Implement llama-pull tool

Complete llama-pull tool with documentation

Signed-off-by: Eric Curtin <eric.curtin@docker.com>

.clang-tidy

@@ -17,7 +17,6 @@ Checks: >
     clang-analyzer-*,
     -clang-analyzer-security.insecureAPI.DeprecatedOrUnsafeBufferHandling,
     performance-*,
-    -performance-enum-size,
     portability-*,
     -portability-simd-intrinsics,
     misc-*,

.devops/cann.Dockerfile

@@ -3,8 +3,7 @@
 # ==============================================================================
 
 # Define the CANN base image for easier version updates later
-ARG CHIP_TYPE=910b
-ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.3.rc1.alpha001-${CHIP_TYPE}-openeuler22.03-py3.11
+ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.1.rc1-910b-openeuler22.03-py3.10
 
 # ==============================================================================
 # BUILD STAGE
@@ -12,6 +11,9 @@ ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.3.rc1.alpha001-${CHIP_TYPE}-openeuler2
 # ==============================================================================
 FROM ${CANN_BASE_IMAGE} AS build
 
+# Define the Ascend chip model for compilation. Default is Ascend910B3
+ARG ASCEND_SOC_TYPE=Ascend910B3
+
 # -- Install build dependencies --
 RUN yum install -y gcc g++ cmake make git libcurl-devel python3 python3-pip && \
     yum clean all && \
@@ -34,21 +36,20 @@ ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/runtime/lib64/stub:$LD_LIBRARY_PATH
 # For brevity, only core variables are listed here. You can paste the original ENV list here.
 
 # -- Build llama.cpp --
-# Use the passed CHIP_TYPE argument and add general build options
-ARG CHIP_TYPE
+# Use the passed ASCEND_SOC_TYPE argument and add general build options
 RUN source /usr/local/Ascend/ascend-toolkit/set_env.sh --force \
     && \
     cmake -B build \
         -DGGML_CANN=ON \
         -DCMAKE_BUILD_TYPE=Release \
-        -DSOC_TYPE=ascend${CHIP_TYPE} \
+        -DSOC_TYPE=${ASCEND_SOC_TYPE} \
         . && \
     cmake --build build --config Release -j$(nproc)
 
 # -- Organize build artifacts for copying in later stages --
 # Create a lib directory to store all .so files
 RUN mkdir -p /app/lib && \
-    find build -name "*.so*" -exec cp -P {} /app/lib \;
+    find build -name "*.so" -exec cp {} /app/lib \;
 
 # Create a full directory to store all executables and Python scripts
 RUN mkdir -p /app/full && \

.devops/cpu.Dockerfile

@@ -20,7 +20,7 @@ RUN if [ "$TARGETARCH" = "amd64" ] || [ "$TARGETARCH" = "arm64" ]; then \
     cmake --build build -j $(nproc)
 
 RUN mkdir -p /app/lib && \
-    find build -name "*.so*" -exec cp -P {} /app/lib \;
+    find build -name "*.so" -exec cp {} /app/lib \;
 
 RUN mkdir -p /app/full \
     && cp build/bin/* /app/full \

.devops/cuda.Dockerfile

@@ -25,7 +25,7 @@ RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
     cmake --build build --config Release -j$(nproc)
 
 RUN mkdir -p /app/lib && \
-    find build -name "*.so*" -exec cp -P {} /app/lib \;
+    find build -name "*.so" -exec cp {} /app/lib \;
 
 RUN mkdir -p /app/full \
     && cp build/bin/* /app/full \

.devops/intel.Dockerfile

@@ -1,8 +1,8 @@
-ARG ONEAPI_VERSION=2025.2.2-0-devel-ubuntu24.04
+ARG ONEAPI_VERSION=2025.1.1-0-devel-ubuntu24.04
 
 ## Build Image
 
-FROM intel/deep-learning-essentials:$ONEAPI_VERSION AS build
+FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build
 
 ARG GGML_SYCL_F16=OFF
 RUN apt-get update && \
@@ -21,7 +21,7 @@ RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
     cmake --build build --config Release -j$(nproc)
 
 RUN mkdir -p /app/lib && \
-    find build -name "*.so*" -exec cp -P {} /app/lib \;
+    find build -name "*.so" -exec cp {} /app/lib \;
 
 RUN mkdir -p /app/full \
     && cp build/bin/* /app/full \
@@ -31,7 +31,7 @@ RUN mkdir -p /app/full \
     && cp requirements.txt /app/full \
     && cp .devops/tools.sh /app/full/tools.sh
 
-FROM intel/deep-learning-essentials:$ONEAPI_VERSION AS base
+FROM intel/oneapi-basekit:$ONEAPI_VERSION AS base
 
 RUN apt-get update \
     && apt-get install -y libgomp1 curl\

.devops/musa.Dockerfile

@@ -1,6 +1,6 @@
 ARG UBUNTU_VERSION=22.04
 # This needs to generally match the container host's environment.
-ARG MUSA_VERSION=rc4.3.0
+ARG MUSA_VERSION=rc4.2.0
 # Target the MUSA build image
 ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}-amd64
 
@@ -32,7 +32,7 @@ RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \
     cmake --build build --config Release -j$(nproc)
 
 RUN mkdir -p /app/lib && \
-    find build -name "*.so*" -exec cp -P {} /app/lib \;
+    find build -name "*.so" -exec cp {} /app/lib \;
 
 RUN mkdir -p /app/full \
     && cp build/bin/* /app/full \

.devops/nix/package.nix

@@ -34,7 +34,6 @@
   rocmGpuTargets ? builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets,
   enableCurl ? true,
   useVulkan ? false,
-  useRpc ? false,
   llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake
 
   # It's necessary to consistently use backendStdenv when building with CUDA support,
@@ -129,6 +128,10 @@ effectiveStdenv.mkDerivation (finalAttrs: {
   };
 
   postPatch = ''
+    substituteInPlace ./ggml/src/ggml-metal/ggml-metal.m \
+      --replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";"
+    substituteInPlace ./ggml/src/ggml-metal/ggml-metal.m \
+      --replace '[bundle pathForResource:@"default" ofType:@"metallib"];' "@\"$out/bin/default.metallib\";"
   '';
 
   # With PR#6015 https://github.com/ggml-org/llama.cpp/pull/6015,
@@ -176,7 +179,6 @@ effectiveStdenv.mkDerivation (finalAttrs: {
       (cmakeBool "GGML_METAL" useMetalKit)
       (cmakeBool "GGML_VULKAN" useVulkan)
       (cmakeBool "GGML_STATIC" enableStatic)
-      (cmakeBool "GGML_RPC" useRpc)
     ]
     ++ optionals useCuda [
       (

.devops/rocm.Dockerfile

@@ -1,8 +1,8 @@
 ARG UBUNTU_VERSION=24.04
 
 # This needs to generally match the container host's environment.
-ARG ROCM_VERSION=7.0
-ARG AMDGPU_VERSION=7.0
+ARG ROCM_VERSION=6.4
+ARG AMDGPU_VERSION=6.4
 
 # Target the ROCm build image
 ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
@@ -13,8 +13,9 @@ FROM ${BASE_ROCM_DEV_CONTAINER} AS build
 # Unless otherwise specified, we make a fat build.
 # List from https://github.com/ggml-org/llama.cpp/pull/1087#issuecomment-1682807878
 # This is mostly tied to rocBLAS supported archs.
-# gfx803, gfx900, gfx906, gfx1032, gfx1101, gfx1102,not officialy supported
-# check https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.1/reference/system-requirements.html
+# gfx803, gfx900, gfx1032, gfx1101, gfx1102,not officialy supported
+# gfx906 is deprecated
+#check https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.1/reference/system-requirements.html
 
 ARG ROCM_DOCKER_ARCH='gfx803;gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1010;gfx1030;gfx1032;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201;gfx1151'
 #ARG ROCM_DOCKER_ARCH='gfx1151'
@@ -35,17 +36,20 @@ WORKDIR /app
 
 COPY . .
 
+RUN git clone https://github.com/rocm/rocwmma --branch develop --depth 1
+
 RUN HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
     cmake -S . -B build \
         -DGGML_HIP=ON \
         -DGGML_HIP_ROCWMMA_FATTN=ON \
+        -DCMAKE_HIP_FLAGS="-I$(pwd)/rocwmma/library/include/" \
         -DAMDGPU_TARGETS="$ROCM_DOCKER_ARCH" \
         -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON \
         -DCMAKE_BUILD_TYPE=Release -DLLAMA_BUILD_TESTS=OFF \
     && cmake --build build --config Release -j$(nproc)
 
 RUN mkdir -p /app/lib \
-    && find build -name "*.so*" -exec cp -P {} /app/lib \;
+    && find build -name "*.so" -exec cp {} /app/lib \;
 
 RUN mkdir -p /app/full \
     && cp build/bin/* /app/full \

.devops/s390x.Dockerfile

@@ -1,126 +0,0 @@
-ARG GCC_VERSION=15.2.0
-ARG UBUNTU_VERSION=24.04
-
-### Build Llama.cpp stage
-FROM gcc:${GCC_VERSION} AS build
-
-RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
-    --mount=type=cache,target=/var/lib/apt/lists,sharing=locked \
-    apt update -y && \
-    apt upgrade -y && \
-    apt install -y --no-install-recommends \
-        git cmake ccache ninja-build \
-        # WARNING: Do not use libopenblas-openmp-dev. libopenblas-dev is faster.
-        libopenblas-dev libcurl4-openssl-dev && \
-    rm -rf /var/lib/apt/lists/*
-
-WORKDIR /app
-COPY . .
-
-RUN --mount=type=cache,target=/root/.ccache \
-    --mount=type=cache,target=/app/build \
-    cmake -S . -B build -G Ninja \
-        -DCMAKE_BUILD_TYPE=Release \
-        -DCMAKE_C_COMPILER_LAUNCHER=ccache \
-        -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
-        -DLLAMA_BUILD_TESTS=OFF \
-        -DGGML_NATIVE=OFF \
-        -DGGML_BACKEND_DL=ON \
-        -DGGML_CPU_ALL_VARIANTS=ON \
-        -DGGML_BLAS=ON \
-        -DGGML_BLAS_VENDOR=OpenBLAS && \
-    cmake --build build --config Release -j $(nproc) && \
-    cmake --install build --prefix /opt/llama.cpp
-
-COPY *.py             /opt/llama.cpp/bin
-COPY .devops/tools.sh /opt/llama.cpp/bin
-
-COPY gguf-py          /opt/llama.cpp/gguf-py
-COPY requirements.txt /opt/llama.cpp/gguf-py
-COPY requirements     /opt/llama.cpp/gguf-py/requirements
-
-
-### Collect all llama.cpp binaries, libraries and distro libraries
-FROM scratch AS collector
-
-# Copy llama.cpp binaries and libraries
-COPY --from=build /opt/llama.cpp/bin     /llama.cpp/bin
-COPY --from=build /opt/llama.cpp/lib     /llama.cpp/lib
-COPY --from=build /opt/llama.cpp/gguf-py /llama.cpp/gguf-py
-
-
-### Base image
-FROM ubuntu:${UBUNTU_VERSION} AS base
-
-RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
-    --mount=type=cache,target=/var/lib/apt/lists,sharing=locked \
-    apt update -y && \
-    apt install -y --no-install-recommends \
-        # WARNING: Do not use libopenblas-openmp-dev. libopenblas-dev is faster.
-        # See: https://github.com/ggml-org/llama.cpp/pull/15915#issuecomment-3317166506
-        curl libgomp1 libopenblas-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 llama.cpp libraries
-COPY --from=collector /llama.cpp/lib /usr/lib/s390x-linux-gnu
-
-
-### Full
-FROM base AS full
-
-ENV PATH="/root/.cargo/bin:${PATH}"
-WORKDIR /app
-
-RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
-    --mount=type=cache,target=/var/lib/apt/lists,sharing=locked \
-    apt update -y && \
-    apt install -y \
-        git cmake libjpeg-dev \
-        python3 python3-pip python3-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
-
-RUN curl https://sh.rustup.rs -sSf | bash -s -- -y
-
-COPY --from=collector /llama.cpp/bin /app
-COPY --from=collector /llama.cpp/gguf-py /app/gguf-py
-
-RUN pip install --no-cache-dir --break-system-packages \
-        -r /app/gguf-py/requirements.txt
-
-ENTRYPOINT [ "/app/tools.sh" ]
-
-
-### CLI Only
-FROM base AS light
-
-WORKDIR /llama.cpp/bin
-
-# Copy llama.cpp binaries and libraries
-COPY --from=collector /llama.cpp/bin/*.so /llama.cpp/bin
-COPY --from=collector /llama.cpp/bin/llama-cli /llama.cpp/bin
-
-ENTRYPOINT [ "/llama.cpp/bin/llama-cli" ]
-
-
-### Server
-FROM base AS server
-
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-WORKDIR /llama.cpp/bin
-
-# Copy llama.cpp binaries and libraries
-COPY --from=collector /llama.cpp/bin/*.so /llama.cpp/bin
-COPY --from=collector /llama.cpp/bin/llama-server /llama.cpp/bin
-
-EXPOSE 8080
-
-ENTRYPOINT [ "/llama.cpp/bin/llama-server" ]

.devops/vulkan.Dockerfile

@@ -1,24 +1,42 @@
-ARG UBUNTU_VERSION=26.04
+ARG UBUNTU_VERSION=24.04
 
 FROM ubuntu:$UBUNTU_VERSION AS build
 
+# Ref: https://vulkan.lunarg.com/doc/sdk/latest/linux/getting_started.html
+
 # Install build tools
 RUN apt update && apt install -y git build-essential cmake wget xz-utils
 
+# Install Vulkan SDK
+ARG VULKAN_VERSION=1.4.321.1
+RUN ARCH=$(uname -m) && \
+    wget -qO /tmp/vulkan-sdk.tar.xz https://sdk.lunarg.com/sdk/download/${VULKAN_VERSION}/linux/vulkan-sdk-linux-${ARCH}-${VULKAN_VERSION}.tar.xz && \
+    mkdir -p /opt/vulkan && \
+    tar -xf /tmp/vulkan-sdk.tar.xz -C /tmp --strip-components=1 && \
+    mv /tmp/${ARCH}/* /opt/vulkan/ && \
+    rm -rf /tmp/*
+
 # Install cURL and Vulkan SDK dependencies
 RUN apt install -y libcurl4-openssl-dev curl \
-    libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libvulkan-dev glslc
+    libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev
+
+# Set environment variables
+ENV VULKAN_SDK=/opt/vulkan
+ENV PATH=$VULKAN_SDK/bin:$PATH
+ENV LD_LIBRARY_PATH=$VULKAN_SDK/lib:$LD_LIBRARY_PATH
+ENV CMAKE_PREFIX_PATH=$VULKAN_SDK:$CMAKE_PREFIX_PATH
+ENV PKG_CONFIG_PATH=$VULKAN_SDK/lib/pkgconfig:$PKG_CONFIG_PATH
 
 # Build it
 WORKDIR /app
 
 COPY . .
 
-RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=ON -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON && \
+RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1  -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON && \
     cmake --build build --config Release -j$(nproc)
 
 RUN mkdir -p /app/lib && \
-    find build -name "*.so*" -exec cp -P {} /app/lib \;
+    find build -name "*.so" -exec cp {} /app/lib \;
 
 RUN mkdir -p /app/full \
     && cp build/bin/* /app/full \
@@ -32,7 +50,7 @@ RUN mkdir -p /app/full \
 FROM ubuntu:$UBUNTU_VERSION AS base
 
 RUN apt-get update \
-    && apt-get install -y libgomp1 curl libvulkan1 mesa-vulkan-drivers \
+    && apt-get install -y libgomp1 curl libvulkan-dev \
     && apt autoremove -y \
     && apt clean -y \
     && rm -rf /tmp/* /var/tmp/* \
@@ -50,7 +68,6 @@ WORKDIR /app
 
 RUN apt-get update \
     && apt-get install -y \
-    build-essential \
     git \
     python3 \
     python3-pip \

.editorconfig

@@ -60,11 +60,3 @@ end_of_line = unset
 charset = unset
 trim_trailing_whitespace = unset
 insert_final_newline = unset
-
-[benches/**]
-indent_style = unset
-indent_size = unset
-end_of_line = unset
-charset = unset
-trim_trailing_whitespace = unset
-insert_final_newline = unset

.github/actions/install-exe/action.yml

@@ -1,36 +0,0 @@
-name: "Install exe"
-description: "Download and install exe"
-inputs:
-  url:
-    description: "URL of the exe installer"
-    required: true
-  args:
-    description: "Installer arguments"
-    required: true
-  timeout:
-    description: "Timeout (in ms)"
-    required: false
-    default: "600000"
-
-runs:
-  using: "composite"
-  steps:
-    - name: Install EXE
-      shell: pwsh
-      run: |
-        $ErrorActionPreference = "Stop"
-        write-host "Downloading Installer EXE"
-        Invoke-WebRequest -Uri "${{ inputs.url }}" -OutFile "${env:RUNNER_TEMP}\temp-install.exe"
-        write-host "Installing"
-        $proc = Start-Process "${env:RUNNER_TEMP}\temp-install.exe" -ArgumentList '${{ inputs.args }}' -NoNewWindow -PassThru
-        $completed = $proc.WaitForExit(${{ inputs.timeout }})
-        if (-not $completed) {
-            Write-Error "Installer timed out. Killing the process"
-            $proc.Kill()
-            exit 1
-        }
-        if ($proc.ExitCode -ne 0) {
-            Write-Error "Installer failed with exit code $($proc.ExitCode)"
-            exit 1
-        }
-        write-host "Completed installation"

.github/actions/linux-setup-spacemit/action.yml

@@ -1,20 +0,0 @@
-name: "Linux - Setup SpacemiT Toolchain"
-description: "Setup SpacemiT Toolchain for Linux"
-inputs:
-  path:
-    description: "Installation path"
-    required: true
-  version:
-    description: "SpacemiT toolchain version"
-    required: true
-
-runs:
-  using: "composite"
-  steps:
-    - name: Setup SpacemiT Toolchain
-      id: setup
-      uses: ./.github/actions/unarchive-tar
-      with:
-        url: https://archive.spacemit.com/toolchain/spacemit-toolchain-linux-glibc-x86_64-v${{ inputs.version }}.tar.xz
-        path: ${{ inputs.path }}
-        strip: 1

.github/actions/linux-setup-vulkan/action.yml

@@ -1,20 +0,0 @@
-name: "Linux - Setup Vulkan SDK"
-description: "Setup Vulkan SDK for Linux"
-inputs:
-  path:
-    description: "Installation path"
-    required: true
-  version:
-    description: "Vulkan SDK version"
-    required: true
-
-runs:
-  using: "composite"
-  steps:
-    - name: Setup Vulkan SDK
-      id: setup
-      uses: ./.github/actions/unarchive-tar
-      with:
-        url: https://sdk.lunarg.com/sdk/download/${{ inputs.version }}/linux/vulkan_sdk.tar.xz
-        path: ${{ inputs.path }}
-        strip: 1

.github/actions/unarchive-tar/action.yml

@@ -1,27 +0,0 @@
-name: "Unarchive tar"
-description: "Download and unarchive tar into directory"
-inputs:
-  url:
-    description: "URL of the tar archive"
-    required: true
-  path:
-    description: "Directory to unarchive into"
-    required: true
-  type:
-    description: "Compression type (tar option)"
-    required: false
-    default: "J"
-  strip:
-    description: "Strip components"
-    required: false
-    default: "0"
-
-runs:
-  using: "composite"
-  steps:
-    - name: Unarchive into directory
-      shell: bash
-      run: |
-        mkdir -p ${{ inputs.path }}
-        cd ${{ inputs.path }}
-        curl --no-progress-meter ${{ inputs.url }} | tar -${{ inputs.type }}x --strip-components=${{ inputs.strip }}

.github/actions/windows-setup-cuda/action.yml

@@ -65,34 +65,3 @@ runs:
           echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\libnvvp" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
           echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
           echo "CUDA_PATH_V12_4=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
-
-    - name: Install Cuda Toolkit 13.1
-      if: ${{ inputs.cuda_version == '13.1' }}
-      shell: pwsh
-      run: |
-          mkdir -p "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1"
-          choco install unzip -y
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_crt/windows-x86_64/cuda_crt-windows-x86_64-13.1.80-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cudart/windows-x86_64/cuda_cudart-windows-x86_64-13.1.80-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvcc/windows-x86_64/cuda_nvcc-windows-x86_64-13.1.80-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvrtc/windows-x86_64/cuda_nvrtc-windows-x86_64-13.1.80-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libcublas/windows-x86_64/libcublas-windows-x86_64-13.2.0.9-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libnvvm/windows-x86_64/libnvvm-windows-x86_64-13.1.80-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvtx/windows-x86_64/cuda_nvtx-windows-x86_64-13.1.68-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_profiler_api/windows-x86_64/cuda_profiler_api-windows-x86_64-13.1.80-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/visual_studio_integration/windows-x86_64/visual_studio_integration-windows-x86_64-13.1.68-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cccl/windows-x86_64/cuda_cccl-windows-x86_64-13.1.78-archive.zip"
-          unzip '*.zip' -d "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1"
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\cuda_crt-windows-x86_64-13.1.80-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\cuda_cudart-windows-x86_64-13.1.80-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\cuda_nvcc-windows-x86_64-13.1.80-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\cuda_nvrtc-windows-x86_64-13.1.80-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\libcublas-windows-x86_64-13.2.0.9-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\libnvvm-windows-x86_64-13.1.80-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\cuda_nvtx-windows-x86_64-13.1.68-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\cuda_profiler_api-windows-x86_64-13.1.80-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\visual_studio_integration-windows-x86_64-13.1.68-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\cuda_cccl-windows-x86_64-13.1.78-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" /E /I /H /Y
-          echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-          echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
-          echo "CUDA_PATH_V13_1=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8

.github/actions/windows-setup-rocm/action.yml

@@ -1,15 +0,0 @@
-name: "Windows - Setup ROCm"
-description: "Setup ROCm for Windows"
-inputs:
-  version:
-    description: "ROCm version"
-    required: true
-
-runs:
-  using: "composite"
-  steps:
-    - name: Setup ROCm
-      uses: ./.github/actions/install-exe
-      with:
-        url: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-${{ inputs.version }}-WinSvr2022-For-HIP.exe
-        args: -install

.github/copilot-instructions.md

@@ -9,7 +9,7 @@ llama.cpp is a large-scale C/C++ project for efficient LLM (Large Language Model
 - **Size**: ~200k+ lines of code across 1000+ files
 - **Architecture**: Modular design with main library (`libllama`) and 40+ executable tools/examples
 - **Core dependency**: ggml tensor library (vendored in `ggml/` directory)
-- **Backends supported**: CPU (AVX/NEON/RVV optimized), CUDA, Metal, Vulkan, SYCL, ROCm, MUSA
+- **Backends supported**: CPU (AVX/NEON optimized), CUDA, Metal, Vulkan, SYCL, ROCm, MUSA
 - **License**: MIT
 
 ## Build Instructions

.github/labeler.yml

@@ -76,10 +76,6 @@ ggml:
     - changed-files:
         - any-glob-to-any-file:
             - ggml/**
-model:
-    - changed-files:
-        - any-glob-to-any-file:
-            - src/models/**
 nix:
     - changed-files:
         - any-glob-to-any-file:

.github/workflows/build-cache.yml

@@ -1,89 +0,0 @@
-name: Build Actions Cache
-
-on:
-  workflow_dispatch: # allows manual triggering
-  schedule:
-    - cron: '0 * * * *'
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  ubuntu-24-vulkan-cache:
-    runs-on: ubuntu-24.04
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-
-      - name: Get latest Vulkan SDK version
-        id: vulkan_sdk_version
-        run: |
-          echo "VULKAN_SDK_VERSION=$(curl https://vulkan.lunarg.com/sdk/latest/linux.txt)" >> "$GITHUB_ENV"
-
-      - name: Setup Cache
-        uses: actions/cache@v4
-        id: cache-sdk
-        with:
-          path: ./vulkan_sdk
-          key: vulkan-sdk-${{ env.VULKAN_SDK_VERSION }}-${{ runner.os }}
-
-      - name: Setup Vulkan SDK
-        if: steps.cache-sdk.outputs.cache-hit != 'true'
-        uses: ./.github/actions/linux-setup-vulkan
-        with:
-          path: ./vulkan_sdk
-          version: ${{ env.VULKAN_SDK_VERSION }}
-
-  ubuntu-24-spacemit-cache:
-    runs-on: ubuntu-24.04
-
-    env:
-      # Make sure this is in sync with build-linux-cross.yml
-      SPACEMIT_IME_TOOLCHAIN_VERSION: "1.1.2"
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-
-      - name: Setup Cache
-        uses: actions/cache@v4
-        id: cache-toolchain
-        with:
-          path: ./spacemit_toolchain
-          key: spacemit-ime-toolchain-v${{ env.SPACEMIT_IME_TOOLCHAIN_VERSION }}-${{ runner.os }}
-
-      - name: Setup SpacemiT Toolchain
-        if: steps.cache-toolchain.outputs.cache-hit != 'true'
-        uses: ./.github/actions/linux-setup-spacemit
-        with:
-          path: ./spacemit_toolchain
-          version: ${{ env.SPACEMIT_IME_TOOLCHAIN_VERSION }}
-
-  windows-2022-rocm-cache:
-    runs-on: windows-2022
-
-    env:
-      # Make sure this is in sync with build.yml
-      HIPSDK_INSTALLER_VERSION: "25.Q3"
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-
-      - name: Setup Cache
-        uses: actions/cache@v4
-        id: cache-rocm
-        with:
-          path: C:\Program Files\AMD\ROCm
-          key: rocm-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ runner.os }}
-
-      - name: Setup ROCm
-        if: steps.cache-rocm.outputs.cache-hit != 'true'
-        uses: ./.github/actions/windows-setup-rocm
-        with:
-          version: ${{ env.HIPSDK_INSTALLER_VERSION }}

.github/workflows/build-linux-cross.yml

@@ -4,49 +4,49 @@ on:
   workflow_call:
 
 jobs:
-  # ubuntu-24-riscv64-cpu-cross:
-  #   runs-on: ubuntu-24.04
+  ubuntu-24-riscv64-cpu-cross:
+    runs-on: ubuntu-24.04
 
-  #   steps:
-  #     - uses: actions/checkout@v4
-  #     - name: Setup Riscv
-  #       run: |
-  #         sudo dpkg --add-architecture riscv64
+    steps:
+      - uses: actions/checkout@v4
+      - name: Setup Riscv
+        run: |
+          sudo dpkg --add-architecture riscv64
 
-  #         # Add arch-specific repositories for non-amd64 architectures
-  #         cat << EOF | sudo tee /etc/apt/sources.list.d/riscv64-ports.list
-  #         deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble main universe
-  #         deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe
-  #         deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe
-  #         deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe
-  #         EOF
+          # Add arch-specific repositories for non-amd64 architectures
+          cat << EOF | sudo tee /etc/apt/sources.list.d/riscv64-ports.list
+          deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble main universe
+          deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe
+          deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe
+          deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe
+          EOF
 
-  #         sudo apt-get update || true    ;# Prevent failure due to missing URLs.
+          sudo apt-get update || true    ;# Prevent failure due to missing URLs.
 
-  #         sudo apt-get install -y --no-install-recommends \
-  #                 build-essential \
-  #                 gcc-14-riscv64-linux-gnu \
-  #                 g++-14-riscv64-linux-gnu
+          sudo apt-get install -y --no-install-recommends \
+                  build-essential \
+                  gcc-14-riscv64-linux-gnu \
+                  g++-14-riscv64-linux-gnu
 
-  #     - name: Build
-  #       run: |
-  #         cmake -B build -DLLAMA_CURL=OFF \
-  #                        -DCMAKE_BUILD_TYPE=Release \
-  #                        -DGGML_OPENMP=OFF \
-  #                        -DLLAMA_BUILD_EXAMPLES=ON \
-  #                        -DLLAMA_BUILD_TOOLS=ON \
-  #                        -DLLAMA_BUILD_TESTS=OFF \
-  #                        -DCMAKE_SYSTEM_NAME=Linux \
-  #                        -DCMAKE_SYSTEM_PROCESSOR=riscv64 \
-  #                        -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \
-  #                        -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14 \
-  #                        -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
-  #                        -DCMAKE_FIND_ROOT_PATH=/usr/lib/riscv64-linux-gnu \
-  #                        -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \
-  #                        -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \
-  #                        -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH
+      - name: Build
+        run: |
+          cmake -B build -DLLAMA_CURL=OFF \
+                         -DCMAKE_BUILD_TYPE=Release \
+                         -DGGML_OPENMP=OFF \
+                         -DLLAMA_BUILD_EXAMPLES=ON \
+                         -DLLAMA_BUILD_TOOLS=ON \
+                         -DLLAMA_BUILD_TESTS=OFF \
+                         -DCMAKE_SYSTEM_NAME=Linux \
+                         -DCMAKE_SYSTEM_PROCESSOR=riscv64 \
+                         -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \
+                         -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14 \
+                         -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
+                         -DCMAKE_FIND_ROOT_PATH=/usr/lib/riscv64-linux-gnu \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH
 
-  #         cmake --build build --config Release -j $(nproc)
+          cmake --build build --config Release -j $(nproc)
 
   # ubuntu-24-riscv64-vulkan-cross:
   #   runs-on: ubuntu-24.04
@@ -141,6 +141,97 @@ jobs:
 
   #         cmake --build build --config Release -j $(nproc)
 
+  ubuntu-24-ppc64el-cpu-cross:
+    runs-on: ubuntu-24.04
+
+    steps:
+      - uses: actions/checkout@v4
+      - name: Setup PowerPC64le
+        run: |
+          sudo dpkg --add-architecture ppc64el
+
+          # Add arch-specific repositories for non-amd64 architectures
+          cat << EOF | sudo tee /etc/apt/sources.list.d/ppc64el-ports.list
+          deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble main universe
+          deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe
+          deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe
+          deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe
+          EOF
+
+          sudo apt-get update || true    ;# Prevent failure due to missing URLs.
+
+          sudo apt-get install -y --no-install-recommends \
+                  build-essential \
+                  gcc-14-powerpc64le-linux-gnu \
+                  g++-14-powerpc64le-linux-gnu
+
+      - name: Build
+        run: |
+          cmake -B build -DLLAMA_CURL=OFF \
+                         -DCMAKE_BUILD_TYPE=Release \
+                         -DGGML_OPENMP=OFF \
+                         -DLLAMA_BUILD_EXAMPLES=ON \
+                         -DLLAMA_BUILD_TOOLS=ON \
+                         -DLLAMA_BUILD_TESTS=OFF \
+                         -DCMAKE_SYSTEM_NAME=Linux \
+                         -DCMAKE_SYSTEM_PROCESSOR=ppc64 \
+                         -DCMAKE_C_COMPILER=powerpc64le-linux-gnu-gcc-14 \
+                         -DCMAKE_CXX_COMPILER=powerpc64le-linux-gnu-g++-14 \
+                         -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
+                         -DCMAKE_FIND_ROOT_PATH=/usr/lib/powerpc64le-linux-gnu \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH
+
+          cmake --build build --config Release -j $(nproc)
+
+  # ubuntu-24-ppc64el-vulkan-cross:
+  #   runs-on: ubuntu-24.04
+
+  #   steps:
+  #     - uses: actions/checkout@v4
+  #     - name: Setup PowerPC64le
+  #       run: |
+  #         sudo dpkg --add-architecture ppc64el
+
+  #         # Add arch-specific repositories for non-amd64 architectures
+  #         cat << EOF | sudo tee /etc/apt/sources.list.d/ppc64el-ports.list
+  #         deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble main universe
+  #         deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe
+  #         deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe
+  #         deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe
+  #         EOF
+
+  #         sudo apt-get update || true    ;# Prevent failure due to missing URLs.
+
+  #         sudo apt-get install -y --no-install-recommends \
+  #                 build-essential \
+  #                 glslc \
+  #                 gcc-14-powerpc64le-linux-gnu \
+  #                 g++-14-powerpc64le-linux-gnu \
+  #                 libvulkan-dev:ppc64el
+
+  #     - name: Build
+  #       run: |
+  #         cmake -B build -DLLAMA_CURL=OFF \
+  #                        -DCMAKE_BUILD_TYPE=Release \
+  #                        -DGGML_VULKAN=ON \
+  #                        -DGGML_OPENMP=OFF \
+  #                        -DLLAMA_BUILD_EXAMPLES=ON \
+  #                        -DLLAMA_BUILD_TOOLS=ON \
+  #                        -DLLAMA_BUILD_TESTS=OFF \
+  #                        -DCMAKE_SYSTEM_NAME=Linux \
+  #                        -DCMAKE_SYSTEM_PROCESSOR=ppc64 \
+  #                        -DCMAKE_C_COMPILER=powerpc64le-linux-gnu-gcc-14 \
+  #                        -DCMAKE_CXX_COMPILER=powerpc64le-linux-gnu-g++-14 \
+  #                        -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
+  #                        -DCMAKE_FIND_ROOT_PATH=/usr/lib/powerpc64le-linux-gnu \
+  #                        -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \
+  #                        -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \
+  #                        -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH
+
+  #         cmake --build build --config Release -j $(nproc)
+
   debian-13-loongarch64-cpu-cross:
     runs-on: ubuntu-24.04
     container: debian@sha256:653dfb9f86c3782e8369d5f7d29bb8faba1f4bff9025db46e807fa4c22903671
@@ -253,46 +344,3 @@ jobs:
                          -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH
 
           cmake --build build --config Release -j $(nproc)
-
-  ubuntu-24-riscv64-cpu-spacemit-ime-cross:
-    runs-on: ubuntu-24.04
-
-    env:
-      # Make sure this is in sync with build-cache.yml
-      SPACEMIT_IME_TOOLCHAIN_VERSION: "1.1.2"
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Use SpacemiT Toolchain Cache
-        uses: actions/cache@v4
-        id: cache-toolchain
-        with:
-          path: ./spacemit_toolchain
-          key: spacemit-ime-toolchain-v${{ env.SPACEMIT_IME_TOOLCHAIN_VERSION }}-${{ runner.os }}
-
-      - name: Setup SpacemiT Toolchain
-        if: steps.cache-toolchain.outputs.cache-hit != 'true'
-        uses: ./.github/actions/linux-setup-spacemit
-        with:
-          path: ./spacemit_toolchain
-          version: ${{ env.SPACEMIT_IME_TOOLCHAIN_VERSION }}
-
-      - name: Build
-        run: |
-          export RISCV_ROOT_PATH=${PWD}/spacemit_toolchain
-          cmake -B build -DLLAMA_CURL=OFF \
-                         -DCMAKE_BUILD_TYPE=Release \
-                         -DGGML_OPENMP=OFF \
-                         -DLLAMA_BUILD_EXAMPLES=ON \
-                         -DLLAMA_BUILD_TOOLS=ON \
-                         -DLLAMA_BUILD_TESTS=OFF \
-                         -DGGML_CPU_RISCV64_SPACEMIT=ON \
-                         -DGGML_RVV=ON \
-                         -DGGML_RV_ZFH=ON \
-                         -DGGML_RV_ZICBOP=ON \
-                         -DGGML_RV_ZIHINTPAUSE=ON \
-                         -DRISCV64_SPACEMIT_IME_SPEC=RISCV64_SPACEMIT_IME1 \
-                         -DCMAKE_TOOLCHAIN_FILE=${PWD}/cmake/riscv64-spacemit-linux-gnu-gcc.cmake
-
-          cmake --build build --config Release -j $(nproc)

.github/workflows/build-riscv-native.yml

@@ -0,0 +1,60 @@
+name: Build on RISCV Linux Machine by Cloud-V
+on:
+  pull_request:
+  workflow_dispatch:
+  workflow_call:
+
+jobs:
+  debian-13-riscv64-native: # Bianbu 2.2
+    runs-on: self-hosted
+
+    steps:
+      - name: Install prerequisites
+        run: |
+          sudo apt-get update || true
+          sudo apt-get install -y libatomic1
+      - uses: actions/checkout@v4
+      - name: Setup Riscv
+        run: |
+          sudo apt-get update || true
+          sudo apt-get install -y --no-install-recommends \
+                  build-essential \
+                  gcc-14-riscv64-linux-gnu \
+                  g++-14-riscv64-linux-gnu \
+                  ccache \
+                  cmake
+
+      - name: Setup ccache
+        run: |
+          mkdir -p $HOME/.ccache
+          ccache -M 5G -d $HOME/.ccache
+          export CCACHE_LOGFILE=/home/runneruser/ccache_debug/ccache.log
+          export CCACHE_DEBUGDIR="/home/runneruser/ccache_debug"
+          echo "$GITHUB_WORKSPACE"
+          echo "CCACHE_LOGFILE=$CCACHE_LOGFILE" >> $GITHUB_ENV
+          echo "CCACHE_DEBUGDIR=$CCACHE_DEBUGDIR" >> $GITHUB_ENV
+          echo "CCACHE_BASEDIR=$GITHUB_WORKSPACE" >> $GITHUB_ENV
+          echo "CCACHE_DIR=$HOME/.ccache" >> $GITHUB_ENV
+
+      - name: Build
+        run: |
+          cmake -B build \
+            -DLLAMA_CURL=OFF \
+            -DCMAKE_BUILD_TYPE=Release \
+            -DGGML_OPENMP=OFF \
+            -DLLAMA_BUILD_EXAMPLES=ON \
+            -DLLAMA_BUILD_TOOLS=ON \
+            -DLLAMA_BUILD_TESTS=OFF \
+            -DCMAKE_SYSTEM_NAME=Linux \
+            -DCMAKE_SYSTEM_PROCESSOR=riscv64 \
+            -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \
+            -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14 \
+            -DCMAKE_C_COMPILER_LAUNCHER=ccache \
+            -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
+            -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
+            -DCMAKE_FIND_ROOT_PATH=/usr/lib/riscv64-linux-gnu \
+            -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \
+            -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \
+            -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH
+
+          cmake --build build --config Release -j $(nproc)

.github/workflows/check-vendor.yml

@@ -1,52 +0,0 @@
-name: Check vendor
-
-on:
-  workflow_dispatch: # allows manual triggering
-  push:
-    branches:
-      - master
-    paths: [
-      'vendor/**',
-      'scripts/sync_vendor.py'
-    ]
-
-  pull_request:
-    types: [opened, synchronize, reopened]
-    paths: [
-      'vendor/**',
-      'scripts/sync_vendor.py'
-    ]
-
-jobs:
-  check-vendor:
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Setup Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: '3.x'
-
-      - name: Run vendor sync
-        run: |
-          set -euo pipefail
-          python3 scripts/sync_vendor.py
-
-      - name: Check for changes
-        run: |
-          set -euo pipefail
-          # detect modified or untracked files
-          changed=$(git status --porcelain --untracked-files=all || true)
-          if [ -n "$changed" ]; then
-            echo "Vendor sync modified files:"
-            echo "$changed" | awk '{ print $2 }' | sed '/^$/d'
-            echo "Failing because vendor files mismatch. Please update scripts/sync_vendor.py"
-            exit 1
-          else
-            echo "Vendor files are up-to-date."
-          fi

.github/workflows/docker.yml

@@ -28,7 +28,7 @@ jobs:
   push_to_registry:
     name: Push Docker image to Docker Hub
 
-    runs-on: ${{ matrix.config.runs_on }}
+    runs-on: ubuntu-22.04
     env:
       COMMIT_SHA: ${{ github.sha }}
     strategy:
@@ -39,12 +39,11 @@ jobs:
           # Note: the arm64 images are failing, which prevents the amd64 images from being built
           # https://github.com/ggml-org/llama.cpp/issues/11888
           #- { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, free_disk_space: false }
-          - { tag: "cpu",    dockerfile: ".devops/cpu.Dockerfile",    platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-22.04" }
-          - { tag: "cuda",   dockerfile: ".devops/cuda.Dockerfile",   platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-22.04" }
-          - { tag: "musa",   dockerfile: ".devops/musa.Dockerfile",   platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-22.04" }
-          - { tag: "intel",  dockerfile: ".devops/intel.Dockerfile",  platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-22.04" }
-          - { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-22.04" }
-          - { tag: "s390x",  dockerfile: ".devops/s390x.Dockerfile",  platforms: "linux/s390x", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-22.04-s390x" }
+          - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false }
+          - { tag: "cuda", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false }
+          - { tag: "musa", dockerfile: ".devops/musa.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true }
+          - { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true }
+          - { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false }
           # 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, free_disk_space: true }
     steps:
@@ -54,7 +53,6 @@ jobs:
           fetch-depth: 0 # preserve git history, so we can determine the build number
 
       - name: Set up QEMU
-        if: ${{ matrix.config.tag != 's390x' }}
         uses: docker/setup-qemu-action@v3
         with:
           image: tonistiigi/binfmt:qemu-v7.0.0-28
@@ -69,19 +67,22 @@ jobs:
           username: ${{ github.repository_owner }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
-      - name: Determine source tag name
-        id: srctag
-        uses: ./.github/actions/get-tag-name
-        env:
-          BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
-
-      - name: Determine image tag name
+      - name: Determine tag name
         id: tag
         shell: bash
         run: |
+          BUILD_NUMBER="$(git rev-list --count HEAD)"
+          SHORT_HASH="$(git rev-parse --short=7 HEAD)"
           REPO_OWNER="${GITHUB_REPOSITORY_OWNER@L}"  # to lower case
           REPO_NAME="${{ github.event.repository.name }}"
 
+          # determine tag name postfix (build number, commit hash)
+          if [[ "${{ env.GITHUB_BRANCH_NAME }}" == "master" ]]; then
+            TAG_POSTFIX="-b${BUILD_NUMBER}"
+          else
+            SAFE_NAME=$(echo "${{ env.GITHUB_BRANCH_NAME }}" | tr '/' '-')
+            TAG_POSTFIX="-${SAFE_NAME}-${SHORT_HASH}"
+          fi
           # list all tags possible
           if [[ "${{ matrix.config.tag }}" == "cpu" ]]; then
               TYPE=""
@@ -89,19 +90,17 @@ jobs:
               TYPE="-${{ matrix.config.tag }}"
           fi
           PREFIX="ghcr.io/${REPO_OWNER}/${REPO_NAME}:"
-          CACHETAGS="${PREFIX}buildcache${TYPE}"
-          FULLTAGS="${PREFIX}full${TYPE},${PREFIX}full${TYPE}-${{ steps.srctag.outputs.name }}"
-          LIGHTTAGS="${PREFIX}light${TYPE},${PREFIX}light${TYPE}-${{ steps.srctag.outputs.name }}"
-          SERVERTAGS="${PREFIX}server${TYPE},${PREFIX}server${TYPE}-${{ steps.srctag.outputs.name }}"
-          echo "cache_output_tags=$CACHETAGS" >> $GITHUB_OUTPUT
+          FULLTAGS="${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" >> $GITHUB_OUTPUT
-          echo "cache_output_tags=$CACHETAGS"  # print out for debugging
           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
         env:
+          GITHUB_BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
           GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}'
 
