.devops/openvino.Dockerfile

@@ -1,138 +0,0 @@
-ARG OPENVINO_VERSION_MAJOR=2026.0
-ARG OPENVINO_VERSION_FULL=2026.0.0.20965.c6d6a13a886
-ARG UBUNTU_VERSION=24.04
-
-# Optional proxy build arguments - empty by default
-ARG http_proxy=
-ARG https_proxy=
-
-## Build Image
-FROM ubuntu:${UBUNTU_VERSION} AS build
-
-# Pass proxy args to build stage
-ARG http_proxy
-ARG https_proxy
-
-RUN apt-get update && \
-    apt-get install -y --no-install-recommends \
-        ca-certificates \
-        gnupg \
-        wget \
-        git \
-        cmake \
-        ninja-build \
-        build-essential \
-        libtbb12 \
-        libssl-dev \
-        ocl-icd-opencl-dev \
-        opencl-headers \
-        opencl-clhpp-headers \
-        intel-opencl-icd && \
-    rm -rf /var/lib/apt/lists/*
-
-# Install OpenVINO for Ubuntu 24.04
-ARG OPENVINO_VERSION_MAJOR
-ARG OPENVINO_VERSION_FULL
-RUN mkdir -p /opt/intel && \
-    wget https://storage.openvinotoolkit.org/repositories/openvino/packages/${OPENVINO_VERSION_MAJOR}/linux/openvino_toolkit_ubuntu24_${OPENVINO_VERSION_FULL}_x86_64.tgz && \
-    tar -xf openvino_toolkit_ubuntu24_${OPENVINO_VERSION_FULL}_x86_64.tgz && \
-    mv openvino_toolkit_ubuntu24_${OPENVINO_VERSION_FULL}_x86_64 /opt/intel/openvino_${OPENVINO_VERSION_MAJOR} && \
-    cd /opt/intel/openvino_${OPENVINO_VERSION_MAJOR} && \
-    echo "Y" | ./install_dependencies/install_openvino_dependencies.sh && \
-    cd - && \
-    ln -s /opt/intel/openvino_${OPENVINO_VERSION_MAJOR} /opt/intel/openvino
-
-ENV OpenVINO_DIR=/opt/intel/openvino
-
-WORKDIR /app
-
-COPY . .
-
-# Build Stage
-RUN bash -c "source ${OpenVINO_DIR}/setupvars.sh && \
-    cmake -B build/ReleaseOV -G Ninja \
-        -DCMAKE_BUILD_TYPE=Release \
-        -DGGML_OPENVINO=ON && \
-    cmake --build build/ReleaseOV -j$(nproc)"
-
-# Copy all necessary libraries
-RUN mkdir -p /app/lib && \
-    find build/ReleaseOV -name '*.so*' -exec cp {} /app/lib \; && \
-    find ${OpenVINO_DIR}/runtime/lib/intel64 -name '*.so*' -exec cp -P {} /app/lib \; 2>/dev/null || \
-    find ${OpenVINO_DIR}/lib/intel64 -name '*.so*' -exec cp -P {} /app/lib \;
-
-# Create runtime directories and copy binaries
-RUN mkdir -p /app/full \
-    && cp build/ReleaseOV/bin/* /app/full/ \
-    && cp *.py /app/full \
-    && cp -r gguf-py /app/full \
-    && cp -r requirements /app/full \
-    && cp requirements.txt /app/full \
-    && cp .devops/tools.sh /app/full/tools.sh
-
-## Base Runtime Image
-FROM ubuntu:${UBUNTU_VERSION} AS base
-
-# Pass proxy args to runtime stage
-ARG http_proxy
-ARG https_proxy
-
-RUN apt-get update \
-    && apt-get install -y libgomp1 libtbb12 curl\
-    && apt autoremove -y \
-    && apt clean -y \
-    && rm -rf /tmp/* /var/tmp/* \
-    && find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
-    && find /var/cache -type f -delete
-
-COPY --from=build /app/lib/ /app/
-
-### Full (all binaries)
-FROM base AS full
-
-ARG http_proxy
-ARG https_proxy
-
-COPY --from=build /app/full /app/
-
-WORKDIR /app
-
-RUN apt-get update && \
-    apt-get install -y --no-install-recommends \
-    git \
-    python3 \
-    python3-venv \
-    python3-pip && \
-    python3 -m venv /ov-venv && \
-    /ov-venv/bin/pip install --no-cache-dir --upgrade pip setuptools wheel && \
-    /ov-venv/bin/pip install --no-cache-dir -r requirements.txt && \
-    apt-get autoremove -y && \
-    apt-get clean && \
-    rm -rf /tmp/* /var/tmp/* && \
-    find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete && \
-    find /var/cache -type f -delete
-
-ENTRYPOINT ["/bin/bash", "-c", "source /ov-venv/bin/activate && exec /app/tools.sh \"$@\"", "--"]
-
-
-### Light, CLI only
-FROM base AS light
-
-COPY --from=build /app/full/llama-cli /app/
-
-WORKDIR /app
-
-ENTRYPOINT [ "/app/llama-cli" ]
-
-### Server, Server only
-FROM base AS server
-
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-COPY --from=build /app/full/llama-server /app/
-
-WORKDIR /app
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/app/llama-server" ]

.devops/vulkan.Dockerfile

@@ -53,11 +53,10 @@ RUN apt-get update \
     && apt-get install -y \
     build-essential \
     git \
-    python3.13 \
-    python3.13-dev \
+    python3 \
+    python3-dev \
     python3-pip \
     python3-wheel \
-    && update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.13 100 \
     && pip install --break-system-packages --upgrade setuptools \
     && pip install --break-system-packages -r requirements.txt \
     && apt autoremove -y \

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

@@ -1,25 +0,0 @@
-name: "Linux - Setup OpenVINO Toolkit"
-description: "Setup OpenVINO Toolkit for Linux"
-inputs:
-  path:
-    description: "Installation path"
-    required: true
-  version_major:
-    description: "OpenVINO major version (e.g., 2025.3)"
-    required: true
-  version_full:
-    description: "OpenVINO full version (e.g., 2025.3.0.19807.44526285f24)"
-    required: true
-
-runs:
-  using: "composite"
-  steps:
-    - name: Setup OpenVINO Toolkit
-      id: setup
-      uses: ./.github/actions/unarchive-tar
-      with:
-        url: https://storage.openvinotoolkit.org/repositories/openvino/packages/${{ inputs.version_major }}/linux/openvino_toolkit_ubuntu24_${{ inputs.version_full }}_x86_64.tgz
-        path: ${{ inputs.path }}
-        type: z
-        strip: 1
-

.github/labeler.yml

@@ -104,20 +104,3 @@ OpenCL:
         - any-glob-to-any-file:
             - ggml/include/ggml-opencl.h
             - ggml/src/ggml-opencl/**
-            - docs/backend/OPENCL.md
-Hexagon:
-    - changed-files:
-        - any-glob-to-any-file:
-            - ggml/include/ggml-hexagon.h
-            - ggml/src/ggml-hexagon/**
-WebGPU:
-    - changed-files:
-        - any-glob-to-any-file:
-            - ggml/include/ggml-webgpu.h
-            - ggml/src/ggml-webgpu/**
-OpenVINO:
-    - changed-files:
-        - any-glob-to-any-file:
-            - ggml/include/ggml-openvino.h
-            - ggml/src/ggml-openvino/**
-            - docs/backend/OPENVINO.md

.github/workflows/ai-issues.yml

@@ -1,87 +0,0 @@
-name: AI review (issues)
-
-on:
-  issues:
-    types: [opened]
-
-jobs:
-  find-related:
-    if: github.event.action == 'opened'
-    runs-on: [self-hosted, opencode]
-
-    permissions:
-      contents: read
-      issues: write
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v6
-        with:
-          fetch-depth: 1
-
-      - name: Find related
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          OPENCODE_PERMISSION: |
-            {
-              "bash": {
-                "*": "deny",
-                "gh issue*": "allow"
-              },
-              "webfetch": "deny"
-            }
-        run: |
-          rm AGENTS.md
-          rm CLAUDE.md
-
-          opencode run -m llama.cpp-dgx/ai-review-issues-find-similar --thinking "A new issue has been created:
-
-          Issue number: ${{ github.event.issue.number }}
-
-          Lookup the contents of the issue using the following `gh` command:
-
-          ```bash
-          gh issue view ${{ github.event.issue.number }} --json title,body,url,number
-          ```
-
-          Next, perform the following task and then post a SINGLE comment (if needed).
-
-          ---
-
-          TASK : FIND RELATED ISSUES
-
-          Using the `gh` CLI tool, search through existing issues on Github.
-          Find related or similar issues to the newly created one and list them.
-          Do not list the new issue itself (it is #${{ github.event.issue.number }}).
-
-          Consider:
-          1. Similar titles or descriptions
-          2. Same error messages or symptoms
-          3. Related functionality or components
-          4. Similar feature requests
-
-          ---
-
-          POSTING YOUR COMMENT:
-
-          Based on your findings, post a SINGLE comment on issue #${{ github.event.issue.number }}. Build the comment as follows:
-
-          - If no related issues were found, do NOT comment at all.
-          - If related issues were found, include a section listing them with links using the following format:
-
-          [comment]
-          This issue might be similar or related to the following issue(s):
-
-            - #[related_issue_number]: [brief description of how they are related]
-            - #[related_issue_number]: [brief description of how they are related]
-            ...
-
-          _This comment was auto-generated locally using **$GA_ENGINE** on **$GA_MACHINE**_
-          [/comment]
-
-          Remember:
-            - Do not include the comment tags in your actual comment.
-            - Post at most ONE comment combining all findings.
-            - If you didn't find issues that are related enough, post nothing.
-            - You have access only to the `gh` CLI tool - don't try to use other tools.
-          "

.github/workflows/build-3rd-party.yml

@@ -1,57 +0,0 @@
-name: CI (3rd-party)
-
-on:
-  workflow_dispatch: # allows manual triggering
-  push:
-    branches:
-      - master
-    paths: [
-      '.github/workflows/build-3rd-party.yml',
-      '**/CMakeLists.txt',
-      '**/.cmake',
-      '**/*.h',
-      '**/*.hpp',
-      '**/*.c',
-      '**/*.cpp'
-    ]
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
-env:
-  GGML_NLOOP: 3
-  GGML_N_THREADS: 1
-  LLAMA_LOG_COLORS: 1
-  LLAMA_LOG_PREFIX: 1
-  LLAMA_LOG_TIMESTAMPS: 1
-
-jobs:
-  ubuntu-24-llguidance:
-    runs-on: ${{ 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Dependencies
-        id: depends
-        run: |
-          sudo apt-get update
-          sudo apt-get install build-essential libssl-dev
-
-      - name: Build
-        id: cmake_build
-        run: |
-          cmake -B build \
-            -DLLAMA_FATAL_WARNINGS=ON \
-            -DLLAMA_LLGUIDANCE=ON
-          cmake --build build --config Release -j $(nproc)
-
-      - name: Test
-        id: cmake_test
-        run: |
-          cd build
-          ctest -L main --verbose --timeout 900
-

.github/workflows/build-android.yml

@@ -1,140 +0,0 @@
-name: CI (android)
-
-on:
-  workflow_dispatch: # allows manual triggering
-  push:
-    branches:
-      - master
-    paths: [
-      '.github/workflows/build-android.yml',
-      '**/CMakeLists.txt',
-      '**/.cmake',
-      '**/*.h',
-      '**/*.hpp',
-      '**/*.c',
-      '**/*.cpp'
-    ]
-
-  pull_request:
-    types: [opened, synchronize, reopened]
-    paths: [
-      '.github/workflows/build-android.yml',
-      'examples/llama.android/**'
-    ]
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
-env:
-  GGML_NLOOP: 3
-  GGML_N_THREADS: 1
-  LLAMA_LOG_COLORS: 1
-  LLAMA_LOG_PREFIX: 1
-  LLAMA_LOG_TIMESTAMPS: 1
-
-jobs:
-  android:
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Clone
-        uses: actions/checkout@v6
-
-      # Disabled due to size (400MB) and always 0 cache hits
-      # - name: ccache
-      #   uses: ggml-org/ccache-action@v1.2.16
-      #   with:
-      #     key: android-build
-      #     evict-old-files: 1d
-
-      - name: Set up JDK
-        uses: actions/setup-java@v5
-        with:
-          java-version: 17
-          distribution: zulu
-
-      - name: Setup Android SDK
-        uses: android-actions/setup-android@v3
-        with:
-          log-accepted-android-sdk-licenses: false
-
-      - name: Build
-        run: |
-          cd examples/llama.android
-          ./gradlew build --no-daemon
-
-  android-ndk:
-    runs-on: ubuntu-latest
-
-    env:
-      OPENCL_VERSION: 2025.07.22
-
-    strategy:
-      matrix:
-        include:
-          - build: 'arm64-cpu'
-            defines: '-D ANDROID_ABI=arm64-v8a -D ANDROID_PLATFORM=android-31 -D CMAKE_TOOLCHAIN_FILE=${ANDROID_NDK_ROOT}/build/cmake/android.toolchain.cmake -D GGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=armv8.5-a+fp16+i8mm -G Ninja -D LLAMA_OPENSSL=OFF -D GGML_OPENMP=OFF'
-          - build: 'arm64-snapdragon'
-            defines: '--preset arm64-android-snapdragon-release'
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Install OpenCL Headers and Libs
-        id: install_opencl
-        if: ${{ matrix.build == 'arm64-snapdragon' }}
-        run: |
-          mkdir opencl
-          curl -L -o opencl/clhpp.tar.gz      https://github.com/KhronosGroup/OpenCL-CLHPP/archive/refs/tags/v${OPENCL_VERSION}.tar.gz
-          curl -L -o opencl/headers.tar.gz    https://github.com/KhronosGroup/OpenCL-Headers/archive/refs/tags/v${OPENCL_VERSION}.tar.gz
-          curl -L -o opencl/icd-loader.tar.gz https://github.com/KhronosGroup/OpenCL-ICD-Loader/archive/refs/tags/v${OPENCL_VERSION}.tar.gz
-          tar -xaf opencl/headers.tar.gz    -C opencl
-          tar -xaf opencl/clhpp.tar.gz      -C opencl
-          tar -xaf opencl/icd-loader.tar.gz -C opencl
-          sudo cp -r opencl/OpenCL-Headers-${OPENCL_VERSION}/CL         ${ANDROID_NDK_ROOT}/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include
-          sudo cp -r opencl/OpenCL-CLHPP-${OPENCL_VERSION}/include/CL/* ${ANDROID_NDK_ROOT}/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include/CL
-          cd opencl/OpenCL-ICD-Loader-${OPENCL_VERSION}
-          cmake -B build -G Ninja -DCMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=${ANDROID_NDK_ROOT}/build/cmake/android.toolchain.cmake -DOPENCL_ICD_LOADER_HEADERS_DIR=${ANDROID_NDK_ROOT}/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=31 -DANDROID_STL=c++_shared
-          cmake --build build
-          sudo cp build/libOpenCL.so ${ANDROID_NDK_ROOT}/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android
-          rm -rf opencl
-
-      - name: Install Hexagon SDK
-        id: install_hexsdk
-        if: ${{ matrix.build == 'arm64-snapdragon' }}
-        env:
-          HEXSDK_VER: 6.4.0.2
-          HEXTLS_VER: 19.0.04
-        run: |
-          curl -L -o hex-sdk.tar.gz https://github.com/snapdragon-toolchain/hexagon-sdk/releases/download/v$HEXSDK_VER/hexagon-sdk-v$HEXSDK_VER-amd64-lnx.tar.xz
-          mkdir hex-sdk
-          tar -xaf hex-sdk.tar.gz -C hex-sdk
-          ls -l hex-sdk
-          sudo mv hex-sdk /opt/hexagon
-          echo "HEXAGON_SDK_ROOT=/opt/hexagon/$HEXSDK_VER"                                     >> "$GITHUB_ENV"
-          echo "HEXAGON_TOOLS_ROOT=/opt/hexagon/$HEXSDK_VER/tools/HEXAGON_Tools/$HEXTLS_VER"   >> "$GITHUB_ENV"
-          echo "DEFAULT_HLOS_ARCH=64"                                                          >> "$GITHUB_ENV"
-          echo "DEFAULT_TOOLS_VARIANT=toolv19"                                                 >> "$GITHUB_ENV"
-          echo "DEFAULT_NO_QURT_INC=0"                                                         >> "$GITHUB_ENV"
-          echo "DEFAULT_DSP_ARCH=v73"                                                          >> "$GITHUB_ENV"
-
-      - name: Update CMake presets
-        id: update_presets
-        if: ${{ matrix.build == 'arm64-snapdragon' }}
-        run: |
-          cp docs/backend/snapdragon/CMakeUserPresets.json .
-
-      - name: Build
-        id: ndk_build
-        run: |
-          cmake ${{ matrix.defines }} -B build
-          cmake --build build
-          cmake --install build --prefix pkg-adb/llama.cpp
-
-      - name: Test
-        id: cmake_test
-        run: |
-          echo "FIXME: test on devices"

.github/workflows/build-apple.yml

@@ -1,214 +0,0 @@
-name: CI (apple)
-
-on:
-  workflow_dispatch: # allows manual triggering
-  push:
-    branches:
-      - master
-    paths: [
-      '.github/workflows/build-apple.yml',
-      '**/CMakeLists.txt',
-      '**/.cmake',
-      '**/*.h',
-      '**/*.hpp',
-      '**/*.c',
-      '**/*.cpp',
-      '**/*.swift',
-      '**/*.m',
-      '**/*.metal'
-    ]
-
-  pull_request:
-    types: [opened, synchronize, reopened]
-    paths: [
-      '.github/workflows/build-apple.yml',
-      'ggml/src/ggml-metal/**'
-    ]
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
-env:
-  GGML_NLOOP: 3
-  GGML_N_THREADS: 1
-  LLAMA_LOG_COLORS: 1
-  LLAMA_LOG_PREFIX: 1
-  LLAMA_LOG_TIMESTAMPS: 1
-
-jobs:
-  macOS-latest-ios:
-    runs-on: macos-latest
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: macOS-latest-ios
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
-      - name: Build
-        id: cmake_build
-        run: |
-          sysctl -a
-          cmake -B build -G Xcode \
-            -DGGML_METAL_USE_BF16=ON \
-            -DGGML_METAL_EMBED_LIBRARY=ON \
-            -DLLAMA_BUILD_COMMON=OFF \
-            -DLLAMA_BUILD_EXAMPLES=OFF \
-            -DLLAMA_BUILD_TOOLS=OFF \
-            -DLLAMA_BUILD_TESTS=OFF \
-            -DLLAMA_BUILD_SERVER=OFF \
-            -DCMAKE_SYSTEM_NAME=iOS \
-            -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
-            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
-          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
-
-  macos-latest-ios-xcode:
-    runs-on: macos-latest
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v6
-
-      - name: Setup Xcode
-        uses: ggml-org/setup-xcode@v1
-        with:
-          xcode-version: latest-stable
-
-      - name: Build
-        id: cmake_build
-        run: |
-          sysctl -a
-          cmake -B build -G Xcode \
-            -DGGML_METAL_USE_BF16=ON \
-            -DGGML_METAL_EMBED_LIBRARY=ON \
-            -DLLAMA_OPENSSL=OFF \
-            -DLLAMA_BUILD_EXAMPLES=OFF \
-            -DLLAMA_BUILD_TOOLS=OFF \
-            -DLLAMA_BUILD_TESTS=OFF \
-            -DLLAMA_BUILD_SERVER=OFF \
-            -DCMAKE_SYSTEM_NAME=iOS \
-            -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
-            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
-          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
-
-      - name: xcodebuild for swift package
-        id: xcodebuild
-        run: |
-          ./build-xcframework.sh
-
-      - name: Upload xcframework artifact
-        uses: actions/upload-artifact@v6
-        with:
-          name: llama-xcframework
-          path: build-apple/llama.xcframework/
-          retention-days: 1
-
-      - name: Build Xcode project
-        run: |
-          xcodebuild -downloadPlatform iOS
-          xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' FRAMEWORK_FOLDER_PATH=./build-ios build
-
-  macOS-latest-tvos:
-    runs-on: macos-latest
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: macOS-latest-tvos
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
-      - name: Build
-        id: cmake_build
-        run: |
-          sysctl -a
-          cmake -B build -G Xcode \
-            -DGGML_METAL_USE_BF16=ON \
-            -DGGML_METAL_EMBED_LIBRARY=ON \
-            -DLLAMA_BUILD_COMMON=OFF \
-            -DLLAMA_BUILD_EXAMPLES=OFF \
-            -DLLAMA_BUILD_TOOLS=OFF \
-            -DLLAMA_BUILD_TESTS=OFF \
-            -DLLAMA_BUILD_SERVER=OFF \
-            -DCMAKE_SYSTEM_NAME=tvOS \
-            -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
-            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
-          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
-
-  macOS-latest-visionos:
-    runs-on: macos-latest
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Build
-        id: cmake_build
-        run: |
-          sysctl -a
-          cmake -B build -G Xcode \
-            -DGGML_METAL_USE_BF16=ON \
-            -DGGML_METAL_EMBED_LIBRARY=ON \
-            -DLLAMA_BUILD_COMMON=OFF \
-            -DLLAMA_BUILD_EXAMPLES=OFF \
-            -DLLAMA_BUILD_TOOLS=OFF \
-            -DLLAMA_BUILD_TESTS=OFF \
-            -DLLAMA_BUILD_SERVER=OFF \
-            -DCMAKE_SYSTEM_NAME=visionOS \
-            -DCMAKE_OSX_DEPLOYMENT_TARGET=1.0 \
-            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
-          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
-
-  macOS-latest-swift:
-    runs-on: macos-latest
-    needs: macos-latest-ios-xcode
-
-    strategy:
-      matrix:
-        destination: ['generic/platform=macOS', 'generic/platform=iOS', 'generic/platform=tvOS']
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: macOS-latest-swift
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
-      - name: Download xcframework artifact
-        uses: actions/download-artifact@v7
-        with:
-          name: llama-xcframework
-          path: build-apple/llama.xcframework/
-
-      - name: Build llama.cpp with CMake
-        id: cmake_build
-        run: |
-          sysctl -a
-          cmake -B build -G Xcode \
-            -DGGML_METAL_USE_BF16=ON \
-            -DGGML_METAL_EMBED_LIBRARY=ON \
-            -DLLAMA_OPENSSL=OFF \
-            -DLLAMA_BUILD_EXAMPLES=OFF \
-            -DLLAMA_BUILD_TOOLS=OFF \
-            -DLLAMA_BUILD_TESTS=OFF \
-            -DLLAMA_BUILD_SERVER=OFF \
-            -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64"
-          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)

.github/workflows/build-cache.yml

@@ -37,39 +37,12 @@ jobs:
           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@v6
-
-  #    - name: Setup Cache
-  #      uses: actions/cache@v5
-  #      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 }}
-
-  ubuntu-24-openvino-cache:
+  ubuntu-24-spacemit-cache:
     runs-on: ubuntu-24.04
 
     env:
-      # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.0"
-      OPENVINO_VERSION_FULL: "2026.0.0.20965.c6d6a13a886"
+      # Make sure this is in sync with build-linux-cross.yml
+      SPACEMIT_IME_TOOLCHAIN_VERSION: "1.1.2"
 
     steps:
       - name: Clone
@@ -78,18 +51,17 @@ jobs:
 
       - name: Setup Cache
         uses: actions/cache@v5
-        id: cache-openvino
+        id: cache-toolchain
         with:
-          path: ./openvino_toolkit
-          key: openvino-toolkit-v${{ env.OPENVINO_VERSION_FULL }}-${{ runner.os }}
+          path: ./spacemit_toolchain
+          key: spacemit-ime-toolchain-v${{ env.SPACEMIT_IME_TOOLCHAIN_VERSION }}-${{ runner.os }}
 
-      - name: Setup OpenVINO Toolkit
-        if: steps.cache-openvino.outputs.cache-hit != 'true'
-        uses: ./.github/actions/linux-setup-openvino
+      - name: Setup SpacemiT Toolchain
+        if: steps.cache-toolchain.outputs.cache-hit != 'true'
+        uses: ./.github/actions/linux-setup-spacemit
         with:
-          path: ./openvino_toolkit
-          version_major: ${{ env.OPENVINO_VERSION_MAJOR }}
-          version_full: ${{ env.OPENVINO_VERSION_FULL }}
+          path: ./spacemit_toolchain
+          version: ${{ env.SPACEMIT_IME_TOOLCHAIN_VERSION }}
 
   windows-2022-rocm-cache:
     runs-on: windows-2022

.github/workflows/build-cann.yml

@@ -1,102 +0,0 @@
-name: CI (cann)
-
-on:
-  workflow_dispatch: # allows manual triggering
-  push:
-    branches:
-      - master
-    paths: [
-      '.github/workflows/build-cann.yml',
-      '**/CMakeLists.txt',
-      '**/.cmake',
-      '**/*.h',
-      '**/*.hpp',
-      '**/*.c',
-      '**/*.cpp'
-    ]
-
-  pull_request:
-    types: [opened, synchronize, reopened]
-    paths: [
-      '.github/workflows/build-cann.yml',
-      'ggml/src/ggml-cann/**'
-    ]
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
-env:
-  GGML_NLOOP: 3
-  GGML_N_THREADS: 1
-  LLAMA_LOG_COLORS: 1
-  LLAMA_LOG_PREFIX: 1
-  LLAMA_LOG_TIMESTAMPS: 1
-
-jobs:
-  openEuler-latest-cann:
-    defaults:
-      run:
-        shell: bash -el {0}
-    strategy:
-      matrix:
-        arch: [x86, aarch64]
-        chip_type: ['910b', '310p']
-        build: ['Release']
-        use_acl_graph: ['on', 'off']
-        exclude:
-          # 310P does not support USE_ACL_GRAPH=on
-          - chip_type: '310p'
-            use_acl_graph: 'on'
-    runs-on: ${{ matrix.arch == 'aarch64' && 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
-
-      - name: Free up disk space
-        uses: ggml-org/free-disk-space@v1.3.1
-        with:
-          tool-cache: true
-
-      - name: Set container image
-        id: cann-image
-        run: |
-          image="ascendai/cann:${{ matrix.chip_type == '910b' &&  '8.3.rc2-910b-openeuler24.03-py3.11' || '8.3.rc2-310p-openeuler24.03-py3.11' }}"
-          echo "image=${image}" >> "${GITHUB_OUTPUT}"
-
-      - name: Pull container image
-        run: docker pull "${{ steps.cann-image.outputs.image }}"
-
-      - name: Build
-        env:
-          BUILD_TYPE: ${{ matrix.build }}
-          SOC_TYPE: ascend${{ matrix.chip_type }}
-          USE_ACL_GRAPH: ${{ matrix.use_acl_graph }}
-        run: |
-          HOST_UID=$(id -u)
-          HOST_GID=$(id -g)
-
-          docker run --rm \
-            -v "${PWD}:/workspace" \
-            -w /workspace \
-            -e SOC_TYPE=${SOC_TYPE} \
-            -e BUILD_TYPE=${BUILD_TYPE} \
-            -e USE_ACL_GRAPH=${USE_ACL_GRAPH} \
-            "${{ steps.cann-image.outputs.image }}" \
-            bash -lc '
-              set -e
-              yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake openssl-devel
-              yum clean all && rm -rf /var/cache/yum
-              git config --global --add safe.directory "/workspace"
-              export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH}
-              cmake -S . -B build \
-                  -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \
-                  -DGGML_CANN=on \
-                  -DSOC_TYPE=${SOC_TYPE} \
-                  -DUSE_ACL_GRAPH=${USE_ACL_GRAPH}
-              cmake --build build -j $(nproc)
-
-              chown -R '"${HOST_UID}"':'"${HOST_GID}"' /workspace/build
-            '

.github/workflows/build-cmake-pkg.yml

@@ -5,7 +5,7 @@ on:
 
 jobs:
   linux:
-    runs-on: ubuntu-slim
+    runs-on: ubuntu-24.04
     steps:
       - uses: actions/checkout@v6
         with:
@@ -14,7 +14,7 @@ jobs:
       - name: Install dependencies
         run: |
           sudo apt update
-          sudo apt install -y build-essential tcl cmake
+          sudo apt install -y build-essential tcl
 
       - name: Build
         run: |

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

@@ -1,24 +1,7 @@
-name: CI (cross)
+name: Build on Linux using cross-compiler
 on:
-  # only manual triggers due to low-importance of the workflows
-  # TODO: for regular runs, provision dedicated self-hosted runners
   workflow_dispatch:
-  push:
-    branches:
-      - master
-    paths: [
-      '.github/workflows/build-cross.yml',
-      'ggml/src/spacemit/*',
-      'ggml/src/arch/loongarch/*'
-    ]
-  # run once every week
-  schedule:
-    - cron: '0 0 * * 0'
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
+  workflow_call:
 
 jobs:
   # ubuntu-24-riscv64-cpu-cross:
@@ -159,7 +142,7 @@ jobs:
   #         cmake --build build --config Release -j $(nproc)
 
   debian-13-loongarch64-cpu-cross:
-    runs-on: ${{ 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}
+    runs-on: ubuntu-24.04
     container: debian@sha256:653dfb9f86c3782e8369d5f7d29bb8faba1f4bff9025db46e807fa4c22903671
 
     steps:
@@ -214,7 +197,7 @@ jobs:
           cmake --build build --config Release -j $(nproc)
 
   debian-13-loongarch64-vulkan-cross:
-    runs-on: ${{ 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}
+    runs-on: ubuntu-24.04
     container: debian@sha256:653dfb9f86c3782e8369d5f7d29bb8faba1f4bff9025db46e807fa4c22903671
 
     steps:
@@ -281,15 +264,15 @@ jobs:
     steps:
       - uses: actions/checkout@v6
 
-      #- name: Use SpacemiT Toolchain Cache
-      #  uses: actions/cache@v5
-      #  id: cache-toolchain
-      #  with:
-      #    path: ./spacemit_toolchain
-      #    key: spacemit-ime-toolchain-v${{ env.SPACEMIT_IME_TOOLCHAIN_VERSION }}-${{ runner.os }}
+      - name: Use SpacemiT Toolchain Cache
+        uses: actions/cache@v5
+        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'
+        if: steps.cache-toolchain.outputs.cache-hit != 'true'
         uses: ./.github/actions/linux-setup-spacemit
         with:
           path: ./spacemit_toolchain

.github/workflows/build-msys.yml

@@ -1,72 +0,0 @@
-name: CI (msys)
-
-on:
-  # only manual triggers due to low-importance of the workflows
-  # TODO: for regular runs, provision dedicated self-hosted runners
-  workflow_dispatch:
-  # run once every week
-  schedule:
-    - cron: '0 0 * * 0'
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
-env:
-  GGML_NLOOP: 3
-  GGML_N_THREADS: 1
-  LLAMA_LOG_COLORS: 1
-  LLAMA_LOG_PREFIX: 1
-  LLAMA_LOG_TIMESTAMPS: 1
-
-jobs:
-  windows-msys2:
-    runs-on: windows-2025
-
-    strategy:
-      fail-fast: false
-      matrix:
-        include:
-          - { sys: UCRT64,  env: ucrt-x86_64,  build: Release }
-          - { sys: CLANG64, env: clang-x86_64, build: Release }
-
-    steps:
-      - name: Clone
-        uses: actions/checkout@v6
-
-      #- name: ccache
-      #  uses: ggml-org/ccache-action@v1.2.16
-      #  with:
-      #    key: windows-msys2
-      #    variant: ccache
-      #    evict-old-files: 1d
-      #    save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
-      - name: Setup ${{ matrix.sys }}
-        uses: msys2/setup-msys2@v2
-        with:
-          update: true
-          msystem: ${{matrix.sys}}
-          install: >-
-            base-devel
-            git
-            mingw-w64-${{matrix.env}}-toolchain
-            mingw-w64-${{matrix.env}}-cmake
-            mingw-w64-${{matrix.env}}-openblas
-
-      - name: Build using CMake
-        shell: msys2 {0}
-        run: |
-            cmake -B build
-            cmake --build build --config ${{ matrix.build }} -j $(nproc)
-
-      - name: Clean after building using CMake
-        shell: msys2 {0}
-        run: |
-            rm -rf build
-
-      - name: Build using CMake w/ OpenBLAS
-        shell: msys2 {0}
-        run: |
-            cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS
-            cmake --build build --config ${{ matrix.build }} -j $(nproc)

.github/workflows/build-riscv.yml

@@ -1,136 +0,0 @@
-name: CI (riscv)
-
-on:
-  workflow_dispatch: # allows manual triggering
-  push:
-    branches:
-      - master
-    paths: [
-      '.github/workflows/build-riscv.yml',
-      '**/CMakeLists.txt',
-      '**/.cmake',
-      '**/*.h',
-      '**/*.hpp',
-      '**/*.c',
-      '**/*.cpp'
-    ]
-
-  pull_request:
-    types: [opened, synchronize, reopened]
-    paths: [
-      '.github/workflows/build-riscv.yml',
-      'ggml/src/ggml-cpu/arch/riscv/**'
-    ]
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
-env:
-  GGML_NLOOP: 3
-  GGML_N_THREADS: 1
-  LLAMA_LOG_COLORS: 1
-  LLAMA_LOG_PREFIX: 1
-  LLAMA_LOG_TIMESTAMPS: 1
-
-jobs:
-  ubuntu-riscv64-native-sanitizer:
-    runs-on: RISCV64
-
-    continue-on-error: true
-
-    strategy:
-      matrix:
-        sanitizer: [ADDRESS, THREAD, UNDEFINED]
-        build_type: [Debug]
-
-    steps:
-      - name: Install dependencies
-        run: |
-          sudo apt-get update
-
-          # Install necessary packages
-          sudo apt-get install -y libatomic1 libtsan2 gcc-14 g++-14 rustup cmake build-essential wget ccache git-lfs
-
-          # Set gcc-14 and g++-14 as the default compilers
-          sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-14 100
-          sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-14 100
-          sudo ln -sf /usr/bin/gcc-14 /usr/bin/gcc
-          sudo ln -sf /usr/bin/g++-14 /usr/bin/g++
-
-          # Install Rust stable version
-          rustup install stable
-          rustup default stable
-
-          git lfs install
-
-      - name: GCC version check
-        run: |
-          gcc --version
-          g++ --version
-
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Setup ccache
-        run: |
-          # Unique cache directory per matrix combination
-          export CCACHE_DIR="$HOME/.ccache/sanitizer-${{ matrix.sanitizer }}-${{ matrix.build_type }}"
-          mkdir -p "$CCACHE_DIR"
-
-          # Configure ccache
-          ccache --set-config=max_size=5G
-          ccache --set-config=compression=true
-          ccache --set-config=compression_level=6
-          ccache --set-config=cache_dir="$CCACHE_DIR"
-          ccache --set-config=sloppiness=file_macro,time_macros,include_file_mtime,include_file_ctime
-          ccache --set-config=hash_dir=false
-
-          # Export for subsequent steps
-          echo "CCACHE_DIR=$CCACHE_DIR" >> $GITHUB_ENV
-          echo "PATH=/usr/lib/ccache:$PATH" >> $GITHUB_ENV
-
-      - name: Build
-        id: cmake_build
-        if: ${{ matrix.sanitizer != 'THREAD' }}
-        run: |
-          cmake -B build \
-            -DLLAMA_OPENSSL=OFF \
-            -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-            -DGGML_OPENMP=ON \
-            -DLLAMA_BUILD_EXAMPLES=ON \
-            -DLLAMA_BUILD_TOOLS=ON \
-            -DLLAMA_BUILD_TESTS=OFF \
-            -DCMAKE_C_COMPILER_LAUNCHER=ccache \
-            -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
-            -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
-            -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \
-            -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14
-
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc)
-
-      - name: Build (no OpenMP)
-        id: cmake_build_no_openmp
-        if: ${{ matrix.sanitizer == 'THREAD' }}
-        run: |
-          cmake -B build \
-            -DLLAMA_OPENSSL=OFF \
-            -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-            -DGGML_OPENMP=OFF \
-            -DLLAMA_BUILD_EXAMPLES=ON \
-            -DLLAMA_BUILD_TOOLS=ON \
-            -DLLAMA_BUILD_TESTS=OFF \
-            -DCMAKE_C_COMPILER_LAUNCHER=ccache \
-            -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
-            -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
-            -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \
-            -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14
-
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc)
-
-      - name: Test
-        id: cmake_test
-        run: |
-          cd build
-          ctest -L main --verbose --timeout 900

.github/workflows/build-sanitize.yml

@@ -1,87 +0,0 @@
-name: CI (sanitize)
-
-on:
-  workflow_dispatch: # allows manual triggering
-  push:
-    branches:
-      - master
-    paths: [
-      '.github/workflows/build-sanitize.yml',
-      '**/CMakeLists.txt',
-      '**/.cmake',
-      '**/*.h',
-      '**/*.hpp',
-      '**/*.c',
-      '**/*.cpp'
-    ]
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
-env:
-  GGML_NLOOP: 3
-  GGML_N_THREADS: 1
-  LLAMA_LOG_COLORS: 1
-  LLAMA_LOG_PREFIX: 1
-  LLAMA_LOG_TIMESTAMPS: 1
-
-jobs:
-  ubuntu-latest-sanitizer:
-    runs-on: ubuntu-latest
-
-    continue-on-error: true
-
-    strategy:
-      matrix:
-        sanitizer: [ADDRESS, THREAD, UNDEFINED]
-        build_type: [Debug]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: ubuntu-latest-sanitizer-${{ matrix.sanitizer }}
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
-      - name: Dependencies
-        id: depends
-        run: |
-          sudo apt-get update
-          sudo apt-get install build-essential libssl-dev
-
-      - name: Build
-        id: cmake_build
-        if: ${{ matrix.sanitizer != 'THREAD' }}
-        run: |
-          cmake -B build \
-            -DLLAMA_FATAL_WARNINGS=ON \
-            -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
-            -DGGML_SANITIZE_${{ matrix.sanitizer }}=ON \
-            -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
-
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc)
-
-      - name: Build (no OpenMP)
-        id: cmake_build_no_openmp
-        if: ${{ matrix.sanitizer == 'THREAD' }}
-        run: |
-          cmake -B build \
-            -DLLAMA_FATAL_WARNINGS=ON \
-            -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
-            -DGGML_SANITIZE_${{ matrix.sanitizer }}=ON \
-            -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-            -DGGML_OPENMP=OFF
-
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc)
-
-      - name: Test
-        id: cmake_test
-        run: |
-          cd build
-          ctest -L main --verbose --timeout 900

.github/workflows/build-self-hosted.yml

@@ -1,245 +0,0 @@
-name: CI (self-hosted)
-
-on:
-  workflow_dispatch: # allows manual triggering
-  push:
-    branches:
-      - master
-    paths: [
-      '.github/workflows/build.yml',
-      '**/CMakeLists.txt',
-      '**/.cmake',
-      '**/*.h',
-      '**/*.hpp',
-      '**/*.c',
-      '**/*.cpp',
-      '**/*.cu',
-      '**/*.cuh',
-      '**/*.swift',
-      '**/*.m',
-      '**/*.metal',
-      '**/*.comp',
-      '**/*.glsl',
-      '**/*.wgsl'
-    ]
-
-  pull_request:
-    types: [opened, synchronize, reopened]
-    paths: [
-      '.github/workflows/build-self-hosted.yml',
-      '**/CMakeLists.txt',
-      '**/.cmake',
-      '**/*.h',
-      '**/*.hpp',
-      '**/*.c',
-      '**/*.cpp',
-      '**/*.cu',
-      '**/*.cuh',
-      '**/*.swift',
-      '**/*.m',
-      '**/*.metal',
-      '**/*.comp',
-      '**/*.glsl',
-      '**/*.wgsl'
-    ]
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
-env:
-  GGML_NLOOP: 3
-  GGML_N_THREADS: 1
-  LLAMA_LOG_COLORS: 1
-  LLAMA_LOG_PREFIX: 1
-  LLAMA_LOG_TIMESTAMPS: 1
-
-jobs:
-  ggml-ci-nvidia-cuda:
-    runs-on: [self-hosted, Linux, NVIDIA]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          nvidia-smi
-          GG_BUILD_CUDA=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
-
-  ggml-ci-nvidia-vulkan-cm:
-    runs-on: [self-hosted, Linux, NVIDIA]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          vulkaninfo --summary
-          GG_BUILD_VULKAN=1 GGML_VK_DISABLE_COOPMAT2=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
-
-  ggml-ci-nvidia-vulkan-cm2:
-    runs-on: [self-hosted, Linux, NVIDIA, COOPMAT2]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          vulkaninfo --summary
-          GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
-
-  # TODO: provision AMX-compatible machine
-  #ggml-ci-cpu-amx:
-  #  runs-on: [self-hosted, Linux, CPU, AMX]
-
-  #  steps:
-  #    - name: Clone
-  #      id: checkout
-  #      uses: actions/checkout@v6
-
-  #    - name: Test
-  #      id: ggml-ci
-  #      run: |
-  #        bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
-
-  # TODO: provision AMD GPU machine
-  # ggml-ci-amd-vulkan:
-  #   runs-on: [self-hosted, Linux, AMD]
-
-  #   steps:
-  #     - name: Clone
-  #       id: checkout
-  #       uses: actions/checkout@v6
-
-  #     - name: Test
-  #       id: ggml-ci
-  #       run: |
-  #         vulkaninfo --summary
-  #         GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
-
-  # TODO: provision AMD GPU machine
-  # ggml-ci-amd-rocm:
-  #   runs-on: [self-hosted, Linux, AMD]
-
-  #   steps:
-  #     - name: Clone
-  #       id: checkout
-  #       uses: actions/checkout@v6
-
-  #     - name: Test
-  #       id: ggml-ci
-  #       run: |
-  #         amd-smi static
-  #         GG_BUILD_ROCM=1 GG_BUILD_AMDGPU_TARGETS="gfx1101" bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
-
-  ggml-ci-mac-metal:
-    runs-on: [self-hosted, macOS, ARM64]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          GG_BUILD_METAL=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  ggml-ci-mac-webgpu:
-    runs-on: [self-hosted, macOS, ARM64]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Dawn Dependency
-        id: dawn-depends
-        run: |
-          DAWN_VERSION="v2.0.0"
-          DAWN_OWNER="reeselevine"
-          DAWN_REPO="dawn"
-          DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-macos-latest-Release"
-          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
-          curl -L -o artifact.zip \
-            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
-          mkdir dawn
-          unzip artifact.zip
-          tar -xvf ${DAWN_ASSET_NAME}.tar.gz -C dawn --strip-components=1
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          GG_BUILD_WEBGPU=1 GG_BUILD_WEBGPU_DAWN_PREFIX="$GITHUB_WORKSPACE/dawn" \
-            bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  ggml-ci-mac-vulkan:
-    runs-on: [self-hosted, macOS, ARM64]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          vulkaninfo --summary
-          GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  ggml-ci-linux-intel-vulkan:
-    runs-on: [self-hosted, Linux, Intel]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-        with:
-          persist-credentials: false
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          vulkaninfo --summary
-          GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  ggml-ci-intel-openvino-gpu-low-perf:
-    runs-on: [self-hosted, Linux, Intel, OpenVINO]
-
-    env:
-      # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.0"
-      OPENVINO_VERSION_FULL: "2026.0.0.20965.c6d6a13a886"
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Setup OpenVINO Toolkit
-        uses: ./.github/actions/linux-setup-openvino
-        with:
-          path: ./openvino_toolkit
-          version_major: ${{ env.OPENVINO_VERSION_MAJOR }}
-          version_full: ${{ env.OPENVINO_VERSION_FULL }}
-
-      - name: Install OpenVINO dependencies
-        run: |
-          cd ./openvino_toolkit
-          chmod +x ./install_dependencies/install_openvino_dependencies.sh
-          echo "Y" | sudo -E ./install_dependencies/install_openvino_dependencies.sh
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          source ./openvino_toolkit/setupvars.sh
-          GG_BUILD_OPENVINO=1 GGML_OPENVINO_DEVICE=GPU GG_BUILD_LOW_PERF=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt

.github/workflows/build-vulkan.yml

@@ -1,96 +0,0 @@
-name: CI (vulkan)
-
-on:
-  workflow_dispatch: # allows manual triggering
-  push:
-    branches:
-      - master
-    paths: [
-      '.github/workflows/build-vulkan.yml',
-      '**/CMakeLists.txt',
-      '**/.cmake',
-      '**/*.h',
-      '**/*.hpp',
-      '**/*.c',
-      '**/*.cpp',
-      '**/*.comp',
-      '**/*.glsl'
-    ]
-
-  pull_request:
-    types: [opened, synchronize, reopened]
-    paths: [
-      '.github/workflows/build-vulkan.yml',
-      'ggml/src/ggml-vulkan/**'
-    ]
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
-env:
-  GGML_NLOOP: 3
-  GGML_N_THREADS: 1
-  LLAMA_LOG_COLORS: 1
-  LLAMA_LOG_PREFIX: 1
-  LLAMA_LOG_TIMESTAMPS: 1
-
-jobs:
-  ubuntu-24-vulkan-llvmpipe:
-    runs-on: ubuntu-24.04
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: ubuntu-24-vulkan-llvmpipe
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
-      - name: Dependencies
-        id: depends
-        run: |
-          sudo add-apt-repository -y ppa:kisak/kisak-mesa
-          sudo apt-get update -y
-          sudo apt-get install -y build-essential mesa-vulkan-drivers libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libssl-dev
-
-      - name: Get latest Vulkan SDK version
-        id: vulkan_sdk_version
-        run: |
-          echo "VULKAN_SDK_VERSION=$(curl https://vulkan.lunarg.com/sdk/latest/linux.txt)" >> "$GITHUB_ENV"
-
-      - name: Use Vulkan SDK Cache
-        uses: actions/cache@v5
-        id: cache-sdk
-        with:
-          path: ./vulkan_sdk
-          key: vulkan-sdk-${{ env.VULKAN_SDK_VERSION }}-${{ runner.os }}
-
-      - name: Setup Vulkan SDK
-        if: steps.cache-sdk.outputs.cache-hit != 'true'
-        uses: ./.github/actions/linux-setup-vulkan-llvmpipe
-        with:
-          path: ./vulkan_sdk
-          version: ${{ env.VULKAN_SDK_VERSION }}
-
-      - name: Build
-        id: cmake_build
-        run: |
-          source ./vulkan_sdk/setup-env.sh
-          cmake -B build \
-            -DGGML_VULKAN=ON
-          cmake --build build --config Release -j $(nproc)
-
-      - name: Test
-        id: cmake_test
-        run: |
-          cd build
-          export GGML_VK_VISIBLE_DEVICES=0
-          export GGML_VK_DISABLE_F16=1
-          export GGML_VK_DISABLE_COOPMAT=1
-          # This is using llvmpipe and runs slower than other backends
-          ctest -L main --verbose --timeout 4800

.github/workflows/copilot-setup-steps.yml

@@ -29,7 +29,7 @@ jobs:
         uses: actions/checkout@v6
 
       - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: copilot-setup-steps
           evict-old-files: 1d
@@ -52,6 +52,6 @@ jobs:
       - name: Install Python dependencies
         run: |
           python3 -m venv .venv
-          source .venv/bin/activate
+          .venv/bin/activate
           pip install -r requirements/requirements-all.txt -r tools/server/tests/requirements.txt
           pip install flake8 pyright pre-commit

.github/workflows/docker.yml

@@ -47,7 +47,6 @@ jobs:
           - { 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: "rocm",   dockerfile: ".devops/rocm.Dockerfile",   platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-22.04" }
-          - { tag: "openvino", dockerfile: ".devops/openvino.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-22.04" }
     steps:
       - name: Check out the repo
         uses: actions/checkout@v6

.github/workflows/hip-quality-check.yml

@@ -1,80 +0,0 @@
-name: HIP quality check
-
-on:
-  workflow_dispatch: # allows manual triggering
-  push:
-    branches:
-      - master
-    paths: [
-      '.github/workflows/hip-quality-check.yml',
-      '**/*.cu',
-      '**/*.cuh'
-    ]
-
-  pull_request:
-    types: [opened, synchronize, reopened]
-    paths: [
-      '.github/workflows/hip-quality-check.yml',
-      '**/*.cu',
-      '**/*.cuh'
-    ]
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
-env:
-  GGML_NLOOP: 3
-  GGML_N_THREADS: 1
-  LLAMA_LOG_COLORS: 1
-  LLAMA_LOG_PREFIX: 1
-  LLAMA_LOG_TIMESTAMPS: 1
-
-jobs:
-  ubuntu-22-hip-quality-check:
-    runs-on: ubuntu-22.04
-    container: rocm/dev-ubuntu-22.04:7.2
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Dependencies
-        id: depends
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y build-essential git cmake rocblas-dev hipblas-dev libssl-dev python3
-
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: ubuntu-22-hip-quality-check
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
-      - name: Build with Werror
-        id: cmake_build
-        run: |
-          cmake -B build -S . \
-            -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" \
-            -DGPU_TARGETS=gfx908 \
-            -DGGML_HIP=ON \
-            -DGGML_HIP_EXPORT_METRICS=Off \
-            -DCMAKE_HIP_FLAGS="-Werror -Wno-tautological-compare" \
-            -DCMAKE_BUILD_TYPE=Release
-          cd build
-          make -j $(nproc)
-
-      - name: Check for major VGPR spills
-        id: vgpr_check
-        run: |
-          cmake -B build -S . \
-            -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" \
-            -DGPU_TARGETS=gfx908 \
-            -DGGML_HIP=ON \
-            -DGGML_HIP_EXPORT_METRICS=On \
-            -DCMAKE_HIP_FLAGS="" \
-            -DCMAKE_BUILD_TYPE=Release
-          cd build
-          make -j $(nproc) 2>&1 | tee metrics.log | grep -v 'Rpass-analysis=kernel-resource-usage\|remark:\|^$'
-          python3 ../scripts/hip/gcn-cdna-vgpr-check.py metrics.log

.github/workflows/python-lint.yml

@@ -4,16 +4,10 @@ on:
   push:
     branches:
       - master
-    paths: [
-      '.github/workflows/python-lint.yml',
-      '**/*.py'
-    ]
+    paths: ['.github/workflows/python-lint.yml', '**/*.py']
   pull_request:
     types: [opened, synchronize, reopened]
-    paths: [
-      '.github/workflows/python-lint.yml',
-      '**/*.py'
-    ]
+    paths: ['.github/workflows/python-lint.yml', '**/*.py']
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}

.github/workflows/release.yml

@@ -10,22 +10,7 @@ on:
   push:
     branches:
       - master
-    paths: [
-      '.github/workflows/release.yml',
-      '**/CMakeLists.txt',
-      '**/.cmake',
-      '**/*.h',
-      '**/*.hpp',
-      '**/*.c',
-      '**/*.cpp',
-      '**/*.cu',
-      '**/*.cuh',
-      '**/*.swift',
-      '**/*.m',
-      '**/*.metal',
-      '**/*.comp',
-      '**/*.glsl'
-    ]
+    paths: ['.github/workflows/release.yml', '**/CMakeLists.txt', '**/.cmake', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal', '**/*.comp']
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
@@ -47,9 +32,9 @@ jobs:
           fetch-depth: 0
 
       - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.16
         with:
-          key: macOS-latest-arm64
+          key: macOS-latest-cmake-arm64
           evict-old-files: 1d
 
       - name: Build
@@ -94,9 +79,9 @@ jobs:
           fetch-depth: 0
 
       - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.16
         with:
-          key: macOS-latest-x64
+          key: macOS-latest-cmake-x64
           evict-old-files: 1d
 
       - name: Build
@@ -153,10 +138,9 @@ jobs:
           fetch-depth: 0
 
       - name: ccache
-        if: ${{ matrix.build != 's390x' }}
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.16
         with:
-          key: ubuntu-cpu-${{ matrix.build }}
+          key: ubuntu-cpu-cmake-${{ matrix.build }}
           evict-old-files: 1d
 
       - name: Dependencies
@@ -205,9 +189,9 @@ jobs:
           fetch-depth: 0
 
       - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.16
         with:
-          key: ubuntu-22-vulkan
+          key: ubuntu-22-cmake-vulkan
           evict-old-files: 1d
 
       - name: Dependencies
@@ -247,86 +231,6 @@ jobs:
           path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.tar.gz
           name: llama-bin-ubuntu-vulkan-x64.tar.gz
 
-  ubuntu-24-openvino:
-    runs-on: ubuntu-24.04
-
-    outputs:
-      openvino_version: ${{ steps.openvino_version.outputs.value }}
-
-    env:
-      # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.0"
-      OPENVINO_VERSION_FULL: "2026.0.0.20965.c6d6a13a886"
-
-    steps:
-      - name: Set OpenVINO version output
-        id: openvino_version
-        run: echo "value=${{ env.OPENVINO_VERSION_MAJOR }}" >> $GITHUB_OUTPUT
-
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
-
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: ubuntu-24-openvino-release-no-preset-v1
-          evict-old-files: 1d
-
-      - name: Dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y build-essential libssl-dev libtbb12 cmake ninja-build python3-pip
-          sudo apt install ocl-icd-opencl-dev opencl-headers opencl-clhpp-headers intel-opencl-icd
-
-      - name: Use OpenVINO Toolkit Cache
-        uses: actions/cache@v5
-        id: cache-openvino
-        with:
-          path: ./openvino_toolkit
-          key: openvino-toolkit-v${{ env.OPENVINO_VERSION_FULL }}-${{ runner.os }}
-
-      - name: Setup OpenVINO Toolkit
-        if: steps.cache-openvino.outputs.cache-hit != 'true'
-        uses: ./.github/actions/linux-setup-openvino
-        with:
-          path: ./openvino_toolkit
-          version_major: ${{ env.OPENVINO_VERSION_MAJOR }}
-          version_full: ${{ env.OPENVINO_VERSION_FULL }}
-
-      - name: Install OpenVINO dependencies
-        run: |
-          cd ./openvino_toolkit
-          chmod +x ./install_dependencies/install_openvino_dependencies.sh
-          echo "Y" | sudo -E ./install_dependencies/install_openvino_dependencies.sh
-
-      - name: Build
-        id: cmake_build
-        run: |
-          source ./openvino_toolkit/setupvars.sh
-          cmake -B build/ReleaseOV -G Ninja \
-            -DCMAKE_BUILD_TYPE=Release \
-            -DGGML_OPENVINO=ON
-          cmake --build build/ReleaseOV --config Release -j $(nproc)
-
-      - name: Determine tag name
-        id: tag
-        uses: ./.github/actions/get-tag-name
-
-      - name: Pack artifacts
-        id: pack_artifacts
-        run: |
-          cp LICENSE ./build/ReleaseOV/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/ReleaseOV/bin .
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v6
-        with:
-          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz
-          name: llama-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz
-
   windows-cpu:
     runs-on: windows-2025
 
@@ -343,9 +247,9 @@ jobs:
           fetch-depth: 0
 
       - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.16
         with:
-          key: windows-latest-cpu-${{ matrix.arch }}
+          key: windows-latest-cmake-cpu-${{ matrix.arch }}
           variant: ccache
           evict-old-files: 1d
 
@@ -404,9 +308,9 @@ jobs:
         uses: actions/checkout@v6
 
       - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.16
         with:
-          key: windows-latest-${{ matrix.backend }}-${{ matrix.arch }}
+          key: windows-latest-cmake-${{ matrix.backend }}-${{ matrix.arch }}
           variant: ccache
           evict-old-files: 1d
 
@@ -474,7 +378,7 @@ jobs:
         uses: actions/checkout@v6
 
       - name: Install ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.16
         with:
           key: windows-cuda-${{ matrix.cuda }}
           variant: ccache
@@ -550,9 +454,9 @@ jobs:
         uses: actions/checkout@v6
 
       - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.16
         with:
-          key: windows-latest-sycl
+          key: windows-latest-cmake-sycl
           variant: ccache
           evict-old-files: 1d
 
@@ -630,9 +534,9 @@ jobs:
           fetch-depth: 0
 
       - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.16
         with:
-          key: ubuntu-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}
+          key: ubuntu-rocm-cmake-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}
           evict-old-files: 1d
 
       - name: Dependencies
@@ -740,9 +644,9 @@ jobs:
           key: rocm-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ runner.os }}
 
       - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.16
         with:
-          key: windows-latest-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ matrix.name }}-x64
+          key: windows-latest-cmake-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ matrix.name }}-x64
           evict-old-files: 1d
 
       - name: Install ROCm
@@ -968,7 +872,7 @@ jobs:
     permissions:
         contents: write # for creating release
 
-    runs-on: ubuntu-slim
+    runs-on: ubuntu-latest
 
     needs:
       - windows
@@ -979,7 +883,6 @@ jobs:
       - ubuntu-22-rocm
       - ubuntu-22-cpu
       - ubuntu-22-vulkan
-      - ubuntu-24-openvino
       - macOS-arm64
       - macOS-x64
       - ios-xcode-build
@@ -1064,7 +967,6 @@ jobs:
             - [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 x64 (ROCm 7.2)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-7.2-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)
-            - [Ubuntu x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ needs.ubuntu-24-openvino.outputs.openvino_version }}-x64.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)

.github/workflows/server-metal.yml

@@ -1,4 +1,4 @@
-name: Server (self-hosted)
+name: Server-Metal
 
 on:
   workflow_dispatch: # allows manual triggering
@@ -14,19 +14,7 @@ on:
   push:
     branches:
       - master
-    paths: [
-      '.github/workflows/server-self-hosted.yml',
-      '**/CMakeLists.txt',
-      '**/Makefile',
-      '**/*.h',
-      '**/*.hpp',
-      '**/*.c',
-      '**/*.cpp',
-      '**/*.cu',
-      '**/*.swift',
-      '**/*.m',
-      'tools/server/**.*'
-    ]
+    paths: ['.github/workflows/server-metal.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'tools/server/**.*']
 
 env:
   LLAMA_LOG_COLORS: 1
@@ -40,7 +28,7 @@ concurrency:
 
 jobs:
   server-metal:
-    runs-on: [self-hosted, llama-server, macOS, ARM64]
+    runs-on: [self-hosted, macOS, ARM64]
 
     name: server-metal (${{ matrix.wf_name }})
     strategy:
@@ -83,42 +71,3 @@ jobs:
           pip install -r requirements.txt
           export ${{ matrix.extra_args }}
           pytest -v -x -m "not slow"
-
-  server-cuda:
-    runs-on: [self-hosted, llama-server, Linux, NVIDIA]
-
-    name: server-cuda (${{ matrix.wf_name }})
-    strategy:
-      matrix:
-        build_type: [Release]
-        wf_name: ["GPUx1"]
-        include:
-          - build_type: Release
-            extra_args: "LLAMA_ARG_BACKEND_SAMPLING=1"
-            wf_name:    "GPUx1, backend-sampling"
-      fail-fast: false
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Build
-        id: cmake_build
-        run: |
-          cmake -B build -DGGML_SCHED_NO_REALLOC=ON
-          cmake --build build --config ${{ matrix.build_type }} -j $(sysctl -n hw.logicalcpu) --target llama-server
-
-      - name: Tests
-        id: server_integration_tests
-        if: ${{ (!matrix.disabled_on_pr || !github.event.pull_request) }}
-        run: |
-          cd tools/server/tests
-          python3 -m venv venv
-          source venv/bin/activate
-          pip install -r requirements.txt
-          export ${{ matrix.extra_args }}
-          pytest -v -x -m "not slow"

.github/workflows/server-sanitize.yml

@@ -1,105 +0,0 @@
-name: Server (sanitize)
-
-on:
-  workflow_dispatch: # allows manual triggering
-    inputs:
-      sha:
-        description: 'Commit SHA1 to build'
-        required: false
-        type: string
-      slow_tests:
-        description: 'Run slow tests'
-        required: true
-        type: boolean
-  push:
-    branches:
-      - master
-    paths: [
-      '.github/workflows/server-sanitize.yml',
-      '**/CMakeLists.txt',
-      '**/Makefile',
-      '**/*.h',
-      '**/*.hpp',
-      '**/*.c',
-      '**/*.cpp',
-      'tools/server/**.*'
-    ]
-
-env:
-  LLAMA_LOG_COLORS: 1
-  LLAMA_LOG_PREFIX: 1
-  LLAMA_LOG_TIMESTAMPS: 1
-  LLAMA_LOG_VERBOSITY: 10
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  server:
-    runs-on: ubuntu-latest
-
-    strategy:
-      matrix:
-        sanitizer: [ADDRESS, UNDEFINED] # THREAD is very slow
-        build_type: [RelWithDebInfo]
-      fail-fast: false
-
-    steps:
-      - name: Dependencies
-        id: depends
-        run: |
-          sudo apt-get update
-          sudo apt-get -y install \
-            build-essential \
-            xxd \
-            git \
-            cmake \
-            curl \
-            wget \
-            language-pack-en \
-            libssl-dev
-
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Build
-        id: cmake_build
-        run: |
-          cmake -B build \
-            -DLLAMA_BUILD_BORINGSSL=ON \
-            -DGGML_SCHED_NO_REALLOC=ON \
-            -DGGML_SANITIZE_ADDRESS=${{ matrix.sanitizer == 'ADDRESS' }} \
-            -DGGML_SANITIZE_THREAD=${{ matrix.sanitizer == 'THREAD' }} \
-            -DGGML_SANITIZE_UNDEFINED=${{ matrix.sanitizer == 'UNDEFINED' }} \
-            -DLLAMA_SANITIZE_ADDRESS=${{ matrix.sanitizer == 'ADDRESS' }} \
-            -DLLAMA_SANITIZE_THREAD=${{ matrix.sanitizer == 'THREAD' }} \
-            -DLLAMA_SANITIZE_UNDEFINED=${{ matrix.sanitizer == 'UNDEFINED' }}
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
-      - name: Python setup
-        id: setup_python
-        uses: actions/setup-python@v6
-        with:
-          python-version: '3.11'
-          pip-install: -r tools/server/tests/requirements.txt
-
-      - name: Tests
-        id: server_integration_tests
-        if: ${{ (!matrix.disabled_on_pr || !github.event.pull_request) }}
-        run: |
-          cd tools/server/tests
-          export ${{ matrix.extra_args }}
-          pytest -v -x -m "not slow"
-
-      - name: Slow tests
-        id: server_integration_tests_slow
-        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
-        run: |
-          cd tools/server/tests
-          export ${{ matrix.extra_args }}
-          SLOW_TESTS=1 pytest -v -x

.github/workflows/server-webui.yml

@@ -1,3 +1,4 @@
+# Server WebUI build and tests
 name: Server WebUI
 
 on:
@@ -10,20 +11,10 @@ on:
   push:
     branches:
       - master
-    paths: [
-      '.github/workflows/server-webui.yml',
-      'tools/server/webui/**.*',
-      'tools/server/tests/**.*',
-      'tools/server/public/**'
-    ]
+    paths: ['.github/workflows/server-webui.yml', 'tools/server/webui/**.*', 'tools/server/tests/**.*', 'tools/server/public/**']
   pull_request:
     types: [opened, synchronize, reopened]
-    paths: [
-      '.github/workflows/server-webui.yml',
-      'tools/server/webui/**.*',
-      'tools/server/tests/**.*',
-      'tools/server/public/**'
-    ]
+    paths: ['.github/workflows/server-webui.yml', 'tools/server/webui/**.*', 'tools/server/tests/**.*', 'tools/server/public/**']
 
 env:
   LLAMA_LOG_COLORS: 1
@@ -38,7 +29,7 @@ concurrency:
 jobs:
   webui-check:
     name: WebUI Checks
-    runs-on: ${{ 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}
+    runs-on: ubuntu-latest
     continue-on-error: true
     steps:
       - name: Checkout code

.github/workflows/server.yml

@@ -1,3 +1,4 @@
+# Server build and tests
 name: Server
 
 on:
@@ -14,34 +15,10 @@ on:
   push:
     branches:
       - master
-    paths: [
-      '.github/workflows/server.yml',
-      '**/CMakeLists.txt',
-      '**/Makefile',
-      '**/*.h',
-      '**/*.hpp',
-      '**/*.c',
-      '**/*.cpp',
-      '**/*.cu',
-      '**/*.swift',
-      '**/*.m',
-      'tools/server/**.*'
-    ]
+    paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'tools/server/**.*']
   pull_request:
     types: [opened, synchronize, reopened]
-    paths: [
-      '.github/workflows/server.yml',
-      '**/CMakeLists.txt',
-      '**/Makefile',
-      '**/*.h',
-      '**/*.hpp',
-      '**/*.c',
-      '**/*.cpp',
-      '**/*.cu',
-      '**/*.swift',
-      '**/*.m',
-      'tools/server/**.*'
-    ]
+    paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'tools/server/**.*']
 
 env:
   LLAMA_LOG_COLORS: 1
@@ -57,18 +34,17 @@ jobs:
   server:
     runs-on: ubuntu-latest
 
-    name: server (${{ matrix.wf_name }})
     strategy:
       matrix:
-        build_type: [Release]
-        wf_name: ["default"]
+        sanitizer: [ADDRESS, UNDEFINED] # THREAD is very slow
+        build_type: [RelWithDebInfo]
         include:
           - build_type: Release
+            sanitizer: ""
             extra_args: ""
-            wf_name:    "default"
           - build_type: Release
+            sanitizer: ""
             extra_args: "LLAMA_ARG_BACKEND_SAMPLING=1"
-            wf_name:    "backend-sampling"
       fail-fast: false
 
     steps:
@@ -98,7 +74,13 @@ jobs:
         run: |
           cmake -B build \
             -DLLAMA_BUILD_BORINGSSL=ON \
-            -DGGML_SCHED_NO_REALLOC=ON
+            -DGGML_SCHED_NO_REALLOC=ON \
+            -DGGML_SANITIZE_ADDRESS=${{ matrix.sanitizer == 'ADDRESS' }} \
+            -DGGML_SANITIZE_THREAD=${{ matrix.sanitizer == 'THREAD' }} \
+            -DGGML_SANITIZE_UNDEFINED=${{ matrix.sanitizer == 'UNDEFINED' }} \
+            -DLLAMA_SANITIZE_ADDRESS=${{ matrix.sanitizer == 'ADDRESS' }} \
+            -DLLAMA_SANITIZE_THREAD=${{ matrix.sanitizer == 'THREAD' }} \
+            -DLLAMA_SANITIZE_UNDEFINED=${{ matrix.sanitizer == 'UNDEFINED' }}
           cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
 
       - name: Python setup

.gitignore

@@ -124,11 +124,6 @@ poetry.toml
 # Scripts
 !/scripts/install-oneapi.bat
 
-# Generated by scripts
-/hellaswag_val_full.txt
-/winogrande-debiased-eval.csv
-/wikitext-2-raw/
-
 # Test models for lora adapters
 /lora-tests
 

CODEOWNERS

@@ -2,13 +2,29 @@
 # multiplie collaborators per item can be specified
 
 /.devops/*.Dockerfile                   @ngxson
-/.github/actions/                       @ggml-org/ci
-/.github/workflows/                     @ggml-org/ci
+/.github/actions/                       @CISC
+/.github/workflows/                     @CISC
 /ci/                                    @ggerganov
 /cmake/                                 @ggerganov
-/common/                                @ggml-org/llama-common
-/common/jinja/                          @CISC
+/common/CMakeLists.txt                  @ggerganov
+/common/arg.*                           @ggerganov
+/common/base64.hpp.*                    @ggerganov
+/common/build-info.*                    @ggerganov
+/common/chat.*                          @pwilkin
+/common/chat-auto*.*                    @pwilkin
+/common/chat-diff-analyzer.*            @pwilkin
+/common/chat-peg-parser.*               @aldehir
+/common/common.*                        @ggerganov
+/common/console.*                       @ggerganov
+/common/http.*                          @angt
+/common/jinja/                          @ngxson @CISC @aldehir
+/common/llguidance.*                    @ggerganov
+/common/log.*                           @ggerganov
 /common/ngram-map.*                     @srogmann
+/common/peg-parser.*                    @aldehir
+/common/sampling.*                      @ggerganov
+/common/speculative.*                   @ggerganov
+/common/unicode.*                       @aldehir
 /convert_*.py                           @CISC
 /examples/batched.swift/                @ggerganov
 /examples/batched/                      @ggerganov
@@ -35,28 +51,29 @@
 /examples/speculative/                  @ggerganov
 /ggml/cmake/                            @ggerganov
 /ggml/include/                          @ggerganov
-/ggml/src/ggml-cann/                    @ggml-org/ggml-cann
 /ggml/src/ggml-common.h                 @ggerganov
 /ggml/src/ggml-cpu/                     @ggerganov
 /ggml/src/ggml-cpu/spacemit/            @alex-spacemit
-/ggml/src/ggml-cuda/                    @ggml-org/ggml-cuda
+/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/                   @ggml-org/ggml-metal
-/ggml/src/ggml-opencl/                  @ggml-org/ggml-opencl
-/ggml/src/ggml-hexagon/                 @ggml-org/ggml-hexagon
+/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/                     @ggml-org/ggml-rpc
-/ggml/src/ggml-sycl/                    @ggml-org/ggml-sycl
+/ggml/src/ggml-rpc/                     @rgerganov
 /ggml/src/ggml-threading.*              @ggerganov
-/ggml/src/ggml-vulkan/                  @ggml-org/ggml-vulkan
+/ggml/src/ggml-vulkan/                  @0cc4m
 /ggml/src/ggml-virtgpu/                 @kpouget
-/ggml/src/ggml-webgpu/                  @ggml-org/ggml-webgpu
-/ggml/src/ggml-zdnn/                    @ggml-org/ggml-zdnn @Andreas-Krebbel @AlekseiNikiforovIBM
-/ggml/src/ggml-openvino/                @cavusmustafa @wine99
+/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
@@ -75,18 +92,16 @@
 /src/models/                            @CISC
 /tests/                                 @ggerganov
 /tests/test-chat.*                      @pwilkin
-/tests/test-llama-archs.cpp             @JohannesGaessler
 /tools/batched-bench/                   @ggerganov
 /tools/cli/                             @ngxson
 /tools/completion/                      @ggerganov
-/tools/mtmd/                            @ggml-org/llama-mtmd
+/tools/mtmd/                            @ngxson
 /tools/perplexity/                      @ggerganov
 /tools/parser/                          @pwilkin
 /tools/quantize/                        @ggerganov
-/tools/rpc/                             @ggml-org/ggml-rpc
-/tools/server/*                         @ggml-org/llama-server # no subdir
-/tools/server/tests/                    @ggml-org/llama-server
-/tools/server/webui/                    @ggml-org/llama-webui
+/tools/rpc/                             @rgerganov
+/tools/server/*                         @ngxson @ggerganov # no subdir
+/tools/server/webui/                    @allozaur
 /tools/tokenize/                        @ggerganov
 /tools/tts/                             @ggerganov
 /vendor/                                @ggerganov

CONTRIBUTING.md

@@ -30,21 +30,15 @@ Before submitting your PR:
 - Search for existing PRs to prevent duplicating efforts
 - 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:
-  - Execute [the full CI locally on your machine](ci/README.md) before publishing
-  - Verify that the perplexity and the performance are not affected negatively by your changes (use `llama-perplexity` and `llama-bench`)
-  - If you modified the `ggml` source, run the `test-backend-ops` tool to check whether different backend implementations of the `ggml` operators produce consistent results (this requires access to at least two different `ggml` backends)
-  - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops`
+    - Execute [the full CI locally on your machine](ci/README.md) before publishing
+    - Verify that the perplexity and the performance are not affected negatively by your changes (use `llama-perplexity` and `llama-bench`)
+    - If you modified the `ggml` source, run the `test-backend-ops` tool to check whether different backend implementations of the `ggml` operators produce consistent results (this requires access to at least two different `ggml` backends)
+    - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops`
 - Create separate PRs for each feature or fix:
-  - Avoid combining unrelated changes in a single PR
-  - For intricate features, consider opening a feature request first to discuss and align expectations
-  - When adding support for a new model or feature, focus on **CPU support only** in the initial PR unless you have a good reason not to. Add support for other backends like CUDA in follow-up PRs
-  - In particular, adding new data types (extension of the `ggml_type` enum) carries with it a disproportionate maintenance burden. As such, to add a new quantization type you will need to meet the following *additional* criteria *at minimum*:
-    - convert a small model to GGUF using the new type and upload it to HuggingFace
-    - provide [perplexity](https://github.com/ggml-org/llama.cpp/tree/master/tools/perplexity) comparisons to FP16/BF16 (whichever is the native precision) as well as to types of similar size
-    - provide KL divergence data calculated vs. the FP16/BF16 (whichever is the native precision) version for both the new type as well as types of similar size
-    - provide [performance data](https://github.com/ggml-org/llama.cpp/tree/master/tools/llama-bench) for the new type in comparison to types of similar size on pure CPU
+    - Avoid combining unrelated changes in a single PR
+    - For intricate features, consider opening a feature request first to discuss and align expectations
+    - When adding support for a new model or feature, focus on **CPU support only** in the initial PR unless you have a good reason not to. Add support for other backends like CUDA in follow-up PRs
 - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly
-- If you are a new contributor, limit your open PRs to 1.
 
 After submitting your PR:
 - Expect requests for modifications to ensure the code meets llama.cpp's standards for quality and long-term maintainability
@@ -178,8 +172,6 @@ Maintainers reserve the right to decline review or close pull requests for any r
 - 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)_
 
-- For changes in server, please make sure to refer to the [server development documentation](./tools/server/README-dev.md)
-
 # Documentation
 
 - Documentation is a community effort

README.md

@@ -279,7 +279,6 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 | [BLAS](docs/build.md#blas-build) | All |
 | [BLIS](docs/backend/BLIS.md) | All |
 | [SYCL](docs/backend/SYCL.md) | Intel and Nvidia GPU |
-| [OpenVINO [In Progress]](docs/backend/OPENVINO.md) | Intel CPUs, GPUs, and NPUs |
 | [MUSA](docs/build.md#musa) | Moore Threads GPU |
 | [CUDA](docs/build.md#cuda) | Nvidia GPU |
 | [HIP](docs/build.md#hip) | AMD GPU |

benches/nemotron/nemotron-dgx-spark.md

@@ -1,117 +0,0 @@
-# NVIDIA DGX Spark
-
-## 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
-Fri Mar  6 11:39:45 2026
-+-----------------------------------------------------------------------------------------+
-| 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   52C    P0             13W /  N/A  | Not Supported          |      0%      Default |
-|                                         |                        |                  N/A |
-+-----------------------------------------+------------------------+----------------------+
-```
-
-## ggml-org/Nemotron-3-Super-120B-GGUF
-
-Model: https://huggingface.co/ggml-org/Nemotron-3-Super-120B-GGUF
-
-- `llama-batched-bench`
-
-main: n_kv_max = 303104, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = 99, 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 |    1.094 |   468.05 |    1.621 |    19.74 |    2.715 |   200.37 |
-|   512 |     32 |    2 |   1088 |    1.463 |   700.16 |    2.437 |    26.26 |    3.900 |   279.01 |
-|   512 |     32 |    4 |   2176 |    2.647 |   773.76 |    4.043 |    31.66 |    6.689 |   325.29 |
-|   512 |     32 |    8 |   4352 |    5.291 |   774.14 |    6.151 |    41.62 |   11.442 |   380.37 |
-|   512 |     32 |   16 |   8704 |   10.603 |   772.62 |   10.385 |    49.30 |   20.987 |   414.72 |
-|   512 |     32 |   32 |  17408 |   21.231 |   771.69 |   18.235 |    56.16 |   39.466 |   441.09 |
-|  4096 |     32 |    1 |   4128 |    5.340 |   767.05 |    1.616 |    19.81 |    6.956 |   593.47 |
-|  4096 |     32 |    2 |   8256 |   10.673 |   767.55 |    2.454 |    26.08 |   13.127 |   628.94 |
-|  4096 |     32 |    4 |  16512 |   21.348 |   767.46 |    4.072 |    31.44 |   25.420 |   649.57 |
-|  4096 |     32 |    8 |  33024 |   42.714 |   767.15 |    6.277 |    40.78 |   48.991 |   674.08 |
-|  4096 |     32 |   16 |  66048 |   85.385 |   767.54 |   10.596 |    48.32 |   95.981 |   688.14 |
-|  4096 |     32 |   32 | 132096 |  170.819 |   767.32 |   18.619 |    55.00 |  189.437 |   697.31 |
-|  8192 |     32 |    1 |   8224 |   10.690 |   766.32 |    1.619 |    19.76 |   12.310 |   668.10 |
-|  8192 |     32 |    2 |  16448 |   21.382 |   766.24 |    2.467 |    25.94 |   23.850 |   689.65 |
-|  8192 |     32 |    4 |  32896 |   42.782 |   765.92 |    4.098 |    31.23 |   46.881 |   701.69 |
-|  8192 |     32 |    8 |  65792 |   85.582 |   765.77 |    6.368 |    40.20 |   91.951 |   715.52 |
-|  8192 |     32 |   16 | 131584 |  171.066 |   766.21 |   10.774 |    47.52 |  181.840 |   723.62 |
-|  8192 |     32 |   32 | 263168 |  342.140 |   766.19 |   18.969 |    53.98 |  361.109 |   728.78 |
-
-- `llama-bench`
-
-| model                   |       size |     params | backend    | n_ubatch | fa |            test |                  t/s |
-| ----------------------- | ---------: | ---------: | ---------- | -------: | -: | --------------: | -------------------: |
-| nemotron 120B.A12B Q4_K |  65.10 GiB |   120.67 B | CUDA       |     2048 |  1 |          pp2048 |        768.84 ± 0.90 |
-| nemotron 120B.A12B Q4_K |  65.10 GiB |   120.67 B | CUDA       |     2048 |  1 |            tg32 |         19.94 ± 0.16 |
-| nemotron 120B.A12B Q4_K |  65.10 GiB |   120.67 B | CUDA       |     2048 |  1 |  pp2048 @ d4096 |        764.51 ± 0.50 |
-| nemotron 120B.A12B Q4_K |  65.10 GiB |   120.67 B | CUDA       |     2048 |  1 |    tg32 @ d4096 |         19.95 ± 0.18 |
-| nemotron 120B.A12B Q4_K |  65.10 GiB |   120.67 B | CUDA       |     2048 |  1 |  pp2048 @ d8192 |        759.53 ± 0.71 |
-| nemotron 120B.A12B Q4_K |  65.10 GiB |   120.67 B | CUDA       |     2048 |  1 |    tg32 @ d8192 |         19.83 ± 0.18 |
-| nemotron 120B.A12B Q4_K |  65.10 GiB |   120.67 B | CUDA       |     2048 |  1 | pp2048 @ d16384 |        747.98 ± 1.58 |
-| nemotron 120B.A12B Q4_K |  65.10 GiB |   120.67 B | CUDA       |     2048 |  1 |   tg32 @ d16384 |         19.84 ± 0.18 |
-| nemotron 120B.A12B Q4_K |  65.10 GiB |   120.67 B | CUDA       |     2048 |  1 | pp2048 @ d32768 |        724.40 ± 2.70 |
-| nemotron 120B.A12B Q4_K |  65.10 GiB |   120.67 B | CUDA       |     2048 |  1 |   tg32 @ d32768 |         19.45 ± 0.18 |
-
-build: 04a65daab (8268)
-
-## ggml-org/Nemotron-3-Nano-4B-GGUF
-
-Model: https://huggingface.co/ggml-org/Nemotron-3-Nano-4B-GGUF
-
-- `llama-batched-bench`
-
-main: n_kv_max = 303104, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = 99, 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 |    0.152 |  3371.61 |    0.597 |    53.64 |    0.748 |   726.90 |
-|   512 |     32 |    2 |   1088 |    0.319 |  3208.68 |    0.857 |    74.66 |    1.176 |   924.89 |
-|   512 |     32 |    4 |   2176 |    0.720 |  2843.56 |    1.323 |    96.78 |    2.043 |  1065.18 |
-|   512 |     32 |    8 |   4352 |    1.428 |  2867.96 |    2.311 |   110.76 |    3.739 |  1163.82 |
-|   512 |     32 |   16 |   8704 |    2.857 |  2866.94 |    4.203 |   121.82 |    7.060 |  1232.82 |
-|   512 |     32 |   32 |  17408 |    5.709 |  2869.76 |    7.964 |   128.58 |   13.673 |  1273.14 |
-|  4096 |     32 |    1 |   4128 |    1.458 |  2809.76 |    0.605 |    52.92 |    2.062 |  2001.52 |
-|  4096 |     32 |    2 |   8256 |    2.905 |  2819.95 |    0.875 |    73.12 |    3.780 |  2183.95 |
-|  4096 |     32 |    4 |  16512 |    5.790 |  2829.74 |    1.361 |    94.07 |    7.151 |  2309.17 |
-|  4096 |     32 |    8 |  33024 |   11.598 |  2825.32 |    2.378 |   107.65 |   13.976 |  2362.89 |
-|  4096 |     32 |   16 |  66048 |   23.208 |  2823.88 |    4.348 |   117.76 |   27.556 |  2396.89 |
-|  4096 |     32 |   32 | 132096 |   46.515 |  2817.85 |    8.279 |   123.69 |   54.794 |  2410.79 |
-|  8192 |     32 |    1 |   8224 |    2.950 |  2776.95 |    0.617 |    51.89 |    3.567 |  2305.75 |
-|  8192 |     32 |    2 |  16448 |    5.921 |  2767.32 |    0.896 |    71.45 |    6.816 |  2413.05 |
-|  8192 |     32 |    4 |  32896 |   11.842 |  2767.21 |    1.401 |    91.34 |   13.243 |  2484.03 |
-|  8192 |     32 |    8 |  65792 |   23.726 |  2762.17 |    2.461 |   104.03 |   26.187 |  2512.38 |
-|  8192 |     32 |   16 | 131584 |   47.777 |  2743.43 |    4.577 |   111.86 |   52.354 |  2513.36 |
-|  8192 |     32 |   32 | 263168 |   96.691 |  2711.16 |    8.772 |   116.73 |  105.463 |  2495.36 |
-
-- `llama-bench`
-
-| model                   |       size |     params | backend    | n_ubatch | fa |            test |                  t/s |
-| ----------------------- | ---------: | ---------: | ---------- | -------: | -: | --------------: | -------------------: |
-| nemotron 4B Q8_0        |   3.94 GiB |     3.97 B | CUDA       |     2048 |  1 |          pp2048 |      2761.90 ± 19.31 |
-| nemotron 4B Q8_0        |   3.94 GiB |     3.97 B | CUDA       |     2048 |  1 |            tg32 |         52.85 ± 0.12 |
-| nemotron 4B Q8_0        |   3.94 GiB |     3.97 B | CUDA       |     2048 |  1 |  pp2048 @ d4096 |      2687.07 ± 21.84 |
-| nemotron 4B Q8_0        |   3.94 GiB |     3.97 B | CUDA       |     2048 |  1 |    tg32 @ d4096 |         52.32 ± 0.23 |
-| nemotron 4B Q8_0        |   3.94 GiB |     3.97 B | CUDA       |     2048 |  1 |  pp2048 @ d8192 |      2564.52 ± 57.69 |
-| nemotron 4B Q8_0        |   3.94 GiB |     3.97 B | CUDA       |     2048 |  1 |    tg32 @ d8192 |         51.27 ± 0.34 |
-| nemotron 4B Q8_0        |   3.94 GiB |     3.97 B | CUDA       |     2048 |  1 | pp2048 @ d16384 |      2334.02 ± 37.83 |
-| nemotron 4B Q8_0        |   3.94 GiB |     3.97 B | CUDA       |     2048 |  1 |   tg32 @ d16384 |         49.71 ± 0.14 |
-| nemotron 4B Q8_0        |   3.94 GiB |     3.97 B | CUDA       |     2048 |  1 | pp2048 @ d32768 |      2041.46 ± 40.45 |
-| nemotron 4B Q8_0        |   3.94 GiB |     3.97 B | CUDA       |     2048 |  1 |   tg32 @ d32768 |         46.71 ± 0.13 |
-
-build: 1bbec6a75 (8382)

ci/run.sh

@@ -25,15 +25,6 @@
 # # with KLEIDIAI support
 # GG_BUILD_KLEIDIAI=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
 #
-# # with BLAS support
-# GG_BUILD_BLAS=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
-#
-# with BLAS support (custom vendor)
-# GG_BUILD_BLAS=1 GG_BUILD_BLAS_VENDOR=Intel10_64lp bash ./ci/run.sh ./tmp/results ./tmp/mnt
-#
-# with OPENVINO support
-# GG_BUILD_OPENVINO=1 GG_BUILD_LOW_PERF=1 GGML_OPENVINO_DEVICE=CPU bash ./ci/run.sh ./tmp/results ./tmp/mnt
-#
 
 if [ -z "$2" ]; then
     echo "usage: $0 <output-dir> <mnt-dir>"
@@ -55,7 +46,6 @@ cd $sd/../
 SRC=`pwd`
 
 CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=${LLAMA_FATAL_WARNINGS:-ON} -DLLAMA_OPENSSL=OFF -DGGML_SCHED_NO_REALLOC=ON"
-CTEST_EXTRA=""
 
 if [ ! -z ${GG_BUILD_METAL} ]; then
     CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON"
@@ -175,22 +165,6 @@ if [ -n "${GG_BUILD_KLEIDIAI}" ]; then
         -DBUILD_SHARED_LIBS=OFF"
 fi
 
-if [ ! -z ${GG_BUILD_BLAS} ]; then
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=${GG_BUILD_BLAS_VENDOR:-OpenBLAS}"
-fi
-
-if [ ! -z ${GG_BUILD_OPENVINO} ]; then
-    if [ -z ${OpenVINO_DIR} ]; then
-        echo "OpenVINO_DIR not found, please install OpenVINO via archives and enable it by:"
-        echo "source /opt/intel/openvino/setupvars.sh"
-        exit 1
-    fi
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_OPENVINO=ON"
-
-    # TODO: fix and re-enable the `test-llama-archs` test below
-    CTEST_EXTRA="-E test-llama-archs"
-fi
-
 ## helpers
 
 # download a file if it does not exist or if it is outdated
@@ -248,7 +222,7 @@ 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" ${CTEST_EXTRA}) 2>&1 | tee -a $OUT/${ci}-ctest.log
+    (time ctest --output-on-failure -L main -E "test-opt|test-backend-ops" ) 2>&1 | tee -a $OUT/${ci}-ctest.log
 
     set +e
 }
@@ -280,9 +254,9 @@ function gg_run_ctest_release {
     (time make -j$(nproc)                                    ) 2>&1 | tee -a $OUT/${ci}-make.log
 
     if [ -z ${GG_BUILD_LOW_PERF} ]; then
-        (time ctest --output-on-failure -L 'main|python' ${CTEST_EXTRA}) 2>&1 | tee -a $OUT/${ci}-ctest.log
+        (time ctest --output-on-failure -L 'main|python' ) 2>&1 | tee -a $OUT/${ci}-ctest.log
     else
-        (time ctest --output-on-failure -L main -E test-opt ${CTEST_EXTRA}) 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
     fi
 
     set +e

common/CMakeLists.txt

@@ -81,8 +81,6 @@ add_library(${TARGET} STATIC
     preset.cpp
     preset.h
     regex-partial.cpp
-    reasoning-budget.cpp
-    reasoning-budget.h
     regex-partial.h
     sampling.cpp
     sampling.h

common/arg.cpp

@@ -732,28 +732,23 @@ static void common_params_print_completion(common_params_context & ctx_arg) {
         "llama-completion",
         "llama-convert-llama2c-to-ggml",
         "llama-cvector-generator",
-        "llama-debug",
-        "llama-diffusion-cli",
         "llama-embedding",
         "llama-eval-callback",
         "llama-export-lora",
-        "llama-finetune",
-        "llama-fit-params",
-        "llama-gemma3-cli",
         "llama-gen-docs",
         "llama-gguf",
         "llama-gguf-hash",
         "llama-gguf-split",
-        "llama-idle",
+        "llama-gritlm",
         "llama-imatrix",
-        "llama-llava-cli",
+        "llama-infill",
+        "llama-mtmd-cli",
+        "llama-llava-clip-quantize-cli",
         "llama-lookahead",
         "llama-lookup",
         "llama-lookup-create",
         "llama-lookup-merge",
         "llama-lookup-stats",
-        "llama-minicpmv-cli",
-        "llama-mtmd-cli",
         "llama-parallel",
         "llama-passkey",
         "llama-perplexity",
@@ -1830,23 +1825,23 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_sparam());
     add_opt(common_arg(
         {"--grammar"}, "GRAMMAR",
-        "BNF-like grammar to constrain generations (see samples in grammars/ dir)",
+        string_format("BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", params.sampling.grammar.c_str()),
         [](common_params & params, const std::string & value) {
-            params.sampling.grammar = {COMMON_GRAMMAR_TYPE_USER, value};
+            params.sampling.grammar = value;
         }
     ).set_sparam());
     add_opt(common_arg(
         {"--grammar-file"}, "FNAME",
         "file to read grammar from",
         [](common_params & params, const std::string & value) {
-            params.sampling.grammar = {COMMON_GRAMMAR_TYPE_USER, read_file(value)};
+            params.sampling.grammar = read_file(value);
         }
     ).set_sparam());
     add_opt(common_arg(
         {"-j", "--json-schema"}, "SCHEMA",
         "JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\nFor schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead",
         [](common_params & params, const std::string & value) {
-            params.sampling.grammar = {COMMON_GRAMMAR_TYPE_OUTPUT_FORMAT, json_schema_to_grammar(json::parse(value))};
+            params.sampling.grammar = json_schema_to_grammar(json::parse(value));
         }
     ).set_sparam());
     add_opt(common_arg(
@@ -1863,7 +1858,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                 std::istreambuf_iterator<char>(),
                 std::back_inserter(schema)
             );
-            params.sampling.grammar = {COMMON_GRAMMAR_TYPE_OUTPUT_FORMAT, json_schema_to_grammar(json::parse(schema))};
+            params.sampling.grammar = json_schema_to_grammar(json::parse(schema));
         }
     ).set_sparam());
     add_opt(common_arg(
@@ -2432,11 +2427,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                 );
             }
             if (split_arg.size() == 1) {
-                std::fill(params.fit_params_target.begin(), params.fit_params_target.end(), std::stoull(split_arg[0]) * 1024*1024);
+                std::fill(params.fit_params_target.begin(), params.fit_params_target.end(), std::stoul(split_arg[0]) * 1024*1024);
                 return;
             }
             for (size_t i = 0; i < split_arg.size(); i++) {
-                params.fit_params_target[i] = std::stoull(split_arg[i]) * 1024*1024;
+                params.fit_params_target[i] = std::stoul(split_arg[i]) * 1024*1024;
             }
         }
     ).set_env("LLAMA_ARG_FIT_TARGET"));
@@ -2671,8 +2666,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.out_file = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA, LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_FINETUNE,
-                    LLAMA_EXAMPLE_RESULTS, LLAMA_EXAMPLE_EXPORT_GRAPH_OPS}));
+    ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA, LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_FINETUNE, LLAMA_EXAMPLE_RESULTS}));
     add_opt(common_arg(
         {"-ofreq", "--output-frequency"}, "N",
         string_format("output the imatrix every N iterations (default: %d)", params.n_out_freq),
@@ -2919,10 +2913,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             auto parsed = json::parse(value);
             for (const auto & item : parsed.items()) {
-                if (item.key() == "enable_thinking") {
-                    LOG_WRN("Setting 'enable_thinking' via --chat-template-kwargs is deprecated. "
-                            "Use --reasoning on / --reasoning off instead.\n");
-                }
                 params.default_template_kwargs[item.key()] = item.value().dump();
             }
         }
@@ -3058,39 +3048,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.reasoning_format = common_reasoning_format_from_name(value);
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_THINK"));
-    add_opt(common_arg(
-        {"-rea", "--reasoning"}, "[on|off|auto]",
-        "Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))",
-        [](common_params & params, const std::string & value) {
-            if (is_truthy(value)) {
-                params.enable_reasoning = 1;
-                params.default_template_kwargs["enable_thinking"] = "true";
-            } else if (is_falsey(value)) {
-                params.enable_reasoning = 0;
-                params.default_template_kwargs["enable_thinking"] = "false";
-            } else if (is_autoy(value)) {
-                params.enable_reasoning = -1;
-            } else {
-                throw std::invalid_argument(
-                    string_format("error: unknown value for --reasoning: '%s'\n", value.c_str()));
-            }
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_REASONING"));
     add_opt(common_arg(
         {"--reasoning-budget"}, "N",
-        "token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)",
+        "controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)",
         [](common_params & params, int value) {
-            if (value < -1) { throw std::invalid_argument("invalid value"); }
+            if (value != 0 && value != -1) { throw std::invalid_argument("invalid value"); }
             params.reasoning_budget = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_THINK_BUDGET"));
-    add_opt(common_arg(
-        {"--reasoning-budget-message"}, "MESSAGE",
-        "message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)",
-        [](common_params & params, const std::string & value) {
-            params.reasoning_budget_message = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_THINK_BUDGET_MESSAGE"));
     add_opt(common_arg(
         {"--chat-template"}, "JINJA_TEMPLATE",
         string_format(
@@ -3115,17 +3080,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.chat_template = read_file(value);
         }
     ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE"));
-    add_opt(common_arg(
-        {"--skip-chat-parsing"},
-        {"--no-skip-chat-parsing"},
-        string_format(
-            "force a pure content parser, even if a Jinja template is specified; model will output everything "
-            "in the content section, including any reasoning and/or tool calls (default: disabled)"
-        ),
-        [](common_params & params, bool value) {
-            params.force_pure_content_parser = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_SKIP_CHAT_PARSING"));
     add_opt(common_arg(
         {"--prefill-assistant"},
         {"--no-prefill-assistant"},
@@ -3494,7 +3448,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                 throw std::invalid_argument("unknown speculative decoding type without draft model");
             }
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_SPEC_TYPE"));
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"--spec-ngram-size-n"}, "N",
         string_format("ngram size N for ngram-simple/ngram-map speculative decoding, length of lookup n-gram (default: %d)", params.speculative.ngram_size_n),

common/chat-auto-parser-generator.cpp

@@ -1,10 +1,8 @@
-#include "chat-auto-parser-helpers.h"
 #include "chat-auto-parser.h"
 #include "chat-peg-parser.h"
 #include "chat.h"
 #include "common.h"
 #include "json-schema-to-grammar.h"
-#include "log.h"
 #include "nlohmann/json.hpp"
 
