quantize: cap working memory size to avoid loading big tensors onto RAM

2026-09-13 17:52:59 +02:00 · 2026-08-27 13:48:24 +02:00
659 changed files with 13808 additions and 73187 deletions
@@ -31,7 +31,7 @@
    ]
    && blas.meta.available,
  useCuda ? config.cudaSupport,
-  useMetalKit ? stdenv.hostPlatform.isAarch64 && stdenv.hostPlatform.isDarwin,
+  useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin,
  # Increases the runtime closure size by ~700M
  useMpi ? false,
  useRocm ? config.rocmSupport,
@@ -92,7 +92,7 @@ let

  cudaBuildInputs = with cudaPackages; [
    cuda_cudart
-    cccl # <nv/target>
+    cuda_cccl # <nv/target>
    libcublas
  ];

@@ -166,7 +166,7 @@ effectiveStdenv.mkDerivation (finalAttrs: {
  # `xcrun` is used find the path of the Metal compiler, which is varible
  # and not on $PATH
  # see https://github.com/ggml-org/llama.cpp/pull/6118 for discussion
-  __noChroot = effectiveStdenv.hostPlatform.isDarwin && useMetalKit && precompileMetalShaders;
+  __noChroot = effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders;

  nativeBuildInputs =
    [
@@ -181,10 +181,10 @@ effectiveStdenv.mkDerivation (finalAttrs: {
      autoAddDriverRunpath
    ]
    ++ optionals (effectiveStdenv.hostPlatform.isGnu && enableStatic) [ glibc.static ]
-    ++ optionals (effectiveStdenv.hostPlatform.isDarwin && useMetalKit && precompileMetalShaders) [ xcrunHost ];
+    ++ optionals (effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders) [ xcrunHost ];

  buildInputs =
-    optionals effectiveStdenv.hostPlatform.isDarwin darwinBuildInputs
+    optionals effectiveStdenv.isDarwin darwinBuildInputs
    ++ optionals useCuda cudaBuildInputs
    ++ optionals useMpi [ mpi ]
    ++ optionals useRocm rocmBuildInputs
@@ -245,7 +245,7 @@ effectiveStdenv.mkDerivation (finalAttrs: {

    # Configurations that are known to result in build failures. Can be
    # overridden by importing Nixpkgs with `allowBroken = true`.
-    broken = (useMetalKit && !effectiveStdenv.hostPlatform.isDarwin);
+    broken = (useMetalKit && !effectiveStdenv.isDarwin);

    description = "Inference of LLaMA model in pure C/C++${descriptionSuffix}";
    homepage = "https://github.com/ggml-org/llama.cpp/";
@@ -1,12 +1,12 @@
-ARG OPENVINO_VERSION_MAJOR=2026.3.1
-ARG OPENVINO_VERSION_FULL=2026.3.1.22476.56d9685302d
+ARG OPENVINO_VERSION_MAJOR=2026.3
+ARG OPENVINO_VERSION_FULL=2026.3.0.22451.bd8d6542e3c
 ARG UBUNTU_VERSION=24.04

 # Intel GPU driver versions. https://github.com/intel/compute-runtime/releases
-ARG IGC_VERSION=v2.40.13
-ARG IGC_VERSION_FULL=2_2.40.13+22418
-ARG COMPUTE_RUNTIME_VERSION=26.31.39395.13
-ARG COMPUTE_RUNTIME_VERSION_FULL=26.31.39395.13-0
+ARG IGC_VERSION=v2.38.2
+ARG IGC_VERSION_FULL=2_2.38.2+22051
+ARG COMPUTE_RUNTIME_VERSION=26.27.39122.11
+ARG COMPUTE_RUNTIME_VERSION_FULL=26.27.39122.11-0
 ARG IGDGMM_VERSION=22.10.0

 # Intel NPU driver versions. https://github.com/intel/linux-npu-driver/releases
@@ -1,4 +1,4 @@
-blank_issues_enabled: false
+blank_issues_enabled: true
 contact_links:
  - name: Got an idea?
    url: https://github.com/ggml-org/llama.cpp/discussions/categories/ideas
@@ -137,19 +137,19 @@ runs:
      run: |
          mkdir -p "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4"
          choco install unzip -y
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cccl/windows-x86_64/cccl-windows-x86_64-13.3.4.2.1-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_crt/windows-x86_64/cuda_crt-windows-x86_64-13.4.59-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvcc/windows-x86_64/cuda_nvcc-windows-x86_64-13.4.59-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libnvvm/windows-x86_64/libnvvm-windows-x86_64-13.4.59-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cudart/windows-arm64/cuda_cudart-windows-arm64-13.4.49-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libcublas/windows-arm64/libcublas-windows-arm64-13.7.0.27-archive.zip"
+          curl -O "https://packages.nvidia.com/bin-archive/pool/windows-x86_64/5B515474-7E78-11F1-8656-C51E4F4B317F/cccl-windows-x86_64-13.3.4.1.2-archive.zip"
+          curl -O "https://packages.nvidia.com/bin-archive/pool/windows-x86_64/5B515474-7E78-11F1-8656-C51E4F4B317F/cuda_crt-windows-x86_64-13.4.46-archive.zip"
+          curl -O "https://packages.nvidia.com/bin-archive/pool/windows-x86_64/5B515474-7E78-11F1-8656-C51E4F4B317F/cuda_nvcc-windows-x86_64-13.4.46-archive.zip"
+          curl -O "https://packages.nvidia.com/bin-archive/pool/windows-x86_64/5B515474-7E78-11F1-8656-C51E4F4B317F/libnvvm-windows-x86_64-13.4.46-archive.zip"
+          curl -O "https://packages.nvidia.com/bin-archive/pool/windows-arm64/5B515474-7E78-11F1-8656-C51E4F4B317F/cuda_cudart-windows-arm64-13.4.46-archive.zip"
+          curl -O "https://packages.nvidia.com/bin-archive/pool/windows-arm64/5B515474-7E78-11F1-8656-C51E4F4B317F/libcublas-windows-arm64-13.7.0.10-archive.zip"
          unzip '*.zip' -d "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4"
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\cccl-windows-x86_64-13.3.4.2.1-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\cuda_crt-windows-x86_64-13.4.59-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\cuda_nvcc-windows-x86_64-13.4.59-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\libnvvm-windows-x86_64-13.4.59-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\cuda_cudart-windows-arm64-13.4.49-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\libcublas-windows-arm64-13.7.0.27-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\cccl-windows-x86_64-13.3.4.1.2-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\cuda_crt-windows-x86_64-13.4.46-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\cuda_nvcc-windows-x86_64-13.4.46-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\libnvvm-windows-x86_64-13.4.46-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\cuda_cudart-windows-arm64-13.4.46-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\libcublas-windows-arm64-13.7.0.10-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
          echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
          echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
          echo "CUDA_PATH_V13_4=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
@@ -24,7 +24,7 @@ runs:

        write-host "Installing ROCm wheels for multi-arch support"
        # Install ROCm wheels for multi-arch support (this may take several minutes)
-        python -m pip install --index-url https://stable.repo.amd.com/rocm/whl-next/ "rocm[libraries,devel]==${{ inputs.version }}"
+        python -m pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "rocm[libraries,devel]==${{ inputs.version }}"

        # Pre-expand the devel tree so it is included in the cache
        write-host "Initializing ROCm devel tree"
@@ -110,7 +110,7 @@ jobs:
      #   cache on:  https://github.com/ggerganov/tmp2/actions/runs/26534713799/job/78224189394
      #
      #- name: ccache
-      #  uses: ggml-org/ccache-action@v1.2.24
+      #  uses: ggml-org/ccache-action@v1.2.21
      #  with:
      #    key: android-ubuntu-arm64
      #    evict-old-files: 1d
@@ -47,19 +47,11 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: apple-arm64
-          save: false
-
-      - name: ccache-buckets-restore
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
-        with:
-          key: apple-arm64
-          folder: llama.cpp
-          hf_bucket: ggml-org/cache
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

      - name: Build
        id: cmake_build
@@ -74,25 +66,7 @@ jobs:
            -DGGML_RPC=ON \
            -DCMAKE_OSX_DEPLOYMENT_TARGET=13.3
          time cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
-
-      - name: ccache-buckets-save
-        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
-        with:
-          key: apple-arm64
-          folder: llama.cpp
-          evict-old-files: 1d
-          hf_bucket: ggml-org/cache
-          save: true
-
-      - name: Check for leaks
-        run: |
-          cmd=(./build/bin/test-thread-safety -hf ggml-org/gemma-3-270m-qat-GGUF -ngl 99 -p "$(printf 'hello %.0s' {1..128})" -n 16 -c 512 -ub 32 -np 2 -t 2 -lv 1)
-          leaks -atExit -- "${cmd[@]}"
-          # Graphics devices are leaked by Metal in Apple code sometimes, so we ignore those leaks
-          OBJC_DEBUG_MISSING_POOLS=YES "${cmd[@]}" 2>&1 | awk '{ print } index($0, "autoreleased with no pool in place") && !/class [a-zA-Z0-9]+Device autoreleased/ { found = 1 } END { exit found }'
+          leaks -atExit -- ./build/bin/test-thread-safety -hf ggml-org/gemma-3-270m-qat-GGUF -ngl 99 -p "$(printf 'hello %.0s' {1..128})" -n 16 -c 512 -ub 32 -np 2 -t 2 -lv 1

      - name: Test
        id: cmake_test
@@ -100,6 +74,16 @@ jobs:
          cd build
          ctest -L main -E "test-llama-archs" --verbose --timeout 900

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        env:
+          GH_TOKEN: ${{ github.token }}
+        with:
+          key: apple-arm64
+          older: 5m
+          min: 1
+          dry-run: ${{ github.event_name != 'push' || github.ref != 'refs/heads/master' }}
+
  macos-latest-x64:
    runs-on: macos-15-intel

@@ -109,19 +93,11 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: apple-x64
-          save: false
-
-      - name: ccache-buckets-restore
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
-        with:
-          key: apple-x64
-          folder: llama.cpp
-          hf_bucket: ggml-org/cache
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

      - name: Build
        id: cmake_build
@@ -138,24 +114,22 @@ jobs:
            -DCMAKE_OSX_DEPLOYMENT_TARGET=13.3
          time cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)

-      - name: ccache-buckets-save
-        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
-        with:
-          key: apple-x64
-          folder: llama.cpp
-          evict-old-files: 1d
-          hf_bucket: ggml-org/cache
-          save: true
-
      - name: Test
        id: cmake_test
        run: |
          cd build
          ctest -L main --verbose --timeout 900

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        env:
+          GH_TOKEN: ${{ github.token }}
+        with:
+          key: apple-x64
+          older: 5m
+          min: 1
+          dry-run: ${{ github.event_name != 'push' || github.ref != 'refs/heads/master' }}
+
  macos-latest-ios-xcode:
    runs-on: macos-latest

@@ -41,8 +41,8 @@ jobs:

    env:
      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.3.1"
-      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"
+      OPENVINO_VERSION_MAJOR: "2026.3"
+      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"

    steps:
      - name: Clone
@@ -69,8 +69,8 @@ jobs:

    env:
      # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.3.1"
-      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"
+      OPENVINO_VERSION_MAJOR: "2026.3"
+      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"

    steps:
      - name: Clone
@@ -62,10 +62,11 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: cpu-${{ matrix.os }}
-          save: false
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

      - name: Build Dependencies
        id: build_depends
@@ -90,15 +91,6 @@ jobs:
          python3 -m pip install --upgrade pip setuptools
          pip3 install ./gguf-py

-      - name: ccache-buckets-restore
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
-        with:
-          key: cpu-${{ matrix.os }}
-          folder: llama.cpp
-          hf_bucket: ggml-org/cache
-
      - name: Build
        id: cmake_build
        run: |
@@ -108,18 +100,6 @@ jobs:
            -DGGML_RPC=ON
          time cmake --build build --config Release -j $(nproc)

-      - name: ccache-buckets-save
-        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
-        with:
-          key: cpu-${{ matrix.os }}
-          folder: llama.cpp
-          evict-old-files: 1d
-          hf_bucket: ggml-org/cache
-          save: true
-
      - name: Test
        id: cmake_test
        run: |
@@ -137,6 +117,18 @@ jobs:
          ./bin/llama-convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf
          ./bin/llama-completion -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256

+      # note: real deletion only on push to master (same condition as the ccache save),
+      #       dry-run otherwise (the token is read-only on PRs from forks)
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        env:
+          GH_TOKEN: ${{ github.token }}
+        with:
+          key: cpu-${{ matrix.os }}
+          older: 5m
+          min: 1
+          dry-run: ${{ github.event_name != 'push' || github.ref != 'refs/heads/master' }}
+
  windows:
    name: windows / ${{ matrix.build }}
    runs-on: windows-2025
@@ -164,7 +156,7 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: cpu-windows-2025-${{ matrix.build }}
          variant: ccache
@@ -53,7 +53,7 @@ jobs:
          apt install -y cmake build-essential ninja-build libgomp1 git libssl-dev jq python3 python3-venv python3-pip

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: cuda-ubuntu-24.04-cuda
          save: false
@@ -61,7 +61,7 @@ jobs:
      - name: ccache-buckets-restore
        uses: ./.github/actions/ccache-buckets
        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
        with:
          key: cuda-ubuntu-24.04-cuda
          folder: llama.cpp
@@ -108,7 +108,7 @@ jobs:
          sudo apt-get install -y build-essential git cmake rocblas-dev hipblas-dev libssl-dev rocwmma-dev jq python3-venv

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: cuda-ubuntu-22.04-hip
          save: false
@@ -116,7 +116,7 @@ jobs:
      - name: ccache-buckets-restore
        uses: ./.github/actions/ccache-buckets
        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
        with:
          key: cuda-ubuntu-22.04-hip
          folder: llama.cpp
@@ -159,7 +159,7 @@ jobs:
          apt-get install -y build-essential git cmake libssl-dev jq

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: cuda-ubuntu-22.04-musa
          save: false
@@ -167,7 +167,7 @@ jobs:
      - name: ccache-buckets-restore
        uses: ./.github/actions/ccache-buckets
        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
+          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
        with:
          key: cuda-ubuntu-22.04-musa
          folder: llama.cpp
@@ -47,7 +47,7 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: release-windows-2022-${{ matrix.arch }}-cuda-${{ matrix.cuda }}

@@ -152,7 +152,7 @@ jobs:
          & "${env:HIP_PATH}\lib\llvm\bin\clang.exe" --version

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          # TODO: this build does not match the build in release.yml, so we use a different cache key
          #       ideally, the builds should match, similar to the CUDA build above so that we would be able
@@ -35,7 +35,7 @@ jobs:
        uses: actions/checkout@v6

      #- name: ccache
-      #  uses: ggml-org/ccache-action@v1.2.24
+      #  uses: ggml-org/ccache-action@v1.2.16
      #  with:
      #    key: msys-windows-2025-x64
      #    variant: ccache
@@ -44,7 +44,7 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: opencl-windows-2025-x64
          variant: ccache
@@ -32,8 +32,6 @@ env:
  LLAMA_ARG_LOG_COLORS: 1
  LLAMA_ARG_LOG_PREFIX: 1
  LLAMA_ARG_LOG_TIMESTAMPS: 1
-  # TODO: fix failing tests on OpenVINO backend
-  CTEST_EXCLUDE: "test-llama-archs|^test-recurrent-state-|test-save-load-state"

 jobs:
  ubuntu-24-openvino:
@@ -41,8 +39,8 @@ jobs:

    env:
      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.3.1"
-      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"
+      OPENVINO_VERSION_MAJOR: "2026.3"
+      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"

    steps:
      - name: Clone
@@ -80,24 +78,26 @@ jobs:

      - name: Test (CPU)
        id: cmake_test_cpu
+        # TODO: fix and re-enable the `test-llama-archs` test below
        run: |
          cd ${{ github.workspace }}
-          ctest --test-dir build/ReleaseOV -L main -E "${{ env.CTEST_EXCLUDE }}" --verbose --timeout 3000
+          ctest --test-dir build/ReleaseOV -L main -E "test-llama-archs|test-recurrent-state-rollback-nemotron-h" --verbose --timeout 2000

      - name: Test (GPU)
        id: cmake_test_gpu
+        # TODO: fix and re-enable the `test-llama-archs` test below
        run: |
          cd ${{ github.workspace }}
          export GGML_OPENVINO_DEVICE=GPU
-          ctest --test-dir build/ReleaseOV -L main -E "${{ env.CTEST_EXCLUDE }}" --verbose --timeout 3000
+          ctest --test-dir build/ReleaseOV -L main -E "test-llama-archs|test-recurrent-state-rollback-nemotron-h" --verbose --timeout 3000

  openvino-windows-2022:
    runs-on: windows-2022

    env:
      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.3.1"
-      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"
+      OPENVINO_VERSION_MAJOR: "2026.3"
+      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"

    steps:
      - name: Clone
@@ -105,7 +105,7 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: openvino-windows-2022
          variant: ccache
@@ -159,13 +159,14 @@ jobs:
      - name: Test (CPU)
        id: cmake_test_cpu
        shell: cmd
+        # TODO: fix and re-enable the `test-llama-archs` test below
        run: |
          REM Find extracted OpenVINO folder dynamically
          for /d %%i in (openvino_toolkit\*) do set OPENVINO_ROOT=%%i
          call "%OPENVINO_ROOT%\setupvars.bat"

          cd build
-          ctest --test-dir ReleaseOV -L main -E "${{ env.CTEST_EXCLUDE }}" -C Release --verbose --timeout 3000
+          ctest --test-dir ReleaseOV -L main -E "test-llama-archs|test-recurrent-state-rollback-nemotron-h" -C Release --verbose --timeout 3000

      - name: ccache-clear
        uses: ./.github/actions/ccache-clear
@@ -67,7 +67,7 @@ jobs:

      # note: sparing some ccache since these jobs run on dedicated runners that are not part of the organitzation
      #- name: ccache
-      #  uses: ggml-org/ccache-action@v1.2.24
+      #  uses: ggml-org/ccache-action@afde29e5b5422e5da23cb1f639e8baecadeadfc3 # https://github.com/ggml-org/ccache-action/pull/1
      #  with:
      #    key: riscv-ubuntu-native
      #    evict-old-files: 1d
@@ -106,3 +106,83 @@ jobs:
          wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/stories260K.bin
          ./bin/llama-convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf
          ./bin/llama-completion -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256
+
+  ubuntu-riscv64-native-sanitizer:
+    runs-on: ubuntu-24.04-riscv
+
+    continue-on-error: true
+
+    strategy:
+      matrix:
+        sanitizer: [ADDRESS, THREAD, UNDEFINED]
+        build_type: [Debug]
+
+    steps:
+      - name: Install dependencies
+        run: |
+          # 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
+
+          git lfs install
+
+      - name: GCC version check
+        run: |
+          gcc --version
+          g++ --version
+
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      # note: sparing some ccache since these jobs run on dedicated runners that are not part of the organitzation
+      #- name: ccache
+      #  uses: ggml-org/ccache-action@afde29e5b5422e5da23cb1f639e8baecadeadfc3 # https://github.com/ggml-org/ccache-action/pull/1
+      #  with:
+      #    key: riscv-ubuntu-native-sanitizer-${{ matrix.sanitizer }}-${{ matrix.build_type }}
+      #    evict-old-files: 1d
+      #    save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+
+      - 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
@@ -55,7 +55,7 @@ jobs:
        uses: actions/checkout@v6

      # - name: ccache
-      #   uses: ggml-org/ccache-action@v1.2.24
+      #   uses: ggml-org/ccache-action@v1.2.21
      #   if: ${{ matrix.sanitizer != 'UNDEFINED' }}
      #   with:
      #     key: ctest-${{ matrix.sanitizer }}-ubuntu-24.04
@@ -101,6 +101,8 @@ jobs:

      - name: Test
        id: cmake_test
+        # skip run in Debug - very slow
+        if: ${{ matrix.sanitizer != 'UNDEFINED' }}
        run: |
          cd build
          ctest -L main -E tokenizer --verbose --timeout 900
@@ -58,48 +58,18 @@ env:

 jobs:
  gpu-cuda:
-    runs-on: "hf-jobs-t4-small:cuda13"
+    runs-on: [self-hosted, Linux, NVIDIA]

    steps:
      - name: Clone
        id: checkout
        uses: actions/checkout@v6

-      - name: Install dependencies
-        run: |
-          sudo apt update
-          sudo apt install -y cmake libssl-dev time unzip wget python3 python3-venv python3-pip
-
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
-        with:
-          restore: false
-          save: false
-
-      - name: ccache-buckets-restore
-        uses: ./.github/actions/ccache-buckets
-        with:
-          key: self-hosted-gpu-cuda
-          folder: llama.cpp
-          hf_bucket: ggml-org/cache
-
      - name: Test
        id: ggml-ci
        run: |
          nvidia-smi
-          GG_BUILD_CUDA=1 CUDACXX=/usr/local/cuda/bin/nvcc bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-      - name: ccache-buckets-save
-        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
-        with:
-          key: self-hosted-gpu-cuda
-          folder: llama.cpp
-          evict-old-files: 1d
-          hf_bucket: ggml-org/cache
-          save: true
+          GG_BUILD_CUDA=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp

  gpu-rocm:
    runs-on: [self-hosted, Linux, AMD]
@@ -160,10 +130,10 @@ jobs:
      - name: Dawn Dependency
        id: dawn-depends
        run: |
-          DAWN_VERSION="v20260908.214631"
+          DAWN_VERSION="v20260317.182325"
          DAWN_OWNER="google"
          DAWN_REPO="dawn"
-          DAWN_ASSET_NAME="Dawn-94c3c9cc0d5fb2e85aebb370fa8d37b71aa34655-ubuntu-latest-Release"
+          DAWN_ASSET_NAME="Dawn-18eb229ef5f707c1464cc581252e7603c73a3ef0-ubuntu-latest-Release"
          echo "Fetching release asset from https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
          curl -L -o artifact.tar.gz \
            "https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
@@ -246,10 +216,10 @@ jobs:
      - name: Dawn Dependency
        id: dawn-depends
        run: |
-          DAWN_VERSION="v20260908.214631"
+          DAWN_VERSION="v20260317.182325"
          DAWN_OWNER="google"
          DAWN_REPO="dawn"
-          DAWN_ASSET_NAME="Dawn-94c3c9cc0d5fb2e85aebb370fa8d37b71aa34655-macos-latest-Release"
+          DAWN_ASSET_NAME="Dawn-18eb229ef5f707c1464cc581252e7603c73a3ef0-macos-latest-Release"
          echo "Fetching release asset from https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
          curl -L -o artifact.tar.gz \
            "https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
@@ -318,8 +288,8 @@ jobs:

    env:
      # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.3.1"
-      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"
+      OPENVINO_VERSION_MAJOR: "2026.3"
+      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"

    steps:
      - name: Clone
@@ -395,11 +365,7 @@ jobs:
      - name: Test
        id: ggml-ci
        run: |
-          LLAMA_ARG_THREADS=$(nproc) \
-          GG_BUILD_HIGH_PERF=1 \
-          GG_BUILD_NO_BF16=1 \
-          GG_BUILD_EXTRA_TESTS_0=1 \
-          bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
+          LLAMA_ARG_THREADS=$(nproc) GG_BUILD_HIGH_PERF=1 GG_BUILD_NO_BF16=1 GG_BUILD_EXTRA_TESTS_0=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp

  cpu-arm64-graviton4-kleidiai:
    runs-on: ah-ubuntu_22_04-c8g_8x
@@ -438,8 +404,6 @@ jobs:
      - name: Test
        id: ggml-ci
        run: |
-          LLAMA_ARG_THREADS=$(nproc) \
          GG_BUILD_KLEIDIAI=1 \
          GG_BUILD_EXTRA_TESTS_0=1 \
-          GG_BUILD_HIGH_PERF=1 \
-          bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
+          bash ./ci/run.sh ./tmp/results ./tmp/mnt
@@ -75,19 +75,11 @@ jobs:
          sudo apt-get install -y ./level-zero.deb ./level-zero-devel.deb

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: sycl-ubuntu-24-${{ matrix.build }}
-          save: false
-
-      - name: ccache-buckets-restore
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
-        with:
-          key: sycl-ubuntu-24-${{ matrix.build }}
-          folder: llama.cpp
-          hf_bucket: ggml-org/cache
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

      - name: Build
        id: cmake_build
@@ -104,17 +96,15 @@ jobs:
            -DGGML_SYCL_F16=${{ matrix.fp16 }}
          time cmake --build build --config Release -j $(nproc)

-      - name: ccache-buckets-save
-        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-        uses: ./.github/actions/ccache-buckets
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+          GH_TOKEN: ${{ github.token }}
        with:
          key: sycl-ubuntu-24-${{ matrix.build }}
-          folder: llama.cpp
-          evict-old-files: 1d
-          hf_bucket: ggml-org/cache
-          save: true
+          older: 5m
+          min: 1
+          dry-run: ${{ github.event_name != 'push' || github.ref != 'refs/heads/master' }}

  windows-latest-sycl:
    runs-on: windows-2022
@@ -147,7 +137,7 @@ jobs:
          "LEVEL_ZERO_V1_SDK_PATH=C:/level-zero-sdk" | Out-File -FilePath $env:GITHUB_ENV -Append

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: sycl-windows-latest
          variant: ccache
@@ -53,20 +53,12 @@ jobs:
          echo "CXX=g++-14" >> "$GITHUB_ENV"

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: vulkan-ubuntu-24.04-arm
          variant: ccache
-          save: false
-
-      - name: ccache-buckets-restore
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
-        with:
-          key: vulkan-ubuntu-24.04-arm
-          folder: llama.cpp
-          hf_bucket: ggml-org/cache
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

      - name: Configure
        id: cmake_configure
@@ -81,17 +73,15 @@ jobs:
        run: |
          time cmake --build build -j $(nproc)

-      - name: ccache-buckets-save
-        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-        uses: ./.github/actions/ccache-buckets
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+          GH_TOKEN: ${{ github.token }}
        with:
          key: vulkan-ubuntu-24.04-arm
-          folder: llama.cpp
-          evict-old-files: 1d
-          hf_bucket: ggml-org/cache
-          save: true
+          older: 5m
+          min: 1
+          dry-run: ${{ github.event_name != 'push' || github.ref != 'refs/heads/master' }}

  ubuntu-llvmpipe:
    runs-on: ubuntu-24.04
@@ -122,19 +112,11 @@ jobs:
          strip: 1

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: vulkan-ubuntu-24.04-llvmpipe
-          save: false
-
-      - name: ccache-buckets-restore
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
-        with:
-          key: vulkan-ubuntu-24.04-llvmpipe
-          folder: llama.cpp
-          hf_bucket: ggml-org/cache
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

      - name: Build
        id: cmake_build
@@ -145,18 +127,6 @@ jobs:
            -DGGML_VULKAN=ON
          cmake --build build --config Release -j $(nproc)

-      - name: ccache-buckets-save
-        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
-        with:
-          key: vulkan-ubuntu-24.04-llvmpipe
-          folder: llama.cpp
-          evict-old-files: 1d
-          hf_bucket: ggml-org/cache
-          save: true
-
      - name: Test
        id: cmake_test
        run: |
@@ -164,7 +134,19 @@ jobs:
          export GGML_VK_VISIBLE_DEVICES=0
          export GGML_VK_DISABLE_F16=1
          export GGML_VK_DISABLE_COOPMAT=1
-          ctest -L main --verbose --timeout 900
+          # This is using llvmpipe and runs slower than other backends
+          # test-backend-ops is too slow on llvmpipe, skip it
+          ctest -L main -E test-backend-ops --verbose --timeout 900
+
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        env:
+          GH_TOKEN: ${{ github.token }}
+        with:
+          key: vulkan-ubuntu-24.04-llvmpipe
+          older: 5m
+          min: 1
+          dry-run: ${{ github.event_name != 'push' || github.ref != 'refs/heads/master' }}

  windows:
    runs-on: windows-2025
@@ -178,7 +160,7 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: cpu-windows-2025-x64-vulkan
          variant: ccache
@@ -54,10 +54,11 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: webgpu-ubuntu-24.04-arm-wasm
-          save: false
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

      - name: Install Emscripten
        run: |
@@ -68,22 +69,13 @@ jobs:

      - name: Fetch emdawnwebgpu
        run: |
-          DAWN_TAG="v20260908.214631"
+          DAWN_TAG="v20260317.182325"
          EMDAWN_PKG="emdawnwebgpu_pkg-${DAWN_TAG}.zip"
          echo "Downloading ${EMDAWN_PKG}"
          curl -L -o emdawn.zip \
            "https://github.com/google/dawn/releases/download/${DAWN_TAG}/${EMDAWN_PKG}"
          unzip emdawn.zip

-      - name: ccache-buckets-restore
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
-        with:
-          key: webgpu-ubuntu-24.04-arm-wasm
-          folder: llama.cpp
-          hf_bucket: ggml-org/cache
-
      - name: Build WASM WebGPU
        run: |
          source emsdk/emsdk_env.sh
@@ -97,14 +89,12 @@ jobs:

          time cmake --build build-wasm --config Release --target test-backend-ops -j $(nproc)

-      - name: ccache-buckets-save
-        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-        uses: ./.github/actions/ccache-buckets
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+          GH_TOKEN: ${{ github.token }}
        with:
          key: webgpu-ubuntu-24.04-arm-wasm
-          folder: llama.cpp
-          evict-old-files: 1d
-          hf_bucket: ggml-org/cache
-          save: true
+          older: 5m
+          min: 1
+          dry-run: ${{ github.event_name != 'push' || github.ref != 'refs/heads/master' }}
@@ -69,33 +69,25 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: webgpu-macos-latest
-          save: false
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

      - name: Dawn Dependency
        id: dawn-depends
        run: |
-          DAWN_VERSION="v20260908.214631"
+          DAWN_VERSION="v20260317.182325"
          DAWN_OWNER="google"
          DAWN_REPO="dawn"
-          DAWN_ASSET_NAME="Dawn-94c3c9cc0d5fb2e85aebb370fa8d37b71aa34655-macos-latest-Release"
+          DAWN_ASSET_NAME="Dawn-18eb229ef5f707c1464cc581252e7603c73a3ef0-macos-latest-Release"
          echo "Fetching release asset from https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
          curl -L -o artifact.tar.gz \
            "https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
          mkdir dawn
          tar -xvf artifact.tar.gz -C dawn --strip-components=1

-      - name: ccache-buckets-restore
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
-        with:
-          key: webgpu-macos-latest
-          folder: llama.cpp
-          hf_bucket: ggml-org/cache
-
      - name: Build
        id: cmake_build
        run: |
@@ -103,24 +95,22 @@ jobs:
          cmake -B build -G "Ninja" -DCMAKE_BUILD_TYPE=Release -DGGML_WEBGPU=ON -DGGML_METAL=OFF -DGGML_BLAS=OFF
          time cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)

-      - name: ccache-buckets-save
-        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
-        with:
-          key: webgpu-macos-latest
-          folder: llama.cpp
-          evict-old-files: 1d
-          hf_bucket: ggml-org/cache
-          save: true
-
      - name: Test
        id: cmake_test
        run: |
          cd build
          ctest -L main --verbose --timeout 900

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        env:
+          GH_TOKEN: ${{ github.token }}
+        with:
+          key: webgpu-macos-latest
+          older: 5m
+          min: 1
+          dry-run: ${{ github.event_name != 'push' || github.ref != 'refs/heads/master' }}
+
  ubuntu:
    runs-on: ubuntu-24.04

@@ -130,10 +120,11 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: webgpu-ubuntu-24.04
-          save: false
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

      - name: Dependencies
        id: depends
@@ -147,25 +138,16 @@ jobs:
        id: dawn-depends
        run: |
          sudo apt-get install -y libxrandr-dev libxinerama-dev libxcursor-dev mesa-common-dev libx11-xcb-dev libxi-dev
-          DAWN_VERSION="v20260908.214631"
+          DAWN_VERSION="v20260317.182325"
          DAWN_OWNER="google"
          DAWN_REPO="dawn"
-          DAWN_ASSET_NAME="Dawn-94c3c9cc0d5fb2e85aebb370fa8d37b71aa34655-ubuntu-latest-Release"
+          DAWN_ASSET_NAME="Dawn-18eb229ef5f707c1464cc581252e7603c73a3ef0-ubuntu-latest-Release"
          echo "Fetching release asset from https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
          curl -L -o artifact.tar.gz \
            "https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
          mkdir dawn
          tar -xvf artifact.tar.gz -C dawn --strip-components=1

-      - name: ccache-buckets-restore
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
-        with:
-          key: webgpu-ubuntu-24.04
-          folder: llama.cpp
-          hf_bucket: ggml-org/cache
-
      - name: Build
        id: cmake_build
        run: |
@@ -174,20 +156,20 @@ jobs:
            -DGGML_WEBGPU=ON
          time cmake --build build --config Release -j $(nproc)

-      - name: ccache-buckets-save
-        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
-        with:
-          key: webgpu-ubuntu-24.04
-          folder: llama.cpp
-          evict-old-files: 1d
-          hf_bucket: ggml-org/cache
-          save: true
-
      - name: Test
        id: cmake_test
        run: |
          cd build
-          ctest -L main --verbose --timeout 900
+          # This is using llvmpipe and runs slower than other backends
+          # test-backend-ops is too slow on llvmpipe, skip it
+          ctest -L main -E test-backend-ops --verbose --timeout 900
+
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        env:
+          GH_TOKEN: ${{ github.token }}
+        with:
+          key: webgpu-ubuntu-24.04
+          older: 5m
+          min: 1
+          dry-run: ${{ github.event_name != 'push' || github.ref != 'refs/heads/master' }}
@@ -29,7 +29,7 @@ jobs:
        uses: actions/checkout@v6

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: copilot-setup-steps
          evict-old-files: 1d
@@ -1,67 +0,0 @@
-name: Fusion
-
-on:
-  workflow_dispatch: # allows manual triggering
-  push:
-    branches:
-      - master
-    paths: [
-      '.github/workflows/fusion.yml',
-      'ggml/**',
-      'tests/fusion/**',
-      'tests/test-fusion.cpp'
-    ]
-
-  pull_request:
-    types: [opened, synchronize, reopened]
-    paths: [
-      '.github/workflows/fusion.yml',
-      'ggml/**',
-      'tests/fusion/**',
-      'tests/test-fusion.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_ARG_LOG_COLORS: 1
-  LLAMA_ARG_LOG_PREFIX: 1
-  LLAMA_ARG_LOG_TIMESTAMPS: 1
-
-jobs:
-  # TODO: add jobs for other backends as they adopt the fusion debug API
-  metal:
-    runs-on: [self-hosted, macOS, ARM64]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Build
-        id: cmake_build
-        run: |
-          cmake -B build \
-            -DCMAKE_BUILD_TYPE=Release \
-            -DLLAMA_FATAL_WARNINGS=ON \
-            -DLLAMA_OPENSSL=OFF \
-            -DGGML_SCHED_NO_REALLOC=ON \
-            -DGGML_BLAS=OFF \
-            -DGGML_METAL=ON
-          time cmake --build build --config Release --target test-llama-archs -j $(sysctl -n hw.logicalcpu)
-          time cmake --build build --config Release --target test-fusion -j $(sysctl -n hw.logicalcpu)
-
-      - name: Generate models
-        id: generate_models
-        run: |
-          rm -rf build-ci-models && mkdir -p build-ci-models
-          ./build/bin/test-llama-archs -o build-ci-models
-
-      - name: Test fusion
-        id: test_fusion
-        run: |
-          ./build/bin/test-fusion --models build-ci-models --device MTL0 --check tests/fusion/MTL.csv
@@ -49,22 +49,14 @@ jobs:
        id: depends
        run: |
          sudo apt-get update
-          sudo apt-get install -y build-essential git cmake rocblas-dev hipblas-dev libssl-dev python3 python3-venv python3-pip jq
+          sudo apt-get install -y build-essential git cmake rocblas-dev hipblas-dev libssl-dev python3

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: hip-quality-check-ubuntu-22.04
-          save: false
-
-      - name: ccache-buckets-restore
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
-        with:
-          key: hip-quality-check-ubuntu-22.04
-          folder: llama.cpp
-          hf_bucket: ggml-org/cache
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

      - name: Build with Werror
        id: cmake_build
@@ -93,14 +85,12 @@ jobs:
          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

-      - name: ccache-buckets-save
-        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-        uses: ./.github/actions/ccache-buckets
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+          GH_TOKEN: ${{ github.token }}
        with:
          key: hip-quality-check-ubuntu-22.04
-          folder: llama.cpp
-          evict-old-files: 1d
-          hf_bucket: ggml-org/cache
-          save: true
+          older: 5m
+          min: 1
+          dry-run: ${{ github.event_name != 'push' || github.ref != 'refs/heads/master' }}
@@ -19,7 +19,6 @@ env:

 permissions:
  contents: write
-  packages: write

 jobs:
  make-release:
@@ -114,29 +113,6 @@ jobs:
              data: await fs.readFileSync('./nightly-tag.txt')
            });

-      - name: Re-tag container images with release version
-        if: ${{ github.event.inputs.dry_run == 'false' && steps.desc.outputs.nightly_tag != '' }}
-        env:
-          GITHUB_REPOSITORY_OWNER: ${{ github.repository_owner }}
-        run: |
-          VERSION="${{ steps.checks.outputs.version }}"
-          NIGHTLY_TAG="${{ steps.desc.outputs.nightly_tag }}"
-          REPO_OWNER="${GITHUB_REPOSITORY_OWNER,,}"
-          IMAGE_REPO="ghcr.io/${REPO_OWNER}/${{ github.event.repository.name }}"
-
-          echo "${{ secrets.GITHUB_TOKEN }}" | docker login ghcr.io -u "${{ github.actor }}" --password-stdin
-
-          VARIANTS=("" "-cuda" "-cuda13" "-vulkan" "-rocm" "-intel" "-musa" "-openvino")
-          TYPES=("full" "light" "server")
-          for type in "${TYPES[@]}"; do
-            for variant in "${VARIANTS[@]}"; do
-              src="${IMAGE_REPO}:${type}${variant}-${NIGHTLY_TAG}"
-              dst="${IMAGE_REPO}:${type}${variant}-${VERSION}"
-              echo "Tagging ${src} -> ${dst}"
-              docker buildx imagetools create --tag "${dst}" "${src}"
-            done
-          done
-
      - name: Dry run summary
        if: ${{ github.event.inputs.dry_run == 'true' }}
        run: |
@@ -31,7 +31,7 @@ jobs:
        uses: actions/setup-python@v6
        with:
          python-version: "3.11"
-          pip-install: -r requirements/requirements-all.txt ty==0.0.78
+          pip-install: -r requirements/requirements-all.txt ty==0.0.35
      # - name: Type-check with Pyright
      #   uses: jakebailey/pyright-action@v2
      #   with:
@@ -40,4 +40,4 @@ jobs:
      #     warnings: true
      - name: Type-check with ty
        run: |
-            ty check --exit-zero-on-warning --output-format=github
+            ty check --output-format=github
@@ -103,7 +103,7 @@ jobs:
          path: tools/ui/dist

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: release-${{ matrix.os }}-${{ matrix.arch }}

@@ -187,7 +187,7 @@ jobs:

      - name: ccache
        if: ${{ matrix.build != 's390x' }}
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: release-${{ matrix.os }}-cpu

@@ -272,7 +272,7 @@ jobs:
          fi

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: release-${{ matrix.os }}-vulkan

@@ -358,7 +358,7 @@ jobs:
      #   cache on:  https://github.com/ggerganov/tmp2/actions/runs/26534713799/job/78224189394
      #
      #- name: ccache
-      #  uses: ggml-org/ccache-action@v1.2.24
+      #  uses: ggml-org/ccache-action@v1.2.21
      #  with:
      #    key: release-android-arm64

@@ -415,8 +415,8 @@ jobs:

    env:
      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.3.1"
-      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"
+      OPENVINO_VERSION_MAJOR: "2026.3"
+      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"

    steps:
      - name: Set OpenVINO version output
@@ -436,7 +436,7 @@ jobs:
          path: tools/ui/dist

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: release-ubuntu-24.04-openvino-release-no-preset-v1

@@ -529,8 +529,8 @@ jobs:

    env:
      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.3.1"
-      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"
+      OPENVINO_VERSION_MAJOR: "2026.3"
+      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"

    steps:
      - name: Set OpenVINO version output
@@ -551,7 +551,7 @@ jobs:
          path: tools/ui/dist

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: release-windows-2022-openvino
          variant: ccache
@@ -679,7 +679,7 @@ jobs:
          choco install ninja

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: release-windows-2025-vs2026-${{ matrix.arch }}-cpu

@@ -714,10 +714,10 @@ jobs:
        with:
          key: release-windows-2025-vs2026-${{ matrix.arch }}-cpu

-  # note: builds only the ggml-hip backend - llama-server is injected from the
-  #       windows-cpu zip during the release "Merge artifacts" step
+  # TODO: build only the ggml-hip backend like the other windows backend jobs
+  #       (windows-cuda, windows-sycl), then drop the ui-build dependency
  windows-rocm:
-    needs: [check-release]
+    needs: [check-release, ui-build]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}

    runs-on: windows-2022
@@ -725,7 +725,7 @@ jobs:
    strategy:
      matrix:
        include:
-          - ROCM_VERSION: "10.0.0"
+          - ROCM_VERSION: "7.14.0"
            gpu_targets: "gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1103;gfx1150;gfx1151;gfx1152;gfx1153;gfx1200;gfx1201"
            build: x64

@@ -736,12 +736,14 @@ jobs:
        with:
          fetch-depth: 0

-      - name: Install Ninja
-        run: |
-          choco install ninja
+      - name: Download UI build
+        uses: actions/download-artifact@v7
+        with:
+          name: llama-ui.zip
+          path: tools/ui/dist

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: windows-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}
          evict-old-files: 1d
@@ -793,28 +795,33 @@ jobs:

      - name: Build
        run: |
-          cmake -S . -B build `
-            -G "Ninja Multi-Config" `
+          mkdir build
+          cd build
+          cmake .. `
+            -G "Unix Makefiles" `
            -DCMAKE_PREFIX_PATH="${env:HIP_PATH}" `
+            -DCMAKE_BUILD_TYPE=Release `
            -DGGML_BACKEND_DL=ON `
            -DGGML_NATIVE=OFF `
-            -DGGML_CPU=OFF `
+            -DGGML_CPU=ON `
+            -DGGML_CPU_ALL_VARIANTS=ON `
            -DGGML_HIP=ON `
            -DCMAKE_C_COMPILER="${env:HIP_PATH}\lib\llvm\bin\clang.exe" `
            -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\lib\llvm\bin\clang++.exe" `
            -DCMAKE_C_FLAGS="-Wno-error=incompatible-pointer-types" `
            -DCMAKE_HIP_COMPILER="${env:HIP_PATH}\lib\llvm\bin\clang.exe" `
            -DHIP_PATH="${env:HIP_PATH}" `
+            -DGGML_HIP_ROCWMMA_FATTN=ON `
            -DAMDGPU_TARGETS="${{ matrix.gpu_targets }}"
-          cmake --build build --config Release --parallel ${env:NUMBER_OF_PROCESSORS} --target ggml-hip
+          cmake --build . --config Release --parallel ${env:NUMBER_OF_PROCESSORS}

      - name: Verify HIP backend was built
        run: |
-          $hipDll = Get-ChildItem -Path build\bin\Release -Filter "ggml-hip*.dll" -ErrorAction SilentlyContinue
+          $hipDll = Get-ChildItem -Path build\bin -Filter "ggml-hip*.dll" -ErrorAction SilentlyContinue
          if (-not $hipDll) {
            Write-Host "##[error]ggml-hip*.dll was NOT produced. The HIP backend silently failed to build."
-            Write-Host "Contents of build\bin\Release:"
-            Get-ChildItem build\bin\Release | Format-Table -AutoSize
+            Write-Host "Contents of build\bin:"
+            Get-ChildItem build\bin | Format-Table -AutoSize
            exit 1
          }
          Write-Host "HIP backend artifact found:"
@@ -829,40 +836,10 @@ jobs:
          $rocmVersionShort = ('${{ matrix.ROCM_VERSION }}'.Split('.')[0..1] -join '.')
          echo "ROCM_VERSION_SHORT=$rocmVersionShort" >> $env:GITHUB_ENV

-      - name: Bundle HIP runtime DLLs (amdhip64_7.dll, rocm_kpack.dll, amd_comgr.dll)
-        run: |
-          $ErrorActionPreference = "Stop"
-          # See issue https://github.com/ggml-org/llama.cpp/issues/26929.
-          # ggml-hip.dll loads amdhip64_7.dll at run time. The Adrenalin driver
-          # ships an amdhip64_7.dll in System32, which the loader searches before PATH,
-          # so a matching DLL from PATH cannot win. Copy amdhip64 next to the
-          # binaries (exe directory is searched before System32) so the correct
-          # runtime is used. rocm_kpack.dll is amdhip64_7's direct dependency, so
-          # copy the matching version too. amd_comgr is copied as well to keep it
-          # in sync with the bundled amdhip64, avoiding a version mismatch with a
-          # amd_comgr from System32.
-          # rocblas/hipblaslt kernels resolve fine via PATH and are not copied.
-          $binPath = (rocm-sdk path --bin).Trim()
-          if (-not $binPath) { throw "rocm-sdk path --bin returned empty" }
-          write-host "ROCm bin path: $binPath"
-
-          $patterns = @("amdhip64_7.dll", "rocm_kpack.dll", "amd_comgr.dll")
-          foreach ($pattern in $patterns) {
-            $files = Get-ChildItem -Path $binPath -Filter $pattern -ErrorAction SilentlyContinue
-            if (-not $files) { throw "no match for $pattern in $binPath" }
-            foreach ($f in $files) {
-              Copy-Item $f.FullName -Destination build\bin\Release -Force
-              write-host "  copied $($f.Name)"
-            }
-          }
-
      - name: Pack artifacts
        run: |
-          7z a -snl llama-bin-win-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.zip `
-            .\build\bin\Release\ggml-hip.dll `
-            .\build\bin\Release\amdhip64_7.dll `
-            .\build\bin\Release\rocm_kpack.dll `
-            .\build\bin\Release\amd_comgr.dll
+          cp "LICENSE" "build\bin\"
+          7z a -snl llama-bin-win-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.zip .\build\bin\*

      - name: Upload artifacts
        uses: actions/upload-artifact@v6
@@ -923,7 +900,7 @@ jobs:

      # TODO: these jobs need to use llvm toolchain in order to utilize the ccache
      #- name: ccache
-      #  uses: ggml-org/ccache-action@v1.2.24
+      #  uses: ggml-org/ccache-action@v1.2.21
      #  with:
      #    key: release-windows-2025-${{ matrix.arch }}-${{ matrix.backend }}

@@ -1011,7 +988,7 @@ jobs:
          choco install ninja

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: release-windows-2022-${{ matrix.arch }}-cuda-${{ matrix.cuda }}

@@ -1107,7 +1084,7 @@ jobs:
          "LEVEL_ZERO_V1_SDK_PATH=C:/level-zero-sdk" | Out-File -FilePath $env:GITHUB_ENV -Append

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: release-windows-2022-x64-sycl

@@ -1225,7 +1202,7 @@ jobs:
          path: tools/ui/dist

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: release-ubuntu-24.04-sycl-${{ matrix.build }}

@@ -1279,7 +1256,7 @@ jobs:
    strategy:
      matrix:
        include:
-          - ROCM_VERSION: "10.0.0"
+          - ROCM_VERSION: "7.14.0"
            gpu_targets: "gfx908;gfx90a;gfx942;gfx950;gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1150;gfx1151;gfx1152;gfx1200;gfx1201"
            build: 'x64'