       - name: Free Disk Space (Ubuntu)
@@ -134,14 +133,11 @@ jobs:
           target: full
           provenance: false
           # using github experimental cache
-          #cache-from: type=gha
-          #cache-to: type=gha,mode=max
+          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
-          # using registry cache (no storage limit)
-          cache-from: type=registry,ref=${{ steps.tag.outputs.cache_output_tags }}
-          cache-to: type=registry,ref=${{ steps.tag.outputs.cache_output_tags }},mode=max
 
       - name: Build and push Light Docker image (tagged + versioned)
         if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.light == true }}
@@ -156,14 +152,11 @@ jobs:
           target: light
           provenance: false
           # using github experimental cache
-          #cache-from: type=gha
-          #cache-to: type=gha,mode=max
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
           # return to this if the experimental github cache is having issues
           #cache-to: type=local,dest=/tmp/.buildx-cache
           #cache-from: type=local,src=/tmp/.buildx-cache
-          # using registry cache (no storage limit)
-          cache-from: type=registry,ref=${{ steps.tag.outputs.cache_output_tags }}
-          cache-to: type=registry,ref=${{ steps.tag.outputs.cache_output_tags }},mode=max
 
       - name: Build and push Server Docker image (tagged + versioned)
         if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.server == true }}
@@ -178,37 +171,8 @@ jobs:
           target: server
           provenance: false
           # using github experimental cache
-          #cache-from: type=gha
-          #cache-to: type=gha,mode=max
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
           # return to this if the experimental github cache is having issues
           #cache-to: type=local,dest=/tmp/.buildx-cache
           #cache-from: type=local,src=/tmp/.buildx-cache
-          # using registry cache (no storage limit)
-          cache-from: type=registry,ref=${{ steps.tag.outputs.cache_output_tags }}
-          cache-to: type=registry,ref=${{ steps.tag.outputs.cache_output_tags }},mode=max
-
-  create_tag:
-    name: Create and push git tag
-    runs-on: ubuntu-22.04
-    permissions:
-      contents: write
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Determine source tag name
-        id: srctag
-        uses: ./.github/actions/get-tag-name
-        env:
-          BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
-
-      - name: Create and push git tag
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: |
-          git tag ${{ steps.srctag.outputs.name }} || exit 0
-          git push origin ${{ steps.srctag.outputs.name }} || exit 0

.github/workflows/release.yml

@@ -66,23 +66,16 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
-          zip -y -r llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip ./build/bin/*
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.tar.gz -s ",./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
+          zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip ./build/bin/*
 
-      - name: Upload artifacts (zip)
+      - name: Upload artifacts
         uses: actions/upload-artifact@v4
         with:
           path: llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip
           name: llama-bin-macos-arm64.zip
 
-      - name: Upload artifacts (tar)
-        uses: actions/upload-artifact@v4
-        with:
-          path: llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.tar.gz
-          name: llama-bin-macos-arm64.tar.gz
-
   macOS-x64:
-    runs-on: macos-15-intel
+    runs-on: macos-13
 
     steps:
       - name: Clone
@@ -127,29 +120,20 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
-          zip -y -r llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip ./build/bin/*
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-macos-x64.tar.gz -s ",./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
+          zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip ./build/bin/*
 
-      - name: Upload artifacts (zip)
+      - name: Upload artifacts
         uses: actions/upload-artifact@v4
         with:
           path: llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip
           name: llama-bin-macos-x64.zip
 
-      - name: Upload artifacts (tar)
-        uses: actions/upload-artifact@v4
-        with:
-          path: llama-${{ steps.tag.outputs.name }}-bin-macos-x64.tar.gz
-          name: llama-bin-macos-x64.tar.gz
-
   ubuntu-22-cpu:
     strategy:
       matrix:
         include:
           - build: 'x64'
             os: ubuntu-22.04
-          - build: 's390x'
-            os: ubuntu-24.04-s390x
           # GGML_BACKEND_DL and GGML_CPU_ALL_VARIANTS are not currently supported on arm
           # - build: 'arm64'
           #   os: ubuntu-22.04-arm
@@ -166,7 +150,7 @@ jobs:
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.16
         with:
-          key: ubuntu-cpu-cmake-${{ matrix.build }}
+          key: ubuntu-cpu-cmake
           evict-old-files: 1d
 
       - name: Dependencies
@@ -196,21 +180,14 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
-          zip -y -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.zip ./build/bin/*
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
+          zip -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.zip ./build/bin/*
 
-      - name: Upload artifacts (zip)
+      - name: Upload artifacts
         uses: actions/upload-artifact@v4
         with:
           path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.zip
           name: llama-bin-ubuntu-${{ matrix.build }}.zip
 
-      - name: Upload artifacts (tar)
-        uses: actions/upload-artifact@v4
-        with:
-          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.tar.gz
-          name: llama-bin-ubuntu-${{ matrix.build }}.tar.gz
-
   ubuntu-22-vulkan:
     runs-on: ubuntu-22.04
 
@@ -256,21 +233,14 @@ jobs:
         id: pack_artifacts
         run: |
           cp LICENSE ./build/bin/
-          zip -y -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.zip ./build/bin/*
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
+          zip -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.zip ./build/bin/*
 
-      - name: Upload artifacts (zip)
+      - name: Upload artifacts
         uses: actions/upload-artifact@v4
         with:
           path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.zip
           name: llama-bin-ubuntu-vulkan-x64.zip
 
-      - name: Upload artifacts (tar)
-        uses: actions/upload-artifact@v4
-        with:
-          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.tar.gz
-          name: llama-bin-ubuntu-vulkan-x64.tar.gz
-
   windows-cpu:
     runs-on: windows-2025
 
@@ -326,7 +296,7 @@ jobs:
         run: |
           Copy-Item $env:CURL_PATH\bin\libcurl-${{ matrix.arch }}.dll .\build\bin\Release\
           Copy-Item "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Redist\MSVC\14.44.35112\debug_nonredist\${{ matrix.arch }}\Microsoft.VC143.OpenMP.LLVM\libomp140.${{ matrix.arch == 'x64' && 'x86_64' || 'aarch64' }}.dll" .\build\bin\Release\
-          7z a -snl llama-bin-win-cpu-${{ matrix.arch }}.zip .\build\bin\Release\*
+          7z a llama-bin-win-cpu-${{ matrix.arch }}.zip .\build\bin\Release\*
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v4
@@ -408,7 +378,7 @@ jobs:
       - name: Pack artifacts
         id: pack_artifacts
         run: |
-          7z a -snl llama-bin-win-${{ matrix.backend }}-${{ matrix.arch }}.zip .\build\bin\Release\${{ matrix.target }}.dll
+          7z a llama-bin-win-${{ matrix.backend }}-${{ matrix.arch }}.zip .\build\bin\Release\${{ matrix.target }}.dll
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v4
@@ -421,7 +391,7 @@ jobs:
 
     strategy:
       matrix:
-        cuda: ['12.4', '13.1']
+        cuda: ['12.4']
 
     steps:
       - name: Clone
@@ -462,7 +432,7 @@ jobs:
       - name: Pack artifacts
         id: pack_artifacts
         run: |
-          7z a -snl llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip .\build\bin\Release\ggml-cuda.dll
+          7z a llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip .\build\bin\Release\ggml-cuda.dll
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v4
@@ -476,7 +446,6 @@ jobs:
           $dst='.\build\bin\cudart\'
           robocopy "${{env.CUDA_PATH}}\bin" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll
           robocopy "${{env.CUDA_PATH}}\lib" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll
-          robocopy "${{env.CUDA_PATH}}\bin\x64" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll
           7z a cudart-llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip $dst\*
 
       - name: Upload Cuda runtime
@@ -493,7 +462,7 @@ jobs:
         shell: bash
 
     env:
-      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/24751ead-ddc5-4479-b9e6-f9fe2ff8b9f2/intel-deep-learning-essentials-2025.2.1.25_offline.exe
+      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/7cd9bba0-7aab-4e30-b3ae-2221006a4a05/intel-oneapi-base-toolkit-2025.1.1.34_offline.exe
       WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel:intel.oneapi.win.dnnl:intel.oneapi.win.tbb.devel
       ONEAPI_ROOT: "C:/Program Files (x86)/Intel/oneAPI"
 
@@ -536,7 +505,6 @@ jobs:
           cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_tbb_thread.2.dll" ./build/bin
 
           cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_level_zero.dll" ./build/bin
-          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_level_zero_v2.dll" ./build/bin
           cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_opencl.dll" ./build/bin
           cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_loader.dll" ./build/bin
           cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_win_proxy_loader.dll" ./build/bin
@@ -545,19 +513,12 @@ jobs:
           cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/svml_dispmd.dll" ./build/bin
           cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libmmd.dll" ./build/bin
           cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libiomp5md.dll" ./build/bin
-          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/sycl-ls.exe" ./build/bin
-          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libsycl-fallback-bfloat16.spv" ./build/bin
-          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libsycl-native-bfloat16.spv" ./build/bin
 
           cp "${{ env.ONEAPI_ROOT }}/dnnl/latest/bin/dnnl.dll" ./build/bin
           cp "${{ env.ONEAPI_ROOT }}/tbb/latest/bin/tbb12.dll" ./build/bin
 
-          cp "${{ env.ONEAPI_ROOT }}/tcm/latest/bin/tcm.dll" ./build/bin
-          cp "${{ env.ONEAPI_ROOT }}/tcm/latest/bin/libhwloc-15.dll" ./build/bin
-          cp "${{ env.ONEAPI_ROOT }}/umf/latest/bin/umf.dll" ./build/bin
-
           echo "cp oneAPI running time dll files to ./build/bin done"
-          7z a -snl llama-bin-win-sycl-x64.zip ./build/bin/*
+          7z a llama-bin-win-sycl-x64.zip ./build/bin/*
 
       - name: Upload the release package
         uses: actions/upload-artifact@v4
@@ -582,12 +543,10 @@ jobs:
         id: checkout
         uses: actions/checkout@v4
 
-      - name: Grab rocWMMA package
-        id: grab_rocwmma
+      - name: Clone rocWMMA repository
+        id: clone_rocwmma
         run: |
-          curl -o rocwmma.deb "https://repo.radeon.com/rocm/apt/7.0.1/pool/main/r/rocwmma-dev/rocwmma-dev_2.0.0.70001-42~24.04_amd64.deb"
-          7z x rocwmma.deb
-          7z x data.tar
+          git clone https://github.com/rocm/rocwmma --branch develop --depth 1
 
       - name: Cache ROCm Installation
         id: cache-rocm
@@ -642,7 +601,7 @@ jobs:
           cmake -G "Unix Makefiles" -B build -S . `
             -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" `
             -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" `
-            -DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/opt/rocm-7.0.1/include/ -Wno-ignored-attributes -Wno-nested-anon-types" `
+            -DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/rocwmma/library/include/ -Wno-ignored-attributes -Wno-nested-anon-types" `
             -DCMAKE_BUILD_TYPE=Release `
             -DGGML_BACKEND_DL=ON `
             -DGGML_NATIVE=OFF `
@@ -663,7 +622,7 @@ jobs:
       - name: Pack artifacts
         id: pack_artifacts
         run: |
-          7z a -snl llama-bin-win-hip-${{ matrix.name }}-x64.zip .\build\bin\*
+          7z a llama-bin-win-hip-${{ matrix.name }}-x64.zip .\build\bin\*
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v4
@@ -716,20 +675,13 @@ jobs:
       - name: Pack artifacts
         id: pack_artifacts
         run: |
-          zip -y -r llama-${{ steps.tag.outputs.name }}-xcframework.zip build-apple/llama.xcframework
-          tar -czvf llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz -C build-apple llama.xcframework
+          zip --symlinks -r llama-${{ steps.tag.outputs.name }}-xcframework.zip build-apple/llama.xcframework
 
-      - name: Upload artifacts (zip)
+      - name: Upload artifacts
         uses: actions/upload-artifact@v4
         with:
           path: llama-${{ steps.tag.outputs.name }}-xcframework.zip
-          name: llama-${{ steps.tag.outputs.name }}-xcframework.zip
-
-      - name: Upload artifacts (tar)
-        uses: actions/upload-artifact@v4
-        with:
-          path: llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz
-          name: llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz
+          name: llama-${{ steps.tag.outputs.name }}-xcframework
 
   release:
     if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
@@ -806,7 +758,6 @@ jobs:
 
           echo "Moving other artifacts..."
           mv -v artifact/*.zip release
-          mv -v artifact/*.tar.gz release
 
       - name: Create release
         id: create_release
@@ -815,34 +766,6 @@ jobs:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
         with:
           tag_name: ${{ steps.tag.outputs.name }}
-          body: |
-            > [!WARNING]
-            > **Release Format Update**: Linux releases will soon use .tar.gz archives instead of .zip. Please make the necessary changes to your deployment scripts.
-
-            <details open>
-
-            ${{ github.event.head_commit.message }}
-
-            </details>
-
-            **macOS/iOS:**
-            - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.tar.gz)
-            - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-macos-x64.tar.gz)
-            - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz)
-
-            **Linux:**
-            - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-x64.tar.gz)
-            - [Ubuntu x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.tar.gz)
-            - [Ubuntu s390x (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-s390x.tar.gz)
-
-            **Windows:**
-            - [Windows x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cpu-x64.zip)
-            - [Windows arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cpu-arm64.zip)
-            - [Windows x64 (CUDA 12)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-12.4-x64.zip)
-            - [Windows x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-13.1-x64.zip)
-            - [Windows x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-vulkan-x64.zip)
-            - [Windows x64 (SYCL)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip)
-            - [Windows x64 (HIP)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-hip-radeon-x64.zip)
 
       - name: Upload release
         id: upload_release
@@ -854,7 +777,7 @@ jobs:
             const fs = require('fs');
             const release_id = '${{ steps.create_release.outputs.id }}';
             for (let file of await fs.readdirSync('./release')) {
-              if (path.extname(file) === '.zip' || file.endsWith('.tar.gz')) {
+              if (path.extname(file) === '.zip') {
                 console.log('uploadReleaseAsset', file);
                 await github.repos.uploadReleaseAsset({
                   owner: context.repo.owner,

.github/workflows/server.yml

@@ -56,7 +56,7 @@ jobs:
             curl \
             wget \
             language-pack-en \
-            libssl-dev
+            libcurl4-openssl-dev
 
       - name: Clone
         id: checkout
@@ -209,7 +209,7 @@ jobs:
         working-directory: tools/server/webui
 
       - name: Run UI tests
-        run: npm run test:ui -- --testTimeout=60000
+        run: npm run test:ui
         working-directory: tools/server/webui
 
       - name: Run E2E tests
@@ -242,7 +242,7 @@ jobs:
             curl \
             wget \
             language-pack-en \
-            libssl-dev
+            libcurl4-openssl-dev
 
       - name: Clone
         id: checkout
@@ -283,8 +283,6 @@ jobs:
         run: |
           cmake -B build \
               -DGGML_NATIVE=OFF \
-              -DLLAMA_CURL=OFF \
-              -DLLAMA_OPENSSL=ON \
               -DLLAMA_BUILD_SERVER=ON \
               -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
               -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
@@ -297,8 +295,6 @@ jobs:
         run: |
           cmake -B build \
               -DGGML_NATIVE=OFF \
-              -DLLAMA_CURL=OFF \
-              -DLLAMA_OPENSSL=ON \
               -DLLAMA_BUILD_SERVER=ON \
               -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
               -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
@@ -310,8 +306,6 @@ jobs:
         run: |
           cmake -B build \
               -DGGML_NATIVE=OFF \
-              -DLLAMA_CURL=OFF \
-              -DLLAMA_OPENSSL=ON \
               -DLLAMA_BUILD_SERVER=ON \
               -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ;
           cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
@@ -351,10 +345,16 @@ jobs:
           fetch-depth: 0
           ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
 
+      - name: libCURL
+        id: get_libcurl
+        uses: ./.github/actions/windows-setup-curl
+
       - name: Build
         id: cmake_build
+        env:
+          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
         run: |
-          cmake -B build -DLLAMA_CURL=OFF -DLLAMA_BUILD_BORINGSSL=ON
+          cmake -B build -DCURL_LIBRARY="$env:CURL_PATH/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:CURL_PATH/include"
           cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target llama-server
 
       - name: Python setup
@@ -368,6 +368,13 @@ jobs:
         run: |
           pip install -r tools/server/tests/requirements.txt
 
+      - name: Copy Libcurl
+        id: prepare_libcurl
+        env:
+          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
+        run: |
+          cp $env:CURL_PATH/bin/libcurl-x64.dll ./build/bin/Release/libcurl-x64.dll
+
       - name: Tests
         id: server_integration_tests
         if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }}

.github/workflows/update-ops-docs.yml

@@ -3,12 +3,10 @@ name: Update Operations Documentation
 on:
     push:
         paths:
-            - 'docs/ops.md'
             - 'docs/ops/**'
             - 'scripts/create_ops_docs.py'
     pull_request:
         paths:
-            - 'docs/ops.md'
             - 'docs/ops/**'
             - 'scripts/create_ops_docs.py'
 

.github/workflows/winget.yml

@@ -9,7 +9,6 @@ jobs:
   update:
     name: Update Winget Package
     runs-on: ubuntu-latest
-    if: github.repository_owner == 'ggml-org'
 
     steps:
       - name: Install cargo binstall

.gitignore

@@ -20,40 +20,52 @@
 *.so
 *.swp
 *.tmp
-*.DS_Store
 
 # IDE / OS
 
-/.cache/
-/.ccls-cache/
-/.direnv/
-/.envrc
-/.idea/
-/.swiftpm
-/.vs/
-/.vscode/
-/nppBackup
+.cache/
+.ccls-cache/
+.direnv/
+.DS_Store
+.envrc
+.idea/
+.swiftpm
+.vs/
+.vscode/
+nppBackup
 
 
 # Coverage
 
-/gcovr-report/
-/lcov-report/
+gcovr-report/
+lcov-report/
 
 # Build Artifacts
 
-/tags
-/.build/
-/build*
-/release
-/debug
+tags
+.build/
+build*
+release
+debug
+!build-info.cmake
+!build-info.cpp.in
+!build-info.sh
+!build.zig
+!docs/build.md
 /libllama.so
 /llama-*
 /vulkan-shaders-gen
+android-ndk-*
+arm_neon.h
+cmake-build-*
+CMakeSettings.json
+compile_commands.json
+ggml-metal-embed.metal
+llama-batched-swift
 /rpc-server
-/out/
-/tmp/
-/autogen-*.md
+out/
+tmp/
+autogen-*.md
 
 # Deprecated
 
@@ -62,38 +74,44 @@
 
 # CI
 
-!/.github/workflows/*.yml
+!.github/workflows/*.yml
 
 # Models
 
-/models/*
-/models-mnt
-!/models/.editorconfig
-!/models/ggml-vocab-*.gguf*
-!/models/templates
+models/*
+models-mnt
+!models/.editorconfig
+!models/ggml-vocab-*.gguf*
+!models/templates
 
 # Zig
-/zig-out/
-/zig-cache/
+zig-out/
+zig-cache/
+
+# Logs
+
+ppl-*.txt
+qnt-*.txt
+perf-*.txt
 
 # Examples
 
-/examples/jeopardy/results.txt
-/tools/server/*.css.hpp
-/tools/server/*.html.hpp
-/tools/server/*.js.hpp
-/tools/server/*.mjs.hpp
-/tools/server/*.gz.hpp
-!/build_64.sh
-!/examples/*.bat
-!/examples/*/*.kts
-!/examples/*/*/*.kts
-!/examples/sycl/*.bat
-!/examples/sycl/*.sh
+examples/jeopardy/results.txt
+tools/server/*.css.hpp
+tools/server/*.html.hpp
+tools/server/*.js.hpp
+tools/server/*.mjs.hpp
+tools/server/*.gz.hpp
+!build_64.sh
+!examples/*.bat
+!examples/*/*.kts
+!examples/*/*/*.kts
+!examples/sycl/*.bat
+!examples/sycl/*.sh
 
 # Server Web UI temporary files
-/tools/server/webui/node_modules
-/tools/server/webui/dist
+node_modules
+tools/server/webui/dist
 
 # Python
 
@@ -129,10 +147,8 @@ poetry.toml
 # Local scripts
 /run-vim.sh
 /run-chat.sh
-/.ccache/
+.ccache/
+
+# Code Workspace
+*.code-workspace
 
-# IDE
-/*.code-workspace
-/.windsurf/
-# emscripten
-a.out.*

.windsurf/rules/css-architecture.md

@@ -0,0 +1,7 @@
+---
+trigger: manual
+---
+
+#### Tailwind & CSS
+
+-   We are using Tailwind v4 which uses oklch colors so we now want to refer to the CSS vars directly, without wrapping it with any color function like `hsla/hsl`, `rgba` etc.

.windsurf/rules/sveltekit-architecture.md

@@ -0,0 +1,48 @@
+---
+trigger: manual
+---
+
+# Coding rules
+
+## Svelte & SvelteKit
+
+### Services vs Stores Separation Pattern
+
+#### `lib/services/` - Pure Business Logic
+
+-   **Purpose**: Stateless business logic and external communication
+-   **Contains**:
+    -   API calls to external services (ApiService)
+    -   Pure business logic functions (ChatService, etc.)
+-   **Rules**:
+    -   NO Svelte runes ($state, $derived, $effect)
+    -   NO reactive state management
+    -   Pure functions and classes only
+    -   Can import types but not stores
+    -   Focus on "how" - implementation details
+
+#### `lib/stores/` - Reactive State Management
+
+-   **Purpose**: Svelte-specific reactive state with runes
+-   **Contains**:
+    -   Reactive state classes with $state, $derived, $effect
+    -   Database operations (DatabaseStore)
+    -   UI-focused state management
+    -   Store orchestration logic
+-   **Rules**:
+    -   USE Svelte runes for reactivity
+    -   Import and use services for business logic
+    -   NO direct database operations
+    -   NO direct API calls (use services)
+    -   Focus on "what" - reactive state for UI
+
+#### Enforcement
+
+-   Services should be testable without Svelte
+-   Stores should leverage Svelte's reactivity system
+-   Clear separation: services handle data, stores handle state
+-   Services can be reused across multiple stores
+
+#### Misc
+
+-   Always use `let` for $derived state variables

.windsurf/rules/tests.md

@@ -0,0 +1,9 @@
+---
+trigger: manual
+---
+
+# Automated Tests
+
+## General rules
+
+-   NEVER include any test code in the production code - we should always have it in a separate dedicated files

.windsurf/rules/typescript-architecture.md

@@ -0,0 +1,7 @@
+---
+trigger: manual
+---
+
+## TypeScript
+
+-   Add JSDocs for functions

CMakeLists.txt

@@ -33,24 +33,10 @@ endif()
 
 option(LLAMA_USE_SYSTEM_GGML "Use system libggml" OFF)
 
-option(LLAMA_WASM_MEM64 "llama: use 64-bit memory in WASM builds" ON)
-
 if (EMSCRIPTEN)
     set(BUILD_SHARED_LIBS_DEFAULT OFF)
 
-    # Use 64-bit memory to support backend_get_memory queries
-    # TODO: analyze performance impact, see https://spidermonkey.dev/blog/2025/01/15/is-memory64-actually-worth-using
-    if (LLAMA_WASM_MEM64)
-      add_compile_options("-sMEMORY64=1")
-      add_link_options("-sMEMORY64=1")
-    endif()
-    add_link_options("-sALLOW_MEMORY_GROWTH=1")
-
-    option(LLAMA_WASM_SINGLE_FILE "llama: embed WASM inside the generated llama.js" OFF)
-    option(LLAMA_BUILD_HTML "llama: build HTML file" ON)
-    if (LLAMA_BUILD_HTML)
-        set(CMAKE_EXECUTABLE_SUFFIX ".html")
-    endif()
+    option(LLAMA_WASM_SINGLE_FILE "llama: embed WASM inside the generated llama.js" ON)
 else()
     if (MINGW)
         set(BUILD_SHARED_LIBS_DEFAULT OFF)
@@ -72,12 +58,6 @@ if (MSVC)
     add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/bigobj>")
 endif()
 
-if (LLAMA_STANDALONE)
-    # enable parallel builds for msbuild
-    list(APPEND CMAKE_VS_GLOBALS UseMultiToolTask=true)
-    list(APPEND CMAKE_VS_GLOBALS EnforceProcessCountAcrossBuilds=true)
-endif()
-
 if (CMAKE_SYSTEM_NAME STREQUAL "iOS")
     set(LLAMA_TOOLS_INSTALL_DEFAULT OFF)
 else()
@@ -112,8 +92,6 @@ option(LLAMA_TOOLS_INSTALL  "llama: install tools"        ${LLAMA_TOOLS_INSTALL_
 
 # 3rd party libs
 option(LLAMA_CURL       "llama: use libcurl to download model from an URL" ON)
-option(LLAMA_HTTPLIB    "llama: if libcurl is disabled, use httplib to download model from an URL" ON)
-option(LLAMA_OPENSSL    "llama: use openssl to support HTTPS" OFF)
 option(LLAMA_LLGUIDANCE "llama-common: include LLGuidance library for structured output in common utils" OFF)
 
 # Required for relocatable CMake package
@@ -199,6 +177,11 @@ if (NOT TARGET ggml AND NOT LLAMA_USE_SYSTEM_GGML)
     # ... otherwise assume ggml is added by a parent CMakeLists.txt
 endif()
 
+if (MINGW)
+    # Target Windows 8 for PrefetchVirtualMemory
+    add_compile_definitions(_WIN32_WINNT=${GGML_WIN_VER})
+endif()
+
 #
 # build the library
 #
@@ -216,9 +199,6 @@ endif()
 
 if (LLAMA_BUILD_COMMON)
     add_subdirectory(common)
-    if (LLAMA_HTTPLIB)
-        add_subdirectory(vendor/cpp-httplib)
-    endif()
 endif()
 
 if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION)

CODEOWNERS

@@ -1,107 +1,12 @@
 # collaborators can optionally add themselves here to indicate their availability for reviewing related PRs
-# multiplie collaborators per item can be specified
 
-/.devops/*.Dockerfile                   @ngxson
-/.github/actions/                       @CISC
-/.github/workflows/                     @CISC
-/ci/                                    @ggerganov
-/cmake/                                 @ggerganov
-/common/CMakeLists.txt                  @ggerganov
-/common/arg.*                           @ggerganov
-/common/base64.hpp.*                    @ggerganov
-/common/build-info.*                    @ggerganov
-/common/chat.*                          @pwilkin
-/common/chat-peg-parser.*               @aldehir
-/common/common.*                        @ggerganov
-/common/console.*                       @ggerganov
-/common/http.*                          @angt
-/common/llguidance.*                    @ggerganov
-/common/log.*                           @ggerganov
-/common/peg-parser.*                    @aldehir
-/common/sampling.*                      @ggerganov
-/common/speculative.*                   @ggerganov
-/common/unicode.*                       @aldehir
-/convert_*.py                           @CISC
-/examples/batched.swift/                @ggerganov
-/examples/batched/                      @ggerganov
-/examples/convert-llama2c-to-ggml/      @ggerganov
-/examples/deprecation-warning/          @ggerganov
-/examples/diffusion/                    @am17an
-/examples/embedding/                    @ggerganov
-/examples/eval-callback/                @ggerganov
-/examples/export-docs/                  @ggerganov
-/examples/gen-docs/                     @ggerganov
-/examples/gguf/                         @ggerganov
-/examples/llama.android/                @ggerganov
-/examples/llama.swiftui/                @ggerganov
-/examples/llama.vim                     @ggerganov
-/examples/lookahead/                    @ggerganov
-/examples/lookup/                       @JohannesGaessler
-/examples/model-conversion/             @danbev
-/examples/parallel/                     @ggerganov
-/examples/passkey/                      @ggerganov
-/examples/retrieval/                    @ggerganov
-/examples/save-load-state/              @ggerganov
-/examples/speculative-simple/           @ggerganov
-/examples/speculative/                  @ggerganov
-/ggml/cmake/                            @ggerganov
-/ggml/include/                          @ggerganov
-/ggml/src/ggml-common.h                 @ggerganov
-/ggml/src/ggml-cpu/                     @ggerganov
-/ggml/src/ggml-cpu/spacemit/            @alex-spacemit
-/ggml/src/ggml-cuda/fattn*              @JohannesGaessler
-/ggml/src/ggml-cuda/mmf.*               @JohannesGaessler @am17an
-/ggml/src/ggml-cuda/mmq.*               @JohannesGaessler
-/ggml/src/ggml-cuda/mmvf.*              @JohannesGaessler
-/ggml/src/ggml-cuda/mmvq.*              @JohannesGaessler
-/ggml/src/ggml-cuda/fattn-wmma*         @IMbackK
-/ggml/src/ggml-hip/                     @IMbackK
-/ggml/src/ggml-cuda/vendors/hip.h       @IMbackK
-/ggml/src/ggml-impl.h                   @ggerganov
-/ggml/src/ggml-metal/                   @ggerganov
-/ggml/src/ggml-opencl/                  @lhez @max-krasnyansky
-/ggml/src/ggml-hexagon/                 @max-krasnyansky @lhez
-/ggml/src/ggml-opt.cpp                  @JohannesGaessler
-/ggml/src/ggml-quants.*                 @ggerganov
-/ggml/src/ggml-rpc/                     @rgerganov
-/ggml/src/ggml-threading.*              @ggerganov
-/ggml/src/ggml-vulkan/                  @0cc4m
-/ggml/src/ggml-webgpu/                  @reeselevine
-/ggml/src/ggml-zdnn/                    @taronaeo @Andreas-Krebbel @AlekseiNikiforovIBM
-/ggml/src/ggml.c                        @ggerganov
-/ggml/src/ggml.cpp                      @ggerganov
-/ggml/src/gguf.cpp                      @JohannesGaessler @Green-Sky
-/gguf-py/                               @CISC
-/media/                                 @ggerganov
-/scripts/gen*                           @ggerganov
-/scripts/get*                           @ggerganov
-/scripts/sync*                          @ggerganov
-/src/                                   @ggerganov
-/src/llama-adapter.*                    @CISC
-/src/llama-arch.*                       @CISC
-/src/llama-chat.*                       @ngxson
-/src/llama-graph.*                      @CISC
-/src/llama-model.*                      @CISC
-/src/llama-vocab.*                      @CISC
-/src/models/                            @CISC
-/tests/                                 @ggerganov
-/tests/test-chat-.*                     @pwilkin
-/tools/batched-bench/                   @ggerganov
-/tools/main/                            @ggerganov
-/tools/mtmd/                            @ngxson
-/tools/perplexity/                      @ggerganov
-/tools/quantize/                        @ggerganov
-/tools/rpc/                             @rgerganov
-/tools/server/*                         @ngxson @ggerganov # no subdir
-/tools/server/webui/                    @allozaur
-/tools/tokenize/                        @ggerganov
-/tools/tts/                             @ggerganov
-/vendor/                                @ggerganov
-/AUTHORS                                @ggerganov
-/CMakeLists.txt                         @ggerganov
-/CONTRIBUTING.md                        @ggerganov
-/LICENSE                                @ggerganov
-/README.md                              @ggerganov
-/SECURITY.md                            @ggerganov
-/build-xcframework.sh                   @danbev
-requirements*.txt                       @CISC
+/ci/ @ggerganov
+/.devops/*.Dockerfile @ngxson
+/tools/server/ @ngxson
+/ggml/src/ggml-cuda/fattn* @JohannesGaessler
+/ggml/src/ggml-cuda/mmq.* @JohannesGaessler
+/ggml/src/ggml-cuda/mmvq.* @JohannesGaessler
+/ggml/src/ggml-opt.cpp @JohannesGaessler
+/ggml/src/gguf.cpp @JohannesGaessler
+/ggml/src/ggml-vulkan/ @0cc4m
+/ggml/src/ggml-zdnn/ @taronaeo

CONTRIBUTING.md

@@ -1,12 +1,4 @@
-# Contributors
-
-The project differentiates between 3 levels of contributors:
-
-- Contributors: people who have contributed before (no special privileges)
-- Collaborators (Triage): people with significant contributions, who may be responsible for some parts of the code, and are expected to maintain and review contributions for the code they own
-- Maintainers: responsible for reviewing and merging PRs, after approval from the code owners
-
-# Pull requests (for contributors & collaborators)
+# Pull requests (for contributors)
 
 - llama.cpp uses the ggml tensor library for model evaluation. If you are unfamiliar with ggml, consider taking a look at the [examples in the ggml repository](https://github.com/ggml-org/ggml/tree/master/examples/). [simple](https://github.com/ggml-org/ggml/tree/master/examples/simple) shows the bare minimum for using ggml. [gpt-2](https://github.com/ggml-org/ggml/tree/master/examples/gpt-2) has minimal implementations for language model inference using GPT-2. [mnist](https://github.com/ggml-org/ggml/tree/master/examples/mnist) demonstrates how to train and evaluate a simple image classifier
 - Test your changes:
@@ -16,18 +8,16 @@ The project differentiates between 3 levels of contributors:
     - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops`
 - Create separate PRs for each feature or fix. Avoid combining unrelated changes in a single PR
 - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly
-- If your PR becomes stale, rebase it on top of latest `master` to get maintainers attention
-- Maintainers will rely on your insights and approval when making a final decision to approve and merge a PR
-- Consider adding yourself to [CODEOWNERS](CODEOWNERS) to indicate your availability for reviewing related PRs
-- Using AI to generate PRs is permitted. However, you must (1) explicitly disclose how AI was used and (2) conduct a thorough manual review before publishing the PR. Note that trivial tab autocompletions do not require disclosure.
+- If your PR becomes stale, don't hesitate to ping the maintainers in the comments
 
-# Pull requests (for maintainers)
+# Pull requests (for collaborators)
 
 - 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/ggml-org/llama.cpp/wiki/Modules
-- Let other maintainers merge their own PRs
-- When merging a PR, make sure you have a good understanding of the changes
+- Consider adding yourself to [CODEOWNERS](CODEOWNERS)
+- Let authors, who are also collaborators, merge their own PRs
+- When merging a PR by a contributor, make sure you have a good understanding of the changes
 - Be mindful of maintenance: most of the work going into a feature happens after the PR is merged. If the PR author is not committed to contribute long-term, someone else needs to take responsibility (you)
 
 # Coding guidelines
@@ -127,21 +117,6 @@ The project differentiates between 3 levels of contributors:
     #endif // FOO
     ```
 
-# Code maintenance
-
-- Existing code should have designated collaborators and/or maintainers specified in the [CODEOWNERS](CODEOWNERS) file reponsible for:
-  - Reviewing and merging related PRs
-  - Fixing related bugs
-  - Providing developer guidance/support
-
-- When adding or modifying a large piece of code:
-  - If you are a collaborator, make sure to add yourself to [CODEOWNERS](CODEOWNERS) to indicate your availability for reviewing related PRs
-  - If you are a contributor, find an existing collaborator who is willing to review and maintain your code long-term
-  - Provide the necessary CI workflow (and hardware) to test your changes (see [ci/README.md](https://github.com/ggml-org/llama.cpp/tree/master/ci))
-
-- New code should follow the guidelines (coding, naming, etc.) outlined in this document. Exceptions are allowed in isolated, backend-specific parts of the code that do not interface directly with the `ggml` interfaces.
-  _(NOTE: for legacy reasons, existing code is not required to follow this guideline)_
-
 # Documentation
 
 - Documentation is a community effort

README.md

@@ -17,13 +17,14 @@ LLM inference in C/C++
 
 ## Hot topics
 
-- **[guide : using the new WebUI of llama.cpp](https://github.com/ggml-org/llama.cpp/discussions/16938)**
-- [guide : running gpt-oss with llama.cpp](https://github.com/ggml-org/llama.cpp/discussions/15396)
-- [[FEEDBACK] Better packaging for llama.cpp to support downstream consumers 🤗](https://github.com/ggml-org/llama.cpp/discussions/15313)
+- **[guide : running gpt-oss with llama.cpp](https://github.com/ggml-org/llama.cpp/discussions/15396)**
+- **[[FEEDBACK] Better packaging for llama.cpp to support downstream consumers 🤗](https://github.com/ggml-org/llama.cpp/discussions/15313)**
 - Support for the `gpt-oss` model with native MXFP4 format has been added | [PR](https://github.com/ggml-org/llama.cpp/pull/15091) | [Collaboration with NVIDIA](https://blogs.nvidia.com/blog/rtx-ai-garage-openai-oss) | [Comment](https://github.com/ggml-org/llama.cpp/discussions/15095)
+- Hot PRs: [All](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Apr+label%3Ahot+) | [Open](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Apr+label%3Ahot+is%3Aopen)
 - Multimodal support arrived in `llama-server`: [#12898](https://github.com/ggml-org/llama.cpp/pull/12898) | [documentation](./docs/multimodal.md)
 - VS Code extension for FIM completions: https://github.com/ggml-org/llama.vscode
 - Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim
+- Introducing GGUF-my-LoRA https://github.com/ggml-org/llama.cpp/discussions/10123
 - Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggml-org/llama.cpp/discussions/9669
 - Hugging Face GGUF editor: [discussion](https://github.com/ggml-org/llama.cpp/discussions/9268) | [tool](https://huggingface.co/spaces/CISCai/gguf-editor)
 
@@ -61,7 +62,6 @@ range 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
 - AVX, AVX2, AVX512 and AMX support for x86 architectures
-- RVV, ZVFH, ZFH, ZICBOP and ZIHINTPAUSE support for RISC-V architectures
 - 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
 - Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads GPUs via MUSA)
 - Vulkan and SYCL backend support
@@ -84,7 +84,6 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1)
 - [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
 - [x] [DBRX](https://huggingface.co/databricks/dbrx-instruct)
-- [x] [Jamba](https://huggingface.co/ai21labs)
 - [X] [Falcon](https://huggingface.co/models?search=tiiuae/falcon)
 - [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)
 - [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
@@ -139,7 +138,6 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [Ling models](https://huggingface.co/collections/inclusionAI/ling-67c51c85b34a7ea0aba94c32)
 - [x] [LFM2 models](https://huggingface.co/collections/LiquidAI/lfm2-686d721927015b2ad73eaa38)
 - [x] [Hunyuan models](https://huggingface.co/collections/tencent/hunyuan-dense-model-6890632cda26b19119c9c5e7)
-- [x] [BailingMoeV2 (Ring/Ling 2.0) models](https://huggingface.co/collections/inclusionAI/ling-v2-68bf1dd2fc34c306c1fa6f86)
 
 #### Multimodal
 
@@ -180,7 +178,6 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj)
 - React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn)
 - Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp)
-- Java: [QuasarByte/llama-cpp-jna](https://github.com/QuasarByte/llama-cpp-jna)
 - Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig)
 - Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart)
 - Flutter: [xuegao-tzx/Fllama](https://github.com/xuegao-tzx/Fllama)
@@ -189,7 +186,6 @@ 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)
 - Delphi [Embarcadero/llama-cpp-delphi](https://github.com/Embarcadero/llama-cpp-delphi)
-- Go (no CGo needed): [hybridgroup/yzma](https://github.com/hybridgroup/yzma)
 
 </details>
 
@@ -242,7 +238,6 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [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)
-- [unslothai/unsloth](https://github.com/unslothai/unsloth) – 🦥 exports/saves fine-tuned and trained models to GGUF (Apache-2.0)
 
 </details>
 
@@ -276,14 +271,11 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 | [MUSA](docs/build.md#musa) | Moore Threads GPU |
 | [CUDA](docs/build.md#cuda) | Nvidia GPU |
 | [HIP](docs/build.md#hip) | AMD GPU |
-| [ZenDNN](docs/build.md#zendnn) | AMD CPU |
 | [Vulkan](docs/build.md#vulkan) | GPU |
 | [CANN](docs/build.md#cann) | Ascend NPU |
 | [OpenCL](docs/backend/OPENCL.md) | Adreno GPU |
-| [IBM zDNN](docs/backend/zDNN.md) | IBM Z & LinuxONE |
 | [WebGPU [In Progress]](docs/build.md#webgpu) | All |
 | [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All |
-| [Hexagon [In Progress]](docs/backend/hexagon/README.md) | Snapdragon |
 
 ## Obtaining and quantizing models
 
@@ -528,8 +520,8 @@ To learn more about model quantization, [read this documentation](tools/quantize
 ## Contributing
 
 - Contributors can open PRs
+- Collaborators can push to branches in the `llama.cpp` repo and merge PRs into the `master` branch
 - Collaborators will be invited based on contributions
-- Maintainers can push to branches in the `llama.cpp` repo and merge PRs into the `master` branch
 - Any help with managing issues, PRs and projects is very appreciated!
 - See [good first issues](https://github.com/ggml-org/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions
 - Read the [CONTRIBUTING.md](CONTRIBUTING.md) for more information
@@ -614,4 +606,3 @@ $ echo "source ~/.llama-completion.bash" >> ~/.bashrc
 - [linenoise.cpp](./tools/run/linenoise.cpp/linenoise.cpp) - C++ library that provides readline-like line editing capabilities, used by `llama-run` - BSD 2-Clause License
 - [curl](https://curl.se/) - Client-side URL transfer library, used by various tools/examples - [CURL License](https://curl.se/docs/copyright.html)
 - [miniaudio.h](https://github.com/mackron/miniaudio) - Single-header audio format decoder, used by multimodal subsystem - Public domain
-- [subprocess.h](https://github.com/sheredom/subprocess.h) - Single-header process launching solution for C and C++ - Public domain

SECURITY.md

@@ -65,6 +65,4 @@ However, If you have discovered a security vulnerability in this project, please
 
 Please disclose it as a private [security advisory](https://github.com/ggml-org/llama.cpp/security/advisories/new).
 