 #include <stdexcept>
@@ -24,13 +22,13 @@ static void foreach_function(const json & tools, const std::function<void(const
 
 namespace autoparser {
 
-parser_build_context::parser_build_context(common_chat_peg_builder & p, const generation_params & inputs) :
+parser_build_context::parser_build_context(common_chat_peg_builder & p, const templates_params & inputs) :
     p(p),
     inputs(inputs),
     reasoning_parser(p.eps()) {}
 
 common_chat_params peg_generator::generate_parser(const common_chat_template &    tmpl,
-                                                  const struct generation_params & inputs) {
+                                                  const struct templates_params & inputs) {
     // Run differential analysis to extract template structure
     struct autoparser autoparser;
     autoparser.analyze_template(tmpl);
@@ -38,16 +36,17 @@ common_chat_params peg_generator::generate_parser(const common_chat_template &
 }
 
 common_chat_params peg_generator::generate_parser(const common_chat_template &    tmpl,
-                                                  const struct generation_params & inputs,
+                                                  const struct templates_params & inputs,
                                                   const autoparser &              autoparser) {
+    // Build the parser using the analysis results
+    auto parser = autoparser.build_parser(inputs);
+
     // Create the result structure
     common_chat_params data;
     data.prompt           = common_chat_template_direct_apply(tmpl, inputs);
     data.format           = COMMON_CHAT_FORMAT_PEG_NATIVE;
     data.preserved_tokens = autoparser.preserved_tokens;
-
-    auto parser = autoparser.build_parser(inputs);
-    data.parser = parser.save();
+    data.parser           = parser.save();
 
     // Build grammar if tools are present
     bool has_tools =
@@ -82,38 +81,44 @@ common_chat_params peg_generator::generate_parser(const common_chat_template &
     return data;
 }
 
-common_peg_arena autoparser::build_parser(const generation_params & inputs) const {
+common_peg_arena autoparser::build_parser(const templates_params & inputs) const {
     if (!analysis_complete) {
         throw std::invalid_argument("Cannot call build_parser on autoparser without performing analysis first, call analyze_template(...)");
     }
     return build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        // If the template uses Python dict format (single-quoted strings in JSON structures),
+        // pre-register a json-string rule that accepts both quote styles. This must happen
+        // before any call to p.json() so that all JSON parsing inherits the flexible rule.
+        if (tools.format.uses_python_dicts) {
+            p.rule("json-string", [&]() { return p.choice({ p.double_quoted_string(), p.single_quoted_string() }); });
+        }
+
         parser_build_context ctx(p, inputs);
         bool                 extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+        bool                 enable_thinking   = inputs.enable_thinking;
 
-        ctx.extracting_reasoning = extract_reasoning && reasoning.mode != reasoning_mode::NONE;
+        ctx.extracting_reasoning = extract_reasoning && enable_thinking && reasoning.mode != reasoning_mode::NONE;
         ctx.content              = &content;
 
         // Build reasoning parser
         ctx.reasoning_parser = reasoning.build_parser(ctx);
 
-        auto parser = p.eps();
-
         bool has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
         bool has_response_format = inputs.json_schema.is_object() && !inputs.json_schema.empty();
 
         if (has_response_format) {
             auto response_format = p.rule("response-format", p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)));
-            parser = ctx.reasoning_parser + p.space() + p.choice({
+            return ctx.reasoning_parser + p.space() + p.choice({
                 p.literal("```json") + p.space() + response_format + p.space() + p.literal("```"),
                 response_format
             }) + p.end();
-        } else if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE && jinja_caps.supports_tool_calls) {
-            parser = tools.build_parser(ctx);
-        } else {
-            parser = content.build_parser(ctx);
         }
-        parser = wrap_for_generation_prompt(p, parser, inputs, reasoning.start);
-        return parser;
+
+        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE && jinja_caps.supports_tool_calls) {
+            return tools.build_parser(ctx);
+        }
+
+        return content.build_parser(ctx);
     });
 }
 
@@ -124,15 +129,22 @@ common_peg_parser analyze_reasoning::build_parser(parser_build_context & ctx) co
         return p.eps();
     }
 
+    bool thinking_forced_open   = (mode == reasoning_mode::FORCED_OPEN);
+    bool thinking_forced_closed = (mode == reasoning_mode::FORCED_CLOSED);
+
+    if (thinking_forced_open || thinking_forced_closed) {
+        // Thinking is forced open OR forced closed with enable_thinking=true
+        // In both cases, expect only the closing tag (opening was in template)
+        return p.reasoning(p.until(end)) + end;
+    }
     if (mode == reasoning_mode::TAG_BASED || mode == reasoning_mode::TOOLS_ONLY) {
-        if (!end.empty()) {
-            if (!start.empty()) {
-                // Standard tag-based: optional(<think>reasoning</think>)
-                return p.optional(start + p.reasoning(p.until(end)) + end + p.space());
-            }
-            // Delimiter-style (empty start)
-            return p.optional(p.reasoning(p.until(end)) + end + p.space());
+        // Standard tag-based reasoning OR tools-only mode (reasoning appears with tools)
+        // Both use the same tag-based pattern if markers are available
+        if (!start.empty() && !end.empty()) {
+            return p.optional(start + p.reasoning(p.until(end)) + end);
         }
+    } else if (mode == reasoning_mode::DELIMITER) {
+        return p.optional(p.reasoning(p.until(end)) + end);
     }
 
     return p.eps();
@@ -168,10 +180,7 @@ common_peg_parser analyze_tools::build_parser(parser_build_context & ctx) const
         case tool_format::TAG_WITH_TAGGED:
             return build_tool_parser_tag_tagged(ctx);
         default:
-            LOG_ERR("[ERROR] Template seems to support tool calls, but failed to determine tool format. Tool calling will not work properly. "
-                "Check for a fixed template for your model in the models/templates directory of your llama.cpp installation or "
-                "report an issue at https://github.com/ggml-org/llama.cpp/issues\n");
-            return ctx.p.eps();
+            GGML_ABORT("Unable to create tool parser");
     }
 }
 
@@ -320,7 +329,7 @@ common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_conte
                                                                      "tool-" + name + "-arg-" + param_name + "-schema",
                                                                      param_schema, true)) :
                                     p.tool_arg_json_value(p.schema(
-                                        p.json(), "tool-" + name + "-arg-" + param_name + "-schema", param_schema, false)) +
+                                        p.json(), "tool-" + name + "-arg-" + param_name + "-schema", param_schema, format.uses_python_dicts)) +
                                         p.space()) +
                 p.tool_arg_close(p.literal(arguments.value_suffix)));
 
@@ -369,9 +378,7 @@ common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_conte
             func_parser = p.atomic(p.tool_open(function.name_prefix + p.tool_name(p.literal(name)) + function.name_suffix) +
                 call_id_section) + p.space() + args_seq;
             matched_atomic = true;
-        } else if (!arguments.name_prefix.empty() && !required_parsers.empty()) {
-            // Only peek for an arg tag when there are required args that must follow.
-            // When all args are optional, the model may emit no arg tags at all (#20650).
+        } else if (!arguments.name_prefix.empty() && properties.size() > 0) {
             func_parser = p.atomic(p.tool_open(function.name_prefix + p.tool_name(p.literal(name)) + function.name_suffix) +
                 call_id_section + p.space() + p.peek(p.literal(arguments.name_prefix))) + args_seq;
             matched_atomic = true;

common/chat-auto-parser-helpers.cpp

@@ -1,11 +1,9 @@
 #include "chat-auto-parser-helpers.h"
 
 #include "chat-auto-parser.h"
-#include "chat-peg-parser.h"
 #include "chat.h"
 #include "log.h"
 #include "nlohmann/json.hpp"
-#include "peg-parser.h"
 
 #include <cctype>
 #include <numeric>
@@ -293,26 +291,10 @@ std::vector<segment> prune_whitespace_segments(const std::vector<segment> & segm
     return result;
 }
 
-common_peg_parser wrap_for_generation_prompt(common_chat_peg_builder &             p,
-                                             const common_peg_parser &             prs,
-                                             const autoparser::generation_params & inputs,
-                                             const std::string &                   reasoning_start) {
-    auto parser = prs;
-    if (!inputs.generation_prompt.empty()) {
-        size_t end_pos = inputs.generation_prompt.size();
-        if (!reasoning_start.empty() && inputs.generation_prompt.find(reasoning_start) != std::string::npos) {
-            end_pos = inputs.generation_prompt.find(reasoning_start);
-        }
-        std::string cut_genprompt = inputs.generation_prompt.substr(0, end_pos);
-        parser                    = p.literal(cut_genprompt) + parser;
-    }
-    return parser;
-}
-
 namespace autoparser {
 
 std::string apply_template(const common_chat_template & tmpl, const template_params & params) {
-    generation_params tmpl_params;
+    templates_params tmpl_params;
     tmpl_params.messages              = params.messages;
     tmpl_params.tools                 = params.tools;
     tmpl_params.add_generation_prompt = params.add_generation_prompt;

common/chat-auto-parser-helpers.h

@@ -1,7 +1,6 @@
 #pragma once
 
 #include "chat-auto-parser.h"
-#include "peg-parser.h"
 #include <functional>
 #include <optional>
 #include <string>
@@ -58,11 +57,6 @@ std::vector<segment> segmentize_markers(const std::string & text);
 //                                   (MARKER, "</function>"), (MARKER, "</tool_call>") ]
 std::vector<segment> prune_whitespace_segments(const std::vector<segment> & segments);
 
-// Wrap parser with generation prompt parser
-common_peg_parser wrap_for_generation_prompt(common_chat_peg_builder &             p,
-                                             const common_peg_parser &             prs,
-                                             const autoparser::generation_params & inputs,
-                                             const std::string &                   reasoning_start = {});
 namespace autoparser {
 
 // Apply a template with the given parameters, returning the rendered string (empty on failure)

common/chat-auto-parser.h

@@ -50,7 +50,7 @@ namespace autoparser {
 // High-level params for parser generation
 // ============================================================================
 
-struct generation_params {
+struct templates_params {
     json                                  messages;
     json                                  tools;
     common_chat_tool_choice               tool_choice = COMMON_CHAT_TOOL_CHOICE_AUTO;
@@ -62,7 +62,6 @@ struct generation_params {
     bool                                  add_generation_prompt = false;
     bool                                  enable_thinking       = true;
     std::chrono::system_clock::time_point now                   = std::chrono::system_clock::now();
-    std::string                           generation_prompt;
     json                                  extra_context;
     bool                                  add_bos       = false;
     bool                                  add_eos       = false;
@@ -78,7 +77,11 @@ struct generation_params {
 // Reasoning handling mode (derived from R1-R3 comparisons)
 enum class reasoning_mode {
     NONE,           // No reasoning markers detected
-    TAG_BASED,      // Tag-based: <think>...</think> (start can be empty for delimiter-style)
+    TAG_BASED,      // Standard tag-based: <think>...</think>
+    DELIMITER,      // Delimiter-based: [BEGIN FINAL RESPONSE] (reasoning ends at delimiter)
+    FORCED_OPEN,    // Template ends with open reasoning tag (empty start, non-empty end)
+    FORCED_CLOSED,  // Template ends with open reasoning tag on enabled thinking but
+                    // with both opened and closed tag for disabled thinking
     TOOLS_ONLY      // Only reason on tool calls, not on normal content
 };
 
@@ -88,6 +91,12 @@ inline std::ostream & operator<<(std::ostream & os, const reasoning_mode & mode)
             return os << "NONE";
         case reasoning_mode::TAG_BASED:
             return os << "TAG_BASED";
+        case reasoning_mode::DELIMITER:
+            return os << "DELIMITER";
+        case reasoning_mode::FORCED_OPEN:
+            return os << "FORCED_OPEN";
+        case reasoning_mode::FORCED_CLOSED:
+            return os << "FORCED_CLOSED";
         case reasoning_mode::TOOLS_ONLY:
             return os << "TOOLS_ONLY";
         default:
@@ -175,6 +184,7 @@ struct tool_format_analysis {
 
     bool fun_name_is_key = false;       // In JSON format function name is JSON key, i.e. { "<funname>": { ... arguments ... } }
     bool tools_array_wrapped = false;   // Tool calls wrapped in JSON array [...]
+    bool uses_python_dicts = false;     // Tool call args use Python dict format (single-quoted strings)
 
     std::string              function_field = "function";
     std::string              name_field     = "name";
@@ -215,12 +225,12 @@ struct analyze_content;
 
 struct parser_build_context {
     common_chat_peg_builder & p;
-    const generation_params &          inputs;
+    const templates_params &          inputs;
     common_peg_parser                 reasoning_parser;
     bool                              extracting_reasoning = false;
     const analyze_content *           content              = nullptr;
 
-    parser_build_context(common_chat_peg_builder & p, const generation_params & inputs);
+    parser_build_context(common_chat_peg_builder & p, const templates_params & inputs);
 };
 
 // ============================================================================
@@ -250,7 +260,6 @@ struct analyze_reasoning : analyze_base {
 
     analyze_reasoning() = default;
     analyze_reasoning(const common_chat_template & tmpl, bool supports_tools);
-    analyze_reasoning(std::string start_, std::string end_) : start(std::move(start_)), end(std::move(end_)) {}
 
     common_peg_parser build_parser(parser_build_context & ctx) const override;
 
@@ -372,7 +381,7 @@ struct autoparser {
     void analyze_template(const common_chat_template & tmpl);
 
     // Build the PEG parser for this template
-    common_peg_arena build_parser(const generation_params & inputs) const;
+    common_peg_arena build_parser(const templates_params & inputs) const;
 
   private:
     // Collect tokens from entire analysis to preserve
@@ -386,10 +395,10 @@ struct autoparser {
 class peg_generator {
   public:
     static common_chat_params generate_parser(const common_chat_template &    tmpl,
-                                              const struct generation_params & inputs);
+                                              const struct templates_params & inputs);
 
     static common_chat_params generate_parser(const common_chat_template &    tmpl,
-                                              const struct generation_params & inputs,
+                                              const struct templates_params & inputs,
                                               const autoparser &              autoparser);
 };
 

common/chat-diff-analyzer.cpp

@@ -2,7 +2,6 @@
 #include "chat-auto-parser-helpers.h"
 #include "chat-peg-parser.h"
 #include "chat.h"
-#include "common.h"
 #include "log.h"
 #include "nlohmann/json.hpp"
 #include "peg-parser.h"
@@ -32,9 +31,8 @@ static std::vector<std::function<void(const common_chat_template & tmpl, autopar
       [](const common_chat_template & tmpl, autoparser & analysis) -> void {
           if (tmpl.src.find("content.split('</think>')") != std::string::npos &&
               tmpl.src.find("reasoning_content") == std::string::npos &&
-              tmpl.src.find("<SPECIAL_12>") == std::string::npos &&
               analysis.reasoning.mode == reasoning_mode::NONE) {
-              analysis.reasoning.mode  = reasoning_mode::TAG_BASED;
+              analysis.reasoning.mode  = reasoning_mode::FORCED_OPEN;
               analysis.reasoning.start = "<think>";
               analysis.reasoning.end   = "</think>";
               analysis.preserved_tokens.push_back("<think>");
@@ -187,6 +185,7 @@ void autoparser::analyze_template(const common_chat_template & tmpl) {
     LOG_DBG("func_name_prefix: '%s'\n", tools.function.name_prefix.c_str());
     LOG_DBG("func_name_suffix: '%s'\n", tools.function.name_suffix.c_str());
     LOG_DBG("func_close: '%s'\n", tools.function.close.c_str());
+    LOG_DBG("python_dict_format: %s\n", tools.format.uses_python_dicts ? "true" : "false");
     LOG_DBG("arg_name_prefix: '%s'\n", tools.arguments.name_prefix.c_str());
     LOG_DBG("arg_name_suffix: '%s'\n", tools.arguments.name_suffix.c_str());
     LOG_DBG("arg_value_prefix: '%s'\n", tools.arguments.value_prefix.c_str());
@@ -296,12 +295,16 @@ void analyze_reasoning::compare_reasoning_presence() {
         }
         if (result.result.success()) {
             if (!result.tags["pre"].empty() && !result.tags["post"].empty()) {
-                mode = reasoning_mode::TAG_BASED;
+                if (parser_wrapped.parse_anywhere_and_extract(diff.right).result.success()) { // both tags in the diff = no forced close
+                    mode = reasoning_mode::TAG_BASED;
+                } else {
+                    mode = reasoning_mode::FORCED_CLOSED;
+                }
                 start = trim_whitespace(result.tags["pre"]);
-                end   = trim_trailing_whitespace(result.tags["post"]);
+                end   = result.tags["post"];
             } else if (!result.tags["post"].empty()) {
-                mode = reasoning_mode::TAG_BASED;
-                end = trim_trailing_whitespace(result.tags["post"]);
+                mode = reasoning_mode::DELIMITER;
+                end = result.tags["post"];
             }
         }
     }
@@ -328,30 +331,53 @@ void analyze_reasoning::compare_thinking_enabled() {
     const auto & diff = comparison->diff;
 
     std::string left_trimmed = trim_whitespace(diff.left);
-    std::string right_trimmed = trim_whitespace(diff.right);
 
     if (left_trimmed.empty() && !diff.right.empty()) {
+        std::string right_trimmed = trim_whitespace(diff.right);
+
         if (!right_trimmed.empty() && string_ends_with(comparison->output_B, right_trimmed)) {
             if (start.empty()) {
                 start = right_trimmed;
-                mode  = reasoning_mode::TAG_BASED;
-            }
-        }
-    } else if (right_trimmed.empty() && !diff.left.empty()) {
-        if (!left_trimmed.empty() && string_ends_with(comparison->output_A, left_trimmed)) {
-            if (end.empty()) {
-                auto seg = prune_whitespace_segments(segmentize_markers(comparison->output_A));
-                if (seg.size() >= 2 && seg[seg.size() - 1].value == left_trimmed && seg[seg.size() - 2].type == segment_type::MARKER) {
-                    start = seg[seg.size() - 2].value;
-                }
-                end = left_trimmed;
-                mode = reasoning_mode::TAG_BASED;
+                mode  = reasoning_mode::FORCED_OPEN;
             }
         }
     }
 
-    if (mode == reasoning_mode::NONE && start.empty() && !end.empty()) {
-        mode = reasoning_mode::TAG_BASED;
+    if (start.empty() && !end.empty()) {
+        mode = reasoning_mode::DELIMITER;
+    }
+
+    // Check for FORCED_CLOSED: when enable_thinking=false produces both start and end markers,
+    // but enable_thinking=true produces only the start marker
+    if (!comparison->output_A.empty() && !comparison->output_B.empty()) {
+        auto parser_start = build_tagged_peg_parser([&](common_peg_parser_builder &p) {
+            return p.literal(start) + p.space() + p.literal(end) + p.rest();
+        });
+        auto parser_start_end = build_tagged_peg_parser([&](common_peg_parser_builder &p) {
+            return p.tag("pre", p.literal(start)) + p.space() + p.negate(p.literal(end)) + p.rest();
+        });
+        if (!start.empty() && parser_start_end.parse_anywhere_and_extract(comparison->output_A).result.success() &&
+            parser_start.parse_anywhere_and_extract(comparison->output_B).result.success()) {
+            mode = reasoning_mode::FORCED_CLOSED;
+        } else if (!end.empty()) { // we extract the starting marker now since we didn't get it earlier
+            auto result = parser_start_end.parse_anywhere_and_extract(comparison->output_A);
+            if (result.result.success()) {
+                start = result.tags["pre"];
+                mode  = reasoning_mode::FORCED_CLOSED;
+            }
+        }
+    }
+
+    if (start.empty() && end.empty()) {  // we might still have the case of "just open" and "just close"
+        if (!diff.left.empty() && !diff.right.empty()) {
+            auto seg_A = segmentize_markers(trim_trailing_whitespace(diff.left));
+            auto seg_B = segmentize_markers(trim_trailing_whitespace(diff.right));
+            if (seg_A.size() == 1 && seg_B.size() == 1) {
+                mode = reasoning_mode::FORCED_CLOSED;
+                start = seg_B[0].value;
+                end = seg_A[0].value;
+            }
+        }
     }
 }
 
@@ -400,14 +426,14 @@ void analyze_reasoning::compare_reasoning_scope() {
         auto result = parser_wrapped.parse_anywhere_and_extract(comparison->output_B);
         if (result.result.success()) {
             start = result.tags["pre"];
-            end = trim_trailing_whitespace(result.tags["post"]);
+            end = result.tags["post"];
         } else {
             auto parser_delimiter = build_tagged_peg_parser([&](common_peg_parser_builder &p) {
                 return p.literal(reasoning_content) + p.space() + p.optional(p.tag("post", (p.marker() + p.space())));
             });
             result = parser_delimiter.parse_anywhere_and_extract(comparison->output_B);
             if (result.result.success()) {
-                end = trim_trailing_whitespace(result.tags["post"]);
+                end = result.tags["post"];
             } else {
                 LOG_DBG(ANSI_ORANGE "%s: Unable to extracft reasoning markers, falling back to reasoning = NONE\n" ANSI_RESET, __func__);
                 mode = reasoning_mode::NONE;
@@ -453,7 +479,6 @@ analyze_content::analyze_content(const common_chat_template & tmpl, const analyz
 
     if (!comparison_with_tools || !comparison_with_reasoning) {
         LOG_DBG(ANSI_ORANGE "%s: Template application failed\n" ANSI_RESET, __func__);
-        return;
     }
 
     const auto & diff_tools     = comparison_with_tools->diff;
@@ -574,23 +599,33 @@ void analyze_tools::analyze_tool_call_format(const std::string &       haystack,
         return;
     }
 
-    auto in_json_haystack = [&haystack](const std::string & needle) -> bool {
+    enum class json_quote_style { NONE, DOUBLE_QUOTES, SINGLE_QUOTES };
+
+    auto in_json_haystack = [&haystack](const std::string & needle) -> json_quote_style {
         auto parser = build_tagged_peg_parser([&](common_peg_parser_builder &p) {
             return p.choice({ p.literal("{"), p.literal(":") }) << p.choice({
+                p.tag("sq", p.literal("'") + p.literal(needle) + p.literal("'")),
                 p.tag("dq", p.literal("\"") + p.literal(needle) + p.literal("\"")) });
         });
         auto result = parser.parse_anywhere_and_extract(haystack);
-        return result.result.success();
+        if (!result.result.success()) {
+            return json_quote_style::NONE;
+        }
+        return result.tags.count("sq") && !result.tags["sq"].empty()
+            ? json_quote_style::SINGLE_QUOTES
+            : json_quote_style::DOUBLE_QUOTES;
     };
 
     auto fun_quote = in_json_haystack(fun_name_needle);
     auto arg_quote = in_json_haystack(arg_name_needle);
 
-    if (fun_quote) {
+    if (fun_quote != json_quote_style::NONE) {
         // no need to check further, we're in JSON land
         format.mode = tool_format::JSON_NATIVE;
-    } else if (arg_quote) {
+        format.uses_python_dicts = (fun_quote == json_quote_style::SINGLE_QUOTES);
+    } else if (arg_quote != json_quote_style::NONE) {
         format.mode = tool_format::TAG_WITH_JSON;
+        format.uses_python_dicts = (arg_quote == json_quote_style::SINGLE_QUOTES);
     } else {
         format.mode = tool_format::TAG_WITH_TAGGED;
     }
@@ -876,10 +911,8 @@ void analyze_tools::extract_function_markers() {
             // we'll have to rely on an extra diff with no-calls version
             auto notool_comp = compare_variants(
                 *tmpl, params, [&](template_params & p) { p.messages = json::array({ user_msg, assistant_nocall }); });
-            if (notool_comp) {
-                auto nt_diff  = notool_comp->diff;
-                closer_suffix = nt_diff.left.substr(nt_diff.left.find("YYYY") + 4);
-            }
+            auto nt_diff  = notool_comp->diff;
+            closer_suffix = nt_diff.left.substr(nt_diff.left.find("YYYY") + 4);
         } else {
             closer_suffix = diff.suffix.substr(0, diff.suffix.find(suffix_marker));
         }

common/chat-peg-parser.cpp

@@ -6,7 +6,7 @@
 
 #include <nlohmann/json.hpp>
 
-using ordered_json = nlohmann::ordered_json;
+using json = nlohmann::ordered_json;
 
 static std::string_view trim_trailing_space(std::string_view sv, int max = -1) {
     int count = 0;
@@ -68,7 +68,7 @@ static int json_brace_depth(const std::string & s) {
 
 // JSON-escape a string and return the inner content (without surrounding quotes).
 static std::string escape_json_string_inner(const std::string & s) {
-    std::string escaped = ordered_json(s).dump();
+    std::string escaped = json(s).dump();
     if (escaped.size() >= 2 && escaped.front() == '"' && escaped.back() == '"') {
         return escaped.substr(1, escaped.size() - 2);
     }
@@ -229,20 +229,6 @@ void common_chat_peg_mapper::from_ast(const common_peg_ast_arena &    arena,
         result.tool_calls.push_back(pending_tool_call.value());
         pending_tool_call.reset();
     }
-
-    // Discard whitespace-only reasoning content (e.g. from <think></think> prefill)
-    if (!result.reasoning_content.empty()) {
-        bool all_whitespace = true;
-        for (char c : result.reasoning_content) {
-            if (c != ' ' && c != '\n' && c != '\r' && c != '\t') {
-                all_whitespace = false;
-                break;
-            }
-        }
-        if (all_whitespace) {
-            result.reasoning_content.clear();
-        }
-    }
 }
 
 void common_chat_peg_mapper::map(const common_peg_ast_node & node) {
@@ -323,7 +309,7 @@ void common_chat_peg_mapper::map(const common_peg_ast_node & node) {
         if (arg_count > 0) {
             arg_entry = ",";
         }
-        arg_entry += ordered_json(trim(node.text)).dump() + ":";
+        arg_entry += json(trim(node.text)).dump() + ":";
         ++arg_count;
 
         auto & target = args_target();
@@ -357,7 +343,7 @@ void common_chat_peg_mapper::map(const common_peg_ast_node & node) {
 
             // Try to parse as JSON value (number, bool, null, object, array)
             try {
-                ordered_json parsed = ordered_json::parse(value_content);
+                json parsed = json::parse(value_content);
                 if (parsed.is_string()) {
                     // Don't add closing quote yet (added by arg_close) for monotonic streaming
                     std::string escaped = parsed.dump();
@@ -422,7 +408,7 @@ void common_chat_peg_mapper::map(const common_peg_ast_node & node) {
 
 common_peg_parser common_chat_peg_builder::standard_constructed_tools(
     const std::map<std::string, std::string> & markers,
-    const ordered_json &                       tools,
+    const nlohmann::json &                     tools,
     bool                                       parallel_tool_calls,
     bool                                       force_tool_calls) {
     if (!tools.is_array() || tools.empty()) {
@@ -453,7 +439,7 @@ common_peg_parser common_chat_peg_builder::standard_constructed_tools(
         }
         const auto &   function = tool_def.at("function");
         std::string    name     = function.at("name");
-        ordered_json   params   = function.contains("parameters") ? function.at("parameters") : ordered_json::object();
+        nlohmann::json params = function.contains("parameters") ? function.at("parameters") : nlohmann::json::object();
 
         // Build argument parsers
         auto args = eps();
@@ -493,8 +479,8 @@ common_peg_parser common_chat_peg_builder::standard_constructed_tools(
 // Python-style tool calls: name(arg1="value1", arg2=123)
 // Used only by LFM2 for now, so we don't merge it into autoparser
 common_peg_parser common_chat_peg_builder::python_style_tool_calls(
-    const ordered_json & tools,
-    bool                 parallel_tool_calls) {
+    const nlohmann::json & tools,
+    bool                   parallel_tool_calls) {
     if (!tools.is_array() || tools.empty()) {
         return eps();
     }
@@ -507,7 +493,7 @@ common_peg_parser common_chat_peg_builder::python_style_tool_calls(
         }
         const auto &   function = tool_def.at("function");
         std::string    name     = function.at("name");
-        ordered_json   params   = function.contains("parameters") ? function.at("parameters") : ordered_json::object();
+        nlohmann::json params = function.contains("parameters") ? function.at("parameters") : nlohmann::json::object();
 
         auto args = eps();
         if (params.contains("properties") && !params["properties"].empty()) {
@@ -521,8 +507,8 @@ common_peg_parser common_chat_peg_builder::python_style_tool_calls(
 
                 common_peg_parser arg_value_parser = eps();
                 auto string_value_parser = choice({
-                    literal("\"") + tool_arg_string_value(string_content('"')) + literal("\""),
-                    literal("'") + tool_arg_string_value(string_content('\'')) + literal("'")
+                    literal("\"") + tool_arg_string_value(json_string_content()) + literal("\""),
+                    literal("'") + tool_arg_string_value(json_string_content()) + literal("'")
                 });
 
                 if (is_string_type) {
@@ -569,11 +555,11 @@ static std::pair<std::string, std::string> parse_key_spec(const std::string & ke
 
 // Mode 1: function_is_key — parse {"function_name": {...}}
 common_peg_parser common_chat_peg_builder::build_json_tools_function_is_key(
-    const ordered_json & tools,
-    const std::string &  args_key,
-    const std::string &  effective_args_key,
-    const std::string &  call_id_key,
-    const std::string &  gen_call_id_key) {
+    const nlohmann::json & tools,
+    const std::string &    args_key,
+    const std::string &    effective_args_key,
+    const std::string &    call_id_key,
+    const std::string &    gen_call_id_key) {
 
     auto tool_choices = choice();
 
@@ -583,7 +569,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_function_is_key(
         }
         const auto &   function = tool_def.at("function");
         std::string    name     = function.at("name");
-        ordered_json   params   = function.contains("parameters") ? function.at("parameters") : ordered_json::object();
+        nlohmann::json params = function.contains("parameters") ? function.at("parameters") : nlohmann::json::object();
 
         // Build inner object fields
         std::vector<common_peg_parser> inner_fields;
@@ -591,7 +577,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_function_is_key(
         if (!call_id_key.empty()) {
             auto id_parser = atomic(
                 literal("\"" + call_id_key + "\"") + space() + literal(":") + space() +
-                literal("\"") + tool_id(string_content('"')) + literal("\"")
+                literal("\"") + tool_id(json_string_content()) + literal("\"")
             );
             inner_fields.push_back(optional(id_parser + space() + optional(literal(",") + space())));
         }
@@ -600,7 +586,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_function_is_key(
             auto gen_id_parser = atomic(
                 literal("\"" + gen_call_id_key + "\"") + space() + literal(":") + space() +
                 choice({
-                    literal("\"") + tool_id(string_content('"')) + literal("\""),
+                    literal("\"") + tool_id(json_string_content()) + literal("\""),
                     tool_id(json_number())
                 })
             );
@@ -648,11 +634,11 @@ common_peg_parser common_chat_peg_builder::build_json_tools_function_is_key(
 
 // Mode 2: Nested keys (dot notation like "function.name")
 common_peg_parser common_chat_peg_builder::build_json_tools_nested_keys(
-    const ordered_json & tools,
-    const std::string &  effective_name_key,
-    const std::string &  effective_args_key,
-    const std::string &  call_id_key,
-    const std::string &  gen_call_id_key) {
+    const nlohmann::json & tools,
+    const std::string &    effective_name_key,
+    const std::string &    effective_args_key,
+    const std::string &    call_id_key,
+    const std::string &    gen_call_id_key) {
 
     auto tool_choices = choice();
 
@@ -669,7 +655,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_nested_keys(
         }
         const auto &   function = tool_def.at("function");
         std::string    name     = function.at("name");
-        ordered_json   params   = function.contains("parameters") ? function.at("parameters") : ordered_json::object();
+        nlohmann::json params = function.contains("parameters") ? function.at("parameters") : nlohmann::json::object();
 
         auto nested_name = literal("\"" + nested_name_field + "\"") + space() + literal(":") + space() +
                           literal("\"") + tool_name(literal(name)) + literal("\"");
@@ -689,7 +675,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_nested_keys(
             if (id_spec.first.empty()) {
                 auto id_parser = atomic(
                     literal("\"" + call_id_key + "\"") + space() + literal(":") + space() +
-                    literal("\"") + tool_id(string_content('"')) + literal("\"")
+                    literal("\"") + tool_id(json_string_content()) + literal("\"")
                 );
                 tool_parser_body = tool_parser_body + optional(id_parser + space() + literal(",") + space());
             }
@@ -701,7 +687,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_nested_keys(
                 auto gen_id_parser = atomic(
                     literal("\"" + gen_call_id_key + "\"") + space() + literal(":") + space() +
                     choice({
-                        literal("\"") + tool_id(string_content('"')) + literal("\""),
+                        literal("\"") + tool_id(json_string_content()) + literal("\""),
                         tool_id(json_number())
                     })
                 );
@@ -720,7 +706,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_nested_keys(
 
 // Mode 3: Flat keys with optional ID fields and parameter ordering
 common_peg_parser common_chat_peg_builder::build_json_tools_flat_keys(
-    const ordered_json &             tools,
+    const nlohmann::json &           tools,
     const std::string &              effective_name_key,
     const std::string &              effective_args_key,
     const std::string &              call_id_key,
@@ -737,7 +723,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_flat_keys(
         }
         const auto &   function = tool_def.at("function");
         std::string    name     = function.at("name");
-        ordered_json   params   = function.contains("parameters") ? function.at("parameters") : ordered_json::object();
+        nlohmann::json params = function.contains("parameters") ? function.at("parameters") : nlohmann::json::object();
 
         auto tool_name_ = name_key_parser + space() + literal(":") + space() +
                          literal("\"") + tool_name(literal(name)) + literal("\"");
@@ -750,7 +736,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_flat_keys(
             id_parser = atomic(
                 literal("\"" + call_id_key + "\"") + space() + literal(":") + space() +
                 choice({
-                    literal("\"") + tool_id(string_content('"')) + literal("\""),
+                    literal("\"") + tool_id(json_string_content()) + literal("\""),
                     tool_id(json_number())
                 })
             );
@@ -761,7 +747,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_flat_keys(
             gen_id_parser = atomic(
                 literal("\"" + gen_call_id_key + "\"") + space() + literal(":") + space() +
                 choice({
-                    literal("\"") + tool_id(string_content('"')) + literal("\""),
+                    literal("\"") + tool_id(json_string_content()) + literal("\""),
                     tool_id(json_number())
                 })
             );
@@ -805,7 +791,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_flat_keys(
 common_peg_parser common_chat_peg_builder::standard_json_tools(
                                                        const std::string &              section_start,
                                                        const std::string &              section_end,
-                                                       const ordered_json &             tools,
+                                                       const nlohmann::json &           tools,
                                                        bool                             parallel_tool_calls,
                                                        bool                             force_tool_calls,
                                                        const std::string &              name_key,

common/chat-peg-parser.h

@@ -94,7 +94,7 @@ class common_chat_peg_builder : public common_peg_parser_builder {
     // parameters_order: order in which JSON fields should be parsed
     common_peg_parser standard_json_tools(const std::string &              section_start,
                                           const std::string &              section_end,
-                                          const nlohmann::ordered_json &   tools,
+                                          const nlohmann::json &           tools,
                                           bool                             parallel_tool_calls,
                                           bool                             force_tool_calls,
                                           const std::string &              name_key = "",
@@ -108,30 +108,30 @@ class common_chat_peg_builder : public common_peg_parser_builder {
     // Legacy-compatible helper for building XML/tagged style tool calls
     // Used by tests and manual parsers
     common_peg_parser standard_constructed_tools(const std::map<std::string, std::string> & markers,
-                                                 const nlohmann::ordered_json &             tools,
+                                                 const nlohmann::json &                     tools,
                                                  bool                                       parallel_tool_calls,
                                                  bool                                       force_tool_calls);
 
     // Helper for Python-style function call format: name(arg1="value1", arg2=123)
     // Used by LFM2 and similar templates
-    common_peg_parser python_style_tool_calls(const nlohmann::ordered_json & tools,
-                                              bool                           parallel_tool_calls);
+    common_peg_parser python_style_tool_calls(const nlohmann::json & tools,
+                                              bool                   parallel_tool_calls);
 
   private:
     // Implementation helpers for standard_json_tools — one per JSON tool call layout mode
-    common_peg_parser build_json_tools_function_is_key(const nlohmann::ordered_json & tools,
-                                                       const std::string &            args_key,
-                                                       const std::string &            effective_args_key,
-                                                       const std::string &            call_id_key,
-                                                       const std::string &            gen_call_id_key);
+    common_peg_parser build_json_tools_function_is_key(const nlohmann::json & tools,
+                                                       const std::string &    args_key,
+                                                       const std::string &    effective_args_key,
+                                                       const std::string &    call_id_key,
+                                                       const std::string &    gen_call_id_key);
 
-    common_peg_parser build_json_tools_nested_keys(const nlohmann::ordered_json & tools,
-                                                   const std::string &            effective_name_key,
-                                                   const std::string &            effective_args_key,
-                                                   const std::string &            call_id_key,
-                                                   const std::string &            gen_call_id_key);
+    common_peg_parser build_json_tools_nested_keys(const nlohmann::json & tools,
+                                                   const std::string &    effective_name_key,
+                                                   const std::string &    effective_args_key,
+                                                   const std::string &    call_id_key,
+                                                   const std::string &    gen_call_id_key);
 
-    common_peg_parser build_json_tools_flat_keys(const nlohmann::ordered_json &   tools,
+    common_peg_parser build_json_tools_flat_keys(const nlohmann::json &           tools,
                                                  const std::string &              effective_name_key,
                                                  const std::string &              effective_args_key,
                                                  const std::string &              call_id_key,

common/chat.cpp

@@ -1,6 +1,5 @@
 #include "chat.h"
 
-#include "chat-auto-parser-helpers.h"
 #include "chat-auto-parser.h"
 #include "chat-peg-parser.h"
 #include "common.h"
@@ -23,7 +22,6 @@
 #include <sstream>
 #include <stdexcept>
 #include <string>
-#include <utility>
 #include <vector>
 
 using json = nlohmann::ordered_json;
@@ -762,7 +760,7 @@ static void foreach_parameter(const json &
 
 std::string common_chat_template_direct_apply(
     const common_chat_template & tmpl,
-    const autoparser::generation_params & inputs,
+    const autoparser::templates_params & inputs,
     const std::optional<json> & messages_override,
     const std::optional<json> & tools_override,
     const std::optional<json> & additional_context) {
@@ -813,7 +811,7 @@ std::string common_chat_template_direct_apply(
 }
 
 static common_chat_params common_chat_params_init_ministral_3(const common_chat_template &    tmpl,
-                                                              const autoparser::generation_params & inputs) {
+                                                              const autoparser::templates_params & inputs) {
     common_chat_params data;
 
     // Build up messages to follow the format: https://huggingface.co/mistralai/Ministral-3-14B-Reasoning-2512/blob/main/chat_template.jinja
@@ -859,9 +857,7 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_
     auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
     auto include_grammar   = true;
 
-    data.supports_thinking  = true;
-    data.thinking_start_tag = "[THINK]";
-    data.thinking_end_tag   = "[/THINK]";
+    data.supports_thinking = true;
     data.prompt            = common_chat_template_direct_apply(tmpl, inputs, /* messages_override = */ adjusted_messages);
     data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
     data.preserved_tokens  = {
@@ -878,8 +874,8 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_
         // Response format parser
         if (inputs.json_schema.is_object() && !inputs.json_schema.empty()) {
             // Ministral wants to emit json surrounded by code fences
-            return wrap_for_generation_prompt(p, reasoning << "```json" << p.content(p.schema(p.json(), "response-format", inputs.json_schema)) << "```",
-                inputs, "[THINK]");
+            return reasoning << "```json" << p.content(p.schema(p.json(), "response-format", inputs.json_schema))
+                             << "```";
         }
 
         // Tool call parser
@@ -899,13 +895,12 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_
             auto max_calls  = inputs.parallel_tool_calls ? -1 : 1;
             auto tool_calls = p.trigger_rule("tool-call", p.repeat("[TOOL_CALLS]" + tool_choice, min_calls, max_calls));
 
-            return wrap_for_generation_prompt(p, reasoning << p.content(p.until("[TOOL_CALLS]")) << tool_calls,
-                inputs, "[THINK]");
+            return reasoning << p.content(p.until("[TOOL_CALLS]")) << tool_calls;
         }
 
         // Content only parser
         include_grammar = false;
-        return wrap_for_generation_prompt(p, reasoning << p.content(p.rest()), inputs, "[THINK]");
+        return reasoning << p.content(p.rest());
     });
 
     data.parser = parser.save();
@@ -931,19 +926,22 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_
 }
 
 static common_chat_params common_chat_params_init_gpt_oss(const common_chat_template &    tmpl,
-                                                          const autoparser::generation_params & inputs) {
+                                                          const autoparser::templates_params & inputs) {
     common_chat_params data;
 
     // Copy reasoning to the "thinking" field as expected by the gpt-oss template
     auto adjusted_messages = json::array();
-    for (auto msg : inputs.messages) {
-        if (msg.contains("reasoning_content") && msg.at("reasoning_content").is_string()) {
-            msg["thinking"] = msg.at("reasoning_content");
-            if (msg.contains("tool_calls") && msg.at("tool_calls").is_array() && !msg.at("tool_calls").empty()) {
-                msg.erase("content");
-            }
+    for (const auto & msg : inputs.messages) {
+        auto has_reasoning_content = msg.contains("reasoning_content") && msg.at("reasoning_content").is_string();
+        auto has_tool_calls        = msg.contains("tool_calls") && msg.at("tool_calls").is_array();
+
+        if (has_reasoning_content && has_tool_calls) {
+            auto adjusted_message        = msg;
+            adjusted_message["thinking"] = msg.at("reasoning_content");
+            adjusted_messages.push_back(adjusted_message);
+        } else {
+            adjusted_messages.push_back(msg);
         }
-        adjusted_messages.push_back(msg);
     }
 
     auto prompt = common_chat_template_direct_apply(tmpl, inputs, /* messages_override= */ adjusted_messages);
@@ -969,32 +967,45 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
         "<|channel|>", "<|constrain|>", "<|message|>", "<|start|>", "<|end|>",
     };
 
