-    set(LLGUIDANCE_SRC ${CMAKE_BINARY_DIR}/llguidance/source)
-    set(LLGUIDANCE_PATH ${LLGUIDANCE_SRC}/target/release)
-    ExternalProject_Add(llguidance_ext
-        GIT_REPOSITORY https://github.com/guidance-ai/llguidance
-        # v0.6.12:
-        GIT_TAG ced1c9023d47ec194fa977932d35ce65c2ebfc09
-        PREFIX ${CMAKE_BINARY_DIR}/llguidance
-        SOURCE_DIR ${LLGUIDANCE_SRC}
-        BUILD_IN_SOURCE TRUE
-        CONFIGURE_COMMAND ""
-        BUILD_COMMAND cargo build --release
-        INSTALL_COMMAND ""
-        BUILD_BYPRODUCTS ${LLGUIDANCE_PATH}/libllguidance.a ${LLGUIDANCE_PATH}/llguidance.h
-        UPDATE_COMMAND ""
-    )
-    target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_LLGUIDANCE)
-
-    add_library(llguidance STATIC IMPORTED)
-    set_target_properties(llguidance PROPERTIES IMPORTED_LOCATION ${LLGUIDANCE_PATH}/libllguidance.a)
-    add_dependencies(llguidance llguidance_ext)
-
-    target_include_directories(${TARGET} PRIVATE ${LLGUIDANCE_PATH})
-    set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} llguidance)
-endif ()
-
 target_include_directories(${TARGET} PUBLIC .)
-target_compile_features   (${TARGET} PUBLIC cxx_std_17)
+target_compile_features   (${TARGET} PUBLIC cxx_std_11)
 target_link_libraries     (${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads)

common/arg.cpp

@@ -22,11 +22,6 @@ common_arg & common_arg::set_examples(std::initializer_list<enum llama_example>
     return *this;
 }
 
-common_arg & common_arg::set_excludes(std::initializer_list<enum llama_example> excludes) {
-    this->excludes = std::move(excludes);
-    return *this;
-}
-
 common_arg & common_arg::set_env(const char * env) {
     help = help + "\n(env: " + env + ")";
     this->env = env;
@@ -42,10 +37,6 @@ bool common_arg::in_example(enum llama_example ex) {
     return examples.find(ex) != examples.end();
 }
 
-bool common_arg::is_exclude(enum llama_example ex) {
-    return excludes.find(ex) != excludes.end();
-}
-
 bool common_arg::get_value_from_env(std::string & output) {
     if (env == nullptr) return false;
     char * value = std::getenv(env);
@@ -128,75 +119,28 @@ std::string common_arg::to_string() {
 // utils
 //
 
-static void common_params_handle_model_default(
-        std::string & model,
-        const std::string & model_url,
-        std::string & hf_repo,
-        std::string & hf_file,
-        const std::string & hf_token,
-        const std::string & model_default) {
-    if (!hf_repo.empty()) {
+static void common_params_handle_model_default(common_params & params) {
+    if (!params.hf_repo.empty()) {
         // short-hand to avoid specifying --hf-file -> default it to --model
-        if (hf_file.empty()) {
-            if (model.empty()) {
-                auto auto_detected = common_get_hf_file(hf_repo, hf_token);
-                if (auto_detected.first.empty() || auto_detected.second.empty()) {
-                    exit(1); // built without CURL, error message already printed
+        if (params.hf_file.empty()) {
+            if (params.model.empty()) {
+                throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
             }
-                hf_repo = auto_detected.first;
-                hf_file = auto_detected.second;
-            } else {
-                hf_file = model;
+            params.hf_file = params.model;
+        } else if (params.model.empty()) {
+            params.model = fs_get_cache_file(string_split<std::string>(params.hf_file, '/').back());
         }
-        }
-        // make sure model path is present (for caching purposes)
-        if (model.empty()) {
-            // this is to avoid different repo having same file name, or same file name in different subdirs
-            std::string filename = hf_repo + "_" + hf_file;
-            // to make sure we don't have any slashes in the filename
-            string_replace_all(filename, "/", "_");
-            model = fs_get_cache_file(filename);
-        }
-    } else if (!model_url.empty()) {
-        if (model.empty()) {
-            auto f = string_split<std::string>(model_url, '#').front();
+    } else if (!params.model_url.empty()) {
+        if (params.model.empty()) {
+            auto f = string_split<std::string>(params.model_url, '#').front();
             f = string_split<std::string>(f, '?').front();
-            model = fs_get_cache_file(string_split<std::string>(f, '/').back());
+            params.model = fs_get_cache_file(string_split<std::string>(f, '/').back());
         }
-    } else if (model.empty()) {
-        model = model_default;
+    } else if (params.model.empty()) {
+        params.model = DEFAULT_MODEL_PATH;
     }
 }
 
-const std::vector<ggml_type> kv_cache_types = {
-    GGML_TYPE_F32,
-    GGML_TYPE_F16,
-    GGML_TYPE_BF16,
-    GGML_TYPE_Q8_0,
-    GGML_TYPE_Q4_0,
-    GGML_TYPE_Q4_1,
-    GGML_TYPE_IQ4_NL,
-    GGML_TYPE_Q5_0,
-    GGML_TYPE_Q5_1,
-};
-
-static ggml_type kv_cache_type_from_str(const std::string & s) {
-    for (const auto & type : kv_cache_types) {
-        if (ggml_type_name(type) == s) {
-            return type;
-        }
-    }
-    throw std::runtime_error("Unsupported cache type: " + s);
-}
-
-static std::string get_all_kv_cache_types() {
-    std::ostringstream msg;
-    for (const auto & type : kv_cache_types) {
-        msg << ggml_type_name(type) << (&type == &kv_cache_types.back() ? "" : ", ");
-    }
-    return msg.str();
-}
-
 //
 // CLI argument parsing functions
 //
@@ -299,10 +243,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
     }
 
-    // TODO: refactor model params in a common struct
-    common_params_handle_model_default(params.model,             params.model_url,             params.hf_repo,             params.hf_file,             params.hf_token, DEFAULT_MODEL_PATH);
-    common_params_handle_model_default(params.speculative.model, params.speculative.model_url, params.speculative.hf_repo, params.speculative.hf_file, params.hf_token, "");
-    common_params_handle_model_default(params.vocoder.model,     params.vocoder.model_url,     params.vocoder.hf_repo,     params.vocoder.hf_file,     params.hf_token, "");
+    common_params_handle_model_default(params);
 
     if (params.escape) {
         string_process_escapes(params.prompt);
@@ -325,14 +266,6 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         throw std::invalid_argument("error: either --embedding or --reranking can be specified, but not both");
     }
 
-    if (!params.chat_template.empty() && !common_chat_verify_template(params.chat_template, params.use_jinja)) {
-        throw std::runtime_error(string_format(
-            "error: the supplied chat template is not supported: %s%s\n",
-            params.chat_template.c_str(),
-            params.use_jinja ? "" : "\nnote: llama.cpp was started without --jinja, we only support commonly used templates"
-        ));
-    }
-
     return true;
 }
 
@@ -386,30 +319,6 @@ static std::vector<ggml_backend_dev_t> parse_device_list(const std::string & val
     return devices;
 }
 
-static void add_rpc_devices(std::string servers) {
-    auto rpc_servers = string_split<std::string>(servers, ',');
-    if (rpc_servers.empty()) {
-        throw std::invalid_argument("no RPC servers specified");
-    }
-    ggml_backend_reg_t rpc_reg = ggml_backend_reg_by_name("RPC");
-    if (!rpc_reg) {
-        throw std::invalid_argument("failed to find RPC backend");
-    }
-    typedef ggml_backend_dev_t (*ggml_backend_rpc_add_device_t)(const char * endpoint);
-    ggml_backend_rpc_add_device_t ggml_backend_rpc_add_device_fn = (ggml_backend_rpc_add_device_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_device");
-    if (!ggml_backend_rpc_add_device_fn) {
-        throw std::invalid_argument("failed to find RPC device add function");
-    }
-    for (const auto & server : rpc_servers) {
-        ggml_backend_dev_t dev = ggml_backend_rpc_add_device_fn(server.c_str());
-        if (dev) {
-            ggml_backend_device_register(dev);
-        } else {
-            throw std::invalid_argument("failed to register RPC device");
-        }
-    }
-}
-
 bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
     auto ctx_arg = common_params_parser_init(params, ex, print_usage);
     const common_params params_org = ctx_arg.params; // the example can modify the default params
@@ -435,18 +344,6 @@ bool common_params_parse(int argc, char ** argv, common_params & params, llama_e
     return true;
 }
 
-static std::string list_builtin_chat_templates() {
-    std::vector<const char *> supported_tmpl;
-    int32_t res = llama_chat_builtin_templates(nullptr, 0);
-    supported_tmpl.resize(res);
-    res = llama_chat_builtin_templates(supported_tmpl.data(), supported_tmpl.size());
-    std::ostringstream msg;
-    for (auto & tmpl : supported_tmpl) {
-        msg << tmpl << (&tmpl == &supported_tmpl.back() ? "" : ", ");
-    }
-    return msg.str();
-}
-
 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
     // load dynamic backends
     ggml_backend_load_all();
@@ -472,7 +369,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
      * - if both {LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_*,} are set, we will prioritize the LLAMA_EXAMPLE_* matching current example
      */
     auto add_opt = [&](common_arg arg) {
-        if ((arg.in_example(ex) || arg.in_example(LLAMA_EXAMPLE_COMMON)) && !arg.is_exclude(ex)) {
+        if (arg.in_example(ex) || arg.in_example(LLAMA_EXAMPLE_COMMON)) {
             ctx_arg.options.push_back(std::move(arg));
         }
     };
@@ -678,7 +575,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.ctx_shift = false;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT"));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT"));
     add_opt(common_arg(
         {"--chunks"}, "N",
         string_format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks),
@@ -701,7 +598,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.prompt = value;
         }
-    ).set_excludes({LLAMA_EXAMPLE_SERVER}));
+    ));
     add_opt(common_arg(
         {"--no-perf"},
         string_format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"),
@@ -725,7 +622,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                 params.prompt.pop_back();
             }
         }
-    ).set_excludes({LLAMA_EXAMPLE_SERVER}));
+    ));
     add_opt(common_arg(
         {"--in-file"}, "FNAME",
         "an input file (repeat to specify multiple files)",
@@ -752,7 +649,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.prompt = ss.str();
             fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), value.c_str());
         }
-    ).set_excludes({LLAMA_EXAMPLE_SERVER}));
+    ));
     add_opt(common_arg(
         {"-e", "--escape"},
         string_format("process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"),
@@ -811,19 +708,15 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-cnv", "--conversation"},
+        string_format(
             "run in conversation mode:\n"
             "- does not print special tokens and suffix/prefix\n"
             "- interactive mode is also enabled\n"
-        "(default: auto enabled if chat template is available)",
+            "(default: %s)",
+            params.conversation ? "true" : "false"
+        ),
         [](common_params & params) {
-            params.conversation_mode = COMMON_CONVERSATION_MODE_ENABLED;
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
-    add_opt(common_arg(
-        {"-no-cnv", "--no-conversation"},
-        "force disable conversation mode (default: false)",
-        [](common_params & params) {
-            params.conversation_mode = COMMON_CONVERSATION_MODE_DISABLED;
+            params.conversation = true;
         }
     ).set_examples({LLAMA_EXAMPLE_MAIN}));
     add_opt(common_arg(
@@ -877,7 +770,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.warmup = false;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_EMBEDDING}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
     add_opt(common_arg(
         {"--spm-infill"},
         string_format(
@@ -904,7 +797,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_sparam());
     add_opt(common_arg(
-        {"--sampling-seq", "--sampler-seq"}, "SEQUENCE",
+        {"--sampling-seq"}, "SEQUENCE",
         string_format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()),
         [](common_params & params, const std::string & value) {
             params.sampling.samplers = common_sampler_types_from_chars(value);
@@ -917,6 +810,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.ignore_eos = true;
         }
     ).set_sparam());
+    add_opt(common_arg(
+        {"--penalize-nl"},
+        string_format("penalize newline tokens (default: %s)", params.sampling.penalize_nl ? "true" : "false"),
+        [](common_params & params) {
+            params.sampling.penalize_nl = true;
+        }
+    ).set_sparam());
     add_opt(common_arg(
         {"--temp"}, "N",
         string_format("temperature (default: %.1f)", (double)params.sampling.temp),
@@ -971,9 +871,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--repeat-last-n"}, "N",
         string_format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sampling.penalty_last_n),
         [](common_params & params, int value) {
-            if (value < -1) {
-                throw std::runtime_error(string_format("error: invalid repeat-last-n = %d\n", value));
-            }
             params.sampling.penalty_last_n = value;
             params.sampling.n_prev = std::max(params.sampling.n_prev, params.sampling.penalty_last_n);
         }
@@ -1028,9 +925,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--dry-penalty-last-n"}, "N",
         string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sampling.dry_penalty_last_n),
         [](common_params & params, int value) {
-            if (value < -1) {
-                throw std::runtime_error(string_format("error: invalid dry-penalty-last-n = %d\n", value));
-            }
             params.sampling.dry_penalty_last_n = value;
         }
     ).set_sparam());
@@ -1264,28 +1158,18 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_env("LLAMA_ARG_NO_KV_OFFLOAD"));
     add_opt(common_arg(
         {"-ctk", "--cache-type-k"}, "TYPE",
-        string_format(
-            "KV cache data type for K\n"
-            "allowed values: %s\n"
-            "(default: %s)",
-            get_all_kv_cache_types().c_str(),
-            ggml_type_name(params.cache_type_k)
-        ),
+        string_format("KV cache data type for K (default: %s)", params.cache_type_k.c_str()),
         [](common_params & params, const std::string & value) {
-            params.cache_type_k = kv_cache_type_from_str(value);
+            // TODO: get the type right here
+            params.cache_type_k = value;
         }
     ).set_env("LLAMA_ARG_CACHE_TYPE_K"));
     add_opt(common_arg(
         {"-ctv", "--cache-type-v"}, "TYPE",
-        string_format(
-            "KV cache data type for V\n"
-            "allowed values: %s\n"
-            "(default: %s)",
-            get_all_kv_cache_types().c_str(),
-            ggml_type_name(params.cache_type_v)
-        ),
+        string_format("KV cache data type for V (default: %s)", params.cache_type_v.c_str()),
         [](common_params & params, const std::string & value) {
-            params.cache_type_v = kv_cache_type_from_str(value);
+            // TODO: get the type right here
+            params.cache_type_v = value;
         }
     ).set_env("LLAMA_ARG_CACHE_TYPE_V"));
     add_opt(common_arg(
@@ -1419,8 +1303,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             {"--rpc"}, "SERVERS",
             "comma separated list of RPC servers",
             [](common_params & params, const std::string & value) {
-                add_rpc_devices(value);
-                GGML_UNUSED(params);
+                params.rpc_servers = value;
             }
         ).set_env("LLAMA_ARG_RPC"));
     }
@@ -1465,28 +1348,15 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--list-devices"},
         "print list of available devices and exit",
         [](common_params &) {
-            std::vector<ggml_backend_dev_t> rpc_devices;
-            std::vector<ggml_backend_dev_t> all_devices;
+            printf("Available devices:\n");
             for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
                 auto * dev = ggml_backend_dev_get(i);
                 if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_GPU) {
-                    ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
-                    if (ggml_backend_reg_name(reg) == std::string("RPC")) {
-                        rpc_devices.push_back(dev);
-                    } else {
-                        all_devices.push_back(dev);
-                    }
-                }
-            }
-            // insert RPC devices in front
-            all_devices.insert(all_devices.begin(), rpc_devices.begin(), rpc_devices.end());
-            printf("Available devices:\n");
-            for (size_t i = 0; i < all_devices.size(); ++i) {
-                auto * dev = all_devices[i];
                     size_t free, total;
                     ggml_backend_dev_memory(dev, &free, &total);
                     printf("  %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024);
                 }
+            }
             exit(0);
         }
     ));
@@ -1496,9 +1366,8 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, int value) {
             params.n_gpu_layers = value;
             if (!llama_supports_gpu_offload()) {
-                fprintf(stderr, "warning: no usable GPU found, --gpu-layers option will be ignored\n");
-                fprintf(stderr, "warning: one possible reason is that llama.cpp was compiled without GPU support\n");
-                fprintf(stderr, "warning: consult docs/build.md for compilation instructions\n");
+                fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers option will be ignored\n");
+                fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
             }
         }
     ).set_env("LLAMA_ARG_N_GPU_LAYERS"));
@@ -1582,7 +1451,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--lora"}, "FNAME",
         "path to LoRA adapter (can be repeated to use multiple adapters)",
         [](common_params & params, const std::string & value) {
-            params.lora_adapters.push_back({ std::string(value), 1.0, nullptr });
+            params.lora_adapters.push_back({ std::string(value), 1.0 });
         }
         // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg
     ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
@@ -1590,7 +1459,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--lora-scaled"}, "FNAME", "SCALE",
         "path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters)",
         [](common_params & params, const std::string & fname, const std::string & scale) {
-            params.lora_adapters.push_back({ fname, std::stof(scale), nullptr });
+            params.lora_adapters.push_back({ fname, std::stof(scale) });
         }
         // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg
     ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
@@ -1644,42 +1513,19 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_env("LLAMA_ARG_MODEL_URL"));
     add_opt(common_arg(
-        {"-hf", "-hfr", "--hf-repo"}, "<user>/<model>[:quant]",
-        "Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.\n"
-        "example: unsloth/phi-4-GGUF:q4_k_m\n"
-        "(default: unused)",
+        {"-hfr", "--hf-repo"}, "REPO",
+        "Hugging Face model repository (default: unused)",
         [](common_params & params, const std::string & value) {
             params.hf_repo = value;
         }
     ).set_env("LLAMA_ARG_HF_REPO"));
-    add_opt(common_arg(
-        {"-hfd", "-hfrd", "--hf-repo-draft"}, "<user>/<model>[:quant]",
-        "Same as --hf-repo, but for the draft model (default: unused)",
-        [](common_params & params, const std::string & value) {
-            params.speculative.hf_repo = value;
-        }
-    ).set_env("LLAMA_ARG_HFD_REPO"));
     add_opt(common_arg(
         {"-hff", "--hf-file"}, "FILE",
-        "Hugging Face model file. If specified, it will override the quant in --hf-repo (default: unused)",
+        "Hugging Face model file (default: unused)",
         [](common_params & params, const std::string & value) {
             params.hf_file = value;
         }
     ).set_env("LLAMA_ARG_HF_FILE"));
-    add_opt(common_arg(
-        {"-hfv", "-hfrv", "--hf-repo-v"}, "<user>/<model>[:quant]",
-        "Hugging Face model repository for the vocoder model (default: unused)",
-        [](common_params & params, const std::string & value) {
-            params.vocoder.hf_repo = value;
-        }
-    ).set_env("LLAMA_ARG_HF_REPO_V"));
-    add_opt(common_arg(
-        {"-hffv", "--hf-file-v"}, "FILE",
-        "Hugging Face model file for the vocoder model (default: unused)",
-        [](common_params & params, const std::string & value) {
-            params.vocoder.hf_file = value;
-        }
-    ).set_env("LLAMA_ARG_HF_FILE_V"));
     add_opt(common_arg(
         {"-hft", "--hf-token"}, "TOKEN",
         "Hugging Face access token (default: value from HF_TOKEN environment variable)",
@@ -1848,13 +1694,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.public_path = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_STATIC_PATH"));
-    add_opt(common_arg(
-        {"--no-webui"},
-        string_format("Disable the Web UI (default: %s)", params.webui ? "enabled" : "disabled"),
-        [](common_params & params) {
-            params.webui = false;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_WEBUI"));
     add_opt(common_arg(
         {"--embedding", "--embeddings"},
         string_format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"),
@@ -1968,44 +1807,22 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}));
-    add_opt(common_arg(
-        {"--jinja"},
-        "use jinja template for chat (default: disabled)",
-        [](common_params & params) {
-            params.use_jinja = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_JINJA"));
     add_opt(common_arg(
         {"--chat-template"}, "JINJA_TEMPLATE",
-        string_format(
         "set custom jinja chat template (default: template taken from model's metadata)\n"
         "if suffix/prefix are specified, template will be disabled\n"
-            "only commonly used templates are accepted (unless --jinja is set before this flag):\n"
-            "list of built-in templates:\n%s", list_builtin_chat_templates().c_str()
-        ),
+        "only commonly used templates are accepted:\nhttps://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template",
         [](common_params & params, const std::string & value) {
+            if (!common_chat_verify_template(value)) {
+                throw std::runtime_error(string_format(
+                    "error: the supplied chat template is not supported: %s\n"
+                    "note: llama.cpp does not use jinja parser, we only support commonly used templates\n",
+                    value.c_str()
+                ));
+            }
             params.chat_template = value;
         }
     ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE"));
-    add_opt(common_arg(
-        {"--chat-template-file"}, "JINJA_TEMPLATE_FILE",
-        string_format(
-            "set custom jinja chat template file (default: template taken from model's metadata)\n"
-            "if suffix/prefix are specified, template will be disabled\n"
-            "only commonly used templates are accepted (unless --jinja is set before this flag):\n"
-            "list of built-in templates:\n%s", list_builtin_chat_templates().c_str()
-        ),
-        [](common_params & params, const std::string & value) {
-            std::ifstream file(value);
-            if (!file) {
-                throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
-            }
-            std::copy(
-                std::istreambuf_iterator<char>(file),
-                std::istreambuf_iterator<char>(),
-                std::back_inserter(params.chat_template));
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE"));
     add_opt(common_arg(
         {"-sps", "--slot-prompt-similarity"}, "SIMILARITY",
         string_format("how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity),
@@ -2240,35 +2057,35 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, int value) {
             params.speculative.n_max = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_MAX"));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"--draft-min", "--draft-n-min"}, "N",
         string_format("minimum number of draft tokens to use for speculative decoding (default: %d)", params.speculative.n_min),
         [](common_params & params, int value) {
             params.speculative.n_min = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_MIN"));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"--draft-p-split"}, "P",
         string_format("speculative decoding split probability (default: %.1f)", (double)params.speculative.p_split),
         [](common_params & params, const std::string & value) {
             params.speculative.p_split = std::stof(value);
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}).set_env("LLAMA_ARG_DRAFT_P_SPLIT"));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
     add_opt(common_arg(
         {"--draft-p-min"}, "P",
         string_format("minimum speculative decoding probability (greedy) (default: %.1f)", (double)params.speculative.p_min),
         [](common_params & params, const std::string & value) {
             params.speculative.p_min = std::stof(value);
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_P_MIN"));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-cd", "--ctx-size-draft"}, "N",
         string_format("size of the prompt context for the draft model (default: %d, 0 = loaded from model)", params.speculative.n_ctx),
         [](common_params & params, int value) {
             params.speculative.n_ctx = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CTX_SIZE_DRAFT"));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-devd", "--device-draft"}, "<dev1,dev2,..>",
         "comma-separated list of devices to use for offloading the draft model (none = don't offload)\n"
@@ -2283,88 +2100,18 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, int value) {
             params.speculative.n_gpu_layers = value;
             if (!llama_supports_gpu_offload()) {
-                fprintf(stderr, "warning: no usable GPU found, --gpu-layers-draft option will be ignored\n");
-                fprintf(stderr, "warning: one possible reason is that llama.cpp was compiled without GPU support\n");
-                fprintf(stderr, "warning: consult docs/build.md for compilation instructions\n");
+                fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers-draft option will be ignored\n");
+                fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
             }
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_N_GPU_LAYERS_DRAFT"));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-md", "--model-draft"}, "FNAME",
         "draft model for speculative decoding (default: unused)",
         [](common_params & params, const std::string & value) {
             params.speculative.model = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODEL_DRAFT"));
-
-    add_opt(common_arg(
-        {"-mv", "--model-vocoder"}, "FNAME",
-        "vocoder model for audio generation (default: unused)",
-        [](common_params & params, const std::string & value) {
-            params.vocoder.model = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER}));
-     add_opt(common_arg(
-        {"--tts-use-guide-tokens"},
-        "Use guide tokens to improve TTS word recall",
-        [](common_params & params) {
-            params.vocoder.use_guide_tokens = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER}));
-
-    // model-specific
-    add_opt(common_arg(
-        {"--tts-oute-default"},
-        string_format("use default OuteTTS models (note: can download weights from the internet)"),
-        [](common_params & params) {
-            params.hf_repo = "OuteAI/OuteTTS-0.2-500M-GGUF";
-            params.hf_file = "OuteTTS-0.2-500M-Q8_0.gguf";
-            params.vocoder.hf_repo = "ggml-org/WavTokenizer";
-            params.vocoder.hf_file = "WavTokenizer-Large-75-F16.gguf";
-        }
-    ).set_examples({LLAMA_EXAMPLE_TTS}));
-
-    add_opt(common_arg(
-        {"--embd-bge-small-en-default"},
-        string_format("use default bge-small-en-v1.5 model (note: can download weights from the internet)"),
-        [](common_params & params) {
-            params.hf_repo = "ggml-org/bge-small-en-v1.5-Q8_0-GGUF";
-            params.hf_file = "bge-small-en-v1.5-q8_0.gguf";
-            params.pooling_type = LLAMA_POOLING_TYPE_NONE;
-            params.embd_normalize = 2;
-            params.n_ctx = 512;
-            params.verbose_prompt = true;
-            params.embedding = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
-
-    add_opt(common_arg(
-        {"--embd-e5-small-en-default"},
-        string_format("use default e5-small-v2 model (note: can download weights from the internet)"),
-        [](common_params & params) {
-            params.hf_repo = "ggml-org/e5-small-v2-Q8_0-GGUF";
-            params.hf_file = "e5-small-v2-q8_0.gguf";
-            params.pooling_type = LLAMA_POOLING_TYPE_NONE;
-            params.embd_normalize = 2;
-            params.n_ctx = 512;
-            params.verbose_prompt = true;
-            params.embedding = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
-
-    add_opt(common_arg(
-        {"--embd-gte-small-default"},
-        string_format("use default gte-small model (note: can download weights from the internet)"),
-        [](common_params & params) {
-            params.hf_repo = "ggml-org/gte-small-Q8_0-GGUF";
-            params.hf_file = "gte-small-q8_0.gguf";
-            params.pooling_type = LLAMA_POOLING_TYPE_NONE;
-            params.embd_normalize = 2;
-            params.n_ctx = 512;
-            params.verbose_prompt = true;
-            params.embedding = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
 
     return ctx_arg;
 }

common/arg.h

@@ -12,7 +12,6 @@
 
 struct common_arg {
     std::set<enum llama_example> examples = {LLAMA_EXAMPLE_COMMON};
-    std::set<enum llama_example> excludes = {};
     std::vector<const char *> args;
     const char * value_hint   = nullptr; // help text or example for arg value
     const char * value_hint_2 = nullptr; // for second arg value
@@ -54,11 +53,9 @@ struct common_arg {
     ) : args(args), value_hint(value_hint), value_hint_2(value_hint_2), help(help), handler_str_str(handler) {}
 
     common_arg & set_examples(std::initializer_list<enum llama_example> examples);
-    common_arg & set_excludes(std::initializer_list<enum llama_example> excludes);
     common_arg & set_env(const char * env);
     common_arg & set_sparam();
     bool in_example(enum llama_example ex);
-    bool is_exclude(enum llama_example ex);
     bool get_value_from_env(std::string & output);
     bool has_value_from_env();
     std::string to_string();