-Please note that using AI to identify vulnerabilities and generate reports is permitted. However, you must (1) explicitly disclose how AI was used and (2) conduct a thorough manual review before submitting the report.
-
 A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure.

benches/dgx-spark/aime25_openai__gpt-oss-120b-high_temp1.0_20251109_094547.json

@@ -1,6 +0,0 @@
-{
-  "chars": 2296.1916666666666,
-  "chars:std": 986.051306946325,
-  "score": 0.925,
-  "score:std": 0.26339134382131846
-}

benches/dgx-spark/dgx-spark.md

@@ -1,264 +0,0 @@
-## System info
-
-```bash
-uname --all
-Linux spark-17ed 6.11.0-1016-nvidia #16-Ubuntu SMP PREEMPT_DYNAMIC Sun Sep 21 16:52:46 UTC 2025 aarch64 aarch64 aarch64 GNU/Linux
-
-g++ --version
-g++ (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
-
-nvidia-smi
-Sun Nov  2 10:43:25 2025
-+-----------------------------------------------------------------------------------------+
-| NVIDIA-SMI 580.95.05              Driver Version: 580.95.05      CUDA Version: 13.0     |
-+-----------------------------------------+------------------------+----------------------+
-| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
-| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
-|                                         |                        |               MIG M. |
-|=========================================+========================+======================|
-|   0  NVIDIA GB10                    On  |   0000000F:01:00.0 Off |                  N/A |
-| N/A   35C    P8              4W /  N/A  | Not Supported          |      0%      Default |
-|                                         |                        |                  N/A |
-+-----------------------------------------+------------------------+----------------------+
-```
-
-## ggml-org/gpt-oss-20b-GGUF
-
-Model: https://huggingface.co/ggml-org/gpt-oss-20b-GGUF
-
-- `llama-batched-bench`
-
-
-main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
-
-|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
-|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
-|   512 |     32 |    1 |    544 |    0.374 |  1369.01 |    0.383 |    83.64 |    0.757 |   719.01 |
-|   512 |     32 |    2 |   1088 |    0.274 |  3741.35 |    0.659 |    97.14 |    0.933 |  1166.66 |
-|   512 |     32 |    4 |   2176 |    0.526 |  3896.47 |    0.817 |   156.73 |    1.342 |  1621.08 |
-|   512 |     32 |    8 |   4352 |    1.044 |  3925.10 |    0.987 |   259.44 |    2.030 |  2143.56 |
-|   512 |     32 |   16 |   8704 |    2.076 |  3945.84 |    1.248 |   410.32 |    3.324 |  2618.60 |
-|   512 |     32 |   32 |  17408 |    4.170 |  3929.28 |    1.630 |   628.40 |    5.799 |  3001.76 |
-|  4096 |     32 |    1 |   4128 |    1.083 |  3782.66 |    0.394 |    81.21 |    1.477 |  2795.13 |
-|  4096 |     32 |    2 |   8256 |    2.166 |  3782.72 |    0.725 |    88.28 |    2.891 |  2856.14 |
-|  4096 |     32 |    4 |  16512 |    4.333 |  3780.88 |    0.896 |   142.82 |    5.230 |  3157.38 |
-|  4096 |     32 |    8 |  33024 |    8.618 |  3802.14 |    1.155 |   221.69 |    9.773 |  3379.08 |
-|  4096 |     32 |   16 |  66048 |   17.330 |  3781.73 |    1.598 |   320.34 |   18.928 |  3489.45 |
-|  4096 |     32 |   32 | 132096 |   34.671 |  3780.48 |    2.336 |   438.35 |   37.007 |  3569.51 |
-|  8192 |     32 |    1 |   8224 |    2.233 |  3668.56 |    0.438 |    72.98 |    2.671 |  3078.44 |
-|  8192 |     32 |    2 |  16448 |    4.425 |  3702.95 |    0.756 |    84.66 |    5.181 |  3174.95 |
-|  8192 |     32 |    4 |  32896 |    8.859 |  3698.64 |    0.967 |   132.38 |    9.826 |  3347.72 |
-|  8192 |     32 |    8 |  65792 |   17.714 |  3699.57 |    1.277 |   200.52 |   18.991 |  3464.35 |
-|  8192 |     32 |   16 | 131584 |   35.494 |  3692.84 |    1.841 |   278.12 |   37.335 |  3524.46 |
-|  8192 |     32 |   32 | 263168 |   70.949 |  3694.82 |    2.798 |   365.99 |   73.747 |  3568.53 |
-
-
-- `llama-bench`
-
-| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap |            test |                  t/s |
-| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |          pp2048 |      3714.25 ± 20.36 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |            tg32 |         86.58 ± 0.43 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d4096 |      3445.17 ± 17.85 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d4096 |         81.72 ± 0.53 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d8192 |      3218.78 ± 11.34 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d8192 |         74.86 ± 0.64 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d16384 |       2732.83 ± 7.17 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d16384 |         71.57 ± 0.51 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d32768 |      2119.75 ± 12.81 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d32768 |         62.33 ± 0.24 |
-
-build: eeee367de (6989)
-
-## ggml-org/gpt-oss-120b-GGUF
-
-Model: https://huggingface.co/ggml-org/gpt-oss-120b-GGUF
-
-- `llama-batched-bench`
-
-
-main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
-
-|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
-|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
-|   512 |     32 |    1 |    544 |    0.571 |   897.18 |    0.543 |    58.96 |    1.113 |   488.60 |
-|   512 |     32 |    2 |   1088 |    0.593 |  1725.37 |    1.041 |    61.45 |    1.635 |   665.48 |
-|   512 |     32 |    4 |   2176 |    1.043 |  1963.15 |    1.334 |    95.95 |    2.377 |   915.36 |
-|   512 |     32 |    8 |   4352 |    2.099 |  1951.63 |    1.717 |   149.07 |    3.816 |  1140.45 |
-|   512 |     32 |   16 |   8704 |    4.207 |  1947.12 |    2.311 |   221.56 |    6.518 |  1335.35 |
-|   512 |     32 |   32 |  17408 |    8.422 |  1945.36 |    3.298 |   310.46 |   11.720 |  1485.27 |
-|  4096 |     32 |    1 |   4128 |    2.138 |  1915.88 |    0.571 |    56.09 |    2.708 |  1524.12 |
-|  4096 |     32 |    2 |   8256 |    4.266 |  1920.25 |    1.137 |    56.27 |    5.404 |  1527.90 |
-|  4096 |     32 |    4 |  16512 |    8.564 |  1913.02 |    1.471 |    86.99 |   10.036 |  1645.29 |
-|  4096 |     32 |    8 |  33024 |   17.092 |  1917.19 |    1.979 |   129.33 |   19.071 |  1731.63 |
-|  4096 |     32 |   16 |  66048 |   34.211 |  1915.65 |    2.850 |   179.66 |   37.061 |  1782.15 |
-|  4096 |     32 |   32 | 132096 |   68.394 |  1916.44 |    4.381 |   233.72 |   72.775 |  1815.13 |
-|  8192 |     32 |    1 |   8224 |    4.349 |  1883.45 |    0.620 |    51.65 |    4.969 |  1655.04 |
-|  8192 |     32 |    2 |  16448 |    8.674 |  1888.83 |    1.178 |    54.33 |    9.852 |  1669.48 |
-|  8192 |     32 |    4 |  32896 |   17.351 |  1888.55 |    1.580 |    81.01 |   18.931 |  1737.68 |
-|  8192 |     32 |    8 |  65792 |   34.743 |  1886.31 |    2.173 |   117.80 |   36.916 |  1782.20 |
-|  8192 |     32 |   16 | 131584 |   69.413 |  1888.29 |    3.297 |   155.28 |   72.710 |  1809.70 |
-|  8192 |     32 |   32 | 263168 |  138.903 |  1887.24 |    5.004 |   204.63 |  143.907 |  1828.73 |
-
-
-- `llama-bench`
-
-| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap |            test |                  t/s |
-| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |          pp2048 |       1919.36 ± 5.01 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |            tg32 |         60.40 ± 0.30 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d4096 |       1825.30 ± 6.37 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d4096 |         56.94 ± 0.29 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d8192 |       1739.19 ± 6.00 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d8192 |         52.51 ± 0.42 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d16384 |       1536.75 ± 4.27 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d16384 |         49.33 ± 0.27 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d32768 |       1255.85 ± 3.26 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d32768 |         42.99 ± 0.18 |
-
-build: eeee367de (6989)
-
-## ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF
-
-Model: https://huggingface.co/ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF
-
-- `llama-batched-bench`
-
-
-main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
-
-|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
-|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
-|   512 |     32 |    1 |    544 |    0.398 |  1285.90 |    0.530 |    60.41 |    0.928 |   586.27 |
-|   512 |     32 |    2 |   1088 |    0.386 |  2651.65 |    0.948 |    67.50 |    1.334 |   815.38 |
-|   512 |     32 |    4 |   2176 |    0.666 |  3076.37 |    1.209 |   105.87 |    1.875 |  1160.71 |
-|   512 |     32 |    8 |   4352 |    1.325 |  3091.39 |    1.610 |   158.98 |    2.935 |  1482.65 |
-|   512 |     32 |   16 |   8704 |    2.664 |  3075.58 |    2.150 |   238.19 |    4.813 |  1808.39 |
-|   512 |     32 |   32 |  17408 |    5.336 |  3070.31 |    2.904 |   352.59 |    8.240 |  2112.50 |
-|  4096 |     32 |    1 |   4128 |    1.444 |  2836.81 |    0.581 |    55.09 |    2.025 |  2038.81 |
-|  4096 |     32 |    2 |   8256 |    2.872 |  2852.14 |    1.084 |    59.06 |    3.956 |  2086.99 |
-|  4096 |     32 |    4 |  16512 |    5.744 |  2852.32 |    1.440 |    88.90 |    7.184 |  2298.47 |
-|  4096 |     32 |    8 |  33024 |   11.463 |  2858.68 |    2.068 |   123.78 |   13.531 |  2440.65 |
-|  4096 |     32 |   16 |  66048 |   22.915 |  2859.95 |    3.018 |   169.67 |   25.933 |  2546.90 |
-|  4096 |     32 |   32 | 132096 |   45.956 |  2852.10 |    4.609 |   222.18 |   50.565 |  2612.39 |
-|  8192 |     32 |    1 |   8224 |    3.063 |  2674.72 |    0.693 |    46.20 |    3.755 |  2189.92 |
-|  8192 |     32 |    2 |  16448 |    6.109 |  2681.87 |    1.214 |    52.71 |    7.323 |  2245.98 |
-|  8192 |     32 |    4 |  32896 |   12.197 |  2686.63 |    1.682 |    76.11 |   13.878 |  2370.30 |
-|  8192 |     32 |    8 |  65792 |   24.409 |  2684.94 |    2.556 |   100.17 |   26.965 |  2439.95 |
-|  8192 |     32 |   16 | 131584 |   48.753 |  2688.50 |    3.994 |   128.20 |   52.747 |  2494.64 |
-|  8192 |     32 |   32 | 263168 |   97.508 |  2688.42 |    6.528 |   156.86 |  104.037 |  2529.57 |
-
-
-- `llama-bench`
-
-| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap |            test |                  t/s |
-| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |          pp2048 |       2925.55 ± 4.25 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |            tg32 |         62.80 ± 0.27 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d4096 |       2531.01 ± 6.79 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d4096 |         55.86 ± 0.33 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d8192 |       2244.39 ± 5.33 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d8192 |         45.95 ± 0.33 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d16384 |       1783.17 ± 3.68 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d16384 |         39.07 ± 0.10 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d32768 |       1241.90 ± 3.13 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d32768 |         29.92 ± 0.06 |
-
-build: eeee367de (6989)
-
-## ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF
-
-Model: https://huggingface.co/ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF
-
-- `llama-batched-bench`
-
-
-main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
-
-|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
-|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
-|   512 |     32 |    1 |    544 |    0.211 |  2421.57 |    1.055 |    30.33 |    1.266 |   429.57 |
-|   512 |     32 |    2 |   1088 |    0.419 |  2441.34 |    1.130 |    56.65 |    1.549 |   702.32 |
-|   512 |     32 |    4 |   2176 |    0.873 |  2345.54 |    1.174 |   108.99 |    2.048 |  1062.74 |
-|   512 |     32 |    8 |   4352 |    1.727 |  2371.85 |    1.254 |   204.22 |    2.980 |  1460.19 |
-|   512 |     32 |   16 |   8704 |    3.452 |  2373.22 |    1.492 |   343.16 |    4.944 |  1760.56 |
-|   512 |     32 |   32 |  17408 |    6.916 |  2368.93 |    1.675 |   611.51 |    8.591 |  2026.36 |
-|  4096 |     32 |    1 |   4128 |    1.799 |  2277.26 |    1.084 |    29.51 |    2.883 |  1431.91 |
-|  4096 |     32 |    2 |   8256 |    3.577 |  2290.01 |    1.196 |    53.50 |    4.774 |  1729.51 |
-|  4096 |     32 |    4 |  16512 |    7.172 |  2284.36 |    1.313 |    97.50 |    8.485 |  1946.00 |
-|  4096 |     32 |    8 |  33024 |   14.341 |  2284.96 |    1.520 |   168.46 |   15.860 |  2082.18 |
-|  4096 |     32 |   16 |  66048 |   28.675 |  2285.44 |    1.983 |   258.21 |   30.658 |  2154.33 |
-|  4096 |     32 |   32 | 132096 |   57.354 |  2285.32 |    2.640 |   387.87 |   59.994 |  2201.82 |
-|  8192 |     32 |    1 |   8224 |    3.701 |  2213.75 |    1.119 |    28.59 |    4.820 |  1706.34 |
-|  8192 |     32 |    2 |  16448 |    7.410 |  2211.19 |    1.272 |    50.31 |    8.682 |  1894.56 |
-|  8192 |     32 |    4 |  32896 |   14.802 |  2213.83 |    1.460 |    87.68 |   16.261 |  2022.96 |
-|  8192 |     32 |    8 |  65792 |   29.609 |  2213.35 |    1.781 |   143.74 |   31.390 |  2095.93 |
-|  8192 |     32 |   16 | 131584 |   59.229 |  2212.96 |    2.495 |   205.17 |   61.725 |  2131.79 |
-|  8192 |     32 |   32 | 263168 |  118.449 |  2213.15 |    3.714 |   275.75 |  122.162 |  2154.25 |
-
-
-- `llama-bench`
-
-| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap |            test |                  t/s |
-| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |          pp2048 |       2272.74 ± 4.68 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |            tg32 |         30.66 ± 0.02 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d4096 |       2107.80 ± 9.55 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d4096 |         29.71 ± 0.05 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d8192 |       1937.80 ± 6.75 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d8192 |         28.86 ± 0.04 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d16384 |       1641.12 ± 1.78 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d16384 |         27.24 ± 0.04 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d32768 |       1296.02 ± 2.67 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d32768 |         23.78 ± 0.03 |
-
-build: eeee367de (6989)
-
-## ggml-org/gemma-3-4b-it-qat-GGUF
-
-Model: https://huggingface.co/ggml-org/gemma-3-4b-it-qat-GGUF
-
-- `llama-batched-bench`
-
-
-main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
-
-|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
-|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
-|   512 |     32 |    1 |    544 |    0.094 |  5434.73 |    0.394 |    81.21 |    0.488 |  1114.15 |
-|   512 |     32 |    2 |   1088 |    0.168 |  6091.68 |    0.498 |   128.52 |    0.666 |  1633.41 |
-|   512 |     32 |    4 |   2176 |    0.341 |  6010.68 |    0.542 |   236.37 |    0.882 |  2466.43 |
-|   512 |     32 |    8 |   4352 |    0.665 |  6161.46 |    0.678 |   377.74 |    1.342 |  3241.72 |
-|   512 |     32 |   16 |   8704 |    1.323 |  6193.19 |    0.902 |   567.41 |    2.225 |  3911.74 |
-|   512 |     32 |   32 |  17408 |    2.642 |  6202.03 |    1.231 |   832.03 |    3.872 |  4495.36 |
-|  4096 |     32 |    1 |   4128 |    0.701 |  5840.49 |    0.439 |    72.95 |    1.140 |  3621.23 |
-|  4096 |     32 |    2 |   8256 |    1.387 |  5906.82 |    0.574 |   111.48 |    1.961 |  4210.12 |
-|  4096 |     32 |    4 |  16512 |    2.758 |  5940.33 |    0.651 |   196.58 |    3.409 |  4843.33 |
-|  4096 |     32 |    8 |  33024 |    5.491 |  5967.56 |    0.876 |   292.40 |    6.367 |  5187.12 |
-|  4096 |     32 |   16 |  66048 |   10.978 |  5969.58 |    1.275 |   401.69 |   12.253 |  5390.38 |
-|  4096 |     32 |   32 | 132096 |   21.944 |  5972.93 |    1.992 |   514.16 |   23.936 |  5518.73 |
-|  8192 |     32 |    1 |   8224 |    1.402 |  5841.91 |    0.452 |    70.73 |    1.855 |  4434.12 |
-|  8192 |     32 |    2 |  16448 |    2.793 |  5865.34 |    0.637 |   100.55 |    3.430 |  4795.51 |
-|  8192 |     32 |    4 |  32896 |    5.564 |  5889.64 |    0.770 |   166.26 |    6.334 |  5193.95 |
-|  8192 |     32 |    8 |  65792 |   11.114 |  5896.44 |    1.122 |   228.07 |   12.237 |  5376.51 |
-|  8192 |     32 |   16 | 131584 |   22.210 |  5901.38 |    1.789 |   286.15 |   24.000 |  5482.74 |
-|  8192 |     32 |   32 | 263168 |   44.382 |  5906.56 |    3.044 |   336.38 |   47.426 |  5549.02 |
-
-
-- `llama-bench`
-
-| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap |            test |                  t/s |
-| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |          pp2048 |      5810.04 ± 21.71 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |            tg32 |         84.54 ± 0.18 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d4096 |       5288.04 ± 3.54 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d4096 |         78.82 ± 1.37 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d8192 |      4960.43 ± 16.64 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d8192 |         74.13 ± 0.30 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d16384 |      4495.92 ± 31.11 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d16384 |         72.37 ± 0.29 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d32768 |      3746.90 ± 40.01 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d32768 |         63.02 ± 0.20 |
-
-build: eeee367de (6989)
-

build-xcframework.sh

@@ -422,7 +422,6 @@ echo "Building for iOS devices..."
 cmake -B build-ios-device -G Xcode \
     "${COMMON_CMAKE_ARGS[@]}" \
     -DCMAKE_OSX_DEPLOYMENT_TARGET=${IOS_MIN_OS_VERSION} \
-    -DCMAKE_SYSTEM_NAME=iOS \
     -DCMAKE_OSX_SYSROOT=iphoneos \
     -DCMAKE_OSX_ARCHITECTURES="arm64" \
     -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=iphoneos \
@@ -454,8 +453,6 @@ cmake -B build-visionos -G Xcode \
     -DCMAKE_C_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_CXX_FLAGS}" \
     -DLLAMA_CURL=OFF \
-    -DLLAMA_HTTPLIB=OFF \
-    -DLLAMA_BUILD_SERVER=OFF \
     -S .
 cmake --build build-visionos --config Release -- -quiet
 
@@ -470,8 +467,6 @@ cmake -B build-visionos-sim -G Xcode \
     -DCMAKE_C_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_CXX_FLAGS}" \
     -DLLAMA_CURL=OFF \
-    -DLLAMA_HTTPLIB=OFF \
-    -DLLAMA_BUILD_SERVER=OFF \
     -S .
 cmake --build build-visionos-sim --config Release -- -quiet
 

ci/README-MUSA.md

@@ -1,35 +0,0 @@
-## Running MUSA CI in a Docker Container
-
-Assuming `$PWD` is the root of the `llama.cpp` repository, follow these steps to set up and run MUSA CI in a Docker container:
-
-### 1. Create a local directory to store cached models, configuration files and venv:
-
-```bash
-mkdir -p $HOME/llama.cpp/ci-cache
-```
-
-### 2. Create a local directory to store CI run results:
-
-```bash
-mkdir -p $HOME/llama.cpp/ci-results
-```
-
-### 3. Start a Docker container and run the CI:
-
-```bash
-docker run --privileged -it \
-    -v $HOME/llama.cpp/ci-cache:/ci-cache \
-    -v $HOME/llama.cpp/ci-results:/ci-results \
-    -v $PWD:/ws -w /ws \
-    mthreads/musa:rc4.3.0-devel-ubuntu22.04-amd64
-```
-
-Inside the container, execute the following commands:
-
-```bash
-apt update -y && apt install -y bc cmake ccache git python3.10-venv time unzip wget
-git config --global --add safe.directory /ws
-GG_BUILD_MUSA=1 bash ./ci/run.sh /ci-results /ci-cache
-```
-
-This setup ensures that the CI runs within an isolated Docker environment while maintaining cached files and results across runs.

ci/README.md

@@ -1,10 +1,18 @@
 # CI
 
-This CI implements heavy-duty workflows that run on self-hosted runners. Typically the purpose of these workflows is to
-cover hardware configurations that are not available from Github-hosted runners and/or require more computational
-resource than normally available.
+In addition to [Github Actions](https://github.com/ggml-org/llama.cpp/actions) `llama.cpp` uses a custom CI framework:
 
-It is a good practice, before publishing changes to execute the full CI locally on your machine. For example:
+https://github.com/ggml-org/ci
+
+It monitors the `master` branch for new commits and runs the
+[ci/run.sh](https://github.com/ggml-org/llama.cpp/blob/master/ci/run.sh) script on dedicated cloud instances. This allows us
+to execute heavier workloads compared to just using Github Actions. Also with time, the cloud instances will be scaled
+to cover various hardware architectures, including GPU and Apple Silicon instances.
+
+Collaborators can optionally trigger the CI run by adding the `ggml-ci` keyword to their commit message.
+Only the branches of this repo are monitored for this keyword.
+
+It is a good practice, before publishing changes to execute the full CI locally on your machine:
 
 ```bash
 mkdir tmp
@@ -21,13 +29,40 @@ GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
 
 # with MUSA support
 GG_BUILD_MUSA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
-
-# etc.
 ```
 
-# Adding self-hosted runners
+## Running MUSA CI in a Docker Container
 
-- Add a self-hosted `ggml-ci` workflow to [[.github/workflows/build.yml]] with an appropriate label
-- Request a runner token from `ggml-org` (for example, via a comment in the PR or email)
-- Set-up a machine using the received token ([docs](https://docs.github.com/en/actions/how-tos/manage-runners/self-hosted-runners/add-runners))
-- Optionally update [ci/run.sh](https://github.com/ggml-org/llama.cpp/blob/master/ci/run.sh) to build and run on the target platform by gating the implementation with a `GG_BUILD_...` env
+Assuming `$PWD` is the root of the `llama.cpp` repository, follow these steps to set up and run MUSA CI in a Docker container:
+
+### 1. Create a local directory to store cached models, configuration files and venv:
+
+```bash
+mkdir -p $HOME/llama.cpp/ci-cache
+```
+
+### 2. Create a local directory to store CI run results:
+
+```bash
+mkdir -p $HOME/llama.cpp/ci-results
+```
+
+### 3. Start a Docker container and run the CI:
+
+```bash
+docker run --privileged -it \
+    -v $HOME/llama.cpp/ci-cache:/ci-cache \
+    -v $HOME/llama.cpp/ci-results:/ci-results \
+    -v $PWD:/ws -w /ws \
+    mthreads/musa:rc4.2.0-devel-ubuntu22.04-amd64
+```
+
+Inside the container, execute the following commands:
+
+```bash
+apt update -y && apt install -y bc cmake ccache git python3.10-venv time unzip wget
+git config --global --add safe.directory /ws
+GG_BUILD_MUSA=1 bash ./ci/run.sh /ci-results /ci-cache
+```
+
+This setup ensures that the CI runs within an isolated Docker environment while maintaining cached files and results across runs.

ci/run.sh

@@ -22,9 +22,6 @@
 # # with MUSA support
 # GG_BUILD_MUSA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
 #
-# # with KLEIDIAI support
-# GG_BUILD_KLEIDIAI=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
-#
 
 if [ -z "$2" ]; then
     echo "usage: $0 <output-dir> <mnt-dir>"
@@ -37,15 +34,15 @@ mkdir -p "$2"
 OUT=$(realpath "$1")
 MNT=$(realpath "$2")
 
-rm -f $OUT/*.log
-rm -f $OUT/*.exit
-rm -f $OUT/*.md
+rm -f "$OUT/*.log"
+rm -f "$OUT/*.exit"
+rm -f "$OUT/*.md"
 
 sd=`dirname $0`
 cd $sd/../
 SRC=`pwd`
 
-CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=${LLAMA_FATAL_WARNINGS:-ON} -DLLAMA_CURL=ON -DGGML_SCHED_NO_REALLOC=ON"
+CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=ON"
 
 if [ ! -z ${GG_BUILD_METAL} ]; then
     CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON"
@@ -68,16 +65,6 @@ if [ ! -z ${GG_BUILD_CUDA} ]; then
     fi
 fi
 
-if [ ! -z ${GG_BUILD_ROCM} ]; then
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_HIP=ON"
-    if [ -z ${GG_BUILD_AMDGPU_TARGETS} ]; then
-        echo "Missing GG_BUILD_AMDGPU_TARGETS, please set it to your GPU architecture (e.g. gfx90a, gfx1100, etc.)"
-        exit 1
-    fi
-
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DGPU_TARGETS=${GG_BUILD_AMDGPU_TARGETS}"
-fi
-
 if [ ! -z ${GG_BUILD_SYCL} ]; then
     if [ -z ${ONEAPI_ROOT} ]; then
         echo "Not detected ONEAPI_ROOT, please install oneAPI base toolkit and enable it by:"
@@ -95,12 +82,6 @@ fi
 
 if [ ! -z ${GG_BUILD_VULKAN} ]; then
     CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_VULKAN=1"
-
-    # if on Mac, disable METAL
-    if [[ "$OSTYPE" == "darwin"* ]]; then
-        CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=OFF -DGGML_BLAS=OFF"
-    fi
-
 fi
 
 if [ ! -z ${GG_BUILD_WEBGPU} ]; then
@@ -112,45 +93,6 @@ if [ ! -z ${GG_BUILD_MUSA} ]; then
     MUSA_ARCH=${MUSA_ARCH:-21}
     CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_MUSA=ON -DMUSA_ARCHITECTURES=${MUSA_ARCH}"
 fi
-
-if [ ! -z ${GG_BUILD_NO_SVE} ]; then
-    # arm 9 and newer enables sve by default, adjust these flags depending on the cpu used
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=armv8.5-a+fp16+i8mm"
-fi
-
-if [ -n "${GG_BUILD_KLEIDIAI}" ]; then
-    echo ">>===== Enabling KleidiAI support"
-
-    CANDIDATES=(
-        "armv9-a+dotprod+i8mm+sve2"
-        "armv9-a+dotprod+i8mm"
-        "armv8.6-a+dotprod+i8mm"
-        "armv8.2-a+dotprod"
-    )
-    CPU=""
-
-    for cpu in "${CANDIDATES[@]}"; do
-        if echo 'int main(){}' | ${CXX:-c++} -march="$cpu" -x c++ - -c -o /dev/null >/dev/null 2>&1; then
-            CPU="$cpu"
-            break
-        fi
-    done
-
-    if [ -z "$CPU" ]; then
-        echo "ERROR: None of the required ARM baselines (armv9/armv8.6/armv8.2 + dotprod) are supported by this compiler."
-        exit 1
-    fi
-
-    echo ">>===== Using ARM baseline: ${CPU}"
-
-    CMAKE_EXTRA="${CMAKE_EXTRA:+$CMAKE_EXTRA } \
-        -DGGML_NATIVE=OFF \
-        -DGGML_CPU_KLEIDIAI=ON \
-        -DGGML_CPU_AARCH64=ON \
-        -DGGML_CPU_ARM_ARCH=${CPU} \
-        -DBUILD_SHARED_LIBS=OFF"
-fi
-
 ## helpers
 
 # download a file if it does not exist or if it is outdated
@@ -208,7 +150,7 @@ function gg_run_ctest_debug {
     (time cmake -DCMAKE_BUILD_TYPE=Debug ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
     (time make -j$(nproc)                                  ) 2>&1 | tee -a $OUT/${ci}-make.log
 
-    (time ctest --output-on-failure -L main -E "test-opt|test-backend-ops" ) 2>&1 | tee -a $OUT/${ci}-ctest.log
+    (time ctest --output-on-failure -L main -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log
 
     set +e
 }
@@ -258,9 +200,33 @@ function gg_sum_ctest_release {
     gg_printf '```\n'
 }
 
-# test_scripts
+# test_scripts_debug
 
-function gg_run_test_scripts {
+function gg_run_test_scripts_debug {
+    cd ${SRC}
+
+    set -e
+
+    (cd ./tools/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
+    (cd ./tools/quantize   && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
+
+    set +e
+}
+
+function gg_sum_test_scripts_debug {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Runs test scripts in debug mode\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '```\n'
+    gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)"
+    gg_printf '```\n'
+    gg_printf '\n'
+}
+
+# test_scripts_release
+
+function gg_run_test_scripts_release {
     cd ${SRC}
 
     set -e
@@ -271,10 +237,10 @@ function gg_run_test_scripts {
     set +e
 }
 
-function gg_sum_test_scripts {
+function gg_sum_test_scripts_release {
     gg_printf '### %s\n\n' "${ci}"
 
-    gg_printf 'Runs test scripts\n'
+    gg_printf 'Runs test scripts in release mode\n'
     gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
     gg_printf '```\n'
     gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)"
@@ -283,9 +249,15 @@ function gg_sum_test_scripts {
 }
 
 function gg_get_model {
-    local gguf_0="$MNT/models/qwen3/0.6B/ggml-model-f16.gguf"
+    local gguf_0="$MNT/models/pythia/1.4B/ggml-model-f16.gguf"
+    local gguf_1="$MNT/models/pythia/2.8B/ggml-model-f16.gguf"
+    local gguf_2="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf"
     if [[ -s $gguf_0 ]]; then
         echo -n "$gguf_0"
+    elif [[ -s $gguf_1 ]]; then
+        echo -n "$gguf_1"
+    elif [[ -s $gguf_2 ]]; then
+        echo -n "$gguf_2"
     else
         echo >&2 "No model found. Can't run gg_run_ctest_with_model."
         exit 1
@@ -344,22 +316,24 @@ function gg_sum_ctest_with_model_release {
     gg_printf '```\n'
 }
 
-# qwen3_0_6b
+# open_llama_7b_v2
 
-function gg_run_qwen3_0_6b {
+function gg_run_open_llama_7b_v2 {
     cd ${SRC}
 
-    gg_wget models-mnt/qwen3/0.6B/ https://huggingface.co/Qwen/Qwen3-0.6B-Base/raw/main/config.json
-    gg_wget models-mnt/qwen3/0.6B/ https://huggingface.co/Qwen/Qwen3-0.6B-Base/raw/main/tokenizer.json
-    gg_wget models-mnt/qwen3/0.6B/ https://huggingface.co/Qwen/Qwen3-0.6B-Base/raw/main/tokenizer_config.json
-   #gg_wget models-mnt/qwen3/0.6B/ https://huggingface.co/Qwen/Qwen3-0.6B-Base/raw/main/special_tokens_map.json
-    gg_wget models-mnt/qwen3/0.6B/ https://huggingface.co/Qwen/Qwen3-0.6B-Base/resolve/main/model.safetensors
-
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/config.json
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/tokenizer.model
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/tokenizer_config.json
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/special_tokens_map.json
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/pytorch_model.bin.index.json
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00001-of-00002.bin
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00002-of-00002.bin
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/generation_config.json
 
     gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
     unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
 
-    path_models="../models-mnt/qwen3/0.6B"
+    path_models="../models-mnt/open-llama/7B-v2"
     path_wiki="../models-mnt/wikitext/wikitext-2-raw"
 
     rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
@@ -369,11 +343,9 @@ function gg_run_qwen3_0_6b {
     (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
     (time make -j$(nproc)                                    ) 2>&1 | tee -a $OUT/${ci}-make.log
 
-    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf  --outtype f16
-    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-bf16.gguf --outtype bf16
+    python3 ../examples/convert_legacy_llama.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
 
     model_f16="${path_models}/ggml-model-f16.gguf"
-    model_bf16="${path_models}/ggml-model-bf16.gguf"
     model_q8_0="${path_models}/ggml-model-q8_0.gguf"
     model_q4_0="${path_models}/ggml-model-q4_0.gguf"
     model_q4_1="${path_models}/ggml-model-q4_1.gguf"
@@ -387,51 +359,179 @@ function gg_run_qwen3_0_6b {
 
     wiki_test="${path_wiki}/wiki.test.raw"
 
-    ./bin/llama-quantize ${model_bf16} ${model_q8_0} q8_0 $(nproc)
-    ./bin/llama-quantize ${model_bf16} ${model_q4_0} q4_0 $(nproc)
-    ./bin/llama-quantize ${model_bf16} ${model_q4_1} q4_1 $(nproc)
-    ./bin/llama-quantize ${model_bf16} ${model_q5_0} q5_0 $(nproc)
-    ./bin/llama-quantize ${model_bf16} ${model_q5_1} q5_1 $(nproc)
-    ./bin/llama-quantize ${model_bf16} ${model_q2_k} q2_k $(nproc)
-    ./bin/llama-quantize ${model_bf16} ${model_q3_k} q3_k $(nproc)
-    ./bin/llama-quantize ${model_bf16} ${model_q4_k} q4_k $(nproc)
-    ./bin/llama-quantize ${model_bf16} ${model_q5_k} q5_k $(nproc)
-    ./bin/llama-quantize ${model_bf16} ${model_q6_k} q6_k $(nproc)
+    ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
+    ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0
+    ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1
+    ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0
+    ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1
+    ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k
+    ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k
+    ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k
+    ./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 1024 -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_bf16} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-bf16.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q8_0} -ngl 99 -c 1024 -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 1024 -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 1024 -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 1024 -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 1024 -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 1024 -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 1024 -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 1024 -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 1024 -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 1024 -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 -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-perplexity --model ${model_f16}  -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    if [ -z ${GG_BUILD_NO_BF16} ]; then
-        (time ./bin/llama-perplexity --model ${model_bf16} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-bf16.log
-    fi
-    (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 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
+    (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
-    (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
+    (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
 
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 1024 -fa off --no-op-offload) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 1024 -fa on  --no-op-offload) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 1024 -fa off                ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 1024 -fa on                 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa on  ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa on  ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+
+    function check_ppl {
+        qnt="$1"
+        ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
+
+        if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then
+            printf '  - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl"
+            return 20
+        fi
+
+        printf '  - %s @ %s OK\n' "$qnt" "$ppl"
+        return 0
+    }
+
+    check_ppl "f16"  "$(cat $OUT/${ci}-tg-f16.log  | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+
+    cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log
+
+    set +e
+}
+
+function gg_sum_open_llama_7b_v2 {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'OpenLLaMA 7B-v2:\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
+    gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)"
+    gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
+    gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
+    gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
+    gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)"
+    gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)"
+    gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)"
+    gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)"
+    gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)"
+    gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
+    gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
+    gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
+    gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)"
+}
+
+# pythia_1.4b
+
+function gg_run_pythia_1_4b {
+    cd ${SRC}
+
+    gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/config.json
+    gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/tokenizer.json
+    gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/tokenizer_config.json
+    gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/special_tokens_map.json
+    gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/resolve/main/pytorch_model.bin
+
+    gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
+    unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
+    head -n 60 models-mnt/wikitext/wikitext-2-raw/wiki.test.raw > models-mnt/wikitext/wikitext-2-raw/wiki.test-60.raw
+
+    path_models="../models-mnt/pythia/1.4B"
+    path_wiki="../models-mnt/wikitext/wikitext-2-raw"
+
+    rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
+
+    set -e
+
+    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j$(nproc)                                    ) 2>&1 | tee -a $OUT/${ci}-make.log
+
+    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
+
+    model_f16="${path_models}/ggml-model-f16.gguf"
+    model_q8_0="${path_models}/ggml-model-q8_0.gguf"
+    model_q4_0="${path_models}/ggml-model-q4_0.gguf"
+    model_q4_1="${path_models}/ggml-model-q4_1.gguf"
+    model_q5_0="${path_models}/ggml-model-q5_0.gguf"
+    model_q5_1="${path_models}/ggml-model-q5_1.gguf"
+    model_q2_k="${path_models}/ggml-model-q2_k.gguf"
+    model_q3_k="${path_models}/ggml-model-q3_k.gguf"
+    model_q4_k="${path_models}/ggml-model-q4_k.gguf"
+    model_q5_k="${path_models}/ggml-model-q5_k.gguf"
+    model_q6_k="${path_models}/ggml-model-q6_k.gguf"
+
+    wiki_test_60="${path_wiki}/wiki.test-60.raw"
+
+    ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
+    ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0
+    ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1
+    ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0
+    ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1
+    ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k
+    ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k
+    ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k
+    ./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-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
+    (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+
+    (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
+
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa on  ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
 
     function check_ppl {
         qnt="$1"
@@ -447,9 +547,6 @@ function gg_run_qwen3_0_6b {
     }
 
     check_ppl "f16"  "$(cat $OUT/${ci}-tg-f16.log  | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    if [ -z ${GG_BUILD_NO_BF16} ]; then
-        check_ppl "bf16" "$(cat $OUT/${ci}-tg-bf16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    fi
     check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
@@ -466,17 +563,147 @@ function gg_run_qwen3_0_6b {
     set +e
 }
 
-function gg_sum_qwen3_0_6b {
+function gg_sum_pythia_1_4b {
     gg_printf '### %s\n\n' "${ci}"
 
-    gg_printf 'Qwen3 0.6B:\n'
+    gg_printf 'Pythia 1.4B:\n'
     gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
     gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
     gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)"
-    gg_printf '- f16:\n```\n%s\n```\n'  "$(cat $OUT/${ci}-tg-f16.log)"
-    if [ -z ${GG_BUILD_NO_BF16} ]; then
-        gg_printf '- bf16:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-bf16.log)"
-    fi
+    gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
+    gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
+    gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
+    gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)"
+    gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)"
+    gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)"
+    gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)"
+    gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)"
+    gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
+    gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
+    gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
+    gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)"
+}
+
+# pythia_2_8b
+
+function gg_run_pythia_2_8b {
+    cd ${SRC}
+
+    gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/config.json
+    gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/tokenizer.json
+    gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/tokenizer_config.json
+    gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/special_tokens_map.json
+    gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/resolve/main/pytorch_model.bin
+
+    gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
+    unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
+
+    path_models="../models-mnt/pythia/2.8B"
+    path_wiki="../models-mnt/wikitext/wikitext-2-raw"
+
+    rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
+
+    set -e
+
+    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j$(nproc)                                    ) 2>&1 | tee -a $OUT/${ci}-make.log
+
+    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
+
+    model_f16="${path_models}/ggml-model-f16.gguf"
+    model_q8_0="${path_models}/ggml-model-q8_0.gguf"
+    model_q4_0="${path_models}/ggml-model-q4_0.gguf"
+    model_q4_1="${path_models}/ggml-model-q4_1.gguf"
+    model_q5_0="${path_models}/ggml-model-q5_0.gguf"
+    model_q5_1="${path_models}/ggml-model-q5_1.gguf"
+    model_q2_k="${path_models}/ggml-model-q2_k.gguf"
+    model_q3_k="${path_models}/ggml-model-q3_k.gguf"
+    model_q4_k="${path_models}/ggml-model-q4_k.gguf"
+    model_q5_k="${path_models}/ggml-model-q5_k.gguf"
+    model_q6_k="${path_models}/ggml-model-q6_k.gguf"
+
+    wiki_test="${path_wiki}/wiki.test.raw"
+
+    ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
+    ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0
+    ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1
+    ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0
+    ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1
+    ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k
+    ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k
+    ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k
+    ./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-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
+    (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+
+    (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
+
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa on  ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa on  ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+
+    function check_ppl {
+        qnt="$1"
+        ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
+
+        if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then
+            printf '  - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl"
+            return 20
+        fi
+
+        printf '  - %s @ %s OK\n' "$qnt" "$ppl"
+        return 0
+    }
+
+    check_ppl "f16"  "$(cat $OUT/${ci}-tg-f16.log  | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+   #check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log # note: ppl > 20.0 for this quant and model
+    check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+
+    cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log
+
+    set +e
+}
+
+function gg_sum_pythia_2_8b {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Pythia 2.8B:\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
+    gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)"
+    gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
     gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
     gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
     gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)"
@@ -523,8 +750,8 @@ function gg_run_embd_bge_small {
 
     ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
 
-    (time ./bin/llama-embedding --model ${model_f16}  -p "I believe the meaning of life is" -ngl 99 -c 0 --no-op-offload) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/llama-embedding --model ${model_q8_0} -p "I believe the meaning of life is" -ngl 99 -c 0 --no-op-offload) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/llama-embedding --model ${model_f16}  -p "I believe the meaning of life is" -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/llama-embedding --model ${model_q8_0} -p "I believe the meaning of life is" -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
 
     set +e
 }
@@ -548,7 +775,12 @@ function gg_run_rerank_tiny {
     gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/tokenizer_config.json
     gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/special_tokens_map.json
     gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/resolve/main/pytorch_model.bin
-    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/vocab.json
+    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/sentence_bert_config.json
+    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/vocab.txt
+    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/modules.json
+    gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/config.json
+
+    gg_wget models-mnt/rerank-tiny/1_Pooling https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/1_Pooling/config.json
 
     path_models="../models-mnt/rerank-tiny"
 
@@ -564,7 +796,7 @@ function gg_run_rerank_tiny {
     model_f16="${path_models}/ggml-model-f16.gguf"
 
     # for this model, the SEP token is "</s>"
-    (time ./bin/llama-embedding --model ${model_f16} -p "what is panda?\thi\nwhat is panda?\tit's a bear\nwhat is panda?\tThe giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." -ngl 99 -c 0 --pooling rank --embd-normalize -1 --no-op-offload --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log
+    (time ./bin/llama-embedding --model ${model_f16} -p "what is panda?\thi\nwhat is panda?\tit's a bear\nwhat is panda?\tThe giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." -ngl 99 -c 0 --pooling rank --embd-normalize -1 --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log
 
     # sample output
     # rerank score 0:    0.029
@@ -638,7 +870,6 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then
 fi
 
 ret=0
-
 test $ret -eq 0 && gg_run ctest_debug
 test $ret -eq 0 && gg_run ctest_release
 
@@ -647,15 +878,20 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then
     test $ret -eq 0 && gg_run rerank_tiny
 
     if [ -z ${GG_BUILD_CLOUD} ] || [ ${GG_BUILD_EXTRA_TESTS_0} ]; then
-        test $ret -eq 0 && gg_run test_scripts
+        test $ret -eq 0 && gg_run test_scripts_debug
+        test $ret -eq 0 && gg_run test_scripts_release
     fi
 
-    test $ret -eq 0 && gg_run qwen3_0_6b
-
-    test $ret -eq 0 && gg_run ctest_with_model_debug
-    test $ret -eq 0 && gg_run ctest_with_model_release
+    if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then
+        if [ -z ${GG_BUILD_CUDA} ] && [ -z ${GG_BUILD_VULKAN} ]; then
+            test $ret -eq 0 && gg_run pythia_1_4b
+        else
+            test $ret -eq 0 && gg_run pythia_2_8b
+            #test $ret -eq 0 && gg_run open_llama_7b_v2
+        fi
+        test $ret -eq 0 && gg_run ctest_with_model_debug
+        test $ret -eq 0 && gg_run ctest_with_model_release
+    fi
 fi
 
-cat $OUT/README.md
-
 exit $ret

cmake/build-info.cmake

@@ -39,10 +39,26 @@ if(Git_FOUND)
     endif()
 endif()
 