-    auto has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
-    auto has_response_format = !inputs.json_schema.is_null() && inputs.json_schema.is_object();
-    auto include_grammar     = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
+    auto has_tools         = inputs.tools.is_array() && !inputs.tools.empty();
+    auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+    auto include_grammar   = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE && has_tools;
 
     auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
-        auto start           = p.rule("start", p.literal("<|start|>assistant"));
-        auto end             = p.rule("end", p.literal("<|end|>"));
-        auto content         = p.rule("message-content", p.until("<|end|>"));
-        auto channel         = p.literal("<|channel|>") + (p.literal("commentary") | p.literal("analysis"));
-        auto constrain_type  = p.chars("[A-Za-z0-9_-]", 1, -1);
+        const std::string END                = "<|end|>";
+        const std::string START              = "<|start|>";
+        const std::string MESSAGE            = "<|message|>";
+        const std::string CHANNEL            = "<|channel|>";
+        const std::string CONSTRAIN          = "<|constrain|>";
+        const std::string START_ASSISTANT    = START + "assistant";
+        const std::string CHANNEL_ANALYSIS   = CHANNEL + "analysis";
+        const std::string CHANNEL_COMMENTARY = CHANNEL + "commentary";
+        const std::string CHANNEL_FINAL      = CHANNEL + "final";
 
-        auto analysis = p.rule("analysis", p.literal("<|channel|>analysis<|message|>") + p.reasoning(content) + end);
-        auto preamble = p.rule("preamble", p.literal("<|channel|>commentary<|message|>") + p.content(content) + end);
-        auto final_msg = p.rule("final", p.literal("<|channel|>final<|message|>") + p.content(content));
-        auto any = p.rule("any", preamble | analysis);
+        auto the_end = END | p.end();
 
-        if (has_response_format) {
-            auto constraint = p.optional(p.space() + p.literal("<|constrain|>") + constrain_type);
-            auto response_format = p.rule("response-format",
-                p.literal("<|channel|>final") + constraint + p.literal("<|message|>") +
-                p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)));
+        const std::string analysis_header  = CHANNEL_ANALYSIS + MESSAGE;
+        auto              segment_content  = p.until(END);
+        auto              analysis_segment = extract_reasoning ?
+                                                 p.literal(analysis_header) + p.reasoning(segment_content) + p.until(END) + the_end :
+                                                 p.content(analysis_header + p.until(END) + the_end);
 
-            return wrap_for_generation_prompt(p, response_format | (analysis + p.zero_or_more(start + analysis) + start + response_format),
-                inputs, "<|channel|>");
+        auto channel_header_content = p.until_one_of({ " to=functions.", MESSAGE });
+        auto content_header         = p.choice({ p.literal(CHANNEL_COMMENTARY), p.literal(CHANNEL_FINAL) });
+        auto content_segment        = p.rule("content-segment", content_header + channel_header_content + MESSAGE +
+                                                                    p.content(segment_content) + the_end);
+
+        if (!inputs.json_schema.is_null()) {
+            auto final_header = p.literal(CHANNEL_FINAL);
+            auto constraint   = p.optional(p.space() + p.literal(CONSTRAIN) + channel_header_content);
+            return p.optional(analysis_segment) + final_header + constraint + MESSAGE +
+                   p.content(p.schema(p.json(), "response-format", inputs.json_schema));
         }
 
+        auto segment  = p.optional(START_ASSISTANT + p.space()) + p.choice({ content_segment, analysis_segment });
+        auto contents = p.optional(segment + p.repeat(p.optional(p.space()) + segment, 0, -1)) + p.end();
+
+        // Tool call parser
         if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
             auto tool_choice = p.choice();
 
@@ -1003,39 +1014,42 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
                 std::string  name     = function.at("name");
                 const auto & params   = function.at("parameters");
 
-                auto func_name  = p.literal(" to=functions.") + p.tool_name(p.literal(name));
-                auto constraint = p.optional(p.space() + p.literal("<|constrain|>") + constrain_type);
+                // Tool call can appear as:
+                // 1. In role header: " to=functions.NAME<|channel|>..."
+                // 2. In channel: "<|channel|>(analysis|commentary) to=functions.NAME..."
+                auto func_name = p.literal(" to=functions.") + p.tool_name(p.literal(name));
+
+                auto channel    = p.literal(CHANNEL_COMMENTARY) | p.literal(CHANNEL_ANALYSIS);
+                auto constraint = p.space() + p.optional(p.literal(CONSTRAIN) + channel_header_content);
                 auto args       = p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", params));
 
-                // recipient in role header
-                //   <|start|>assistant to=functions.NAME<|channel|>(commentary|analysis)[constraint]<|message|>ARGS
-                auto tool_in_role = p.tool(p.tool_open(func_name + channel + constraint + p.literal("<|message|>")) + args);
+                // Pattern 1: recipient in role header
+                // " to=functions.NAME<|channel|>(analysis|commentary)[constraint]<|message|>ARGS"
+                auto tool_in_role = p.tool(p.tool_open(func_name + channel) + constraint + MESSAGE + args);
 
-                // recipient in channel header
-                //   <|channel|>(commentary|analysis) to=functions.NAME[constraint]<|message|>ARGS
-                auto tool_in_channel = p.tool(p.tool_open(channel + func_name + constraint + p.literal("<|message|>")) + args);
+                // Pattern 2: recipient in channel header
+                // "<|channel|>(analysis|commentary) to=functions.NAME[constraint]<|message|>ARGS"
+                auto tool_in_channel = p.tool(channel + p.tool_open(func_name + constraint + MESSAGE) + args);
 
-                tool_choice |= p.rule("tool-" + name, tool_in_role | tool_in_channel);
+                tool_choice |= tool_in_role | tool_in_channel;
             });
 
-            auto tool_call  = p.trigger_rule("tool-call", tool_choice);
+            auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0;
+            auto max_calls = inputs.parallel_tool_calls ? -1 : 1;
 
-            if (inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED) {
-                return tool_call | ( any + p.zero_or_more(start + any) + start + tool_call);
-            }
+            auto role_start = p.optional(p.space() + p.literal(START_ASSISTANT));
+            auto tool_call  = p.rule("tool-call", p.repeat(role_start + tool_choice, min_calls, max_calls) + p.end());
 
-            return wrap_for_generation_prompt(p, tool_call | final_msg | (any + p.zero_or_more(start + any) + start + (tool_call | final_msg)),
-                inputs, "<|channel|>");
+            return p.choice({ p.trigger_rule("single-tool", tool_call), p.trigger_rule("tools", p.one_or_more(segment) + tool_call) });
         }
 
-        return wrap_for_generation_prompt(p, final_msg | (any + p.zero_or_more(start + any) + start + final_msg),
-            inputs, "<|channel|>");
+        return contents;
     });
 
     data.parser = parser.save();
 
     if (include_grammar) {
-        data.grammar_lazy = !(has_response_format || (has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED));
+        data.grammar_lazy = has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
         data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
             foreach_function(inputs.tools, [&](const json & tool) {
                 const auto & function = tool.at("function");
@@ -1046,9 +1060,10 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
         });
 
         data.grammar_triggers = {
-            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "^\\s+to$" },
-            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "<\\|start\\|>assistant(\\s+to)" },
-            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "<\\|start\\|>assistant(<\\|channel\\|>(?:commentary|analysis)\\s+to)" }
+            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "^(?:<\\|start\\|>assistant\\s*)?(\\s+to=functions)"               },
+            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "(?:<\\|end\\|>)(?:<\\|start\\|>assistant\\s*)?(\\s+to=functions)" },
+            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN,
+             "(?:<\\|start\\|>assistant\\s*)?(<\\|channel\\|>(?:commentary|analysis)\\s+to=functions)"                }
         };
     }
 
@@ -1057,7 +1072,7 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
 
 // Functionary v3.2 - uses recipient-based format: >>>recipient\n{content}
 static common_chat_params common_chat_params_init_functionary_v3_2(const common_chat_template &    tmpl,
-                                                                   const autoparser::generation_params & inputs) {
+                                                                   const autoparser::templates_params & inputs) {
     common_chat_params data;
 
     data.prompt           = common_chat_template_direct_apply(tmpl, inputs);
@@ -1078,13 +1093,13 @@ static common_chat_params common_chat_params_init_functionary_v3_2(const common_
         // Build content parser for >>>all\n{content}
         // When tools are present, content stops before the next ">>>" (tool call)
         // When no tools, content goes until end
-        auto content_until_tool = p.literal("all\n") + p.content(p.until(">>>"));
-        auto content_until_end  = p.literal("all\n") + p.content(p.rest());
+        auto content_until_tool = p.literal(">>>all\n") + p.content(p.until(">>>"));
+        auto content_until_end  = p.literal(">>>all\n") + p.content(p.rest());
 
         // If no tools or tool_choice is NONE, just parse content
         if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
             // When no tools, just match the prefix and capture everything after
-            return wrap_for_generation_prompt(p, content_until_end + p.end(), inputs);
+            return content_until_end + p.end();
         }
 
         // Build tool call parsers for each available function
@@ -1096,7 +1111,7 @@ static common_chat_params common_chat_params_init_functionary_v3_2(const common_
 
             // Tool format: >>>function_name\n{json_args}
             auto tool_parser = p.tool(
-                p.tool_open(p.tool_name(p.literal(name)) + p.literal("\n")) +
+                p.tool_open(p.literal(">>>") + p.tool_name(p.literal(name)) + p.literal("\n")) +
                 p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema))
             );
 
@@ -1107,20 +1122,17 @@ static common_chat_params common_chat_params_init_functionary_v3_2(const common_
         auto tools_only = p.trigger_rule("tools", p.one_or_more(tool_choice));
         auto content_and_tools = content_until_tool + tools_only;
 
-        auto ret = p.eps();
         if (inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED) {
             if (inputs.parallel_tool_calls) {
-                ret = p.choice({ content_and_tools, tools_only }) + p.end();
-            } else {
-                ret = p.choice({ content_until_tool + tool_choice, tools_only }) + p.end();
+                return p.choice({ content_and_tools, tools_only }) + p.end();
             }
-        } else if (inputs.parallel_tool_calls) {
-            ret = p.choice({ content_and_tools, content_only, tools_only }) + p.end();
-        } else {
-            auto content_and_tool = content_until_tool + tool_choice;
-            ret = p.choice({ content_and_tool, content_only, tool_choice }) + p.end();
+            return p.choice({ content_until_tool + tool_choice, tools_only }) + p.end();
         }
-        return wrap_for_generation_prompt(p, ret, inputs);
+        if (inputs.parallel_tool_calls) {
+            return p.choice({ content_and_tools, content_only, tools_only }) + p.end();
+        }
+        auto content_and_tool = content_until_tool + tool_choice;
+        return p.choice({ content_and_tool, content_only, tool_choice }) + p.end();
     });
 
     data.parser = parser.save();
@@ -1150,12 +1162,12 @@ static common_chat_params common_chat_params_init_functionary_v3_2(const common_
 // Kimi K2 Thinking - uses unique tool call ID format: functions.<name>:<index>
 // The ID contains both the function name and an incrementing counter
 static common_chat_params common_chat_params_init_kimi_k2(const common_chat_template &    tmpl,
-                                                          const autoparser::generation_params & inputs) {
+                                                          const autoparser::templates_params & inputs) {
     common_chat_params data;
 
-    data.prompt             = common_chat_template_direct_apply(tmpl, inputs);
-    data.format             = COMMON_CHAT_FORMAT_PEG_NATIVE;
-    data.supports_thinking  = true;
+    data.prompt            = common_chat_template_direct_apply(tmpl, inputs);
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.supports_thinking = true;
     data.preserved_tokens  = {
         "<|tool_calls_section_begin|>",
         "<|tool_calls_section_end|>",
@@ -1170,18 +1182,6 @@ static common_chat_params common_chat_params_init_kimi_k2(const common_chat_temp
     auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
     auto include_grammar   = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
 
-    const std::string SECTION_BEGIN = "<|tool_calls_section_begin|>";
-    const std::string SECTION_END   = "<|tool_calls_section_end|>";
-    const std::string CALL_BEGIN    = "<|tool_call_begin|>";
-    const std::string ARGS_BEGIN    = "<|tool_call_argument_begin|>";
-    const std::string CALL_END      = "<|tool_call_end|>";
-
-    const std::string THINK_START = "<think>";
-    const std::string THINK_END   = "</think>";
-
-    data.thinking_start_tag = THINK_START;
-    data.thinking_end_tag   = THINK_END;
-
     auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
         // Kimi K2 Thinking format:
         // - Reasoning: <think>{reasoning}</think>
@@ -1193,7 +1193,16 @@ static common_chat_params common_chat_params_init_kimi_k2(const common_chat_temp
         //   <|tool_calls_section_end|>
         // The ID format is: functions.<function_name>:<counter> where counter is 0, 1, 2, ...
 
-        // Tool call markers
+                // Tool call markers
+        const std::string SECTION_BEGIN = "<|tool_calls_section_begin|>";
+        const std::string SECTION_END   = "<|tool_calls_section_end|>";
+        const std::string CALL_BEGIN    = "<|tool_call_begin|>";
+        const std::string ARGS_BEGIN    = "<|tool_call_argument_begin|>";
+        const std::string CALL_END      = "<|tool_call_end|>";
+
+        const std::string THINK_START   = "<think>";
+        const std::string THINK_END     = "</think>";
+
         auto end = p.end();
 
         // Note: this model is CRAZY. It can diverge from its supposed tool calling pattern in so many ways it's not funny.
@@ -1205,8 +1214,7 @@ static common_chat_params common_chat_params_init_kimi_k2(const common_chat_temp
 
         // Content only parser (no tools)
         if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
-            return wrap_for_generation_prompt(p, reasoning + p.content(p.rest()) + end,
-                inputs, THINK_START);
+            return reasoning + p.content(p.rest()) + end;
         }
 
         // Build tool call parsers for each available function
@@ -1242,8 +1250,7 @@ static common_chat_params common_chat_params_init_kimi_k2(const common_chat_temp
 
         auto content_before_tools = p.content(p.until_one_of({ SECTION_BEGIN, CALL_BEGIN }));
 
-        return wrap_for_generation_prompt(p, reasoning + content_before_tools + tool_calls + end,
-            inputs, THINK_START);
+        return reasoning + content_before_tools + tool_calls + end;
     });
 
     data.parser = parser.save();
@@ -1273,7 +1280,7 @@ static common_chat_params common_chat_params_init_kimi_k2(const common_chat_temp
 // - Tool calls: <|tool_call_start|>[function_name(arg1="value1", arg2="value2")]<|tool_call_end|>
 // Tool calls can appear multiple times (parallel tool calls)
 static common_chat_params common_chat_params_init_lfm2(const common_chat_template &    tmpl,
-                                                       const autoparser::generation_params & inputs) {
+                                                       const autoparser::templates_params & inputs) {
     common_chat_params data;
 
     data.prompt            = common_chat_template_direct_apply(tmpl, inputs);
@@ -1292,15 +1299,13 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat
     auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
     auto include_grammar   = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
 
+
     const std::string TOOL_CALL_START = "<|tool_call_start|>";
     const std::string TOOL_CALL_END   = "<|tool_call_end|>";
     const std::string THINK_START     = "<think>";
     const std::string THINK_END       = "</think>";
-
-    data.thinking_start_tag = THINK_START;
-    data.thinking_end_tag   = THINK_END;
-
     auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+
         auto end = p.end();
 
         auto reasoning = p.eps();
@@ -1309,8 +1314,7 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat
         }
 
         if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
-            return wrap_for_generation_prompt(p, reasoning + p.content(p.rest()) + end, inputs,
-                THINK_START);
+            return reasoning + p.content(p.rest()) + end;
         }
 
         auto tool_calls = p.rule("tool-calls",
@@ -1322,8 +1326,7 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat
 
         auto content = p.content(p.until(TOOL_CALL_START));
 
-        return wrap_for_generation_prompt(p, reasoning + content + tool_calls + end, inputs,
-            THINK_START);
+        return reasoning + content + tool_calls + end;
     });
 
     data.parser = parser.save();
@@ -1347,92 +1350,8 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat
     return data;
 }
 
-static common_chat_params common_chat_params_init_gigachat_v3(
-        const common_chat_template & tmpl,
-        const autoparser::generation_params & inputs) {
-
-    common_chat_params data;
-
-    data.prompt            = common_chat_template_direct_apply(tmpl, inputs);
-    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
-    data.supports_thinking = false;
-    data.preserved_tokens  = {
-        "<|message_sep|>\n\n",
-        "<|role_sep|>\n",
-    };
-
-    auto has_tools         = inputs.tools.is_array() && !inputs.tools.empty();
-    auto include_grammar   = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
-    const auto *tool_call_start_prefix = "<|message_sep|>\n\nfunction call<|role_sep|>\n";
-
-    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
-        auto ret = p.eps();
-        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
-            // Build a choice of all available tools
-            auto tool_choice = p.choice();
-            for (const auto & tool : inputs.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.tool_name(p.literal(name)) + "\"");
-                auto tool_args = p.json_member("arguments", p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema)));
-
-                auto tool_open = p.tool_open(p.literal("{") << tool_name);
-
-                tool_choice |= p.rule("tool-" + name, tool_open << "," << tool_args << "}");
-            }
-
-            // Define the tool call structure
-            auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0;
-            auto max_calls = 1; // parallel toolcalls are not supported
-            auto tool_call = p.rule("tool-call", p.literal(tool_call_start_prefix) + tool_choice);
-            auto tool_calls = p.trigger_rule("tool-call-root", p.repeat(tool_call, /* min = */ min_calls, /* max = */ max_calls));
-
-            ret = p.content(p.until("<|message_sep|>\n\n")) << tool_calls;
-        } else {
-            // Content only parser
-            include_grammar = false;
-            ret = p.content(p.rest());
-        }
-
-        return wrap_for_generation_prompt(p, ret, inputs);
-    });
-
-    data.parser = parser.save();
-
-    if (include_grammar) {
-        data.grammar_lazy = has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
-
-        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-            foreach_function(inputs.tools, [&](const json & tool) {
-                const auto & function = tool.at("function");
-                auto schema = function.at("parameters");
-                builder.resolve_refs(schema);
-            });
-            parser.build_grammar(builder, data.grammar_lazy);
-        });
-
-        data.grammar_triggers = {
-            {COMMON_GRAMMAR_TRIGGER_TYPE_WORD, tool_call_start_prefix}
-        };
-    }
-    return data;
-}
-
 namespace workaround {
 
-static void map_developer_role_to_system(json & messages) {
-    for (auto & message : messages) {
-        if (message.contains("role")) {
-            if (message["role"] == "developer") {
-                message["role"] = "system";
-            }
-        }
-    }
-}
-
-
 // if first message is system and template does not support it, merge it with next message
 static void system_message_not_supported(json & messages) {
     if (!messages.empty() && messages.front().at("role") == "system") {
@@ -1491,10 +1410,65 @@ static json common_chat_extra_context() {
     return ctx;
 }
 
-static std::optional<common_chat_params> try_specialized_template(
-        const common_chat_template &          tmpl,
-        const std::string &                   src,
-        const autoparser::generation_params & params) {
+static common_chat_params common_chat_templates_apply_jinja(const struct common_chat_templates *        tmpls,
+                                                            const struct common_chat_templates_inputs & inputs) {
+    autoparser::templates_params params;
+    params.tools = common_chat_tools_to_json_oaicompat(inputs.tools);
+    const auto & tmpl = params.tools.is_array() && tmpls->template_tool_use
+        ? *tmpls->template_tool_use
+        : *tmpls->template_default;
+    const auto & src = tmpl.source();
+    const auto & caps = tmpl.original_caps();
+    params.messages = render_message_to_json(inputs.messages, tmpl.original_caps());
+    params.add_generation_prompt = inputs.add_generation_prompt;
+    params.tool_choice = inputs.tool_choice;
+    params.reasoning_format = inputs.reasoning_format;
+    params.enable_thinking = inputs.enable_thinking;
+    params.grammar = inputs.grammar;
+    params.now = inputs.now;
+    params.add_bos = tmpls->add_bos;
+    params.add_eos = tmpls->add_eos;
+
+    workaround::func_args_not_string(params.messages);
+
+    if (!tmpl.original_caps().supports_system_role) {
+        workaround::system_message_not_supported(params.messages);
+    }
+
+    if (tmpl.original_caps().supports_tool_calls) {
+        // some templates will require the content field in tool call messages
+        // to still be non-null, this puts an empty string everywhere where the
+        // content field is null
+        workaround::requires_non_null_content(params.messages);
+    }
+
+    params.extra_context = common_chat_extra_context();
+    for (auto el : inputs.chat_template_kwargs) {
+        params.extra_context[el.first] = json::parse(el.second);
+    }
+
+    if (!inputs.json_schema.empty()) {
+        params.json_schema = json::parse(inputs.json_schema);
+    }
+
+    // if (inputs.parallel_tool_calls && !tmpl.original_caps().supports_parallel_tool_calls) {
+    //     LOG_DBG("Disabling parallel_tool_calls because the template does not support it\n");
+    //     params.parallel_tool_calls = false;
+    // } else {
+    params.parallel_tool_calls = inputs.parallel_tool_calls;
+    //}
+
+    if (params.tools.is_array()) {
+        if (params.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE && !params.grammar.empty()) {
+            throw std::runtime_error("Cannot specify grammar with tools");
+        }
+        if (caps.supports_tool_calls && !caps.supports_tools) {
+            LOG_WRN(
+                "Template supports tool calls but does not natively describe tools. The fallback behaviour used may "
+                "produce bad results, inspect prompt w/ --verbose & consider overriding the template.\n");
+        }
+    }
+
     // Ministral/Mistral Large 3 - uses special reasoning structure fixes, can't use autoparser
     // Note: Mistral Small 3.2 uses [CALL_ID] which Ministral doesn't have, so we can distinguish them
     if (src.find("[SYSTEM_PROMPT]") != std::string::npos && src.find("[TOOL_CALLS]") != std::string::npos &&
@@ -1532,120 +1506,12 @@ static std::optional<common_chat_params> try_specialized_template(
         return common_chat_params_init_lfm2(tmpl, params);
     }
 
-    // GigaChatV3 format detection
-    if (src.find("<|role_sep|>") != std::string::npos &&
-        src.find("<|message_sep|>") != std::string::npos &&
-        src.find("<|function_call|>") == std::string::npos) {
-        LOG_DBG("Using specialized template: GigaChatV3\n");
-        return common_chat_params_init_gigachat_v3(tmpl, params);
-    }
-
-    return std::nullopt;
-}
-
-static common_chat_params common_chat_templates_apply_jinja(const struct common_chat_templates *        tmpls,
-                                                            const struct common_chat_templates_inputs & inputs) {
-    autoparser::generation_params params;
-    params.tools = common_chat_tools_to_json_oaicompat(inputs.tools);
-    const auto & tmpl =
-        params.tools.is_array() && tmpls->template_tool_use ? *tmpls->template_tool_use : *tmpls->template_default;
-    const auto & src        = tmpl.source();
-    const auto & caps       = tmpl.original_caps();
-    params.messages         = render_message_to_json(inputs.messages, tmpl.original_caps());
-    params.tool_choice      = inputs.tool_choice;
-    params.reasoning_format = inputs.reasoning_format;
-    params.enable_thinking  = inputs.enable_thinking;
-    params.grammar          = inputs.grammar;
-    params.now              = inputs.now;
-    params.add_bos          = tmpls->add_bos;
-    params.add_eos          = tmpls->add_eos;
-
-    if (src.find("<|channel|>") == std::string::npos) {
-        // map developer to system for all models except for GPT-OSS
-        workaround::map_developer_role_to_system(params.messages);
-    }
-
-    if (!tmpl.original_caps().supports_system_role) {
-        workaround::system_message_not_supported(params.messages);
-    }
-
-    if (tmpl.original_caps().supports_tool_calls) {
-        // some templates will require the content field in tool call messages
-        // to still be non-null, this puts an empty string everywhere where the
-        // content field is null
-        workaround::requires_non_null_content(params.messages);
-    }
-
-    if (tmpl.original_caps().supports_object_arguments) {
-        workaround::func_args_not_string(params.messages);
-    }
-
-    params.add_generation_prompt = false;
-    std::string no_gen_prompt    = common_chat_template_direct_apply(tmpl, params);
-    params.add_generation_prompt = true;
-    std::string gen_prompt       = common_chat_template_direct_apply(tmpl, params);
-    auto        diff             = calculate_diff_split(no_gen_prompt, gen_prompt);
-    params.generation_prompt     = diff.right;
-
-    params.add_generation_prompt = inputs.add_generation_prompt;
-
-    params.extra_context = common_chat_extra_context();
-    for (auto el : inputs.chat_template_kwargs) {
-        params.extra_context[el.first] = json::parse(el.second);
-    }
-
-    if (!inputs.json_schema.empty()) {
-        params.json_schema = json::parse(inputs.json_schema);
-    }
-
-    params.parallel_tool_calls = inputs.parallel_tool_calls;
-
-    if (params.tools.is_array()) {
-        if (params.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE && !params.grammar.empty()) {
-            throw std::runtime_error("Cannot specify grammar with tools");
-        }
-        if (caps.supports_tool_calls && !caps.supports_tools) {
-            LOG_WRN(
-                "Template supports tool calls but does not natively describe tools. The fallback behaviour used may "
-                "produce bad results, inspect prompt w/ --verbose & consider overriding the template.\n");
-        }
-    }
-
-    if (inputs.force_pure_content) {
-        LOG_WRN("Forcing pure content template, will not render reasoning or tools separately.");
-        // Create the result structure
-        common_chat_params data;
-        auto params_copy               = params;
-        params_copy.reasoning_format   = COMMON_REASONING_FORMAT_NONE;
-        data.prompt                    = common_chat_template_direct_apply(tmpl, params_copy);
-        data.format                    = COMMON_CHAT_FORMAT_PEG_NATIVE;
-        data.generation_prompt         = params.generation_prompt;
-        auto parser                    = build_chat_peg_parser([&params](common_chat_peg_builder &p) {
-            return wrap_for_generation_prompt(p, p.content(p.rest()), params);
-        });
-        data.parser                    = parser.save();
-        return data;
-    }
-
-    if (auto result = try_specialized_template(tmpl, src, params)) {
-        result->generation_prompt = params.generation_prompt;
-        return *result;
-    }
-
     try {
-        LOG_DBG("%s: using differential autoparser\n", __func__);
+        LOG_DBG("Using differential autoparser\n");
         struct autoparser::autoparser autoparser;
         autoparser.analyze_template(tmpl);
         auto auto_params = autoparser::peg_generator::generate_parser(tmpl, params, autoparser);
         auto_params.supports_thinking = autoparser.reasoning.mode != autoparser::reasoning_mode::NONE;
-        if (auto_params.supports_thinking) {
-            auto_params.thinking_start_tag = autoparser.reasoning.start;
-            auto_params.thinking_end_tag   = autoparser.reasoning.end;
-        }
-        auto_params.generation_prompt = params.generation_prompt;
-        common_peg_arena arena;
-        arena.load(auto_params.parser);
-        LOG_DBG("%s: generated parser:\n%s\n\nparser generation prompt: %s\n", __func__, arena.dump(arena.root()).c_str(), auto_params.generation_prompt.c_str());
         return auto_params;
     } catch (const std::exception & e) {
         throw std::invalid_argument(std::string("Unable to generate parser for this template. Automatic parser generation failed: ") + e.what());
@@ -1739,22 +1605,18 @@ common_chat_msg common_chat_peg_parse(const common_peg_arena &          src_pars
         build_chat_peg_parser([](common_chat_peg_builder & p) { return p.content(p.rest()) + p.end(); }) :
         src_parser;
 
-    if (src_parser.empty()) {
-        LOG_DBG("No parser definition detected, assuming pure content parser.");
+        if (src_parser.empty()) {
+        LOG_WRN("No parser definition detected, assuming pure content parser.");
     }
 
-    const std::string effective_input = params.generation_prompt.empty()
-        ? input
-        : params.generation_prompt + input;
-
-    LOG_DBG("Parsing PEG input with format %s: %s\n", common_chat_format_name(params.format), effective_input.c_str());
+    LOG_DBG("Parsing PEG input with format %s: %s\n", common_chat_format_name(params.format), input.c_str());
 
     common_peg_parse_flags flags = COMMON_PEG_PARSE_FLAG_LENIENT;
     if (params.debug) {
         flags |= COMMON_PEG_PARSE_FLAG_DEBUG;
     }
 
-    common_peg_parse_context ctx(effective_input, flags);
+    common_peg_parse_context ctx(input, flags);
     auto result = parser.parse(ctx);
 
     if (result.fail()) {
@@ -1774,7 +1636,7 @@ common_chat_msg common_chat_peg_parse(const common_peg_arena &          src_pars
             return msg;
         }
         throw std::runtime_error(std::string("Failed to parse input at pos ") + std::to_string(result.end) + ": " +
-                                 effective_input.substr(result.end));
+                                 input.substr(result.end));
     }
 
     common_chat_msg msg;

common/chat.h

@@ -24,7 +24,7 @@ using json = nlohmann::ordered_json;
 struct common_chat_templates;
 
 namespace autoparser {
-struct generation_params;
+struct templates_params;
 }  // namespace autoparser
 
 struct common_chat_tool_call {
@@ -204,7 +204,6 @@ struct common_chat_templates_inputs {
     std::map<std::string, std::string>    chat_template_kwargs;
     bool                                  add_bos = false;
     bool                                  add_eos = false;
-    bool                                  force_pure_content = false;
 };
 
 struct common_chat_params {
@@ -212,10 +211,8 @@ struct common_chat_params {
     std::string                         prompt;
     std::string                         grammar;
     bool                                grammar_lazy         = false;
-    std::string                         generation_prompt;
+    bool                                thinking_forced_open = false;
     bool                                supports_thinking    = false;
-    std::string                         thinking_start_tag;  // e.g., "<think>"
-    std::string                         thinking_end_tag;    // e.g., "</think>"
     std::vector<common_grammar_trigger> grammar_triggers;
     std::vector<std::string>            preserved_tokens;
     std::vector<std::string>            additional_stops;
@@ -229,14 +226,14 @@ struct common_chat_parser_params {
     common_reasoning_format reasoning_format     = COMMON_REASONING_FORMAT_NONE; // TODO: refactor this to "bool parse_reasoning"
     // Whether reasoning_content should be inlined in the content (e.g. for reasoning_format=deepseek in stream mode)
     bool                    reasoning_in_content = false;
-    std::string             generation_prompt;
+    bool                    thinking_forced_open = false;
     bool                    parse_tool_calls     = true;
     bool                    debug                = false;  // Enable debug output for PEG parser
     common_peg_arena        parser               = {};
     common_chat_parser_params() = default;
     common_chat_parser_params(const common_chat_params & chat_params) {
-        format  = chat_params.format;
-        generation_prompt = chat_params.generation_prompt;
+        format               = chat_params.format;
+        thinking_forced_open = chat_params.thinking_forced_open;
     }
 };
 
@@ -302,7 +299,7 @@ std::map<std::string, bool> common_chat_templates_get_caps(const common_chat_tem
 
 std::string common_chat_template_direct_apply(
     const common_chat_template & tmpl,
-    const autoparser::generation_params & inputs,
+    const autoparser::templates_params & inputs,
     const std::optional<json> & messages_override = std::nullopt,
     const std::optional<json> & tools_override = std::nullopt,
     const std::optional<json> & additional_context = std::nullopt);

common/common.cpp

@@ -1067,7 +1067,7 @@ common_init_result::common_init_result(common_params & params) :
 
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
-    // load and optionally apply lora adapters
+    // load and optionally apply lora adapters (must be loaded before context creation)
     for (auto & la : params.lora_adapters) {
         llama_adapter_lora_ptr lora;
         lora.reset(llama_adapter_lora_init(model, la.path.c_str()));

common/common.h

@@ -3,14 +3,12 @@
 #pragma once
 
 #include "ggml-opt.h"
-#include "ggml.h"
 #include "llama-cpp.h"
 
 #include <set>
 #include <sstream>
 #include <string>
 #include <string_view>
-#include <variant>
 #include <vector>
 #include <map>
 
@@ -107,7 +105,6 @@ enum llama_example {
     LLAMA_EXAMPLE_FINETUNE,
     LLAMA_EXAMPLE_FIT_PARAMS,
     LLAMA_EXAMPLE_RESULTS,
-    LLAMA_EXAMPLE_EXPORT_GRAPH_OPS,
 
     LLAMA_EXAMPLE_COUNT,
 };
@@ -180,43 +177,6 @@ enum common_speculative_type {
     COMMON_SPECULATIVE_TYPE_COUNT          // number of types, unknown type
 };
 
-// Grammar type enumeration
-enum common_grammar_type {
-    COMMON_GRAMMAR_TYPE_NONE,           // no grammar set
-    COMMON_GRAMMAR_TYPE_USER,           // user-provided GBNF (--grammar / "grammar" API field)
-    COMMON_GRAMMAR_TYPE_OUTPUT_FORMAT,  // auto-generated from JSON schema (--json-schema / "json_schema" API field)
-    COMMON_GRAMMAR_TYPE_TOOL_CALLS,     // auto-generated by chat template parser for function calling
-};
-
-// Grammar variant struct with type and grammar string
-struct common_grammar {
-    common_grammar_type type = COMMON_GRAMMAR_TYPE_NONE;
-    std::string grammar;
-
-    // Default constructor - no grammar
-    common_grammar() = default;
-
-    // Constructor with type and grammar string
-    common_grammar(common_grammar_type t, std::string g) : type(t), grammar(std::move(g)) {
-        GGML_ASSERT(type != COMMON_GRAMMAR_TYPE_NONE || !grammar.empty());
-    }
-
-    // Check if a grammar is set
-    bool empty() const { return type == COMMON_GRAMMAR_TYPE_NONE || grammar.empty(); }
-};
-
-// Returns the raw grammar string, or empty string if no grammar is set.
-inline const std::string & common_grammar_value(const common_grammar & g) {
-    return g.grammar;
-}
-
-// Returns true when the generation_prompt should be prefilled into the grammar sampler.
-// Only output-format and tool-call grammars need prefill; user-supplied grammars must not be prefilled.
-inline bool common_grammar_needs_prefill(const common_grammar & g) {
-    return g.type == COMMON_GRAMMAR_TYPE_OUTPUT_FORMAT
-        || g.type == COMMON_GRAMMAR_TYPE_TOOL_CALLS;
-}
-
 // sampling parameters
 struct common_params_sampling {
     uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
@@ -267,7 +227,7 @@ struct common_params_sampling {
         COMMON_SAMPLER_TYPE_TEMPERATURE,
     };
 
-    common_grammar              grammar;      // optional grammar constraint (user / output-format / tool-calls)
+    std::string                         grammar; // optional BNF-like grammar to constrain sampling
     bool                                grammar_lazy = false;
     std::vector<common_grammar_trigger> grammar_triggers; // optional triggers (for lazy grammars)
     std::set<llama_token>               preserved_tokens;
@@ -275,19 +235,6 @@ struct common_params_sampling {
     std::vector<llama_logit_bias> logit_bias;     // logit biases to apply
     std::vector<llama_logit_bias> logit_bias_eog; // pre-calculated logit biases for EOG tokens
 
-    // The assistant generation prompt already prefilled into the prompt.
-    // Fed to the grammar sampler (to advance past pre-existing tokens) and used
-    // to determine the reasoning budget sampler's initial state.
-    // Only applied when the grammar is of output-format or tool-calls type.
-    std::string generation_prompt;
-
-    // reasoning budget sampler parameters
-    // these are populated by the server/CLI based on chat template params
-    int32_t                  reasoning_budget_tokens   = -1;   // -1 = disabled, >= 0 = token budget
-    std::vector<llama_token> reasoning_budget_start;           // start tag token sequence
-    std::vector<llama_token> reasoning_budget_end;             // end tag token sequence
-    std::vector<llama_token> reasoning_budget_forced;          // forced sequence (message + end tag)
-
     bool backend_sampling = false;
 
     bool has_logit_bias() const {
@@ -588,11 +535,8 @@ struct common_params {
     std::string chat_template = "";                                                                         // NOLINT
     bool use_jinja = true;                                                                                  // NOLINT
     bool enable_chat_template = true;
-    bool force_pure_content_parser = false;
     common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
-    int enable_reasoning = -1; // -1 = auto, 0 = disable, 1 = enable
     int reasoning_budget = -1;
-    std::string reasoning_budget_message; // message injected before end tag when budget exhausted
     bool prefill_assistant = true; // if true, any trailing assistant message will be prefilled into the response
     int sleep_idle_seconds = -1;   // if >0, server will sleep after this many seconds of idle time
 
@@ -972,7 +916,7 @@ const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
 // MoE utils
 //
 
-const char * const LLM_FFN_EXPS_REGEX = "\\.ffn_(up|down|gate|gate_up)_(ch|)exps";
+const char * const LLM_FFN_EXPS_REGEX = "\\.ffn_(up|down|gate)_(ch|)exps";
 
 inline std::string llm_ffn_exps_block_regex(int idx) {
     return string_format("blk\\.%d%s", idx, LLM_FFN_EXPS_REGEX);

common/http.h

@@ -7,7 +7,6 @@ struct common_http_url {
     std::string user;
     std::string password;
     std::string host;
-    int port;
     std::string path;
 };
 
@@ -48,20 +47,6 @@ static common_http_url common_http_parse_url(const std::string & url) {
         parts.host = rest;
         parts.path = "/";
     }
-
-    auto colon_pos = parts.host.find(':');
-
-    if (colon_pos != std::string::npos) {
-        parts.port = std::stoi(parts.host.substr(colon_pos + 1));
-        parts.host = parts.host.substr(0, colon_pos);
-    } else if (parts.scheme == "http") {
-        parts.port = 80;
-    } else if (parts.scheme == "https") {
-        parts.port = 443;
-    } else {
-        throw std::runtime_error("unsupported URL scheme: " + parts.scheme);
-    }
-
     return parts;
 }
 
@@ -83,7 +68,7 @@ static std::pair<httplib::Client, common_http_url> common_http_client(const std:
     }
 #endif
 
-    httplib::Client cli(parts.scheme + "://" + parts.host + ":" + std::to_string(parts.port));
+    httplib::Client cli(parts.scheme + "://" + parts.host);
 
     if (!parts.user.empty()) {
         cli.set_basic_auth(parts.user, parts.password);

common/jinja/caps.cpp

@@ -75,7 +75,6 @@ std::map<std::string, bool> caps::to_map() const {
         {"supports_parallel_tool_calls", supports_parallel_tool_calls},
         {"supports_system_role", supports_system_role},
         {"supports_preserve_reasoning", supports_preserve_reasoning},
-        {"supports_object_arguments", supports_object_arguments},
     };
 }
 
@@ -159,9 +158,9 @@ caps caps_get(jinja::program & prog) {
         }
     );
 
-    JJ_DEBUG("%s\n", ">>> Running capability check: single tool with object arguments support");
+    JJ_DEBUG("%s\n", ">>> Running capability check: single tool support");
 
-    // case: tools support: single call with object arguments
+    // case: tools support: single call
     caps_try_execute(
         prog,
         [&]() {
@@ -227,7 +226,9 @@ caps caps_get(jinja::program & prog) {
         },
         [&](bool success, value & messages, value & tools) {
             if (!success) {
-                return; // Nothing can be inferred
+                result.supports_tool_calls = false;
+                result.supports_tools = false;
+                return;
             }
 
             auto & tool_name = tools->at(0)->at("function")->at("name");
@@ -241,117 +242,16 @@ caps caps_get(jinja::program & prog) {
             caps_print_stats(tool_calls, "messages[1].tool_calls");
             if (!tool_calls->stats.used) {
                 result.supports_tool_calls = false;
-                return;
-            }
-
-            auto & tool_arg = tool_calls->at(0)->at("function")->at("arguments")->at("arg");
-            caps_print_stats(tool_arg, "messages[1].tool_calls[0].function.arguments.arg");
-            if (tool_arg->stats.used) {
-                result.supports_object_arguments = true;
             }
         }
     );
 
-    if (!result.supports_object_arguments) {
-        JJ_DEBUG("%s\n", ">>> Running capability check: single tool with string arguments support");
-
-        // case: tools support: single call with string arguments
-        caps_try_execute(
-            prog,
-            [&]() {
-                // messages
-                return json::array({
-                    {
-                        {"role", "user"},
-                        {"content", "User message"},
-                    },
-                    {
-                        {"role", "assistant"},
-                        {"content", ""}, // Some templates expect content to be empty with tool calls
-                        {"tool_calls", json::array({
-                            {
-                                {"id", "call00001"},
-                                {"type", "function"},
-                                {"function", {
-                                    {"name", "tool1"},
-                                    {"arguments", R"({"arg": "value"})"}
-                                }}
-                            }
-                        })}
-                    },
-                    {
-                        {"role", "tool"},
-                        {"content", "Tool response"},
-                        {"tool_call_id", "call00001"}
-                    },
-                    {
-                        {"role", "assistant"},
-                        {"content", "The tool response was 'tool response'"}
-                    },
-                    {
-                        {"role", "user"},
-                        {"content", "User message"},
-                    },
-                });
-            },
-            [&]() {
-                // tools
-                return json::array({
-                    {
-                        {"name", "tool"},
-                        {"type", "function"},
-                        {"function", {
-                            {"name", "tool1"},
-                            {"description", "Tool description"},
-                            {"parameters", {
-                                {"type", "object"},
-                                {"properties", {
-                                    {"arg", {
-                                        {"type", "string"},
-                                        {"description", "Arg description"},
-                                    }},
-                                }},
-                                {"required", json::array({ "arg" })},
-                            }},
-                        }},
-                    },
-                });
-            },
-            [&](bool success, value & messages, value & tools) {
-                if (!success) {
-                    result.supports_tool_calls = false;
-                    result.supports_tools = false;
-                    return;
-                }
-
-                auto & tool_name = tools->at(0)->at("function")->at("name");
-                caps_print_stats(tool_name, "tools[0].function.name");
-                caps_print_stats(tools, "tools");
-                if (!tool_name->stats.used) {
-                    result.supports_tools = false;
-                }
-
-                auto & tool_calls = messages->at(1)->at("tool_calls");
-                caps_print_stats(tool_calls, "messages[1].tool_calls");
-                if (!tool_calls->stats.used) {
-                    result.supports_tool_calls = false;
-                    return;
-                }
-            }
-        );
-    }
-
     JJ_DEBUG("%s\n", ">>> Running capability check: parallel tool support");
 
     // case: tools support: parallel calls
     caps_try_execute(
         prog,
         [&]() {
-            json args = json(R"({"arg": "value"})");
-            if (result.supports_object_arguments) {
-                args = json{{"arg", "value"}};
-            }
-
             // messages
             return json::array({
                 {
@@ -367,7 +267,9 @@ caps caps_get(jinja::program & prog) {
                             {"type", "function"},
                             {"function", {
                                 {"name", "tool1"},
-                                {"arguments", args}
+                                {"arguments", {
+                                    {"arg", "value"}
+                                }}
                             }}
                         },
                         {
@@ -375,7 +277,9 @@ caps caps_get(jinja::program & prog) {
                             {"type", "function"},
                             {"function", {
                                 {"name", "tool1"},
-                                {"arguments", args}
+                                {"arguments", {
+                                    {"arg", "value"}
+                                }}
                             }}
                         }
                     })}
@@ -424,7 +328,7 @@ caps caps_get(jinja::program & prog) {
                 return;
             }
 
-            auto & tool_calls = messages->at(1)->at("tool_calls");
+            auto & tool_calls = messages->at(1)->at("tool_calls");;
             caps_print_stats(tool_calls, "messages[1].tool_calls");
 
             // check for second tool call usage

common/jinja/caps.h

@@ -18,8 +18,6 @@ struct caps {
     bool supports_string_content = true;
     bool supports_typed_content = false;
 
-    bool supports_object_arguments = false;
-
     // for reporting on server
     std::map<std::string, bool> to_map() const;
 

common/jinja/value.h

@@ -451,7 +451,7 @@ struct value_array_t : public value_t {
     }
 protected:
     virtual bool equivalent(const value_t & other) const override {
-        return typeid(*this) == typeid(other) && is_hashable() && other.is_hashable() && std::equal(val_arr.begin(), val_arr.end(), other.val_arr.begin(), other.val_arr.end(), value_equivalence());
+        return typeid(*this) == typeid(other) && is_hashable() && other.is_hashable() && std::equal(val_arr.begin(), val_arr.end(), other.val_arr.begin(), value_equivalence());
     }
 };
 using value_array = std::shared_ptr<value_array_t>;
@@ -587,7 +587,7 @@ struct value_object_t : public value_t {
     }
 protected:
     virtual bool equivalent(const value_t & other) const override {
-        return typeid(*this) == typeid(other) && is_hashable() && other.is_hashable() && std::equal(val_obj.begin(), val_obj.end(), other.val_obj.begin(), other.val_obj.end(), value_equivalence());
+        return typeid(*this) == typeid(other) && is_hashable() && other.is_hashable() && std::equal(val_obj.begin(), val_obj.end(), other.val_obj.begin(), value_equivalence());
     }
 };
 using value_object = std::shared_ptr<value_object_t>;

common/json-schema-to-grammar.cpp

@@ -790,7 +790,7 @@ public:
                             } else if (target.is_array()) {
                                 size_t sel_index;
                                 try {
-                                    sel_index = std::stoull(sel);
+                                    sel_index = std::stoul(sel);
                                 } catch (const std::invalid_argument & e) {
                                     sel_index = target.size();
                                 }

common/peg-parser.cpp

@@ -658,7 +658,7 @@ struct parser_executor {
         return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos);
     }
 