common/chat-template.hpp

@@ -1,529 +0,0 @@
-/*
-    Copyright 2024 Google LLC
-
-    Use of this source code is governed by an MIT-style
-    license that can be found in the LICENSE file or at
-    https://opensource.org/licenses/MIT.
-*/
-// SPDX-License-Identifier: MIT
-#pragma once
-
-#include "minja.hpp"
-#include <json.hpp>
-#include <string>
-#include <vector>
-
-using json = nlohmann::ordered_json;
-
-namespace minja {
-
-struct chat_template_caps {
-    bool supports_tools = false;
-    bool supports_tool_calls = false;
-    bool supports_tool_responses = false;
-    bool supports_system_role = false;
-    bool supports_parallel_tool_calls = false;
-    bool supports_tool_call_id = false;
-    // meta-llama/Llama-3.1-8B-Instruct expects arguments to be an object.
-    // Most other templates (and OpenAI's API) expect the arguments object to be stringified.
-    bool requires_object_arguments = false;
-    // CohereForAI/c4ai-command-r-plus simple variant
-    bool requires_non_null_content = false;
-    // MiniMaxAI/MiniMax-Text-01 special
-    bool requires_typed_content = false;
-};
-
-struct chat_template_inputs {
-    nlohmann::ordered_json messages;
-    nlohmann::ordered_json tools;
-    bool add_generation_prompt = true;
-    nlohmann::ordered_json extra_context;
-    std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
-};
-
-struct chat_template_options {
-    bool apply_polyfills = true;
-    bool use_bos_token = true;
-    bool use_eos_token = true;
-    bool define_strftime_now = true;
-
-    bool polyfill_tools = true;
-    bool polyfill_tool_call_examples = true;
-    bool polyfill_tool_calls = true;
-    bool polyfill_tool_responses = true;
-    bool polyfill_system_role = true;
-    bool polyfill_object_arguments = true;
-    bool polyfill_typed_content = true;
-};
-
-class chat_template {
-
-  private:
-    chat_template_caps caps_;
-    std::string source_;
-    std::string bos_token_;
-    std::string eos_token_;
-    std::shared_ptr<minja::TemplateNode> template_root_;
-    std::string tool_call_example_;
-
-    std::string try_raw_render(
-        const nlohmann::ordered_json & messages,
-        const nlohmann::ordered_json & tools,
-        bool add_generation_prompt,
-        const nlohmann::ordered_json & extra_context = nlohmann::ordered_json()) const
-    {
-        try {
-            chat_template_inputs inputs;
-            inputs.messages = messages;
-            inputs.tools = tools;
-            inputs.add_generation_prompt = add_generation_prompt;
-            inputs.extra_context = extra_context;
-            // Use fixed date for tests
-            inputs.now = std::chrono::system_clock::from_time_t(0);
-
-            chat_template_options opts;
-            opts.apply_polyfills = false;
-
-            auto prompt = apply(inputs, opts);
-            // fprintf(stderr, "try_raw_render: %s\n", prompt.c_str());
-            return prompt;
-        } catch (const std::exception & e) {
-            // fprintf(stderr, "try_raw_render error: %s\n", e.what());
-            return "";
-        }
-    }
-
-  public:
-
-    chat_template(const std::string & source, const std::string & bos_token, const std::string & eos_token)
-        : source_(source), bos_token_(bos_token), eos_token_(eos_token)
-    {
-        template_root_ = minja::Parser::parse(source_, {
-            /* .trim_blocks = */ true,
-            /* .lstrip_blocks = */ true,
-            /* .keep_trailing_newline = */ false,
-        });
-
-        auto contains = [](const std::string & haystack, const std::string & needle) {
-            return haystack.find(needle) != std::string::npos;
-        };
-
-        const std::string user_needle = "<User Needle>";
-        const std::string sys_needle = "<System Needle>";
-        const json dummy_str_user_msg = {{"role", "user"}, {"content", user_needle}};
-        const json dummy_typed_user_msg = {{"role", "user"}, {"content", json::array({{{"type", "text"}, {"text", user_needle}}})}};
-
-        caps_.requires_typed_content =
-            !contains(try_raw_render(json::array({dummy_str_user_msg}), {}, false), user_needle)
-            && contains(try_raw_render(json::array({dummy_typed_user_msg}), {}, false), user_needle);
-
-        const auto dummy_user_msg = caps_.requires_typed_content
-            ? dummy_typed_user_msg
-            : dummy_str_user_msg;
-        const json needle_system_msg = {
-            {"role", "system"},
-            {"content", caps_.requires_typed_content ? json::array({{{"type", "text"}, {"text", sys_needle}}}) : json(sys_needle)},
-        };
-
-        caps_.supports_system_role = contains(try_raw_render({needle_system_msg, dummy_user_msg,}, {}, false), sys_needle);
-
-        auto out = try_raw_render(json::array({
-            dummy_user_msg
-        }), json::array({
-            {
-                {"name", "some_tool"},
-                {"type", "function"},
-                {"function", {
-                    {"name", "some_tool"},
-                    {"description", "Some tool."},
-                    {"parameters", {
-                        {"type", "object"},
-                        {"properties", {
-                            {"arg", {
-                                {"type", "string"},
-                                {"description", "Some argument."},
-                            }},
-                        }},
-                        {"required", json::array({ "arg" })},
-                    }},
-                }},
-            },
-        }), false);
-        caps_.supports_tools = contains(out, "some_tool");
-
-        auto make_tool_calls_msg = [&](const json & tool_calls) {
-            return json {
-                {"role", "assistant"},
-                {"content", nullptr},
-                {"tool_calls", tool_calls},
-            };
-        };
-        auto make_tool_call = [](const std::string & tool_name, const json & arguments) {
-            return json {
-                {"id", "call_1___"},
-                {"type", "function"},
-                {"function", {
-                    {"arguments", arguments},
-                    {"name", tool_name},
-                }},
-            };
-        };
-        const json dummy_args_obj {{"argument_needle", "print('Hello, World!')"}};
-
-        // Note: the arguments are rendered in both cases, but may be double-escaped, which we don't want.
-        out = try_raw_render(json::array({
-            dummy_user_msg,
-            make_tool_calls_msg(json::array({make_tool_call("ipython", dummy_args_obj.dump())})),
-        }), {}, false);
-        auto tool_call_renders_str_arguments = contains(out, "\"argument_needle\":") || contains(out, "'argument_needle':");
-        out = try_raw_render(json::array({
-            dummy_user_msg,
-            make_tool_calls_msg(json::array({make_tool_call("ipython", dummy_args_obj)})),
-        }), {}, false);
-        auto tool_call_renders_obj_arguments = contains(out, "\"argument_needle\":") || contains(out, "'argument_needle':");
-
-        caps_.supports_tool_calls = tool_call_renders_str_arguments || tool_call_renders_obj_arguments;
-        caps_.requires_object_arguments = !tool_call_renders_str_arguments && tool_call_renders_obj_arguments;
-        auto out_empty = try_raw_render(json::array({dummy_user_msg, {{"role", "assistant"}, {"content", ""}}}), {}, false);
-        auto out_null = try_raw_render(json::array({dummy_user_msg, {{"role", "assistant"}, {"content", nullptr}}}), {}, false);
-        caps_.requires_non_null_content = contains(out_empty, user_needle) && !contains(out_null, user_needle);
-
-        if (caps_.supports_tool_calls) {
-            auto dummy_args = caps_.requires_object_arguments ? dummy_args_obj : json(dummy_args_obj.dump());
-            auto tc1 = make_tool_call("test_tool1", dummy_args);
-            auto tc2 = make_tool_call("test_tool2", dummy_args);
-            auto out = try_raw_render(json::array({
-                dummy_user_msg,
-                make_tool_calls_msg(json::array({tc1, tc2})),
-            }), {}, false);
-            caps_.supports_parallel_tool_calls = contains(out, "test_tool1") && contains(out, "test_tool2");
-
-            out = try_raw_render(json::array({
-                dummy_user_msg,
-                make_tool_calls_msg(json::array({tc1})),
-                {
-                    {"role", "tool"},
-                    {"name", "test_tool1"},
-                    {"content", "Some response!"},
-                    {"tool_call_id", "call_911_"},
-                }
-            }), {}, false);
-            caps_.supports_tool_responses = contains(out, "Some response!");
-            caps_.supports_tool_call_id = contains(out, "call_911_");
-        }
-
-        try {
-            if (!caps_.supports_tools) {
-                const json user_msg {
-                    {"role", "user"},
-                    {"content", "Hey"},
-                };
-                const json args {
-                    {"arg1", "some_value"},
-                };
-                const json tool_call_msg {
-                    {"role", "assistant"},
-                    {"content", nullptr},
-                    {"tool_calls", json::array({
-                        {
-                            // TODO: detect if requires numerical id or fixed length == 6 like Nemo
-                            {"id", "call_1___"},
-                            {"type", "function"},
-                            {"function", {
-                                {"name", "tool_name"},
-                                {"arguments", (caps_.requires_object_arguments ? args : json(minja::Value(args).dump(-1, /* to_json= */ true)))},
-                            }},
-                        },
-                    })},
-                };
-                std::string prefix, full;
-                {
-                    chat_template_inputs inputs;
-                    inputs.messages = json::array({user_msg});
-                    inputs.add_generation_prompt = true;
-                    prefix = apply(inputs);
-                }
-                {
-                    chat_template_inputs inputs;
-                    inputs.messages = json::array({user_msg, tool_call_msg});
-                    inputs.add_generation_prompt = false;
-                    full = apply(inputs);
-                }
-                auto eos_pos_last = full.rfind(eos_token_);
-                if (eos_pos_last == prefix.size() - eos_token_.size() ||
-                      (full[full.size() - 1] == '\n' && (eos_pos_last == full.size() - eos_token_.size() - 1))) {
-                    full = full.substr(0, eos_pos_last);
-                }
-                size_t common_prefix_length = 0;
-                for (size_t i = 0; i < prefix.size() && i < full.size(); ++i) {
-                    if (prefix[i] != full[i]) {
-                        break;
-                    }
-                    if (prefix[i] == '<') {
-                        // DeepSeek R1's template (as of 20250209) adds a trailing <think> if add_generation_prompt,
-                        // but it removes thinking tags for past messages.
-                        // The prefix and full strings diverge at <think> vs. <｜tool▁calls▁begin｜>, we avoid consuming the leading <.
-                        continue;
-                    }
-                    common_prefix_length = i + 1;
-                }
-                auto example = full.substr(common_prefix_length);
-                if (example.find("tool_name") == std::string::npos && example.find("some_value") == std::string::npos) {
-                    fprintf(stderr, "Failed to infer a tool call example (possible template bug)\n");
-                } else {
-                    tool_call_example_ = example;
-                }
-            }
-        } catch (const std::exception & e) {
-            fprintf(stderr, "Failed to generate tool call example: %s\n", e.what());
-        }
-    }
-
-    const std::string & source() const { return source_; }
-    const std::string & bos_token() const { return bos_token_; }
-    const std::string & eos_token() const { return eos_token_; }
-    const chat_template_caps & original_caps() const { return caps_; }
-
-    // Deprecated, please use the form with chat_template_inputs and chat_template_options
-    std::string apply(
-        const nlohmann::ordered_json & messages,
-        const nlohmann::ordered_json & tools,
-        bool add_generation_prompt,
-        const nlohmann::ordered_json & extra_context = nlohmann::ordered_json(),
-        bool apply_polyfills = true)
-    {
-        fprintf(stderr, "[%s] Deprecated!\n", __func__);
-        chat_template_inputs inputs;
-        inputs.messages = messages;
-        inputs.tools = tools;
-        inputs.add_generation_prompt = add_generation_prompt;
-        inputs.extra_context = extra_context;
-        inputs.now = std::chrono::system_clock::now();
-
-        chat_template_options opts;
-        opts.apply_polyfills = apply_polyfills;
-
-        return apply(inputs, opts);
-    }
-
-    std::string apply(
-        const chat_template_inputs & inputs,
-        const chat_template_options & opts = chat_template_options()) const
-    {
-        json actual_messages;
-
-        auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
-        auto has_tool_calls = false;
-        auto has_tool_responses = false;
-        auto has_string_content = false;
-        for (const auto & message : inputs.messages) {
-            if (message.contains("tool_calls") && !message["tool_calls"].is_null()) {
-                has_tool_calls = true;
-            }
-            if (message.contains("role") && message["role"] == "tool") {
-                has_tool_responses = true;
-            }
-            if (message.contains("content") && message["content"].is_string()) {
-                has_string_content = true;
-            }
-        }
-
-        auto polyfill_system_role = opts.polyfill_system_role && !caps_.supports_system_role;
-        auto polyfill_tools = opts.polyfill_tools && has_tools && !caps_.supports_tools;
-        auto polyfill_tool_call_example = polyfill_tools && opts.polyfill_tool_call_examples;
-        auto polyfill_tool_calls = opts.polyfill_tool_calls && has_tool_calls && !caps_.supports_tool_calls;
-        auto polyfill_tool_responses = opts.polyfill_tool_responses && has_tool_responses && !caps_.supports_tool_responses;
-        auto polyfill_object_arguments = opts.polyfill_object_arguments && has_tool_calls && caps_.requires_object_arguments;
-        auto polyfill_typed_content = opts.polyfill_typed_content && has_string_content && caps_.requires_typed_content;
-
-        auto needs_polyfills = opts.apply_polyfills && (false
-            || polyfill_system_role
-            || polyfill_tools
-            || polyfill_tool_calls
-            || polyfill_tool_responses
-            || polyfill_object_arguments
-            || polyfill_typed_content
-        );
-
-        if (needs_polyfills) {
-            actual_messages = json::array();
-
-            auto add_message = [&](const json & msg) {
-                if (polyfill_typed_content && msg.contains("content") && !msg.at("content").is_null() && msg.at("content").is_string()) {
-                    actual_messages.push_back({
-                        {"role", msg.at("role")},
-                        {"content", {{
-                            {"type", "text"},
-                            {"text", msg.at("content")},
-                        }}},
-                    });
-                } else {
-                    actual_messages.push_back(msg);
-                }
-            };
-
-            std::string pending_system;
-            auto flush_sys = [&]() {
-                if (!pending_system.empty()) {
-                    add_message({
-                        {"role", "user"},
-                        {"content", pending_system},
-                    });
-                    pending_system.clear();
-                }
-            };
-
-            json adjusted_messages;
-            if (polyfill_tools) {
-                adjusted_messages = add_system(inputs.messages,
-                    "You can call any of the following tools to satisfy the user's requests: " + minja::Value(inputs.tools).dump(2, /* to_json= */ true) +
-                    (!polyfill_tool_call_example || tool_call_example_.empty() ? "" : "\n\nExample tool call syntax:\n\n" + tool_call_example_ + "\n\n"));
-            } else {
-                adjusted_messages = inputs.messages;
-            }
-
-            for (const auto & message_ : adjusted_messages) {
-                auto message = message_;
-                if (!message.contains("role") || !message.contains("content")) {
-                    throw std::runtime_error("message must have 'role' and 'content' fields: " + message.dump());
-                }
-                std::string role = message.at("role");
-
-                if (message.contains("tool_calls")) {
-                    if (polyfill_object_arguments || polyfill_tool_calls) {
-                        for (auto & tool_call : message.at("tool_calls")) {
-                            if (tool_call["type"] == "function") {
-                                auto & function = tool_call.at("function");
-                                auto & arguments = function.at("arguments");
-                                if (arguments.is_string()) {
-                                    try {
-                                        arguments = json::parse(arguments.get<std::string>());
-                                    } catch (const std::exception & ecvt) {
-                                        fprintf(stderr, "Failed to parse arguments: %s\n", ecvt.what());
-                                    }
-                                }
-                            }
-                        }
-                    }
-                    if (polyfill_tool_calls) {
-                        auto content = message.at("content");
-                        auto tool_calls = json::array();
-                        for (const auto & tool_call : message.at("tool_calls")) {
-                            if (tool_call.at("type") != "function") {
-                                continue;
-                            }
-                            const auto & function = tool_call.at("function");
-                            auto tc = json {
-                                {"name", function.at("name")},
-                                {"arguments", function.at("arguments")},
-                            };
-                            if (tool_call.contains("id")) {
-                                tc["id"] = tool_call["id"];
-                            }
-                            tool_calls.push_back(tc);
-                        }
-                        auto obj = json {
-                            {"tool_calls", tool_calls},
-                        };
-                        if (!content.is_null() && content != "") {
-                            obj["content"] = content;
-                        }
-                        message["content"] = obj.dump(2);
-                        message.erase("tool_calls");
-                    }
-                }
-                if (polyfill_tool_responses && role == "tool") {
-                    message["role"] = "user";
-                    auto obj = json {
-                        {"tool_response", {
-                            {"content", message.at("content")},
-                        }},
-                    };
-                    if (message.contains("name")) {
-                        obj["tool_response"]["name"] = message.at("name");
-                    }
-                    if (message.contains("tool_call_id")) {
-                        obj["tool_response"]["tool_call_id"] = message.at("tool_call_id");
-                    }
-                    message["content"] = obj.dump(2);
-                    message.erase("name");
-                }
-
-                if (!message["content"].is_null() && polyfill_system_role) {
-                    std::string content = message.at("content");
-                    if (role == "system") {
-                        if (!pending_system.empty()) pending_system += "\n";
-                        pending_system += content;
-                        continue;
-                    } else {
-                        if (role == "user") {
-                            if (!pending_system.empty()) {
-                                message["content"] = pending_system + (content.empty() ? "" : "\n" + content);
-                                pending_system.clear();
-                            }
-                        } else {
-                            flush_sys();
-                        }
-                    }
-                }
-                add_message(message);
-            }
-            flush_sys();
-        } else {
-            actual_messages = inputs.messages;
-        }
-
-        auto context = minja::Context::make(json({
-            {"messages", actual_messages},
-            {"add_generation_prompt", inputs.add_generation_prompt},
-        }));
-        context->set("bos_token", opts.use_bos_token ? bos_token_ : "");
-        context->set("eos_token", opts.use_eos_token ? eos_token_ : "");
-        if (opts.define_strftime_now) {
-            auto now = inputs.now;
-            context->set("strftime_now", Value::callable([now](const std::shared_ptr<minja::Context> &, minja::ArgumentsValue & args) {
-                args.expectArgs("strftime_now", {1, 1}, {0, 0});
-                auto format = args.args[0].get<std::string>();
-
-                auto time = std::chrono::system_clock::to_time_t(now);
-                auto local_time = *std::localtime(&time);
-                std::ostringstream ss;
-                ss << std::put_time(&local_time, format.c_str());
-                return ss.str();
-            }));
-        }
-        if (!inputs.tools.is_null()) {
-            context->set("tools", minja::Value(inputs.tools));
-        }
-        if (!inputs.extra_context.is_null()) {
-            for (auto & kv : inputs.extra_context.items()) {
-                context->set(kv.key(), minja::Value(kv.value()));
-            }
-        }
-
-        auto ret = template_root_->render(context);
-        // fprintf(stderr, "actual_messages: %s\n", actual_messages.dump(2).c_str());
-        // fprintf(stderr, "apply: %s\n\n", ret.c_str());
-        return ret;
-    }
-
-    static nlohmann::ordered_json add_system(const nlohmann::ordered_json & messages, const std::string & system_prompt) {
-        json messages_with_system = messages;
-
-        if (messages_with_system.size() > 0 && messages_with_system[0].at("role") == "system") {
-            std::string existing_system = messages_with_system.at(0).at("content");
-            messages_with_system[0] = json {
-                {"role", "system"},
-                {"content", existing_system + "\n\n" + system_prompt},
-            };
-        } else {
-            messages_with_system.insert(messages_with_system.begin(), json {
-                {"role", "system"},
-                {"content", system_prompt},
-            });
-        }
-        return messages_with_system;
-    }
-};
-
-}  // namespace minja