-set(BUILD_COMPILER "${CMAKE_C_COMPILER_ID} ${CMAKE_C_COMPILER_VERSION}")
-
-if(CMAKE_VS_PLATFORM_NAME)
-    set(BUILD_TARGET ${CMAKE_VS_PLATFORM_NAME})
+if(MSVC)
+    set(BUILD_COMPILER "${CMAKE_C_COMPILER_ID} ${CMAKE_C_COMPILER_VERSION}")
+    if (CMAKE_VS_PLATFORM_NAME)
+        set(BUILD_TARGET ${CMAKE_VS_PLATFORM_NAME})
+    else()
+        set(BUILD_TARGET "${CMAKE_SYSTEM_NAME} ${CMAKE_SYSTEM_PROCESSOR}")
+    endif()
 else()
-    set(BUILD_TARGET "${CMAKE_SYSTEM_NAME} ${CMAKE_SYSTEM_PROCESSOR}")
+    execute_process(
+        COMMAND ${CMAKE_C_COMPILER} --version
+        OUTPUT_VARIABLE OUT
+        OUTPUT_STRIP_TRAILING_WHITESPACE
+    )
+    string(REGEX REPLACE " *\n.*" "" OUT "${OUT}")
+    set(BUILD_COMPILER ${OUT})
+
+    execute_process(
+        COMMAND ${CMAKE_C_COMPILER} -dumpmachine
+        OUTPUT_VARIABLE OUT
+        OUTPUT_STRIP_TRAILING_WHITESPACE
+    )
+    set(BUILD_TARGET ${OUT})
 endif()

cmake/riscv64-spacemit-linux-gnu-gcc.cmake

@@ -1,29 +0,0 @@
-set(CMAKE_SYSTEM_NAME Linux)
-set(CMAKE_SYSTEM_PROCESSOR riscv64)
-set(CMAKE_SYSTEM_VERSION 1)
-
-if (CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "^(riscv)")
-    message(STATUS "HOST SYSTEM ${CMAKE_HOST_SYSTEM_PROCESSOR}")
-else()
-    set(GNU_MACHINE riscv64-unknown-linux-gnu CACHE STRING "GNU compiler triple")
-    if (DEFINED ENV{RISCV_ROOT_PATH})
-        file(TO_CMAKE_PATH $ENV{RISCV_ROOT_PATH} RISCV_ROOT_PATH)
-    else()
-        message(FATAL_ERROR "RISCV_ROOT_PATH env must be defined")
-    endif()
-
-    set(RISCV_ROOT_PATH ${RISCV_ROOT_PATH} CACHE STRING "root path to riscv toolchain")
-    set(CMAKE_C_COMPILER ${RISCV_ROOT_PATH}/bin/riscv64-unknown-linux-gnu-gcc)
-    set(CMAKE_CXX_COMPILER ${RISCV_ROOT_PATH}/bin/riscv64-unknown-linux-gnu-g++)
-    set(CMAKE_STRIP ${RISCV_ROOT_PATH}/bin/riscv64-unknown-linux-gnu-strip)
-    set(CMAKE_FIND_ROOT_PATH "${RISCV_ROOT_PATH}/riscv64-unknown-linux-gnu")
-    set(CMAKE_SYSROOT "${RISCV_ROOT_PATH}/sysroot")
-endif()
-
-set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER)
-set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY)
-set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY)
-set(CMAKE_FIND_ROOT_PATH_MODE_PACKAGE ONLY)
-set(CMAKE_C_FLAGS "-march=rv64gcv_zfh_zba_zicbop -mabi=lp64d ${CMAKE_C_FLAGS}")
-set(CMAKE_CXX_FLAGS "-march=rv64gcv_zfh_zba_zicbop -mabi=lp64d ${CXX_FLAGS}")
-set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -latomic")

common/CMakeLists.txt

@@ -50,19 +50,12 @@ add_library(${TARGET} STATIC
     base64.hpp
     chat-parser.cpp
     chat-parser.h
-    chat-parser-xml-toolcall.h
-    chat-parser-xml-toolcall.cpp
-    chat-peg-parser.cpp
-    chat-peg-parser.h
     chat.cpp
     chat.h
     common.cpp
     common.h
     console.cpp
     console.h
-    download.cpp
-    download.h
-    http.h
     json-partial.cpp
     json-partial.h
     json-schema-to-grammar.cpp
@@ -71,27 +64,22 @@ add_library(${TARGET} STATIC
     log.h
     ngram-cache.cpp
     ngram-cache.h
-    peg-parser.cpp
-    peg-parser.h
     regex-partial.cpp
     regex-partial.h
     sampling.cpp
     sampling.h
     speculative.cpp
     speculative.h
-    unicode.cpp
-    unicode.h
     )
 
 if (BUILD_SHARED_LIBS)
     set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
 endif()
 
-# TODO: use list(APPEND LLAMA_COMMON_EXTRA_LIBS ...)
 set(LLAMA_COMMON_EXTRA_LIBS build_info)
 
+# Use curl to download model url
 if (LLAMA_CURL)
-    # Use curl to download model url
     find_package(CURL)
     if (NOT CURL_FOUND)
         message(FATAL_ERROR "Could NOT find CURL. Hint: to disable this feature, set -DLLAMA_CURL=OFF")
@@ -99,11 +87,7 @@ if (LLAMA_CURL)
     target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_CURL)
     include_directories(${CURL_INCLUDE_DIRS})
     set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARIES})
-elseif (LLAMA_HTTPLIB)
-    # otherwise, use cpp-httplib
-    target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_HTTPLIB)
-    set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} cpp-httplib)
-endif()
+endif ()
 
 if (LLAMA_LLGUIDANCE)
     include(ExternalProject)

common/arg.h

@@ -59,8 +59,8 @@ struct common_arg {
     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) const;
-    bool has_value_from_env() const;
+    bool get_value_from_env(std::string & output);
+    bool has_value_from_env();
     std::string to_string();
 };
 
@@ -78,6 +78,7 @@ bool common_params_parse(int argc, char ** argv, common_params & params, llama_e
 
 // function to be used by test-arg-parser
 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
+bool common_has_curl();
 
 struct common_remote_params {
     std::vector<std::string> headers;

common/chat-parser-xml-toolcall.cpp

@@ -1,879 +0,0 @@
-#include "chat.h"
-#include "chat-parser.h"
-#include "common.h"
-#include "json-partial.h"
-#include "json-schema-to-grammar.h"
-#include "log.h"
-#include "regex-partial.h"
-
-using json = nlohmann::ordered_json;
-
-class xml_toolcall_syntax_exception : public std::runtime_error {
-  public:
-    xml_toolcall_syntax_exception(const std::string & message) : std::runtime_error(message) {}
-};
-
-template<typename T>
-inline void sort_uniq(std::vector<T> &vec) {
-    std::sort(vec.begin(), vec.end());
-    vec.erase(std::unique(vec.begin(), vec.end()), vec.end());
-}
-
-template<typename T>
-inline bool all_space(const T &str) {
-    return std::all_of(str.begin(), str.end(), [](unsigned char ch) { return std::isspace(ch); });
-}
-
-static size_t utf8_truncate_safe(const std::string_view s) {
-    size_t len = s.size();
-    if (len == 0) return 0;
-    size_t i = len;
-    for (size_t back = 0; back < 4 && i > 0; ++back) {
-        --i;
-        unsigned char c = s[i];
-        if ((c & 0x80) == 0) {
-            return len;
-        } else if ((c & 0xC0) == 0xC0) {
-            size_t expected_len = 0;
-            if ((c & 0xE0) == 0xC0) expected_len = 2;
-            else if ((c & 0xF0) == 0xE0) expected_len = 3;
-            else if ((c & 0xF8) == 0xF0) expected_len = 4;
-            else return i;
-            if (len - i >= expected_len) {
-                return len;
-            } else {
-                return i;
-            }
-        }
-    }
-    return len - std::min(len, size_t(3));
-}
-
-inline void utf8_truncate_safe_resize(std::string &s) {
-    s.resize(utf8_truncate_safe(s));
-}
-
-inline std::string_view utf8_truncate_safe_view(const std::string_view s) {
-    return s.substr(0, utf8_truncate_safe(s));
-}
-
-static std::optional<common_chat_msg_parser::find_regex_result> try_find_2_literal_splited_by_spaces(common_chat_msg_parser & builder, const std::string & literal1, const std::string & literal2) {
-    if (literal1.size() == 0) return builder.try_find_literal(literal2);
-    const auto saved_pos = builder.pos();
-    while (auto res = builder.try_find_literal(literal1)) {
-        builder.consume_spaces();
-        const auto match_len = std::min(literal2.size(), builder.input().size() - builder.pos());
-        if (builder.input().compare(builder.pos(), match_len, literal2, 0, match_len) == 0) {
-            if (res->prelude.size() != res->groups[0].begin - saved_pos) {
-                res->prelude = builder.str({saved_pos, res->groups[0].begin});
-            }
-            builder.move_to(builder.pos() + match_len);
-            res->groups[0].end = builder.pos();
-            GGML_ASSERT(res->groups[0].begin != res->groups[0].end);
-            return res;
-        }
-        builder.move_to(res->groups[0].begin + 1);
-    }
-    builder.move_to(saved_pos);
-    return std::nullopt;
-}
-
-/**
- * make a GBNF that accept any strings except those containing any of the forbidden strings.
- */
-std::string make_gbnf_excluding(std::vector<std::string> forbids) {
-    constexpr auto charclass_escape = [](unsigned char c) -> std::string {
-        if (c == '\\' || c == ']' || c == '^' || c == '-') {
-            std::string s = "\\";
-            s.push_back((char)c);
-            return s;
-        }
-        if (isprint(c)) {
-            return std::string(1, (char)c);
-        }
-        char buf[16];
-        snprintf(buf, 15, "\\x%02X", c);
-        return std::string(buf);
-    };
-    constexpr auto build_expr = [charclass_escape](auto self, const std::vector<std::string>& forbids, int l, int r, int depth) -> std::string {
-        std::vector<std::pair<unsigned char, std::pair<int,int>>> children;
-        int i = l;
-        while (i < r) {
-            const std::string &s = forbids[i];
-            if ((int)s.size() == depth) {
-                ++i;
-                continue;
-            }
-            unsigned char c = (unsigned char)s[depth];
-            int j = i;
-            while (j < r && (int)forbids[j].size() > depth &&
-                   (unsigned char)forbids[j][depth] == c) {
-                ++j;
-            }
-            children.push_back({c, {i, j}});
-            i = j;
-        }
-        std::vector<std::string> alts;
-        if (!children.empty()) {
-            std::string cls;
-            for (auto &ch : children) cls += charclass_escape(ch.first);
-            alts.push_back(std::string("[^") + cls + "]");
-        }
-        for (auto &ch : children) {
-            std::string childExpr = self(self, forbids, ch.second.first, ch.second.second, depth+1);
-            if (!childExpr.empty()) {
-                std::string quoted_ch = "\"";
-                if (ch.first == '\\') quoted_ch += "\\\\";
-                else if (ch.first == '"') quoted_ch += "\\\"";
-                else if (isprint(ch.first)) quoted_ch.push_back(ch.first);
-                else {
-                    char buf[16];
-                    snprintf(buf, 15, "\\x%02X", ch.first);
-                    quoted_ch += buf;
-                }
-                quoted_ch += "\"";
-                std::string branch = quoted_ch + std::string(" ") + childExpr;
-                alts.push_back(branch);
-            }
-        }
-        if (alts.empty()) return "";
-        std::ostringstream oss;
-        oss << "( ";
-        for (size_t k = 0; k < alts.size(); ++k) {
-            if (k) oss << " | ";
-            oss << alts[k];
-        }
-        oss << " )";
-        return oss.str();
-    };
-    if (forbids.empty()) return "( . )*";
-    sort(forbids.begin(), forbids.end());
-    std::string expr = build_expr(build_expr, forbids, 0, forbids.size(), 0);
-    if (expr.empty()) {
-        std::string cls;
-        for (auto &s : forbids) if (!s.empty()) cls += charclass_escape((unsigned char)s[0]);
-        expr = std::string("( [^") + cls + "] )";
-    }
-    if (forbids.size() == 1)
-        return expr + "*";
-    else
-        return std::string("( ") + expr + " )*";
-}
-
-/**
- * Build grammar for xml-style tool call
- * form.scope_start and form.scope_end can be empty.
- * Requires data.format for model-specific hacks.
- */
-void build_grammar_xml_tool_call(common_chat_params & data, const json & tools, const struct xml_tool_call_format & form) {
-    GGML_ASSERT(!form.tool_start.empty());
-    GGML_ASSERT(!form.tool_sep.empty());
-    GGML_ASSERT(!form.key_start.empty());
-    GGML_ASSERT(!form.val_end.empty());
-    GGML_ASSERT(!form.tool_end.empty());
-
-    std::string key_val_sep = form.key_val_sep;
-    if (form.key_val_sep2) {
-        key_val_sep += "\n";
-        key_val_sep += *form.key_val_sep2;
-    }
-    GGML_ASSERT(!key_val_sep.empty());
-
-    if (tools.is_array() && !tools.empty()) {
-        data.grammar = build_grammar([&](const common_grammar_builder &builder) {
-            auto string_arg_val = form.last_val_end ?
-                    builder.add_rule("string-arg-val", make_gbnf_excluding({form.val_end, *form.last_val_end})) :
-                    builder.add_rule("string-arg-val", make_gbnf_excluding({form.val_end}));
-
-            std::vector<std::string> tool_rules;
-            for (const auto & tool : tools) {
-                if (!tool.contains("type") || tool.at("type") != "function" || !tool.contains("function")) {
-                    LOG_WRN("Skipping tool without function: %s", tool.dump(2).c_str());
-                    continue;
-                }
-                const auto & function = tool.at("function");
-                if (!function.contains("name") || !function.at("name").is_string()) {
-                    LOG_WRN("Skipping invalid function (invalid name): %s", function.dump(2).c_str());
-                    continue;
-                }
-                if (!function.contains("parameters") || !function.at("parameters").is_object()) {
-                    LOG_WRN("Skipping invalid function (invalid parameters): %s", function.dump(2).c_str());
-                    continue;
-                }
-                std::string name = function.at("name");
-                auto parameters = function.at("parameters");
-                builder.resolve_refs(parameters);
-
-                struct parameter_rule {
-                    std::string symbol_name;
-                    bool is_required;
-                };
-                std::vector<parameter_rule> arg_rules;
-                if (!parameters.contains("properties") || !parameters.at("properties").is_object()) {
-                    LOG_WRN("Skipping invalid function (invalid properties): %s", function.dump(2).c_str());
-                    continue;
-                } else {
-                    std::vector<std::string> requiredParameters;
-                    if (parameters.contains("required")) {
-                        try { parameters.at("required").get_to(requiredParameters); }
-                        catch (const std::runtime_error&) {
-                            LOG_WRN("Invalid function required parameters, ignoring: %s", function.at("required").dump(2).c_str());
-                        }
-                    }
-                    sort_uniq(requiredParameters);
-                    for (const auto & [key, value] : parameters.at("properties").items()) {
-                        std::string quoted_key = key;
-                        bool required = std::binary_search(requiredParameters.begin(), requiredParameters.end(), key);
-                        if (form.key_start.back() == '"' && key_val_sep[0] == '"') {
-                            quoted_key = gbnf_format_literal(key);
-                            quoted_key = quoted_key.substr(1, quoted_key.size() - 2);
-                        }
-                        arg_rules.push_back(parameter_rule {builder.add_rule("func-" + name + "-kv-" + key,
-                            gbnf_format_literal(form.key_start) + " " +
-                            gbnf_format_literal(quoted_key) + " " +
-                            gbnf_format_literal(key_val_sep) + " " +
-                            ((value.contains("type") && value["type"].is_string() && value["type"] == "string" && (!form.raw_argval || *form.raw_argval)) ?
-                                    (form.raw_argval ?
-                                            string_arg_val :
-                                            "( " + string_arg_val + " | " + builder.add_schema(name + "-arg-" + key, value) + " )"
-                                    ) :
-                                    builder.add_schema(name + "-arg-" + key, value)
-                            )
-                        ), required});
-                    }
-                }
-
-                auto next_arg_with_sep = builder.add_rule(name + "-last-arg-end", form.last_val_end ? gbnf_format_literal(*form.last_val_end) : gbnf_format_literal(form.val_end));
-                decltype(next_arg_with_sep) next_arg = "\"\"";
-                for (auto i = arg_rules.size() - 1; /* i >= 0 && */ i < arg_rules.size(); --i) {
-                    std::string include_this_arg = arg_rules[i].symbol_name + " " + next_arg_with_sep;
-                    next_arg = builder.add_rule(name + "-arg-after-" + std::to_string(i), arg_rules[i].is_required ?
-                            include_this_arg : "( " + include_this_arg + " ) | " + next_arg
-                    );
-                    include_this_arg = gbnf_format_literal(form.val_end) + " " + include_this_arg;
-                    next_arg_with_sep = builder.add_rule(name + "-arg-after-" + std::to_string(i) + "-with-sep", arg_rules[i].is_required ?
-                            include_this_arg : "( " + include_this_arg + " ) | " + next_arg_with_sep
-                    );
-                }
-
-                std::string quoted_name = name;
-                if (form.tool_start.back() == '"' && form.tool_sep[0] == '"') {
-                    quoted_name = gbnf_format_literal(name);
-                    quoted_name = quoted_name.substr(1, quoted_name.size() - 2);
-                }
-                quoted_name = gbnf_format_literal(quoted_name);
-                // Kimi-K2 uses functions.{{ tool_call['function']['name'] }}:{{ loop.index }} as function name
-                if (data.format == COMMON_CHAT_FORMAT_KIMI_K2) {
-                    quoted_name = "\"functions.\" " + quoted_name + " \":\" [0-9]+";
-                }
-                tool_rules.push_back(builder.add_rule(name + "-call",
-                        gbnf_format_literal(form.tool_start) + " " +
-                        quoted_name + " " +
-                        gbnf_format_literal(form.tool_sep) + " " +
-                        next_arg
-                ));
-            }
-
-            auto tool_call_once = builder.add_rule("root-tool-call-once", string_join(tool_rules, " | "));
-            auto tool_call_more = builder.add_rule("root-tool-call-more", gbnf_format_literal(form.tool_end) + " " + tool_call_once);
-            auto call_end = builder.add_rule("root-call-end", form.last_tool_end ? gbnf_format_literal(*form.last_tool_end) : gbnf_format_literal(form.tool_end));
-            auto tool_call_multiple_with_end = builder.add_rule("root-tool-call-multiple-with-end", tool_call_once + " " + tool_call_more + "* " + call_end);
-            builder.add_rule("root",
-                (form.scope_start.empty() ? "" : gbnf_format_literal(form.scope_start) + " ") +
-                tool_call_multiple_with_end  + "?" +
-                (form.scope_end.empty() ? "" : " " + gbnf_format_literal(form.scope_end))
-            );
-        });
-
-        // grammar trigger for tool call
-        data.grammar_triggers.push_back({ COMMON_GRAMMAR_TRIGGER_TYPE_WORD, form.scope_start + form.tool_start });
-    }
-}
-
-/**
- * Parse XML-Style tool call for given xml_tool_call_format. Return false for invalid syntax and get the position untouched.
- * Throws xml_toolcall_syntax_exception if there is invalid syntax and cannot recover the original status for common_chat_msg_parser.
- * form.scope_start, form.tool_sep and form.scope_end can be empty.
- */
-inline bool parse_xml_tool_calls(common_chat_msg_parser & builder, const struct xml_tool_call_format & form) {
-    GGML_ASSERT(!form.tool_start.empty());
-    GGML_ASSERT(!form.key_start.empty());
-    GGML_ASSERT(!form.key_val_sep.empty());
-    GGML_ASSERT(!form.val_end.empty());
-    GGML_ASSERT(!form.tool_end.empty());
-
-    // Helper to choose return false or throw error
-    constexpr auto return_error = [](common_chat_msg_parser & builder, auto &start_pos, const bool &recovery) {
-        LOG_DBG("Failed to parse XML-Style tool call at position: %s\n", gbnf_format_literal(builder.consume_rest().substr(0, 20)).c_str());
-        if (recovery) {
-            builder.move_to(start_pos);
-            return false;
-        } else throw xml_toolcall_syntax_exception("Tool call parsing failed with unrecoverable errors. Try using a grammar to constrain the model’s output.");
-    };
-    // Drop substring from needle to end from a JSON
-    constexpr auto partial_json = [](std::string &json_str, std::string_view needle = "XML_TOOL_CALL_PARTIAL_FLAG") {
-        auto pos = json_str.rfind(needle);
-        if (pos == std::string::npos) {
-            return false;
-        }
-        for (auto i = pos + needle.size(); i < json_str.size(); ++i) {
-            unsigned char ch = static_cast<unsigned char>(json_str[i]);
-            if (ch != '\'' && ch != '"' && ch != '}' && ch != ':' && !std::isspace(ch)) {
-                return false;
-            }
-        }
-        if (pos != 0 && json_str[pos - 1] == '"') {
-            --pos;
-        }
-        json_str.resize(pos);
-        return true;
-    };
-    // Helper to generate a partial argument JSON
-    constexpr auto gen_partial_json = [partial_json](auto set_partial_arg, auto &arguments, auto &builder, auto &function_name) {
-        auto rest = builder.consume_rest();
-        utf8_truncate_safe_resize(rest);
-        set_partial_arg(rest, "XML_TOOL_CALL_PARTIAL_FLAG");
-        auto tool_str = arguments.dump();
-        if (partial_json(tool_str)) {
-            if (builder.add_tool_call(function_name, "", tool_str)) {
-                return;
-            }
-        }
-        LOG_DBG("Failed to parse partial XML-Style tool call, fallback to non-partial: %s\n", tool_str.c_str());
-    };
-    // Helper to find a close (because there may be form.last_val_end or form.last_tool_end)
-    constexpr auto try_find_close = [](
-            common_chat_msg_parser & builder,
-            const std::string & end,
-            const std::optional<std::string> & alt_end,
-            const std::string & end_next,
-            const std::optional<std::string> & alt_end_next
-    ) {
-        auto saved_pos = builder.pos();
-        auto tc = builder.try_find_literal(end);
-        auto val_end_size = end.size();
-        if (alt_end) {
-            auto pos_1 = builder.pos();
-            builder.move_to(saved_pos);
-            auto tc2 = try_find_2_literal_splited_by_spaces(builder, *alt_end, end_next);
-            if (alt_end_next) {
-                builder.move_to(saved_pos);
-                auto tc3 = try_find_2_literal_splited_by_spaces(builder, *alt_end, *alt_end_next);
-                if (tc3 && (!tc2 || tc2->prelude.size() > tc3->prelude.size())) {
-                    tc2 = tc3;
-                }
-            }
-            if (tc2 && (!tc || tc->prelude.size() > tc2->prelude.size())) {
-                tc = tc2;
-                tc->groups[0].end = std::min(builder.input().size(), tc->groups[0].begin + alt_end->size());
-                builder.move_to(tc->groups[0].end);
-                val_end_size = alt_end->size();
-            } else {
-                builder.move_to(pos_1);
-            }
-        }
-        return std::make_pair(val_end_size, tc);
-    };
-    // Helper to find a val_end or last_val_end, returns matched pattern size
-    const auto try_find_val_end = [try_find_close, &builder, &form]() {
-        return try_find_close(builder, form.val_end, form.last_val_end, form.tool_end, form.last_tool_end);
-    };
-    // Helper to find a tool_end or last_tool_end, returns matched pattern size
-    const auto try_find_tool_end = [try_find_close, &builder, &form]() {
-        return try_find_close(builder, form.tool_end, form.last_tool_end, form.scope_end, std::nullopt);
-    };
-
-    bool recovery = true;
-    const auto start_pos = builder.pos();
-    if (!all_space(form.scope_start)) {
-        if (auto tc = builder.try_find_literal(form.scope_start)) {
-            if (all_space(tc->prelude)) {
-                if (form.scope_start.size() != tc->groups[0].end - tc->groups[0].begin)
-                    throw common_chat_msg_partial_exception("Partial literal: " + gbnf_format_literal(form.scope_start));
-            } else {
-                builder.move_to(start_pos);
-                return false;
-            }
-        } else return false;
-    }
-    while (auto tc = builder.try_find_literal(form.tool_start)) {
-        if (!all_space(tc->prelude)) {
-            LOG_DBG("XML-Style tool call: Expected %s, but found %s, trying to match next pattern\n",
-                    gbnf_format_literal(form.tool_start).c_str(),
-                    gbnf_format_literal(tc->prelude).c_str()
-            );
-            builder.move_to(tc->groups[0].begin - tc->prelude.size());
-            break;
-        }
-
-        // Find tool name
-        auto func_name = builder.try_find_literal(all_space(form.tool_sep) ? form.key_start : form.tool_sep);
-        if (!func_name) {
-            auto [sz, tc] = try_find_tool_end();
-            func_name = tc;
-        }
-        if (!func_name) {
-            // Partial tool name not supported
-            throw common_chat_msg_partial_exception("incomplete tool_call");
-        }
-        // If the model generate multiple tool call and the first tool call has no argument
-        if (func_name->prelude.find(form.tool_end) != std::string::npos || (form.last_tool_end ? func_name->prelude.find(*form.last_tool_end) != std::string::npos : false)) {
-            builder.move_to(func_name->groups[0].begin - func_name->prelude.size());
-            auto [sz, tc] = try_find_tool_end();
-            func_name = tc;
-        }
-
-        // Parse tool name
-        builder.move_to(all_space(form.tool_sep) ? func_name->groups[0].begin : func_name->groups[0].end);
-        std::string function_name = string_strip(func_name->prelude);
-        // Kimi-K2 uses functions.{{ tool_call['function']['name'] }}:{{ loop.index }} as function name
-        if (builder.syntax().format == COMMON_CHAT_FORMAT_KIMI_K2) {
-            if (string_starts_with(function_name, "functions.")) {
-                static const std::regex re(":\\d+$");
-                if (std::regex_search(function_name, re)) {
-                    function_name = function_name.substr(10, function_name.rfind(":") - 10);
-                }
-            }
-        }
-
-        // Argument JSON
-        json arguments = json::object();
-
-        // Helper to generate a partial argument JSON
-        const auto gen_partial_args = [&](auto set_partial_arg) {
-            gen_partial_json(set_partial_arg, arguments, builder, function_name);
-        };
-
-        // Parse all arg_key/arg_value pairs
-        while (auto tc = builder.try_find_literal(form.key_start)) {
-            if (!all_space(tc->prelude)) {
-                LOG_DBG("XML-Style tool call: Expected %s, but found %s, trying to match next pattern\n",
-                        gbnf_format_literal(form.key_start).c_str(),
-                        gbnf_format_literal(tc->prelude).c_str()
-                );
-                builder.move_to(tc->groups[0].begin - tc->prelude.size());
-                break;
-            }
-            if (tc->groups[0].end - tc->groups[0].begin != form.key_start.size()) {
-                auto tool_call_arg = arguments.dump();
-                if (tool_call_arg.size() != 0 && tool_call_arg[tool_call_arg.size() - 1] == '}') {
-                    tool_call_arg.resize(tool_call_arg.size() - 1);
-                }
-                builder.add_tool_call(function_name, "", tool_call_arg);
-                throw common_chat_msg_partial_exception("Partial literal: " + gbnf_format_literal(form.key_start));
-            }
-
-            // Parse arg_key
-            auto key_res = builder.try_find_literal(form.key_val_sep);
-            if (!key_res) {
-                gen_partial_args([&](auto &rest, auto &needle) {arguments[rest + needle] = "";});
-                throw common_chat_msg_partial_exception("Expected " + gbnf_format_literal(form.key_val_sep) + " after " + gbnf_format_literal(form.key_start));
-            }
-            if (key_res->groups[0].end - key_res->groups[0].begin != form.key_val_sep.size()) {
-                gen_partial_args([&](auto &, auto &needle) {arguments[key_res->prelude + needle] = "";});
-                throw common_chat_msg_partial_exception("Partial literal: " + gbnf_format_literal(form.key_val_sep));
-            }
-            auto &key = key_res->prelude;
-            recovery = false;
-
-            // Parse arg_value
-            if (form.key_val_sep2) {
-                if (auto tc = builder.try_find_literal(*form.key_val_sep2)) {
-                    if (!all_space(tc->prelude)) {
-                        LOG_DBG("Failed to parse XML-Style tool call: Unexcepted %s between %s and %s\n",
-                                gbnf_format_literal(tc->prelude).c_str(),
-                                gbnf_format_literal(form.key_val_sep).c_str(),
-                                gbnf_format_literal(*form.key_val_sep2).c_str()
-                        );
-                        return return_error(builder, start_pos, false);
-                    }
-                    if (tc->groups[0].end - tc->groups[0].begin != form.key_val_sep2->size()) {
-                        gen_partial_args([&](auto &, auto &needle) {arguments[key] = needle;});
-                        throw common_chat_msg_partial_exception("Partial literal: " + gbnf_format_literal(*form.key_val_sep2));
-                    }
-                } else {
-                    gen_partial_args([&](auto &, auto &needle) {arguments[key] = needle;});
-                    throw common_chat_msg_partial_exception("Expected " + gbnf_format_literal(*form.key_val_sep2) + " after " + gbnf_format_literal(form.key_val_sep));
-                }
-            }
-            auto val_start = builder.pos();
-
-            // Test if arg_val is a partial JSON
-            std::optional<common_json> value_json = std::nullopt;
-            if (!form.raw_argval || !*form.raw_argval) {
-                try { value_json = builder.try_consume_json(); }
-                catch (const std::runtime_error&) { builder.move_to(val_start); }
-                // TODO: Delete this when json_partial adds top-level support for null/true/false
-                if (builder.pos() == val_start) {
-                    const static std::regex number_regex(R"([0-9-][0-9]*(\.\d*)?([eE][+-]?\d*)?)");
-                    builder.consume_spaces();
-                    std::string_view sv = utf8_truncate_safe_view(builder.input());
-                    sv.remove_prefix(builder.pos());
-                    std::string rest = "a";
-                    if (sv.size() < 6) rest = sv;
-                    if (string_starts_with("null", rest) || string_starts_with("true", rest) || string_starts_with("false", rest) || std::regex_match(sv.begin(), sv.end(), number_regex)) {
-                        value_json = {123, {"123", "123"}};
-                        builder.consume_rest();
-                    } else {
-                        builder.move_to(val_start);
-                    }
-                }
-            }
-
-            // If it is a JSON and followed by </arg_value>, parse as json
-            // cannot support streaming because it may be a plain text starting with JSON
-            if (value_json) {
-                auto json_end = builder.pos();
-                builder.consume_spaces();
-                if (builder.pos() == builder.input().size()) {
-                    if (form.raw_argval && !*form.raw_argval && (value_json->json.is_string() || value_json->json.is_object() || value_json->json.is_array())) {
-                        arguments[key] = value_json->json;
-                        auto json_str = arguments.dump();
-                        if (!value_json->healing_marker.json_dump_marker.empty()) {
-                            GGML_ASSERT(std::string::npos != json_str.rfind(value_json->healing_marker.json_dump_marker));
-                            json_str.resize(json_str.rfind(value_json->healing_marker.json_dump_marker));
-                        } else {
-                            GGML_ASSERT(json_str.back() == '}');
-                            json_str.resize(json_str.size() - 1);
-                        }
-                        builder.add_tool_call(function_name, "", json_str);
-                    } else {
-                        gen_partial_args([&](auto &, auto &needle) {arguments[key] = needle;});
-                    }
-                    LOG_DBG("Possible JSON arg_value: %s\n", value_json->json.dump().c_str());
-                    throw common_chat_msg_partial_exception("JSON arg_value detected. Waiting for more tokens for validations.");
-                }
-                builder.move_to(json_end);
-                auto [val_end_size, tc] = try_find_val_end();
-                if (tc && all_space(tc->prelude) && value_json->healing_marker.marker.empty()) {
-                    if (tc->groups[0].end - tc->groups[0].begin != val_end_size) {
-                        gen_partial_args([&](auto &, auto &needle) {arguments[key] = needle;});
-                        LOG_DBG("Possible terminated JSON arg_value: %s\n", value_json->json.dump().c_str());
-                        throw common_chat_msg_partial_exception("Partial literal: " + gbnf_format_literal(form.val_end) + (form.last_val_end ? gbnf_format_literal(*form.last_val_end) : ""));
-                    } else arguments[key] = value_json->json;
-                } else builder.move_to(val_start);
-            }
-
-            // If not, parse as plain text
-            if (val_start == builder.pos()) {
-                if (auto [val_end_size, value_plain] = try_find_val_end(); value_plain) {
-                    auto &value_str = value_plain->prelude;
-                    if (form.trim_raw_argval) value_str = string_strip(value_str);
-                    if (value_plain->groups[0].end - value_plain->groups[0].begin != val_end_size) {
-                        gen_partial_args([&](auto &, auto &needle) {arguments[key] = value_str + needle;});
-                        throw common_chat_msg_partial_exception(
-                                "Expected " + gbnf_format_literal(form.val_end) +
-                                " after " + gbnf_format_literal(form.key_val_sep) +
-                                (form.key_val_sep2 ? " " + gbnf_format_literal(*form.key_val_sep2) : "")
-                        );
-                    }
-                    arguments[key] = value_str;
-                } else {
-                    if (form.trim_raw_argval) {
-                        gen_partial_args([&](auto &rest, auto &needle) {arguments[key] = string_strip(rest) + needle;});
-                    } else {
-                        gen_partial_args([&](auto &rest, auto &needle) {arguments[key] = rest + needle;});
-                    }
-                    throw common_chat_msg_partial_exception(
-                            "Expected " + gbnf_format_literal(form.val_end) +
-                            " after " + gbnf_format_literal(form.key_val_sep) +
-                            (form.key_val_sep2 ? " " + gbnf_format_literal(*form.key_val_sep2) : "")
-                    );
-                }
-            }
-        }
-
-        // Consume closing tag
-        if (auto [tool_end_size, tc] = try_find_tool_end(); tc) {
-            if (!all_space(tc->prelude)) {
-                LOG_DBG("Failed to parse XML-Style tool call: Expected %s, but found %s\n",
-                        gbnf_format_literal(form.tool_end).c_str(),
-                        gbnf_format_literal(tc->prelude).c_str()
-                );
-                return return_error(builder, start_pos, recovery);
-            }
-            if (tc->groups[0].end - tc->groups[0].begin == tool_end_size) {
-                // Add the parsed tool call
-                if (!builder.add_tool_call(function_name, "", arguments.dump())) {
-                    throw common_chat_msg_partial_exception("Failed to add XML-Style tool call");
-                }
-                recovery = false;
-                continue;
-            }
-        }
-
-        auto tool_call_arg = arguments.dump();
-        if (tool_call_arg.size() != 0 && tool_call_arg[tool_call_arg.size() - 1] == '}') {
-            tool_call_arg.resize(tool_call_arg.size() - 1);
-        }
-        builder.add_tool_call(function_name, "", tool_call_arg);
-        throw common_chat_msg_partial_exception("Expected " + gbnf_format_literal(form.tool_end) + " after " + gbnf_format_literal(form.val_end));
-    }
-    if (auto tc = builder.try_find_literal(form.scope_end)) {
-        if (!all_space(tc->prelude)) {
-            LOG_DBG("Failed to parse XML-Style tool call: Expected %s, but found %s\n",
-                    gbnf_format_literal(form.scope_end).c_str(),
-                    gbnf_format_literal(tc->prelude).c_str()
-            );
-            return return_error(builder, start_pos, recovery);
-        }
-    } else {
-        if (all_space(form.scope_end)) return true;
-        builder.consume_spaces();
-        if (builder.pos() == builder.input().size())
-            throw common_chat_msg_partial_exception("incomplete tool calls");
-        LOG_DBG("Failed to parse XML-Style tool call: Expected %s, but found %s\n",
-                gbnf_format_literal(form.scope_end).c_str(),
-                gbnf_format_literal(builder.consume_rest()).c_str()
-        );
-        return return_error(builder, start_pos, recovery);
-    }
-
-    return true;
-}
-
-/**
- * Parse XML-Style tool call for given xml_tool_call_format. Return false for invalid syntax and get the position untouched.
- * May cause std::runtime_error if there is invalid syntax because partial valid tool call is already sent out to client.
- * form.scope_start, form.tool_sep and form.scope_end can be empty.
- */
-bool common_chat_msg_parser::try_consume_xml_tool_calls(const struct xml_tool_call_format & form) {
-    auto pos = pos_;
-    auto tsize = result_.tool_calls.size();
-    try { return parse_xml_tool_calls(*this, form); }
-    catch (const xml_toolcall_syntax_exception&) {}
-    move_to(pos);
-    result_.tool_calls.resize(tsize);
-    return false;
-}
-
-/**
- * Parse content uses reasoning and XML-Style tool call
- * TODO: Note that form.allow_toolcall_in_think is not tested yet. If anyone confirms it works, this comment can be removed.
- */
-inline void parse_msg_with_xml_tool_calls(common_chat_msg_parser & builder, const struct xml_tool_call_format & form, const std::string & start_think = "<think>", const std::string & end_think = "</think>") {
-    constexpr auto rstrip = [](std::string &s) {
-        s.resize(std::distance(s.begin(), std::find_if(s.rbegin(), s.rend(), [](unsigned char ch) { return !std::isspace(ch); }).base()));
-    };
-    // Erase substring from l to r, along with additional spaces nearby
-    constexpr auto erase_spaces = [](auto &str, size_t l, size_t r) {
-        while (/* l > -1 && */ --l < str.size() && std::isspace(static_cast<unsigned char>(str[l])));
-        ++l;
-        while (++r < str.size() && std::isspace(static_cast<unsigned char>(str[r])));
-        if (l < r) str[l] = '\n';
-        if (l + 1 < r) str[l + 1] = '\n';
-        if (l != 0) l += 2;
-        str.erase(l, r - l);
-        return l;
-    };
-    constexpr auto trim_suffix = [](std::string &content, std::initializer_list<std::string_view> list) {
-        auto best_match = content.size();
-        for (auto pattern: list) {
-            if (pattern.size() == 0) continue;
-            for (auto match_idx = content.size() - std::min(pattern.size(), content.size()); content.size() > match_idx; match_idx++) {
-                auto match_len = content.size() - match_idx;
-                if (content.compare(match_idx, match_len, pattern.data(), match_len) == 0 && best_match > match_idx) {
-                    best_match = match_idx;
-                }
-            }
-        }
-        if (content.size() > best_match) {
-            content.erase(best_match);
-        }
-    };
-    const auto trim_potential_partial_word = [&start_think, &end_think, &form, trim_suffix](std::string &content) {
-        return trim_suffix(content, {
-            start_think, end_think, form.scope_start, form.tool_start, form.tool_sep, form.key_start,
-            form.key_val_sep, form.key_val_sep2 ? form.key_val_sep2->c_str() : "",
-            form.val_end, form.last_val_end ? form.last_val_end->c_str() : "",
-            form.tool_end, form.last_tool_end ? form.last_tool_end->c_str() : "",
-            form.scope_end
-        });
-    };
-
-
-    // Trim leading spaces without affecting keyword matching
-    static const common_regex spaces_regex("\\s*");
-    {
-        auto tc = builder.consume_regex(spaces_regex);
-        auto spaces = builder.str(tc.groups[0]);
-        auto s1 = spaces.size();
-        trim_potential_partial_word(spaces);
-        auto s2 = spaces.size();
-        builder.move_to(builder.pos() - (s1 - s2));
-    }
-
-    // Parse content
-    bool reasoning_unclosed = builder.syntax().thinking_forced_open;
-    std::string unclosed_reasoning_content("");
-    for (;;) {
-        auto tc = try_find_2_literal_splited_by_spaces(builder, form.scope_start, form.tool_start);
-        std::string content;
-        std::string tool_call_start;
-
-        if (tc) {
-            content = std::move(tc->prelude);
-            tool_call_start = builder.str(tc->groups[0]);
-            LOG_DBG("Matched tool start: %s\n", gbnf_format_literal(tool_call_start).c_str());
-        } else {
-            content = builder.consume_rest();
-            utf8_truncate_safe_resize(content);
-        }
-
-        // Handle unclosed think block
-        if (reasoning_unclosed) {
-            if (auto pos = content.find(end_think); pos == std::string::npos && builder.pos() != builder.input().size()) {
-                unclosed_reasoning_content += content;
-                if (!(form.allow_toolcall_in_think && tc)) {
-                    unclosed_reasoning_content += tool_call_start;
-                    continue;
-                }
-            } else {
-                reasoning_unclosed = false;
-                std::string reasoning_content;
-                if (pos == std::string::npos) {
-                    reasoning_content = std::move(content);
-                } else {
-                    reasoning_content = content.substr(0, pos);
-                    content.erase(0, pos + end_think.size());
-                }
-                if (builder.pos() == builder.input().size() && all_space(content)) {
-                    rstrip(reasoning_content);
-                    trim_potential_partial_word(reasoning_content);
-                    rstrip(reasoning_content);
-                    if (reasoning_content.empty()) {
-                        rstrip(unclosed_reasoning_content);
-                        trim_potential_partial_word(unclosed_reasoning_content);
-                        rstrip(unclosed_reasoning_content);
-                        if (unclosed_reasoning_content.empty()) continue;
-                    }
-                }
-                if (builder.syntax().reasoning_format == COMMON_REASONING_FORMAT_NONE || builder.syntax().reasoning_in_content) {
-                    builder.add_content(start_think);
-                    builder.add_content(unclosed_reasoning_content);
-                    builder.add_content(reasoning_content);
-                    if (builder.pos() != builder.input().size() || !all_space(content))
-                        builder.add_content(end_think);
-                } else {
-                    builder.add_reasoning_content(unclosed_reasoning_content);
-                    builder.add_reasoning_content(reasoning_content);
-                }
-                unclosed_reasoning_content.clear();
-            }
-        }
-
-        // Handle multiple think block
-        bool toolcall_in_think = false;
-        for (auto think_start = content.find(start_think); think_start != std::string::npos; think_start = content.find(start_think, think_start)) {
-            if (auto think_end = content.find(end_think, think_start + start_think.size()); think_end != std::string::npos) {
-                if (builder.syntax().reasoning_format != COMMON_REASONING_FORMAT_NONE && !builder.syntax().reasoning_in_content) {
-                    auto reasoning_content = content.substr(think_start + start_think.size(), think_end - think_start - start_think.size());
-                    builder.add_reasoning_content(reasoning_content);
-                    think_start = erase_spaces(content, think_start, think_end + end_think.size() - 1);
-                } else {
-                    think_start = think_end + end_think.size() - 1;
-                }
-            } else {
-                // This <tool_call> start is in thinking block, skip this tool call
-                // This <tool_call> start is in thinking block
-                if (form.allow_toolcall_in_think) {
-                    unclosed_reasoning_content = content.substr(think_start + start_think.size());
-                } else {
-                    unclosed_reasoning_content = content.substr(think_start + start_think.size()) + tool_call_start;
-                }
-                reasoning_unclosed = true;
-                content.resize(think_start);
-                toolcall_in_think = true;
-            }
-        }
-
-        if (builder.syntax().reasoning_format != COMMON_REASONING_FORMAT_NONE && !builder.syntax().reasoning_in_content) {
-            rstrip(content);
-            // Handle unclosed </think> token from content: delete all </think> token
-            if (auto pos = content.rfind(end_think); pos != std::string::npos) {
-                while (pos != std::string::npos) {
-                    pos = erase_spaces(content, pos, pos + end_think.size() - 1);
-                    pos = content.rfind(end_think, pos);
-                }
-            }
-            // Strip if needed
-            if (content.size() > 0 && std::isspace(static_cast<unsigned char>(content[0]))) {
-                content = string_strip(content);
-            }
-        }
-
-        // remove potential partial suffix
-        if (builder.pos() == builder.input().size()) {
-            if (unclosed_reasoning_content.empty()) {
-                rstrip(content);
-                trim_potential_partial_word(content);
-                rstrip(content);
-            } else {
-                rstrip(unclosed_reasoning_content);
-                trim_potential_partial_word(unclosed_reasoning_content);
-                rstrip(unclosed_reasoning_content);
-            }
-        }
-
-        // consume unclosed_reasoning_content if allow_toolcall_in_think is set
-        if (form.allow_toolcall_in_think && !unclosed_reasoning_content.empty()) {
-            if (builder.syntax().reasoning_format != COMMON_REASONING_FORMAT_NONE && !builder.syntax().reasoning_in_content) {
-                builder.add_reasoning_content(unclosed_reasoning_content);
-            } else {
-                if (content.empty()) {
-                    content = start_think + unclosed_reasoning_content;
-                } else {
-                    content += "\n\n" + start_think;
-                    content += unclosed_reasoning_content;
-                }
-            }
-            unclosed_reasoning_content.clear();
-        }
-
-        // Add content
-        if (!content.empty()) {
-            // If there are multiple content blocks
-            if (builder.syntax().reasoning_format != COMMON_REASONING_FORMAT_NONE && !builder.syntax().reasoning_in_content && builder.result().content.size() != 0) {
-                builder.add_content("\n\n");
-            }
-            builder.add_content(content);
-        }
-
-        // This <tool_call> start is in thinking block and toolcall_in_think not set, skip this tool call
-        if (toolcall_in_think && !form.allow_toolcall_in_think) {
-            continue;
-        }
-
-        // There is no tool call and all content is parsed
-        if (!tc) {
-            GGML_ASSERT(builder.pos() == builder.input().size());
-            GGML_ASSERT(unclosed_reasoning_content.empty());
-            if (!form.allow_toolcall_in_think) GGML_ASSERT(!reasoning_unclosed);
-            break;
-        }
-
-        builder.move_to(tc->groups[0].begin);
-        if (builder.try_consume_xml_tool_calls(form)) {
-            auto end_of_tool = builder.pos();
-            builder.consume_spaces();
-            if (builder.pos() != builder.input().size()) {
-                builder.move_to(end_of_tool);
-                if (!builder.result().content.empty()) {
-                    builder.add_content("\n\n");
-                }
-            }
-        } else {
-            static const common_regex next_char_regex(".");
-            auto c = builder.str(builder.consume_regex(next_char_regex).groups[0]);
-            rstrip(c);
-            builder.add_content(c);
-        }
-    }
-}
-
-/**
- * Parse content uses reasoning and XML-Style tool call
- */
-void common_chat_msg_parser::consume_reasoning_with_xml_tool_calls(const struct xml_tool_call_format & form, const std::string & start_think, const std::string & end_think) {
-    parse_msg_with_xml_tool_calls(*this, form, start_think, end_think);
-}

common/chat-parser-xml-toolcall.h

@@ -1,45 +0,0 @@
-#pragma once
-
-#include "chat.h"
-
-#include <nlohmann/json.hpp>
-
-#include <optional>
-#include <string>
-#include <vector>
-
-
-// Sample config:
-// MiniMax-M2 (left): <minimax:tool_call>\n<invoke name="tool-name">\n<parameter name="key">value</parameter>\n...</invoke>\n...</minimax:tool_call>
-// GLM 4.5   (right): <tool_call>function_name\n<arg_key>key</arg_key>\n<arg_value>value</arg_value>\n</tool_call>
-struct xml_tool_call_format {
-    std::string scope_start; // <minimax:tool_call>\n  // \n                      // can be empty
-    std::string tool_start;  // <invoke name=\"        // <tool_call>
-    std::string tool_sep;    // \">\n                  // \n                      // can be empty only for parse_xml_tool_calls
-    std::string key_start;   // <parameter name=\"     // <arg_key>
-    std::string key_val_sep; // \">                    // </arg_key>\n<arg_value>
-    std::string val_end;     // </parameter>\n         // </arg_value>\n
-    std::string tool_end;    // </invoke>\n            // </tool_call>\n
-    std::string scope_end;   // </minimax:tool_call>   //                         // can be empty
-    // Set this if there can be dynamic spaces inside key_val_sep.
-    // e.g. key_val_sep=</arg_key> key_val_sep2=<arg_value> for GLM4.5
-    std::optional<std::string> key_val_sep2 = std::nullopt;
-    // Set true if argval should only be raw string. e.g. Hello "world" hi
-    // Set false if argval should only be json string. e.g. "Hello \"world\" hi"
-    // Defaults to std::nullopt, both will be allowed.
-    std::optional<bool> raw_argval = std::nullopt;
-    std::optional<std::string> last_val_end = std::nullopt;
-    std::optional<std::string> last_tool_end = std::nullopt;
-    bool trim_raw_argval = false;
-    bool allow_toolcall_in_think = false;
-};
-
-// make a GBNF that accept any strings except those containing any of the forbidden strings.
-std::string make_gbnf_excluding(std::vector<std::string> forbids);
-
-/**
- * Build grammar for xml-style tool call
- * form.scope_start and form.scope_end can be empty.
- * Requires data.format for model-specific hacks.
- */
-void build_grammar_xml_tool_call(common_chat_params & data, const nlohmann::ordered_json & tools, const struct xml_tool_call_format & form);

common/chat-parser.h

@@ -1,7 +1,6 @@
 #pragma once
 
 #include "chat.h"
-#include "chat-parser-xml-toolcall.h"
 #include "json-partial.h"
 #include "regex-partial.h"
 