@@ -1302,7 +1279,7 @@ jobs:
          tool-cache: true

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: release-ubuntu-24.04-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}
          evict-old-files: 1d
@@ -1333,7 +1310,7 @@ jobs:
          # libraries = HIP runtime and CMake configs needed for linking
          # devel = compilers, headers, static libs
          python -m pip install --upgrade pip
-          python -m pip install --index-url https://stable.repo.amd.com/rocm/whl-next/ "rocm[libraries,devel]==${{ matrix.ROCM_VERSION }}"
+          python -m pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "rocm[libraries,devel]==${{ matrix.ROCM_VERSION }}"

          # Get ROCm installation paths using the rocm-sdk CLI tool
          ROCM_PATH=$(rocm-sdk path --root)
@@ -1703,7 +1680,7 @@ jobs:
            - [Ubuntu s390x (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-s390x.tar.gz)
            - [Ubuntu x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.tar.gz)
            - [Ubuntu arm64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-arm64.tar.gz)
-            - [Ubuntu x64 (ROCm 10.0)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-10.0-x64.tar.gz)
+            - [Ubuntu x64 (ROCm 7.14)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-7.14-x64.tar.gz)
            - [Ubuntu x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ needs.ubuntu-24-openvino.outputs.openvino_version }}-x64.tar.gz)
            - [Ubuntu x64 (SYCL FP32)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-fp32-x64.tar.gz)
            - [Ubuntu x64 (SYCL FP16)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-fp16-x64.tar.gz)
@@ -1717,11 +1694,11 @@ jobs:
            - [Windows arm64 (OpenCL Adreno)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-opencl-adreno-arm64.zip)
            - [Windows x64 (CUDA 12)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-12.4-x64.zip) - [CUDA 12.4 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-12.4-x64.zip)
            - [Windows x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-13.3-x64.zip) - [CUDA 13.3 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-13.3-x64.zip)
-            - [Windows arm64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-13.4-arm64.zip) - [CUDA 13.4 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-13.4-arm64.zip)
+            - [Windows arm64 (CUDA 13) (preview)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-13.4-arm64.zip) - [CUDA 13.4 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-13.4-arm64.zip)
            - [Windows x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-vulkan-x64.zip)
            - [Windows x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-openvino-${{ needs.windows-openvino.outputs.openvino_version }}-x64.zip)
            - [Windows x64 (SYCL)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip)
-            - [Windows x64 (ROCm 10.0)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-rocm-10.0-x64.zip)
+            - [Windows x64 (ROCm 7.14)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-rocm-7.14-x64.zip)

            **openEuler:**
            - [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23705)
@@ -32,8 +32,6 @@ on:
    ]

 env:
-  # note: this is dud token to avoid rate limiting (https://github.com/ggml-org/llama.cpp/pull/25706#issuecomment-4979941302)
-  HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
  LLAMA_ARG_LOG_COLORS: 1
  LLAMA_ARG_LOG_PREFIX: 1
  LLAMA_ARG_LOG_TIMESTAMPS: 1
@@ -45,7 +43,7 @@ concurrency:

 jobs:
  server:
-    runs-on: hf-jobs-cpu-upgrade
+    runs-on: [self-hosted, CPU, Linux, llama-server]

    strategy:
      matrix:
@@ -54,6 +52,20 @@ jobs:
      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
@@ -61,24 +73,6 @@ jobs:
          fetch-depth: 0
          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}

-      - name: Install dependencies
-        run: |
-          sudo apt update
-          sudo apt install -y build-essential cmake python3-full
-
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
-        with:
-          restore: false
-          save: false
-
-      - name: ccache-buckets-restore
-        uses: ./.github/actions/ccache-buckets
-        with:
-          key: server-sanitize-${{ matrix.sanitizer }}
-          folder: llama.cpp
-          hf_bucket: ggml-org/cache
-
      - name: Build
        id: cmake_build
        run: |
@@ -93,17 +87,9 @@ jobs:
            -DLLAMA_SANITIZE_UNDEFINED=${{ matrix.sanitizer == 'UNDEFINED' }}
          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server

-      - name: ccache-buckets-save
-        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
-        with:
-          key: server-sanitize-${{ matrix.sanitizer }}
-          folder: llama.cpp
-          evict-old-files: 1d
-          hf_bucket: ggml-org/cache
-          save: true
+      - name: Python setup
+        id: setup_python
+        uses: actions/setup-python@v7

      - name: Install Python dependencies
        run: |
@@ -117,7 +103,7 @@ jobs:
          source .venv/bin/activate
          cd tools/server/tests
          export ${{ matrix.extra_args }}
-          PYTEST_WORKERS=1 ./tests.sh
+          ./tests.sh

      - name: Slow tests
        id: server_integration_tests_slow
@@ -126,4 +112,4 @@ jobs:
          source .venv/bin/activate
          cd tools/server/tests
          export ${{ matrix.extra_args }}
-          PYTEST_WORKERS=1 SLOW_TESTS=1 ./tests.sh
+          SLOW_TESTS=1 ./tests.sh
@@ -72,7 +72,7 @@ jobs:
        run: |
          cd tools/server/tests
          source venv/bin/activate
-          PYTEST_WORKERS=1 ./tests.sh
+          ./tests.sh

      - name: Tests (GPUx1, backend-sampling)
        id: server_integration_tests_backend_sampling
@@ -81,7 +81,7 @@ jobs:
          cd tools/server/tests
          source venv/bin/activate
          export LLAMA_ARG_BACKEND_SAMPLING=1
-          PYTEST_WORKERS=1 ./tests.sh
+          ./tests.sh

      - name: Tests (GPUx2)
        id: server_integration_tests_gpu2
@@ -90,7 +90,7 @@ jobs:
          cd tools/server/tests
          source venv/bin/activate
          export GGML_METAL_DEVICES=2
-          PYTEST_WORKERS=1 ./tests.sh
+          ./tests.sh

      - name: Tests (GPUx2, backend-sampling)
        id: server_integration_tests_gpu2_backend_sampling
@@ -99,10 +99,10 @@ jobs:
          cd tools/server/tests
          source venv/bin/activate
          export GGML_METAL_DEVICES=2 LLAMA_ARG_BACKEND_SAMPLING=1
-          PYTEST_WORKERS=1 ./tests.sh
+          ./tests.sh

  server-cuda:
-    runs-on: "hf-jobs-t4-small:cuda13"
+    runs-on: [self-hosted, llama-server, Linux, NVIDIA]

    steps:
      - name: Clone
@@ -112,42 +112,12 @@ jobs:
          fetch-depth: 0
          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}

-      - name: Install dependencies
-        run: |
-          sudo apt update
-          sudo apt install -y cmake libssl-dev python3 python3-venv python3-pip
-
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
-        with:
-          restore: false
-          save: false
-
-      - name: ccache-buckets-restore
-        uses: ./.github/actions/ccache-buckets
-        with:
-          key: self-hosted-server-cuda
-          folder: llama.cpp
-          hf_bucket: ggml-org/cache
-
      - name: Build
        id: cmake_build
        run: |
-          cmake -B build -DGGML_CUDA=ON -DGGML_SCHED_NO_REALLOC=ON -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc
+          cmake -B build -DGGML_CUDA=ON -DGGML_SCHED_NO_REALLOC=ON
          cmake --build build --config Release -j $(nproc) --target llama-server

-      - name: ccache-buckets-save
-        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
-        with:
-          key: self-hosted-server-cuda
-          folder: llama.cpp
-          evict-old-files: 1d
-          hf_bucket: ggml-org/cache
-          save: true
-
      - name: Python setup
        id: setup_python
        run: |
@@ -162,7 +132,7 @@ jobs:
        run: |
          cd tools/server/tests
          source venv/bin/activate
-          PYTEST_WORKERS=1 ./tests.sh
+          ./tests.sh

      - name: Tests (GPUx1, backend-sampling)
        id: server_integration_tests_backend_sampling
@@ -171,7 +141,7 @@ jobs:
          cd tools/server/tests
          source venv/bin/activate
          export LLAMA_ARG_BACKEND_SAMPLING=1
-          PYTEST_WORKERS=1 ./tests.sh
+          ./tests.sh

      - name: Tests (GPUx2)
        id: server_integration_tests_gpu2
@@ -180,7 +150,7 @@ jobs:
          cd tools/server/tests
          source venv/bin/activate
          export GGML_CUDA_DEVICES=2
-          PYTEST_WORKERS=1 ./tests.sh
+          ./tests.sh

      - name: Tests (GPUx2, backend-sampling)
        id: server_integration_tests_gpu2_backend_sampling
@@ -189,7 +159,7 @@ jobs:
          cd tools/server/tests
          source venv/bin/activate
          export GGML_CUDA_DEVICES=2 LLAMA_ARG_BACKEND_SAMPLING=1
-          PYTEST_WORKERS=1 ./tests.sh
+          ./tests.sh

  server-kleidiai:
    runs-on: ah-ubuntu_22_04-c8g_8x
@@ -80,19 +80,11 @@ jobs:
          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: server-ubuntu-24.04-arm
-          save: false
-
-      - name: ccache-buckets-restore
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
-        with:
-          key: server-ubuntu-24.04-arm
-          folder: llama.cpp
-          hf_bucket: ggml-org/cache
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

      - name: Build
        id: cmake_build
@@ -101,18 +93,6 @@ jobs:
            -DGGML_SCHED_NO_REALLOC=ON
          cmake --build build --config Release -j $(nproc) --target llama-server

-      - name: ccache-buckets-save
-        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-        uses: ./.github/actions/ccache-buckets
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
-        with:
-          key: server-ubuntu-24.04-arm
-          folder: llama.cpp
-          evict-old-files: 1d
-          hf_bucket: ggml-org/cache
-          save: true
-
      - name: Python setup
        id: setup_python
        uses: actions/setup-python@v6
@@ -148,6 +128,16 @@ jobs:
          export LLAMA_ARG_BACKEND_SAMPLING=1
          SLOW_TESTS=1 ./tests.sh

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        env:
+          GH_TOKEN: ${{ github.token }}
+        with:
+          key: server-ubuntu-24.04-arm
+          older: 5m
+          min: 1
+          dry-run: ${{ github.event_name != 'push' || github.ref != 'refs/heads/master' }}
+
  windows:
    runs-on: windows-2025

@@ -160,7 +150,7 @@ jobs:
          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.24
+        uses: ggml-org/ccache-action@v1.2.21
        with:
          key: server-windows-2025-x64
          evict-old-files: 1d
@@ -17,9 +17,8 @@ jobs:
        uses: actions/setup-node@v6
        with:
          node-version: "24"
-          # cache: "npm"
-          # cache-dependency-path: "tools/ui/package-lock.json"
-          package-manager-cache: false
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"

      - name: Install dependencies
        run: npm ci
@@ -33,9 +33,8 @@ jobs:
        uses: actions/setup-node@v6
        with:
          node-version: "24"
-          # cache: "npm"
-          # cache-dependency-path: "tools/ui/package-lock.json"
-          package-manager-cache: false
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"

      - name: Install dependencies
        run: npm ci
@@ -57,9 +57,8 @@ jobs:
        uses: actions/setup-node@v6
        with:
          node-version: "24"
-          # cache: "npm"
-          # cache-dependency-path: "tools/ui/package-lock.json"
-          package-manager-cache: false
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"

      - name: Download built UI artifacts
        uses: actions/download-artifact@v6
@@ -115,9 +114,8 @@ jobs:
        uses: actions/setup-node@v6
        with:
          node-version: "24"
-          # cache: "npm"
-          # cache-dependency-path: "tools/ui/package-lock.json"
-          package-manager-cache: false
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"

      - name: Install dependencies
        id: setup
@@ -1,4 +1,4 @@
-# date: Fri Sep  4 10:06:46 EEST 2026
+# date: Tue Aug 18 14:32:43 EEST 2026
 # this file is auto-generated by scripts/gen-authors.sh

 Нияз Гарифзянов <112617865+garrnizon@users.noreply.github.com>
@@ -46,7 +46,6 @@ Abhijit Ramesh <abhijitramesh2k@gmail.com>
 abhijitb11 <113058133+abhijitb11@users.noreply.github.com>
 Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>
 Abhinay Krishna <abhinaykrishna60@gmail.com>
-Abhiram <78226909+geckguy@users.noreply.github.com>
 Abhishek Gopinath K <31348521+overtunned@users.noreply.github.com>
 abotsis <github@bots.is>
 Abraham Gonzalez <theabecaster0@gmail.com>
@@ -88,7 +87,6 @@ akleine <alb.kleine@gmx.de>
 Al G <toasting@gmail.com>
 Al Mochkin <14274697+amochkin@users.noreply.github.com>
 Alan Gray <agray3@users.noreply.github.com>
-Alan Tseng <alanhc.tseng1999@gmail.com>
 Alawode Oluwandabira <dabiraalawode@yahoo.com>
 Albert Jin <albert.jin@gmail.com>
 Alberto <57916483+albbus-stack@users.noreply.github.com>
@@ -138,9 +136,7 @@ alonfaraj <alonfaraj@gmail.com>
 AlpinDale <52078762+AlpinDale@users.noreply.github.com>
 alwqx <kenan3015@gmail.com>
 Aman <amangupta052@gmail.com>
-Aman Chadha(IVIXMMI) <79802170+ac-mmi@users.noreply.github.com>
 Aman Gupta <amangupta052@gmail.com>
-Aman Karki <itsamankarki@gmail.com>
 amd-dwang <dong.wang@amd.com>
 amd-lalithnc <lalithnc@amd.com>
 Amir <amir_zia@outlook.com>
@@ -191,7 +187,6 @@ Anton Mitkov <anton.mitkov@codeplay.com>
 Antonis Makropoulos <benuix@gmail.com>
 Anudit Nagar <nagaranudit@gmail.com>
 Anuj Attri <anujattri01@gmail.com>
-anujj <ajalota@nvidia.com>
 anzz1 <anzz1@live.com>
 Aparna M P <aparmp@qti.qualcomm.com>
 Aparna M P <quic_aparmp@quicinc.com>
@@ -201,7 +196,6 @@ arch-btw <57669023+arch-btw@users.noreply.github.com>
 arcrank <arcrank@gmail.com>
 ardfork <134447697+ardfork@users.noreply.github.com>
 Arik Poznanski <arikpoz@users.noreply.github.com>
-Aritro Bandyopadhyay <71339004+AriBandyo@users.noreply.github.com>
 arlo-phoenix <140345165+arlo-phoenix@users.noreply.github.com>
 Armen Kaleshian <kriation@users.noreply.github.com>
 Arsen Arutunan <58118221+limloop@users.noreply.github.com>
@@ -236,7 +230,6 @@ bandoti <141645996+bandoti@users.noreply.github.com>
 Bar Haim <barvhaim@gmail.com>
 BarfingLemurs <128182951+BarfingLemurs@users.noreply.github.com>
 Bart Louwers <bart.louwers@gmail.com>
-Bartosz Taudul <wolf@nereid.pl>
 Bartowski <3266127+bartowski1182@users.noreply.github.com>
 Bartowski <ckealty1182@gmail.com>
 Bas Nijholt <basnijholt@gmail.com>
@@ -284,7 +277,6 @@ Bono Lv <lvscar@users.noreply.github.com>
 Borislav Stanimirov <b.stanimirov@abv.bg>
 Borislav Stanimirov <b@ibob.bg>
 Bowen Han <fancycode@gmail.com>
-Brad Smith <1472326+infinitewarp@users.noreply.github.com>
 Branden Butler <bwtbutler@hotmail.com>
 Brandon Squizzato <35474886+bsquizz@users.noreply.github.com>
 Brian <mofosyne@gmail.com>
@@ -295,7 +287,6 @@ Bryan Honof <bryanhonof@gmail.com>
 bryanSwk <93190252+bryanSwk@users.noreply.github.com>
 bsilvereagle <bsilvereagle@users.noreply.github.com>
 bssrdf <merlintiger@hotmail.com>
-Buğra Özgürsoy <13810383+ozgursoy@users.noreply.github.com>
 byte-6174 <88070277+byte-6174@users.noreply.github.com>
 Caleb DeLeeuw <143902425+SolshineCode@users.noreply.github.com>
 Calvin Laurenson <calvin@laurenson.dev>
@@ -335,7 +326,6 @@ Chenguang Li <757486878@qq.com>
 Chenguang Li <87689256+noemotiovon@users.noreply.github.com>
 Chipmunk <101038159+CHIPMUNK-T0T@users.noreply.github.com>
 chiranko <96988916+chiranko@users.noreply.github.com>
-Chris Danis <cdanis@gmail.com>
 Chris Elrod <elrodc@gmail.com>
 Chris Kuehl <ckuehl@ckuehl.me>
 Chris Lee <clee@mg8.org>
@@ -366,7 +356,6 @@ clyang <clyang@clyang.net>
 cmdr2 <secondary.cmdr2@gmail.com>
 cmdr2 <shashank.shekhar.global@gmail.com>
 cocktailpeanut <121128867+cocktailpeanut@users.noreply.github.com>
-codemonkey <441345965@qq.com>
 codezjx <code.zjx@gmail.com>
 coezbek <c.oezbek@gmail.com>
 comex <comexk@gmail.com>
@@ -378,8 +367,6 @@ Copilot <198982749+Copilot@users.noreply.github.com>
 Corentin REGAL <corentin.regal@gmail.com>
 cphlipot <9103367+cphlipot@users.noreply.github.com>
 cpumaxx <163466046+cpumaxx@users.noreply.github.com>
-cqderek <cqderek@gmail.com>
-cqderek <cqiang@qti.qualcomm.com>
 crasm <crasm@git.vczf.net>
 crasm <crasm@git.vczf.us>
 crat0z <11581854+crat0z@users.noreply.github.com>
@@ -440,7 +427,6 @@ DavidKorczynski <david@adalogics.com>
 davidrhodus <david@vacovideo.com>
 Dawid Potocki <github@dawidpotocki.com>
 Dawid Wysocki <62249621+TortillaZHawaii@users.noreply.github.com>
-Daya Adianto <addianto@users.noreply.github.com>
 ddh0 <chemist-mulches-39@icloud.com>
 ddh0 <dylanhalladay02@icloud.com>
 ddpasa <112642920+ddpasa@users.noreply.github.com>
@@ -477,7 +463,6 @@ Dmytro Romanov <casteldazur@gmail.com>
 Dobri Danchev <12420863+danchev@users.noreply.github.com>
 DocShotgun <126566557+DocShotgun@users.noreply.github.com>
 Doctor Shotgun <126566557+DocShotgun@users.noreply.github.com>
-Dominik Pantaleoni <95251853+dpantaleoni@users.noreply.github.com>
 Don Mahurin <dmahurin@users.noreply.github.com>
 Dong Won Kim <63934649+ddwkim@users.noreply.github.com>
 Donghyeon Jeong <54725479+djeong20@users.noreply.github.com>
@@ -519,7 +504,6 @@ Emmanuel Ferdman <emmanuelferdman@gmail.com>
 Emreerdog <34742675+Emreerdog@users.noreply.github.com>
 Engininja2 <139037756+Engininja2@users.noreply.github.com>
 Equim <sayaka@ekyu.moe>
-Eric A Stalee <87948564+Eric-A-Stalee@users.noreply.github.com>
 Eric Curtin <ecurtin@redhat.com>
 Eric Curtin <eric.curtin@docker.com>
 Eric Curtin <ericcurtin17@gmail.com>
@@ -535,7 +519,6 @@ Esko Toivonen <eskot98@gmail.com>
 Ethan Turner <eturner64@gmail.com>
 Ettore Di Giacinto <mudler@users.noreply.github.com>
 EugeoSynthesisThirtyTwo <gabriel.dhimoila@gmail.com>
-Eurekatic <eurekatic@eurekatic.eu>
 Evan Huus <eapache@gmail.com>
 Evan Jones <evan.q.jones@gmail.com>
 Evan Miller <emmiller@gmail.com>
@@ -694,7 +677,6 @@ HimariO <dsfhe49854@gmail.com>
 hipudding <huafengchun@gmail.com>
 Hitesh Chopra <34310832+hiteshchopra11@users.noreply.github.com>
 hksdpc255 <43977088+hksdpc255@users.noreply.github.com>
-hmirin <hmirin@users.noreply.github.com>
 hmscider <201289679+hmscider@users.noreply.github.com>
 Hoang Nguyen <hugo53@users.noreply.github.com>
 hoangmit <hoangmit@users.noreply.github.com>
@@ -719,7 +701,6 @@ Huawei Lin <huaweilin.cs@gmail.com>
 Hugo <hugo@whynothugo.nl>
 Hugo Roussel <hugo.rous@gmail.com>
 Huifeng Ou <79071290+ho2103@users.noreply.github.com>
-HumerousGorgon <31957201+HumerousGorgon@users.noreply.github.com>
 hutli <6594598+hutli@users.noreply.github.com>
 hutli <hutli@hutli.hu>
 hutli <jensstaermose@hotmail.com>
@@ -757,15 +738,12 @@ intelmatt <61025942+intelmatt@users.noreply.github.com>
 iohub <rickyang.pro@gmail.com>
 Ionoclast Laboratories <brigham@ionoclast.com>
 iron <lizhenneng@gmail.com>
-Isaac <34376531+init-22@users.noreply.github.com>
 Isaac McFadyen <isaac@imcf.me>
 IsaacDynamo <61521674+IsaacDynamo@users.noreply.github.com>
 Ishaan Gandhi <Ishaangandhi@gmail.com>
 iSma <ismail.senhaji@gmail.com>
 Ismail <115064057+AlrIsmail@users.noreply.github.com>
 issixx <46835150+issixx@users.noreply.github.com>
-itsnotoger <19309683+itsnotoger@users.noreply.github.com>
-itterative <190138728+itterative@users.noreply.github.com>
 Ivan <nekotekina@gmail.com>
 Ivan Chikish <nekotekina@gmail.com>
 Ivan Filipov <159561759+vanaka11@users.noreply.github.com>
@@ -790,7 +768,6 @@ Jakkala Mahesh <155058658+MaheshJakkala@users.noreply.github.com>
 Jakub N <jakubniemczyk97@gmail.com>
 JamePeng <jame_peng@sina.com>
 James A Capozzoli <157492257+jac-jim@users.noreply.github.com>
-James Francis <6763899+JamesFranc@users.noreply.github.com>
 James O'Leary <65884233+jpohhhh@users.noreply.github.com>
 James Reynolds <magnusviri@users.noreply.github.com>
 jameswu2014 <545426914@qq.com>
@@ -821,7 +798,6 @@ Jed Fox <git@jedfox.com>
 Jeff Bolz <jbolz@nvidia.com>
 Jeffrey Morgan <jmorganca@gmail.com>
 Jeffrey Quesnelle <emozilla@nousresearch.com>
-Jeremie Miller <jeremie.miller@gmail.com>
 Jeremy Demeule <jdemeule@users.noreply.github.com>
 Jeremy Rand <244188+JeremyRand@users.noreply.github.com>
 Jeroen Mostert <jeroen.mostert@cm.com>
@@ -833,7 +809,6 @@ Jesse Jojo Johnson <williamsaintgeorge@gmail.com>
 Jesse LaRose <jesse@taey.ai>
 Jesse Posner <jesse.posner@gmail.com>
 Jesus Talavera <145992175+jesus-talavera-ibm@users.noreply.github.com>
-Jetson Tan <tanzongyouyi@outlook.com>
 Jett Janiak <jettjaniak@gmail.com>
 Jeximo <jeximo@gmail.com>
 JFLFY2255 <JFLFY2255@163.com>
@@ -850,7 +825,6 @@ Jie Fu (傅杰) <jiefu@tencent.com>
 jiez <373447296@qq.com>
 Jillis ter Hove <j.terhove@gmail.com>
 Jim Wu <jimw567@users.noreply.github.com>
-Jingxin (Philip) Li <philipaslee@gmail.com>
 Jinwoo Jeong <33892306+williamjeong2@users.noreply.github.com>
 Jinyang He <hejinyang@loongson.cn>
 jinzihao <jinzihao1996@gmail.com>
@@ -876,13 +850,11 @@ John Balis <phobossystems@gmail.com>
 John Bean <113509988+johnbean393@users.noreply.github.com>
 John Eismeier <42679190+jeis4wpi@users.noreply.github.com>
 John Smith <67539080+kingsidelee@users.noreply.github.com>
-John-Henry Lim <42513874+Interpause@users.noreply.github.com>
 Johnathan Craig Maudlin <13183098+jcmdln@users.noreply.github.com>
 JohnnyB <jboero@users.noreply.github.com>
 johnson442 <56517414+johnson442@users.noreply.github.com>
 jojorne <jojorne@users.noreply.github.com>
 jon-chuang <9093549+jon-chuang@users.noreply.github.com>
-Jonas J <111707981+John-194@users.noreply.github.com>
 Jonas Jankaitis <111707981+John-194@users.noreply.github.com>
 Jonas Wunderlich <32615971+jonas-w@users.noreply.github.com>
 Jonathan <47618606+jbuchananr@users.noreply.github.com>
@@ -952,7 +924,6 @@ Karsten Weiss <knweiss@gmail.com>
 Karthick <j.karthic2004@gmail.com>
 Karthik Kumar Viswanathan <195178+guilt@users.noreply.github.com>
 Karthik Sethuraman <k.seth1993@gmail.com>
-Kartik Gulia <kgulia@nvidia.com>
 Kartik Sirohi <99896785+sirohikartik@users.noreply.github.com>
 Kashif Rasul <kashif.rasul@gmail.com>
 KASR <karim.asrih@gmail.com>
@@ -960,7 +931,6 @@ Kasumi <90275229+kasumi-1@users.noreply.github.com>
 Katostrofik <georgiopapairo@gmail.com>
 katsu560 <118887472+katsu560@users.noreply.github.com>
 Kawrakow <48489457+ikawrakow@users.noreply.github.com>
-kbenkhaled <khalilbenkhaled01@gmail.com>
 kchro3 <62481661+kchro3@users.noreply.github.com>
 kdkd <2569413+kdkd@users.noreply.github.com>
 Keiichi Tabata <keiichi.tabata@outlook.com>
@@ -969,7 +939,6 @@ Kenvix ⭐ <kenvixzure@live.com>
 Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
 Kevin Gibbons <bakkot@gmail.com>
 Kevin Hannon <kehannon@redhat.com>
-Kevin Hopper <93635715+kh0pper@users.noreply.github.com>
 Kevin Ji <1146876+kevinji@users.noreply.github.com>
 Kevin Kwok <antimatter15@gmail.com>
 Kevin Liu <4396kevinliu@gmail.com>
@@ -995,14 +964,12 @@ Konstantin Herud <konstantin.herud@denkbares.com>
 Konstantin Zhuravlyov <konstantin.zhuravlyov@amd.com>
 Krishna Sridhar <99914379+srikris-sridhar@users.noreply.github.com>
 krystiancha <krystian@krystianch.com>
-krzsztf <krzysztof@witkowscy.org>
 kubawoo <k-wach@o2.pl>
 kumaal <44551860+kumaal@users.noreply.github.com>
 kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com>
 kunnis <kunnis@users.noreply.github.com>
 Kunshang Ji <kunshang.ji@intel.com>
 kuronekosaiko <EvanChanJ@163.com>
-kurquhar <kurquhar@qti.qualcomm.com>
 Kusha Gharahi <3326002+kushagharahi@users.noreply.github.com>
 kustaaya <58045274+kustaaya@users.noreply.github.com>
 kuvaus <22169537+kuvaus@users.noreply.github.com>
@@ -1014,7 +981,6 @@ Kyle Liang <liangmanlai@gmail.com>
 Kyle Mistele <kyle@mistele.com>
 KyleHagy <59183061+KyleHagy@users.noreply.github.com>
 Kylin <56434533+KyL0N@users.noreply.github.com>
-Kyozzz <1147385157@qq.com>
 l-austenfeld <53152202+l-austenfeld@users.noreply.github.com>
 l3utterfly <gc.pthzfoldr@gmail.com>
 l8bloom <l8bloomapi@gmail.com>
@@ -1026,7 +992,6 @@ Lars Sonchocky-Helldorf <lars.sonchocky-helldorf@hamburg.de>
 las7 <98077186+las7@users.noreply.github.com>
 Lasse Lauwerys <65569591+Iemand005@users.noreply.github.com>
 Laura <Tijntje_7@msn.com>
-Laurent Zuijdwijk <laurent.zuijdwijk@gmail.com>
 Law Po Ying <30721578+yingying0906@users.noreply.github.com>
 lcy <lcy0321@users.noreply.github.com>
 ldwang <ftgreat@163.com>
@@ -1074,8 +1039,6 @@ Ludovic Henry <git@ludovic.dev>
 Ludovic Henry <ludovic@rivosinc.com>
 Lukas Straub <lukasstraub2@web.de>
 Łukasz Ślusarczyk <112692748+lslusarczyk@users.noreply.github.com>
-Lukasz Stolcman <4583553+lstolcman@users.noreply.github.com>
-LunalFresh <165352784+LunalFresh@users.noreply.github.com>
 Luo Tian <lt@basecity.com>
 luoyu-intel <yu.luo@intel.com>
 luyhcsu <110711054+luyhcsu@users.noreply.github.com>
@@ -1091,7 +1054,6 @@ Maarten ter Huurne <maarten@treewalker.org>
 Maciej Lisowski <39798354+MaciejDromin@users.noreply.github.com>
 Mack Straight <eiz@users.noreply.github.com>
 maddes8cht <55592906+maddes8cht@users.noreply.github.com>
-Mads Marquart <mads@marquart.dk>
 Maël Kerbiriou <m431.kerbiriou@gmail.com>
 MaggotHATE <clay1326@gmail.com>
 MagicExists <106458387+gugugiyu@users.noreply.github.com>
@@ -1253,8 +1215,6 @@ Naco Siren <naco-siren@users.noreply.github.com>
 Nam D. Tran <42194884+namtranase@users.noreply.github.com>
 nanahi <130121847+na-na-hi@users.noreply.github.com>
 Nathan Epstein <nate2@umbc.edu>
-Nathan Wilson <67372905+Nathanw1014@users.noreply.github.com>
-Nathanw1014 <67372905+Nathanw1014@users.noreply.github.com>
 Natsu <chino@hotococoa.moe>
 Nauful Shaikh <nauful@gmail.com>
 NawafAlansari <72708095+NawafAlansari@users.noreply.github.com>
@@ -1277,7 +1237,6 @@ niansa/tuxifan <tuxifan@posteo.de>
 Nicholai Tukanov <nicholaitukanov@gmail.com>
 Nicholas Sparks <157740354+nisparks@users.noreply.github.com>
 Nick <0x0b4ac@gmail.com>
-Nick Farrell <nick.farrell@aiven.io>
 nick huang <nickhuang99@hotmail.com>
 Nick Lafleur <55208706+nicklafleur@users.noreply.github.com>
 Nick Towle <ntowle@gmail.com>
@@ -1300,7 +1259,6 @@ NikolaiLyssogor <59844691+NikolaiLyssogor@users.noreply.github.com>
 Nikolaos Pothitos <pothitos@di.uoa.gr>
 Nikolas <127742645+nneubacher@users.noreply.github.com>
 Nikolay Popov <131475237+npopov-vst@users.noreply.github.com>
-Nils Gladitz <nilsgladitz@gmail.com>
 Nindaleth <Nindaleth@users.noreply.github.com>
 ningshanwutuobang <ningshanwutuobang@gmail.com>
 Noah <99681487+NoahOksuz@users.noreply.github.com>
@@ -1397,7 +1355,6 @@ Pop Flamingo <trevor.annedenise@icloud.com>
 postmasters <namnguyen@google.com>
 Pouya <PooyaGhahramanian@Gmail.com>
 pqnet <119850+pqnet@users.noreply.github.com>
-Prabhsimran Singh <pskrunner14@gmail.com>
 Prabod <prabod@maincode.com>
 Prajwal B Mehendarkar <prajwal.b.mehendarkar@ibm.com>
 Pranav Dhinakar <pdhinaka@qti.qualcomm.com>
@@ -1421,7 +1378,6 @@ qouoq <qouoq@fastmail.com>
 Qu Zongfu <43257352+yancaoweidaode@users.noreply.github.com>
 quei <56998528+quei4r@users.noreply.github.com>
 Quentin Bramas <quentin.bramas@gmail.com>
-QuintinShaw <github@xyt.email>
 QuintinShaw <yx6f20@soton.ac.uk>
 qunash <anzoria@gmail.com>
 quyentonndbs <raynaedgar8677@outlook.com>
@@ -1506,7 +1462,6 @@ robertomeroni <150194833+robertomeroni@users.noreply.github.com>
 Robey Holderith <robey@flaminglunchbox.net>
 Robin Davidsson <40024429+R-Dson@users.noreply.github.com>
 Robyn <robyngraf@users.noreply.github.com>
-Rock Chen <rockchen.tw@gmail.com>
 Rőczey Barnabás <31726601+An0nie@users.noreply.github.com>
 RodriMora <bullerwins@gmail.com>
 Roger Chen <chenrui@gmail.com>
@@ -1544,21 +1499,17 @@ runfuture <runfuture@users.noreply.github.com>
 RunningLeon <maningsheng@sensetime.com>
 RunningLeon <mnsheng@yeah.net>
 Russyyds <161207317+Russyyds@users.noreply.github.com>
-Ryan C <ryan5rdx@users.noreply.github.com>
 Ryan Goulden <percontation@gmail.com>
 Ryan Landay <rlanday@gmail.com>
 Ryan Mangeno <160974989+ryan-mangeno@users.noreply.github.com>
 Ryder Wishart <ryderwishart@gmail.com>
 Ryuei <louixs@users.noreply.github.com>
 s-goto-11 <206795233+s-goto-11@users.noreply.github.com>
-s0mecode <213953308+s0mecode@users.noreply.github.com>
 s8322 <s0527684199@gmail.com>
-Saad Ali <NIXKnight@users.noreply.github.com>
 Saba Fallah <10401143+sfallah@users.noreply.github.com>
 Saba Fallah <sabafallah@gmail.com>
 Sachin Desai <smdesai@gmail.com>
 Sachin Sharma <sachin@zettabolt.com>
-Safi Ullah <safiullah.3915@gmail.com>
 safranowith <bsh155762@gmail.com>
 SakuraUmi <yukinon244@gmail.com>
 Salvador E. Tropea <stropea@inti.gob.ar>
@@ -1601,7 +1552,6 @@ Sergey Alirzaev <l29ah@riseup.net>
 Sergey Alirzaev <zl29ah@gmail.com>
 Sergey Fedorov <vital.had@gmail.com>
 Sergey Malinin <sergmalinin@gmail.com>
-Sergey Sklyarov <sergey.sklyarov@gmail.com>
 Sergio López <slp@redhat.com>
 Sergio López <slp@sinrega.org>
 Sergiu <8598216+mzsergiu@users.noreply.github.com>
@@ -1632,13 +1582,11 @@ Shawn Gu <shawngu@qti.qualcomm.com>
 Shawn yang <137684499+Yangxiaoz@users.noreply.github.com>
 Shelby Jenkins <47464908+ShelbyJenkins@users.noreply.github.com>
 Sheldon Robinson <sheldon.robinson@live.com>
-Shenghan Yang <ysharke@sjtu.edu.cn>
 shibe2 <shibe@tuta.io>
 Shijie <821898965@qq.com>
 Shin-myoung-serp <relent95@naver.com>
 Shintarou Okada <kokuzen@gmail.com>
 shivamkumard-ctrl <shivamkumard@nvidia.com>
-Shobhit <sobhit.me@gmail.com>
 Shouyu <65317431+joeldushouyu@users.noreply.github.com>
 Shouzheng Liu <61452103+lshzh-ww@users.noreply.github.com>
 Shouzheng Liu <lshzh.hi@gmail.com>
@@ -1659,7 +1607,6 @@ Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
 Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
 simevo <github@simevo.com>
 Simon Redman <simon@ergotech.com>
-Simon Teixidor <simon@flaskpost.me>
 Simon Willison <swillison@gmail.com>
 simon886212 <37953122+simon886212@users.noreply.github.com>
 Simranjeet Singh <105192966+simrnsingh@users.noreply.github.com>
@@ -1716,7 +1663,6 @@ stevenkuang <stevenkuang@tencent.com>
 Steward Garcia <57494570+FSSRepo@users.noreply.github.com>
 StrangeBytesDev <141275258+StrangeBytesDev@users.noreply.github.com>
 strawberrymelonpanda <152940198+strawberrymelonpanda@users.noreply.github.com>
-Strongtut <Strongtut@users.noreply.github.com>
 Suaj Carrot <72162667+SuajCarrot@users.noreply.github.com>
 sudhiarm <sudhi.sathyavathy@arm.com>
 Sukriti Sharma <Ssukriti@users.noreply.github.com>
@@ -1741,7 +1687,6 @@ Tamar <Tamar0812@outlook.co.il>
 tamarPal <tamarp3385@gmail.com>
 Tameem <113388789+AhmadTameem@users.noreply.github.com>
 Tamotsu Takahashi <ttakah+github@gmail.com>
-Tanner Bruhn <66120666+tannerbruhn@users.noreply.github.com>
 tarcey <cey.tarik@gmail.com>
 Tarek Dakhran <t.dakhran@gmail.com>
 Tarek Dakhran <tarek@liquid.ai>
@@ -1751,7 +1696,6 @@ Taylor <quantumtraveling@gmail.com>
 tc-mb <157115220+tc-mb@users.noreply.github.com>
 TecJesh <qdvm5gl@163.com>
 Tei Home <taiteitonghome@proton.me>
-Tekin Ertekin <tekin.ertekin@gmail.com>
 Tekin Ertekin <tekinertekin@gmail.com>
 tempstudio <49735574+tempstudio@users.noreply.github.com>
 teo <TeoZosa@users.noreply.github.com>
@@ -1793,7 +1737,6 @@ Ting Lou <louting@189.cn>
 Ting Lou <ting.lou@gmail.com>
 Ting Sun <suntcrick@gmail.com>
 Titaniumtown <titaniumtown@proton.me>
-Tiwei Bie <tiwei.btw@antgroup.com>
 tjohnman <tjohnman@users.noreply.github.com>
 Tobias Lütke <tobi@shopify.com>
 Toby <25832191+aetherbird@users.noreply.github.com>
@@ -1870,7 +1813,6 @@ Vishal Agarwal <vishalagarwal.jss@gmail.com>
 Vishal Singh <vishal@zettabolt.com>
 Vitali Lovich <vlovich+github@gmail.com>
 Vivian <vynride@gmail.com>
-vk <89937361+itsvedantkumar@users.noreply.github.com>
 Vlad <spitfireage@gmail.com>
 Vladimir <bogdad@gmail.com>
 Vladimir Malyutin <first-leon@yandex.ru>
@@ -1955,7 +1897,6 @@ Yaiko <elyaiko@hotmail.com>
 Yakine Tahtah <96926916+ReinforcedKnowledge@users.noreply.github.com>
 YangLe <smilingpoplar@gmail.com>
 yangli2 <yangli2@gmail.com>
-Yaniss Amazouz <yaniss91600@gmail.com>
 Yann Follet <131855179+YannFollet@users.noreply.github.com>
 Yanzhao Wang <yanzhaow@qti.qualcomm.com>
 Yarden Tal <yardent@qti.qualcomm.com>
@@ -4,7 +4,7 @@ include(CheckIncludeFileCXX)

 ### llama.cpp version
 set(LLAMA_VERSION_MAJOR 0)
-set(LLAMA_VERSION_MINOR 4)
+set(LLAMA_VERSION_MINOR 3)
 set(LLAMA_VERSION_PATCH 0)
 set(LLAMA_VERSION_BASE "${LLAMA_VERSION_MAJOR}.${LLAMA_VERSION_MINOR}.${LLAMA_VERSION_PATCH}")

@@ -57,7 +57,6 @@
 /ggml/src/ggml-cann/                    @ggml-org/ggml-cann
 /ggml/src/ggml-common.h                 @ggerganov
 /ggml/src/ggml-cpu/                     @ggerganov
-/ggml/src/ggml-cpu/iqp.*                @bartowski1182
 /ggml/src/ggml-cpu/spacemit/            @alex-spacemit
 /ggml/src/ggml-cuda/                    @ggml-org/ggml-cuda
 /ggml/src/ggml-cuda/vendors/hip.h       @IMbackK
@@ -13,7 +13,7 @@
 [![Docker](https://img.shields.io/github/actions/workflow/status/ggml-org/llama.cpp/docker.yml?label=Docker)](https://github.com/ggml-org/llama.cpp/actions/workflows/docker.yml)
 [![Winget](https://img.shields.io/github/actions/workflow/status/ggml-org/llama.cpp/winget.yml?label=Winget)](https://github.com/ggml-org/llama.cpp/actions/workflows/winget.yml)

-[ggml](https://github.com/ggml-org/ggml) / [ops](https://github.com/ggml-org/llama.cpp/blob/master/docs/ops.md) / [maintainer PRs](https://github.com/ggml-org/llama.cpp/issues?q=is%3Apr%20is%3Aopen%20draft%3AFalse%20(author%3Argerganov%20OR%20author%3AKitaitiMakoto%20OR%20author%3Adanbev%20OR%20author%3Aaldehir%20OR%20author%3Amax-krasnyansky%20OR%20author%3ACISC%20OR%20author%3Aggerganov%20OR%20author%3Aam17an%20OR%20author%3Ajhen0409%20OR%20author%3Abartowski1182%20OR%20author%3Anikwen%20OR%20author%3Ahipudding%20OR%20author%3Aravi9%20OR%20author%3AServeurpersoCom%20OR%20author%3Apwilkin%20OR%20author%3Areeselevine%20OR%20author%3Angxson%20OR%20author%3Ajeffbolznv%20OR%20author%3Amarty1885%20OR%20author%3A0cc4m%20OR%20author%3ATitaniumtown%20OR%20author%3Aangt%20OR%20author%3AIMbackK%20OR%20author%3Aarthw%20OR%20author%3AJohannesGaessler%20OR%20author%3AORippler%20OR%20author%3Aruixiang63%20OR%20author%3Axctan%20OR%20author%3Aallozaur%20OR%20author%3Ayomaytk%20OR%20author%3Aaendk%20OR%20author%3Awine99%20OR%20author%3Agaugarg-nv%20OR%20author%3Ataronaeo%20OR%20author%3Aforforever73%20OR%20author%3Alhez%20OR%20author%3Anetrunnereve%20OR%20author%3Afairydreaming)%20sort%3Aupdated-desc) / [dev stats](https://github.com/ggml-org/llama.cpp-dev) / [lib llama API](https://github.com/ggml-org/llama.cpp/issues/9289) / [llama-server REST API](https://github.com/ggml-org/llama.cpp/issues/9291)
+[ggml](https://github.com/ggml-org/ggml) / [ops](https://github.com/ggml-org/llama.cpp/blob/master/docs/ops.md) / [maintainer PRs](https://github.com/ggml-org/llama.cpp/issues?q=is%3Apr%20is%3Aopen%20draft%3AFalse%20(author%3Argerganov%20OR%20author%3AKitaitiMakoto%20OR%20author%3Adanbev%20OR%20author%3Aaldehir%20OR%20author%3Amax-krasnyansky%20OR%20author%3ACISC%20OR%20author%3Aggerganov%20OR%20author%3Aam17an%20OR%20author%3Abartowski1182%20OR%20author%3Anikwen%20OR%20author%3Ahipudding%20OR%20author%3AServeurpersoCom%20OR%20author%3Apwilkin%20OR%20author%3Areeselevine%20OR%20author%3Angxson%20OR%20author%3Ajeffbolznv%20OR%20author%3Amarty1885%20OR%20author%3A0cc4m%20OR%20author%3ATitaniumtown%20OR%20author%3Aangt%20OR%20author%3AIMbackK%20OR%20author%3Aarthw%20OR%20author%3AJohannesGaessler%20OR%20author%3AORippler%20OR%20author%3Aruixiang63%20OR%20author%3Axctan%20OR%20author%3Aallozaur%20OR%20author%3Ayomaytk%20OR%20author%3Aaendk%20OR%20author%3Agaugarg-nv%20OR%20author%3Ataronaeo%20OR%20author%3Aforforever73%20OR%20author%3Alhez%20OR%20author%3Anetrunnereve%20OR%20author%3Afairydreaming)%20sort%3Aupdated-desc) / [dev stats](https://github.com/ggml-org/llama.cpp-dev) / [lib llama API](https://github.com/ggml-org/llama.cpp/issues/9289) / [llama-server REST API](https://github.com/ggml-org/llama.cpp/issues/9291)

 </div>

@@ -74,7 +74,7 @@ The `llama.cpp` project is build on top of the [ggml](https://github.com/ggml-or
 | [CANN](docs/build.md#cann) | Ascend NPU |
 | [CUDA](docs/build.md#cuda) | Nvidia GPU |
 | [HIP](docs/build.md#hip) | AMD GPU |
-| [Hexagon](docs/backend/snapdragon/README.md) | Snapdragon |
+| [Hexagon [In Progress]](docs/backend/snapdragon/README.md) | Snapdragon |
 | [IBM zDNN](docs/backend/zDNN.md) | IBM Z & LinuxONE |
 | [MUSA](docs/build.md#musa) | Moore Threads GPU |
 | [Metal](docs/build.md#metal-build) | Apple Silicon |
@@ -80,7 +80,7 @@ static const command cmds[] = {
 #undef UPDATE_HIDDEN

 static int version(int /*argc*/, char ** /*argv*/) {
-    llama_print_build_info(llama_version(), stdout);
+    llama_print_build_info(llama_version());
    return 0;
 }

@@ -18,7 +18,7 @@ LLAMA_BUILD_TESTS=OFF
 LLAMA_BUILD_SERVER=OFF
 LLAMA_BUILD_MTMD=ON
 GGML_METAL=ON
-GGML_METAL_EMBED_LIBRARY=${GGML_METAL_EMBED_LIBRARY:-ON}
+GGML_METAL_EMBED_LIBRARY=ON
 GGML_BLAS_DEFAULT=ON
 GGML_OPENMP=OFF

@@ -169,14 +169,6 @@ setup_framework_structure() {
    cp tools/mtmd/mtmd.h           ${header_path}
    cp tools/mtmd/mtmd-helper.h    ${header_path}

-    if [[ "$GGML_METAL_EMBED_LIBRARY" == "OFF" ]]; then
-        if [[ "$platform" == "macos" ]]; then
-            cp ${build_dir}/bin/*.metallib ${build_dir}/framework/${framework_name}.framework/Versions/A/Resources/
-        else
-            cp ${build_dir}/bin/*.metallib ${build_dir}/framework/${framework_name}.framework/
-        fi
-    fi
-
    # Create module map (common for all platforms)
    cat > ${module_path}module.modulemap << EOF
 framework module llama {
@@ -458,7 +450,6 @@ build_ios_sim() {
        -DIOS=ON \
        -DCMAKE_SYSTEM_NAME=iOS \
        -DCMAKE_OSX_SYSROOT=iphonesimulator \
-        -DGGML_METAL_TARGET_OS=ios \
        -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
        -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=iphonesimulator \
        -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
@@ -476,7 +467,6 @@ build_ios_device() {
        -DCMAKE_OSX_DEPLOYMENT_TARGET=${IOS_MIN_OS_VERSION} \
        -DCMAKE_SYSTEM_NAME=iOS \
        -DCMAKE_OSX_SYSROOT=iphoneos \
-        -DGGML_METAL_TARGET_OS=ios \
        -DCMAKE_OSX_ARCHITECTURES="arm64" \
        -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=iphoneos \
        -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
@@ -508,7 +498,6 @@ build_visionos() {
        -DCMAKE_OSX_ARCHITECTURES="arm64" \
        -DCMAKE_SYSTEM_NAME=visionOS \
        -DCMAKE_OSX_SYSROOT=xros \
-        -DGGML_METAL_TARGET_OS=xros \
        -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=xros \
        -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
        -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
@@ -527,7 +516,6 @@ build_visionos_sim() {
        -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
        -DCMAKE_SYSTEM_NAME=visionOS \
        -DCMAKE_OSX_SYSROOT=xrsimulator \
-        -DGGML_METAL_TARGET_OS=xros \
        -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=xrsimulator \
        -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
        -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
@@ -546,7 +534,6 @@ build_tvos_sim() {
        -DCMAKE_OSX_DEPLOYMENT_TARGET=${TVOS_MIN_OS_VERSION} \
        -DCMAKE_SYSTEM_NAME=tvOS \
        -DCMAKE_OSX_SYSROOT=appletvsimulator \
-        -DGGML_METAL_TARGET_OS=tvos \
        -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
        -DGGML_METAL=ON \
        -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=appletvsimulator \
@@ -565,7 +552,6 @@ build_tvos_device() {
        -DCMAKE_OSX_DEPLOYMENT_TARGET=${TVOS_MIN_OS_VERSION} \
        -DCMAKE_SYSTEM_NAME=tvOS \
        -DCMAKE_OSX_SYSROOT=appletvos \
-        -DGGML_METAL_TARGET_OS=tvos \
        -DCMAKE_OSX_ARCHITECTURES="arm64" \
        -DGGML_METAL=ON \
        -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=appletvos \
@@ -189,8 +189,8 @@ if [ ! -z ${GG_BUILD_OPENVINO} ]; then
    fi
    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_OPENVINO=ON"