common/chat.cpp

@@ -1,966 +0,0 @@
-#include "chat.hpp"
-#include "chat-template.hpp"
-#include "json-schema-to-grammar.h"
-#include "log.h"
-#include "minja.hpp"
-
-std::string common_chat_format_name(common_chat_format format) {
-    switch (format) {
-        case COMMON_CHAT_FORMAT_CONTENT_ONLY: return "Content-only";
-        case COMMON_CHAT_FORMAT_GENERIC: return "Generic";
-        case COMMON_CHAT_FORMAT_MISTRAL_NEMO: return "Mistral Nemo";
-        case COMMON_CHAT_FORMAT_LLAMA_3_X: return "Llama 3.x";
-        case COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS: return "Llama 3.x with builtin tools";
-        case COMMON_CHAT_FORMAT_DEEPSEEK_R1: return "DeepSeek R1";
-        case COMMON_CHAT_FORMAT_FIREFUNCTION_V2: return "FireFunction v2";
-        case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2: return "Functionary v3.2";
-        case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: return "Functionary v3.1 Llama 3.1";
-        case COMMON_CHAT_FORMAT_HERMES_2_PRO: return "Hermes 2 Pro";
-        case COMMON_CHAT_FORMAT_COMMAND_R7B: return "Command R7B";
-        default:
-            throw std::runtime_error("Unknown chat format");
-    }
-}
-
-const common_grammar_options grammar_options {
-    /* .dotall = */ false,
-    /* .compact_spaces = */ false,
-    // /* .compact_spaces = */ true,
-};
-
-static bool parse_json(std::string::const_iterator & it, const std::string::const_iterator & end, json & out) {
-    // // https://json.nlohmann.me/features/parsing/sax_interface/
-    struct json_error_locator : public nlohmann::json_sax<json> {
-        std::size_t position;
-        bool found_error;
-
-        json_error_locator() : position(0), found_error(false) {}
-
-        bool parse_error(std::size_t position, const std::string &, const json::exception &) override {
-            this->position = position - 1;
-            this->found_error = true;
-            return false;
-        }
-        bool null() override { return true; }
-        bool boolean(bool) override { return true; }
-        bool number_integer(number_integer_t) override { return true; }
-        bool number_unsigned(number_unsigned_t) override { return true; }
-        bool number_float(number_float_t, const string_t &) override { return true; }
-        bool string(string_t &) override { return true; }
-        bool binary(binary_t &) override { return true; }
-        bool start_object(std::size_t) override { return true; }
-        bool key(string_t &) override { return true; }
-        bool end_object() override { return true; }
-        bool start_array(std::size_t) override { return true; }
-        bool end_array() override { return true; }
-    };
-    json_error_locator err_loc;
-    json::sax_parse(it, end, &err_loc);
-
-    std::string::const_iterator temptative_end;
-    if (err_loc.found_error) {
-        temptative_end = it + err_loc.position;
-    } else {
-        temptative_end = end;
-    }
-    std::string json_sub {it, temptative_end};
-    try {
-        out = json::parse(json_sub);
-        it = temptative_end;
-        return true;
-    } catch (const std::exception &) {
-        return false;
-    }
-}
-
-
-/**
- * Takes a prefix regex that must have 1 group to capture the function name, a closing suffix, and expects json parameters in between.
- * Aggregates the prefix, suffix and in-between text into the content.
- */
-static common_chat_msg parse_json_tool_calls(
-    const std::string& input,
-    const std::optional<std::regex> & trigger_opt,
-    const std::regex & function_regex,
-    const std::regex & close_regex) {
-    std::smatch match;
-
-    common_chat_msg result;
-    result.role = "assistant";
-
-
-    auto end = input.end();
-    auto it = input.begin();
-
-    if (trigger_opt) {
-        if (!std::regex_search(it, end, match, *trigger_opt)) {
-            result.content = input;
-            return result;
-        }
-        result.content = match.prefix().str();
-        it = match.suffix().first;
-    }
-
-    while (it != end) {
-        std::sregex_iterator rend;
-        std::sregex_iterator rit(it, end, function_regex);
-        if (rit == rend) {
-            fprintf(stderr, "No more tool calls found\n");
-            result.content += std::string(it, end);
-            break;
-        }
-        auto name = rit->str(1);
-        result.content += std::string(it, rit->prefix().second);
-        it = rit->suffix().first;
-
-        json arguments;
-        if (!parse_json(it, end, arguments)) {
-            throw std::runtime_error("Failed to parse json tool call arguments");
-        }
-        if (!std::regex_search(it, end, match, close_regex)) {
-            throw std::runtime_error("Malformed input, missing closing pattern");
-        }
-        it = match.suffix().first;
-        result.tool_calls.push_back({name, arguments.is_string() ? arguments.get<std::string>() : arguments.dump(), /* id= */ ""});
-    }
-    return result;
-}
-
-static common_chat_msg parse_prefixed_json_tool_call_array(const std::string& input, const std::string & prefix, size_t rstrip_prefix = 0) {
-    auto content_end = input.find(prefix);
-    size_t tc_start = std::string::npos;
-
-    common_chat_msg result;
-    result.role = "assistant";
-    const auto process_tool_calls = [&](const json & tool_calls) {
-        for (const auto & tool_call : tool_calls) {
-            const auto & arguments = tool_call["arguments"];
-            result.tool_calls.push_back({
-                tool_call["name"],
-                arguments.is_string() ? arguments.get<std::string>() : arguments.dump(),
-                tool_call.contains("id") ? tool_call["id"] : "",
-            });
-        }
-    };
-    if (content_end == std::string::npos) {
-        result.content = input;
-    } else {
-        tc_start = content_end + prefix.size() - rstrip_prefix;
-        result.content = input.substr(0, content_end);
-        auto tool_calls = json::parse(input.substr(tc_start));
-        process_tool_calls(tool_calls);
-    }
-    return result;
-}
-
-static void foreach_function(const json & tools, const std::function<void(const json &)> & fn) {
-    for (const auto & tool : tools) {
-        if (!tool.contains("type") || tool["type"] != "function" || !tool.contains("function")) {
-            LOG_INF("Skipping tool without function: %s", tool.dump(2).c_str());
-            continue;
-        }
-        fn(tool);
-    }
-}
-
-static std::string apply(
-    const common_chat_template & tmpl,
-    const nlohmann::ordered_json & messages,
-    const nlohmann::ordered_json & tools,
-    bool add_generation_prompt,
-    const nlohmann::ordered_json & extra_context = nlohmann::ordered_json())
-{
-    minja::chat_template_inputs tmpl_inputs;
-    tmpl_inputs.messages = messages;
-    tmpl_inputs.tools = tools;
-    tmpl_inputs.add_generation_prompt = add_generation_prompt;
-    tmpl_inputs.extra_context = extra_context;
-    // TODO: add flag to control date/time, if only for testing purposes.
-    // tmpl_inputs.now = std::chrono::system_clock::now();
-
-    minja::chat_template_options tmpl_opts;
-    tmpl_opts.use_bos_token = false;
-    tmpl_opts.use_eos_token = false;
-
-    return tmpl.apply(tmpl_inputs, tmpl_opts);
-}
-
-static common_chat_params common_chat_params_init_generic(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
-    common_chat_params data;
-
-    auto tool_call_schemas = json::array();
-    foreach_function(inputs.tools, [&](const json & tool) {
-        const auto & function = tool["function"];
-        auto tool_schema = json {
-            {"type", "object"},
-            {"properties", {
-                {"name", {
-                    {"type", "string"},
-                    {"const", function["name"]},
-                }},
-                {"arguments", function["parameters"]},
-            }},
-            {"required", json::array({"name", "arguments"})},
-        };
-        if (function.contains("description")) {
-            tool_schema["description"] = function["description"];
-        }
-        if (inputs.parallel_tool_calls) {
-            tool_schema["properties"]["id"] = {
-                {"type", "string"},
-                {"minLength", 4},
-            };
-            tool_schema["required"].push_back("id");
-        }
-        tool_call_schemas.emplace_back(tool_schema);
-    });
-    const auto tool_call =
-        inputs.parallel_tool_calls
-            ? json {
-                {"type", "object"},
-                {"properties", {
-                    {"tool_calls", {
-                        {"type", "array"},
-                        {"items", tool_call_schemas.size() == 1 ? tool_call_schemas[0] : json {
-                            {"anyOf", tool_call_schemas},
-                        }},
-                        {"minItems", 1},
-                    }},
-                }},
-                {"required", json::array({"tool_calls"})},
-            }
-            : json {
-                {"type", "object"},
-                {"properties", {
-                    {"tool_call", tool_call_schemas.size() == 1 ? tool_call_schemas[0] : json {
-                        {"anyOf", tool_call_schemas},
-                    }},
-                }},
-                {"required", json::array({"tool_call"})},
-            };
-    const auto schema =
-        inputs.tool_choice != "required"
-            ? json {
-                {"anyOf", json::array({
-                    tool_call,
-                    {
-                        {"type", "object"},
-                        {"properties", {
-                            {"response", inputs.json_schema.is_null()
-                                ? json {{"type", "string"}}
-                                : inputs.json_schema
-                            },
-                        }},
-                        {"required", json::array({"response"})},
-                    },
-                })}
-            }
-            : tool_call;
-
-    data.grammar_lazy = false;
-    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-        builder.add_schema("root", schema);
-    }, grammar_options);
-
-    auto tweaked_messages = common_chat_template::add_system(
-        inputs.messages,
-        "Respond in JSON format, either with `tool_call` (a request to call tools) or with `response` reply to the user's request");
-
-    data.prompt = apply(tmpl, tweaked_messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
-    data.format = COMMON_CHAT_FORMAT_GENERIC;
-    return data;
-}
-static common_chat_msg common_chat_parse_generic(const std::string & input) {
-    json data = json::parse(input);
-    common_chat_msg result;
-    result.role = "assistant";
-    if (data.contains("tool_calls")) {
-        for (const auto & tool_call : data["tool_calls"]) {
-            result.tool_calls.push_back({
-                tool_call["name"],
-                tool_call["arguments"].dump(),
-                tool_call.contains("id") ? tool_call["id"] : "",
-            });
-        }
-    } else if (data.contains("tool_call")) {
-        result.tool_calls.push_back({
-            data["tool_call"]["name"],
-            data["tool_call"]["arguments"].dump(),
-            /* id= */ "",
-        });
-    } else if (data.contains("response")) {
-        const auto & response = data["response"];
-        result.content = response.is_string() ? response.get<std::string>() : response.dump(2);
-    }
-    return result;
-}
-
-static common_chat_params common_chat_params_init_mistral_nemo(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
-    common_chat_params data;
-    data.grammar_lazy = inputs.tool_choice != "required";
-    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-        auto schemas = json::array();
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const auto & function = tool["function"];
-            schemas.push_back({
-                {"type", "object"},
-                {"properties", {
-                    // Important note: the model is probably trained to take a JSON stringified arguments value.
-                    // It's hard to constrain that for now (while reusing the JSON schema conversion), so we're just expecting a plain object.
-                    {"name", {
-                        {"type", "string"},
-                        {"const", function["name"]},
-                    }},
-                    {"arguments", function["parameters"]},
-                    {"id", {
-                        {"type", "string"},
-                        // Nemo's template expects a 9-character alphanumeric ID.
-                        {"pattern", "^[a-zA-Z0-9]{9}$"},
-                    }},
-                }},
-                {"required", json::array({"name", "arguments", "id"})},
-            });
-        });
-        auto schema = json {
-            {"type", "array"},
-            {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}},
-            {"minItems", 1},
-        };
-        if (!inputs.parallel_tool_calls) {
-            schema["maxItems"] = 1;
-        }
-        builder.add_rule("root", "\"[TOOL_CALLS]\" " + builder.add_schema("tool_calls", schema));
-    }, grammar_options);
-    data.grammar_triggers.push_back({"[TOOL_CALLS]", /* .at_start = */ true});
-    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
-    data.format = COMMON_CHAT_FORMAT_MISTRAL_NEMO;
-    return data;
-}
-static common_chat_msg common_chat_parse_mistral_nemo(const std::string & input) {
-    return parse_prefixed_json_tool_call_array(input, "[TOOL_CALLS]");
-}
-
-static common_chat_params common_chat_params_init_command_r7b(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
-    common_chat_params data;
-    data.grammar_lazy = inputs.tool_choice != "required";
-    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-        auto schemas = json::array();
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const auto & function = tool["function"];
-            schemas.push_back({
-                {"type", "object"},
-                {"properties", {
-                    {"tool_call_id", {
-                        {"type", "string"},
-                        // Command-R's template expects an integer string.
-                        {"pattern", "^[0-9]{1,10}$"},
-                    }},
-                    {"tool_name", {
-                        {"type", "string"},
-                        {"const", function["name"]},
-                    }},
-                    {"parameters", function["parameters"]},
-                }},
-                {"required", json::array({"tool_call_id", "tool_name", "parameters"})},
-            });
-        });
-        auto schema = json {
-            {"type", "array"},
-            {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}},
-            {"minItems", 1},
-        };
-        if (!inputs.parallel_tool_calls) {
-            schema["maxItems"] = 1;
-        }
-        builder.add_rule("root", "\"<|START_ACTION|>\" " + builder.add_schema("tool_calls", schema) + " \"<|END_ACTION|>\"");
-    }, grammar_options);
-    data.grammar_triggers.push_back({"<|START_ACTION|>", /* .at_start = */ false});
-    data.preserved_tokens = {
-        "<|START_RESPONSE|>",
-        "<|END_RESPONSE|>",
-        "<|START_THINKING|>",
-        "<|END_THINKING|>",
-        "<|END_ACTION|>",
-    };
-    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
-    data.format = COMMON_CHAT_FORMAT_COMMAND_R7B;
-    return data;
-}
-static common_chat_msg common_chat_parse_command_r7b(const std::string & input) {
-    static std::regex response_regex("<\\|START_RESPONSE\\|>([\\s\\S\\n\\r]*?)<\\|END_RESPONSE\\|>");
-    static std::regex thought_action_regex("<\\|START_THINKING\\|>([\\s\\S\\n\\r]*?)<\\|END_THINKING\\|><\\|START_ACTION\\|>([\\s\\S\\n\\r]*?)<\\|END_ACTION\\|>");
-    std::smatch match;
-
-    common_chat_msg result;
-    result.role = "assistant";
-    if (std::regex_match(input, match, response_regex)) {
-        result.content = match[1].str();
-    } else if (std::regex_match(input, match, thought_action_regex)) {
-        result.tool_plan = match[1].str();
-        auto actions_str = match[2].str();
-        auto actions = json::parse(actions_str);
-        for (const auto & action : actions) {
-            result.tool_calls.push_back({
-                /* .name = */      action["tool_name"],
-                /* .arguments = */ action["parameters"].dump(),
-                /* .id = */        action["tool_call_id"],
-            });
-        }
-    } else {
-        LOG_ERR("Failed to parse command_r output");
-        result.content = input;
-    }
-    return result;
-}
-
-static void expect_tool_parameters(const std::string & name, const json & parameters, const std::vector<std::string> & expected_properties) {
-    if (!parameters.is_object() || !parameters.contains("type") || parameters["type"] != "object" || !parameters.contains("properties") || !parameters.contains("required")) {
-        throw std::runtime_error("Parameters of tool " + name + " must be an object w/ required properties");
-    }
-    const auto & parameters_properties = parameters.at("properties");
-    const auto & parameters_required = parameters.at("required");
-    for (const auto & prop : expected_properties) {
-        if (!parameters_properties.contains(prop)) {
-            throw std::runtime_error("Parameters of tool " + name + " is missing property: " + prop);
-        }
-        if (std::find(parameters_required.begin(), parameters_required.end(), json(prop)) == parameters_required.end()) {
-            throw std::runtime_error("Parameters of tool " + name + " must have property marked as required: " + prop);
-        }
-    }
-    if (parameters_properties.size() != expected_properties.size()) {
-        throw std::runtime_error("Parameters of tool " + name + " must only have these properties:" + string_join(expected_properties, ", "));
-    }
-}
-
-static common_chat_params common_chat_params_init_llama_3_1_tool_calls(const common_chat_template & tmpl, const struct common_chat_inputs & inputs, bool allow_python_tag_builtin_tools) {
-    auto builtin_tools = json::array();
-    common_chat_params data;
-    data.grammar_lazy = inputs.tool_choice != "required";
-    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-        std::vector<std::string> tool_rules;
-
-        auto handle_builtin_tool = [&](const std::string & name, const json & parameters) {
-            if (name == "wolfram_alpha") {
-                // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py
-                expect_tool_parameters(name, parameters, {"query"});
-            } else if (name == "web_search" || name == "brave_search") {
-                // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py
-                expect_tool_parameters(name, parameters, {"query"});
-            } else if (name == "python" || name == "code_interpreter") {
-                // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/inline/tool_runtime/code_interpreter/code_interpreter.py
-                expect_tool_parameters(name, parameters, {"code"});
-            } else {
-                return false;
-            }
-
-            std::vector<std::string> kvs;
-            for (const auto & [key, value] : parameters.at("properties").items()) {
-                kvs.push_back("\"" + key + "=\" " + builder.add_schema(name + "-args-" + key, value));
-            }
-
-            tool_rules.push_back(
-                builder.add_rule(
-                    name + "-call",
-                    "\"<|python_tag|>" + name + ".call(\" " + string_join(kvs, " \", \" ") + " \")\""));
-            builtin_tools.push_back(name);
-
-            return true;
-        };
-
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const auto & function = tool["function"];
-            std::string name = function["name"];
-            auto parameters = function["parameters"];
-            builder.resolve_refs(parameters);
-
-            // https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/remote/tool_runtime
-            if (allow_python_tag_builtin_tools) {
-                handle_builtin_tool(name, parameters);
-            }
-            tool_rules.push_back(
-                builder.add_rule(
-                    name + "-call",
-                    "\"{\" space "
-                    "( \"\\\"type\\\":\" space \"\\\"function\\\",\" space )? "
-                    "\"\\\"name\\\": \\\"" + name + "\\\", \\\"parameters\\\": \" " +
-                        builder.add_schema(name + "-args", parameters) +
-                    " \"}\""));
-            data.grammar_triggers.push_back({"{\"name\": \"" + name + "\"", /* .at_start = */ true});
-        });
-        data.grammar_triggers.push_back({"{\"name\":", /* .at_start = */ true});
-        data.grammar_triggers.push_back({"{\n  \"name\":", /* .at_start = */ true});
-        data.grammar_triggers.push_back({"{\n    \"name\":", /* .at_start = */ true});
-        data.grammar_triggers.push_back({"{\"type\": \"function\"", /* .at_start = */ true});
-        data.grammar_triggers.push_back({"{\n  \"type\": \"function\"", /* .at_start = */ true});
-        data.grammar_triggers.push_back({"{\n    \"type\": \"function\"", /* .at_start = */ true});
-        if (!builtin_tools.empty()) {
-            data.grammar_triggers.push_back({"<|python_tag|>", /* .at_start = */ false});
-        }
-        builder.add_rule("root", string_join(tool_rules, " | "));
-    }, grammar_options);
-    data.additional_stops.push_back("<|eom_id|>");
-    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt, {
-        {"tools_in_user_message", false},
-        {"builtin_tools", builtin_tools.empty() ? json() : builtin_tools},
-    });
-    data.format = allow_python_tag_builtin_tools && !builtin_tools.empty()
-        ? COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS
-        : COMMON_CHAT_FORMAT_LLAMA_3_X;
-    return data;
-}
-static common_chat_msg common_chat_parse_llama_3_1(const std::string & input, bool with_builtin_tools = false) {
-    // TODO: tighten & simplify the parser, don't accept leading text context.
-    static std::regex function_regex("\\{[\\s\\n\\r]*(?:\"type\"[\\s\\n\\r]*:[\\s\\n\\r]*\"function\"[\\s\\n\\r]*,[\\s\\n\\r]*|[\\s\\n\\r]*)\"name\"[\\s\\n\\r]*:[\\s\\n\\r]*\"([^\"]+)\"[\\s\\n\\r]*,[\\s\\n\\r]*\"parameters\": ");
-    static std::regex close_regex("\\}");
-    static std::regex builtin_call_regex("<\\|python_tag\\|>([^.(]+)\\.call\\((.*)\\)");
-
-    if (with_builtin_tools) {
-        std::smatch match;
-        if (std::regex_match(input, match, builtin_call_regex)) {
-            auto name = match[1].str();
-            auto raw_args = match[2].str();
-
-            // TODO: if/when builtin tools start accepting more than 1 argument, use parse_json for real parsing.
-            auto it_eq = raw_args.find('=');
-            auto arg_name = raw_args.substr(0, it_eq);
-            auto arg_value_str = raw_args.substr(it_eq + 1);
-            auto arg_value = json::parse(arg_value_str);
-
-            return {
-                /* .role = */ "assistant",
-                /* .content = */ match.prefix().str(),
-                /* .tool_calls = */ {
-                    {
-                        /* .name = */ match[1],
-                        /* .arguments = */ (json {
-                            {arg_name, arg_value},
-                        }).dump(),
-                        /* .id = */ "",
-                    },
-                },
-            };
-        }
-    }
-    return parse_json_tool_calls(input, std::nullopt, function_regex, close_regex);
-}
-
-static common_chat_params common_chat_params_init_deepseek_r1(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
-    common_chat_params data;
-    data.grammar_lazy = inputs.tool_choice != "required";
-    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-        std::vector<std::string> tool_rules;
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const auto & function = tool["function"];
-            std::string name = function["name"];
-            auto parameters = function["parameters"];
-            auto args_rule = builder.add_schema(name + "-args", parameters);
-            tool_rules.push_back(builder.add_rule(name + "-call",
-                "\"<｜tool▁call▁begin｜>function<｜tool▁sep｜>" + name + "\\n```json\\n\" " + args_rule + " \"```<｜tool▁call▁end｜>\""));
-        });
-        data.grammar_triggers.push_back({"<｜tool▁calls▁begin｜>", /* .at_start = */ false});
-        data.preserved_tokens = {
-            "<｜tool▁sep｜>",
-            "<｜tool▁call▁end｜>",
-        };
-        builder.add_rule("root", "\"<｜tool▁calls▁begin｜>\" (" + string_join(tool_rules, " | ") + ")" + (inputs.parallel_tool_calls ? "*" : "") + " space");
-    }, grammar_options);
-    auto prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
-    data.prompt = prompt;
-    data.format = COMMON_CHAT_FORMAT_DEEPSEEK_R1;
-    return data;
-}
-static common_chat_msg common_chat_parse_deepseek_r1(const std::string & input) {
-    static std::regex trigger_regex("<｜tool▁calls▁begin｜>");
-    static std::regex function_regex("<｜tool▁call▁begin｜>function<｜tool▁sep｜>([^\n]+)\n```json\n");
-    static std::regex close_regex("```<｜tool▁call▁end｜>");
-    return parse_json_tool_calls(input, trigger_regex, function_regex, close_regex);
-}
-
-static common_chat_params common_chat_params_init_firefunction_v2(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
-    fprintf(stderr, "%s\n", __func__);
-    common_chat_params data;
-    data.prompt = apply(tmpl, inputs.messages, /* tools= */ nullptr, inputs.add_generation_prompt, {
-        {"datetime", "Jan 29 2025 13:00:00 GMT"},
-        {"functions", json(inputs.tools.empty() ? "" : inputs.tools.dump(2))},
-    });
-    if (!inputs.tools.is_null() && !inputs.tools.empty()) {
-        data.grammar_lazy = inputs.tool_choice != "required";
-        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-            auto schemas = json::array();
-            foreach_function(inputs.tools, [&](const json & tool) {
-                const auto & function = tool["function"];
-                schemas.push_back({
-                    {"type", "object"},
-                    {"properties", {
-                        {"name", {
-                            {"type", "string"},
-                            {"const", function["name"]},
-                        }},
-                        {"arguments", function["parameters"]},
-                    }},
-                    {"required", json::array({"name", "arguments", "id"})},
-                });
-            });
-            auto schema = json {
-                {"type", "array"},
-                {"items", schemas.size() == 1 ? schemas[0] : json {{"anyOf", schemas}}},
-                {"minItems", 1},
-            };
-            if (!inputs.parallel_tool_calls) {
-                schema["maxItems"] = 1;
-            }
-            builder.add_rule("root", "\" functools\"? " + builder.add_schema("tool_calls", schema));
-        }, grammar_options);
-        data.grammar_triggers.push_back({" functools[", /* .at_start = */ false});
-        data.format = COMMON_CHAT_FORMAT_FIREFUNCTION_V2;
-    } else {
-        data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
-    }
-    return data;
-}
-static common_chat_msg common_chat_parse_firefunction_v2(const std::string & input) {
-    return parse_prefixed_json_tool_call_array(input, " functools[", /* rstrip_prefix= */ 1);
-}
-
-static common_chat_params common_chat_params_init_functionary_v3_2(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
-    // >>>all\nlet's call functions>>>fn1\n{"arg1": 1...}\n>>>fn2\n{"arg1": 1...}...
-    // Using ">>>f1\n", ">>>f2\n"... as trigger words for the grammar
-    common_chat_params data;
-    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
-    data.format = COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2;
-    if (!inputs.tools.is_null() && !inputs.tools.empty()) {
-        data.grammar_lazy = inputs.tool_choice != "required";
-        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-            std::vector<std::string> first_tool_rules;
-            std::vector<std::string> subsequent_tool_rules;
-            foreach_function(inputs.tools, [&](const json & tool) {
-                const auto & function = tool["function"];
-                std::string name = function["name"];
-                auto parameters = function["parameters"];
-                auto args_rule = builder.add_schema(name + "-args", parameters);
-                first_tool_rules.push_back(builder.add_rule(name + "-call", "\"" + name + "\\n\" " + args_rule));
-                subsequent_tool_rules.push_back(builder.add_rule(name + "-call2", "\">>>" + name + "\\n\" " + args_rule));
-                data.grammar_triggers.push_back({name, /* .at_start = */ true});
-                data.grammar_triggers.push_back({">>>" + name, /* .at_start = */ false});
-            });
-            auto first_rule = first_tool_rules.empty() ? "" : builder.add_rule("first_tool_call", string_join(first_tool_rules, " | ")) + " space";
-            if (inputs.parallel_tool_calls) {
-                auto subsequent_rule = builder.add_rule("subsequent_tool_call", string_join(subsequent_tool_rules, " | ")) + " space";
-                builder.add_rule("root", first_rule + " (" + subsequent_rule + ")*");
-            } else {
-                builder.add_rule("root", first_rule);
-            }
-
-        }, grammar_options);
-    }
-    return data;
-}
-
-static bool consume(std::string::const_iterator & it, const std::string::const_iterator & end, const std::string & expected) {
-    auto expected_it = expected.begin();
-    auto tmp_it = it;
-    while (tmp_it != end && expected_it != expected.end() && *tmp_it == *expected_it) {
-        ++tmp_it;
-        ++expected_it;
-    }
-    if (expected_it == expected.end()) {
-        it = tmp_it;
-        return true;
-    }
-    return false;
-}
-
-static common_chat_msg common_chat_parse_functionary_v3_2(const std::string & input) {
-    static std::regex function_regex(R"((?:>>>)?(\w+)\n)");
-    static std::regex close_regex(R"($|(?=>>>))");
-
-    std::string content;
-    auto it = input.begin();
-    const auto end = input.end();
-
-    if (consume(it, end, "all\n")) {
-        std::smatch match;
-        if (std::regex_search(it, end, match, function_regex)) {
-            auto fun_it = match.prefix().second;
-            content = std::string(it, fun_it);
-            it = fun_it;
-        } else {
-            common_chat_msg res;
-            res.role = "assistant";
-            res.content = std::string(it, end);
-            return res;
-        }
-    }
-    // TODO: tighten & simplify.
-    try {
-        auto res = parse_json_tool_calls(std::string(it, end), std::nullopt, function_regex, close_regex);
-        res.content = content + res.content;
-        return res;
-    } catch (const std::exception & e) {
-        LOG_ERR("Failed to parse functionary v3.2 input: %s\n", e.what());
-        common_chat_msg res;
-        res.role = "assistant";
-        res.content = input;
-        return res;
-    }
-}
-
-static common_chat_params common_chat_params_init_functionary_v3_1_llama_3_1(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
-    // https://github.com/MeetKai/functionary/blob/main/tests/prompt_test_v3-llama3.1.txt
-    common_chat_params data;
-    json tools = inputs.tools.is_null() ? inputs.tools : json::array();
-    std::string python_code_argument_name;
-    auto has_raw_python = false;
-
-    data.grammar_lazy = inputs.tool_choice != "required";
-    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-        std::vector<std::string> tool_rules;
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const auto & function = tool["function"];
-            const auto & parameters = function["parameters"];
-            std::string name = function["name"];
-            if (name == "python" || name == "ipython") {
-                if (!parameters.contains("type")) {
-                    throw std::runtime_error("Missing type in python tool");
-                }
-                has_raw_python = true;
-                auto type = parameters.at("type");
-                if (type == "object") {
-                    auto properties = parameters.at("properties");
-                    for (auto it = properties.begin(); it != properties.end(); ++it) {
-                        if (it.value().at("type") == "string") {
-                            if (!python_code_argument_name.empty()) {
-                                throw std::runtime_error("Multiple string arguments found in python tool");
-                            }
-                            python_code_argument_name = it.key();
-                        }
-                    }
-                    if (python_code_argument_name.empty()) {
-                        throw std::runtime_error("No string argument found in python tool");
-                    }
-                } else if (type != "string") {
-                    throw std::runtime_error("Invalid type in python tool: " + type.dump());
-                }
-            }
-            tool_rules.push_back(builder.add_rule(name + "-call", "\"<function=" + name + ">\" " + builder.add_schema(name + "-args", parameters) + " \"</function>\" space"));
-        });
-        if (has_raw_python) {
-            tool_rules.push_back(builder.add_rule("python-call", "\"<|python_tag|>\" .*"));
-            data.grammar_triggers.push_back({"<|python_tag|>", /* .at_start = */ false});
-        }
-        auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | ")) + " space";
-        builder.add_rule("root", inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call);
-        data.grammar_triggers.push_back({"<function=", /* .at_start = */ false});
-    }, grammar_options);
-
-    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
-    // TODO: if (has_raw_python)
-    data.format = COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1;
-    return data;
-}
-static common_chat_msg common_chat_parse_functionary_v3_1_llama_3_1(const std::string & input) {
-    // This version of Functionary still supports the llama 3.1 tool call format for the python tool.
-    static std::regex python_tag_regex(R"(<\|python_tag\|>([\s\S\n]*)$)");
-    std::smatch match;
-    if (std::regex_search(input, match, python_tag_regex)) {
-        auto code = match[1].str();
-        return {
-            /* .role = */ "assistant",
-            /* .content = */ match.prefix().str(),
-            /* .tool_calls = */ {
-                {
-                    /* .name = */ "python",
-                    /* .arguments = */ (json {{"code", code}}).dump(),
-                    /* .id = */ "",
-                },
-            }
-        };
-    }
-    static std::regex function_regex(R"(<function=(\w+)>)");
-    static std::regex close_regex(R"(</function>)");
-    // TODO: tighten & simplify.
-    return parse_json_tool_calls(input, std::nullopt, function_regex, close_regex);
-}
-
-static common_chat_params common_chat_params_init_hermes_2_pro(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
-    common_chat_params data;
-    // (content)?(<tool_call>{"name": "foo", "arguments": {"a": 1}}</tool_call>)*
-    data.grammar_lazy = inputs.tool_choice != "required";
-    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-        std::vector<std::string> tool_rules;
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const auto & function = tool["function"];
-            std::string name = function["name"];
-            auto parameters = function["parameters"];
-            builder.resolve_refs(parameters);
-            tool_rules.push_back(builder.add_schema(name + "-call", {
-                {"type", "object"},
-                {"properties", json {
-                    {"name", json {{"const", name}}},
-                    {"arguments", parameters},
-                }},
-                {"required", json::array({"name", "arguments"})},
-            }));
-        });
-        auto tool_call = "\"<tool_call>\" space " + builder.add_rule("tool_call", string_join(tool_rules, " | ")) + " \"</tool_call>\" space";
-        builder.add_rule("root", inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call);
-        data.grammar_triggers.push_back({"<tool_call>", /* .at_start = */ false});
-        data.preserved_tokens = { "</tool_call>" };
-    }, grammar_options);
-
-    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
-    data.format = COMMON_CHAT_FORMAT_HERMES_2_PRO;
-    return data;
-}
-static common_chat_msg common_chat_parse_hermes_2_pro(const std::string & input) {
-    try {
-        std::regex start_pattern(R"([\n\s]*<tool_call>)");
-        std::regex middle_pattern(R"([\n\s]*</tool_call>[\n\s]*<tool_call>)");
-        std::regex end_pattern(R"([\n\s]*</tool_call>[\n\s]*$)");
-
-        auto end = input.end();
-        std::sregex_iterator rend;
-        std::sregex_iterator rit(input.begin(), end, start_pattern);
-        if (rit == rend) {
-            return {
-                /* .role = */ "assistant",
-                /* .content = */ input,
-                /* .tool_calls = */ {},
-            };
-        }
-
-        common_chat_msg result;
-        result.role = "assistant";
-        result.content = rit->prefix();
-
-        auto it = rit->suffix().first;
-        while (it != end) {
-            json call;
-            if (!parse_json(it, end, call)) {
-                throw std::runtime_error("Failed to parse json tool call");
-            }
-            const auto & arguments = call["arguments"];
-            result.tool_calls.push_back({
-                call["name"],
-                arguments.dump(),
-                // arguments.is_string() ? arguments.get<std::string>() : arguments.dump(),
-                /* id= */ "",
-            });
-            rit = {it, end, middle_pattern};
-            if (rit != rend) {
-                it = rit->suffix().first;
-            } else {
-                rit = {it, end, end_pattern};
-                if (rit == rend) {
-                    throw std::runtime_error("Malformed input, missing </tool_call>");
-                }
-                break;
-            }
-        }
-        return result;
-    } catch (const std::exception & e) {
-        return {
-            /* .role = */ "assistant",
-            /* .content = */ input,
-            /* .tool_calls = */ {},
-        };
-    }
-}
-
-static common_chat_params common_chat_params_init_without_tools(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
-    common_chat_params data;
-    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
-    data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
-    data.grammar_lazy = false;
-    if (!inputs.json_schema.is_null()) {
-        if (!inputs.grammar.empty()) {
-            throw std::runtime_error("Either \"json_schema\" or \"grammar\" can be specified, but not both");
-        }
-        data.grammar = json_schema_to_grammar(inputs.json_schema);
-    } else {
-        data.grammar = inputs.grammar.empty();
-    }
-    return data;
-}
-
-common_chat_params common_chat_params_init(const common_chat_template & tmpl, const struct common_chat_inputs & inputs) {
-    auto has_tools = !inputs.tools.is_null() && inputs.tool_choice != "none";
-    LOG_DBG("[%s] has_tools=%s\n", __func__, has_tools ? "true" : "false");
-
-    if (has_tools && !inputs.grammar.empty()) {
-        throw std::runtime_error("Cannot specify grammar with tools");
-    }
-
-    const auto & src = tmpl.source();
-    if (src.find(">>>all") != std::string::npos) {
-        // Functionary prepends "all\n" to plain content outputs, so we use the parser no matter when
-        return common_chat_params_init_functionary_v3_2(tmpl, inputs);
-    }
-    if (src.find(" functools[") != std::string::npos) {
-        // Firefunction v2 requires datetime and functions in the context, even w/o tools.
-        return common_chat_params_init_firefunction_v2(tmpl, inputs);
-    }
-
-    if (!has_tools) {
-        return common_chat_params_init_without_tools(tmpl, inputs);
-    }
-
-    if (src.find("<tool_call>") != std::string::npos) {
-        return common_chat_params_init_hermes_2_pro(tmpl, inputs);
-    }
-    if (src.find("<|start_header_id|>") != std::string::npos
-        && src.find("<function=") != std::string::npos) {
-        return common_chat_params_init_functionary_v3_1_llama_3_1(tmpl, inputs);
-    }
-    if (src.find("<|start_header_id|>ipython<|end_header_id|>") != std::string::npos) {
-        auto allow_python_tag_builtin_tools = src.find("<|python_tag|>") != std::string::npos;
-        return common_chat_params_init_llama_3_1_tool_calls(tmpl, inputs, allow_python_tag_builtin_tools);
-    }
-    if (src.find("<｜tool▁calls▁begin｜>") != std::string::npos) {
-        return common_chat_params_init_deepseek_r1(tmpl, inputs);
-    }
-    if (src.find("[TOOL_CALLS]") != std::string::npos) {
-        return common_chat_params_init_mistral_nemo(tmpl, inputs);
-    }
-    if (src.find("<|END_THINKING|><|START_ACTION|>") != std::string::npos) {
-        return common_chat_params_init_command_r7b(tmpl, inputs);
-    }
-    return common_chat_params_init_generic(tmpl, inputs);
-}
-
-static common_chat_msg common_chat_parse_content_only(const std::string & input) {
-    return {
-        /* .role = */ "assistant",
-        /* .content = */ input,
-        /* .tool_calls = */ {},
-    };
-}
-
-common_chat_msg common_chat_parse(const std::string & input, common_chat_format format) {
-    switch (format) {
-        case COMMON_CHAT_FORMAT_CONTENT_ONLY:
-            return common_chat_parse_content_only(input);
-        case COMMON_CHAT_FORMAT_GENERIC:
-            return common_chat_parse_generic(input);
-        case COMMON_CHAT_FORMAT_MISTRAL_NEMO:
-            return common_chat_parse_mistral_nemo(input);
-        case COMMON_CHAT_FORMAT_LLAMA_3_X:
-            return common_chat_parse_llama_3_1(input);
-        case COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS:
-            return common_chat_parse_llama_3_1(input, /* with_builtin_tools= */ true);
-        case COMMON_CHAT_FORMAT_DEEPSEEK_R1:
-            return common_chat_parse_deepseek_r1(input);
-        case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2:
-            return common_chat_parse_functionary_v3_2(input);
-        case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1:
-            return common_chat_parse_functionary_v3_1_llama_3_1(input);
-        case COMMON_CHAT_FORMAT_HERMES_2_PRO:
-            return common_chat_parse_hermes_2_pro(input);
-        case COMMON_CHAT_FORMAT_FIREFUNCTION_V2:
-            return common_chat_parse_firefunction_v2(input);
-        case COMMON_CHAT_FORMAT_COMMAND_R7B:
-            return common_chat_parse_command_r7b(input);
-        default:
-            throw std::runtime_error("Unsupported format: " + common_chat_format_name(format));
-    }
-}

common/chat.hpp

@@ -1,52 +0,0 @@
-// Chat support (incl. tool call grammar constraining & output parsing) w/ generic & custom template handlers.
-
-#pragma once
-
-#include "common.h"
-#include <json.hpp>
-#include <optional>
-#include <string>
-#include <vector>
-
-using json = nlohmann::ordered_json;
-
-struct common_chat_inputs {
-    json messages;
-    json tools;
-    json tool_choice;
-    json json_schema;
-    bool parallel_tool_calls;
-    bool stream;
-    std::string grammar;
-    bool add_generation_prompt = true;
-};
-
-enum common_chat_format {
-    COMMON_CHAT_FORMAT_CONTENT_ONLY,
-    COMMON_CHAT_FORMAT_GENERIC,
-    COMMON_CHAT_FORMAT_MISTRAL_NEMO,
-    COMMON_CHAT_FORMAT_LLAMA_3_X,
-    COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS,
-    COMMON_CHAT_FORMAT_DEEPSEEK_R1,
-    COMMON_CHAT_FORMAT_FIREFUNCTION_V2,
-    COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2,
-    COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1,
-    COMMON_CHAT_FORMAT_HERMES_2_PRO,
-    COMMON_CHAT_FORMAT_COMMAND_R7B,
-
-    COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats
-};
-
-struct common_chat_params {
-    common_chat_format                  format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
-    json                                prompt;
-    std::string                         grammar;
-    bool                                grammar_lazy = false;
-    std::vector<common_grammar_trigger> grammar_triggers;
-    std::vector<std::string>            preserved_tokens;
-    std::vector<std::string>            additional_stops;
-};
-
-struct common_chat_params common_chat_params_init(const common_chat_template & tmpl, const struct common_chat_inputs & params);
-std::string               common_chat_format_name(common_chat_format format);
-common_chat_msg           common_chat_parse(      const std::string & input, common_chat_format format);

common/common.cpp

@@ -2,9 +2,6 @@
 #define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
 #endif
 