@@ -65,9 +64,6 @@ class common_chat_msg_parser {
     // Adds an array of tool calls using their "name", "id" and "arguments" fields.
     bool add_tool_calls(const nlohmann::ordered_json & arr);
 
-    // Adds a tool call using the short form: { "tool_name": { "arg1": val, "arg2": val } }
-    bool add_tool_call_short_form(const nlohmann::ordered_json & tool_call);
-
     void finish();
 
     bool consume_spaces();
@@ -120,14 +116,5 @@ class common_chat_msg_parser {
         const std::vector<std::vector<std::string>> & content_paths = {}
     );
 
-    /**
-     * Parse XML-Style tool call for given xml_tool_call_format. Return false for invalid syntax and get the position untouched.
-     * form.scope_start, form.tool_sep and form.scope_end can be empty.
-     */
-    bool try_consume_xml_tool_calls(const struct xml_tool_call_format & form);
-
-    // Parse content uses reasoning and XML-Style tool call
-    void consume_reasoning_with_xml_tool_calls(const struct xml_tool_call_format & form, const std::string & start_think = "<think>", const std::string & end_think = "</think>");
-
     void clear_tools();
 };

common/chat-peg-parser.cpp

@@ -1,114 +0,0 @@
-#include "chat-peg-parser.h"
-
-#include <nlohmann/json.hpp>
-
-using json = nlohmann::json;
-
-static std::string_view trim_trailing_space(std::string_view sv) {
-    while (!sv.empty() && std::isspace(static_cast<unsigned char>(sv.back()))) {
-        sv.remove_suffix(1);
-    }
-    return sv;
-}
-
-void common_chat_peg_mapper::from_ast(const common_peg_ast_arena & arena, const common_peg_parse_result & result) {
-    arena.visit(result, [this](const common_peg_ast_node & node) {
-        map(node);
-    });
-}
-
-void common_chat_peg_mapper::map(const common_peg_ast_node & node) {
-    bool is_reasoning = node.tag == common_chat_peg_builder::REASONING;
-    bool is_content = node.tag == common_chat_peg_builder::CONTENT;
-
-    if (is_reasoning) {
-        result.reasoning_content = std::string(trim_trailing_space(node.text));
-    }
-
-    if (is_content) {
-        result.content = std::string(trim_trailing_space(node.text));
-    }
-}
-
-void common_chat_peg_native_mapper::map(const common_peg_ast_node & node) {
-    common_chat_peg_mapper::map(node);
-
-    bool is_tool_open = node.tag == common_chat_peg_native_builder::TOOL_OPEN;
-    bool is_tool_name = node.tag == common_chat_peg_native_builder::TOOL_NAME;
-    bool is_tool_id = node.tag == common_chat_peg_native_builder::TOOL_ID;
-    bool is_tool_args = node.tag == common_chat_peg_native_builder::TOOL_ARGS;
-
-    if (is_tool_open) {
-        result.tool_calls.emplace_back();
-        current_tool = &result.tool_calls.back();
-    }
-
-    if (is_tool_id && current_tool) {
-        current_tool->id = std::string(trim_trailing_space(node.text));
-    }
-
-    if (is_tool_name && current_tool) {
-        current_tool->name = std::string(trim_trailing_space(node.text));
-    }
-
-    if (is_tool_args && current_tool) {
-        current_tool->arguments = std::string(trim_trailing_space(node.text));
-    }
-}
-
-void common_chat_peg_constructed_mapper::map(const common_peg_ast_node & node) {
-    common_chat_peg_mapper::map(node);
-
-    bool is_tool_open = node.tag == common_chat_peg_constructed_builder::TOOL_OPEN;
-    bool is_tool_name = node.tag == common_chat_peg_constructed_builder::TOOL_NAME;
-    bool is_tool_close = node.tag == common_chat_peg_constructed_builder::TOOL_CLOSE;
-    bool is_arg_open = node.tag == common_chat_peg_constructed_builder::TOOL_ARG_OPEN;
-    bool is_arg_close = node.tag == common_chat_peg_constructed_builder::TOOL_ARG_CLOSE;
-    bool is_arg_name = node.tag == common_chat_peg_constructed_builder::TOOL_ARG_NAME;
-    bool is_arg_string = node.tag == common_chat_peg_constructed_builder::TOOL_ARG_STRING_VALUE;
-    bool is_arg_json = node.tag == common_chat_peg_constructed_builder::TOOL_ARG_JSON_VALUE;
-
-    if (is_tool_open) {
-        result.tool_calls.emplace_back();
-        current_tool = &result.tool_calls.back();
-        arg_count = 0;
-    }
-
-    if (is_tool_name) {
-        current_tool->name = std::string(node.text);
-        current_tool->arguments = "{";
-    }
-
-    if (is_arg_open) {
-        needs_closing_quote = false;
-    }
-
-    if (is_arg_name && current_tool) {
-        if (arg_count > 0) {
-            current_tool->arguments += ",";
-        }
-        current_tool->arguments += json(trim_trailing_space(node.text)).dump() + ":";
-        ++arg_count;
-    }
-
-    if (is_arg_string && current_tool) {
-        // Serialize to JSON, but exclude the end quote
-        std::string dumped = json(node.text).dump();
-        current_tool->arguments += dumped.substr(0, dumped.size() - 1);
-        needs_closing_quote = true;
-    }
-
-    if (is_arg_close && current_tool) {
-        if (needs_closing_quote) {
-            current_tool->arguments += "\"";
-        }
-    }
-
-    if (is_arg_json && current_tool) {
-        current_tool->arguments += std::string(trim_trailing_space(node.text));
-    }
-
-    if (is_tool_close && current_tool) {
-        current_tool->arguments += "}";
-    }
-}

common/chat-peg-parser.h

@@ -1,105 +0,0 @@
-#pragma once
-
-#include "chat.h"
-#include "peg-parser.h"
-
-class common_chat_peg_builder : public common_peg_parser_builder {
-  public:
-    static constexpr const char * REASONING_BLOCK = "reasoning-block";
-    static constexpr const char * REASONING = "reasoning";
-    static constexpr const char * CONTENT = "content";
-
-    common_peg_parser reasoning_block(const common_peg_parser & p) { return tag(REASONING_BLOCK, p); }
-    common_peg_parser reasoning(const common_peg_parser & p) { return tag(REASONING, p); }
-    common_peg_parser content(const common_peg_parser & p) { return tag(CONTENT, p); }
-};
-
-inline common_peg_arena build_chat_peg_parser(const std::function<common_peg_parser(common_chat_peg_builder & builder)> & fn) {
-    common_chat_peg_builder builder;
-    builder.set_root(fn(builder));
-    return builder.build();
-}
-
-class common_chat_peg_mapper {
-  public:
-    common_chat_msg & result;
-
-    common_chat_peg_mapper(common_chat_msg & msg) : result(msg) {}
-
-    virtual void from_ast(const common_peg_ast_arena & arena, const common_peg_parse_result & result);
-    virtual void map(const common_peg_ast_node & node);
-};
-
-class common_chat_peg_native_builder : public common_chat_peg_builder {
-  public:
-    static constexpr const char * TOOL = "tool";
-    static constexpr const char * TOOL_OPEN = "tool-open";
-    static constexpr const char * TOOL_CLOSE = "tool-close";
-    static constexpr const char * TOOL_ID = "tool-id";
-    static constexpr const char * TOOL_NAME = "tool-name";
-    static constexpr const char * TOOL_ARGS = "tool-args";
-
-    common_peg_parser tool(const common_peg_parser & p) { return tag(TOOL, p); }
-    common_peg_parser tool_open(const common_peg_parser & p) { return atomic(tag(TOOL_OPEN, p)); }
-    common_peg_parser tool_close(const common_peg_parser & p) { return atomic(tag(TOOL_CLOSE, p)); }
-    common_peg_parser tool_id(const common_peg_parser & p) { return atomic(tag(TOOL_ID, p)); }
-    common_peg_parser tool_name(const common_peg_parser & p) { return atomic(tag(TOOL_NAME, p)); }
-    common_peg_parser tool_args(const common_peg_parser & p) { return tag(TOOL_ARGS, p); }
-};
-
-class common_chat_peg_native_mapper : public common_chat_peg_mapper {
-    common_chat_tool_call * current_tool;
-
-  public:
-    common_chat_peg_native_mapper(common_chat_msg & msg) : common_chat_peg_mapper(msg) {}
-
-    void map(const common_peg_ast_node & node) override;
-};
-
-inline common_peg_arena build_chat_peg_native_parser(const std::function<common_peg_parser(common_chat_peg_native_builder & builder)> & fn) {
-    common_chat_peg_native_builder builder;
-    builder.set_root(fn(builder));
-    return builder.build();
-}
-
-class common_chat_peg_constructed_builder : public common_chat_peg_builder {
-  public:
-    static constexpr const char * TOOL = "tool";
-    static constexpr const char * TOOL_OPEN = "tool-open";
-    static constexpr const char * TOOL_CLOSE = "tool-close";
-    static constexpr const char * TOOL_NAME = "tool-name";
-    static constexpr const char * TOOL_ARG = "tool-arg";
-    static constexpr const char * TOOL_ARG_OPEN = "tool-arg-open";
-    static constexpr const char * TOOL_ARG_CLOSE = "tool-arg-close";
-    static constexpr const char * TOOL_ARG_NAME = "tool-arg-name";
-    static constexpr const char * TOOL_ARG_STRING_VALUE = "tool-arg-string-value";
-    static constexpr const char * TOOL_ARG_JSON_VALUE = "tool-arg-json-value";
-
-    common_peg_parser tool(const common_peg_parser & p) { return tag(TOOL, p); }
-    common_peg_parser tool_open(const common_peg_parser & p) { return atomic(tag(TOOL_OPEN, p)); }
-    common_peg_parser tool_close(const common_peg_parser & p) { return atomic(tag(TOOL_CLOSE, p)); }
-    common_peg_parser tool_name(const common_peg_parser & p) { return atomic(tag(TOOL_NAME, p)); }
-    common_peg_parser tool_arg(const common_peg_parser & p) { return tag(TOOL_ARG, p); }
-    common_peg_parser tool_arg_open(const common_peg_parser & p) { return atomic(tag(TOOL_ARG_OPEN, p)); }
-    common_peg_parser tool_arg_close(const common_peg_parser & p) { return atomic(tag(TOOL_ARG_CLOSE, p)); }
-    common_peg_parser tool_arg_name(const common_peg_parser & p) { return atomic(tag(TOOL_ARG_NAME, p)); }
-    common_peg_parser tool_arg_string_value(const common_peg_parser & p) { return tag(TOOL_ARG_STRING_VALUE, p); }
-    common_peg_parser tool_arg_json_value(const common_peg_parser & p) { return tag(TOOL_ARG_JSON_VALUE, p); }
-};
-
-class common_chat_peg_constructed_mapper : public common_chat_peg_mapper {
-    common_chat_tool_call * current_tool;
-    int arg_count = 0;
-    bool needs_closing_quote = false;
-
-  public:
-    common_chat_peg_constructed_mapper(common_chat_msg & msg) : common_chat_peg_mapper(msg) {}
-
-    void map(const common_peg_ast_node & node) override;
-};
-
-inline common_peg_arena build_chat_peg_constructed_parser(const std::function<common_peg_parser(common_chat_peg_constructed_builder & builder)> & fn) {
-    common_chat_peg_constructed_builder builder;
-    builder.set_root(fn(builder));
-    return builder.build();
-}

common/chat.h

@@ -3,7 +3,6 @@
 #pragma once
 
 #include "common.h"
-#include "peg-parser.h"
 #include <functional>
 #include <chrono>
 #include <string>
@@ -34,8 +33,8 @@ struct common_chat_msg_content_part {
 struct common_chat_msg {
     std::string role;
     std::string content;
-    std::vector<common_chat_msg_content_part> content_parts;
-    std::vector<common_chat_tool_call> tool_calls;
+    std::vector<common_chat_msg_content_part> content_parts = {};
+    std::vector<common_chat_tool_call> tool_calls = {};
     std::string reasoning_content;
     std::string tool_name;
     std::string tool_call_id;
@@ -45,7 +44,7 @@ struct common_chat_msg {
     bool empty() const {
         return content.empty() && content_parts.empty() && tool_calls.empty() && reasoning_content.empty() && tool_name.empty() && tool_call_id.empty();
     }
-    void set_tool_call_ids(std::vector<std::string> & ids_cache, const std::function<std::string()> & gen_tool_call_id) {
+    void ensure_tool_call_ids_set(std::vector<std::string> & ids_cache, const std::function<std::string()> & gen_tool_call_id) {
         for (auto i = 0u; i < tool_calls.size(); i++) {
             if (ids_cache.size() <= i) {
                 auto id = tool_calls[i].id;
@@ -77,7 +76,7 @@ struct common_chat_msg_diff {
     size_t tool_call_index = std::string::npos;
     common_chat_tool_call tool_call_delta;
 
-    static std::vector<common_chat_msg_diff> compute_diffs(const common_chat_msg & msg_prv, const common_chat_msg & msg_new);
+    static std::vector<common_chat_msg_diff> compute_diffs(const common_chat_msg & previous_msg, const common_chat_msg & new_msg);
 
     bool operator==(const common_chat_msg_diff & other) const {
         return content_delta == other.content_delta
@@ -102,7 +101,6 @@ enum common_chat_format {
     COMMON_CHAT_FORMAT_CONTENT_ONLY,
     COMMON_CHAT_FORMAT_GENERIC,
     COMMON_CHAT_FORMAT_MISTRAL_NEMO,
-    COMMON_CHAT_FORMAT_MAGISTRAL,
     COMMON_CHAT_FORMAT_LLAMA_3_X,
     COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS,
     COMMON_CHAT_FORMAT_DEEPSEEK_R1,
@@ -116,19 +114,6 @@ enum common_chat_format {
     COMMON_CHAT_FORMAT_GPT_OSS,
     COMMON_CHAT_FORMAT_SEED_OSS,
     COMMON_CHAT_FORMAT_NEMOTRON_V2,
-    COMMON_CHAT_FORMAT_APERTUS,
-    COMMON_CHAT_FORMAT_LFM2_WITH_JSON_TOOLS,
-    COMMON_CHAT_FORMAT_GLM_4_5,
-    COMMON_CHAT_FORMAT_MINIMAX_M2,
-    COMMON_CHAT_FORMAT_KIMI_K2,
-    COMMON_CHAT_FORMAT_QWEN3_CODER_XML,
-    COMMON_CHAT_FORMAT_APRIEL_1_5,
-    COMMON_CHAT_FORMAT_XIAOMI_MIMO,
-
-    // These are intended to be parsed by the PEG parser
-    COMMON_CHAT_FORMAT_PEG_SIMPLE,
-    COMMON_CHAT_FORMAT_PEG_NATIVE,
-    COMMON_CHAT_FORMAT_PEG_CONSTRUCTED,
 
     COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats
 };
@@ -160,7 +145,6 @@ struct common_chat_params {
     std::vector<common_grammar_trigger> grammar_triggers;
     std::vector<std::string>            preserved_tokens;
     std::vector<std::string>            additional_stops;
-    std::string                         parser;
 };
 
 struct common_chat_syntax {
@@ -170,7 +154,6 @@ struct common_chat_syntax {
     bool                     reasoning_in_content  = false;
     bool                     thinking_forced_open  = false;
     bool                     parse_tool_calls      = true;
-    common_peg_arena         parser                = {};
 };
 
 // Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
@@ -214,7 +197,6 @@ const char*               common_chat_format_name(common_chat_format format);
 const char*               common_reasoning_format_name(common_reasoning_format format);
 common_reasoning_format   common_reasoning_format_from_name(const std::string & format);
 common_chat_msg           common_chat_parse(const std::string & input, bool is_partial, const common_chat_syntax & syntax);
-common_chat_msg           common_chat_peg_parse(const common_peg_arena & parser, const std::string & input, bool is_partial, const common_chat_syntax & syntax);
 
 common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice);
 

common/common.cpp

@@ -8,14 +8,12 @@
 #include "common.h"
 #include "log.h"
 #include "llama.h"
-#include "sampling.h"
 
 #include <algorithm>
 #include <cinttypes>
 #include <climits>
 #include <cmath>
 #include <codecvt>
-#include <chrono>
 #include <cstdarg>
 #include <cstring>
 #include <ctime>
@@ -27,6 +25,7 @@
 #include <sstream>
 #include <string>
 #include <thread>
+#include <unordered_map>
 #include <unordered_set>
 #include <vector>
 
@@ -51,23 +50,10 @@
 #include <unistd.h>
 #endif
 
-#if defined(__linux__)
-#include <sys/types.h>
-#include <pwd.h>
-#endif
-
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
-common_time_meas::common_time_meas(int64_t & t_acc, bool disable) : t_start_us(disable ? -1 : ggml_time_us()), t_acc(t_acc) {}
-
-common_time_meas::~common_time_meas() {
-    if (t_start_us >= 0) {
-        t_acc += ggml_time_us() - t_start_us;
-    }
-}
-
 //
 // CPU utils
 //
@@ -363,7 +349,11 @@ bool parse_cpu_mask(const std::string & mask, bool (&boolmask)[GGML_MAX_N_THREAD
 }
 
 void common_init() {
-    llama_log_set(common_log_default_callback, NULL);
+    llama_log_set([](ggml_log_level level, const char * text, void * /*user_data*/) {
+        if (LOG_DEFAULT_LLAMA <= common_log_verbosity_thold) {
+            common_log_add(common_log_main(), level, "%s", text);
+        }
+    }, NULL);
 
 #ifdef NDEBUG
     const char * build_type = "";
@@ -694,7 +684,7 @@ bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_over
 
 // Validate if a filename is safe to use
 // To validate a full path, split the path by the OS-specific path separator, and validate each part with this function
-bool fs_validate_filename(const std::string & filename, bool allow_subdirs) {
+bool fs_validate_filename(const std::string & filename) {
     if (!filename.length()) {
         // Empty filename invalid
         return false;
@@ -754,14 +744,10 @@ bool fs_validate_filename(const std::string & filename, bool allow_subdirs) {
             || (c >= 0xD800 && c <= 0xDFFF) // UTF-16 surrogate pairs
             || c == 0xFFFD // Replacement Character (UTF-8)
             || c == 0xFEFF // Byte Order Mark (BOM)
-            || c == ':' || c == '*' // Illegal characters
+            || c == '/' || c == '\\' || c == ':' || c == '*' // Illegal characters
             || c == '?' || c == '"' || c == '<' || c == '>' || c == '|') {
             return false;
         }
-        if (!allow_subdirs && (c == '/' || c == '\\')) {
-            // Subdirectories not allowed, reject path separators
-            return false;
-        }
     }
 
     // Reject any leading or trailing ' ', or any trailing '.', these are stripped on Windows and will cause a different filename
@@ -786,29 +772,11 @@ bool fs_validate_filename(const std::string & filename, bool allow_subdirs) {
 #include <iostream>
 
 
-#ifdef _WIN32
-static std::wstring utf8_to_wstring(const std::string & str) {
-    if (str.empty()) {
-        return std::wstring();
-    }
-
-    int size = MultiByteToWideChar(CP_UTF8, 0, str.c_str(), (int)str.size(), NULL, 0);
-
-    if (size <= 0) {
-        return std::wstring();
-    }
-
-    std::wstring wstr(size, 0);
-    MultiByteToWideChar(CP_UTF8, 0, str.c_str(), (int)str.size(), &wstr[0], size);
-
-    return wstr;
-}
-#endif
-
 // returns true if successful, false otherwise
 bool fs_create_directory_with_parents(const std::string & path) {
 #ifdef _WIN32
-    std::wstring wpath = utf8_to_wstring(path);
+    std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
+    std::wstring wpath = converter.from_bytes(path);
 
     // if the path already exists, check whether it's a directory
     const DWORD attributes = GetFileAttributesW(wpath.c_str());
@@ -881,11 +849,6 @@ bool fs_create_directory_with_parents(const std::string & path) {
 #endif // _WIN32
 }
 
-bool fs_is_directory(const std::string & path) {
-    std::filesystem::path dir(path);
-    return std::filesystem::exists(dir) && std::filesystem::is_directory(dir);
-}
-
 std::string fs_get_cache_directory() {
     std::string cache_directory = "";
     auto ensure_trailing_slash = [](std::string p) {
@@ -901,27 +864,13 @@ std::string fs_get_cache_directory() {
 #if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX) || defined(__OpenBSD__)
         if (std::getenv("XDG_CACHE_HOME")) {
             cache_directory = std::getenv("XDG_CACHE_HOME");
-        } else if (std::getenv("HOME")) {
-            cache_directory = std::getenv("HOME") + std::string("/.cache/");
         } else {
-#if defined(__linux__)
-            /* no $HOME is defined, fallback to getpwuid */
-            struct passwd *pw = getpwuid(getuid());
-            if ((!pw) || (!pw->pw_dir)) {
-                throw std::runtime_error("Failed to find $HOME directory");
-            }
-
-            cache_directory = std::string(pw->pw_dir) + std::string("/.cache/");
-#else /* defined(__linux__) */
-            throw std::runtime_error("Failed to find $HOME directory");
-#endif /* defined(__linux__) */
+            cache_directory = std::getenv("HOME") + std::string("/.cache/");
         }
 #elif defined(__APPLE__)
         cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
 #elif defined(_WIN32)
         cache_directory = std::getenv("LOCALAPPDATA");
-#elif defined(__EMSCRIPTEN__)
-        GGML_ABORT("not implemented on this platform");
 #else
 #  error Unknown architecture
 #endif
@@ -941,130 +890,11 @@ std::string fs_get_cache_file(const std::string & filename) {
     return cache_directory + filename;
 }
 
-std::vector<common_file_info> fs_list(const std::string & path, bool include_directories) {
-    std::vector<common_file_info> files;
-    if (path.empty()) return files;
-
-    std::filesystem::path dir(path);
-    if (!std::filesystem::exists(dir) || !std::filesystem::is_directory(dir)) {
-        return files;
-    }
-
-    for (const auto & entry : std::filesystem::directory_iterator(dir)) {
-        try {
-            // Only include regular files (skip directories)
-            const auto & p = entry.path();
-            if (std::filesystem::is_regular_file(p)) {
-                common_file_info info;
-                info.path   = p.string();
-                info.name   = p.filename().string();
-                info.is_dir = false;
-                try {
-                    info.size = static_cast<size_t>(std::filesystem::file_size(p));
-                } catch (const std::filesystem::filesystem_error &) {
-                    info.size = 0;
-                }
-                files.push_back(std::move(info));
-            } else if (include_directories && std::filesystem::is_directory(p)) {
-                common_file_info info;
-                info.path   = p.string();
-                info.name   = p.filename().string();
-                info.size   = 0; // Directories have no size
-                info.is_dir = true;
-                files.push_back(std::move(info));
-            }
-        } catch (const std::filesystem::filesystem_error &) {
-            // skip entries we cannot inspect
-            continue;
-        }
-    }
-
-    return files;
-}
-
-//
-// TTY utils
-//
-
-bool tty_can_use_colors() {
-    // Check NO_COLOR environment variable (https://no-color.org/)
-    if (const char * no_color = std::getenv("NO_COLOR")) {
-        if (no_color[0] != '\0') {
-            return false;
-        }
-    }
-
-    // Check TERM environment variable
-    if (const char * term = std::getenv("TERM")) {
-        if (std::strcmp(term, "dumb") == 0) {
-            return false;
-        }
-    }
-
-    // Check if stdout and stderr are connected to a terminal
-    // We check both because log messages can go to either
-    bool stdout_is_tty = isatty(fileno(stdout));
-    bool stderr_is_tty = isatty(fileno(stderr));
-
-    return stdout_is_tty || stderr_is_tty;
-}
 
 //
 // Model utils
 //
 
-static inline void common_init_sampler_from_model(
-    const llama_model * model,
-    common_params_sampling & sparams) {
-
-    const uint64_t config = sparams.user_sampling_config;
-
-    auto get_int32 = [&](const char * key, int32_t & dst, uint64_t user_config) {
-        if (config & user_config) return;
-
-        char buf[64] = {0};
-        if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) {
-            char * end = nullptr;
-            int32_t v = strtol(buf, &end, 10);
-            if (end && end != buf) dst = v;
-        }
-    };
-
-    auto get_float = [&](const char * key, float & dst, uint64_t user_config) {
-        if (config & user_config) return;
-
-        char buf[128] = {0};
-        if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) {
-            char * end = nullptr;
-            float v = strtof(buf, &end);
-            if (end && end != buf) dst = v;
-        }
-    };
-
-    // Sampling sequence
-    if (!(config & common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_SAMPLERS)) {
-        char buf[512] = {0};
-        if (llama_model_meta_val_str(model, llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_SEQUENCE), buf, sizeof(buf)) > 0) {
-            const std::vector<std::string> sampler_names = string_split<std::string>(std::string(buf), ';');
-            if (!sampler_names.empty()) {
-                sparams.samplers = common_sampler_types_from_names(sampler_names, true);
-            }
-        }
-    }
-
-    get_int32(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_TOP_K),           sparams.top_k,           common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_K);
-    get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_TOP_P),           sparams.top_p,           common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_P);
-    get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIN_P),           sparams.min_p,           common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIN_P);
-    get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_XTC_PROBABILITY), sparams.xtc_probability, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_XTC_PROBABILITY);
-    get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_XTC_THRESHOLD),   sparams.xtc_threshold,   common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_XTC_THRESHOLD);
-    get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_TEMP),            sparams.temp,            common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TEMP);
-    get_int32(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_LAST_N),  sparams.penalty_last_n,  common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_LAST_N);
-    get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_REPEAT),  sparams.penalty_repeat,  common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_REPEAT);
-    get_int32(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT),        sparams.mirostat,        common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT);
-    get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_TAU),    sparams.mirostat_tau,    common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_TAU);
-    get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA),    sparams.mirostat_eta,    common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA);
-}
-
 struct common_init_result common_init_from_params(common_params & params) {
     common_init_result iparams;
     auto mparams = common_model_params_to_llama(params);
@@ -1076,8 +906,6 @@ struct common_init_result common_init_from_params(common_params & params) {
         return iparams;
     }
 
-    common_init_sampler_from_model(model, params.sampling);
-
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
     auto cparams = common_context_params_to_llama(params);
@@ -1132,13 +960,15 @@ struct common_init_result common_init_from_params(common_params & params) {
 
         bool has_eos = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL;
         bool has_sep = llama_vocab_sep(vocab) != LLAMA_TOKEN_NULL;
-        bool has_rerank_prompt = llama_model_chat_template(model, "rerank") != NULL;
 
-        if (!has_eos && !has_sep && !has_rerank_prompt) {
-            LOG_WRN("%s: warning: vocab does not have an EOS token, SEP token, or rerank prompt. Reranking will not work\n", __func__);
+        if (!has_eos && !has_sep) {
+            LOG_WRN("%s: warning: vocab does not have an EOS token or SEP token, reranking will not work\n", __func__);
             ok = false;
         } else if (!has_eos) {
             LOG_WRN("%s: warning: vocab does not have an EOS token, using SEP token as fallback\n", __func__);
+        } else if (!has_sep) {
+            LOG_WRN("%s: warning: vocab does not have a SEP token, reranking will not work\n", __func__);
+            ok = false;
         }
 
         if (!ok) {
@@ -1287,7 +1117,6 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
     mparams.use_mlock       = params.use_mlock;
     mparams.check_tensors   = params.check_tensors;
     mparams.use_extra_bufts = !params.no_extra_bufts;
-    mparams.no_host         = params.no_host;
 
     if (params.kv_overrides.empty()) {
         mparams.kv_overrides = NULL;

common/common.h

@@ -2,19 +2,17 @@
 
 #pragma once
 
-#include "ggml-opt.h"
-#include "llama-cpp.h"
-
 #include <set>
 #include <sstream>
 #include <string>
 #include <string_view>
 #include <vector>
 #include <map>
+#include <sstream>
+#include <cmath>
 
-#if defined(_WIN32) && !defined(_WIN32_WINNT)
-#define _WIN32_WINNT 0x0A00
-#endif
+#include "ggml-opt.h"
+#include "llama-cpp.h"
 
 #ifdef _WIN32
 #define DIRECTORY_SEPARATOR '\\'
@@ -30,14 +28,7 @@
     fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET);    \
 } while(0)
 
-struct common_time_meas {
-    common_time_meas(int64_t & t_acc, bool disable = false);
-    ~common_time_meas();
-
-    const int64_t t_start_us;
-
-    int64_t & t_acc;
-};
+#define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf"
 
 struct common_adapter_lora_info {
     std::string path;
@@ -142,22 +133,6 @@ struct common_grammar_trigger {
     llama_token token = LLAMA_TOKEN_NULL;
 };
 
-enum common_params_sampling_config : uint64_t {
-    COMMON_PARAMS_SAMPLING_CONFIG_SAMPLERS        = 1 << 0,
-    COMMON_PARAMS_SAMPLING_CONFIG_TOP_K           = 1 << 1,
-    COMMON_PARAMS_SAMPLING_CONFIG_TOP_P           = 1 << 2,
-    COMMON_PARAMS_SAMPLING_CONFIG_MIN_P           = 1 << 3,
-    COMMON_PARAMS_SAMPLING_CONFIG_XTC_PROBABILITY = 1 << 4,
-    COMMON_PARAMS_SAMPLING_CONFIG_XTC_THRESHOLD   = 1 << 5,
-    COMMON_PARAMS_SAMPLING_CONFIG_TEMP            = 1 << 6,
-    COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_LAST_N  = 1 << 7,
-    COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_REPEAT  = 1 << 8,
-    COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT        = 1 << 9,
-    COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_TAU    = 1 << 10,
-    COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA    = 1 << 11,
-};
-
-
 // sampling parameters
 struct common_params_sampling {
     uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
@@ -190,8 +165,6 @@ struct common_params_sampling {
     bool    no_perf            = false; // disable performance metrics
     bool    timing_per_token   = false;
 
-    uint64_t user_sampling_config = 0; // bitfield to track user-specified samplers
-
     std::vector<std::string> dry_sequence_breakers = {"\n", ":", "\"", "*"};     // default sequence breakers for DRY
 
 
@@ -225,7 +198,6 @@ struct common_params_model {
     std::string hf_repo     = ""; // HF repo                                                // NOLINT
     std::string hf_file     = ""; // HF file                                                // NOLINT
     std::string docker_repo = ""; // Docker repo                                            // NOLINT
-    std::string name        = ""; // in format <user>/<model>[:<tag>] (tag is optional)     // NOLINT
 };
 
 struct common_params_speculative {
@@ -372,7 +344,7 @@ struct common_params {
 
     std::vector<common_control_vector_load_info> control_vectors; // control vector with user defined scale
 
-    int32_t verbosity                  = 3;  // LOG_LEVEL_INFO
+    int32_t verbosity                  = 0;
     int32_t control_vector_layer_start = -1; // layer range for control vector
     int32_t control_vector_layer_end   = -1; // layer range for control vector
     bool    offline                    = false;
@@ -406,7 +378,7 @@ struct common_params {
     bool simple_io         = false; // improves compatibility with subprocesses and limited consoles
     bool cont_batching     = true;  // insert new sequences for decoding on-the-fly
     bool no_perf           = false; // disable performance metrics
-    bool ctx_shift         = false; // context shift on infinite text generation
+    bool ctx_shift         = false;  // context shift on infinite text generation
     bool swa_full          = false; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
     bool kv_unified        = false; // enable unified KV cache
 
@@ -420,7 +392,6 @@ struct common_params {
     bool check_tensors     = false; // validate tensor data
     bool no_op_offload     = false; // globally disable offload host tensor operations to device
     bool no_extra_bufts    = false; // disable extra buffer types (used for weight repacking)
-    bool no_host           = false; // bypass host buffer allowing extra buffers to be used
 
     bool single_turn       = false; // single turn chat conversation
 
@@ -434,8 +405,6 @@ struct common_params {
     bool mmproj_use_gpu = true;     // use GPU for multimodal model
     bool no_mmproj = false;         // explicitly disable multimodal model
     std::vector<std::string> image; // path to image file(s)
-    int image_min_tokens = -1;
-    int image_max_tokens = -1;
 
     // finetune
     struct lr_opt lr;
@@ -455,8 +424,7 @@ struct common_params {
     int32_t timeout_write     = timeout_read; // http write timeout in seconds
     int32_t n_threads_http    = -1;           // number of threads to process HTTP requests (TODO: support threadpool)
     int32_t n_cache_reuse     = 0;            // min chunk size to reuse from the cache via KV shifting
-    int32_t n_ctx_checkpoints = 8;            // max number of context checkpoints per slot
-    int32_t cache_ram_mib     = 8192;         // -1 = no limit, 0 - disable, 1 = 1 MiB, etc.
+    int32_t n_swa_checkpoints = 3;            // max number of SWA checkpoints per slot
 
     std::string hostname      = "127.0.0.1";
     std::string public_path   = "";                                                                         // NOLINT
@@ -464,7 +432,7 @@ struct common_params {
     std::string chat_template = "";                                                                         // NOLINT
     bool use_jinja = false;                                                                                 // NOLINT
     bool enable_chat_template = true;
-    common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
+    common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_AUTO;
     int reasoning_budget = -1;
     bool prefill_assistant = true;                                                                          // if true, any trailing assistant message will be prefilled into the response
 
@@ -481,21 +449,14 @@ struct common_params {
     bool endpoint_props   = false; // only control POST requests, not GET
     bool endpoint_metrics = false;
 
-    // router server configs
-    std::string models_dir = ""; // directory containing models for the router server
-    int models_max = 4;          // maximum number of models to load simultaneously
-    bool models_autoload = true; // automatically load models when requested via the router server
-
     bool log_json = false;
 
     std::string slot_save_path;
-    std::string media_path; // path to directory for loading media files
 
     float slot_prompt_similarity = 0.1f;
 
     // batched-bench params
-    bool is_pp_shared   = false;
-    bool is_tg_separate = false;
+    bool is_pp_shared = false;
 
     std::vector<int32_t> n_pp;
     std::vector<int32_t> n_tg;
@@ -542,10 +503,6 @@ struct common_params {
     // return false from callback to abort model loading or true to continue
     llama_progress_callback load_progress_callback = NULL;
     void *                  load_progress_callback_user_data = NULL;
-
-    bool has_speculative() const {
-        return !speculative.model.path.empty() || !speculative.model.hf_repo.empty();
-    }
 };
 
 // call once at the start of a program if it uses libcommon
@@ -640,28 +597,12 @@ std::string string_from(const struct llama_context * ctx, const struct llama_bat
 // Filesystem utils
 //
 
-bool fs_validate_filename(const std::string & filename, bool allow_subdirs = false);
+bool fs_validate_filename(const std::string & filename);
 bool fs_create_directory_with_parents(const std::string & path);
-bool fs_is_directory(const std::string & path);
 
 std::string fs_get_cache_directory();
 std::string fs_get_cache_file(const std::string & filename);
 
-struct common_file_info {
-    std::string path;
-    std::string name;
-    size_t      size = 0; // in bytes
-    bool        is_dir = false;
-};
-std::vector<common_file_info> fs_list(const std::string & path, bool include_directories);
-
-//
-// TTY utils
-//
-
-// Auto-detect if colors can be enabled based on terminal and environment
-bool tty_can_use_colors();
-
 //
 // Model utils
 //
@@ -797,7 +738,7 @@ const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
 // MoE utils
 //
 
-const char * const LLM_FFN_EXPS_REGEX = "\\.ffn_(up|down|gate)_(ch|)exps";
+const char * const LLM_FFN_EXPS_REGEX = "\\.ffn_(up|down|gate)_exps";
 
 static std::string llm_ffn_exps_block_regex(int idx) {
     return string_format("blk\\.%d%s", idx, LLM_FFN_EXPS_REGEX);

common/download.h

@@ -1,57 +0,0 @@
-#pragma once
-
-#include <string>
-
-struct common_params_model;
-
-//
-// download functionalities
-//
-
-struct common_cached_model_info {
-    std::string manifest_path;
-    std::string user;
-    std::string model;
-    std::string tag;
-    size_t      size = 0; // GGUF size in bytes
-    // return string representation like "user/model:tag"
-    // if tag is "latest", it will be omitted
-    std::string to_string() const {
-        return user + "/" + model + (tag == "latest" ? "" : ":" + tag);
-    }
-};
-
-struct common_hf_file_res {
-    std::string repo; // repo name with ":tag" removed
-    std::string ggufFile;
-    std::string mmprojFile;
-};
-
-/**
- * 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.
- */
-common_hf_file_res common_get_hf_file(
-    const std::string & hf_repo_with_tag,
-    const std::string & bearer_token,
-    bool offline);
-
-// returns true if download succeeded
-bool common_download_model(
-    const common_params_model & model,
-    const std::string & bearer_token,
-    bool offline);
-
-// returns list of cached models
-std::vector<common_cached_model_info> common_list_cached_models();
-
-// resolve and download model from Docker registry
-// return local path to downloaded model file
-std::string common_docker_resolve_model(const std::string & docker);

common/http.h

@@ -1,73 +0,0 @@
-#pragma once
-
-#include <cpp-httplib/httplib.h>
-
-struct common_http_url {
-    std::string scheme;
-    std::string user;
-    std::string password;
-    std::string host;
-    std::string path;
-};
-
-static common_http_url common_http_parse_url(const std::string & url) {
-    common_http_url parts;
-    auto scheme_end = url.find("://");
-
-    if (scheme_end == std::string::npos) {
-        throw std::runtime_error("invalid URL: no scheme");
-    }
-    parts.scheme = url.substr(0, scheme_end);
-
-    if (parts.scheme != "http" && parts.scheme != "https") {
-        throw std::runtime_error("unsupported URL scheme: " + parts.scheme);
-    }
-
-    auto rest = url.substr(scheme_end + 3);
-    auto at_pos = rest.find('@');
-
-    if (at_pos != std::string::npos) {
-        auto auth = rest.substr(0, at_pos);
-        auto colon_pos = auth.find(':');
-        if (colon_pos != std::string::npos) {
-            parts.user = auth.substr(0, colon_pos);
-            parts.password = auth.substr(colon_pos + 1);
-        } else {
-            parts.user = auth;
-        }
-        rest = rest.substr(at_pos + 1);
-    }
-
-    auto slash_pos = rest.find('/');
-
-    if (slash_pos != std::string::npos) {
-        parts.host = rest.substr(0, slash_pos);
-        parts.path = rest.substr(slash_pos);
-    } else {
-        parts.host = rest;
-        parts.path = "/";
-    }
-    return parts;
-}
-
-static std::pair<httplib::Client, common_http_url> common_http_client(const std::string & url) {
-    common_http_url parts = common_http_parse_url(url);
-
-    if (parts.host.empty()) {
-        throw std::runtime_error("error: invalid URL format");
-    }
-
-    httplib::Client cli(parts.scheme + "://" + parts.host);
-
-    if (!parts.user.empty()) {
-        cli.set_basic_auth(parts.user, parts.password);
-    }
-
-    cli.set_follow_location(true);
-
-    return { std::move(cli), std::move(parts) };
-}
-
-static std::string common_http_show_masked_url(const common_http_url & parts) {
-    return parts.scheme + "://" + (parts.user.empty() ? "" : "****:****@") + parts.host + parts.path;
-}

common/json-partial.cpp

@@ -5,7 +5,6 @@
 #include <nlohmann/json.hpp>
 
 #include <string>
-#include <regex>
 
 using json = nlohmann::ordered_json;
 