-    # TODO: fix failing tests on OpenVINO backend
-    CTEST_EXTRA="-E test-llama-archs|^test-recurrent-state-|test-save-load-state"
+    # TODO: fix and re-enable the `test-llama-archs` test below
+    CTEST_EXTRA="-E test-llama-archs|test-recurrent-state-rollback-nemotron-h"
 fi

 ## helpers
@@ -250,7 +250,7 @@ function gg_run_ctest_debug {
    (cmake -G "${CMAKE_GENERATOR}" -DCMAKE_BUILD_TYPE=Debug ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
    (time cmake --build . --config Debug -j$(nproc)) 2>&1 | tee -a $OUT/${ci}-make.log

-    (time ctest -C Debug --output-on-failure -L main -E "test-opt|test-llama-archs" ${CTEST_EXTRA}) 2>&1 | tee -a $OUT/${ci}-ctest.log
+    (time ctest -C Debug --output-on-failure -L main -E "test-opt|test-backend-ops|test-llama-archs" ${CTEST_EXTRA}) 2>&1 | tee -a $OUT/${ci}-ctest.log

    set +e
 }
@@ -334,35 +334,6 @@ function gg_sum_test_llama_archs_tensor_split {
    gg_printf '```\n'
 }

-# test_llama_archs_models
-
-function gg_run_test_llama_archs_models {
-    cd ${SRC}
-
-    set -e
-
-    # TODO: fix and re-enable `test-llama-archs` on OpenVINO
-    # TODO: the `test-llama-archs` currently does not build on Windows, so we check if the binary exists
-    if [ -z ${GG_BUILD_OPENVINO} ] && [ -f ./build-ci-release/bin/test-llama-archs ]; then
-        rm -rf build-ci-models && mkdir -p build-ci-models
-
-        # generate the dummy models used by the model-dependent tests
-        ./build-ci-release/bin/test-llama-archs -o build-ci-models 2>&1
-    fi
-
-    set +e
-}
-
-function gg_sum_test_llama_archs_models {
-    gg_printf '### %s\n\n' "${ci}"
-
-    gg_printf 'Generates the dummy models used by the model-dependent tests\n'
-    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
-    gg_printf '```\n'
-    gg_printf '%s\n' "$(cat $OUT/${ci}.log)"
-    gg_printf '```\n'
-}
-
 # test_scripts

 function gg_run_test_scripts {
@@ -761,50 +732,27 @@ function gg_check_build_requirements {
        gg_printf 'ctest not found, please install\n'
        exit 1
    fi
-
-    if ! command -v unzip &> /dev/null; then
-        gg_printf 'unzip not found, please install\n'
-        exit 1
-    fi
 }

-function gg_run_test_backend_ops {
+function gg_run_test_backend_ops_cpu {
    cd ${SRC}

    cd build-ci-release

    set -e

-    local args_extra="-j $(nproc)"
-
-    # TODO: fix multi-threaded for ROCm
-    #       https://github.com/ggml-org/llama.cpp/actions/runs/34576278519/job/103297889044?pr=28740#step:3:4865
-    if [ ! -z ${GG_BUILD_ROCM} ]; then
-        args_extra=""
-    fi
-
-    # TODO: MoltenVK bug?
-    #       https://github.com/ggml-org/llama.cpp/actions/runs/34611260059/job/103302413736?pr=28740#step:3:5897
-    if [ ! -z "${GG_BUILD_VULKAN}" ] && [ "$(uname -s)" = "Darwin" ]; then
-        args_extra=""
-    fi
-
-    if [ ! -z ${GG_BUILD_HIGH_PERF} ]; then
-        (time ./bin/test-backend-ops ${args_extra} -b CPU) 2>&1 | tee -a $OUT/${ci}-test-backend-ops.log
-    else
-        (time ./bin/test-backend-ops ${args_extra}       ) 2>&1 | tee -a $OUT/${ci}-test-backend-ops.log
-    fi
+    (time ./bin/test-backend-ops -b CPU ) 2>&1 | tee -a $OUT/${ci}-test-backend-ops-cpu.log

    set +e
 }

-function gg_sum_test_backend_ops {
+function gg_sum_test_backend_ops_cpu {
    gg_printf '### %s\n\n' "${ci}"

-    gg_printf 'Runs test-backend-ops\n'
+    gg_printf 'Runs test-backend-ops for CPU backend\n'
    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
    gg_printf '```\n'
-    gg_printf '%s\n' "$(cat $OUT/${ci}-test-backend-ops.log)"
+    gg_printf '%s\n' "$(cat $OUT/${ci}-test-backend-ops-cpu.log)"
    gg_printf '```\n'
    gg_printf '\n'
 }
@@ -837,11 +785,12 @@ ret=0
 test $ret -eq 0 && gg_run ctest_debug
 test $ret -eq 0 && gg_run ctest_release

-test $ret -eq 0 && gg_run test_backend_ops
-
-test $ret -eq 0 && gg_run test_llama_archs_models
 test $ret -eq 0 && gg_run test_llama_archs_tensor_split

+if [ ! -z ${GG_BUILD_HIGH_PERF} ]; then
+    test $ret -eq 0 && gg_run test_backend_ops_cpu
+fi
+
 if [ -z ${GG_BUILD_LOW_PERF} ]; then
    test $ret -eq 0 && gg_run embd_bge_small
    test $ret -eq 0 && gg_run rerank_tiny
@@ -53,10 +53,7 @@ endif()

 set(TARGET llama-common)

-include(parsers/sources.cmake)
-
 add_library(${TARGET}
-    ${LLAMA_CHAT_PARSERS_SOURCES}
    arg.cpp
    arg.h
    base64.hpp
@@ -134,8 +131,6 @@ set_target_properties(${TARGET} PROPERTIES
 target_include_directories(${TARGET} PUBLIC .)
 target_link_libraries     (${TARGET} PUBLIC vendor::nlohmann vendor::sheredom)
 target_compile_features   (${TARGET} PUBLIC cxx_std_17)
-target_precompile_headers (${TARGET} PRIVATE common.h)
-target_precompile_headers (${TARGET} PRIVATE chat.h)

 if (LLAMA_SUBPROCESS)
    target_compile_definitions(${TARGET} PUBLIC LLAMA_SUBPROCESS)
@@ -872,6 +872,17 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
                    arg.c_str(), e.what(), opt.to_string().c_str()));
            }
        }
+
+        // TODO: remove this check after deprecating --mmap|mlock|dio
+        auto has_arg = [&](std::initializer_list<const char *> names) {
+            return std::any_of(names.begin(), names.end(), [&](const char * name) {
+                return seen_args.count(name);
+            });
+        };
+        if (has_arg({"-lm", "--load-mode"}) &&
+            has_arg({"--mlock", "--mmap", "--no-mmap", "-dio", "--direct-io", "-ndio", "--no-direct-io"})) {
+            LOG_WRN("DEPRECATED: `--load-mode` and `--mlock`/`--mmap`/`--direct-io` should not be combined; only the last flag on the command line will take effect\n");
+        }
    };

    // parse all CLI args now, so that -hf is available below for remote preset resolution
@@ -883,12 +894,6 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
    postprocess_cpu_params(params.speculative.draft.cpuparams,       &params.cpuparams);
    postprocess_cpu_params(params.speculative.draft.cpuparams_batch, &params.cpuparams_batch);

-    // default the mmproj device to the global device selection if not set explicitly with -mmdev
-    if (params.mmproj_use_gpu && params.mmproj_device == nullptr && !params.devices.empty()) {
-        params.mmproj_device = params.devices.front();
-        params.mmproj_use_gpu = params.mmproj_device != nullptr;
-    }
-
    if (params.prompt_cache_all && (params.interactive || params.interactive_first)) {
        throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
    }
@@ -955,11 +960,6 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
        ));
    }

-    // if the preserve_reasoning kwarg was not specified explicitly, enable it by default
-    if (!params.default_template_kwargs.count("preserve_reasoning")) {
-        params.default_template_kwargs["preserve_reasoning"] = "true";
-    }
-
    return true;
 }

@@ -1643,14 +1643,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            }
        }
    ).set_env("LLAMA_ARG_CTX_SIZE"));
-    add_opt(common_arg(
-        { "--kv-unified-per-slot" }, "N",
-        "context limit per parallel slot (default: unset, behavior unchanged).\n"
-        "when set without -c/--ctx-size, the shared KV pool is sized to n_parallel*N",
-        [](common_params & params, int value) {
-            params.kv_unified_per_slot = value;
-        }
-    ).set_env("LLAMA_ARG_KV_UNIFIED_PER_SLOT").set_examples({ LLAMA_EXAMPLE_SERVER }));
    add_opt(common_arg(
        {"-n", "--predict", "--n-predict"}, "N",
        string_format(
@@ -2605,7 +2597,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    add_opt(common_arg(
        // note: "-mmdev" must sort after "--rpc" in the preset map, else RPC devices are not registered yet
        {"-mmdev", "--mmproj-device"}, "DEVICE",
-        "device to use for multimodal projector (none = don't offload, default: follows --device)\n"
+        "device to use for multimodal projector (none = don't offload, default: auto)\n"
        "use --list-devices to see a list of available devices",
        [](common_params & params, const std::string & value) {
            if (value == "none") {
@@ -2683,6 +2675,32 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            }
        ).set_env("LLAMA_ARG_RPC"));
    }
+    add_opt(common_arg(
+        {"--mlock"},
+        "DEPRECATED in favor of `--load-mode`: force system to keep model in RAM rather than swapping or compressing",
+        [](common_params & params) {
+            LOG_WRN("DEPRECATED: --mlock is deprecated. use --load-mode mlock instead\n");
+            params.load_mode = LLAMA_LOAD_MODE_MLOCK;
+        }
+    ).set_env("LLAMA_ARG_MLOCK"));
+    add_opt(common_arg(
+        {"--mmap"},
+        {"--no-mmap"},
+        "DEPRECATED in favor of `--load-mode`: whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock)",
+        [](common_params & params, bool value) {
+            LOG_WRN("DEPRECATED: --mmap and --no-mmap are deprecated. use --load-mode mmap instead\n");
+            params.load_mode = value ? LLAMA_LOAD_MODE_MMAP : LLAMA_LOAD_MODE_NONE;
+        }
+    ).set_env("LLAMA_ARG_MMAP"));
+    add_opt(common_arg(
+        {"-dio", "--direct-io"},
+        {"-ndio", "--no-direct-io"},
+        "DEPRECATED in favor of `--load-mode`: use DirectIO if available",
+        [](common_params & params, bool value) {
+            LOG_WRN("DEPRECATED: --direct-io and --no-direct-io are deprecated. use --load-mode dio instead\n");
+            params.load_mode = value ? LLAMA_LOAD_MODE_DIRECT_IO : LLAMA_LOAD_MODE_NONE;
+        }
+    ).set_env("LLAMA_ARG_DIO"));
    add_opt(common_arg(
        {"-lm", "--load-mode"}, "MODE",
        "model loading mode (default: auto)\n"
@@ -2702,19 +2720,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            else { throw std::invalid_argument("invalid value"); }
        }
    ).set_env("LLAMA_ARG_LOAD_MODE"));
-    add_opt(common_arg(
-        {"-lzm", "--lazy-mode"}, "MODE",
-        "on-demand reading of certain tensors, for example per-layer embeddings (default: auto)\n"
-        "- on: read the rows of such tensors from disk on demand instead of keeping them resident (requires mmap)\n"
-        "- auto: on, but only for tensors larger than 4 GiB\n"
-        "- off: always keep them resident",
-        [](common_params & params, const std::string & value) {
-            /**/ if (value == "on")   { params.lazy_mode = LLAMA_LAZY_MODE_ON;   }
-            else if (value == "auto") { params.lazy_mode = LLAMA_LAZY_MODE_AUTO; }
-            else if (value == "off")  { params.lazy_mode = LLAMA_LAZY_MODE_OFF;  }
-            else { throw std::invalid_argument("invalid value"); }
-        }
-    ).set_env("LLAMA_ARG_LAZY_MODE"));
    add_opt(common_arg(
        {"--numa"}, "TYPE",
        "attempt optimizations that help on some NUMA systems\n"
@@ -3527,10 +3532,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                    LOG_WRN("Setting 'enable_thinking' via --chat-template-kwargs is deprecated. "
                            "Use --reasoning on / --reasoning off instead.\n");
                }
-                if (item.key() == "preserve_reasoning") {
-                    LOG_WRN("Setting 'preserve_reasoning' via --chat-template-kwargs is deprecated. "
-                            "Use --reasoning-preserve / --no-reasoning-preserve instead.\n");
-                }
                params.default_template_kwargs[item.key()] = item.value().dump();
            }
        }
@@ -3721,7 +3722,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    add_opt(common_arg(
        {"--reasoning-preserve"},
        {"--no-reasoning-preserve"},
-        "preserve reasoning trace in the full history, not just the last assistant message (default: enabled)\n"
+        "preserve reasoning trace in the full history, not just the last assistant message (default: template default)\n"
        "compatible with certain templates having 'supports_preserve_reasoning' capability\n"
        "example: https://docs.z.ai/guides/capabilities/thinking-mode#preserved-thinking",
        [](common_params & params, bool value) {
@@ -3730,7 +3731,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            } else {
                params.default_template_kwargs["preserve_reasoning"] = "false";
            }
-            params.preserve_reasoning_specified = true;
        }
    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_REASONING_PRESERVE"));
    add_opt(common_arg(
@@ -3870,14 +3870,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            common_log_set_file(common_log_main(), value.c_str());
        }
    ).set_env("LLAMA_ARG_LOG_FILE"));
-    add_opt(common_arg(
-        {"--log-jsonl"},
-        {"--no-log-jsonl"},
-        "Log as JSONL (one JSON object per line) to stdout, this also disables colored logging (default: disabled)",
-        [](common_params &, bool value) {
-            common_log_set_jsonl(common_log_main(), value);
-        }
-    ).set_env("LLAMA_ARG_LOG_JSONL"));
    add_opt(common_arg(
        {"--log-prompts-dir"}, "PATH",
        "Log prompts to directory (auto-created if not present; only used for debugging, default: disabled)",
@@ -4140,38 +4132,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.speculative.draft.n_min = value;
        }
    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_N_MIN"));
-    add_opt(common_arg(
-        {"--spec-synth-len"}, "L",
-        "target mean synthetic acceptance length, including the target token (benchmarking only)",
-        [](common_params & params, const std::string & value) {
-            const std::string text = string_strip(value);
-            size_t pos = 0;
-            const double length = std::stod(text, &pos);
-            if (pos != text.size() || length == -1.0) {
-                throw std::invalid_argument("invalid value");
-            }
-            params.speculative.synth_len = length;
-        }
-    ).set_spec().set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_SYNTH_LEN"));
-    add_opt(common_arg(
-        {"--spec-synth-rates"}, "P0,P1,...",
-        "comma-separated unconditional per-position synthetic acceptance probabilities (benchmarking only)",
-        [](common_params & params, const std::string & value) {
-            const auto values = string_split<std::string>(value, ',');
-            std::vector<double> rates;
-            rates.reserve(values.size());
-            for (const auto & raw : values) {
-                const std::string text = string_strip(raw);
-                size_t pos = 0;
-                const double rate = std::stod(text, &pos);
-                if (pos != text.size()) {
-                    throw std::invalid_argument("invalid value");
-                }
-                rates.push_back(rate);
-            }
-            params.speculative.synth_rates = std::move(rates);
-        }
-    ).set_spec().set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_SYNTH_RATES"));

    add_opt(common_arg(
        {"--spec-draft-p-split", "--draft-p-split"}, "P",
@@ -4198,7 +4158,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_BACKEND_SAMPLING"));
    add_opt(common_arg(
        {"--spec-draft-device", "-devd", "--device-draft"}, "<dev1,dev2,..>",
-        "comma-separated list of devices to use for offloading the draft model (none = don't offload, default: follows --device)\n"
+        "comma-separated list of devices to use for offloading the draft model (none = don't offload)\n"
        "use --list-devices to see a list of available devices",
        [](common_params & params, const std::string & value) {
            params.speculative.draft.devices = parse_device_list(value);
@@ -29,7 +29,7 @@ const char * llama_build_info(void) {
    return s.c_str();
 }

-void llama_print_build_info(const char * llama_version, FILE * stream) {
-    fprintf(stream, "version: %s (build %d, commit %s)\n", llama_version, llama_build_number(), llama_commit());
-    fprintf(stream, "built with %s for %s\n", llama_compiler(), llama_build_target());
+void llama_print_build_info(const char * llama_version) {
+    fprintf(stderr, "version: %s (build %d, commit %s)\n", llama_version, llama_build_number(), llama_commit());
+    fprintf(stderr, "built with %s for %s\n", llama_compiler(), llama_build_target());
 }
@@ -1,7 +1,5 @@
 #pragma once

-#include <cstdio>
-
 int llama_build_number(void);

 const char * llama_commit(void);
@@ -10,4 +8,4 @@ const char * llama_compiler(void);
 const char * llama_build_target(void);
 const char * llama_build_info(void);

-void llama_print_build_info(const char *, FILE * = stderr);
+void llama_print_build_info(const char *);
@@ -1688,7 +1688,6 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
    mparams.main_gpu        = params.main_gpu;
    mparams.split_mode      = params.split_mode;
    mparams.load_mode       = params.load_mode;
-    mparams.lazy_mode = params.lazy_mode;
    mparams.tensor_split    = params.tensor_split;
    mparams.check_tensors   = params.check_tensors;
    mparams.use_extra_bufts = !params.no_extra_bufts;
@@ -270,7 +270,7 @@ struct common_params_sampling {
        COMMON_SAMPLER_TYPE_TEMPERATURE,
    };

-    common_grammar                      grammar;          // optional grammar constraint (user / output-format / tool-calls)
+    common_grammar              grammar;      // optional grammar constraint (user / output-format / tool-calls)
    bool                                grammar_lazy = false;
    std::vector<common_grammar_trigger> grammar_triggers; // optional triggers (for lazy grammars)
    std::set<llama_token>               preserved_tokens;
@@ -370,9 +370,6 @@ struct common_params_speculative_ngram_cache {
 struct common_params_speculative {
    std::vector<enum common_speculative_type> types = { COMMON_SPECULATIVE_TYPE_NONE };

-    double synth_len = -1.0;
-    std::vector<double> synth_rates;
-
    // used by Simple, MTP, Eagle3, etc. - all methods that require some kind of draft model
    common_params_speculative_draft draft;

@@ -387,10 +384,6 @@ struct common_params_speculative {
        return !draft.mparams.empty();
    }

-    bool has_synth() const {
-        return synth_len != -1.0 || !synth_rates.empty();
-    }
-
    uint32_t need_n_rs_seq() const {
        bool needs_rs_seq = std::any_of(types.begin(), types.end(), [&](auto t) {
            return t == COMMON_SPECULATIVE_TYPE_DRAFT_MTP || t == COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3 || t == COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH || t == COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK;
@@ -483,8 +476,6 @@ struct common_params {
    enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
    enum llama_load_mode  load_mode  = LLAMA_LOAD_MODE_AUTO; // how to load the model

-    enum llama_lazy_mode lazy_mode = LLAMA_LAZY_MODE_AUTO; // on-demand reading of tensors marked by the arch
-
    common_cpu_params cpuparams;
    common_cpu_params cpuparams_batch;

@@ -627,7 +618,6 @@ struct common_params {
    bool    cache_prompt        = true;  // whether to enable prompt caching
    bool    cache_idle_slots    = true;  // save and clear idle slots upon starting a new task
    int32_t n_ctx_checkpoints   = 32;    // max number of context checkpoints per slot
-    int32_t kv_unified_per_slot = 0;     // max context per parallel slot; 0 = unset
    int32_t checkpoint_min_step = 8192;  // minimum spacing between context checkpoints
    int32_t cache_ram_mib       = 8192;  // -1 = no limit, 0 - disable, 1 = 1 MiB, etc.

@@ -657,7 +647,6 @@ struct common_params {
    std::string ssl_file_cert = "";                                                                         // NOLINT

    std::map<std::string, std::string> default_template_kwargs;
-    bool preserve_reasoning_specified = false;

    // CLI params
    std::string server_base; // if set, connect to this server instead of starting a new one
@@ -117,7 +117,6 @@ caps caps_get(jinja::program & prog) {

    JJ_DEBUG("%s\n", ">>> Running capability check: typed content");

-    bool checks_for_string = false;
    static const std::string content_marker = "STRING_MARKER";

    // case: typed content support
@@ -137,10 +136,6 @@ caps caps_get(jinja::program & prog) {
        [&](context &, bool success, value & messages, value &, const std::string & rendered) {
            auto & content = messages->at(0)->at("content");
            caps_print_stats(content, "messages[0].content");
-            if (has_op(content, "test_is_string")) {
-                // checked if content is string
-                checks_for_string = true;
-            }
            bool used_as_array = has_op(content, "selectattr") || has_op(content, "array_access");
            if (used_as_array) {
                // accessed as an array
@@ -156,33 +151,6 @@ caps caps_get(jinja::program & prog) {
        }
    );

-    if (checks_for_string) {
-        caps_try_execute(
-            prog,
-            [&]() {
-                // messages
-                return json::array({
-                    {
-                        {"role", "user"},
-                        {"content", json::array({
-                        })}
-                    }
-                });
-            },
-            nullptr, // ctx_fn
-            nullptr, // tools_fn
-            [&](context &, bool success, value & messages, value &, const std::string &) {
-                auto & content = messages->at(0)->at("content");
-                caps_print_stats(content, "messages[0].content");
-                bool used_as_array = has_op(content, "selectattr") || has_op(content, "array_access");
-                if (used_as_array && success) {
-                    // accessed as an array
-                    result.supports_typed_content = true;
-                }
-            }
-        );
-    }
-
    JJ_DEBUG("%s\n", ">>> Running capability check: system prompt");

    // case: system prompt support
@@ -167,12 +167,6 @@ value binary_expression::execute_impl(context & ctx) {
        }
        throw std::runtime_error("Cannot perform operation " + op.value + " on undefined values");
    } else if (is_val<value_none>(left_val) || is_val<value_none>(right_val)) {
-        if (!is_val<value_none>(right_val) && (op.value == "in" || op.value == "not in")) {
-            // case: none in {'low': 1}
-            // A null left operand is looked up like any other value.
-            bool member = test_is_in();
-            return mk_val<value_bool>(op.value == "in" ? member : !member);
-        }
        if (op.value == "+" || op.value == "~") {
            value res = mk_val<value_undefined>();
            if (workaround_concat_null_with_str(res)) {
@@ -418,18 +412,12 @@ value test_expression::execute_impl(context & ctx) {
        throw std::runtime_error("Invalid test expression");
    }

-    const std::string test_name = "test_is_" + test_id;
-    auto it = builtins.find(test_name);
-    JJ_DEBUG("Test expression %s '%s' %s (using function '%s')", operand->type().c_str(), test_id.c_str(), negate ? "(negate)" : "", test_name.c_str());
+    auto it = builtins.find("test_is_" + test_id);
+    JJ_DEBUG("Test expression %s '%s' %s (using function 'test_is_%s')", operand->type().c_str(), test_id.c_str(), negate ? "(negate)" : "", test_id.c_str());
    if (it == builtins.end()) {
        throw std::runtime_error("Unknown test '" + test_id + "'");
    }

-    if (ctx.is_get_stats) {
-        value_t::stats_t::mark_used(input);
-        input->stats.ops.insert(test_name);
-    }
-
    auto res = it->second(args);

    if (negate) {
@@ -748,10 +748,6 @@ private:
            optional_props.push_back("*");
        }

-        if (required_props.empty() && optional_props.empty()) {
-            return "\"{\" space \"}\"";
-        }
-
        std::string rule = "\"{\" space ";
        for (size_t i = 0; i < required_props.size(); i++) {
            if (i > 0) {
@@ -1,6 +1,5 @@
 #include "common.h"
 #include "log.h"
-#include "json.h"

 #include <chrono>
 #include <condition_variable>
@@ -67,17 +66,6 @@ static const char* g_col[] = {
    "",
 };

-static const char * level_str(enum ggml_log_level level) {
-    switch (level) {
-        case GGML_LOG_LEVEL_DEBUG: return "debug";
-        case GGML_LOG_LEVEL_INFO:  return "info";
-        case GGML_LOG_LEVEL_WARN:  return "warn";
-        case GGML_LOG_LEVEL_ERROR: return "error";
-        case GGML_LOG_LEVEL_CONT:  return "cont";
-        default:                   return "none";
-    }
-}
-
 struct common_log_entry {
    enum ggml_log_level level {GGML_LOG_LEVEL_INFO};

@@ -86,7 +74,6 @@ struct common_log_entry {
    int64_t timestamp { 0 };
    bool is_end       { false }; // signals the worker thread to stop
    bool prefix       { false };
-    bool jsonl        { false };

    common_log_entry(size_t size = 256) : msg(size) { }

@@ -101,23 +88,11 @@ struct common_log_entry {

            fcur = stdout;

-            if (level != GGML_LOG_LEVEL_NONE && !jsonl) {
+            if (level != GGML_LOG_LEVEL_NONE) {
                fcur = stderr;
            }
        }

-        if (jsonl) {
-            common_json obj = {
-                {"type",  "log"},
-                {"time",  timestamp},
-                {"level", level_str(level)},
-                {"msg",   msg.data()},
-            };
-            fprintf(fcur, "%s\n", obj.dump_safe().c_str());
-            fflush(fcur);
-            return;
-        }
-
        if (level != GGML_LOG_LEVEL_NONE && level != GGML_LOG_LEVEL_CONT && prefix) {
            if (timestamp) {
                // [M.s.ms.us]
@@ -156,7 +131,6 @@ struct common_log {
        file       = nullptr;
        prefix     = false;
        timestamps = false;
-        jsonl      = false;
        running    = false;
        t_start    = t_us();

@@ -184,7 +158,6 @@ private:

    bool prefix;
    bool timestamps;
-    bool jsonl;
    bool running;

    int64_t t_start;
@@ -273,7 +246,6 @@ public:
        entry.is_end    = false;
        entry.level     = level;
        entry.prefix    = prefix;
-        entry.jsonl     = jsonl;
        entry.timestamp = 0;
        if (timestamps) {
            entry.timestamp = t_us() - t_start;
@@ -388,12 +360,6 @@ public:

        this->timestamps = timestamps;
    }
-
-    void set_jsonl(bool jsonl) {
-        std::lock_guard<std::mutex> lock(mtx);
-
-        this->jsonl = jsonl;
-    }
 };

 //
@@ -467,16 +433,12 @@ void common_log_set_timestamps(struct common_log * log, bool timestamps) {
    log->set_timestamps(timestamps);
 }

-void common_log_set_jsonl(struct common_log * log, bool jsonl) {
-    log->set_jsonl(jsonl);
-}
-
 void common_log_flush(struct common_log * log) {
    log->pause();
    log->resume();
 }

-int common_log_get_verbosity(enum ggml_log_level level) {
+static int common_get_verbosity(enum ggml_log_level level) {
    switch (level) {
        case GGML_LOG_LEVEL_DEBUG: return LOG_LEVEL_DEBUG;
        case GGML_LOG_LEVEL_INFO:  return LOG_LEVEL_TRACE;
@@ -490,7 +452,7 @@ int common_log_get_verbosity(enum ggml_log_level level) {
 }

 void common_log_default_callback(enum ggml_log_level level, const char * text, void * /*user_data*/) {
-    auto verbosity = common_log_get_verbosity(level);
+    auto verbosity = common_get_verbosity(level);
    if (verbosity <= common_log_verbosity_thold) {
        common_log_add(common_log_main(), level, "%s", text);
    }
@@ -43,8 +43,6 @@ int  common_log_get_verbosity_thold(void);

 void common_log_set_verbosity_thold(int verbosity); // not thread-safe

-int common_log_get_verbosity(enum ggml_log_level level);
-
 void common_log_default_callback(enum ggml_log_level level, const char * text, void * user_data);

 // the common_log uses an internal worker thread to print/write log messages
@@ -91,7 +89,6 @@ void common_log_set_file      (struct common_log * log, const char * file); // n
 void common_log_set_colors    (struct common_log * log, log_colors colors); // not thread-safe
 void common_log_set_prefix    (struct common_log * log, bool prefix);       // whether to output prefix to each log
 void common_log_set_timestamps(struct common_log * log, bool timestamps);   // whether to output timestamps in the prefix
-void common_log_set_jsonl     (struct common_log * log, bool jsonl);        // print each log as a JSON object on one line, not thread-safe
 void common_log_flush         (struct common_log * log);                    // flush all pending log messages

 // helper macros for logging
@@ -1,150 +0,0 @@
-#include "parsers.h"
-
-// Cohere2 MoE (a.k.a. "North Code") parser.
-//
-// The assistant turn is fully marker-wrapped:
-//   <|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
-//     <|START_THINKING|>{reasoning}<|END_THINKING|>
-//     then EITHER content:    <|START_TEXT|>{content}<|END_TEXT|>
-//          OR     tool calls: <|START_ACTION|>[
-//                                 {"tool_call_id": "0", "tool_name": "f", "parameters": {...}}, ...
-//                             ]<|END_ACTION|>
-//   <|END_OF_TURN_TOKEN|>
-//
-// The generation prompt forces a leading <|START_THINKING|> (when reasoning is enabled, which is
-// the template default), so the model's output continues from *inside* the thinking block. The
-// parser literal therefore only covers the stable <|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|> prefix
-// and the reasoning rule consumes the <|START_THINKING|> ... <|END_THINKING|> markers itself,
-// regardless of whether they came from the generation prompt or the generated text.
-common_chat_params common_chat_params_init_cohere2moe(const common_chat_template &          tmpl,
-                                                              const autoparser::generation_params & inputs) {
-    common_chat_params data;
-
-    const std::string TURN_START    = "<|START_OF_TURN_TOKEN|>";
-    const std::string TURN_END      = "<|END_OF_TURN_TOKEN|>";
-    const std::string CHATBOT       = "<|CHATBOT_TOKEN|>";
-    const std::string USER          = "<|USER_TOKEN|>";
-    const std::string SYSTEM        = "<|SYSTEM_TOKEN|>";
-    const std::string THINK_START   = "<|START_THINKING|>";
-    const std::string THINK_END     = "<|END_THINKING|>";
-    const std::string TEXT_START    = "<|START_TEXT|>";
-    const std::string TEXT_END      = "<|END_TEXT|>";
-    const std::string ACTION_START  = "<|START_ACTION|>";
-    const std::string ACTION_END    = "<|END_ACTION|>";
-    const std::string RESULT_START  = "<|START_TOOL_RESULT|>";
-    const std::string RESULT_END    = "<|END_TOOL_RESULT|>";
-
-    // Stable prefix of the generation prompt that precedes the (forced) <|START_THINKING|> marker.
-    const std::string GEN_PREFIX = TURN_START + CHATBOT;
-
-    data.prompt             = common_chat_template_direct_apply_impl(tmpl, inputs);
-    data.generation_prompt  = common_chat_template_generation_prompt_impl(tmpl, inputs);
-    data.format             = COMMON_CHAT_FORMAT_PEG_NATIVE;
-    data.supports_thinking  = true;
-    data.thinking_start_tag = THINK_START;
-    data.thinking_end_tags  = {THINK_END};
-    data.preserved_tokens   = {
-        TURN_START, TURN_END, CHATBOT, USER, SYSTEM,
-        THINK_START, THINK_END,
-        TEXT_START, TEXT_END,
-        ACTION_START, ACTION_END,
-        RESULT_START, RESULT_END,
-    };
-
-    // Declare per-role message delimiters. Tool results are rendered with the
-    // system token followed by <|START_TOOL_RESULT|>, so the "tool" delimiter must be listed before
-    // the plain "system" one (it is a strict superset, and the role split tries delimiters in order).
-    data.message_delimiters = {
-        { COMMON_CHAT_ROLE_ASSISTANT, GEN_PREFIX },
-        { COMMON_CHAT_ROLE_USER,      TURN_START + USER },
-        { COMMON_CHAT_ROLE_TOOL,      TURN_START + SYSTEM + RESULT_START },
-        { COMMON_CHAT_ROLE_SYSTEM,    TURN_START + SYSTEM },
-    };
-
-    auto has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
-    auto has_response_format = inputs.json_schema.is_object() && !inputs.json_schema.empty();
-    auto extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
-    auto include_grammar     = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
-
-    if (inputs.has_continuation()) {
-        const auto & msg = inputs.continue_msg;
-
-        data.generation_prompt = GEN_PREFIX + THINK_START + msg.reasoning_content;
-        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
-            data.generation_prompt += THINK_END + TEXT_START + msg.render_content();
-        }
-
-        data.prompt += data.generation_prompt;
-    }
-
-    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
-        auto generation_prompt = p.literal(GEN_PREFIX);
-        auto end               = p.end();
-
-        // The thinking block is always present (the generation prompt forces <|START_THINKING|>).
-        // When extracting reasoning, capture its body; otherwise keep the whole block (markers
-        // included) inline as content, matching reasoning_format=NONE conventions.
-        common_peg_parser reasoning = p.eps();
-        if (extract_reasoning) {
-            reasoning = p.optional(p.literal(THINK_START) +
-                                   p.reasoning(p.until_one_of({ THINK_END, TEXT_START, ACTION_START })) +
-                                   p.optional(p.literal(THINK_END)));
-        } else {
-            reasoning = p.optional(p.content(p.literal(THINK_START) +
-                                             p.until_one_of({ THINK_END, TEXT_START, ACTION_START }) +
-                                             p.optional(p.literal(THINK_END))));
-        }
-
-        auto text_content = has_response_format
-            ? p.literal(TEXT_START) +
-                p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)) +
-                p.optional(p.literal(TEXT_END))
-            : p.literal(TEXT_START) + p.content(p.until(TEXT_END)) + p.optional(p.literal(TEXT_END));
-
-        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
-            return generation_prompt + reasoning + text_content + p.optional(p.literal(TURN_END)) + end;
-        }
-
-        auto require_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED;
-
-        // <|START_ACTION|>[ {"tool_call_id": "0", "tool_name": "f", "parameters": {...}}, ... ]<|END_ACTION|>
-        auto tool_calls = p.standard_json_tools(ACTION_START, ACTION_END, inputs.tools, inputs.parallel_tool_calls,
-                                                /* force_tool_calls = */ true,
-                                                /* name_key         = */ "tool_name",
-                                                /* args_key         = */ "parameters",
-                                                /* array_wrapped    = */ true,
-                                                /* function_is_key  = */ false,
-                                                /* call_id_key      = */ "",
-                                                /* gen_call_id_key  = */ "tool_call_id",
-                                                /* parameters_order = */ { "tool_call_id", "tool_name", "parameters" });
-
-        // Content and tool calls are mutually exclusive in this format.
-        common_peg_parser body = require_tools ? tool_calls : p.choice({ tool_calls, text_content });
-
-        return generation_prompt + reasoning + body + p.optional(p.literal(TURN_END)) + end;
-    });
-
-    data.parser = parser.save();
-
-    if (include_grammar) {
-        data.grammar_lazy = !has_response_format && 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);
-            });
-            if (has_response_format) {
-                auto schema = inputs.json_schema;
-                builder.resolve_refs(schema);
-            }
-            parser.build_grammar(builder, data.grammar_lazy);
-        });
-
-        data.grammar_triggers = {
-            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, ACTION_START }
-        };
-    }
-
-    return data;
-}
@@ -1,287 +0,0 @@
-#include "parsers.h"
-
-// The DeepSeek V4 reference implementation renders consecutive tool results into a single
-// user block, ordered by the tool call order of the preceding assistant message (matched
-// by tool call id) rather than by the order they appear in the conversation.
-static json deepseek_v4_sort_tool_results(const json & messages) {
-    json adjusted = messages;
-    std::map<std::string, size_t> call_order;
-
-    for (size_t i = 0; i < adjusted.size();) {
-        const auto & msg  = adjusted[i];
-        const auto   role = msg.value("role", "");
-
-        if (role == "assistant" && msg.contains("tool_calls") &&
-                msg.at("tool_calls").is_array() && !msg.at("tool_calls").empty()) {
-            call_order.clear();
-            const auto & tool_calls = msg.at("tool_calls");
-            for (size_t idx = 0; idx < tool_calls.size(); idx++) {
-                auto id = tool_calls[idx].value("id", "");
-                if (!id.empty()) {
-                    call_order[id] = idx;
-                }
-            }
-            i++;
-            continue;
-        }
-
-        if (role != "user" && role != "tool") {
-            i++;
-            continue;
-        }
-
-        // collect a maximal run of user/tool messages - they render into one user block
-        std::vector<size_t> tool_positions;
-        size_t run_end = i;
-        for (; run_end < adjusted.size(); run_end++) {
-            const auto r = adjusted[run_end].value("role", "");
-            if (r == "tool") {
-                tool_positions.push_back(run_end);
-            } else if (r != "user") {
-                break;
-            }
-        }
-
-        if (tool_positions.size() > 1 && !call_order.empty()) {
-            std::vector<json> results;
-            results.reserve(tool_positions.size());
-            for (auto pos : tool_positions) {
-                results.push_back(adjusted[pos]);
-            }
-            std::stable_sort(results.begin(), results.end(), [&](const json & a, const json & b) {
-                const auto order = [&](const json & m) {
-                    auto it = call_order.find(m.value("tool_call_id", ""));
-                    return it == call_order.end() ? (size_t) 0 : it->second;
-                };
-                return order(a) < order(b);
-            });
-            for (size_t k = 0; k < tool_positions.size(); k++) {
-                adjusted[tool_positions[k]] = std::move(results[k]);
-            }
-        }
-
-        i = run_end;
-    }
-
-    return adjusted;
-}
-
-common_chat_params common_chat_params_init_deepseek_v3_2(const common_chat_template &    tmpl,
-                                                                 const autoparser::generation_params & inputs) {
-    common_chat_params data;
-
-    // V4 uses the same DSML markup as V3.2, but names the tool call block "tool_calls"
-    // instead of "function_calls", renders tool results in tool call order and its
-    // non-thinking generation prompt ends with a bare </think> instead of an empty
-    // <think></think> pair.
-    const bool is_v4 = tmpl.source().find("function_calls") == std::string::npos;
-
-    std::optional<json> adjusted_messages;
-    if (is_v4) {
-        adjusted_messages = deepseek_v4_sort_tool_results(inputs.messages);
-    }
-
-    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 extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
-    auto include_grammar     = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
-
-    std::optional<json> additional_context;
-    if (is_v4 && has_response_format) {
-        additional_context = json{ { "response_format", inputs.json_schema } };
-    }
-
-    const std::string DSML         = "｜DSML｜";
-    const std::string THINK_START  = "<think>";
-    const std::string THINK_END    = "</think>";
-    const std::string TC_BLOCK     = is_v4 ? "tool_calls" : "function_calls";
-    const std::string FC_START     = "<" + DSML + TC_BLOCK + ">";
-    const std::string FC_END       = "</" + DSML + TC_BLOCK + ">";
-    const std::string INVOKE_START = "<" + DSML + "invoke";
-    const std::string INVOKE_END   = "</" + DSML + "invoke>";
-    const std::string PARAM_START  = "<" + DSML + "parameter";
-    const std::string PARAM_END    = "</" + DSML + "parameter>";
-    const std::string GEN_PROMPT   = "<｜Assistant｜>";
-    const std::string TC_SEPARATOR = "\n\n";
-
-    data.prompt = common_chat_template_direct_apply_impl(
-        tmpl, inputs, adjusted_messages, std::nullopt, additional_context);
-    data.generation_prompt = common_chat_template_generation_prompt_impl(
-        tmpl, inputs, adjusted_messages, std::nullopt, additional_context);
-    data.format             = COMMON_CHAT_FORMAT_PEG_NATIVE;
-    data.supports_thinking  = true;
-    data.thinking_start_tag = THINK_START;
-    data.thinking_end_tags  = {THINK_END, FC_START};
-    data.preserved_tokens   = {
-        DSML,
-        THINK_START,
-        THINK_END,
-    };
-
-    if (inputs.has_continuation()) {
-        const auto & msg = inputs.continue_msg;
-
-        if (is_v4 && msg.reasoning_content.empty()) {
-            data.generation_prompt = GEN_PROMPT + THINK_END;
-            if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
-                data.generation_prompt += msg.render_content();
-            }
-        } else {
-            data.generation_prompt = GEN_PROMPT + THINK_START + msg.reasoning_content;
-            if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
-                data.generation_prompt += THINK_END + msg.render_content();
-            }
-        }
-
-        data.prompt += data.generation_prompt;
-    }
-
-    bool require_tools   = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED;
-    bool has_tool_calls = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
-
-    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
-        auto generation_prompt = p.literal(GEN_PROMPT);
-        auto end               = p.end();
-
-        // build tool call section first since we might need it in reasoning
-        auto tool_choice = p.choice();
-        if (has_tool_calls) {
-            foreach_function(inputs.tools, [&](const json & tool) {
-                const auto & function = tool.at("function");
-                std::string  name     = function.at("name");
-                auto         params   = function.contains("parameters") ? function.at("parameters") : json::object();
-                const auto & props    = params.contains("properties") ? params.at("properties") : json::object();
-
-                std::set<std::string> required;
-                if (params.contains("required")) {
-                    required = params.at("required").get<std::set<std::string>>();
-                }
-
-                auto schema_info = common_schema_info();
-                schema_info.resolve_refs(params);
-
-                std::vector<common_peg_parser> required_parsers;
-                std::vector<common_peg_parser> optional_parsers;
-                for (const auto & [param_name, param_schema] : props.items()) {
-                    bool is_required = required.find(param_name) != required.end();
-                    bool is_string   = schema_info.resolves_to_string(param_schema);
-
-                    auto arg = p.tool_arg(
-                        p.tool_arg_open(p.literal(PARAM_START + " name=\"") + p.tool_arg_name(p.literal(param_name)) +
-                                        p.literal("\" string=\"" + std::string(is_string ? "true" : "false") + "\">")) +
-                        (is_string ?
-                             p.tool_arg_string_value(p.until(PARAM_END)) :
-                             p.tool_arg_json_value(p.schema(p.json(), "tool-" + name + "-arg-" + param_name + "-schema",
-                                                            param_schema, false))) +
-                        p.tool_arg_close(p.literal(PARAM_END)));
-
-                    auto named_arg = p.rule("tool-" + name + "-arg-" + param_name, arg);
-                    if (is_required) {
-                        required_parsers.push_back(named_arg);
-                    } else {
-                        optional_parsers.push_back(named_arg);
-                    }
-                }
-
-                common_peg_parser args_seq = p.eps();
-                for (size_t i = 0; i < required_parsers.size(); i++) {
-                    if (i > 0) {
-                        args_seq = args_seq + p.space();
-                    }
-                    args_seq = args_seq + required_parsers[i];
-                }
-
-                if (!optional_parsers.empty()) {
-                    common_peg_parser any_opt = p.choice();
-                    for (const auto & opt : optional_parsers) {
-                        any_opt |= opt;
-                    }
-                    args_seq = args_seq + p.repeat(p.space() + any_opt, 0, -1);
-                }
-
-                common_peg_parser invoke_body = args_seq;
-                auto              func_parser = p.tool(p.tool_open(p.literal(INVOKE_START + " name=\"") +
-                                                                   p.tool_name(p.literal(name)) + p.literal("\">\n")) +
-                                                       invoke_body + p.space() + p.tool_close(p.literal(INVOKE_END)));
-
-                tool_choice |= p.rule("tool-" + name, func_parser);
-            });
-        }
-
-        common_peg_parser tool_calls = p.eps();
-        if (inputs.parallel_tool_calls) {
-            tool_calls = p.trigger_rule("tool-call",
-                p.literal(FC_START) + p.space() + tool_choice +
-                p.zero_or_more(p.space() + tool_choice) + p.space() + p.literal(FC_END));
-        } else {
-            tool_calls = p.trigger_rule("tool-call",
-                p.literal(FC_START) + p.space() + tool_choice + p.space() + p.literal(FC_END));
-        }
-
-        auto reasoning = p.eps();
-        auto reasoning_with_tc = p.eps();
-        auto obligatory_tool_calls = tool_calls;
-        bool allow_reasoning_with_tc = false;
-
-        if (!require_tools) {
-            tool_calls = p.optional(tool_calls);
-        }
-
-        if (extract_reasoning && inputs.enable_thinking) {
-            reasoning = p.optional(THINK_START + p.reasoning(p.until(THINK_END)) + THINK_END);
-            reasoning_with_tc = THINK_START +
-                p.reasoning(p.until_one_of({ TC_SEPARATOR + FC_START, FC_START, THINK_END })) +
-                p.space() + obligatory_tool_calls;
-            allow_reasoning_with_tc = true;
-        } else if (extract_reasoning) {
-            // Thinking disabled but reasoning extraction requested: the generation prompt
-            // contains an empty <think></think> pair (V3.2) or a bare </think> (V4) that
-            // must still be consumed.
-            reasoning = is_v4
-                ? p.optional(p.literal(THINK_END))
-                : p.optional(p.literal(THINK_START) + p.until(THINK_END) + p.literal(THINK_END));
-        }
-
-        if (has_response_format) {
-            auto response_format = p.rule("response-format",
-                p.literal("```json") + p.space() +
-                p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)) +
-                p.space() + p.literal("```"));
-            return generation_prompt + reasoning + response_format + end;
-        }
-
-        if (!has_tool_calls) {
-            return generation_prompt + reasoning + p.content(p.rest()) + end;
-        }
-
-        auto content_before_tools = p.negate(p.literal(THINK_START)) +
-            p.content(p.until_one_of({ TC_SEPARATOR + FC_START, FC_START })) +
-            p.space();
-        return allow_reasoning_with_tc ? generation_prompt + (reasoning_with_tc | (reasoning + content_before_tools + tool_calls)) + end :
-            generation_prompt + reasoning + content_before_tools + tool_calls + end;
-    });
-
-    data.parser = parser.save();
-
-    if (include_grammar) {
-        data.grammar_lazy = has_tools && !require_tools;
-        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.contains("parameters") ? function.at("parameters") : json::object();
-                builder.resolve_refs(schema);
-            });
-            if (has_response_format) {
-                auto schema = inputs.json_schema;
-                builder.resolve_refs(schema);
-            }
-            parser.build_grammar(builder, data.grammar_lazy);
-        });
-
-        data.grammar_triggers = {
-            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, FC_START },
-        };
-    }
-
-    return data;
-}
@@ -1,101 +0,0 @@
-#include "parsers.h"
-
-// Functionary v3.2 - uses recipient-based format: >>>recipient\n{content}
-common_chat_params common_chat_params_init_functionary_v3_2(const common_chat_template &    tmpl,
-                                                                   const autoparser::generation_params & inputs) {
-    common_chat_params data;
-
-    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
-    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
-    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
-    data.preserved_tokens  = {
-        ">>>all",
-    };
-
-    auto has_tools         = inputs.tools.is_array() && !inputs.tools.empty();
-    auto include_grammar   = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
-
-    if (inputs.has_continuation()) {
-        const auto & msg = inputs.continue_msg;
-        data.generation_prompt = "<|start_header_id|>assistant<|end_header_id|>\n\n>>>all\n" + msg.render_content();
-        data.prompt += data.generation_prompt;
-    }
-
-    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
-        // Functionary v3.2 format:
-        // - Normal content: >>>all\n{content}
-        // - Tool calls: >>>function_name\n{json_args}
-        // Generation prompt ends with ">>>" so model outputs recipient immediately
-
-        // 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 generation_prompt  = p.literal("<|start_header_id|>assistant<|end_header_id|>\n\n>>>");
-
-        // 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 generation_prompt + content_until_end + p.end();
-        }
-
-        // Build tool call parsers for each available function
-        auto tool_choice = p.choice();
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const auto & function = tool.at("function");
-            std::string  name     = function.at("name");
-            const auto & schema   = function.at("parameters");
-
-            // 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_args(p.schema(p.json(), "tool-" + name + "-schema", schema))
-            );
-
-            tool_choice |= p.rule("tool-" + name, tool_parser);
-        });
-
-        auto content_only = content_until_end;
-        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();
-            }
-        } 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 generation_prompt + ret;
-    });
-
-    data.parser = parser.save();
-
-    if (include_grammar) {
-        data.grammar_lazy = 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);
-        });
-
-        // Grammar trigger for when the model starts outputting a tool call
-        // (after the initial ">>>" in the generation prompt but recipient other than "all")
-        data.grammar_triggers = {
-            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, ">>>(?!all)" }
-        };
-    }
-
-    return data;
-}
@@ -1,312 +0,0 @@
-#include "parsers.h"
-
-namespace workaround {
-
-// Gemma4 uses a custom tool_responses field instead of role:tool messages.
-//
-// This will transform a sequence of messages:
-//   assistant(tool_call+) -> tool+ -> assistant(content)
-//
-// Into a single assistant message containing a tool_responses field:
-//   assistant(content + tool_call + tool_responses)
-//
-// This is necessary for the Gemma4 chat template to properly format the prompt.
-// See https://ai.google.dev/gemma/docs/core/prompt-formatting-gemma4
-struct gemma4_model_turn_builder {
-    json & messages;
-    size_t pos;
-    json tool_calls = json::array();
-    json tool_responses = json::array();
-    json content;
-    json reasoning_content;
-
-    gemma4_model_turn_builder(json & msgs, size_t pos) : messages(msgs), pos(pos) {}
-
-    void collect() {
-        // Collect the first assistant message
-        auto & msg = messages[pos];
-        if (msg.contains("reasoning_content") && msg.at("reasoning_content").is_string()) {
-            // According to the prompt formatting guide, we need to preserve reasoning_content
-            // between function calls. The current chat templates do not support this, but we will do it anyway.
-            reasoning_content = msg.at("reasoning_content");
-        }
-        for (auto & tc : msg.at("tool_calls")) {
-            tool_calls.push_back(tc);
-        }
-        pos++;
-
-        // Collect tool call results
-        while (pos < messages.size() && messages[pos].value("role", "") == "tool") {
-            collect_result(messages[pos]);
-            pos++;
-        }
-
-        // Check if the next assistant message is the final message
-        if (pos < messages.size() && messages[pos].value("role", "") == "assistant") {
-            auto & next = messages[pos];
-            if (!has_tool_calls(next) && has_content(next)) {
-                content = next.at("content");
-                pos++;
-            }
-        }
-    }
-
-    void collect_result(const json & curr) {
-        json response;
-        if (curr.contains("content")) {
-            const auto & content = curr.at("content");
-            if (content.is_string()) {
-                // Try to parse the content as JSON; fall back to raw string
-                try {
-                    response = json::parse(content.get<std::string>());
-                } catch (...) {
-                    response = content;
-                }
-            } else {
-                response = content;
-            }
-        }
-
-        std::string name;
-
-        // Match name with corresponding tool call
-        size_t idx = tool_responses.size();
-        if (idx < tool_calls.size()) {
-            auto & tc = tool_calls[idx];
-            if (tc.contains("function")) {
-                name = tc.at("function").value("name", "");
-            }
-        }
-
-        // Fallback to the tool call id
-        if (name.empty()) {
-            name = curr.value("tool_call_id", "");
-        }
-
-        tool_responses.push_back({{"name", name}, {"response", response}});
-    }
-
-    json build() {
-        collect();
-
-        json msg = {
-            {"role", "assistant"},
-            {"tool_calls", tool_calls},
-        };
-        if (!tool_responses.empty()) {
-            msg["tool_responses"] = tool_responses;
-        }
-        if (!content.is_null()) {
-            msg["content"] = content;
-        }
-        if (!reasoning_content.is_null()) {
-            msg["reasoning_content"] = reasoning_content;
-        }
-        return msg;
-    }
-
-    static bool has_content(const json & msg) {
-        if (!msg.contains("content") || msg.at("content").is_null()) {
-            return false;
-        }
-        const auto & content = msg.at("content");
-        if (content.is_string() && !content.get<std::string>().empty()) {
-            return true;
-        }
-        if (content.is_array() && !content.empty()) {
-            return true;
-        }
-        return false;
-    }
-
-    static bool has_tool_calls(const json & msg) {
-        return msg.contains("tool_calls") && msg.at("tool_calls").is_array() && !msg.at("tool_calls").empty();
-    }
-};
-
-void convert_tool_responses_gemma4(json & messages) {
-    json result = json::array();
-    size_t i = 0;
-
-    while (i < messages.size()) {
-        auto & msg = messages[i];
-
-        if (msg.value("role", "") != "assistant" || !msg.contains("tool_calls") ||
-            !msg.at("tool_calls").is_array() || msg.at("tool_calls").empty()) {
-            result.push_back(msg);
-            i++;
-            continue;
-        }
-
-        gemma4_model_turn_builder builder(messages, i);
-        result.push_back(builder.build());
-        i = builder.pos;
-    }
-
-    messages = result;
-}
-
-}
-
-common_chat_params common_chat_params_init_gemma4(const common_chat_template &    tmpl,
-                                                         const autoparser::generation_params & inputs) {
-    common_chat_params data;
-
-    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
-    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
-
-    if (inputs.add_generation_prompt && string_ends_with(data.prompt, "<turn|>\n")) {
-        // This may happen if the model generates content + tool_call, the
-        // template does not add the model's next turn and confuses the model
-        // from emitting its proper reasoning token sequence.
-        data.generation_prompt = "<|turn>model\n";
-        data.prompt += data.generation_prompt;
-    }
-
-    data.message_delimiters = {
-        { COMMON_CHAT_ROLE_USER,      "<|turn>user"  },
-        { COMMON_CHAT_ROLE_ASSISTANT, "<|turn>model" },
-    };
-
-    data.format            = COMMON_CHAT_FORMAT_PEG_GEMMA4;
-    data.supports_thinking  = true;
-    data.thinking_start_tag = "<|channel>thought";
-    data.thinking_end_tags  = {"<channel|>"};
-
-    data.preserved_tokens = {
-        "<|channel>",
-        "<channel|>",
-        "<|tool_call>",
-        "<tool_call|>",
-        "<|turn>",
-    };
-
-    if (inputs.has_continuation()) {
-        const auto & msg = inputs.continue_msg;
-
-        data.generation_prompt = string_ends_with(data.prompt, "<turn|>\n") ? "<|turn>model\n" : "";
-        data.generation_prompt += "<|channel>thought\n" + msg.reasoning_content;
-        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
-            data.generation_prompt += "<channel|>" + msg.render_content();
-        }
-
-        data.prompt += data.generation_prompt;
-    }
-
-    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 extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
-
-    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
-        auto start = p.rule("start", p.optional(p.literal("<|turn>model\n")));
-
-        if (extract_reasoning) {
-            p.rule("thought", p.literal("<|channel>thought") + p.space() + p.reasoning(p.until("<channel|>")) + p.literal("<channel|>"));
-        } else {
-            p.rule("thought", p.content(p.literal("<|channel>thought") + p.space() + p.until("<channel|>") + p.literal("<channel|>")));
-        }
-
-        auto consume_empty_channels = p.gbnf(p.zero_or_more(p.literal("<|channel>") + p.negate(p.literal("thought"))), "");
-        auto thought = (p.peek(p.literal("<|channel>")) + consume_empty_channels + p.ref("thought")) | p.negate(p.literal("<|channel>"));
-
-        if (has_response_format) {
-            auto response_format = p.literal("```json") <<
-                p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)) <<
-                p.literal("```");
-            return start + p.optional(thought) + response_format;
-        }
-
-        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
-            // Gemma4 tool calling syntax
-            // Rules should match traversal logic in gemma4_to_json()
-            p.rule("gemma4-string-content", p.until("<|\"|>"));
-            p.rule("gemma4-string", p.literal("<|\"|>") + p.ref("gemma4-string-content") + p.literal("<|\"|>"));
-            p.rule("gemma4-bool", p.json_bool());
-            p.rule("gemma4-null", p.json_null());
-            p.rule("gemma4-number", p.json_number());
-            p.rule("gemma4-dict-key", p.rule("gemma4-dict-key-name", p.chars("[^:}]", 1, -1)) + p.literal(":"));
-            p.rule("gemma4-dict-kv", p.ref("gemma4-dict-key") + p.space() + p.ref("gemma4-value"));
-            p.rule("gemma4-dict", [&]() {
-                auto ws = p.space();
-                auto member = p.ref("gemma4-dict-kv");
-                auto members = p.sequence({member, p.zero_or_more(p.sequence({p.literal(","), ws, member}))});
-                return p.sequence({
-                    p.literal("{"), ws,
-                    p.choice({p.literal("}"), p.sequence({members, ws, p.literal("}")})})
-                });
-            });
-            p.rule("gemma4-array", [&]() {
-                auto ws = p.space();
-                auto value = p.ref("gemma4-value");
-                auto elements = p.sequence({value, p.zero_or_more(p.sequence({p.literal(","), ws, value}))});
-                return p.sequence({
-                    p.literal("["), ws,
-                    p.choice({p.literal("]"), p.sequence({elements, ws, p.literal("]")})})
-                });
-            });
-            p.rule("gemma4-value", [&]() {
-                return p.choice({
-                    p.ref("gemma4-string"), p.ref("gemma4-dict"), p.ref("gemma4-array"),
-                    p.ref("gemma4-number"), p.ref("gemma4-bool"), p.ref("gemma4-null")
-                });
-            });
-
-            auto tool_choice = p.choice();
-
-            foreach_function(inputs.tools, [&](const json & tool) {
-                const auto & function = tool.at("function");
-                std::string  name     = function.at("name");
-                // TODO @aldehir : need to extend json-schema-to-grammar to produce more than JSON rules
-                // const auto & params   = function.at("parameters");
-
-                tool_choice |= p.rule("tool-" + name, p.tool(p.sequence({
-                    p.tool_open(p.tool_name(p.literal(name)) + p.peek(p.literal("{"))),
-                    p.tool_args(p.ref("gemma4-dict")),
-                })));
-            });
-
-            auto tool_call = p.trigger_rule("tool-call", p.repeat(
-                "<|tool_call>call:" + tool_choice + "<tool_call|>",
-                /* min = */ inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0,
-                /* max = */ inputs.parallel_tool_calls ? -1 : 1
-            ));
-
-            auto scan_to_toolcall = p.rule("scan-to-toolcall", p.until("<|tool_call>"));
-            auto content = p.rule("content", p.content(p.until_one_of({"<|channel>", "<channel|>", "<|tool_call>"})));
-            auto message = p.rule("message", thought + content);
-            return start + p.zero_or_more(message) + scan_to_toolcall + tool_call;
-        }
-
-        // Gemma 4 may emit an extra <|channel>thought\n<channel|> at the end of the content. It may
-        // also emit a single trailing <channel|> token. Consume all complete reasoning blocks and
-        // then stop at the first unmatched <channel|> token.
-        auto content = p.rule("content", p.content(p.until_one_of({"<|channel>", "<channel|>"})));
-        auto message = p.rule("message", thought + content);
-        return start + p.one_or_more(message);
-    });
-
-    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      = 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);
-            });
-            if (has_response_format) {
-                auto schema = inputs.json_schema;
-                builder.resolve_refs(schema);
-            }
-            parser.build_grammar(builder, data.grammar_lazy);
-        });
-
-        data.grammar_triggers = {
-            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<|tool_call>" },
-        };
-    }
-
-    return data;
-}
@@ -1,81 +0,0 @@
-#include "parsers.h"
-
-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_impl(tmpl, inputs);
-    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
-    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
-    data.supports_thinking = false;
-    data.preserved_tokens  = {
-        "<|message_sep|>\n\n",
-        "<|role_sep|>\n",
-    };
-
-    if (inputs.has_continuation()) {
-        const auto & msg = inputs.continue_msg;
-        data.generation_prompt = "assistant<|role_sep|>\n" + msg.render_content();
-        data.prompt += data.generation_prompt;
-    }
-
-    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 p.literal("assistant<|role_sep|>\n") + ret;
-    });
-
-    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;
-}
@@ -1,167 +0,0 @@
-#include "parsers.h"
-
-common_chat_params common_chat_params_init_gpt_oss(const common_chat_template &    tmpl,
-                                                          const autoparser::generation_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");
-            }
-        }
-        adjusted_messages.push_back(msg);
-    }
-
-    auto prompt = common_chat_template_direct_apply_impl(tmpl, inputs, /* messages_override= */ adjusted_messages);
-
-    // Check if we need to replace the return token with end token during
-    // inference and without generation prompt. For more details see:
-    // https://github.com/ggml-org/llama.cpp/issues/15417
-    if (inputs.is_inference && !inputs.add_generation_prompt) {
-        static constexpr std::string_view return_token = "<|return|>";
-        static constexpr std::string_view end_token    = "<|end|>";
-        if (size_t pos = prompt.rfind(return_token); pos != std::string::npos) {
-            prompt.replace(pos, return_token.length(), end_token);
-        }
-    }
-
-    data.prompt            = prompt;
-    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs, /* messages_override= */ adjusted_messages);
-    data.message_delimiters = {
-        { COMMON_CHAT_ROLE_ASSISTANT, "<|start|>assistant" },
-        { COMMON_CHAT_ROLE_USER,      "<|start|>user"      },
-        { COMMON_CHAT_ROLE_SYSTEM,    "<|start|>developer" },
-        { COMMON_CHAT_ROLE_SYSTEM,    "<|start|>system"    },
-        { COMMON_CHAT_ROLE_TOOL,      "<|start|>functions" },
-    };
-
-    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
-    data.supports_thinking = true;
-
-    data.thinking_start_tag = "<|channel|>analysis<|message|>";
-    data.thinking_end_tags  = {"<|end|>"};
-
-    // These special tokens are required to parse properly, so we include them
-    // even if parse_tool_calls is false.
-    data.preserved_tokens = {
-        "<|channel|>", "<|constrain|>", "<|message|>", "<|start|>", "<|end|>",
-    };
-
-    // Adjust prompt for continuation
-    if (inputs.has_continuation()) {
-        const auto & msg = inputs.continue_msg;
-
-        data.generation_prompt = "<|start|>assistant<|channel|>analysis<|message|>" + msg.reasoning_content;
-        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
-            data.generation_prompt += "<|end|><|start|>assistant<|channel|>final<|message|>" + msg.render_content();
-        }
-
-        data.prompt += data.generation_prompt;
-    }
-
-    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 extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
-
-    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);
-
-        // Occasionally, gpt-oss-20b will prefix channels with this commentary
-        auto stray_commentary = p.optional(p.literal("<|channel|>commentary") + p.optional(p.literal(" to=assistant")));
-        auto start_analysis = stray_commentary + p.literal("<|channel|>analysis<|message|>");
-
-        if (extract_reasoning) {
-            p.rule("analysis", start_analysis + p.reasoning(content) + end);
-        } else {
-            p.rule("analysis", p.content(start_analysis + content + end));
-        }
-
-        auto analysis = p.ref("analysis");
-        auto preamble = p.rule("preamble", p.literal("<|channel|>commentary<|message|>") + p.content(content) + end);
-        auto final_msg = p.rule("final", stray_commentary + p.literal("<|channel|>final<|message|>") + p.content(content));
-
-        // Consume any unsolicited tool calls, e.g. builtin functions
-        auto unsolicited = p.rule("unsolicited", p.atomic(p.optional(channel) + p.literal(" to=") + content + end));
-
-        auto any = p.rule("any", preamble | analysis);
-
-        if (has_response_format) {
-            auto constraint = p.optional(p.space() + p.optional(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)));
-
-            return p.zero_or_more(start + analysis) + start + response_format;
-        }
-
-        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
-            auto tool_choice = p.choice();
-
-            foreach_function(inputs.tools, [&](const json & tool) {
-                const auto & function = tool.at("function");
-                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.optional(p.literal("<|constrain|>")) + constrain_type);
-                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);
-
-                // 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);
-
-                tool_choice |= p.rule("tool-" + name, tool_in_role | tool_in_channel);
-            });
-
-            auto tool_call  = p.trigger_rule("tool-call", tool_choice);
-
-            if (inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED) {
-                return p.zero_or_more(start + any) + start + tool_call;
-            }
-
-            return p.zero_or_more(start + any) + start + (tool_call | final_msg);
-        }
-
-        return p.zero_or_more(start + any) + start + (final_msg | unsolicited);
-    });
-
-    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      = 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);
-            });
-            if (has_response_format) {
-                auto schema = inputs.json_schema;
-                builder.resolve_refs(schema);
-            }
-            parser.build_grammar(builder, data.grammar_lazy);
-        });
-
-        data.grammar_triggers = {
-            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "^\\s+to$" },
-            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "^<\\|channel\\|>(?:commentary|analysis)\\s+to=functions$" },
-            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "<\\|start\\|>assistant(\\s+to)" },
-            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "<\\|start\\|>assistant(<\\|channel\\|>(?:commentary|analysis)\\s+to)" }
-        };
-    }
-
-    return data;
-}
@@ -1,133 +0,0 @@
-#include "parsers.h"
-
-// Kimi K2 Thinking - uses unique tool call ID format: functions.<name>:<index>
-// The ID contains both the function name and an incrementing counter
-common_chat_params common_chat_params_init_kimi_k2(const common_chat_template &    tmpl,
-                                                          const autoparser::generation_params & inputs) {
-    common_chat_params data;
-
-    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
-    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
-    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
-    data.supports_thinking = true;
-    data.preserved_tokens  = {
-        "<|tool_calls_section_begin|>",
-        "<|tool_calls_section_end|>",
-        "<|tool_call_begin|>",
-        "<|tool_call_argument_begin|>",
-        "<|tool_call_end|>",
-        "<think>",
-        "</think>",
-    };
-
-    auto has_tools         = inputs.tools.is_array() && !inputs.tools.empty();
-    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>";
-    const std::string GEN_PROMPT  = "<|im_assistant|>assistant<|im_middle|>";
-
-    data.thinking_start_tag = THINK_START;
-    data.thinking_end_tags  = {THINK_END};
-
-    if (inputs.has_continuation()) {
-        const auto & msg = inputs.continue_msg;
-
-        data.generation_prompt = GEN_PROMPT + THINK_START + msg.reasoning_content;
-        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
-            data.generation_prompt += THINK_END + msg.render_content();
-        }
-
-        data.prompt += data.generation_prompt;
-    }
-
-    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
-        // Kimi K2 Thinking format:
-        // - Reasoning: <think>{reasoning}</think>
-        // - Content: text after reasoning
-        // - Tool calls section:
-        //   <|tool_calls_section_begin|>
-        //   <|tool_call_begin|>functions.<name>:<index><|tool_call_argument_begin|>{json_args}<|tool_call_end|>
-        //   ...
-        //   <|tool_calls_section_end|>
-        // The ID format is: functions.<function_name>:<counter> where counter is 0, 1, 2, ...
-
-        // Tool call markers
-        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.
-        // For example, it can call tools at the end of reasoning without closing reasoning...
-        auto reasoning = extract_reasoning ? p.optional(THINK_START + p.reasoning(
-            p.until_one_of({ THINK_END, "<|tool_calls_section_begin|>", "<|tool_call_begin|>" })) +
-            p.optional(p.literal(THINK_END))) : p.eps();
-        auto generation_prompt = p.literal(GEN_PROMPT);
-
-
-        // Content only parser (no tools)
-        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
-            return generation_prompt + reasoning + p.content(p.rest()) + end;
-        }
-
-        // Build tool call parsers for each available function
-        // The ID format is: functions.<name>:<index>
-        // We need to match: functions.<name>:<digits>
-        auto tool_choice = p.choice();
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const auto & function = tool.at("function");
-            std::string  name     = function.at("name");
-            const auto & schema   = function.at("parameters");
-
-            // Match: functions.<name>:<digits>
-            // Capture the full call id (functions.<name>:<digits>) using tool_id tag
-            auto tool_id = p.tool_id(p.literal("functions.") + p.tool_name(p.literal(name)) + p.literal(":") + p.chars("[0-9]", 1, -1));
-            auto tool_parser = p.tool(
-                p.tool_open(tool_id + p.literal(ARGS_BEGIN)) +
-                p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema)) +
-                p.tool_close(p.optional((p.literal(CALL_END))))
-            );
-
-            tool_choice |= p.rule("tool-" + name, tool_parser);
-        });
-
-        // Tool calls section: <|tool_calls_section_begin|> tool_calls <|tool_calls_section_end|>
-        auto min_calls  = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0;
-        auto max_calls  = inputs.parallel_tool_calls ? -1 : 1;
-        // Use trigger_rule so grammar generator knows where to start generating rules
-        auto tool_calls = p.rule("tool-calls",
-            p.optional(p.literal(SECTION_BEGIN)) +
-            p.trigger_rule("tool-call", p.repeat(CALL_BEGIN + tool_choice, min_calls, max_calls) +
-                p.optional(p.literal(SECTION_END)))
-        );
-
-        auto content_before_tools = p.content(p.until_one_of({ SECTION_BEGIN, CALL_BEGIN }));
-
-        return generation_prompt + reasoning + content_before_tools + tool_calls + end;
-    });
-
-    data.parser = parser.save();
-
-    if (include_grammar) {
-        data.grammar_lazy = 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_begin|>" }
-        };
-    }
-
-    return data;
-}
@@ -1,174 +0,0 @@
-#include "parsers.h"
-
-// Kimi K3 - XTML tagged format, built by open_tag/close_tag macros:
-//   open_tag(t, attrs) = <|open|>t k="v"...<|sep|>   close_tag(t) = <|close|>t<|sep|>
-//   assistant := [think] [response] [tools] close_tag(message) <|end_of_msg|>
-// the generation prompt already opens the think (or response) section, so the
-// section opener is optional here - same as Kimi K2 Thinking
-common_chat_params common_chat_params_init_kimi_k3(const common_chat_template &          tmpl,
-                                                          const autoparser::generation_params & inputs) {
-    common_chat_params data;
-
-    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
-    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
-    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
-    data.supports_thinking = true;
-
-    const std::string SEP         = "<|sep|>";
-    const std::string MSG_START   = "<|open|>message role=\"assistant\"<|sep|>";
-    const std::string THINK_START = "<|open|>think<|sep|>";
-    const std::string THINK_END   = "<|close|>think<|sep|>";
-    const std::string RESP_START  = "<|open|>response<|sep|>";
-    const std::string RESP_END    = "<|close|>response<|sep|>";
-    const std::string TOOLS_START = "<|open|>tools<|sep|>";
-    const std::string TOOLS_END   = "<|close|>tools<|sep|>";
-    const std::string CALL_START  = "<|open|>call tool=\"";
-    const std::string CALL_END    = "<|close|>call<|sep|>";
-    const std::string ARG_START   = "<|open|>argument key=\"";
-    const std::string ARG_END     = "<|close|>argument<|sep|>";
-    const std::string MSG_END     = "<|close|>message<|sep|>";
-    const std::string EOM_TOKEN   = "<|end_of_msg|>";
-
-    // only the markers are special tokens. tag names ("think", "response", ...) are
-    // normal tokens and must not be preserved, or prose with those words is broken
-    data.preserved_tokens = {
-        "<|open|>",
-        "<|close|>",
-        "<|sep|>",
-        "<|end_of_msg|>",
-    };
-
-    data.thinking_start_tag = THINK_START;
-    data.thinking_end_tags  = { THINK_END };
-
-    // per-role message-start delimiters. user/assistant messages only have the role
-    // attribute, so the full opener is used. system and tool messages have more
-    // attributes, so those delimiters stop after the closing quote of the role
-    data.message_delimiters = {
-        { COMMON_CHAT_ROLE_ASSISTANT, "<|open|>message role=\"assistant\"<|sep|>" },
-        { COMMON_CHAT_ROLE_USER,      "<|open|>message role=\"user\"<|sep|>"      },
-        { COMMON_CHAT_ROLE_TOOL,      "<|open|>message role=\"tool\""             },
-        { COMMON_CHAT_ROLE_SYSTEM,    "<|open|>message role=\"system\""           },
-    };
-
-    auto has_tools         = inputs.tools.is_array() && !inputs.tools.empty();
-    auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
-    auto include_grammar   = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
-
-    if (inputs.has_continuation()) {
-        const auto & msg = inputs.continue_msg;
-
-        data.generation_prompt = MSG_START + THINK_START + msg.reasoning_content;
-        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
-            data.generation_prompt += THINK_END + RESP_START + msg.render_content();
-        }
-
-        data.prompt += data.generation_prompt;
-    }
-
-    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
-        auto end = p.end();
-
-        auto start = p.optional(p.literal(MSG_START));
-
-        // the think section is always consumed, even with reasoning extraction off:
-        // the generation prompt ends with open_tag('think'), so it is always present.
-        // reasoning stops at its own closer, or at the response opener if the model
-        // skips the closer
-        auto think_body = extract_reasoning ? p.reasoning(p.until_one_of({ THINK_END, RESP_START })) :
-                                              p.content(p.until_one_of({ THINK_END, RESP_START }));
-
-        auto reasoning = p.optional(p.optional(p.literal(THINK_START)) + think_body +
-                                    p.optional(p.literal(THINK_END)));
-
-        // content runs to the response closer, or to the next section if truncated
-        auto response = p.optional(p.literal(RESP_START)) +
-                        p.content(p.until_one_of({ RESP_END, TOOLS_START, MSG_END })) +
-                        p.optional(p.literal(RESP_END));
-
-        // the EOG token after the message closer reaches the parser as text,
-        // so it must be consumed or the parse stays incomplete
-        auto trailer = p.optional(p.literal(MSG_END)) + p.optional(p.literal(EOM_TOKEN));
-
-        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
-            return start + reasoning + response + trailer + end;
-        }
-
-        auto tool_choices = p.choice();
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const auto & function = tool.at("function");
-            std::string  name     = function.at("name");
-            const json   schema   = function.contains("parameters") ? function.at("parameters") : json::object();
-
-            // arguments come one tag per key, with the JSON type in a type="..."
-            // attribute. the type is taken from the tool schema instead, as it tells
-            // us if the value is JSON or a literal string
-            auto args = p.eps();
-            if (schema.contains("properties") && !schema.at("properties").empty()) {
-                auto arg_choices = p.choice();
-                for (const auto & prop : schema.at("properties").items()) {
-                    const std::string & key = prop.key();
-
-                    std::string type = "string";
-                    if (prop.value().is_object() && prop.value().contains("type") &&
-                        prop.value().at("type").is_string()) {
-                        type = prop.value().at("type").get<std::string>();
-                    }
-
-                    auto value = type == "string" ? p.tool_arg_string_value(p.until(ARG_END)) :
-                                                    p.tool_arg_value(p.until(ARG_END));
-
-                    // skip the trailing type="..." attribute: anything up to <|sep|>
-                    arg_choices |= p.rule("kimi-k3-arg-" + name + "-" + key,
-                                          p.tool_arg(p.tool_arg_open(p.literal(ARG_START)) +
-                                                     p.tool_arg_name(p.literal(key)) + p.literal("\"") +
-                                                     p.until(SEP) + p.literal(SEP) + value +
-                                                     p.tool_arg_close(p.literal(ARG_END))));
-                }
-                args = p.zero_or_more(arg_choices);
-            }
-
-            // skip the trailing index="N" attribute the same way
-            auto call = p.tool(p.tool_open(p.literal(CALL_START) + p.tool_name(p.literal(name)) + p.literal("\"") +
-                                           p.until(SEP) + p.literal(SEP)) +
-                               p.tool_args(args) + p.tool_close(p.literal(CALL_END)));
-
-            tool_choices |= p.rule("kimi-k3-tool-" + name, call);
-        });
-
-        // all calls go inside one tools section, then the message is closed. the
-        // message closer is part of the trigger rule, or else the lazy grammar
-        // rejects it once tool calls have started
-        auto tools_section =
-            p.trigger_rule("kimi-k3-tool-call", p.literal(TOOLS_START) + p.one_or_more(tool_choices) +
-                                                    p.literal(TOOLS_END) + p.optional(p.literal(MSG_END)) +
-                                                    p.optional(p.literal(EOM_TOKEN)));
-
-        auto tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? tools_section :
-                                                                              p.optional(tools_section);
-
-        return start + reasoning + response + tools + trailer + end;
-    });
-
-    data.parser = parser.save();
-
-    if (include_grammar) {
-        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
-        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
-            foreach_function(inputs.tools, [&](const json & tool) {
-                const auto & function = tool.at("function");
-                if (function.contains("parameters")) {
-                    auto schema = function.at("parameters");
-                    builder.resolve_refs(schema);
-                }
-            });
-            parser.build_grammar(builder, data.grammar_lazy);
-        });
-
-        data.grammar_triggers = {
-            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, TOOLS_START },
-        };
-    }
-
-    return data;
-}
@@ -1,119 +0,0 @@
-#include "parsers.h"
-
-// LFM2 format detection: template uses <|tool_list_start|>[...]<|tool_list_end|> around the tool list
-// and <|tool_call_start|>[...]<|tool_call_end|> around each tool call
-bool is_lfm2_template(const std::string & src) {
-    return src.find("<|tool_list_start|>") != std::string::npos &&
-           src.find("<|tool_list_end|>")   != std::string::npos;
-}
-
-// LFM2/LFM2.5 parser. Tool calls are almost Python-style and parallel-capable
-// (except dotted names and JSON literals true/false/null).
-// Always wrapped in <|tool_call_start|>[name(args)]<|tool_call_end|> with optional <think> reasoning.
-// tool_list_tokens preserves LFM2 system tool-list markers.
-common_chat_params common_chat_params_init_lfm2(const common_chat_template &          tmpl,
-                                                       const autoparser::generation_params & inputs,
-                                                       bool tool_list_tokens) {
-    common_chat_params data;
-
-    const std::string TOOL_CALL_START = "<|tool_call_start|>";
-    const std::string TOOL_CALL_END   = "<|tool_call_end|>";
-    const std::string TOOL_LIST_START = "<|tool_list_start|>";
-    const std::string TOOL_LIST_END   = "<|tool_list_end|>";
-    const std::string THINK_START     = "<think>";
-    const std::string THINK_END       = "</think>";
-    const std::string GEN_PROMPT      = "<|im_start|>assistant\n";
-
-    // Copy reasoning to the "thinking" field the template expects
-    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");
-        }
-        adjusted_messages.push_back(msg);
-    }
-
-    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs, adjusted_messages);
-    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs, adjusted_messages);
-    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
-    data.supports_thinking = true;
-    data.preserved_tokens  = { TOOL_CALL_START, TOOL_CALL_END, THINK_START, THINK_END };
-    if (tool_list_tokens) {
-        data.preserved_tokens.push_back(TOOL_LIST_START);
-        data.preserved_tokens.push_back(TOOL_LIST_END);
-    }
-
-    data.thinking_start_tag = THINK_START;
-    data.thinking_end_tags  = {THINK_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();
-    // Gate by reasoning format and whether the template supports <think>
-    auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE &&
-                             tmpl.source().find(THINK_START) != std::string::npos;
-    auto include_grammar   = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
-
-    if (inputs.has_continuation()) {
-        const auto & msg = inputs.continue_msg;
-
-        data.generation_prompt = GEN_PROMPT + THINK_START + msg.reasoning_content;
-        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
-            data.generation_prompt += THINK_END + msg.render_content();
-        }
-
-        data.prompt += data.generation_prompt;
-    }
-
-    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
-        auto generation_prompt = p.literal(GEN_PROMPT);
-        auto end = p.end();
-
-        auto reasoning = p.eps();
-        if (extract_reasoning) {
-            reasoning = p.optional(THINK_START + p.reasoning(p.until(THINK_END)) + THINK_END);
-        }
-
-        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
-            if (has_response_format) {
-                auto response_format = p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema));
-                return generation_prompt + reasoning + response_format + end;
-            }
-            return generation_prompt + reasoning + p.content(p.rest()) + end;
-        }
-        auto tool_calls = p.rule("tool-calls",
-            p.trigger_rule("tool-call",
-                p.literal(TOOL_CALL_START) +
-                p.python_style_tool_calls(inputs.tools, inputs.parallel_tool_calls, /* allow_json_literals = */ true) +
-                p.literal(TOOL_CALL_END)
-            )
-        );
-
-        auto content = p.content(p.until(TOOL_CALL_START));
-
-        return generation_prompt + reasoning + content + tool_calls + end;
-    });
-
-    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      = 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);
-            });
-            if (has_response_format) {
-                auto schema = inputs.json_schema;
-                builder.resolve_refs(schema);
-            }
-            parser.build_grammar(builder, data.grammar_lazy);
-        });
-
-        data.grammar_triggers = {
-            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, TOOL_CALL_START }
-        };
-    }
-
-    return data;
-}
@@ -1,144 +0,0 @@
-#include "parsers.h"
-
-// MiniCPM5 format:
-// - Reasoning: <think>{reasoning}</think> (optional)
-// - Tool calls: <function name="foo"><param name="bar">value</param></function>
-common_chat_params common_chat_params_init_minicpm5(const common_chat_template &          tmpl,
-                                                           const autoparser::generation_params & inputs) {
-    common_chat_params data;
-
-    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
-    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
-    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
-    data.supports_thinking = true;
-    data.preserved_tokens  = {
-        "<function",
-        "<param",
-        "</function>",
-        "</param>",
-        "<think>",
-        "</think>",
-    };
-
-    data.thinking_start_tag = "<think>";
-    data.thinking_end_tags  = {"</think>"};
-
-    data.message_delimiters = {
-        { COMMON_CHAT_ROLE_ASSISTANT, "<|im_start|>assistant"             },
-        { COMMON_CHAT_ROLE_TOOL,      "<|im_start|>user\n<tool_response>" },
-        { COMMON_CHAT_ROLE_USER,      "<|im_start|>user"                  },
-        { COMMON_CHAT_ROLE_SYSTEM,    "<|im_start|>system"                },
-    };
-
-    auto has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
-    auto has_response_format = inputs.json_schema.is_object() && !inputs.json_schema.empty();
-    auto extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
-    auto include_grammar     = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
-
-    if (inputs.has_continuation()) {
-        const auto & msg = inputs.continue_msg;
-
-        data.generation_prompt = "<|im_start|>assistant\n<think>\n" + msg.reasoning_content;
-        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
-            data.generation_prompt += "\n</think>\n\n" + msg.render_content();
-        }
-
-        data.prompt += data.generation_prompt;
-    }
-
-    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
-        auto generation_prompt = p.literal("<|im_start|>assistant\n");
-
-        auto reasoning = p.eps();
-        if (extract_reasoning) {
-            reasoning = ("<think>" << p.reasoning(p.until("</think>")) << "</think>") + p.space();
-        }
-
-        // Response format parser
-        if (has_response_format) {
-            return generation_prompt + reasoning + p.content(p.schema(p.json(), "response-format", inputs.json_schema));
-        }
-
-        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
-            // CDATA lets a value carry characters that would otherwise close the tag (e.g.
-            // </param>); capture the inner text only, excluding the CDATA markers.
-            auto string_value = p.choice({
-                p.literal("<![CDATA[") + p.ac(p.tool_arg_string_value(p.until("]]>")) + p.literal("]]>"), "]]>") + p.tool_arg_close(p.literal("</param>")),
-                p.negate(p.literal("<![CDATA[")) + p.ac(p.tool_arg_string_value(p.until("</param>")) + p.tool_arg_close(p.literal("</param>")), "</param>")
-            });
-
-            auto tool_choice = p.choice();
-            foreach_function(inputs.tools, [&](const json & tool) {
-                const auto &      function = tool.at("function");
-                const std::string name     = function.at("name");
-                auto              params   = function.contains("parameters") ? function.at("parameters") : json::object();
-
-                auto args = p.eps();
-                if (params.contains("properties") && params.at("properties").is_object() && !params.at("properties").empty()) {
-                    auto schema_info = common_schema_info();
-                    schema_info.resolve_refs(params);
-
-                    auto arg_choice = p.choice();
-                    for (const auto & [prop_name, prop_schema] : params.at("properties").items()) {
-                        auto value_parser = p.eps();
-                        if (schema_info.resolves_to_string(prop_schema)) {
-                            value_parser = string_value;
-                        } else {
-                            value_parser = p.tool_arg_json_value(
-                                    p.schema(p.json(), "tool-" + name + "-arg-" + prop_name + "-schema", prop_schema, false)
-                                ) + p.tool_arg_close(p.literal("</param>"));
-                        }
-
-                        auto arg_rule = p.tool_arg(
-                            p.tool_arg_open(p.literal("<param name=\"") + p.tool_arg_name(p.literal(prop_name)) + p.literal("\">")) +
-                            value_parser
-                        );
-
-                        arg_choice |= arg_rule;
-                    }
-                    args = p.zero_or_more(arg_choice + p.space());
-                }
-
-                auto tool_parser = p.tool(
-                    p.tool_open(p.literal("<function name=\"") + p.tool_name(p.literal(name)) + p.literal("\">"))
-                    << p.tool_args(args)
-                    << p.tool_close(p.literal("</function>")));
-
-                tool_choice |= p.rule("tool-" + name, tool_parser);
-            });
-
-            auto max_calls  = inputs.parallel_tool_calls ? -1 : 1;
-            auto tool_calls = p.trigger_rule("tool-call", p.repeat(tool_choice + p.space(), 1, max_calls));
-
-            auto content = p.content(p.until("<function"));
-
-            return generation_prompt + reasoning + content + tool_calls + p.end();
-        }
-
-        return generation_prompt + reasoning + p.content(p.rest()) + p.end();
-    });
-
-    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      = build_grammar([&](const common_grammar_builder & builder) {
-            foreach_function(inputs.tools, [&](const json & tool) {
-                const auto & function = tool.at("function");
-                auto         schema   = function.contains("parameters") ? function.at("parameters") : json::object();
-                builder.resolve_refs(schema);
-            });
-            if (has_response_format) {
-                auto schema = inputs.json_schema;
-                builder.resolve_refs(schema);
-            }
-            parser.build_grammar(builder, data.grammar_lazy);
-        });
-
-        data.grammar_triggers = {
-            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<function" },
-        };
-    }
-
-    return data;
-}
@@ -1,259 +0,0 @@
-#include "parsers.h"
-
-common_chat_params common_chat_params_init_minimax_m3(const common_chat_template &          tmpl,
-                                                             const autoparser::generation_params & inputs) {
-    common_chat_params data;
-
-    data.prompt             = common_chat_template_direct_apply_impl(tmpl, inputs);
-    data.generation_prompt  = common_chat_template_generation_prompt_impl(tmpl, inputs);
-    data.format             = COMMON_CHAT_FORMAT_PEG_MINIMAX_M3;
-    data.supports_thinking  = true;
-    data.thinking_start_tag = "<mm:think>";
-    data.thinking_end_tags  = {"</mm:think>"};
-
-    // M3 prefixes every tool tag with the namespace token "]<]minimax[>[";
-    // params use the parameter name as the tag (<file_path>...</file_path>).
-    const std::string NS          = "]<]minimax[>[";
-    const std::string THINK_START = "<mm:think>";
-    const std::string THINK_END   = "</mm:think>";
-    const std::string FC_START    = NS + "<tool_call>";
-    const std::string FC_END      = NS + "</tool_call>";
-    const std::string INVOKE_END  = NS + "</invoke>";
-
-    data.preserved_tokens = {
-        NS,
-        "<tool_call>",
-        "</tool_call>",
-        THINK_START,
-        THINK_END,
-    };
-
-    data.message_delimiters = {
-        { COMMON_CHAT_ROLE_ASSISTANT, "]~b]ai"        },
-        { COMMON_CHAT_ROLE_USER,      "]~b]user"      },
-        { COMMON_CHAT_ROLE_TOOL,      "]~b]tool"      },
-        { COMMON_CHAT_ROLE_SYSTEM,    "]~b]developer" },
-        { COMMON_CHAT_ROLE_SYSTEM,    "]~b]system"    },
-    };
-
-    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 extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
-    auto include_grammar     = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
-
-    const std::string GEN_PROMPT = data.generation_prompt;
-
-    using mm3 = common_chat_peg_minimax_m3_mapper;
-
-    if (inputs.has_continuation()) {
-        const auto & msg = inputs.continue_msg;
-
-        data.generation_prompt = GEN_PROMPT + THINK_START + msg.reasoning_content;
-        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
-            data.generation_prompt += THINK_END + msg.render_content();
-        }
-
-        data.prompt += data.generation_prompt;
-    }
-
-    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
-        auto generation_prompt = p.prefix(GEN_PROMPT, THINK_START);
-        auto end = p.end();
-
-        auto reasoning = p.eps();
-        if (extract_reasoning) {
-            auto block = inputs.enable_thinking
-                             ? p.literal(THINK_START) + p.space() +
-                                   p.ac(p.reasoning(p.until(THINK_END)) + p.literal(THINK_END), THINK_END)
-                             : p.literal(THINK_START) + p.ac(p.until(THINK_END) + p.literal(THINK_END), THINK_END);
-
-            // A turn without reasoning is prefixed with a bare </mm:think>, written either by the
-            // generation prompt (thinking_mode = "disabled") or by the model itself.
-            reasoning = p.optional(p.choice({ block, p.literal(THINK_END) }));
-        }
-
-        if (has_response_format) {
-            auto response_format = p.rule("response-format",
-                p.literal("```json") + p.space() +
-                p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)) +
-                p.space() + p.literal("```"));
-            return generation_prompt + reasoning + response_format + end;
-        }
-
-        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
-            return generation_prompt + reasoning + p.content(p.rest()) + end;
-        }
-
-        auto alternatives_of = [](const json & schema) -> std::optional<json> {
-            for (const auto * keyword : { "oneOf", "anyOf" }) {
-                if (schema.contains(keyword) && schema.at(keyword).is_array() && !schema.at(keyword).empty()) {
-                    return schema.at(keyword);
-                }
-            }
-            return std::nullopt;
-        };
-
-        auto tool_choice = p.choice();
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const auto & function = tool.at("function");
-            std::string  name     = function.at("name");
-            auto         params   = function.contains("parameters") ? function.at("parameters") : json::object();
-
-            auto schema_info = common_schema_info();
-            schema_info.resolve_refs(params);
-
-            // The template expands argument values recursively in XML (see the to_xml() macro)
-            std::function<common_peg_parser(const json &, const std::string &, const std::string &)> value_of;
-            std::function<common_peg_parser(const json &, const std::string &)>                      members_of;
-
-            auto element_of = [&](const std::string & tag, const json & schema, const std::string & rule_name) {
-                const std::string close = NS + "</" + tag + ">";
-                return p.rule(rule_name,
-                    p.tool_arg(
-                        p.tool_arg_open(
-                            p.literal(NS + "<") +
-                            p.tool_arg_name(p.literal(tag)) +
-                            p.literal(">")) +
-                        value_of(schema, rule_name, close)));
-            };
-
-            value_of = [&](const json & schema,
-                           const std::string & rule_name,
-                           const std::string & close) -> common_peg_parser {
-                auto close_tag = p.tool_arg_close(p.literal(close));
-
-                // A string accepts anything, so a union with a string alternative is a string
-                if (schema_info.resolves_to_string(schema)) {
-                    return p.ac(p.tool_arg_string_value(p.until(close)) + close_tag, close);
-                }
-
-                if (auto alternatives = alternatives_of(schema)) {
-                    std::vector<common_peg_parser> choices;
-
-                    size_t index = 0;
-                    for (const auto & alternative : *alternatives) {
-                        const std::string alt_name = rule_name + "-" + std::to_string(index++);
-
-                        // There is a risk that this breaks streaming deltas, but that's a risk we
-                        // assume to provide tool arg streaming.
-                        choices.push_back(value_of(alternative, alt_name, close));
-                    }
-
-                    return p.choice(choices);
-                }
-
-                const std::string type = schema.contains("type") && schema.at("type").is_string()
-                                             ? schema.at("type").get<std::string>()
-                                             : "";
-
-                if (type == "object" && schema.contains("properties")) {
-                    return p.tag(mm3::TOOL_ARG_OBJECT, members_of(schema, rule_name)) + p.space() + close_tag;
-                }
-
-                if (type == "array" && schema.contains("items")) {
-                    const std::string item_close = NS + "</item>";
-                    auto item = p.rule(rule_name + "-item",
-                        p.tag(mm3::TOOL_ARG_ITEM,
-                              p.literal(NS + "<item>") +
-                                  value_of(schema.at("items"), rule_name + "-item", item_close)));
-                    return p.tag(mm3::TOOL_ARG_ARRAY, p.repeat(p.space() + item, 0, -1)) + p.space() + close_tag;
-                }
-
-                return p.tool_arg_json_value(p.schema(p.json(), rule_name + "-schema", schema, false)) + close_tag;
-            };
-
-            // Required properties in schema order, then any number of optional ones in any order.
-            members_of = [&](const json & schema, const std::string & rule_prefix) -> common_peg_parser {
-                const auto & props = schema.at("properties");
-
-                std::set<std::string> required;
-                if (schema.contains("required")) {
-                    required = schema.at("required").get<std::set<std::string>>();
-                }
-
-                std::vector<common_peg_parser> required_elements;
-                std::vector<common_peg_parser> optional_elements;
-                for (const auto & [key, key_schema] : props.items()) {
-                    auto element = element_of(key, key_schema, rule_prefix + "-" + key);
-                    if (required.find(key) != required.end()) {
-                        required_elements.push_back(element);
-                    } else {
-                        optional_elements.push_back(element);
-                    }
-                }
-
-                common_peg_parser members = p.eps();
-                for (size_t i = 0; i < required_elements.size(); i++) {
-                    if (i > 0) {
-                        members = members + p.space();
-                    }
-                    members = members + required_elements[i];
-                }
-
-                if (!optional_elements.empty()) {
-                    common_peg_parser any_optional = p.choice();
-                    for (const auto & element : optional_elements) {
-                        any_optional |= element;
-                    }
-                    members = members + p.repeat(p.space() + any_optional, 0, -1);
-                }
-
-                return members;
-            };
-
-            common_peg_parser invoke_body =
-                params.contains("properties") ? members_of(params, "tool-" + name + "-arg") : p.eps();
-
-            auto func_parser = p.tool(
-                p.tool_open(p.literal(NS + "<invoke name=\"") +
-                            p.tool_name(p.literal(name)) + p.literal("\">")) +
-                p.space() + invoke_body + p.space() +
-                p.tool_close(p.literal(INVOKE_END)));
-
-            tool_choice |= p.rule("tool-" + name, func_parser);
-        });
-
-        auto require_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED;
-
-        common_peg_parser tool_calls = p.eps();
-        if (inputs.parallel_tool_calls) {
-            tool_calls = p.trigger_rule("tool-call",
-                p.literal(FC_START) + p.space() + tool_choice +
-                p.zero_or_more(p.space() + tool_choice) + p.space() + p.literal(FC_END));
-        } else {
-            tool_calls = p.trigger_rule("tool-call",
-                p.literal(FC_START) + p.space() + tool_choice + p.space() + p.literal(FC_END));
-        }
-
-        if (!require_tools) {
-            tool_calls = p.optional(tool_calls);
-        }
-
-        auto content_before_tools = p.content(p.until(FC_START));
-        return generation_prompt + reasoning + content_before_tools + tool_calls + end;
-    });
-
-    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      = build_grammar([&](const common_grammar_builder & builder) {
-            foreach_function(inputs.tools, [&](const json & tool) {
-                const auto & function = tool.at("function");
-                auto         schema   = function.contains("parameters") ? function.at("parameters") : json::object();
-                builder.resolve_refs(schema);
-            });
-            if (has_response_format) {
-                auto schema = inputs.json_schema;
-                builder.resolve_refs(schema);
-            }
-            parser.build_grammar(builder, data.grammar_lazy);
-        });
-
-        data.grammar_triggers = {
-            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, FC_START },
-        };
-    }
-
-    return data;
-}
@@ -1,135 +0,0 @@
-#include "parsers.h"
-
-common_chat_params common_chat_params_init_ministral_3(const common_chat_template &    tmpl,
-                                                              const autoparser::generation_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
-    auto adjusted_messages = json::array();
-    for (const auto & msg : inputs.messages) {
-        auto role = msg.value("role", "");
-        if (role != "system" && role != "assistant") {
-            // Only adjust system and assistant messages. Interestingly, the system message may contain thinking.
-            adjusted_messages.push_back(msg);
-            continue;
-        }
-
-        auto content = json::array();
-
-        // If message contains `reasoning_content`, add it as a block of type `thinking`
-        if (msg.contains("reasoning_content") && msg.at("reasoning_content").is_string()) {
-            content.push_back({
-                { "type",     "thinking"                                     },
-                { "thinking", msg.at("reasoning_content").get<std::string>() },
-            });
-        }
-
-        // If message contains `content`, add it as a block of type `text`
-        if (msg.contains("content")) {
-            if (msg.at("content").is_string()) {
-                content.push_back({
-                    { "type", "text"                               },
-                    { "text", msg.at("content").get<std::string>() },
-                });
-            } else if (msg.at("content").is_array()) {
-                auto blocks = msg.at("content");
-                content.insert(blocks);
-            }
-        }
-
-        auto adjusted       = msg;
-        adjusted["content"] = content;
-        adjusted.erase("reasoning_content");
-        adjusted_messages.push_back(adjusted);
-    }
-
-    auto has_tools            = inputs.tools.is_array() && !inputs.tools.empty();
-    auto has_response_format  = inputs.json_schema.is_object() && !inputs.json_schema.empty();
-    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_tags  = {"[/THINK]"};
-    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs, /* messages_override = */ adjusted_messages);
-    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs, /* messages_override = */ adjusted_messages);
-    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
-    data.preserved_tokens  = {
-        "[THINK]",
-        "[/THINK]",
-        "[TOOL_CALLS]",
-        "[ARGS]",
-    };
-
-    if (inputs.has_continuation()) {
-        const auto & msg = inputs.continue_msg;
-
-        data.generation_prompt = "[THINK]" + msg.reasoning_content;
-        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
-            data.generation_prompt += "[/THINK]" + msg.render_content();
-        }
-
-        data.prompt += data.generation_prompt;
-    }
-
-    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
-        auto generation_prompt = p.eps();
-        auto reasoning =
-            extract_reasoning ? p.optional("[THINK]" + p.reasoning(p.until("[/THINK]")) + "[/THINK]") : p.eps();
-
-        // Response format parser
-        if (has_response_format) {
-            // Ministral wants to emit json surrounded by code fences
-            return generation_prompt + (reasoning << "```json" << p.content(p.schema(p.json(), "response-format", inputs.json_schema)) << "```");
-        }
-
-        // Tool call parser
-        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
-            auto tool_choice = p.choice();
-            foreach_function(inputs.tools, [&](const json & tool) {
-                const auto & function = tool.at("function");
-                std::string  name     = function.at("name");
-                const auto & schema   = function.at("parameters");
-
-                tool_choice |=
-                    p.rule("tool-" + name, p.tool_open(p.tool_name(p.literal(name)) + "[ARGS]") +
-                                               p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema)));
-            });
-
-            auto min_calls  = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0;
-            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 generation_prompt + (reasoning << p.content(p.until("[TOOL_CALLS]")) << tool_calls);
-        }
-
-        // Content only parser
-        include_grammar = false;
-        return generation_prompt + (reasoning << p.content(p.rest()));
-    });
-
-    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);
-            });
-            if (has_response_format) {
-                auto schema = inputs.json_schema;
-                builder.resolve_refs(schema);
-            }
-            parser.build_grammar(builder, data.grammar_lazy);
-        });
-
-        data.grammar_triggers = {
-            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "[TOOL_CALLS]" }
-        };
-    }
-
-    return data;
-}
@@ -1,148 +0,0 @@
-#include "parsers.h"
-
-// An assistant turn is rendered as one or more messages, each
-// "<|start|>assistant to=<recipient><|message|>{content}{END}" where END is
-// <|eom|> (more messages follow) or <|eot|> (end of turn):
-//   - chain-of-thought: to=self, terminated by <|eom|>
-//   - final answer:     to=user, terminated by <|eot|>
-// The generation prompt is just "<|start|>assistant"; the model emits its own
-// " to=...<|message|>".
-common_chat_params common_chat_params_init_muse_glimmer(const common_chat_template &          tmpl,
-                                                               const autoparser::generation_params & inputs) {
-    common_chat_params data;
-
-    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
-    data.generation_prompt = "<|start|>assistant";
-    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
-    data.supports_thinking = true;
-
-    data.preserved_tokens = {
-        "<|start|>", "<|message|>", "<|eom|>", "<|eot|>",
-        // ATEM tool-call markup emitted on " to=<tool>" turns.
-        "<atem:function_calls>", "<atem:invoke", "<atem:parameter", "</atem:parameter>",
-        "</atem:invoke>", "</atem:function_calls>",
-    };
-
-    data.message_delimiters = {
-        { COMMON_CHAT_ROLE_ASSISTANT, "<|start|>assistant" },
-        { COMMON_CHAT_ROLE_USER,      "<|start|>user"      },
-        { COMMON_CHAT_ROLE_SYSTEM,    "<|start|>system"    },
-        { COMMON_CHAT_ROLE_TOOL,      "<|start|>tool"      },
-    };
-
-    if (inputs.has_continuation()) {
-        const auto & msg = inputs.continue_msg;
-
-        data.generation_prompt = "<|start|>assistant to=self<|message|>" + msg.reasoning_content;
-        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
-            data.generation_prompt += "<|eom|><|start|>assistant to=user<|message|>" + msg.render_content();
-        }
-
-        data.prompt += data.generation_prompt;
-    }
-
-    auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
-
-    auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
-    // Constrained grammar whenever tools are offered.
-    auto include_grammar = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
-
-    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
-        auto start = p.rule("start", p.literal("<|start|>assistant"));
-
-        if (!extract_reasoning && !include_grammar) {
-            return start + p.content(p.rest());
-        }
-
-        if (extract_reasoning) {
-            p.rule("analysis", p.literal(" to=self<|message|>") + p.reasoning(p.until("<|eom|>")) + p.literal("<|eom|>"));
-        } else {
-            p.rule("analysis", p.literal(" to=self<|message|>") + p.content(p.until("<|eom|>")) + p.literal("<|eom|>"));
-        }
-        auto analysis = p.ref("analysis");
-
-        auto recipient  = p.optional(p.literal(" to=user"));
-        auto final_msg  = p.rule("final", recipient + p.literal("<|message|>") +
-                                              p.content(p.until_one_of({ "<|eot|>", "<|eom|>" })));
-
-        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
-            auto string_value = p.ac(
-                p.tool_arg_string_value(p.until("</atem:parameter>")) + p.tool_arg_close(p.literal("</atem:parameter>")),
-                "</atem:parameter>");
-
-            auto tool_choice = p.choice();
-            foreach_function(inputs.tools, [&](const json & tool) {
-                const auto &      function = tool.at("function");
-                const std::string name     = function.at("name");
-                auto              params   = function.contains("parameters") ? function.at("parameters") : json::object();
-
-                auto args = p.eps();
-                if (params.contains("properties") && params.at("properties").is_object() && !params.at("properties").empty()) {
-                    auto schema_info = common_schema_info();
-                    schema_info.resolve_refs(params);
-
-                    auto arg_choice = p.choice();
-                    for (const auto & [prop_name, prop_schema] : params.at("properties").items()) {
-                        auto value_parser = p.eps();
-                        if (schema_info.resolves_to_string(prop_schema)) {
-                            value_parser = string_value;
-                        } else {
-                            value_parser = p.tool_arg_json_value(
-                                    p.schema(p.json(), "tool-" + name + "-arg-" + prop_name + "-schema", prop_schema, false))
-                                + p.tool_arg_close(p.literal("</atem:parameter>"));
-                        }
-
-                        auto arg_rule = p.tool_arg(
-                            p.tool_arg_open(p.literal("<atem:parameter name=\"") + p.tool_arg_name(p.literal(prop_name)) + p.literal("\">")) +
-                            value_parser);
-
-                        arg_choice |= arg_rule;
-                    }
-                    args = p.zero_or_more(arg_choice + p.space());
-                }
-
-                auto tool_parser = p.tool(
-                    p.tool_open(p.literal(" to=") + p.until("<|message|>") +
-                                p.literal("<|message|><atem:function_calls>") + p.space() +
-                                p.literal("<atem:invoke name=\"") + p.tool_name(p.literal(name)) + p.literal("\">") + p.space())
-                    << p.tool_args(args)
-                    << p.tool_close(p.literal("</atem:invoke>") + p.space() + p.literal("</atem:function_calls>")));
-
-                tool_choice |= p.rule("tool-" + name, tool_parser);
-            });
-
-            auto tool_calls = inputs.parallel_tool_calls
-                ? p.trigger_rule("tool-call", tool_choice + p.zero_or_more(p.literal("<|eom|>") + start + tool_choice))
-                : p.trigger_rule("tool-call", tool_choice);
-
-
-            if (inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED) {
-                return p.zero_or_more(start + analysis) + start + tool_calls;
-            }
-            auto trailing_calls = p.optional(p.literal("<|eom|>") + start + tool_calls);
-            return p.zero_or_more(start + analysis) + start + (tool_calls | (final_msg + trailing_calls));
-        }
-
-        return p.zero_or_more(start + analysis) + start + final_msg;
-    });
-
-    data.parser = parser.save();
-
-    if (include_grammar) {
-        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
-        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.contains("parameters") ? function.at("parameters") : json::object();
-                builder.resolve_refs(schema);
-            });
-            parser.build_grammar(builder, data.grammar_lazy);
-        });
-        data.grammar_triggers = {
-            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN,
-              "<\\|start\\|>assistant( to=(?!self<\\|message\\|>)(?!user<\\|message\\|>)[^<]*?<\\|message\\|>)" },
-        };
-    }
-
-    return data;
-}
@@ -1,34 +0,0 @@
-#include "parsers.h"
-
-#include "log.h"
-
-#include <set>
-
-void foreach_function(const json & tools, const std::function<void(const json &)> & fn) {
-    for (const auto & tool : tools) {
-        if (!tool.contains("type") || tool.at("type") != "function" || !tool.contains("function")) {
-            LOG_INF("Skipping tool without function: %s", tool.dump(2).c_str());
-            continue;
-        }
-        fn(tool);
-    }
-}
-
-void foreach_parameter(const json & function, const std::function<void(const std::string &, const json &, bool)> & fn) {
-    if (!function.contains("parameters") || !function.at("parameters").is_object()) {
-        return;
-    }
-    const auto & params = function.at("parameters");
-    if (!params.contains("properties") || !params.at("properties").is_object()) {
-        return;
-    }
-    const auto & props = params.at("properties");
-    std::set<std::string> required;
-    if (params.contains("required") && params.at("required").is_array()) {
-        required = params.at("required").get<std::set<std::string>>();
-    }
-    for (const auto & [name, prop] : props.items()) {
-        bool is_required = (required.find(name) != required.end());
-        fn(name, prop, is_required);
-    }
-}
@@ -1,77 +0,0 @@
-#pragma once
-
-#include "chat.h"
-#include "chat-auto-parser.h"
-#include "chat-auto-parser-helpers.h"
-#include "chat-peg-parser.h"
-#include "common.h"
-#include "ggml.h"
-#include "json-schema-to-grammar.h"
-#include "json.h"
-
-#include <functional>
-#include <optional>
-#include <set>
-#include <string>
-#include <vector>
-
-using json = common_json;
-
-// iterate over the function tools of an OpenAI-style tools array
-void foreach_function(const json & tools, const std::function<void(const json &)> & fn);
-
-// iterate over the parameters of a function tool, flagging the ones listed as required
-void foreach_parameter(const json & function, const std::function<void(const std::string &, const json &, bool)> & fn);
-
-// render a template; the override arguments let a parser feed in messages, tools or context it has rewritten
-std::string common_chat_template_direct_apply_impl(
-    const common_chat_template & tmpl,
-    const autoparser::generation_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);
-
-// the suffix a template appends when add_generation_prompt is set
-std::string common_chat_template_generation_prompt_impl(
-    const common_chat_template & tmpl,
-    const autoparser::generation_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);
-
-bool is_lfm2_template(const std::string & src);
-
-namespace workaround {
-
-void convert_tool_responses_gemma4(json & messages);
-
-}
-
-common_chat_params common_chat_params_init_cohere2moe(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
-
-common_chat_params common_chat_params_init_deepseek_v3_2(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
-
-common_chat_params common_chat_params_init_functionary_v3_2(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
-
-common_chat_params common_chat_params_init_gemma4(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
-
-common_chat_params common_chat_params_init_gigachat_v3(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
-
-common_chat_params common_chat_params_init_gpt_oss(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
-
-common_chat_params common_chat_params_init_kimi_k2(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
-
-common_chat_params common_chat_params_init_kimi_k3(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
-
-// tool_list_tokens preserves the LFM2 system tool-list markers; LFM2.5 renders without them
-common_chat_params common_chat_params_init_lfm2(const common_chat_template & tmpl, const autoparser::generation_params & inputs, bool tool_list_tokens);
-
-common_chat_params common_chat_params_init_minicpm5(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
-
-common_chat_params common_chat_params_init_minimax_m3(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
-
-common_chat_params common_chat_params_init_ministral_3(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
-
-common_chat_params common_chat_params_init_muse_glimmer(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
-
-common_chat_params common_chat_params_init_qwen3_coder(const common_chat_template & tmpl, const autoparser::generation_params & inputs);
@@ -1,181 +0,0 @@
-#include "parsers.h"
-
-common_chat_params common_chat_params_init_qwen3_coder(const common_chat_template &          tmpl,
-                                                              const autoparser::generation_params & inputs) {
-    common_chat_params data;
-
-    const std::string GEN_PREFIX = "<|im_start|>assistant\n";
-
-    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
-    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
-    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
-
-    auto supports_reasoning = tmpl.source().find("<think>") != std::string::npos;
-
-    data.supports_thinking = supports_reasoning;
-    data.preserved_tokens  = {
-        "<tool_call>",
-        "</tool_call>",
-    };
-
-    auto is_qwen3_coder  = !supports_reasoning;
-
-    if (supports_reasoning) {
-        data.thinking_start_tag = "<think>";
-        // Support both </think> and <tool_call> as reasoning end sequences.
-        // <function= is omitted, as it is a workaround for Qwen3-Coder which is not a thinking model
-        data.thinking_end_tags = { "</think>", "<tool_call>" };
-        data.preserved_tokens.insert(data.preserved_tokens.end(), { "<think>", "</think>" });
-    }
-
-    data.message_delimiters = {
-        { COMMON_CHAT_ROLE_ASSISTANT, "<|im_start|>assistant"             },
-        { COMMON_CHAT_ROLE_TOOL,      "<|im_start|>user\n<tool_response>" }, // Qwen3-Coder, Qwen3.5, Nemotron Nano 3
-        { COMMON_CHAT_ROLE_TOOL,      "<|im_start|>tool_response"         }, // StepFun-3.5-Flash
-        { COMMON_CHAT_ROLE_USER,      "<|im_start|>user"                  },
-        { COMMON_CHAT_ROLE_SYSTEM,    "<|im_start|>system"                },
-    };
-
-    auto has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
-    auto has_response_format = inputs.json_schema.is_object() && !inputs.json_schema.empty();
-    auto extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
-    auto include_grammar     = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
-
-    if (inputs.has_continuation()) {
-        const auto & msg = inputs.continue_msg;
-
-        data.generation_prompt = GEN_PREFIX;
-        if (supports_reasoning) {
-            data.generation_prompt += "<think>\n" + msg.reasoning_content;
-            if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
-                data.generation_prompt += "\n</think>\n\n";
-            }
-        }
-        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
-            data.generation_prompt += msg.render_content();
-        }
-
-        data.prompt += data.generation_prompt;
-    }
-
-    std::vector<std::string> tool_call_starts = { "<tool_call>" };
-
-    if (is_qwen3_coder) {
-        // Match complete <function=name> opener for Qwen3-Coder models that occasionally omit the
-        // starting <tool_call>. The model may hallucinate a tool name, but it is preferable over
-        // constraining on <function which may occur in valid content generation, e.g. #include <functional>
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const std::string name = tool.at("function").at("name");
-            tool_call_starts.push_back("<function=" + name + ">");
-        });
-    }
-
-    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
-        auto generation_prompt = p.literal(GEN_PREFIX);
-
-        auto reasoning = p.eps();
-        if (supports_reasoning && extract_reasoning) {
-            reasoning = p.optional("<think>" + p.space() +
-                                   p.reasoning(p.until_one_of({ "</think>", "<tool_call>" })) +
-                                   (p.literal("</think>") | p.peek(p.literal("<tool_call>"))));
-        }
-
-        // Response format parser
-        if (has_response_format) {
-            return generation_prompt + (reasoning << p.content(p.schema(p.json(), "response-format", inputs.json_schema)));
-        }
-
-        // Tool call parser
-        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
-            auto arg_close  = p.tool_arg_close(p.literal("\n</parameter>\n"));
-            auto arg_string = p.rule("xml-arg-string",
-                p.ac(p.tool_arg_string_value(p.until("\n</parameter>\n")) + arg_close, "\n</parameter>\n"));
-
-            auto tool_choice = p.choice();
-            foreach_function(inputs.tools, [&](const json & tool) {
-                const auto & function   = tool.at("function");
-                std::string  name       = function.at("name");
-                auto         parameters = function.contains("parameters") ? function.at("parameters") : json::object();
-
-                auto schema_info = common_schema_info();
-                schema_info.resolve_refs(parameters);
-
-                std::vector<common_peg_parser> required_args;
-                std::vector<common_peg_parser> optional_args;
-
-                foreach_parameter(function, [&](const std::string & param_name, const json & param_schema, bool is_required) {
-                    auto rule_name = "tool-" + name + "-arg-" + param_name;
-
-                    auto arg_open = p.tool_arg_open("<parameter=" + p.tool_arg_name(p.literal(param_name)) + ">\n");
-
-                    auto arg_value = schema_info.resolves_to_string(param_schema) ?
-                        arg_string :
-                        p.tool_arg_json_value(p.schema(p.json(), rule_name + "-schema", param_schema)) + arg_close;
-
-                    auto arg_rule = p.rule(rule_name, p.tool_arg(arg_open + arg_value));
-
-                    (is_required ? required_args : optional_args).push_back(arg_rule);
-                });
-
-                // Accept required arguments in any order, as Qwen does not always adhere to the
-                // order provided.
-                auto args = p.permute("tool-" + name + "-args", required_args);
-                if (!optional_args.empty()) {
-                    args = args + p.zero_or_more(p.choice(optional_args));
-                }
-
-                auto func = p.tool(p.tool_open("<function=" + p.tool_name(p.literal(name)) + ">\n") +
-                                   p.tool_args(args) +
-                                   p.tool_close(p.literal("</function>\n")));
-
-                tool_choice |= p.rule("tool-" + name, func);
-            });
-
-            auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0;
-
-            auto tool_call_body = tool_choice + "</tool_call>" + p.space();
-            auto tool_call      = p.rule("tool-call", "<tool_call>\n" + tool_call_body);
-
-            // Qwen3-Coder models may occasionally omit the <tool_call> token.
-            auto tool_call_first = is_qwen3_coder ?
-                p.rule("tool-call-first", p.optional(p.literal("<tool_call>\n")) + tool_call_body) :
-                tool_call;
-
-            auto calls      = inputs.parallel_tool_calls ? tool_call_first + p.zero_or_more(tool_call) : tool_call_first;
-            auto tool_calls = p.trigger_rule("tool-call-root", p.repeat(calls, min_calls, 1));
-
-            return generation_prompt +
-                   (reasoning << p.content(p.until_one_of(tool_call_starts)) << tool_calls);
-        }
-
-        // Content only parser
-        return generation_prompt + (reasoning << p.content(p.rest()));
-    });
-
-    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.contains("parameters") ? function.at("parameters") : json::object();
-                builder.resolve_refs(schema);
-            });
-            if (has_response_format) {
-                auto schema = inputs.json_schema;
-                builder.resolve_refs(schema);
-            }
-            parser.build_grammar(builder, data.grammar_lazy);
-        });
-
-        if (data.grammar_lazy) {
-            for (const auto & start : tool_call_starts) {
-                data.grammar_triggers.push_back({ COMMON_GRAMMAR_TRIGGER_TYPE_WORD, start });
-            }
-        }
-    }
-
-    return data;
-}
@@ -1,20 +0,0 @@
-# Specialized chat template parsers, listed explicitly so that adding or removing one re-runs CMake instead of leaving an incremental build stale.
-
-set(LLAMA_CHAT_PARSERS_SOURCES
-    ${CMAKE_CURRENT_LIST_DIR}/parsers.cpp
-    ${CMAKE_CURRENT_LIST_DIR}/parsers.h
-    ${CMAKE_CURRENT_LIST_DIR}/cohere2moe.cpp
-    ${CMAKE_CURRENT_LIST_DIR}/deepseek.cpp
-    ${CMAKE_CURRENT_LIST_DIR}/functionary-v3-2.cpp
-    ${CMAKE_CURRENT_LIST_DIR}/gemma4.cpp
-    ${CMAKE_CURRENT_LIST_DIR}/gigachat-v3.cpp
-    ${CMAKE_CURRENT_LIST_DIR}/gpt-oss.cpp
-    ${CMAKE_CURRENT_LIST_DIR}/kimi-k2.cpp
-    ${CMAKE_CURRENT_LIST_DIR}/kimi-k3.cpp
-    ${CMAKE_CURRENT_LIST_DIR}/lfm2.cpp
-    ${CMAKE_CURRENT_LIST_DIR}/minicpm5.cpp
-    ${CMAKE_CURRENT_LIST_DIR}/minimax-m3.cpp
-    ${CMAKE_CURRENT_LIST_DIR}/ministral3.cpp
-    ${CMAKE_CURRENT_LIST_DIR}/muse-glimmer.cpp
-    ${CMAKE_CURRENT_LIST_DIR}/qwen3-coder.cpp
-)
@@ -14,7 +14,6 @@

 #include <algorithm>
 #include <cassert>
-#include <cmath>
 #include <cstring>
 #include <iomanip>
 #include <map>
@@ -139,7 +138,6 @@ struct common_speculative_impl {
    const common_speculative_type type;

    uint32_t n_seq;
-    int32_t n_max; // maximum draft length after implementation-specific limits

    size_t n_call_begin  = 0; // number of times this implementation was called for refresh.
    size_t n_call_draft  = 0; // number of times this implementation was called for generation.
@@ -159,7 +157,7 @@ struct common_speculative_impl {
    int64_t t_draft_us  = 0; // total time spent in generating drafts in this implementation in microseconds.
    int64_t t_accept_us = 0; // total time spent in accumulation of this implementation in microseconds.

-    common_speculative_impl(common_speculative_type type, uint32_t n_seq, int32_t n_max) : type(type), n_seq(n_seq), n_max(n_max) {}
+    common_speculative_impl(common_speculative_type type, uint32_t n_seq) : type(type), n_seq(n_seq) {}

    virtual ~common_speculative_impl() = default;

@@ -184,7 +182,7 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl {
    std::vector<common_sampler_ptr> smpls;

    common_speculative_impl_draft_simple(const common_params_speculative & params, uint32_t n_seq)
-        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE, n_seq, params.draft.n_max)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE, n_seq)
        , params(params.draft)
    {
        auto * ctx_dft = this->params.ctx_dft;
@@ -296,7 +294,7 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl {
            drafting[seq_id] = true;
            common_sampler_reset(smpls[seq_id].get());

-            common_batch_add(batch, dp.id_last, dp.pos0, { seq_id }, true);
+            common_batch_add(batch, dp.id_last, dp.n_past, { seq_id }, true);
        }

        int ret = llama_decode(ctx_dft, batch);
@@ -355,7 +353,7 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl {
                    continue;
                }

-                common_batch_add(batch, id, dp.pos0 + i + 1, { seq_id }, true);
+                common_batch_add(batch, id, dp.n_past + i + 1, { seq_id }, true);
            }

            if (batch.n_tokens == 0) {
@@ -454,7 +452,7 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
    std::vector<float> g_embd_buf;

    common_speculative_impl_draft_eagle3(const common_params_speculative & params, uint32_t n_seq)
-        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, n_seq, params.draft.n_max)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, n_seq)
        , params(params.draft)
    {
        SPC_TRC("%s", "adding speculative implementation 'draft-eagle3'\n");
@@ -925,25 +923,21 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
    int32_t     block_size    = 0;
    llama_token mask_token_id = 0;

-    bool    is_dflash2     = false;
-    bool    is_mrope       = false;
-    int32_t selector_top_k = 0;
-
    // draft-dspark: the draft carries a Markov head and uses an anchor-first block layout
    const bool is_dspark;

    // dspark speculators
    bool sample_from_anchor = true;

-    // block-internal attention
-    bool causal_attn = false;
-
    const int32_t * target_layer_ids   = nullptr; // model_dft's extract layer indices
    uint32_t        target_layer_ids_n = 0;

+    // scratch buffer for concatenated target features [n_tokens, n_embd_enc]
+    std::vector<float> features_buf;
+
    common_speculative_impl_draft_dflash(const common_params_speculative & params, uint32_t n_seq,
            common_speculative_type type = COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH)
-        : common_speculative_impl(type, n_seq, params.draft.n_max)
+        : common_speculative_impl(type, n_seq)
        , params(params.draft)
        , is_dspark(type == COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK)
    {
@@ -972,25 +966,9 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
            if (llama_model_meta_val_str(model_dft, "dflash.sample_from_anchor", buf, sizeof(buf)) >= 0) {
                sample_from_anchor = std::strcmp(buf, "true") == 0;
            }
-            if (llama_model_meta_val_str(model_dft, "dflash.attention.causal", buf, sizeof(buf)) >= 0) {
-                causal_attn = std::strcmp(buf, "true") == 0;
-            }
        }
-
-        selector_top_k = llama_model_dflash_selector_top_k(model_dft);
-        is_dflash2     = selector_top_k > 0;
        mask_token_id = llama_vocab_mask(llama_model_get_vocab(model_dft));

-        if (is_dspark && this->params.p_min > 0.0f) {
-            char buf[16] = {};
-            const bool has_conf =
-                llama_model_meta_val_str(model_dft, "dflash.has_confidence_head", buf, sizeof(buf)) < 0 ||
-                std::strcmp(buf, "true") == 0;
-            if (!has_conf) {
-                throw std::runtime_error("DSpark draft has no confidence head: please set --spec-draft-p-min 0");
-            }
-        }
-
        LOG_INF("%s: adding speculative implementation '%s'\n", __func__, common_speculative_type_to_str(type).c_str());
        LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%.2f\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min);
        LOG_INF("%s: - block_size=%d, mask_token_id=%d, n_extract=%u, sample_from_anchor=%s\n", __func__,
@@ -1005,17 +983,9 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
            this->params.n_max = std::min(this->params.n_max, n_draft_max);
            this->params.n_min = std::min(this->params.n_min, n_draft_max);
        }
-        this->n_max = this->params.n_max;

        batch        = llama_batch_init(llama_n_batch(ctx_dft), 0,          n_seq);
-        batch_inject = llama_batch_init(llama_n_ubatch(ctx_dft), n_embd_enc, n_seq);
-
-        // embd batches on an M-RoPE draft need 4 position rows per token
-        is_mrope = llama_model_rope_type(model_dft) == LLAMA_ROPE_TYPE_MROPE;
-        if (is_mrope) {
-            free(batch_inject.pos);
-            batch_inject.pos = (llama_pos *) malloc(sizeof(llama_pos) * 4 * llama_n_batch(ctx_dft));
-        }
+        batch_inject = llama_batch_init(llama_n_batch(ctx_dft), n_embd_dec, n_seq);

        smpls.resize(n_seq);
        for (auto & s : smpls) {
@@ -1028,7 +998,7 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {

        // offload draft sampling to the backend
        backend_chains.assign(n_seq, nullptr);
-        if (this->params.backend_sampling && !is_dflash2) {
+        if (this->params.backend_sampling) {
            for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
                llama_sampler * chain = llama_sampler_chain_init(llama_sampler_chain_default_params());
                llama_sampler_chain_add(chain, llama_sampler_init_top_k(10));
@@ -1047,9 +1017,8 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
            llama_set_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k], true);
        }

-        // DFlash2 reads its selector lattice from h_nextn and never consumes raw logits.
-        llama_set_embeddings_nextn(ctx_dft, true, /*masked*/ !is_dflash2);
-        llama_set_causal_attn(ctx_dft, causal_attn); // DFlash needs non-causal attention unless the model says otherwise
+        llama_set_embeddings_nextn(ctx_dft, true, /*masked*/ true);
+        llama_set_causal_attn(ctx_dft, false); // DFlash needs non-causal attention
    }

    ~common_speculative_impl_draft_dflash() override {
@@ -1094,7 +1063,8 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {

        // Target prefill may contain token IDs or multimodal embeddings. Both
        // produce the target-layer features used to seed the draft KV cache, so
-        // embeddings are injected too, except the pinned ones skipped below.
+        // skipping the embedding batches leaves a hole in the draft's cache and
+        // the next injection fails to initialize.
        // TODO: revisit after https://github.com/ggml-org/llama.cpp/pull/24669 is merged
        const bool has_tokens     = batch_in.token != nullptr;
        const bool has_embeddings = batch_in.embd  != nullptr;
@@ -1130,44 +1100,55 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
            }
            const int32_t n_rows = i_batch_end[seq_id] - i_batch_beg[seq_id] + 1;

-            // an M-RoPE image pins all its rows to one position, so a windowed draft
-            // cache cannot free cells for it - skip it, the draft can jump over the gap
-            const bool pos_pinned = batch_in.pos[i_batch_beg[seq_id]] == batch_in.pos[i_batch_end[seq_id]];
-            if (has_embeddings && n_rows > 1 && pos_pinned) {
-                continue;
-            }
-
            for (int32_t offset = 0; offset < n_rows; offset += n_ubatch) {
                const int32_t n_chunk = std::min(n_ubatch, n_rows - offset);

-                // gather target features per extract layer; the fused decode encodes and
-                // injects them into the K/V cache at the target positions
-                batch_inject.n_tokens = n_chunk;
+                // gather this chunk's target features, interleaved by extract layer
+                features_buf.resize((size_t) n_chunk * n_embd_enc);
                for (uint32_t k = 0; k < target_layer_ids_n; ++k) {
                    const float * layer = llama_get_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k]);
                    if (!layer) {
                        GGML_ABORT("DFlash: target layer %d input not extracted.", target_layer_ids[k]);
                    }
                    for (int32_t i = 0; i < n_chunk; ++i) {
-                        float       * dst = batch_inject.embd + (size_t) i * n_embd_enc + k * (size_t) n_embd_tgt;
+                        float       * dst = features_buf.data() + (size_t) i * n_embd_enc + k * (size_t) n_embd_tgt;
                        const float * src = layer + (size_t) (i_batch_beg[seq_id] + offset + i) * n_embd_tgt;
                        std::memcpy(dst, src, (size_t) n_embd_tgt * sizeof(float));
                    }
                }

+                // fuse extracted features through DFlash encoder
+                llama_batch enc_batch = {
+                    /*.n_tokens =*/ n_chunk,
+                    /*.token    =*/ nullptr,
+                    /*.embd     =*/ features_buf.data(),
+                    /*.pos      =*/ nullptr,
+                    /*.n_seq_id =*/ nullptr,
+                    /*.seq_id   =*/ nullptr,
+                    /*.logits   =*/ nullptr,
+                };
+
+                int32_t rc = llama_encode(ctx_dft, enc_batch);
+                if (rc != 0) {
+                    LOG_ERR("%s: llama_encode(ctx_dft) failed rc=%d (n_tokens=%d, offset=%d)\n",
+                            __func__, rc, (int) n_chunk, (int) offset);
+                    return false;
+                }
+
+                const float * inp_g = llama_get_embeddings_nextn(ctx_dft);
+                GGML_ASSERT(inp_g && "DFlash encoder produced no output.");
+
+                // inject the DFlash decoder K/V cache at the tokens' target positions
+                batch_inject.n_tokens = n_chunk;
+                std::memcpy(batch_inject.embd, inp_g, (size_t) n_chunk * n_embd_dec * sizeof(float));
+
                for (int32_t i = 0; i < n_chunk; ++i) {
-                    const llama_pos p = batch_in.pos[i_batch_beg[seq_id] + offset + i];
-                    batch_inject.pos[i] = p;
-                    if (is_mrope) {
-                        batch_inject.pos[1 * n_chunk + i] = p;
-                        batch_inject.pos[2 * n_chunk + i] = p;
-                        batch_inject.pos[3 * n_chunk + i] = 0;
-                    }
+                    batch_inject.pos[i]       = batch_in.pos[i_batch_beg[seq_id] + offset + i];
                    batch_inject.n_seq_id[i]  = 1;
                    batch_inject.seq_id[i][0] = seq_id;
                    batch_inject.logits[i]    = false;
                }
-                const int32_t rc = llama_decode(ctx_dft, batch_inject);
+                rc = llama_decode(ctx_dft, batch_inject);
                if (rc != 0) {
                    LOG_ERR("%s: llama_decode(ctx_dft) failed rc=%d (n_tokens=%d, offset=%d)\n",
                            __func__, rc, (int) n_chunk, (int) offset);
@@ -1197,7 +1178,7 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {

            common_sampler_reset(smpls[seq_id].get());

-            const int32_t n = (int32_t) dp.pos0;
+            const int32_t n = (int32_t) dp.n_past;

            const int32_t n_draft = params.n_max;

@@ -1205,7 +1186,7 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
            i_block_beg[seq_id] = batch.n_tokens;
            n_block    [seq_id] = n_block_tokens;
            for (int32_t i = 0; i < n_block_tokens; ++i) {
-                common_batch_add(batch, i == 0 ? dp.id_last : mask_token_id, n + i, { seq_id }, !is_dflash2);
+                common_batch_add(batch, i == 0 ? dp.id_last : mask_token_id, n + i, { seq_id }, true);
            }
        }

@@ -1233,36 +1214,6 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {

            auto & result = *dp.result;

-            if (is_dflash2) {
-                const float * lattice = llama_get_embeddings_nextn(ctx_dft);
-                GGML_ASSERT(lattice && "DFlash2 selector produced no lattice");
-
-                int32_t predecessor = 0;
-                for (int32_t i = 1; i < n_block_tokens; ++i) {
-                    const float * row = lattice + (size_t) (beg + i) * n_embd_dec;
-                    const float * scores = row + selector_top_k + (size_t) predecessor * selector_top_k;
-
-                    predecessor = (int32_t) std::distance(scores,
-                            std::max_element(scores, scores + selector_top_k));
-                    if (params.p_min > 0.0f) {
-                        // softmax(scores) at the argmax, i.e. 1 / sum(exp(s_k - s_max))
-                        float sum = 0.0f;
-                        for (int32_t k = 0; k < selector_top_k; ++k) {
-                            sum += std::exp(scores[k] - scores[predecessor]);
-                        }
-                        if (1.0f / sum < params.p_min) {
-                            break;
-                        }
-                    }
-                    result.push_back((llama_token) row[predecessor]);
-                }
-
-                if (result.size() < (size_t) params.n_min) {
-                    result.clear();
-                }
-                continue;
-            }
-
            if (is_dspark) {
                // DSpark: read from the first draft slot, truncate below the confidence threshold
                const float * conf = params.p_min > 0.0f ? llama_get_embeddings_nextn(ctx_dft) : nullptr;
@@ -1364,7 +1315,7 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
    std::vector<std::vector<float>> chain_h;

    common_speculative_impl_draft_mtp(const common_params_speculative & params, uint32_t n_seq)
-        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, n_seq, params.draft.n_max)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, n_seq)
        , params(params.draft)
    {
        auto * ctx_tgt = this->params.ctx_tgt;
@@ -1431,7 +1382,6 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
                c.reserve((size_t) (this->params.n_max + 1) * n_embd);
            }
        }
-        this->n_max = this->params.n_max;

        pending_h.assign(n_seq, std::vector<float>(n_embd, 0.0f));

@@ -1493,7 +1443,7 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {

        const int32_t n_tokens = batch_in.n_tokens;

-        // remember the first and last batch index for each sequence
+        // remember the frist and last batch index for each sequence
        std::fill(i_batch_beg.begin(), i_batch_beg.end(), -1);
        std::fill(i_batch_end.begin(), i_batch_end.end(), -1);

@@ -1621,7 +1571,7 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
            drafting[seq_id] = true;
            common_sampler_reset(smpls[seq_id].get());

-            common_batch_add(batch, dp.id_last, dp.pos0, { seq_id }, true);
+            common_batch_add(batch, dp.id_last, dp.n_past, { seq_id }, true);
            std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd, pending_h[seq_id].data(), row_bytes);

            i_last[seq_id] = batch.n_tokens - 1;
@@ -1635,16 +1585,16 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {

        while (n_drafting > 0) {
            // each step decodes under a different head, i.e. a different decoder layer, and
-            // KV is per layer. process() filled this layer's KV only for positions < pos0
+            // KV is per layer. process() filled this layer's KV only for positions < n_past
            // (prompt + accepted prefix) — nothing in the draft region yet. so reset the
-            // draft region (the seq_rm lower bound is pos0, leaving the prompt KV intact)
+            // draft region (the seq_rm lower bound is n_past, leaving the prompt KV intact)
            // and select head i so it rebuilds its own layer's KV there; decoding just the
            // latest token would leave its attention reading cells only another head wrote.
            if (chain_heads) {
                auto * mem_dft = llama_get_memory(ctx_dft);
                for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
                    if (drafting[seq_id]) {
-                        llama_memory_seq_rm(mem_dft, seq_id, dparams[seq_id].pos0, -1);
+                        llama_memory_seq_rm(mem_dft, seq_id, dparams[seq_id].n_past, -1);
                    }
                }
                llama_set_nextn_layer_offset(ctx_dft, i);
@@ -1710,17 +1660,17 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
                    const int n_rows = (int) result.size() + 1; // id_last + tokens drafted so far
                    for (int t = 0; t < n_rows; ++t) {
                        const llama_token tok = (t == 0) ? dp.id_last : result[t - 1];
-                        common_batch_add(batch, tok, dp.pos0 + t, { seq_id }, t == n_rows - 1);
+                        common_batch_add(batch, tok, dp.n_past + t, { seq_id }, t == n_rows - 1);
                        std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd,
                                    chain_h[seq_id].data() + (size_t) t * n_embd, row_bytes);
                    }
                } else if (is_mem_shared) {
                    // note: with shared memory (e.g. Gemma4 assistants) we use the same position for all draft tokens
                    // ref: https://github.com/huggingface/transformers/blob/effde20942e3f82a1b97449f60b3a48c5ff96145/docs/source/en/model_doc/gemma4_assistant.md?plain=1#L36-L37
-                    common_batch_add(batch, id, dp.pos0, { seq_id }, true);
+                    common_batch_add(batch, id, dp.n_past, { seq_id }, true);
                    std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd, h_row, row_bytes);
                } else {
-                    common_batch_add(batch, id, dp.pos0 + i + 1, { seq_id }, true);
+                    common_batch_add(batch, id, dp.n_past + i + 1, { seq_id }, true);
                    std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd, h_row, row_bytes);
                }

@@ -1776,7 +1726,7 @@ struct common_speculative_impl_ngram_simple : public common_speculative_impl {
    common_speculative_impl_ngram_simple(
            const common_params_speculative & params, uint32_t n_seq,
            common_ngram_simple_config config)
-        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE, n_seq, params.ngram_simple.size_m)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE, n_seq)
        , params(params.ngram_simple)
        , config(config)
    {
@@ -1820,7 +1770,7 @@ struct common_speculative_impl_ngram_map_k : public common_speculative_impl {
            const common_ngram_map & config,
            uint32_t n_seq)
        : common_speculative_impl(config.key_only ? COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K
-            : COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, n_seq, config.size_value)
+            : COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, n_seq)
    {
        for (uint32_t i = 0; i < n_seq; i++) {
            this->config.push_back(config);
@@ -1891,7 +1841,7 @@ struct common_speculative_impl_ngram_mod : public common_speculative_impl {
    common_speculative_impl_ngram_mod(
            const common_params_speculative & params,
            uint32_t n_seq)
-        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_MOD, n_seq, params.ngram_mod.n_max)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_MOD, n_seq)
        , params(params.ngram_mod)
        , mod(params.ngram_mod.n_match, 4*1024*1024)
        , verbose(std::getenv("LLAMA_TRACE") != nullptr) {
@@ -2067,7 +2017,7 @@ struct common_speculative_impl_ngram_cache : public common_speculative_impl {
            const std::string & path_dynamic,
            bool save_dynamic,
            bool save_static)
-        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_CACHE, n_seq, n_draft)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_NGRAM_CACHE, n_seq)
        , params(params.ngram_cache)
        , n_draft(n_draft)
        , save_dynamic(save_dynamic)
@@ -2188,8 +2138,6 @@ struct common_speculative {

    // which implementaion was used for a given seq_id
    std::vector<common_speculative_impl *> impl_last;
-
-    std::vector<double> synth_probs;
 };

 static common_ngram_map get_common_ngram_map(
@@ -2368,101 +2316,6 @@ int32_t common_speculative_n_max(const common_params_speculative * spec) {
    return n_max;
 }

-int32_t common_speculative_n_max(const common_speculative * spec) {
-    int32_t n_max = 0;
-
-    if (spec == nullptr) {
-        return n_max;
-    }
-
-    for (const auto & impl : spec->impls) {
-        n_max = std::max(n_max, std::max(0, impl->n_max));
-    }
-
-    return n_max;
-}
-
-std::vector<double> common_speculative_synth_rates_resolve(const common_params_speculative * spec, int32_t n_max) {
-    const bool has_length = spec->synth_len != -1.0;
-    const bool has_rates  = !spec->synth_rates.empty();
-
-    if (!has_length && !has_rates) {
-        return {};
-    }
-    if (has_length && has_rates) {
-        throw std::invalid_argument("synthetic acceptance length and rates are mutually exclusive");
-    }
-
-    if (n_max <= 0) {
-        throw std::invalid_argument("synthetic acceptance requires at least one speculative token");
-    }
-
-    if (has_rates) {
-        const auto & rates = spec->synth_rates;
-        if (rates.size() != (size_t) n_max) {
-            throw std::invalid_argument(string_format(
-                    "synthetic acceptance rates must contain %d values, got %zu", n_max, rates.size()));
-        }
-
-        for (size_t i = 0; i < rates.size(); ++i) {
-            if (!std::isfinite(rates[i]) || rates[i] < 0.0 || rates[i] > 1.0) {
-                throw std::invalid_argument("synthetic acceptance rates must be finite and within [0, 1]");
-            }
-            if (i > 0 && rates[i] > rates[i - 1]) {
-                throw std::invalid_argument("synthetic acceptance rates must be monotonically non-increasing");
-            }
-        }
-
-        return rates;
-    }
-
-    const double length = spec->synth_len;
-    const double length_max = (double) n_max + 1.0;
-    if (!std::isfinite(length) || length < 1.0 || length > length_max) {
-        throw std::invalid_argument(string_format(
-                "synthetic acceptance length must be finite and within [1, %.0f]", length_max));
-    }
-
-    double p = 0.0;
-    if (length == length_max) {
-        p = 1.0;
-    } else if (length > 1.0) {
-        double p_min = 0.0;
-        double p_max = 1.0;
-        for (int i = 0; i < 32; ++i) {
-            const double p_mid = 0.5 * (p_min + p_max);
-            double sum = 0.0;
-            double term = p_mid;
-            for (int32_t j = 0; j < n_max; ++j) {
-                sum += term;
-                term *= p_mid;
-            }
-
-            if (sum < length - 1.0) {
-                p_min = p_mid;
-            } else {
-                p_max = p_mid;
-            }
-        }
-        p = 0.5 * (p_min + p_max);
-    }
-
-    std::vector<double> rates;
-    rates.reserve(n_max);
-    double rate = p;
-    for (int32_t i = 0; i < n_max; ++i) {
-        rates.push_back(rate);
-        rate *= p;
-    }
-
-    return rates;
-}
-
-const std::vector<double> & common_speculative_get_synth_probs(const common_speculative * spec) {
-    GGML_ASSERT(spec);
-    return spec->synth_probs;
-}
-
 common_params common_base_params_to_speculative(const common_params & params) {
    const bool has_draft = params.speculative.has_dft();

@@ -2473,22 +2326,11 @@ common_params common_base_params_to_speculative(const common_params & params) {
    result.pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED;

    if (has_draft) {
-        // default to global devices value
-        if (!params_spec.devices.empty()) {
-            result.devices           = params_spec.devices;
-        }
+        result.devices               = params_spec.devices;
        result.model                 = params_spec.mparams;
        result.n_gpu_layers          = params_spec.n_gpu_layers;
        result.tensor_buft_overrides = params_spec.tensor_buft_overrides;

-        // a draft pinned to a single device doesn't need the meta wrapper an inherited -sm tensor would give it
-        // (the device list is null-terminated, so a single device means size 2)
-        const size_t n_devs = std::count_if(params_spec.devices.begin(), params_spec.devices.end(),
-                [](ggml_backend_dev_t d) { return d != nullptr; });
-        if (n_devs == 1) {
-            result.split_mode = LLAMA_SPLIT_MODE_LAYER;
-        }
-
        if (params_spec.cpuparams.n_threads > 0) {
            result.cpuparams.n_threads       = params_spec.cpuparams.n_threads;
            result.cpuparams_batch.n_threads = params_spec.cpuparams_batch.n_threads;
@@ -2726,39 +2568,13 @@ common_speculative * common_speculative_init(common_params_speculative & params,
        return nullptr;
    }

-    common_speculative_ptr result(new common_speculative {
-        /* .dparams     = */ common_speculative_draft_params_vec(n_seq),
-        /* .impls       = */ std::move(impls),
-        /* .impl_last   = */ std::vector<common_speculative_impl *>(n_seq, nullptr),
-        /* .synth_probs = */ {},
-    });
+    auto * result = new common_speculative {
+        /* .dparams   = */ common_speculative_draft_params_vec(n_seq),
+        /* .impls     = */ std::move(impls),
+        /* .impl_last = */ std::vector<common_speculative_impl *>(n_seq, nullptr)
+    };

-    const int32_t n_max_configured = common_speculative_n_max(&params);
-    const int32_t n_max_effective  = common_speculative_n_max(result.get());
-    const auto rates = common_speculative_synth_rates_resolve(&params, n_max_effective);
-
-    std::vector<std::string> rates_str;
-    rates_str.reserve(rates.size());
-    result->synth_probs.reserve(rates.size());
-    double rate_prev = 1.0;
-    double acceptance_length = 1.0;
-    for (const double rate : rates) {
-        result->synth_probs.push_back(rate_prev > 0.0 ? rate / rate_prev : 0.0);
-        rates_str.push_back(string_format("%.6g", rate));
-        rate_prev = rate;
-        acceptance_length += rate;
-    }
-    if (!result->synth_probs.empty()) {
-        SPC_WRN("%s", "synthetic speculative acceptance is enabled for benchmarking; generated output is not valid\n");
-        if (n_max_effective != n_max_configured) {
-            SPC_WRN("synthetic acceptance draft limit was reduced from %d to %d by the initialized speculative implementations\n",
-                    n_max_configured, n_max_effective);
-        }
-        SPC_INF("synthetic acceptance: n_max = %zu, mean length = %.6f, rates = [%s]\n",
-                rates.size(), acceptance_length, string_join(rates_str, ", ").c_str());
-    }
-
-    return result.release();
+    return result;
 }

 void common_speculative_free(common_speculative * spec) {
@@ -26,15 +26,6 @@ std::string common_speculative_type_to_str(enum common_speculative_type type);
 // return the max number of draft tokens based on the speculative parameters
 int32_t common_speculative_n_max(const common_params_speculative * spec);

-// return the max number of draft tokens from the initialized implementations
-int32_t common_speculative_n_max(const common_speculative * spec);
-
-// validate and resolve the unconditional synthetic acceptance rates
-std::vector<double> common_speculative_synth_rates_resolve(const common_params_speculative * spec, int32_t n_max);
-
-// return the conditional synthetic acceptance probabilities
-const std::vector<double> & common_speculative_get_synth_probs(const common_speculative * spec);
-
 common_params common_base_params_to_speculative(const common_params & params);

 struct common_speculative_output_limits {
@@ -61,7 +52,7 @@ struct common_speculative_draft_params {
    // can be used to constraint the max draft based on the remaining context size
    int32_t n_max = -1;

-    llama_pos   pos0;
+    llama_pos   n_past;
    llama_token id_last;

    // TODO: remove in the future by keeping track of the prompt from the _begin() call and the consecutive accept calls
@@ -54,7 +54,6 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "DeepseekV3ForCausalLM": "deepseek",
    "DeepseekV32ForCausalLM": "deepseek",
    "DFlashDraftModel": "qwen",
-    "DFlash2DraftModel": "qwen",
    "Qwen3DSparkModel": "qwen",
    "DSparkDraftModel": "qwen",
    "DSparkSpeculator": "qwen",
@@ -124,7 +123,6 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "HunYuanMoEV1ForCausalLM": "hunyuan",
    "HunYuanVLForConditionalGeneration": "hunyuan",
    "HYV3ForCausalLM": "hunyuan",
-    "HYV4ForCausalLM": "hy_v4",
    "IQuestCoderForCausalLM": "llama",
    "InternLM2ForCausalLM": "internlm",
    "InternLM3ForCausalLM": "internlm",
@@ -189,7 +187,6 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "NanbeigeForCausalLM": "nanbeige",
    "NemotronForCausalLM": "nemotron",
    "NemotronHForCausalLM": "nemotron",
-    "NemotronHPuzzleForCausalLM": "nemotron",
    "NeoBERT": "bert",
    "NeoBERTForSequenceClassification": "bert",
    "NeoBERTLMHead": "bert",
@@ -238,8 +235,6 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "Qwen3_5ForConditionalGeneration": "qwen",
    "Qwen3_5MoeForCausalLM": "qwen",
    "Qwen3_5MoeForConditionalGeneration": "qwen",
-    "Qwen4ExpForCausalLM": "qwen4exp",
-    "Qwen4ExpForConditionalGeneration": "qwen4exp",
    "RND1": "qwen",
    "RWForCausalLM": "falcon",
    "RWKV6Qwen2ForCausalLM": "rwkv",
@@ -255,7 +250,6 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "SeedOssForCausalLM": "olmo",
    "SmallThinkerForCausalLM": "smallthinker",
    "SmolLM3ForCausalLM": "llama",
-    "Spark2_5ForCausalLM": "spark2_5",
    "SolarOpenForCausalLM": "glm",
    "StableLMEpochForCausalLM": "stablelm",
    "StableLmForCausalLM": "stablelm",
@@ -289,7 +283,6 @@ MMPROJ_MODEL_MAP: dict[str, str] = {
    "CogVLMForCausalLM": "cogvlm",
    "DeepseekOCR2ForCausalLM": "deepseek",
    "DeepseekOCRForCausalLM": "deepseek",
-    "DeepseekV4ForCausalLM": "deepseek",
    "Dots3NoteForCausalLM": "dots3",
    "Dots3NoteForConditionalGeneration": "dots3",
    "DotsOCRForCausalLM": "dotsocr",
@@ -339,7 +332,6 @@ MMPROJ_MODEL_MAP: dict[str, str] = {
    "Qwen3VLMoeForConditionalGeneration": "qwen3vl",
    "Qwen3_5ForConditionalGeneration": "qwen3vl",
    "Qwen3_5MoeForConditionalGeneration": "qwen3vl",
-    "Qwen4ExpForConditionalGeneration": "qwen4exp",
    "RADIOModel": "nemotron",
    "Sarashina2VisionForCausalLM": "sarashina2",
    "SmolVLMForConditionalGeneration": "smolvlm",
@@ -130,8 +130,7 @@ class ModelBase:
                 sentence_transformers_dense_modules: bool = False,
                 target_model_dir: Path | None = None,
                 fuse_gate_up_exps: bool = False,
-                 fp8_as_q8: bool = False,
-                 fuse_qkv: bool = False):
+                 fp8_as_q8: bool = False):
        if type(self) is ModelBase or \
                type(self) is TextModel or \
                type(self) is MmprojModel:
@@ -154,15 +153,6 @@ class ModelBase:
        self.fuse_gate_up_exps = fuse_gate_up_exps
        self._gate_exp_buffer: dict[int, Tensor] = {}
        self._up_exp_buffer: dict[int, Tensor] = {}
-        self.fuse_qkv = fuse_qkv
-        self._q_buffer: dict[int, Tensor] = {}
-        self._k_buffer: dict[int, Tensor] = {}
-        self._v_buffer: dict[int, Tensor] = {}
-        self._q_bias_buffer: dict[int, Tensor] = {}
-        self._k_bias_buffer: dict[int, Tensor] = {}
-        self._v_bias_buffer: dict[int, Tensor] = {}
-        self._fusable_qkv_weight_layers: set[int] = set()
-        self._fusable_qkv_bias_layers: set[int] = set()
        self.hparams = ModelBase.load_hparams(self.dir_model, self.is_mistral_format) if hparams is None else hparams
        self.model_tensors = self.index_tensors(remote_hf_model_id=remote_hf_model_id)
        self.metadata_override = metadata_override
@@ -627,43 +617,6 @@ class ModelBase:
            raise ValueError(f"Can not map tensor {name!r}")
        return new_name

-    def prepare_qkv_fusion(self) -> None:
-        self._fusable_qkv_weight_layers.clear()
-        self._fusable_qkv_bias_layers.clear()
-        if not self.fuse_qkv or gguf.MODEL_TENSOR.ATTN_QKV not in gguf.MODEL_TENSORS[self.model_arch]:
-            return
-
-        qkv_types = {
-            gguf.MODEL_TENSOR.ATTN_Q,
-            gguf.MODEL_TENSOR.ATTN_K,
-            gguf.MODEL_TENSOR.ATTN_V,
-        }
-        weights: dict[int, set[gguf.MODEL_TENSOR]] = {}
-        biases: dict[int, set[gguf.MODEL_TENSOR]] = {}
-
-        for name in self.model_tensors:
-            mapped = self.tensor_map.get_type_and_name(name, try_suffixes=(".weight", ".bias"))
-            if mapped is None:
-                continue
-            tensor_type, new_name = mapped
-            if tensor_type not in qkv_types:
-                continue
-
-            bid = next((int(part) for part in new_name.split(".") if part.isdecimal()), None)
-            if bid is None:
-                continue
-            if new_name.endswith(".weight"):
-                weights.setdefault(bid, set()).add(tensor_type)
-            elif new_name.endswith(".bias"):
-                biases.setdefault(bid, set()).add(tensor_type)
-
-        for bid, weight_types in weights.items():
-            bias_types = biases.get(bid, set())
-            if weight_types == qkv_types and (not bias_types or bias_types == qkv_types):
-                self._fusable_qkv_weight_layers.add(bid)
-                if bias_types:
-                    self._fusable_qkv_bias_layers.add(bid)
-
    def set_gguf_parameters(self):
        raise NotImplementedError("set_gguf_parameters() must be implemented in subclasses")

@@ -692,40 +645,6 @@ class ModelBase:
               self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.FFN_UP_EXP, bid):
                return []

-        # Handle Q/K/V tensor fusion if enabled
-        qkv_bid = next((int(part) for part in new_name.split(".") if part.isdecimal()), None) if self.fuse_qkv else None
-        if qkv_bid is not None:
-            is_bias = new_name.endswith('.bias')
-            suffix = '.bias' if is_bias else '.weight'
-            fusable_layers = self._fusable_qkv_bias_layers if is_bias else self._fusable_qkv_weight_layers
-            if qkv_bid not in fusable_layers:
-                return [(new_name, data_torch)]
-
-            buf_q = self._q_bias_buffer if is_bias else self._q_buffer
-            buf_k = self._k_bias_buffer if is_bias else self._k_buffer
-            buf_v = self._v_bias_buffer if is_bias else self._v_buffer
-
-            if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.ATTN_Q, qkv_bid, suffix):
-                buf_q[qkv_bid] = data_torch
-            elif self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.ATTN_K, qkv_bid, suffix):
-                buf_k[qkv_bid] = data_torch
-            elif self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.ATTN_V, qkv_bid, suffix):
-                buf_v[qkv_bid] = data_torch
-
-            if qkv_bid in buf_q and qkv_bid in buf_k and qkv_bid in buf_v:
-                q_data = buf_q.pop(qkv_bid)
-                k_data = buf_k.pop(qkv_bid)
-                v_data = buf_v.pop(qkv_bid)
-                fused_data = torch.cat([q_data, k_data, v_data], dim=0)
-                fused_name = self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_QKV, qkv_bid, suffix=suffix)
-                logger.info(f"Fused Q, K, V {suffix[1:]} into QKV for layer {qkv_bid}")
-                return [(fused_name, fused_data)]
-
-            if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.ATTN_Q, qkv_bid, suffix) or \
-               self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.ATTN_K, qkv_bid, suffix) or \
-               self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.ATTN_V, qkv_bid, suffix):
-                return []
-
        return [(new_name, data_torch)]

    def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
@@ -980,8 +899,6 @@ class ModelBase:

        self.dequant_model()

-        self.prepare_qkv_fusion()
-
        # Handle empty tensor_map for models with block_count=0 (like MobileNetV5)
        if self.tensor_map.mapping:
            max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
@@ -1089,16 +1006,12 @@ class ModelBase:
                    else:
                        raise ValueError(f"Unknown file type: {self.ftype.name}")

-                # a chunked tensor quantizes as one chunk at a time, while it is written
-                quantize = data.quantize if isinstance(data, gguf.LazyChunkedTensor) else (
-                    lambda qtype, d=data: gguf.quants.quantize(d, qtype))
-
                try:
-                    data = quantize(data_qtype)
+                    data = gguf.quants.quantize(data, data_qtype)
                except gguf.QuantError as e:
                    logger.warning("%s, %s", e, "falling back to F16")
                    data_qtype = gguf.GGMLQuantizationType.F16
-                    data = quantize(data_qtype)
+                    data = gguf.quants.quantize(data, data_qtype)

                shape = gguf.quant_shape_from_byte_shape(data.shape, data_qtype) if data.dtype == np.uint8 else data.shape

@@ -1110,13 +1023,6 @@ class ModelBase:

                self.gguf_writer.add_tensor(new_name, data, raw_dtype=data_qtype)

-        qkv_buffers = (
-            self._q_buffer, self._k_buffer, self._v_buffer,
-            self._q_bias_buffer, self._k_bias_buffer, self._v_bias_buffer,
-        )
-        if any(qkv_buffers):
-            raise ValueError("QKV fusion did not consume all buffered tensors")
-
    def set_type(self):
        self.gguf_writer.add_type(gguf.GGUFType.MODEL)

@@ -1597,9 +1503,6 @@ class TextModel(ModelBase):
        if chkhsh == "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6":
            # ref: https://huggingface.co/tencent/Hunyuan-4B-Instruct
            res = "hunyuan-dense"
-        if chkhsh == "e6ddf9c6686791c12d698d34c31ab9be1fea9af5a3d9a6909783ab382198ae1c":
-            # ref: https://huggingface.co/tencent/Hy4-preview
-            res = "hy_v4"
        if chkhsh == "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6":
            # ref: https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base
            res = "falcon-h1"
@@ -1633,9 +1536,6 @@ class TextModel(ModelBase):
        if chkhsh == "9e454714343b69b99b71795c1d27a68c2a1d15dab111f4d353109f966af29da7":
            # ref: https://huggingface.co/LiquidAI/LFM2.5-8B-A1B
            res = "lfm2"
-        if chkhsh == "0a766d034107bc736a3f2dc4968fd62e54a3570f1454443e0c5a4cc6bd7941ed":
-            # ref: https://huggingface.co/XHToken/Spark-X2.5-1.7B
-            res = "spark2_5"
        if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
            # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
            res = "llama-bpe"
@@ -578,8 +578,6 @@ class DeepseekV4Model(TextModel):
    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item
-        if name.startswith(("aligner.", "image_")):
-            return None
        if name.startswith("mtp."):
            if not cls.mtp_only:
                cls._skipped_mtp_tensors += 1
@@ -855,7 +853,6 @@ class DeepseekV4Model(TextModel):
            "ffn_norm.weight": (gguf.MODEL_TENSOR.FFN_NORM, ".weight"),
            "ffn.gate.weight": (gguf.MODEL_TENSOR.FFN_GATE_INP, ".weight"),
            "ffn.gate.bias": (gguf.MODEL_TENSOR.FFN_EXP_PROBS_B, ".bias"),
-            "ffn.gate.bias_vl": (gguf.MODEL_TENSOR.FFN_EXP_PROBS_B_VL, ".bias"),
            "ffn.gate.tid2eid": (gguf.MODEL_TENSOR.FFN_GATE_TID2EID, ".weight"),
            "ffn.shared_experts.w1.weight": (gguf.MODEL_TENSOR.FFN_GATE_SHEXP, ".weight"),
            "ffn.shared_experts.w2.weight": (gguf.MODEL_TENSOR.FFN_DOWN_SHEXP, ".weight"),
@@ -881,10 +878,6 @@ class DeepseekV4Model(TextModel):
        if re.match(r"layers\.\d+\.ffn\.experts\.\d+\.w[123]\.(weight|scale)$", name):
            return []

-        # hash layers route text tokens via tid2eid and image tokens via bias_vl; gate.bias is unused
-        if name.endswith(".ffn.gate.bias") and bid is not None and bid < self.hparams["num_hash_layers"]:
-            return []
-
        tensor_key, suffix = self._map_dsv4_tensor_name(name, bid)
        if tensor_key == gguf.MODEL_TENSOR.FFN_GATE_TID2EID:
            return []
@@ -1007,13 +1000,6 @@ class DeepseekV4DSparkModel(DeepseekV4Model):
            return self._DSPARK_ROOT_MAP[name]
        return super()._map_dsv4_tensor_name(name, bid)

-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # the DFlash draft uses the plain exp-probs bias (ffn.gate.bias -> FFN_EXP_PROBS_B);
-        # the mtmd-only hash routing tensors (bias_vl, tid2eid) are not part of the DFLASH arch
-        if name.endswith(".ffn.gate.bias_vl"):
-            return
-        yield from super().modify_tensors(data_torch, name, bid)
-
    def set_vocab(self):
        if self.target_model_dir is None:
            raise ValueError("DeepSeek-V4 DSpark requires --target-model-dir with the target tokenizer")
@@ -1032,73 +1018,3 @@ class DeepseekV4DSparkModel(DeepseekV4Model):

        self.gguf_writer.add_block_size(self.hparams["dspark_block_size"])
        self.gguf_writer.add_target_layers([layer + 1 for layer in self.hparams["dspark_target_layer_ids"]])
-
-
-@ModelBase.register("DeepseekV4ForCausalLM")
-@ModelBase.example("deepseek-ai/DeepSeek-V4-Flash-Vision-Exp")
-class DeepseekV4FlashVisionModel(MmprojModel):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        assert self.hparams_vision is not None
-        # no preprocessor_config.json in the repo; normalization is (x/255 - 0.5) / 0.5
-        # ref: inference/image_processor.py (load_image)
-        self.preprocessor_config = {
-            "image_mean": [0.5, 0.5, 0.5],
-            "image_std":  [0.5, 0.5, 0.5],
-            **self.preprocessor_config,
-        }
-
-    def get_vision_config(self) -> dict[str, Any] | None:
-        cfg = self.global_config
-        if cfg.get("vision_n_layers", 0) == 0:
-            raise ValueError("DeepseekV4FlashVisionModel requires vision_n_layers > 0 in the model config")
-        return {
-            "num_hidden_layers":   cfg["vision_n_layers"],
-            "hidden_size":         cfg["vision_dim"],
-            "num_attention_heads": cfg["vision_n_heads"],
-            "intermediate_size":   cfg["vision_inter_dim"],
-            "patch_size":          cfg["vision_patch_size"],
-            # dynamic resolution; only used for compat / warmup
-            "image_size":          cfg["vision_patch_size"] * cfg["vision_downsample_ratio"] * 16,
-            "rope_theta":          cfg.get("vision_rope_theta", 10000.0),
-            "downsample_ratio":    cfg["vision_downsample_ratio"],
-            "min_pixels":          cfg["vision_min_pixels"],
-        }
-
-    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.DEEPSEEK4V)
-        # vision RMSNorm eps is the pytorch default, NOT the LLM's rms_norm_eps (1e-20)
-        # ref: inference/vision.py (RMSNorm)
-        self.gguf_writer.add_vision_attention_layernorm_eps(1e-6)
-        self.gguf_writer.add_vision_use_silu(True) # SwiGLU MLP
-        self.gguf_writer.add_vision_projector_scale_factor(self.hparams_vision["downsample_ratio"])
-        self.gguf_writer.add_vision_min_pixels(self.hparams_vision["min_pixels"])
-        # hardcoded on the C++ side (see PROJECTOR_TYPE_DEEPSEEK4V in clip.cpp)
-        # if future models use different values, add GGUF keys for those
-        assert self.global_config["vision_max_n_token"] == 384
-        assert self.global_config["vision_max_wh_ratio"] == 8
-
-    @classmethod
-    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
-        name, _ = item
-        if not (name.startswith(("vision.", "aligner.", "image_"))):
-            return None
-        return super().filter_tensors(item)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        assert self.hparams_vision is not None
-        if name == "vision.patch_embed.proj.weight":
-            # nn.Linear over flattened (3, p, p) patches == conv2d weight
-            p = self.hparams_vision["patch_size"]
-            data_torch = data_torch.reshape(data_torch.shape[0], 3, p, p)
-
-        if ".mlp.w1." in name:
-            # fused SwiGLU gate+up
-            gate, up = data_torch.chunk(2, dim=0)
-            yield from super().modify_tensors(gate, name.replace("w1", "w1_gate"), bid)
-            yield from super().modify_tensors(up, name.replace("w1", "w1_up"), bid)
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
@@ -1,244 +0,0 @@
-from __future__ import annotations
-
-import re
-from typing import Iterable
-
-import torch
-
-from .base import ModelBase, gguf, logger
-from .deepseek import DeepseekV2Model
-
-
-def split_gate_up(weight: torch.Tensor, moe_intermediate_size: int):
-    """Split a fused stacked gate_up expert tensor into (gate, up).
-
-    weight: [n_expert, 2*moe_intermediate_size, hidden] (gate first, up second).
-    Returns (gate, up) each [n_expert, moe_intermediate_size, hidden].
-    """
-    assert weight.shape[1] == 2 * moe_intermediate_size, f"{weight.shape[1]} != 2*{moe_intermediate_size}"
-    gate = weight[:, :moe_intermediate_size, :].contiguous()
-    up = weight[:, moe_intermediate_size:, :].contiguous()
-    return gate, up
-
-
-@ModelBase.register("HYV4ForCausalLM")
-@ModelBase.example("tencent/Hy4-preview")
-class HYV4Model(DeepseekV2Model):
-    """HY_V4: DeepSeek-V3 style MLA + MoE with iHC, a gated MLA output and a learnable sink.
-
-    Reuses DeepseekV2Model for the vocab and the MLA metadata, but overrides the tensor mapping
-    because HY_V4 ships pre-stacked / fused experts plus extra iHC, gate and sink tensors. The
-    rope rows are mapped straight through (no permute) - the graph rotates consecutive pairs.
-
-    DSA is supported: indexer weights are exported for the layers marked "full" in indexer_types.
-    "shared" layers reuse the top-k of the last preceding full layer at inference time, so they
-    carry no indexer weights.
-
-    MTP (num_nextn_predict_layers) is dropped, so the GGUF cannot be used for speculative
-    decoding. The reference only runs the MTP layers while training or while speculating, so they
-    cannot change single-token logits.
-    """
-
-    model_arch = gguf.MODEL_ARCH.HY_V4
-
-    merge_expert = False
-
-    # tensors a "full" indexer layer must carry
-    INDEXER_SUFFIXES = frozenset({
-        "self_attn.indexer.wq_b.weight",
-        "self_attn.indexer.wk.weight",
-        "self_attn.indexer.k_norm.weight",
-        "self_attn.indexer.k_norm.bias",
-        "self_attn.indexer.weights_proj.weight",
-    })
-
-    @classmethod
-    def filter_tensors(cls, item):
-        # drop MTP here, not in modify_tensors, so the weights are never read
-        if item[0].startswith("model.mtp_layers."):
-            return None
-        return super().filter_tensors(item)
-
-    def _check_indexer_hparams(self):
-        for key in ("index_n_heads", "index_head_dim", "index_topk"):
-            if key not in self.hparams:
-                raise ValueError(f"HY_V4 has DSA layers but no {key}")
-
-    def indexer_is_full(self) -> list[bool] | None:
-        """Per-layer indexer ownership, or None when the checkpoint has no DSA.
-
-        indexer_types entries are "full" (owns an indexer) or "shared" (reuses the preceding
-        full layer's top-k). Missing indexer_types with sparse layers means every sparse layer
-        owns one.
-        """
-        hparams = self.hparams
-        n_layer = hparams["num_hidden_layers"]
-        indexer_types = hparams.get("indexer_types")
-
-        # the reference drives DSA off indexer_types alone; layer_types is only a fallback for
-        # checkpoints predating it (it was renamed to deepseek_sparse_attention upstream)
-        if indexer_types is None:
-            layer_types = hparams.get("layer_types") or []
-            sparse = {"sparse_attention", "deepseek_sparse_attention"}
-            if not any(t in sparse for t in layer_types):
-                return None
-            if len(layer_types) < n_layer:
-                raise ValueError(f"HY_V4 layer_types has {len(layer_types)} entries, need {n_layer}")
-            self._check_indexer_hparams()
-            return [t in sparse for t in layer_types[:n_layer]]
-
-        self._check_indexer_hparams()
-
-        if len(indexer_types) < n_layer:
-            raise ValueError(f"HY_V4 indexer_types has {len(indexer_types)} entries, need {n_layer}")
-        unknown = {t for t in indexer_types[:n_layer]} - {"full", "shared"}
-        if unknown:
-            raise ValueError(f"HY_V4 unknown indexer_types values: {sorted(unknown)}")
-        is_full = [t == "full" for t in indexer_types[:n_layer]]
-        if is_full and not is_full[0]:
-            raise ValueError("HY_V4 layer 0 must be indexer_types 'full' (nothing precedes it to share)")
-        return is_full
-
-    def set_gguf_parameters(self):
-        hparams = self.hparams
-
-        # HY4 has n_group == topk_group == 1 (no group routing). Drop the keys so the base does
-        # not emit expert_group_count/used; llama.cpp then takes the ungrouped MoE path.
-        if hparams.get("n_group") == 1 and hparams.get("topk_group") == 1:
-            hparams.pop("n_group", None)
-            hparams.pop("topk_group", None)
-
-        # HY_V4 config expresses dense/sparse layers via mlp_layer_types, but DeepseekV2Model
-        # needs first_k_dense_replace. Derive it as the contiguous leading "dense" block
-        # (the real config.json also carries first_k_dense_replace; prefer it when present,
-        # but assert the two agree so a mismatch fails loudly).
-        mlp_types = hparams.get("mlp_layer_types")
-        explicit = hparams.get("first_k_dense_replace")
-        derived = None
-        if mlp_types is not None:
-            lead = 0
-            for t in mlp_types:
-                if t == "dense":
-                    lead += 1
-                else:
-                    break
-            if any(t == "dense" for t in mlp_types[lead:]):
-                raise NotImplementedError("HY_V4 converter expects a contiguous leading dense block")
-            derived = lead
-        if explicit is not None and derived is not None and explicit != derived:
-            raise ValueError(
-                f"HY_V4 first_k_dense_replace ({explicit}) disagrees with mlp_layer_types "
-                f"leading-dense count ({derived})"
-            )
-        if explicit is None:
-            if derived is None:
-                raise ValueError("HY_V4 needs first_k_dense_replace or mlp_layer_types to place dense layers")
-            hparams["first_k_dense_replace"] = derived
-
-        # reuse DeepseekV2 MLA + MoE metadata (forces num_key_value_heads=1, writes q/kv lora,
-        # key/value lengths, expert counts, weights scale/norm, rope dims, etc.)
-        super().set_gguf_parameters()
-
-        # HY4 uses DeepSeek-V3 sigmoid routing with e_score_correction_bias. The config has no
-        # scoring_func key, so the base does not write a gating func; set it explicitly.
-        self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
-
-        # routed-expert SwiGLU logits clamp (only routed experts; shared/dense are not clamped,
-        # so swiglu_clamp_shexp is intentionally not written). 0.0 disables the clamp.
-        swiglu_limit = float(hparams.get("swiglu_limit", 0.0) or 0.0)
-        if swiglu_limit > 0.0:
-            self.gguf_writer.add_swiglu_clamp_exp([swiglu_limit] * self.block_count)
-
-        # iHC (independent Hyper-Connections)
-        self.gguf_writer.add_hyper_connection_count(hparams["hc_mult"])
-        self.gguf_writer.add_hyper_connection_epsilon(hparams["hc_eps"])
-        self.gguf_writer.add_hyper_connection_magnitude(hparams["hc_magnitude"])
-
-        # is_full is written explicitly; the graph must not infer it from tensor presence
-        is_full = self.indexer_is_full()
-        if is_full is not None:
-            self.gguf_writer.add_indexer_head_count(hparams["index_n_heads"])
-            self.gguf_writer.add_indexer_key_length(hparams["index_head_dim"])
-            self.gguf_writer.add_indexer_top_k(hparams["index_topk"])
-            self.gguf_writer.add_indexer_types(is_full)
-            logger.info(
-                "HY_V4 DSA: %d/%d layers own an indexer (top_k=%d, n_heads=%d, head_dim=%d)",
-                sum(is_full), len(is_full), hparams["index_topk"],
-                hparams["index_n_heads"], hparams["index_head_dim"],
-            )
-
-        if hparams.get("num_nextn_predict_layers", 0):
-            logger.warning(
-                "HY_V4: dropping %d MTP (nextn) layer(s) - the reference runs them only under "
-                "training / speculative decoding. This GGUF cannot be used for speculative decoding.",
-                hparams["num_nextn_predict_layers"],
-            )
-
-    def prepare_tensors(self):
-        # Hy4-preview for some reason has num_key_value_heads equal to 8, so override it here
-        # without this conversion/deepseek.py fails on assert
-        self.hparams["num_key_value_heads"] = self.hparams["num_attention_heads"]
-
-        # validate before the base materializes tensors, so a mismatch fails early
-        is_full = self.indexer_is_full()
-        if is_full is not None:
-            present: dict[int, set[str]] = {}
-            for name in self.model_tensors:
-                m = re.match(r"model\.layers\.(\d+)\.(self_attn\.indexer\..+)$", name)
-                if m:
-                    present.setdefault(int(m.group(1)), set()).add(m.group(2))
-            for il, expect_full in enumerate(is_full):
-                seen = present.get(il, set())
-                if expect_full and seen != self.INDEXER_SUFFIXES:
-                    raise ValueError(
-                        f"HY_V4 layer {il} is indexer_types 'full' but is missing indexer tensors: "
-                        f"{sorted(self.INDEXER_SUFFIXES - seen)}"
-                    )
-                if not expect_full and seen:
-                    raise ValueError(
-                        f"HY_V4 layer {il} is indexer_types 'shared' but carries indexer tensors: "
-                        f"{sorted(seen)}"
-                    )
-
-        super().prepare_tensors()
-
-    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        # iHC mixing matrices are 2D .weight tensors that the reference keeps in fp32
-        # (_keep_in_fp32_modules_strict). 1D tensors (hc_base/scale, attn_sinks,
-        # e_score_correction_bias) and the router (FFN_GATE_INP) are already forced F32 by the
-        # base rules. Force the HC *_fn matrices here.
-        if new_name.endswith(("hc_attn_fn.weight", "hc_ffn_fn.weight", "output_hc_fn.weight")):
-            return gguf.GGMLQuantizationType.F32
-        # indexer k_norm is fp32 in the reference; the base rules already cover
-        # *_norm.weight and INDEXER_PROJ, but not this bias
-        if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.INDEXER_K_NORM, bid, suffix=".bias"):
-            return gguf.GGMLQuantizationType.F32
-        # enable_lm_head_fp32: mirror the reference fp32 LM-head matmul by keeping output F32.
-        if new_name == "output.weight" and self.hparams.get("enable_lm_head_fp32", False):
-            return gguf.GGMLQuantizationType.F32
-        return super().tensor_force_quant(name, new_name, bid, n_dims)
-
-    def modify_tensors(self, data_torch: torch.Tensor, name: str, bid: int | None) -> Iterable[tuple[str, torch.Tensor]]:
-        hparams = self.hparams
-        moe_inter = hparams["moe_intermediate_size"]
-
-        tn = self.format_tensor_name
-
-        # fused stacked experts: split gate_up into gate/up
-        if name.endswith("mlp.experts.gate_up_proj"):
-            gate, up = split_gate_up(data_torch, moe_inter)
-            yield from super().modify_tensors(gate, tn(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), bid)
-            yield from super().modify_tensors(up,   tn(gguf.MODEL_TENSOR.FFN_UP_EXP,   bid), bid)
-            return
-
-        # add .weight suffixes
-        if name.endswith("mlp.experts.down_proj") or name.endswith(".self_attn.learnable_sink_param"):
-            name += ".weight"
-
-        if re.search(r"\.hc_head\.hc_head_(?:fn|base|scale)$", name):
-            name += ".weight"
-
-        if re.search(r"\.hc_(?:attn|mlp)_layer\.hc_pre\.hc_(?:fn|base|scale)$", name):
-            name += ".weight"
-
-        yield from super().modify_tensors(data_torch, name, bid)
@@ -25,7 +25,7 @@ class MiniMaxText01Model(TextModel):
        # they get in the way of the token sampling process and must be suppressed

        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
-        tokenizer_vocab_size = tokenizer.vocab_size  # ty: ignore[unresolved-attribute]
+        tokenizer_vocab_size = tokenizer.vocab_size

        with open(self.dir_model / "model.safetensors.index.json", "r", encoding="utf-8") as f:
            weight_map = json.load(f)["weight_map"]
@@ -37,7 +37,7 @@ class MuseGlimmerModel(TextModel):

        from transformers import AutoTokenizer
        tok = AutoTokenizer.from_pretrained(self.dir_model)
-        eot_id = tok.convert_tokens_to_ids("<|eot|>")  # ty: ignore[unresolved-attribute]
+        eot_id = tok.convert_tokens_to_ids("<|eot|>")
        if isinstance(eot_id, int) and eot_id >= 0:
            self.gguf_writer.add_eot_token_id(eot_id)

@@ -5,7 +5,6 @@ from typing import Any, Callable, Iterable, TYPE_CHECKING
 import torch

 if TYPE_CHECKING:
-    from pathlib import Path
    from torch import Tensor

 from .base import MmprojModel, ModelBase, TextModel, gguf, logger
@@ -202,7 +201,6 @@ class NemotronHModel(GraniteHybridModel):
    model_arch = gguf.MODEL_ARCH.NEMOTRON_H
    is_moe: bool = False
    supports_mtp_export = True
-    _experts: list[dict[str, Tensor]] | None = None

    _SSM_LAYER_TYPES = {"mamba", "linear_attention"}
    _ATTN_LAYER_TYPES = {"attention", "full_attention"}
@@ -216,14 +214,14 @@ class NemotronHModel(GraniteHybridModel):
        hparams = kwargs.pop("hparams", None)
        if hparams is None:
            hparams = ModelBase.load_hparams(args[0], self.is_mistral_format)
-        llm_config = {**hparams, **(hparams.get("llm_config") or {})}
-
-        has_moe_params = "num_experts_per_tok" in llm_config
-        layers_block_type = llm_config.get("layers_block_type")
-
+        has_moe_params = (
+            "num_experts_per_tok" in hparams
+            or (isinstance(hparams.get("llm_config"), dict) and "num_experts_per_tok" in hparams["llm_config"])
+        )
        if has_moe_params:
            self.model_arch = gguf.MODEL_ARCH.NEMOTRON_H_MOE
            self.is_moe = True
+        layers_block_type = hparams.get("layers_block_type")
        if layers_block_type is not None:
            hparams["num_hidden_layers"] = len(layers_block_type)

@@ -304,10 +302,6 @@ class NemotronHModel(GraniteHybridModel):
            )
            if not keep:
                return None
-        # PEFT names adapter tensors using model.layers.*, while Nemotron-H checkpoints
-        # and the GGUF tensor map use backbone.layers.*
-        if name.startswith("model.layers.") and ".mixer." in name:
-            name = name.replace("model.layers.", "backbone.layers.", 1)
        return super().filter_tensors((name, gen))

    def prepare_metadata(self, vocab_only: bool):
@@ -515,88 +509,3 @@ class NemotronHModel(GraniteHybridModel):
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")
-
-
-@ModelBase.register("NemotronHPuzzleForCausalLM")
-@ModelBase.example("nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16")
-class NemotronHPuzzleModel(NemotronHModel):
-    """NVIDIA Puzzle: NemotronH with a per-block MoE config (block_configs).
-
-    The checkpoint also ships an MTP draft head (mtp.safetensors). It is skipped
-    here: there is no Puzzle MTP inference path in tree, and the head is laid out
-    by mtp_block_configs rather than the mtp.layers.* form NemotronHModel maps."""
-
-    model_arch = gguf.MODEL_ARCH.NEMOTRON_H_MOE
-    is_moe: bool = True
-    supports_mtp_export = False
-
-    def __init__(self, dir_model: "Path", *args, **kwargs):
-        hparams = dict(kwargs.pop("hparams", None) or ModelBase.load_hparams(dir_model, self.is_mistral_format))
-
-        self.block_configs: list[dict] = hparams["block_configs"]
-        self.n_layer_trunk = len(self.block_configs)
-
-        # block_configs carries the per-block MoE shape, and is the authority on the
-        # block pattern too: the layers_block_type the HF config wrapper computes is
-        # not sized to it.
-        hparams["num_hidden_layers"] = self.n_layer_trunk
-        hparams["layers_block_type"] = [bc["block_type"] for bc in self.block_configs]
-
-        self.model_arch = gguf.MODEL_ARCH.NEMOTRON_H_MOE
-
-        # Bypass NemotronHModel.__init__: it assumes a flat num_experts_per_tok /
-        # moe_intermediate_size and a layers_block_type sized to block_count, neither
-        # of which hold for Puzzle's per-block config.
-        GraniteHybridModel.__init__(self, dir_model, *args, hparams=hparams, **kwargs)
-
-        self.head_dim = self.find_hparam(["head_dim", "attention_head_dim"])
-        self.d_inner = self.find_hparam(["num_heads"]) * self.d_model
-
-        # NemotronHModel.__init__ folds an MTP block into block_count when the
-        # config carries num_nextn_predict_layers; Puzzle's config does, but its
-        # head has a different layout and no inference path, so stay opted out.
-        self._mtp_bid = None
-
-    def set_gguf_parameters(self):
-        GraniteHybridModel.set_gguf_parameters(self)
-
-        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)
-
-        ffn_lengths = [bc.get("moe_intermediate_size") or 0 for bc in self.block_configs]
-        experts_used = [bc.get("num_experts_per_tok") or 0 for bc in self.block_configs]
-
-        self.gguf_writer.add_feed_forward_length(ffn_lengths)
-        self.gguf_writer.add_expert_feed_forward_length(ffn_lengths)
-        self.gguf_writer.add_expert_used_count(experts_used)
-
-        self.gguf_writer.add_expert_shared_feed_forward_length(self.hparams["moe_shared_expert_intermediate_size"])
-        self.gguf_writer.add_expert_count(self.hparams["n_routed_experts"])
-        self.gguf_writer.add_expert_shared_count(self.hparams["n_shared_experts"])
-        self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
-        self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
-        self.gguf_writer.add_expert_group_count(self.hparams["n_group"])
-        self.gguf_writer.add_moe_latent_size(self.hparams["moe_latent_size"])
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # The official BF16 checkpoint (NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16)
-        # names the trunk "model.*" (model.layers.*, model.embeddings, model.norm_f)
-        # where the original release used the NemotronH-style "backbone.*", and spells
-        # the router bias "e_score_correction_bias" instead of "e_score_correction.bias";
-        # normalize so both convert identically.
-        if name.startswith("model."):
-            name = "backbone." + name[len("model."):]
-        if name.endswith("mixer.gate.e_score_correction_bias"):
-            name = name[: -len("e_score_correction_bias")] + "e_score_correction.bias"
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    @classmethod
-    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
-        # Drop the MTP head unconditionally; see the class docstring.
-        if item[0].startswith("mtp."):
-            return None
-        return super().filter_tensors(item)
@@ -379,13 +379,6 @@ class Qwen3NextModel(_QwenMtpMixin, Qwen2MoeModel):
        self.gguf_writer.add_ssm_group_count(self.hparams["linear_num_key_heads"])
        self.gguf_writer.add_ssm_time_step_rank(self.hparams["linear_num_value_heads"])
        self.gguf_writer.add_ssm_inner_size(self.hparams["linear_value_head_dim"] * self.hparams["linear_num_value_heads"])
-        if (layer_types := self.hparams.get("layer_types")) is not None:
-            n_layer = self.hparams["num_hidden_layers"]
-            if len(layer_types) != n_layer:
-                raise ValueError(f"layer_types has {len(layer_types)} entries, expected num_hidden_layers ({n_layer})")
-            recurrent = [t == "linear_attention" for t in layer_types]
-            recurrent += [False] * (self.block_count - n_layer)
-            self.gguf_writer.add_recurrent_layers(recurrent)
        self.gguf_writer.add_full_attention_interval(self.hparams.get("full_attention_interval", 4))
        if (rope_dim := self.hparams.get("head_dim")) is None:
            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
@@ -646,7 +639,7 @@ class Qwen3_5MoeTextModel(_Qwen35MRopeMixin, _LinearAttentionVReorderBase):
    model_arch = gguf.MODEL_ARCH.QWEN35MOE


-@ModelBase.register("DFlashDraftModel", "DFlash2DraftModel")
+@ModelBase.register("DFlashDraftModel")
@ModelBase.example("z-lab/Qwen3.5-9B-DFlash")
 class DFlashModel(Qwen3Model):
    model_arch = gguf.MODEL_ARCH.DFLASH
@@ -685,98 +678,34 @@ class DFlashModel(Qwen3Model):
    def set_gguf_parameters(self):
        super().set_gguf_parameters()

-        dflash_config = self.hparams.get("dflash_config", {})
-        block_size = dflash_config.get("block_size", self.hparams.get("block_size", 16))
+        block_size = self.hparams.get("block_size", 16)
        self.gguf_writer.add_block_size(block_size)
-
-        if "conv_kernel_size" in dflash_config:
-            self.gguf_writer.add_conv_kernel_size(int(dflash_config["conv_kernel_size"]))
-            self.gguf_writer.add_conv_group_size(int(dflash_config["conv_group_size"]))
-            self.gguf_writer.add_selector_rank(int(dflash_config["selector_rank"]))
-            self.gguf_writer.add_selector_top_k(int(dflash_config["selector_top_k"]))
-
-        output_multiplier = dflash_config.get(
-            "output_multiplier", self.hparams.get("output_multiplier")
-        )
-        if output_multiplier is not None:
-            self.gguf_writer.add_logit_scale(float(output_multiplier))
-        softcap = dflash_config.get(
-            "final_logit_softcapping", self.hparams.get("final_logit_softcapping")
-        )
-        if softcap is not None and float(softcap) > 0:
-            self.gguf_writer.add_final_logit_softcapping(float(softcap))
-        embedding_scale = dflash_config.get(
-            "input_embedding_scale", self.hparams.get("input_embedding_scale")
-        )
-        if embedding_scale is not None:
-            self.gguf_writer.add_embedding_scale(float(embedding_scale))
+        dflash_config = self.hparams.get("dflash_config", {})

        target_layer_ids = dflash_config.get("target_layer_ids", [])
        if target_layer_ids:
            extract_layer_ids = [i + 1 for i in target_layer_ids]
            self.gguf_writer.add_target_layers(extract_layer_ids)

-        use_sliding_window = self.hparams.get("use_sliding_window", False) or dflash_config.get("use_swa", False)
-        sliding_window = dflash_config.get("swa_window_size") or self.hparams.get("sliding_window")
+        use_sliding_window = self.hparams.get("use_sliding_window", False)
+        sliding_window = self.hparams.get("sliding_window")
        layer_types = self.hparams.get("layer_types")
        if use_sliding_window and sliding_window and layer_types:
            is_swa = [lt == "sliding_attention" for lt in layer_types]
            self.gguf_writer.add_sliding_window(sliding_window)
            self.gguf_writer.add_sliding_window_pattern(is_swa)

-        causal = self.hparams.get("is_causal")
-        if causal is None:
-            causal = dflash_config.get("causal")
-        if causal is not None:
-            self.gguf_writer.add_causal_attention(bool(causal))
-
-        # M-RoPE target: the draft ropes on the temporal dim only, so write
-        # degenerate sections [n_rot/2, 0, 0, 0]
-        if self._target_uses_mrope():
-            head_dim = self.hparams.get("head_dim") or self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-            self.gguf_writer.add_rope_dimension_sections([head_dim // 2, 0, 0, 0])
-
-    def _target_uses_mrope(self) -> bool:
-        if self.target_model_dir is None:
-            return False
-        with open(self.target_model_dir / "config.json", "r", encoding="utf-8") as f:
-            cfg = json.load(f)
-        cfg = cfg.get("text_config", cfg)
-        rope = cfg.get("rope_parameters") or cfg.get("rope_scaling") or {}
-        return "mrope_section" in rope
-
    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item
        if not name.startswith("model."):
            name = "model." + name
-        if "sink" in name and not name.endswith(".weight"):
-            name += ".weight"
        return super().filter_tensors((name, gen))

-    _ROPE_PERMUTE_SUFFIXES = (
-        "self_attn.q_proj.weight",
-        "self_attn.k_proj.weight",
-        "self_attn.q_norm.weight",
-        "self_attn.k_norm.weight",
-    )
-
    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if name == "model.embed_tokens.weight" and not self.hparams.get("has_embed_tokens", True):
            return

-        # interleaved-rope checkpoints (rope_is_neox_style = false) -> NeoX layout: per head, even dims first then odd
-        if not self.hparams.get("rope_is_neox_style", True) and name.endswith(self._ROPE_PERMUTE_SUFFIXES):
-            head_dim = self.hparams["head_dim"]
-            shape = data_torch.shape
-            data_torch = data_torch.reshape(-1, head_dim // 2, 2, *shape[1:]).transpose(1, 2).reshape(shape)
-
-        if name in (
-            "model.candidate_selector.predecessor_codebook",
-            "model.candidate_selector.successor_codebook",
-        ):
-            name += ".weight"
-
        yield from super().modify_tensors(data_torch, name, bid)


@@ -830,10 +759,6 @@ class DSparkModel(DFlashModel):
        super().set_gguf_parameters()
        self.gguf_writer.add_sample_from_anchor(self._sample_from_anchor)

-        # confidence head is optional: vanilla-markov exports ship without it
-        has_conf = any("confidence_head.proj" in name for name in self.model_tensors)
-        self.gguf_writer.add_has_confidence_head(has_conf)
-
    @classmethod
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        if item[0] == "t2d":  # not used at runtime
@@ -852,7 +777,7 @@ class DSparkModel(DFlashModel):
            self._d2t = data_torch
            return

-        if self._n_vocab_draft == self.hparams["vocab_size"] and name.endswith("lm_head.weight"):
+        if self._n_vocab_draft == self.hparams["vocab_size"] and name.endswith(("embed_tokens.weight", "lm_head.weight")):
            return

        # interleaved-rope checkpoints (rope_is_neox_style = false) -> NeoX layout: per head, even dims first then odd
@@ -276,10 +276,6 @@ class Qwen3TTSSpeakerEncoderModel(MmprojModel):
        # ConvTranspose1d kernels: only F16/F32 are implemented, no BF16
        if new_name.endswith(".conv.weight") and (".up.blk." in new_name or ".dac.blk." in new_name):
            return gguf.GGMLQuantizationType.F32
-        # the code predictor FFN intermediate peaks around 1.5e5, above the F16 range, and mul_mat
-        # casts its input to the weight type
-        if new_name.startswith("a.gen.code.blk.") and new_name.endswith(".ffn_down.weight"):
-            return gguf.GGMLQuantizationType.F32
        return super().tensor_force_quant(name, new_name, bid, n_dims)

    @classmethod
@@ -1,195 +0,0 @@
-from __future__ import annotations
-
-from typing import Iterable, cast
-
-import torch
-from torch import Tensor
-
-import gguf
-import numpy as np
-
-from .base import ModelBase
-from .qwen import _LinearAttentionVReorderBase, _Qwen35MRopeMixin
-from .qwen3vl import Qwen3VLVisionModel
-
-
-@ModelBase.register("Qwen4ExpForConditionalGeneration", "Qwen4ExpForCausalLM")
-@ModelBase.example("Qwen/Qwen3.8-Flash-Next")
-class Qwen4ExpTextModel(_Qwen35MRopeMixin, _LinearAttentionVReorderBase):
-    """Qwen3.8-Flash-Next.
-
-    Shares the Qwen3.5 gated delta net and interleaved mrope, and adds three things:
-    hyper-connections in place of every layer norm, QSA sparse attention on the full
-    attention layers, and PLE n-gram hash embeddings on a single layer.
-    """
-
-    model_arch = gguf.MODEL_ARCH.QWEN4EXP
-
-    # the MTP block is a separate draft head; vLLM drops it too
-    supports_mtp_export = False
-    no_mtp = True
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        # only the shard names, so the table itself is never held
-        self._ple_shards: dict[int, str] = {}
-        self._ple_row_dim: int | None = None
-
-    def _read_hash_constants(self, suffix: str) -> list[int]:
-        """Read an int64 PLE constant straight from the checkpoint.
-
-        prepare_tensors() casts every non-float dtype to float32 before
-        modify_tensors() sees it (base.py), which would silently round these
-        45-bit multipliers. Reading the lazy tensor here bypasses that.
-        """
-        for name, gen in self.model_tensors.items():
-            if name.endswith(suffix):
-                t = gen()
-                if t.dtype != torch.int64:
-                    t = t.to(torch.int64)
-                return [int(x) for x in t.tolist()]
-        raise ValueError(f"PLE constant {suffix!r} missing from the checkpoint")
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hp = self.hparams
-
-        self.gguf_writer.add_hyper_connection_count(hp["hc_count"])
-        self.gguf_writer.add_hyper_connection_low_rank(hp["hc_lowrank"])
-
-        n_layer = hp["num_hidden_layers"]
-        self.gguf_writer.add_indexer_head_count(hp["indexer_n_heads"])
-        self.gguf_writer.add_indexer_key_length(hp["indexer_head_dim"])
-        self.gguf_writer.add_indexer_top_k(hp["indexer_budget"])
-        ratio = hp["indexer_compress_ratio"]
-        layer_types = hp["layer_types"]
-        self.gguf_writer.add_attention_compress_ratios(
-            [ratio if layer_types[i] == "full_attention" else 0 for i in range(n_layer)]
-        )
-
-        # ple_layer_ids is 1-based in the HF config; empty means no n-gram table,
-        # so emit no PLE keys rather than optional ones
-        ple_layers = [i - 1 for i in hp["ple_layer_ids"]]
-        if not ple_layers:
-            return
-        self.gguf_writer.add_ple_layers(ple_layers)
-        self.gguf_writer.add_ple_ngram_size(hp["ngram_size"])
-        self.gguf_writer.add_ple_heads_per_ngram(hp["heads_per_ngram"])
-        self.gguf_writer.add_ple_conv_kernel(hp["ple_conv_kernel_size"])
-        self.gguf_writer.add_ple_eos_token_id(self._eos_token_id())
-        # an image is decoded as an embeddings-only batch, so the graph has no placeholder
-        # ids to hash; carry the id and let it stand in for those positions
-        _img = self._image_token_id()
-        if _img is not None:
-            self.gguf_writer.add_ple_image_token_id(int(_img))
-        if self._ple_row_dim is not None:
-            self.gguf_writer.add_embedding_length_per_layer_input(self._ple_row_dim)
-
-        self.gguf_writer.add_ple_layer_multipliers(
-            self._read_hash_constants("ple_embedding.layer_multipliers"))
-        self.gguf_writer.add_ple_head_offsets(
-            self._read_hash_constants("ple_embedding.ngram_heads_offsets"))
-        self.gguf_writer.add_ple_head_vocab_sizes(
-            self._read_hash_constants("ple_embedding.ngram_heads_vocab_sizes"))
-
-    def _image_token_id(self) -> int | None:
-        img = self.hparams.get("image_token_id")
-        return None if img is None else int(img)
-
-    def _eos_token_id(self) -> int:
-        eos = self.hparams.get("eos_token_id")
-        if isinstance(eos, list):
-            # the PLE hash resets n-grams on the primary EOS
-            return int(eos[-1])
-        if eos is None:
-            raise ValueError("eos_token_id is required: the PLE hash resets its n-grams on it")
-        return int(eos)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # int64 hash constants must stay exact; 1-D tensors force F32, so use KV
-        if name.endswith("ple_embedding.layer_multipliers"):
-            self._ple_multipliers = [int(x) for x in data_torch.tolist()]
-            return []
-        if name.endswith("ple_embedding.ngram_heads_offsets"):
-            self._ple_head_offsets = [int(x) for x in data_torch.tolist()]
-            return []
-        if name.endswith("ple_embedding.ngram_heads_vocab_sizes"):
-            self._ple_head_vocab_sizes = [int(x) for x in data_torch.tolist()]
-            return []
-
-        if ".ngram_embedding.shard_" in name:
-            return self._place_ple_shard(data_torch, name)
-
-        # one projection feeds indexer q and k; split it, as minimax-m3 does
-        if ".indexer.index_qk_proj.weight" in name:
-            n_q = self.hparams["indexer_n_heads"] * self.hparams["indexer_head_dim"]
-            q = data_torch[:n_q]
-            k = data_torch[n_q:]
-            return [
-                (self.format_tensor_name(gguf.MODEL_TENSOR.INDEXER_Q_PROJ, bid, ".weight"), q),
-                (self.format_tensor_name(gguf.MODEL_TENSOR.INDEXER_K_PROJ, bid, ".weight"), k),
-            ]
-
-        # Gemma zero-centred gammas the inherited norm.weight rule misses
-        if name.endswith((".ple.norm_key.weight", ".ple.norm_query.weight", ".ple.norm_conv.weight",
-                          ".indexer.q_layernorm.weight", ".indexer.k_layernorm.weight")):
-            return [(self.map_tensor_name(name), data_torch + 1)]
-
-        if name.endswith(".ple.conv1d.weight"):
-            return [(self.map_tensor_name(name), data_torch.squeeze())]
-
-        return super().modify_tensors(data_torch, name, bid)
-
-    # the shards concatenate into a tensor of well over 100 GB
-    # use LazyChunkedTensor here, a single shard resident at a time
-    def _place_ple_shard(self, data_torch: Tensor, name: str) -> Iterable[tuple[str, Tensor]]:
-
-        idx = int(name.rpartition(".shard_")[2].partition(".")[0])
-        n_parts = self.hparams["split_ngram_parts"]
-
-        self._ple_shards[idx] = name
-        self._ple_row_dim = int(data_torch.shape[-1])
-
-        if len(self._ple_shards) < n_parts:
-            return []
-
-        # the checkpoint may yield the shards in any order, the row order is by index
-        shards = [self._ple_shards[i] for i in sorted(self._ple_shards)]
-        rows = 0
-        for shard in shards:
-            shape = self.model_tensors[shard]().shape
-            if int(shape[-1]) != self._ple_row_dim:
-                raise ValueError(
-                    f"PLE shard {shard} has row dim {int(shape[-1])}, expected {self._ple_row_dim}")
-            rows += int(shape[0])
-
-        table = gguf.LazyChunkedTensor(
-            [self._load_ple_shard(shard) for shard in shards],
-            shape=(rows, self._ple_row_dim),
-            dtype=np.float32,
-        )
-        gguf_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.PER_LAYER_TOKEN_EMBD]
-        return [(gguf_name + ".weight", cast(Tensor, table))]
-
-    def _load_ple_shard(self, name: str):
-        def load() -> np.ndarray:
-            from .base import LazyTorchTensor
-
-            # a fresh lazy tensor every call, or to_eager() memoizes every shard
-            eager = LazyTorchTensor.to_eager(self.model_tensors[name]())
-            return eager.to(torch.float32).contiguous().numpy()
-        return load
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-        n_parts = self.hparams.get("split_ngram_parts", 0)
-        if self._ple_shards and len(self._ple_shards) != n_parts:
-            raise ValueError(
-                f"got {len(self._ple_shards)} PLE embedding shards, expected {n_parts}"
-            )
-
-
-@ModelBase.register("Qwen4ExpForConditionalGeneration")
-@ModelBase.example("Qwen/Qwen3.8-Flash-Next")
-class Qwen4ExpVisionModel(Qwen3VLVisionModel):
-    """The vision tower is an unmodified Qwen3-VL ViT."""
@@ -1,65 +0,0 @@
-from __future__ import annotations
-
-from collections.abc import Iterable
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from torch import Tensor
-
-from .base import ModelBase, TextModel, gguf
-
-
-@ModelBase.register("Spark2_5ForCausalLM")
-@ModelBase.example("XHToken/Spark-X2.5-1.7B")
-class Spark2_5Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.SPARK2_5
-
-    def set_gguf_parameters(self) -> None:
-        super().set_gguf_parameters()
-
-        hparams = self.hparams
-        layer_types = hparams["layer_types"]
-        if len(layer_types) != self.block_count:
-            raise ValueError(
-                f"Spark2_5 layer_types length {len(layer_types)} != num_hidden_layers {self.block_count}"
-            )
-        if any(layer_type not in ("sliding_attention", "full_attention") for layer_type in layer_types):
-            raise ValueError(f"Spark2_5 has unsupported layer_types: {layer_types}")
-        if hparams.get("gate_attn_act_mode") != "sigmoid" or hparams.get("headwise_attn_output_gate") is not True:
-            raise ValueError("Spark2_5 conversion requires head-wise sigmoid attention gates")
-        if hparams.get("hidden_act") != "gelu":
-            raise ValueError(f"Spark2_5 conversion requires GELU, got {hparams.get('hidden_act')!r}")
-
-        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
-        self.gguf_writer.add_sliding_window(hparams["sliding_window"])
-        self.gguf_writer.add_sliding_window_pattern(
-            [layer_type == "sliding_attention" for layer_type in layer_types]
-        )
-
-        head_dim = hparams["head_dim"]
-        full_rope = self.rope_parameters["full_attention"]
-        swa_rope = self.rope_parameters["sliding_attention"]
-        self.gguf_writer.add_rope_dimension_count(
-            int(head_dim * float(full_rope["partial_rotary_factor"]))
-        )
-        self.gguf_writer.add_rope_dimension_count_swa(
-            int(head_dim * float(swa_rope["partial_rotary_factor"]))
-        )
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.endswith(".self_attn.q_k_v_proj.weight"):
-            if bid is None:
-                raise ValueError(f"Spark2_5 fused QKV tensor has no block id: {name}")
-            yield self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_QKV, bid), data_torch
-            return
-
-        if name.endswith(".self_attn.g_proj.weight"):
-            if bid is None:
-                raise ValueError(f"Spark2_5 attention gate tensor has no block id: {name}")
-            expected = self.hparams["num_attention_heads"]
-            if data_torch.shape[0] != expected:
-                raise ValueError(
-                    f"Spark2_5 layer {bid} attention gate width {data_torch.shape[0]} != head count {expected}"
-                )
-
-        yield from super().modify_tensors(data_torch, name, bid)
@@ -157,10 +157,6 @@ def parse_args() -> argparse.Namespace:
        help="Store tensors dequantized from FP8 as Q8_0 instead of BF16/F16.",
    )

-    parser.add_argument(
-        "--fuse-qkv", action="store_true",
-        help="Fuse separate Q, K, V weight tensors into a single QKV tensor.",
-    )
    parser.add_argument(
        "--target-model-dir", type=str, default=None,
        help=(
@@ -294,7 +290,6 @@ def main() -> None:
                                     target_model_dir=Path(args.target_model_dir) if args.target_model_dir else None,
                                     fuse_gate_up_exps=args.fuse_gate_up_exps,
                                     fp8_as_q8=args.fp8_as_q8,
-                                     fuse_qkv=args.fuse_qkv,
                                     )

        if args.vocab_only:
@@ -176,7 +176,6 @@ pre_computed_hashes = [
    {"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", "chkhsh": "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35"},
    {"name": "hunyuan", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-A13B-Instruct", "chkhsh": "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664"},
    {"name": "hunyuan-dense", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-4B-Instruct", "chkhsh": "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6"},
-    {"name": "hy_v4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hy4-preview", "chkhsh": "e6ddf9c6686791c12d698d34c31ab9be1fea9af5a3d9a6909783ab382198ae1c"},
    # falcon-h1 series uses 4 different tokenizers across model sizes (0.5b - 34b), hence we need to define 4 different hashes
    {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base", "chkhsh": "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6"},
    {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-1B-Base", "chkhsh": "60476e1243776c4fb1b993dbd7a5f15ac22f83c80afdf425fa5ae01c8d44ef86"},
@@ -191,7 +190,6 @@ pre_computed_hashes = [
    {"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/evilfreelancer/ruGPT3XL", "chkhsh": "0fe1cf6eda062318a1af7270f3331a85c539a01778ff948e24388e949c5282f4"},
    # lfm2 variants
    {"name": "lfm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LiquidAI/LFM2.5-8B-A1B", "chkhsh": "9e454714343b69b99b71795c1d27a68c2a1d15dab111f4d353109f966af29da7"},
-    {"name": "spark2_5", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/XHToken/Spark-X2.5-1.7B", "chkhsh": "0a766d034107bc736a3f2dc4968fd62e54a3570f1454443e0c5a4cc6bd7941ed"},
 ]


@@ -53,7 +53,7 @@ To see what it might look like visually, here's an old demo of an interactive se
 https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4

 ## Cross-compile CLI using Android NDK
-It's possible to build `llama.cpp` for Android on your host system via CMake and the Android NDK. If you are interested in this path, ensure you already have an environment prepared to cross-compile programs for Android (i.e., install the Android SDK/NDK and set `ANDROID_NDK` to the NDK root). Note that, unlike desktop environments, the Android environment ships with a limited set of native libraries, and so only those libraries are available to CMake when building with the Android NDK (see: https://developer.android.com/ndk/guides/stable_apis.)
+It's possible to build `llama.cpp` for Android on your host system via CMake and the Android NDK. If you are interested in this path, ensure you already have an environment prepared to cross-compile programs for Android (i.e., install the Android SDK). Note that, unlike desktop environments, the Android environment ships with a limited set of native libraries, and so only those libraries are available to CMake when building with the Android NDK (see: https://developer.android.com/ndk/guides/stable_apis.)

 Once you're ready and have cloned `llama.cpp`, invoke the following in the project directory:

@@ -62,22 +62,18 @@ $ cmake \
  -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \
  -DANDROID_ABI=arm64-v8a \
  -DANDROID_PLATFORM=android-28 \
-  -DGGML_NATIVE=OFF \
+  -DCMAKE_C_FLAGS="-march=armv8.7a" \
+  -DCMAKE_CXX_FLAGS="-march=armv8.7a" \
  -DGGML_OPENMP=OFF \
  -DGGML_LLAMAFILE=OFF \
-  -DLLAMA_OPENSSL=OFF \
  -B build-android
 ```

 Notes:
-  - `GGML_NATIVE=OFF` is required for cross-compilation because the host CPU is not the Android target CPU
  - While later versions of Android NDK ship with OpenMP, it must still be installed by CMake as a dependency, which is not supported at this time
  - `llamafile` does not appear to support Android devices (see: https://github.com/Mozilla-Ocho/llamafile/issues/325)
-  - `LLAMA_OPENSSL=OFF` avoids depending on OpenSSL, which is not part of the Android NDK stable native API set

-The above command configures a portable Android `arm64-v8a` build. Do not add a global `-march` flag unless you intentionally want to raise the baseline instruction set for every compiled source.
-
-For optional KleidiAI acceleration on Android `arm64-v8a`, see the [Arm KleidiAI section in build.md](./build.md#arm-kleidiai).
+The above command should configure `llama.cpp` with the most performant options for modern devices. Even if your device is not running `armv8.7a`, `llama.cpp` includes runtime checks for available CPU features it can use.

 Feel free to adjust the Android ABI for your target. Once the project is configured:

@@ -514,7 +514,6 @@ The following templates have active tests in `tests/test-chat.cpp`:
 | Mistral Small 3.2 | JSON_NATIVE | `[TOOL_CALLS]func[ARGS]{...}` with call ID |
 | Devstral | JSON_NATIVE | `[TOOL_CALLS]func[ARGS]{...}` without call ID |
 | StepFun 3.5 Flash | TAG_WITH_TAGGED | `<function=X><parameter=Y>` format |
-| Spark2.5 | TAG_WITH_TAGGED | `<tool_call>name<arg_key>...<arg_value>...` format |

 ## Adding Support for New Templates

@@ -22,8 +22,8 @@ The OpenVINO backend is implemented in `ggml/src/ggml-openvino` and provides a t
  - [0. Prerequisites](#0-prerequisites)
  - [1. Install OpenVINO Runtime](#1-install-openvino-runtime)
  - [2. Build llama.cpp with OpenVINO Backend](#2-build-llamacpp-with-openvino-backend)
-    - [Ubuntu Build Script](#ubuntu-build-script)
-    - [Windows Build Script](#windows-build-script)
+    - [Automated Ubuntu Build Script](#automated-ubuntu-build-script)
+    - [Automated Windows Build Script](#automated-windows-build-script)
  - [3. Download Sample Model](#3-download-sample-model)
  - [4. Run Inference with OpenVINO Backend](#4-run-inference-with-openvino-backend)
  - [5. Docker Build](#5-docker-build)
@@ -96,7 +96,7 @@ Although, the validated models below were tested with `llama-cli` using the `Q4_
  - **SL** = Stateless (`GGML_OPENVINO_STATEFUL_EXECUTION=0`)
  - **SF** = Stateful (`GGML_OPENVINO_STATEFUL_EXECUTION=1`)
  - Note: The NPU operates in stateless mode only.
- **Validation system:** Intel® Core™ Ultra 5 238V (Lunar Lake) | 32 GB RAM | Ubuntu 24.04 | Intel OpenCL GPU Driver 26.31.39395.13-0 | Intel NPU Driver 1.35.0.
+- **Validation system:** Intel® Core™ Ultra 5 238V (Lunar Lake) | 32 GB RAM | Ubuntu 24.04 | Intel OpenCL GPU Driver 26.18.38308.1 | Intel NPU Driver 1.33.0.
 - See [Known Limitations](#known-limitations) for context on observed failures.

 | Model | CPU (SL / SF) | GPU (SL / SF) | NPU (SL) |
@@ -105,32 +105,27 @@ Although, the validated models below were tested with `llama-cli` using the `Q4_
 | [bartowski/Llama-3.2-3B-Instruct-Q4_K_M](https://huggingface.co/bartowski/Llama-3.2-3B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
 | [bartowski/Meta-Llama-3.1-8B-Instruct-Q4_K_M](https://huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
 |  |  |  |  |
-| [Qwen/qwen2.5-1.5b-instruct-q4_k_m](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
-| [Qwen/qwen2.5-coder-7b-instruct-q4_k_m](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
-| [bartowski/Qwen_Qwen3-0.6B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3-0.6B-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
-| [bartowski/Qwen_Qwen3-1.7B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3-1.7B-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
-| [Qwen/Qwen3-4B-Q4_K_M](https://huggingface.co/Qwen/Qwen3-4B-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
-| [lm-kit/Qwen3-8B-Q4_K_M](https://huggingface.co/lm-kit/qwen-3-8b-instruct-gguf) | ✓ / ✓ | ✓ / ✓ | ✓ |
-| [bartowski/Qwen_Qwen3.5-0.8B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3.5-0.8B-GGUF) | ✓ / ✗ | ✓ / ✗ | ✗ |
-| [bartowski/Qwen_Qwen3.5-2B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3.5-2B-GGUF) | ✓ / ✗ | ✓ / ✗ | ✗ |
-| [bartowski/Qwen_Qwen3.5-4B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3.5-4B-GGUF) | ✓ / ✗ | ✓ / ✗ | ✗ |
-| [lmstudio-community/Qwen3.5-9B-Q4_K_M](https://huggingface.co/lmstudio-community/Qwen3.5-9B-GGUF) | ✓ / ✗ | ✓ / ✗ | ✗ |
+| [Qwen/qwen2.5-1.5b-instruct-q4_k_m](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+| [Qwen/qwen2.5-coder-7b-instruct-q4_k_m](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+| [bartowski/Qwen_Qwen3-0.6B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3-0.6B-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+| [bartowski/Qwen_Qwen3-1.7B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3-1.7B-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+| [Qwen/Qwen3-4B-Q4_K_M](https://huggingface.co/Qwen/Qwen3-4B-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+| [lm-kit/Qwen3-8B-Q4_K_M](https://huggingface.co/lm-kit/qwen-3-8b-instruct-gguf) | ✓ / ✓ | ✓ / ✗ | ✓ |
 |  |  |  |  |
-| [unsloth/gemma-3-4b-it-Q4_K_M](https://huggingface.co/unsloth/gemma-3-4b-it-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
-| [bartowski/google_gemma-4-E2B-it-Q4_K_M](https://huggingface.co/bartowski/google_gemma-4-E2B-it-GGUF) | ✓ / ✗ | ✓ / ✗ | ✗ |
+| [unsloth/gemma-3-4b-it-Q4_K_M](https://huggingface.co/unsloth/gemma-3-4b-it-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+| [bartowski/google_gemma-4-E2B-it-Q4_K_M](https://huggingface.co/bartowski/google_gemma-4-E2B-it-GGUF) | ✓ / ✗ | ✓ / ✗ | ✓ |
 | [bartowski/google_gemma-4-E4B-it-Q4_K_M](https://huggingface.co/bartowski/google_gemma-4-E4B-it-GGUF) | ✓ / ✗ | ✓ / ✗ | ✓ |
-| [bartowski/gemma-4-12B-it-Q4_K_M](https://huggingface.co/bartowski/gemma-4-12B-it-GGUF) | ✓ / ✗ | ✓ / ✗ | ✓ |
+| [bartowski/gemma-4-12B-it-Q4_K_M](https://huggingface.co/bartowski/gemma-4-12B-it-GGUF) | ✓ / ✗ | ✓ / ✗ | ✗ |
 |  |  |  |  |
-| [bartowski/Phi-3-mini-4k-instruct-Q4_K_M](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
-| [bartowski/Phi-3.5-mini-instruct-Q4_K_M](https://huggingface.co/bartowski/Phi-3.5-mini-instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
-| [bartowski/microsoft_Phi-4-mini-instruct-Q4_K_M](https://huggingface.co/bartowski/microsoft_Phi-4-mini-instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
+| [bartowski/Phi-3-mini-4k-instruct-Q4_K_M](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+| [bartowski/Phi-3.5-mini-instruct-Q4_K_M](https://huggingface.co/bartowski/Phi-3.5-mini-instruct-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
 |  |  |  |  |
 | [bartowski/Mistral-7B-Instruct-v0.3-Q4_K_M](https://huggingface.co/bartowski/Mistral-7B-Instruct-v0.3-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
 | [QuantFactory/Ministral-3b-instruct.Q4_K_M](https://huggingface.co/QuantFactory/Ministral-3b-instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
 | [bartowski/Ministral-8B-Instruct-2410-Q4_K_M](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
 |  |  |  |  |
 | [bartowski/DeepSeek-R1-Distill-Llama-8B-Q4_K_M](https://huggingface.co/bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
-| [bartowski/DeepSeek-R1-Distill-Qwen-7B-Q4_K_M](https://huggingface.co/bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
+| [bartowski/DeepSeek-R1-Distill-Qwen-7B-Q4_K_M](https://huggingface.co/bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
 |  |  |  |  |
 | [ibm-granite/granite-4.0-350m-Q4_K_M](https://huggingface.co/ibm-granite/granite-4.0-350m-GGUF) | ✓ / ✓ | ✗ / ✗ | ✓ |
 | [ibm-granite/granite-4.0-micro-Q4_K_M](https://huggingface.co/ibm-granite/granite-4.0-micro-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
@@ -138,10 +133,10 @@ Although, the validated models below were tested with `llama-cli` using the `Q4_
 | [ibm-research/granite-3.2-8b-instruct-Q4_K_M](https://huggingface.co/ibm-research/granite-3.2-8b-instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
 |  |  |  |  |
 | [HuggingFaceTB/smollm2-1.7b-instruct-q4_k_m](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
-| [openbmb/MiniCPM-V-2_6-Q4_K_M](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) | ✓ / ✓ | ✓ / ✓ | ✓ |
-| [bartowski/tencent_Hunyuan-7B-Instruct-Q4_K_M](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
-| [LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct-Q4_K_M](https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
-| [bartowski/prism-ml_Bonsai-8B-unpacked-Q4_K_M](https://huggingface.co/bartowski/prism-ml_Bonsai-8B-unpacked-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
+| [openbmb/MiniCPM-V-2_6-Q4_K_M](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) | ✓ / ✓ | ✓ / ✗ | ✓ |
+| [bartowski/tencent_Hunyuan-7B-Instruct-Q4_K_M](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+| [LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct-Q4_K_M](https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+| [bartowski/prism-ml_Bonsai-8B-unpacked-Q4_K_M](https://huggingface.co/bartowski/prism-ml_Bonsai-8B-unpacked-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
 |  |  |  |  |
 | [gpustack/bge-m3-Q4_K_M.gguf](https://huggingface.co/gpustack/bge-m3-GGUF) | ✓ | ✗ | ✗ |

@@ -222,18 +217,18 @@ cmake --build build\ReleaseOV --parallel
 > [!NOTE]
 > The Windows install path is `C:\Intel\openvino` (no spaces) to avoid quoting problems some CMake/Ninja toolchains have with `C:\Program Files (x86)\...`. Adjust to wherever you installed OpenVINO Runtime. From `cmd`, run `C:\Intel\openvino\setupvars.bat`; from PowerShell, run `& "C:\Intel\openvino\setupvars.ps1"` instead. Once the build is finished you can launch the binaries from any `cmd` or `PowerShell` window after sourcing the matching `setupvars` script for that shell.

-#### Ubuntu Build Script
+#### Automated Ubuntu Build Script

 For Ubuntu24 users, the following shell script automates the prerequisite installs (build tools, OpenCL ICD), the OpenVINO Runtime download/extract/setup, and the Ninja-based llama.cpp build.
-Save the following as `build-llamacpp-ov.sh` next to where you want the `llama.cpp` folder to land, then run it:
+Save the following as `ubuntu-llamacpp-ov-install.sh` next to where you want the `llama.cpp` folder to land, then run it:

 ```bash
-chmod +x build-llamacpp-ov.sh
-./build-llamacpp-ov.sh
+chmod +x ubuntu-llamacpp-ov-install.sh
+./ubuntu-llamacpp-ov-install.sh
 ```

 <details>
-<summary>Click to expand <code>build-llamacpp-ov.sh</code></summary>
+<summary>Click to expand <code>ubuntu-llamacpp-ov-install.sh</code></summary>

 ```bash
 #!/usr/bin/env bash
@@ -242,8 +237,8 @@ chmod +x build-llamacpp-ov.sh
 # ============================================
 set -euo pipefail

-OPENVINO_VERSION_MAJOR="2026.3.1"
-OPENVINO_VERSION_FULL="2026.3.1.22476.56d9685302d"
+OPENVINO_VERSION_MAJOR="2026.3"
+OPENVINO_VERSION_FULL="2026.3.0.22451.bd8d6542e3c"

 SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
 OPENVINO_INSTALL_DIR="/opt/intel/openvino_${OPENVINO_VERSION_MAJOR}"
@@ -318,9 +313,8 @@ fi
 echo "============================================"
 echo "Configuring with CMake..."
 echo "============================================"
-set +u
+# shellcheck disable=SC1091
 source "${OPENVINO_ROOT}/setupvars.sh"
-set -u

 cmake -B build/ReleaseOV -G Ninja \
    -DCMAKE_BUILD_TYPE=Release \
@@ -340,27 +334,27 @@ echo "  ./build/ReleaseOV/bin/llama-cli -m model.gguf"
 ```

 > [!NOTE]
-> The script pins OpenVINO `2026.3.1` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release.
+> The script pins OpenVINO `2026.3` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release.

 </details>

-#### Windows Build Script
+#### Automated Windows Build Script

 For Windows users, the following `.bat` script automates the prerequisite installs (Git, Ninja, CMake, Visual Studio 2022 Build Tools, vcpkg + OpenCL), the OpenVINO Runtime download/extract, and the Ninja-based llama.cpp build.
-Save the following as `build-llamacpp-ov.bat` next to where you want the `llama.cpp` to land, then run it from either **Command Prompt** or **PowerShell**:
+Save the following as `windows-llamacpp-ov-install.bat` next to where you want the `llama.cpp` to land, then run it from either **Command Prompt** or **PowerShell**:

 ```cmd
 :: Command Prompt
-build-llamacpp-ov.bat
+windows-llamacpp-ov-install.bat
 ```

 ```powershell
 # PowerShell
-.\build-llamacpp-ov.bat
+.\windows-llamacpp-ov-install.bat
 ```

 <details>
-<summary>Click to expand <code>build-llamacpp-ov.bat</code></summary>
+<summary>Click to expand <code>windows-llamacpp-ov-install.bat</code></summary>

 ```bat
@echo off
@@ -370,8 +364,8 @@ REM ============================================
 REM llama.cpp OpenVINO Build Script (Ninja)
 REM ============================================

-set "OPENVINO_VERSION_MAJOR=2026.3.1"
-set "OPENVINO_VERSION_FULL=2026.3.1.22476.56d9685302d"
+set "OPENVINO_VERSION_MAJOR=2026.3"
+set "OPENVINO_VERSION_FULL=2026.3.0.22451.bd8d6542e3c"

 set "SCRIPT_DIR=%~dp0"
 set "VCPKG_DIR=C:\vcpkg"
@@ -459,6 +453,9 @@ if exist "%OPENVINO_INSTALL_DIR%\setupvars.bat" (
    )

    REM Move the single top-level folder contents into the versioned install dir.
+    REM NOTE: delayed expansion (!VAR!) is required because the surrounding else( ... )
+    REM block is parsed once up-front, so %OPENVINO_EXTRACTED% would expand to "" here
+    REM and xcopy would then treat "\*" as C:\* and fail with "Cannot perform a cyclic copy".
    set "OPENVINO_EXTRACTED="
    for /d %%i in ("%OPENVINO_EXTRACT_TMP%\*") do set "OPENVINO_EXTRACTED=%%i"
    if not defined OPENVINO_EXTRACTED (
@@ -550,7 +547,7 @@ endlocal
 ```

 > [!NOTE]
-> The script pins OpenVINO `2026.3.1` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release. From any new shell, source the matching `setupvars` script via the junction — `call "C:\Intel\openvino\setupvars.bat"` from `cmd`, or `& "C:\Intel\openvino\setupvars.ps1"` from PowerShell. If `winget` cannot register Visual Studio Build Tools on first run, install them once manually and re-run the script from an elevated **Developer Command Prompt for VS 2022**.
+> The script pins OpenVINO `2026.3` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release. From any new shell, source the matching `setupvars` script via the junction — `call "C:\Intel\openvino\setupvars.bat"` from `cmd`, or `& "C:\Intel\openvino\setupvars.ps1"` from PowerShell. If `winget` cannot register Visual Studio Build Tools on first run, install them once manually and re-run the script from an elevated **Developer Command Prompt for VS 2022**.

 </details>

@@ -715,7 +712,6 @@ Boolean flags follow a uniform convention: set to a **positive integer** (e.g. `
 | `GGML_OPENVINO_CACHE_DIR`         | String    | `not set`  | Directory for OpenVINO model caching (recommended: `/tmp/ov_cache`). Enables model caching when set. **Not supported on NPU devices.** |
 | `GGML_OPENVINO_COMPILED_MODEL_CACHE_DIR` | String | `not set` | Directory for the frontend compiled-model cache. When set, OpenVINO compiled models are exported as blobs and imported on later runs to skip weight requantization, graph conversion, and compilation for matching single-graph models. |
 | `GGML_OPENVINO_PREFILL_CHUNK_SIZE`| Integer   | `256`      | Token chunk size for **NPU** prefill (NPU-only; ignored on CPU/GPU). Must be a positive integer; otherwise the default is used. |
-| `GGML_OPENVINO_NPU_COMPILE_CONFIG` | String | `not set` | NPU-only compiler mode parameters forwarded to OpenVINO as `NPU_COMPILATION_MODE_PARAMS`, for example `optimization-level=3`. |
 | `GGML_OPENVINO_STATEFUL_EXECUTION`| Boolean   | `0`        | Enable stateful KV cache for better performance. Recommended on CPU, GPU.                                   |
 | `GGML_OPENVINO_DISABLE_CACHE`     | Boolean   | `0`        | Disable the in-process compiled-model / decoder cache (cache is on by default). Set to `1` to disable.      |
 | `GGML_OPENVINO_DISABLE_KV_SLICE`  | Boolean   | `0`        | Disable the KV-cache input-tensor slicing optimization (slicing is on by default on CPU/GPU). Set to `1` to disable. |
@@ -729,11 +725,9 @@ Boolean flags follow a uniform convention: set to a **positive integer** (e.g. `
 | `GGML_OPENVINO_DEBUG_INPUT`       | Boolean   | `0`        | Enable input debugging and print input tensor info.                                                         |
 | `GGML_OPENVINO_DEBUG_OUTPUT`      | Boolean   | `0`        | Enable output debugging and print output tensor info.                                                       |
 | `GGML_OPENVINO_PRINT_CGRAPH_TENSOR_ADDRESS` | Boolean | `0` | Print tensor address map once.                                                                           |
-| `GGML_OPENVINO_LOG_UNSUPPORTED_OPS`| Boolean   | `0`        | Log warning messages with tensor details and rejection reasons for any ops not supported by the OpenVINO backend. Emits at `WARN` level (requires `--log-verbosity >= 2`, enabled by default). |

 > [!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.
-> - `GGML_OPENVINO_LOG_UNSUPPORTED_OPS` emits logs at `WARN` level (`GGML_LOG_WARN`), which requires application log verbosity `--log-verbosity >= 2` (or `-lv 2`).
+>`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

@@ -795,9 +795,7 @@ User can use the device management in [docs/multi-gpu.md](https://github.com/ggm
 | GGML_SYCL_ENABLE_FLASH_ATTN | 1 (default) or 0| Enable Flash-Attention. It can reduce memory usage. The performance impact depends on the LLM.|
 | GGML_SYCL_ENABLE_OPT | 0 or 1 (default)| Enable optimize features for Intel GPUs. (Recommended to 0 for Intel devices older than Gen 10) |
 | GGML_SYCL_ENABLE_GRAPH | 0 (default) or 1 | Enable running computations through SYCL Graphs feature. Disabled by default because SYCL Graph is still on development, no better performance. |
-| GGML_SYCL_ENABLE_HOST_PINNED_MEM | 0 or 1 (default) | Enable host pinned memory to speed up copy data from host to device. When disable it, host memory will common malloc() on CPU. Disable it when use `--load-model mlock`.|
-| GGML_SYCL_HOST_PINNED_MEM_2G | 0 (default) or 1 | Limit the max memory allocation to be no more than 2GB when enable host pinned memory. USM allocations above 2 GiB take the relaxed/large-allocation path, which serializes H2D copies with compute and prevents copy/compute overlap. It will impact the startup time. Need more test. Depend on `GGML_SYCL_ENABLE_HOST_PINNED_MEM=1`.|
-| GGML_SYCL_GET_MEM_API | 0 (default) or 1  | Set to get memory info (free, total) by Level Zero or SYCL API:<br>0 - Level Zero API: support more GPUs, only run on Level Zero running time. When there is an error, fallback to call SYCL API. Depend on GGML_SYCL_SUPPORT_LEVEL_ZERO_API.<br>1 - SYCL API: legacy, support more running time, it can't get the free size of some GPUs (like Arc770). In such case, return total size for free size.|
+| GGML_SYCL_ENABLE_HOST_PINNED_MEM | 0 or 1 (default) | Enable host pinned memory to speed up copy data from host to device. When disable it, host memory will common malloc() on CPU.|
 | GGML_SYCL_USE_LEVEL_ZERO_API | 1 (default) or 0 | Use Level Zero API for device memory allocation instead of SYCL. Reduces system RAM usage on Intel dGPUs by avoiding DMA-buf/TTM host memory staging. Requires GGML_SYCL_SUPPORT_LEVEL_ZERO_API=ON at build time. SYCL backend always runs on Level Zero running time even if it's set as OFF (The SYCL api will be usage for memory allocation).|
 | GGML_SYCL_ENABLE_DNN | 0 or 1 (default)| Enable running computations through oneDNN and always use oneMKL. |
 | GGML_SYCL_FA_ONEDNN | 1 (default) or 0 | Enable the oneDNN fused SDPA (flash-attention) path on supported GPUs. Set to 0 to always use the native SYCL flash-attention kernel. |
@@ -805,10 +803,8 @@ User can use the device management in [docs/multi-gpu.md](https://github.com/ggm
 | GGML_SYCL_ENABLE_VMM | 0 or 1 (default) | Enable the virtual-memory device pool. |
 | GGML_SYCL_ENABLE_MKL_FA | 1 (default) or 0 | Enable oneMKL GEMM flash attention for XMX-accelerated prompt processing with quantized KV cache. Automatically activates during prefill (prompt processing) when all conditions are met: (1) flash-attn enabled (`-fa` or `--flash-attn on`), (2) KV cache quantized (`--cache-type-k q8_0 --cache-type-v q8_0` or other `*_0/*_1` types), (3) batch size ≥ 1024 (`--batch-size 1024`), (4) prompt length ≥ 1024 tokens. Set to 0 to force the TILE kernel for A/B testing. Example minimum command: `llama-cli -m model.gguf -fa -ngl 99 --cache-type-k q8_0 --cache-type-v q8_0 --batch-size 1024 -p "your prompt"` |
 | GGML_SYCL_MKL_FA_DEBUG | 0 (default) or 1 | Enable per-call diagnostic logging for MKL flash attention: GEMM/softmax timings, interleaved-head detection, and buffer memory usage. |
-| GGML_SYCL_MEMTRACE | 0 (default), 1, 2 | Enable record and output memory allocation diagnostics. Requires `-lv 4`. <br>0 -  Disable<br>1 - Basic memory info, including current and peak allocations, as well allocations from other sources, around 50 lines per model load.<br>2 - More verbose, logging around 900 specific allocations and deallocations. |
-| GGML_SYCL_MEMTRACE_STEP | 64 (default) or positive integer | With GGML_SYCL_MEMTRACE=1, the minimum growth in memory usage to trigger another log record. |
 | GGML_SYCL_MKL_FA_DIAG | 0 (default) or 1 | Enable output fingerprinting for MKL flash attention. Dumps the first 64 float output values for the first 6 FA calls with n_kv ≥ 1024, labeled with kernel type (MKL/TILE/VEC) for cross-kernel comparison. |
-| GGML_SYCL_ENABLE_FUSION | 0 or 1 (default) | Enable fused-kernel dispatch in graph compute. Unsupported types and layouts fall back to the standalone op kernels. See `ggml_sycl_can_fuse()`. |
+| GGML_SYCL_ENABLE_FUSION | 0 or 1 (default) | Enable fused-kernel dispatch in graph compute. |
 | GGML_SYCL_ENABLE_ESIMD | 0 or 1 (default)| Enable ESIMD kernels when available. |
 | ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer |
 | UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS | 0 (default) or 1 | Allow SYCL/Unified Runtime Level Zero device allocations larger than 4 GiB. llama.cpp's direct Level Zero allocation path requests the relaxed maximum-size limit itself when GGML_SYCL_ENABLE_LEVEL_ZERO=1. |
@@ -188,7 +188,7 @@ llama_memory_breakdown_print: |   - Host               |                  439 =
 Op test for MUL_MAT:

 ```
-~/src/llama.cpp$ ./scripts/snapdragon/run.py --target adb --devices HTP0:0 -- test-backend-ops -b HTP0:0 -o MUL_MAT
+~/src/llama.cpp$ ./scripts/snapdragon/run.py --target adb --hex-hostbuf 0 --devices HTP0:0 -- test-backend-ops -b HTP0:0 -o MUL_MAT
 ...
 Backend 2/3: HTP0:0
 Device description: Hexagon
@@ -213,109 +213,14 @@ ggml-hex: new session: HTP0 : session-id 0 domain-id 3 uri file:///libggml-htp-v
 | llama 1B Q4_0  | 729.75 MiB | 1.24 B | HTP        |  99 |       4 |     128 |    0 |  tg64 |  51.54 ± 1.13 |
 ```

-## Multi-Device Execution Modes
-
-The Hexagon backend supports multiple execution and partitioning modes to accommodate different model sizes, memory
-constraints, and single- or multi-NPU hardware topologies:
-
-### 1. Single-Device Mode with Dynamic Buffer Mapping
-
-Runs the model on a single NPU session (e.g. `HTP0` or `HTP0:0`).
-
-A single NPU session provides ~3.5GB of available virtual address space. For models larger than 3.5GB, the backend
-automatically maps and unmaps weight buffers during graph execution. This allows large models to run on a single NPU
-without manual configuration:
-
-```bash
-./scripts/snapdragon/run.py --target adb --devices HTP0:0 -- \
-    llama-cli -m models/Llama-3.2-3B-Instruct-Q4_0.gguf -ngl 99 -p "Hello"
-```
-
-### 2. Layer-Split Mode across Virtual Sessions (`HTP0,HTP1,...` or `HTP0:0,HTP0:1,...`)
-
-Partitions model layers at load time across multiple virtual sessions hosted on a single physical NPU.
-
-Each virtual session acts as an independent backend device from llama.cpp's perspective (similar to multiple GPUs).
-Because layers are permanently distributed across sessions, each session's allocated weights remain within its private 3.5GB
-address space window, eliminating runtime buffer re-mapping overhead.
-
-Here is an example of running the GPT-OSS-20B model on a Snapdragon device using 4 virtual sessions on a single NPU:
-
-```bash
-./scripts/snapdragon/run.py --target adb \
-    --devices HTP0:0,HTP0:1,HTP0:2,HTP0:3 -- \
-    llama-cli --load-mode none -m /data/local/tmp/gguf/gpt-oss-20b-Q4_0.gguf -t 4 \
-    --ctx-size 8192 --batch-size 128 -ctk q8_0 -ctv q8_0 -fa on -ngl 99 -no-cnv -f surfing.txt
-```
-
-Log output snippet:
-
-```
-...
-llama_model_loader: - type  f32:  289 tensors
-llama_model_loader: - type q4_0:   96 tensors
-llama_model_loader: - type q8_0:    2 tensors
-llama_model_loader: - type mxfp4:  72 tensors
-...
-load_tensors: offloaded 25/25 layers to GPU
-load_tensors:          CPU model buffer size =  1182.09 MiB
-load_tensors:       HTP0:1 model buffer size =  2512.58 MiB
-load_tensors:       HTP0:3 model buffer size =  2093.83 MiB
-load_tensors:       HTP0:0 model buffer size =  2931.34 MiB
-load_tensors:       HTP0:2 model buffer size =  2512.58 MiB
-...
-llama_perf_context_print: prompt eval time =    3843.67 ms /   197 tokens ( 19.51 ms per token, 51.25 tokens per second)
-llama_perf_context_print:        eval time =    1686.13 ms /    31 runs   ( 54.39 ms per token, 18.39 tokens per second)
-llama_perf_context_print:       total time =    6266.30 ms /   228 tokens
-llama_memory_breakdown_print: | memory breakdown [MiB] | total   free    self   model   context   compute    unaccounted |
-llama_memory_breakdown_print: |   - HTP0:0 (Hexagon)   |  2048 = 2048 + (   0 =     0 +       0 +       0) +           0 |
-llama_memory_breakdown_print: |   - HTP0:1 (Hexagon)   |  2048 = 2048 + (   0 =     0 +       0 +       0) +           0 |
-llama_memory_breakdown_print: |   - HTP0:2 (Hexagon)   |  2048 = 2048 + (   0 =     0 +       0 +       0) +           0 |
-llama_memory_breakdown_print: |   - HTP0:3 (Hexagon)   |  2048 = 2048 + (   0 =     0 +       0 +       0) +           0 |
-llama_memory_breakdown_print: |   - Host               |                 1476 =  1208 +     105 +     162                |
-```
-
-### 3. Tensor-Split Mode across Physical Devices (`HTP0:0,HTP1:0,...`)
-
-Distributes model tensors across distinct physical NPU hardware cores using llama.cpp's tensor parallelism
-(`--split-mode tensor`).
-
-Tensors are partitioned across physical NPUs for parallel execution (proportions are distributed equally by default without
-needing an explicit `--tensor-split` option):
-
-```bash
-./scripts/snapdragon/run.py --target adb \
-    --devices HTP0:0,HTP1:0 -- \
-    llama-cli -m models/Llama-3.2-3B-Instruct-Q4_0.gguf --split-mode tensor -ngl 99 -p "Hello"
-```
-
-### 4. Row-Split Multi-Device Mode via Device Grouping (`HTP0[0-1]`)
-
-Groups multiple physical NPU cores into a single logical device using bracket notation (`HTP0[0-1]` or `HTP0[0,1]`).
-
-Unlike host-level tensor-splitting, row-splitting is executed entirely inside the Hexagon backend:
-
-```bash
-./scripts/snapdragon/run.py --target adb \
-    --devices 'HTP0[0-1]' -- \
-    llama-cli -m models/Llama-3.2-3B-Instruct-Q4_0.gguf -ngl 99 -p "Hello"
-```
-
-You can also combine row-splitting with layer-splitting across multiple grouped devices (e.g. `--devices 'HTP0[0-1],HTP1[2-3]'`
-on 4 physical NPUs, or `--devices 'HTP0[0-1:0],HTP1[0-1:1]'` on 2 physical NPUs using virtual sessions 0 and 1).
-
 ## Environment variables

 - `GGML_HEXAGON_DEVICES` (default: not set, defaults to HTP0 session)
-  Controls which NPU devices and sessions to allocate. Configurable via `--devices` in `run.py`:
-  - `N` (single integer): Allocates `N` virtual sessions named `HTP0`, `HTP1`, ..., `HTP<N-1>` on physical NPU 0.
-  - `HTP<phys>:<virt>,...`: Comma-separated list of individual devices specifying physical and virtual index:
-    - `HTP0:0,HTP0:1`: Two virtual sessions on physical NPU 0 (layer-split on single NPU).
-    - `HTP0:0,HTP1:0`: One session on physical NPU 0 and one on physical NPU 1 (tensor-split across physical cores).
-  - `HTP<name>[<phys_spec>]`: Device grouping syntax for row-split multi-device execution:
-    - `HTP0[0-1]`: A single logical device `HTP0` that groups physical cores 0 and 1.
-    - `HTP0[0-1],HTP1[2-3]`: Two layer-split devices across 4 physical NPUs (cores 0-1 and 2-3).
-    - `HTP0[0-1:0],HTP1[0-1:1]`: Two layer-split devices across 2 physical NPUs using virtual sessions 0 and 1.
+  Controls which NPU devices and sessions to allocate. Can be configured as:
+  - A single integer `N`: Allocates `N` sessions named `HTP0`, `HTP1`, ..., `HTP<N-1>` (behaves identically to `GGML_HEXAGON_NDEV=N`).
+  - A comma-separated list of device names in `HTP<physical_idx>:<virtual_idx>` format (or legacy `HTP<idx>` format). For example, `HTP0:0,HTP0:1` creates two virtual
+    sessions on the first physical NPU (useful for memory limits). `HTP0:0,HTP1:0` allocates one session on each of the two physical NPUs
+    on a dual-NPU device.

 - `GGML_HEXAGON_NDEV` (deprecated)
  Replaced by `GGML_HEXAGON_DEVICES`. Controls the number of virtual sessions to allocate on physical NPU `0`.
@@ -324,8 +229,9 @@ on 4 physical NPUs, or `--devices 'HTP0[0-1:0],HTP1[0-1:1]'` on 2 physical NPUs
 - `GGML_HEXAGON_NHVX=0`
  Controls the number of HVX hardware threads to use. The default is all (actual number varies depending on the hardware version).

- `GGML_HEXAGON_HOSTBUF=1` (default: 0, disabled)
-  Enables allocating host buffers for debugging. By default, host buffers are disabled.
+- `GGML_HEXAGON_HOSTBUF=1`
+  Controls whether the Hexagon backend allocates host buffers. By default, all buffers except for REPACK are host buffers.
+  This option is required for testing Ops that require REPACK buffers (MUL_MAT and MUL_MAT_ID).

 - `GGML_HEXAGON_VERBOSE=1`
  Enables verbose logging of Ops from the backend. Example output:
@@ -340,26 +246,23 @@ on 4 physical NPUs, or `--devices 'HTP0[0-1:0],HTP1[0-1:1]'` on 2 physical NPUs
  ```

 - `GGML_HEXAGON_PROFILE=1`
-  Enables Op profiling (configurable via `--hex-profile` in `run.py`):
+  Enables Op profiling:

-  - `1`: Basic profile with per-op `usecs` and `cycles` counters
-  - `2`: Extended profile with per-op `usecs`, `cycles` and default PMU counter data
-  - `0x1,...,0x8`: Extended profile with per-op `usecs`, `cycles` and custom PMU counter data
+  - `1` Basic profile with per-op `usecs` and `cycles` counters
+  - `2` Extended profile with per-op `usecs`, `cycles` and default PMU counter data
+  - `0x1,...,0x8` Extended profile with per-op `usecs`, `cycles` and custom PMU counter data

-  The logging output can be saved to a file or piped directly into the post-processing script:
+  The logging output can be either saved into a file for post-processing or it can be piped directly into the post-processing tool
+  to generate the report.
+  Examples:

-  ```bash
-  ./scripts/snapdragon/run.py --target adb --hex-profile 1 -- llama-cli ... |& \
-      ./scripts/snapdragon/ggml-hexagon-profile.py -
-  ```
+      `GGML_HEXAGON_PROFILE=1 ./scripts/snapdragon/run.py --target adb -- llama-cli ... |& ./scripts/snapdragon/ggml-hexagon-profile.py -`

 - `GGML_HEXAGON_OPFILTER=regex`
-  Filters (disables) Ops matching the regex pattern (configurable via `--hex-opfilter` in `run.py`):
+  Allows filtering (disabling) Ops that match the regex pattern:

-  ```bash
-  # Disable Flash Attention on Hexagon (falls back to CPU or GPU)
-  ./scripts/snapdragon/run.py --target adb --hex-opfilter "FLASH_ATTN_EXT" -- llama-cli ...
+  Examples:
+
+      `GGML_HEXAGON_OPFILTER="FLASH_ATTN_EXT" ./scripts/snapdragon/run.py --target adb -- llama-cli ...` - Disable Flash Attention on Hexagon (falls back to CPU or GPU)
+      `GGML_HEXAGON_OPFILTER="ADD\|SUB" ./scripts/snapdragon/run.py --target adb -- llama-cli ...` - Disable ADD and SUB on Hexagon (fall back to CPU or GPU)

-  # Disable ADD and SUB on Hexagon (fall back to CPU or GPU)
-  ./scripts/snapdragon/run.py --target adb --hex-opfilter "ADD|SUB" -- llama-cli ...
-  ```
@@ -2,16 +2,16 @@

 ## Backend libraries

-The Hexagon backend consists of two parts:
+The Hexagon backend consist of two parts:

  - `libggml-hexagon`
-    This is the regular CPU-side GGML backend library, either shared or statically linked.
+    This is the regular CPU-side GGML backend library, either shared or statically linked

  - `libggml-htp-vNN`
    This is the NPU-side (HTP stands for Hexagon Tensor Processor) shared library that contains the Op dispatcher and kernels.
    The correct library is selected automatically at runtime based on the HW version.