-#include "ggml.h"
-#include "gguf.h"
-
 #include "common.h"
 #include "log.h"
 // Change JSON_ASSERT from assert() to GGML_ASSERT:
@@ -12,8 +9,6 @@
 #include "json.hpp"
 #include "json-schema-to-grammar.h"
 #include "llama.h"
-#include "chat.hpp"
-#include "chat-template.hpp"
 
 #include <algorithm>
 #include <cinttypes>
@@ -23,7 +18,6 @@
 #include <cstdarg>
 #include <cstring>
 #include <ctime>
-#include <filesystem>
 #include <fstream>
 #include <iostream>
 #include <iterator>
@@ -68,29 +62,11 @@
 #ifdef __linux__
 #include <linux/limits.h>
 #elif defined(_WIN32)
-#   if !defined(PATH_MAX)
-#   define PATH_MAX MAX_PATH
-#   endif
+#define PATH_MAX MAX_PATH
 #else
 #include <sys/syslimits.h>
 #endif
 #define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
-
-//
-// CURL utils
-//
-
-using curl_ptr = std::unique_ptr<CURL, decltype(&curl_easy_cleanup)>;
-
-// cannot use unique_ptr for curl_slist, because we cannot update without destroying the old one
-struct curl_slist_ptr {
-    struct curl_slist * ptr = nullptr;
-    ~curl_slist_ptr() {
-        if (ptr) {
-            curl_slist_free_all(ptr);
-        }
-    }
-};
 #endif // LLAMA_USE_CURL
 
 using json = nlohmann::ordered_json;
@@ -485,48 +461,6 @@ void string_replace_all(std::string & s, const std::string & search, const std::
     s = std::move(builder);
 }
 
-std::string string_join(const std::vector<std::string> & values, const std::string & separator) {
-    std::ostringstream result;
-    for (size_t i = 0; i < values.size(); ++i) {
-        if (i > 0) {
-            result << separator;
-        }
-        result << values[i];
-    }
-    return result.str();
-}
-
-std::vector<std::string> string_split(const std::string & str, const std::string & delimiter) {
-    std::vector<std::string> parts;
-    size_t start = 0;
-    size_t end = str.find(delimiter);
-
-    while (end != std::string::npos) {
-        parts.push_back(str.substr(start, end - start));
-        start = end + delimiter.length();
-        end = str.find(delimiter, start);
-    }
-
-    parts.push_back(str.substr(start));
-
-    return parts;
-}
-
-std::string string_repeat(const std::string & str, size_t n) {
-    if (n == 0) {
-        return "";
-    }
-
-    std::string result;
-    result.reserve(str.length() * n);
-
-    for (size_t i = 0; i < n; ++i) {
-        result += str;
-    }
-
-    return result;
-}
-
 std::string string_from(bool value) {
     return value ? "true" : "false";
 }
@@ -718,17 +652,7 @@ bool fs_validate_filename(const std::string & filename) {
 
     std::u32string filename_utf32;
     try {
-#if defined(__clang__)
-        // disable C++17 deprecation warning for std::codecvt_utf8
-#    pragma clang diagnostic push
-#    pragma clang diagnostic ignored "-Wdeprecated-declarations"
-#endif
         std::wstring_convert<std::codecvt_utf8<char32_t>, char32_t> converter;
-
-#if defined(__clang__)
-#    pragma clang diagnostic pop
-#endif
-
         filename_utf32 = converter.from_bytes(filename);
 
         // If the reverse conversion mismatches, it means overlong UTF-8 sequences were used,
@@ -905,11 +829,11 @@ struct common_init_result common_init_from_params(common_params & params) {
     llama_model * model = nullptr;
 
     if (!params.hf_repo.empty() && !params.hf_file.empty()) {
-        model = common_load_model_from_hf(params.hf_repo, params.hf_file, params.model, params.hf_token, mparams);
+        model = common_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
     } else if (!params.model_url.empty()) {
-        model = common_load_model_from_url(params.model_url, params.model, params.hf_token, mparams);
+        model = common_load_model_from_url(params.model_url.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
     } else {
-        model = llama_model_load_from_file(params.model.c_str(), mparams);
+        model = llama_load_model_from_file(params.model.c_str(), mparams);
     }
 
     if (model == NULL) {
@@ -917,28 +841,26 @@ struct common_init_result common_init_from_params(common_params & params) {
         return iparams;
     }
 
-    const llama_vocab * vocab = llama_model_get_vocab(model);
-
     if (params.reranking) {
         bool ok = true;
 
-        if (llama_vocab_bos(vocab) == LLAMA_TOKEN_NULL) {
-            LOG_WRN("%s: warning: vocab does not have a  BOS token, reranking will not work\n", __func__);
+        if (llama_token_bos(model) == LLAMA_TOKEN_NULL) {
+            LOG_WRN("%s: warning: model does not have a  BOS token, reranking will not work\n", __func__);
             ok = false;
         }
 
-        if (llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
-            LOG_WRN("%s: warning: vocab does not have an EOS token, reranking will not work\n", __func__);
+        if (llama_token_eos(model) == LLAMA_TOKEN_NULL) {
+            LOG_WRN("%s: warning: model does not have an EOS token, reranking will not work\n", __func__);
             ok = false;
         }
 
-        if (llama_vocab_sep(vocab) == LLAMA_TOKEN_NULL) {
-            LOG_WRN("%s: warning: vocab does not have a  SEP token, reranking will not work\n", __func__);
+        if (llama_token_sep(model) == LLAMA_TOKEN_NULL) {
+            LOG_WRN("%s: warning: model does not have a  SEP token, reranking will not work\n", __func__);
             ok = false;
         }
 
         if (!ok) {
-            llama_model_free(model);
+            llama_free_model(model);
 
             return iparams;
         }
@@ -946,32 +868,32 @@ struct common_init_result common_init_from_params(common_params & params) {
 
     auto cparams = common_context_params_to_llama(params);
 
-    llama_context * lctx = llama_init_from_model(model, cparams);
+    llama_context * lctx = llama_new_context_with_model(model, cparams);
     if (lctx == NULL) {
         LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.c_str());
-        llama_model_free(model);
+        llama_free_model(model);
         return iparams;
     }
 
     if (params.ctx_shift && !llama_kv_cache_can_shift(lctx)) {
-        LOG_WRN("%s: KV cache shifting is not supported for this model, disabling KV cache shifting\n", __func__);
-        params.ctx_shift = false;
+        LOG_ERR("%s: KV cache shifting is not supported for this model (--no-context-shift to disable)'\n", __func__);
+        llama_free_model(model);
+        return iparams;
     }
 
     if (!params.control_vectors.empty()) {
         if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
-        if (params.control_vector_layer_end   <= 0) params.control_vector_layer_end   = llama_model_n_layer(model);
+        if (params.control_vector_layer_end   <= 0) params.control_vector_layer_end   = llama_n_layer(model);
 
         const auto cvec = common_control_vector_load(params.control_vectors);
         if (cvec.n_embd == -1) {
             llama_free(lctx);
-            llama_model_free(model);
+            llama_free_model(model);
 
             return iparams;
         }
 
-        int err = llama_apply_adapter_cvec(
-                lctx,
+        int err = llama_control_vector_apply(lctx,
                                              cvec.data.data(),
                                              cvec.data.size(),
                                              cvec.n_embd,
@@ -979,7 +901,7 @@ struct common_init_result common_init_from_params(common_params & params) {
                                              params.control_vector_layer_end);
         if (err) {
             llama_free(lctx);
-            llama_model_free(model);
+            llama_free_model(model);
 
             return iparams;
         }
@@ -987,54 +909,33 @@ struct common_init_result common_init_from_params(common_params & params) {
 
     // load and optionally apply lora adapters
     for (auto & la : params.lora_adapters) {
-        llama_adapter_lora_ptr lora;
-        lora.reset(llama_adapter_lora_init(model, la.path.c_str()));
-        if (lora == nullptr) {
+        common_lora_adapter_container loaded_la;
+        loaded_la.path = la.path;
+        loaded_la.scale = la.scale;
+        loaded_la.adapter = llama_lora_adapter_init(model, la.path.c_str());
+        if (loaded_la.adapter == nullptr) {
             LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
             llama_free(lctx);
-            llama_model_free(model);
+            llama_free_model(model);
             return iparams;
         }
-
-        la.ptr = lora.get();
-        iparams.lora.emplace_back(std::move(lora)); // copy to list of loaded adapters
+        iparams.lora_adapters.push_back(loaded_la); // copy to list of loaded adapters
     }
-
     if (!params.lora_init_without_apply) {
-        common_set_adapter_lora(lctx, params.lora_adapters);
+        common_lora_adapters_apply(lctx, iparams.lora_adapters);
     }
 
-    if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
-        LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__);
+    if (params.sampling.ignore_eos && llama_token_eos(model) == LLAMA_TOKEN_NULL) {
+        LOG_WRN("%s: warning: model does not have an EOS token, ignoring --ignore-eos\n", __func__);
         params.sampling.ignore_eos = false;
     }
 
-    if (params.sampling.ignore_eos) {
-        for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
-            if (llama_vocab_is_eog(vocab, i)) {
-                LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY);
-                params.sampling.logit_bias.push_back({i, -INFINITY});
-            }
-        }
-    }
-
-    if (params.sampling.penalty_last_n == -1) {
-        LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
-        params.sampling.penalty_last_n = llama_n_ctx(lctx);
-    }
-
-    if (params.sampling.dry_penalty_last_n == -1) {
-        LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
-        params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
-    }
-
     if (params.warmup) {
         LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);
 
         std::vector<llama_token> tmp;
-        llama_token bos = llama_vocab_bos(vocab);
-        llama_token eos = llama_vocab_eos(vocab);
-
+        llama_token bos = llama_token_bos(model);
+        llama_token eos = llama_token_eos(model);
         // some models (e.g. T5) don't have a BOS token
         if (bos != LLAMA_TOKEN_NULL) {
             tmp.push_back(bos);
@@ -1049,7 +950,7 @@ struct common_init_result common_init_from_params(common_params & params) {
         if (llama_model_has_encoder(model)) {
             llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size()));
             llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
-            if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
+            if (decoder_start_token_id == -1) {
                 decoder_start_token_id = bos;
             }
             tmp.clear();
@@ -1063,17 +964,17 @@ struct common_init_result common_init_from_params(common_params & params) {
         llama_perf_context_reset(lctx);
     }
 
-    iparams.model.reset(model);
-    iparams.context.reset(lctx);
+    iparams.model   = model;
+    iparams.context = lctx;
 
     return iparams;
 }
 
-void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora) {
-    llama_clear_adapter_lora(ctx);
-    for (auto & la : lora) {
+void common_lora_adapters_apply(struct llama_context * ctx, std::vector<common_lora_adapter_container> & lora_adapters) {
+    llama_lora_adapter_clear(ctx);
+    for (auto & la : lora_adapters) {
         if (la.scale != 0.0f) {
-            llama_set_adapter_lora(ctx, la.ptr, la.scale);
+            llama_lora_adapter_set(ctx, la.adapter, la.scale);
         }
     }
 }
@@ -1087,6 +988,7 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
     if (params.n_gpu_layers != -1) {
         mparams.n_gpu_layers = params.n_gpu_layers;
     }
+    mparams.rpc_servers     = params.rpc_servers.c_str();
     mparams.main_gpu        = params.main_gpu;
     mparams.split_mode      = params.split_mode;
     mparams.tensor_split    = params.tensor_split;
@@ -1103,6 +1005,38 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
     return mparams;
 }
 
+static ggml_type kv_cache_type_from_str(const std::string & s) {
+    if (s == "f32") {
+        return GGML_TYPE_F32;
+    }
+    if (s == "f16") {
+        return GGML_TYPE_F16;
+    }
+    if (s == "bf16") {
+        return GGML_TYPE_BF16;
+    }
+    if (s == "q8_0") {
+        return GGML_TYPE_Q8_0;
+    }
+    if (s == "q4_0") {
+        return GGML_TYPE_Q4_0;
+    }
+    if (s == "q4_1") {
+        return GGML_TYPE_Q4_1;
+    }
+    if (s == "iq4_nl") {
+        return GGML_TYPE_IQ4_NL;
+    }
+    if (s == "q5_0") {
+        return GGML_TYPE_Q5_0;
+    }
+    if (s == "q5_1") {
+        return GGML_TYPE_Q5_1;
+    }
+
+    throw std::runtime_error("Unsupported cache type: " + s);
+}
+
 struct llama_context_params common_context_params_to_llama(const common_params & params) {
     auto cparams = llama_context_default_params();
 
@@ -1137,8 +1071,8 @@ struct llama_context_params common_context_params_to_llama(const common_params &
         cparams.pooling_type  = LLAMA_POOLING_TYPE_RANK;
     }
 
-    cparams.type_k = params.cache_type_k;
-    cparams.type_v = params.cache_type_v;
+    cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
+    cparams.type_v = kv_cache_type_from_str(params.cache_type_v);
 
     return cparams;
 }
@@ -1164,7 +1098,13 @@ struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_p
 #define CURL_MAX_RETRY 3
 #define CURL_RETRY_DELAY_SECONDS 2
 
-static bool curl_perform_with_retry(const std::string & url, CURL * curl, int max_attempts, int retry_delay_seconds) {
+
+static bool starts_with(const std::string & str, const std::string & prefix) {
+    // While we wait for C++20's std::string::starts_with...
+    return str.rfind(prefix, 0) == 0;
+}
+
+static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_attempts, int retry_delay_seconds) {
     int remaining_attempts = max_attempts;
 
     while (remaining_attempts > 0) {
@@ -1188,9 +1128,9 @@ static bool curl_perform_with_retry(const std::string & url, CURL * curl, int ma
 }
 
 static bool common_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {
+
     // Initialize libcurl
-    curl_ptr       curl(curl_easy_init(), &curl_easy_cleanup);
-    curl_slist_ptr http_headers;
+    std::unique_ptr<CURL, decltype(&curl_easy_cleanup)> curl(curl_easy_init(), &curl_easy_cleanup);
     if (!curl) {
         LOG_ERR("%s: error initializing libcurl\n", __func__);
         return false;
@@ -1204,9 +1144,11 @@ static bool common_download_file(const std::string & url, const std::string & pa
 
     // Check if hf-token or bearer-token was specified
     if (!hf_token.empty()) {
-        std::string auth_header = "Authorization: Bearer " + hf_token;
-        http_headers.ptr = curl_slist_append(http_headers.ptr, auth_header.c_str());
-        curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr);
+      std::string auth_header = "Authorization: Bearer ";
+      auth_header += hf_token.c_str();
+      struct curl_slist *http_headers = NULL;
+      http_headers = curl_slist_append(http_headers, auth_header.c_str());
+      curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers);
     }
 
 #if defined(_WIN32)
@@ -1216,7 +1158,8 @@ static bool common_download_file(const std::string & url, const std::string & pa
 #endif
 
     // Check if the file already exists locally
-    auto file_exists = std::filesystem::exists(path);
+    struct stat model_file_info;
+    auto file_exists = (stat(path.c_str(), &model_file_info) == 0);
 
     // If the file exists, check its JSON metadata companion file.
     std::string metadata_path = path + ".json";
@@ -1258,13 +1201,11 @@ static bool common_download_file(const std::string & url, const std::string & pa
         std::string etag;
         std::string last_modified;
     };
-
     common_load_model_from_url_headers headers;
-
     {
         typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
         auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
-            common_load_model_from_url_headers * headers = (common_load_model_from_url_headers *) userdata;
+            common_load_model_from_url_headers *headers = (common_load_model_from_url_headers *) userdata;
 
             static std::regex header_regex("([^:]+): (.*)\r\n");
             static std::regex etag_regex("ETag", std::regex_constants::icase);
@@ -1401,17 +1342,17 @@ static bool common_download_file(const std::string & url, const std::string & pa
 }
 
 struct llama_model * common_load_model_from_url(
-        const std::string & model_url,
-        const std::string & local_path,
-        const std::string & hf_token,
+        const char * model_url,
+        const char * path_model,
+        const char * hf_token,
         const struct llama_model_params & params) {
     // Basic validation of the model_url
-    if (model_url.empty()) {
+    if (!model_url || strlen(model_url) == 0) {
         LOG_ERR("%s: invalid model_url\n", __func__);
         return NULL;
     }
 
-    if (!common_download_file(model_url, local_path, hf_token)) {
+    if (!common_download_file(model_url, path_model, hf_token)) {
         return NULL;
     }
 
@@ -1422,9 +1363,9 @@ struct llama_model * common_load_model_from_url(
             /*.no_alloc = */ true,
             /*.ctx      = */ NULL,
         };
-        auto * ctx_gguf = gguf_init_from_file(local_path.c_str(), gguf_params);
+        auto * ctx_gguf = gguf_init_from_file(path_model, gguf_params);
         if (!ctx_gguf) {
-            LOG_ERR("\n%s:  failed to load input GGUF from %s\n", __func__, local_path.c_str());
+            LOG_ERR("\n%s:  failed to load input GGUF from %s\n", __func__, path_model);
             return NULL;
         }
 
@@ -1443,13 +1384,13 @@ struct llama_model * common_load_model_from_url(
         // Verify the first split file format
         // and extract split URL and PATH prefixes
         {
-            if (!llama_split_prefix(split_prefix, sizeof(split_prefix), local_path.c_str(), 0, n_split)) {
-                LOG_ERR("\n%s: unexpected model file name: %s n_split=%d\n", __func__, local_path.c_str(), n_split);
+            if (!llama_split_prefix(split_prefix, sizeof(split_prefix), path_model, 0, n_split)) {
+                LOG_ERR("\n%s: unexpected model file name: %s n_split=%d\n", __func__, path_model, n_split);
                 return NULL;
             }
 
-            if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model_url.c_str(), 0, n_split)) {
-                LOG_ERR("\n%s: unexpected model url: %s n_split=%d\n", __func__, model_url.c_str(), n_split);
+            if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model_url, 0, n_split)) {
+                LOG_ERR("\n%s: unexpected model url: %s n_split=%d\n", __func__, model_url, n_split);
                 return NULL;
             }
         }
@@ -1476,14 +1417,14 @@ struct llama_model * common_load_model_from_url(
         }
     }
 
-    return llama_model_load_from_file(local_path.c_str(), params);
+    return llama_load_model_from_file(path_model, params);
 }
 
 struct llama_model * common_load_model_from_hf(
-        const std::string & repo,
-        const std::string & remote_path,
-        const std::string & local_path,
-        const std::string & hf_token,
+        const char * repo,
+        const char * model,
+        const char * path_model,
+        const char * hf_token,
         const struct llama_model_params & params) {
     // construct hugging face model url:
     //
@@ -1497,111 +1438,32 @@ struct llama_model * common_load_model_from_hf(
     std::string model_url = "https://huggingface.co/";
     model_url += repo;
     model_url += "/resolve/main/";
-    model_url += remote_path;
+    model_url += model;
 
-    return common_load_model_from_url(model_url, local_path, hf_token, params);
-}
-
-/**
- * Allow getting the HF file from the HF repo with tag (like ollama), for example:
- * - bartowski/Llama-3.2-3B-Instruct-GGUF:q4
- * - bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
- * - bartowski/Llama-3.2-3B-Instruct-GGUF:q5_k_s
- * Tag is optional, default to "latest" (meaning it checks for Q4_K_M first, then Q4, then if not found, return the first GGUF file in repo)
- *
- * Return pair of <repo, file> (with "repo" already having tag removed)
- *
- * Note: we use the Ollama-compatible HF API, but not using the blobId. Instead, we use the special "ggufFile" field which returns the value for "hf_file". This is done to be backward-compatible with existing cache files.
- */
-std::pair<std::string, std::string> common_get_hf_file(const std::string & hf_repo_with_tag, const std::string & hf_token) {
-    auto parts = string_split<std::string>(hf_repo_with_tag, ':');
-    std::string tag = parts.size() > 1 ? parts.back() : "latest";
-    std::string hf_repo = parts[0];
-    if (string_split<std::string>(hf_repo, '/').size() != 2) {
-        throw std::invalid_argument("error: invalid HF repo format, expected <user>/<model>[:quant]\n");
-    }
-
-    // fetch model info from Hugging Face Hub API
-    json model_info;
-    curl_ptr       curl(curl_easy_init(), &curl_easy_cleanup);
-    curl_slist_ptr http_headers;
-    std::string res_str;
-    std::string url = "https://huggingface.co/v2/" + hf_repo + "/manifests/" + tag;
-    curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
-    curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L);
-    typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * ptr, size_t size, size_t nmemb, void * data);
-    auto write_callback = [](void * ptr, size_t size, size_t nmemb, void * data) -> size_t {
-        static_cast<std::string *>(data)->append((char * ) ptr, size * nmemb);
-        return size * nmemb;
-    };
-    curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
-    curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, &res_str);
-#if defined(_WIN32)
-    curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
-#endif
-    if (!hf_token.empty()) {
-        std::string auth_header = "Authorization: Bearer " + hf_token;
-        http_headers.ptr = curl_slist_append(http_headers.ptr, auth_header.c_str());
-    }
-    // Important: the User-Agent must be "llama-cpp" to get the "ggufFile" field in the response
-    http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp");
-    http_headers.ptr = curl_slist_append(http_headers.ptr, "Accept: application/json");
-    curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr);
-
-    CURLcode res = curl_easy_perform(curl.get());
-
-    if (res != CURLE_OK) {
-        throw std::runtime_error("error: cannot make GET request to HF API");
-    }
-
-    long res_code;
-    curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &res_code);
-    if (res_code == 200) {
-        model_info = json::parse(res_str);
-    } else if (res_code == 401) {
-        throw std::runtime_error("error: model is private or does not exist; if you are accessing a gated model, please provide a valid HF token");
-    } else {
-        throw std::runtime_error(string_format("error from HF API, response code: %ld, data: %s", res_code, res_str.c_str()));
-    }
-
-    // check response
-    if (!model_info.contains("ggufFile")) {
-        throw std::runtime_error("error: model does not have ggufFile");
-    }
-    json & gguf_file = model_info.at("ggufFile");
-    if (!gguf_file.contains("rfilename")) {
-        throw std::runtime_error("error: ggufFile does not have rfilename");
-    }
-
-    return std::make_pair(hf_repo, gguf_file.at("rfilename"));
+    return common_load_model_from_url(model_url.c_str(), path_model, hf_token, params);
 }
 
 #else
 
 struct llama_model * common_load_model_from_url(
-        const std::string & /*model_url*/,
-        const std::string & /*local_path*/,
-        const std::string & /*hf_token*/,
+        const char * /*model_url*/,
+        const char * /*path_model*/,
+        const char * /*hf_token*/,
         const struct llama_model_params & /*params*/) {
     LOG_WRN("%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__);
     return nullptr;
 }
 
 struct llama_model * common_load_model_from_hf(
-        const std::string & /*repo*/,
-        const std::string & /*remote_path*/,
-        const std::string & /*local_path*/,
-        const std::string & /*hf_token*/,
+        const char * /*repo*/,
+        const char * /*model*/,
+        const char * /*path_model*/,
+        const char * /*hf_token*/,
         const struct llama_model_params & /*params*/) {
     LOG_WRN("%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__);
     return nullptr;
 }
 
-std::pair<std::string, std::string> common_get_hf_file(const std::string &, const std::string &) {
-    LOG_WRN("%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__);
-    return std::make_pair("", "");
-}
-
 #endif // LLAMA_USE_CURL
 
 //
@@ -1700,23 +1562,21 @@ std::vector<llama_token> common_tokenize(
            const std::string & text,
                         bool   add_special,
                         bool   parse_special) {
-    const llama_model * model = llama_get_model(ctx);
-    const llama_vocab * vocab = llama_model_get_vocab(model);
-    return common_tokenize(vocab, text, add_special, parse_special);
+    return common_tokenize(llama_get_model(ctx), text, add_special, parse_special);
 }
 
 std::vector<llama_token> common_tokenize(
-    const struct llama_vocab * vocab,
+    const struct llama_model * model,
            const std::string & text,
                         bool   add_special,
                         bool   parse_special) {
     // upper limit for the number of tokens
     int n_tokens = text.length() + 2 * add_special;
     std::vector<llama_token> result(n_tokens);
-    n_tokens = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
+    n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
     if (n_tokens < 0) {
         result.resize(-n_tokens);
-        int check = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
+        int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
         GGML_ASSERT(check == -n_tokens);
     } else {
         result.resize(n_tokens);
@@ -1725,18 +1585,12 @@ std::vector<llama_token> common_tokenize(
 }
 
 std::string common_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
-    const llama_model * model = llama_get_model(ctx);
-    const llama_vocab * vocab = llama_model_get_vocab(model);
-    return common_token_to_piece(vocab, token, special);
-}
-
-std::string common_token_to_piece(const struct llama_vocab * vocab, llama_token token, bool special) {
     std::string piece;
     piece.resize(piece.capacity());  // using string internal cache, 15 bytes + '\n'
-    const int n_chars = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special);
+    const int n_chars = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
     if (n_chars < 0) {
         piece.resize(-n_chars);
-        int check = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special);
+        int check = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
         GGML_ASSERT(check == -n_chars);
     }
     else {
@@ -1746,19 +1600,13 @@ std::string common_token_to_piece(const struct llama_vocab * vocab, llama_token
     return piece;
 }
 
-std::string common_detokenize(const struct llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
-    const llama_model * model = llama_get_model(ctx);
-    const llama_vocab * vocab = llama_model_get_vocab(model);
-    return common_detokenize(vocab, tokens, special);
-}
-
-std::string common_detokenize(const struct llama_vocab * vocab, const std::vector<llama_token> & tokens, bool special) {
+std::string common_detokenize(llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
     std::string text;
     text.resize(std::max(text.capacity(), tokens.size()));
-    int32_t n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
+    int32_t n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
     if (n_chars < 0) {
         text.resize(-n_chars);
-        n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
+        n_chars = llama_detokenize(llama_get_model(ctx), tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
         GGML_ASSERT(n_chars <= (int32_t)text.size());  // whitespace trimming is performed after per-token detokenization
     }
 
@@ -1772,80 +1620,63 @@ std::string common_detokenize(const struct llama_vocab * vocab, const std::vecto
 // Chat template utils
 //
 
-bool common_chat_verify_template(const std::string & tmpl, bool use_jinja) {
-    if (use_jinja) {
-        try {
-            auto chat_template = common_chat_template(tmpl, "<s>", "</s>");
-            common_chat_inputs inputs;
-            inputs.messages = json::array({{
-                {"role", "user"},
-                {"content", "test"},
-            }});
-            common_chat_params_init(chat_template, inputs);
-            return true;
-        } catch (const std::exception & e) {
-            LOG_ERR("%s: failed to apply template: %s\n", __func__, e.what());
-            return false;
-        }
-    }
+bool common_chat_verify_template(const std::string & tmpl) {
     llama_chat_message chat[] = {{"user", "test"}};
-    const int res = llama_chat_apply_template(tmpl.c_str(), chat, 1, true, nullptr, 0);
+    int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
     return res >= 0;
 }
 
-std::string common_chat_apply_template(
-        const common_chat_template & tmpl,
+std::string common_chat_apply_template(const struct llama_model * model,
+        const std::string & tmpl,
         const std::vector<common_chat_msg> & msgs,
-        bool add_ass,
-        bool use_jinja) {
-    if (use_jinja) {
-        auto messages = json::array();
-        for (const auto & msg : msgs) {
-            messages.push_back({{"role", msg.role}, {"content", msg.content}});
-        }
-        common_chat_inputs inputs;
-        inputs.messages = messages;
-        inputs.add_generation_prompt = add_ass;
-        return common_chat_params_init(tmpl, inputs).prompt;
-    }
-
+        bool add_ass) {
     int alloc_size = 0;
+    bool fallback = false; // indicate if we must fallback to default chatml
     std::vector<llama_chat_message> chat;
-    for (const auto & msg : msgs) {
+    for (auto & msg : msgs) {
         chat.push_back({msg.role.c_str(), msg.content.c_str()});
         alloc_size += (msg.role.size() + msg.content.size()) * 1.25;
     }
 
+    const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
     std::vector<char> buf(alloc_size);
 
     // run the first time to get the total output length
-    int32_t res = llama_chat_apply_template(tmpl.source().c_str(), chat.data(), chat.size(), add_ass, buf.data(), buf.size());
+    int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), add_ass, buf.data(), buf.size());
 
     // error: chat template is not supported
     if (res < 0) {
+        if (ptr_tmpl != nullptr) {
             // if the custom "tmpl" is not supported, we throw an error
             // this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template()
             throw std::runtime_error("this custom template is not supported");
+        } else {
+            // If the built-in template is not supported, we default to chatml
+            res = llama_chat_apply_template(nullptr, "chatml", chat.data(), chat.size(), add_ass, buf.data(), buf.size());
+            fallback = true;
+        }
     }
 
     // if it turns out that our buffer is too small, we resize it
     if ((size_t) res > buf.size()) {
         buf.resize(res);
-        res = llama_chat_apply_template(tmpl.source().c_str(), chat.data(), chat.size(), add_ass, buf.data(), buf.size());
+        res = llama_chat_apply_template(
+            fallback ? nullptr : model,
+            fallback ? "chatml" : ptr_tmpl,
+            chat.data(), chat.size(), add_ass, buf.data(), buf.size());
     }
 
     std::string formatted_chat(buf.data(), res);
     return formatted_chat;
 }
 
-std::string common_chat_format_single(
-        const common_chat_template & tmpl,
+std::string common_chat_format_single(const struct llama_model * model,
+        const std::string & tmpl,
         const std::vector<common_chat_msg> & past_msg,
         const common_chat_msg & new_msg,
-        bool add_ass,
-        bool use_jinja) {
+        bool add_ass) {
     std::ostringstream ss;
-    auto fmt_past_msg = past_msg.empty() ? "" : common_chat_apply_template(tmpl, past_msg, false, use_jinja);
+    auto fmt_past_msg = past_msg.empty() ? "" : common_chat_apply_template(model, tmpl, past_msg, false);
     std::vector<common_chat_msg> chat_new(past_msg);
     // if the past_msg ends with a newline, we must preserve it in the formatted version
     if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') {
@@ -1853,87 +1684,21 @@ std::string common_chat_format_single(
     };
     // format chat with new_msg
     chat_new.push_back(new_msg);
-    auto fmt_new_msg = common_chat_apply_template(tmpl, chat_new, add_ass, use_jinja);
+    auto fmt_new_msg = common_chat_apply_template(model, tmpl, chat_new, add_ass);
     // get the diff part
     ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size());
     return ss.str();
 }
 
-std::string common_chat_format_example(const common_chat_template & tmpl, bool use_jinja) {
+std::string common_chat_format_example(const struct llama_model * model,
+        const std::string & tmpl) {
     std::vector<common_chat_msg> msgs = {
-        {"system",    "You are a helpful assistant", {}},
-        {"user",      "Hello", {}},
-        {"assistant", "Hi there", {}},
-        {"user",      "How are you?", {}},
+        {"system",    "You are a helpful assistant"},
+        {"user",      "Hello"},
+        {"assistant", "Hi there"},
+        {"user",      "How are you?"},
     };
-    return common_chat_apply_template(tmpl, msgs, true, use_jinja);
-}
-
-#define CHATML_TEMPLATE_SRC \
-    "{%- for message in messages -%}\n" \
-    "  {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' -}}\n" \
-    "{%- endfor -%}\n" \
-    "{%- if add_generation_prompt -%}\n" \
-    "  {{- '<|im_start|>assistant\n' -}}\n" \
-    "{%- endif -%}"
-
-common_chat_templates common_chat_templates_from_model(const struct llama_model * model, const std::string & chat_template_override)
-{
-    std::string default_template_src;
-    std::string template_tool_use_src;
-
-    bool has_explicit_template = !chat_template_override.empty();
-    if (chat_template_override.empty()) {
-        auto str = llama_model_chat_template(model, /* name */ nullptr);
-        if (str) {
-            default_template_src = str;
-            has_explicit_template = true;
-        }
-        str = llama_model_chat_template(model, /* name */ "tool_use");
-        if (str) {
-            template_tool_use_src = str;
-            has_explicit_template = true;
-        }
-    } else {
-        default_template_src = chat_template_override;
-    }
-    if (default_template_src.empty() || default_template_src == "chatml") {
-        if (!template_tool_use_src.empty()) {
-            default_template_src = template_tool_use_src;
-        } else {
-            default_template_src = CHATML_TEMPLATE_SRC;
-        }
-    }
-    auto vocab = llama_model_get_vocab(model);
-    const auto get_token = [&](llama_token token, const char * name, const char * jinja_variable_name) {
-        if (token == LLAMA_TOKEN_NULL) {
-            if (default_template_src.find(jinja_variable_name) != std::string::npos
-                || template_tool_use_src.find(jinja_variable_name) != std::string::npos) {
-                LOG_WRN("%s: warning: vocab does not have a %s token, jinja template won't work as intended.\n", __func__, name);
-            }
-            return std::string();
-        } else {
-            return common_token_to_piece(vocab, token, true);
-        }
-    };
-    auto token_bos = get_token(llama_vocab_bos(vocab), "BOS", "bos_token");
-    auto token_eos = get_token(llama_vocab_eos(vocab), "EOS", "eos_token");
-    try {
-        return {
-            has_explicit_template,
-            std::make_unique<minja::chat_template>(default_template_src, token_bos, token_eos),
-            template_tool_use_src.empty()
-                ? nullptr
-                : std::make_unique<minja::chat_template>(template_tool_use_src, token_bos, token_eos),
-        };
-    } catch (const std::exception & e) {
-        LOG_ERR("%s: failed to parse chat template: %s\n", __func__, e.what());
-        return {
-            has_explicit_template,
-            std::make_unique<minja::chat_template>(CHATML_TEMPLATE_SRC, token_bos, token_eos),
-            nullptr,
-        };
-    }
+    return common_chat_apply_template(model, tmpl, msgs, true);
 }
 
 //
@@ -2024,9 +1789,7 @@ void common_embd_normalize(const float * inp, float * out, int n, int embd_norm)
             break;
         case 0: // max absolute
             for (int i = 0; i < n; i++) {
-                if (sum < std::abs(inp[i])) {
-                    sum = std::abs(inp[i]);
-                }
+                if (sum < std::abs(inp[i])) sum = std::abs(inp[i]);
             }
             sum /= 32760.0; // make an int16 range
             break;

common/common.h

@@ -2,9 +2,8 @@
 
 #pragma once
 
-#include "llama-cpp.h"
+#include "llama.h"
 
-#include <set>
 #include <string>
 #include <vector>
 #include <sstream>
@@ -25,20 +24,22 @@
 
 #define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf"
 
-struct common_adapter_lora_info {
+struct common_lora_adapter_info {
     std::string path;
     float scale;
+};
 
-    struct llama_adapter_lora * ptr;
+struct common_lora_adapter_container : common_lora_adapter_info {
+    struct llama_lora_adapter * adapter;
 };
 
 using llama_tokens = std::vector<llama_token>;
 
 // build info
 extern int LLAMA_BUILD_NUMBER;
-extern const char * LLAMA_COMMIT;
-extern const char * LLAMA_COMPILER;
-extern const char * LLAMA_BUILD_TARGET;
+extern char const * LLAMA_COMMIT;
+extern char const * LLAMA_COMPILER;
+extern char const * LLAMA_BUILD_TARGET;
 
 struct common_control_vector_load_info;
 
@@ -79,7 +80,6 @@ enum llama_example {
     LLAMA_EXAMPLE_LLAVA,
     LLAMA_EXAMPLE_LOOKUP,
     LLAMA_EXAMPLE_PARALLEL,
-    LLAMA_EXAMPLE_TTS,
 
     LLAMA_EXAMPLE_COUNT,
 };
@@ -95,7 +95,6 @@ enum common_sampler_type {
     COMMON_SAMPLER_TYPE_TEMPERATURE = 7,
     COMMON_SAMPLER_TYPE_XTC         = 8,
     COMMON_SAMPLER_TYPE_INFILL      = 9,
-    COMMON_SAMPLER_TYPE_PENALTIES   = 10,
 };
 
 // dimensionality reduction methods, used by cvector-generator
@@ -104,17 +103,6 @@ enum dimre_method {
     DIMRE_METHOD_MEAN,
 };
 
-enum common_conversation_mode {
-    COMMON_CONVERSATION_MODE_DISABLED = 0,
-    COMMON_CONVERSATION_MODE_ENABLED  = 1,
-    COMMON_CONVERSATION_MODE_AUTO     = 2,
-};
-
-struct common_grammar_trigger {
-    std::string word;
-    bool at_start;
-};
-
 // sampling parameters
 struct common_params_sampling {
     uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
@@ -142,15 +130,14 @@ struct common_params_sampling {
     int32_t mirostat           = 0;     // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
     float   mirostat_tau       = 5.00f; // target entropy
     float   mirostat_eta       = 0.10f; // learning rate
+    bool    penalize_nl        = false; // consider newlines as a repeatable token
     bool    ignore_eos         = false;
     bool    no_perf            = false; // disable performance metrics
-    bool    timing_per_token   = false;
 
     std::vector<std::string> dry_sequence_breakers = {"\n", ":", "\"", "*"};     // default sequence breakers for DRY
 
 
     std::vector<enum common_sampler_type> samplers = {
-        COMMON_SAMPLER_TYPE_PENALTIES,
         COMMON_SAMPLER_TYPE_DRY,
         COMMON_SAMPLER_TYPE_TOP_K,
         COMMON_SAMPLER_TYPE_TYPICAL_P,
@@ -161,10 +148,6 @@ struct common_params_sampling {
     };
 
     std::string grammar; // optional BNF-like grammar to constrain sampling
-    bool                                grammar_lazy = false;
-    std::vector<common_grammar_trigger> grammar_trigger_words;  // optional trigger words to trigger lazy grammar
-    std::vector<llama_token>            grammar_trigger_tokens; // optional trigger tokens to trigger lazy grammar and print trigger special tokens.
-    std::set<llama_token>               preserved_tokens;
 
     std::vector<llama_logit_bias> logit_bias; // logit biases to apply
 
@@ -174,7 +157,6 @@ struct common_params_sampling {
 
 struct common_params_speculative {
     std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
-
     int32_t n_ctx        =     0; // draft context size
     int32_t n_max        =    16; // maximum number of tokens to draft during speculative decoding
     int32_t n_min        =     5; // minimum number of draft tokens to use for speculative decoding
@@ -185,21 +167,7 @@ struct common_params_speculative {
     struct cpu_params cpuparams;
     struct cpu_params cpuparams_batch;
 
-    std::string hf_repo = ""; // HF repo                                                     // NOLINT
-    std::string hf_file = ""; // HF file                                                     // NOLINT
-
     std::string model = ""; // draft model for speculative decoding                          // NOLINT
-    std::string model_url = ""; // model url to download                                     // NOLINT
-};
-
-struct common_params_vocoder {
-    std::string hf_repo = ""; // HF repo                                                     // NOLINT
-    std::string hf_file = ""; // HF file                                                     // NOLINT
-
-    std::string model     = ""; // model path                                                // NOLINT
-    std::string model_url = ""; // model url to download                                     // NOLINT
-
-    bool use_guide_tokens = false; // enable guide tokens to improve TTS accuracy            // NOLINT
 };
 
 struct common_params {
@@ -225,11 +193,9 @@ struct common_params {
 
     // offload params
     std::vector<ggml_backend_dev_t> devices;         // devices to use for offloading
-
     int32_t n_gpu_layers                    =    -1; // number of layers to store in VRAM (-1 - use default)
     int32_t main_gpu                        =     0; // the GPU that is used for scratch and small tensors
     float   tensor_split[128]               =   {0}; // how split tensors should be distributed across GPUs
-
     enum llama_split_mode        split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
 
     struct cpu_params cpuparams;
@@ -246,10 +212,9 @@ struct common_params {
 
     struct common_params_sampling sampling;
     struct common_params_speculative speculative;
-    struct common_params_vocoder     vocoder;
 
     std::string model                = ""; // model path                                                    // NOLINT
-    std::string model_alias          = ""; // model alias                                                   // NOLINT
+    std::string model_alias          = "unknown"; // model alias                                            // NOLINT
     std::string model_url            = ""; // model url to download                                         // NOLINT
     std::string hf_token             = ""; // HF token                                                      // NOLINT
     std::string hf_repo              = ""; // HF repo                                                       // NOLINT
@@ -262,13 +227,14 @@ struct common_params {
     std::string lookup_cache_static  = ""; // path of static ngram cache file for lookup decoding           // NOLINT
     std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding          // NOLINT
     std::string logits_file          = ""; // file for saving *all* logits                                  // NOLINT
+    std::string rpc_servers          = ""; // comma separated list of RPC servers                           // NOLINT
 
     std::vector<std::string> in_files;   // all input files
     std::vector<std::string> antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts)
     std::vector<llama_model_kv_override> kv_overrides;
 
-    bool lora_init_without_apply = false; // only load lora to memory, but do not apply it to ctx (user can manually apply lora later using llama_adapter_lora_apply)
-    std::vector<common_adapter_lora_info> lora_adapters; // lora adapter path with user defined scale
+    bool lora_init_without_apply = false; // only load lora to memory, but do not apply it to ctx (user can manually apply lora later using llama_lora_adapter_apply)
+    std::vector<common_lora_adapter_info> lora_adapters; // lora adapter path with user defined scale
 
     std::vector<common_control_vector_load_info> control_vectors; // control vector with user defined scale
 
@@ -296,6 +262,7 @@ struct common_params {
     bool special           = false; // enable special token output
     bool interactive       = false; // interactive mode
     bool interactive_first = false; // wait for user input immediately
+    bool conversation      = false; // conversation mode (does not print special tokens and suffix/prefix)
     bool prompt_cache_all  = false; // save user input and generations to prompt cache
     bool prompt_cache_ro   = false; // open the prompt cache read-only and do not update it
 
@@ -318,10 +285,8 @@ struct common_params {
     bool warmup            = true;  // warmup run
     bool check_tensors     = false; // validate tensor data
 
-    ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K
-    ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V
-
-    common_conversation_mode conversation_mode = COMMON_CONVERSATION_MODE_AUTO;
+    std::string cache_type_k = "f16"; // KV cache data type for the K
+    std::string cache_type_v = "f16"; // KV cache data type for the V
 
     // multimodal models (see examples/llava)
     std::string mmproj = "";        // path to multimodal projector                                         // NOLINT
@@ -344,7 +309,6 @@ struct common_params {
     std::string hostname      = "127.0.0.1";
     std::string public_path   = "";                                                                         // NOLINT
     std::string chat_template = "";                                                                         // NOLINT
-    bool use_jinja = false;                                                                                 // NOLINT
     bool enable_chat_template = true;
 
     std::vector<std::string> api_keys;
@@ -439,10 +403,6 @@ std::string string_format(const char * fmt, ...);
 std::string string_strip(const std::string & str);
 std::string string_get_sortable_timestamp();
 
-std::string string_join(const std::vector<std::string> & values, const std::string & separator);
-std::vector<std::string> string_split(const std::string & str, const std::string & delimiter);
-std::string string_repeat(const std::string & str, size_t n);
-
 void string_replace_all(std::string & s, const std::string & search, const std::string & replace);
 
 template<class T>
@@ -476,16 +436,6 @@ std::vector<std::string> string_split<std::string>(const std::string & input, ch
     return parts;
 }
 
-static bool string_starts_with(const std::string & str,
-                               const std::string & prefix) {  // While we wait for C++20's std::string::starts_with...
-    return str.rfind(prefix, 0) == 0;
-}
-
-static bool string_ends_with(const std::string & str,
-                               const std::string & suffix) {  // While we wait for C++20's std::string::ends_with...
-    return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;
-}
-
 bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
 void string_process_escapes(std::string & input);
 
@@ -508,12 +458,10 @@ std::string fs_get_cache_file(const std::string & filename);
 // Model utils
 //
 
-// note: defines object's lifetime
 struct common_init_result {
-    llama_model_ptr   model;
-    llama_context_ptr context;
-
-    std::vector<llama_adapter_lora_ptr> lora;
+    struct llama_model   * model   = nullptr;
+    struct llama_context * context = nullptr;
+    std::vector<common_lora_adapter_container> lora_adapters;
 };
 
 struct common_init_result     common_init_from_params(common_params & params);
@@ -522,25 +470,11 @@ struct llama_model_params     common_model_params_to_llama  (      common_params
 struct llama_context_params   common_context_params_to_llama(const common_params & params);
 struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params);
 
-struct llama_model * common_load_model_from_url(
-    const std::string & model_url,
-    const std::string & local_path,
-    const std::string & hf_token,
-    const struct llama_model_params & params);
-
-struct llama_model * common_load_model_from_hf(
-    const std::string & repo,
-    const std::string & remote_path,
-    const std::string & local_path,
-    const std::string & hf_token,
-    const struct llama_model_params & params);
-
-std::pair<std::string, std::string> common_get_hf_file(
-    const std::string & hf_repo_with_tag,
-    const std::string & hf_token);
+struct llama_model * common_load_model_from_url(const char * model_url, const char * path_model, const char * hf_token, const struct llama_model_params & params);
+struct llama_model * common_load_model_from_hf(const char * repo, const char * file, const char * path_model, const char * hf_token, const struct llama_model_params & params);
 
 // clear LoRA adapters from context, then apply new list of adapters
-void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora);
+void common_lora_adapters_apply(struct llama_context * ctx, std::vector<common_lora_adapter_container> & lora_adapters);
 
 //
 // Batch utils
@@ -578,7 +512,7 @@ std::vector<llama_token> common_tokenize(
                         bool   parse_special = false);
 
 std::vector<llama_token> common_tokenize(
-    const struct llama_vocab * vocab,
+    const struct llama_model * model,
            const std::string & text,
                         bool   add_special,
                         bool   parse_special = false);
@@ -590,21 +524,11 @@ std::string common_token_to_piece(
                        llama_token   token,
                        bool          special = true);
 
-std::string common_token_to_piece(
-          const struct llama_vocab * vocab,
-                       llama_token   token,
-                       bool          special = true);
-
 // detokenizes a vector of tokens into a string
 // should work similar to Python's `tokenizer.decode`
 // optionally renders special/control tokens
 std::string common_detokenize(
-            const struct llama_context * ctx,
-        const std::vector<llama_token> & tokens,
-                                  bool   special = true);
-
-std::string common_detokenize(
-              const struct llama_vocab * vocab,
+                         llama_context * ctx,
         const std::vector<llama_token> & tokens,
                                   bool   special = true);
 
@@ -612,57 +536,33 @@ std::string common_detokenize(
 // Chat template utils
 //
 
-struct common_tool_call {
-    std::string name;
-    std::string arguments;
-    std::string id;
-};
-
 // same with llama_chat_message, but uses std::string
 struct common_chat_msg {
     std::string role;
     std::string content;
-    std::vector<common_tool_call> tool_calls;
-    std::string tool_plan = "";
 };
 
 // Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
-bool common_chat_verify_template(const std::string & tmpl, bool use_jinja);
-
-namespace minja {
-    class chat_template;
-}
-
-typedef minja::chat_template common_chat_template;
-
-struct common_chat_templates {
-    bool has_explicit_template; // Model had builtin template or template overridde was specified.
-    std::unique_ptr<common_chat_template> template_default; // always set (defaults to chatml)
-    std::unique_ptr<common_chat_template> template_tool_use;
-};
+bool common_chat_verify_template(const std::string & tmpl);
 
 // CPP wrapper for llama_chat_apply_template
 // If the built-in template is not supported, we default to chatml
 // If the custom "tmpl" is not supported, we throw an error
-std::string common_chat_apply_template(
-        const common_chat_template & tmpl,
+std::string common_chat_apply_template(const struct llama_model * model,
+        const std::string & tmpl,
         const std::vector<common_chat_msg> & chat,
-        bool add_ass,
-        bool use_jinja);
+        bool add_ass);
 
 // Format single message, while taking into account the position of that message in chat history
-std::string common_chat_format_single(
-        const common_chat_template & tmpl,
+std::string common_chat_format_single(const struct llama_model * model,
+        const std::string & tmpl,
         const std::vector<common_chat_msg> & past_msg,
         const common_chat_msg & new_msg,
-        bool add_ass,
-        bool use_jinja);
+        bool add_ass);
 
 // Returns an example of formatted chat
-std::string common_chat_format_example(
-    const common_chat_template & tmpl, bool use_jinja);
-
-common_chat_templates common_chat_templates_from_model(const struct llama_model * model, const std::string & chat_template_override);
+std::string common_chat_format_example(const struct llama_model * model,
+        const std::string & tmpl);
 
 //
 // KV cache utils
@@ -678,8 +578,7 @@ void common_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_si
 // Embedding utils
 //
 
-// TODO: repace embd_norm with an enum
-void common_embd_normalize(const float * inp, float * out, int n, int embd_norm);
+void common_embd_normalize(const float * inp, float * out, int n, int embd_norm = 2);
 
 float common_embd_similarity_cos(const float * embd1, const float * embd2, int n);
 
@@ -708,10 +607,6 @@ common_control_vector_data common_control_vector_load(const std::vector<common_c
 // Split utils
 //
 
-namespace {
-
-const char * const LLM_KV_SPLIT_NO            = "split.no";
-const char * const LLM_KV_SPLIT_COUNT         = "split.count";
-const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
-
-}
+static const char * const LLM_KV_SPLIT_NO            = "split.no";
+static const char * const LLM_KV_SPLIT_COUNT         = "split.count";
+static const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";

common/json-schema-to-grammar.cpp

@@ -1,6 +1,4 @@
 #include "json-schema-to-grammar.h"
-#include "common.h"
-
 #include <algorithm>
 #include <fstream>
 #include <map>
@@ -13,6 +11,11 @@
 
 using json = nlohmann::ordered_json;
 
+template <typename Iterator>
+static std::string join(Iterator begin, Iterator end, const std::string & separator);
+
+static std::string repeat(const std::string & str, size_t n);
+
 static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "") {
     auto has_max = max_items != std::numeric_limits<int>::max();
 
@@ -125,8 +128,8 @@ static void _build_min_max_int(int min_value, int max_value, std::stringstream &
                 if (sub_len > 0) {
                     auto from_sub = from.substr(i + 1);
                     auto to_sub = to.substr(i + 1);
-                    auto sub_zeros = string_repeat("0", sub_len);
-                    auto sub_nines = string_repeat("9", sub_len);
+                    auto sub_zeros = repeat("0", sub_len);
+                    auto sub_nines = repeat("9", sub_len);
 
                     auto to_reached = false;
                     out << "(";
@@ -185,8 +188,8 @@ static void _build_min_max_int(int min_value, int max_value, std::stringstream &
         auto max_digits = max_s.length();
 
         for (auto digits = min_digits; digits < max_digits; digits++) {
-            uniform_range(min_s, string_repeat("9", digits));
-            min_s = "1" + string_repeat("0", digits);
+            uniform_range(min_s, repeat("9", digits));
+            min_s = "1" + repeat("0", digits);
             out << " | ";
         }
         uniform_range(min_s, max_s);
@@ -315,6 +318,49 @@ std::unordered_map<char, std::string> GRAMMAR_LITERAL_ESCAPES = {
 std::unordered_set<char> NON_LITERAL_SET = {'|', '.', '(', ')', '[', ']', '{', '}', '*', '+', '?'};
 std::unordered_set<char> ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = {'^', '$', '.', '[', ']', '(', ')', '|', '{', '}', '*', '+', '?'};
 
+template <typename Iterator>
+std::string join(Iterator begin, Iterator end, const std::string & separator) {
+    std::ostringstream result;
+    if (begin != end) {
+        result << *begin;
+        for (Iterator it = begin + 1; it != end; ++it) {
+            result << separator << *it;
+        }
+    }
+    return result.str();
+}
+
+static std::vector<std::string> split(const std::string & str, const std::string & delimiter) {
+    std::vector<std::string> tokens;
+    size_t start = 0;
+    size_t end = str.find(delimiter);
+
+    while (end != std::string::npos) {
+        tokens.push_back(str.substr(start, end - start));
+        start = end + delimiter.length();
+        end = str.find(delimiter, start);
+    }
+
+    tokens.push_back(str.substr(start));
+
+    return tokens;
+}
+
+static std::string repeat(const std::string & str, size_t n) {
+    if (n == 0) {
+        return "";
+    }
+
+    std::string result;
+    result.reserve(str.length() * n);
+
+    for (size_t i = 0; i < n; ++i) {
+        result += str;
+    }
+
+    return result;
+}
+
 static std::string replacePattern(const std::string & input, const std::regex & regex, const std::function<std::string(const std::smatch  &)> & replacement) {
     std::smatch match;
     std::string result;
@@ -343,7 +389,6 @@ static std::string format_literal(const std::string & literal) {
 
 class SchemaConverter {
 private:
-    friend std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options);
     std::function<json(const std::string &)> _fetch_json;
     bool _dotall;
     std::map<std::string, std::string> _rules;
@@ -373,7 +418,7 @@ private:
         for (size_t i = 0; i < alt_schemas.size(); i++) {
             rules.push_back(visit(alt_schemas[i], name + (name.empty() ? "alternative-" : "-") + std::to_string(i)));
         }
-        return string_join(rules, " | ");
+        return join(rules.begin(), rules.end(), " | ");
     }
 
     std::string _visit_pattern(const std::string & pattern, const std::string & name) {
@@ -436,7 +481,7 @@ private:
                 for (const auto & item : ret) {
                     results.push_back(to_rule(item));
                 }
-                return std::make_pair(string_join(results, " "), false);
+                return std::make_pair(join(results.begin(), results.end(), " "), false);
             };
 
             while (i < length) {
@@ -494,7 +539,7 @@ private:
                     }
                     curly_brackets += '}';
                     i++;
-                    auto nums = string_split(curly_brackets.substr(1, curly_brackets.length() - 2), ",");
+                    auto nums = split(curly_brackets.substr(1, curly_brackets.length() - 2), ",");
                     int min_times = 0;
                     int max_times = std::numeric_limits<int>::max();
                     try {
@@ -764,11 +809,10 @@ private:
 public:
     SchemaConverter(
         const std::function<json(const std::string &)> & fetch_json,
-        bool dotall,
-        bool compact_spaces)
+        bool dotall)
           : _fetch_json(fetch_json), _dotall(dotall)
     {
-        _rules["space"] = compact_spaces ? "\" \"?" : SPACE_RULE;
+        _rules["space"] = SPACE_RULE;
     }
 
     void resolve_refs(json & schema, const std::string & url) {
@@ -810,7 +854,7 @@ public:
                             return;
                         }
                         std::string pointer = ref.substr(ref.find('#') + 1);
-                        std::vector<std::string> tokens = string_split(pointer, "/");
+                        std::vector<std::string> tokens = split(pointer, "/");
                         for (size_t i = 1; i < tokens.size(); ++i) {
                             std::string sel = tokens[i];
                             if (target.is_null() || !target.contains(sel)) {
@@ -861,7 +905,7 @@ public:
             for (const auto & v : schema["enum"]) {
                 enum_values.push_back(_generate_constant_rule(v));
             }
-            return _add_rule(rule_name, "(" + string_join(enum_values, " | ") + ") space");
+            return _add_rule(rule_name, "(" + join(enum_values.begin(), enum_values.end(), " | ") + ") space");
         } else if ((schema_type.is_null() || schema_type == "object")
                 && (schema.contains("properties") ||
                     (schema.contains("additionalProperties") && schema["additionalProperties"] != true))) {
@@ -975,10 +1019,10 @@ public:
 
     void check_errors() {
         if (!_errors.empty()) {
-            throw std::runtime_error("JSON schema conversion failed:\n" + string_join(_errors, "\n"));
+            throw std::runtime_error("JSON schema conversion failed:\n" + join(_errors.begin(), _errors.end(), "\n"));
         }
         if (!_warnings.empty()) {
-            fprintf(stderr, "WARNING: JSON schema conversion was incomplete: %s\n", string_join(_warnings, "; ").c_str());
+            fprintf(stderr, "WARNING: JSON schema conversion was incomplete: %s\n", join(_warnings.begin(), _warnings.end(), "; ").c_str());
         }
     }
 