@@ -169,47 +168,6 @@ bool common_json_parse(
                 }
             }
 
-            // Matches a potentially partial unicode escape sequence, e.g. \u, \uX, \uXX, \uXXX, \uXXXX
-            static const std::regex partial_unicode_regex(R"(\\u(?:[0-9a-fA-F](?:[0-9a-fA-F](?:[0-9a-fA-F](?:[0-9a-fA-F])?)?)?)?$)");
-
-            auto is_high_surrogate = [&](const std::string & s) {
-                // Check if a partial of a high surrogate (U+D800-U+DBFF)
-                return s.length() >= 4 &&
-                    s[0] == '\\' && s[1] == 'u' &&
-                    std::tolower(s[2]) == 'd' &&
-                    (s[3] == '8' || s[3] == '9' || std::tolower(s[3]) == 'a' || std::tolower(s[3]) == 'b');
-            };
-
-            // Initialize the unicode marker to a low surrogate to handle the edge case
-            // where a high surrogate (U+D800-U+DBFF) is immediately followed by a
-            // backslash (\)
-            std::string unicode_marker_padding = "udc00";
-            std::smatch last_unicode_seq;
-
-            if (std::regex_search(str, last_unicode_seq, partial_unicode_regex)) {
-                std::smatch second_last_seq;
-                std::string prelude = str.substr(0, last_unicode_seq.position());
-
-                // Pad the escape sequence with 0s until it forms a complete sequence of 6 characters
-                unicode_marker_padding = std::string(6 - last_unicode_seq.length(), '0');
-
-                if (is_high_surrogate(last_unicode_seq.str())) {
-                    // If the sequence is a partial match for a high surrogate, add a low surrogate (U+DC00-U+UDFF)
-                    unicode_marker_padding += "\\udc00";
-                } else if (std::regex_search(prelude, second_last_seq, partial_unicode_regex)) {
-                    if (is_high_surrogate(second_last_seq.str())) {
-                        // If this follows a high surrogate, pad it to be a low surrogate
-                        if (last_unicode_seq.length() == 2) {
-                            unicode_marker_padding = "dc00";
-                        } else if (last_unicode_seq.length() == 3) {
-                            unicode_marker_padding = "c00";
-                        } else {
-                            // The original unicode_marker_padding is already padded with 0s
-                        }
-                    }
-                }
-            }
-
             const auto & magic_seed = out.healing_marker.marker = healing_marker;//"$llama.cpp.json$";
 
             if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_KEY) {
@@ -228,9 +186,6 @@ bool common_json_parse(
                 } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"" + closing)) {
                     // Was inside an object value string after an escape
                     str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"" + closing;
-                } else if (can_parse(str + unicode_marker_padding + "\"" + closing)) {
-                    // Was inside an object value string after a partial unicode escape
-                    str += (out.healing_marker.json_dump_marker = unicode_marker_padding + magic_seed) + "\"" + closing;
                 } else {
                     // find last :
                     auto last_pos = str.find_last_of(':');
@@ -250,9 +205,6 @@ bool common_json_parse(
                 } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"" + closing)) {
                     // Was inside an array value string after an escape
                     str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"" + closing;
-                } else if (can_parse(str + unicode_marker_padding + "\"" + closing)) {
-                    // Was inside an array value string after a partial unicode escape
-                    str += (out.healing_marker.json_dump_marker = unicode_marker_padding + magic_seed) + "\"" + closing;
                 } else if (!was_maybe_number() && can_parse(str + ", 1" + closing)) {
                     // Had just finished a value
                     str += (out.healing_marker.json_dump_marker = ",\"" + magic_seed) + "\"" + closing;
@@ -278,9 +230,6 @@ bool common_json_parse(
                 } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\": 1" + closing)) {
                     // Was inside an object key string after an escape
                     str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\": 1" + closing;
-                } else if (can_parse(str + unicode_marker_padding + "\": 1" + closing)) {
-                    // Was inside an object key string after a partial unicode escape
-                    str += (out.healing_marker.json_dump_marker = unicode_marker_padding + magic_seed) + "\": 1" + closing;
                 } else {
                     auto last_pos = str.find_last_of(':');
                     if (last_pos == std::string::npos) {
@@ -297,25 +246,8 @@ bool common_json_parse(
             it = temptative_end;
             return true;
         }
-        // handle unclosed top-level primitive
-        if (err_loc.position != 0 && !healing_marker.empty() && err_loc.stack.empty()) {
-            std::string str(it, temptative_end);
-            const auto & magic_seed = out.healing_marker.marker = healing_marker;
-            if (can_parse(str + "\"")) {
-                // Was inside an string
-                str += (out.healing_marker.json_dump_marker = magic_seed) + "\"";
-            } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"")) {
-                // Was inside an string after an escape
-                str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"";
-            } else {
-                // TODO: handle more unclosed top-level primitive if the stack was empty but we got an error (e.g. "tru", "\"", etc...)
-                // fprintf(stderr, "Closing: TODO\n");
-                return false;
-            }
-            out.json = json::parse(str);
-            it = temptative_end;
-            return true;
-        }
+        // TODO: handle unclosed top-level primitive if the stack was empty but we got an error (e.g. "tru", "\"", etc...)
+        // fprintf(stderr, "Closing: TODO\n");
         return false;
     }
     out.json = json::parse(it, end);

common/json-schema-to-grammar.cpp

@@ -41,9 +41,9 @@ static std::string build_repetition(const std::string & item_rule, int min_items
     return result;
 }
 
-static void _build_min_max_int(int64_t min_value, int64_t max_value, std::stringstream & out, int decimals_left = 16, bool top_level = true) {
-    auto has_min = min_value != std::numeric_limits<int64_t>::min();
-    auto has_max = max_value != std::numeric_limits<int64_t>::max();
+static void _build_min_max_int(int min_value, int max_value, std::stringstream & out, int decimals_left = 16, bool top_level = true) {
+    auto has_min = min_value != std::numeric_limits<int>::min();
+    auto has_max = max_value != std::numeric_limits<int>::max();
 
     auto digit_range = [&](char from, char to) {
         out << "[";
@@ -159,7 +159,7 @@ static void _build_min_max_int(int64_t min_value, int64_t max_value, std::string
     if (has_min) {
         if (min_value < 0) {
             out << "\"-\" (";
-            _build_min_max_int(std::numeric_limits<int64_t>::min(), -min_value, out, decimals_left, /* top_level= */ false);
+            _build_min_max_int(std::numeric_limits<int>::min(), -min_value, out, decimals_left, /* top_level= */ false);
             out << ") | [0] | [1-9] ";
             more_digits(0, decimals_left - 1);
         } else if (min_value == 0) {
@@ -194,7 +194,7 @@ static void _build_min_max_int(int64_t min_value, int64_t max_value, std::string
             }
             digit_range(c, c);
             out << " (";
-            _build_min_max_int(std::stoll(min_s.substr(1)), std::numeric_limits<int64_t>::max(), out, less_decimals, /* top_level= */ false);
+            _build_min_max_int(std::stoi(min_s.substr(1)), std::numeric_limits<int>::max(), out, less_decimals, /* top_level= */ false);
             out << ")";
             if (c < '9') {
                 out << " | ";
@@ -216,7 +216,7 @@ static void _build_min_max_int(int64_t min_value, int64_t max_value, std::string
             _build_min_max_int(0, max_value, out, decimals_left, /* top_level= */ true);
         } else {
             out << "\"-\" (";
-            _build_min_max_int(-max_value, std::numeric_limits<int64_t>::max(), out, decimals_left, /* top_level= */ false);
+            _build_min_max_int(-max_value, std::numeric_limits<int>::max(), out, decimals_left, /* top_level= */ false);
             out << ")";
         }
         return;
@@ -268,10 +268,10 @@ static bool is_reserved_name(const std::string & name) {
 }
 
 std::regex INVALID_RULE_CHARS_RE("[^a-zA-Z0-9-]+");
-std::regex GRAMMAR_LITERAL_ESCAPE_RE("[\r\n\"\\\\]");
+std::regex GRAMMAR_LITERAL_ESCAPE_RE("[\r\n\"]");
 std::regex GRAMMAR_RANGE_LITERAL_ESCAPE_RE("[\r\n\"\\]\\-\\\\]");
 std::unordered_map<char, std::string> GRAMMAR_LITERAL_ESCAPES = {
-    {'\r', "\\r"}, {'\n', "\\n"}, {'"', "\\\""}, {'-', "\\-"}, {']', "\\]"}, {'\\', "\\\\"}
+    {'\r', "\\r"}, {'\n', "\\n"}, {'"', "\\\""}, {'-', "\\-"}, {']', "\\]"}
 };
 
 std::unordered_set<char> NON_LITERAL_SET = {'|', '.', '(', ')', '[', ']', '{', '}', '*', '+', '?'};
@@ -303,8 +303,6 @@ static std::string format_literal(const std::string & literal) {
     return "\"" + escaped + "\"";
 }
 
-std::string gbnf_format_literal(const std::string & literal) { return format_literal(literal); }
-
 class SchemaConverter {
 private:
     friend std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options);
@@ -603,10 +601,7 @@ private:
     }
 
     std::string _resolve_ref(const std::string & ref) {
-        auto it = ref.find('#');
-        std::string ref_fragment = it != std::string::npos ? ref.substr(it + 1) : ref;
-        static const std::regex nonalphanumeric_regex(R"([^a-zA-Z0-9-]+)");
-        std::string ref_name = "ref" + std::regex_replace(ref_fragment, nonalphanumeric_regex, "-");
+        std::string ref_name = ref.substr(ref.find_last_of('/') + 1);
         if (_rules.find(ref_name) == _rules.end() && _refs_being_resolved.find(ref) == _refs_being_resolved.end()) {
             _refs_being_resolved.insert(ref);
             json resolved = _refs[ref];
@@ -779,24 +774,11 @@ public:
                         std::vector<std::string> tokens = string_split(pointer, "/");
                         for (size_t i = 1; i < tokens.size(); ++i) {
                             std::string sel = tokens[i];
-                            if (target.is_object() && target.contains(sel)) {
-                                target = target[sel];
-                            } else if (target.is_array()) {
-                                size_t sel_index;
-                                try {
-                                    sel_index = std::stoul(sel);
-                                } catch (const std::invalid_argument & e) {
-                                    sel_index = target.size();
-                                }
-                                if (sel_index >= target.size()) {
-                                    _errors.push_back("Error resolving ref " + ref + ": " + sel + " not in " + target.dump());
-                                    return;
-                                }
-                                target = target[sel_index];
-                            } else {
+                            if (target.is_null() || !target.contains(sel)) {
                                 _errors.push_back("Error resolving ref " + ref + ": " + sel + " not in " + target.dump());
                                 return;
                             }
+                            target = target[sel];
                         }
                         _refs[ref] = target;
                     }
@@ -943,17 +925,17 @@ public:
             int max_len = schema.contains("maxLength") ? schema["maxLength"].get<int>() : std::numeric_limits<int>::max();
             return _add_rule(rule_name, "\"\\\"\" " + build_repetition(char_rule, min_len, max_len) + " \"\\\"\" space");
         } else if (schema_type == "integer" && (schema.contains("minimum") || schema.contains("exclusiveMinimum") || schema.contains("maximum") || schema.contains("exclusiveMaximum"))) {
-            int64_t min_value = std::numeric_limits<int64_t>::min();
-            int64_t max_value = std::numeric_limits<int64_t>::max();
+            int min_value = std::numeric_limits<int>::min();
+            int max_value = std::numeric_limits<int>::max();
             if (schema.contains("minimum")) {
-                min_value = schema["minimum"].get<int64_t>();
+                min_value = schema["minimum"].get<int>();
             } else if (schema.contains("exclusiveMinimum")) {
-                min_value = schema["exclusiveMinimum"].get<int64_t>() + 1;
+                min_value = schema["exclusiveMinimum"].get<int>() + 1;
             }
             if (schema.contains("maximum")) {
-                max_value = schema["maximum"].get<int64_t>();
+                max_value = schema["maximum"].get<int>();
             } else if (schema.contains("exclusiveMaximum")) {
-                max_value = schema["exclusiveMaximum"].get<int64_t>() - 1;
+                max_value = schema["exclusiveMaximum"].get<int>() - 1;
             }
             std::stringstream out;
             out << "(";
@@ -974,7 +956,7 @@ public:
 
     void check_errors() {
         if (!_errors.empty()) {
-            throw std::invalid_argument("JSON schema conversion failed:\n" + string_join(_errors, "\n"));
+            throw std::runtime_error("JSON schema conversion failed:\n" + string_join(_errors, "\n"));
         }
         if (!_warnings.empty()) {
             fprintf(stderr, "WARNING: JSON schema conversion was incomplete: %s\n", string_join(_warnings, "; ").c_str());

common/json-schema-to-grammar.h

@@ -18,6 +18,4 @@ struct common_grammar_options {
     bool dotall = false;
 };
 
-std::string gbnf_format_literal(const std::string & literal);
-
 std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options = {});

common/log.cpp

@@ -1,4 +1,3 @@
-#include "common.h"
 #include "log.h"
 
 #include <chrono>
@@ -27,6 +26,30 @@ void common_log_set_verbosity_thold(int verbosity) {
     common_log_verbosity_thold = verbosity;
 }
 
+// Auto-detect if colors should be enabled based on terminal and environment
+static bool common_log_should_use_colors_auto() {
+    // Check NO_COLOR environment variable (https://no-color.org/)
+    if (const char * no_color = std::getenv("NO_COLOR")) {
+        if (no_color[0] != '\0') {
+            return false;
+        }
+    }
+
+    // Check TERM environment variable
+    if (const char * term = std::getenv("TERM")) {
+        if (std::strcmp(term, "dumb") == 0) {
+            return false;
+        }
+    }
+
+    // Check if stdout and stderr are connected to a terminal
+    // We check both because log messages can go to either
+    bool stdout_is_tty = isatty(fileno(stdout));
+    bool stderr_is_tty = isatty(fileno(stderr));
+
+    return stdout_is_tty || stderr_is_tty;
+}
+
 static int64_t t_us() {
     return std::chrono::duration_cast<std::chrono::microseconds>(std::chrono::system_clock::now().time_since_epoch()).count();
 }
@@ -368,7 +391,7 @@ struct common_log * common_log_main() {
     static std::once_flag    init_flag;
     std::call_once(init_flag, [&]() {
         // Set default to auto-detect colors
-        log.set_colors(tty_can_use_colors());
+        log.set_colors(common_log_should_use_colors_auto());
     });
 
     return &log;
@@ -399,7 +422,7 @@ void common_log_set_file(struct common_log * log, const char * file) {
 
 void common_log_set_colors(struct common_log * log, log_colors colors) {
     if (colors == LOG_COLORS_AUTO) {
-        log->set_colors(tty_can_use_colors());
+        log->set_colors(common_log_should_use_colors_auto());
         return;
     }
 
@@ -419,23 +442,3 @@ void common_log_set_prefix(struct common_log * log, bool prefix) {
 void common_log_set_timestamps(struct common_log * log, bool timestamps) {
     log->set_timestamps(timestamps);
 }
-
-static int common_get_verbosity(enum ggml_log_level level) {
-    switch (level) {
-        case GGML_LOG_LEVEL_DEBUG: return LOG_LEVEL_DEBUG;
-        case GGML_LOG_LEVEL_INFO:  return LOG_LEVEL_INFO;
-        case GGML_LOG_LEVEL_WARN:  return LOG_LEVEL_WARN;
-        case GGML_LOG_LEVEL_ERROR: return LOG_LEVEL_ERROR;
-        case GGML_LOG_LEVEL_CONT:  return LOG_LEVEL_INFO; // same as INFO
-        case GGML_LOG_LEVEL_NONE:
-        default:
-            return LOG_LEVEL_OUTPUT;
-    }
-}
-
-void common_log_default_callback(enum ggml_log_level level, const char * text, void * /*user_data*/) {
-    auto verbosity = common_get_verbosity(level);
-    if (verbosity <= common_log_verbosity_thold) {
-        common_log_add(common_log_main(), level, "%s", text);
-    }
-}

common/log.h

@@ -21,14 +21,8 @@
 #    define LOG_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
 #endif
 
-#define LOG_LEVEL_DEBUG  4
-#define LOG_LEVEL_INFO   3
-#define LOG_LEVEL_WARN   2
-#define LOG_LEVEL_ERROR  1
-#define LOG_LEVEL_OUTPUT 0 // output data from tools
-
-#define LOG_DEFAULT_DEBUG LOG_LEVEL_DEBUG
-#define LOG_DEFAULT_LLAMA LOG_LEVEL_INFO
+#define LOG_DEFAULT_DEBUG 1
+#define LOG_DEFAULT_LLAMA 0
 
 enum log_colors {
     LOG_COLORS_AUTO     = -1,
@@ -42,8 +36,6 @@ extern int common_log_verbosity_thold;
 
 void common_log_set_verbosity_thold(int verbosity); // not thread-safe
 
-void common_log_default_callback(enum ggml_log_level level, const char * text, void * user_data);
-
 // the common_log uses an internal worker thread to print/write log messages
 // when the worker thread is paused, incoming log messages are discarded
 struct common_log;
@@ -73,11 +65,10 @@ void common_log_add(struct common_log * log, enum ggml_log_level level, const ch
 //   0.00.090.578 I llm_load_tensors: offloading 32 repeating layers to GPU
 //   0.00.090.579 I llm_load_tensors: offloading non-repeating layers to GPU
 //
+// I - info    (stdout, V = 0)
+// W - warning (stderr, V = 0)
+// E - error   (stderr, V = 0)
 // D - debug   (stderr, V = LOG_DEFAULT_DEBUG)
-// I - info    (stdout, V = LOG_DEFAULT_INFO)
-// W - warning (stderr, V = LOG_DEFAULT_WARN)
-// E - error   (stderr, V = LOG_DEFAULT_ERROR)
-// O - output  (stdout, V = LOG_DEFAULT_OUTPUT)
 //
 
 void common_log_set_file      (struct common_log * log, const char * file); // not thread-safe
@@ -102,14 +93,14 @@ void common_log_set_timestamps(struct common_log * log, bool timestamps);   // w
         } \
     } while (0)
 
-#define LOG(...)             LOG_TMPL(GGML_LOG_LEVEL_NONE, LOG_LEVEL_OUTPUT, __VA_ARGS__)
-#define LOGV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_NONE, verbosity,        __VA_ARGS__)
+#define LOG(...)             LOG_TMPL(GGML_LOG_LEVEL_NONE, 0,         __VA_ARGS__)
+#define LOGV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_NONE, verbosity, __VA_ARGS__)
 
-#define LOG_DBG(...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, LOG_LEVEL_DEBUG,  __VA_ARGS__)
-#define LOG_INF(...) LOG_TMPL(GGML_LOG_LEVEL_INFO,  LOG_LEVEL_INFO,   __VA_ARGS__)
-#define LOG_WRN(...) LOG_TMPL(GGML_LOG_LEVEL_WARN,  LOG_LEVEL_WARN,   __VA_ARGS__)
-#define LOG_ERR(...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, LOG_LEVEL_ERROR,  __VA_ARGS__)
-#define LOG_CNT(...) LOG_TMPL(GGML_LOG_LEVEL_CONT,  LOG_LEVEL_INFO,   __VA_ARGS__) // same as INFO
+#define LOG_INF(...) LOG_TMPL(GGML_LOG_LEVEL_INFO,  0,                 __VA_ARGS__)
+#define LOG_WRN(...) LOG_TMPL(GGML_LOG_LEVEL_WARN,  0,                 __VA_ARGS__)
+#define LOG_ERR(...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, 0,                 __VA_ARGS__)
+#define LOG_DBG(...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, LOG_DEFAULT_DEBUG, __VA_ARGS__)
+#define LOG_CNT(...) LOG_TMPL(GGML_LOG_LEVEL_CONT,  0,                 __VA_ARGS__)
 
 #define LOG_INFV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_INFO,  verbosity, __VA_ARGS__)
 #define LOG_WRNV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_WARN,  verbosity, __VA_ARGS__)

common/peg-parser.h

@@ -1,459 +0,0 @@
-#pragma once
-
-#include <nlohmann/json_fwd.hpp>
-
-#include <memory>
-#include <unordered_map>
-#include <string>
-#include <string_view>
-#include <functional>
-#include <vector>
-#include <variant>
-
-struct common_grammar_builder;
-
-class common_peg_parser_builder;
-
-using common_peg_parser_id = size_t;
-constexpr common_peg_parser_id COMMON_PEG_INVALID_PARSER_ID = static_cast<common_peg_parser_id>(-1);
-
-using common_peg_ast_id = size_t;
-constexpr common_peg_ast_id COMMON_PEG_INVALID_AST_ID = static_cast<common_peg_ast_id>(-1);
-
-// Lightweight wrapper around common_peg_parser_id for convenience
-class common_peg_parser {
-    common_peg_parser_id id_;
-    common_peg_parser_builder & builder_;
-
-  public:
-    common_peg_parser(const common_peg_parser & other) : id_(other.id_), builder_(other.builder_) {}
-    common_peg_parser(common_peg_parser_id id, common_peg_parser_builder & builder) : id_(id), builder_(builder) {}
-
-    common_peg_parser & operator=(const common_peg_parser & other);
-    common_peg_parser & operator+=(const common_peg_parser & other);
-    common_peg_parser & operator|=(const common_peg_parser & other);
-
-    operator common_peg_parser_id() const { return id_; }
-    common_peg_parser_id id() const { return id_; }
-
-    common_peg_parser_builder & builder() const { return builder_; }
-
-    // Creates a sequence
-    common_peg_parser operator+(const common_peg_parser & other) const;
-
-    // Creates a sequence separated by spaces.
-    common_peg_parser operator<<(const common_peg_parser & other) const;
-
-    // Creates a choice
-    common_peg_parser operator|(const common_peg_parser & other) const;
-
-    common_peg_parser operator+(const char * str) const;
-    common_peg_parser operator+(const std::string & str) const;
-    common_peg_parser operator<<(const char * str) const;
-    common_peg_parser operator<<(const std::string & str) const;
-    common_peg_parser operator|(const char * str) const;
-    common_peg_parser operator|(const std::string & str) const;
-};
-
-common_peg_parser operator+(const char * str, const common_peg_parser & p);
-common_peg_parser operator+(const std::string & str, const common_peg_parser & p);
-common_peg_parser operator<<(const char * str, const common_peg_parser & p);
-common_peg_parser operator<<(const std::string & str, const common_peg_parser & p);
-common_peg_parser operator|(const char * str, const common_peg_parser & p);
-common_peg_parser operator|(const std::string & str, const common_peg_parser & p);
-
-enum common_peg_parse_result_type {
-    COMMON_PEG_PARSE_RESULT_FAIL            = 0,
-    COMMON_PEG_PARSE_RESULT_SUCCESS         = 1,
-    COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT = 2,
-};
-
-const char * common_peg_parse_result_type_name(common_peg_parse_result_type type);
-
-struct common_peg_ast_node {
-    common_peg_ast_id id;
-    std::string rule;
-    std::string tag;
-    size_t start;
-    size_t end;
-    std::string_view text;
-    std::vector<common_peg_ast_id> children;
-
-    bool is_partial = false;
-};
-
-struct common_peg_parse_result;
-
-using common_peg_ast_visitor = std::function<void(const common_peg_ast_node & node)>;
-
-class common_peg_ast_arena {
-    std::vector<common_peg_ast_node> nodes_;
-  public:
-    common_peg_ast_id add_node(
-        const std::string & rule,
-        const std::string & tag,
-        size_t start,
-        size_t end,
-        std::string_view text,
-        std::vector<common_peg_ast_id> children,
-        bool is_partial = false
-    ) {
-        common_peg_ast_id id = nodes_.size();
-        nodes_.push_back({id, rule, tag, start, end, text, std::move(children), is_partial});
-        return id;
-    }
-
-    const common_peg_ast_node & get(common_peg_ast_id id) const { return nodes_.at(id); }
-
-    size_t size() const { return nodes_.size(); }
-
-    void clear() { nodes_.clear(); }
-
-    void visit(common_peg_ast_id id, const common_peg_ast_visitor & visitor) const;
-    void visit(const common_peg_parse_result & result, const common_peg_ast_visitor & visitor) const;
-};
-
-struct common_peg_parse_result {
-    common_peg_parse_result_type type = COMMON_PEG_PARSE_RESULT_FAIL;
-    size_t start = 0;
-    size_t end = 0;
-
-    std::vector<common_peg_ast_id> nodes;
-
-    common_peg_parse_result() = default;
-
-    common_peg_parse_result(common_peg_parse_result_type type, size_t start)
-        : type(type), start(start), end(start) {}
-
-    common_peg_parse_result(common_peg_parse_result_type type, size_t start, size_t end)
-        : type(type), start(start), end(end) {}
-
-    common_peg_parse_result(common_peg_parse_result_type type, size_t start, size_t end, std::vector<common_peg_ast_id> nodes)
-        : type(type), start(start), end(end), nodes(std::move(nodes)) {}
-
-    bool fail() const { return type == COMMON_PEG_PARSE_RESULT_FAIL; }
-    bool need_more_input() const { return type == COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT; }
-    bool success() const { return type == COMMON_PEG_PARSE_RESULT_SUCCESS; }
-};
-
-struct common_peg_parse_context {
-    std::string input;
-    bool is_partial;
-    common_peg_ast_arena ast;
-
-    int parse_depth;
-
-    common_peg_parse_context()
-        : is_partial(false), parse_depth(0) {}
-
-    common_peg_parse_context(const std::string & input)
-        : input(input), is_partial(false), parse_depth(0) {}
-
-    common_peg_parse_context(const std::string & input, bool is_partial)
-        : input(input), is_partial(is_partial), parse_depth(0) {}
-};
-
-class common_peg_arena;
-
-// Parser variants
-struct common_peg_epsilon_parser {};
-
-struct common_peg_start_parser {};
-
-struct common_peg_end_parser {};
-
-struct common_peg_literal_parser {
-    std::string literal;
-};
-
-struct common_peg_sequence_parser {
-    std::vector<common_peg_parser_id> children;
-};
-
-struct common_peg_choice_parser {
-    std::vector<common_peg_parser_id> children;
-};
-
-struct common_peg_repetition_parser {
-    common_peg_parser_id child;
-    int min_count;
-    int max_count;  // -1 for unbounded
-};
-
-struct common_peg_and_parser {
-    common_peg_parser_id child;
-};
-
-struct common_peg_not_parser {
-    common_peg_parser_id child;
-};
-
-struct common_peg_any_parser {};
-
-struct common_peg_space_parser {};
-
-struct common_peg_chars_parser {
-    struct char_range {
-        uint32_t start;
-        uint32_t end;
-        bool contains(uint32_t codepoint) const { return codepoint >= start && codepoint <= end; }
-    };
-
-    std::string pattern;
-    std::vector<char_range> ranges;
-    bool negated;
-    int min_count;
-    int max_count;  // -1 for unbounded
-};
-
-struct common_peg_json_string_parser {};
-
-struct common_peg_until_parser {
-    std::vector<std::string> delimiters;
-};
-
-struct common_peg_schema_parser {
-    common_peg_parser_id child;
-    std::string name;
-    std::shared_ptr<nlohmann::ordered_json> schema;
-
-    // Indicates if the GBNF should accept a raw string that matches the schema.
-    bool raw;
-};
-
-struct common_peg_rule_parser {
-    std::string name;
-    common_peg_parser_id child;
-    bool trigger;
-};
-
-struct common_peg_ref_parser {
-    std::string name;
-};
-
-struct common_peg_atomic_parser {
-    common_peg_parser_id child;
-};
-
-struct common_peg_tag_parser {
-    common_peg_parser_id child;
-    std::string tag;
-};
-
-// Variant holding all parser types
-using common_peg_parser_variant = std::variant<
-    common_peg_epsilon_parser,
-    common_peg_start_parser,
-    common_peg_end_parser,
-    common_peg_literal_parser,
-    common_peg_sequence_parser,
-    common_peg_choice_parser,
-    common_peg_repetition_parser,
-    common_peg_and_parser,
-    common_peg_not_parser,
-    common_peg_any_parser,
-    common_peg_space_parser,
-    common_peg_chars_parser,
-    common_peg_json_string_parser,
-    common_peg_until_parser,
-    common_peg_schema_parser,
-    common_peg_rule_parser,
-    common_peg_ref_parser,
-    common_peg_atomic_parser,
-    common_peg_tag_parser
->;
-
-class common_peg_arena {
-    std::vector<common_peg_parser_variant> parsers_;
-    std::unordered_map<std::string, common_peg_parser_id> rules_;
-    common_peg_parser_id root_ = COMMON_PEG_INVALID_PARSER_ID;
-
-  public:
-    const common_peg_parser_variant & get(common_peg_parser_id id) const { return parsers_.at(id); }
-    common_peg_parser_variant & get(common_peg_parser_id id) { return parsers_.at(id); }
-
-    size_t size() const { return parsers_.size(); }
-    bool empty() const { return parsers_.empty(); }
-
-    common_peg_parser_id get_rule(const std::string & name) const;
-    bool has_rule(const std::string & name) const { return rules_.find(name) != rules_.end(); }
-
-    common_peg_parser_id root() const { return root_; }
-    void set_root(common_peg_parser_id id) { root_ = id; }
-
-    common_peg_parse_result parse(common_peg_parse_context & ctx, size_t start = 0) const;
-    common_peg_parse_result parse(common_peg_parser_id id, common_peg_parse_context & ctx, size_t start) const;
-
-    void resolve_refs();
-
-    void build_grammar(const common_grammar_builder & builder, bool lazy = false) const;
-
-    std::string dump(common_peg_parser_id id) const;
-
-    nlohmann::json to_json() const;
-    static common_peg_arena from_json(const nlohmann::json & j);
-
-    std::string save() const;
-    void load(const std::string & data);
-
-    friend class common_peg_parser_builder;
-
-  private:
-    common_peg_parser_id add_parser(common_peg_parser_variant parser);
-    void add_rule(const std::string & name, common_peg_parser_id id);
-
-    common_peg_parser_id resolve_ref(common_peg_parser_id id);
-};
-
-class common_peg_parser_builder {
-    common_peg_arena arena_;
-
-    common_peg_parser wrap(common_peg_parser_id id) { return common_peg_parser(id, *this); }
-    common_peg_parser add(const common_peg_parser_variant & p) { return wrap(arena_.add_parser(p)); }
-
-  public:
-    common_peg_parser_builder();
-
-    // Match nothing, always succeed.
-    //   S -> ε
-    common_peg_parser eps() { return add(common_peg_epsilon_parser{}); }
-
-    // Matches the start of the input.
-    //   S -> ^
-    common_peg_parser start() { return add(common_peg_start_parser{}); }
-
-    // Matches the end of the input.
-    //   S -> $
-    common_peg_parser end() { return add(common_peg_end_parser{}); }
-
-    // Matches an exact literal string.
-    //   S -> "hello"
-    common_peg_parser literal(const std::string & literal) { return add(common_peg_literal_parser{literal}); }
-
-    // Matches a sequence of parsers in order, all must succeed.
-    //   S -> A B C
-    common_peg_parser sequence() { return add(common_peg_sequence_parser{}); }
-    common_peg_parser sequence(const std::vector<common_peg_parser_id> & parsers);
-    common_peg_parser sequence(const std::vector<common_peg_parser> & parsers);
-    common_peg_parser sequence(std::initializer_list<common_peg_parser> parsers);
-
-    // Matches the first parser that succeeds from a list of alternatives.
-    //   S -> A | B | C
-    common_peg_parser choice() { return add(common_peg_choice_parser{}); }
-    common_peg_parser choice(const std::vector<common_peg_parser_id> & parsers);
-    common_peg_parser choice(const std::vector<common_peg_parser> & parsers);
-    common_peg_parser choice(std::initializer_list<common_peg_parser> parsers);
-
-    // Matches one or more repetitions of a parser.
-    //   S -> A+
-    common_peg_parser one_or_more(const common_peg_parser & p) { return repeat(p, 1, -1); }
-
-    // Matches zero or more repetitions of a parser, always succeeds.
-    //   S -> A*
-    common_peg_parser zero_or_more(const common_peg_parser & p) { return repeat(p, 0, -1); }
-
-    // Matches zero or one occurrence of a parser, always succeeds.
-    //   S -> A?
-    common_peg_parser optional(const common_peg_parser & p) { return repeat(p, 0, 1); }
-
-    // Positive lookahead: succeeds if child parser succeeds, consumes no input.
-    //   S -> &A
-    common_peg_parser peek(const common_peg_parser & p) { return add(common_peg_and_parser{p}); }
-
-    // Negative lookahead: succeeds if child parser fails, consumes no input.
-    //   S -> !A
-    common_peg_parser negate(const common_peg_parser & p) { return add(common_peg_not_parser{p}); }
-
-    // Matches any single character.
-    //   S -> .
-    common_peg_parser any() { return add(common_peg_any_parser{}); }
-
-    // Matches between min and max repetitions of characters from a character class.
-    //   S -> [a-z]{m,n}
-    //
-    // Use -1 for max to represent unbounded repetition (equivalent to {m,})
-    common_peg_parser chars(const std::string & classes, int min = 1, int max = -1);
-
-    // Creates a lightweight reference to a named rule (resolved during build()).
-    // Use this for forward references in recursive grammars.
-    //   expr_ref -> expr
-    common_peg_parser ref(const std::string & name) { return add(common_peg_ref_parser{name}); }
-
-    // Matches zero or more whitespace characters (space, tab, newline).
-    //   S -> [ \t\n]*
-    common_peg_parser space() { return add(common_peg_space_parser{}); }
-
-    // Matches all characters until a delimiter is found (delimiter not consumed).
-    //   S -> (!delim .)*
-    common_peg_parser until(const std::string & delimiter) { return add(common_peg_until_parser{{delimiter}}); }
-
-    // Matches all characters until one of the delimiters in the list is found (delimiter not consumed).
-    //   S -> (!delim .)*
-    common_peg_parser until_one_of(const std::vector<std::string> & delimiters) { return add(common_peg_until_parser{delimiters}); }
-
-    // Matches everything
-    //   S -> .*
-    common_peg_parser rest() { return until_one_of({}); }
-
-    // Matches between min and max repetitions of a parser (inclusive).
-    //   S -> A{m,n}
-    // Use -1 for max to represent unbounded repetition (equivalent to {m,})
-    common_peg_parser repeat(const common_peg_parser & p, int min, int max) { return add(common_peg_repetition_parser{p, min,max}); }
-
-    // Matches exactly n repetitions of a parser.
-    //   S -> A{n}
-    common_peg_parser repeat(const common_peg_parser & p, int n) { return repeat(p, n, n); }
-
-    // Creates a complete JSON parser supporting objects, arrays, strings, numbers, booleans, and null.
-    //   value -> object | array | string | number | true | false | null
-    common_peg_parser json();
-    common_peg_parser json_object();
-    common_peg_parser json_string();
-    common_peg_parser json_array();
-    common_peg_parser json_number();
-    common_peg_parser json_bool();
-    common_peg_parser json_null();
-
-    // Matches JSON string content without the surrounding quotes.
-    // Useful for extracting content within a JSON string.
-    common_peg_parser json_string_content();
-
-    // Matches a JSON object member with a key and associated parser as the
-    // value.
-    common_peg_parser json_member(const std::string & key, const common_peg_parser & p);
-
-    // Wraps a parser with JSON schema metadata for grammar generation.
-    // Used internally to convert JSON schemas to GBNF grammar rules.
-    common_peg_parser schema(const common_peg_parser & p, const std::string & name, const nlohmann::ordered_json & schema, bool raw = false);
-
-    // Creates a named rule, stores it in the grammar, and returns a ref.
-    // If trigger=true, marks this rule as an entry point for lazy grammar generation.
-    //   auto json = p.rule("json", json_obj | json_arr | ...)
-    common_peg_parser rule(const std::string & name, const common_peg_parser & p, bool trigger = false);
-
-    // Creates a named rule using a builder function, and returns a ref.
-    // If trigger=true, marks this rule as an entry point for lazy grammar generation.
-    //   auto json = p.rule("json", [&]() { return json_object() | json_array() | ... })
-    common_peg_parser rule(const std::string & name, const std::function<common_peg_parser()> & builder, bool trigger = false);
-
-    // Creates a trigger rule. When generating a lazy grammar from the parser,
-    // only trigger rules and descendents are emitted.
-    common_peg_parser trigger_rule(const std::string & name, const common_peg_parser & p) { return rule(name, p, true); }
-    common_peg_parser trigger_rule(const std::string & name, const std::function<common_peg_parser()> & builder) { return rule(name, builder, true); }
-
-    // Creates an atomic parser. Atomic parsers do not create an AST node if
-    // the child results in a partial parse, i.e. NEEDS_MORE_INPUT. This is
-    // intended for situations where partial output is undesirable.
-    common_peg_parser atomic(const common_peg_parser & p) { return add(common_peg_atomic_parser{p}); }
-
-    // Tags create nodes in the generated AST for semantic purposes.
-    // Unlike rules, you can tag multiple nodes with the same tag.
-    common_peg_parser tag(const std::string & tag, const common_peg_parser & p) { return add(common_peg_tag_parser{p.id(), tag}); }
-
-    void set_root(const common_peg_parser & p);
-
-    common_peg_arena build();
-};
-
-// Helper function for building parsers
-common_peg_arena build_peg_parser(const std::function<common_peg_parser(common_peg_parser_builder & builder)> & fn);

common/sampling.cpp

@@ -3,10 +3,9 @@
 #include "common.h"
 #include "log.h"
 
-#include <algorithm>
 #include <cmath>
-#include <cstring>
 #include <unordered_map>
+#include <algorithm>
 
 // the ring buffer works similarly to std::deque, but with a fixed capacity
 // TODO: deduplicate with llama-impl.h
@@ -113,13 +112,6 @@ struct common_sampler {
 
     llama_token_data_array cur_p;
 
-    void reset() {
-        prev.clear();
-
-        llama_sampler_reset(grmr);
-        llama_sampler_reset(chain);
-    }
-
     void set_logits(struct llama_context * ctx, int idx) {
         const auto * logits = llama_get_logits_ith(ctx, idx);
 
@@ -136,12 +128,6 @@ struct common_sampler {
 
         cur_p = { cur.data(), cur.size(), -1, false };
     }
-
-    common_time_meas tm() {
-        return common_time_meas(t_total_us, params.no_perf);
-    }
-
-    mutable int64_t t_total_us = 0;
 };
 
 std::string common_params_sampling::print() const {
@@ -312,8 +298,6 @@ void common_sampler_free(struct common_sampler * gsmpl) {
 }
 
 void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) {
-    const auto tm = gsmpl->tm();
-
     if (accept_grammar) {
         llama_sampler_accept(gsmpl->grmr, token);
     }
@@ -324,7 +308,9 @@ void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, boo
 }
 
 void common_sampler_reset(struct common_sampler * gsmpl) {
-    gsmpl->reset();
+    llama_sampler_reset(gsmpl->grmr);
+
+    llama_sampler_reset(gsmpl->chain);
 }
 
 struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
@@ -341,54 +327,15 @@ struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
 void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl) {
     // TODO: measure grammar performance
 