-    static common_peg_parse_result handle_escape_sequence(common_peg_parse_context & ctx, size_t start, size_t & pos, const char delimiter) {
+    static common_peg_parse_result handle_escape_sequence(common_peg_parse_context & ctx, size_t start, size_t & pos) {
         ++pos; // consume '\'
         if (pos >= ctx.input.size()) {
             if (!ctx.is_lenient()) {
@@ -667,14 +667,23 @@ struct parser_executor {
             return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start, pos);
         }
 
-        char c = ctx.input[pos];
-        if (c == delimiter || c == '\\' || c == '/' || c == 'b' || c == 'f' || c == 'n' || c == 'r' || c == 't') {
-            ++pos;
-            return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start, pos);
-        } else if (c == 'u') {
-            return handle_unicode_escape(ctx, start, pos);
-        } else {
-            return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start);
+        switch (ctx.input[pos]) {
+            case '"':
+            case '\'':
+            case '\\':
+            case '/':
+            case 'b':
+            case 'f':
+            case 'n':
+            case 'r':
+            case 't':
+                ++pos;
+                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start, pos);
+            case 'u':
+                return handle_unicode_escape(ctx, start, pos);
+            default:
+                // Invalid escape sequence
+                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start);
         }
     }
 
@@ -695,20 +704,62 @@ struct parser_executor {
         return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start, pos);
     }
 
-    common_peg_parse_result operator()(const common_peg_string_parser & p) {
+    common_peg_parse_result operator()(const common_peg_json_string_parser & /* p */) {
         auto pos = start_pos;
 
         // Parse string content (without quotes)
         while (pos < ctx.input.size()) {
             char c = ctx.input[pos];
 
-            if (c == p.delimiter) {
-                // Found closing delimiter - success (don't consume it)
+            if (c == '"') {
+                // Found closing quote - success (don't consume it)
                 return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos);
             }
 
             if (c == '\\') {
-                auto result = handle_escape_sequence(ctx, start_pos, pos, p.delimiter);
+                auto result = handle_escape_sequence(ctx, start_pos, pos);
+                if (!result.success()) {
+                    return result;
+                }
+            } else {
+                auto utf8_result = common_parse_utf8_codepoint(ctx.input, pos);
+
+                if (utf8_result.status == utf8_parse_result::INCOMPLETE) {
+                    if (!ctx.is_lenient()) {
+                        return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos);
+                    }
+                    return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, pos);
+                }
+
+                if (utf8_result.status == utf8_parse_result::INVALID) {
+                    return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos);
+                }
+
+                pos += utf8_result.bytes_consumed;
+            }
+        }
+
+        // Reached end without finding closing quote
+        if (!ctx.is_lenient()) {
+            return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos, pos);
+        }
+        return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, pos);
+    }
+
+    common_peg_parse_result operator()(const common_peg_python_dict_string_parser & /* p */) {
+        auto pos = start_pos;
+
+        // Parse string content (without quotes)
+        while (pos < ctx.input.size()) {
+            char c = ctx.input[pos];
+
+            if (c == '\'') {
+                // Found closing quote - success (don't consume it)
+                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos);
+            }
+
+            if (c == '\\') {
+                auto result = handle_escape_sequence(ctx, start_pos, pos);
                 if (!result.success()) {
                     return result;
                 }
@@ -937,7 +988,8 @@ void common_peg_arena::resolve_refs() {
                                  std::is_same_v<T, common_peg_ref_parser> ||
                                  std::is_same_v<T, common_peg_until_parser> ||
                                  std::is_same_v<T, common_peg_literal_parser> ||
-                                 std::is_same_v<T, common_peg_string_parser> ||
+                                 std::is_same_v<T, common_peg_json_string_parser> ||
+                                 std::is_same_v<T, common_peg_python_dict_string_parser> ||
                                  std::is_same_v<T, common_peg_chars_parser> ||
                                  std::is_same_v<T, common_peg_any_parser> ||
                                  std::is_same_v<T, common_peg_space_parser>) {
@@ -1013,8 +1065,10 @@ std::string common_peg_arena::dump_impl(common_peg_parser_id
                 return "CharRepeat(" + p.pattern + ", " + std::to_string(p.min_count) + ", unbounded)";
             }
             return "CharRepeat(" + p.pattern + ", " + std::to_string(p.min_count) + ", " + std::to_string(p.max_count) + ")";
-        } else if constexpr (std::is_same_v<T, common_peg_string_parser>) {
-            return "String(" + std::string(1, p.delimiter) + ")";
+        } else if constexpr (std::is_same_v<T, common_peg_json_string_parser>) {
+            return "JsonString()";
+        } else if constexpr (std::is_same_v<T, common_peg_python_dict_string_parser>) {
+            return "PythonDictString()";
         } else if constexpr (std::is_same_v<T, common_peg_until_parser>) {
             return "Until(" + string_join(p.delimiters, " | ") + ")";
         } else if constexpr (std::is_same_v<T, common_peg_schema_parser>) {
@@ -1227,25 +1281,47 @@ common_peg_arena common_peg_parser_builder::build() {
 
 // String primitives
 
-common_peg_parser common_peg_parser_builder::string_content(char delimiter) {
-    return wrap(arena_.add_parser(common_peg_string_parser{delimiter}));
+common_peg_parser common_peg_parser_builder::json_string_content() {
+    return wrap(arena_.add_parser(common_peg_json_string_parser{}));
+}
+
+common_peg_parser common_peg_parser_builder::single_quoted_string_content() {
+    return wrap(arena_.add_parser(common_peg_python_dict_string_parser{}));
 }
 
 common_peg_parser common_peg_parser_builder::double_quoted_string() {
-    return rule("double-quoted-string", [this]() {
-        return sequence({literal("\""), string_content('"'), literal("\""), space()});
-    });
+    return rule("dq-string",
+                [this]() { return sequence({ literal("\""), json_string_content(), literal("\""), space() }); });
 }
 
 common_peg_parser common_peg_parser_builder::single_quoted_string() {
-    return rule("single-quoted-string", [this]() {
-        return sequence({literal("'"), string_content('\''), literal("'"), space()});
+    return rule("sq-string",
+                [this]() { return sequence({ literal("'"), single_quoted_string_content(), literal("'"), space() }); });
+}
+
+common_peg_parser common_peg_parser_builder::flexible_string() {
+    return rule("flexible-string", [this]() { return choice({ double_quoted_string(), single_quoted_string() }); });
+}
+
+// Generic helpers for object/array structure
+
+common_peg_parser common_peg_parser_builder::generic_object(const std::string &            name,
+                                                             const common_peg_parser & string_parser,
+                                                             const common_peg_parser & value_parser) {
+    return rule(name, [this, string_parser, value_parser]() {
+        auto ws      = space();
+        auto member  = sequence({ string_parser, ws, literal(":"), ws, value_parser });
+        auto members = sequence({ member, zero_or_more(sequence({ ws, literal(","), ws, member })) });
+        return sequence({ literal("{"), ws, choice({ literal("}"), sequence({ members, ws, literal("}") }) }) });
     });
 }
 
-common_peg_parser common_peg_parser_builder::quoted_string() {
-    return rule("quoted-string", [this]() {
-        return choice({double_quoted_string(), single_quoted_string()});
+common_peg_parser common_peg_parser_builder::generic_array(const std::string &            name,
+                                                            const common_peg_parser & value_parser) {
+    return rule(name, [this, value_parser]() {
+        auto ws       = space();
+        auto elements = sequence({ value_parser, zero_or_more(sequence({ literal(","), ws, value_parser })) });
+        return sequence({ literal("["), ws, choice({ literal("]"), sequence({ elements, ws, literal("]") }) }) });
     });
 }
 
@@ -1268,7 +1344,7 @@ common_peg_parser common_peg_parser_builder::json_number() {
 
 common_peg_parser common_peg_parser_builder::json_string() {
     return rule("json-string", [this]() {
-        return sequence({literal("\""), string_content('"'), literal("\""), space()});
+        return sequence({literal("\""), json_string_content(), literal("\""), space()});
     });
 }
 
@@ -1285,36 +1361,11 @@ common_peg_parser common_peg_parser_builder::json_null() {
 }
 
 common_peg_parser common_peg_parser_builder::json_object() {
-    return rule("json-object", [this]() {
-        auto ws = space();
-        auto member = sequence({json_string(), ws, literal(":"), ws, json()});
-        auto members = sequence({member, zero_or_more(sequence({ws, literal(","), ws, member}))});
-        return sequence({
-            literal("{"),
-            ws,
-            choice({
-                literal("}"),
-                sequence({members, ws, literal("}")})
-            }),
-            ws
-        });
-    });
+    return generic_object("json-object", json_string(), json());
 }
 
 common_peg_parser common_peg_parser_builder::json_array() {
-    return rule("json-array", [this]() {
-        auto ws = space();
-        auto elements = sequence({json(), zero_or_more(sequence({literal(","), ws, json()}))});
-        return sequence({
-            literal("["),
-            ws,
-            choice({
-                literal("]"),
-                sequence({elements, ws, literal("]")})
-            }),
-            ws
-        });
-    });
+    return generic_array("json-array", json());
 }
 
 common_peg_parser common_peg_parser_builder::json() {
@@ -1331,9 +1382,7 @@ common_peg_parser common_peg_parser_builder::json() {
 }
 
 common_peg_parser common_peg_parser_builder::python_string() {
-    return rule("python-string", [this]() {
-        return choice({double_quoted_string(), single_quoted_string()});
-    });
+    return rule("python-string", [this]() { return choice({ double_quoted_string(), single_quoted_string() }); });
 }
 
 common_peg_parser common_peg_parser_builder::python_number() {
@@ -1341,63 +1390,24 @@ common_peg_parser common_peg_parser_builder::python_number() {
 }
 
 common_peg_parser common_peg_parser_builder::python_bool() {
-    return rule("python-bool", [this]() {
-        return sequence({
-            choice({literal("True"), literal("False")}),
-            space()
-        });
-    });
+    return rule("python-bool", [this]() { return sequence({ choice({ literal("True"), literal("False") }), space() }); });
 }
 
 common_peg_parser common_peg_parser_builder::python_null() {
-    return rule("python-none", [this]() {
-        return sequence({literal("None"), space()});
-    });
+    return rule("python-none", [this]() { return sequence({ literal("None"), space() }); });
 }
 
 common_peg_parser common_peg_parser_builder::python_dict() {
-    return rule("python-dict", [this]() {
-        auto ws = space();
-        auto member = sequence({python_string(), ws, literal(":"), ws, python_value()});
-        auto members = sequence({member, zero_or_more(sequence({ws, literal(","), ws, member}))});
-        return sequence({
-            literal("{"),
-            ws,
-            choice({
-                literal("}"),
-                sequence({members, ws, literal("}")})
-            }),
-            ws
-        });
-    });
+    return generic_object("python-dict", python_string(), python_value());
 }
 
 common_peg_parser common_peg_parser_builder::python_array() {
-    return rule("python-array", [this]() {
-        auto ws = space();
-        auto elements = sequence({python_value(), zero_or_more(sequence({literal(","), ws, python_value()}))});
-        return sequence({
-            literal("["),
-            ws,
-            choice({
-                literal("]"),
-                sequence({elements, ws, literal("]")})
-            }),
-            ws
-        });
-    });
+    return generic_array("python-array", python_value());
 }
 
 common_peg_parser common_peg_parser_builder::python_value() {
     return rule("python-value", [this]() {
-        return choice({
-            python_dict(),
-            python_array(),
-            python_string(),
-            python_number(),
-            python_bool(),
-            python_null()
-        });
+        return choice({ python_dict(), python_array(), python_string(), python_number(), python_bool(), python_null() });
     });
 }
 
@@ -1518,7 +1528,8 @@ static std::unordered_set<std::string> collect_reachable_rules(
                           std::is_same_v<T, common_peg_chars_parser> ||
                           std::is_same_v<T, common_peg_space_parser> ||
                           std::is_same_v<T, common_peg_any_parser> ||
-                          std::is_same_v<T, common_peg_string_parser>) {
+                          std::is_same_v<T, common_peg_json_string_parser> ||
+                          std::is_same_v<T, common_peg_python_dict_string_parser>) {
                 // These parsers do not have any children
             } else if constexpr (std::is_same_v<T, common_peg_sequence_parser>) {
                 for (auto child : p.children) {
@@ -1654,9 +1665,10 @@ void common_peg_arena::build_grammar(const common_grammar_builder & builder, boo
                     return result + "{" + std::to_string(p.min_count) + "}";
                 }
                 return result + "{" + std::to_string(p.min_count) + "," + std::to_string(p.max_count) + "}";
-            } else if constexpr (std::is_same_v<T, common_peg_string_parser>) {
-                const std::string delim(1, p.delimiter);
-                return R"(( [^)" + delim + R"(\\] | "\\" ( [)" + delim + R"(\\/ bfnrt] | "u" [0-9a-fA-F]{4} ) )*)";
+            } else if constexpr (std::is_same_v<T, common_peg_json_string_parser>) {
+                return R"(( [^"\\] | "\\" ( ["\\/ bfnrt] | "u" [0-9a-fA-F]{4} ) )*)";
+            } else if constexpr (std::is_same_v<T, common_peg_python_dict_string_parser>) {
+                return R"(( [^"\\] | "\\" ( ["\\/ bfnrt] | "u" [0-9a-fA-F]{4} ) )*)";
             } else if constexpr (std::is_same_v<T, common_peg_until_parser>) {
                 if (p.delimiters.empty()) {
                     return ".*";
@@ -1786,8 +1798,10 @@ static nlohmann::json serialize_parser_variant(const common_peg_parser_variant &
                 {"min_count", p.min_count},
                 {"max_count", p.max_count}
             };
-        } else if constexpr (std::is_same_v<T, common_peg_string_parser>) {
-            return json{{"type", "string"}, {"delimiter", std::string(1, p.delimiter)}};
+        } else if constexpr (std::is_same_v<T, common_peg_json_string_parser>) {
+            return json{{"type", "json_string"}};
+        } else if constexpr (std::is_same_v<T, common_peg_python_dict_string_parser>) {
+            return json{{ "type", "python_dict_string" }};
         } else if constexpr (std::is_same_v<T, common_peg_until_parser>) {
             return json{{"type", "until"}, {"delimiters", p.delimiters}};
         } else if constexpr (std::is_same_v<T, common_peg_schema_parser>) {
@@ -1914,15 +1928,11 @@ static common_peg_parser_variant deserialize_parser_variant(const nlohmann::json
         }
         return parser;
     }
-    if (type == "string") {
-        if (!j.contains("delimiter")) {
-            throw std::runtime_error("string parser missing delimiter field.");
-        }
-        std::string delimiter = j["delimiter"];
-        if (delimiter.empty()) {
-            throw std::runtime_error("string parser delimiter is empty.");
-        }
-        return common_peg_string_parser{delimiter[0]};
+    if (type == "json_string") {
+        return common_peg_json_string_parser{};
+    }
+    if (type == "python_dict_string") {
+        return common_peg_python_dict_string_parser{};
     }
     if (type == "until") {
         if (!j.contains("delimiters") || !j["delimiters"].is_array()) {

common/peg-parser.h

@@ -231,9 +231,8 @@ struct common_peg_chars_parser {
     int max_count;  // -1 for unbounded
 };
 
-struct common_peg_string_parser {
-    char delimiter;
-};
+struct common_peg_json_string_parser {};
+struct common_peg_python_dict_string_parser {};
 
 struct common_peg_until_parser {
     std::vector<std::string> delimiters;
@@ -281,7 +280,8 @@ using common_peg_parser_variant = std::variant<
     common_peg_any_parser,
     common_peg_space_parser,
     common_peg_chars_parser,
-    common_peg_string_parser,
+    common_peg_json_string_parser,
+    common_peg_python_dict_string_parser,
     common_peg_until_parser,
     common_peg_schema_parser,
     common_peg_rule_parser,
@@ -340,6 +340,10 @@ class common_peg_parser_builder {
     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)); }
 
+    // Generic helpers for building object/array structures with configurable string/value parsers.
+    common_peg_parser generic_object(const std::string & name, const common_peg_parser & string_parser, const common_peg_parser & value_parser);
+    common_peg_parser generic_array(const std::string & name, const common_peg_parser & value_parser);
+
   public:
     common_peg_parser_builder();
 
@@ -440,10 +444,13 @@ class common_peg_parser_builder {
     common_peg_parser single_quoted_string();
 
     // Matches a string that accepts both double-quoted and single-quoted styles.
-    common_peg_parser quoted_string();
+    common_peg_parser flexible_string();
 
-    // Matches string content without the surrounding delimiter.
-    common_peg_parser string_content(char delimiter);
+    // Matches double-quoted string content without the surrounding quotes.
+    common_peg_parser json_string_content();
+
+    // Matches single-quoted string content without the surrounding quotes.
+    common_peg_parser single_quoted_string_content();
 
     // Creates a complete JSON parser supporting objects, arrays, strings, numbers, booleans, and null.
     //   value -> object | array | string | number | true | false | null

common/reasoning-budget.cpp

@@ -1,263 +0,0 @@
-#include "reasoning-budget.h"
-#include "common.h"
-#include "unicode.h"
-
-#include "log.h"
-
-#include <cmath>
-#include <cstdint>
-#include <string>
-#include <vector>
-
-struct token_matcher {
-    std::vector<llama_token> tokens;
-    size_t pos = 0;
-
-    bool advance(llama_token token) {
-        if (tokens.empty()) {
-            return false;
-        }
-
-        if (token == tokens[pos]) {
-            pos++;
-            if (pos >= tokens.size()) {
-                pos = 0;
-                return true;
-            }
-        } else {
-            pos = 0;
-            if (token == tokens[0]) {
-                pos = 1;
-            }
-        }
-        return false;
-    }
-
-    void reset() { pos = 0; }
-};
-
-struct common_reasoning_budget_ctx {
-    const llama_vocab * vocab;
-
-    token_matcher start_matcher;
-    token_matcher end_matcher;
-    std::vector<llama_token> forced_tokens;
-
-    int32_t budget;           // maximum tokens in reasoning block
-    int32_t remaining;        // tokens remaining in budget
-
-    common_reasoning_budget_state state;
-
-    // for forcing
-    size_t force_pos;         // next position in forced_tokens to force
-};
-
-static const char * common_reasoning_budget_name(const struct llama_sampler * /*smpl*/) {
-    return "reasoning-budget";
-}
-
-static void common_reasoning_budget_accept(struct llama_sampler * smpl, llama_token token) {
-    auto * ctx = (common_reasoning_budget_ctx *) smpl->ctx;
-
-    switch (ctx->state) {
-        case REASONING_BUDGET_IDLE:
-        {
-            if (ctx->start_matcher.advance(token)) {
-                ctx->state = REASONING_BUDGET_COUNTING;
-                ctx->remaining = ctx->budget;
-                LOG_INF("reasoning-budget: activated, budget=%d tokens\n", ctx->budget);
-
-                if (ctx->remaining <= 0) {
-                    ctx->state = REASONING_BUDGET_FORCING;
-                    ctx->force_pos = 0;
-                    LOG_INF("reasoning-budget: budget=0, forcing immediately\n");
-                }
-            }
-            break;
-        }
-        case REASONING_BUDGET_COUNTING:
-        case REASONING_BUDGET_WAITING_UTF8:
-        {
-            if (ctx->end_matcher.advance(token)) {
-                ctx->state = REASONING_BUDGET_DONE;
-                LOG_INF("reasoning-budget: deactivated (natural end)\n");
-                break;
-            }
-
-            bool utf8_complete = true;
-            if (ctx->vocab != nullptr) {
-                const std::string piece = common_token_to_piece(ctx->vocab, token, false);
-                utf8_complete = common_utf8_is_complete(piece);
-            }
-
-            if (ctx->state == REASONING_BUDGET_WAITING_UTF8) {
-                if (utf8_complete) {
-                    ctx->state = REASONING_BUDGET_FORCING;
-                    ctx->force_pos = 0;
-                    ctx->end_matcher.reset();
-                    LOG_INF("reasoning-budget: UTF-8 complete, now forcing end sequence\n");
-                }
-            } else if (ctx->state == REASONING_BUDGET_COUNTING) {
-                ctx->remaining--;
-                if (ctx->remaining <= 0) {
-                    if (utf8_complete) {
-                        ctx->state = REASONING_BUDGET_FORCING;
-                        ctx->force_pos = 0;
-                        ctx->end_matcher.reset();
-                        LOG_INF("reasoning-budget: budget exhausted, forcing end sequence\n");
-                    } else {
-                        ctx->state = REASONING_BUDGET_WAITING_UTF8;
-                        ctx->end_matcher.reset();
-                        LOG_INF("reasoning-budget: budget exhausted, waiting for UTF-8 completion\n");
-                    }
-                }
-            }
-            break;
-        }
-        case REASONING_BUDGET_FORCING:
-            // force_pos is advanced in apply(), not here.
-            // This ensures the first forced token isn't skipped when the sampler
-            // is initialized directly in FORCING state (e.g. COUNTING + budget=0)
-            break;
-        case REASONING_BUDGET_DONE:
-            break;
-    }
-}
-
-static void common_reasoning_budget_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
-    auto * ctx = (common_reasoning_budget_ctx *) smpl->ctx;
-
-    if (ctx->state != REASONING_BUDGET_FORCING) {
-        // passthrough — don't modify logits
-        return;
-    }
-
-    if (ctx->force_pos >= ctx->forced_tokens.size()) {
-        return;
-    }
-
-    const llama_token forced = ctx->forced_tokens[ctx->force_pos];
-
-    // set all logits to -inf except the forced token
-    for (size_t i = 0; i < cur_p->size; i++) {
-        if (cur_p->data[i].id != forced) {
-            cur_p->data[i].logit = -INFINITY;
-        }
-    }
-
-    // advance to next forced token (done here rather than in accept so that
-    // the first forced token isn't skipped when starting in FORCING state)
-    ctx->force_pos++;
-    if (ctx->force_pos >= ctx->forced_tokens.size()) {
-        ctx->state = REASONING_BUDGET_DONE;
-        LOG_INF("reasoning-budget: forced sequence complete, done\n");
-    }
-}
-
-static void common_reasoning_budget_reset(struct llama_sampler * smpl) {
-    auto * ctx = (common_reasoning_budget_ctx *) smpl->ctx;
-    ctx->state = REASONING_BUDGET_IDLE;
-    ctx->remaining = ctx->budget;
-    ctx->start_matcher.reset();
-    ctx->end_matcher.reset();
-    ctx->force_pos = 0;
-}
-
-// forward declaration for use in clone
-static struct llama_sampler * common_reasoning_budget_init_state(
-        const struct llama_vocab * vocab, const std::vector<llama_token> & start_tokens,
-        const std::vector<llama_token> & end_tokens, const std::vector<llama_token> & forced_tokens,
-        int32_t budget, common_reasoning_budget_state initial_state);
-
-static struct llama_sampler * common_reasoning_budget_clone(const struct llama_sampler * smpl) {
-    const auto * ctx = (const common_reasoning_budget_ctx *) smpl->ctx;
-    return common_reasoning_budget_init_state(
-        ctx->vocab,
-        ctx->start_matcher.tokens,
-        ctx->end_matcher.tokens,
-        ctx->forced_tokens,
-        ctx->budget,
-        ctx->state);
-}
-
-static void common_reasoning_budget_free(struct llama_sampler * smpl) {
-    delete (common_reasoning_budget_ctx *) smpl->ctx;
-}
-
-static struct llama_sampler_i common_reasoning_budget_i = {
-    /* .name              = */ common_reasoning_budget_name,
-    /* .accept            = */ common_reasoning_budget_accept,
-    /* .apply             = */ common_reasoning_budget_apply,
-    /* .reset             = */ common_reasoning_budget_reset,
-    /* .clone             = */ common_reasoning_budget_clone,
-    /* .free              = */ common_reasoning_budget_free,
-    /* .backend_init      = */ nullptr,
-    /* .backend_accept    = */ nullptr,
-    /* .backend_apply     = */ nullptr,
-    /* .backend_set_input = */ nullptr,
-};
-
-static struct llama_sampler * common_reasoning_budget_init_state(
-        const struct llama_vocab             * vocab,
-        const std::vector<llama_token>       & start_tokens,
-        const std::vector<llama_token>       & end_tokens,
-        const std::vector<llama_token>       & forced_tokens,
-        int32_t                                budget,
-        common_reasoning_budget_state          initial_state) {
-    // promote COUNTING with budget <= 0 to FORCING
-    if (initial_state == REASONING_BUDGET_COUNTING && budget <= 0) {
-        initial_state = REASONING_BUDGET_FORCING;
-    }
-
-    return llama_sampler_init(
-        /* .iface = */ &common_reasoning_budget_i,
-        /* .ctx   = */ new common_reasoning_budget_ctx {
-            /* .vocab         = */ vocab,
-            /* .start_matcher = */ { start_tokens, 0 },
-            /* .end_matcher   = */ { end_tokens, 0 },
-            /* .forced_tokens = */ forced_tokens,
-            /* .budget        = */ budget,
-            /* .remaining     = */ budget,
-            /* .state         = */ initial_state,
-            /* .force_pos     = */ 0,
-        }
-    );
-}
-
-struct llama_sampler * common_reasoning_budget_init(
-        const struct llama_vocab       * vocab,
-        const std::vector<llama_token> & start_tokens,
-        const std::vector<llama_token> & end_tokens,
-        const std::vector<llama_token> & forced_tokens,
-        int32_t                          budget,
-        const std::vector<llama_token> & prefill_tokens) {
-    // Determine initial state from prefill: COUNTING if the prefill begins with
-    // the start sequence but does not also contain the end sequence after it.
-    common_reasoning_budget_state initial_state = REASONING_BUDGET_IDLE;
-    if (!prefill_tokens.empty() && !start_tokens.empty() &&
-            prefill_tokens.size() >= start_tokens.size() &&
-            std::equal(start_tokens.begin(), start_tokens.end(), prefill_tokens.begin())) {
-        initial_state = REASONING_BUDGET_COUNTING;
-        // If the end sequence also follows the start in the prefill, reasoning
-        // was opened and immediately closed — stay IDLE.
-        if (!end_tokens.empty() &&
-                prefill_tokens.size() >= start_tokens.size() + end_tokens.size()) {
-            auto end_start = prefill_tokens.end() - (ptrdiff_t) end_tokens.size();
-            if (end_start >= prefill_tokens.begin() + (ptrdiff_t) start_tokens.size() &&
-                    std::equal(end_tokens.begin(), end_tokens.end(), end_start)) {
-                initial_state = REASONING_BUDGET_IDLE;
-            }
-        }
-    }
-    return common_reasoning_budget_init_state(vocab, start_tokens, end_tokens, forced_tokens, budget, initial_state);
-}
-
-struct llama_sampler * common_reasoning_budget_init(
-        const struct llama_vocab       * vocab,
-        const std::vector<llama_token> & start_tokens,
-        const std::vector<llama_token> & end_tokens,
-        const std::vector<llama_token> & forced_tokens,
-        int32_t                          budget,
-        common_reasoning_budget_state    initial_state) {
-    return common_reasoning_budget_init_state(vocab, start_tokens, end_tokens, forced_tokens, budget, initial_state);
-}

common/reasoning-budget.h

@@ -1,53 +0,0 @@
-#pragma once
-
-#include "llama.h"
-
-#include <cstdint>
-#include <vector>
-
-enum common_reasoning_budget_state {
-    REASONING_BUDGET_IDLE,         // waiting for start sequence
-    REASONING_BUDGET_COUNTING,     // counting down tokens
-    REASONING_BUDGET_FORCING,      // forcing budget message + end sequence
-    REASONING_BUDGET_WAITING_UTF8, // budget exhausted, waiting for UTF-8 completion
-    REASONING_BUDGET_DONE,         // passthrough forever
-};
-
-// Creates a reasoning budget sampler that limits token generation inside a
-// reasoning block (e.g. between <think> and </think>).
-//
-// State machine: IDLE -> COUNTING -> WAITING_UTF8 -> FORCING -> DONE
-//   IDLE:         passthrough, watching for start_tokens sequence
-//   COUNTING:     counting down remaining tokens, watching for natural end_tokens
-//   WAITING_UTF8: budget exhausted, allowing tokens to complete a UTF-8 sequence
-//   FORCING:      forces forced_tokens token-by-token (all other logits -> -inf)
-//   DONE:         passthrough forever
-//
-// Parameters:
-//   vocab          - vocabulary (used for UTF-8 boundary detection; can be nullptr)
-//   start_tokens   - token sequence that activates counting
-//   end_tokens     - token sequence for natural deactivation
-//   forced_tokens  - token sequence forced when budget expires
-//   budget         - max tokens allowed in the reasoning block
-//   prefill_tokens - tokens already present in the prompt (generation prompt);
-//                    used to determine the initial state: COUNTING if they begin
-//                    with start_tokens (but don't also end with end_tokens),
-//                    IDLE otherwise. COUNTING with budget <= 0 is promoted to FORCING.
-//
-struct llama_sampler * common_reasoning_budget_init(
-        const struct llama_vocab       * vocab,
-        const std::vector<llama_token> & start_tokens,
-        const std::vector<llama_token> & end_tokens,
-        const std::vector<llama_token> & forced_tokens,
-        int32_t                          budget,
-        const std::vector<llama_token> & prefill_tokens = {});
-
-// Variant that takes an explicit initial state (used by tests and clone).
-// COUNTING with budget <= 0 is promoted to FORCING.
-struct llama_sampler * common_reasoning_budget_init(
-        const struct llama_vocab       * vocab,
-        const std::vector<llama_token> & start_tokens,
-        const std::vector<llama_token> & end_tokens,
-        const std::vector<llama_token> & forced_tokens,
-        int32_t                          budget,
-        common_reasoning_budget_state    initial_state);

common/regex-partial.cpp

@@ -102,7 +102,7 @@ std::string regex_to_reversed_partial_regex(const std::string & pattern) {
                 auto is_star = *it == '*';
                 ++it;
                 if (is_star) {
-                    if (it != end && *it == '?') {
+                    if (*it == '?') {
                         ++it;
                     }
                 }

common/sampling.cpp

@@ -1,16 +1,12 @@
 #include "sampling.h"
 
 #include "common.h"
-#include "ggml.h"
 #include "log.h"
-#include "reasoning-budget.h"
 
 #include <algorithm>
-#include <cctype>
 #include <cmath>
 #include <cstring>
 #include <unordered_map>
-#include <vector>
 
 // the ring buffer works similarly to std::deque, but with a fixed capacity
 // TODO: deduplicate with llama-impl.h
@@ -192,10 +188,9 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
 
     std::vector<llama_sampler *> samplers;
 
-    const std::string & grammar_str = common_grammar_value(params.grammar);
-    if (grammar_str.compare(0, 11, "%llguidance") == 0) {
+    if (params.grammar.compare(0, 11, "%llguidance") == 0) {
 #ifdef LLAMA_USE_LLGUIDANCE
-        grmr = llama_sampler_init_llg(vocab, "lark", grammar_str.c_str());
+        grmr = llama_sampler_init_llg(vocab, "lark", params.grammar.c_str());
 #else
         GGML_ABORT("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
 #endif // LLAMA_USE_LLGUIDANCE
@@ -244,57 +239,17 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
             trigger_patterns_c.push_back(regex.c_str());
         }
 
-        if (!grammar_str.empty()) {
+        if (!params.grammar.empty()) {
              if (params.grammar_lazy) {
-                 grmr = llama_sampler_init_grammar_lazy_patterns(vocab, grammar_str.c_str(), "root",
+                 grmr = llama_sampler_init_grammar_lazy_patterns(vocab, params.grammar.c_str(), "root",
                          trigger_patterns_c.data(), trigger_patterns_c.size(),
                          trigger_tokens.data(), trigger_tokens.size());
              } else {
-                 grmr = llama_sampler_init_grammar(vocab, grammar_str.c_str(), "root");
+                 grmr = llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root");
              }
         }
     }
 
-    // Feed generation prompt tokens to the grammar sampler so it advances past
-    // tokens the template already placed in the prompt.
-    // Only applies to output-format and tool-call grammars; user-supplied grammars must not be prefilled.
-    std::vector<llama_token> prefill_tokens;
-    if (!params.generation_prompt.empty() && common_grammar_needs_prefill(params.grammar)) {
-        GGML_ASSERT(vocab != nullptr);
-        prefill_tokens = common_tokenize(vocab, params.generation_prompt, false, true);
-        if (!prefill_tokens.empty()) {
-            std::string first_token = common_token_to_piece(vocab, prefill_tokens[0], true);
-            if (std::isspace(first_token[0]) && !std::isspace(params.generation_prompt[0])) {
-                // Some tokenizers will add a space before the first special token, need to remove
-                prefill_tokens = std::vector<llama_token>(prefill_tokens.begin() + 1, prefill_tokens.end());
-            }
-        }
-
-        if (grmr) {
-            try {
-                for (const auto & token : prefill_tokens) {
-                    llama_sampler_accept(grmr, token);
-                    LOG_DBG("%s: accepted prefill token (%d)\n", __func__, token);
-                }
-            } catch (std::exception &e) {
-                LOG_ERR("%s: error initializing grammar sampler for grammar:\n%s\n\nGeneration prompt:\n'%s'\n", __func__,
-                    common_grammar_value(params.grammar).c_str(), params.generation_prompt.c_str());
-                throw e;
-            }
-        }
-    }
-
-    // reasoning budget sampler — added first so it can force tokens before other samplers
-    if (params.reasoning_budget_tokens >= 0 && !params.reasoning_budget_forced.empty()) {
-        samplers.push_back(common_reasoning_budget_init(
-            vocab,
-            params.reasoning_budget_start,
-            params.reasoning_budget_end,
-            params.reasoning_budget_forced,
-            params.reasoning_budget_tokens,
-            prefill_tokens));
-    }
-
     if (params.has_logit_bias()) {
         samplers.push_back(llama_sampler_init_logit_bias(llama_vocab_n_tokens(vocab), params.logit_bias.size(), params.logit_bias.data()));
     }

common/unicode.cpp

@@ -1,10 +1,8 @@
 #include "unicode.h"
-
-#include <algorithm>
 #include <cassert>
 #include <stdexcept>
-#include <string>
 #include <vector>
+#include <string>
 
 // implementation adopted from src/unicode.cpp
 
@@ -69,20 +67,6 @@ utf8_parse_result common_parse_utf8_codepoint(std::string_view input, size_t off
     return utf8_parse_result(utf8_parse_result::INVALID);
 }
 
-bool common_utf8_is_complete(const std::string & s) {
-    if (s.empty()) {
-        return true;
-    }
-    for (int i = 1; i <= std::min(4, (int)s.size()); i++) {
-        unsigned char c = s[s.size() - i];
-        if ((c & 0xC0) != 0x80) {
-            int expected = (c >= 0xF0) ? 4 : (c >= 0xE0) ? 3 : (c >= 0xC0) ? 2 : 1;
-            return i >= expected;
-        }
-    }
-    return false;
-}
-
 std::string common_unicode_cpts_to_utf8(const std::vector<uint32_t> & cps) {
     std::string result;
     for (size_t i = 0; i < cps.size(); ++i) {

common/unicode.h

@@ -20,9 +20,6 @@ struct utf8_parse_result {
 // Returns 0 for invalid first bytes
 size_t common_utf8_sequence_length(unsigned char first_byte);
 
-// Check if a string ends with a complete UTF-8 sequence.
-bool common_utf8_is_complete(const std::string & s);
-
 // Parse a single UTF-8 codepoint from input
 utf8_parse_result common_parse_utf8_codepoint(std::string_view input, size_t offset);
 

convert_hf_to_gguf.py

@@ -144,7 +144,6 @@ class ModelBase:
         self.metadata_override = metadata_override
         self.model_name = model_name
         self.dir_model_card = dir_model  # overridden in convert_lora_to_gguf.py
-        self._is_nvfp4 = False
 
         # Apply heuristics to figure out typical tensor encoding based on first tensor's dtype
         # NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie.
@@ -272,10 +271,6 @@ class ModelBase:
         return tensors
 
     def dequant_model(self):
-        # If all quantized tensors were already handled (e.g. pure NVFP4), skip
-        if self._is_nvfp4 and not any(k.endswith((".weight_scale", ".weight_scale_inv")) for k in self.model_tensors):
-            return
-
         tensors_to_remove: list[str] = []
         new_tensors: dict[str, Callable[[], Tensor]] = {}
 
@@ -298,16 +293,11 @@ class ModelBase:
                 scale = scale.float()
 
                 if block_size is not None:
-                    dim_offset = scale.ndim - len(block_size)
                     for i, size in enumerate(block_size):
-                        scale = scale.repeat_interleave(size, dim_offset + i)
+                        scale = scale.repeat_interleave(size, i)
                     # unpad the scale (e.g. when the tensor size isn't a multiple of the block size)
                     scale = scale[tuple(slice(0, size) for size in weight.shape)]
 
-                # align scale dims to weight for correct broadcasting (e.g. [128] -> [128, 1, 1])
-                while scale.ndim < weight.ndim:
-                    scale = scale.unsqueeze(-1)
-
                 return weight.float() * scale
 
             # ref: https://github.com/ModelCloud/GPTQModel/blob/037c5c0f6c9e33c500d975b038d02e7ca437546d/gptqmodel/nn_modules/qlinear/__init__.py#L437-L476
@@ -398,7 +388,7 @@ class ModelBase:
             elif quant_method == "fp8":
                 block_size = quant_config.get("weight_block_size")
                 for name in self.model_tensors.keys():
-                    if name.endswith("_scale_inv"):
+                    if name.endswith(".weight_scale_inv"):
                         weight_name = name.removesuffix("_scale_inv")
                         w = self.model_tensors[weight_name]
                         s = self.model_tensors[name]
@@ -406,8 +396,6 @@ class ModelBase:
                         tensors_to_remove.append(name)
                     if name.endswith(".activation_scale"):  # unused
                         tensors_to_remove.append(name)
-                    if name.endswith("_activation_scale"):  # Mistral-Small-4-119B-2602, unused
-                        tensors_to_remove.append(name)
                     # mistral format
                     if name.endswith(".qscale_weight"):
                         weight_name = name.removesuffix("qscale_weight") + "weight"
@@ -482,20 +470,7 @@ class ModelBase:
                                 tensors_to_remove.append(base_name + "_zero_point")
                 else:
                     raise NotImplementedError(f"Quant format {quant_format!r} for method {quant_method!r} is not yet supported")
-            elif quant_method == "modelopt":
-                # Mixed-precision ModelOpt models: NVFP4 tensors are handled by
-                # _generate_nvfp4_tensors; FP8 tensors have 1D weight_scale and
-                # are dequantized here. input_scale tensors are unused.
-                for name in self.model_tensors.keys():
-                    if name.endswith(".weight_scale"):
-                        weight_name = name.removesuffix("_scale")
-                        w = self.model_tensors[weight_name]
-                        s = self.model_tensors[name]
-                        self.model_tensors[weight_name] = lambda w=w, s=s: dequant_simple(w(), s(), None)
-                        tensors_to_remove.append(name)
-                    if name.endswith((".input_scale", ".k_scale", ".v_scale")):
-                        tensors_to_remove.append(name)
-            elif quant_method is not None:
+            else:
                 raise NotImplementedError(f"Quant method is not yet supported: {quant_method!r}")
 
         for name in tensors_to_remove:
@@ -541,7 +516,6 @@ class ModelBase:
         raise NotImplementedError("set_gguf_parameters() must be implemented in subclasses")
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-
         new_name = self.map_tensor_name(name)
 
         # Handle gate/up expert tensor fusion if enabled
@@ -577,164 +551,7 @@ class ModelBase:
     def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
         return ()
 
-    @staticmethod
-    def _nvfp4_pack(weight: Tensor, scale: Tensor) -> tuple[np.ndarray, list[int]]:
-        """Repack NVFP4 ModelOpt tensors into ggml super-block layout.
-        Preserves original E4M3 scale bits as UE4M3 (strip sign bit).
-        The per-tensor scale2 factor is stored as a separate tensor and applied at inference time via ggml_mul().
-        Returns (raw_data, logical_shape)."""
-
-        out_features = weight.shape[0]
-        n_blocks = scale.shape[1]
-
-        # Unpack ModelOpt nibble-packed weights
-        w = weight.reshape(out_features, n_blocks, 8)
-        vals = torch.stack([w & 0x0F, w >> 4], dim=-1).reshape(out_features, n_blocks, 16)
-
-        # Preserve original E4M3 scale bits as UE4M3 (strip sign bit)
-        d_ue = scale.view(torch.uint8).numpy().reshape(out_features, n_blocks) & 0x7F
-        qs = (vals[:, :, :8] | (vals[:, :, 8:] << 4)).to(torch.uint8).numpy()
-
-        # Pack into super-blocks: [4 UE4M3 scales, 32 qs bytes] = 36 bytes per 64 elements
-        n_super = n_blocks // 4
-        d_grouped = d_ue.reshape(out_features, n_super, 4)
-        qs_grouped = qs.reshape(out_features, n_super, 4, 8).reshape(out_features, n_super, 32)
-        raw = np.concatenate([d_grouped, qs_grouped], axis=-1).reshape(out_features, n_super * 36)
-        return raw, [out_features, n_super * 64]
-
-    @staticmethod
-    def _nvfp4_scale2_is_trivial(scale2: Tensor) -> bool:
-        return scale2.numel() <= 1 and abs(float(scale2.float().sum()) - 1.0) < 1e-6
-
-    def _repack_nvfp4(self, new_name: str, weight: Tensor, scale: Tensor, scale2: Tensor):
-        raw, shape = self._nvfp4_pack(weight, scale)
-        logger.info(f"Repacked {new_name} with shape {shape} and quantization NVFP4")
-        self.gguf_writer.add_tensor(new_name, raw, raw_dtype=gguf.GGMLQuantizationType.NVFP4)
-
-        # Emit per-tensor scale2 as a separate F32 tensor when non-trivial
-        if not self._nvfp4_scale2_is_trivial(scale2):
-            scale2_f32 = scale2.float().numpy().flatten()
-            scale_name = new_name.replace(".weight", ".scale")
-            logger.info(f"  + {scale_name} (per-tensor NVFP4 scale2, shape [{scale2_f32.size}])")
-            self.gguf_writer.add_tensor(scale_name, scale2_f32)
-
-    def _generate_nvfp4_tensors(self):
-        # Per-layer expert merging to avoid holding all experts in memory
-        expert_blocks: dict[tuple[int, str], list[tuple[int, np.ndarray]]] = {}
-        expert_scales: dict[tuple[int, str], list[tuple[int, float]]] = {}
-        expert_shapes: dict[tuple[int, str], list[int]] = {}
-        n_experts = self.find_hparam(["num_local_experts", "num_experts"], optional=True) or 0
-        consumed: list[str] = []
-
-        for name in list(self.model_tensors.keys()):
-            if not name.endswith(".weight"):
-                continue
-            scale_name = name.replace(".weight", ".weight_scale")
-            scale2_name = name.replace(".weight", ".weight_scale_2")
-            if scale_name not in self.model_tensors:
-                continue
-            # Force eager materialization of lazy tensors
-            weight = LazyTorchTensor.to_eager(self.model_tensors[name]())
-            scale = LazyTorchTensor.to_eager(self.model_tensors[scale_name]())
-
-            # Skip non-NVFP4 tensors (e.g. FP8 with per-channel 1D scales)
-            if scale.ndim < 2:
-                continue
-
-            scale2 = LazyTorchTensor.to_eager(self.model_tensors.get(scale2_name, lambda: torch.tensor(1.0))())
-
-            # Mark tensors for removal from model_tensors (already written to gguf)
-            consumed.extend([name, scale_name])
-            if scale2_name in self.model_tensors:
-                consumed.append(scale2_name)
-
-            # Check if this is a per-expert tensor
-            m = re.search(r'\.experts\.(\d+)\.(gate_proj|up_proj|down_proj)\.weight$', name)
-            if m:
-                expert_id = int(m.group(1))
-                proj_type = m.group(2)
-                bid_m = re.search(r'\.layers\.(\d+)\.', name)
-                bid = int(bid_m.group(1)) if bid_m else 0
-                key = (bid, proj_type)
-
-                raw, shape = self._nvfp4_pack(weight, scale)
-
-                if key not in expert_blocks:
-                    expert_blocks[key] = []
-                    expert_scales[key] = []
-                    expert_shapes[key] = shape
-                expert_blocks[key].append((expert_id, raw.copy()))
-                # Collect per-expert scale2 (scalar per expert)
-                expert_scales[key].append((expert_id, float(scale2.float().sum())))
-
-                # Flush when all experts for this (layer, proj) are collected
-                if n_experts > 0 and len(expert_blocks[key]) >= n_experts:
-                    self._flush_nvfp4_experts(key, expert_blocks, expert_scales, expert_shapes, bid, proj_type)
-            else:
-                new_name = self.map_tensor_name(name)
-                self._repack_nvfp4(new_name, weight, scale, scale2)
-
-        # Flush any remaining experts (fallback if n_experts was unknown)
-        for (bid, proj_type) in list(expert_blocks.keys()):
-            self._flush_nvfp4_experts((bid, proj_type), expert_blocks, expert_scales, expert_shapes, bid, proj_type)
-
-        # Remove consumed tensors so get_tensors/modify_tensors won't see them
-        for name in consumed:
-            self.model_tensors.pop(name, None)
-
-        # Remove unused auxiliary tensors (input_scale, k_scale, v_scale)
-        for name in list(self.model_tensors.keys()):
-            if name.endswith((".input_scale", ".k_scale", ".v_scale")):
-                del self.model_tensors[name]
-
-    def _flush_nvfp4_experts(self, key, expert_blocks, expert_scales, expert_shapes, bid, proj_type):
-        experts = expert_blocks.pop(key)
-        scales = expert_scales.pop(key)
-        shape = expert_shapes.pop(key)
-
-        experts.sort(key=lambda x: x[0])
-        merged = np.stack([e[1] for e in experts], axis=0)
-        merged_name = f"model.layers.{bid}.mlp.experts.{proj_type}.weight"
-        new_name = self.map_tensor_name(merged_name)
-        logger.info(f"Repacked {new_name} with shape [{len(experts)}, {shape[0]}, {shape[1]}] and quantization NVFP4")
-        self.gguf_writer.add_tensor(new_name, merged, raw_dtype=gguf.GGMLQuantizationType.NVFP4)
-
-        # Emit per-expert scale2 tensor if any expert has non-trivial scale2
-        scales.sort(key=lambda x: x[0])
-        scale_vals = np.array([s[1] for s in scales], dtype=np.float32)
-        if not np.allclose(scale_vals, 1.0, atol=1e-6):
-            scale_name = new_name.replace(".weight", ".scale")
-            logger.info(f"  + {scale_name} (per-expert NVFP4 scale2, shape [{len(scales)}])")
-            self.gguf_writer.add_tensor(scale_name, scale_vals)
-
-        del experts, merged
-
     def prepare_tensors(self):
-        # detect NVFP4 quantization (ModelOpt format)
-        quant_algo = (self.hparams.get("quantization_config") or {}).get("quant_algo")
-        quant_layers = (self.hparams.get("quantization_config") or {}).get("quantized_layers") or {}
-        quant_config_file = self.dir_model / "hf_quant_config.json"
-
-        if (not quant_algo or not quant_layers) and quant_config_file.is_file():
-            with open(quant_config_file, "r", encoding="utf-8") as f:
-                quant_config = json.load(f).get("quantization") or {}
-                quant_algo = quant_config.get("quant_algo", quant_algo)
-                quant_layers = quant_config.get("quantized_layers", quant_layers) or {}
-
-        # Some models use per-tensor quant_algo (e.g. "MIXED_PRECISION" with
-        # per-layer NVFP4/FP8) instead of a single global "NVFP4" value.
-        if quant_algo != "NVFP4":
-            if any(v.get("quant_algo") == "NVFP4" for v in quant_layers.values() if isinstance(v, dict)):
-                quant_algo = "NVFP4"
-
-        self._is_nvfp4 = quant_algo == "NVFP4"
-
-        # NVFP4 weights are repacked and written directly to gguf_writer.
-        # This must run before dequant_model so NVFP4 tensors are removed
-        # from model_tensors, leaving only non-NVFP4 (e.g. FP8) for dequant.
-        if self._is_nvfp4:
-            self._generate_nvfp4_tensors()
-
         self.dequant_model()
 