@@ -991,35 +1035,11 @@ public:
     }
 };
 
-std::string json_schema_to_grammar(const json & schema, bool force_gbnf) {
-#ifdef LLAMA_USE_LLGUIDANCE
-    if (!force_gbnf) {
-        return "%llguidance {}\nstart: %json " + schema.dump();
-    }
-#else
-    (void)force_gbnf;
-#endif // LLAMA_USE_LLGUIDANCE
-    return build_grammar([&](const common_grammar_builder & callbacks) {
+std::string json_schema_to_grammar(const json & schema) {
+    SchemaConverter converter([](const std::string &) { return json::object(); }, /* dotall= */ false);
     auto copy = schema;
-        callbacks.resolve_refs(copy);
-        callbacks.add_schema("", copy);
-    });
-}
-
-std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options) {
-    SchemaConverter converter([&](const std::string &) { return json(); }, options.dotall, options.compact_spaces);
-    common_grammar_builder builder {
-        /* .add_rule = */ [&](const std::string & name, const std::string & rule) {
-            return converter._add_rule(name, rule);
-        },
-        /* .add_schema = */ [&](const std::string & name, const nlohmann::ordered_json & schema) {
-            return converter.visit(schema, name == "root" ? "" : name);
-        },
-        /* .resolve_refs = */ [&](nlohmann::ordered_json & schema) {
-            converter.resolve_refs(schema, "");
-        }
-    };
-    cb(builder);
+    converter.resolve_refs(copy, "input");
+    converter.visit(copy, "");
     converter.check_errors();
     return converter.format_grammar();
 }

common/json-schema-to-grammar.h

@@ -5,18 +5,4 @@
 #define JSON_ASSERT GGML_ASSERT
 #include "json.hpp"
 
-std::string json_schema_to_grammar(const nlohmann::ordered_json & schema,
-                                   bool force_gbnf = false);
-
-struct common_grammar_builder {
-    std::function<std::string(const std::string &, const std::string &)> add_rule;
-    std::function<std::string(const std::string &, const nlohmann::ordered_json &)> add_schema;
-    std::function<void(nlohmann::ordered_json &)> resolve_refs;
-};
-
-struct common_grammar_options {
-    bool dotall = false;
-    bool compact_spaces = false;
-};
-
-std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options = {});
+std::string json_schema_to_grammar(const nlohmann::ordered_json& schema);

common/llguidance.cpp

@@ -1,270 +0,0 @@
-#include "sampling.h"
-#include "log.h"
-
-#ifdef LLAMA_USE_LLGUIDANCE
-
-#    include "llguidance.h"
-#    include <cmath>
-
-struct llama_sampler_llg {
-    const llama_vocab * vocab;
-    std::string         grammar_kind;
-    std::string         grammar_data;
-    LlgTokenizer *      tokenizer;
-    LlgConstraint *     grammar;
-    LlgMaskResult       llg_res;
-    bool                has_llg_res;
-};
-
-static LlgConstraint * llama_sampler_llg_new(LlgTokenizer * tokenizer, const char * grammar_kind,
-                                             const char * grammar_data) {
-    LlgConstraintInit cinit;
-    llg_constraint_init_set_defaults(&cinit, tokenizer);
-    const char * log_level = getenv("LLGUIDANCE_LOG_LEVEL");
-    if (log_level && *log_level) {
-        cinit.log_stderr_level = atoi(log_level);
-    }
-    auto c = llg_new_constraint_any(&cinit, grammar_kind, grammar_data);
-    if (llg_get_error(c)) {
-        LOG_ERR("llg error: %s\n", llg_get_error(c));
-        llg_free_constraint(c);
-        return nullptr;
-    }
-    return c;
-}
-
-static const char * llama_sampler_llg_name(const llama_sampler * /*smpl*/) {
-    return "llguidance";
-}
-
-static void llama_sampler_llg_accept_impl(llama_sampler * smpl, llama_token token) {
-    auto * ctx = (llama_sampler_llg *) smpl->ctx;
-    if (ctx->grammar) {
-        LlgCommitResult res;
-        llg_commit_token(ctx->grammar, token, &res);
-        ctx->has_llg_res = false;
-    }
-}
-
-static void llama_sampler_llg_apply(llama_sampler * smpl, llama_token_data_array * cur_p) {
-    auto * ctx = (llama_sampler_llg *) smpl->ctx;
-    if (ctx->grammar) {
-        if (!ctx->has_llg_res) {
-            if (llg_compute_mask(ctx->grammar, &ctx->llg_res) == 0) {
-                ctx->has_llg_res = true;
-            } else {
-                LOG_ERR("llg error: %s\n", llg_get_error(ctx->grammar));
-                llg_free_constraint(ctx->grammar);
-                ctx->grammar = nullptr;
-            }
-        }
-        if (ctx->has_llg_res) {
-            if (ctx->llg_res.is_stop) {
-                for (size_t i = 0; i < cur_p->size; ++i) {
-                    if (!llama_vocab_is_eog(ctx->vocab, cur_p->data[i].id)) {
-                        cur_p->data[i].logit = -INFINITY;
-                    }
-                }
-            } else {
-                const uint32_t * mask = ctx->llg_res.sample_mask;
-                for (size_t i = 0; i < cur_p->size; ++i) {
-                    auto token = cur_p->data[i].id;
-                    if ((mask[token / 32] & (1 << (token % 32))) == 0) {
-                        cur_p->data[i].logit = -INFINITY;
-                    }
-                }
-            }
-        }
-    }
-}
-
-static void llama_sampler_llg_reset(llama_sampler * smpl) {
-    auto * ctx = (llama_sampler_llg *) smpl->ctx;
-    if (!ctx->grammar) {
-        return;
-    }
-
-    auto * grammar_new = llama_sampler_llg_new(ctx->tokenizer, ctx->grammar_kind.c_str(), ctx->grammar_data.c_str());
-    llg_free_constraint(ctx->grammar);
-    ctx->grammar     = grammar_new;
-    ctx->has_llg_res = false;
-}
-
-static llama_sampler * llama_sampler_llg_clone(const llama_sampler * smpl) {
-    const auto * ctx = (const llama_sampler_llg *) smpl->ctx;
-
-    auto * result = llama_sampler_init_llg(ctx->vocab, nullptr, nullptr);
-
-    // copy the state
-    {
-        auto * result_ctx = (llama_sampler_llg *) result->ctx;
-
-        if (ctx->grammar) {
-            result_ctx->grammar_kind = ctx->grammar_kind;
-            result_ctx->grammar_data = ctx->grammar_data;
-            result_ctx->grammar      = llg_clone_constraint(ctx->grammar);
-            result_ctx->tokenizer    = llg_clone_tokenizer(ctx->tokenizer);
-        }
-    }
-
-    return result;
-}
-
-static void llama_sampler_llg_free(llama_sampler * smpl) {
-    const auto * ctx = (llama_sampler_llg *) smpl->ctx;
-
-    if (ctx->grammar) {
-        llg_free_constraint(ctx->grammar);
-        llg_free_tokenizer(ctx->tokenizer);
-    }
-
-    delete ctx;
-}
-
-static llama_sampler_i llama_sampler_llg_i = {
-    /* .name   = */ llama_sampler_llg_name,
-    /* .accept = */ llama_sampler_llg_accept_impl,
-    /* .apply  = */ llama_sampler_llg_apply,
-    /* .reset  = */ llama_sampler_llg_reset,
-    /* .clone  = */ llama_sampler_llg_clone,
-    /* .free   = */ llama_sampler_llg_free,
-};
-
-static size_t llama_sampler_llg_tokenize_fn(const void * user_data, const uint8_t * bytes, size_t bytes_len,
-                                            uint32_t * output_tokens, size_t output_tokens_len) {
-    const llama_vocab * vocab = (const llama_vocab *) user_data;
-    int                 r     = 0;
-    try {
-        r = llama_tokenize(vocab, (const char *) bytes, bytes_len, (int32_t *) output_tokens, output_tokens_len, false,
-                           true);
-    } catch (const std::exception & e) {
-        GGML_ABORT("llama_tokenize failed: %s\n", e.what());
-    }
-    if (r < 0) {
-        return -r;
-    }
-    return r;
-}
-
-static LlgTokenizer * llama_sampler_llg_new_tokenizer(const llama_vocab * vocab) {
-    // TODO store the tokenizer in the vocab somehow
-    static const llama_vocab * vocab_cache;
-    static LlgTokenizer *      tokenizer_cache;
-
-    if (vocab_cache == vocab) {
-        return llg_clone_tokenizer(tokenizer_cache);
-    }
-
-    auto tok_eos = llama_vocab_eot(vocab);
-    if (tok_eos == LLAMA_TOKEN_NULL) {
-        tok_eos = llama_vocab_eos(vocab);
-    }
-
-    size_t vocab_size = llama_vocab_n_tokens(vocab);
-
-    auto token_lens       = new uint32_t[vocab_size];
-    // we typically have ~7 bytes per token; let's go on the safe side here
-    auto token_bytes_size = vocab_size * 16 + 1024 * 1024;
-    auto token_bytes      = new uint8_t[token_bytes_size];
-
-    size_t offset = 0;
-    for (size_t i = 0; i < vocab_size; i++) {
-        size_t max_token = 1024;
-        if (token_bytes_size - offset < max_token) {
-            GGML_ABORT("token_bytes buffer too small\n");
-        }
-
-        llama_token token = i;
-        auto        dp    = (char *) token_bytes + offset;
-        auto        size  = llama_detokenize(vocab, &token, 1, dp, max_token, false, false);
-        if (size < 0) {
-            GGML_ABORT("llama_detokenize failed\n");
-        }
-        if (size == 0) {
-            size = llama_detokenize(vocab, &token, 1, dp + 1, max_token - 1, false, true);
-            if (size < 0) {
-                GGML_ABORT("llama_detokenize failed\n");
-            }
-            if (size != 0) {
-                *dp = '\xff';  // special token prefix marker
-                size += 1;
-            }
-        }
-
-        token_lens[i] = size;
-        offset += size;
-    }
-
-    LlgTokenizerInit tinit = {
-        /* .vocab_size                         = */ (uint32_t) vocab_size,
-        /* .tok_eos                            = */ (uint32_t) tok_eos,
-        /* .token_lens                         = */ token_lens,
-        /* .token_bytes                        = */ token_bytes,
-        /* .tokenizer_json                     = */ nullptr,
-        /* .tokenize_assumes_string            = */ true,
-        /* .tokenize_fn                        = */ llama_sampler_llg_tokenize_fn,
-        /* .use_approximate_greedy_tokenize_fn = */ false,
-        /* .tokenize_user_data                 = */ vocab,
-    };
-
-    char           error_buffer[1024];
-    LlgTokenizer * tokenizer = llg_new_tokenizer(&tinit, error_buffer, sizeof(error_buffer));
-
-    delete[] token_bytes;
-    delete[] token_lens;
-
-    if (tokenizer == nullptr) {
-        LOG_ERR("llg tokenizer error: %s\n", error_buffer);
-        return tokenizer;
-    }
-
-    if (tokenizer_cache) {
-        llg_free_tokenizer(tokenizer_cache);
-    }
-    vocab_cache     = vocab;
-    tokenizer_cache = tokenizer;
-
-    return llg_clone_tokenizer(tokenizer_cache);
-}
-
-llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab, const char * grammar_kind,
-                                       const char * grammar_data) {
-    auto * ctx = new llama_sampler_llg;
-
-    if (grammar_kind != nullptr && grammar_kind[0] != '\0') {
-        auto tokenizer = llama_sampler_llg_new_tokenizer(vocab);
-        *ctx           = {
-            /* .vocab        = */ vocab,
-            /* .grammar_kind = */ grammar_kind,
-            /* .grammar_data = */ grammar_data,
-            /* .tokenizer    = */ tokenizer,
-            /* .grammar      = */ llama_sampler_llg_new(tokenizer, grammar_kind, grammar_data),
-            /* .llg_res      = */ {},
-            /* .has_llg_res  = */ false,
-        };
-    } else {
-        *ctx = {
-            /* .vocab        = */ vocab,
-            /* .grammar_kind = */ {},
-            /* .grammar_data = */ {},
-            /* .tokenizer    = */ nullptr,
-            /* .grammar      = */ nullptr,
-            /* .llg_res      = */ {},
-            /* .has_llg_res  = */ false,
-        };
-    }
-
-    return llama_sampler_init(
-        /* .iface = */ &llama_sampler_llg_i,
-        /* .ctx   = */ ctx
-    );
-}
-
-#else
-
-llama_sampler * llama_sampler_init_llg(const llama_vocab *, const char *, const char *) {
-    LOG_WRN("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
-    return nullptr;
-}
-
-#endif  // LLAMA_USE_LLGUIDANCE

common/log.cpp

@@ -14,6 +14,16 @@ void common_log_set_verbosity_thold(int verbosity) {
     common_log_verbosity_thold = verbosity;
 }
 
+#define LOG_COL_DEFAULT "\033[0m"
+#define LOG_COL_BOLD    "\033[1m"
+#define LOG_COL_RED     "\033[31m"
+#define LOG_COL_GREEN   "\033[32m"
+#define LOG_COL_YELLOW  "\033[33m"
+#define LOG_COL_BLUE    "\033[34m"
+#define LOG_COL_MAGENTA "\033[35m"
+#define LOG_COL_CYAN    "\033[36m"
+#define LOG_COL_WHITE   "\033[37m"
+
 static int64_t t_us() {
     return std::chrono::duration_cast<std::chrono::microseconds>(std::chrono::system_clock::now().time_since_epoch()).count();
 }
@@ -196,7 +206,6 @@ public:
                 vsnprintf(entry.msg.data(), entry.msg.size(), ss.str().c_str(), args_copy);
             }
 #endif
-            va_end(args_copy);
         }
 
         entry.level = level;

common/log.h

@@ -2,17 +2,6 @@
 
 #include "ggml.h" // for ggml_log_level
 
-#define LOG_CLR_TO_EOL  "\033[K\r"
-#define LOG_COL_DEFAULT "\033[0m"
-#define LOG_COL_BOLD    "\033[1m"
-#define LOG_COL_RED     "\033[31m"
-#define LOG_COL_GREEN   "\033[32m"
-#define LOG_COL_YELLOW  "\033[33m"
-#define LOG_COL_BLUE    "\033[34m"
-#define LOG_COL_MAGENTA "\033[35m"
-#define LOG_COL_CYAN    "\033[36m"
-#define LOG_COL_WHITE   "\033[37m"
-
 #ifndef __GNUC__
 #    define LOG_ATTRIBUTE_FORMAT(...)
 #elif defined(__MINGW32__)

common/ngram-cache.cpp

@@ -65,13 +65,13 @@ constexpr int     draft_min_percent_strict[LLAMA_NGRAM_MAX] = {75, 66, 66, 66};
 static llama_token try_draft(common_ngram_cache & nc_static, const common_ngram ngram_static) {
     common_ngram_cache::iterator part_static_it = nc_static.find(ngram_static);
     if (part_static_it == nc_static.end()) {
-        return LLAMA_TOKEN_NULL;
+        return -1;
     }
     const common_ngram_cache_part part_static = part_static_it->second;
 
     int max_count_static  = 0;
     int sum_count_static  = 0;
-    llama_token max_token = LLAMA_TOKEN_NULL;
+    llama_token max_token = -1;
 
     for (std::pair<llama_token, int> token_count_static : part_static) {
         const llama_token token = token_count_static.first;
@@ -85,10 +85,10 @@ static llama_token try_draft(common_ngram_cache & nc_static, const common_ngram
     }
 
     if (sum_count_static < draft_min_sample_size_lax[LLAMA_NGRAM_STATIC-1]) {
-        return LLAMA_TOKEN_NULL;
+        return -1;
     }
     if (100*max_count_static < draft_min_percent_lax[LLAMA_NGRAM_STATIC-1]*sum_count_static) {
-        return LLAMA_TOKEN_NULL;
+        return -1;
     }
     return max_token;
 }
@@ -98,9 +98,9 @@ static llama_token try_draft(
     common_ngram_cache & nc_primary, const std::vector<common_ngram> & ngrams_primary, common_ngram_cache_part & part_static,
     const int * min_sample_size, const int * min_percent) {
 
-    llama_token drafted_token = LLAMA_TOKEN_NULL;
+    llama_token drafted_token = -1;
 
-    for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == LLAMA_TOKEN_NULL; --i) {
+    for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == -1; --i) {
         const common_ngram ngram_primary = ngrams_primary[i];
 
         common_ngram_cache::iterator part_primary_it = nc_primary.find(ngram_primary);
@@ -112,7 +112,7 @@ static llama_token try_draft(
         int max_count_primary = 0;
         int max_count_static  = 0;
         int sum_count_primary = 0;
-        llama_token max_token = LLAMA_TOKEN_NULL;
+        llama_token max_token = -1;
 
         for (std::pair<llama_token, int> token_count_primary : part_primary) {
             const llama_token token = token_count_primary.first;
@@ -154,7 +154,7 @@ void common_ngram_cache_draft(
     }
 
     while ((int) draft.size()-1 < n_draft) {
-        llama_token drafted_token = LLAMA_TOKEN_NULL;
+        llama_token drafted_token = -1;
 
         const int ngram_start_static = inp_size-LLAMA_NGRAM_STATIC + draft.size()-1;
         common_ngram ngram_static;
@@ -177,17 +177,17 @@ void common_ngram_cache_draft(
             }
             ngrams_cd.push_back(ngram_cd);
         }
-        if (drafted_token == LLAMA_TOKEN_NULL) {
+        if (drafted_token == -1) {
             drafted_token = try_draft(nc_context, ngrams_cd, part_static, draft_min_sample_size_lax, draft_min_percent_lax);
         }
-        if (drafted_token == LLAMA_TOKEN_NULL) {
+        if (drafted_token == -1) {
             drafted_token = try_draft(nc_dynamic, ngrams_cd, part_static, draft_min_sample_size_strict, draft_min_percent_strict);
         }
-        if (drafted_token == LLAMA_TOKEN_NULL) {
+        if (drafted_token == -1) {
             drafted_token = try_draft(nc_static, ngram_static);
         }
 
-        if (drafted_token == LLAMA_TOKEN_NULL) {
+        if (drafted_token == -1) {
             break;
         }
 

common/ngram-cache.h

@@ -17,13 +17,13 @@ struct common_ngram {
 
     common_ngram() {
         for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
-            tokens[i] = LLAMA_TOKEN_NULL;
+            tokens[i] = -1;
         }
     }
 
     common_ngram(const llama_token * input, const int ngram_size) {
         for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
-            tokens[i] = i < ngram_size ? input[i] : LLAMA_TOKEN_NULL;
+            tokens[i] = i < ngram_size ? input[i] : -1;
         }
     }
 

common/sampling.cpp

@@ -113,10 +113,7 @@ struct common_sampler {
     void set_logits(struct llama_context * ctx, int idx) {
         const auto * logits = llama_get_logits_ith(ctx, idx);
 
-        const llama_model * model = llama_get_model(ctx);
-        const llama_vocab * vocab = llama_model_get_vocab(model);
-
-        const int n_vocab = llama_vocab_n_tokens(vocab);
+        const int n_vocab = llama_n_vocab(llama_get_model(ctx));
 
         cur.resize(n_vocab);
 
@@ -145,36 +142,13 @@ std::string common_params_sampling::print() const {
 }
 
 struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params) {
-    const llama_vocab * vocab = llama_model_get_vocab(model);
-
     llama_sampler_chain_params lparams = llama_sampler_chain_default_params();
 
     lparams.no_perf = params.no_perf;
 
-    std::vector<const char *> trigger_words;
-    trigger_words.reserve(params.grammar_trigger_words.size());
-    for (const auto & str : params.grammar_trigger_words) {
-        trigger_words.push_back(str.word.c_str());
-    }
-
-    struct llama_sampler * grmr;
-    if (params.grammar.compare(0, 11, "%llguidance") == 0) {
-#ifdef LLAMA_USE_LLGUIDANCE
-        grmr = llama_sampler_init_llg(vocab, "lark", params.grammar.c_str());
-#else
-        GGML_ABORT("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
-#endif // LLAMA_USE_LLGUIDANCE
-    } else {
-        grmr = params.grammar_lazy
-             ? llama_sampler_init_grammar_lazy(vocab, params.grammar.c_str(), "root",
-                                               trigger_words.data(), trigger_words.size(),
-                                               params.grammar_trigger_tokens.data(), params.grammar_trigger_tokens.size())
-             :      llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root");
-    }
-
     auto * result = new common_sampler {
         /* .params = */ params,
-        /* .grmr   = */ grmr,
+        /* .grmr   = */ llama_sampler_init_grammar(model, params.grammar.c_str(), "root"),
         /* .chain  = */ llama_sampler_chain_init(lparams),
         /* .prev   = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
         /* .cur    = */ {},
@@ -183,22 +157,34 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
 
     llama_sampler_chain_add(result->chain,
             llama_sampler_init_logit_bias(
-                llama_vocab_n_tokens(vocab),
+                llama_n_vocab(model),
                 params.logit_bias.size(),
                 params.logit_bias.data()));
 
+    llama_sampler_chain_add(result->chain,
+            llama_sampler_init_penalties(
+                llama_n_vocab  (model),
+                llama_token_eos(model),
+                llama_token_nl (model),
+                params.penalty_last_n,
+                params.penalty_repeat,
+                params.penalty_freq,
+                params.penalty_present,
+                params.penalize_nl,
+                params.ignore_eos));
+
     if (params.mirostat == 0) {
         for (const auto & cnstr : params.samplers) {
             switch (cnstr) {
                     case COMMON_SAMPLER_TYPE_DRY:
                     {
-                        std::vector<const char *> c_breakers;
+                        std::vector<const char*> c_breakers;
                         c_breakers.reserve(params.dry_sequence_breakers.size());
-                        for (const auto & str : params.dry_sequence_breakers) {
+                        for (const auto& str : params.dry_sequence_breakers) {
                             c_breakers.push_back(str.c_str());
                         }
 
-                        llama_sampler_chain_add(result->chain, llama_sampler_init_dry      (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
+                        llama_sampler_chain_add(result->chain, llama_sampler_init_dry      (model, params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
                     }
                         break;
                 case COMMON_SAMPLER_TYPE_TOP_K:
@@ -220,10 +206,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                     llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent));
                     break;
                 case COMMON_SAMPLER_TYPE_INFILL:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_infill   (vocab));
-                    break;
-                case COMMON_SAMPLER_TYPE_PENALTIES:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
+                    llama_sampler_chain_add(result->chain, llama_sampler_init_infill   (model));
                     break;
                 default:
                     GGML_ASSERT(false && "unknown sampler type");
@@ -232,7 +215,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
         llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
     } else if (params.mirostat == 1) {
         llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
-        llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
+        llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_n_vocab(model), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
     } else if (params.mirostat == 2) {
         llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
         llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta));
@@ -432,7 +415,6 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) {
         case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't';
         case COMMON_SAMPLER_TYPE_XTC:         return 'x';
         case COMMON_SAMPLER_TYPE_INFILL:      return 'i';
-        case COMMON_SAMPLER_TYPE_PENALTIES:   return 'e';
         default : return '?';
     }
 }
@@ -447,7 +429,6 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) {
         case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature";
         case COMMON_SAMPLER_TYPE_XTC:         return "xtc";
         case COMMON_SAMPLER_TYPE_INFILL:      return "infill";
-        case COMMON_SAMPLER_TYPE_PENALTIES:   return "penalties";
         default : return "";
     }
 }
@@ -462,7 +443,6 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
         { "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE },
         { "xtc",         COMMON_SAMPLER_TYPE_XTC },
         { "infill",      COMMON_SAMPLER_TYPE_INFILL },
-        { "penalties",   COMMON_SAMPLER_TYPE_PENALTIES },
     };
 
     // since samplers names are written multiple ways
@@ -509,7 +489,6 @@ std::vector<common_sampler_type> common_sampler_types_from_chars(const std::stri
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC),         COMMON_SAMPLER_TYPE_XTC },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL),      COMMON_SAMPLER_TYPE_INFILL },
-        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_PENALTIES),   COMMON_SAMPLER_TYPE_PENALTIES },
     };
 
     std::vector<common_sampler_type> samplers;

common/sampling.h

@@ -102,6 +102,3 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr);
 
 std::vector<enum common_sampler_type> common_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
 std::vector<enum common_sampler_type> common_sampler_types_from_chars(const std::string & chars);
-
-llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab,
-                const char * grammar_kind, const char * grammar_data);

common/speculative.cpp

@@ -62,10 +62,6 @@ struct common_speculative * common_speculative_init(
 }
 
 void common_speculative_free(struct common_speculative * spec) {
-    if (spec == nullptr) {
-        return;
-    }
-
     common_sampler_free(spec->smpl);
 
     llama_batch_free(spec->batch);
@@ -79,13 +75,10 @@ bool common_speculative_are_compatible(
     const struct llama_model * model_tgt = llama_get_model(ctx_tgt);
     const struct llama_model * model_dft = llama_get_model(ctx_dft);
 
-    const struct llama_vocab * vocab_tgt = llama_model_get_vocab(model_tgt);
-    const struct llama_vocab * vocab_dft = llama_model_get_vocab(model_dft);
-
-    const bool vocab_type_tgt = llama_vocab_type(vocab_tgt);
+    const bool vocab_type_tgt = llama_vocab_type(model_tgt);
     LOG_DBG("%s: vocab_type tgt: %d\n", __func__, vocab_type_tgt);
 
-    const bool vocab_type_dft = llama_vocab_type(vocab_dft);
+    const bool vocab_type_dft = llama_vocab_type(model_dft);
     LOG_DBG("%s: vocab_type dft: %d\n", __func__, vocab_type_dft);
 
     if (vocab_type_tgt != vocab_type_dft) {
@@ -94,34 +87,34 @@ bool common_speculative_are_compatible(
         return false;
     }
 
-    if (llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) ||
-        llama_vocab_get_add_eos(vocab_tgt) != llama_vocab_get_add_eos(vocab_dft) ||
-        llama_vocab_bos(vocab_tgt) != llama_vocab_bos(vocab_dft) ||
-        llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft)) {
-        LOG_ERR("%s: draft vocab special tokens must match target vocab to use speculation\n", __func__);
-        LOG_ERR("%s: tgt: bos = %d (%d), eos = %d (%d)\n", __func__, llama_vocab_bos(vocab_tgt), llama_vocab_get_add_bos(vocab_tgt), llama_vocab_eos(vocab_tgt), llama_vocab_get_add_eos(vocab_tgt));
-        LOG_ERR("%s: dft: bos = %d (%d), eos = %d (%d)\n", __func__, llama_vocab_bos(vocab_dft), llama_vocab_get_add_bos(vocab_dft), llama_vocab_eos(vocab_dft), llama_vocab_get_add_eos(vocab_dft));
+    if (llama_add_bos_token(model_tgt) != llama_add_bos_token(model_dft) ||
+        llama_add_eos_token(model_tgt) != llama_add_eos_token(model_dft) ||
+        llama_token_bos(model_tgt) != llama_token_bos(model_dft) ||
+        llama_token_eos(model_tgt) != llama_token_eos(model_dft)) {
+        LOG_ERR("%s: draft model special tokens must match target model to use speculation\n", __func__);
+        LOG_ERR("%s: tgt: bos = %d (%d), eos = %d (%d)\n", __func__, llama_token_bos(model_tgt), llama_add_bos_token(model_tgt), llama_token_eos(model_tgt), llama_add_eos_token(model_tgt));
+        LOG_ERR("%s: dft: bos = %d (%d), eos = %d (%d)\n", __func__, llama_token_bos(model_dft), llama_add_bos_token(model_dft), llama_token_eos(model_dft), llama_add_eos_token(model_dft));
         return false;
     }
 
     {
-        const int n_vocab_tgt = llama_vocab_n_tokens(vocab_tgt);
-        const int n_vocab_dft = llama_vocab_n_tokens(vocab_dft);
+        const int n_vocab_tgt = llama_n_vocab(model_tgt);
+        const int n_vocab_dft = llama_n_vocab(model_dft);
 
         const int vocab_diff = std::abs(n_vocab_tgt - n_vocab_dft);
 
         if (vocab_diff > SPEC_VOCAB_MAX_SIZE_DIFFERENCE) {
             LOG_ERR("%s: draft model vocab must closely match target model to use speculation but "
                          "target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n",
-                    __func__, n_vocab_tgt, llama_vocab_n_tokens(vocab_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
+                    __func__, n_vocab_tgt, llama_n_vocab(model_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
             return false;
         }
 
         for (int i = SPEC_VOCAB_CHECK_START_TOKEN_ID; i < std::min(n_vocab_tgt, n_vocab_dft); ++i) {
-            const char * token_text_tgt = llama_vocab_get_text(vocab_tgt, i);
-            const char * token_text_dft = llama_vocab_get_text(vocab_dft, i);
+            const char * token_text_tgt = llama_token_get_text(model_tgt, i);
+            const char * token_text_dft = llama_token_get_text(model_dft, i);
             if (std::strcmp(token_text_tgt, token_text_dft) != 0) {
-                LOG_ERR("%s: draft vocab vocab must match target vocab to use speculation but "
+                LOG_ERR("%s: draft model vocab must match target model to use speculation but "
                              "token %d content differs - target '%s', draft '%s'\n", __func__, i,
                         common_token_to_piece(ctx_tgt, i).c_str(),
                         common_token_to_piece(ctx_dft, i).c_str());

convert_hf_to_gguf_update.py

@@ -17,7 +17,7 @@
 #
 #   python3 convert_hf_to_gguf_update.py <huggingface_token>
 #
-# - The convert_hf_to_gguf.py script will have had its get_vocab_base_pre() function updated
+# - Copy-paste the generated get_vocab_base_pre() function into convert_hf_to_gguf.py
 # - Update llama.cpp with the new pre-tokenizer if necessary
 #
 # TODO: generate tokenizer tests for llama.cpp
@@ -72,7 +72,6 @@ models = [
     {"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
     {"name": "falcon",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
     {"name": "bert-bge",       "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
-    {"name": "falcon3",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon3-7B-Base", },
     {"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", },
     {"name": "mpt",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
     {"name": "starcoder",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
@@ -103,12 +102,6 @@ models = [
     {"name": "exaone",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct", },
     {"name": "phi-2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/microsoft/phi-2", },
     {"name": "chameleon",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/facebook/chameleon-7b", },
-    {"name": "minerva-7b",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", },
-    {"name": "roberta-bpe",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"},
-    {"name": "gigachat",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"},
-    {"name": "megrez",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Infinigence/Megrez-3B-Instruct"},
-    {"name": "deepseek-v3",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V3"},
-    {"name": "deepseek-r1-qwen", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"},
 ]
 
 

convert_lora_to_gguf.py

@@ -226,9 +226,6 @@ def get_base_tensor_name(lora_tensor_name: str) -> str:
     base_name = lora_tensor_name.replace("base_model.model.", "")
     base_name = base_name.replace(".lora_A.weight", ".weight")
     base_name = base_name.replace(".lora_B.weight", ".weight")
-    # models produced by mergekit-extract-lora have token embeddings in the adapter
-    base_name = base_name.replace(".lora_embedding_A", ".weight")
-    base_name = base_name.replace(".lora_embedding_B", ".weight")
     return base_name
 
 
@@ -263,10 +260,6 @@ def parse_args() -> argparse.Namespace:
         "--base", type=Path,
         help="directory containing Hugging Face model config files (config.json, tokenizer.json) for the base model that the adapter is based on - only config is needed, actual model weights are not required. If base model is unspecified, it will be loaded from Hugging Face hub based on the adapter config",
     )
-    parser.add_argument(
-        "--base-model-id", type=str,
-        help="the model ID of the base model, if it is not available locally or in the adapter config. If specified, it will ignore --base and load the base model config from the Hugging Face hub (Example: 'meta-llama/Llama-3.2-1B-Instruct')",
-    )
     parser.add_argument(
         "lora_path", type=Path,
         help="directory containing Hugging Face PEFT LoRA config (adapter_model.json) and weights (adapter_model.safetensors or adapter_model.bin)",
@@ -297,7 +290,6 @@ if __name__ == '__main__':
 
     dir_base_model: Path | None = args.base
     dir_lora: Path = args.lora_path
-    base_model_id: str | None = args.base_model_id
     lora_config = dir_lora / "adapter_config.json"
     input_model = dir_lora / "adapter_model.safetensors"
 
@@ -321,10 +313,7 @@ if __name__ == '__main__':
         lparams: dict[str, Any] = json.load(f)
 
     # load base model
-    if base_model_id is not None:
-        logger.info(f"Loading base model from Hugging Face: {base_model_id}")
-        hparams = load_hparams_from_hf(base_model_id)
-    elif dir_base_model is None:
+    if dir_base_model is None:
         if "base_model_name_or_path" in lparams:
             model_id = lparams["base_model_name_or_path"]
             logger.info(f"Loading base model from Hugging Face: {model_id}")
@@ -382,16 +371,11 @@ if __name__ == '__main__':
                     if self.lazy:
                         tensor = LazyTorchTensor.from_eager(tensor)
                     base_name = get_base_tensor_name(name)
-                    # note: mergekit-extract-lora also adds token embeddings to the adapter
-                    is_lora_a = ".lora_A.weight" in name or ".lora_embedding_A" in name
-                    is_lora_b = ".lora_B.weight" in name or ".lora_embedding_B" in name
+                    is_lora_a = ".lora_A.weight" in name
+                    is_lora_b = ".lora_B.weight" in name
                     if not is_lora_a and not is_lora_b:
                         if ".base_layer.weight" in name:
                             continue
-                        # mergekit-extract-lora add these layernorm to the adapter, we need to keep them
-                        if "_layernorm" in name or ".norm" in name:
-                            yield (base_name, tensor)
-                            continue
                         logger.error(f"Unexpected name '{name}': Not a lora_A or lora_B tensor")
                         if ".embed_tokens.weight" in name or ".lm_head.weight" in name:
                             logger.error("Embeddings is present in the adapter. This can be due to new tokens added during fine tuning")
@@ -423,21 +407,9 @@ if __name__ == '__main__':
                 if name == "lm_head.weight" and len(dest) == 0:
                     raise ValueError("lm_head is present in adapter, but is ignored in base model")
                 for dest_name, dest_data in dest:
-                    # mergekit-extract-lora add these layernorm to the adapter
-                    if "_norm" in dest_name:
-                        assert dest_data.dim() == 1
-                        yield (dest_name, dest_data)
-                        continue
-
-                    # otherwise, we must get the lora_A and lora_B tensors
                     assert isinstance(dest_data, LoraTorchTensor)
                     lora_a, lora_b = dest_data.get_lora_A_B()
 
-                    # note: mergekit-extract-lora flip and transpose A and B
-                    # here we only need to transpose token_embd.lora_a, see llm_build_inp_embd()
-                    if "token_embd.weight" in dest_name:
-                        lora_a = lora_a.T
-
                     yield (dest_name + ".lora_a", lora_a)
                     yield (dest_name + ".lora_b", lora_b)
 

docs/android.md

@@ -23,10 +23,10 @@ $ curl -L {model-url} -o ~/{model}.gguf
 Then, if you are not already in the repo directory, `cd` into `llama.cpp` and:
 
 ```
-$ ./build/bin/llama-cli -m ~/{model}.gguf -c {context-size} -p "{your-prompt}"
+$ ./build/bin/llama-simple -m ~/{model}.gguf -c {context-size} -p "{your-prompt}"
 ```
 
-Here, we show `llama-cli`, but any of the executables under `examples` should work, in theory. Be sure to set `context-size` to a reasonable number (say, 4096) to start with; otherwise, memory could spike and kill your terminal.
+Here, we show `llama-simple`, but any of the executables under `examples` should work, in theory. Be sure to set `context-size` to a reasonable number (say, 4096) to start with; otherwise, memory could spike and kill your terminal.
 
 To see what it might look like visually, here's an old demo of an interactive session running on a Pixel 5 phone:
 

docs/backend/BLIS.md

@@ -27,6 +27,13 @@ We recommend using openmp since it's easier to modify the cores being used.
 
 ### llama.cpp compilation
 
+Makefile:
+
+```bash
+make GGML_BLIS=1 -j
+# make GGML_BLIS=1 llama-benchmark-matmult
+```
+
 CMake:
 
 ```bash

docs/backend/CANN.md

@@ -23,8 +23,6 @@ The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the abi
 
 ## News
 
-- 2024.11
-  - Support F16 and F32 data type model for Ascend 310P NPU.
 - 2024.8
   - Support `Q4_0` and `Q8_0` data type for Ascend NPU.
 - 2024.7
@@ -42,11 +40,9 @@ The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the abi
 ### Ascend NPU
 
 **Verified devices**
-
 | Ascend NPU                    | Status  |
 |:-----------------------------:|:-------:|
 | Atlas 300T A2                 | Support |
-| Atlas 300I Duo                | Support |
 
 *Notes:*
 

docs/backend/SYCL.md

@@ -133,7 +133,7 @@ The docker build option is currently limited to *intel GPU* targets.
 ### Build image
 ```sh
 # Using FP16
-docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" --target light -f .devops/intel.Dockerfile .
+docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" -f .devops/llama-cli-intel.Dockerfile .
 ```
 
 *Notes*:

docs/build.md

@@ -7,22 +7,45 @@ git clone https://github.com/ggerganov/llama.cpp
 cd llama.cpp
 ```
 
-The following sections describe how to build with different backends and options.
+In order to build llama.cpp you have four different options.
 
-## CPU Build
+- Using `make`:
+  - On Linux or MacOS:
 
-Build llama.cpp using `CMake`:
+      ```bash
+      make
+      ```
 
-```bash
-cmake -B build
-cmake --build build --config Release
-```
+  - On Windows (x86/x64 only, arm64 requires cmake):
 
-**Notes**:
+    1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
+    2. Extract `w64devkit` on your pc.
+    3. Run `w64devkit.exe`.
+    4. Use the `cd` command to reach the `llama.cpp` folder.
+    5. From here you can run:
+        ```bash
+        make
+        ```
 
-- For faster compilation, add the `-j` argument to run multiple jobs in parallel, or use a generator that does this automatically such as Ninja. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel.
-- For faster repeated compilation, install [ccache](https://ccache.dev/)
-- For debug builds, there are two cases:
+  - Notes:
+    - For `Q4_0_4_4` quantization type build, add the `GGML_NO_LLAMAFILE=1` flag. For example, use `make GGML_NO_LLAMAFILE=1`.
+    - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `make -j 8` will run 8 jobs in parallel.
+    - For faster repeated compilation, install [ccache](https://ccache.dev/).
+    - For debug builds, run `make LLAMA_DEBUG=1`
+
+- Using `CMake`:
+
+  ```bash
+  cmake -B build
+  cmake --build build --config Release
+  ```
+
+  **Notes**:
+
+    - For `Q4_0_4_4` quantization type build, add the `-DGGML_LLAMAFILE=OFF` cmake option. For example, use `cmake -B build -DGGML_LLAMAFILE=OFF`.
+    - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel.
+    - For faster repeated compilation, install [ccache](https://ccache.dev/).
+    - For debug builds, there are two cases:
 
       1. Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag):
 
@@ -37,15 +60,7 @@ cmake --build build --config Release
       cmake -B build -G "Xcode"
       cmake --build build --config Debug
       ```
-
-    For more details and a list of supported generators, see the [CMake documentation](https://cmake.org/cmake/help/latest/manual/cmake-generators.7.html).
-- For static builds, add `-DBUILD_SHARED_LIBS=OFF`:
-  ```
-  cmake -B build -DBUILD_SHARED_LIBS=OFF
-  cmake --build build --config Release
-  ```
-
-- Building for Windows (x86, x64 and arm64) with MSVC or clang as compilers:
+    - Building for Windows (x86, x64 and arm64) with MSVC or clang as compilers:
       - Install Visual Studio 2022, e.g. via the [Community Edition](https://visualstudio.microsoft.com/de/vs/community/). In the installer, select at least the following options (this also automatically installs the required additional tools like CMake,...):
         - Tab Workload: Desktop-development with C++
         - Tab Components (select quickly via search): C++-_CMake_ Tools for Windows, _Git_ for Windows, C++-_Clang_ Compiler for Windows, MS-Build Support for LLVM-Toolset (clang)
@@ -55,27 +70,61 @@ cmake --build build --config Release
         cmake --preset arm64-windows-llvm-release -D GGML_OPENMP=OFF
         cmake --build build-arm64-windows-llvm-release
         ```
-    Building for arm64 can also be done with the MSVC compiler with the build-arm64-windows-MSVC preset, or the standard CMake build instructions. However, note that the MSVC compiler does not support inline ARM assembly code, used e.g. for the accelerated Q4_0_N_M CPU kernels.
+        Note: Building for arm64 could also be done just with MSVC (with the build-arm64-windows-MSVC preset, or the standard CMake build instructions). But MSVC does not support inline ARM assembly-code, used e.g. for the accelerated Q4_0_4_8 CPU kernels.
+
+-   Using `gmake` (FreeBSD):
+
+    1. Install and activate [DRM in FreeBSD](https://wiki.freebsd.org/Graphics)
+    2. Add your user to **video** group
+    3. Install compilation dependencies.
 
-    For building with ninja generator and clang compiler as default:
-      -set path:set LIB=C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\um\x64;C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.41.34120\lib\x64\uwp;C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\ucrt\x64
         ```bash
-      cmake --preset x64-windows-llvm-release
-      cmake --build build-x64-windows-llvm-release
+        sudo pkg install gmake automake autoconf pkgconf llvm15 openblas
+
+        gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4
         ```
 
+## Metal Build
+
+On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU.
+To disable the Metal build at compile time use the `GGML_NO_METAL=1` flag or the `GGML_METAL=OFF` cmake option.
+
+When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers|-ngl 0` command-line
+argument.
+
 ## BLAS Build
 
-Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Using BLAS doesn't affect the generation performance. There are currently several different BLAS implementations available for build and use:
+Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS. There are currently several different BLAS implementations available for build and use:
 
-### Accelerate Framework
+### Accelerate Framework:
 
 This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions.
 
-### OpenBLAS
+### OpenBLAS:
 
 This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine.
 
+- Using `make`:
+  - On Linux:
+    ```bash
+    make GGML_OPENBLAS=1
+    ```
+
+  - On Windows:
+
+    1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
+    2. Download the latest version of [OpenBLAS for Windows](https://github.com/xianyi/OpenBLAS/releases).
+    3. Extract `w64devkit` on your pc.
+    4. From the OpenBLAS zip that you just downloaded copy `libopenblas.a`, located inside the `lib` folder, inside `w64devkit\x86_64-w64-mingw32\lib`.
+    5. From the same OpenBLAS zip copy the content of the `include` folder inside `w64devkit\x86_64-w64-mingw32\include`.
+    6. Run `w64devkit.exe`.
+    7. Use the `cd` command to reach the `llama.cpp` folder.
+    8. From here you can run:
+
+        ```bash
+        make GGML_OPENBLAS=1
+        ```
+
 - Using `CMake` on Linux:
 
     ```bash
@@ -87,6 +136,14 @@ This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS i
 
 Check [BLIS.md](./backend/BLIS.md) for more information.
 
+### SYCL
+
+SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators.
+
+llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU).
+
+For detailed info, please refer to [llama.cpp for SYCL](./backend/SYCL.md).
+
 ### Intel oneMKL
 
 Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./backend/SYCL.md).
@@ -104,87 +161,27 @@ Building through oneAPI compilers will make avx_vnni instruction set available f
 
 Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information.
 
-### Other BLAS libraries
+### CUDA
 
-Any other BLAS library can be used by setting the `GGML_BLAS_VENDOR` option. See the [CMake documentation](https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors) for a list of supported vendors.
+This provides GPU acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).
 
-## Metal Build
+For Jetson user, if you have Jetson Orin, you can try this: [Offical Support](https://www.jetson-ai-lab.com/tutorial_text-generation.html). If you are using an old model(nano/TX2), need some additional operations before compiling.
 
-On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU.
-To disable the Metal build at compile time use the `-DGGML_METAL=OFF` cmake option.
+- Using `make`:
+  ```bash
+  make GGML_CUDA=1
+  ```
+- Using `CMake`:
 
-When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers 0` command-line argument.
+  ```bash
+  cmake -B build -DGGML_CUDA=ON
+  cmake --build build --config Release
+  ```
 
-## SYCL
-
-SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators.
-
-llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU).
-
-For detailed info, please refer to [llama.cpp for SYCL](./backend/SYCL.md).
-
-## CUDA
-
-This provides GPU acceleration using an NVIDIA GPU. Make sure to have the [CUDA toolkit](https://developer.nvidia.com/cuda-toolkit) installed.
-
-#### Download directly from NVIDIA
-You may find the official downloads here: [NVIDIA developer site](https://developer.nvidia.com/cuda-downloads).
-
-
-#### Compile and run inside a Fedora Toolbox Container
-We also have a [guide](./cuda-fedora.md) for setting up CUDA toolkit in a Fedora [toolbox container](https://containertoolbx.org/).
-
-**Recommended for:**
-
-- ***Particularly*** *convenient* for users of [Atomic Desktops for Fedora](https://fedoraproject.org/atomic-desktops/); such as: [Silverblue](https://fedoraproject.org/atomic-desktops/silverblue/) and [Kinoite](https://fedoraproject.org/atomic-desktops/kinoite/).
-- Toolbox is installed by default: [Fedora Workstation](https://fedoraproject.org/workstation/) or [Fedora KDE Plasma Desktop](https://fedoraproject.org/spins/kde).
-- *Optionally* toolbox packages are available: [Arch Linux](https://archlinux.org/), [Red Hat Enterprise Linux >= 8.5](https://www.redhat.com/en/technologies/linux-platforms/enterprise-linux), or [Ubuntu](https://ubuntu.com/download)
-
-
-### Compilation
-```bash
-cmake -B build -DGGML_CUDA=ON
-cmake --build build --config Release
-```
-
-### Override Compute Capability Specifications
-
-If `nvcc` cannot detect your gpu, you may get compile-warnings such as:
- ```text
-nvcc warning : Cannot find valid GPU for '-arch=native', default arch is used
-```
-
-To override the `native` GPU detection:
-
-#### 1. Take note of the `Compute Capability` of your NVIDIA devices: ["CUDA: Your GPU Compute > Capability"](https://developer.nvidia.com/cuda-gpus).
-
-```text
-GeForce RTX 4090      8.9
-GeForce RTX 3080 Ti   8.6
-GeForce RTX 3070      8.6
-```
-
-#### 2. Manually list each varying `Compute Capability` in the `CMAKE_CUDA_ARCHITECTURES` list.
-
-```bash
-cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES="86;89"
-```
-
-### Runtime CUDA environmental variables
-
-You may set the [cuda environmental variables](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) at runtime.
-
-```bash
-# Use `CUDA_VISIBLE_DEVICES` to hide the first compute device.
-CUDA_VISIBLE_DEVICES="-0" ./build/bin/llama-server --model /srv/models/llama.gguf
-```
-
-### Unified Memory
+The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used.
 
 The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted. In Windows this setting is available in the NVIDIA control panel as `System Memory Fallback`.
 
-### Performance Tuning
-
 The following compilation options are also available to tweak performance:
 
 | Option                        | Legal values           | Default | Description                                                                                                                                                                                                                                                                             |
@@ -195,10 +192,14 @@ The following compilation options are also available to tweak performance:
 | GGML_CUDA_PEER_MAX_BATCH_SIZE | Positive integer       | 128     | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial.                                                                         |
 | GGML_CUDA_FA_ALL_QUANTS       | Boolean                | false   | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer.                                                                                                  |
 
-## MUSA
+### MUSA
 
 This provides GPU acceleration using the MUSA cores of your Moore Threads MTT GPU. Make sure to have the MUSA SDK installed. You can download it from here: [MUSA SDK](https://developer.mthreads.com/sdk/download/musa).
 
+- Using `make`:
+  ```bash
+  make GGML_MUSA=1
+  ```
 - Using `CMake`:
 
   ```bash
@@ -212,12 +213,16 @@ The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enab
 
 Most of the compilation options available for CUDA should also be available for MUSA, though they haven't been thoroughly tested yet.
 
-## HIP
+### hipBLAS
 
-This provides GPU acceleration on HIP-supported AMD GPUs.
+This provides BLAS acceleration on HIP-supported AMD GPUs.
 Make sure to have ROCm installed.
 You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html#rocm-install-quick).
 
+- Using `make`:
+  ```bash
+  make GGML_HIPBLAS=1
+  ```
 - Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU):
   ```bash
   HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
@@ -242,6 +247,11 @@ You can download it from your Linux distro's package manager or from here: [ROCm
       && cmake --build build -- -j 16
   ```
 
+- Using `make` (example for target gfx1030, build with 16 CPU threads):
+  ```bash
+  make -j16 GGML_HIPBLAS=1 GGML_HIP_UMA=1 AMDGPU_TARGETS=gfx1030
+  ```
+
 - Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU):
   ```bash
   set PATH=%HIP_PATH%\bin;%PATH%
@@ -255,11 +265,11 @@ You can download it from your Linux distro's package manager or from here: [ROCm
 The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used.
 If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3.
 
-## Vulkan
+### Vulkan
 
 **Windows**
 
-### w64devkit
+#### w64devkit
 
 Download and extract [`w64devkit`](https://github.com/skeeto/w64devkit/releases).
 
@@ -279,14 +289,9 @@ Libs: -lvulkan-1
 EOF
 
 ```
+Switch into the `llama.cpp` directory and run `make GGML_VULKAN=1`.
 
-Switch into the `llama.cpp` directory and build using CMake.
-```sh
-cmake -B build -DGGML_VULKAN=ON
-cmake --build build --config Release
-```
-
-### Git Bash MINGW64
+#### Git Bash MINGW64
 
 Download and install [`Git-SCM`](https://git-scm.com/downloads/win) with the default settings
 
@@ -305,21 +310,20 @@ cmake --build build --config Release
 
 Now you can load the model in conversation mode using `Vulkan`
 
-```sh
-build/bin/Release/llama-cli -m "[PATH TO MODEL]" -ngl 100 -c 16384 -t 10 -n -2 -cnv
+```
+build/bin/release/llama-cli -m "[PATH TO MODEL]" -ngl 100 -c 16384 -t 10 -n -2 -cnv
 ```
 
-### MSYS2
+#### MSYS2
 Install [MSYS2](https://www.msys2.org/) and then run the following commands in a UCRT terminal to install dependencies.
-```sh
-pacman -S git \
+  ```sh
+  pacman -S git \
       mingw-w64-ucrt-x86_64-gcc \
       mingw-w64-ucrt-x86_64-cmake \
       mingw-w64-ucrt-x86_64-vulkan-devel \
       mingw-w64-ucrt-x86_64-shaderc
-```
-
-Switch into the `llama.cpp` directory and build using CMake.
+  ```
+Switch into `llama.cpp` directory and build using CMake.
 ```sh
 cmake -B build -DGGML_VULKAN=ON
 cmake --build build --config Release
@@ -331,7 +335,7 @@ You don't need to install Vulkan SDK. It will be installed inside the container.
 
 ```sh
 # Build the image
-docker build -t llama-cpp-vulkan --target light -f .devops/vulkan.Dockerfile .
+docker build -t llama-cpp-vulkan -f .devops/llama-cli-vulkan.Dockerfile .
 
 # Then, use it:
 docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
@@ -368,7 +372,7 @@ cmake --build build --config Release
 # ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32
 ```
 
-## CANN
+### CANN
 This provides NPU acceleration using the AI cores of your Ascend NPU. And [CANN](https://www.hiascend.com/en/software/cann) is a hierarchical APIs to help you to quickly build AI applications and service based on Ascend NPU.
 
 For more information about Ascend NPU in [Ascend Community](https://www.hiascend.com/en/).
@@ -383,26 +387,22 @@ cmake --build build --config release
 
 You can test with:
 
-```bash
-./build/bin/llama-cli -m PATH_TO_MODEL -p "Building a website can be done in 10 steps:" -ngl 32
-```
+`./build/bin/llama-cli -m PATH_TO_MODEL -p "Building a website can be done in 10 steps:" -ngl 32`
 
-If the following info is output on screen, you are using `llama.cpp` with the CANN backend:
+If the fllowing info is output on screen, you are using `llama.cpp by CANN backend`:
 ```bash
-llm_load_tensors:       CANN model buffer size = 13313.00 MiB
+llm_load_tensors:       CANN buffer size = 13313.00 MiB
 llama_new_context_with_model:       CANN compute buffer size =  1260.81 MiB
 ```
 
 For detailed info, such as model/device supports, CANN install, please refer to [llama.cpp for CANN](./backend/CANN.md).
 
-## Android
+### Android
 
 To read documentation for how to build on Android, [click here](./android.md)
 
-## Notes about GPU-accelerated backends
+### Arm CPU optimized mulmat kernels
 
-The GPU may still be used to accelerate some parts of the computation even when using the `-ngl 0` option. You can fully disable GPU acceleration by using `--device none`.
+Llama.cpp includes a set of optimized mulmat kernels for the Arm architecture, leveraging Arm® Neon™, int8mm and SVE instructions. These kernels are enabled at build time through the appropriate compiler cpu-type flags, such as `-DCMAKE_C_FLAGS=-march=armv8.2a+i8mm+sve`. Note that these optimized kernels require the model to be quantized into one of the formats: `Q4_0_4_4` (Arm Neon), `Q4_0_4_8` (int8mm) or `Q4_0_8_8` (SVE). The SVE mulmat kernel specifically requires a vector width of 256 bits. When running on devices with a different vector width, it is recommended to use the `Q4_0_4_8` (int8mm) or `Q4_0_4_4` (Arm Neon) formats for better performance. Refer to [examples/quantize/README.md](../examples/quantize/README.md) for more information on the quantization formats.
 
-In most cases, it is possible to build and use multiple backends at the same time. For example, you can build llama.cpp with both CUDA and Vulkan support by using the `-DGGML_CUDA=ON -DGGML_VULKAN=ON` options with CMake. At runtime, you can specify which backend devices to use with the `--device` option. To see a list of available devices, use the `--list-devices` option.
-
-Backends can be built as dynamic libraries that can be loaded dynamically at runtime. This allows you to use the same llama.cpp binary on different machines with different GPUs. To enable this feature, use the `GGML_BACKEND_DL` option when building.
+To support `Q4_0_4_4`, you must build with `GGML_NO_LLAMAFILE=1` (`make`) or `-DGGML_LLAMAFILE=OFF` (`cmake`).

docs/cuda-fedora.md

@@ -1,270 +0,0 @@
-# Setting Up CUDA on Fedora
-
-In this guide we setup [Nvidia CUDA](https://docs.nvidia.com/cuda/) in a toolbox container. This guide is applicable for:
-
-- [Fedora Workstation](https://fedoraproject.org/workstation/)
-- [Atomic Desktops for Fedora](https://fedoraproject.org/atomic-desktops/)
-- [Fedora Spins](https://fedoraproject.org/spins)
-- [Other Distributions](https://containertoolbx.org/distros/), including `Red Hat Enterprise Linux >= 8.5`, `Arch Linux`, and `Ubuntu`.
-
-## Table of Contents
-
-- [Prerequisites](#prerequisites)
-- [Using the Fedora 41 CUDA Repository](#using-the-fedora-41-cuda-repository)
-- [Creating a Fedora Toolbox Environment](#creating-a-fedora-toolbox-environment)
-- [Installing Essential Development Tools](#installing-essential-development-tools)
-- [Adding the CUDA Repository](#adding-the-cuda-repository)
-- [Installing `nvidia-driver-libs`](#installing-nvidia-driver-libs)
-- [Manually Resolving Package Conflicts](#manually-resolving-package-conflicts)
-- [Finalizing the Installation of `nvidia-driver-libs`](#finalizing-the-installation-of-nvidia-driver-libs)
-- [Installing the CUDA Meta-Package](#installing-the-cuda-meta-package)
-- [Configuring the Environment](#configuring-the-environment)
-- [Verifying the Installation](#verifying-the-installation)
-- [Conclusion](#conclusion)
-- [Troubleshooting](#troubleshooting)
-- [Additional Notes](#additional-notes)
-- [References](#references)
-
-## Prerequisites
-
-- **Toolbox Installed on the Host System** `Fedora Silverblue` and `Fedora Workstation` both have toolbox by default, other distributions may need to install the [toolbox package](https://containertoolbx.org/install/).
-- **NVIDIA Drivers and Graphics Card installed on Host System (recommended)** To run CUDA program, such as `llama.cpp`, the host should be setup to access your NVIDIA hardware. Fedora Hosts can use the [RPM Fusion Repository](https://rpmfusion.org/Howto/NVIDIA).
-- **Internet connectivity** to download packages.
-
-### Using the Fedora 41 CUDA Repository
-
-The latest release is 41.
-
-- [Fedora 41 CUDA Repository](https://developer.download.nvidia.com/compute/cuda/repos/fedora41/x86_64/)
-
-**Note:** We recommend using a toolbox environment to prevent system conflicts.
-
-## Creating a Fedora Toolbox Environment
-
-This guide focuses on Fedora hosts, but with small adjustments, it can work for other hosts. Using the Fedora Toolbox allows us to install the necessary packages without affecting the host system.
-
-**Note:** Toolbox is available for other systems, and even without Toolbox, it is possible to use Podman or Docker.
-
-1. **Create a Fedora 41 Toolbox:**
-
-   ```bash
-   toolbox create --image registry.fedoraproject.org/fedora-toolbox:41 --container fedora-toolbox-41-cuda
-   ```
-
-2. **Enter the Toolbox:**
-
-   ```bash
-   toolbox enter --container fedora-toolbox-41-cuda
-   ```
-
-   Inside the toolbox, you have root privileges and can install packages without affecting the host system.
-
-## Installing Essential Development Tools
-
-1. **Synchronize the DNF Package Manager:**
-
-   ```bash
-   sudo dnf distro-sync
-   ```
-
-2. **Install the Default Text Editor (Optional):**
-
-   ```bash
-   sudo dnf install vim-default-editor --allowerasing
-   ```
-
-   The `--allowerasing` flag will allow the removal of the conflicting `nano-default-editor` package.
-
-3. **Install Development Tools and Libraries:**
-
-   ```bash
-   sudo dnf install @c-development @development-tools cmake
-   ```
-
-   This installs essential packages for compiling software, including `gcc`, `make`, and other development headers.
-
-## Adding the CUDA Repository
-
-Add the NVIDIA CUDA repository to your DNF configuration:
-
-```bash
-sudo dnf config-manager addrepo --from-repofile=https://developer.download.nvidia.com/compute/cuda/repos/fedora41/x86_64/cuda-fedora41.repo
-```
-
-After adding the repository, synchronize the package manager again:
-
-```bash
-sudo dnf distro-sync
-```
-
-## Installing `nvidia-driver-libs` and `nvidia-driver-cuda-libs`
-
-We need to detect if the host is supplying the [NVIDIA driver libraries into the toolbox](https://github.com/containers/toolbox/blob/main/src/pkg/nvidia/nvidia.go).
-
-```bash
-ls -la /usr/lib64/libcuda.so.1
-```
-
-**Explanation:**
-
-- `nvidia-driver-libs` and `nvidia-driver-cuda-libs` contains necessary NVIDIA driver libraries required by CUDA,
-  on hosts with NVIDIA drivers installed the Fedora Container will supply the host libraries.
-
-### Install Nvidia Driver Libraries on Guest (if `libcuda.so.1` was NOT found).
-
-```bash
-sudo dnf install nvidia-driver-libs nvidia-driver-cuda-libs
-```
-
-### Manually Updating the RPM database for host-supplied NVIDIA drivers (if `libcuda.so.1` was found).
-
-If the installation fails due to conflicts, we'll manually download and install the required packages, excluding conflicting files.
-
-#### 1. Download `nvidia-driver-libs` and `nvidia-driver-cuda-libs` RPM's (with dependencies)
-
-```bash
-sudo dnf download --destdir=/tmp/nvidia-driver-libs --resolve --arch x86_64 nvidia-driver-libs nvidia-driver-cuda-libs
-```
-
-#### 2. Update the RPM database to assume the installation of these packages.
-
-```bash
-sudo rpm --install --verbose --hash --justdb /tmp/nvidia-driver-libs/*
-```
-
-**Note:**
-
-- The `--justdb` option only updates the RPM database, without touching the filesystem.
-
-#### Finalizing the Installation of `nvidia-driver-libs` and `nvidia-driver-cuda-libs`
-
-After manually installing the dependencies, run:
-
-```bash
-sudo dnf install nvidia-driver-libs nvidia-driver-cuda-libs
-```
-
-You should receive a message indicating the package is already installed:
-
-```
-Updating and loading repositories:
-Repositories loaded.
-Package "nvidia-driver-libs-3:570.86.10-1.fc41.x86_64" is already installed.
-Package "nvidia-driver-cuda-libs-3:570.86.10-1.fc41.x86_64" is already installed.
-
-Nothing to do.
-```
-
-## Installing the CUDA Meta-Package
-
-Now that the driver libraries are installed, proceed to install CUDA:
-
-```bash
-sudo dnf install cuda
-```
-
-This installs the CUDA toolkit and associated packages.
-
-## Configuring the Environment
-
-To use CUDA, add its binary directory to your system's `PATH`.
-
-1. **Create a Profile Script:**
-
-   ```bash
-   sudo sh -c 'echo "export PATH=\$PATH:/usr/local/cuda/bin" >> /etc/profile.d/cuda.sh'
-   ```
-
-   **Explanation:**
-
-   - We add to `/etc/profile.d/` as the `/etc/` folder is unique to this particular container, and is not shared with other containers or the host system.
-   - The backslash `\` before `$PATH` ensures the variable is correctly written into the script.
-
-2. **Make the Script Executable:**
-
-   ```bash
-   sudo chmod +x /etc/profile.d/cuda.sh
-   ```
-
-3. **Source the Script to Update Your Environment:**
-
-   ```bash
-   source /etc/profile.d/cuda.sh
-   ```
-
-   **Note:** This command updates your current shell session with the new `PATH`. The `/etc/profile.d/cuda.sh` script ensures that the CUDA binaries are available in your `PATH` for all future sessions.
-
-## Verifying the Installation
-
-To confirm that CUDA is correctly installed and configured, check the version of the NVIDIA CUDA Compiler (`nvcc`):
-
-```bash
-nvcc --version
-```
-
-You should see output similar to:
-
-```
-nvcc: NVIDIA (R) Cuda compiler driver
-Copyright (c) 2005-2025 NVIDIA Corporation
-Built on Wed_Jan_15_19:20:09_PST_2025
-Cuda compilation tools, release 12.8, V12.8.61
-Build cuda_12.8.r12.8/compiler.35404655_0
-```
-
-This output confirms that the CUDA compiler is accessible and indicates the installed version.
-
-## Conclusion
-
-You have successfully set up CUDA on Fedora within a toolbox environment using the Fedora 41 CUDA repository. By manually updating the RPM db and configuring the environment, you can develop CUDA applications without affecting your host system.
-
-## Troubleshooting
-
-- **Installation Failures:**
-
-  - If you encounter errors during installation, carefully read the error messages. They often indicate conflicting files or missing dependencies.
-  - You may use the `--excludepath` option with `rpm` to exclude conflicting files during manual RPM installations.
-
-- **Rebooting the Container:**
-
-  - Sometimes there may be a bug in the NVIDIA driver host passthrough (such as missing a shared library). Rebooting the container may solve this issue:
-
-  ```bash
-  # on the host system
-  podman container restart --all
-  ```
-
-- **Environment Variables Not Set:**
-  - If `nvcc` is not found after installation, ensure that `/usr/local/cuda/bin` is in your `PATH`.
-  - Run `echo $PATH` to check if the path is included.
-  - Re-source the profile script or open a new terminal session.
-
-## Additional Notes
-
-- **Updating CUDA in the Future:**
-
-  - Keep an eye on the official NVIDIA repositories for updates to your Fedora version.
-  - When an updated repository becomes available, adjust your `dnf` configuration accordingly.
-
-- **Building `llama.cpp`:**
-
-  - With CUDA installed, you can follow these [build instructions for `llama.cpp`](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md) to compile it with CUDA support.
-  - Ensure that any CUDA-specific build flags or paths are correctly set in your build configuration.
-
-- **Using the Toolbox Environment:**
-  - The toolbox environment is isolated from your host system, which helps prevent conflicts.
-  - Remember that system files and configurations inside the toolbox are separate from the host. By default the home directory of the user is shared between the host and the toolbox.
-
----
-
-**Disclaimer:** Manually installing and modifying system packages can lead to instability of the container. The above steps are provided as a guideline and may need adjustments based on your specific system configuration. Always back up important data before making significant system changes, especially as your home folder is writable and shared with he toolbox.
-
-**Acknowledgments:** Special thanks to the Fedora community and NVIDIA documentation for providing resources that assisted in creating this guide.
-
-## References
-
-- [Fedora Toolbox Documentation](https://docs.fedoraproject.org/en-US/fedora-silverblue/toolbox/)
-- [NVIDIA CUDA Installation Guide](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
-- [Podman Documentation](https://podman.io/get-started)
-
----

docs/development/HOWTO-add-model.md

@@ -28,7 +28,7 @@ The required steps to implement for an HF model are:
 ```python
 @Model.register("MyModelForCausalLM")
 class MyModel(Model):
-    model_arch = gguf.MODEL_ARCH.MYMODEL
+    model_arch = gguf.MODEL_ARCH.GROK
 ```
 
 2. Define the layout of the GGUF tensors in [constants.py](/gguf-py/gguf/constants.py)
@@ -79,14 +79,14 @@ Depending on the model configuration, tokenizer, code and tensors layout, you wi
 - `Model#set_vocab`
 - `Model#write_tensors`
 
-NOTE: Tensor names must end with `.weight` or `.bias` suffixes, that is the convention and several tools like `quantize` expect this to proceed the weights.
+NOTE: Tensor names must end with `.weight` suffix, that is the convention and several tools like `quantize` expect this to proceed the weights.
 
 ### 2. Define the model architecture in `llama.cpp`
 
 The model params and tensors layout must be defined in `llama.cpp`:
 1. Define a new `llm_arch`
 2. Define the tensors layout in `LLM_TENSOR_NAMES`
-3. Add any non-standard metadata in `llm_load_hparams`
+3. Add any non standard metadata in `llm_load_hparams`
 4. Create the tensors for inference in `llm_load_tensors`
 5. If the model has a RoPE operation, add the rope type in `llama_rope_type`
 
@@ -96,9 +96,9 @@ NOTE: The dimensions in `ggml` are typically in the reverse order of the `pytorc
 
 This is the funniest part, you have to provide the inference graph implementation of the new model architecture in `llama_build_graph`.
 
-Have a look at existing implementations like `build_llama`, `build_dbrx` or `build_bert`.
+Have a look at existing implementation like `build_llama`, `build_dbrx` or `build_bert`.
 
-Some `ggml` backends do not support all operations. Backend implementations can be added in a separate PR.
+When implementing a new graph, please note that the underlying `ggml` backends might not support them all, support for missing backend operations can be added in another PR.
 
 Note: to debug the inference graph: you can use [llama-eval-callback](/examples/eval-callback/).
 

docs/docker.md

@@ -60,9 +60,9 @@ Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia
 ## Building Docker locally
 
 ```bash
-docker build -t local/llama.cpp:full-cuda --target full -f .devops/cuda.Dockerfile .
-docker build -t local/llama.cpp:light-cuda --target light -f .devops/cuda.Dockerfile .
-docker build -t local/llama.cpp:server-cuda --target server -f .devops/cuda.Dockerfile .
+docker build -t local/llama.cpp:full-cuda -f .devops/full-cuda.Dockerfile .
+docker build -t local/llama.cpp:light-cuda -f .devops/llama-cli-cuda.Dockerfile .
+docker build -t local/llama.cpp:server-cuda -f .devops/llama-server-cuda.Dockerfile .
 ```
 
 You may want to pass in some different `ARGS`, depending on the CUDA environment supported by your container host, as well as the GPU architecture.
@@ -95,9 +95,9 @@ Assuming one has the [mt-container-toolkit](https://developer.mthreads.com/musa/
 ## Building Docker locally
 
 ```bash
-docker build -t local/llama.cpp:full-musa --target full -f .devops/musa.Dockerfile .
-docker build -t local/llama.cpp:light-musa --target light -f .devops/musa.Dockerfile .
-docker build -t local/llama.cpp:server-musa --target server -f .devops/musa.Dockerfile .
+docker build -t local/llama.cpp:full-musa -f .devops/full-musa.Dockerfile .
+docker build -t local/llama.cpp:light-musa -f .devops/llama-cli-musa.Dockerfile .
+docker build -t local/llama.cpp:server-musa -f .devops/llama-server-musa.Dockerfile .
 ```
 
 You may want to pass in some different `ARGS`, depending on the MUSA environment supported by your container host, as well as the GPU architecture.

docs/llguidance.md

@@ -1,51 +0,0 @@
-# LLGuidance Support in llama.cpp
-
-[LLGuidance](https://github.com/guidance-ai/llguidance) is a library for constrained decoding (also called constrained sampling or structured outputs) for Large Language Models (LLMs). Initially developed as the backend for the [Guidance](https://github.com/guidance-ai/guidance) library, it can also be used independently.
-
-LLGuidance supports JSON Schemas and arbitrary context-free grammars (CFGs) written in a [variant](https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md) of Lark syntax. It is [very fast](https://github.com/guidance-ai/jsonschemabench/tree/main/maskbench) and has [excellent](https://github.com/guidance-ai/llguidance/blob/main/docs/json_schema.md) JSON Schema coverage but requires the Rust compiler, which complicates the llama.cpp build process.
-
-## Building
-
-To enable LLGuidance support, build llama.cpp with the `LLAMA_LLGUIDANCE` option:
-
-```sh
-cmake -B build -DLLAMA_LLGUIDANCE=ON
-make -C build -j
-```
-
-This requires the Rust compiler and the `cargo` tool to be [installed](https://www.rust-lang.org/tools/install).
-
-## Interface
-
-There are no new command-line arguments or modifications to `common_params`. When enabled, grammars starting with `%llguidance` are passed to LLGuidance instead of the [current](../grammars/README.md) llama.cpp grammars. Additionally, JSON Schema requests (e.g., using the `-j` argument in `llama-cli`) are also passed to LLGuidance.
-
-For your existing GBNF grammars, you can use [gbnf_to_lark.py script](https://github.com/guidance-ai/llguidance/blob/main/scripts/gbnf_to_lark.py) to convert them to LLGuidance Lark-like format.
-
-## Performance
-
-Computing a "token mask" (i.e., the set of allowed tokens) for a llama3 tokenizer with 128k tokens takes, on average, 50μs of single-core CPU time for the [JSON Schema Bench](https://github.com/guidance-ai/jsonschemabench). The p99 time is 0.5ms, and the p100 time is 20ms. These results are due to the lexer/parser split and several [optimizations](https://github.com/guidance-ai/llguidance/blob/main/docs/optimizations.md).
-
-## JSON Schema
-
-LLGuidance adheres closely to the JSON Schema specification. For example:
-
-- `additionalProperties` defaults to `true`, unlike current grammars, though you can set `"additionalProperties": false` if needed.
-- any whitespace is allowed.
-- The definition order in the `"properties": {}` object is maintained, regardless of whether properties are required (current grammars always puts required properties first).
-
-Unsupported schemas result in an error message—no keywords are silently ignored.
-
-## Why Not Reuse GBNF Format?
-
-GBNF lacks the concept of a lexer.
-
-Most programming languages, including JSON, use a two-step process: a lexer (built with regular expressions) converts a byte stream into lexemes, which are then processed by a CFG parser. This approach is faster because lexers are cheaper to evaluate, and there is ~10x fewer lexemes than bytes.
-LLM tokens often align with lexemes, so the parser is engaged in under 0.5% of tokens, with the lexer handling the rest.
-
-However, the user has to provide the distinction between lexemes and CFG symbols. In [Lark](https://github.com/lark-parser/lark), lexeme names are uppercase, while CFG symbols are lowercase.
-The [gbnf_to_lark.py script](https://github.com/guidance-ai/llguidance/blob/main/scripts/gbnf_to_lark.py) can often take care of this automatically.
-See [LLGuidance syntax docs](https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md#terminals-vs-rules) for more details.
-
-## Error Handling
-
-Errors are currently printed to `stderr`, and generation continues. Improved error handling may be added in the future.

examples/CMakeLists.txt

@@ -6,10 +6,6 @@ find_package(Threads REQUIRED)
 
 # ...
 
-# flags
-
-llama_add_compile_flags()
-
 # examples
 
 include_directories(${CMAKE_CURRENT_SOURCE_DIR})
@@ -20,12 +16,7 @@ else()
     add_subdirectory(batched)
     add_subdirectory(embedding)
     add_subdirectory(eval-callback)
-
-    if (NOT WIN32)
-        # disabled on Windows because it uses internal functions not exported with LLAMA_API
     add_subdirectory(gbnf-validator)
-    endif()
-
     add_subdirectory(gguf-hash)
     add_subdirectory(gguf-split)
     add_subdirectory(gguf)
@@ -51,17 +42,12 @@ else()
     add_subdirectory(speculative)
     add_subdirectory(speculative-simple)
     add_subdirectory(tokenize)
-    add_subdirectory(tts)
-    add_subdirectory(gen-docs)
     if (NOT GGML_BACKEND_DL)
         # these examples use the backends directly and cannot be built with dynamic loading
         add_subdirectory(convert-llama2c-to-ggml)
         add_subdirectory(cvector-generator)
         add_subdirectory(export-lora)
-        if (NOT WIN32)
-            # disabled on Windows because it uses internal functions not exported with LLAMA_API
         add_subdirectory(quantize-stats)
-        endif()
         add_subdirectory(llava)
         if (GGML_RPC)
             add_subdirectory(rpc)

examples/base-translate.sh

@@ -0,0 +1,61 @@
+#!/bin/bash
+#
+# Few-shot translation example.
+# Requires a base model (i.e. no fine-tuned or instruct models).
+#
+# Usage:
+#
+#   cd llama.cpp
+#   make -j
+#
+#   ./examples/base-translate.sh <model-base> "<text>" [extra-main-args]
+#
+
+if [ $# -lt 2 ]; then
+  echo "Usage: ./base-translate.sh <model-base> \"<text>\" [extra-main-args]"
+  exit 1
+fi
+
+eargs=""
+if [ $# -gt 2 ]; then
+  eargs="${@:3}"
+fi
+
+ftmp="__llama.cpp_example_tmp__.txt"
+trap "rm -f $ftmp" EXIT
+
+echo "Translate from English to French:
+
+===
+
+sea otter, peppermint, plush girafe:
+
+sea otter => loutre de mer
+peppermint => menthe poivrée
+plush girafe => girafe peluche
+
+===
+
+violin
+
+violin => violon
+
+===
+
+phone, computer, mouse, keyboard:
+
+phone => téléphone
+computer => ordinateur
+mouse => souris
+keyboard => clavier
+
+===
+" > $ftmp
+
+echo "$2
+" >> $ftmp
+
+model=$1
+
+# generate the most likely continuation until the string "===" is found
+./llama-cli -m $model -f $ftmp -n 64 --temp 0 --repeat-penalty 1.0 --no-penalize-nl -r "===" $eargs

examples/batched-bench/CMakeLists.txt

@@ -2,4 +2,4 @@ set(TARGET llama-batched-bench)
 add_executable(${TARGET} batched-bench.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_17)
+target_compile_features(${TARGET} PRIVATE cxx_std_11)

examples/batched-bench/batched-bench.cpp

@@ -38,7 +38,7 @@ int main(int argc, char ** argv) {
 
     llama_model_params model_params = common_model_params_to_llama(params);
 
-    llama_model * model = llama_model_load_from_file(params.model.c_str(), model_params);
+    llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
 
     if (model == NULL) {
         fprintf(stderr , "%s: error: unable to load model\n" , __func__);
@@ -50,7 +50,7 @@ int main(int argc, char ** argv) {
     // ensure enough sequences are available
     ctx_params.n_seq_max = n_pl.empty() ? 1 : *std::max_element(n_pl.begin(), n_pl.end());
 
-    llama_context * ctx = llama_init_from_model(model, ctx_params);
+    llama_context * ctx = llama_new_context_with_model(model, ctx_params);
 
     if (ctx == NULL) {
         fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
@@ -194,7 +194,7 @@ int main(int argc, char ** argv) {
     llama_batch_free(batch);
 
     llama_free(ctx);
-    llama_model_free(model);
+    llama_free_model(model);
 
     llama_backend_free();
 

examples/batched.swift/Sources/main.swift

@@ -23,17 +23,12 @@ defer {
 }
 
 let model_params = llama_model_default_params()
-guard let model = llama_model_load_from_file(modelPath.cString(using: .utf8), model_params) else {
+guard let model = llama_load_model_from_file(modelPath.cString(using: .utf8), model_params) else {
     print("Failed to load model")
     exit(1)
 }
 defer {
-    llama_model_free(model)
-}
-
-guard let vocab = llama_model_get_vocab(model) else {
-    print("Failed to get vocab")
-    exit(1)
+    llama_free_model(model)
 }
 
 var tokens = tokenize(text: prompt, add_bos: true)
@@ -46,7 +41,7 @@ context_params.n_batch = UInt32(max(n_len, n_parallel))
 context_params.n_threads = 8
 context_params.n_threads_batch = 8
 
-let context = llama_init_from_model(model, context_params)
+let context = llama_new_context_with_model(model, context_params)
 guard context != nil else {
     print("Failed to initialize context")
     exit(1)
@@ -146,7 +141,7 @@ while n_cur <= n_len {
         let new_token_id = llama_sampler_sample(smpl, context, i_batch[i])
 
         // is it an end of stream? -> mark the stream as finished
-        if llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len {
+        if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
             i_batch[i] = -1
             // print("")
             if n_parallel > 1 {
@@ -212,7 +207,7 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
     let utf8Count = text.utf8.count
     let n_tokens = utf8Count + (add_bos ? 1 : 0)
     let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
-    let tokenCount = llama_tokenize(vocab, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false)
+    let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false)
     var swiftTokens: [llama_token] = []
     for i in 0 ..< tokenCount {
         swiftTokens.append(tokens[Int(i)])
@@ -223,12 +218,12 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
 
 private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String? {
     var result = [CChar](repeating: 0, count: 8)
-    let nTokens = llama_token_to_piece(vocab, token, &result, Int32(result.count), 0, false)
+    let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count), 0, false)
     if nTokens < 0 {
         let actualTokensCount = -Int(nTokens)
         result = .init(repeating: 0, count: actualTokensCount)
         let check = llama_token_to_piece(
-            vocab,
+            model,
             token,
             &result,
             Int32(result.count),

examples/batched/CMakeLists.txt

@@ -2,4 +2,4 @@ set(TARGET llama-batched)
 add_executable(${TARGET} batched.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_17)
+target_compile_features(${TARGET} PRIVATE cxx_std_11)

examples/batched/batched.cpp

@@ -41,19 +41,17 @@ int main(int argc, char ** argv) {
 
     llama_model_params model_params = common_model_params_to_llama(params);
 
-    llama_model * model = llama_model_load_from_file(params.model.c_str(), model_params);
+    llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
 
     if (model == NULL) {
         LOG_ERR("%s: error: unable to load model\n" , __func__);
         return 1;
     }
 
-    const llama_vocab * vocab = llama_model_get_vocab(model);
-
     // tokenize the prompt
 
     std::vector<llama_token> tokens_list;
-    tokens_list = common_tokenize(vocab, params.prompt, true);
+    tokens_list = common_tokenize(model, params.prompt, true);
 
     const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size())*n_parallel;
 
@@ -64,10 +62,9 @@ int main(int argc, char ** argv) {
     ctx_params.n_ctx   = n_kv_req;
     ctx_params.n_batch = std::max(n_predict, n_parallel);
 
-    llama_context * ctx = llama_init_from_model(model, ctx_params);
+    llama_context * ctx = llama_new_context_with_model(model, ctx_params);
 
     auto sparams = llama_sampler_chain_default_params();
-    sparams.no_perf = false;
 
     llama_sampler * smpl = llama_sampler_chain_init(sparams);
 
@@ -122,8 +119,8 @@ int main(int argc, char ** argv) {
         }
 
         llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
-        if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
-            decoder_start_token_id = llama_vocab_bos(vocab);
+        if (decoder_start_token_id == -1) {
+            decoder_start_token_id = llama_token_bos(model);
         }
 
         common_batch_clear(batch);
@@ -176,7 +173,7 @@ int main(int argc, char ** argv) {
             const llama_token new_token_id = llama_sampler_sample(smpl, ctx, i_batch[i]);
 
             // is it an end of generation? -> mark the stream as finished
-            if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_predict) {
+            if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) {
                 i_batch[i] = -1;
                 LOG("\n");
                 if (n_parallel > 1) {
@@ -238,7 +235,7 @@ int main(int argc, char ** argv) {
 
     llama_sampler_free(smpl);
     llama_free(ctx);
-    llama_model_free(model);
+    llama_free_model(model);
 
     llama_backend_free();
 

examples/convert-llama2c-to-ggml/CMakeLists.txt

@@ -2,4 +2,4 @@ set(TARGET llama-convert-llama2c-to-ggml)
 add_executable(${TARGET} convert-llama2c-to-ggml.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_17)
+target_compile_features(${TARGET} PRIVATE cxx_std_11)

examples/convert-llama2c-to-ggml/README.md

@@ -2,8 +2,11 @@
 
 This example reads weights from project [llama2.c](https://github.com/karpathy/llama2.c) and saves them in ggml compatible format. The vocab that is available in `models/ggml-vocab.bin` is used by default.
 
-To convert the model first download the models from the [llama2.c](https://github.com/karpathy/llama2.c) repository.
+To convert the model first download the models from the [llama2.c](https://github.com/karpathy/llama2.c) repository:
 
+`$ make -j`
+
+After successful compilation, following usage options are available:
 ```
 usage: ./llama-convert-llama2c-to-ggml [options]
 

examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp

@@ -1,6 +1,4 @@
 #include "ggml.h"
-#include "gguf.h"
-
 #include "llama.h"
 #include "common.h"
 #include "log.h"
@@ -436,12 +434,12 @@ static void print_matrix(struct ggml_tensor * probs) {
     }
 }
 
-struct my_llama_file {
+struct llama_file {
     // use FILE * so we don't have to re-open the file to mmap
     FILE * fp;
     size_t size;
 
-    my_llama_file(const char * fname, const char * mode) {
+    llama_file(const char * fname, const char * mode) {
         fp = std::fopen(fname, mode);
         if (fp == NULL) {
             size = 0;
@@ -502,7 +500,7 @@ struct my_llama_file {
         return std::string(chars.data(), len);
     }
 
-    ~my_llama_file() {
+    ~llama_file() {
         if (fp) {
             std::fclose(fp);
         }
@@ -510,7 +508,7 @@ struct my_llama_file {
 };
 
 static bool is_ggml_file(const char * filename) {
-    my_llama_file file(filename, "rb");
+    llama_file file(filename, "rb");
     if (file.size < 4) {
         return false;
     }
@@ -578,7 +576,7 @@ static void load_vocab(const char * filename, const Config * config, struct my_l
     } else {
         // assume llama2.c vocabulary
         LOG_INF("%s: Assuming llama2.c vocabulary since %s is not a gguf file\n", __func__, filename);
-        my_llama_file file(filename, "rb");
+        llama_file file(filename, "rb");
         if (!file.fp) {
             die_fmt("%s: %s", strerror(errno), filename);
         }
@@ -691,8 +689,8 @@ static void save_as_llama_model(
     gguf_set_val_u32(ctx, KV_TOKENIZER_UNK_ID, UNKNOWN_TOKEN_ID);
     gguf_set_val_u32(ctx, KV_TOKENIZER_BOS_ID, BOS_TOKEN_ID);
     gguf_set_val_u32(ctx, KV_TOKENIZER_EOS_ID, EOS_TOKEN_ID);
-    gguf_set_val_u32(ctx, KV_TOKENIZER_SEP_ID, LLAMA_TOKEN_NULL);
-    gguf_set_val_u32(ctx, KV_TOKENIZER_PAD_ID, LLAMA_TOKEN_NULL);
+    gguf_set_val_u32(ctx, KV_TOKENIZER_SEP_ID, -1);
+    gguf_set_val_u32(ctx, KV_TOKENIZER_PAD_ID, -1);
 
     gguf_set_val_u32(ctx, KV_CONTEXT_LENGTH, model->hparams.n_ctx);
     gguf_set_val_u32(ctx, KV_EMBEDDING_LENGTH, model->hparams.n_embd);
@@ -911,7 +909,7 @@ int main(int argc, char ** argv) {
     load_vocab(params.fn_vocab_model, &config, &vocab);
 
     struct my_llama_model model;
-    model.hparams.n_vocab   = config.vocab_size; //llama_vocab_n_vocab(lctx);
+    model.hparams.n_vocab   = config.vocab_size; //llama_n_vocab(lctx);
     model.hparams.n_ctx     = params.n_ctx;
     model.hparams.n_embd    = config.dim; //params.n_embd;
     model.hparams.n_ff      = config.hidden_dim;

examples/cvector-generator/CMakeLists.txt

@@ -2,4 +2,4 @@ set(TARGET llama-cvector-generator)
 add_executable(${TARGET} cvector-generator.cpp pca.hpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_17)
+target_compile_features(${TARGET} PRIVATE cxx_std_11)