-    const double t_sampling_ms = gsmpl ? 1e-3*gsmpl->t_total_us : 0;
-
-    llama_perf_sampler_data data_smpl;
-    llama_perf_context_data data_ctx;
-
-    memset(&data_smpl, 0, sizeof(data_smpl));
-    memset(&data_ctx,  0, sizeof(data_ctx));
-
     if (gsmpl) {
-        auto & data = data_smpl;
-
-        data = llama_perf_sampler(gsmpl->chain);
-
-        // note: the sampling time includes the samplers time + extra time spent in common/sampling
-        LOG_INF("%s:    sampling time = %10.2f ms\n", __func__, t_sampling_ms);
-        LOG_INF("%s:    samplers time = %10.2f ms / %5d tokens\n", __func__, data.t_sample_ms, data.n_sample);
+        llama_perf_sampler_print(gsmpl->chain);
     }
-
     if (ctx) {
-        auto & data = data_ctx;
-
-        data = llama_perf_context(ctx);
-
-        const double t_end_ms = 1e-3 * ggml_time_us();
-
-        const double t_total_ms = t_end_ms - data.t_start_ms;
-        const double t_unacc_ms = t_total_ms - (t_sampling_ms + data.t_p_eval_ms + data.t_eval_ms);
-        const double t_unacc_pc = 100.0 * t_unacc_ms /  t_total_ms;
-
-        LOG_INF("%s:        load time = %10.2f ms\n", __func__, data.t_load_ms);
-        LOG_INF("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
-                __func__, data.t_p_eval_ms, data.n_p_eval, data.t_p_eval_ms / data.n_p_eval, 1e3 / data.t_p_eval_ms * data.n_p_eval);
-        LOG_INF("%s:        eval time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
-                __func__, data.t_eval_ms, data.n_eval, data.t_eval_ms / data.n_eval, 1e3 / data.t_eval_ms * data.n_eval);
-        LOG_INF("%s:       total time = %10.2f ms / %5d tokens\n", __func__, (t_end_ms - data.t_start_ms), (data.n_p_eval + data.n_eval));
-        LOG_INF("%s: unaccounted time = %10.2f ms / %5.1f %%      (total - sampling - prompt eval - eval) / (total)\n", __func__, t_unacc_ms, t_unacc_pc);
-        LOG_INF("%s:    graphs reused = %10d\n", __func__, data.n_reused);
-
-        llama_memory_breakdown_print(ctx);
+        llama_perf_context_print(ctx);
     }
 }
 
 llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) {
-    llama_synchronize(ctx);
-
-    // start measuring sampling time after the llama_context synchronization in order to not measure any ongoing async operations
-    const auto tm = gsmpl->tm();
-
     gsmpl->set_logits(ctx, idx);
 
     auto & grmr  = gsmpl->grmr;
@@ -480,8 +427,6 @@ uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) {
 // helpers
 
 llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl, bool do_sort) {
-    const auto tm = gsmpl->tm();
-
     auto * res = &gsmpl->cur_p;
 
     if (do_sort && !res->sorted) {

common/unicode.cpp

@@ -1,64 +0,0 @@
-#include "unicode.h"
-
-// implementation adopted from src/unicode.cpp
-
-size_t utf8_sequence_length(unsigned char first_byte) {
-    const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
-    uint8_t highbits = static_cast<uint8_t>(first_byte) >> 4;
-    return lookup[highbits];
-}
-
-utf8_parse_result parse_utf8_codepoint(std::string_view input, size_t offset) {
-    if (offset >= input.size()) {
-        return utf8_parse_result(utf8_parse_result::INCOMPLETE);
-    }
-
-    // ASCII fast path
-    if (!(input[offset] & 0x80)) {
-        return utf8_parse_result(utf8_parse_result::SUCCESS, input[offset], 1);
-    }
-
-    // Invalid: continuation byte as first byte
-    if (!(input[offset] & 0x40)) {
-        return utf8_parse_result(utf8_parse_result::INVALID);
-    }
-
-    // 2-byte sequence
-    if (!(input[offset] & 0x20)) {
-        if (offset + 1 >= input.size()) {
-            return utf8_parse_result(utf8_parse_result::INCOMPLETE);
-        }
-        if ((input[offset + 1] & 0xc0) != 0x80) {
-            return utf8_parse_result(utf8_parse_result::INVALID);
-        }
-        auto result = ((input[offset] & 0x1f) << 6) | (input[offset + 1] & 0x3f);
-        return utf8_parse_result(utf8_parse_result::SUCCESS, result, 2);
-    }
-
-    // 3-byte sequence
-    if (!(input[offset] & 0x10)) {
-        if (offset + 2 >= input.size()) {
-            return utf8_parse_result(utf8_parse_result::INCOMPLETE);
-        }
-        if ((input[offset + 1] & 0xc0) != 0x80 || (input[offset + 2] & 0xc0) != 0x80) {
-            return utf8_parse_result(utf8_parse_result::INVALID);
-        }
-        auto result = ((input[offset] & 0x0f) << 12) | ((input[offset + 1] & 0x3f) << 6) | (input[offset + 2] & 0x3f);
-        return utf8_parse_result(utf8_parse_result::SUCCESS, result, 3);
-    }
-
-    // 4-byte sequence
-    if (!(input[offset] & 0x08)) {
-        if (offset + 3 >= input.size()) {
-            return utf8_parse_result(utf8_parse_result::INCOMPLETE);
-        }
-        if ((input[offset + 1] & 0xc0) != 0x80 || (input[offset + 2] & 0xc0) != 0x80 || (input[offset + 3] & 0xc0) != 0x80) {
-            return utf8_parse_result(utf8_parse_result::INVALID);
-        }
-        auto result = ((input[offset] & 0x07) << 18) | ((input[offset + 1] & 0x3f) << 12) | ((input[offset + 2] & 0x3f) << 6) | (input[offset + 3] & 0x3f);
-        return utf8_parse_result(utf8_parse_result::SUCCESS, result, 4);
-    }
-
-    // Invalid first byte
-    return utf8_parse_result(utf8_parse_result::INVALID);
-}

common/unicode.h

@@ -1,22 +0,0 @@
-#pragma once
-
-#include <cstdint>
-#include <string_view>
-
-// UTF-8 parsing utilities for streaming-aware unicode support
-
-struct utf8_parse_result {
-    uint32_t codepoint;      // Decoded codepoint (only valid if status == SUCCESS)
-    size_t bytes_consumed;   // How many bytes this codepoint uses (1-4)
-    enum status { SUCCESS, INCOMPLETE, INVALID } status;
-
-    utf8_parse_result(enum status s, uint32_t cp = 0, size_t bytes = 0)
-        : codepoint(cp), bytes_consumed(bytes), status(s) {}
-};
-
-// Determine the expected length of a UTF-8 sequence from its first byte
-// Returns 0 for invalid first bytes
-size_t utf8_sequence_length(unsigned char first_byte);
-
-// Parse a single UTF-8 codepoint from input
-utf8_parse_result parse_utf8_codepoint(std::string_view input, size_t offset);

convert_hf_to_gguf_update.py

@@ -139,10 +139,7 @@ models = [
     {"name": "lfm2",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LiquidAI/LFM2-Tokenizer"},
     {"name": "exaone4",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B", },
     {"name": "mellum",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/JetBrains/Mellum-4b-base", },
-    {"name": "afmoe",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/arcee-ai/Trinity-Tokenizer", },
-    {"name": "bailingmoe2",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/Ling-mini-base-2.0", },
-    {"name": "granite-docling",  "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ibm-granite/granite-docling-258M", },
-    {"name": "minimax-m2",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2", },
+    {"name": "llada-moe",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/LLaDA-MoE-7B-A1B-Base", },
 ]
 
 # some models are known to be broken upstream, so we will skip them as exceptions
@@ -437,7 +434,7 @@ for model in models:
             tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
         else:
             tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
-    except (OSError, TypeError) as e:
+    except OSError as e:
         logger.error(f"Failed to load tokenizer for model {name}. Error: {e}")
         continue  # Skip this model and continue with the next one in the loop
 

convert_lora_to_gguf.py

@@ -242,7 +242,7 @@ def parse_args() -> argparse.Namespace:
         help="path to write to; default: based on input. {ftype} will be replaced by the outtype.",
     )
     parser.add_argument(
-        "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "auto"], default="f32",
+        "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "auto"], default="f16",
         help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type",
     )
     parser.add_argument(
@@ -277,15 +277,10 @@ def parse_args() -> argparse.Namespace:
     return parser.parse_args()
 
 
-def load_hparams_from_hf(hf_model_id: str) -> tuple[dict[str, Any], Path | None]:
-    from huggingface_hub import try_to_load_from_cache
-
+def load_hparams_from_hf(hf_model_id: str) -> dict[str, Any]:
     # normally, adapter does not come with base model config, we need to load it from AutoConfig
     config = AutoConfig.from_pretrained(hf_model_id)
-    cache_dir = try_to_load_from_cache(hf_model_id, "config.json")
-    cache_dir = Path(cache_dir).parent if isinstance(cache_dir, str) else None
-
-    return config.to_dict(), cache_dir
+    return config.to_dict()
 
 
 if __name__ == '__main__':
@@ -330,13 +325,13 @@ if __name__ == '__main__':
     # load base model
     if base_model_id is not None:
         logger.info(f"Loading base model from Hugging Face: {base_model_id}")
-        hparams, dir_base_model = load_hparams_from_hf(base_model_id)
+        hparams = load_hparams_from_hf(base_model_id)
     elif 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}")
             try:
-                hparams, dir_base_model = load_hparams_from_hf(model_id)
+                hparams = load_hparams_from_hf(model_id)
             except OSError as e:
                 logger.error(f"Failed to load base model config: {e}")
                 logger.error("Please try downloading the base model and add its path to --base")
@@ -485,7 +480,6 @@ if __name__ == '__main__':
             dir_lora_model=dir_lora,
             lora_alpha=alpha,
             hparams=hparams,
-            remote_hf_model_id=base_model_id,
         )
 
         logger.info("Exporting model...")

docs/backend/CANN.md

@@ -313,12 +313,7 @@ Converting the matmul weight format from ND to NZ to improve performance. Enable
 
 ### GGML_CANN_ACL_GRAPH
 
-Operators are executed using ACL graph execution, rather than in op-by-op (eager) mode. Enabled by default. This option is only effective if `USE_ACL_GRAPH` was enabled at compilation time. To enable it, recompile using:
-
-```sh
-cmake -B build -DGGML_CANN=on -DCMAKE_BUILD_TYPE=release -DUSE_ACL_GRAPH=ON
-cmake --build build --config release
-```
+Operators are executed using ACL graph execution, rather than in op-by-op (eager) mode. Enabled by default.
 
 ### GGML_CANN_GRAPH_CACHE_CAPACITY
 

docs/backend/OPENCL.md

@@ -39,23 +39,18 @@ The llama.cpp OpenCL backend is designed to enable llama.cpp on **Qualcomm Adren
 | Adreno 830 (Snapdragon 8 Elite)      | Support |
 | Adreno X85 (Snapdragon X Elite)      | Support |
 
-> A6x GPUs with a recent driver and compiler are supported; they are usually found in IoT platforms.
-However, A6x GPUs in phones are likely not supported due to the outdated driver and compiler.
-
 ## DataType Supports
 
 | DataType               | Status                     |
 |:----------------------:|:--------------------------:|
 | Q4_0                   | Support                    |
 | Q6_K                   | Support, but not optimized |
-| Q8_0                   | Support                    |
-| MXFP4                  | Support                    |
 
 ## Model Preparation
 
-You can refer to the general [llama-quantize tool](/tools/quantize/README.md) for steps to convert a model in Hugging Face safetensor format to GGUF with quantization.
+You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model prepration.
 
-Currently we support `Q4_0` quantization and have optimized for it. To achieve best performance on Adreno GPU, add `--pure` to `llama-quantize` (i.e., make all weights in `Q4_0`). For example,
+Currently we support `Q4_0` quantization and have optimize for it. To achieve best performance on Adreno GPU, add `--pure` to `llama-quantize`. For example,
 
 ```sh
 ./llama-quantize --pure ggml-model-qwen2.5-3b-f16.gguf ggml-model-qwen-3b-Q4_0.gguf Q4_0
@@ -63,17 +58,6 @@ Currently we support `Q4_0` quantization and have optimized for it. To achieve b
 
 Since `Q6_K` is also supported, `Q4_0` quantization without `--pure` will also work. However, the performance will be worse compared to pure `Q4_0` quantization.
 
-### `MXFP4` MoE Models
-
-OpenAI gpt-oss models are MoE models in `MXFP4`. The quantized model will be in `MXFP4_MOE`, a mixture of `MXFP4` and `Q8_0`.
-For this quantization, there is no need to specify `--pure`.
-For gpt-oss-20b model, you can directly [download](https://huggingface.co/ggml-org/gpt-oss-20b-GGUF) the quantized GGUF file in `MXFP4_MOE` from Hugging Face.
-
-Although it is possible to quantize gpt-oss-20b model in pure `Q4_0` (all weights in `Q4_0`), it is not recommended since `MXFP4` has been optimized for MoE while `Q4_0` is not. In addition, accuracy should degrade with such pure `Q4_0` quantization.
-Hence, using the default `MXFP4_MOE` quantization (see the link above) is recommended for this model.
-
-> Note that the `Q4_0` model found [here](https://huggingface.co/unsloth/gpt-oss-20b-GGUF/blob/main/gpt-oss-20b-Q4_0.gguf) is a mixture of `Q4_0`, `Q8_0` and `MXFP4` and gives better performance than `MXFP4_MOE` quantization.
-
 ## CMake Options
 
 The OpenCL backend has the following CMake options that control the behavior of the backend.
@@ -162,13 +146,10 @@ A Snapdragon X Elite device with Windows 11 Arm64 is used. Make sure the followi
 * Ninja
 * Visual Studio 2022
 * Powershell 7
-* Python
 
 Visual Studio provides necessary headers and libraries although it is not directly used for building.
 Alternatively, Visual Studio Build Tools can be installed instead of the full Visual Studio.
 
-> Note that building using Visual Studio's cl compiler is not supported. Clang must be used. Clang depends on libraries provided by Visual Studio to work. Therefore, Visual Studio must be installed. Alternatively, Visual Studio Build Tools can be installed instead of the full Visual Studio.
-
 Powershell 7 is used for the following commands.
 If an older version of Powershell is used, these commands may not work as they are.
 
@@ -220,12 +201,9 @@ ninja
 
 ## Known Issues
 
-- Flash attention does not always improve performance.
-- Currently OpenCL backend works on A6xx GPUs with recent drivers and compilers (usually found in IoT platforms).
-  However, it does not work on A6xx GPUs found in phones with old drivers and compilers.
+- Currently OpenCL backend does not work on Adreno 6xx GPUs.
 
 ## TODO
 
 - Optimization for Q6_K
 - Support and optimization for Q4_K
-- Improve flash attention

docs/backend/SYCL.md

@@ -42,9 +42,6 @@ The following releases are verified and recommended:
 
 ## News
 
-- 2025.11
-  - Support malloc memory on device more than 4GB.
-
 - 2025.2
   - Optimize MUL_MAT Q4_0 on Intel GPU for all dGPUs and built-in GPUs since MTL. Increase the performance of LLM (llama-2-7b.Q4_0.gguf) 21%-87% on Intel GPUs (MTL, ARL-H, Arc, Flex, PVC).
     |GPU|Base tokens/s|Increased tokens/s|Percent|
@@ -148,13 +145,12 @@ The docker build option is currently limited to *Intel GPU* targets.
 ```sh
 # Using FP16
 docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" --target light -f .devops/intel.Dockerfile .
-
-# Using FP32
-docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=OFF" --target light -f .devops/intel.Dockerfile .
 ```
 
 *Notes*:
 
+To build in default FP32 *(Slower than FP16 alternative)*, set `--build-arg="GGML_SYCL_F16=OFF"` in the previous command.
+
 You can also use the `.devops/llama-server-intel.Dockerfile`, which builds the *"server"* alternative.
 Check the [documentation for Docker](../docker.md) to see the available images.
 
@@ -164,7 +160,7 @@ Check the [documentation for Docker](../docker.md) to see the available images.
 # First, find all the DRI cards
 ls -la /dev/dri
 # Then, pick the card that you want to use (here for e.g. /dev/dri/card1).
-docker run -it --rm -v "/path/to/models:/models" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card0:/dev/dri/card0 llama-cpp-sycl -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -c 4096 -s 0
+docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-sycl -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
 ```
 
 *Notes:*
@@ -219,19 +215,9 @@ To target AMD GPUs with SYCL, the ROCm stack must be installed first.
 
 2. **Install Intel® oneAPI Base toolkit**
 
-SYCL backend depends on:
-  - Intel® oneAPI DPC++/C++ compiler/running-time.
-  - Intel® oneAPI DPC++/C++ library (oneDPL).
-  - Intel® oneAPI Deep Neural Network Library (oneDNN).
-  - Intel® oneAPI Math Kernel Library (oneMKL).
-
 - **For Intel GPU**
 
-All above are included in both **Intel® oneAPI Base toolkit** and **Intel® Deep Learning Essentials** packages.
-
-It's recommended to install **Intel® Deep Learning Essentials** which only provides the necessary libraries with less size.
-
-The **Intel® oneAPI Base toolkit** and **Intel® Deep Learning Essentials** can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page.
+The base toolkit can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page.
 
 Please follow the instructions for downloading and installing the Toolkit for Linux, and preferably keep the default installation values unchanged, notably the installation path *(`/opt/intel/oneapi` by default)*.
 
@@ -239,12 +225,6 @@ Following guidelines/code snippets assume the default installation values. Other
 
 Upon a successful installation, SYCL is enabled for the available intel devices, along with relevant libraries such as oneAPI oneDNN for Intel GPUs.
 
-|Verified release|
-|-|
-|2025.2.1|
-|2025.1|
-|2024.1|
-
 - **Adding support to Nvidia GPUs**
 
 **oneAPI Plugin**: In order to enable SYCL support on Nvidia GPUs, please install the [Codeplay oneAPI Plugin for Nvidia GPUs](https://developer.codeplay.com/products/oneapi/nvidia/download). User should also make sure the plugin version matches the installed base toolkit one *(previous step)* for a seamless "oneAPI on Nvidia GPU" setup.
@@ -275,11 +255,10 @@ sycl-ls
 When targeting an intel GPU, the user should expect one or more devices among the available SYCL devices. Please make sure that at least one GPU is present via `sycl-ls`, for instance `[level_zero:gpu]` in the sample output below:
 
 ```
-[level_zero:gpu][level_zero:0] Intel(R) oneAPI Unified Runtime over Level-Zero, Intel(R) Arc(TM) A770 Graphics 12.55.8 [1.3.29735+27]
-[level_zero:gpu][level_zero:1] Intel(R) oneAPI Unified Runtime over Level-Zero, Intel(R) UHD Graphics 730 12.2.0 [1.3.29735+27]
-[opencl:cpu][opencl:0] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i5-13400 OpenCL 3.0 (Build 0) [2025.20.8.0.06_160000]
-[opencl:gpu][opencl:1] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics OpenCL 3.0 NEO  [24.39.31294]
-[opencl:gpu][opencl:2] Intel(R) OpenCL Graphics, Intel(R) UHD Graphics 730 OpenCL 3.0 NEO  [24.39.31294]
+[opencl:acc][opencl:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2  [2023.16.10.0.17_160000]
+[opencl:cpu][opencl:1] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i7-13700K OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000]
+[opencl:gpu][opencl:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics OpenCL 3.0 NEO  [23.30.26918.50]
+[level_zero:gpu][level_zero:0] Intel(R) Level-Zero, Intel(R) Arc(TM) A770 Graphics 1.3 [1.3.26918]
 ```
 
 - **Nvidia GPU**
@@ -374,7 +353,7 @@ cmake --build build --config Release -j -v
 
 #### Retrieve and prepare model
 
-You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model preparation, or download an already quantized model like [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/resolve/main/llama-2-7b.Q4_0.gguf?download=true) or [Meta-Llama-3-8B-Instruct-Q4_0.gguf](https://huggingface.co/aptha/Meta-Llama-3-8B-Instruct-Q4_0-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-Q4_0.gguf).
+You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model preparation, or download an already quantized model like [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) or [Meta-Llama-3-8B-Instruct-Q4_0.gguf](https://huggingface.co/aptha/Meta-Llama-3-8B-Instruct-Q4_0-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-Q4_0.gguf).
 
 ##### Check device
 
@@ -487,17 +466,7 @@ If you already have a recent version of Microsoft Visual Studio, you can skip th
 
 3. Install Intel® oneAPI Base toolkit
 
-SYCL backend depends on:
-  - Intel® oneAPI DPC++/C++ compiler/running-time.
-  - Intel® oneAPI DPC++/C++ library (oneDPL).
-  - Intel® oneAPI Deep Neural Network Library (oneDNN).
-  - Intel® oneAPI Math Kernel Library (oneMKL).
-
-All above are included in both **Intel® oneAPI Base toolkit** and **Intel® Deep Learning Essentials** packages.
-
-It's recommended to install **Intel® Deep Learning Essentials** which only provides the necessary libraries with less size.
-
-The **Intel® oneAPI Base toolkit** and **Intel® Deep Learning Essentials** can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page.
+The base toolkit can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page.
 
 Please follow the instructions for downloading and installing the Toolkit for Windows, and preferably keep the default installation values unchanged, notably the installation path *(`C:\Program Files (x86)\Intel\oneAPI` by default)*.
 
@@ -792,8 +761,6 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 | GGML_SYCL_DISABLE_GRAPH | 0 or 1 (default) | Disable running computations through SYCL Graphs feature. Disabled by default because graph performance isn't yet better than non-graph performance. |
 | GGML_SYCL_DISABLE_DNN | 0 (default) or 1 | Disable running computations through oneDNN and always use oneMKL. |
 | ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer |
-| UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS | 0 (default) or 1 | Support malloc device memory more than 4GB.|
-
 
 
 ## Known Issues
@@ -840,14 +807,6 @@ use 1 SYCL GPUs: [0] with Max compute units:512
   | The default context is too big. It leads to excessive memory usage.|Set `-c 8192` or a smaller value.|
   | The model is too big and requires more memory than what is available.|Choose a smaller model or change to a smaller quantization, like Q5 -> Q4;<br>Alternatively, use more than one device to load model.|
 
-- `ggml_backend_sycl_buffer_type_alloc_buffer: can't allocate 5000000000 Bytes of memory on device`
-
-  You need to enable to support 4GB memory malloc by:
-  ```
-    export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
-    set UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
-  ```
-
 ### **GitHub contribution**:
 Please add the `SYCL :` prefix/tag in issues/PRs titles to help the SYCL contributors to check/address them without delay.
 

docs/backend/ZenDNN.md

@@ -1,258 +0,0 @@
-# llama.cpp for AMD ZenDNN
-
-> [!WARNING]
-> **Note:** ZenDNN is **not** the same as zDNN.
-> - **ZenDNN** (this page): AMD's deep learning library for AMD EPYC CPUs
-> - **zDNN**: IBM's Deep Neural Network acceleration library for IBM Z & LinuxONE Mainframes ([see zDNN documentation](zDNN.md))
-
-- [Background](#background)
-- [OS](#os)
-- [Hardware](#hardware)
-- [Supported Operations](#supported-operations)
-- [DataType Supports](#datatype-supports)
-- [Linux](#linux)
-- [Environment Variable](#environment-variable)
-- [Performance Optimization](#performance-optimization)
-- [Known Issues](#known-issues)
-- [TODO](#todo)
-
-## Background
-
-**ZenDNN** (Zen Deep Neural Network Library) is AMD's high-performance deep learning inference library optimized for AMD EPYC™ CPUs. It provides optimized implementations of key deep learning primitives and operations, delivering significant performance improvements for neural network workloads on AMD Zen-based processor architectures.
-
-**Llama.cpp + ZenDNN**
-
-The llama.cpp ZenDNN backend leverages AMD's optimized matrix multiplication primitives to accelerate inference on AMD CPUs. It utilizes ZenDNN's **LowOHA (Low Overhead Hardware Accelerated)** MatMul operator for efficient GEMM operations with minimal execution overhead, built-in weight caching, and direct access to backend libraries (AOCL BLIS, LibXSMM, OneDNN).
-
-For more information about ZenDNN, visit: https://www.amd.com/en/developer/zendnn.html
-
-## OS
-
-| OS      | Status  | Verified                                       |
-|:-------:|:-------:|:----------------------------------------------:|
-| Linux   | Support | Ubuntu 20.04, 22.04, 24.04                     |
-
-For the latest list of supported operating systems, see the [ZenDNN Supported OS](https://github.com/amd/ZenDNN/blob/zendnnl/README.md#15-supported-os).
-
-## Hardware
-
-### AMD CPUs
-
-**Recommended Processors**
-
-ZenDNN is optimized for AMD EPYC™ processors and AMD Ryzen™ processors based on "Zen" microarchitecture and newer.
-
-| CPU Family                    | Status  | Notes                              |
-|:-----------------------------:|:-------:|:----------------------------------:|
-| AMD EPYC™ 9005 Series (Turin)| Support | 5th Gen - Zen 5 architecture       |
-| AMD EPYC™ 9004 Series (Genoa)| Support | 4th Gen - Zen 4 architecture       |
-| AMD EPYC™ 7003 Series (Milan)| Support | 3rd Gen - Zen 3 architecture       |
-| AMD Ryzen™ AI MAX (Strix Halo)| Support | High-performance mobile processors |
-
-*Notes:*
-
-- Best performance is achieved on AMD EPYC™ processors with high core counts (e.g., EPYC 9005 series).
-- ZenDNN leverages AMD's advanced CPU features including AVX2 and AVX-512 instruction sets.
-- For optimal performance, ensure your system has sufficient memory bandwidth.
-
-## Supported Operations
-
-The ZenDNN backend currently accelerates **matrix multiplication (MUL_MAT)** operations only. Other operations are handled by the standard CPU backend.
-
-| Operation    | Status  | Notes                                          |
-|:-------------|:-------:|:----------------------------------------------:|
-| MUL_MAT      |    ✓    | Accelerated via ZenDNN LowOHA MatMul           |
-
-*Note:* Since only MUL_MAT is accelerated, models will benefit most from ZenDNN when matrix multiplications dominate the computational workload (which is typical for transformer-based LLMs).
-
-## DataType Supports
-
-| DataType               | Status  | Notes                                         |
-|:----------------------:|:-------:|:---------------------------------------------:|
-| FP32                   | Support | Full precision floating point                 |
-| BF16                   | Support | BFloat16 (best performance on Zen 4/Zen 5)    |
-
-*Notes:*
-
-- **BF16** provides best performance on Zen 4 and Zen 5 EPYC™ processors (Genoa, Turin).
-
-## Linux
-
-### I. Setup Environment
-
-You have two options to set up ZenDNN:
-
-#### Option 1: Automatic Download and Build (Recommended)
-
-CMake will automatically download and build ZenDNN for you:
-
-```sh
-# Build llama.cpp - ZenDNN will be automatically downloaded and built
-cmake -B build -DGGML_ZENDNN=ON -DCMAKE_BUILD_TYPE=Release
-cmake --build build --config Release -j $(nproc)
-```
-
-No manual ZenDNN installation required. CMake will handle everything automatically.
-
-#### Option 2: Use Custom ZenDNN Installation
-
-If you want to build ZenDNN yourself or use a specific version:
-
-**Step 1: Build ZenDNN from source**
-
-```sh
-# Clone ZenDNN repository
-git clone https://github.com/amd/ZenDNN.git
-cd ZenDNN
-git checkout zendnnl
-
-# Build and install (requires CMake >= 3.25)
-mkdir build && cd build
-cmake ..
-cmake --build . --target all
-```
-
-Default installation path: `ZenDNN/build/install`
-
-**For detailed build instructions**, refer to the [ZenDNN README](https://github.com/amd/ZenDNN/blob/zendnnl/README.md).
-
-**Step 2: Build llama.cpp with custom ZenDNN path**
-
-```sh
-# Using environment variable
-export ZENDNN_ROOT=/path/to/ZenDNN/build/install
-cmake -B build -DGGML_ZENDNN=ON -DCMAKE_BUILD_TYPE=Release
-cmake --build build --config Release -j $(nproc)
-
-# OR specify path directly in CMake
-cmake -B build -DGGML_ZENDNN=ON -DZENDNN_ROOT=/path/to/ZenDNN/build/install -DCMAKE_BUILD_TYPE=Release
-cmake --build build --config Release -j $(nproc)
-```
-
-### II. Run the Server
-
-#### 1. Download Model
-
-Download LLaMA 3.1 8B Instruct BF16 model:
-
-```sh
-# Download from Hugging Face
-huggingface-cli download meta-llama/Llama-3.1-8B-Instruct-GGUF --local-dir models/
-```
-
-#### 2. Start Server
-
-Run llama.cpp server with ZenDNN acceleration:
-
-```sh
-# Set optimal configuration
-export OMP_NUM_THREADS=64  # Adjust to your CPU core count
-export ZENDNNL_MATMUL_ALGO=2  # Blocked AOCL BLIS for best performance
-
-# Start server
-./build/bin/llama-server \
-    -m models/Llama-3.1-8B-Instruct.BF16.gguf \
-    --host 0.0.0.0 \
-    --port 8080 \
-    -t 64
-```
-
-Access the server at `http://localhost:8080`.
-
-**Performance tips**:
-- Set `OMP_NUM_THREADS` to match your physical core count
-- Use `ZENDNNL_MATMUL_ALGO=2` for optimal performance
-- For NUMA systems: `numactl --cpunodebind=0 --membind=0 ./build/bin/llama-server ...`
-
-## Environment Variable
-
-### Build Time
-
-| Name               | Value                                 | Function                                    |
-|--------------------|---------------------------------------|---------------------------------------------|
-| GGML_ZENDNN        | ON/OFF                                | Enable ZenDNN backend support               |
-| ZENDNN_ROOT        | Path to ZenDNN installation           | Set ZenDNN installation directory           |
-| GGML_OPENMP        | ON/OFF (recommended: ON)              | Enable OpenMP for multi-threading           |
-
-### Runtime
-
-| Name                    | Value                    | Function                                                          |
-|-------------------------|--------------------------|-------------------------------------------------------------------|
-| OMP_NUM_THREADS         | Number (e.g., 64)        | Set number of OpenMP threads (recommended: physical core count)   |
-| ZENDNNL_MATMUL_ALGO     | 0-5                      | Select MatMul backend algorithm (see Performance Optimization)    |
-| ZENDNNL_PROFILE_LOG_LEVEL | 0-4                    | Profiling log level (0=disabled, 4=verbose)                       |
-| ZENDNNL_ENABLE_PROFILER | 0 or 1                   | Enable detailed profiling (1=enabled)                             |
-| ZENDNNL_API_LOG_LEVEL   | 0-4                      | API log level (0=disabled, 4=verbose)                             |
-
-**Example**:
-
-```sh
-export OMP_NUM_THREADS=64
-export ZENDNNL_MATMUL_ALGO=2  # Use Blocked AOCL BLIS for best performance
-./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "Test" -n 100
-```
-
-## Performance Optimization
-
-### MatMul Algorithm Selection
-
-ZenDNN's LowOHA MatMul supports multiple backend algorithms. For **best performance**, use the **Blocked AOCL BLIS** algorithm:
-
-```sh
-export ZENDNNL_MATMUL_ALGO=2  # Blocked AOCL BLIS (recommended)
-```
-
-**Available algorithms**:
-
-| Value | Algorithm              | Description                                    |
-|:-----:|:-----------------------|:----------------------------------------------|
-| 0     | Dynamic Dispatch       | Automatic backend selection (default)         |
-| 1     | AOCL BLIS              | AOCL BLIS backend                             |
-| 2     | AOCL BLIS Blocked      | **Blocked AOCL BLIS (recommended)**           |
-| 3     | OneDNN                 | OneDNN backend                                |
-| 4     | OneDNN Blocked         | Blocked OneDNN                                |
-| 5     | LibXSMM                | LibXSMM backend                               |
-
-### Profiling and Debugging
-
-For detailed profiling and logging options, refer to the [ZenDNN Logging Documentation](https://github.com/amd/ZenDNN/blob/zendnnl/docs/logging.md).
-
-## Known Issues
-
-- **Limited operation support**: Currently only matrix multiplication (MUL_MAT) is accelerated via ZenDNN. Other operations fall back to the standard CPU backend.
-- **BF16 support**: BF16 operations require AMD Zen 4 or Zen 5 architecture (EPYC 9004/9005 series). On older CPUs, operations will use FP32.
-- **NUMA awareness**: For multi-socket systems, manual NUMA binding may be required for optimal performance.
-
-## Q&A
-
-**Q: How do I verify that ZenDNN backend is being used?**
-
-A: Check the log output when running llama.cpp. You should see messages indicating the ZenDNN backend is initialized. You can also check the backend name in the output.
-
-**Q: What performance improvement can I expect?**
-
-A: Performance gains vary depending on the model size, batch size, and CPU architecture. On AMD EPYC processors, you can typically expect 1.1x-2x speedup compared to standard CPU inference for matrix multiplication operations.
-
-**Q: Can I use ZenDNN on non-AMD processors?**
-
-A: ZenDNN is optimized specifically for AMD processors. While it may work on other x86-64 CPUs, performance benefits are only guaranteed on AMD Zen-based architectures.
-
-**Q: Does ZenDNN support quantized models?**
-
-A: Currently, ZenDNN primarily supports FP32 and BF16 data types. Quantized model support is not available at this time.
-
-**Q: Why is my inference not faster with ZenDNN?**
-
-A: Ensure:
-1. You're using an AMD EPYC or Ryzen processor (Zen 2 or newer)
-2. `OMP_NUM_THREADS` is set appropriately (physical core count)
-3. `ZENDNNL_MATMUL_ALGO=2` is set for best performance (Blocked AOCL BLIS)
-4. You're using a sufficiently large model (small models may not benefit as much)
-5. Enable profiling to verify ZenDNN MatMul is being called
-
-### **GitHub Contribution**:
-Please add the **[ZenDNN]** prefix/tag in issues/PRs titles to help the ZenDNN-team check/address them without delay.
-
-## TODO
-
-- Expand operation support beyond MUL_MAT (attention operations, activations, etc.)

docs/backend/hexagon/CMakeUserPresets.json

@@ -1,49 +0,0 @@
-{
-  "version": 4,
-  "configurePresets": [
-    {
-        "name": "arm64-android-snapdragon",
-        "hidden": true,
-        "architecture": { "value": "arm64",       "strategy": "external" },
-        "toolset":      { "value": "host=x86_64", "strategy": "external" },
-        "cacheVariables": {
-            "ANDROID_ABI":      "arm64-v8a",
-            "ANDROID_PLATFORM": "android-31",
-            "CMAKE_TOOLCHAIN_FILE": "$env{ANDROID_NDK_ROOT}/build/cmake/android.toolchain.cmake",
-            "CMAKE_C_FLAGS":   "-march=armv8.7a+fp16 -fvectorize -ffp-model=fast -fno-finite-math-only -flto -D_GNU_SOURCE",
-            "CMAKE_CXX_FLAGS": "-march=armv8.7a+fp16 -fvectorize -ffp-model=fast -fno-finite-math-only -flto -D_GNU_SOURCE",
-            "CMAKE_C_FLAGS_RELEASE":          "-O3 -DNDEBUG",
-            "CMAKE_CXX_FLAGS_RELEASE":        "-O3 -DNDEBUG",
-            "CMAKE_C_FLAGS_RELWITHDEBINFO":   "-O3 -DNDEBUG -g",
-            "CMAKE_CXX_FLAGS_RELWITHDEBINFO": "-O3 -DNDEBUG -g",
-            "HEXAGON_SDK_ROOT": "$env{HEXAGON_SDK_ROOT}",
-            "PREBUILT_LIB_DIR": "android_aarch64",
-            "GGML_OPENMP":      "OFF",
-            "GGML_LLAMAFILE":   "OFF",
-            "GGML_OPENCL":      "ON",
-            "GGML_HEXAGON":     "ON",
-            "LLAMA_CURL":       "OFF"
-        }
-    },
-
-    {
-        "name": "arm64-windows-snapdragon",
-        "inherits": [ "base", "arm64-windows-llvm" ],
-        "cacheVariables": {
-            "HEXAGON_SDK_ROOT": "$env{HEXAGON_SDK_ROOT}",
-            "PREBUILT_LIB_DIR": "windows_aarch64",
-            "GGML_OPENMP":      "OFF",
-            "GGML_LLAMAFILE":   "OFF",
-            "GGML_OPENCL":      "ON",
-            "GGML_HEXAGON":     "ON",
-            "LLAMA_CURL":       "OFF"
-        }
-    },
-
-    { "name": "arm64-android-snapdragon-debug"  , "inherits": [ "base", "arm64-android-snapdragon", "debug" ] },
-    { "name": "arm64-android-snapdragon-release", "inherits": [ "base", "arm64-android-snapdragon", "release" ] },
-
-    { "name": "arm64-windows-snapdragon-debug"  , "inherits": [ "base", "arm64-windows-snapdragon", "debug" ] },
-    { "name": "arm64-windows-snapdragon-release", "inherits": [ "base", "arm64-windows-snapdragon", "release" ] }
-  ]
-}

docs/backend/hexagon/README.md

@@ -1,239 +0,0 @@
-# Snapdragon-based Android devices
-
-## How to Build
-
-The easiest way to build llama.cpp for a Snapdragon-based Android device is using the toolchain Docker image (see github.com/snapdragon-toolchain).
-This image includes Android NDK, OpenCL SDK, Hexagon SDK, CMake, etc.
-
-This method works on Linux, macOS, and Windows. macOS and Windows users should install Docker Desktop.
-
-```
-~/src/llama.cpp$ docker run -it -u $(id -u):$(id -g) --volume $(pwd):/workspace --platform linux/amd64 ghcr.io/snapdragon-toolchain/arm64-android:v0.3
-[d]/> cd /workspace
-```
-
-The rest of the Android build process assumes that you're running inside the toolchain container.
-Let's build llama.cpp with CPU, OpenCL, and Hexagon backends via CMake presets:
-
-```
-[d]/workspace> cp docs/backend/hexagon/CMakeUserPresets.json .
-
-[d]/workspace> cmake --preset arm64-android-snapdragon-release -B build-snapdragon
-Preset CMake variables:
-  ANDROID_ABI="arm64-v8a"
-  ...
-  CMAKE_TOOLCHAIN_FILE="/opt/android-ndk-r28b/build/cmake/android.toolchain.cmake"
-  GGML_HEXAGON="ON"
-  GGML_OPENCL="ON"
-  GGML_OPENMP="OFF"
-  HEXAGON_SDK_ROOT="/opt/hexagon/6.4.0.2"
-...
--- Including OpenCL backend
--- Including Hexagon backend
-...
--- Build files have been written to: /workspace/build-snapdragon
-
-[d]/workspace> cmake --build build-snapdragon
-...
-[144/356] Performing build step for 'htp-v73'
-[1/16] Generating htp_iface_skel.c, htp_iface_stub.c, htp_iface.h
-[2/16] Building C object CMakeFiles/ggml-htp-v73.dir/hvx-sigmoid.c.obj
-[3/16] Building C object CMakeFiles/ggml-htp-v73.dir/htp-dma.c.obj
-[4/16] Building C object CMakeFiles/ggml-htp-v73.dir/worker-pool.c.obj
-...
--- Installing: /workspace/build-snapdragon/ggml/src/ggml-hexagon/libggml-htp-v73.so
--- Installing: /workspace/build-snapdragon/ggml/src/ggml-hexagon/libggml-htp-v75.so
-...
-```
-
-To generate an installable "package" simply use cmake --install:
-
-```
-[d]/workspace> cmake --install build-snapdragon --prefix pkg-adb/llama.cpp
--- Install configuration: "Release"
--- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-cpu.so
--- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-opencl.so
--- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-hexagon.so
--- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-htp-v73.so
--- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-htp-v75.so
--- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-htp-v79.so
--- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-htp-v81.so
--- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml.so
-...
--- Installing: /workspace/pkg-adb/llama.cpp/bin/llama-bench
--- Installing: /workspace/pkg-adb/llama.cpp/bin/llama-cli
-...
-```
-
-## How to Install
-
-For this step, your device needs to be configured for on-device development.
-Please see https://developer.android.com/studio/debug/dev-options for details.
-
-Once ADB is enabled, use `adb push` to install `pkg-snapdragon` on the device.
-**Note that the toolchain Docker image doesn't have ADB and doesn't set up the ADB bridge. Please use native ADB on the host.**
-
-```
-~/src/llama.cpp$ adb push pkg-adb/llama.cpp /data/local/tmp/
-pkg-adb/llama.cpp/bin/: 67 files pushed, 0 skipped. 190.2 MB/s (919095042 bytes in 4.607s)
-pkg-adb/llama.cpp/include/: 19 files pushed, 0 skipped. 20.5 MB/s (255173 bytes in 0.012s)
-pkg-adb/llama.cpp/lib/: 16 files pushed, 0 skipped. 144.4 MB/s (43801382 bytes in 0.289s)
-102 files pushed, 0 skipped. 186.9 MB/s (963151597 bytes in 4.914s)
-```
-
-At this point, you should also install some models:
-
-```
-~/src/llama.cpp$ wget https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_0.gguf
-...
-2025-10-11 12:04:52 (10.7 MB/s) - ‘Llama-3.2-1B-Instruct-Q4_0.gguf’ saved [773025920/773025920]
-
-~/src/llama.cpp$ adb push Llama-3.2-1B-Instruct-Q4_0.gguf /data/local/tmp/gguf
-Llama-3.2-1B-Instruct-Q4_0.gguf: 1 file pushed, 0 skipped. 38.3 MB/s (773025920 bytes in 19.250s)
-```
-
-## How to Run
-
-The easiest way to run llama.cpp cli tools is using provided wrapper scripts that properly set up all required environment variables.
-
-llama.cpp supports three backends on Snapdragon-based devices: CPU, Adreno GPU (GPUOpenCL), and Hexagon NPU (HTP0-4).
-You can select which backend to run the model on using the `D=` variable, which maps to the `--device` option.
-
-Hexagon NPU behaves as a "GPU" device when it comes to `-ngl` and other offload-related options.
-
-Here are some examples of running various llama.cpp tools via ADB.
-
-Simple question for Llama-3.2-1B
-
-```
-~/src/llama.cpp$ M=Llama-3.2-1B-Instruct-Q4_0.gguf D=HTP0 ./scripts/snapdragon/adb/run-cli.sh -no-cnv -p "what is the most popular cookie in the world?"
-...
-ggml-hex: Hexagon backend (experimental) : allocating new registry : ndev 1
-ggml-hex: Hexagon Arch version v79
-ggml-hex: allocating new session: HTP0
-ggml-hex: new session: HTP0 : session-id 0 domain-id 3 uri file:///libggml-htp-v79.so?htp_iface_skel_handle_invoke&_modver=1.0&_dom=cdsp&_session=0 handle 0xb4000072c7955e50
-...
-load_tensors: offloading output layer to GPU
-load_tensors: offloaded 17/17 layers to GPU
-load_tensors:          CPU model buffer size =   225.49 MiB
-load_tensors:         HTP0 model buffer size =     0.26 MiB
-load_tensors:  HTP0-REPACK model buffer size =   504.00 MiB
-...
-I hope this helps you understand the world's most popular cookies! [end of text]
-...
-llama_perf_sampler_print:    sampling time =      30.08 ms /   487 runs   (    0.06 ms per token, 16191.77 tokens per second)
-llama_perf_context_print:        load time =     617.94 ms
-llama_perf_context_print: prompt eval time =      80.76 ms /    11 tokens (    7.34 ms per token,   136.21 tokens per second)
-llama_perf_context_print:        eval time =    9210.59 ms /   475 runs   (   19.39 ms per token,    51.57 tokens per second)
-llama_perf_context_print:       total time =    9454.92 ms /   486 tokens
-llama_perf_context_print:    graphs reused =        473
-llama_memory_breakdown_print: | memory breakdown [MiB] | total   free    self   model   context   compute    unaccounted |
-llama_memory_breakdown_print: |   - HTP0 (Hexagon)     |  2048 = 2048 + (   0 =     0 +       0 +       0) +           0 |
-llama_memory_breakdown_print: |   - Host               |                  439 =   225 +     136 +      77                |
-llama_memory_breakdown_print: |   - HTP0-REPACK        |                  504 =   504 +       0 +       0                |
-```
-
-Summary request for OLMoE-1B-7B. This is a large model that requires two HTP sessions/devices
-
-```
-~/src/llama.cpp$ M=OLMoE-1B-7B-0125-Instruct-Q4_0.gguf NDEV=2 D=HTP0,HTP1 ./scripts/snapdragon/adb/run-cli.sh -f surfing.txt -no-cnv
-...
-ggml-hex: Hexagon backend (experimental) : allocating new registry : ndev 1
-ggml-hex: Hexagon Arch version v81
-ggml-hex: allocating new session: HTP0
-ggml-hex: allocating new session: HTP1
-...
-load_tensors: offloading output layer to GPU
-load_tensors: offloaded 17/17 layers to GPU
-load_tensors:          CPU model buffer size =   143.86 MiB
-load_tensors:         HTP1 model buffer size =     0.23 MiB
-load_tensors:  HTP1-REPACK model buffer size =  1575.00 MiB
-load_tensors:         HTP0 model buffer size =     0.28 MiB
-load_tensors:  HTP0-REPACK model buffer size =  2025.00 MiB
-...
-llama_context:        CPU  output buffer size =     0.19 MiB
-llama_kv_cache:       HTP1 KV buffer size =   238.00 MiB
-llama_kv_cache:       HTP0 KV buffer size =   306.00 MiB
-llama_kv_cache: size =  544.00 MiB (  8192 cells,  16 layers,  1/1 seqs), K (q8_0):  272.00 MiB, V (q8_0):  272.00 MiB
-llama_context:       HTP0 compute buffer size =    15.00 MiB
-llama_context:       HTP1 compute buffer size =    15.00 MiB
-llama_context:        CPU compute buffer size =    24.56 MiB
-...
-llama_perf_context_print: prompt eval time =    1730.57 ms /   212 tokens (    8.16 ms per token,   122.50 tokens per second)
-llama_perf_context_print:        eval time =    5624.75 ms /   257 runs   (   21.89 ms per token,    45.69 tokens per second)
-llama_perf_context_print:       total time =    7377.33 ms /   469 tokens
-llama_perf_context_print:    graphs reused =        255
-llama_memory_breakdown_print: | memory breakdown [MiB] | total   free    self   model   context   compute    unaccounted |
-llama_memory_breakdown_print: |   - HTP0 (Hexagon)     |  2048 = 2048 + (   0 =     0 +       0 +       0) +           0 |
-llama_memory_breakdown_print: |   - HTP1 (Hexagon)     |  2048 = 2048 + (   0 =     0 +       0 +       0) +           0 |
-llama_memory_breakdown_print: |   - Host               |                  742 =   144 +     544 +      54                |
-llama_memory_breakdown_print: |   - HTP1-REPACK        |                 1575 =  1575 +       0 +       0                |
-llama_memory_breakdown_print: |   - HTP0-REPACK        |                 2025 =  2025 +       0 +       0                |
-```
-
-Op test for MUL_MAT
-
-```
-~/src/llama.cpp$ HB=0 ./scripts/snapdragon/adb/run-tool.sh test-backend-ops -b HTP0 -o MUL_MAT
-...
-Backend 2/3: HTP0
-Device description: Hexagon
-Device memory: 2048 MB (2048 MB free)
-MUL_MAT(type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
-MUL_MAT(type_a=q4_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
-MUL_MAT(type_a=q4_0,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
-
-~/src/llama.cpp-hexagon$ M=Llama-3.2-1B-Instruct-Q4_0.gguf ./scripts/snapdragon/adb/run-bench.sh -p 128 -n 64
-...
-ggml-hex: Hexagon backend (experimental) : allocating new registry : ndev 1
-ggml-hex: Hexagon Arch version v79
-ggml-hex: allocating new session: HTP0
-ggml-hex: new session: HTP0 : session-id 0 domain-id 3 uri file:///libggml-htp-v79.so?htp_iface_skel_handle_invoke&_modver=1.0&_dom=cdsp&_session=0 handle 0xb400007d4b231090
-| model          |       size | params | backend    | ngl | threads | n_batch | mmap |  test |           t/s |
-| ---------------| ---------: | -----: | ---------- | --: | ------: | ------: | ---: | ----: | ------------: |
-| llama 1B Q4_0  | 729.75 MiB | 1.24 B | HTP        |  99 |       4 |     128 |    0 | pp128 | 169.42 ± 1.75 |
-| llama 1B Q4_0  | 729.75 MiB | 1.24 B | HTP        |  99 |       4 |     128 |    0 |  tg64 |  51.54 ± 1.13 |
-
-build: 6a8cf8914 (6733)
-```
-
-## Environment variables
-
-- `GGML_HEXAGON_NDEV=1`
-  Controls the number of devices/sessions to allocate. The default is 1.
-  Most quantized models under 4B fit into a single session; an 8B model needs two, and a 20B model needs four.
-
-- `GGML_HEXAGON_NHVX=0`
-  Controls the number of HVX hardware threads to use. The default is all (actual number varies depending on the hardware version).
-
-- `GGML_HEXAGON_HOSTBUF=1`
-  Controls whether the Hexagon backend allocates host buffers. By default, all buffers except for REPACK are host buffers.
-  This option is required for testing Ops that require REPACK buffers (MUL_MAT and MUL_MAT_ID).
-
-- `GGML_HEXAGON_VERBOSE=1`
-  Enables verbose logging of Ops from the backend. Example output:
-
-  ```
-  ggml-hex: HTP0 graph-compute n_nodes 2
-  ggml-hex: HTP0 matmul : blk.27.ffn_up.weight x ffn_norm-27 -> ffn_up-27 : 3072:8192 x 3072:1 -> 8192:1 : q4_0 x f32 -> f32 : HTP0 x HTP0 -> HTP0 : flags 0x1
-  ggml-hex: HTP0 matmul : blk.27.ffn_gate.weight x ffn_norm-27 -> ffn_gate-27 : 3072:8192 x 3072:1 -> 8192:1 : q4_0 x f32 -> f32 : HTP0 x HTP0 -> HTP0 : flags 0x3
-  ggml-hex: HTP0 graph-compute n_nodes 1
-  ggml-hex: HTP0 matmul : blk.27.ffn_down.weight x ffn_gate_par-27 -> ffn_out-27 : 8192:3072 x 8192:1 -> 3072:1 : q4_0 x f32 -> f32 : HTP0 x HTP0 -> HTP0 : flags 0x0
-  ggml-hex: HTP0 get-tensor result_output : data 0x7592487000 offset 0 size 513024
-  ```
-
-- `GGML_HEXAGON_PROFILE=1`
-  Generates a host-side profile for the ggml-hexagon Ops.
-
-- `GGML_HEXAGON_OPMASK=0x0`
-  Allows enabling specific stages of the processing pipeline:
-
-  - `0x1` Enable Op Queue (i.e., queuing Ops into NPU)
-  - `0x2` Enable Dynamic Quantizer (if needed for the Op)
-  - `0x4` Enable Op Compute (MUL_MAT, etc.)
-
-  Examples:
-
-      `GGML_HEXAGON_OPMASK=0x1 llama-cli ...` - Ops are enqueued but NPU-side processing is stubbed out
-      `GGML_HEXAGON_OPMASK=0x3 llama-cli ...` - NPU performs dynamic quantization and skips the rest
-      `GGML_HEXAGON_OPMASK=0x7 llama-cli ...` - Full queuing and processing of Ops (default)