-Here is an example of the build artifacts:
+Here is an example of the build artifacts

 ```
 ~/src/llama.cpp$ ls -l pkg-adb/llama.cpp/lib/libggml*
@@ -26,307 +26,75 @@ pkg-adb/llama.cpp/lib/libggml-htp-v81.so

 ## Memory buffers

-The Hexagon NPU backend takes advantage of Snapdragon unified memory where all DDR buffers are accessible by CPU, GPU, and NPU.
-The NPU has dedicated tightly-coupled memory called VTCM (Vector Tightly-Coupled Memory). VTCM is used for intermediate data (such as
-dynamically quantized activations) and streaming buffers (chunks of weight and activation tensors fetched via DMA).
+Hexagon NPU backend takes advantage of the Snapdragon's unified memory model where all buffers are fully accessible by the CPU and GPU.
+The NPU does have a dedicated tightly-coupled memory called VTCM but that memory is used only for intermediate data (e.g. dynamically
+quantized tensors) or temporary data (chunks of the weight tensors fetched via DMA).
+
+Please note that currently the Hexagon backend does not implement SET/GET_ROWS Ops because there is no advantage in offloading those
+to the NPU at this point.
+
+The backend does allocates non-host buffers for the tensors with datatypes that require repacking: Q4_0, Q8_0, MXFP4.
+From the MMU perspective these buffers are still regular buffers (normal access by the CPU) they are marked as non-host simply to force
+the repacking.

 ## Large model handling

-Hexagon NPU sessions have a 32-bit virtual address space window of around 3.5GB.
+Hexagon NPU sessions (aka Process Domains (PD) in the Hexagon SDK) are limited to a maximum memory mapping window of around 3.5GB.
 In llama.cpp/GGML, each Hexagon session is mapped to a single GGML backend device (e.g., `HTP0:0`, `HTP0:1`, etc. when using
 `GGML_HEXAGON_DEVICES`, or `HTP0`, `HTP1` in legacy mode).

-To support running models larger than 3.5GB on a single device, the Hexagon backend dynamically maps and unmaps buffers:
- Buffers are allocated in shared DDR (RPCMEM) via file descriptors (`fastrpc_mmap` using `FASTRPC_MAP_FD_DELAYED`).
- Pinned buffers (such as KV cache and active compute buffers) remain mapped throughout execution.
- Inactive weight buffers are dynamically mapped into the NPU session via `HAP_mmap()` during batch buffer preparation
-  (`prep_op_bufs()` in `htp/main.c`) and unmapped via `htp_iface_munmap()` when no longer needed by the active batch.
- This dynamic sliding window allows a single NPU session to execute models that exceed the 3.5GB window.
+To support running models larger than 3.5GB on a single device, the Hexagon backend dynamically maps and unmaps execution buffers
+during the graph execution cycle to stay within the Process Domain window. This enables large models to run successfully on a single
+NPU device.

-Alternatively, users can partition and split the model across multiple virtual sessions or physical NPUs using layer-splitting,
-tensor-splitting, or row-splitting modes. For user-facing execution modes and examples, see the
-[Snapdragon user guide](README.md#multi-device-execution-modes).
+Alternatively, users can choose to use standard llama.cpp/GGML layer-splitting mode to partition and split the model across
+multiple Hexagon devices or virtual sessions (which behave like multiple GPUs from the offload and splitting perspective).

-## Op and Kernel Development Guidelines
-
-Writing high-performance operators for Hexagon requires following specific guidelines.
-
-### DDR -> DMA -> VTCM Execution Pipeline
-
- Strongly prefer the `DDR -> DMA -> VTCM -> compute (HVX/HMX) -> VTCM -> DMA -> DDR` data flow.
- Direct HVX reads/writes from/to DDR are less efficient and should only be used as a fallback.
- The DMA queue is a strict FIFO where operations must be pushed and popped in strict order.
- Follow the pipelined multi-buffering sequence properly (typically 2x to 16x buffering) so every push has a corresponding pop:
-
-  1. In the prologue, push initial DDR -> VTCM transfers to prime the pipeline.
-  2. In the loop body, wait for buffer N via DMA pop, launch HVX/HMX compute on buffer N, push VTCM -> DDR writeback of result N,
-     and push DDR -> VTCM prefetch of buffer N+2.
-  3. In the epilogue, pop all remaining in-flight transfers to drain the pipeline.
-
- Because every push must be matched by a pop, `dma_queue_flush()` is not required when the pipeline sequence is followed
-  properly. Flushing is only used in rare exceptions where a batch of operations is pushed without individual pops.
- Use the DMA queue interface from [`dma-queue.h`](../../../ggml/src/ggml-hexagon/htp/dma-queue.h)
-  (`dma_queue_push_ddr_to_vtcm`, `dma_queue_pop`, `dma_queue_push_vtcm_to_ddr`).
-  See [`cumsum-ops.c`](../../../ggml/src/ggml-hexagon/htp/cumsum-ops.c) and
-  [`act-ops.c`](../../../ggml/src/ggml-hexagon/htp/act-ops.c) for reference implementations.
-
-### Avoid Scalar Reads and Writes to VTCM
-
- Access VTCM data using DMA transfers or HVX/HMX vector instructions rather than scalar reads and writes.
-
-### Avoid Scalar Division in Inner Loops
-
- Hexagon cores do not have hardware division instructions.
- For recurring divisions across iterations or threads, use `fastdiv` from
-  [`hex-fastdiv.h`](../../../ggml/src/ggml-hexagon/htp/hex-fastdiv.h) with precomputed divisors (such as
-  `octx->ctx->mdev.count_div` or `octx->n_threads_div`).
- Do not call `init_fastdiv_values()` for single-use divisions; use standard compiler division (`/`) instead.
-
-### Host-Side Precomputation via `kernel_params`
-
- Precompute tensor shapes, strides, scale conversions, tiling layouts, and validation checks on the host CPU during graph
-  preparation in [`ggml-hexagon.cpp`](../../../ggml/src/ggml-hexagon/ggml-hexagon.cpp).
- Pack precomputed parameters into the operator's fixed `kernel_params` structure in `htp_op_node` (such as
-  `htp_mm_kernel_params`, `htp_unary_kernel_params`, `htp_fa_kernel_params`, `htp_get_rows_kernel_params`).
- The NPU executes directly using `octx->kernel_params` without redundant runtime metadata extraction or validation.
- **Strict Host-Kernel Alignment**:
-  - Verify that parameters calculated by the host CPU are strictly honored by the NPU kernel.
-  - Ensure the kernel does not ignore host-computed fields (for example, falling back to `octx->n_threads` instead of
-    using `kparams->n_threads`, or ignoring precomputed `tasks_per_thread` and chunk counts).
-  - Both human developers and coding agents must audit both sides of the interface: ensure fields populated in `kernel_params`
-    in [`ggml-hexagon.cpp`](../../../ggml/src/ggml-hexagon/ggml-hexagon.cpp) are actively and consistently utilized by the
-    corresponding operator entry point and worker threads in `htp/*-ops.c`.
-
-### Tracing Instrumentation
-
- All kernels must include trace events for performance profiling and timeline visualization in Perfetto
-  ([`hex-profile.h`](../../../ggml/src/ggml-hexagon/htp/hex-profile.h)).
- Surround compute sections with `htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) info)` and
-  `htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, (uint16_t) info)`.
- Use specific event types for major phases:
-  - `HTP_TRACE_EVT_HVX_COMP`: Vector compute execution.
-  - `HTP_TRACE_EVT_DMA`: DMA transfer wait or poll cycles.
-  - `HTP_TRACE_EVT_FENCE`: Multi-device fence barrier synchronization.
-  - `HTP_TRACE_EVT_L2FLUSH`: L2 cache cleaning operations.
- Pass meaningful progress metrics (such as row index, chunk index, or token index) in the 16-bit `info` parameter.
-
-### Work Queue and Threading
-
- Distribute parallel work across NPU worker threads using the thread pool work queue:
-
-  ```c
-  work_queue_run(ctx->work_queue, worker_func, &op_ctx, n_threads);
-  ```
-
- Keep worker functions independent and re-entrant. Worker threads should only operate on their designated chunk of rows or elements.
-
-### Avoid Redundant Defensive NULL Checks
-
- Do not add defensive NULL checks or assertions for internal framework pointers or required graph operands and outputs.
-  Internal pointers include `ctx`, `octx`, local context structs like `*ctx`, `kparams`, and worker callback `data`.
- These pointers are architectural invariants during kernel execution and host-side graph preparation.
-  Graph compute receives allocated nodes with valid required `node->src[N]` and `node->data` pointers.
- Do not turn an invariant violation into an unsupported operation or missed fusion.
-  Checks such as `if (!octx || !octx->ctx)` clutter the code, obscure intent, and hide upstream errors.
- **Distinction**: `octx->src[N]` pointers *can* be NULL by design and must be checked when optional.
-  Examples include attention masks, optional bias or weights in fused kernels, and frequency factors.
-
-### Multiline Macro Formatting
-
- Keep trailing backslashes in multiline `#define` macros cleanly aligned to a consistent column.
- Avoid trailing whitespace after macro backslashes.
- Use [`scripts/snapdragon/ggml-hexagon-align-macros.py`](../../../scripts/snapdragon/ggml-hexagon-align-macros.py) to inspect, diff,
-  or automatically align macro definitions across Hexagon kernel sources:
-
-  ```bash
-  # Check for misaligned macros
-  python3 scripts/snapdragon/ggml-hexagon-align-macros.py ggml/src/ggml-hexagon/htp/
-
-  # Fix misaligned macros in-place
-  python3 scripts/snapdragon/ggml-hexagon-align-macros.py --fix ggml/src/ggml-hexagon/htp/
-  ```
-
-## Multi-Device Partitioning (mdev)
-
-Multi-device (mdev) mode enables row-level tensor parallel execution across multiple physical NPU cores or virtual NPU
-sessions.
-
-### 128-Byte Cache Line Alignment
-
- Shared tensor buffers reside in DDR (RPCMEM) with a 128-byte cache line granularity
-  (`HEX_L2_LINE_SIZE` = 128 bytes, `HTP_TENSOR_MDEV_LINE_SIZE`).
- **Rule**: Multi-device work partitions must align destination write regions to 128-byte cache line boundaries so distinct
-  devices never share or overwrite the same cache line.
-
-### Partitioning Helpers in `htp-tensor.h`
-
-Common partitioning logic is factored into reusable inline helpers in
-[`htp-tensor.h`](../../../ggml/src/ggml-hexagon/htp/htp-tensor.h):
-
-1. [`htp_tensor_mdev_rows_per_chunk`](../../../ggml/src/ggml-hexagon/htp/htp-tensor.h#L67):
-   Determines the minimum number of rows per chunk so that the chunk byte size is a multiple of 128 bytes:
-
-   ```
-   rows_per_chunk = 128 / hex_gcd_u32(row_size, 128)
-   ```
-
-   If row stride `nb[1]` is already a multiple of 128 bytes, `rows_per_chunk = 1`.
-   Returns `false` if the tensor cannot be safely row-partitioned (such as unaligned base pointer, permuted layout,
-   or non-128-byte aligned outer strides).
-
-2. [`htp_tensor_mdev_partition`](../../../ggml/src/ggml-hexagon/htp/htp-tensor.h#L94):
-   Calculates the per-device work range `struct htp_tensor_mdev_range { uint32_t start; uint32_t count; }` given
-   `total_units`, `units_per_chunk`, `mdev_idx`, `mdev_count`, and the precomputed `mdev_count_div`.
-   Handles chunk distribution across devices, assigns remainder units to the last device, and automatically triggers
-   single-device fallback when partitioning is unsafe.
-
-### Row-Partitioned Operators
-
-For row-wise operators
-(such as activations in [`act-ops.c`](../../../ggml/src/ggml-hexagon/htp/act-ops.c),
-binary ops in [`binary-ops.c`](../../../ggml/src/ggml-hexagon/htp/binary-ops.c),
-unary ops in [`unary-ops.c`](../../../ggml/src/ggml-hexagon/htp/unary-ops.c), and
-sameshape copies in [`cpy-ops.c`](../../../ggml/src/ggml-hexagon/htp/cpy-ops.c)):
-
-```c
-const uint32_t total_rows   = ne01 * ne02 * ne03;
-const size_t   dst_row_size = dst->ne[0] * elem_size;
-
-uint32_t row_start = 0;
-uint32_t nrows     = total_rows;
-
-if (octx->ctx->mdev.count > 1) {
-    uint32_t rows_per_chunk = 0;
-    htp_tensor_mdev_rows_per_chunk(dst, elem_size, (uint32_t) dst_row_size, &rows_per_chunk);
-    const struct htp_tensor_mdev_range range = htp_tensor_mdev_partition(
-        total_rows, rows_per_chunk, octx->ctx->mdev.idx, octx->ctx->mdev.count, &octx->ctx->mdev.count_div);
-    row_start = range.start;
-    nrows     = range.count;
-}
-
-if (nrows == 0) {
-    return HTP_STATUS_OK;
-}
-```
-
-### Element-Partitioned Operators
-
-For flat element-wise operations (such as reshape copies in
-[`cpy-ops.c`](../../../ggml/src/ggml-hexagon/htp/cpy-ops.c)):
- Partition total linear elements N = ne0 * ne1 * ne2 * ne3 in 128-byte cache line chunks (`elems_per_line = (elem_size == 4) ? 32 : 64`).
- Requires strict 1D contiguity:
-  [`htp_tensor_is_contiguous(dst, elem_size)`](../../../ggml/src/ggml-hexagon/htp/htp-tensor.h#L28)
-  and 128-byte aligned destination pointer
-  [`htp_tensor_mdev_data_aligned(dst)`](../../../ggml/src/ggml-hexagon/htp/htp-tensor.h#L47).
- If contiguous and aligned, pass `elems_per_line` to
-  [`htp_tensor_mdev_partition`](../../../ggml/src/ggml-hexagon/htp/htp-tensor.h#L94);
-  otherwise pass 0 to trigger Device 0 fallback.
-
-### Single-Device Fallback (Device 0)
-
- Fallback to Device 0 (`mdev.idx == 0`) when partitioning would cause cache line tearing or when work cannot be evenly distributed.
- Triggers:
-  1. Destination tensor cannot be safely partitioned (`rows_per_chunk == 0` or non-contiguous/unaligned buffer).
-  2. Total aligned chunks < `mdev_count`.
- Device 0 processes the entire tensor `[0, total_units)`.
- Devices 1 ... N-1 receive `count = 0` and return `HTP_STATUS_OK` immediately.
-
-### Flatten Outer Dimensions Globally
-
- **Never partition solely on `ne01` (dimension 1).**
- Partitioning only on `ne01` repeats the device boundary across every 2D slice (`ne02`, `ne03`). If each 2D slice is small,
-  false sharing occurs repeatedly throughout the tensor.
- Always flatten outer dimensions globally: `total_rows = ne01 * ne02 * ne03` and partition once across the combined row space.
-
-### Stateless Starting Coordinates
-
- Do not use incremental state variables across slices that assume the thread or device starts at index 0.
- Precompute starting multidimensional coordinates at `r = row_start` (or `e = elem_start`) once using `fastdiv`.
- In inner loops, step base pointers directly (`ptr += stride`) or reset/wrap coordinates explicitly (`if (++i01 == ne01) { ... }`).
-
-### Clean Range Encapsulation
-
- Initialize single-device default ranges at declaration:
-
-  ```c
-  uint32_t row_start = 0;
-  uint32_t nrows     = total_rows;
-  ```
-
- Encapsulate all multi-device logic inside `if (octx->ctx->mdev.count > 1)`. If the block is omitted or compiled out,
-  the operator runs standard single-device execution untouched.
- Do not propagate `mdev_` prefixes to worker functions or context structs. Worker threads are device-agnostic and
-  should only receive standard range parameters (`ctx.row_start`, `ctx.nrows`).
- In worker threads, calculate row intervals using standard arithmetic:
-
-  ```c
-  const uint32_t ir0 = ctx->row_start + dr * ith;
-  const uint32_t ir1 = MIN(ir0 + dr, ctx->row_start + ctx->nrows);
-  ```
-
-  In single-device mode (`row_start == 0`), this naturally simplifies to `dr * ith` and `MIN(ir0 + dr, ctx->nrows)` with zero overhead.
-
-## Multi-Device Synchronization
-
-Multi-device execution synchronizes worker sessions across devices using explicit barriers and tensor cache flushing.
-
-### Synchronization Fence Protocol
-
-Multi-device execution synchronizes worker sessions through atomic fence slots and barriers defined in
-[`htp-fence.h`](../../../ggml/src/ggml-hexagon/htp/htp-fence.h):
+Here is an example of running GPT-OSS-20B model on a Snapdragon device using 4 virtual sessions on a single NPU (physical index 0).

 ```
-[NPU Session 0]                              [NPU Session 1]
-       |                                            |
-  (Input Prep)                                 (Input Prep)
-       |                                            |
-  Pre-Op Barrier ----------------------------- Pre-Op Barrier
-  (mdev_sync_fence)                            (mdev_sync_fence)
-       |                                            |
-  Kernel Execution                             Kernel Execution
-  (Output Slice 0)                             (Output Slice 1)
-       |                                            |
-  Tensor Cache Flush                           Tensor Cache Flush
-  (htp_tensor_flush_all)                       (htp_tensor_flush_all)
-       |                                            |
-  Post-Op/Batch Barrier ---------------------- Post-Op/Batch Barrier
-  (htp_mdev_group_barrier)                     (htp_mdev_group_barrier)
-       |                                            |
-  Return Response to Host                      Return Response to Host
+~/src/llama.cpp$ ./scripts/snapdragon/run.py --target adb --devices HTP0:0,HTP0:1,HTP0:2,HTP0:3 -- llama-cli --load-mode none -m /data/local/tmp/gguf/gpt-oss-20b-Q4_0.gguf -t 4 --ctx-size 8192 --batch-size 128 -ctk q8_0 -ctv q8_0 -fa on -ngl 99 -no-cnv -f surfing.txt
+...
+llama_model_loader: - type  f32:  289 tensors
+llama_model_loader: - type q4_0:   96 tensors
+llama_model_loader: - type q8_0:    2 tensors
+llama_model_loader: - type mxfp4:  72 tensors
+...
+load_tensors: offloaded 25/25 layers to GPU
+load_tensors:          CPU model buffer size =  1182.09 MiB
+load_tensors:       HTP0:1 model buffer size =  2512.58 MiB
+load_tensors:       HTP0:3 model buffer size =  2093.83 MiB
+load_tensors:       HTP0:0 model buffer size =  2931.34 MiB
+load_tensors:       HTP0:2 model buffer size =  2512.58 MiB
+...
+llama_context: n_ctx_per_seq (8192) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
+llama_context:        CPU  output buffer size =     0.77 MiB
+llama_kv_cache_iswa: creating non-SWA KV cache, size = 8192 cells
+llama_kv_cache:     HTP0:1 KV buffer size =    25.50 MiB
+llama_kv_cache:     HTP0:3 KV buffer size =    25.50 MiB
+llama_kv_cache:     HTP0:0 KV buffer size =    25.50 MiB
+llama_kv_cache:     HTP0:2 KV buffer size =    25.50 MiB
+llama_kv_cache: size =  102.00 MiB (  8192 cells,  12 layers,  1/1 seqs), K (q8_0):   51.00 MiB, V (q8_0):   51.00 MiB
+llama_kv_cache_iswa: creating     SWA KV cache, size = 256 cells
+llama_kv_cache:     HTP0:1 KV buffer size =     0.80 MiB
+llama_kv_cache:     HTP0:3 KV buffer size =     0.53 MiB
+llama_kv_cache:     HTP0:0 KV buffer size =     1.06 MiB
+llama_kv_cache:     HTP0:2 KV buffer size =     0.80 MiB
+llama_kv_cache: size =    3.19 MiB (   256 cells,  12 layers,  1/1 seqs), K (q8_0):    1.59 MiB, V (q8_0):    1.59 MiB
+llama_context:     HTP0:0 compute buffer size =    16.06 MiB
+llama_context:     HTP0:1 compute buffer size =    16.06 MiB
+llama_context:     HTP0:2 compute buffer size =    16.06 MiB
+llama_context:     HTP0:3 compute buffer size =    16.06 MiB
+llama_context:        CPU compute buffer size =    98.19 MiB
+...
+llama_perf_context_print: prompt eval time =    3843.67 ms /   197 tokens ( 19.51 ms per token, 51.25 tokens per second)
+llama_perf_context_print:        eval time =    1686.13 ms /    31 runs   ( 54.39 ms per token, 18.39 tokens per second)
+llama_perf_context_print:       total time =    6266.30 ms /   228 tokens
+llama_perf_context_print:    graphs reused =         30
+llama_memory_breakdown_print: | memory breakdown [MiB] | total   free    self   model   context   compute    unaccounted |
+llama_memory_breakdown_print: |   - HTP0:0 (Hexagon)   |  2048 = 2048 + (   0 =     0 +       0 +       0) +           0 |
+llama_memory_breakdown_print: |   - HTP0:1 (Hexagon)   |  2048 = 2048 + (   0 =     0 +       0 +       0) +           0 |
+llama_memory_breakdown_print: |   - HTP0:2 (Hexagon)   |  2048 = 2048 + (   0 =     0 +       0 +       0) +           0 |
+llama_memory_breakdown_print: |   - HTP0:3 (Hexagon)   |  2048 = 2048 + (   0 =     0 +       0 +       0) +           0 |
+llama_memory_breakdown_print: |   - Host               |                 1476 =  1208 +     105 +     162                |
 ```
-
-### Atomic Fence Slots and Cache Invalidation
-
- Fence synchronization operates on dedicated RPCMEM shared memory mapped across all participating sessions (`ctx->mdev.fence_base`).
- Each device owns a dedicated 128-byte cache-line aligned fence slot:
-
-  ```c
-  atomic_uint * my_fence = htp_mdev_fence_slot(fence_base, mdev_idx);
-  ```
-
- **Writing to fence ([`htp_fence_write`](../../../ggml/src/ggml-hexagon/htp/htp-fence.h#L18))**:
-  Stores `seq` and `status`, issues a `syncht` thread synchronization barrier, and flushes/invalidates the line
-  using `Q6_dccleaninva_A(fence)`.
- **Reading from peer fence ([`htp_fence_read`](../../../ggml/src/ggml-hexagon/htp/htp-fence.h#L26))**:
-  Executes `Q6_dccleaninva_A(fence)` and `syncht` before reading atomic values to ensure fresh data from DDR.
-
-### Deterministic Monotonic Sequence Numbers
-
- Barrier fences use monotonically increasing sequence numbers:
-
-  ```c
-  const uint32_t seq = ++ctx->mdev.fence_seq;
-  ```
-
- Comparing sequence numbers with signed arithmetic `(int32_t)(peer_seq - seq) >= 0` prevents race conditions or
-  misaligned barrier arrivals across iterations.
- If any peer reports an error status (`peer_status > HTP_STATUS_OK`), the barrier propagates the error and unblocks immediately.
-
-### Tensor Cache Flush and Pipeline Completion
-
- In the kernel, ensure all pushed DMA operations have been popped in strict FIFO order to drain the queue.
- Use [`htp_tensor_flush_all()`](../../../ggml/src/ggml-hexagon/htp/htp-tensor.h) to flush specific dirty tensors back to DDR:
-  - [`htp_tensor_flush_all()`](../../../ggml/src/ggml-hexagon/htp/htp-tensor.h) flushes only modified tensor address ranges,
-    ensuring peer devices and the host CPU observe consistent data in DDR.
- Never signal completion before all DMA transfers are drained and dirty tensor flushes have completed.
-
@@ -24,18 +24,7 @@ must be included in the .cat file digitally signed with a trusted certificate.
 This document covers details on how to generate personal certificate files (.pfx) and how to configure the system
 to allow for test signatures (aka test-signing).

-## Install Windows SDKs
-
-The recommended method is `setup-sdk.py`:
-
-```
-> python scripts\snapdragon\setup-sdk.py --list-sdk-releases
-> python scripts\snapdragon\setup-sdk.py --hexagon --opencl
-```
-
-It installs the selected SDKs under `C:\Qualcomm` and sets their corresponding environment variables for the current user. Start a new terminal after it completes; native Windows builds check all SDK paths before CMake runs.
-
-Select the SDKs to install with `--hexagon` and `--opencl`; use both to prepare a dual-backend build. To select a different available version, pass it to the SDK option, for example `--hexagon 6.4.0.2`. SDK versions install side by side, so you can switch versions without deleting an existing installation. Use `--force` to reinstall the selected SDKs. Use a new CMake build directory after each switch because CMake caches the SDK paths.
+## Install the latest Adreno OpenCL SDK

 Either use the trimmed down version (optimized for CI) from

@@ -1,122 +0,0 @@
-## Build profiling
-This page is a working document for analyzing the current build and try to
-identify ways to improve the build time.
-
-### Requirements
-The profiling script requires clang to be used as the compiler tool chain and
-also requires that ClangBuildAnalyzer is installed.
-
-Mac:
-```console
-brew install clang-build-analyzer
-```
-
-Linux:
-```console
-git clone https://github.com/aras-p/ClangBuildAnalyzer.git
-cd ClangBuildAnalyzer
-cmake -B build -DCMAKE_BUILD_TYPE=Release
-cmake --build build -j$(nproc)
-sudo cp build/ClangBuildAnalyzer /usr/local/bin/
-```
-
-Windows: install LLVM/clang and Ninja (e.g. via the
-[LLVM releases page](https://github.com/llvm/llvm-project/releases) and
-`winget install Ninja-build.Ninja`), then build ClangBuildAnalyzer the same
-way as on Linux:
-```console
-git clone https://github.com/aras-p/ClangBuildAnalyzer.git
-cd ClangBuildAnalyzer
-cmake -B build -G Ninja -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release
-cmake --build build --config Release
-```
-Then add `ClangBuildAnalyzer\build` to `PATH`.
-
-### Usage
-Mac/Linux:
-```console
-$ ./scripts/build-profile.sh
-```
-
-Windows:
-```console
-> .\scripts\build-profile.ps1
-```
-
-Both accept `--full`/`-Full` (include Server, Tools, and Tests) and a jobs
-override (`-jN` / `-Jobs N`).
-
-Note: on Windows, `cmake` defaults to the Visual Studio generator, which
-ignores `CMAKE_C_COMPILER`/`CMAKE_CXX_COMPILER` and silently falls back to
-MSVC. `build-profile.ps1` passes `-G Ninja` so clang is actually used, this
-is required on ARM64.
-
-### Linux (Ubuntu 24.04)
-
-Environment:
- Clang:     18.1.3 (Ubuntu clang version 18.1.3 (1ubuntu1))
- libstdc++: GCC 13.3.0 (Ubuntu 13.3.0-6ubuntu2~24.04.1)
- Target:    x86_64-pc-linux-gnu
-
-```console
-+------------------------+-----+------------+------------+------------+
-| Build                  | TUs | Frontend   | Backend    | Total      |
-+------------------------+-----+------------+------------+------------+
-| Minimal, master        | 249 |   468.2 s  |   270.3 s  |   738.5 s  |
-| Minimal, with PCH      | 253 |   177.1 s  |   265.8 s  |   442.9 s  |
-| Full,    master        | 396 |   811.0 s  |   692.2 s  | 1,503.2 s  |
-| Full,    with PCH      | 405 |   380.0 s  |   664.7 s  | 1,044.7 s  |
-| Full,    with PCH + UB | 264 |   357.7 s  |   635.7 s  |   993.4 s  |
-+------------------------+-----+------------+------------+------------+
-
-PCH  = precompiled header.
-Full = includes building Server, Tools, and Tests.
-UB   = unity build for models
-```
-Note that the number of translation units (TUs) increases when using precompiled
-headers — each PCH target adds one extra TU for the precompilation step itself.
-
-### Mac (Apple M3)
-
-Environment:
- Clang:  Apple clang version 17.0.0 (clang-1700.3.19.1)
- libc++: ships with Apple clang 17.0.0 (Xcode toolchain)
- Target: arm64-apple-macosx15.6
-
-```console
-+------------------------+-----+------------+------------+------------+
-| Build                  | TUs | Frontend   | Backend    | Total      |
-+------------------------+-----+------------+------------+------------+
-| Minimal, master        | 256 |   154.5 s  |    94.8 s  |   249.3 s  |
-| Minimal, with PCH      | 261 |    65.9 s  |    90.0 s  |   155.9 s  |
-| Full,    master        | 407 |   265.7 s  |   209.7 s  |   475.4 s  |
-| Full,    with PCH      | 414 |   154.6 s  |   197.5 s  |   352.1 s  |
-| Full,    with PCH + UB | 274 |   143.0 s  |   192.2 s  |   335.2 s  |
-+------------------------+-----+------------+------------+------------+
-
-PCH = precompiled header.
-Full = includes building Server, Tools, and Tests.
-UB   = unity build for models
-```
-
-### Windows (ARM64)
-
-Environment:
- Clang:  clang version 22.1.8 (LLVM, `C:\Program Files\LLVM`)
- STL:    MSVC STL (Visual Studio 2022 Build Tools 14.44.35207)
- Target: aarch64-pc-windows-msvc
-
-```console
-+------------------------+-----+------------+------------+------------+
-| Build                  | TUs | Frontend   | Backend    | Total      |
-+------------------------+-----+------------+------------+------------+
-| Minimal, master        | 249 |   159.4 s  |    82.2 s  |   241.6 s  |
-| Full,    master        | 373 |   337.2 s  |   167.4 s  |   504.6 s  |
-| Minimal, with PCH + UB | 113 |    62.3 s  |    82.4 s  |   144.7 s  |
-| Full,    with PCH + UB | 240 |   233.0 s  |   185.1 s  |   418.1 s  |
-+------------------------+-----+------------+------------+------------+
-
-PCH = precompiled header.
-Full = includes building Server, Tools, and Tests.
-UB   = unity build for models
-```
@@ -243,7 +243,6 @@ IBM VXE/VXE2 SIMD acceleration depends on the BLAS implementation. It is strongl
 | FP32       | ✅           | ✅    | ❓     |
 | FP16       | ✅           | ✅    | ❓     |
 | BF16       | ✅           | ✅    | ❓     |
-| Q1_0       | ✅           | ❓    | ❓     |
 | Q4_0       | ✅           | ❓    | ❓     |
 | Q4_1       | ✅           | ❓    | ❓     |
 | MXFP4      | ✅           | ❓    | ❓     |
@@ -273,4 +272,4 @@ IBM VXE/VXE2 SIMD acceleration depends on the BLAS implementation. It is strongl
 -   🚫 - acceleration unavailable, will still run using scalar implementation
 -   ❓ - acceleration unknown, please contribute if you can test it yourself

-Last Updated by **Aaron Teo (aaron.teo1@ibm.com)** on Sep 8, 2026.
+Last Updated by **Aaron Teo (aaron.teo1@ibm.com)** on Feb 15, 2026.
@@ -27,7 +27,6 @@ The following sections describe how to build with different backends and options
 * [OpenCL](#opencl)
 * [Android](#android-1)
 * [OpenVINO](#openvino)
-* [Hexagon](#hexagon)
 * [Notes about GPU-accelerated backends](#notes-about-gpu-accelerated-backends)

 ## CPU Build
@@ -300,8 +299,8 @@ The following compilation options are also available to tweak performance:
 |-------------------------------|------------------------|---------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | 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_FA_QUANTS           | `all` or `type_K-type_V` list | q4_0-q4_0;q8_0-q8_0;f16-f16;bf16-bf16 | Select which K/V type combinations to compile the FlashAttention CUDA kernels for. `all` compiles every combination, but compilation takes much longer. Otherwise a `;`-separated list of `type_K-type_V` pairs; f16-f16 is always compiled. Combinations that were not compiled fall back to f16-f16 kernel with a warning. Legal types: f16, bf16, q4_0, q4_1, q5_0, q5_1, q8_0. |
-| GGML_CUDA_FA_ALL_QUANTS       | Boolean                | false   | Deprecated alias for `GGML_CUDA_FA_QUANTS=all`.                                                                                                                                                                                                                                                                                                                               |
+| GGML_CUDA_PEER_MAX_BATCH_SIZE | Positive integer       | 128     | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial.                                                                                                                                                                  |
+| GGML_CUDA_FA_ALL_QUANTS       | Boolean                | false   | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer.                                                                                                                                                                                           |

 ## MUSA

@@ -615,100 +614,30 @@ You can test with:
 For detailed information about hardware support, setup instructions, and performance optimization, refer to [llama.cpp for ZenDNN](./backend/ZenDNN.md).

 ## Arm® KleidiAI™
-KleidiAI provides optimized Arm CPU microkernels used by the ggml CPU backend. Enabling it at build time makes those kernels available; it does not force every operation to use KleidiAI. At runtime, llama.cpp selects the best compatible CPU kernel from the detected CPU features, tensor type, operation shape, and active backend priority.
-
-Supported targets:
-
-| Platform | Supported ABI / architecture | Notes |
-| --- | --- | --- |
-| Linux | AArch64 / arm64 | Runtime CPU feature detection is automatic. |
-| Android | `arm64-v8a` | Use the Android NDK command below for a portable build. |
-| Apple | arm64 | Runtime CPU feature detection is automatic. Non-streaming SVE vector length is treated as unavailable. |
-| Windows | arm64 | Runtime CPU feature detection is automatic. SMCU count is treated as unknown until a detection path is verified. |
-
-`GGML_CPU_KLEIDIAI=ON` is valid only for AArch64/arm64 builds. Do not enable it for x86, 32-bit Arm, or Android ABIs other than `arm64-v8a`.
-
-### Native AArch64/arm64 build
-
-From the llama.cpp source directory:
+KleidiAI is a library of optimized microkernels for AI workloads, specifically designed for Arm CPUs. These microkernels enhance performance and can be enabled for use by the CPU backend.

+To enable KleidiAI, go to the llama.cpp directory and build using CMake
 ```bash
-cmake -S . -B build -DGGML_CPU_KLEIDIAI=ON
+cmake -B build -DGGML_CPU_KLEIDIAI=ON
 cmake --build build --config Release
 ```
-
-### Android arm64-v8a NDK build
-
-Set `ANDROID_NDK` to the Android NDK root, then run the following from the llama.cpp source directory. This command configures a portable Android `arm64-v8a` build with KleidiAI enabled and avoids Android dependencies that are not part of the NDK stable native API set.
-
-```bash
-cmake -S . -B build-android \
-  -DCMAKE_BUILD_TYPE=Release \
-  -DCMAKE_TOOLCHAIN_FILE="$ANDROID_NDK/build/cmake/android.toolchain.cmake" \
-  -DANDROID_ABI=arm64-v8a \
-  -DANDROID_PLATFORM=android-28 \
-  -DGGML_CPU_KLEIDIAI=ON \
-  -DGGML_NATIVE=OFF \
-  -DGGML_OPENMP=OFF \
-  -DGGML_LLAMAFILE=OFF \
-  -DLLAMA_OPENSSL=OFF
-cmake --build build-android --config Release --parallel
-cmake --install build-android --prefix {install-dir} --config Release
-```
-
-Important Android options:
-
- `GGML_CPU_KLEIDIAI=ON` enables KleidiAI for Android `arm64-v8a`.
- `GGML_NATIVE=OFF` is required for cross-compilation because the build host CPU is not the Android target CPU.
- `GGML_OPENMP=OFF` avoids adding an OpenMP runtime dependency to this NDK command-line build.
- `GGML_LLAMAFILE=OFF` avoids the llamafile backend, which is not supported on Android.
- `LLAMA_OPENSSL=OFF` avoids depending on OpenSSL, which is not part of the Android NDK stable native API set.
-
-The Android Studio project under `examples/llama.android` enables KleidiAI automatically for `arm64-v8a`. For Android command-line CMake builds on `arm64-v8a`, pass `-DGGML_CPU_KLEIDIAI=ON` explicitly.
-
-Global -march flags such as `-march=armv8.7a` flag are not required for a portable Android `arm64-v8a` build. Global `-march` flags raise the baseline instruction set for generic code. No manual architecture-specific source selection is required; llama.cpp selects compatible KleidiAI kernels at runtime. The KleidiAI libraries internal CMake handles the -march flags for each particular kernel.
-
-### Verifying the build
-
-Run an installed or in-tree binary:
-
+You can verify that KleidiAI is being used by running
 ```bash
 ./build/bin/llama-cli -m PATH_TO_MODEL -p "What is a car?"
 ```
-
-If KleidiAI is enabled, the output contains a line similar to:
-
+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.

-This confirms that the model has tensors allocated through the KleidiAI CPU buffer. It does not prove that every operation, or any specific SME-family operation, used a KleidiAI microkernel. Runtime CPU features, tensor type, operation shape, and backend priority still control dispatch.
-
-Depending on the build target, another backend may have higher priority than the CPU backend. To force CPU execution for a run, disable higher priority backends at build time, for example `-DGGML_METAL=OFF`, or use a runtime device option such as `--device none` where supported.
-
-### Runtime dispatch
-
-KleidiAI microkernels use Arm CPU features such as dotprod, i8mm, SVE, and SME/SME2. Build-time configuration makes the kernels available. Runtime dispatch selects a compatible kernel for the detected CPU and operation. Older or lower-feature CPUs fall back automatically to compatible kernels.
-
-KleidiAI accelerates selected `GGML_OP_MUL_MAT` paths for F32 and common quantized formats. Exact coverage depends on the bundled KleidiAI version and the llama.cpp runtime selector, so unsupported tensor types, unsupported operation shapes, or higher priority backends may bypass KleidiAI even when the CPU supports the required Arm feature. This is also why a model may not use SME-family kernels on SME-capable hardware.
-
-The current llama.cpp KleidiAI SVE selector only enables SVE kernels when the runtime SVE vector length is known to be QK8_0 bytes, currently 32 bytes. Linux and Android query this at runtime. Apple reports SVE capability separately from userspace non-streaming SVE availability, so llama.cpp treats the SVE vector length as unknown there. Windows exposes SVE feature presence but not the runtime SVE vector length used by this selector, so that value is also treated as unknown. Windows arm64 also treats SMCU count as unknown until a detection mechanism is verified.
-
-The set of available SME-family kernels depends on the bundled KleidiAI version and the detected CPU capabilities. Production configuration does not require any KleidiAI runtime environment variables.
-
-### Diagnostics and debug overrides
-
-KleidiAI runtime environment variables are diagnostics/debug overrides, not production configuration. Leave them unset for normal use.
-
-`GGML_KLEIDIAI_SME` controls SME-family kernel selection and overrides the maximum number of threads assigned to selected quantized SME-family kernels:
-
- Not set: use automatic runtime detection.
- `0`: disable SME-family kernels.
- `<n> > 0`: enable compatible SME-family kernels and allow up to `<n>` threads for quantized SME-family kernels.
-
-On Windows arm64, use `GGML_KLEIDIAI_SME=<n>` as the temporary diagnostics/debug override for SME thread-cap calibration until automatic SMCU count detection is verified.
-
-If the CPU does not support the required SME-family capability for a bundled kernel, that kernel is disabled regardless of the environment variable.
+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`.

 ## OpenCL

@@ -806,7 +735,7 @@ To read documentation for how to build on Android, [click here](./android.md)

 ## WebGPU

-The WebGPU backend relies on [Dawn](https://dawn.googlesource.com/dawn). Follow the instructions [here](https://dawn.googlesource.com/dawn/+/refs/heads/main/docs/quickstart-cmake.md) to install Dawn locally so that llama.cpp can find it using CMake. The current implementation is up-to-date with Dawn commit `94c3c9c`.
+The WebGPU backend relies on [Dawn](https://dawn.googlesource.com/dawn). Follow the instructions [here](https://dawn.googlesource.com/dawn/+/refs/heads/main/docs/quickstart-cmake.md) to install Dawn locally so that llama.cpp can find it using CMake. The current implementation is up-to-date with Dawn commit `18eb229`.

 In the llama.cpp directory, build with CMake:

@@ -831,9 +760,6 @@ To read documentation for how to build on IBM Z & LinuxONE, [click here](./build

 For build instructions and usage examples, refer to [OPENVINO.md](backend/OPENVINO.md).

-### Hexagon
-
-Check [README.md](./backend/snapdragon/README.md) for target specific build and run info.

 ---
 ## Notes about GPU-accelerated backends
@@ -12,117 +12,116 @@ Legend:
 - 🟡 Partially supported by this backend
 - ❌ Not supported by this backend

-| Operation | BLAS | CANN | CPU | CUDA | ET | HTP | MTL | OpenCL | SYCL | Vulkan | WebGPU | ZenDNN | zDNN |
-|-----------|------|------|------|------|------|------|------|------|------|------|------|------|------|
-|                              ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                              ACC | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                           ARANGE | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             CEIL | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                        COL2IM_1D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                           CONCAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
-|                             CONT | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
-|                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              COS | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                      COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
-|               CROSS_ENTROPY_LOSS | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|          CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                           CUMSUM | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             DIAG | ❌ | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              DIV | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                     DSV4_HC_COMB | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                     DSV4_HC_POST | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                      DSV4_HC_PRE | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                              DUP | ❌ | ✅ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              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 | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
-|                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
-|                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                           IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                        IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                          L2_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | 🟡 | ❌ | ❌ |
-|                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                LIGHTNING_INDEXER | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                              LOG | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                 MUL_MAT_HADAMARD | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ✅ | 🟡 | 🟡 | ❌ |
-|                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ |
-|                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                     OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                         OUT_PROD | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 |
-|                              PAD | ❌ | 🟡 | ✅ | 🟡 | ❌ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                          POOL_1D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                           REPEAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
-|                      REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                         RMS_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                    RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                             ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                             ROPE | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                        ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                            ROUND | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                        RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                        RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                            SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                              SET | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                         SET_ROWS | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ |
-|                              SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                              SIN | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
-|                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             SQRT | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                         SSM_CONV | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
-|                             STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                              SUM | ❌ | 🟡 | ✅ | 🟡 | ❌ | ❌ | 🟡 | ❌ | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
-|                         SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                     SWIGLU_CLAMP | ❌ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                            TOP_K | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
-|                              TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                            TRUNC | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                            XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+| Operation | BLAS | CANN | CPU | CUDA | ET | MTL | OpenCL | SYCL | Vulkan | WebGPU | ZenDNN | zDNN |
+|-----------|------|------|------|------|------|------|------|------|------|------|------|------|
+|                              ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              ACC | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                           ARANGE | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             CEIL | ❌ | ❌ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                        COL2IM_1D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                           CONCAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
+|                             CONT | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
+|                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                              COS | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                      COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
+|               CROSS_ENTROPY_LOSS | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|          CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                           CUMSUM | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             DIAG | ❌ | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                              DIV | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                     DSV4_HC_COMB | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                     DSV4_HC_POST | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                      DSV4_HC_PRE | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                              DUP | ❌ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                              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 | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
+|                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
+|                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                           IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                        IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                          L2_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | 🟡 | ❌ | ❌ |
+|                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                LIGHTNING_INDEXER | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                              LOG | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
+|                 MUL_MAT_HADAMARD | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | 🟡 | 🟡 | ❌ |
+|                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ |
+|                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                     OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                         OUT_PROD | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 |
+|                              PAD | ❌ | 🟡 | ✅ | 🟡 | ❌ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
+|                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                          POOL_1D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                           REPEAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
+|                      REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                         RMS_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                    RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                             ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                             ROPE | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                        ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                            ROUND | ❌ | ❌ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                        RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                        RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                            SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              SET | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
+|                         SET_ROWS | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ |
+|                              SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                              SIN | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
+|                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | 🟡 | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
+|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             SQRT | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                         SSM_CONV | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                             STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              SUM | ❌ | 🟡 | ✅ | 🟡 | ❌ | 🟡 | ❌ | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
+|                         SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
+|                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                            TOP_K | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                              TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                            TRUNC | ❌ | ❌ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | ❌ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                            XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
@@ -212,15 +212,6 @@ Use `--backend-sampling` to run supported target-model samplers on the model bac

 Unsupported samplers and device layouts fall back to CPU sampling. Tensor split mode does not support backend sampling. A fixed seed produces repeatable random draws, but stochastic CPU and backend sampling can still select different tokens because floating-point operations can differ between implementations and devices. Use greedy sampling when exact output matching is required.

-### Synthetic Acceptance
-
-`llama-server` and `llama-cli` can replace normal speculative verification with synthetic decisions for benchmarking. The generated output is not valid model output because accepted draft tokens do not have to match the target model.
-
-Use exactly one of these options:
-
- `--spec-synth-rates P0,P1,...` sets unconditional per-position acceptance probabilities. Entry `i` is the probability that the first `i+1` draft tokens are all accepted. The number of entries must match the effective maximum draft length. Values must be finite, within `[0, 1]`, and monotonically non-increasing.
- `--spec-synth-len L` sets the target mean acceptance length, including the target token. For `K` maximum draft tokens, `L` must be within `[1, K+1]`. The server finds a constant conditional probability `p` such that `p + p^2 + ... + p^K = L - 1`, then uses unconditional rates `[p, p^2, ..., p^K]`.
-
 ### General Speculative Parameters

 ```
@@ -734,9 +734,6 @@ class SchemaConverter:
            )
            optional_props.append("*")

-        if not required_props and not optional_props:
-            return '"{" space "}"'
-
        rule = '"{" space '
        rule += ' "," space '.join(prop_kv_rule_names[k] for k in required_props)

@@ -1177,7 +1177,7 @@ def create_dynamic_model_from_function(func: Callable[..., Any]):
        dynamic_fields[param.name] = (
            param.annotation if param.annotation != inspect.Parameter.empty else str, default_value)
    # Creating the dynamic model
-    dynamic_model = create_model(f"{getattr(func, '__name__')}", **dynamic_fields)  # ty: ignore[no-matching-overload]
+    dynamic_model = create_model(f"{getattr(func, '__name__')}", **dynamic_fields)

    for name, param_doc in param_docs:
        dynamic_model.model_fields[name].description = param_doc.description
@@ -188,7 +188,7 @@ int main(int argc, char ** argv) {
            common_speculative_get_draft_params(spec, seq_id) = {
                /* .drafting   = */ true,
                /* .n_max      = */ n_draft_max,
-                /* .pos0       = */ n_past,
+                /* .n_past     = */ n_past,
                /* .id_last    = */ id_last,
                /* .prompt     = */ &prompt_tgt,
                /* .result     = */ &draft, // output
--- a/Show More
+++ b/Show More