         # Handle empty tensor_map for models with block_count=0 (like MobileNetV5)
@@ -1062,10 +879,6 @@ class TextModel(ModelBase):
             self.gguf_writer.add_head_count_kv(n_head_kv)
             logger.info(f"gguf: key-value head count = {n_head_kv}")
 
-        if self.hparams.get("is_causal") is False:
-            self.gguf_writer.add_causal_attention(False)
-            logger.info("gguf: causal attention = False")
-
         # TODO: Handle "sliding_attention" similarly when models start implementing it
         rope_params = self.rope_parameters.get("full_attention", self.rope_parameters)
         if (rope_type := rope_params.get("rope_type")) is not None:
@@ -2244,8 +2057,6 @@ class GPTNeoXModel(TextModel):
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
         n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
-        assert n_head is not None
-        assert n_embed is not None
 
         if re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.weight", name):
             # Map bloom-style qkv_linear to gpt-style qkv_linear
@@ -2283,8 +2094,6 @@ class BloomModel(TextModel):
     def set_gguf_parameters(self):
         n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
         n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
-        assert n_head is not None
-        assert n_embed is not None
         self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
         self.gguf_writer.add_embedding_length(n_embed)
         self.gguf_writer.add_feed_forward_length(4 * n_embed)
@@ -2297,8 +2106,6 @@ class BloomModel(TextModel):
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
         n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
-        assert n_head is not None
-        assert n_embed is not None
 
         name = re.sub(r'transformer\.', '', name)
 
@@ -3042,16 +2849,10 @@ class LlavaVisionModel(MmprojModel):
     def get_token_id(self, token: str) -> int:
         tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
         with open(tokenizer_config_file, "r", encoding="utf-8") as f:
-            added_tokens_decoder = json.load(f).get('added_tokens_decoder') or {}
+            added_tokens_decoder = json.load(f)['added_tokens_decoder']
             for id_, token_data in added_tokens_decoder.items():
-                if token_data.get("content") == token:
-                    return int(id_)
-            # fallthrough to tokenizer.json
-        with open(self.dir_model / "tokenizer.json", "r", encoding="utf-8") as f:
-            tokenizer_json = json.load(f)
-            for token_data in tokenizer_json["added_tokens"]:
                 if token_data["content"] == token:
-                    return int(token_data["id"])
+                    return int(id_)
         raise ValueError(f"Token '{token}' not found in tokenizer config.")
 
     def set_gguf_parameters(self):
@@ -3215,6 +3016,40 @@ class Llama4VisionModel(MmprojModel):
                 yield from super().modify_tensors(data_torch, name, bid)
 
 
+@ModelBase.register(
+    "Mistral3ForConditionalGeneration",
+    "Ministral3ForCausalLM",
+)
+class Mistral3Model(LlamaModel):
+    model_arch = gguf.MODEL_ARCH.MISTRAL3
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # for compatibility, we use LLAMA arch for older models
+        # TODO: remove this once everyone has migrated to newer version of llama.cpp
+        if self.hparams.get("model_type") != "ministral3":
+            self.model_arch = gguf.MODEL_ARCH.LLAMA
+            self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[self.model_arch]
+            self.gguf_writer.add_architecture()
+            self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        rope_params = self.rope_parameters
+        if self.hparams.get("model_type") == "ministral3":
+            assert rope_params, "ministral3 must have 'rope_parameters' config"
+            assert rope_params["rope_type"] == "yarn", "ministral3 rope_type must be 'yarn'"
+            self.gguf_writer.add_rope_scaling_yarn_log_mul(rope_params["mscale_all_dim"])
+            self.gguf_writer.add_attn_temperature_scale(rope_params["llama_4_scaling_beta"])
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
+        name = name.replace("language_model.", "")
+        if "multi_modal_projector" in name or "vision_tower" in name:
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
 @ModelBase.register("DeciLMForCausalLM")
 class DeciModel(TextModel):
     model_arch = gguf.MODEL_ARCH.DECI
@@ -3881,7 +3716,6 @@ class LLaDAModel(TextModel):
 
         if (rope_dim := hparams.get("head_dim")) is None:
             n_heads = hparams.get("num_attention_heads", hparams.get("n_heads"))
-            assert n_heads is not None
             rope_dim = hparams.get("hidden_size", hparams.get("d_model")) // n_heads
         self.gguf_writer.add_rope_dimension_count(rope_dim)
 
@@ -3913,7 +3747,6 @@ class LLaDAModel(TextModel):
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         n_head = self.hparams.get("num_attention_heads", self.hparams.get("n_heads"))
-        assert n_head is not None
         n_kv_head = self.hparams.get("num_key_value_heads", self.hparams.get("n_kv_heads"))
 
         if self.undo_permute:
@@ -4470,14 +4303,6 @@ class Qwen2MoeModel(TextModel):
         # process the experts separately
         name = name.replace("language_model.", "") # InternVL
 
-        # NVFP4 expert weights are handled in _generate_nvfp4_tensors
-        if self._is_nvfp4 and "experts" in name:
-            if name.endswith((".weight", ".weight_scale", ".weight_scale_2", ".input_scale")):
-                if name.endswith(".weight") and name.replace(".weight", ".weight_scale") in self.model_tensors:
-                    return
-                if not name.endswith(".weight"):
-                    return
-
         # handle aggregated expert tensors
         # GGUF stores dimensions reversed from PyTorch, so:
         # PyTorch (A,B,C) -> GGUF writes [C,B,A] -> GGML reads ne={C,B,A}
@@ -4565,31 +4390,15 @@ class Qwen3Model(Qwen2Model):
         hparams = ModelBase.load_hparams(self.dir_model, is_mistral_format=False)
         self.origin_hf_arch = hparams.get('architectures', [None])[0]
 
-        if self._is_qwen3_reranker():
-            self._find_rerank_config()
-
-    def _is_qwen3_reranker(self) -> bool:
+        # a bit hacky, but currently the only way to detect if this is a rerank model
+        # ref: https://huggingface.co/Qwen/Qwen3-Reranker-0.6B
         readme_path = self.dir_model / "README.md"
         readme_text = ""
         if readme_path.exists():
             with readme_path.open("r", encoding="utf-8") as f:
                 readme_text = f.read()
-
-        name_hints = [
-            str(self.dir_model.name),
-            str(self.hparams.get("_name_or_path", "")),
-            str(self.hparams.get("model_type", "")),
-            str(self.origin_hf_arch or ""),
-        ]
-        name_hints = [hint.lower() for hint in name_hints if hint]
-
-        if "# qwen3-reranker" in readme_text.lower() or "# qwen3-vl-reranker" in readme_text.lower():
-            return True
-
-        if any("qwen3-reranker" in hint or "qwen3-vl-reranker" in hint for hint in name_hints):
-            return True
-
-        return "sequenceclassification" in (self.origin_hf_arch or "").lower()
+        if "# Qwen3-Reranker" in readme_text:
+            self._find_rerank_config()
 
     def set_vocab(self):
         # deal with intern-s1-mini
@@ -5092,7 +4901,7 @@ class Phi2Model(TextModel):
         self.gguf_writer.add_add_bos_token(False)
 
 
-@ModelBase.register("Phi3ForCausalLM", "Phi4ForCausalLMV")
+@ModelBase.register("Phi3ForCausalLM")
 class Phi3MiniModel(TextModel):
     model_arch = gguf.MODEL_ARCH.PHI3
 
@@ -5267,129 +5076,6 @@ class Phi3MiniModel(TextModel):
         yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32))
         yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32))
 
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith(("model.vision_tower.", "vision_tower.", "model.mm_projector.", "mm_projector.")):
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Phi4ForCausalLMV")
-class Phi4VisionMmprojModel(MmprojModel):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        assert self.hparams_vision is not None
-
-        self.vision_total_layers = int(self.find_vparam(self.n_block_keys))
-        if self.vision_total_layers < 2:
-            raise ValueError(
-                f"Phi-4 vision mmproj conversion requires at least 2 vision layers, got {self.vision_total_layers}"
-            )
-
-        # Phi-4 uses SigLIP2 hidden_states[-2], so export one fewer encoder block and
-        # drop post-layernorm/head weights. This makes the GGUF runtime output match
-        # the feature map consumed by the patched siglip.cpp Phi-4 projector path.
-        self.vision_export_layers = self.vision_total_layers - 1
-        self.vision_last_layer_idx = self.vision_total_layers - 1
-
-        for key in self.n_block_keys:
-            if key in self.hparams_vision:
-                self.hparams_vision[key] = self.vision_export_layers
-                break
-
-        self.block_count = self.vision_export_layers
-        self.tensor_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.MMPROJ, self.block_count)
-
-        patch_size = self.preprocessor_config.get("patch_size")
-        if patch_size is None:
-            raise KeyError("Phi-4 vision mmproj conversion requires patch_size in preprocessor_config.json")
-
-        self.hparams_vision["patch_size"] = patch_size
-
-        pos_emb_name = next(
-            (
-                name for name in self.model_tensors
-                if name.endswith("vision_model.embeddings.position_embedding.weight")
-            ),
-            None,
-        )
-        if pos_emb_name is None:
-            raise KeyError("Phi-4 vision mmproj conversion could not find position_embedding.weight")
-
-        pos_emb_shape = self.model_tensors[pos_emb_name]().shape
-        base_grid_tokens = int(pos_emb_shape[0])
-        grid_side = math.isqrt(base_grid_tokens)
-        if grid_side * grid_side != base_grid_tokens:
-            raise ValueError(f"Unexpected Phi-4 position embedding shape: {tuple(pos_emb_shape)}")
-
-        self.hparams_vision["image_size"] = grid_side * patch_size
-
-        min_num_patches = self.preprocessor_config.get("min_num_patches", self.global_config.get("min_num_patches"))
-        max_num_patches = self.preprocessor_config.get("max_num_patches", self.global_config.get("max_num_patches"))
-        if min_num_patches is None or max_num_patches is None:
-            raise KeyError("Phi-4 vision mmproj conversion requires min_num_patches and max_num_patches")
-
-        self.min_pixels = int(min_num_patches) * patch_size * patch_size
-        self.max_pixels = int(max_num_patches) * patch_size * patch_size
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        assert self.hparams_vision is not None
-
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PHI4)
-        self.gguf_writer.add_vision_min_pixels(self.min_pixels)
-        self.gguf_writer.add_vision_max_pixels(self.max_pixels)
-        self.gguf_writer.add_vision_use_gelu(True)
-        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-6))
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith(("model.vision_tower.vision_tower.", "vision_tower.")):
-            if ".vision_model.head." in name:
-                return
-
-            new_name = name.replace("model.vision_tower.vision_tower.", "vision_tower.")
-
-            if ".vision_model.post_layernorm." in new_name:
-                return
-
-            if bid is not None and bid == self.vision_last_layer_idx:
-                return
-
-            if new_name.endswith("vision_model.embeddings.patch_embedding.weight"):
-                assert self.hparams_vision is not None
-                if data_torch.ndim != 2:
-                    raise ValueError(f"Unexpected Phi-4 patch embedding shape: {tuple(data_torch.shape)}")
-
-                patch_area = self.hparams_vision["patch_size"] ** 2
-                in_features = data_torch.shape[1]
-                if in_features % patch_area != 0:
-                    raise ValueError(
-                        f"Phi-4 patch embedding input dim {in_features} is not divisible by patch area {patch_area}"
-                    )
-
-                num_channels = in_features // patch_area
-                patch_size = self.hparams_vision["patch_size"]
-                data_torch = data_torch.view(data_torch.shape[0], patch_size, patch_size, num_channels)
-                data_torch = data_torch.permute(0, 3, 1, 2)
-
-            yield from super().modify_tensors(data_torch, new_name, bid)
-            return
-
-        if name.startswith(("model.mm_projector.", "mm_projector.")):
-            local_name = name
-            local_name = local_name.replace("model.mm_projector.", "")
-            local_name = local_name.replace("mm_projector.", "")
-
-            if not (local_name.startswith("0.") or local_name.startswith("2.")):
-                return
-
-            suffix = ".bias" if local_name.endswith(".bias") else ".weight"
-            mm_idx = int(local_name.split(".", maxsplit=1)[0])
-            yield (self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, mm_idx, suffix=suffix), data_torch)
-            return
-
-        return
-
 
 @ModelBase.register("PhiMoEForCausalLM")
 class PhiMoeModel(Phi3MiniModel):
@@ -8254,8 +7940,6 @@ class DeepseekV2Model(TextModel):
     # TODO @ngxson : remove this when we support MTP for deepseek models
     skip_mtp = True
 
-    merge_expert = True
-
     def set_vocab(self):
         try:
             self._set_vocab_gpt2()
@@ -8394,7 +8078,7 @@ class DeepseekV2Model(TextModel):
                 return
 
         # process the experts separately
-        if self.merge_expert and name.find("mlp.experts") != -1:
+        if name.find("mlp.experts") != -1:
             n_experts = self.hparams["n_routed_experts"]
             assert bid is not None
 
@@ -8453,69 +8137,6 @@ class DeepseekV2Model(TextModel):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
-@ModelBase.register(
-    "Mistral3ForConditionalGeneration",
-    "Ministral3ForCausalLM",
-)
-class Mistral3Model(TextModel):
-    class Ministral3Model(LlamaModel):
-        model_arch = gguf.MODEL_ARCH.MISTRAL3
-
-        def set_gguf_parameters(self):
-            super().set_gguf_parameters()
-            rope_params = self.rope_parameters
-            if self.hparams.get("model_type") == "ministral3":
-                assert rope_params, "ministral3 must have 'rope_parameters' config"
-                assert rope_params["rope_type"] == "yarn", "ministral3 rope_type must be 'yarn'"
-                self.gguf_writer.add_rope_scaling_yarn_log_mul(rope_params["mscale_all_dim"])
-                self.gguf_writer.add_attn_temperature_scale(rope_params["llama_4_scaling_beta"])
-
-        def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
-            name = name.replace("language_model.", "")
-            if "multi_modal_projector" in name or "vision_tower" in name:
-                return
-
-            yield from super().modify_tensors(data_torch, name, bid)
-
-    class Mistral4Model(DeepseekV2Model):
-        model_arch = gguf.MODEL_ARCH.MISTRAL4
-        skip_mtp = False # model contains no MTP layers, so no need to skip
-        merge_expert = False # experts are already stacked as 3D
-
-        def modify_tensors(self, data_torch, name, bid):
-            if name.endswith(".down_proj") or name.endswith(".gate_up_proj"):
-                name = name + ".weight"
-            yield from super().modify_tensors(data_torch, name, bid)
-
-    model_arch = gguf.MODEL_ARCH.MISTRAL3 # unused
-    impl: TextModel
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        if self.hparams.get("model_type") == "mistral4":
-            self.impl = Mistral3Model.Mistral4Model(*args, **kwargs)
-        else:
-            self.impl = Mistral3Model.Ministral3Model(*args, **kwargs)
-
-    def set_vocab(self):
-        self.impl.set_vocab()
-
-    def set_gguf_parameters(self):
-        self.impl.set_gguf_parameters()
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
-        yield from self.impl.modify_tensors(data_torch, name, bid)
-
-    def prepare_tensors(self):
-        self.impl.prepare_tensors()
-
-    def write_vocab(self):
-        self.impl.write_vocab()
-
-    def write(self):
-        self.impl.write()
-
-
 @ModelBase.register("MiniMaxM2ForCausalLM")
 class MiniMaxM2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.MINIMAXM2
@@ -9580,9 +9201,7 @@ class ChatGLMModel(TextModel):
 
     def set_gguf_parameters(self):
         n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
-        assert n_embed is not None
         n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
-        assert n_head is not None
         n_head_kv = self.hparams.get("multi_query_group_num", self.hparams.get("num_key_value_heads", n_head))
         self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
         self.gguf_writer.add_embedding_length(n_embed)
@@ -10108,35 +9727,20 @@ class NemotronHModel(GraniteHybridModel):
         # M: Mamba2, *: Attention, -: MLP
         # MoE:
         # M: Mamba2, *: Attention, E: Expert
-        pattern = self.hparams.get("hybrid_override_pattern") or self.hparams.get("layers_block_type")
-        if pattern is None:
-            self._ssm_layers = []
-            self._mlp_layers = []
-        elif isinstance(pattern, str):
-            self._ssm_layers = [i for i, val in enumerate(pattern) if val == "M"]
-            self._mlp_layers = [i for i, val in enumerate(pattern) if val == ("E" if self.is_moe else "-")]
-        else:
-            self._ssm_layers = [i for i, val in enumerate(pattern) if val == "mamba"]
-            self._mlp_layers = [i for i, val in enumerate(pattern) if val == "moe"]
+        hybrid_override_pattern = self.hparams["hybrid_override_pattern"]
+        self._ssm_layers = [i for i, val in enumerate(hybrid_override_pattern) if val == "M"]
+        self._mlp_layers = [i for i, val in enumerate(hybrid_override_pattern) if val == ("E" if self.is_moe else "-")]
 
     def get_attn_layers(self):
-        pattern = self.hparams.get("hybrid_override_pattern") or self.hparams.get("layers_block_type")
-        if pattern is None:
-            return []
-        assert len(pattern) == self.block_count, f"Mismatch between pattern ({len(pattern)}) and block_count ({self.block_count})!"
-        if isinstance(pattern, str):
-            return [i for i, val in enumerate(pattern) if val == "*"]
-
-        return [i for i, val in enumerate(pattern) if val == "attention"]
+        hybrid_override_pattern = self.hparams["hybrid_override_pattern"]
+        assert len(hybrid_override_pattern) == self.block_count, "Mismatch between hybrid override and num_hidden_layers!"
+        return [i for i, val in enumerate(hybrid_override_pattern) if val == "*"]
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
 
-        head_dim = self.head_dim
-        if head_dim is None:
-            raise ValueError("Could not find the attention head dim in config")
-        self.gguf_writer.add_key_length(head_dim)
-        self.gguf_writer.add_value_length(head_dim)
+        self.gguf_writer.add_key_length(self.head_dim)
+        self.gguf_writer.add_value_length(self.head_dim)
 
         # Set feed_forward_length
         # NOTE: This will trigger an override warning. This is preferable to
@@ -10164,9 +9768,6 @@ class NemotronHModel(GraniteHybridModel):
             if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
                 self.gguf_writer.add_expert_used_count(n_experts_used)
 
-            if (latent_size := self.hparams.get("moe_latent_size")) is not None:
-                self.gguf_writer.add_moe_latent_size(latent_size)
-
     def set_vocab(self):
         super().set_vocab()
 
@@ -10186,13 +9787,6 @@ class NemotronHModel(GraniteHybridModel):
             name = name[len("language_model."):]
 
         if self.is_moe and bid is not None:
-            # Skip Multi-Token Prediction (MTP) tensors. These are used for
-            # for speculative decoding but we don't include them in this model
-            # conversion. See https://github.com/ggml-org/llama.cpp/pull/18886
-            if name.startswith("mtp."):
-                logger.info(f"gguf: Skipping MTP (Speculative) layer: {name}")
-                return
-
             if name.endswith("mixer.gate.e_score_correction_bias"):
                 new_name = name.replace("e_score_correction_bias", "e_score_correction.bias")
                 yield from ModelBase.modify_tensors(self, data_torch, new_name, bid)

convert_lora_to_gguf.py

@@ -128,12 +128,6 @@ class LoraTorchTensor:
         assert dim is None
         return self.shape
 
-    def contiguous(self) -> LoraTorchTensor:
-        return LoraTorchTensor(
-            self._lora_A.contiguous(),
-            self._lora_B.contiguous(),
-        )
-
     def reshape(self, *shape: int | tuple[int, ...]) -> LoraTorchTensor:
         if isinstance(shape[0], tuple):
             new_shape: tuple[int, ...] = shape[0]

docs/autoparser.md

@@ -14,7 +14,7 @@ The unified auto-parser uses a pure differential, compositional approach (inspir
 **Analysis + Parser Building in Two Steps**:
 
 1. `autoparser::autoparser tmpl_analysis(tmpl)` — runs all differential comparisons and populates the analysis structs
-2. `autoparser::peg_generator::generate_parser(tmpl, generation_params, tmpl_analysis)` — uses the analysis to build a PEG parser and optional GBNF grammar
+2. `autoparser::peg_generator::generate_parser(tmpl, params, tmpl_analysis)` — uses the analysis to build a PEG parser and optional GBNF grammar
 
 ## Data Structures
 
@@ -34,7 +34,7 @@ All structs are defined in [common/chat-auto-parser.h](common/chat-auto-parser.h
 
 ### `analyze_tools` and its sub-structs
 
-- [common/chat-auto-parser.h:176-194](common/chat-auto-parser.h#L176-L194) — `tool_format_analysis`: `mode` enum, `section_start/end`, `per_call_start/end`, JSON field names (`function_field`, `name_field`, `args_field`, `id_field`, `gen_id_field`), and format flags (`fun_name_is_key`, `tools_array_wrapped`)
+- [common/chat-auto-parser.h:176-194](common/chat-auto-parser.h#L176-L194) — `tool_format_analysis`: `mode` enum, `section_start/end`, `per_call_start/end`, JSON field names (`function_field`, `name_field`, `args_field`, `id_field`, `gen_id_field`), and format flags (`fun_name_is_key`, `tools_array_wrapped`, `uses_python_dicts`)
 - [common/chat-auto-parser.h:196-200](common/chat-auto-parser.h#L196-L200) — `tool_function_analysis`: `name_prefix`, `name_suffix`, `close` markers around function names
 - [common/chat-auto-parser.h:202-210](common/chat-auto-parser.h#L202-L210) — `tool_arguments_analysis`: `start/end` container markers, `name_prefix/suffix`, `value_prefix/suffix`, `separator`
 - [common/chat-auto-parser.h:212-217](common/chat-auto-parser.h#L212-L217) — `tool_id_analysis`: `pos` enum, `prefix`/`suffix` markers around call ID values
@@ -47,21 +47,12 @@ All structs are defined in [common/chat-auto-parser.h](common/chat-auto-parser.h
 | Value           | Description                                                                       |
 |-----------------|-----------------------------------------------------------------------------------|
 | `NONE`          | No reasoning markers detected                                                     |
-| `TAG_BASED`     | Tag-based: `<think>...</think>` (start can be empty for delimiter-style formats)  |
+| `TAG_BASED`     | Standard tag-based: `<think>...</think>`                                          |
+| `DELIMITER`     | Delimiter-based: reasoning ends at a delimiter (e.g., `[BEGIN FINAL RESPONSE]`)   |
+| `FORCED_OPEN`   | Template ends with open reasoning tag when `enable_thinking=true`                 |
+| `FORCED_CLOSED` | `enable_thinking=false` emits both tags; `enable_thinking=true` emits only start  |
 | `TOOLS_ONLY`    | Reasoning only appears in tool call responses, not plain content                  |
 
-**Generation Prompt & Reasoning Prefill**: Computed in `common_chat_templates_apply_jinja` before invoking either the specialized handlers or the auto-parser, by rendering the template twice — once with `add_generation_prompt=false` and once with `add_generation_prompt=true` — and storing the diff suffix as `generation_params::generation_prompt`. This string is propagated into `common_chat_params::generation_prompt` and `common_chat_parser_params::generation_prompt`.
-
-The generation prompt is prepended to model output before PEG parsing via `wrap_for_generation_prompt()`. The portion *before* the reasoning start marker (if any) is prepended as a literal to ensure any boilerplate added by the template is consumed. The full string is also fed to the grammar sampler via `llama_sampler_accept` (stored in `common_params_sampling::grammar_prefill`), advancing the grammar past tokens already in the prompt. It is used to determine the reasoning budget sampler's initial state — COUNTING if the prefill tokens begin with the reasoning start sequence (but don't also contain the end sequence), IDLE otherwise.
-
-**`grammar_prefill`** (`common_params_sampling`): The generation prompt string tokenized and accepted by the grammar sampler at init time. Only applied when `grammar_external` is false (i.e., the grammar was not set explicitly by the user).
-
-Three outcomes for reasoning-prefill handling (in `generate_parser()`):
-
-1. **Start+end in generation prompt** (e.g. `<think></think>\n`): the parser sees reasoning as opened and immediately closed; whitespace-only reasoning content is discarded.
-2. **Only start in generation prompt** (e.g. `<think>\n`): the parser sees reasoning as already open.
-3. **Start marker present but not at the end** (e.g. Apriel's `<|begin_assistant|>` followed by boilerplate): the marker is a template artifact; the start literal is cleared so reasoning uses delimiter-style (end-only). For templates that ignore `add_generation_prompt` (empty diff), the rendered `data.prompt` is used as fallback — but only for non-TOOLS_ONLY modes, since in TOOLS_ONLY the start tag is model-generated and may appear in prior conversation turns.
-
 **`content_mode`**: How the template wraps assistant content.
 
 | Value                    | Description                                                    |
@@ -270,16 +261,16 @@ Text is segmentized into markers and non-marker fragments using `segmentize_mark
 
 - Searches `diff.right` (output with reasoning) for the reasoning content needle
 - Uses PEG parsers to find surrounding markers:
-  - If both pre/post markers found in `diff.right` → `TAG_BASED`
-  - If both found but post marker only in the full output B → `TAG_BASED` (template forces markers; handled via prefill)
-  - If only post marker found → `TAG_BASED` (delimiter-style, empty start)
+  - If both pre/post markers found in `diff.right` → `TAG_BASED` (both tags visible in diff = no forced close)
+  - If both found but post marker only in the full output B → `FORCED_CLOSED`
+  - If only post marker found → `DELIMITER`
 - Sets `reasoning.start` and `reasoning.end`
 
 **R2 — `compare_thinking_enabled()`**: Compares `enable_thinking=false` vs `true` with a generation prompt.
 
-- Detects template-added reasoning markers: `enable_thinking=true` appends a non-empty marker → sets `reasoning.start`, mode = `TAG_BASED`
-- Handles the reverse case (`enable_thinking=false` appends the marker instead): extracts both start (from the preceding segment) and end markers; mode = `TAG_BASED`
-- The reasoning prefill (markers added by the template) is later extracted in `common_chat_templates_apply_jinja` and prepended to model output before parsing
+- Detects `FORCED_OPEN`: `enable_thinking=true` adds a non-empty marker at the end of the prompt (where model will start generating) — sets `reasoning.start`, mode = `FORCED_OPEN`
+- Detects `FORCED_CLOSED`: `enable_thinking=false` produces both start+end markers; `enable_thinking=true` produces only start marker
+- Handles the reverse case: if both start and end are still empty, looks for a single-segment diff on each side to extract both markers
 
 **R3 — `compare_reasoning_scope()`**: Compares assistant message with reasoning+text-content vs reasoning+tool-calls.
 
@@ -352,7 +343,7 @@ Classification logic:
 
 A workaround array in `common/chat-diff-analyzer.cpp` applies post-hoc patches after analysis. Each workaround is a lambda that inspects the template source and overrides analysis results. Current workarounds:
 
-1. **Old Qwen/DeepSeek thinking templates** — source contains `content.split('</think>')` but not `<SPECIAL_12>`: sets `reasoning.mode = TAG_BASED` with `<think>`/`</think>` markers if no reasoning was detected
+1. **Old Qwen/DeepSeek thinking templates** — source contains `content.split('</think>')`: sets `reasoning.mode = FORCED_OPEN` with `<think>`/`</think>` markers if no reasoning was detected
 2. **Granite 3.3** — source contains specific "Write your thoughts" text: forces `TAG_BASED` reasoning with `<think>`/`</think>` and `WRAPPED_WITH_REASONING` content with `<response>`/`</response>`
 3. **Cohere Command R+** — source contains `<|CHATBOT_TOKEN|>`: sets `ALWAYS_WRAPPED` content mode if no content start is already set
 4. **Functionary 3.1** — source contains `set has_code_interpreter`: forces `PLAIN` content, specific `per_call_start/end`, clears preserved tokens to only keep Functionary-specific markers
@@ -364,13 +355,12 @@ Each analyzer struct (`analyze_reasoning`, `analyze_content`, `analyze_tools`) i
 
 #### Reasoning Parser (`analyze_reasoning::build_parser`)
 
-| Mode                                          | Parser                                                                    |
-|-----------------------------------------------|---------------------------------------------------------------------------|
-| Not extracting reasoning                      | `eps()`                                                                   |
-| `TAG_BASED` or `TOOLS_ONLY` (non-empty start) | `optional(start + reasoning(until(end)) + end + space())`                 |
-| `TAG_BASED` or `TOOLS_ONLY` (empty start)     | `optional(reasoning(until(end)) + end + space())` — delimiter-style       |
-
-Note: The start marker may be empty either because the analyzer detected delimiter-style reasoning, or because `generate_parser()` cleared a template artifact start marker (see Generation Prompt & Reasoning Prefill above). Whitespace-only reasoning content (e.g. from a `<think></think>` prefill) is discarded by the mapper.
+| Mode                              | Parser                                                              |
+|-----------------------------------|---------------------------------------------------------------------|
+| Not extracting reasoning          | `eps()`                                                             |
+| `FORCED_OPEN` or `FORCED_CLOSED`  | `reasoning(until(end)) + end` — opening tag was in the prompt       |
+| `TAG_BASED` or `TOOLS_ONLY`       | `optional(start + reasoning(until(end)) + end)`                     |
+| `DELIMITER`                       | `optional(reasoning(until(end)) + end)` — no start marker           |
 
 #### Content Parser (`analyze_content::build_parser`)
 
@@ -420,7 +410,9 @@ All three tool parsers return:
 reasoning + optional(content(until(trigger_marker))) + tool_calls + end()
 ```
 
-Each returned parser is wrapped by `wrap_for_generation_prompt()`, which prepends a literal for any boilerplate prefix of the generation prompt (the portion before the reasoning start marker).
+### Python Dict Format
+
+When `format.uses_python_dicts` is true (detected when single-quoted strings appear in JSON argument context), `build_parser()` pre-registers a `json-string` rule that accepts both single-quoted and double-quoted strings. This is done before any `p.json()` call so all JSON parsing inherits the flexible rule.
 
 ## Mapper
 
@@ -429,22 +421,22 @@ Each returned parser is wrapped by `wrap_for_generation_prompt()`, which prepend
 - **Buffered arguments**: Before `tool_name` is known, argument text goes to `args_buffer`; once the name is set, the buffer is flushed to `current_tool->arguments`
 - **`args_target()`**: Returns a reference to whichever destination is currently active (buffer or tool args), eliminating branching
 - **`closing_quote_pending`**: Tracks whether a closing `"` needs to be appended when a string argument value is finalized (for schema-declared string types in tagged format)
-- **Whitespace-only reasoning**: Reasoning content that consists entirely of whitespace (e.g. from a `<think></think>` prefill) is cleared so the message shows no reasoning
+- **Quote normalization**: Python-style quotes (`'key': 'value'`) are converted to JSON (`"key": "value"`)
 - **Brace auto-closing**: At tool close, unclosed `{` braces are closed automatically
 
 ## Files
 
-| File                                      | Purpose                                                                         |
-|-------------------------------------------|---------------------------------------------------------------------------------|
-| `common/chat-auto-parser.h`               | All analysis structs, enums, `autoparser`, `peg_generator`, `generation_params` |
-| `common/chat-auto-parser-generator.cpp`   | Parser generator: `generate_parser()` and `build_parser()` methods              |
-| `common/chat-diff-analyzer.cpp`           | Differential analysis implementation and workarounds                            |
-| `common/chat-auto-parser-helpers.h/cpp`   | `calculate_diff_split()`, `segmentize_markers()`, `compare_variants()`,         |
-|                                           | `wrap_for_generation_prompt()`, string helpers                                  |
-| `common/chat-peg-parser.h/cpp`            | `common_chat_peg_builder`, `common_chat_peg_mapper`, and helpers                |
-| `common/chat.cpp`                         | Entry point: `common_chat_templates_apply_jinja()`                              |
-| `tools/parser/debug-template-parser.cpp`  | Debug tool for template analysis                                                |
-| `tools/parser/template-analysis.cpp`      | Template analysis tool                                                          |
+| File                                      | Purpose                                                              |
+|-------------------------------------------|----------------------------------------------------------------------|
+| `common/chat-auto-parser.h`               | All analysis structs, enums, `autoparser`, `peg_generator`, `templates_params` |
+| `common/chat-auto-parser-generator.cpp`   | Parser generator: `generate_parser()` and `build_parser()` methods   |
+| `common/chat-diff-analyzer.cpp`           | Differential analysis implementation and workarounds                 |
+| `common/chat-auto-parser-helpers.h/cpp`   | `calculate_diff_split()`, `segmentize_markers()`,                    |
+|                                           | `compare_variants()`, string helpers                                 |
+| `common/chat-peg-parser.h/cpp`            | `common_chat_peg_builder`, `common_chat_peg_mapper`, and helpers     |
+| `common/chat.cpp`                         | Entry point: `common_chat_templates_apply_jinja()`                   |
+| `tools/parser/debug-template-parser.cpp`  | Debug tool for template analysis                                     |
+| `tools/parser/template-analysis.cpp`      | Template analysis tool                                               |
 
 ## Testing & Debugging
 
@@ -524,10 +516,10 @@ To support a new template format:
 
 ## Edge Cases and Quirks
 
-1. **Generation Prompt & Reasoning Prefill**: The generation prompt is extracted by diffing `add_generation_prompt=false` vs `true` in `common_chat_templates_apply_jinja`, so it contains exactly what the template appends — avoiding false positives from prior conversation turns.
+1. **Forced Thinking**: When `enable_thinking=true` and the model prompt ends with an open reasoning tag (e.g., `<think>`), the parser enters forced thinking mode and immediately expects reasoning content without waiting for a start marker.
 2. **Per-Call vs Per-Section Markers**: Some templates wrap each tool call individually (`per_call_start/end`); others wrap the entire section (`section_start/end`). T2 (`check_per_call_markers()`) disambiguates by checking if the second call in a two-call output starts with the section marker.
-3. **Tag Boundary Fixing**: `calculate_diff_split()` iteratively adjusts prefix/suffix boundaries to avoid splitting `<tag>` or `[marker]` tokens, ensuring clean extraction.
-4. **Call ID Side Effects**: When a call ID is detected, `per_call_end` may have been incorrectly set to include the call ID suffix. T7 clears `per_call_end` in this case.
-5. **Tool Analysis Gating**: `analyze_tools` is only constructed (and all tool analysis phases run) when `jinja_caps.supports_tool_calls` is true. Within tool analysis, `check_per_call_markers()` (T2) only runs if `jinja_caps.supports_parallel_tool_calls`.
-6. **`analyze_arguments()` Gating**: Within tool analysis, A1 and A2 (argument name/value marker extraction) only run for `TAG_WITH_TAGGED` format. `extract_argument_separator()` and `extract_args_markers()` run for all non-`JSON_NATIVE` formats.
-7. **Undetected Tool Format**: If `analyze_tools` concludes tool calling is supported but cannot determine the format, `build_parser()` logs an error and returns `eps()` (graceful degradation) rather than aborting.
+3. **Python Dict Format**: The Seed template family uses single-quoted JSON (`'key': 'value'`). The `uses_python_dicts` flag causes the PEG builder to register a flexible `json-string` rule accepting both quote styles before any JSON rules are built.
+4. **Tag Boundary Fixing**: `calculate_diff_split()` iteratively adjusts prefix/suffix boundaries to avoid splitting `<tag>` or `[marker]` tokens, ensuring clean extraction.
+5. **Call ID Side Effects**: When a call ID is detected, `per_call_end` may have been incorrectly set to include the call ID suffix. T7 clears `per_call_end` in this case.
+6. **Tool Analysis Gating**: `analyze_tools` is only constructed (and all tool analysis phases run) when `jinja_caps.supports_tool_calls` is true. Within tool analysis, `check_per_call_markers()` (T2) only runs if `jinja_caps.supports_parallel_tool_calls`.
+7. **`analyze_arguments()` Gating**: Within tool analysis, A1 and A2 (argument name/value marker extraction) only run for `TAG_WITH_TAGGED` format. `extract_argument_separator()` and `extract_args_markers()` run for all non-`JSON_NATIVE` formats.

docs/backend/OPENVINO.md

@@ -1,343 +0,0 @@
-# OpenVINO Backend for llama.cpp
-[OpenVINO](https://docs.openvino.ai/) is an open-source toolkit for optimizing and deploying high-performance AI inference, specifically designed for Intel hardware, including CPUs, GPUs, and NPUs, in the cloud, on-premises, and on the edge.
-This document describes the [OpenVINO backend for llama.cpp](../../src/ggml-openvino), which enables hardware-accelerated inference on **Intel® CPUs, GPUs, and NPUs** while remaining compatible with the existing **GGUF model ecosystem**. The backend translates GGML compute graphs into OpenVINO graphs and leverages graph compilation, kernel fusion, and device-specific optimizations to improve inference performance on supported Intel hardware.
-
-The OpenVINO backend is implemented in `ggml/src/ggml-openvino` and provides a translation layer for core GGML operations. The OpenVINO backend replaces the standard GGML graph execution path with Intel's OpenVINO inference engine. This approach allows the same GGUF model file to run on Intel CPUs, Intel GPUs (integrated and discrete), and Intel NPUs without changes to the model or the rest of the llama.cpp stack. When a `ggml_cgraph` is dispatched to OpenVINO backend, it:
-
-- Walks the GGML graph and identifies inputs, outputs, weights, and KV cache tensors.
-- Translates the GGML operations into an `ov::Model` using OpenVINO's frontend API.
-- Compiles and caches the model for the target device.
-- Binds GGML tensor memory to OpenVINO inference tensors and runs inference.
-
-## Supported Devices
-
-OpenVINO backend supports the following hardware:
-
-- Intel CPUs
-- Intel GPUs (integrated and discrete)
-- Intel NPUs
-
-Although OpenVINO supports a wide range of [Intel hardware](https://docs.openvino.ai/2026/about-openvino/release-notes-openvino/system-requirements.html), the llama.cpp OpenVINO backend has been validated specifically on AI PCs such as the Intel® Core™ Ultra Series 1 and Series 2.
-
-## Supported Model Precisions
-
-- `FP16`
-- `BF16` (on Intel Xeon)
-- `Q8_0`
-- `Q4_0`
-- `Q4_1`
-- `Q4_K`
-- `Q4_K_M`
-- `Q5_K` (converted to Q8_0_C at runtime)
-- `Q6_K` (converted to Q8_0_C at runtime)
-
-> [!NOTE]
-> Accuracy validation and performance optimizations for quantized models are a work in progress.
-
-## Quantization Support Details
-
-### CPU and GPU
-
-- **`Q4_0`, `Q4_1`, `Q4_K_M`, `Q6_K` models are supported**
-- `Q5_K` and `Q6_K` tensors are converted to `Q8_0_C`
-
-### NPU
-
-- **Primary supported quantization scheme is `Q4_0`**
-- `Q6_K` tensors are requantized to `Q4_0_128` in general. For embedding weights, `Q6_K` tensors are requantized to `Q8_0_C` except for the token embedding matrix which is dequantized to fp16
-
-### Additional Notes
-
-- Both `Q4_0` and `Q4_1` models use `Q6_K` for the token embedding tensor and the final matmul weight tensor (often the same tensor)
-- `Q4_0` models may produce some `Q4_1` tensors if an imatrix is provided during quantization using `llama-quantize`
-- `Q4_K_M` models may include both `Q6_K` and `Q5_K` tensors (observed in Phi-3)
-
-## Validated Models
-
-The following models have been validated for functionality on Intel® Core™ Ultra Series 1 and Series 2:
-
-- [Llama-3.2-1B-Instruct-GGUF](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-GGUF/)
-- [Llama-3.1-8B-Instruct](https://huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF)
-- [microsoft/Phi-3-mini-4k-instruct-gguf](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf)
-- [Qwen/Qwen2.5-1.5B-Instruct-GGUF](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF)
-- [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B-GGUF)
-- [openbmb/MiniCPM-1B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-S-1B-sft-gguf)
-- [tencent/Hunyuan-7B-Instruct](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF)
-- [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/bartowski/Mistral-7B-Instruct-v0.3-GGUF)
-- [bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF](https://huggingface.co/bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF)
-
-## Build Instructions
-
-### Prerequisites
-
-- Linux or Windows system with Intel hardware (CPU, GPU, or NPU)
-- **For Intel GPU or NPU Usage**: Install the appropriate hardware drivers for your Intel GPU or NPU. For detailed instructions, see: [Additional Configurations for Hardware Acceleration](https://docs.openvino.ai/2025/get-started/install-openvino/configurations.html).
-
-- **Linux:**
-    - Git, CMake, and Ninja software tools are needed for building.
-    ```bash
-      sudo apt-get update
-      sudo apt-get install -y build-essential libcurl4-openssl-dev libtbb12 cmake ninja-build python3-pip curl wget tar
-    ```
-    - OpenCL
-    ```bash
-      sudo apt install ocl-icd-opencl-dev opencl-headers opencl-clhpp-headers intel-opencl-icd
-    ```
-
-- **Windows:**
-  - Download and install [Microsoft Visual Studio 2022 Build Tools](https://aka.ms/vs/17/release/vs_BuildTools.exe). During installation, select the **"Desktop development with C++"** workload.
-
-  - Install required tools:
-    ```powershell
-    # Windows PowerShell
-    winget install Git.Git
-    winget install GNU.Wget
-    winget install Ninja-build.Ninja
-    ```
-
-  - Install **OpenCL** using **vcpkg**:
-    ```powershell
-    # Windows PowerShell
-    cd C:\
-    git clone https://github.com/microsoft/vcpkg
-    cd vcpkg
-    .\bootstrap-vcpkg.bat
-    .\vcpkg install opencl
-    # Optional but recommended: Integrate vcpkg with Visual Studio / CMake:
-    .\vcpkg integrate install
-    ```
-
-### 1. Install OpenVINO Runtime
-
-- Follow the guide to install OpenVINO Runtime from an archive file: [Linux](https://docs.openvino.ai/2026/get-started/install-openvino/install-openvino-archive-linux.html) | [Windows](https://docs.openvino.ai/2026/get-started/install-openvino/install-openvino-archive-windows.html)
-
-- **Linux:**
-
-    <details>
-    <summary>📦 Click to expand OpenVINO installation from an archive file on Ubuntu</summary>
-    <br>
-
-    ```bash
-    wget https://raw.githubusercontent.com/ravi9/misc-scripts/main/openvino/ov-archive-install/install-openvino-from-archive.sh
-    chmod +x install-openvino-from-archive.sh
-    ./install-openvino-from-archive.sh
-    ```
-
-    Verify OpenVINO is initialized properly:
-    ```bash
-    echo $OpenVINO_DIR
-    ```
-    </details>
-
-
-### 2. Build llama.cpp with OpenVINO Backend
-
-Clone the OpenVINO-enabled llama.cpp fork and build it:
-
-```bash
-git clone https://github.com/ggml-org/llama.cpp
-cd llama.cpp
-```
-
-- **Linux:**
-    ```bash
-    source /opt/intel/openvino/setupvars.sh
-    cmake -B build/ReleaseOV -G Ninja -DCMAKE_BUILD_TYPE=Release -DGGML_OPENVINO=ON
-    cmake --build build/ReleaseOV --parallel
-    ```
-
-- **Windows:**
-    ```cmd
-    # x64 Native Tools Command Prompt for VS 2022
-    "C:\Program Files (x86)\Intel\openvino_2026.0\setupvars.bat"
-    cmake -B build\ReleaseOV -G Ninja -DCMAKE_BUILD_TYPE=Release -DGGML_OPENVINO=ON -DLLAMA_CURL=OFF -DCMAKE_TOOLCHAIN_FILE=C:\vcpkg\scripts\buildsystems\vcpkg.cmake
-    cmake --build build\ReleaseOV --parallel
-    ```
-> [!NOTE]
-> Use `x64 Native Tools Command Prompt` for Windows build. After building, you could use either `cmd` or `PowerShell` to run the OpenVINO backend.
-
-### 3. Download Sample Model
-
-Download models for testing:
-
-```bash
-# Linux
-mkdir -p ~/models/
-wget https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_0.gguf \
-     -O ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf
-
-# Windows PowerShell
-mkdir C:\models
-Invoke-WebRequest -Uri https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_0.gguf -OutFile C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf
-
-# Windows Command Line
-mkdir C:\models
-curl -L https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_0.gguf -o C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf
-```
-
-### 4. Run Inference with OpenVINO Backend
-
-When using the OpenVINO backend, the first inference token may have slightly higher latency due to on-the-fly conversion to the OpenVINO graph. Subsequent tokens and runs will be faster.
-
-```bash
-# If device is unset or unavailable, defaults to CPU.
-# If the system has multiple GPUs, use GPU.0 or GPU.1 to explicitly target a specific GPU.
-
-# Linux
-export GGML_OPENVINO_DEVICE=GPU
-# To run llama-simple:
-./build/ReleaseOV/bin/llama-simple -m ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf -n 50 "The story of AI is "
-# To run in chat mode:
-./build/ReleaseOV/bin/llama-cli -m ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf
-
-# Windows Command Line
-set GGML_OPENVINO_DEVICE=GPU
-# Windows PowerShell
-$env:GGML_OPENVINO_DEVICE = "GPU"
-
-# To run llama-simple
-build\ReleaseOV\bin\llama-simple.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf" -n 50 "The story of AI is "
-# To run in chat mode:
-build\ReleaseOV\bin\llama-cli.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf"
-
-```
-> [!NOTE]
-> On systems with multiple GPUs, use `GPU.0` or `GPU.1` to explicitly target specific GPU. See [OpenVINO GPU Device](https://docs.openvino.ai/2026/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html) for more details.
-
-
-### Docker Build
-
-You can build and run llama.cpp with OpenVINO backend using Docker.
-
-```bash
-# Build the base runtime image with compiled shared libraries and minimal dependencies.
-docker build -t llama-openvino:base -f .devops/openvino.Dockerfile .
-
-# Build the complete image with all binaries, Python tools, gguf-py library, and model conversion utilities.
-docker build --target=full -t llama-openvino:full -f .devops/openvino.Dockerfile .
-
-# Build a minimal CLI-only image containing just the llama-cli executable.
-docker build --target=light -t llama-openvino:light -f .devops/openvino.Dockerfile .
-
-# Builds a server-only image with llama-server executable, health check endpoint, and REST API support.
-docker build --target=server -t llama-openvino:server -f .devops/openvino.Dockerfile .
-
-# If you are behind a proxy:
-docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy --target=light -t llama-openvino:light -f .devops/openvino.Dockerfile .
-```
-
-Run llama.cpp with OpenVINO backend Docker container.
-Save sample models in `~/models` as [shown above](#3-download-sample-model). It will be mounted to the container in the examples below.
-
-```bash
-#  Run Docker container
-docker run --rm -it -v ~/models:/models llama-openvino:light --no-warmup -m /models/Llama-3.2-1B-Instruct-Q4_0.gguf
-
-# With Intel GPU access (iGPU or dGPU)
-docker run --rm -it -v ~/models:/models \
---device=/dev/dri --group-add=$(stat -c "%g" /dev/dri/render* | head -n 1) -u $(id -u):$(id -g) \
-llama-openvino:light --no-warmup -m /models/Llama-3.2-1B-Instruct-Q4_0.gguf
-
-# With Intel NPU access
-docker run --rm -it --env GGML_OPENVINO_DEVICE=NPU -v ~/models:/models \
---device=/dev/accel --group-add=$(stat -c "%g" /dev/dri/render* | head -n 1) -u $(id -u):$(id -g) \
-llama-openvino:light --no-warmup -m /models/Llama-3.2-1B-Instruct-Q4_0.gguf
-```
-
-Run Llama.cpp Server with OpenVINO Backend:
-```bash
-# Run the Server Docker container
-docker run --rm -it -p 8080:8080 -v ~/models:/models llama-openvino:server --no-warmup -m /models/Llama-3.2-1B-Instruct-Q4_0.gguf
-
-# In a NEW terminal, test the server with curl
-
-# If you are behind a proxy, make sure to set NO_PROXY to avoid proxy for localhost
-export NO_PROXY=localhost,127.0.0.1
-
-# Test health endpoint
-curl -f http://localhost:8080/health
-
-# Test with a simple prompt
-curl -X POST "http://localhost:8080/v1/chat/completions" -H "Content-Type: application/json" \
- -d '{"messages":[{"role":"user","content":"Write a poem about OpenVINO"}],"max_tokens":100}' | jq .
-```
-
-## Runtime Configuration
-
-The OpenVINO backend can be configured using the following environment variables at runtime to control device selection, caching, debugging, and profiling behavior.
-
-### Configuration Options
-
-| Variable                          | Default    | Description                                                                                                 |
-|-----------------------------------|------------|-------------------------------------------------------------------------------------------------------------|
-| `GGML_OPENVINO_DEVICE`            | `CPU`      | Specify the target device (CPU, GPU, NPU). On systems with multiple GPUs, use `GPU.0` or `GPU.1` to explicitly target specific GPU. See [OpenVINO GPU Device](https://docs.openvino.ai/2026/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html). When set to **NPU**, static compilation mode is enabled for optimal performance. |
-| `GGML_OPENVINO_CACHE_DIR`         | `not set`  | Directory for OpenVINO model caching (recommended: `/tmp/ov_cache`). Enables model caching when set. **Not supported on NPU devices.** |
-| `GGML_OPENVINO_PREFILL_CHUNK_SIZE`| `256`      | Token chunk size for **NPU** prefill.                                                                       |
-| `GGML_OPENVINO_STATEFUL_EXECUTION`| `0`        | Enable stateful KV cache on for better performance. Recommended on CPU, GPU.                                |
-| `GGML_OPENVINO_PROFILING`         | `0`        | Enable execution-time profiling.                                                                            |
-| `GGML_OPENVINO_DUMP_CGRAPH`       | `0`        | Dump the GGML compute graph to `cgraph_ov.txt`.                                                             |
-| `GGML_OPENVINO_DUMP_IR`           | `0`        | Serialize OpenVINO IR files with timestamps.                                                                |
-| `GGML_OPENVINO_DEBUG_INPUT`       | `0`        | Enable input debugging and print input tensor info.                                                         |
-| `GGML_OPENVINO_DEBUG_OUTPUT`      | `0`        | Enable output debugging and print output tensor info.                                                       |
-| `GGML_OPENVINO_PRINT_CGRAPH_TENSOR_ADDRESS` | `0` | Print tensor address map once.                                                                           |
-
-> [!NOTE]
->`GGML_OPENVINO_STATEFUL_EXECUTION` is an **Experimental** feature to allow stateful execution for managing the KV cache internally inside the OpenVINO model, improving performance on CPUs and GPUs. Stateful execution is not effective on NPUs, and not all models currently support this feature. This feature is experimental and has been validated only with the llama-simple, llama-cli, llama-bench, and llama-run applications and is recommended to enable for the best performance. Other applications, such as llama-server and llama-perplexity, are not yet supported.
-
-### Example Usage
-
-#### GPU Inference with Profiling
-
-```bash
-# If the system has multiple GPUs, use GPU.0 or GPU.1 to explicitly target a specific GPU.
-
-# Linux
-export GGML_OPENVINO_CACHE_DIR=/tmp/ov_cache
-export GGML_OPENVINO_PROFILING=1
-export GGML_OPENVINO_DEVICE=GPU
-
-./build/ReleaseOV/bin/llama-simple -m ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf -n 50 "The story of AI is "
-
-# Windows Command Line
-set GGML_OPENVINO_CACHE_DIR=C:\tmp\ov_cache
-set GGML_OPENVINO_PROFILING=1
-set GGML_OPENVINO_DEVICE=GPU
-
-# Windows PowerShell
-$env:GGML_OPENVINO_CACHE_DIR = "C:\tmp\ov_cache"
-$env:GGML_OPENVINO_PROFILING = "1"
-$env:GGML_OPENVINO_DEVICE = "GPU"
-
-build\ReleaseOV\bin\llama-simple.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf" -n 50 "The story of AI is "
-
-```
-
-#### llama-bench
-
-```bash
-# -fa 1 is required when running llama-bench with the OpenVINO backend.
-GGML_OPENVINO_DEVICE=GPU ./llama-bench -fa 1
-```
-
-### NPU Notes
-
-- Model caching is not yet supported
-- Does not support llama-server -np > 1 (multiple parallel sequences)
-- Only supports llama-perplexity -b 512 or smaller
-
-## Llama.cpp Tools
-
-The following tools work with the OpenVINO backend on CPU, GPU, NPU:
-- llama-simple
-- llama-run
-- llama-cli
-- llama-server
-- llama-bench
-- llama-perplexity
-
-## Work in Progress
-
-- Performance and memory optimizations
-- Accuracy validation
-- Broader quantization coverage
-- Support for additional model architectures

docs/backend/SYCL.md

@@ -382,27 +382,17 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 
 ## Windows
 
-### Install GPU driver
+### I. Setup Environment
+
+1. Install GPU driver
 
 Intel GPU drivers instructions guide and download page can be found here: [Get Intel GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html).
 
-### Option 1: download the binary package directly
-
-Download the binary package for Windows from: https://github.com/ggml-org/llama.cpp/releases.
-
-Extract the package to local folder, run the llama tools directly. Refer to [Run the inference](#iii-run-the-inference-1).
-
-Note, the package includes the SYCL running time and all depended dll files, no need to install oneAPI package and activte them.
-
-### Option 2: build locally from the source code.
-
-#### I. Setup environment
-
-1. Install Visual Studio
+2. Install Visual Studio
 
 If you already have a recent version of Microsoft Visual Studio, you can skip this step. Otherwise, please refer to the official download page for [Microsoft Visual Studio](https://visualstudio.microsoft.com/).
 
-2. Install Intel® oneAPI Base toolkit
+3. Install Intel® oneAPI Base toolkit
 
 SYCL backend depends on:
   - Intel® oneAPI DPC++/C++ compiler/running-time.
@@ -453,25 +443,25 @@ Output (example):
 [ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044]
 ```
 
-3. Install build tools
+4. Install build tools
 
 a. Download & install cmake for Windows: https://cmake.org/download/ (CMake can also be installed from Visual Studio Installer)
 b. The new Visual Studio will install Ninja as default. (If not, please install it manually: https://ninja-build.org/)
 
 
-#### II. Build llama.cpp
+### II. Build llama.cpp
 
 You could download the release package for Windows directly, which including binary files and depended oneAPI dll files.
 
 Choose one of following methods to build from source code.
 
-##### Option 1: Script
+#### 1. Script
 
 ```sh
 .\examples\sycl\win-build-sycl.bat
 ```
 
-##### Option 2: CMake
+#### 2. CMake
 
 On the oneAPI command line window, step into the llama.cpp main directory and run the following:
 
@@ -500,7 +490,7 @@ cmake --preset x64-windows-sycl-debug
 cmake --build build-x64-windows-sycl-debug -j --target llama-completion
 ```
 
-##### Option 3: Visual Studio
+#### 3. Visual Studio
 
 You have two options to use Visual Studio to build llama.cpp:
 - As CMake Project using CMake presets.
@@ -510,7 +500,7 @@ You have two options to use Visual Studio to build llama.cpp:
 
 All following commands are executed in PowerShell.
 
-###### - Open as a CMake Project
+##### - Open as a CMake Project
 
 You can use Visual Studio to open the `llama.cpp` folder directly as a CMake project. Before compiling, select one of the SYCL CMake presets:
 
@@ -525,7 +515,7 @@ You can use Visual Studio to open the `llama.cpp` folder directly as a CMake pro
     cmake --build build --config Release -j --target llama-completion
     ```
 
-###### - Generating a Visual Studio Solution
+##### - Generating a Visual Studio Solution
 
 You can use Visual Studio solution to build and work on llama.cpp on Windows. You need to convert the CMake Project into a `.sln` file.
 
@@ -613,7 +603,7 @@ found 2 SYCL devices:
 
 ```
 
-##### Choose level-zero devices
+#### Choose level-zero devices
 
 |Chosen Device ID|Setting|
 |-|-|
@@ -621,7 +611,7 @@ found 2 SYCL devices:
 |1|`set ONEAPI_DEVICE_SELECTOR="level_zero:1"`|
 |0 & 1|`set ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"` or `set ONEAPI_DEVICE_SELECTOR="level_zero:*"`|
 
-##### Execute
+#### Execute
 
 Choose one of following methods to run.
 
@@ -679,7 +669,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 
 ## Environment Variable
 
-### Build
+#### Build
 
 | Name               | Value                                 | Function                                    |
 |--------------------|---------------------------------------|---------------------------------------------|
@@ -694,7 +684,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 
 1. FP32 or FP16 have different performance impact to LLM. Recommended to test them for better prompt processing performance on your models. You need to rebuild the code after change `GGML_SYCL_F16=OFF/ON`.
 
-### Runtime
+#### Runtime
 
 | Name              | Value            | Function                                                                                                                  |
 |-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------|
@@ -787,7 +777,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
   ```
 
 ### **GitHub contribution**:
-Please add the `[SYCL]` prefix/tag in issues/PRs titles to help the SYCL contributors to check/address them without delay.
+Please add the `SYCL :` prefix/tag in issues/PRs titles to help the SYCL contributors to check/address them without delay.
 
 ## TODO
 

docs/backend/VirtGPU/development.md

@@ -55,8 +55,7 @@ LLAMA_MAC_BUILD=$PWD/build/ggml-virtgpu-backend
 cmake -S . -B $LLAMA_MAC_BUILD \
       -DGGML_NATIVE=OFF \
       -DLLAMA_CURL=ON \
-      -DGGML_VIRTGPU=ON \
-      -DGGML_VIRTGPU_BACKEND=ONLY \
+      -DGGML_REMOTINGBACKEND=ONLY \
       -DGGML_METAL=ON
 
 TARGETS="ggml-metal"
@@ -72,7 +71,6 @@ cmake --build $LLAMA_MAC_BUILD --parallel 8 --target $EXTRA_TARGETS
 ```bash
 # Build virglrenderer with APIR support
 mkdir virglrenderer
-cd virglrenderer
 git clone https://gitlab.freedesktop.org/kpouget/virglrenderer -b main-macos src
 cd src
 
@@ -97,7 +95,7 @@ mkdir llama.cpp
 git clone https://github.com/ggml-org/llama.cpp.git src
 cd src
 
-LLAMA_LINUX_BUILD=$PWD/build-virtgpu
+LLAMA_LINUX_BUILD=$PWD//build-virtgpu
 
 cmake -S . -B $LLAMA_LINUX_BUILD \
       -DGGML_VIRTGPU=ON

docs/build.md

@@ -13,21 +13,6 @@ cd llama.cpp
 
 The following sections describe how to build with different backends and options.
 
-* [CPU Build](#cpu-build)
-* [BLAS Build](#blas-build)
-* [Metal Build](#metal-build)
-* [SYCL](#sycl)
-* [CUDA](#cuda)
-* [MUSA](#musa)
-* [HIP](#hip)
-* [Vulkan](#vulkan)
-* [CANN](#cann)
-* [Arm® KleidiAI™](#arm-kleidiai)
-* [OpenCL](#opencl)
-* [Android](#android-1)
-* [OpenVINO](#openvino)
-* [Notes about GPU-accelerated backends](#notes-about-gpu-accelerated-backends)
-
 ## CPU Build
 
 Build llama.cpp using `CMake`:
@@ -269,14 +254,6 @@ The environment variable [`CUDA_SCALE_LAUNCH_QUEUES`](https://docs.nvidia.com/cu
 
 Consider setting `CUDA_SCALE_LAUNCH_QUEUES=4x`, which increases the CUDA command buffer to 4 times its default size. This optimization is particularly beneficial for **Multi-GPU setups with pipeline parallelism**, where it significantly improves prompt processing throughput by allowing more operations to be enqueued across GPUs.
 
-#### GGML_CUDA_FORCE_CUBLAS_COMPUTE_32F
-
-Use `GGML_CUDA_FORCE_CUBLAS_COMPUTE_32F` environment variable to use FP32 compute type on all GPUs in FP16 cuBLAS for preventing possible numerical overflows in exchange for slower prompt processing (small impact on RTX PRO/Datacenter products and significant on GeForce products).
-
-#### GGML_CUDA_FORCE_CUBLAS_COMPUTE_16F
-
-Use `GGML_CUDA_FORCE_CUBLAS_COMPUTE_16F` environment variable to force use FP16 compute type (instead of default FP32) in FP16 cuBLAS for V100, CDNA and RDNA4.
-
 ### Unified Memory
 
 The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted. In Windows this setting is available in the NVIDIA control panel as `System Memory Fallback`.
@@ -288,7 +265,7 @@ The following compilation options are also available to tweak performance:
 | Option                        | Legal values           | Default | Description                                                                                                                                                                                                                                                                                                                                                                      |
 |-------------------------------|------------------------|---------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | GGML_CUDA_FORCE_MMQ           | Boolean                | false   | Force the use of custom matrix multiplication kernels for quantized models instead of FP16 cuBLAS even if there is no int8 tensor core implementation available (affects V100, CDNA and RDNA3+). MMQ kernels are enabled by default on GPUs with int8 tensor core support. With MMQ force enabled, speed for large batch sizes will be worse but VRAM consumption will be lower. |
-| GGML_CUDA_FORCE_CUBLAS        | Boolean                | false   | Force the use of FP16 cuBLAS instead of custom matrix multiplication kernels for quantized models. There may be issues with numerical overflows (except for V100, CDNA and RDNA4 which use FP32 compute type by default) and memory use will be higher. Prompt processing may become faster on recent datacenter GPUs (the custom kernels were tuned primarily for RTX 3000/4000).   |
+| GGML_CUDA_FORCE_CUBLAS        | Boolean                | false   | Force the use of FP16 cuBLAS instead of custom matrix multiplication kernels for quantized models. There may be issues with numerical overflows (except for CDNA and RDNA4) and memory use will be higher. Prompt processing may become faster on recent datacenter GPUs (the custom kernels were tuned primarily for RTX 3000/4000).                                            |
 | GGML_CUDA_PEER_MAX_BATCH_SIZE | Positive integer       | 128     | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial.                                                                                                                                                                  |
 | GGML_CUDA_FA_ALL_QUANTS       | Boolean                | false   | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer.                                                                                                                                                                                           |
 
@@ -622,13 +599,7 @@ If KleidiAI is enabled, the output will contain a line similar to:
 ```
 load_tensors: CPU_KLEIDIAI model buffer size =  3474.00 MiB
 ```
-KleidiAI’s microkernels implement optimized tensor operations using Arm CPU features such as dotprod, int8mm, SVE, and SME. Llama.cpp selects the most efficient kernels at runtime based on detected CPU capabilities.
-On CPUs that support SME, SME microkernels are enabled automatically using runtime detection.
-The environment variable GGML_KLEIDIAI_SME can be used to control SME behavior:
-- Not set: enable SME automatically if supported and detected.
-- 0: disable SME.
-- <n> > 0: enable SME and assume <n> available SME units (override auto detection).
-If SME is not supported by the CPU, SME microkernels are always disabled.
+KleidiAI's microkernels implement optimized tensor operations using Arm CPU features such as dotprod, int8mm and SME. llama.cpp selects the most efficient kernel based on runtime CPU feature detection. However, on platforms that support SME, you must manually enable SME microkernels by setting the environment variable `GGML_KLEIDIAI_SME=1`.
 
 Depending on your build target, other higher priority backends may be enabled by default. To ensure the CPU backend is used, you must disable the higher priority backends either at compile time, e.g. -DGGML_METAL=OFF, or during run-time using the command line option `--device none`.
 
@@ -747,14 +718,6 @@ Follow the instructions [here](https://dawn.googlesource.com/dawn/+/refs/heads/m
 
 To read documentation for how to build on IBM Z & LinuxONE, [click here](./build-s390x.md)
 
-## OpenVINO
-
-[OpenVINO](https://docs.openvino.ai/) is an open-source toolkit for optimizing and deploying high-performance AI inference, specifically designed for Intel hardware (CPUs, GPUs, and NPUs).
-
-For build instructions and usage examples, refer to [OPENVINO.md](backend/OPENVINO.md).
-
-
----
 ## Notes about GPU-accelerated backends
 
 The GPU may still be used to accelerate some parts of the computation even when using the `-ngl 0` option. You can fully disable GPU acceleration by using `--device none`.

docs/docker.md

@@ -28,9 +28,6 @@ Additionally, there the following images, similar to the above:
 - `ghcr.io/ggml-org/llama.cpp:full-vulkan`: Same as `full` but compiled with Vulkan support. (platforms: `linux/amd64`)
 - `ghcr.io/ggml-org/llama.cpp:light-vulkan`: Same as `light` but compiled with Vulkan support. (platforms: `linux/amd64`)
 - `ghcr.io/ggml-org/llama.cpp:server-vulkan`: Same as `server` but compiled with Vulkan support. (platforms: `linux/amd64`)
-- `ghcr.io/ggml-org/llama.cpp:full-openvino`: Same as `full` but compiled with OpenVino support. (platforms: `linux/amd64`)
-- `ghcr.io/ggml-org/llama.cpp:light-openvino`: Same as `light` but compiled with OpenVino support. (platforms: `linux/amd64`)
-- `ghcr.io/ggml-org/llama.cpp:server-openvino`: Same as `server` but compiled with OpenVino support. (platforms: `linux/amd64`)
 
 The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](../.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](../.github/workflows/docker.yml). If you need different settings (for example, a different CUDA, ROCm or MUSA library, you'll need to build the images locally for now).
 

docs/ops.md

@@ -12,10 +12,10 @@ Legend:
 - 🟡 Partially supported by this backend
 - ❌ Not supported by this backend
 
-| Operation | BLAS | CANN | CPU | CUDA | MTL | OpenCL | SYCL | Vulkan | WebGPU | ZenDNN | zDNN |
+| Operation | BLAS | CANN | CPU | CUDA | Metal | OpenCL | SYCL | Vulkan | WebGPU | ZenDNN | zDNN |
 |-----------|------|------|------|------|------|------|------|------|------|------|------|
-|                              ABS | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                              ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
+|                              ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                              ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
@@ -23,99 +23,98 @@ Legend:
 |                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
 |                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                            CLAMP | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             CONT | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
+|                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
 |                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                              COS | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                              COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                      COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |               CROSS_ENTROPY_LOSS | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |          CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                           CUMSUM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                             DIAG | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
+|                             DIAG | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              ELU | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                              EXP | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                            EXPM1 | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
+|                              ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                              EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                            EXPM1 | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
 |                             FILL | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
 |                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |                            FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
-|                  GATED_DELTA_NET | ❌ | ❌ | ✅ | ❌ | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ |
 |                GATED_LINEAR_ATTN | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                        GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                      GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                             GELU | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                         GELU_ERF | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                       GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
+|                             GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                         GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                       GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
 |                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                           IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                        IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                          L2_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                          L2_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
-|                              LOG | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
+|                              LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
-|                              NEG | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
+|                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
+|                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                             NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ |
 |                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                     OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                         OUT_PROD | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | 🟡 |
 |                              PAD | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
 |                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                          POOL_1D | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                          POOL_1D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                             RELU | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                           REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                             RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                           REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ❌ | ❌ | ❌ |
 |                      REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                         RMS_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             ROPE | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                        ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                        ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                            ROUND | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                        RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                        RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                              SET | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
+|                              SET | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                         SET_ROWS | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
-|                              SGN | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                             SILU | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                              SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ |
+|                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                              SIN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
-|                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                              SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
-|                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                              SQR | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
-|                             SQRT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
-|                         SSM_CONV | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                             SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                         SSM_CONV | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
-|                             STEP | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                             STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              SUM | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |                         SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
 |                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                             TANH | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
-|                              TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
-|                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                            XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |

docs/ops/Vulkan.csv

@@ -1,8 +1,8 @@
 "backend_name","op_name","op_params","test_mode","supported","error_message","backend_reg_name"
 "Vulkan0","ABS","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan"
 "Vulkan0","ABS","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan"
-"Vulkan0","SGN","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan"
-"Vulkan0","SGN","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan"
+"Vulkan0","SGN","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","Vulkan"
+"Vulkan0","SGN","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","Vulkan"
 "Vulkan0","NEG","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan"
 "Vulkan0","NEG","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan"
 "Vulkan0","STEP","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan"
@@ -85,8 +85,8 @@
 "Vulkan0","TRUNC","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","Vulkan"
 "Vulkan0","ABS","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan"
 "Vulkan0","ABS","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan"
-"Vulkan0","SGN","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan"
-"Vulkan0","SGN","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan"
+"Vulkan0","SGN","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","Vulkan"
+"Vulkan0","SGN","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","Vulkan"
 "Vulkan0","NEG","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan"
 "Vulkan0","NEG","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","Vulkan"
 "Vulkan0","STEP","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","Vulkan"
@@ -13591,16 +13591,3 @@
 "Vulkan0","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[30000,1,1,1]","support","0","no","Vulkan"
 "Vulkan0","OPT_STEP_ADAMW","type=f32,ne=[10,5,4,3]","support","1","yes","Vulkan"
 "Vulkan0","OPT_STEP_SGD","type=f32,ne=[10,5,4,3]","support","1","yes","Vulkan"
-"Vulkan0","GATED_DELTA_NET","type=f32,head_count=32,head_size=128,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","0","no","Vulkan"
-"Vulkan0","GATED_DELTA_NET","type=f32,head_count=16,head_size=64,n_seq_tokens=1,n_seqs=2,v_repeat=1,permuted=0,kda=0","support","0","no","Vulkan"
-"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=0,kda=0","support","0","no","Vulkan"
-"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=0,kda=0","support","0","no","Vulkan"
-"Vulkan0","GATED_DELTA_NET","type=f32,head_count=8,head_size=32,n_seq_tokens=4,n_seqs=2,v_repeat=2,permuted=0,kda=0","support","0","no","Vulkan"
-"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=1,kda=0","support","0","no","Vulkan"
-"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=1,kda=0","support","0","no","Vulkan"
-"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=1,n_seqs=1,v_repeat=1,permuted=0,kda=1","support","0","no","Vulkan"
-"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=1,n_seqs=2,v_repeat=1,permuted=0,kda=1","support","0","no","Vulkan"
-"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=32,n_seq_tokens=4,n_seqs=1,v_repeat=1,permuted=0,kda=1","support","0","no","Vulkan"
-"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=0,kda=1","support","0","no","Vulkan"
-"Vulkan0","GATED_DELTA_NET","type=f32,head_count=8,head_size=32,n_seq_tokens=4,n_seqs=2,v_repeat=2,permuted=0,kda=1","support","0","no","Vulkan"
-"Vulkan0","GATED_DELTA_NET","type=f32,head_count=4,head_size=64,n_seq_tokens=4,n_seqs=2,v_repeat=1,permuted=1,kda=1","support","0","no","Vulkan"

examples/json_schema_to_grammar.py

@@ -633,7 +633,7 @@ class SchemaConverter:
             return self._add_rule(rule_name, self._build_object_rule(properties, required, hybrid_name, additional_properties=None))
 
         elif schema_type in (None, 'array') and ('items' in schema or 'prefixItems' in schema):
-            items = schema.get('items', schema.get('prefixItems'))
+            items = schema.get('items') or schema['prefixItems']
             if isinstance(items, list):
                 return self._add_rule(
                     rule_name,

ggml/CMakeLists.txt

@@ -4,7 +4,7 @@ project("ggml" C CXX ASM)
 ### GGML Version
 set(GGML_VERSION_MAJOR 0)
 set(GGML_VERSION_MINOR 9)
-set(GGML_VERSION_PATCH 8)
+set(GGML_VERSION_PATCH 7)
 set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")
 
 find_program(GIT_EXE NAMES git git.exe NO_CMAKE_FIND_ROOT_PATH)
@@ -248,14 +248,12 @@ set   (GGML_SYCL_TARGET "INTEL" CACHE STRING
 set   (GGML_SYCL_DEVICE_ARCH "" CACHE STRING
                                             "ggml: sycl device architecture")
 
-option(GGML_OPENVINO                        "ggml: use OPENVINO"                              OFF)
-
 option(GGML_OPENCL                          "ggml: use OpenCL"                                OFF)
 option(GGML_OPENCL_PROFILING                "ggml: use OpenCL profiling (increases overhead)" OFF)
 option(GGML_OPENCL_EMBED_KERNELS            "ggml: embed kernels"                             ON)
 option(GGML_OPENCL_USE_ADRENO_KERNELS       "ggml: use optimized kernels for Adreno"          ON)
 set   (GGML_OPENCL_TARGET_VERSION "300" CACHE STRING
-                                            "ggml: OpenCL API version to target")
+                                            "gmml: OpenCL API version to target")
 
 option(GGML_HEXAGON                         "ggml: enable Hexagon backend"                    OFF)
 set(GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE 128 CACHE STRING "ggml: quantize group size (32, 64, or 128)")
@@ -329,7 +327,6 @@ set(GGML_PUBLIC_HEADERS
     include/ggml-vulkan.h
     include/ggml-webgpu.h
     include/ggml-zendnn.h
-    include/ggml-openvino.h
     include/gguf.h)
 
 set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}")

ggml/include/ggml-openvino.h

@@ -1,37 +0,0 @@
-#pragma once
-
-#include "ggml-backend.h"
-
-#include <cstring>
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-#define GGML_OPENVINO_NAME "OPENVINO"
-
-// backend API
-GGML_BACKEND_API ggml_backend_t ggml_backend_openvino_init(int device);
-
-GGML_BACKEND_API bool ggml_backend_is_openvino(ggml_backend_t backend);
-
-GGML_BACKEND_API bool ggml_backend_buffer_is_openvino(ggml_backend_buffer_t buffer);
-
-GGML_BACKEND_API bool ggml_backend_buft_is_openvino(ggml_backend_buffer_type_t buft);
-
-GGML_BACKEND_API bool ggml_backend_buft_is_openvino_host(ggml_backend_buffer_type_t buft);
-
-GGML_BACKEND_API size_t ggml_backend_openvino_buffer_get_ctx_id(ggml_backend_buffer_t buffer);
-
-// device buffer
-GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_openvino_buffer_type(int device);
-
-GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_openvino_host_buffer_type(int device);
-
-GGML_BACKEND_API int ggml_backend_openvino_get_device_count(void);
-
-GGML_BACKEND_API ggml_backend_reg_t ggml_backend_openvino_reg(void);
-
-#ifdef __cplusplus
-}
-#endif

ggml/include/ggml-rpc.h

@@ -8,12 +8,7 @@ extern "C" {
 
 #define RPC_PROTO_MAJOR_VERSION    3
 #define RPC_PROTO_MINOR_VERSION    6
-#define RPC_PROTO_PATCH_VERSION    1
-
-#ifdef  __cplusplus
-static_assert(GGML_OP_COUNT == 96, "GGML_OP_COUNT has changed - update RPC_PROTO_PATCH_VERSION");
-#endif
-
+#define RPC_PROTO_PATCH_VERSION    0
 #define GGML_RPC_MAX_SERVERS       16
 
 // backend API

ggml/include/ggml.h

@@ -427,8 +427,7 @@ extern "C" {
         // GGML_TYPE_IQ4_NL_4_8 = 37,
         // GGML_TYPE_IQ4_NL_8_8 = 38,
         GGML_TYPE_MXFP4   = 39, // MXFP4 (1 block)
-        GGML_TYPE_NVFP4   = 40, // NVFP4 (4 blocks, E4M3 scale)
-        GGML_TYPE_COUNT   = 41,
+        GGML_TYPE_COUNT   = 40,
     };
 
     // precision
@@ -464,7 +463,6 @@ extern "C" {
         GGML_FTYPE_MOSTLY_IQ1_M   = 23, // except 1d tensors
         GGML_FTYPE_MOSTLY_BF16    = 24, // except 1d tensors
         GGML_FTYPE_MOSTLY_MXFP4   = 25, // except 1d tensors
-        GGML_FTYPE_MOSTLY_NVFP4   = 26, // except 1d tensors
     };
 
     // available tensor operations:
@@ -733,10 +731,6 @@ extern "C" {
     GGML_API size_t  ggml_type_size(enum ggml_type type);             // size in bytes for all elements in a block
     GGML_API size_t  ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row
 
-    GGML_DEPRECATED(
-    GGML_API double ggml_type_sizef(enum ggml_type type), // ggml_type_size()/ggml_blck_size() as float
-    "use ggml_row_size() instead");
-
     GGML_API const char * ggml_type_name(enum ggml_type type);
     GGML_API const char * ggml_op_name  (enum ggml_op   op);
     GGML_API const char * ggml_op_symbol(enum ggml_op   op);
@@ -2470,8 +2464,6 @@ extern "C" {
         bool                  lower,
         bool                  uni);
 
-    // TODO: add ggml_gated_delta_net_set_bcast() to be able to configure Q, K broadcast type: tiled vs interleaved [TAG_GGML_GDN_BCAST]
-    // ref: https://github.com/ggml-org/llama.cpp/pull/19468#discussion_r2786394306
     GGML_API struct ggml_tensor * ggml_gated_delta_net(
             struct ggml_context * ctx,
             struct ggml_tensor  * q,

ggml/src/CMakeLists.txt

@@ -460,7 +460,6 @@ ggml_add_backend(zDNN)
 ggml_add_backend(OpenCL)
 ggml_add_backend(Hexagon)
 ggml_add_backend(ZenDNN)
-ggml_add_backend(OPENVINO)
 
 foreach (target ggml-base ggml)
     target_include_directories(${target} PUBLIC    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/../include> $<INSTALL_INTERFACE:include>)

ggml/src/ggml-backend-reg.cpp

@@ -82,10 +82,6 @@
 #include "ggml-zendnn.h"
 #endif
 
-#ifdef GGML_USE_OPENVINO
-#include "ggml-openvino.h"
-#endif
-
 namespace fs = std::filesystem;
 
 static std::string path_str(const fs::path & path) {
@@ -158,9 +154,6 @@ struct ggml_backend_registry {
 #ifdef GGML_USE_RPC
         register_backend(ggml_backend_rpc_reg());
 #endif
-#ifdef GGML_USE_OPENVINO
-        register_backend(ggml_backend_openvino_reg());
-#endif
 #ifdef GGML_USE_CPU
         register_backend(ggml_backend_cpu_reg());
 #endif
@@ -564,7 +557,6 @@ void ggml_backend_load_all_from_path(const char * dir_path) {
     ggml_backend_load_best("opencl", silent, dir_path);
     ggml_backend_load_best("hexagon", silent, dir_path);
     ggml_backend_load_best("musa", silent, dir_path);
-    ggml_backend_load_best("openvino", silent, dir_path);
     ggml_backend_load_best("cpu", silent, dir_path);
     // check the environment variable GGML_BACKEND_PATH to load an out-of-tree backend
     const char * backend_path = std::getenv("GGML_BACKEND_PATH");

ggml/src/ggml-backend.cpp

@@ -1455,6 +1455,10 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
         int split_backend_id = split->backend_id;
         ggml_backend_t split_backend = sched->backends[split_backend_id];
 
+        if (sched->events[split_backend_id][sched->cur_copy] == NULL) {
+            ggml_backend_synchronize(split_backend);
+        }
+
         // copy the input tensors to the split backend
         for (int input_id = 0; input_id < split->n_inputs; input_id++) {
             ggml_backend_t input_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[input_id]);
@@ -1465,16 +1469,12 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
                 // inputs from the user must be copied immediately to prevent the user overwriting the data before the copy is done
                 if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
                     ggml_backend_event_synchronize(sched->events[split_backend_id][sched->cur_copy]);
-                } else {
-                    ggml_backend_synchronize(split_backend);
                 }
-                ggml_backend_tensor_copy(input, input_cpy);
+                ggml_backend_tensor_copy_async(input_backend, split_backend, input, input_cpy);
             } else {
                 // wait for the split backend to finish using the input before overwriting it
                 if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
                     ggml_backend_event_wait(split_backend, sched->events[split_backend_id][sched->cur_copy]);
-                } else {
-                    ggml_backend_synchronize(split_backend);
                 }
 
                 // when offloading MoE weights, we can reduce the amount of data copied by copying only the experts that are used
@@ -1578,6 +1578,10 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
             }
         }
 
+        if (sched->events[split_backend_id][sched->cur_copy] == NULL) {
+            ggml_backend_synchronize(split_backend);
+        }
+
         if (!sched->callback_eval) {
             enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &split->graph);
             if (ec != GGML_STATUS_SUCCESS) {

ggml/src/ggml-blas/ggml-blas.cpp

@@ -121,8 +121,6 @@ static void ggml_backend_blas_mul_mat(ggml_backend_blas_context * ctx, struct gg
     bli_thread_set_num_threads(ctx->n_threads);
 #elif defined(GGML_BLAS_USE_NVPL)
     nvpl_blas_set_num_threads(ctx->n_threads);
-#elif defined(GGML_BLAS_USE_MKL)
-    mkl_set_num_threads(ctx->n_threads);
 #endif
 
     for (int64_t i13 = 0; i13 < ne13; i13++) {

ggml/src/ggml-cann/aclnn_ops.cpp

@@ -1544,8 +1544,8 @@ static void aclnn_get_slope(ggml_backend_cann_context & ctx,
         end   = 2 * ((n_head - 1) - n_head_log2) + 1;
         step  = 2;
         count = n_head - n_head_log2;
-        aclnn_get_slope_inner(ctx, (char *) slope_buffer + n_head_log2 * ggml_type_size(dtype), m1, count, start, end + 1,
-                              step, dtype);
+        aclnn_get_slope_inner(ctx, (char *) slope_buffer + n_head_log2 * sizeof(float), m1, count, start, end + 1, step,
+                              dtype);
     }
 }
 
@@ -1788,11 +1788,9 @@ void ggml_cann_get_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     ggml_tensor * src0 = dst->src[0];  // src
     ggml_tensor * src1 = dst->src[1];  // index
 
-    GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16
-                || dst->type == GGML_TYPE_BF16);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16);
 
     switch (src0->type) {
-        case GGML_TYPE_BF16:
         case GGML_TYPE_F16:
         case GGML_TYPE_F32:
             if (src0->type == dst->type) {
@@ -1883,7 +1881,6 @@ void ggml_cann_set_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
                 break;
             }
         case GGML_TYPE_F16:
-        case GGML_TYPE_BF16:
             {
                 acl_tensor_ptr       acl_src0 = ggml_cann_create_tensor(src0);
                 ggml_cann_pool_alloc src_buffer_allocator(ctx.pool(), ggml_nelements(src0) * sizeof(uint16_t));
@@ -1894,7 +1891,7 @@ void ggml_cann_set_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
                     src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
                 }
                 acl_tensor_ptr src_trans_tensor = ggml_cann_create_tensor(
-                    src_trans_buffer, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), src0->ne, src_trans_nb, GGML_MAX_DIMS);
+                    src_trans_buffer, ACL_FLOAT16, ggml_type_size(dst->type), src0->ne, src_trans_nb, GGML_MAX_DIMS);
                 aclnn_cast(ctx, acl_src0.get(), src_trans_tensor.get(), ggml_cann_type_mapping(dst->type));
                 aclnn_index_copy_4d(ctx, src_trans_buffer, src0->ne, src_trans_nb, dst->data, dst->ne, dst->nb, src1,
                                     dst->type);
@@ -1968,7 +1965,7 @@ static void ggml_cann_mat_mul_fp(ggml_backend_cann_context & ctx, ggml_tensor *
 
     // Only check env once.
     static bool weight_to_nz = parse_bool(get_env_as_lowercase("GGML_CANN_WEIGHT_NZ").value_or("on"));
-    if (weight_to_nz && weight->type != GGML_TYPE_BF16 && is_matmul_weight(weight)) {
+    if (weight_to_nz && is_matmul_weight(weight)) {
         acl_weight_tensor = ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, n_dims, ACL_FORMAT_FRACTAL_NZ);
     } else {
         acl_weight_tensor = ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, n_dims, ACL_FORMAT_ND);
@@ -2149,9 +2146,6 @@ void ggml_cann_mul_mat(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     switch (type) {
         case GGML_TYPE_F32:
         case GGML_TYPE_F16:
-#ifndef ASCEND_310P
-        case GGML_TYPE_BF16:
-#endif
             ggml_cann_mat_mul_fp(ctx, dst);
             break;
         case GGML_TYPE_Q4_0:
@@ -2949,27 +2943,6 @@ void ggml_cann_rope(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
         // Rotate full tensor (no tail), using trans tensors
         GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src_trans_tensor.get(), acl_cos_reshape_tensor.get(),
                                 acl_sin_reshape_tensor.get(), acl_mode, acl_dst_trans_tensor.get());
-    } else if (src0->data == dst->data && !ggml_is_contiguous(src0)) {
-        // In-place on non-contiguous tensor: RotaryPositionEmbedding cannot safely
-        // read and write the same non-contiguous buffer. Use contiguous temporaries.
-        size_t contiguous_nb[GGML_MAX_DIMS];
-        contiguous_nb[0] = sizeof(float);
-        for (int i = 1; i < GGML_MAX_DIMS; i++) {
-            contiguous_nb[i] = contiguous_nb[i - 1] * src0->ne[i - 1];
-        }
-        int64_t              total_elements = ggml_nelements(src0);
-        ggml_cann_pool_alloc inplace_src_alloc(ctx.pool(), total_elements * sizeof(float));
-        ggml_cann_pool_alloc inplace_dst_alloc(ctx.pool(), total_elements * sizeof(float));
-
-        acl_tensor_ptr acl_src_contig = ggml_cann_create_tensor(inplace_src_alloc.get(), ACL_FLOAT, sizeof(float),
-                                                                src0->ne, contiguous_nb, GGML_MAX_DIMS);
-        acl_tensor_ptr acl_dst_contig = ggml_cann_create_tensor(inplace_dst_alloc.get(), ACL_FLOAT, sizeof(float),
-                                                                dst->ne, contiguous_nb, GGML_MAX_DIMS);
-
-        cann_copy(ctx, acl_src.get(), acl_src_contig.get());
-        GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src_contig.get(), acl_cos_reshape_tensor.get(),
-                                acl_sin_reshape_tensor.get(), acl_mode, acl_dst_contig.get());
-        cann_copy(ctx, acl_dst_contig.get(), acl_dst.get());
     } else {
         // Rotate full tensor (no tail), using original tensors
         GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src.get(), acl_cos_reshape_tensor.get(),
@@ -3626,44 +3599,6 @@ void ggml_cann_flash_attn_ext(ggml_backend_cann_context & ctx, ggml_tensor * dst
         acl_k_tensor = ggml_cann_create_tensor(src1, src1_bsnd_ne, src1_bsnd_nb, GGML_MAX_DIMS);
         acl_v_tensor = ggml_cann_create_tensor(src2, src2_bsnd_ne, src2_bsnd_nb, GGML_MAX_DIMS);
 
-        // Step 2.5: Pad Q, K, V along head dimension if D is not a multiple of 16
-        //           (required by FusedInferAttentionScoreV2)
-        const int64_t D         = src0->ne[0];
-        const int64_t D_padded  = GGML_PAD(D, 16);
-        const bool needs_padding = (D != D_padded);
-
-        ggml_cann_pool_alloc q_pad_allocator(ctx.pool());
-        ggml_cann_pool_alloc k_pad_allocator(ctx.pool());
-        ggml_cann_pool_alloc v_pad_allocator(ctx.pool());
-
-        if (needs_padding) {
-            int64_t paddings[] = { 0, D_padded - D, 0, 0, 0, 0, 0, 0 };
-
-            auto pad_fa_tensor = [&](acl_tensor_ptr & tensor, const int64_t * bsnd_ne,
-                                     ggml_cann_pool_alloc & allocator) {
-                int64_t pad_ne[GGML_MAX_DIMS] = { D_padded, bsnd_ne[1], bsnd_ne[2], bsnd_ne[3] };
-                size_t  pad_nb[GGML_MAX_DIMS];
-                pad_nb[0] = faElemSize;
-                for (int i = 1; i < GGML_MAX_DIMS; ++i) {
-                    pad_nb[i] = pad_nb[i - 1] * pad_ne[i - 1];
-                }
-                int64_t nelements = pad_ne[0] * pad_ne[1] * pad_ne[2] * pad_ne[3];
-                void *  buffer    = allocator.alloc(nelements * faElemSize);
-                acl_tensor_ptr padded =
-                    ggml_cann_create_tensor(buffer, faDataType, faElemSize, pad_ne, pad_nb, GGML_MAX_DIMS);
-                aclnn_pad(ctx, tensor.get(), padded.get(), paddings);
-                tensor = std::move(padded);
-            };
-
-            pad_fa_tensor(acl_q_tensor, src0_bsnd_ne, q_pad_allocator);
-            pad_fa_tensor(acl_k_tensor, src1_bsnd_ne, k_pad_allocator);
-            pad_fa_tensor(acl_v_tensor, src2_bsnd_ne, v_pad_allocator);
-
-            src0_bsnd_ne[0] = D_padded;
-            src1_bsnd_ne[0] = D_padded;
-            src2_bsnd_ne[0] = D_padded;
-        }
-
         // Step 3: create the PSEShift tensor if needed
         //         this tensor is considered as mask (f16) in the llama.cpp
         acl_tensor_ptr       bcast_pse_tensor;
@@ -3753,16 +3688,17 @@ void ggml_cann_flash_attn_ext(ggml_backend_cann_context & ctx, ggml_tensor * dst
 
         GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16);
         acl_tensor_ptr       fa_dst_tensor;
+        acl_tensor_ptr       acl_dst_tensor;
         ggml_cann_pool_alloc out_f16_allocator(ctx.pool());
-        if (dst->type == GGML_TYPE_F32 || needs_padding) {
+        if (dst->type == GGML_TYPE_F32) {
+            void * out_f16_buffer = out_f16_allocator.alloc(ggml_nelements(dst) * faElemSize);
+
             int64_t * out_f16_ne = src0_bsnd_ne;
             size_t    out_f16_nb[GGML_MAX_DIMS];
             out_f16_nb[0] = faElemSize;
             for (int i = 1; i < GGML_MAX_DIMS; ++i) {
                 out_f16_nb[i] = out_f16_nb[i - 1] * out_f16_ne[i - 1];
             }
-            int64_t out_nelements = out_f16_ne[0] * out_f16_ne[1] * out_f16_ne[2] * out_f16_ne[3];
-            void *  out_f16_buffer = out_f16_allocator.alloc(out_nelements * faElemSize);
 
             fa_dst_tensor =
                 ggml_cann_create_tensor(out_f16_buffer, faDataType, faElemSize, out_f16_ne, out_f16_nb, GGML_MAX_DIMS);
@@ -3794,33 +3730,8 @@ void ggml_cann_flash_attn_ext(ggml_backend_cann_context & ctx, ggml_tensor * dst
                                 nullptr                                // softmaxLse
         );
 
-        // Step 6: post-processing — slice padded output and/or cast to f32
-        if (needs_padding) {
-            ggml_cann_pool_alloc sliced_f16_allocator(ctx.pool());
-
-            if (dst->type == GGML_TYPE_F32) {
-                int64_t sliced_ne[GGML_MAX_DIMS] = { D, src0_bsnd_ne[1], src0_bsnd_ne[2], src0_bsnd_ne[3] };
-                size_t  sliced_nb[GGML_MAX_DIMS];
-                sliced_nb[0] = faElemSize;
-                for (int i = 1; i < GGML_MAX_DIMS; ++i) {
-                    sliced_nb[i] = sliced_nb[i - 1] * sliced_ne[i - 1];
-                }
-                int64_t sliced_nelements = sliced_ne[0] * sliced_ne[1] * sliced_ne[2] * sliced_ne[3];
-                void *  sliced_buffer    = sliced_f16_allocator.alloc(sliced_nelements * faElemSize);
-                acl_tensor_ptr sliced_f16_tensor = ggml_cann_create_tensor(sliced_buffer, faDataType, faElemSize,
-                                                                           sliced_ne, sliced_nb, GGML_MAX_DIMS);
-
-                GGML_CANN_CALL_ACLNN_OP(ctx, Slice, fa_dst_tensor.get(),
-                                        (int64_t) -1, (int64_t) 0, D, (int64_t) 1, sliced_f16_tensor.get());
-
-                acl_tensor_ptr acl_dst_tensor = ggml_cann_create_tensor(dst);
-                aclnn_cast(ctx, sliced_f16_tensor.get(), acl_dst_tensor.get(), ggml_cann_type_mapping(dst->type));
-            } else {
-                acl_tensor_ptr acl_dst_tensor = ggml_cann_create_tensor(dst);
-                GGML_CANN_CALL_ACLNN_OP(ctx, Slice, fa_dst_tensor.get(),
-                                        (int64_t) -1, (int64_t) 0, D, (int64_t) 1, acl_dst_tensor.get());
-            }
-        } else if (dst->type == GGML_TYPE_F32) {
+        if (dst->type == GGML_TYPE_F32) {
+            // Step 6: post-processing, permute and cast to f32
             acl_tensor_ptr acl_dst_tensor = ggml_cann_create_tensor(dst);
             aclnn_cast(ctx, fa_dst_tensor.get(), acl_dst_tensor.get(), ggml_cann_type_mapping(dst->type));
         }

ggml/src/ggml-cann/ggml-cann.cpp

@@ -1234,8 +1234,7 @@ static void ggml_backend_cann_buffer_set_tensor(ggml_backend_buffer_t buffer,
     static bool weight_to_nz = parse_bool(get_env_as_lowercase("GGML_CANN_WEIGHT_NZ").value_or("on"));
     if (!need_transform(tensor->type)) {
         ACL_CHECK(aclrtMemcpy((char *) tensor->data + offset, size, data, size, ACL_MEMCPY_HOST_TO_DEVICE));
-        if (weight_to_nz && tensor->type != GGML_TYPE_BF16
-            && is_matmul_weight((const ggml_tensor *) tensor)) {
+        if (weight_to_nz && is_matmul_weight((const ggml_tensor *) tensor)) {
             GGML_ASSERT(tensor->ne[2] == 1);
             GGML_ASSERT(tensor->ne[3] == 1);
             weight_format_to_nz(tensor, offset, ctx->device);
@@ -1444,8 +1443,7 @@ static size_t ggml_backend_cann_buffer_type_get_alloc_size(ggml_backend_buffer_t
         if (ne0 % MATRIX_ROW_PADDING != 0) {
             size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
         }
-    } else if (weight_to_nz && tensor->type != GGML_TYPE_BF16
-               && is_matmul_weight((const ggml_tensor *) tensor)) {
+    } else if (weight_to_nz && is_matmul_weight((const ggml_tensor *) tensor)) {
         // NZ format weight are not support quantized yet.
         // If ND tensor transform to NZ, size may changed.
         int64_t shape[] = { tensor->ne[1], tensor->ne[0] };
@@ -2285,9 +2283,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
         case GGML_OP_MUL_MAT:
             {
                 switch (op->src[0]->type) {
-#ifndef ASCEND_310P
-                    case GGML_TYPE_BF16:
-#endif
                     case GGML_TYPE_F16:
                     case GGML_TYPE_F32:
                         return true;
@@ -2325,9 +2320,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
                 switch (op->src[0]->type) {
                     case GGML_TYPE_F32:
                     case GGML_TYPE_F16:
-#ifndef ASCEND_310P
-                    case GGML_TYPE_BF16:
-#endif
                     case GGML_TYPE_Q8_0:
                         return true;
                     default:
@@ -2340,9 +2332,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
                 switch (op->type) {
                     case GGML_TYPE_F32:
                     case GGML_TYPE_F16:
-#ifndef ASCEND_310P
-                    case GGML_TYPE_BF16:
-#endif
                         return true;
                     default:
                         return false;
@@ -2352,30 +2341,20 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
         case GGML_OP_CPY:
             {
                 ggml_tensor * src = op->src[0];
-#ifdef ASCEND_310P
                 if ((op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_F16) ||
                     (src->type != GGML_TYPE_F32 && src->type != GGML_TYPE_F16)) {
-                    // only support F32 and F16 on 310P.
+                    // only support F32 and F16.
                     return false;
                 }
-#else
-                if ((op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_F16 && op->type != GGML_TYPE_BF16) ||
-                    (src->type != GGML_TYPE_F32 && src->type != GGML_TYPE_F16 && src->type != GGML_TYPE_BF16)) {
-                    // only support F32, F16 and BF16.
-                    return false;
-                }
-#endif
                 return true;
             }
             break;
         case GGML_OP_CONT:
             {
+                // TODO: support GGML_TYPE_BF16
                 switch (op->src[0]->type) {
                     case GGML_TYPE_F32:
                     case GGML_TYPE_F16:
-#ifndef ASCEND_310P
-                    case GGML_TYPE_BF16:
-#endif
                         return true;
                     default:
                         return false;
@@ -2524,6 +2503,10 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
                     // different head sizes of K and V are not supported yet
                     return false;
                 }
+                if (op->src[0]->ne[0] % 16 != 0) {
+                    // TODO: padding to support
+                    return false;
+                }
                 float logitSoftcap = 0.0f;
                 memcpy(&logitSoftcap, (const float *) (op->op_params) + 2, sizeof(float));
                 if (logitSoftcap != 0.0f) {

ggml/src/ggml-common.h

@@ -102,9 +102,6 @@ typedef sycl::half2 ggml_half2;
 #define QI_MXFP4 (QK_MXFP4 / (4 * QR_MXFP4))
 #define QR_MXFP4 2
 
-#define QI_NVFP4 (QK_NVFP4 / (4 * QR_NVFP4))
-#define QR_NVFP4 2
-
 #define QI5_0 (QK5_0 / (4 * QR5_0))
 #define QR5_0 2
 
@@ -197,14 +194,6 @@ typedef struct {
 } block_mxfp4;
 static_assert(sizeof(block_mxfp4) == sizeof(uint8_t) + QK_MXFP4/2, "wrong mxfp4 block size/padding");
 
-#define QK_NVFP4 64
-#define QK_NVFP4_SUB 16  // sub-block size for per-group scales
-typedef struct {
-    uint8_t d[QK_NVFP4/QK_NVFP4_SUB]; // UE4M3 scales (4 bytes, one per 16-element sub-block)
-    uint8_t qs[QK_NVFP4/2];           // packed 4-bit E2M1 values (32 bytes)
-} block_nvfp4;
-static_assert(sizeof(block_nvfp4) == sizeof(uint8_t)*(QK_NVFP4/QK_NVFP4_SUB) + QK_NVFP4/2, "wrong nvfp4 block size/padding");
-
 #define QK5_0 32
 typedef struct {
     ggml_half d;           // delta

ggml/src/ggml-cpu/CMakeLists.txt

@@ -570,36 +570,24 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
         set(KLEIDIAI_DOWNLOAD_URL "https://github.com/ARM-software/kleidiai/archive/refs/tags/${KLEIDIAI_COMMIT_TAG}.tar.gz")
         set(KLEIDIAI_ARCHIVE_MD5  "54049037570ab0ee0a0d126b2ba5ece1")
 
-        set(KLEIDIAI_FETCH_ARGS
-            URL ${KLEIDIAI_DOWNLOAD_URL}
-            URL_HASH MD5=${KLEIDIAI_ARCHIVE_MD5}
-        )
-        if (CMAKE_VERSION VERSION_GREATER_EQUAL "3.24")
-            list(APPEND KLEIDIAI_FETCH_ARGS DOWNLOAD_EXTRACT_TIMESTAMP NEW)
+        if (POLICY CMP0135)
+            cmake_policy(SET CMP0135 NEW)
         endif()
 
-        if (CMAKE_VERSION VERSION_GREATER_EQUAL "3.28")
-            FetchContent_Declare(KleidiAI_Download
-                ${KLEIDIAI_FETCH_ARGS}
-                EXCLUDE_FROM_ALL
-            )
+        # TODO: Use FetchContent_MakeAvailable with EXCLUDE_FROM_ALL after bumping minimum CMake version to 3.28+
+        # Using FetchContent_Populate instead to avoid EXCLUDE_FROM_ALL which requires CMake 3.28
+        FetchContent_Declare(KleidiAI_Download
+            URL ${KLEIDIAI_DOWNLOAD_URL}
+            DOWNLOAD_EXTRACT_TIMESTAMP NEW
+            URL_HASH MD5=${KLEIDIAI_ARCHIVE_MD5})
 
-            FetchContent_MakeAvailable(KleidiAI_Download)
+        FetchContent_GetProperties(KleidiAI_Download
+            SOURCE_DIR  KLEIDIAI_SRC
+            POPULATED   KLEIDIAI_POPULATED)
+
+        if (NOT KLEIDIAI_POPULATED)
+            FetchContent_Populate(KleidiAI_Download)
             FetchContent_GetProperties(KleidiAI_Download SOURCE_DIR KLEIDIAI_SRC)
-        else()
-            FetchContent_Declare(KleidiAI_Download
-                ${KLEIDIAI_FETCH_ARGS}
-            )
-
-            FetchContent_GetProperties(KleidiAI_Download
-                SOURCE_DIR KLEIDIAI_SRC
-                POPULATED KLEIDIAI_POPULATED
-            )
-
-            if (NOT KLEIDIAI_POPULATED)
-                FetchContent_Populate(KleidiAI_Download)
-                FetchContent_GetProperties(KleidiAI_Download SOURCE_DIR KLEIDIAI_SRC)
-            endif()
         endif()
 
         add_compile_definitions(GGML_USE_CPU_KLEIDIAI)

ggml/src/ggml-cpu/arch-fallback.h

@@ -15,7 +15,6 @@
 #define ggml_vec_dot_q5_1_q8_1_generic ggml_vec_dot_q5_1_q8_1
 #define ggml_vec_dot_q8_0_q8_0_generic ggml_vec_dot_q8_0_q8_0
 #define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
-#define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0
 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
 #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
 #define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K
@@ -80,8 +79,6 @@
 #define ggml_gemm_mxfp4_8x8_q8_0_generic ggml_gemm_mxfp4_8x8_q8_0
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #elif defined(__x86_64__) || defined(__i386__) || defined(_M_IX86) || defined(_M_X64)
-// quants.c
-#define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_K_4x4_generic ggml_quantize_mat_q8_K_4x4
@@ -111,7 +108,6 @@
 // ref: https://github.com/ggml-org/llama.cpp/pull/14146#issuecomment-2972561679
 // quants.c
 #define quantize_row_q8_K_generic quantize_row_q8_K
-#define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0
 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
 #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
@@ -159,7 +155,6 @@
 #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
 #define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
-#define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
@@ -199,11 +194,16 @@
 #define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0
 #elif defined(__riscv)
 // quants.c
-#define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0
+#define quantize_row_q8_K_generic quantize_row_q8_K
+#define ggml_vec_dot_iq2_xxs_q8_K_generic ggml_vec_dot_iq2_xxs_q8_K
+#define ggml_vec_dot_iq2_xs_q8_K_generic ggml_vec_dot_iq2_xs_q8_K
+#define ggml_vec_dot_iq3_xxs_q8_K_generic ggml_vec_dot_iq3_xxs_q8_K
+#define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
+#define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K
+#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // repack.cpp
-#define ggml_quantize_mat_q8_0_4x1_generic ggml_quantize_mat_q8_0_4x1
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
-#define ggml_quantize_mat_q8_K_4x1_generic ggml_quantize_mat_q8_K_4x1
+#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
 #define ggml_quantize_mat_q8_K_4x4_generic ggml_quantize_mat_q8_K_4x4
 #define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
 #define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
@@ -239,7 +239,6 @@
 #elif defined(__s390x__)
 // quants.c
 #define quantize_row_q8_K_generic quantize_row_q8_K
-#define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0
 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
 #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
 #define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K
@@ -302,7 +301,6 @@
 #define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
 #define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K
 #define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
-#define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8

ggml/src/ggml-cpu/arch/arm/quants.c

@@ -650,90 +650,6 @@ void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = sumf;
 }
 
-void ggml_vec_dot_nvfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
-    assert(nrc == 1);
-    UNUSED(nrc);
-    UNUSED(bx);
-    UNUSED(by);
-    UNUSED(bs);
-    assert(n % QK_NVFP4 == 0);
-
-    const block_nvfp4 * GGML_RESTRICT x = vx;
-    const block_q8_0 * GGML_RESTRICT y = vy;
-
-    // Each NVFP4 super-block (64 elements) spans 2 q8_0 blocks
-    const int nb = n / QK_NVFP4;
-
-    float sumf = 0;
-
-#if defined(__ARM_NEON) && defined(__ARM_FEATURE_FMA)
-    const int8x16_t values = vld1q_s8(kvalues_mxfp4);
-    const uint8x16_t m4b = vdupq_n_u8(0x0f);
-    float32x4_t acc = vdupq_n_f32(0.0f);
-
-    for (int ib = 0; ib < nb; ++ib) {
-        const uint8x16_t q4bits_0 = vld1q_u8(x[ib].qs);
-        const uint8x16_t q4bits_1 = vld1q_u8(x[ib].qs + 16);
-
-        const int8x16_t q4_lo_0 = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits_0, m4b));
-        const int8x16_t q4_hi_0 = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits_0, 4));
-        const int8x16_t q4_lo_1 = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits_1, m4b));
-        const int8x16_t q4_hi_1 = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits_1, 4));
-
-        const int8x16_t q8_0a = vld1q_s8(y[2*ib].qs);
-        const int8x16_t q8_0b = vld1q_s8(y[2*ib].qs + 16);
-        const int8x16_t q8_lo_0 = vcombine_s8(vget_low_s8(q8_0a), vget_low_s8(q8_0b));
-        const int8x16_t q8_hi_0 = vcombine_s8(vget_high_s8(q8_0a), vget_high_s8(q8_0b));
-
-        const int8x16_t q8_1a = vld1q_s8(y[2*ib+1].qs);
-        const int8x16_t q8_1b = vld1q_s8(y[2*ib+1].qs + 16);
-        const int8x16_t q8_lo_1 = vcombine_s8(vget_low_s8(q8_1a), vget_low_s8(q8_1b));
-        const int8x16_t q8_hi_1 = vcombine_s8(vget_high_s8(q8_1a), vget_high_s8(q8_1b));
-
-        const int32x4_t p0 = vaddq_s32(
-            ggml_vdotq_s32(vdupq_n_s32(0), q4_lo_0, q8_lo_0),
-            ggml_vdotq_s32(vdupq_n_s32(0), q4_hi_0, q8_hi_0));
-        const int32x4_t p1 = vaddq_s32(
-            ggml_vdotq_s32(vdupq_n_s32(0), q4_lo_1, q8_lo_1),
-            ggml_vdotq_s32(vdupq_n_s32(0), q4_hi_1, q8_hi_1));
-
-        const int32x4_t sums = vpaddq_s32(p0, p1);
-
-        // Decode 4 UE4M3 scales to f32 and multiply with q8 scales
-        const float dy0 = GGML_CPU_FP16_TO_FP32(y[2*ib].d);
-        const float dy1 = GGML_CPU_FP16_TO_FP32(y[2*ib+1].d);
-        const float32x4_t nvsc = {
-            ggml_ue4m3_to_fp32(x[ib].d[0]),
-            ggml_ue4m3_to_fp32(x[ib].d[1]),
-            ggml_ue4m3_to_fp32(x[ib].d[2]),
-            ggml_ue4m3_to_fp32(x[ib].d[3])
-        };
-        const float32x4_t scales = vmulq_f32(nvsc, (float32x4_t){dy0, dy0, dy1, dy1});
-
-        acc = vfmaq_f32(acc, vcvtq_f32_s32(sums), scales);
-    }
-    sumf = vaddvq_f32(acc);
-#else
-    for (int ib = 0; ib < nb; ++ib) {
-        for (int si = 0; si < 4; ++si) {
-            const float d = ggml_ue4m3_to_fp32(x[ib].d[si]);
-            const int q8b = si / 2;
-            const int q8o = (si % 2) * QK_NVFP4_SUB;
-            const float dy = GGML_CPU_FP16_TO_FP32(y[2*ib + q8b].d);
-
-            int sumi_lo = 0, sumi_hi = 0;
-            for (int j = 0; j < QK_NVFP4_SUB/2; ++j) {
-                const uint8_t qv = x[ib].qs[si*(QK_NVFP4_SUB/2) + j];
-                sumi_lo += y[2*ib + q8b].qs[q8o + j +               0] * kvalues_mxfp4[qv & 0xf];
-                sumi_hi += y[2*ib + q8b].qs[q8o + j + QK_NVFP4_SUB/2] * kvalues_mxfp4[qv >>  4];
-            }
-            sumf += dy * d * (sumi_lo + sumi_hi);
-        }
-    }
-#endif
-    *s = sumf;
-}
-
 void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
     const int qk = QK8_0;
     const int nb = n / qk;

ggml/src/ggml-cpu/arch/x86/repack.cpp

@@ -531,6 +531,7 @@ static void gemv_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t
 
     UNUSED(bs);
 
+    __m128i changemask = _mm_set_epi8(15, 14, 7, 6, 13, 12, 5, 4, 11, 10, 3, 2, 9, 8, 1, 0);
     __m256i finalpermutemask = _mm256_set_epi32(7, 5, 3, 1, 6, 4, 2, 0);
 
     // Permute mask used for easier vector processing at later stages
@@ -579,7 +580,6 @@ static void gemv_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t
                 if constexpr (
                         std::is_same_v<block_tx8, block_q4_0x8> ||
                         std::is_same_v<block_tx8, block_iq4_nlx8>) {
-                    const __m128i changemask = _mm_set_epi8(15, 14, 7, 6, 13, 12, 5, 4, 11, 10, 3, 2, 9, 8, 1, 0);
                     col_scale_f32 = GGML_F32Cx8_REARRANGE_LOAD(b_ptr[b].d, changemask);
                 } else if constexpr (std::is_same_v<block_tx8, block_mxfp4x8>) {
                     // Load 8 E8M0 exponents and convert to float via LUT

ggml/src/ggml-cpu/ggml-cpu.c

@@ -270,12 +270,6 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_0,
         .nrows                    = 1,
     },
-    [GGML_TYPE_NVFP4] = {
-        .from_float               = quantize_row_nvfp4,
-        .vec_dot                  = ggml_vec_dot_nvfp4_q8_0,
-        .vec_dot_type             = GGML_TYPE_Q8_0,
-        .nrows                    = 1,
-    },
     [GGML_TYPE_Q2_K] = {
         .from_float               = quantize_row_q2_K,
         .vec_dot                  = ggml_vec_dot_q2_K_q8_K,

ggml/src/ggml-cpu/kleidiai/kernels.cpp

@@ -520,7 +520,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .packed_stride_ex      = */ &rhs_stride_fn4<kai_get_rhs_packed_stride_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0>,
             /* .pack_func_ex          = */ &rhs_pack_fn12<kai_run_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0>,
         },
-        /* .required_cpu       = */ CPU_FEATURE_I8MM,
+        /* .required_cpu       = */ CPU_FEATURE_DOTPROD | CPU_FEATURE_I8MM,
         /* .lhs_type           = */ GGML_TYPE_F32,
         /* .rhs_type           = */ GGML_TYPE_Q4_0,
         /* .op_type            = */ GGML_TYPE_F32,
@@ -631,7 +631,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .packed_stride_ex      = */ &rhs_stride_fn4<kai_get_rhs_packed_stride_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0>,
             /* .pack_func_ex          = */ &rhs_pack_fn12<kai_run_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0>,
         },
-        /* .required_cpu       = */ CPU_FEATURE_I8MM,
+        /* .required_cpu       = */ CPU_FEATURE_DOTPROD | CPU_FEATURE_I8MM,
         /* .lhs_type           = */ GGML_TYPE_F32,
         /* .rhs_type           = */ GGML_TYPE_Q4_0,
         /* .op_type            = */ GGML_TYPE_F32,
@@ -801,7 +801,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels_q8[] = {
             /* .packed_stride_ex      = */ &rhs_stride_fn4<kai_get_rhs_packed_stride_rhs_pack_nxk_qsi8cxp_qsi8cx_neon>,
             /* .pack_func_ex          = */ &rhs_pack_scale_fn12<kai_run_rhs_pack_nxk_qsi8cxp_qsi8cx_neon>,
         },
-        /* .required_cpu       = */ CPU_FEATURE_I8MM,
+        /* .required_cpu       = */ CPU_FEATURE_DOTPROD | CPU_FEATURE_I8MM,
         /* .lhs_type           = */ GGML_TYPE_F32,
         /* .rhs_type           = */ GGML_TYPE_Q8_0,
         /* .op_type            = */ GGML_TYPE_F32,

ggml/src/ggml-cpu/ops.cpp

@@ -670,7 +670,6 @@ void ggml_compute_forward_add(
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_MXFP4:
-        case GGML_TYPE_NVFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -1120,7 +1119,6 @@ void ggml_compute_forward_add1(
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q8_1:
         case GGML_TYPE_MXFP4:
-        case GGML_TYPE_NVFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -1249,7 +1247,6 @@ void ggml_compute_forward_acc(
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q8_1:
         case GGML_TYPE_MXFP4:
-        case GGML_TYPE_NVFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -4337,7 +4334,6 @@ void ggml_compute_forward_out_prod(
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_MXFP4:
-        case GGML_TYPE_NVFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -4613,7 +4609,6 @@ void ggml_compute_forward_set(
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q8_1:
         case GGML_TYPE_MXFP4:
-        case GGML_TYPE_NVFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -4836,7 +4831,6 @@ void ggml_compute_forward_get_rows(
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q8_1:
         case GGML_TYPE_MXFP4:
-        case GGML_TYPE_NVFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -5561,7 +5555,6 @@ void ggml_compute_forward_clamp(
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q8_1:
         case GGML_TYPE_MXFP4:
-        case GGML_TYPE_NVFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -6205,7 +6198,7 @@ static void ggml_compute_forward_im2col_f16(
     const ggml_tensor * src1 = dst->src[1];
 
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
-    GGML_ASSERT(src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT( dst->type == GGML_TYPE_F16);
 
     GGML_TENSOR_BINARY_OP_LOCALS;
@@ -6236,7 +6229,7 @@ static void ggml_compute_forward_im2col_f16(
     int ofs1 = is_2D ? nb12 : nb11;
 
     GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
-    GGML_ASSERT(nb10 == ggml_type_size(src1->type));
+    GGML_ASSERT(nb10 == sizeof(float));
 
     // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW]
     {
@@ -6249,12 +6242,7 @@ static void ggml_compute_forward_im2col_f16(
 
                         // micro kernel
                         ggml_fp16_t * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW]
-                        const float * const src_data_f32 = src1->type == GGML_TYPE_F32
-                            ? (const float *)((const char *) src1->data + in*ofs0 + iic*ofs1)
-                            : nullptr; // [IH, IW]
-                        const ggml_fp16_t * const src_data_f16 = src1->type == GGML_TYPE_F16
-                            ? (const ggml_fp16_t *)((const char *) src1->data + in*ofs0 + iic*ofs1)
-                            : nullptr; // [IH, IW]
+                        const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW]
 
                         for (int64_t ikh = 0; ikh < KH; ikh++) {  // 1
                             for (int64_t ikw = 0; ikw < KW; ikw++) {
@@ -6264,11 +6252,7 @@ static void ggml_compute_forward_im2col_f16(
                                 if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
                                     dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0;
                                 } else {
-                                    if (src_data_f32 != nullptr) {
-                                        dst_data[iic*(KH*KW) + ikh*KW + ikw] = GGML_CPU_FP32_TO_FP16(src_data_f32[iih*IW + iiw]);
-                                    } else {
-                                        dst_data[iic*(KH*KW) + ikh*KW + ikw] = src_data_f16[iih*IW + iiw];
-                                    }
+                                    dst_data[iic*(KH*KW) + ikh*KW + ikw] = GGML_CPU_FP32_TO_FP16(src_data[iih*IW + iiw]);
                                 }
                             }
                         }
@@ -9633,7 +9617,7 @@ void ggml_compute_forward_win_unpart(
     }
 }
 
-//ggml_compute_forward_unary
+//gmml_compute_forward_unary
 
 void ggml_compute_forward_unary(
         const ggml_compute_params * params,
@@ -10452,8 +10436,8 @@ static void ggml_compute_forward_gated_delta_net_one_chunk(
 
     const float * state_in_base = (const float *)src_state->data;
 
-  //const int64_t rq1 = nev1 / neq1;
-  //const int64_t rk1 = nev1 / nek1;
+    const int64_t rq1 = nev1 / neq1;
+    const int64_t rk1 = nev1 / nek1;
     const int64_t rq3 = nev3 / neq3;
     const int64_t rk3 = nev3 / nek3;
 
@@ -10463,8 +10447,8 @@ static void ggml_compute_forward_gated_delta_net_one_chunk(
         const int64_t iv1 = ir % H; // head_index
         const int64_t iv3 = ir / H; // sequence
 
-        const int64_t iq1 = iv1 % neq1;
-        const int64_t ik1 = iv1 % nek1;
+        const int64_t iq1 = iv1 / rq1;
+        const int64_t ik1 = iv1 / rk1;
 
         const int64_t iq3 = iv3 / rq3;
         const int64_t ik3 = iv3 / rk3;
@@ -10484,45 +10468,40 @@ static void ggml_compute_forward_gated_delta_net_one_chunk(
             const float * v_d = (const float *)((const char *)src_v->data + iv3 * nbv3 + t * nbv2 + iv1 * nbv1);
 
             const float beta_val = *(const float *)((const char *)src_beta->data + iv3 * nbb3 + t * nbb2 + iv1 * nbb1);
-            const float * g_d    =  (const float *)((const char *)src_g->data    + iv3 * nbg3 + t * nbg2 + iv1 * nbg1);
-
-            // state is stored transposed: s_out[j*S_v + i] = S[i][j]
-            // so row j of s_out = column j of S (contiguous access)
+            const float * g_d   =  (const float *)((const char *)src_g->data    + iv3 * nbg3 + t * nbg2 + iv1 * nbg1);
 
             if (kda) {
-                // precompute exp(g) into delta scratch (reused below)
                 for (int64_t i = 0; i < S_v; ++i) {
-                    delta[i] = expf(g_d[i]);
-                }
-                // S[i][:] *= exp(g[i]) => for each row j of M: M[j][i] *= exp(g[i])
-                for (int64_t j = 0; j < S_v; ++j) {
-                    ggml_vec_mul_f32(S_v, &s_out[j * S_v], &s_out[j * S_v], delta);
+                    ggml_vec_scale_f32(S_v, &s_out[i * S_v], expf(g_d[i]));
                 }
             } else {
                 ggml_vec_scale_f32(S_v * S_v, s_out, expf(g_d[0]));
             }
 
-            // delta[j] = sum_i S[i][j] * k[i] = dot(row j of M, k)
+            // delta[j] = sum_i S[j][i] * k[i]
+            memset(delta, 0, S_v * sizeof(float));
+            for (int64_t i = 0; i < S_v; ++i) {
+                ggml_vec_mad_f32(S_v, delta, &s_out[i * S_v], k_d[i]);
+            }
             for (int64_t j = 0; j < S_v; ++j) {
-                float sum = 0.0f;
-                ggml_vec_dot_f32(S_v, &sum, 0, &s_out[j * S_v], 0, k_d, 0, 1);
-                delta[j] = (v_d[j] - sum) * beta_val;
+                delta[j] = (v_d[j] - delta[j]) * beta_val;
             }
 
-            // outer product: S[i][j] += k[i] * delta[j] => M[j][i] += delta[j] * k[i]
-            for (int64_t j = 0; j < S_v; ++j) {
-                ggml_vec_mad_f32(S_v, &s_out[j * S_v], k_d, delta[j]);
+            // outer product: S[j][i] += k[i] * delta[j]
+            for (int64_t i = 0; i < S_v; ++i) {
+                ggml_vec_mad_f32(S_v, &s_out[i * S_v], delta, k_d[i]);
             }
 
-            // attn_out[j] = sum_i S[i][j] * q[i] = dot(row j of M, q)
-            for (int64_t j = 0; j < S_v; ++j) {
-                float sum = 0.0f;
-                ggml_vec_dot_f32(S_v, &sum, 0, &s_out[j * S_v], 0, q_d, 0, 1);
-                attn_data[j] = sum * scale;
+            // attn_out[j] = sum_i S[j][i] * q[i]
+            memset(attn_data, 0, S_v * sizeof(float));
+            for (int64_t i = 0; i < S_v; ++i) {
+                ggml_vec_mad_f32(S_v, attn_data, &s_out[i * S_v], q_d[i]);
             }
+            ggml_vec_scale_f32(S_v, attn_data, scale);
 
             attn_data += S_v * H; // advance to next token
         }
+
     }
 }
 

ggml/src/ggml-cpu/quants.c

@@ -50,10 +50,6 @@ void quantize_row_mxfp4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, i
     quantize_row_mxfp4_ref(x, y, k);
 }
 
-void quantize_row_nvfp4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k) {
-    quantize_row_nvfp4_ref(x, y, k);
-}
-
 //
 // 2-6 bit quantization in super-blocks
 //
@@ -220,42 +216,6 @@ void ggml_vec_dot_mxfp4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
     *s = sumf;
 }
 
-// NVFP4: super-block of 64 elements = 4 sub-blocks of 16 = 2 q8_0 blocks
-void ggml_vec_dot_nvfp4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
-    assert(nrc == 1);
-    UNUSED(nrc);
-    UNUSED(bx);
-    UNUSED(by);
-    UNUSED(bs);
-    assert(n % QK_NVFP4 == 0);
-
-    const block_nvfp4 * GGML_RESTRICT x = vx;
-    const block_q8_0 * GGML_RESTRICT y = vy;
-
-    const int nb = n / QK_NVFP4;
-
-    float sumf = 0;
-
-    for (int ib = 0; ib < nb; ++ib) {
-        for (int s_idx = 0; s_idx < 4; ++s_idx) {
-            const float d = ggml_ue4m3_to_fp32(x[ib].d[s_idx]);
-            const int q8_block = s_idx / 2;
-            const int q8_off   = (s_idx % 2) * QK_NVFP4_SUB;
-            const float dy = GGML_CPU_FP16_TO_FP32(y[2*ib + q8_block].d);
-
-            int sumi_lo = 0, sumi_hi = 0;
-            for (int j = 0; j < QK_NVFP4_SUB/2; ++j) {
-                const uint8_t qv = x[ib].qs[s_idx*(QK_NVFP4_SUB/2) + j];
-                sumi_lo += y[2*ib + q8_block].qs[q8_off + j +               0] * kvalues_mxfp4[qv & 0xf];
-                sumi_hi += y[2*ib + q8_block].qs[q8_off + j + QK_NVFP4_SUB/2] * kvalues_mxfp4[qv >>  4];
-            }
-
-            sumf += dy * d * (sumi_lo + sumi_hi);
-        }
-    }
-    *s = sumf;
-}
-
 void ggml_vec_dot_q5_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
     const int qk = QK8_0;
     const int nb = n / qk;

ggml/src/ggml-cpu/quants.h

@@ -20,7 +20,6 @@ void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in
 void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 
 void quantize_row_mxfp4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
-void quantize_row_nvfp4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 
 void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
@@ -43,7 +42,6 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
 void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 
 void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
-void ggml_vec_dot_nvfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 
 void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
@@ -75,7 +73,6 @@ void ggml_vec_dot_q5_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, c
 void ggml_vec_dot_q8_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 
 void ggml_vec_dot_mxfp4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
-void ggml_vec_dot_nvfp4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 
 void ggml_vec_dot_tq1_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_tq2_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);

ggml/src/ggml-cpu/repack.h

@@ -28,17 +28,13 @@ template <int K, int N> struct block {
 // control size
 static_assert(sizeof(block<4, 4>) == 4 * sizeof(ggml_half) + QK8_0 * 2, "wrong block<4,4> size/padding");
 static_assert(sizeof(block<4, 8>) == 8 * sizeof(ggml_half) + QK8_0 * 4, "wrong block<4,8> size/padding");
-static_assert(sizeof(block<4, 16>) == 16 * sizeof(ggml_half) + QK8_0 * 8, "wrong block<4,16> size/padding");
 static_assert(sizeof(block<8, 4>) == 4 * sizeof(ggml_half) + QK8_0 * 4, "wrong block<8,4> size/padding");
 static_assert(sizeof(block<8, 8>) == 8 * sizeof(ggml_half) + QK8_0 * 8, "wrong block<8,8> size/padding");
-static_assert(sizeof(block<8, 16>) == 16 * sizeof(ggml_half) + QK8_0 * 16, "wrong block<8,16> size/padding");
 
 using block_q4_0x4 = block<4, 4>;
 using block_q4_0x8 = block<4, 8>;
-using block_q4_0x16 = block<4, 16>;
 using block_q8_0x4 = block<8, 4>;
 using block_q8_0x8 = block<8, 8>;
-using block_q8_0x16 = block<8, 16>;
 
 struct block_q4_Kx8 {
     ggml_half d[8];      // super-block scale for quantized scales
@@ -48,14 +44,7 @@ struct block_q4_Kx8 {
 };
 
 static_assert(sizeof(block_q4_Kx8) == sizeof(ggml_half) * 16 + K_SCALE_SIZE * 8 + QK_K * 4, "wrong q4_K block size/padding");
-struct block_q4_Kx16 {
-    ggml_half d[16];      // super-block scale for quantized scales
-    ggml_half dmin[16];   // super-block scale for quantized mins
-    uint8_t scales[192];  // scales and mins, quantized with 6 bits
-    uint8_t qs[2048];    // 4--bit quants
-};
 
-static_assert(sizeof(block_q4_Kx16) == sizeof(ggml_half) * 32 + K_SCALE_SIZE * 16 + QK_K * 8, "wrong q4_K block size/padding");
 struct block_q2_Kx8 {
     ggml_half d[8];      // super-block scale for quantized scales
     ggml_half dmin[8];   // super-block scale for quantized mins
@@ -64,13 +53,6 @@ struct block_q2_Kx8 {
 };
 
 static_assert(sizeof(block_q2_Kx8) == sizeof(ggml_half) * 16 + QK_K/2 + QK_K * 2, "wrong q2_K block size/padding");
-struct block_q2_Kx16 {
-    ggml_half d[16];       // Super-block scale for quantized scales
-    ggml_half dmin[16];    // Super-block scale for quantized mins
-    uint8_t   scales[256]; // Sub-block scales (16 cols * 16 sub-blocks)
-    uint8_t   qs[1024];    // Data (16 cols * 64 bytes per block)
-};
-static_assert(sizeof(block_q2_Kx16) == sizeof(ggml_half) * 32 + QK_K + QK_K * 4, "wrong q2_K block size/padding");
 
 struct block_q5_Kx8 {
     ggml_half d[8];              // super-block scale for quantized scales
@@ -115,12 +97,6 @@ struct block_iq4_nlx8 {
 
 static_assert(sizeof(block_iq4_nlx8) == 8 * sizeof(ggml_half) + QK4_NL * 4, "wrong iq4_nlx8 block size/padding");
 
-struct block_iq4_nlx16 {
-    ggml_half d[16];            // deltas for 16 iq4_nl blocks
-    uint8_t   qs[QK4_NL * 8];  // nibbles / quants for 16 iq4_nl blocks
-};
-
-static_assert(sizeof(block_iq4_nlx16) == 16 * sizeof(ggml_half) + QK4_NL * 8, "wrong iq4_nlx16 block size/padding");
 struct block_mxfp4x4 {
     uint8_t e[4];
     uint8_t qs[QK_MXFP4 * 2];
@@ -133,6 +109,7 @@ struct block_mxfp4x8 {
 };
 static_assert(sizeof(block_mxfp4x8) == 8 + QK_MXFP4 * 4, "wrong mxfp4x8 block size/padding");
 
+
 #if defined(__cplusplus)
 extern "C" {
 #endif
@@ -155,8 +132,6 @@ void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const
 void ggml_gemv_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_mxfp4_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_mxfp4_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_q8_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_q8_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
@@ -171,22 +146,10 @@ void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const
 void ggml_gemm_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_mxfp4_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_mxfp4_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q8_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q8_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q8_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q8_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-#if defined __riscv_zvfh
-void ggml_quantize_mat_q8_0_4x1(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
-void ggml_quantize_mat_q8_K_4x1(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
-void ggml_gemv_q4_0_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_q4_K_16x1_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_iq4_nl_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_q8_0_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_q2_K_16x1_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemm_q4_0_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemm_q4_K_16x1_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemm_iq4_nl_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemm_q8_0_16x1_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemm_q2_K_16x1_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-#endif
 
 // Native implementations
 void ggml_quantize_mat_q8_0_4x4_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
@@ -207,8 +170,6 @@ void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs
 void ggml_gemv_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_mxfp4_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_mxfp4_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_q8_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_q8_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
@@ -223,22 +184,10 @@ void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs
 void ggml_gemm_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_mxfp4_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_mxfp4_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q8_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q8_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q8_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q8_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-#if defined __riscv_zvfh
-void ggml_quantize_mat_q8_0_4x1_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
-void ggml_quantize_mat_q8_K_4x1_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
-void ggml_gemv_q4_0_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_q4_K_16x1_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_q8_0_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_q2_K_16x1_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_iq4_nl_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemm_q4_0_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemm_q4_K_16x1_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemm_q8_0_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemm_q2_K_16x1_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemm_iq4_nl_16x1_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-#endif
 
 #if defined(__cplusplus)
 } // extern "C"

ggml/src/ggml-cpu/simd-mappings.h

@@ -479,51 +479,13 @@ do {                                                                  \
 
 // F16 AVX512
 
-#if defined(__AVX512FP16__)
-
-#define GGML_F16_STEP 128
-#define GGML_F16_EPR  32
-
-#define GGML_F16x32              __m512h
-#define GGML_F16x32_ZERO         _mm512_setzero_ph()
-#define GGML_F16x32_SET1(x)      _mm512_set1_ph(__extension__(_Float16)(x))
-#define GGML_F16x32_LOAD(x)      _mm512_loadu_ph(x)
-#define GGML_F16x32_STORE(x, y)  _mm512_storeu_ph(x, y)
-#define GGML_F16x32_FMA(a, b, c) _mm512_fmadd_ph(b, c, a)
-#define GGML_F16x32_ADD          _mm512_add_ph
-#define GGML_F16x32_MUL          _mm512_mul_ph
-#define GGML_F16x32_REDUCE(res, x)                                     \
-do {                                                                   \
-    int offset = GGML_F16_ARR >> 1;                                    \
-    for (int i = 0; i < offset; ++i) {                                 \
-        x[i] = _mm512_add_ph(x[i], x[offset+i]);                       \
-    }                                                                  \
-    offset >>= 1;                                                      \
-    for (int i = 0; i < offset; ++i) {                                 \
-        x[i] = _mm512_add_ph(x[i], x[offset+i]);                       \
-    }                                                                  \
-    offset >>= 1;                                                      \
-    for (int i = 0; i < offset; ++i) {                                 \
-        x[i] = _mm512_add_ph(x[i], x[offset+i]);                       \
-    }                                                                  \
-    res = (ggml_float) _mm512_reduce_add_ph(x[0]);                     \
-} while (0)
-
-#define GGML_F16_VEC                GGML_F16x32
-#define GGML_F16_VEC_ZERO           GGML_F16x32_ZERO
-#define GGML_F16_VEC_SET1           GGML_F16x32_SET1
-#define GGML_F16_VEC_LOAD(p, i)     GGML_F16x32_LOAD(p)
-#define GGML_F16_VEC_STORE(p, r, i) GGML_F16x32_STORE(p, r[i])
-#define GGML_F16_VEC_FMA            GGML_F16x32_FMA
-#define GGML_F16_VEC_ADD            GGML_F16x32_ADD
-#define GGML_F16_VEC_MUL            GGML_F16x32_MUL
-#define GGML_F16_VEC_REDUCE         GGML_F16x32_REDUCE
-
-#else // Fallback FP16 <-> FP32
+// F16 AVX
 
 #define GGML_F16_STEP 64
 #define GGML_F16_EPR  16
 
+// AVX512 has FP16 extension (AVX512_FP16) but I don't have it on my machine so I use FP32 instead
+
 #define GGML_F32Cx16             __m512
 #define GGML_F32Cx16_ZERO        _mm512_setzero_ps()
 #define GGML_F32Cx16_SET1(x)     _mm512_set1_ps(x)
@@ -563,8 +525,6 @@ do {                                                              \
 #define GGML_F16_VEC_MUL            GGML_F32Cx16_MUL
 
 #define GGML_F16_VEC_REDUCE         GGML_F32Cx16_REDUCE
-
-#endif // __AVX512FP16__
 #elif defined(__AVX__)
 
 #define GGML_SIMD