docs/backend/hexagon/developer.md

@@ -1,109 +0,0 @@
-# Hexagon backend developer details
-
-## Backend libraries
-
-The Hexagon backend consist of two parts:
-
-  - `libggml-hexagon`
-    This is the regular CPU-side GGML backend library, either shared or statically linked
-
-  - `libggml-htp-vNN`
-    This is the NPU-side (HTP stands for Hexagon Tensor Processor) shared library that contains the Op dispatcher and kernels.
-    The correct library is selected automatically at runtime based on the HW version.
-
-Here is an example of the build artifacts
-
-```
-~/src/llama.cpp$ ls -l pkg-adb/llama.cpp/lib/libggml*
-pkg-adb/llama.cpp/lib/libggml-base.so
-pkg-adb/llama.cpp/lib/libggml-cpu.so
-pkg-adb/llama.cpp/lib/libggml-hexagon.so      <<< CPU library
-pkg-adb/llama.cpp/lib/libggml-htp-v73.so      <<< HTP op/kernels for Hexagon v73
-pkg-adb/llama.cpp/lib/libggml-htp-v75.so
-pkg-adb/llama.cpp/lib/libggml-htp-v79.so
-pkg-adb/llama.cpp/lib/libggml-htp-v81.so
-```
-
-## Memory buffers
-
-Hexagon NPU backend takes advantage of the Snapdragon's unified memory model where all buffers are fully accessible by the CPU and GPU.
-The NPU does have a dedicated tightly-coupled memory called VTCM but that memory is used only for intermediate data (e.g. dynamically
-quantized tensors) or temporary data (chunks of the weight tensors fetched via DMA).
-
-Please note that currently the Hexagon backend does not implement SET/GET_ROWS Ops because there is no advantage in offloading those
-to the NPU at this point.
-
-The backend does allocates non-host buffers for the tensors with datatypes that require repacking: Q4_0, Q8_0, MXFP4.
-From the MMU perspective these buffers are still regular buffers (normal access by the CPU) they are marked as non-host simply to force
-the repacking.
-
-## Large model handling
-
-Hexagon NPU session (aka Process Domain (PD) in the Hexagon docs) is limited to a memory mapping of around 3.5GB.
-In llama.cpp/GGML the Hexagon session is mapped to a single GGML backend device (HTP0, HTP1, etc).
-
-In order to map models larger than 3.5GB we need to allocate multiple devices and split the model.
-For this we're taking advantage of the llama.cpp/GGML multi-GPU layer-splitting support.
-Each Hexagon device behaves like a GPU from the offload and model splitting perspective.
-
-Here is an example of running GPT-OSS-20B model on a newer Snapdragon device with 16GB of DDR.
-
-```
-M=gpt-oss-20b-Q4_0.gguf NDEV=4 D=HTP0,HTP1,HTP2,HTP3 P=surfing.txt scripts/snapdragon/adb/run-cli.sh -no-cnv -f surfing.txt -n 32
-...
-LD_LIBRARY_PATH=/data/local/tmp/llama.cpp/lib
-ADSP_LIBRARY_PATH=/data/local/tmp/llama.cpp/lib
-GGML_HEXAGON_NDEV=4 ./bin/llama-cli --no-mmap -m /data/local/tmp/llama.cpp/../gguf/gpt-oss-20b-Q4_0.gguf
-      -t 4 --ctx-size 8192 --batch-size 128 -ctk q8_0 -ctv q8_0 -fa on -ngl 99 --device HTP0,HTP1,HTP2,HTP3 -no-cnv -f surfing.txt
-...
-llama_model_loader: - type  f32:  289 tensors
-llama_model_loader: - type q4_0:   96 tensors
-llama_model_loader: - type q8_0:    2 tensors
-llama_model_loader: - type mxfp4:  72 tensors
-...
-load_tensors: offloaded 25/25 layers to GPU
-load_tensors:          CPU model buffer size =  1182.09 MiB
-load_tensors:         HTP1 model buffer size =     6.64 MiB
-load_tensors:  HTP1-REPACK model buffer size =  2505.94 MiB
-load_tensors:         HTP3 model buffer size =     5.55 MiB
-load_tensors:  HTP3-REPACK model buffer size =  2088.28 MiB
-load_tensors:         HTP0 model buffer size =     7.75 MiB
-load_tensors:  HTP0-REPACK model buffer size =  2923.59 MiB
-load_tensors:         HTP2 model buffer size =     6.64 MiB
-load_tensors:  HTP2-REPACK model buffer size =  2505.94 MiB
-...
-llama_context: n_ctx_per_seq (8192) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
-llama_context:        CPU  output buffer size =     0.77 MiB
-llama_kv_cache_iswa: creating non-SWA KV cache, size = 8192 cells
-llama_kv_cache:       HTP1 KV buffer size =    25.50 MiB
-llama_kv_cache:       HTP3 KV buffer size =    25.50 MiB
-llama_kv_cache:       HTP0 KV buffer size =    25.50 MiB
-llama_kv_cache:       HTP2 KV buffer size =    25.50 MiB
-llama_kv_cache: size =  102.00 MiB (  8192 cells,  12 layers,  1/1 seqs), K (q8_0):   51.00 MiB, V (q8_0):   51.00 MiB
-llama_kv_cache_iswa: creating     SWA KV cache, size = 256 cells
-llama_kv_cache:       HTP1 KV buffer size =     0.80 MiB
-llama_kv_cache:       HTP3 KV buffer size =     0.53 MiB
-llama_kv_cache:       HTP0 KV buffer size =     1.06 MiB
-llama_kv_cache:       HTP2 KV buffer size =     0.80 MiB
-llama_kv_cache: size =    3.19 MiB (   256 cells,  12 layers,  1/1 seqs), K (q8_0):    1.59 MiB, V (q8_0):    1.59 MiB
-llama_context:       HTP0 compute buffer size =    16.06 MiB
-llama_context:       HTP1 compute buffer size =    16.06 MiB
-llama_context:       HTP2 compute buffer size =    16.06 MiB
-llama_context:       HTP3 compute buffer size =    16.06 MiB
-llama_context:        CPU compute buffer size =    98.19 MiB
-...
-llama_perf_context_print: prompt eval time =    3843.67 ms /   197 tokens ( 19.51 ms per token, 51.25 tokens per second)
-llama_perf_context_print:        eval time =    1686.13 ms /    31 runs   ( 54.39 ms per token, 18.39 tokens per second)
-llama_perf_context_print:       total time =    6266.30 ms /   228 tokens
-llama_perf_context_print:    graphs reused =         30
-llama_memory_breakdown_print: | memory breakdown [MiB] | total   free    self   model   context   compute    unaccounted |
-llama_memory_breakdown_print: |   - HTP0 (Hexagon)     |  2048 = 2048 + (   0 =     0 +       0 +       0) +           0 |
-llama_memory_breakdown_print: |   - HTP1 (Hexagon)     |  2048 = 2048 + (   0 =     0 +       0 +       0) +           0 |
-llama_memory_breakdown_print: |   - HTP2 (Hexagon)     |  2048 = 2048 + (   0 =     0 +       0 +       0) +           0 |
-llama_memory_breakdown_print: |   - HTP3 (Hexagon)     |  2048 = 2048 + (   0 =     0 +       0 +       0) +           0 |
-llama_memory_breakdown_print: |   - Host               |                 1476 =  1208 +     105 +     162                |
-llama_memory_breakdown_print: |   - HTP1-REPACK        |                 2505 =  2505 +       0 +       0                |
-llama_memory_breakdown_print: |   - HTP3-REPACK        |                 2088 =  2088 +       0 +       0                |
-llama_memory_breakdown_print: |   - HTP0-REPACK        |                 2923 =  2923 +       0 +       0                |
-llama_memory_breakdown_print: |   - HTP2-REPACK        |                 2505 =  2505 +       0 +       0                |
-```

docs/backend/zDNN.md

@@ -1,66 +0,0 @@
-# llama.cpp for IBM zDNN Accelerator
-
-> [!WARNING]
-> **Note:** zDNN is **not** the same as ZenDNN.
-> - **zDNN** (this page): IBM's Deep Neural Network acceleration library for IBM Z & LinuxONE Mainframes
-> - **ZenDNN**: AMD's deep learning library for AMD EPYC CPUs ([see ZenDNN documentation](ZenDNN.md))
-
-## Background
-
-IBM zDNN (Z Deep Neural Network) is a hardware acceleration library designed specifically to leverage the IBM NNPA (Neural Network Processor Assist) accelerator located within IBM Telum I and II processors. It provides significant performance improvements for neural network inference operations.
-
-### Llama.cpp + IBM zDNN
-
-The llama.cpp zDNN backend is designed to enable llama.cpp on IBM z17 and later systems via the IBM zDNN hardware acceleration library.
-
-## Software & Hardware Support
-
-| Hardware Level       | Status        | Verified                   |
-| -------------------- | ------------- | -------------------------- |
-| IBM z17 / LinuxONE 5 | Supported     | RHEL 9.6, IBM z17, 40 IFLs |
-| IBM z16 / LinuxONE 4 | Not Supported |                            |
-
-## Data Types Supported
-
-| Data Type | Status    |
-| --------- | --------- |
-| F32       | Supported |
-| F16       | Supported |
-| BF16      | Supported |
-
-## CMake Options
-
-The IBM zDNN backend has the following CMake options that control the behaviour of the backend.
-
-| CMake Option | Default Value | Description                         |
-| ------------ | ------------- | ----------------------------------- |
-| `GGML_ZDNN`  | `OFF`         | Compile llama.cpp with zDNN support |
-| `ZDNN_ROOT`  | `""`          | Override zDNN library lookup        |
-
-## 1. Install zDNN Library
-
-Note: Using the zDNN library provided via `apt` or `yum` may not work correctly as reported in [#15772](https://github.com/ggml-org/llama.cpp/issues/15772). It is preferred that you compile from source.
-
-```sh
-git clone --recurse-submodules https://github.com/IBM/zDNN
-cd zDNN
-
-autoreconf .
-./configure --prefix=/opt/zdnn-libs
-
-make build
-sudo make install
-```
-
-## 2. Build llama.cpp
-
-```sh
-git clone https://github.com/ggml-org/llama.cpp
-cd llama.cpp
-
-cmake -S . -G Ninja -B build \
-    -DCMAKE_BUILD_TYPE=Release \
-    -DGGML_ZDNN=ON \
-    -DZDNN_ROOT=/opt/zdnn-libs
-cmake --build build --config Release -j$(nproc)
-```

docs/build-riscv64-spacemit.md

@@ -1,90 +0,0 @@
-> [!IMPORTANT]
-> This build documentation is specific only to RISC-V SpacemiT SOCs.
-
-## Build llama.cpp locally (for riscv64)
-
-1. Prepare Toolchain For RISCV
-~~~
-wget https://archive.spacemit.com/toolchain/spacemit-toolchain-linux-glibc-x86_64-v1.1.2.tar.xz
-~~~
-
-2. Build
-Below is the build script: it requires utilizing RISC-V vector instructions for acceleration. Ensure the `GGML_CPU_RISCV64_SPACEMIT` compilation option is enabled. The currently supported optimization version is `RISCV64_SPACEMIT_IME1`, corresponding to the `RISCV64_SPACEMIT_IME_SPEC` compilation option. Compiler configurations are defined in the `riscv64-spacemit-linux-gnu-gcc.cmake` file. Please ensure you have installed the RISC-V compiler and set the environment variable via `export RISCV_ROOT_PATH={your_compiler_path}`.
-```bash
-
-cmake -B build \
-    -DCMAKE_BUILD_TYPE=Release \
-    -DGGML_CPU_RISCV64_SPACEMIT=ON \
-    -DLLAMA_CURL=OFF \
-    -DGGML_RVV=ON \
-    -DGGML_RV_ZFH=ON \
-    -DGGML_RV_ZICBOP=ON \
-    -DGGML_RV_ZIHINTPAUSE=ON \
-    -DRISCV64_SPACEMIT_IME_SPEC=RISCV64_SPACEMIT_IME1 \
-    -DCMAKE_TOOLCHAIN_FILE=${PWD}/cmake/riscv64-spacemit-linux-gnu-gcc.cmake \
-    -DCMAKE_INSTALL_PREFIX=build/installed
-
-cmake --build build --parallel $(nproc) --config Release
-
-pushd build
-make install
-popd
-```
-
-## Simulation
-You can use QEMU to perform emulation on non-RISC-V architectures.
-
-1. Download QEMU
-~~~
-wget https://archive.spacemit.com/spacemit-ai/qemu/jdsk-qemu-v0.0.14.tar.gz
-~~~
-
-2. Run Simulation
-After build your llama.cpp, you can run the executable file via QEMU for simulation, for example:
-~~~
-export QEMU_ROOT_PATH={your QEMU file path}
-export RISCV_ROOT_PATH_IME1={your RISC-V compiler path}
-
-${QEMU_ROOT_PATH}/bin/qemu-riscv64 -L ${RISCV_ROOT_PATH_IME1}/sysroot -cpu max,vlen=256,elen=64,vext_spec=v1.0 ${PWD}/build/bin/llama-cli -m ${PWD}/models/Qwen2.5-0.5B-Instruct-Q4_0.gguf -t 1
-~~~
-## Performance
-#### Quantization Support For Matrix
-~~~
-model name      : Spacemit(R) X60
-isa             : rv64imafdcv_zicbom_zicboz_zicntr_zicond_zicsr_zifencei_zihintpause_zihpm_zfh_zfhmin_zca_zcd_zba_zbb_zbc_zbs_zkt_zve32f_zve32x_zve64d_zve64f_zve64x_zvfh_zvfhmin_zvkt_sscofpmf_sstc_svinval_svnapot_svpbmt
-mmu             : sv39
-uarch           : spacemit,x60
-mvendorid       : 0x710
-marchid         : 0x8000000058000001
-~~~
-
-Q4_0
-|   Model    |   Size   | Params | backend | threads | test | t/s |
-| -----------| -------- | ------ | ------- | ------- | ---- |------|
-Qwen2.5 0.5B |403.20 MiB|630.17 M|   cpu   |    4    | pp512|64.12 ± 0.26|
-Qwen2.5 0.5B |403.20 MiB|630.17 M|   cpu   |    4    | tg128|10.03 ± 0.01|
-Qwen2.5 1.5B |1011.16 MiB| 1.78 B |   cpu   |    4    | pp512|24.16 ± 0.02|
-Qwen2.5 1.5B |1011.16 MiB| 1.78 B |   cpu   |    4    | tg128|3.83 ± 0.06|
-Qwen2.5 3B   | 1.86 GiB  | 3.40 B |   cpu   |    4    | pp512|12.08 ± 0.02|
-Qwen2.5 3B   | 1.86 GiB  | 3.40 B |   cpu   |    4    | tg128|2.23 ± 0.02|
-
-Q4_1
-|   Model    |   Size   | Params | backend | threads | test | t/s |
-| -----------| -------- | ------ | ------- | ------- | ---- |------|
-Qwen2.5 0.5B |351.50 MiB|494.03 M|   cpu   |    4    | pp512|62.07 ± 0.12|
-Qwen2.5 0.5B |351.50 MiB|494.03 M|   cpu   |    4    | tg128|9.91 ± 0.01|
-Qwen2.5 1.5B |964.06 MiB| 1.54 B |   cpu   |    4    | pp512|22.95 ± 0.25|
-Qwen2.5 1.5B |964.06 MiB| 1.54 B |   cpu   |    4    | tg128|4.01 ± 0.15|
-Qwen2.5 3B   | 1.85 GiB | 3.09 B |   cpu   |    4    | pp512|11.55 ± 0.16|
-Qwen2.5 3B   | 1.85 GiB | 3.09 B |   cpu   |    4    | tg128|2.25 ± 0.04|
-
-
-Q4_K
-|   Model    |   Size   | Params | backend | threads | test | t/s |
-| -----------| -------- | ------ | ------- | ------- | ---- |------|
-Qwen2.5 0.5B |462.96 MiB|630.17 M|   cpu   |    4    | pp512|9.29 ± 0.05|
-Qwen2.5 0.5B |462.96 MiB|630.17 M|   cpu   |    4    | tg128|5.67 ± 0.04|
-Qwen2.5 1.5B | 1.04 GiB | 1.78 B |   cpu   |    4    | pp512|10.38 ± 0.10|
-Qwen2.5 1.5B | 1.04 GiB | 1.78 B |   cpu   |    4    | tg128|3.17 ± 0.08|
-Qwen2.5 3B   | 1.95 GiB | 3.40 B |   cpu   |    4    | pp512|4.23 ± 0.04|
-Qwen2.5 3B   | 1.95 GiB | 3.40 B |   cpu   |    4    | tg128|1.73 ± 0.00|

docs/build.md

@@ -178,48 +178,6 @@ GeForce RTX 3070      8.6
 cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES="86;89"
 ```
 
-### Overriding the CUDA Version
-
-If you have multiple CUDA installations on your system and want to compile llama.cpp for a specific one, e.g. for CUDA 11.7 installed under `/opt/cuda-11.7`:
-
-```bash
-cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_COMPILER=/opt/cuda-11.7/bin/nvcc -DCMAKE_INSTALL_RPATH="/opt/cuda-11.7/lib64;\$ORIGIN" -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON
-```
-
-#### Fixing Compatibility Issues with Old CUDA and New glibc
-
-If you try to use an old CUDA version (e.g. v11.7) with a new glibc version you can get errors like this:
-
-```
-/usr/include/bits/mathcalls.h(83): error: exception specification is
-  incompatible with that of previous function "cospi"
-
-
-  /opt/cuda-11.7/bin/../targets/x86_64-linux/include/crt/math_functions.h(5545):
-  here
-```
-
-It seems the least bad solution is to patch the CUDA installation to declare the correct signatures.
-Replace the following lines in `/path/to/your/cuda/installation/targets/x86_64-linux/include/crt/math_functions.h`:
-
-```C++
-// original lines
-extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ double                 cospi(double x);
-extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ float                  cospif(float x);
-extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ double                 sinpi(double x);
-extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ float                  sinpif(float x);
-extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ double                 rsqrt(double x);
-extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ float                  rsqrtf(float x);
-
-// edited lines
-extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ double                 cospi(double x) noexcept (true);
-extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ float                  cospif(float x) noexcept (true);
-extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ double                 sinpi(double x) noexcept (true);
-extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ float                  sinpif(float x) noexcept (true);
-extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ double                 rsqrt(double x) noexcept (true);
-extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ float                  rsqrtf(float x) noexcept (true);
-```
-
 ### 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.
@@ -303,12 +261,10 @@ You can download it from your Linux distro's package manager or from here: [ROCm
 - Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU):
   ```bash
   HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
-      cmake -S . -B build -DGGML_HIP=ON -DGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
+      cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
       && cmake --build build --config Release -- -j 16
   ```
 
-  Note: `GPU_TARGETS` is optional, omitting it will build the code for all GPUs in the current system.
-
   To enhance flash attention performance on RDNA3+ or CDNA architectures, you can utilize the rocWMMA library by enabling the `-DGGML_HIP_ROCWMMA_FATTN=ON` option. This requires rocWMMA headers to be installed on the build system.
 
   The rocWMMA library is included by default when installing the ROCm SDK using the `rocm` meta package provided by AMD. Alternatively, if you are not using the meta package, you can install the library using the `rocwmma-dev` or `rocwmma-devel` package, depending on your system's package manager.
@@ -326,17 +282,17 @@ You can download it from your Linux distro's package manager or from here: [ROCm
   ```bash
   HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -p)" \
   HIP_DEVICE_LIB_PATH=<directory-you-just-found> \
-      cmake -S . -B build -DGGML_HIP=ON -DGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
+      cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
       && cmake --build build -- -j 16
   ```
 
 - 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%
-  cmake -S . -B build -G Ninja -DGPU_TARGETS=gfx1100 -DGGML_HIP=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release
+  cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DGGML_HIP=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release
   cmake --build build
   ```
-  If necessary, adapt `GPU_TARGETS` to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors)
+  Make sure that `AMDGPU_TARGETS` is set to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors)
   Find your gpu version string by matching the most significant version information from `rocminfo | grep gfx | head -1 | awk '{print $2}'` with the list of processors, e.g. `gfx1035` maps to `gfx1030`.
 
 
@@ -431,22 +387,11 @@ docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/ren
 
 ### For Linux users:
 
-#### Using the LunarG Vulkan SDK
-
 First, follow the official LunarG instructions for the installation and setup of the Vulkan SDK in the [Getting Started with the Linux Tarball Vulkan SDK](https://vulkan.lunarg.com/doc/sdk/latest/linux/getting_started.html) guide.
 
 > [!IMPORTANT]
 > After completing the first step, ensure that you have used the `source` command on the `setup_env.sh` file inside of the Vulkan SDK in your current terminal session. Otherwise, the build won't work. Additionally, if you close out of your terminal, you must perform this step again if you intend to perform a build. However, there are ways to make this persistent. Refer to the Vulkan SDK guide linked in the first step for more information about any of this.
 
-#### Using system packages
-
-On Debian / Ubuntu, you can install the required dependencies using:
-```sh
-sudo apt-get install libvulkan-dev glslc
-```
-
-#### Common steps
-
 Second, after verifying that you have followed all of the SDK installation/setup steps, use this command to make sure before proceeding:
 ```bash
 vulkaninfo
@@ -495,38 +440,6 @@ 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).
 
-## ZenDNN
-
-ZenDNN provides optimized deep learning primitives for AMD EPYC™ CPUs. It accelerates matrix multiplication operations for inference workloads.
-
-### Compilation
-
-- Using `CMake` on Linux (automatic build):
-
-    ```bash
-    cmake -B build -DGGML_ZENDNN=ON
-    cmake --build build --config Release
-    ```
-
-    The first build will automatically download and build ZenDNN, which may take 5-10 minutes. Subsequent builds will be much faster.
-
-- Using `CMake` with custom ZenDNN installation:
-
-    ```bash
-    cmake -B build -DGGML_ZENDNN=ON -DZENDNN_ROOT=/path/to/zendnn/install
-    cmake --build build --config Release
-    ```
-
-### Testing
-
-You can test with:
-
-```bash
-./build/bin/llama-cli -m PATH_TO_MODEL -p "Building a website can be done in 10 steps:" -n 50
-```
-
-For detailed information about hardware support, setup instructions, and performance optimization, refer to [llama.cpp for ZenDNN](./backend/ZenDNN.md).
-
 ## Arm® KleidiAI™
 KleidiAI is a library of optimized microkernels for AI workloads, specifically designed for Arm CPUs. These microkernels enhance performance and can be enabled for use by the CPU backend.
 

docs/development/parsing.md

@@ -1,288 +0,0 @@
-# Parsing Model Output
-
-The `common` library contains a PEG parser implementation suitable for parsing
-model output.
-
-Types with the prefix `common_peg_*` are intended for general use and may have
-applications beyond parsing model output, such as parsing user-provided regex
-patterns.
-
-Types with the prefix `common_chat_peg_*` are specialized helpers for model
-output.
-
-The parser features:
-
-- Partial parsing of streaming input
-- Built-in JSON parsers
-- AST generation with semantics via "tagged" nodes
-
-## Example
-
-Below is a contrived example demonstrating how to use the PEG parser to parse
-output from a model that emits arguments as JSON.
-
-```cpp
-auto parser = build_chat_peg_native_parser([&](common_chat_peg_native_builder & p) {
-    // Build a choice of all available tools
-    auto tool_choice = p.choice();
-    for (const auto & tool : tools) {
-        const auto & function = tool.at("function");
-        std::string name = function.at("name");
-        const auto & schema = function.at("parameters");
-
-        auto tool_name = p.json_member("name", "\"" + p.literal(name) + "\"");
-        auto tool_args = p.json_member("arguments", p.schema(p.json(), "tool-" + name + "-schema", schema));
-
-        tool_choice |= p.rule("tool-" + name, "{" << tool_name << "," << tool_args << "}");
-    }
-
-    // Define the tool call structure: <tool_call>[{tool}]</tool_call>
-    auto tool_call = p.trigger_rule("tool-call",
-        p.sequence({
-            p.literal("<tool_call>["),
-            tool_choice,
-            p.literal("]</tool_call>")
-        })
-    );
-
-    // Parser accepts content, optionally followed by a tool call
-    return p.sequence({
-        p.content(p.until("<tool_call>")),
-        p.optional(tool_call),
-        p.end()
-    });
-});
-```
-
-For a more complete example, see `test_example_native()` in
-[tests/test-chat-peg-parser.cpp](tests/test-chat-peg-parser.cpp).
-
-## Parsers/Combinators
-
-### Basic Matchers
-
-- **`eps()`** - Matches nothing and always succeeds (epsilon/empty match)
-- **`start()`** - Matches the start of input (anchor `^`)
-- **`end()`** - Matches the end of input (anchor `$`)
-- **`literal(string)`** - Matches an exact literal string
-- **`any()`** - Matches any single character (`.`)
-
-### Combinators
-
-- **`sequence(...)`** - Matches parsers in order; all must succeed
-- **`choice(...)`** - Matches the first parser that succeeds from alternatives (ordered choice)
-- **`one_or_more(p)`** - Matches one or more repetitions (`+`)
-- **`zero_or_more(p)`** - Matches zero or more repetitions (`*`)
-- **`optional(p)`** - Matches zero or one occurrence (`?`)
-- **`repeat(p, min, max)`** - Matches between min and max repetitions (use `-1` for unbounded)
-- **`repeat(p, n)`** - Matches exactly n repetitions
-
-### Lookahead
-
-- **`peek(p)`** - Positive lookahead: succeeds if parser succeeds without consuming input (`&`)
-- **`negate(p)`** - Negative lookahead: succeeds if parser fails without consuming input (`!`)
-
-### Character Classes & Utilities
-
-- **`chars(classes, min, max)`** - Matches repetitions of characters from a character class
-- **`space()`** - Matches zero or more whitespace characters (space, tab, newline)
-- **`until(delimiter)`** - Matches characters until delimiter is found (delimiter not consumed)
-- **`until_one_of(delimiters)`** - Matches characters until any delimiter in the list is found
-- **`rest()`** - Matches everything remaining (`.*`)
-
-### JSON Parsers
-
-- **`json()`** - Complete JSON parser (objects, arrays, strings, numbers, booleans, null)
-- **`json_object()`** - JSON object parser
-- **`json_array()`** - JSON array parser
-- **`json_string()`** - JSON string parser
-- **`json_number()`** - JSON number parser
-- **`json_bool()`** - JSON boolean parser
-- **`json_null()`** - JSON null parser
-- **`json_string_content()`** - JSON string content without surrounding quotes
-- **`json_member(key, p)`** - JSON object member with specific key and value parser
-
-### Grammar Building
-
-- **`ref(name)`** - Creates a lightweight reference to a named rule (for recursive grammars)
-- **`rule(name, p, trigger)`** - Creates a named rule and returns a reference
-- **`trigger_rule(name, p)`** - Creates a trigger rule (entry point for lazy grammar generation)
-- **`schema(p, name, schema, raw)`** - Wraps parser with JSON schema metadata for grammar generation
-
-### AST Control
-
-- **`atomic(p)`** - Prevents AST node creation for partial parses
-- **`tag(tag, p)`** - Creates AST nodes with semantic tags (multiple nodes can share tags)
-
-## GBNF Grammar Generation
-
-The PEG parser also acts as a convenient DSL for generating GBNF grammars, with
-some exceptions.
-
-```cpp
-data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-    foreach_function(params.tools, [&](const json & fn) {
-        builder.resolve_refs(fn.at("parameters"));
-    });
-    parser.build_grammar(builder, data.grammar_lazy);
-});
-```
-
-The notable exception is the `negate(p)` lookahead parser, which cannot be
-defined as a CFG grammar and therefore does not produce a rule. Its usage
-should be limited and preferably hidden behind a `schema()` parser. In many
-cases, `until(delimiter)` or `until_one_of(delimiters)` is a better choice.
-
-Another limitation is that the PEG parser requires an unambiguous grammar. In
-contrast, the `llama-grammar` implementation can support ambiguous grammars,
-though they are difficult to parse.
-
-### Lazy Grammars
-
-During lazy grammar generation, only rules reachable from a `trigger_rule(p)`
-are emitted in the grammar. All trigger rules are added as alternations in the
-root rule. It is still necessary to define trigger patterns, as the parser has
-no interaction with the grammar sampling.
-
-### JSON Schema
-
-The `schema(p, name, schema, raw)` parser will use the `json-schema-to-grammar`
-implementation to generate the grammar instead of the underlying parser.
-
-The `raw` option emits a grammar suitable for a raw string instead of a JSON
-string. In other words, it won't be wrapped in quotes or require escaping
-quotes. It should only be used when `type == "string"`.
-
-The downside is that it can potentially lead to ambiguous grammars. For
-example, if a user provides the pattern `^.*$`, the following grammar may be
-generated:
-
-```
-root ::= "<arg>" .* "</arg>"
-```
-
-This creates an ambiguous grammar that cannot be parsed by the PEG parser. To
-help mitigate this, if `.*` is found in the pattern, the grammar from the
-underlying parser will be emitted instead.
-
-## Common AST Shapes for Chat Parsing
-
-Most model output can be placed in one of the following categories:
-
-- Content only
-- Tool calling with arguments emitted as a single JSON object
-- Tool calling with arguments emitted as separate entities, either XML
-  (Qwen3-Coder, MiniMax M2) or pseudo-function calls (LFM2)
-
-To provide broad coverage,
-[`common/chat-peg-parser.h`](common/chat-peg-parser.h) contains builders and
-mappers that help create parsers and visitors/extractors for these types. They
-require parsers to tag nodes to conform to an AST "shape". This normalization
-makes it easy to extract information and generalize parsing.
-
-### Simple
-
-The `common_chat_peg_builder` builds a `simple` parser that supports
-content-only models with optional reasoning.
-
-- **`reasoning(p)`** - Tag node for extracting `reasoning_content`
-- **`content(p)`** - Tag node for extracting `content`
-
-```cpp
-build_chat_peg_parser([&](common_chat_peg_parser & p) {
-    return p.sequence({
-        p.optional("<think>" + p.reasoning(p.until("</think>")) + "</think>"),
-        p.content(p.until("<tool_call>")),
-        p.end()
-    });
-});
-```
-
-Use `common_chat_peg_mapper` to extract the content. Note that this is already
-done for you in `common_chat_peg_parser` when
-`chat_format == COMMON_CHAT_FORMAT_PEG_SIMPLE`.
-
-```cpp
-auto result = parser.parse(ctx);
-
-common_chat_msg msg;
-auto mapper = common_chat_peg_mapper(msg);
-mapper.from_ast(ctx.ast, result);
-```
-
-### Native
-
-The `common_chat_peg_native_builder` builds a `native` parser suitable for
-models that emit tool arguments as a direct JSON object.
-
-- **`reasoning(p)`** - Tag node for `reasoning_content`
-- **`content(p)`** - Tag node for `content`
-- **`tool(p)`** - Tag entirety of a single tool call
-- **`tool_open(p)`** - Tag start of a tool call
-- **`tool_close(p)`** - Tag end of a tool call
-- **`tool_id(p)`** - Tag the tool call ID (optional)
-- **`tool_name(p)`** - Tag the tool name
-- **`tool_args(p)`** - Tag the tool arguments
-
-```cpp
-build_chat_peg_native_parser([&](common_chat_peg_native_parser & p) {
-    auto get_weather_tool = p.tool(p.sequence({
-        p.tool_open(p.literal("{")),
-        p.json_member("name", "\"" + p.tool_name(p.literal("get_weather")) + "\""),
-        p.literal(","),
-        p.json_member("arguments", p.tool_args(p.json())),
-        p.tool_close(p.literal("}"))
-    }));
-
-    return p.sequence({
-        p.content(p.until("<tool_call>")),
-        p.literal("<tool_call>"),
-        get_weather_tool,
-        p.literal("</tool_call>"),
-        p.end()
-    });
-});
-```
-
-### Constructed
-
-The `common_chat_peg_constructed_builder` builds a `constructed` parser
-suitable for models that emit tool arguments as separate entities, such as XML
-tags.
-
-- **`reasoning(p)`** - Tag node for `reasoning_content`
-- **`content(p)`** - Tag node for `content`
-- **`tool(p)`** - Tag entirety of a single tool call
-- **`tool_open(p)`** - Tag start of a tool call
-- **`tool_close(p)`** - Tag end of a tool call
-- **`tool_name(p)`** - Tag the tool name
-- **`tool_arg(p)`** - Tag a complete tool argument (name + value)
-- **`tool_arg_open(p)`** - Tag start of a tool argument
-- **`tool_arg_close(p)`** - Tag end of a tool argument
-- **`tool_arg_name(p)`** - Tag the argument name
-- **`tool_arg_string_value(p)`** - Tag string value for the argument
-- **`tool_arg_json_value(p)`** - Tag JSON value for the argument
-
-```cpp
-build_chat_peg_constructed_parser([&](common_chat_peg_constructed_builder & p) {
-    auto location_arg = p.tool_arg(
-        p.tool_arg_open("<parameter name=\"" + p.tool_arg_name(p.literal("location")) + "\">"),
-        p.tool_arg_string_value(p.until("</parameter>")),
-        p.tool_arg_close(p.literal("</parameter>"))
-    );
-
-    auto get_weather_tool = p.tool(p.sequence({
-        p.tool_open("<function name=\"" + p.tool_name(p.literal("get_weather")) + "\">"),
-        location_arg,
-        p.tool_close(p.literal("</function>"))
-    }));
-
-    return p.sequence({
-        p.content(p.until("<tool_call>")),
-        p.literal("<tool_call>"),
-        get_weather_tool,
-        p.literal("</tool_call>"),
-        p.end()
-    });
-});
-```

docs/docker.md

@@ -7,9 +7,9 @@
 ## Images
 We have three Docker images available for this project:
 
-1. `ghcr.io/ggml-org/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`, `linux/s390x`)
-2. `ghcr.io/ggml-org/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`, `linux/s390x`)
-3. `ghcr.io/ggml-org/llama.cpp:server`: This image only includes the server executable file. (platforms: `linux/amd64`, `linux/arm64`, `linux/s390x`)
+1. `ghcr.io/ggml-org/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`)
+2. `ghcr.io/ggml-org/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`)
+3. `ghcr.io/ggml-org/llama.cpp:server`: This image only includes the server executable file. (platforms: `linux/amd64`, `linux/arm64`)
 
 Additionally, there the following images, similar to the above:
 
@@ -110,7 +110,7 @@ You may want to pass in some different `ARGS`, depending on the MUSA environment
 
 The defaults are:
 
-- `MUSA_VERSION` set to `rc4.3.0`
+- `MUSA_VERSION` set to `rc4.2.0`
 
 The resulting images, are essentially the same as the non-MUSA images:
 

docs/ops.md

@@ -12,111 +12,99 @@ Legend:
 - 🟡 Partially supported by this backend
 - ❌ Not supported by this backend
 
-| Operation | BLAS | CANN | CPU | CUDA | Metal | OpenCL | SYCL | Vulkan | WebGPU | ZenDNN | zDNN |
-|-----------|------|------|------|------|------|------|------|------|------|------|------|
-|                              ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                              ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
-|                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ |
-|                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
-|                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                              COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
-|                      COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
-|               CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|          CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                           CUMSUM | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                              DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ |
-|                              EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                            EXPM1 | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                             FILL | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
-|                            FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
-|                GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                        GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                      GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                             GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                         GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                       GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
-|                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|               GROUP_NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                           IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                        IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                          L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
-|                              LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
-|                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
-|                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                             NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ |
-|                     NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                     OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                         OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ |
-|                              PAD | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | ❌ |
-|                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                             RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                           REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ❌ | ❌ | ❌ |
-|                      REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                         RMS_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                    RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                 RMS_NORM_MUL_ADD | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                             ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                             ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                        ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                            ROUND | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
-|                        RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                        RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                            SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                              SET | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ |
-|                         SET_ROWS | ❌ | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
-|                              SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ |
-|                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                              SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
-|                          SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
-|                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
-|                        SOLVE_TRI | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
-|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
-|                             SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
-|                         SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
-|                             STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                              SUM | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
-|                         SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | ❌ |
-|                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ |
-|                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                            TOP_K | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
-|                              TRI | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                            TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
-|                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
-|                            XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Operation | BLAS | CANN | CPU | CUDA | Metal | OpenCL | SYCL | Vulkan | zDNN |
+|-----------|------|------|------|------|------|------|------|------|------|
+|                              ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
+|                              ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
+|                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ |
+|                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
+|                           ADD_ID | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
+|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
+|                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ |
+|                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ❌ |
+|                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ❌ |
+|                          CONV_2D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ |
+|                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
+|                          CONV_3D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
+|                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                              COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ |
+|                      COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
+|                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
+|               CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|          CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ |
+|                              DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ |
+|                              DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ |
+|                              ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
+|                              EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
+|                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ |
+|                GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
+|                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
+|                        GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
+|                      GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
+|                             GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
+|                         GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
+|                       GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
+|                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | ❌ |
+|                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
+|               GROUP_NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
+|                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
+|                           IM2COL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ |
+|                        IM2COL_3D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                          L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
+|                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
+|                              LOG | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
+|                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ |
+|                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
+|                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ |
+|                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
+|                             NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
+|                     NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
+|                     OPT_STEP_SGD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                         OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ |
+|                              PAD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
+|                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
+|                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
+|                             RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
+|                           REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ❌ |
+|                      REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
+|                         RMS_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ |
+|                    RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
+|                 RMS_NORM_MUL_ADD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
+|                             ROLL | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ |
+|                             ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
+|                        ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
+|                        RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
+|                        RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
+|                            SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
+|                              SET | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                         SET_ROWS | ❌ | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
+|                              SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
+|                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
+|                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
+|                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
+|                              SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ |
+|                          SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ |
+|                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ✅ | ❌ |
+|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ |
+|                             SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | ❌ | ❌ |
+|                         SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                             STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
+|                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ |
+|                              SUM | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ |
+|                         SUM_ROWS | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
+|                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
+|                       SWIGLU_OAI | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | ❌ |
+|               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
+|                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ |