vendor: attempt to ignore warnings from vendored files

Signed-off-by: Aaron Teo <[email protected]>
ggml-zdnn: fix compiler errors
2026-09-27 21:46:57 +02:00 · 2026-09-28 03:22:59 +08:00 · 2026-09-28 02:57:13 +08:00 · 2026-09-28 02:56:50 +08:00 · 2026-09-28 02:39:23 +08:00 · 2026-09-28 02:36:07 +08:00
545 changed files with 70679 additions and 44698 deletions
@@ -1,10 +1,9 @@
 ARG UBUNTU_VERSION=22.04
 # This needs to generally match the container host's environment.
-ARG MUSA_VERSION=rc4.3.0
 # Target the MUSA build image
-ARG BASE_MUSA_DEV_CONTAINER=docker.io/mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}-amd64
+ARG BASE_MUSA_DEV_CONTAINER=registry.mthreads.com/mcconline/inference/pytorch:2.9.1.post1-py3.10-musa5.2.0-mp31-devel-ubuntu${UBUNTU_VERSION}-amd64

-ARG BASE_MUSA_RUN_CONTAINER=docker.io/mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}-amd64
+ARG BASE_MUSA_RUN_CONTAINER=${BASE_MUSA_DEV_CONTAINER}

 ARG BUILD_DATE=N/A
 ARG APP_VERSION=N/A
@@ -1,5 +1,5 @@
-ARG OPENVINO_VERSION_MAJOR=2026.3.1
-ARG OPENVINO_VERSION_FULL=2026.3.1.22476.56d9685302d
+ARG OPENVINO_VERSION_MAJOR=2026.4
+ARG OPENVINO_VERSION_FULL=2026.4.0.22959.99c81491cc3
 ARG UBUNTU_VERSION=24.04

 # Intel GPU driver versions. https://github.com/intel/compute-runtime/releases
@@ -10,9 +10,9 @@ ARG COMPUTE_RUNTIME_VERSION_FULL=26.31.39395.13-0
 ARG IGDGMM_VERSION=22.10.0

 # Intel NPU driver versions. https://github.com/intel/linux-npu-driver/releases
-ARG NPU_DRIVER_VERSION=v1.35.0
-ARG NPU_DRIVER_FULL=v1.35.0.20260722-29947505341
-ARG LIBZE1_VERSION=1.28.2-1~24.04~ppa1
+ARG NPU_DRIVER_VERSION=v1.38.0
+ARG NPU_DRIVER_FULL=v1.38.0.20260910-34487311128
+ARG LIBZE1_VERSION=1.32.0-1~24.04~ppa1

 # Optional proxy build arguments
 ARG http_proxy=
@@ -173,7 +173,7 @@ RUN --mount=type=cache,target=/var/cache/intel-npu,sharing=locked \
    fi; \
    DEB=/var/cache/intel-npu/libze1_${LIBZE1_VERSION}_amd64.deb; \
    if [ ! -f "$DEB" ]; then \
-        wget -q -O "$DEB" https://snapshot.ppa.launchpadcontent.net/kobuk-team/intel-graphics/ubuntu/20260606T100000Z/pool/main/l/level-zero-loader/libze1_${LIBZE1_VERSION}_amd64.deb; \
+        wget -q -O "$DEB" https://snapshot.ppa.launchpadcontent.net/kobuk-team/intel-graphics/ubuntu/20260830T100000Z/pool/main/l/level-zero-loader/libze1_${LIBZE1_VERSION}_amd64.deb; \
    fi; \
    mkdir /tmp/npu/ && cd /tmp/npu/ && tar -xf "$TGZ" && cp "$DEB" .; \
    apt-get update; \
@@ -1,5 +1,5 @@
 {
-  "Exclude": ["^\\.gitmodules$", "stb_image\\.h"],
+  "Exclude": ["^\\.gitmodules$", "stb_image\\.h", "examples/test-cmake/build/", "examples/test-cmake/build-subdir/"],
  "Disable": {
    "IndentSize": true
  }
@@ -14,7 +14,7 @@ runs:
      run: |
        BUILD_NUMBER="$(git rev-list --count HEAD)"
        SHORT_HASH="$(git rev-parse --short=7 HEAD)"
-        if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then
+        if [[ "${{ env.BRANCH_NAME }}" == "master" || "${{ env.BRANCH_NAME }}" == "b${BUILD_NUMBER}" ]]; then
          echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT
        else
          SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-')
@@ -100,36 +100,36 @@ runs:
          echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
          echo "CUDA_PATH_V13_1=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8

-    - name: Install Cuda Toolkit 13.3
-      if: ${{ inputs.cuda_version == '13.3' }}
+    - name: Install Cuda Toolkit 13.4 for x64
+      if: ${{ inputs.cuda_version == '13.4' && inputs.cuda_arch == 'x64' }}
      shell: pwsh
      run: |
-          mkdir -p "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3"
+          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/cuda_crt/windows-x86_64/cuda_crt-windows-x86_64-13.3.33-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cudart/windows-x86_64/cuda_cudart-windows-x86_64-13.3.29-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvcc/windows-x86_64/cuda_nvcc-windows-x86_64-13.3.33-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvrtc/windows-x86_64/cuda_nvrtc-windows-x86_64-13.3.33-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libcublas/windows-x86_64/libcublas-windows-x86_64-13.5.1.27-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libnvvm/windows-x86_64/libnvvm-windows-x86_64-13.3.33-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvtx/windows-x86_64/cuda_nvtx-windows-x86_64-13.3.29-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_profiler_api/windows-x86_64/cuda_profiler_api-windows-x86_64-13.3.27-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/visual_studio_integration/windows-x86_64/visual_studio_integration-windows-x86_64-13.3.27-archive.zip"
-          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cccl/windows-x86_64/cccl-windows-x86_64-13.3.3.3.1-archive.zip"
-          unzip '*.zip' -d "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3"
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\cuda_crt-windows-x86_64-13.3.33-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\cuda_cudart-windows-x86_64-13.3.29-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\cuda_nvcc-windows-x86_64-13.3.33-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\cuda_nvrtc-windows-x86_64-13.3.33-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\libcublas-windows-x86_64-13.5.1.27-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\libnvvm-windows-x86_64-13.3.33-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\cuda_nvtx-windows-x86_64-13.3.29-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\cuda_profiler_api-windows-x86_64-13.3.27-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\visual_studio_integration-windows-x86_64-13.3.27-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" /E /I /H /Y
-          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\cccl-windows-x86_64-13.3.3.3.1-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" /E /I /H /Y
-          echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-          echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
-          echo "CUDA_PATH_V13_3=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
+          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_cudart/windows-x86_64/cuda_cudart-windows-x86_64-13.4.49-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/cuda_nvrtc/windows-x86_64/cuda_nvrtc-windows-x86_64-13.4.59-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libcublas/windows-x86_64/libcublas-windows-x86_64-13.7.0.27-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_nvtx/windows-x86_64/cuda_nvtx-windows-x86_64-13.4.49-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_profiler_api/windows-x86_64/cuda_profiler_api-windows-x86_64-13.4.49-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/visual_studio_integration/windows-x86_64/visual_studio_integration-windows-x86_64-13.4.49-archive.zip"
+          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"
+          unzip '*.zip' -d "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4"
+          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_cudart-windows-x86_64-13.4.49-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\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\cuda_nvrtc-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\libcublas-windows-x86_64-13.7.0.27-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\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_nvtx-windows-x86_64-13.4.49-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\cuda_profiler_api-windows-x86_64-13.4.49-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\visual_studio_integration-windows-x86_64-13.4.49-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\cccl-windows-x86_64-13.3.4.2.1-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

    - name: Install Cuda Toolkit 13.4 for ARM64
      if: ${{ inputs.cuda_version == '13.4' && inputs.cuda_arch == 'arm64' }}
@@ -29,7 +29,7 @@ concurrency:

 jobs:
  android-ndk-snapdragon:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04  # previously ubuntu-latest
    container:
      image: 'ghcr.io/snapdragon-toolchain/arm64-android:v0.7'
    defaults:
@@ -59,7 +59,7 @@ jobs:
          path: pkg-snapdragon/llama.cpp

  linux-iot-snapdragon:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04  # previously ubuntu-latest
    container:
      image: 'ghcr.io/snapdragon-toolchain/arm64-linux:v0.7'
    defaults:
@@ -33,7 +33,7 @@ env:

 jobs:
  default:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04  # previously ubuntu-latest

    steps:
      - name: Clone
@@ -49,7 +49,7 @@ jobs:
          distribution: zulu

      - name: Setup Android SDK
-        uses: android-actions/setup-android@40fd30fb8d7440372e1316f5d1809ec01dcd3699 # v4.0.1
+        uses: android-actions/setup-android@be39fa834029ff78f1a44aa3bb0819b8fc2bd8fd # v4.0.4
        with:
          log-accepted-android-sdk-licenses: false

@@ -59,7 +59,7 @@ jobs:
          ./gradlew build --no-daemon

  ndk:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04  # previously ubuntu-latest
    container:
      image: 'ghcr.io/snapdragon-toolchain/arm64-android:v0.3'
    defaults:
@@ -93,7 +93,7 @@ jobs:
          path: pkg-adb/llama.cpp

  arm64:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04  # previously ubuntu-latest

    env:
      NDK_VERSION: "29.0.14206865"
@@ -123,7 +123,7 @@ jobs:
          distribution: temurin

      - name: Setup Android SDK
-        uses: android-actions/setup-android@40fd30fb8d7440372e1316f5d1809ec01dcd3699 # v4.0.1
+        uses: android-actions/setup-android@be39fa834029ff78f1a44aa3bb0819b8fc2bd8fd # v4.0.4
        with:
          log-accepted-android-sdk-licenses: false

@@ -33,6 +33,7 @@ concurrency:
 env:
  GGML_NLOOP: 3
  GGML_N_THREADS: 1
+  GGML_SCHED_DEBUG_REALLOC: 1
  LLAMA_ARG_LOG_COLORS: 1
  LLAMA_ARG_LOG_PREFIX: 1
  LLAMA_ARG_LOG_TIMESTAMPS: 1
@@ -98,7 +99,8 @@ jobs:
        id: cmake_test
        run: |
          cd build
-          ctest -L main -E "test-llama-archs" --verbose --timeout 900
+          # ref: https://github.com/ggml-org/llama.cpp/pull/19802#issuecomment-4013704023
+          ctest -L main -E "test-llama-archs|test-save-load-state" --verbose --timeout 900

  macos-latest-x64:
    runs-on: macos-15-intel
@@ -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: "2026.4"
+      OPENVINO_VERSION_FULL: "2026.4.0.22959.99c81491cc3"

    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: "2026.4"
+      OPENVINO_VERSION_FULL: "2026.4.0.22959.99c81491cc3"

    steps:
      - name: Clone
@@ -5,7 +5,7 @@ on:

 jobs:
  linux:
-    runs-on: [self-hosted, Linux]
+    runs-on: [self-hosted, Linux, CPU]
    steps:
      - uses: actions/checkout@v6
        with:
@@ -37,6 +37,7 @@ concurrency:
 env:
  GGML_NLOOP: 3
  GGML_N_THREADS: 1
+  GGML_SCHED_DEBUG_REALLOC: 1
  LLAMA_ARG_LOG_COLORS: 1
  LLAMA_ARG_LOG_PREFIX: 1
  LLAMA_ARG_LOG_TIMESTAMPS: 1
@@ -88,7 +89,7 @@ jobs:
        run: |
          export PIP_BREAK_SYSTEM_PACKAGES="1"
          python3 -m pip install --upgrade pip setuptools
-          pip3 install ./gguf-py
+          pip3 install ./gguf-py jinja2==3.1.6

      - name: ccache-buckets-restore
        uses: ./.github/actions/ccache-buckets
@@ -124,7 +125,7 @@ jobs:
        id: cmake_test
        run: |
          cd build
-          ctest -L main --verbose --timeout 900
+          ctest -L 'main|python' --verbose --timeout 900

      - name: Test llama2c conversion
        id: llama2c_test
@@ -145,7 +145,7 @@ jobs:

  musa:
    runs-on: ubuntu-22.04
-    container: mthreads/musa:rc4.3.0-devel-ubuntu22.04-amd64
+    container: registry.mthreads.com/mcconline/inference/pytorch:2.9.1.post1-py3.10-musa5.2.0-mp31-devel-ubuntu22.04-amd64

    steps:
      - name: Clone
@@ -156,7 +156,7 @@ jobs:
        id: depends
        run: |
          apt-get update
-          apt-get install -y build-essential git cmake libssl-dev jq
+          apt-get install -y build-essential git cmake libssl-dev jq python3-venv

      - name: ccache
        uses: ggml-org/[email protected]
@@ -177,8 +177,9 @@ jobs:
        id: cmake_build
        run: |
          cmake -B build -S . \
-            -DGGML_MUSA=ON
-          time cmake --build build --config Release -j $(nproc)
+            -DGGML_MUSA=ON \
+            -DMUSA_ARCHITECTURES=31
+          cmake --build build --config Release -j $(nproc)

      - name: ccache-buckets-save
        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
@@ -34,7 +34,7 @@ jobs:
          - cuda: '12.4'
            arch: x64
            defines: '-DGGML_CUDA_CUB_3DOT2=ON'
-          - cuda: '13.3'
+          - cuda: '13.4'
            arch: x64
            defines: ''
          - cuda: '13.4'
@@ -34,10 +34,15 @@ env:
  LLAMA_ARG_LOG_TIMESTAMPS: 1

 jobs:
-
  ubuntu-24-s390x:
+    name: ubuntu-24-s390x (VXE ${{ matrix.vxe }})
    runs-on: ubuntu-24.04-s390x

+    strategy:
+      fail-fast: false
+      matrix:
+        vxe: ["ON", "OFF"]  # `-DGGML_VXE=ON/OFF`
+
    steps:
      - name: Clone
        id: checkout
@@ -77,7 +82,8 @@ jobs:
        run: |
          cmake -B build \
            -DLLAMA_FATAL_WARNINGS=ON \
-            -DGGML_RPC=ON
+            -DGGML_RPC=ON \
+            -DGGML_VXE=${{ matrix.vxe }}
          time cmake --build build --config Release -j $(nproc)

      - name: Test
@@ -94,6 +100,46 @@ jobs:
          wget https://huggingface.co/ggml-org/models/resolve/main/tinyllamas/stories260K-be.gguf
          ./bin/llama-completion -m stories260K-be.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256

+  ubuntu-26-zdnn-s390x:
+    name: ubuntu-26-zdnn-s390x
+    runs-on: ubuntu-24.04-s390x
+    container: ubuntu:26.04  # required to get GCC 15.1 and binutils 2.44
+    defaults:
+      run:
+        shell: bash
+
+    steps:
+      - name: Build Dependencies
+        id: build_depends
+        run: |
+          apt-get update
+          apt-get install -y --no-install-recommends \
+            build-essential cmake git ca-certificates \
+            libssl-dev libzdnn-dev
+
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Toolchain workaround (GCC 15)
+        run: |
+          apt-get install -y gcc-15 g++-15
+          echo "CC=gcc-15" >> "$GITHUB_ENV"
+          echo "CXX=g++-15" >> "$GITHUB_ENV"
+
+      - name: Build with zDNN Backend
+        id: cmake_build
+        run: |
+          cmake -B build \
+            -DLLAMA_FATAL_WARNINGS=ON \
+            -DGGML_NATIVE=OFF \
+            -DGGML_VXE=ON \
+            -DGGML_ZDNN=ON \
+            -DGGML_RPC=ON \
+            -DCMAKE_C_FLAGS="-march=arch15" \
+            -DCMAKE_CXX_FLAGS="-march=arch15"
+          time cmake --build build --config Release -j $(nproc)
+
  ubuntu-24-ppc64le:
    runs-on: ubuntu-24.04-ppc64le

@@ -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: "2026.4"
+      OPENVINO_VERSION_FULL: "2026.4.0.22959.99c81491cc3"

    steps:
      - name: Clone
@@ -96,8 +96,8 @@ 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: "2026.4"
+      OPENVINO_VERSION_FULL: "2026.4.0.22959.99c81491cc3"

    steps:
      - name: Clone
@@ -1,447 +0,0 @@
-name: CI (self-hosted)
-
-on:
-  workflow_dispatch: # allows manual triggering
-  push:
-    branches:
-      - master
-    paths: [
-      '.github/workflows/build-self-hosted.yml',
-      'ci/run.sh',
-      '**/CMakeLists.txt',
-      '**/.cmake',
-      '**/*.h',
-      '**/*.hpp',
-      '**/*.c',
-      '**/*.cpp',
-      '**/*.cu',
-      '**/*.cuh',
-      '**/*.swift',
-      '**/*.m',
-      '**/*.metal',
-      '**/*.comp',
-      '**/*.glsl',
-      '**/*.wgsl'
-    ]
-
-  pull_request:
-    types: [opened, synchronize, reopened]
-    paths: [
-      '.github/workflows/build-self-hosted.yml',
-      'ci/run.sh',
-      '**/CMakeLists.txt',
-      '**/.cmake',
-      '**/*.h',
-      '**/*.hpp',
-      '**/*.c',
-      '**/*.cpp',
-      '**/*.cu',
-      '**/*.cuh',
-      '**/*.swift',
-      '**/*.m',
-      '**/*.metal',
-      '**/*.comp',
-      '**/*.glsl',
-      '**/*.wgsl'
-    ]
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
-env:
-  # 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 }}
-  GGML_NLOOP: 3
-  GGML_N_THREADS: 1
-  LLAMA_ARG_LOG_COLORS: 1
-  LLAMA_ARG_LOG_PREFIX: 1
-  LLAMA_ARG_LOG_TIMESTAMPS: 1
-
-jobs:
-  gpu-cuda:
-    runs-on: "hf-jobs-t4-small:cuda13"
-
-    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/[email protected]
-        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
-
-  gpu-rocm:
-    runs-on: [self-hosted, Linux, AMD]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Test
-        id: ggml-ci
-        # HIP_LAUNCH_BLOCKING=1: workaround for an async-execution correctness
-        # issue on integrated RDNA3.5 (gfx1151) where batched inference returns
-        # incorrect output (perplexity ~88 vs ~9.4). Serializing kernel launches
-        # restores correctness. Remove once the underlying ROCm/HIP issue is fixed.
-        env:
-          HIP_LAUNCH_BLOCKING: "1"
-        run: |
-          rocminfo
-          GG_BUILD_ROCM=1 GG_BUILD_AMDGPU_TARGETS=gfx1151 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  gpu-vulkan-nvidia-cm:
-    runs-on: [self-hosted, Linux, NVIDIA]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          vulkaninfo --summary
-          GG_BUILD_VULKAN=1 GGML_VK_DISABLE_COOPMAT2=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  gpu-vulkan-nvidia-cm2:
-    runs-on: [self-hosted, Linux, NVIDIA, COOPMAT2]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          vulkaninfo --summary
-          GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  gpu-webgpu-nvidia:
-    runs-on: [self-hosted, Linux, NVIDIA, X64]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Dawn Dependency
-        id: dawn-depends
-        run: |
-          DAWN_VERSION="v20260908.214631"
-          DAWN_OWNER="google"
-          DAWN_REPO="dawn"
-          DAWN_ASSET_NAME="Dawn-94c3c9cc0d5fb2e85aebb370fa8d37b71aa34655-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}/${DAWN_ASSET_NAME}.tar.gz"
-          mkdir dawn
-          tar -xvf artifact.tar.gz -C dawn --strip-components=1
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          GG_BUILD_WEBGPU=1 \
-          GG_BUILD_WEBGPU_DAWN_PREFIX="$GITHUB_WORKSPACE/dawn" \
-          GG_BUILD_WEBGPU_DAWN_DIR="$GITHUB_WORKSPACE/dawn/lib64/cmake/Dawn" \
-            bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  # TODO: provision AMX-compatible machine
-  #cpu-amx:
-  #  runs-on: [self-hosted, Linux, CPU, AMX]
-
-  #  steps:
-  #    - name: Clone
-  #      id: checkout
-  #      uses: actions/checkout@v6
-
-  #    - name: Test
-  #      id: ggml-ci
-  #      run: |
-  #        bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  # TODO: provision AMD GPU machine
-  # amd-vulkan:
-  #   runs-on: [self-hosted, Linux, AMD]
-
-  #   steps:
-  #     - name: Clone
-  #       id: checkout
-  #       uses: actions/checkout@v6
-
-  #     - name: Test
-  #       id: ggml-ci
-  #       run: |
-  #         vulkaninfo --summary
-  #         GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  # TODO: provision AMD GPU machine
-  # amd-rocm:
-  #   runs-on: [self-hosted, Linux, AMD]
-
-  #   steps:
-  #     - name: Clone
-  #       id: checkout
-  #       uses: actions/checkout@v6
-
-  #     - name: Test
-  #       id: ggml-ci
-  #       run: |
-  #         amd-smi static
-  #         GG_BUILD_ROCM=1 GG_BUILD_AMDGPU_TARGETS="gfx1101" bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  gpu-metal:
-    runs-on: [self-hosted, macOS, ARM64]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          GG_BUILD_METAL=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  gpu-webgpu-apple:
-    runs-on: [self-hosted, macOS, ARM64]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Dawn Dependency
-        id: dawn-depends
-        run: |
-          DAWN_VERSION="v20260908.214631"
-          DAWN_OWNER="google"
-          DAWN_REPO="dawn"
-          DAWN_ASSET_NAME="Dawn-94c3c9cc0d5fb2e85aebb370fa8d37b71aa34655-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}/${DAWN_ASSET_NAME}.tar.gz"
-          mkdir dawn
-          tar -xvf artifact.tar.gz -C dawn --strip-components=1
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          GG_BUILD_WEBGPU=1 GG_BUILD_WEBGPU_DAWN_PREFIX="$GITHUB_WORKSPACE/dawn" \
-            bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  gpu-vulkan-apple:
-    runs-on: [self-hosted, macOS, ARM64]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          vulkaninfo --summary
-          GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  gpu-vulkan-intel-linux:
-    runs-on: [self-hosted, Linux, Intel]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-        with:
-          persist-credentials: false
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          vulkaninfo --summary
-          GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  gpu-vulkan-intel-windows:
-    runs-on: [self-hosted, Windows, X64, Intel]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Test
-        id: ggml-ci
-        shell: C:\msys64\usr\bin\bash.exe --noprofile --norc -eo pipefail "{0}"
-        env:
-          MSYSTEM: UCRT64
-          CHERE_INVOKING: 1
-          PATH: C:\msys64\ucrt64\bin;C:\msys64\usr\bin;C:\Windows\System32;${{ env.PATH }}
-        run: |
-          vulkaninfo --summary
-          # Skip python related tests with GG_BUILD_LOW_PERF=1 since Windows MSYS2 UCRT64 currently fails to create
-          # a valid python environment for testing
-          LLAMA_FATAL_WARNINGS=OFF GG_BUILD_NINJA=1 GG_BUILD_VULKAN=1 GG_BUILD_LOW_PERF=1 ./ci/run.sh ./results/llama.cpp ./mnt/llama.cpp
-
-  gpu-openvino-low-perf:
-    runs-on: [self-hosted, Linux, Intel, OpenVINO]
-
-    env:
-      # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.3.1"
-      OPENVINO_VERSION_FULL: "2026.3.1.22476.56d9685302d"
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Setup OpenVINO Toolkit
-        uses: ./.github/actions/linux-setup-openvino
-        with:
-          path: ./openvino_toolkit
-          version_major: ${{ env.OPENVINO_VERSION_MAJOR }}
-          version_full: ${{ env.OPENVINO_VERSION_FULL }}
-
-      - name: Install OpenVINO dependencies
-        run: |
-          cd ./openvino_toolkit
-          chmod +x ./install_dependencies/install_openvino_dependencies.sh
-          echo "Y" | sudo -E ./install_dependencies/install_openvino_dependencies.sh
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          source ./openvino_toolkit/setupvars.sh
-          GG_BUILD_OPENVINO=1 GGML_OPENVINO_DEVICE=GPU GG_BUILD_LOW_PERF=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  cpu-x64-high-perf:
-    runs-on: [self-hosted, Linux, X64]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          LLAMA_ARG_THREADS=$(nproc) GG_BUILD_HIGH_PERF=1 GG_BUILD_EXTRA_TESTS_0=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  cpu-arm64-high-perf-graviton4:
-    runs-on: ah-ubuntu_22_04-c8g_8x
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Dependencies
-        id: depends
-        run: |
-          set -euxo pipefail
-          sudo apt-get update
-          sudo DEBIAN_FRONTEND=noninteractive NEEDRESTART_MODE=a \
-          apt-get install -y \
-          build-essential \
-          python3-venv \
-          gpg \
-          wget \
-          time \
-          git-lfs
-
-          git lfs install
-
-          # install the latest cmake
-          sudo install -d /usr/share/keyrings
-          wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc \
-            | gpg --dearmor \
-            | sudo tee /usr/share/keyrings/kitware-archive-keyring.gpg >/dev/null
-          echo 'deb [signed-by=/usr/share/keyrings/kitware-archive-keyring.gpg] https://apt.kitware.com/ubuntu/ jammy main' \
-            | sudo tee /etc/apt/sources.list.d/kitware.list
-          sudo apt-get update
-          sudo apt-get install -y cmake
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          LLAMA_ARG_THREADS=$(nproc) \
-          GG_BUILD_HIGH_PERF=1 \
-          GG_BUILD_NO_BF16=1 \
-          GG_BUILD_EXTRA_TESTS_0=1 \
-          bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  cpu-arm64-graviton4-kleidiai:
-    runs-on: ah-ubuntu_22_04-c8g_8x
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Dependencies
-        id: depends
-        run: |
-          set -euxo pipefail
-          sudo apt-get update
-          sudo DEBIAN_FRONTEND=noninteractive NEEDRESTART_MODE=a \
-          apt-get install -y \
-          build-essential \
-          python3-venv \
-          gpg \
-          wget \
-          time \
-          git-lfs
-
-          git lfs install
-
-          # install the latest cmake
-          sudo install -d /usr/share/keyrings
-          wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc \
-            | gpg --dearmor \
-            | sudo tee /usr/share/keyrings/kitware-archive-keyring.gpg >/dev/null
-          echo 'deb [signed-by=/usr/share/keyrings/kitware-archive-keyring.gpg] https://apt.kitware.com/ubuntu/ jammy main' \
-            | sudo tee /etc/apt/sources.list.d/kitware.list
-          sudo apt-get update
-          sudo apt-get install -y cmake
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          LLAMA_ARG_THREADS=$(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
@@ -49,7 +49,7 @@ jobs:
    env:
      ONEAPI_ROOT: /opt/intel/oneapi/
      ONEAPI_INSTALLER_VERSION: "2025.3.3"
-      LEVEL_ZERO_VERSION: "1.28.2"
+      LEVEL_ZERO_VERSION: "1.33.1"
      LEVEL_ZERO_UBUNTU_VERSION: "u24.04"

    continue-on-error: true
@@ -70,9 +70,10 @@ jobs:
        shell: bash
        run: |
          cd /tmp
-          wget -q "https://github.com/oneapi-src/level-zero/releases/download/v${LEVEL_ZERO_VERSION}/level-zero_${LEVEL_ZERO_VERSION}%2B${LEVEL_ZERO_UBUNTU_VERSION}_amd64.deb" -O level-zero.deb
-          wget -q "https://github.com/oneapi-src/level-zero/releases/download/v${LEVEL_ZERO_VERSION}/level-zero-devel_${LEVEL_ZERO_VERSION}%2B${LEVEL_ZERO_UBUNTU_VERSION}_amd64.deb" -O level-zero-devel.deb
-          sudo apt-get install -y ./level-zero.deb ./level-zero-devel.deb
+          # v1.33.x renamed the Debian packages to libze1 / libze-dev
+          wget -q "https://github.com/oneapi-src/level-zero/releases/download/v${LEVEL_ZERO_VERSION}/libze1_${LEVEL_ZERO_VERSION}%2B${LEVEL_ZERO_UBUNTU_VERSION}_amd64.deb" -O libze1.deb
+          wget -q "https://github.com/oneapi-src/level-zero/releases/download/v${LEVEL_ZERO_VERSION}/libze-dev_${LEVEL_ZERO_VERSION}%2B${LEVEL_ZERO_UBUNTU_VERSION}_amd64.deb" -O libze-dev.deb
+          sudo apt-get install -y ./libze1.deb ./libze-dev.deb

      - name: ccache
        uses: ggml-org/[email protected]
@@ -101,7 +102,11 @@ jobs:
            -DCMAKE_CXX_COMPILER=icpx \
            -DLLAMA_OPENSSL=OFF \
            -DGGML_NATIVE=OFF \
-            -DGGML_SYCL_F16=${{ matrix.fp16 }}
+            -DGGML_SYCL_F16=${{ matrix.fp16 }} \
+            -DGGML_SYCL_SUPPORT_LEVEL_ZERO_API=ON \
+            -DGGML_SYCL_DNN=ON \
+            -DCMAKE_CXX_FLAGS="-fsycl-unnamed-lambda" \
+            -DCMAKE_EXE_LINKER_FLAGS="-fsycl-unnamed-lambda"
          time cmake --build build --config Release -j $(nproc)

      - name: ccache-buckets-save
@@ -126,7 +131,7 @@ jobs:
    env:
      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/b60765d1-2b85-4e85-86b6-cb0e9563a699/intel-deep-learning-essentials-2025.3.3.18_offline.exe
      WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel:intel.oneapi.win.dnnl:intel.oneapi.win.tbb.devel
-      LEVEL_ZERO_SDK_URL: https://github.com/oneapi-src/level-zero/releases/download/v1.28.2/level-zero-win-sdk-1.28.2.zip
+      LEVEL_ZERO_SDK_URL: https://github.com/oneapi-src/level-zero/releases/download/v1.33.1/level-zero-win-sdk-1.33.1.zip
      ONEAPI_ROOT: "C:/Program Files (x86)/Intel/oneAPI"
      ONEAPI_INSTALLER_VERSION: "2025.3.3"
    steps:
@@ -31,6 +31,7 @@ concurrency:
 env:
  GGML_NLOOP: 3
  GGML_N_THREADS: 1
+  GGML_SCHED_DEBUG_REALLOC: 1
  LLAMA_ARG_LOG_COLORS: 1
  LLAMA_ARG_LOG_PREFIX: 1
  LLAMA_ARG_LOG_TIMESTAMPS: 1
@@ -19,7 +19,7 @@ on:

 jobs:
  check-vendor:
-    runs-on: [self-hosted, fast]
+    runs-on: ubuntu-slim

    steps:
      - name: Checkout
@@ -0,0 +1,112 @@
+name: CI (self-hosted CPU backend)
+
+on:
+  workflow_dispatch: # allows manual triggering
+  push:
+    branches:
+      - master
+    paths: [
+      '.github/workflows/ci-self-hosted-cpu.yml',
+      'ci/run.sh',
+      '**/CMakeLists.txt',
+      '**/.cmake',
+      '**/*.h',
+      '**/*.hpp',
+      '**/*.c',
+      '**/*.cpp'
+    ]
+
+  pull_request:
+    types: [opened, synchronize, reopened]
+    paths: [
+      '.github/workflows/ci-self-hosted-cpu.yml',
+      'ci/run.sh',
+      '**/CMakeLists.txt',
+      '**/.cmake',
+      'ggml/src/*',
+      'ggml/src/ggml-cpu/**'
+    ]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
+env:
+  # note: this is dud token to avoid rate limiting (https://github.com/ggml-org/llama.cpp/pull/25706#issuecomment-4979941302)
+  HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
+  GGML_NLOOP: 3
+  GGML_N_THREADS: 1
+  LLAMA_ARG_LOG_COLORS: 1
+  LLAMA_ARG_LOG_PREFIX: 1
+  LLAMA_ARG_LOG_TIMESTAMPS: 1
+
+jobs:
+  cpu-x64-high-perf:
+    runs-on: [self-hosted, Linux, X64]
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Test
+        id: ggml-ci
+        run: |
+          LLAMA_ARG_THREADS=$(nproc) GG_BUILD_HIGH_PERF=1 GG_BUILD_EXTRA_TESTS_0=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
+
+  cpu-arm64-high-perf-graviton4:
+    runs-on: ah-ubuntu_24_04-c8g_8x
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Dependencies
+        id: depends
+        run: |
+          set -euxo pipefail
+          sudo apt-get update
+          sudo DEBIAN_FRONTEND=noninteractive NEEDRESTART_MODE=a \
+          apt-get install -y \
+          build-essential \
+          python3-venv \
+          gpg \
+          wget \
+          time \
+          git-lfs
+
+          git lfs install
+
+          # install the latest cmake
+          sudo install -d /usr/share/keyrings
+          wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc \
+            | gpg --dearmor \
+            | sudo tee /usr/share/keyrings/kitware-archive-keyring.gpg >/dev/null
+          echo 'deb [signed-by=/usr/share/keyrings/kitware-archive-keyring.gpg] https://apt.kitware.com/ubuntu/ jammy main' \
+            | sudo tee /etc/apt/sources.list.d/kitware.list
+          sudo apt-get update
+          sudo apt-get install -y cmake
+
+      - 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
+
+  # TODO: provision AMX-compatible machine
+  #cpu-amx:
+  #  runs-on: [self-hosted, Linux, CPU, AMX]
+
+  #  steps:
+  #    - name: Clone
+  #      id: checkout
+  #      uses: actions/checkout@v6
+
+  #    - name: Test
+  #      id: ggml-ci
+  #      run: |
+  #        bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
@@ -0,0 +1,124 @@
+name: CI (self-hosted CUDA backend)
+
+on:
+  workflow_dispatch: # allows manual triggering
+  push:
+    branches:
+      - master
+    paths: [
+      '.github/workflows/ci-self-hosted-cuda.yml',
+      'ci/run.sh',
+      '**/CMakeLists.txt',
+      '**/.cmake',
+      '**/*.h',
+      '**/*.hpp',
+      '**/*.c',
+      '**/*.cpp',
+      '**/*.cu',
+      '**/*.cuh'
+    ]
+
+  pull_request:
+    types: [opened, synchronize, reopened]
+    paths: [
+      '.github/workflows/ci-self-hosted-cuda.yml',
+      'ci/run.sh',
+      '**/CMakeLists.txt',
+      '**/.cmake',
+      'ggml/src/*',
+      'ggml/src/ggml-cpu/**',
+      'ggml/src/ggml-cuda/**'
+    ]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
+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 }}
+  GGML_NLOOP: 3
+  GGML_N_THREADS: 1
+  LLAMA_ARG_LOG_COLORS: 1
+  LLAMA_ARG_LOG_PREFIX: 1
+  LLAMA_ARG_LOG_TIMESTAMPS: 1
+
+jobs:
+  gpu-cuda:
+    runs-on: "hf-jobs-t4-small:cuda13"
+
+    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/[email protected]
+        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
+
+  gpu-rocm:
+    runs-on: [self-hosted, Linux, AMD]
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Test
+        id: ggml-ci
+        # HIP_LAUNCH_BLOCKING=1: workaround for an async-execution correctness
+        # issue on integrated RDNA3.5 (gfx1151) where batched inference returns
+        # incorrect output (perplexity ~88 vs ~9.4). Serializing kernel launches
+        # restores correctness. Remove once the underlying ROCm/HIP issue is fixed.
+        env:
+          HIP_LAUNCH_BLOCKING: "1"
+        run: |
+          rocminfo
+          GG_BUILD_ROCM=1 GG_BUILD_AMDGPU_TARGETS=gfx1151 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
+
+  # TODO: provision AMD GPU machine
+  # amd-rocm:
+  #   runs-on: [self-hosted, Linux, AMD]
+
+  #   steps:
+  #     - name: Clone
+  #       id: checkout
+  #       uses: actions/checkout@v6
+
+  #     - name: Test
+  #       id: ggml-ci
+  #       run: |
+  #         amd-smi static
+  #         GG_BUILD_ROCM=1 GG_BUILD_AMDGPU_TARGETS="gfx1101" bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
@@ -0,0 +1,85 @@
+name: CI (self-hosted KleidiAI backend)
+
+on:
+  workflow_dispatch: # allows manual triggering
+  push:
+    branches:
+      - master
+    paths: [
+      '.github/workflows/ci-self-hosted-kleidiai.yml',
+      'ci/run.sh',
+      '**/CMakeLists.txt',
+      '**/.cmake',
+      '**/*.h',
+      '**/*.hpp',
+      '**/*.c',
+      '**/*.cpp'
+    ]
+
+  pull_request:
+    types: [opened, synchronize, reopened]
+    paths: [
+      '.github/workflows/ci-self-hosted-kleidiai.yml',
+      'ci/run.sh',
+      '**/CMakeLists.txt',
+      '**/.cmake',
+      'ggml/src/*',
+      'ggml/src/ggml-cpu/**'
+    ]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
+env:
+  # note: this is dud token to avoid rate limiting (https://github.com/ggml-org/llama.cpp/pull/25706#issuecomment-4979941302)
+  HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
+  GGML_NLOOP: 3
+  GGML_N_THREADS: 1
+  LLAMA_ARG_LOG_COLORS: 1
+  LLAMA_ARG_LOG_PREFIX: 1
+  LLAMA_ARG_LOG_TIMESTAMPS: 1
+
+jobs:
+  cpu-arm64-graviton4-kleidiai:
+    runs-on: ah-ubuntu_24_04-c8g_8x
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Dependencies
+        id: depends
+        run: |
+          set -euxo pipefail
+          sudo apt-get update
+          sudo DEBIAN_FRONTEND=noninteractive NEEDRESTART_MODE=a \
+          apt-get install -y \
+          build-essential \
+          python3-venv \
+          gpg \
+          wget \
+          time \
+          git-lfs
+
+          git lfs install
+
+          # install the latest cmake
+          sudo install -d /usr/share/keyrings
+          wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc \
+            | gpg --dearmor \
+            | sudo tee /usr/share/keyrings/kitware-archive-keyring.gpg >/dev/null
+          echo 'deb [signed-by=/usr/share/keyrings/kitware-archive-keyring.gpg] https://apt.kitware.com/ubuntu/ jammy main' \
+            | sudo tee /etc/apt/sources.list.d/kitware.list
+          sudo apt-get update
+          sudo apt-get install -y cmake
+
+      - name: Test
+        id: ggml-ci
+        run: |
+          LLAMA_ARG_THREADS=$(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
@@ -0,0 +1,59 @@
+name: CI (self-hosted Metal backend)
+
+on:
+  workflow_dispatch: # allows manual triggering
+  push:
+    branches:
+      - master
+    paths: [
+      '.github/workflows/ci-self-hosted-metal.yml',
+      'ci/run.sh',
+      '**/CMakeLists.txt',
+      '**/.cmake',
+      '**/*.h',
+      '**/*.hpp',
+      '**/*.c',
+      '**/*.cpp',
+      '**/*.swift',
+      '**/*.m',
+      '**/*.metal'
+    ]
+
+  pull_request:
+    types: [opened, synchronize, reopened]
+    paths: [
+      '.github/workflows/ci-self-hosted-metal.yml',
+      'ci/run.sh',
+      '**/CMakeLists.txt',
+      '**/.cmake',
+      'ggml/src/*',
+      'ggml/src/ggml-cpu/**',
+      'ggml/src/ggml-metal/**'
+    ]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
+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 }}
+  GGML_NLOOP: 3
+  GGML_N_THREADS: 1
+  LLAMA_ARG_LOG_COLORS: 1
+  LLAMA_ARG_LOG_PREFIX: 1
+  LLAMA_ARG_LOG_TIMESTAMPS: 1
+
+jobs:
+  gpu-metal:
+    runs-on: [self-hosted, macOS, ARM64]
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Test
+        id: ggml-ci
+        run: |
+          GG_BUILD_METAL=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
@@ -0,0 +1,75 @@
+name: CI (self-hosted OpenVINO backend)
+
+on:
+  workflow_dispatch: # allows manual triggering
+  push:
+    branches:
+      - master
+    paths: [
+      '.github/workflows/ci-self-hosted-openvino.yml',
+      'ci/run.sh',
+      '**/CMakeLists.txt',
+      '**/.cmake',
+      '**/*.h',
+      '**/*.hpp',
+      '**/*.c',
+      '**/*.cpp'
+    ]
+
+  pull_request:
+    types: [opened, synchronize, reopened]
+    paths: [
+      '.github/workflows/ci-self-hosted-openvino.yml',
+      'ci/run.sh',
+      '**/CMakeLists.txt',
+      '**/.cmake',
+      'ggml/src/*',
+      'ggml/src/ggml-cpu/**',
+      'ggml/src/ggml-openvino/**'
+    ]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
+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 }}
+  GGML_NLOOP: 3
+  GGML_N_THREADS: 1
+  LLAMA_ARG_LOG_COLORS: 1
+  LLAMA_ARG_LOG_PREFIX: 1
+  LLAMA_ARG_LOG_TIMESTAMPS: 1
+
+jobs:
+  gpu-openvino-low-perf:
+    runs-on: [self-hosted, Linux, Intel, OpenVINO]
+
+    env:
+      # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
+      OPENVINO_VERSION_MAJOR: "2026.4"
+      OPENVINO_VERSION_FULL: "2026.4.0.22959.99c81491cc3"
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Setup OpenVINO Toolkit
+        uses: ./.github/actions/linux-setup-openvino
+        with:
+          path: ./openvino_toolkit
+          version_major: ${{ env.OPENVINO_VERSION_MAJOR }}
+          version_full: ${{ env.OPENVINO_VERSION_FULL }}
+
+      - name: Install OpenVINO dependencies
+        run: |
+          cd ./openvino_toolkit
+          chmod +x ./install_dependencies/install_openvino_dependencies.sh
+          echo "Y" | sudo -E ./install_dependencies/install_openvino_dependencies.sh
+
+      - name: Test
+        id: ggml-ci
+        run: |
+          source ./openvino_toolkit/setupvars.sh
+          GG_BUILD_OPENVINO=1 GGML_OPENVINO_DEVICE=GPU GG_BUILD_LOW_PERF=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
@@ -0,0 +1,201 @@
+name: CI (self-hosted Vulkan backend)
+
+on:
+  workflow_dispatch: # allows manual triggering
+  push:
+    branches:
+      - master
+    paths: [
+      '.github/workflows/ci-self-hosted-vulkan.yml',
+      'ci/run.sh',
+      '**/CMakeLists.txt',
+      '**/.cmake',
+      '**/*.h',
+      '**/*.hpp',
+      '**/*.c',
+      '**/*.cpp',
+      '**/*.comp',
+      '**/*.glsl'
+    ]
+
+  pull_request:
+    types: [opened, synchronize, reopened]
+    paths: [
+      '.github/workflows/ci-self-hosted-vulkan.yml',
+      'ci/run.sh',
+      '**/CMakeLists.txt',
+      '**/.cmake',
+      'ggml/src/*',
+      'ggml/src/ggml-cpu/**',
+      'ggml/src/ggml-vulkan/**'
+    ]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
+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 }}
+  GGML_NLOOP: 3
+  GGML_N_THREADS: 1
+  LLAMA_ARG_LOG_COLORS: 1
+  LLAMA_ARG_LOG_PREFIX: 1
+  LLAMA_ARG_LOG_TIMESTAMPS: 1
+
+jobs:
+  gpu-vulkan-nvidia-cm:
+    # runs-on: "hf-jobs-t4-small:ubuntu26_04"
+    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 build-essential cmake libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libvulkan-dev glslc spirv-headers vulkan-tools mesa-vulkan-drivers libglvnd0 libgl1 libglx0 libegl1 libgles2 libssl-dev time unzip wget python3 python3-venv python3-pip
+
+      # - name: ccache
+      #   uses: ggml-org/[email protected]
+      #   with:
+      #     restore: false
+      #     save: false
+
+      # - name: ccache-buckets-restore
+      #   uses: ./.github/actions/ccache-buckets
+      #   with:
+      #     key: self-hosted-vulkan-nvidia-cm
+      #     folder: llama.cpp
+      #     hf_bucket: ggml-org/cache
+
+      - name: Test
+        id: ggml-ci
+        run: |
+          vulkaninfo --summary
+          GG_BUILD_VULKAN=1 GGML_VK_DISABLE_COOPMAT2=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
+
+      # - 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-vulkan-nvidia-cm
+      #     folder: llama.cpp
+      #     evict-old-files: 1d
+      #     hf_bucket: ggml-org/cache
+      #     save: true
+
+  gpu-vulkan-nvidia-cm2:
+    # runs-on: "hf-jobs-t4-small:ubuntu26_04"
+    runs-on: [self-hosted, Linux, NVIDIA, COOPMAT2]
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      # - name: Install dependencies
+      #   run: |
+      #     sudo apt update
+      #     sudo apt install -y build-essential cmake libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libvulkan-dev glslc spirv-headers vulkan-tools mesa-vulkan-drivers libglvnd0 libgl1 libglx0 libegl1 libgles2 libssl-dev time unzip wget python3 python3-venv python3-pip
+
+      # - name: ccache
+      #   uses: ggml-org/[email protected]
+      #   with:
+      #     restore: false
+      #     save: false
+
+      # - name: ccache-buckets-restore
+      #   uses: ./.github/actions/ccache-buckets
+      #   with:
+      #     key: self-hosted-vulkan-nvidia-cm2
+      #     folder: llama.cpp
+      #     hf_bucket: ggml-org/cache
+
+      - name: Test
+        id: ggml-ci
+        run: |
+          vulkaninfo --summary
+          GG_BUILD_VULKAN=1 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: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+      #   with:
+      #     key: self-hosted-vulkan-nvidia-cm2
+      #     folder: llama.cpp
+      #     evict-old-files: 1d
+      #     hf_bucket: ggml-org/cache
+      #     save: true
+
+  gpu-vulkan-apple:
+    runs-on: [self-hosted, macOS, ARM64]
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Test
+        id: ggml-ci
+        run: |
+          vulkaninfo --summary
+          GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
+
+  gpu-vulkan-intel-linux:
+    runs-on: [self-hosted, Linux, Intel]
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+        with:
+          persist-credentials: false
+
+      - name: Test
+        id: ggml-ci
+        run: |
+          vulkaninfo --summary
+          GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
+
+  gpu-vulkan-intel-windows:
+    runs-on: [self-hosted, Windows, X64, Intel]
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Test
+        id: ggml-ci
+        shell: C:\msys64\usr\bin\bash.exe --noprofile --norc -eo pipefail "{0}"
+        env:
+          MSYSTEM: UCRT64
+          CHERE_INVOKING: 1
+          PATH: C:\msys64\ucrt64\bin;C:\msys64\usr\bin;C:\Windows\System32;${{ env.PATH }}
+        run: |
+          vulkaninfo --summary
+          # Skip python related tests with GG_BUILD_LOW_PERF=1 since Windows MSYS2 UCRT64 currently fails to create
+          # a valid python environment for testing
+          LLAMA_FATAL_WARNINGS=OFF GG_BUILD_NINJA=1 GG_BUILD_VULKAN=1 GG_BUILD_LOW_PERF=1 ./ci/run.sh ./results/llama.cpp ./mnt/llama.cpp
+
+  # TODO: provision AMD GPU machine
+  # amd-vulkan:
+  #   runs-on: [self-hosted, Linux, AMD]
+
+  #   steps:
+  #     - name: Clone
+  #       id: checkout
+  #       uses: actions/checkout@v6
+
+  #     - name: Test
+  #       id: ggml-ci
+  #       run: |
+  #         vulkaninfo --summary
+  #         GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
@@ -0,0 +1,130 @@
+name: CI (self-hosted WebGPU backend)
+
+on:
+  workflow_dispatch: # allows manual triggering
+  push:
+    branches:
+      - master
+    paths: [
+      '.github/workflows/ci-self-hosted-webgpu.yml',
+      'ci/run.sh',
+      '**/CMakeLists.txt',
+      '**/.cmake',
+      '**/*.h',
+      '**/*.hpp',
+      '**/*.c',
+      '**/*.cpp',
+      '**/*.wgsl'
+    ]
+
+  pull_request:
+    types: [opened, synchronize, reopened]
+    paths: [
+      '.github/workflows/ci-self-hosted-webgpu.yml',
+      'ci/run.sh',
+      '**/CMakeLists.txt',
+      '**/.cmake',
+      'ggml/src/*',
+      'ggml/src/ggml-cpu/**',
+      'ggml/src/ggml-webgpu/**'
+    ]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
+env:
+  # note: this is dud token to avoid rate limiting (https://github.com/ggml-org/llama.cpp/pull/25706#issuecomment-4979941302)
+  HF_TOKEN: ${{ secrets.HF_TOKEN_CI }}
+  GGML_NLOOP: 3
+  GGML_N_THREADS: 1
+  LLAMA_ARG_LOG_COLORS: 1
+  LLAMA_ARG_LOG_PREFIX: 1
+  LLAMA_ARG_LOG_TIMESTAMPS: 1
+
+jobs:
+  gpu-webgpu-nvidia:
+    runs-on: "hf-jobs-t4-small:ubuntu26_04"
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Install dependencies
+        run: |
+          sudo apt update
+          sudo apt install -y build-essential cmake libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libvulkan1 mesa-vulkan-drivers libglvnd0 libgl1 libglx0 libegl1 libgles2 libssl-dev time unzip wget python3 python3-venv python3-pip
+
+      - name: ccache
+        uses: ggml-org/[email protected]
+        with:
+          restore: false
+          save: false
+
+      - name: ccache-buckets-restore
+        uses: ./.github/actions/ccache-buckets
+        with:
+          key: self-hosted-webgpu-nvidia
+          folder: llama.cpp
+          hf_bucket: ggml-org/cache
+
+      - name: Dawn Dependency
+        id: dawn-depends
+        run: |
+          DAWN_VERSION="v20260908.214631"
+          DAWN_OWNER="google"
+          DAWN_REPO="dawn"
+          DAWN_ASSET_NAME="Dawn-94c3c9cc0d5fb2e85aebb370fa8d37b71aa34655-ubuntu-latest-Release"
+          echo "Fetching release asset from https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
+          curl -L -o artifact.tar.gz \
+            "https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
+          mkdir dawn
+          tar -xvf artifact.tar.gz -C dawn --strip-components=1
+
+      - name: Test
+        id: ggml-ci
+        run: |
+          GG_BUILD_WEBGPU=1 \
+          GG_BUILD_WEBGPU_DAWN_PREFIX="$GITHUB_WORKSPACE/dawn" \
+          GG_BUILD_WEBGPU_DAWN_DIR="$GITHUB_WORKSPACE/dawn/lib64/cmake/Dawn" \
+            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: ${{ secrets.HF_TOKEN_CACHE_OUTPUT }}
+        with:
+          key: self-hosted-webgpu-nvidia
+          folder: llama.cpp
+          evict-old-files: 1d
+          hf_bucket: ggml-org/cache
+          save: true
+
+  gpu-webgpu-apple:
+    runs-on: [self-hosted, macOS, ARM64]
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Dawn Dependency
+        id: dawn-depends
+        run: |
+          DAWN_VERSION="v20260908.214631"
+          DAWN_OWNER="google"
+          DAWN_REPO="dawn"
+          DAWN_ASSET_NAME="Dawn-94c3c9cc0d5fb2e85aebb370fa8d37b71aa34655-macos-latest-Release"
+          echo "Fetching release asset from https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
+          curl -L -o artifact.tar.gz \
+            "https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
+          mkdir dawn
+          tar -xvf artifact.tar.gz -C dawn --strip-components=1
+
+      - name: Test
+        id: ggml-ci
+        run: |
+          GG_BUILD_WEBGPU=1 GG_BUILD_WEBGPU_DAWN_PREFIX="$GITHUB_WORKSPACE/dawn" \
+            bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
@@ -11,10 +11,12 @@ on:
    paths:
      - .github/workflows/copilot-setup-steps.yml

+cache-mode: none
+
 jobs:
  # The job MUST be called `copilot-setup-steps` or it will not be picked up by Copilot.
  copilot-setup-steps:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04

    # Set the permissions to the lowest permissions possible needed for your steps.
    # Copilot will be given its own token for its operations.
@@ -31,8 +33,8 @@ jobs:
      - name: ccache
        uses: ggml-org/[email protected]
        with:
-          key: copilot-setup-steps
-          evict-old-files: 1d
+          restore: false
+          save: false

      - name: Dependencies
        id: depends
@@ -90,8 +90,8 @@ jobs:
            { "tag": "cpu", "dockerfile": ".devops/s390x.Dockerfile", "platforms": "linux/s390x", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04-s390x", "prebuilt_ui": true },
            { "tag": "cuda cuda12", "dockerfile": ".devops/cuda.Dockerfile", "cuda_version": "12.8.1", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
            { "tag": "cuda cuda12", "dockerfile": ".devops/cuda.Dockerfile", "cuda_version": "12.8.1", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04-arm" },
-            { "tag": "cuda13", "dockerfile": ".devops/cuda.Dockerfile", "cuda_version": "13.3.0", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
-            { "tag": "cuda13", "dockerfile": ".devops/cuda.Dockerfile", "cuda_version": "13.3.0", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04-arm" },
+            { "tag": "cuda13", "dockerfile": ".devops/cuda.Dockerfile", "cuda_version": "13.4.1", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
+            { "tag": "cuda13", "dockerfile": ".devops/cuda.Dockerfile", "cuda_version": "13.4.1", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04-arm" },
            { "tag": "musa", "dockerfile": ".devops/musa.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
            { "tag": "intel", "dockerfile": ".devops/intel.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
            { "tag": "vulkan", "dockerfile": ".devops/vulkan.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04" },
@@ -9,7 +9,9 @@ on:
      '.github/workflows/fusion.yml',
      'ggml/**',
      'tests/fusion/**',
-      'tests/test-fusion.cpp'
+      'tests/test-fusion.cpp',
+      'tests/test-llama-archs.cpp',
+      'src/models/**'
    ]

  pull_request:
@@ -18,7 +20,9 @@ on:
      '.github/workflows/fusion.yml',
      'ggml/**',
      'tests/fusion/**',
-      'tests/test-fusion.cpp'
+      'tests/test-fusion.cpp',
+      'tests/test-llama-archs.cpp',
+      'src/models/**'
    ]

 concurrency:
@@ -21,7 +21,7 @@ on:
 jobs:
  deploy:

-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04  # previously ubuntu-latest

    steps:
    - uses: actions/checkout@v6
@@ -13,6 +13,16 @@ on:
        required: true
        type: boolean
        default: true
+      skip_apiabi_check:
+        description: 'Skip API/ABI compatibility check'
+        required: false
+        type: boolean
+        default: false
+      apiabi_compare_tag:
+        description: 'Tag to compare against for API/ABI check (default: latest release)'
+        required: false
+        type: string
+        default: ''

 env:
  GH_TOKEN: ${{ github.token }}
@@ -23,7 +33,7 @@ permissions:

 jobs:
  make-release:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04  # previously ubuntu-latest

    steps:
      - name: Checkout
@@ -33,12 +43,18 @@ jobs:
          ref: ${{ inputs.commit != '' && inputs.commit || github.ref_name }}
          fetch-depth: 0

+      - name: Install API/ABI check tools
+        if: ${{ github.event.inputs.skip_apiabi_check != 'true' }}
+        run: sudo apt-get install -y abi-compliance-checker abigail-tools
+
      - name: Run release checks
        id: checks
        run: bash scripts/make-release-checks.sh ${{ github.event.inputs.dry_run == 'true' && '--dry-run' || '' }}
        env:
          GITHUB_REPOSITORY: ${{ github.repository }}
          RELEASE_BRANCH: ${{ github.ref_name }}
+          SKIP_APIABI_CHECK: ${{ github.event.inputs.skip_apiabi_check }}
+          APIABI_COMPARE_TAG: ${{ github.event.inputs.apiabi_compare_tag }}

      - name: Create release tag
        if: ${{ github.event.inputs.dry_run == 'false' }}
@@ -12,7 +12,7 @@ on:

 jobs:
    pre-tokenizer-hashes:
-        runs-on: [self-hosted, fast]
+        runs-on: ubuntu-slim

        steps:
        - name: Checkout repository
@@ -20,7 +20,7 @@ concurrency:

 jobs:
  python-check-requirements:
-    runs-on: [self-hosted, CPU, fast]
+    runs-on: ${{ 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}
    name: check-requirements
    steps:
      - name: Check out source repository
@@ -21,7 +21,7 @@ concurrency:

 jobs:
  flake8-lint:
-    runs-on: [self-hosted, fast]
+    runs-on: ubuntu-slim
    name: Lint
    steps:
      - name: Check out source repository
@@ -22,7 +22,7 @@ concurrency:

 jobs:
  python-type-check:
-    runs-on: [self-hosted, fast]
+    runs-on: ubuntu-slim
    name: python type-check
    steps:
      - name: Check out source repository
@@ -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==0.0.84
      # - name: Type-check with Pyright
      #   uses: jakebailey/pyright-action@v2
      #   with:
@@ -106,6 +106,7 @@ jobs:
        uses: ggml-org/[email protected]
        with:
          key: release-${{ matrix.os }}-${{ matrix.arch }}
+          evict-old-files: 1d

      - name: Build
        id: cmake_build
@@ -190,6 +191,7 @@ jobs:
        uses: ggml-org/[email protected]
        with:
          key: release-${{ matrix.os }}-cpu
+          evict-old-files: 1d

      - name: Build
        id: cmake_build
@@ -275,6 +277,7 @@ jobs:
        uses: ggml-org/[email protected]
        with:
          key: release-${{ matrix.os }}-vulkan
+          evict-old-files: 1d

      - name: Build
        id: cmake_build
@@ -310,11 +313,150 @@ jobs:
        with:
          key: release-${{ matrix.os }}-vulkan

+  ubuntu-cuda:
+    name: ubuntu-cuda (${{ matrix.label }}, ${{ matrix.build }})
+    needs: [check-release, ui-build]
+    if: ${{ needs.check-release.outputs.should_release == 'true' }}
+
+    strategy:
+      matrix:
+        include:
+          # label = short version used in artifact names / release body
+          # cuda  = full container image tag
+          - build: 'x64'
+            os: ubuntu-24.04
+            cuda: '12.8.2'
+            label: '12.8'
+            defines: '-DGGML_CUDA_CUB_3DOT2=ON'
+          - build: 'x64'
+            os: ubuntu-24.04
+            cuda: '13.4.1'
+            label: '13.4'
+            defines: ''
+          - build: 'arm64'
+            os: ubuntu-24.04-arm
+            cuda: '13.4.1'
+            label: '13.4'
+            defines: ''
+
+    runs-on: ${{ matrix.os }}
+    container: nvidia/cuda:${{ matrix.cuda }}-devel-ubuntu24.04
+
+    permissions:
+      actions: write
+
+    steps:
+      # the container has no git; install it before checkout so that a real git
+      # repository is created (the get-tag-name action and the build both need it)
+      - name: Install git
+        run: |
+          apt-get update
+          apt-get install -y --no-install-recommends git
+
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+
+      # checkout runs as the host user; in-container steps run as root, so git
+      # refuses to touch a repo it does not own. Mark the workspace as safe.
+      # use the env var: the github.workspace context holds the HOST path,
+      # GITHUB_WORKSPACE the container path
+      - name: Git safe directory
+        run: git config --global --add safe.directory "$GITHUB_WORKSPACE"
+
+      - name: Download UI build
+        uses: actions/download-artifact@v7
+        with:
+          name: llama-ui.zip
+          path: tools/ui/dist
+
+      - name: Dependencies
+        id: depends
+        # container jobs default to sh (dash); need bash for the [[ ]] below
+        shell: bash
+        run: |
+          apt-get update
+          apt-get install -y --no-install-recommends build-essential cmake ninja-build libssl-dev jq python3-venv
+          # the container ships GCC 13, which does not know the 'sme' march
+          # feature used by the armv9.2 CPU variant of GGML_CPU_ALL_VARIANTS
+          if [[ "${{ matrix.build }}" == "arm64" ]]; then
+            apt-get install -y --no-install-recommends gcc-14 g++-14
+            echo "CC=gcc-14" >> "$GITHUB_ENV"
+            echo "CXX=g++-14" >> "$GITHUB_ENV"
+          fi
+
+      - name: ccache
+        uses: ggml-org/[email protected]
+        with:
+          key: release-ubuntu-${{ matrix.os }}-cuda-${{ matrix.label }}-${{ matrix.build }}
+          evict-old-files: 1d
+          max-size: "1G"
+
+      - name: Build
+        id: cmake_build
+        # no CMAKE_CUDA_ARCHITECTURES: use the broad default arch set from
+        # ggml/src/ggml-cuda/CMakeLists.txt so the release binary covers many GPUs
+        run: |
+          cmake -B build \
+            -DCMAKE_INSTALL_RPATH='$ORIGIN' \
+            -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
+            -DGGML_BACKEND_DL=ON \
+            -DGGML_NATIVE=OFF \
+            -DGGML_CPU_ALL_VARIANTS=ON \
+            -DGGML_CUDA=ON \
+            -DGGML_CUDA_NCCL=OFF \
+            ${{ env.CMAKE_ARGS }} ${{ matrix.defines }}
+          cmake --build build --config Release -j $(nproc)
+
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        run: |
+          cp LICENSE ./build/bin/
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-${{ matrix.label }}-${{ matrix.build }}.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
+
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v6
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-${{ matrix.label }}-${{ matrix.build }}.tar.gz
+          name: llama-bin-ubuntu-cuda-${{ matrix.label }}-${{ matrix.build }}.tar.gz
+
+      # ship the CUDA runtime libraries the backend links against, mirroring
+      # the windows-cuda cudart zip - extract next to the binaries ($ORIGIN rpath)
+      - name: Pack CUDA runtime
+        id: pack_cuda_runtime
+        run: |
+          major="${{ matrix.label }}"
+          major="${major%%.*}"
+          mkdir -p ./cudart
+          # cp -L dereferences the SONAME symlinks into plain files, so the
+          # tarball holds exactly 3 files with no versioned duplicates
+          cp -L /usr/local/cuda/lib64/libcudart.so.${major} ./cudart/
+          cp -L /usr/local/cuda/lib64/libcublas.so.${major} ./cudart/
+          cp -L /usr/local/cuda/lib64/libcublasLt.so.${major} ./cudart/
+          tar -czvf cudart-llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-${{ matrix.label }}-${{ matrix.build }}.tar.gz --transform "s,^\.,cudart-llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-${{ matrix.label }}-${{ matrix.build }}," -C ./cudart .
+
+      - name: Upload CUDA runtime
+        uses: actions/upload-artifact@v6
+        with:
+          path: cudart-llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-${{ matrix.label }}-${{ matrix.build }}.tar.gz
+          name: cudart-llama-bin-ubuntu-cuda-${{ matrix.label }}-${{ matrix.build }}.tar.gz
+
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-ubuntu-${{ matrix.os }}-cuda-${{ matrix.label }}-${{ matrix.build }}
+
  android-arm64:
    needs: [check-release, ui-build]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}

-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04  # previously ubuntu-latest

    #permissions:
    #  actions: write
@@ -342,7 +484,7 @@ jobs:
          distribution: temurin

      - name: Setup Android SDK
-        uses: android-actions/setup-android@40fd30fb8d7440372e1316f5d1809ec01dcd3699 # v4.0.1
+        uses: android-actions/setup-android@be39fa834029ff78f1a44aa3bb0819b8fc2bd8fd # v4.0.4
        with:
          log-accepted-android-sdk-licenses: false

@@ -361,6 +503,7 @@ jobs:
      #  uses: ggml-org/[email protected]
      #  with:
      #    key: release-android-arm64
+      #    evict-old-files: 1d

      - name: Build
        id: cmake_build
@@ -401,6 +544,120 @@ jobs:
          path: llama-${{ steps.tag.outputs.name }}-bin-android-arm64.tar.gz
          name: llama-bin-android-arm64.tar.gz

+  android-arm64-snapdragon:
+    needs: [check-release, ui-build]
+    if: ${{ needs.check-release.outputs.should_release == 'true' }}
+
+    runs-on: ubuntu-latest
+    container: 'ghcr.io/snapdragon-toolchain/arm64-android:v0.7'
+
+    defaults:
+      run:
+        shell: bash
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+
+      # checkout runs as the host user; in-container steps run as root, so git
+      # refuses to touch a repo it does not own. Mark the workspace as safe.
+      - name: Git safe directory
+        run: git config --global --add safe.directory "$GITHUB_WORKSPACE"
+
+      - name: Download UI build
+        uses: actions/download-artifact@v7
+        with:
+          name: llama-ui.zip
+          path: tools/ui/dist
+
+      - name: Build
+        id: cmake_build
+        run: |
+          cp docs/backend/snapdragon/CMakeUserPresets.json .
+          cmake --preset arm64-android-snapdragon-release -B build \
+            -DCMAKE_INSTALL_RPATH='$ORIGIN;$ORIGIN/../lib' \
+            -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
+            -DLLAMA_BUILD_BORINGSSL=ON \
+            ${{ env.CMAKE_ARGS }}
+          cmake --build build -j $(nproc)
+          cmake --install build --prefix pkg-snapdragon/llama.cpp
+
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        run: |
+          cp LICENSE pkg-snapdragon/llama.cpp/
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-android-arm64-snapdragon.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C pkg-snapdragon/llama.cpp .
+
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v6
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-android-arm64-snapdragon.tar.gz
+          name: llama-bin-android-arm64-snapdragon.tar.gz
+
+  linux-arm64-snapdragon:
+    needs: [check-release, ui-build]
+    if: ${{ needs.check-release.outputs.should_release == 'true' }}
+
+    runs-on: ubuntu-latest
+    container: 'ghcr.io/snapdragon-toolchain/arm64-linux:v0.7'
+
+    defaults:
+      run:
+        shell: bash
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+
+      # checkout runs as the host user; in-container steps run as root, so git
+      # refuses to touch a repo it does not own. Mark the workspace as safe.
+      - name: Git safe directory
+        run: git config --global --add safe.directory "$GITHUB_WORKSPACE"
+
+      - name: Download UI build
+        uses: actions/download-artifact@v7
+        with:
+          name: llama-ui.zip
+          path: tools/ui/dist
+
+      - name: Build
+        id: cmake_build
+        run: |
+          cp docs/backend/snapdragon/CMakeUserPresets.json .
+          cmake --preset arm64-linux-snapdragon-release -B build -DGGML_OPENCL=ON \
+            -DCMAKE_INSTALL_RPATH='$ORIGIN;$ORIGIN/../lib' \
+            -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
+            -DLLAMA_BUILD_BORINGSSL=ON \
+            ${{ env.CMAKE_ARGS }}
+          cmake --build build -j $(nproc)
+          cmake --install build --prefix pkg-snapdragon/llama.cpp
+
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        run: |
+          cp LICENSE pkg-snapdragon/llama.cpp/
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-linux-arm64-snapdragon.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C pkg-snapdragon/llama.cpp .
+
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v6
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-linux-arm64-snapdragon.tar.gz
+          name: llama-bin-linux-arm64-snapdragon.tar.gz
+
  ubuntu-24-openvino:
    needs: [check-release, ui-build]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}
@@ -415,8 +672,8 @@ 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: "2026.4"
+      OPENVINO_VERSION_FULL: "2026.4.0.22959.99c81491cc3"

    steps:
      - name: Set OpenVINO version output
@@ -439,6 +696,7 @@ jobs:
        uses: ggml-org/[email protected]
        with:
          key: release-ubuntu-24.04-openvino-release-no-preset-v1
+          evict-old-files: 1d

      - name: Dependencies
        run: |
@@ -529,8 +787,8 @@ 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: "2026.4"
+      OPENVINO_VERSION_FULL: "2026.4.0.22959.99c81491cc3"

    steps:
      - name: Set OpenVINO version output
@@ -682,6 +940,7 @@ jobs:
        uses: ggml-org/[email protected]
        with:
          key: release-windows-2025-vs2026-${{ matrix.arch }}-cpu
+          evict-old-files: 1d

      - name: Build
        shell: cmd
@@ -926,6 +1185,7 @@ jobs:
      #  uses: ggml-org/[email protected]
      #  with:
      #    key: release-windows-2025-${{ matrix.arch }}-${{ matrix.backend }}
+      #    evict-old-files: 1d

      - name: Install OpenCL Headers and Libs
        id: install_opencl
@@ -987,7 +1247,7 @@ jobs:
          - cuda: '12.4'
            arch: x64
            defines: '-DGGML_CUDA_CUB_3DOT2=ON'
-          - cuda: '13.3'
+          - cuda: '13.4'
            arch: x64
            defines: ''
          - cuda: '13.4'
@@ -1014,6 +1274,7 @@ jobs:
        uses: ggml-org/[email protected]
        with:
          key: release-windows-2022-${{ matrix.arch }}-cuda-${{ matrix.cuda }}
+          evict-old-files: 1d

      - name: Build
        id: cmake_build
@@ -1110,6 +1371,7 @@ jobs:
        uses: ggml-org/[email protected]
        with:
          key: release-windows-2022-x64-sycl
+          evict-old-files: 1d

      - name: Build
        id: cmake_build
@@ -1228,6 +1490,7 @@ jobs:
        uses: ggml-org/[email protected]
        with:
          key: release-ubuntu-24.04-sycl-${{ matrix.build }}
+          evict-old-files: 1d

      - name: Build
        id: cmake_build
@@ -1280,7 +1543,7 @@ jobs:
      matrix:
        include:
          - ROCM_VERSION: "10.0.0"
-            gpu_targets: "gfx908;gfx90a;gfx942;gfx950;gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1150;gfx1151;gfx1152;gfx1200;gfx1201"
+            gpu_targets: "gfx908;gfx90a;gfx942;gfx950;gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1103;gfx1150;gfx1151;gfx1152;gfx1200;gfx1201"
            build: 'x64'

    steps:
@@ -1572,9 +1835,12 @@ jobs:
      - ubuntu-24-rocm
      - ubuntu-cpu
      - ubuntu-vulkan
+      - ubuntu-cuda
      - ubuntu-24-openvino
      - ubuntu-24-sycl
      - android-arm64
+      - android-arm64-snapdragon
+      - linux-arm64-snapdragon
      - macos-cpu
      - ios-xcode
      #- openEuler-cann
@@ -1703,20 +1969,25 @@ 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 (CUDA 12)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-12.8-x64.tar.gz) - [CUDA 12.8 libraries](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-12.8-x64.tar.gz)
+            - [Ubuntu x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-13.4-x64.tar.gz) - [CUDA 13.4 libraries](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-13.4-x64.tar.gz)
+            - [Ubuntu arm64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-13.4-arm64.tar.gz) - [CUDA 13.4 libraries](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-${{ steps.tag.outputs.name }}-bin-ubuntu-cuda-13.4-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 (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-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-fp16-x64.tar.gz)
+            - [Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-linux-arm64-snapdragon.tar.gz) - [setup guide](https://github.com/ggml-org/llama.cpp/blob/master/docs/backend/snapdragon/linux.md)

            **Android:**
            - [Android arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-android-arm64.tar.gz)
+            - [Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-android-arm64-snapdragon.tar.gz) - [setup guide](https://github.com/ggml-org/llama.cpp/blob/master/docs/backend/snapdragon/README.md)

            **Windows:**
            - [Windows x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cpu-x64.zip)
            - [Windows arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cpu-arm64.zip)
            - [Windows 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 x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-13.4-x64.zip) - [CUDA 13.4 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-13.4-x64.zip)
            - [Windows arm64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ 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 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)
@@ -45,7 +45,7 @@ concurrency:

 jobs:
  server:
-    runs-on: hf-jobs-cpu-upgrade
+    runs-on: hf-jobs-cpu-performance

    strategy:
      matrix:
@@ -116,7 +116,7 @@ jobs:
        run: |
          source .venv/bin/activate
          cd tools/server/tests
-          PYTEST_WORKERS=1 ./tests.sh
+          PYTEST_WORKERS=4 ./tests.sh

      - name: Slow tests
        id: server_integration_tests_slow
@@ -124,4 +124,4 @@ jobs:
        run: |
          source .venv/bin/activate
          cd tools/server/tests
-          PYTEST_WORKERS=1 SLOW_TESTS=1 ./tests.sh
+          PYTEST_WORKERS=4 SLOW_TESTS=1 ./tests.sh
@@ -192,7 +192,7 @@ jobs:
          PYTEST_WORKERS=1 ./tests.sh

  server-kleidiai:
-    runs-on: ah-ubuntu_22_04-c8g_8x
+    runs-on: ah-ubuntu_24_04-c8g_8x

    steps:
      - name: Clone
@@ -232,7 +232,7 @@ jobs:
      - name: Build
        id: cmake_build
        run: |
-          cmake -B build -DGGML_SCHED_NO_REALLOC=ON -DGGML_CPU_KLEIDIAI=ON
+          cmake -B build -DGGML_SCHED_NO_REALLOC=ON -DGGML_CPU_KLEIDIAI=ON -DLLAMA_FATAL_WARNINGS=ON
          cmake --build build --config Release -j $(nproc) --target llama-server

      - name: Python setup
@@ -16,7 +16,7 @@ on:

 jobs:
    update-ops-docs:
-        runs-on: [self-hosted, fast, ARM64]
+        runs-on: ubuntu-slim

        steps:
        - name: Checkout repository
@@ -8,7 +8,7 @@ on:
 jobs:
  update:
    name: Update Winget Package
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04  # previously ubuntu-latest
    if: github.repository_owner == 'ggml-org'

    steps:
@@ -7,6 +7,7 @@ General:
 - Don't try to build or run the code unless you are explicitly asked to do so
 - Use the `gh` CLI tool when querying PRs, issues, or other GitHub resources
 - When [MODEL] is needed, first try to get it from the `PI_MODEL_NAME` env var before asking the user
+- Never read the `AGENTS.md` file

 Coding:
 - When in doubt, always refer to the CONTRIBUTING.md file of the project
@@ -23,6 +24,7 @@ Pull requests (PRs):
 - For the AI usage disclosure section, write "YES. pi:llama.cpp/[MODEL]"
 - If `PI_MODEL_NAME` env var is not set, ask the user to tell you what model was used and write it in place of [MODEL]
 - Always create the pull requests in draft mode
+- Never reply to review comments or post comments on issues/PRs without explicit permission from the user

 Commits:
 - On every commit that you make, include a "Assisted-by: pi:llama.cpp/[MODEL]" tag
@@ -4,7 +4,7 @@ include(CheckIncludeFileCXX)

 ### llama.cpp version
 set(LLAMA_VERSION_MAJOR 0)
-set(LLAMA_VERSION_MINOR 4)
+set(LLAMA_VERSION_MINOR 5)
 set(LLAMA_VERSION_PATCH 0)
 set(LLAMA_VERSION_BASE "${LLAMA_VERSION_MAJOR}.${LLAMA_VERSION_MINOR}.${LLAMA_VERSION_PATCH}")

@@ -57,7 +57,7 @@
 /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/tiled/               @jbooth @bartowski1182
 /ggml/src/ggml-cpu/spacemit/            @alex-spacemit
 /ggml/src/ggml-cuda/                    @ggml-org/ggml-cuda
 /ggml/src/ggml-cuda/vendors/hip.h       @IMbackK
@@ -97,7 +97,6 @@
 /src/models/                            @CISC
 /tests/                                 @ggerganov
 /tests/test-chat.*                      @pwilkin
-/tests/test-llama-archs.cpp             @JohannesGaessler
 /tools/batched-bench/                   @ggerganov
 /tools/cli/                             @ngxson
 /tools/completion/                      @ggerganov
@@ -20,8 +20,8 @@ If AI is used to generate any portion of the code, contributors must adhere to t

 1. Explicitly disclose the manner in which AI was employed.
 2. Check for an existing PR addressing the same change; if one exists, comment there to work with its author instead of opening a duplicate.
-3. Perform a comprehensive manual review prior to submitting the pull request.
-4. Be prepared to explain every line of code they submitted when asked about it by a maintainer.
+3. Perform a comprehensive manual review prior to submitting the pull request. A proper code review usually takes something like one hour per 200-400 LOC and you should be spending **at least that much time on code review alone**.
+4. Be prepared to explain every line of code you submit when asked about it by a maintainer.
 5. It is strictly prohibited to use AI to write your posts for you (bug reports, feature requests, pull request descriptions, Github discussions, responding to humans, ...).

 For more info, please refer to the [AGENTS.md](AGENTS.md) file.
@@ -16,7 +16,7 @@ target_link_libraries(${TARGET} PRIVATE
 target_compile_features(${TARGET} PRIVATE cxx_std_17)

 # Automatically add all files from the 'licenses' directory
-file(GLOB EXTRA_LICENSES "${CMAKE_SOURCE_DIR}/licenses/LICENSE-*")
+file(GLOB EXTRA_LICENSES "${PROJECT_SOURCE_DIR}/licenses/LICENSE-*")

 foreach(FILE_PATH ${EXTRA_LICENSES})
    get_filename_component(FILE_NAME "${FILE_PATH}" NAME)
@@ -21,7 +21,7 @@ docker run --privileged -it \
    -v $HOME/llama.cpp/ci-cache:/ci-cache \
    -v $HOME/llama.cpp/ci-results:/ci-results \
    -v $PWD:/ws -w /ws \
-    mthreads/musa:rc4.3.0-devel-ubuntu22.04-amd64
+    registry.mthreads.com/mcconline/inference/pytorch:2.9.1.post1-py3.10-musa5.2.0-mp31-devel-ubuntu22.04-amd64
 ```

 Inside the container, execute the following commands:
@@ -158,8 +158,8 @@ if [ ! -z ${GG_BUILD_WEBGPU} ]; then
 fi

 if [ ! -z ${GG_BUILD_MUSA} ]; then
-    # Use qy1 by default (MTT S80)
-    MUSA_ARCH=${MUSA_ARCH:-21}
+    # Use ph1 by default (MTT S5000)
+    MUSA_ARCH=${MUSA_ARCH:-31}
    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_MUSA=ON -DMUSA_ARCHITECTURES=${MUSA_ARCH}"
 fi

@@ -669,6 +669,11 @@ function gg_run_test_backend_ops {
        args_extra=""
    fi

+    # TODO: OpenVINO GPU plugin crashes (CL_OUT_OF_RESOURCES) with 2 concurrent workers on GPU.
+    if [ ! -z "${GG_BUILD_OPENVINO}" ] && [ "${GGML_OPENVINO_DEVICE:-}" = "GPU" ]; then
+        args_extra=""
+    fi
+
    # TODO: reduce the test-backend-ops timeout to 1800s
    if [ ! -z ${GG_BUILD_HIGH_PERF} ]; then
        (time timeout 3600 ./bin/test-backend-ops ${args_extra} -b CPU) 2>&1 | tee -a $OUT/${ci}-test-backend-ops.log
@@ -17,14 +17,16 @@ find_library(llama_LIBRARY llama
    NO_CMAKE_FIND_ROOT_PATH
 )

-add_library(llama UNKNOWN IMPORTED)
-set_target_properties(llama
-    PROPERTIES
-        INTERFACE_INCLUDE_DIRECTORIES "${LLAMA_INCLUDE_DIR}"
-        INTERFACE_LINK_LIBRARIES "ggml::ggml;ggml::ggml-base;"
-        IMPORTED_LINK_INTERFACE_LANGUAGES "CXX"
-        IMPORTED_LOCATION "${llama_LIBRARY}"
-        INTERFACE_COMPILE_FEATURES c_std_90
-        POSITION_INDEPENDENT_CODE ON)
+if(NOT TARGET llama)
+    add_library(llama UNKNOWN IMPORTED)
+    set_target_properties(llama
+        PROPERTIES
+            INTERFACE_INCLUDE_DIRECTORIES "${LLAMA_INCLUDE_DIR}"
+            INTERFACE_LINK_LIBRARIES "ggml::ggml;ggml::ggml-base;"
+            IMPORTED_LINK_INTERFACE_LANGUAGES "CXX"
+            IMPORTED_LOCATION "${llama_LIBRARY}"
+            INTERFACE_COMPILE_FEATURES c_std_90
+            POSITION_INDEPENDENT_CODE ON)
+endif()

 check_required_components(Llama)
@@ -136,8 +136,6 @@ 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)
@@ -2016,7 +2016,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.sampling.temp = std::max(params.sampling.temp, 0.0f);
            params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TEMP;
        }
-    ).set_sampling());
+    ).set_sampling().set_env("LLAMA_ARG_TEMPERATURE"));
    add_opt(common_arg(
        {"--top-k"}, "N",
        string_format("top-k sampling (default: %d, 0 = disabled)", params.sampling.top_k),
@@ -2032,7 +2032,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.sampling.top_p = std::stof(value);
            params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_P;
        }
-    ).set_sampling());
+    ).set_sampling().set_env("LLAMA_ARG_TOP_P"));
    add_opt(common_arg(
        {"--min-p"}, "N",
        string_format("min-p sampling (default: %.2f, 0.0 = disabled)", (double)params.sampling.min_p),
@@ -2040,7 +2040,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.sampling.min_p = std::stof(value);
            params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIN_P;
        }
-    ).set_sampling());
+    ).set_sampling().set_env("LLAMA_ARG_MIN_P"));
    add_opt(common_arg(
        {"--top-nsigma", "--top-n-sigma"}, "N",
        string_format("top-n-sigma sampling (default: %.2f, -1.0 = disabled)", params.sampling.top_n_sigma),
@@ -2096,7 +2096,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.sampling.penalty_repeat = penalty_repeat;
            params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_REPEAT;
        }
-    ).set_sampling());
+    ).set_sampling().set_env("LLAMA_ARG_REPEAT_PENALTY"));
    add_opt(common_arg(
        {"--presence-penalty"}, "N",
        string_format("repeat alpha presence penalty (default: %.2f, 0.0 = disabled)", (double)params.sampling.penalty_present),
@@ -2107,7 +2107,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            }
            params.sampling.penalty_present = penalty_present;
        }
-    ).set_sampling());
+    ).set_sampling().set_env("LLAMA_ARG_PRESENCE_PENALTY"));
    add_opt(common_arg(
        {"--frequency-penalty"}, "N",
        string_format("repeat alpha frequency penalty (default: %.2f, 0.0 = disabled)", (double)params.sampling.penalty_freq),
@@ -2118,7 +2118,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            }
            params.sampling.penalty_freq = penalty_freq;
        }
-    ).set_sampling());
+    ).set_sampling().set_env("LLAMA_ARG_FREQUENCY_PENALTY"));
    add_opt(common_arg(
        {"--dry-multiplier"}, "N",
        string_format("set DRY sampling multiplier (default: %.2f, 0.0 = disabled)", (double)params.sampling.dry_multiplier),
@@ -2673,16 +2673,17 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.video_ffmpeg_bin_dir = value;
        }
    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_VIDEO_FFMPEG_DIR"));
-    if (params.is_gen_docs || llama_supports_rpc()) {
-        add_opt(common_arg(
-            {"--rpc"}, "SERVERS",
-            "comma-separated list of RPC servers (host:port)",
-            [](common_params & params, const std::string & value) {
-                add_rpc_devices(value);
-                GGML_UNUSED(params);
+    add_opt(common_arg(
+        {"--rpc"}, "SERVERS",
+        "comma-separated list of RPC servers (host:port)",
+        [](common_params & params, const std::string & value) {
+            if (!llama_supports_rpc()) {
+                throw std::invalid_argument("RPC not supported in this build");
            }
-        ).set_env("LLAMA_ARG_RPC"));
-    }
+            add_rpc_devices(value);
+            GGML_UNUSED(params);
+        }
+    ).set_env("LLAMA_ARG_RPC"));
    add_opt(common_arg(
        {"-lm", "--load-mode"}, "MODE",
        "model loading mode (default: auto)\n"
@@ -3308,9 +3309,18 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
    add_opt(common_arg(
        {"--host"}, "HOST",
-        string_format("ip address to listen, or bind to an UNIX socket if the address ends with .sock (default: %s)", params.hostname.c_str()),
+        string_format("IP addresses to listen on, comma-separated, or UNIX socket paths ending in .sock; with multiple TCP addresses, :: binds IPv6 only; overlapping addresses result in undefined behavior (default: %s)", params.hostnames[0].c_str()),
        [](common_params & params, const std::string & value) {
-            params.hostname = value;
+            params.hostnames.clear();
+            for (auto & host : parse_csv_row(value)) {
+                host = string_strip(host);
+                if (!host.empty()) {
+                    params.hostnames.push_back(host);
+                }
+            }
+            if (params.hostnames.empty()) {
+                throw std::invalid_argument("--host requires at least one address");
+            }
        }
    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_HOST"));
    add_opt(common_arg(
@@ -122,6 +122,8 @@ struct common_params_context {

 // parse input arguments from CLI
 // if one argument has invalid value, it will automatically display usage of the specific argument (and not the full usage message)
+// TODO: this function can load ggml backend (by calling llama_support_rpc)
+//       this is a side-effect that should be avoided
 bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);

 // load all backends and print the list of available (non-CPU) devices to stdout
@@ -318,13 +318,13 @@ void common_chat_peg_mapper::map(const common_peg_ast_node & node) {
    bool is_content   = node.tag == common_chat_peg_builder::CONTENT;

    if (is_reasoning) { // GPT OSS can have more than 1 reasoning block, so concatenate here
-        result.reasoning_content += std::string(node.text);
+        result.reasoning_content += node.sanitized_text();
    }

    if (is_content) {
        // Concatenate content from multiple content nodes (e.g., when reasoning markers
        // are preserved before content markers in reasoning_format=NONE mode)
-        result.content += std::string(node.text);
+        result.content += node.sanitized_text();
    }

    // Handle tool-related tags (supporting both JSON and tagged formats)
@@ -1058,12 +1058,12 @@ void common_chat_peg_gemma4_mapper::visit(const common_peg_ast_arena & arena, co
    const auto & node = arena.get(id);

    if (node.tag == "reasoning") {
-        result.reasoning_content += std::string(node.text);
+        result.reasoning_content += node.sanitized_text();
        return;
    }

    if (node.tag == "content") {
-        result.content += std::string(node.text);
+        result.content += node.sanitized_text();
        return;
    }

@@ -1206,12 +1206,12 @@ void common_chat_peg_minimax_m3_mapper::visit(const common_peg_ast_arena & arena
    const auto & node = arena.get(id);

    if (node.tag == common_chat_peg_builder::REASONING) {
-        result.reasoning_content += std::string(node.text);
+        result.reasoning_content += node.sanitized_text();
        return;
    }

    if (node.tag == common_chat_peg_builder::CONTENT) {
-        result.content += std::string(node.text);
+        result.content += node.sanitized_text();
        return;
    }

@@ -1133,6 +1133,14 @@ std::optional<common_chat_params> common_chat_try_specialized_template(
        return common_chat_params_init_kimi_k3(tmpl, params);
    }

+    // Ling 3.0 / Bailing V3 - <role>X</role> sections with <arg_key>/<arg_value> tagged
+    // tool calls. <role> sections are unique to this family among the tagged-arg templates.
+    if (src.find("<role>ASSISTANT</role>") != std::string::npos &&
+        src.find("<arg_key>") != std::string::npos) {
+        LOG_DBG("Using specialized template: Ling 3.0 (Bailing V3)\n");
+        return common_chat_params_init_ling3(tmpl, params);
+    }
+
    // Cohere2 MoE / North Code - marker-wrapped format with <|START_TEXT|> content and
    // <|START_ACTION|> JSON tool calls. <|START_TEXT|> is unique to this template (the older
    // Command-R templates use <|START_RESPONSE|>).
@@ -1204,7 +1212,9 @@ std::optional<common_chat_params> common_chat_try_specialized_template(
    // Qwen3-Coder XML tool calls, also used by Nemotron Nano 3, Qwen3.5 and StepFun-3.5-Flash
    if (src.find("<tool_call>") != std::string::npos &&
        src.find("<function=") != std::string::npos &&
-        src.find("<parameter=") != std::string::npos) {
+        src.find("<parameter=") != std::string::npos &&
+        // Exclude models that don't use \n between tags
+        src.find("'<tool_call><function=' ~ tool_call.name ~ '>'") == std::string::npos) {
        LOG_DBG("Using specialized template: Qwen3-Coder\n");
        return common_chat_params_init_qwen3_coder(tmpl, params);
    }
@@ -46,7 +46,6 @@
 #include <io.h>
 #else
 #include <sys/ioctl.h>
-#include <sys/stat.h>
 #include <unistd.h>
 #endif

@@ -900,7 +899,7 @@ bool fs_validate_filename(const std::string & filename, bool allow_subdirs) {


 #ifdef _WIN32
-static std::wstring utf8_to_wstring(const std::string & str) {
+std::wstring utf8_to_wstring(const std::string & str) {
    if (str.empty()) {
        return std::wstring();
    }
@@ -916,82 +915,36 @@ static std::wstring utf8_to_wstring(const std::string & str) {

    return wstr;
 }
+
+std::string wstring_to_utf8(const std::wstring & str) {
+    if (str.empty()) {
+        return std::string();
+    }
+
+    int size = WideCharToMultiByte(CP_UTF8, 0, str.c_str(), (int)str.size(), NULL, 0, NULL, NULL);
+
+    if (size <= 0) {
+        return std::string();
+    }
+
+    std::string utf8(size, 0);
+    WideCharToMultiByte(CP_UTF8, 0, str.c_str(), (int)str.size(), &utf8[0], size, NULL, NULL);
+
+    return utf8;
+}
 #endif

+// returns the path as a UTF-8 string, preserving its separators
+std::string fs_path_to_utf8(const std::filesystem::path & path) {
+    const auto value = path.u8string();
+    return std::string(value.begin(), value.end());
+}
+
 // returns true if successful, false otherwise
 bool fs_create_directory_with_parents(const std::string & path) {
-#ifdef _WIN32
-    std::wstring wpath = utf8_to_wstring(path);
-
-    // if the path already exists, check whether it's a directory
-    const DWORD attributes = GetFileAttributesW(wpath.c_str());
-    if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) {
-        return true;
-    }
-
-    size_t pos_slash = 0;
-
-    // process path from front to back, procedurally creating directories
-    while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
-        const std::wstring subpath = wpath.substr(0, pos_slash);
-
-        pos_slash += 1;
-
-        // skip the drive letter, in some systems it can return an access denied error
-        if (subpath.length() == 2 && subpath[1] == ':') {
-            continue;
-        }
-
-        const bool success = CreateDirectoryW(subpath.c_str(), NULL);
-
-        if (!success) {
-            const DWORD error = GetLastError();
-
-            // if the path already exists, ensure that it's a directory
-            if (error == ERROR_ALREADY_EXISTS) {
-                const DWORD attributes = GetFileAttributesW(subpath.c_str());
-                if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) {
-                    return false;
-                }
-            } else {
-                return false;
-            }
-        }
-    }
-
-    return true;
-#else
-    // if the path already exists, check whether it's a directory
-    struct stat info;
-    if (stat(path.c_str(), &info) == 0) {
-        return S_ISDIR(info.st_mode);
-    }
-
-    size_t pos_slash = 1; // skip leading slashes for directory creation
-
-    // process path from front to back, procedurally creating directories
-    while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) {
-        const std::string subpath = path.substr(0, pos_slash);
-        struct stat info;
-
-        // if the path already exists, ensure that it's a directory
-        if (stat(subpath.c_str(), &info) == 0) {
-            if (!S_ISDIR(info.st_mode)) {
-                return false;
-            }
-        } else {
-            // create parent directories
-            const int ret = mkdir(subpath.c_str(), 0755);
-            if (ret != 0) {
-                return false;
-            }
-        }
-
-        pos_slash += 1;
-    }
-
-    return true;
-#endif // _WIN32
+    std::error_code ec;
+    std::filesystem::create_directories(std::filesystem::u8path(path), ec);
+    return !ec;
 }

 bool fs_is_directory(const std::string & path) {
@@ -2198,9 +2151,28 @@ bool common_replay_last_token(struct llama_context * ctx, llama_token last_token
    return true;
 }

+llama_batch_ext_ptr common_batch_ext_get_one(llama_context * ctx, const llama_tokens & tokens) {
+    llama_batch_ext_ptr batch(llama_batch_ext_init(ctx));
+
+    auto mem = llama_get_memory(ctx);
+    llama_pos pos = mem ? llama_memory_seq_pos_max(mem, 0) + 1 : 0;
+
+    for (size_t i = 0; i < tokens.size(); ++i) {
+        const int32_t idx = llama_batch_ext_add_token(batch.get(), 0, tokens[i]);
+        llama_batch_ext_set_pos(batch.get(), idx, &pos);
+        pos++;
+    }
+
+    if (!tokens.empty()) {
+        llama_batch_ext_set_output_logits(batch.get(), (int32_t) tokens.size() - 1, true);
+    }
+
+    return batch;
+}
+
 bool common_prompt_batch_decode(
              struct llama_context * ctx,
-    const std::vector<llama_token> & all_tokens,
+                const llama_tokens & all_tokens,
                               int   n_new,
                               int & n_past,
                               int   n_batch,
@@ -2221,7 +2193,9 @@ bool common_prompt_batch_decode(
        // Memory implementations in recurrent/hybrid models don't support removing tokens from their
        // memory, so we can't just remove the last token from the memory and replay the last token which
        // is the reason for this logic.
-        if (llama_decode(ctx, llama_batch_get_one(const_cast<llama_token*>(all_tokens.data() + offset), n_tokens_before_last))) {
+        llama_tokens prefix_tokens(all_tokens.begin() + offset, all_tokens.begin() + offset + n_tokens_before_last);
+        llama_batch_ext_ptr batch_prefix = common_batch_ext_get_one(ctx, prefix_tokens);
+        if (llama_process(ctx, LLAMA_PROCESS_TYPE_DECODE, batch_prefix.get())) {
            COM_ERR("%s", "failed to eval\n");
            return false;
        }
@@ -2231,17 +2205,19 @@ bool common_prompt_batch_decode(
        COM_INF("saved session before last token to %s, n_new = %zu\n", state_path.data(), all_tokens.size());

        llama_token last_token = all_tokens.back();
-        llama_batch batch = llama_batch_get_one(&last_token, 1);
-        int32_t pos = n_past;
-        batch.pos = &pos;
+        llama_batch_ext_ptr batch_last = common_batch_ext_get_one(ctx, { last_token });
+        llama_pos pos = n_past;
+        llama_batch_ext_set_pos(batch_last.get(), 0, &pos);

-        if (llama_decode(ctx, batch)) {
+        if (llama_process(ctx, LLAMA_PROCESS_TYPE_DECODE, batch_last.get())) {
            COM_ERR("%s", "failed to eval last token\n");
            return false;
        }
        n_past++;
    } else {
-        if (llama_decode(ctx, llama_batch_get_one(const_cast<llama_token*>(all_tokens.data() + offset), n_new))) {
+        llama_tokens new_tokens(all_tokens.begin() + offset, all_tokens.begin() + offset + n_new);
+        llama_batch_ext_ptr batch = common_batch_ext_get_one(ctx, new_tokens);
+        if (llama_process(ctx, LLAMA_PROCESS_TYPE_DECODE, batch.get())) {
            COM_ERR("%s", "failed to eval\n");
            return false;
        }
@@ -16,6 +16,7 @@
 #include <vector>
 #include <map>
 #include <algorithm>
+#include <filesystem>
 #include <fstream>

 #if defined(_WIN32) && !defined(_WIN32_WINNT)
@@ -631,10 +632,10 @@ struct common_params {
    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.

-    std::string hostname      = "127.0.0.1";
    std::string public_path   = "";                                                                         // NOLINT
    std::string api_prefix    = "";                                                                         // NOLINT
    std::string chat_template = "";                                                                         // NOLINT
+    std::vector<std::string> hostnames = {"127.0.0.1"};
    bool use_jinja = true;                                                                                  // NOLINT

    // server CORS params
@@ -808,7 +809,9 @@ static std::vector<T> string_split(const std::string & str, char delim) {
    while (std::getline(str_stream, token, delim)) {
        T value;
        std::istringstream token_stream(token);
-        token_stream >> value;
+        if (!(token_stream >> value)) {
+            throw std::invalid_argument("invalid value: \"" + token + "\"");
+        }
        values.push_back(value);
    }
    return values;
@@ -880,6 +883,18 @@ std::string string_from(const struct llama_context * ctx, const struct llama_bat

 bool glob_match(const std::string & pattern, const std::string & str);

+//
+// Unicode utils
+//
+
+#ifdef _WIN32
+std::wstring utf8_to_wstring(const std::string & str);
+std::string  wstring_to_utf8(const std::wstring & str);
+#endif
+
+// returns the path as a UTF-8 string, preserving its separators
+std::string fs_path_to_utf8(const std::filesystem::path & path);
+
 //
 // Environment utils
 //
@@ -1021,6 +1036,10 @@ void common_batch_add(
    const std::vector<llama_seq_id> & seq_ids,
                               bool   logits);

+// create a single-sequence batch from a list of tokens
+// last token always have output_logits set to true
+llama_batch_ext_ptr common_batch_ext_get_one(struct llama_context * ctx, const llama_tokens & tokens);
+
 // decodes a single batch of tokens for a prompt and manages session tokens
 //
 // Note: We save state before the last token so that we can replay it to ensure
@@ -1028,7 +1047,7 @@ void common_batch_add(
 // tokens from memory, so this approach works across all model architectures.
 bool common_prompt_batch_decode(
              struct llama_context * ctx,
-    const std::vector<llama_token> & all_tokens,
+                const llama_tokens & all_tokens,
                               int   n_new,
                               int & n_past,
                               int   n_batch,
@@ -1,4 +1,5 @@
 #include "console.h"
+#include "common.h"
 #include "log.h"
 #include <vector>
 #include <iostream>
@@ -1053,9 +1054,7 @@ namespace console {
            return false;
        }

-        int size_needed = WideCharToMultiByte(CP_UTF8, 0, &wline[0], (int)wline.size(), NULL, 0, NULL, NULL);
-        line.resize(size_needed);
-        WideCharToMultiByte(CP_UTF8, 0, &wline[0], (int)wline.size(), &line[0], size_needed, NULL, NULL);
+        line = wstring_to_utf8(wline);
 #else
        if (!std::getline(std::cin, line)) {
            // Input stream is bad or EOF received
@@ -62,6 +62,10 @@ static fs::path get_cache_directory() {
    return cache;
 }

+std::string get_cache_path() {
+    return fs_path_to_utf8(get_cache_directory());
+}
+
 static std::string folder_name_to_repo(const std::string & folder) {
    constexpr std::string_view prefix = "models--";
    if (folder.rfind(prefix, 0)) {
@@ -393,8 +397,8 @@ static std::string get_cached_ref(const fs::path & repo_path) {
 }

 hf_files get_cached_files(const std::string & repo_id) {
-    fs::path cache_dir = get_cache_directory();
-    if (!fs::exists(cache_dir)) {
+    const fs::path cache_path = get_cache_directory();
+    if (!fs::exists(cache_path)) {
        return {};
    }

@@ -405,7 +409,7 @@ hf_files get_cached_files(const std::string & repo_id) {

    hf_files files;

-    for (const auto & repo : fs::directory_iterator(cache_dir)) {
+    for (const auto & repo : fs::directory_iterator(cache_path)) {
        if (!repo.is_directory()) {
            continue;
        }
@@ -32,4 +32,7 @@ std::string finalize_file(const hf_file & file);
 // Remove the entire cached directory for a repo, returns true if removed
 bool remove_cached_repo(const std::string & repo_id);

+// Returns the HuggingFace hub cache path
+std::string get_cache_path();
+
 } // namespace hf_cache
@@ -429,15 +429,23 @@ private:
            bool negate = false;
            if (is_identifier("not")) { ++current; negate = true; }
            auto test_id = parse_primary_expression();
-            // FIXME: tests can also be expressed like this: if x is eq 3
-            if (is(token::open_paren)) test_id = parse_call_expression(std::move(test_id));
+            if (is(token::open_paren)) {
+                test_id = parse_call_expression(std::move(test_id));
+            } else if (is(token::numeric_literal) || is(token::string_literal) || is(token::open_curly_bracket) || is(token::open_square_bracket) ||
+                    (is(token::identifier) && !is_identifier("and") && !is_identifier("or") && !is_identifier("else"))) {
+                size_t call_pos = current;
+                statements args;
+                args.push_back(parse_unary_expression());
+                test_id = mk_stmt<call_expression>(call_pos, std::move(test_id), std::move(args));
+            }
            operand = mk_stmt<test_expression>(start_pos, std::move(operand), negate, std::move(test_id));
        }
        return operand;
    }

    statement_ptr parse_filter_expression() {
-        auto operand = parse_call_member_expression();
+        // Filters/tests bind outside unary so -n|abs is (-n)|abs, not -(n|abs).
+        auto operand = parse_unary_expression();
        while (is(token::pipe)) {
            size_t start_pos = current;
            ++current; // consume pipe
@@ -448,6 +456,15 @@ private:
        return operand;
    }

+    statement_ptr parse_unary_expression() {
+        if (is(token::unary_operator)) {
+            size_t start_pos = current;
+            auto op = next();
+            return mk_stmt<unary_expression>(start_pos, op, parse_unary_expression());
+        }
+        return parse_call_member_expression();
+    }
+
    statement_ptr parse_call_member_expression() {
        // Handle member expressions recursively
        auto member = parse_member_expression(parse_primary_expression());
@@ -51,7 +51,7 @@ static void ensure_key_type_allowed(const value & val) {
 }

 // execute with error handling
-value statement::execute(context & ctx) {
+value statement::execute(context & ctx) const {
    try {
        return execute_impl(ctx);
    } catch (const continue_statement::signal & /* ex */) {
@@ -80,7 +80,7 @@ value statement::execute(context & ctx) {
    }
 }

-value identifier::execute_impl(context & ctx) {
+value identifier::execute_impl(context & ctx) const {
    auto it = ctx.get_val(val);
    auto builtins = global_builtins();
    if (!it->is_undefined()) {
@@ -98,7 +98,7 @@ value identifier::execute_impl(context & ctx) {
    }
 }

-value object_literal::execute_impl(context & ctx) {
+value object_literal::execute_impl(context & ctx) const {
    auto obj = mk_val<value_object>();
    for (const auto & pair : val) {
        value key = pair.first->execute(ctx);
@@ -109,7 +109,7 @@ value object_literal::execute_impl(context & ctx) {
    return obj;
 }

-value binary_expression::execute_impl(context & ctx) {
+value binary_expression::execute_impl(context & ctx) const {
    value left_val = left->execute(ctx);

    // Logical operators
@@ -317,9 +317,7 @@ static value try_builtin_func(context & ctx, const std::string & name, value & i
    throw std::runtime_error("Unknown (built-in) filter '" + name + "' for type " + input->type());
 }

-value filter_expression::execute_impl(context & ctx) {
-    value input = operand ? operand->execute(ctx) : val;
-
+static value apply_filter(context & ctx, const statement_ptr & filter, value input) {
    JJ_DEBUG("Applying filter to %s", input->type().c_str());

    auto set_filter_alias = [](auto & filter_id) {
@@ -375,22 +373,21 @@ value filter_expression::execute_impl(context & ctx) {
    }
 }

-value filter_statement::execute_impl(context & ctx) {
+value filter_expression::execute_impl(context & ctx) const {
+    return apply_filter(ctx, filter, operand->execute(ctx));
+}
+
+value filter_statement::execute_impl(context & ctx) const {
    // eval body as string, then apply filter
    auto body_val = exec_statements(body, ctx);
    value_string parts = mk_val<value_string>();
    gather_string_parts_recursive(body_val, parts);

    JJ_DEBUG("FilterStatement: applying filter to body string of length %zu", parts->val_str.length());
-    filter_expression filter_expr(std::move(parts), std::move(filter));
-    value out = filter_expr.execute(ctx);
-
-    // this node can be reused later, make sure filter is preserved
-    this->filter = std::move(filter_expr.filter);
-    return out;
+    return apply_filter(ctx, filter, parts);
 }

-value test_expression::execute_impl(context & ctx) {
+value test_expression::execute_impl(context & ctx) const {
    // NOTE: "value is something" translates to function call "test_is_something(value)"
    const auto & builtins = global_builtins();

@@ -439,7 +436,7 @@ value test_expression::execute_impl(context & ctx) {
    }
 }

-value unary_expression::execute_impl(context & ctx) {
+value unary_expression::execute_impl(context & ctx) const {
    value operand_val = argument->execute(ctx);
    JJ_DEBUG("Executing unary expression with operator '%s'", op.value.c_str());

@@ -453,12 +450,17 @@ value unary_expression::execute_impl(context & ctx) {
        } else {
            throw std::runtime_error("Unary - operator requires numeric operand");
        }
+    } else if (op.value == "+") {
+        if (is_val<value_int>(operand_val) || is_val<value_float>(operand_val)) {
+            return operand_val;
+        }
+        throw std::runtime_error("Unary + operator requires numeric operand");
    }

    throw std::runtime_error("Unknown unary operator '" + op.value + "'");
 }

-value if_statement::execute_impl(context & ctx) {
+value if_statement::execute_impl(context & ctx) const {
    value test_val = test->execute(ctx);

    auto out = mk_val<value_array>();
@@ -479,20 +481,14 @@ value if_statement::execute_impl(context & ctx) {
    return str;
 }

-value for_statement::execute_impl(context & ctx) {
+value for_statement::execute_impl(context & ctx) const {
    context scope(ctx); // new scope for loop variables

-    jinja::select_expression * select_expr = cast_stmt<select_expression>(iterable);
+    const jinja::select_expression * select_expr = cast_stmt<select_expression>(iterable);
    statement_ptr test_expr_nullptr;

-    statement_ptr & iter_expr = [&]() -> statement_ptr & {
-        auto tmp = cast_stmt<select_expression>(iterable);
-        return tmp ? tmp->lhs : iterable;
-    }();
-    statement_ptr & test_expr = [&]() -> statement_ptr & {
-        auto tmp = cast_stmt<select_expression>(iterable);
-        return tmp ? tmp->test : test_expr_nullptr;
-    }();
+    const statement_ptr & iter_expr = select_expr ? select_expr->lhs : iterable;
+    const statement_ptr & test_expr = select_expr ? select_expr->test : test_expr_nullptr;

    JJ_DEBUG("Executing for statement, iterable type: %s", iter_expr->type().c_str());

@@ -645,7 +641,7 @@ value for_statement::execute_impl(context & ctx) {
    return str;
 }

-value set_statement::execute_impl(context & ctx) {
+value set_statement::execute_impl(context & ctx) const {
    auto rhs = val ? val->execute(ctx) : exec_statements(body, ctx);

    if (is_stmt<identifier>(assignee)) {
@@ -744,7 +740,7 @@ static inline void bind_parameters(const std::string & name, const statements &
    }
 }

-value macro_statement::execute_impl(context & ctx) {
+value macro_statement::execute_impl(context & ctx) const {
    if (!is_stmt<identifier>(this->name)) {
        throw std::runtime_error("Macro name must be an identifier");
    }
@@ -767,7 +763,7 @@ value macro_statement::execute_impl(context & ctx) {
    return mk_val<value_undefined>();
 }

-value call_statement::execute_impl(context & ctx) {
+value call_statement::execute_impl(context & ctx) const {
    auto call_expr = cast_stmt<call_expression>(this->call);
    if (!call_expr) {
        throw std::runtime_error("Call statement requires a valid call expression");
@@ -807,7 +803,7 @@ value call_statement::execute_impl(context & ctx) {
    return callee_func->invoke(args);
 }

-value member_expression::execute_impl(context & ctx) {
+value member_expression::execute_impl(context & ctx) const {
    value object = this->object->execute(ctx);

    value property;
@@ -940,7 +936,7 @@ value member_expression::execute_impl(context & ctx) {
    return val;
 }

-value call_expression::execute_impl(context & ctx) {
+value call_expression::execute_impl(context & ctx) const {
    // gather arguments
    func_args args(ctx);
    for (auto & arg_stmt : this->args) {
@@ -958,7 +954,7 @@ value call_expression::execute_impl(context & ctx) {
    return callee_func->invoke(args);
 }

-value keyword_argument_expression::execute_impl(context & ctx) {
+value keyword_argument_expression::execute_impl(context & ctx) const {
    if (!is_stmt<identifier>(key)) {
        throw std::runtime_error("Keyword argument key must be identifiers");
    }
@@ -982,7 +978,7 @@ std::string runtime::debug_dump_program(const program & prog, const std::string
        return std::string(lvl * 2, ' ');
    };

-    ctx.visitor = [&](bool is_leaf, statement * node, std::vector<visitor_pair> children) {
+    ctx.visitor = [&](bool is_leaf, const statement * node, std::vector<visitor_pair> children) {
        oss << indent(lvl) << node->type() << ":\n";
        lvl++;
        if (is_leaf) {
@@ -48,9 +48,9 @@ const T * cast_stmt(const statement_ptr & ptr) {
 void enable_debug(bool enable);

 // for visiting AST nodes
-// function signature: void(bool is_leaf, statement * node, pair of <label, children>)
-using visitor_pair = std::pair<std::string, std::vector<statement *>>;
-using visitor_fn = std::function<void(bool, statement *, std::vector<visitor_pair>)>;
+// function signature: void(bool is_leaf, const statement * node, pair of <label, children>)
+using visitor_pair = std::pair<std::string, std::vector<const statement *>>;
+using visitor_fn = std::function<void(bool, const statement *, std::vector<visitor_pair>)>;

 struct context {
    std::shared_ptr<std::string> src; // for debugging; use shared_ptr to avoid copying on scope creation
@@ -107,8 +107,8 @@ private:
 };

 // utils for visiting AST nodes
-static std::vector<statement *> stmts_to_ptr(const statements & stmts) {
-    std::vector<statement *> children;
+static std::vector<const statement *> stmts_to_ptr(const statements & stmts) {
+    std::vector<const statement *> children;
    for (const auto & stmt : stmts) {
        children.push_back(stmt.get());
    }
@@ -117,17 +117,18 @@ static std::vector<statement *> stmts_to_ptr(const statements & stmts) {

 /**
 * Base class for all nodes in the AST.
+ * The AST is shared between threads, so visit and execute must be const.
 */
 struct statement {
    size_t pos; // position in source, for debugging
    virtual ~statement() = default;
    virtual std::string type() const { return "Statement"; }
-    virtual void visit(context & ctx) { ctx.visitor(true, this, {}); }
+    virtual void visit(context & ctx) const { ctx.visitor(true, this, {}); }

    // execute_impl must be overridden by derived classes
-    virtual value execute_impl(context &) { throw_exec_error(); }
+    virtual value execute_impl(context &) const { throw_exec_error(); }
    // execute is the public method to execute a statement with error handling
-    value execute(context &);
+    value execute(context &) const;

 private:
    [[noreturn]] void throw_exec_error() const {
@@ -166,7 +167,7 @@ struct program : public statement {
    program() = default;
    explicit program(statements && body) : body(std::move(body)) {}
    std::string type() const override { return "Program"; }
-    [[noreturn]] value execute_impl(context &) override {
+    [[noreturn]] value execute_impl(context &) const override {
        throw std::runtime_error("Cannot execute program directly, use jinja::runtime instead");
    }
 };
@@ -182,8 +183,8 @@ struct if_statement : public statement {
    }

    std::string type() const override { return "If"; }
-    value execute_impl(context & ctx) override;
-    void visit(context & ctx) override {
+    value execute_impl(context & ctx) const override;
+    void visit(context & ctx) const override {
        ctx.visitor(false, this, {
            {"test", {test.get()}},
            {"body", stmts_to_ptr(body)},
@@ -213,8 +214,8 @@ struct for_statement : public statement {
    }

    std::string type() const override { return "For"; }
-    value execute_impl(context & ctx) override;
-    void visit(context & ctx) override {
+    value execute_impl(context & ctx) const override;
+    void visit(context & ctx) const override {
        ctx.visitor(false, this, {
            {"loopvar", {loopvar.get()}},
            {"iterable", {iterable.get()}},
@@ -233,7 +234,7 @@ struct break_statement : public statement {
        }
    };

-    [[noreturn]] value execute_impl(context &) override {
+    [[noreturn]] value execute_impl(context &) const override {
        throw break_statement::signal();
    }
 };
@@ -247,7 +248,7 @@ struct continue_statement : public statement {
        }
    };

-    [[noreturn]] value execute_impl(context &) override {
+    [[noreturn]] value execute_impl(context &) const override {
        throw continue_statement::signal();
    }
 };
@@ -255,7 +256,7 @@ struct continue_statement : public statement {
 // do nothing
 struct noop_statement : public statement {
    std::string type() const override { return "Noop"; }
-    value execute_impl(context &) override {
+    value execute_impl(context &) const override {
        return mk_val<value_undefined>();
    }
 };
@@ -272,8 +273,8 @@ struct set_statement : public statement {
    }

    std::string type() const override { return "Set"; }
-    value execute_impl(context & ctx) override;
-    void visit(context & ctx) override {
+    value execute_impl(context & ctx) const override;
+    void visit(context & ctx) const override {
        ctx.visitor(false, this, {
            {"assignee", {assignee.get()}},
            {"value", {val.get()}},
@@ -294,8 +295,8 @@ struct macro_statement : public statement {
    }

    std::string type() const override { return "Macro"; }
-    value execute_impl(context & ctx) override;
-    void visit(context & ctx) override {
+    value execute_impl(context & ctx) const override;
+    void visit(context & ctx) const override {
        ctx.visitor(false, this, {
            {"name", {name.get()}},
            {"args", stmts_to_ptr(args)},
@@ -308,7 +309,7 @@ struct comment_statement : public statement {
    std::string val;
    explicit comment_statement(const std::string & v) : val(v) {}
    std::string type() const override { return "Comment"; }
-    value execute_impl(context &) override {
+    value execute_impl(context &) const override {
        return mk_val<value_undefined>();
    }
 };
@@ -318,7 +319,7 @@ struct comment_statement : public statement {
 // Represents an omitted expression in a computed member, e.g. `a[]`.
 struct blank_expression : public expression {
    std::string type() const override { return "BlankExpression"; }
-    value execute_impl(context &) override {
+    value execute_impl(context &) const override {
        return mk_val<value_undefined>();
    }
 };
@@ -334,8 +335,8 @@ struct member_expression : public expression {
        chk_type<expression>(this->property);
    }
    std::string type() const override { return "MemberExpression"; }
-    value execute_impl(context & ctx) override;
-    void visit(context & ctx) override {
+    value execute_impl(context & ctx) const override;
+    void visit(context & ctx) const override {
        ctx.visitor(false, this, {
            {"object", {object.get()}},
            {"property", {property.get()}}
@@ -353,8 +354,8 @@ struct call_expression : public expression {
        for (const auto& arg : this->args) chk_type<expression>(arg);
    }
    std::string type() const override { return "CallExpression"; }
-    value execute_impl(context & ctx) override;
-    void visit(context & ctx) override {
+    value execute_impl(context & ctx) const override;
+    void visit(context & ctx) const override {
        ctx.visitor(false, this, {
            {"callee", {callee.get()}},
            {"args", stmts_to_ptr(args)}
@@ -369,7 +370,7 @@ struct identifier : public expression {
    std::string val;
    explicit identifier(const std::string & val) : val(val) {}
    std::string type() const override { return "Identifier"; }
-    value execute_impl(context & ctx) override;
+    value execute_impl(context & ctx) const override;
 };

 // Literals
@@ -378,7 +379,7 @@ struct integer_literal : public expression {
    int64_t val;
    explicit integer_literal(int64_t val) : val(val) {}
    std::string type() const override { return "IntegerLiteral"; }
-    value execute_impl(context &) override {
+    value execute_impl(context &) const override {
        return mk_val<value_int>(val);
    }
 };
@@ -387,7 +388,7 @@ struct float_literal : public expression {
    double val;
    explicit float_literal(double val) : val(val) {}
    std::string type() const override { return "FloatLiteral"; }
-    value execute_impl(context &) override {
+    value execute_impl(context &) const override {
        return mk_val<value_float>(val);
    }
 };
@@ -396,7 +397,7 @@ struct string_literal : public expression {
    std::string val;
    explicit string_literal(const std::string & val) : val(val) {}
    std::string type() const override { return "StringLiteral"; }
-    value execute_impl(context &) override {
+    value execute_impl(context &) const override {
        return mk_val<value_string>(val);
    }
 };
@@ -407,7 +408,7 @@ struct array_literal : public expression {
        for (const auto& item : this->val) chk_type<expression>(item);
    }
    std::string type() const override { return "ArrayLiteral"; }
-    value execute_impl(context & ctx) override {
+    value execute_impl(context & ctx) const override {
        auto arr = mk_val<value_array>();
        for (const auto & item_stmt : val) {
            arr->push_back(item_stmt->execute(ctx));
@@ -422,7 +423,7 @@ struct tuple_literal : public expression {
        for (const auto& item : this->val) chk_type<expression>(item);
    }
    std::string type() const override { return "TupleLiteral"; }
-    value execute_impl(context & ctx) override {
+    value execute_impl(context & ctx) const override {
        auto arr = mk_val<value_array>();
        for (const auto & item_stmt : val) {
            arr->push_back(item_stmt->execute(ctx));
@@ -441,7 +442,7 @@ struct object_literal : public expression {
        }
    }
    std::string type() const override { return "ObjectLiteral"; }
-    value execute_impl(context & ctx) override;
+    value execute_impl(context & ctx) const override;
 };

 // Complex Expressions
@@ -462,8 +463,8 @@ struct binary_expression : public expression {
        chk_type<expression>(this->right);
    }
    std::string type() const override { return "BinaryExpression"; }
-    value execute_impl(context & ctx) override;
-    void visit(context & ctx) override {
+    value execute_impl(context & ctx) const override;
+    void visit(context & ctx) const override {
        ctx.visitor(false, this, {
            {"left", {left.get()}},
            {"right", {right.get()}}
@@ -476,10 +477,7 @@ struct binary_expression : public expression {
 * Operator precedence: https://github.com/pallets/jinja/issues/379#issuecomment-168076202
 */
 struct filter_expression : public expression {
-    // either an expression or a value is allowed
    statement_ptr operand;
-    value_string val; // will be set by filter_statement
-
    statement_ptr filter;

    filter_expression(statement_ptr && operand, statement_ptr && filter)
@@ -488,14 +486,9 @@ struct filter_expression : public expression {
        chk_type<identifier, call_expression>(this->filter);
    }

-    filter_expression(value_string && val, statement_ptr && filter)
-        : val(std::move(val)), filter(std::move(filter)) {
-        chk_type<identifier, call_expression>(this->filter);
-    }
-
    std::string type() const override { return "FilterExpression"; }
-    value execute_impl(context & ctx) override;
-    void visit(context & ctx) override {
+    value execute_impl(context & ctx) const override;
+    void visit(context & ctx) const override {
        ctx.visitor(false, this, {
            {"operand", {operand.get()}},
            {"filter", {filter.get()}}
@@ -512,8 +505,8 @@ struct filter_statement : public statement {
        chk_type<identifier, call_expression>(this->filter);
    }
    std::string type() const override { return "FilterStatement"; }
-    value execute_impl(context & ctx) override;
-    void visit(context & ctx) override {
+    value execute_impl(context & ctx) const override;
+    void visit(context & ctx) const override {
        ctx.visitor(false, this, {
            {"filter", {filter.get()}},
            {"body", stmts_to_ptr(body)}
@@ -537,14 +530,14 @@ struct select_expression : public expression {
        chk_type<expression>(this->test);
    }
    std::string type() const override { return "SelectExpression"; }
-    value execute_impl(context & ctx) override {
+    value execute_impl(context & ctx) const override {
        auto predicate = test->execute_impl(ctx);
        if (!predicate->as_bool()) {
            return mk_val<value_undefined>();
        }
        return lhs->execute_impl(ctx);
    }
-    void visit(context & ctx) override {
+    void visit(context & ctx) const override {
        ctx.visitor(false, this, {
            {"lhs", {lhs.get()}},
            {"test", {test.get()}}
@@ -567,8 +560,8 @@ struct test_expression : public expression {
        chk_type<identifier, call_expression>(this->test);
    }
    std::string type() const override { return "TestExpression"; }
-    value execute_impl(context & ctx) override;
-    void visit(context & ctx) override {
+    value execute_impl(context & ctx) const override;
+    void visit(context & ctx) const override {
        ctx.visitor(false, this, {
            {"operand", {operand.get()}},
            {"test", {test.get()}}
@@ -588,8 +581,8 @@ struct unary_expression : public expression {
        chk_type<expression>(this->argument);
    }
    std::string type() const override { return "UnaryExpression"; }
-    value execute_impl(context & ctx) override;
-    void visit(context & ctx) override {
+    value execute_impl(context & ctx) const override;
+    void visit(context & ctx) const override {
        ctx.visitor(false, this, {
            {"argument", {argument.get()}}
        });
@@ -608,10 +601,10 @@ struct slice_expression : public expression {
        chk_type<expression>(this->step_expr);
    }
    std::string type() const override { return "SliceExpression"; }
-    [[noreturn]] value execute_impl(context &) override {
+    [[noreturn]] value execute_impl(context &) const override {
        throw std::runtime_error("must be handled by MemberExpression");
    }
-    void visit(context & ctx) override {
+    void visit(context & ctx) const override {
        ctx.visitor(false, this, {
            {"start_expr", {start_expr.get()}},
            {"stop_expr", {stop_expr.get()}},
@@ -630,8 +623,8 @@ struct keyword_argument_expression : public expression {
        chk_type<expression>(this->val);
    }
    std::string type() const override { return "KeywordArgumentExpression"; }
-    value execute_impl(context & ctx) override;
-    void visit(context & ctx) override {
+    value execute_impl(context & ctx) const override;
+    void visit(context & ctx) const override {
        ctx.visitor(false, this, {
            {"key", {key.get()}},
            {"val", {val.get()}}
@@ -645,7 +638,7 @@ struct spread_expression : public expression {
        chk_type<expression>(this->argument);
    }
    std::string type() const override { return "SpreadExpression"; }
-    void visit(context & ctx) override {
+    void visit(context & ctx) const override {
        ctx.visitor(false, this, {
            {"argument", {argument.get()}}
        });
@@ -663,8 +656,8 @@ struct call_statement : public statement {
        for (const auto & arg : this->caller_args) chk_type<expression>(arg);
    }
    std::string type() const override { return "CallStatement"; }
-    value execute_impl(context & ctx) override;
-    void visit(context & ctx) override {
+    value execute_impl(context & ctx) const override;
+    void visit(context & ctx) const override {
        ctx.visitor(false, this, {
            {"call", {call.get()}},
            {"caller_args", stmts_to_ptr(caller_args)},
@@ -685,7 +678,7 @@ struct ternary_expression : public expression {
        chk_type<expression>(this->false_expr);
    }
    std::string type() const override { return "Ternary"; }
-    value execute_impl(context & ctx) override {
+    value execute_impl(context & ctx) const override {
        value cond_val = condition->execute(ctx);
        if (cond_val->as_bool()) {
            return true_expr->execute(ctx);
@@ -693,7 +686,7 @@ struct ternary_expression : public expression {
            return false_expr->execute(ctx);
        }
    }
-    void visit(context & ctx) override {
+    void visit(context & ctx) const override {
        ctx.visitor(false, this, {
            {"condition", {condition.get()}},
            {"true_expr", {true_expr.get()}},
@@ -348,6 +348,43 @@ static value default_value(const func_args & args) {
    return no_value ? args.get_pos(1) : args.get_pos(0);
 }

+static value toobject(const func_args & args) {
+    auto out = mk_val<value_object>();
+    value iter = args.get_pos(0, mk_val<value_undefined>());
+    bool iter_first = false;
+    if (is_val<value_array>(iter)) {
+        iter_first = true;
+        for (const auto & it : iter->as_array()) {
+            if (is_val<value_array>(it) && it->as_array().size() == 2) {
+                auto tuple = it->as_array();
+                auto key = tuple[0];
+                auto val = tuple[1];
+                JJ_DEBUG("namespace/dict: adding key '%s'", key->as_string().str().c_str());
+                out->insert(key, val);
+            } else {
+                throw raised_exception("namespace/dict() iterable argument must consist of tuples, not " + it->type());
+            }
+        }
+    } else if (is_val<value_object>(iter)) {
+        iter_first = true;
+        for (const auto & pair : iter->as_ordered_object()) {
+            JJ_DEBUG("namespace/dict: adding key '%s'", pair.first->as_string().str().c_str());
+            out->insert(pair.first, pair.second);
+        }
+    }
+    for (const auto & arg : args.get_args()) {
+        if (is_val<value_kwarg>(arg)) {
+            auto kwarg = cast_val<value_kwarg>(arg);
+            JJ_DEBUG("namespace/dict: adding key '%s'", kwarg->key.c_str());
+            out->insert(kwarg->key, kwarg->val);
+        } else if (!iter_first) {
+            throw raised_exception("namespace/dict() arguments must be kwargs, dict and/or iterable of tuples, not " + arg->type());
+        }
+        iter_first = false;
+    }
+    return out;
+}
+
 const func_builtins & global_builtins() {
    static const func_builtins builtins = {
        {"raise_exception", [](const func_args & args) -> value {
@@ -355,18 +392,8 @@ const func_builtins & global_builtins() {
            std::string msg = args.get_pos(0)->as_string().str();
            throw raised_exception("Jinja Exception: " + msg);
        }},
-        {"namespace", [](const func_args & args) -> value {
-            auto out = mk_val<value_object>();
-            for (const auto & arg : args.get_args()) {
-                if (!is_val<value_kwarg>(arg)) {
-                    throw raised_exception("namespace() arguments must be kwargs");
-                }
-                auto kwarg = cast_val<value_kwarg>(arg);
-                JJ_DEBUG("namespace: adding key '%s'", kwarg->key.c_str());
-                out->insert(kwarg->key, kwarg->val);
-            }
-            return out;
-        }},
+        {"dict", toobject},
+        {"namespace", toobject},
        {"strftime_now", [](const func_args & args) -> value {
            args.ensure_vals<value_string>();
            std::string format = args.get_pos(0)->as_string().str();
@@ -515,8 +542,28 @@ const func_builtins & global_builtins() {
        }},
        {"test_is_sameas", [](const func_args & args) -> value {
            // Check if an object points to the same memory address as another object
-            (void)args;
-            throw not_implemented_exception("sameas test not implemented");
+            args.ensure_count(2);
+            auto a = args.get_pos(0);
+            auto b = args.get_pos(1);
+            bool res = false;
+            if (!is_val<value_undefined>(a) && !is_val<value_undefined>(b)) {
+                if (is_val<value_none>(a) && is_val<value_none>(b)) {
+                    res = true;
+                } else if (is_val<value_bool>(a) && is_val<value_bool>(b)) {
+                    if (a->as_bool() == b->as_bool()) {
+                        res = true;
+                    }
+                } else if (is_val<value_int>(a) && is_val<value_int>(b)) {
+                    const int64_t x = a->as_int();
+                    // Allow comparison within small-int cache range
+                    if (x >= -5 && x <= 256 && x == b->as_int()) {
+                        res = true;
+                    }
+                } else if (a == b) {
+                    res = true;
+                }
+            }
+            return mk_val<value_bool>(res);
        }},
        {"test_is_escaped", [](const func_args & args) -> value {
            (void)args;
@@ -321,7 +321,8 @@ static size_t gbnf_escape_length(const std::string & pattern, size_t pos) {
        case 'x': n_hex = 2; break;
        case 'u': n_hex = 4; break;
        case 'U': n_hex = 8; break;
-        case 't': case 'r': case 'n': case '\\': case '"': case '[': case ']':
+        // keep in sync with parse_char() in src/llama-grammar.cpp
+        case 't': case 'r': case 'n': case '\\': case '"': case '[': case ']': case '-':
            return 2;
        default:
            return 0;
@@ -82,6 +82,9 @@ struct common_json_value {
    // note: a nested pair {"a", "b"} does not build, use common_json::array({"a", "b"}) for an array
    common_json_value(std::initializer_list<common_json_item> items);

+    template <typename T, typename std::enable_if<std::is_enum<T>::value, int>::type = 0>
+    common_json_value(T val) : common_json_value((typename std::underlying_type<T>::type) val) {}
+
    template <typename T, typename std::enable_if<std::is_integral<T>::value && !std::is_same<T, bool>::value, int>::type = 0>
    common_json_value(T val) : type(std::is_signed<T>::value ? VAL_INT : VAL_UINT) {
        if (std::is_signed<T>::value) {
@@ -111,6 +114,7 @@ struct common_json_item {
 // the types common_json_value holds on its own
 // anything else reaches its common_json ctor and recurses forever
 template <typename T> struct common_json_is_value : std::integral_constant<bool,
+    std::is_enum<T>::value ||
    std::is_arithmetic<T>::value ||
    std::is_same<T, std::nullptr_t>::value ||
    std::is_same<T, std::string>::value ||
@@ -104,6 +104,12 @@ common_chat_params common_chat_params_init_deepseek_v3_2(const common_chat_templ
    const std::string GEN_PROMPT   = "<｜Assistant｜>";
    const std::string TC_SEPARATOR = "\n\n";

+    // lets the server find user turns in the prompt and place context checkpoints there
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_ASSISTANT, GEN_PROMPT },
+        { COMMON_CHAT_ROLE_USER, "<｜User｜>" },
+    };
+
    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(
@@ -272,6 +272,10 @@ common_chat_params common_chat_params_init_gemma4(const common_chat_template &
                /* max = */ inputs.parallel_tool_calls ? -1 : 1
            ));

+            if (inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED) {
+                return start + thought + tool_call;
+            }
+
            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);
@@ -0,0 +1,194 @@
+#include "parsers.h"
+
+// Ling 3.0 / Bailing V3 - <role>X</role> sections with tagged tool calls:
+//   assistant := [<think> ... </think>] [content] {<tool_call>name
+//                  <arg_key>k</arg_key>\n<arg_value>v</arg_value> ...</tool_call>}
+// The generation prompt ends with "<role>ASSISTANT</role>\n<think>", so the model
+// never emits the opening think tag, and a tool call can arrive before any
+// </think>. Reasoning therefore terminates at the think close tag or at a tool
+// call start, like the Qwen3-Coder and Kimi K3 parsers. With thinking off the
+// template pre-closes the think block instead, and the model emits bare content.
+common_chat_params common_chat_params_init_ling3(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 ROLE        = "<role>ASSISTANT</role>";
+    const std::string THINK_START = "<think>";
+    const std::string THINK_END   = "</think>";
+    const std::string CALL_START  = "<tool_call>";
+    const std::string CALL_END    = "</tool_call>";
+    const std::string ARG_KEY     = "<arg_key>";
+    const std::string ARG_KEY_END = "</arg_key>";
+    const std::string ARG_VAL     = "<arg_value>";
+    const std::string ROLE_END    = "<|role_end|>";
+    const std::string ARG_VAL_END = "</arg_value>";
+
+    data.preserved_tokens = {
+        THINK_START, THINK_END, CALL_START, CALL_END,
+        ARG_KEY, ARG_KEY_END, ARG_VAL, ARG_VAL_END, ROLE_END,
+    };
+
+    data.thinking_start_tag = THINK_START;
+    // Support both </think> and <tool_call> as reasoning end sequences: a call
+    // can be emitted before the think block is closed.
+    data.thinking_end_tags  = { THINK_END, CALL_START };
+
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_ASSISTANT, "<role>ASSISTANT</role>" },
+        { COMMON_CHAT_ROLE_USER,      "<role>HUMAN</role>"     },
+        { COMMON_CHAT_ROLE_TOOL,      "<role>OBSERVATION</role>" },
+        { COMMON_CHAT_ROLE_SYSTEM,    "<role>SYSTEM</role>"    },
+    };
+
+    // the model may spell the end-of-turn control token out as text tokens,
+    // which does not stop generation; a literal stop string catches it either
+    // way (as the Laguna patch does for its </assistant> token)
+    data.additional_stops = { ROLE_END };
+
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = ROLE + "\n" + 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;
+    }
+
+    // The generation prompt pre-opens the think block when thinking is on, so
+    // the opening tag is optional here and reasoning runs until </think> or a
+    // tool call start; with thinking off the template pre-closes the block and
+    // everything the model emits is content.
+    bool think_open = false;
+    if (inputs.has_continuation()) {
+        think_open = inputs.continue_final_message != COMMON_CHAT_CONTINUATION_CONTENT;
+    } else {
+        auto last_open  = data.generation_prompt.rfind(THINK_START);
+        auto last_close = data.generation_prompt.rfind(THINK_END);
+        think_open      = last_open != std::string::npos &&
+                     (last_close == std::string::npos || last_open > last_close);
+    }
+
+    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;
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto end = p.end();
+
+        // the effective parse input is generation_prompt + model output, so the
+        // assistant opener is optionally consumed here
+        auto opener = p.optional(p.literal(ROLE) + p.optional(p.space()));
+
+        // the generation prompt pre-opens the think block, so the opening tag
+        // is optional; a missing close tag does not swallow a tool call
+        auto body_end   = think_open ? p.until_one_of({ THINK_END, CALL_START }) : p.until_one_of({ THINK_END });
+        auto think_body = extract_reasoning ? p.reasoning(body_end) : p.content(body_end);
+
+        auto reasoning = p.optional(p.optional(p.literal(THINK_START)) + think_body +
+                                    p.optional(p.literal(THINK_END)));
+
+        // content between the think block and the first tool call, plus any
+        // trailing text after the last tool call, are plain content
+        auto content = p.optional(p.content(p.until_one_of({ CALL_START })));
+
+        // a trailing end-of-turn token is consumed instead of leaking into content
+        auto tail = p.optional(p.content(p.until(ROLE_END))) + p.optional(p.literal(ROLE_END));
+
+        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
+            return opener + reasoning + tail + end;
+        }
+
+        auto tool_choices = p.choice();
+        auto arg_close    = p.tool_arg_close(p.literal(ARG_VAL_END));
+        auto arg_string   = p.rule("ling3-arg-string",
+                                   p.tool_arg_string_value(p.until(ARG_VAL_END)) + arg_close);
+
+        foreach_function(inputs.tools, [&](const json & tool) {
+            const auto & function = tool.at("function");
+            std::string  name     = function.at("name");
+
+            std::vector<common_peg_parser> required_args;
+            std::vector<common_peg_parser> optional_args;
+
+            // each argument may be preceded by whitespace: the model emits
+            // newlines between arguments, the template history does not
+            foreach_parameter(function, [&](const common_chat_schema_property & param, const common_chat_schema_document_ptr & doc) {
+                auto rule_name = "ling3-arg-" + name + "-" + param.name;
+
+                auto types = param.schema->value_types();
+
+                // string arguments are raw text up to the closing tag, other
+                // types parse as JSON per their schema; each alternative
+                // consumes the closing tag itself so a JSON prefix can not
+                // commit the choice before the tag matches
+                auto arg_value = p.eps();
+                if (!types.has(common_chat_schema::TYPE_STRING)) {
+                    arg_value = p.tool_arg_json_value(p.schema(p.json(), rule_name + "-schema", doc, *param.schema)) + arg_close;
+                } else if (types.is_only(common_chat_schema::TYPE_STRING)) {
+                    arg_value = arg_string;
+                } else {
+                    // the parser tries the JSON alternative first to type the value
+                    arg_value = p.gbnf(p.atomic(p.tool_arg_json_value(p.schema(p.json(), rule_name + "-schema", doc, *param.schema)) + arg_close) | arg_string,
+                                       "ling3-arg-string");
+                }
+
+                auto arg = p.rule(rule_name,
+                    p.optional(p.space()) +
+                    p.tool_arg(p.tool_arg_open(p.literal(ARG_KEY) + p.tool_arg_name(p.literal(param.name)) +
+                                               p.literal(ARG_KEY_END)) +
+                               p.optional(p.space()) + p.literal(ARG_VAL) +
+                               arg_value));
+
+                (param.required ? required_args : optional_args).push_back(arg);
+            });
+
+            // required arguments in any order (as Qwen3-Coder does), then
+            // optional ones in any order and number
+            auto args = p.permute("ling3-" + name + "-args", required_args);
+            if (!optional_args.empty()) {
+                args = args + p.zero_or_more(p.choice(optional_args));
+            }
+
+            auto call = p.tool(p.tool_open(p.literal(CALL_START) + p.tool_name(p.literal(name)) +
+                                           p.optional(p.space())) +
+                               p.tool_args(args) +
+                               p.tool_close(p.optional(p.space()) + p.literal(CALL_END)));
+
+            tool_choices |= p.rule("ling3-tool-" + name, call);
+        });
+
+        auto calls = inputs.parallel_tool_calls ?
+                     tool_choices + p.zero_or_more(p.space() + tool_choices) :
+                     tool_choices;
+
+        auto tools_section = p.trigger_rule("ling3-tool-call", calls + p.space() +
+                                        p.optional(p.content(p.until(ROLE_END))) + p.optional(p.literal(ROLE_END)));
+
+        auto tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? tools_section :
+                                                                             p.optional(tools_section);
+
+        return opener + reasoning + content + tools + tail + 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) {
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        data.grammar_triggers = {
+            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, CALL_START },
+        };
+    }
+
+    return data;
+}
@@ -130,7 +130,7 @@ common_chat_params common_chat_params_init_muse_glimmer(const common_chat_templa
        });
        data.grammar_triggers = {
            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN,
-              "<\\|start\\|>assistant( to=(?!self<\\|message\\|>)(?!user<\\|message\\|>)[^<]*?<\\|message\\|>)" },
+              "(?:^|<\\|start\\|>assistant)( to=(?!self<\\|message\\|>)(?!user<\\|message\\|>)[^<]*?<\\|message\\|>)" },
        };
    }

@@ -63,6 +63,8 @@ common_chat_params common_chat_params_init_kimi_k2(const common_chat_template &

 common_chat_params common_chat_params_init_kimi_k3(const common_chat_template & tmpl, const autoparser::generation_params & inputs);

+common_chat_params common_chat_params_init_ling3(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);

@@ -23,8 +23,9 @@ common_chat_params common_chat_params_init_qwen3_coder(const common_chat_templat
    if (supports_reasoning) {
        data.thinking_start_tag = "<think>";
        // Support both </think> and <tool_call> as reasoning end sequences.
+        // The newline variant comes first so it is included in the forced message
        // <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.thinking_end_tags = { "\n</think>", "</think>", "<tool_call>" };
        data.preserved_tokens.insert(data.preserved_tokens.end(), { "<think>", "</think>" });
    }

@@ -11,6 +11,7 @@ set(LLAMA_CHAT_PARSERS_SOURCES
    ${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}/ling3.cpp
    ${CMAKE_CURRENT_LIST_DIR}/lfm2.cpp
    ${CMAKE_CURRENT_LIST_DIR}/minicpm5.cpp
    ${CMAKE_CURRENT_LIST_DIR}/minimax-m3.cpp
@@ -166,6 +166,25 @@ common_peg_ast_id common_peg_ast_arena::find_by_rule(const common_peg_ast_node &
    return COMMON_PEG_INVALID_AST_ID;
 }

+std::string common_peg_ast_node::sanitized_text() const {
+    if (invalid_utf8.empty()) {
+        return std::string(text);
+    }
+
+    std::string out;
+    out.reserve(text.size() + 2 * invalid_utf8.size());
+
+    size_t seg_start = start;
+    for (const auto & invalid : invalid_utf8) {
+        out.append(text.data() + (seg_start - start), invalid.pos - seg_start);
+        out.append("\xEF\xBF\xBD");
+        seg_start = invalid.pos + invalid.len;
+    }
+    out.append(text.data() + (seg_start - start), end - seg_start);
+
+    return out;
+}
+
 void common_peg_ast_arena::visit(common_peg_ast_id id, const common_peg_ast_visitor & visitor) const {
    if (id == COMMON_PEG_INVALID_AST_ID) {
        return;
@@ -282,6 +301,7 @@ struct parser_executor {

        auto pos = start_pos;
        std::vector<common_peg_ast_id> nodes;
+        std::vector<common_peg_invalid_utf8> invalid_utf8;

        for (size_t i = 0; i < p.children.size(); i++) {
            const auto & child_id = p.children[i];
@@ -306,13 +326,14 @@ struct parser_executor {
            if (!result.nodes.empty()) {
                nodes.insert(nodes.end(), result.nodes.begin(), result.nodes.end());
            }
+            invalid_utf8.insert(invalid_utf8.end(), result.invalid_utf8.begin(), result.invalid_utf8.end());

            if (result.need_more_input()) {
                ctx.parse_depth--;
                if (ctx.is_debug()) {
                    fprintf(stderr, "%sSEQ -> NEED_MORE\n", debug_indent().c_str());
                }
-                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, result.end, std::move(nodes));
+                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, result.end, std::move(nodes), std::move(invalid_utf8));
            }

            pos = result.end;
@@ -322,7 +343,7 @@ struct parser_executor {
        if (ctx.is_debug()) {
            fprintf(stderr, "%sSEQ -> SUCCESS at %zu->%zu\n", debug_indent().c_str(), start_pos, pos);
        }
-        return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos, std::move(nodes));
+        return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos, std::move(nodes), std::move(invalid_utf8));
    }

    common_peg_parse_result operator()(const common_peg_choice_parser & p) {
@@ -370,6 +391,7 @@ struct parser_executor {
        auto pos = start_pos;
        int match_count = 0;
        std::vector<common_peg_ast_id> nodes;
+        std::vector<common_peg_invalid_utf8> invalid_utf8;

        // Try to match up to max_count times (or unlimited if max_count is -1)
        while (p.max_count == -1 || match_count < p.max_count) {
@@ -400,6 +422,7 @@ struct parser_executor {
                if (!result.nodes.empty()) {
                    nodes.insert(nodes.end(), result.nodes.begin(), result.nodes.end());
                }
+                invalid_utf8.insert(invalid_utf8.end(), result.invalid_utf8.begin(), result.invalid_utf8.end());

                pos = result.end;
                match_count++;
@@ -410,13 +433,14 @@ struct parser_executor {
                if (!result.nodes.empty()) {
                    nodes.insert(nodes.end(), result.nodes.begin(), result.nodes.end());
                }
+                invalid_utf8.insert(invalid_utf8.end(), result.invalid_utf8.begin(), result.invalid_utf8.end());

                ctx.parse_depth--;
                if (ctx.is_debug()) {
                    fprintf(stderr, "%sREPEAT -> NEED_MORE (count=%d, nodes=%zu)\n", debug_indent().c_str(),
                            match_count, nodes.size());
                }
-                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, result.end, std::move(nodes));
+                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, result.end, std::move(nodes), std::move(invalid_utf8));
            }

            // Child failed - stop trying
@@ -434,7 +458,7 @@ struct parser_executor {
                    fprintf(stderr, "%sREPEAT -> NEED_MORE (not enough matches: %d < %d)\n", debug_indent().c_str(),
                            match_count, p.min_count);
                }
-                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, pos, std::move(nodes));
+                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, pos, std::move(nodes), std::move(invalid_utf8));
            }
            if (ctx.is_debug()) {
                fprintf(stderr, "%sREPEAT -> FAIL (not enough matches: %d < %d)\n", debug_indent().c_str(), match_count,
@@ -448,7 +472,7 @@ struct parser_executor {
            fprintf(stderr, "%sREPEAT -> SUCCESS (count=%d, nodes=%zu)\n", debug_indent().c_str(), match_count,
                    nodes.size());
        }
-        return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos, std::move(nodes));
+        return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos, std::move(nodes), std::move(invalid_utf8));
    }

    common_peg_parse_result operator()(const common_peg_and_parser & p) {
@@ -664,23 +688,23 @@ struct parser_executor {
        // Scan input and check for delimiters
        size_t pos = start_pos;
        size_t last_valid_pos = start_pos;
+        std::vector<common_peg_invalid_utf8> invalid_utf8;

        while (pos < ctx.input.size()) {
            auto utf8_result = common_parse_utf8_codepoint(ctx.input, pos);

-            if (utf8_result.status == utf8_parse_result::INCOMPLETE) {
-                // Incomplete UTF-8 sequence
-                if (!ctx.is_lenient()) {
-                    // Input is complete but UTF-8 is incomplete = malformed
-                    return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos);
-                }
-                // Return what we have so far (before incomplete sequence)
-                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, last_valid_pos);
+            if (utf8_result.status == utf8_parse_result::INCOMPLETE && ctx.is_lenient()) {
+                // The rest of the sequence may still arrive, return what we have so far
+                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, last_valid_pos, {}, std::move(invalid_utf8));
            }

-            if (utf8_result.status == utf8_parse_result::INVALID) {
-                // Malformed UTF-8
-                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start_pos);
+            if (utf8_result.status != utf8_parse_result::SUCCESS) {
+                // Malformed UTF-8, or a sequence truncated by the end of a complete input.
+                // A delimiter cannot start inside bytes that fail to decode, so consume them and move on
+                invalid_utf8.push_back({pos, utf8_result.bytes_consumed});
+                pos += utf8_result.bytes_consumed;
+                last_valid_pos = pos;
+                continue;
            }

            // Check if a delimiter starts at this position
@@ -688,12 +712,12 @@ struct parser_executor {

            if (match == common_trie::COMPLETE_MATCH) {
                // Found a complete delimiter, return everything before it
-                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos);
+                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos, {}, std::move(invalid_utf8));
            }

            if (match == common_trie::PARTIAL_MATCH) {
                // Found a partial match extending to end of input, return everything before it
-                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos);
+                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, pos, {}, std::move(invalid_utf8));
            }

            pos += utf8_result.bytes_consumed;
@@ -702,9 +726,9 @@ struct parser_executor {

        if (last_valid_pos == ctx.input.size() && ctx.is_lenient()) {
            // Reached the end of a partial stream, there might still be more input that we need to consume.
-            return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, last_valid_pos);
+            return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos, last_valid_pos, {}, std::move(invalid_utf8));
        }
-        return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, last_valid_pos);
+        return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start_pos, last_valid_pos, {}, std::move(invalid_utf8));
    }

    common_peg_parse_result operator()(const common_peg_schema_parser & p) {
@@ -728,10 +752,11 @@ struct parser_executor {
                result.end,
                text,
                std::move(result.nodes),
-                result.need_more_input()
+                result.need_more_input(),
+                result.invalid_utf8
            );

-            return common_peg_parse_result(result.type, result.start, result.end, { node_id });
+            return common_peg_parse_result(result.type, result.start, result.end, { node_id }, std::move(result.invalid_utf8));
        }

        return result;
@@ -757,10 +782,11 @@ struct parser_executor {
                result.end,
                text,
                std::move(result.nodes),
-                result.need_more_input()
+                result.need_more_input(),
+                result.invalid_utf8
            );

-            return common_peg_parse_result(result.type, result.start, result.end, { node_id });
+            return common_peg_parse_result(result.type, result.start, result.end, { node_id }, std::move(result.invalid_utf8));
        }

        return result;
@@ -72,6 +72,12 @@ enum common_peg_parse_result_type {

 const char * common_peg_parse_result_type_name(common_peg_parse_result_type type);

+// A run of input bytes that does not decode as UTF-8
+struct common_peg_invalid_utf8 {
+    size_t pos;
+    size_t len;
+};
+
 struct common_peg_ast_node {
    common_peg_ast_id id;
    std::string rule;
@@ -82,6 +88,12 @@ struct common_peg_ast_node {
    std::vector<common_peg_ast_id> children;

    bool is_partial = false;
+
+    // Invalid UTF-8 inside the node, in ascending order
+    std::vector<common_peg_invalid_utf8> invalid_utf8;
+
+    // Returns the text with every invalid run replaced by U+FFFD
+    std::string sanitized_text() const;
 };

 struct common_peg_parse_result;
@@ -98,10 +110,11 @@ class common_peg_ast_arena {
        size_t end,
        std::string_view text,
        std::vector<common_peg_ast_id> children,
-        bool is_partial = false
+        bool is_partial = false,
+        std::vector<common_peg_invalid_utf8> invalid_utf8 = {}
    ) {
        common_peg_ast_id id = nodes_.size();
-        nodes_.push_back({id, rule, tag, start, end, text, std::move(children), is_partial});
+        nodes_.push_back({id, rule, tag, start, end, text, std::move(children), is_partial, std::move(invalid_utf8)});
        return id;
    }

@@ -127,6 +140,9 @@ struct common_peg_parse_result {

    std::vector<common_peg_ast_id> nodes;

+    // Invalid UTF-8 consumed by this result, carried up to the enclosing AST nodes
+    std::vector<common_peg_invalid_utf8> invalid_utf8;
+
    common_peg_parse_result() = default;

    common_peg_parse_result(common_peg_parse_result_type type, size_t start)
@@ -135,8 +151,8 @@ struct common_peg_parse_result {
    common_peg_parse_result(common_peg_parse_result_type type, size_t start, size_t end)
        : type(type), start(start), end(end) {}

-    common_peg_parse_result(common_peg_parse_result_type type, size_t start, size_t end, std::vector<common_peg_ast_id> nodes)
-        : type(type), start(start), end(end), nodes(std::move(nodes)) {}
+    common_peg_parse_result(common_peg_parse_result_type type, size_t start, size_t end, std::vector<common_peg_ast_id> nodes, std::vector<common_peg_invalid_utf8> invalid_utf8 = {})
+        : type(type), start(start), end(end), nodes(std::move(nodes)), invalid_utf8(std::move(invalid_utf8)) {}

    bool fail() const { return type == COMMON_PEG_PARSE_RESULT_FAIL; }
    bool need_more_input() const { return type == COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT; }
@@ -430,6 +446,7 @@ class common_peg_parser_builder {
    common_peg_parser space() { return add(common_peg_space_parser{}); }

    // Matches all characters until a delimiter is found (delimiter not consumed).
+    // Invalid UTF-8 is consumed and recorded on the AST nodes.
    //   S -> (!delim .)*
    common_peg_parser until(const std::string & delimiter) { return add(common_peg_until_parser{{delimiter}}); }

@@ -214,7 +214,7 @@ struct common_sampler * common_sampler_init(
 #ifdef LLAMA_USE_LLGUIDANCE
        grmr = llama_sampler_init_llg(vocab, "lark", grammar_str.c_str());
 #else
-        GGML_ABORT("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
+        throw std::runtime_error("failed to parse grammar: llguidance is not enabled");
 #endif // LLAMA_USE_LLGUIDANCE
    } else {
        std::vector<std::string> trigger_patterns;
@@ -228,9 +228,7 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl {
            common_params_sampling params;
            params.no_perf = false;
            params.top_k = 10;
-            params.samplers = {
-                COMMON_SAMPLER_TYPE_TOP_K,
-            };
+            params.samplers.assign(1, COMMON_SAMPLER_TYPE_TOP_K);

            smpl.reset(common_sampler_init(llama_get_model(ctx_dft), params));
        }
@@ -26,16 +26,16 @@ utf8_parse_result common_parse_utf8_codepoint(std::string_view input, size_t off

    // Invalid: continuation byte as first byte
    if (!(input[offset] & 0x40)) {
-        return utf8_parse_result(utf8_parse_result::INVALID);
+        return utf8_parse_result(utf8_parse_result::INVALID, 0, 1);
    }

    // 2-byte sequence
    if (!(input[offset] & 0x20)) {
        if (offset + 1 >= input.size()) {
-            return utf8_parse_result(utf8_parse_result::INCOMPLETE);
+            return utf8_parse_result(utf8_parse_result::INCOMPLETE, 0, 1);
        }
        if ((input[offset + 1] & 0xc0) != 0x80) {
-            return utf8_parse_result(utf8_parse_result::INVALID);
+            return utf8_parse_result(utf8_parse_result::INVALID, 0, 1);
        }
        auto result = ((input[offset] & 0x1f) << 6) | (input[offset + 1] & 0x3f);
        return utf8_parse_result(utf8_parse_result::SUCCESS, result, 2);
@@ -43,11 +43,14 @@ utf8_parse_result common_parse_utf8_codepoint(std::string_view input, size_t off

    // 3-byte sequence
    if (!(input[offset] & 0x10)) {
-        if (offset + 2 >= input.size()) {
-            return utf8_parse_result(utf8_parse_result::INCOMPLETE);
-        }
-        if ((input[offset + 1] & 0xc0) != 0x80 || (input[offset + 2] & 0xc0) != 0x80) {
-            return utf8_parse_result(utf8_parse_result::INVALID);
+        // Check one byte at a time so a bad byte is reported before a short input
+        for (size_t i = 1; i < 3; i++) {
+            if (offset + i >= input.size()) {
+                return utf8_parse_result(utf8_parse_result::INCOMPLETE, 0, i);
+            }
+            if ((input[offset + i] & 0xc0) != 0x80) {
+                return utf8_parse_result(utf8_parse_result::INVALID, 0, i);
+            }
        }
        auto result = ((input[offset] & 0x0f) << 12) | ((input[offset + 1] & 0x3f) << 6) | (input[offset + 2] & 0x3f);
        return utf8_parse_result(utf8_parse_result::SUCCESS, result, 3);
@@ -55,18 +58,20 @@ utf8_parse_result common_parse_utf8_codepoint(std::string_view input, size_t off

    // 4-byte sequence
    if (!(input[offset] & 0x08)) {
-        if (offset + 3 >= input.size()) {
-            return utf8_parse_result(utf8_parse_result::INCOMPLETE);
-        }
-        if ((input[offset + 1] & 0xc0) != 0x80 || (input[offset + 2] & 0xc0) != 0x80 || (input[offset + 3] & 0xc0) != 0x80) {
-            return utf8_parse_result(utf8_parse_result::INVALID);
+        for (size_t i = 1; i < 4; i++) {
+            if (offset + i >= input.size()) {
+                return utf8_parse_result(utf8_parse_result::INCOMPLETE, 0, i);
+            }
+            if ((input[offset + i] & 0xc0) != 0x80) {
+                return utf8_parse_result(utf8_parse_result::INVALID, 0, i);
+            }
        }
        auto result = ((input[offset] & 0x07) << 18) | ((input[offset + 1] & 0x3f) << 12) | ((input[offset + 2] & 0x3f) << 6) | (input[offset + 3] & 0x3f);
        return utf8_parse_result(utf8_parse_result::SUCCESS, result, 4);
    }

    // Invalid first byte
-    return utf8_parse_result(utf8_parse_result::INVALID);
+    return utf8_parse_result(utf8_parse_result::INVALID, 0, 1);
 }

 bool common_utf8_is_complete(const std::string & s) {
@@ -9,7 +9,7 @@

 struct utf8_parse_result {
    uint32_t codepoint;      // Decoded codepoint (only valid if status == SUCCESS)
-    size_t bytes_consumed;   // How many bytes this codepoint uses (1-4)
+    size_t bytes_consumed;   // How many bytes this codepoint uses (1-4), or the length of the valid prefix if status != SUCCESS
    enum status { SUCCESS, INCOMPLETE, INVALID } status;

    utf8_parse_result(enum status s, uint32_t cp = 0, size_t bytes = 0)
@@ -28,6 +28,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "BailingMoeForCausalLM": "bailingmoe",
    "BailingMoeV2ForCausalLM": "bailingmoe",
    "BailingMoeV3ForCausalLM": "bailingmoe3",
+    "BailingMoeV3VLForConditionalGeneration": "bailingmoe3",
    "BambaForCausalLM": "granite",
    "BertForMaskedLM": "bert",
    "BertForSequenceClassification": "bert",
@@ -94,6 +95,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "Gemma3nForCausalLM": "gemma",
    "Gemma3nForConditionalGeneration": "gemma",
    "Gemma4AssistantForCausalLM": "gemma",
+    "Gemma4DSparkModel": "gemma",
    "Gemma4ForConditionalGeneration": "gemma",
    "Gemma4ForCausalLM": "gemma",
    "Gemma4UnifiedForConditionalGeneration": "gemma",
@@ -123,6 +125,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "HunYuanDenseV1ForCausalLM": "hunyuan",
    "HunYuanMoEV1ForCausalLM": "hunyuan",
    "HunYuanVLForConditionalGeneration": "hunyuan",
+    "HrmTextForCausalLM": "hrm_text",
    "HYV3ForCausalLM": "hunyuan",
    "HYV4ForCausalLM": "hy_v4",
    "IQuestCoderForCausalLM": "llama",
@@ -168,6 +171,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "Mamba2ForCausalLM": "mamba",
    "MambaForCausalLM": "mamba",
    "MambaLMHeadModel": "mamba",
+    "MapleForCausalLM": "maple",
    "MellumForCausalLM": "mellum",
    "MiMoV2FlashForCausalLM": "mimo",
    "MiMoV2ForCausalLM": "mimo",
@@ -299,6 +303,7 @@ MMPROJ_MODEL_MAP: dict[str, str] = {
    "Gemma4ForConditionalGeneration": "gemma",
    "Gemma4UnifiedForConditionalGeneration": "gemma",
    "Glm4vForConditionalGeneration": "qwen3vl",
+    "BailingMoeV3VLForConditionalGeneration": "bailingmoe3",
    "Glm4vMoeForConditionalGeneration": "qwen3vl",
    "Glm5vForConditionalGeneration": "kimivl",
    "GlmOcrForConditionalGeneration": "qwen3vl",
@@ -9,7 +9,9 @@ import torch
 if TYPE_CHECKING:
    from torch import Tensor

-from .base import ModelBase, TextModel, gguf
+from .base import ModelBase, MmprojModel, TextModel, gguf
+
+from .qwen3vl import Qwen3VLVisionModel


@ModelBase.register("BailingMoeV3ForCausalLM")
@@ -74,7 +76,7 @@ class BailingMoeV3Model(TextModel):

        self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
        self.gguf_writer.add_expert_shared_feed_forward_length(self.hparams["moe_shared_expert_intermediate_size"])
-        self.gguf_writer.add_expert_shared_count(self.hparams["num_shared_experts"])
+        self.gguf_writer.add_expert_shared_count(self.hparams.get("num_shared_experts", 1))
        self.gguf_writer.add_leading_dense_block_count(self.hparams["first_k_dense_replace"])
        self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
        self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
@@ -191,3 +193,111 @@ class BailingMoeV3Model(TextModel):
            experts = [name for layer in self._experts for name in layer]
            if experts:
                raise ValueError(f"Unprocessed experts: {experts}")
+
+
+@ModelBase.register("BailingMoeV3VLForConditionalGeneration")
+@ModelBase.example("inclusionAI/Ling-3.0-flash-VL")
+class BailingMoeV3VLModel(BailingMoeV3Model):
+    model_arch = gguf.MODEL_ARCH.BAILINGMOE3
+
+    def index_tensors(self, remote_hf_model_id: str | None = None):
+        # hoist text_config before the shared BailingMoeV3 logic runs:
+        # ModelBase.__init__ calls this with the raw VL config, where the text
+        # dims still live under text_config
+        if "text_config" in self.hparams:
+            self.hparams = {**self.hparams, **self.hparams["text_config"]}
+        return super().index_tensors(remote_hf_model_id=remote_hf_model_id)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        mrope_section = self.hparams.get("mrope_section")
+        if mrope_section is None:
+            raise ValueError("BailingMoeV3VL requires mrope_section in the config")
+        if sum(mrope_section[:3]) * 2 != self.hparams["qk_rope_head_dim"]:
+            raise ValueError(
+                f"mrope_section {mrope_section[:3]} counts rope pairs and must sum to"
+                f" qk_rope_head_dim / 2 = {self.hparams['qk_rope_head_dim'] // 2}"
+            )
+        # mrope_section is [t, h, w]; pad to the 4-wide sections array
+        self.gguf_writer.add_rope_dimension_sections(list(mrope_section[:3]) + [0])
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        # Skip projector tensors; the vision tower is skipped by TextModel.filter_tensors
+        if name.startswith("linear_proj"):
+            return None
+
+        return super().filter_tensors(item)
+
+
+@ModelBase.register("BailingMoeV3VLForConditionalGeneration")
+@ModelBase.example("inclusionAI/Ling-3.0-flash-VL")
+class BailingMoeV3VLVisionModel(Qwen3VLVisionModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+
+        if self.hparams_vision.get("disable_merger_proj") is not True:
+            raise ValueError("BailingMoeV3VL requires disable_merger_proj=true")
+
+        # out_hidden_size is the vision encoder output (post spatial merge, pre linear_proj)
+        self.image_emb_dim = self.hparams_vision.get("out_hidden_size")
+        if self.image_emb_dim is None:
+            raise ValueError("BailingMoeV3VL vision config requires out_hidden_size")
+
+    def set_gguf_parameters(self):
+        assert self.hparams_vision is not None
+        MmprojModel.set_gguf_parameters(self)  # skip Qwen3VLVisionModel parameters
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LING3VL)
+        self.gguf_writer.add_vision_use_gelu(True)
+
+        merge_size = self.hparams_vision.get("spatial_merge_size")
+        if merge_size is not None:
+            self.gguf_writer.add_vision_spatial_merge_size(int(merge_size))
+
+        rms_norm_eps = self.global_config.get("text_config", {}).get("rms_norm_eps", 1e-6)
+        self.gguf_writer.add_vision_attention_layernorm_eps(rms_norm_eps)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if name.startswith("lm_head."):
+            return None
+
+        if name.startswith("linear_proj"):
+            # top-level projector MLP: linear_proj.0 -> mm.0, linear_proj.2 -> mm.2
+            parts = name.split(".")
+            if len(parts) != 3:
+                raise ValueError(f"Unexpected linear_proj tensor: {name}")
+            idx, suffix = int(parts[1]), parts[2]
+            name = f"mm.{idx}.{suffix}"
+            # the qwen3vl filter keeps only visual.*; skip it for the renamed projector tensors
+            return MmprojModel.filter_tensors((name, gen))
+
+        if name.startswith("model.visual."):
+            name = name.replace("model.visual.", "visual.", 1)
+
+        if not name.startswith("visual."):
+            return None
+
+        return super().filter_tensors((name, gen))
+
+    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.startswith("mm.0.") or name.startswith("mm.2."):
+            # top-level projector MLP (linear_proj.0 / linear_proj.2, renamed by filter_tensors)
+            yield (name, data_torch)
+            return
+
+        if name == "visual.merger.norm.weight" or name == "visual.merger.norm.bias":
+            # the merger is norm-only for Ling: per-patch LayerNorm before the spatial merge
+            new_name = f"mm.input_norm.{name.split('.')[-1]}"
+            yield (new_name, data_torch)
+            return
+
+        # Ling has no patch bias; the Conv3D split below matches the stock qwen3vl path
+        yield from Qwen3VLVisionModel.modify_tensors(self, data_torch, name, bid)
@@ -170,6 +170,9 @@ class ModelBase:
        self.dir_model_card = dir_model  # overridden in convert_lora_to_gguf.py
        self._is_nvfp4 = False
        self._is_mxfp4 = False
+        self._nvfp4_global_algo: str | None = None # checkpoint-wide NVFP4 quant_algo
+        self._nvfp4_layer_algo: dict[str, str | None] = {} # per-layer quant_algo, keyed by HF module path
+        self._prec_a4: dict[str, bool] = {} # gguf tensor name -> can use 4-bit (A4) activations
        self._fp8_as_q8 = fp8_as_q8
        self._fp8_dequantized: set[str] = set()

@@ -664,6 +667,18 @@ class ModelBase:
                if bias_types:
                    self._fusable_qkv_bias_layers.add(bid)

+    def _tag_prec_a4(self, hf_name: str, gguf_name: str) -> None:
+        # W4A16_NVFP4 should not use 4-bit activations
+        name = hf_name.removesuffix(".weight").removesuffix(".bias")
+        algo = self._nvfp4_global_algo
+        while name:
+            if name in self._nvfp4_layer_algo:
+                algo = self._nvfp4_layer_algo[name]
+                break
+            name = name.rpartition(".")[0]
+        if algo == "W4A16_NVFP4":
+            self._prec_a4[gguf_name] = False
+
    def set_gguf_parameters(self):
        raise NotImplementedError("set_gguf_parameters() must be implemented in subclasses")

@@ -776,6 +791,36 @@ class ModelBase:
        raw = torch.cat((s.unsqueeze(-1), qs.to(torch.uint8)), dim=-1)
        return raw.reshape(rows, n_blocks * 17).cpu().numpy()

+    def _mxfp4_expert_tensor(self, loaders: list[tuple[Callable[[], Tensor], Callable[[], Tensor]]]):
+        """
+        One stacked [n_expert, rows, cols] MXFP4 tensor, built lazily.
+
+        gguf_writer holds every added tensor until the final write, so building
+        this eagerly (like the DeepSeek-V4 path does) keeps every expert in
+        memory at once. lazy means only the tensor being written is resident.
+        """
+        # meta shapes, so this does not read any weights
+        rows, packed_cols = loaders[0][0]().shape
+        n_blocks = (packed_cols * 2) // 32
+        byte_shape = (len(loaders), rows, n_blocks * 17)
+
+        def load(fns: list[tuple[Callable[[], Tensor], Callable[[], Tensor]]]) -> np.ndarray:
+            out = np.empty(byte_shape, dtype=np.uint8)
+            for eid, (packed_fn, scale_fn) in enumerate(fns):
+                out[eid] = self.repack_mxfp4_blocks(
+                    LazyTorchTensor.to_eager(packed_fn()),
+                    LazyTorchTensor.to_eager(scale_fn()),
+                )
+            return out
+
+        # loaders goes through args, not the closure, so that `func` matches
+        # LazyBase's single-argument shape
+        return gguf.LazyNumpyTensor(
+            meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(np.uint8, byte_shape),
+            args=(loaders,),
+            func=load,
+        )
+
    @staticmethod
    def _nvfp4_pack(weight: Tensor, scale: Tensor) -> tuple[np.ndarray, list[int]]:
        """Repack NVFP4 ModelOpt tensors into ggml super-block layout.
@@ -807,6 +852,7 @@ class ModelBase:
        raw, shape = self._nvfp4_pack(weight, scale)
        logger.info(f"Repacked {new_name} with shape {shape} and quantization NVFP4")
        self.gguf_writer.add_tensor(new_name, raw, raw_dtype=gguf.GGMLQuantizationType.NVFP4)
+        self._tag_prec_a4(name, new_name)

        self._write_scale_tensor(new_name.replace(".weight", ".scale"), scale2)
        self._write_scale_tensor(new_name.replace(".weight", ".input_scale"), input_scale)
@@ -899,6 +945,7 @@ class ModelBase:
        new_name = self.map_tensor_name(merged_name)
        logger.info(f"Repacked {new_name} with shape [{len(experts)}, {shape[0]}, {shape[1]}] and quantization NVFP4")
        self.gguf_writer.add_tensor(new_name, merged, raw_dtype=gguf.GGMLQuantizationType.NVFP4)
+        self._tag_prec_a4(merged_name, new_name)

        scales.sort(key=lambda x: x[0])
        self._write_scales_tensor(new_name.replace(".weight", ".scale"), [s[1] for s in scales])
@@ -941,6 +988,9 @@ class ModelBase:
            and bool(quant_groups)
            and all(g.get("format") == "nvfp4-pack-quantized" for g in quant_groups.values() if isinstance(g, dict))
        )
+
+        self._nvfp4_global_algo = quant_algo
+
        if quant_algo != "NVFP4":
            if nvfp4_compressed_tensors:
                quant_algo = "NVFP4"
@@ -950,6 +1000,22 @@ class ModelBase:
        self._is_nvfp4 = quant_algo in ("NVFP4", "W4A16_NVFP4")
        self._is_mxfp4 = quant_method == "mxfp4"

+        # Per-tensor NVFP4 precision.
+        self._nvfp4_layer_algo = {}
+        if quant_layers:
+            # store all possible module paths and assert if a quantized layer is not in the model
+            modules: set[str] = set()
+            for name in self.model_tensors:
+                while name := name.rpartition(".")[0]:
+                    modules.add(name)
+
+            for layer_name, entry in quant_layers.items():
+                if not isinstance(entry, dict):
+                    continue
+                if titem := self.filter_tensors((layer_name, lambda: torch.empty(0))):
+                    assert titem[0] in modules, f"quantized_layers entry {layer_name!r} is not in the model tensors"
+                    self._nvfp4_layer_algo[titem[0]] = entry.get("quant_algo")
+
        # NVFP4 weights are repacked and written directly to gguf_writer.
        # This must run before dequant_model so NVFP4 tensors are removed
        # from model_tensors, leaving only non-NVFP4 (e.g. FP8) for dequant.
@@ -1155,6 +1221,12 @@ class ModelBase:
        logger.info("Set model quantization version")
        self.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION)

+        if self._prec_a4:
+            names = sorted(self._prec_a4.keys())
+            values = [self._prec_a4[n] for n in names]
+            logger.info(f"Set prec_a4 metadata for {len(names)} tensor(s)")
+            self.gguf_writer.add_tensor_extra_prec_a4(names, values)
+
    def write_vocab(self):
        raise NotImplementedError("write_vocab() must be implemented in subclasses")

@@ -1633,6 +1705,9 @@ class TextModel(ModelBase):
        if chkhsh == "9e454714343b69b99b71795c1d27a68c2a1d15dab111f4d353109f966af29da7":
            # ref: https://huggingface.co/LiquidAI/LFM2.5-8B-A1B
            res = "lfm2"
+        if chkhsh == "846deafc5b0fa786186fa4ae6c7b49903cf2f1d1895bdb80b9120d60be135252":
+            # ref: https://huggingface.co/danish-foundation-models/DFM-Mimir
+            res = "gemma4"
        if chkhsh == "0a766d034107bc736a3f2dc4968fd62e54a3570f1454443e0c5a4cc6bd7941ed":
            # ref: https://huggingface.co/XHToken/Spark-X2.5-1.7B
            res = "spark2_5"
@@ -1858,6 +1933,9 @@ class TextModel(ModelBase):
        if chkhsh == "972da7b59cec44d1f0a490a86c96df53859e486e481563e5dddac155013d87ac":
            # ref: https://huggingface.co/poolside/Laguna-XS.2
            res = "laguna"
+        if chkhsh == "653660222fb704f61cbf2b618a8ae6502b7f8b20c980f9a5de07ed78e13319cd":
+            # ref: https://huggingface.co/ufakai/ufakzeka-1
+            res = "ufakzeka"

        if res is None:
            logger.warning("\n")
@@ -341,7 +341,7 @@ class NomicBertModel(BertModel):
        else:
            raise ValueError(f"unrecognized parameters: n_positions={npos}, max_trained_positions={mtp}")

-        assert self.hparams["activation_function"] == "gelu" if self.is_moe else "swiglu"
+        assert self.hparams["activation_function"] == ("gelu" if self.is_moe else "swiglu")

        # this doesn't do anything in the HF version
        assert self.hparams["causal"] is False
@@ -11,6 +11,7 @@ if TYPE_CHECKING:
    from torch import Tensor

 from .base import MmprojModel, ModelBase, TextModel, gguf, logger
+from .qwen import DFlashModel


@ModelBase.register("GemmaForCausalLM")
@@ -809,6 +810,105 @@ class Gemma4Model(Gemma3Model):
        yield from super().modify_tensors(data_torch, name, bid)


+@ModelBase.register("Gemma4DSparkModel")
+class Gemma4DSparkModel(DFlashModel):
+    model_arch = gguf.MODEL_ARCH.DFLASH
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        if not self.hparams.get("attention_k_eq_v", False):
+            raise ValueError("Gemma4 DSpark currently requires attention_k_eq_v")
+        if self.hparams.get("layer_types") != ["full_attention"] * self.block_count:
+            raise ValueError("Gemma4 DSpark currently requires uniform full_attention layer types")
+        if self.hparams.get("hidden_activation", "gelu_pytorch_tanh") != "gelu_pytorch_tanh":
+            raise ValueError("Gemma4 DSpark currently requires hidden_activation=gelu_pytorch_tanh")
+        if self.hparams.get("attention_bias", False) or self.hparams.get("enable_moe_block", False):
+            raise ValueError("Gemma4 DSpark attention bias and MoE are not supported")
+        if (self.hparams.get("draft_vocab_size") or self.hparams["vocab_size"]) != self.hparams["vocab_size"]:
+            raise ValueError("Gemma4 DSpark currently requires a full draft vocabulary")
+        if "model.lm_head.weight" not in self.model_tensors and self.hparams.get("tie_word_embeddings") is not True:
+            raise ValueError("Gemma4 DSpark requires lm_head.weight unless tie_word_embeddings is true")
+
+        self.dflash_config = self.hparams.get("dflash_config", {})
+        markov_type = self.dflash_config.get("markov_head_type", self.hparams.get("markov_head_type", "vanilla"))
+        if markov_type != "vanilla":
+            raise ValueError("Gemma4 DSpark currently requires a vanilla Markov head")
+
+        # Gemma4TextConfig supplies these defaults when rope_parameters is absent.
+        rope = self.hparams.get("rope_parameters") or {
+            "full_attention": {"rope_type": "proportional", "partial_rotary_factor": 0.25, "rope_theta": 1000000.0},
+        }
+        self.rope_parameters = rope.get("full_attention", rope)
+        if self.rope_parameters.get("rope_type") not in ("default", "proportional"):
+            raise ValueError("Gemma4 DSpark requires default or proportional RoPE")
+
+    def set_vocab(self):
+        super().set_vocab()
+        mask_id = self.dflash_config.get("mask_token_id", self.hparams.get("mask_token_id"))
+        if mask_id is None:
+            raise ValueError("Gemma4 DSpark requires mask_token_id")
+        if "mask_token_id" not in self.dflash_config:
+            self.gguf_writer.add_mask_token_id(mask_id)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        head_dim = int(self.hparams["global_head_dim"])
+        self.gguf_writer.add_head_count_kv(self.hparams["num_global_key_value_heads"])
+        self.gguf_writer.add_key_length(head_dim)
+        self.gguf_writer.add_value_length(head_dim)
+        self.gguf_writer.add_rope_dimension_count(head_dim)
+        self.gguf_writer.add_embedding_scale(self.hparams["hidden_size"] ** 0.5)
+        self.gguf_writer.add_attention_scale(1.0)
+        self.gguf_writer.add_hidden_act("gelu_pytorch_tanh")
+
+        self.gguf_writer.add_sample_from_anchor(self.hparams.get("sample_from_anchor", True))
+        target_layers = self.dflash_config.get("target_layer_ids", self.hparams.get("target_layer_ids"))
+        if not target_layers:
+            raise ValueError("Gemma4 DSpark requires target_layer_ids")
+        self.gguf_writer.add_has_confidence_head(any("confidence_head.proj" in name for name in self.model_tensors))
+
+        if self.hparams.get("final_logit_softcapping"):
+            raise ValueError("Gemma4 DSpark logit softcapping is not supported")
+        # The top-level sliding_window is inert unless the draft enables SWA.
+        if self.dflash_config.get("use_swa", False):
+            window = self.dflash_config["swa_window_size"]
+            if window <= 0:
+                raise ValueError("Gemma4 DSpark swa_window_size must be positive")
+
+    @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 name.endswith(".layer_scalar"):
+            name += ".weight"
+        name = name.replace("model.confidence_proj.", "model.confidence_head.proj.")
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # The shared DFlash map assigns this name to Qwen's pre-FFN norm.
+        if name.endswith(".post_attention_layernorm.weight"):
+            name = self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_POST_NORM, bid)
+        elif name.endswith(".pre_feedforward_layernorm.weight"):
+            name = self.format_tensor_name(gguf.MODEL_TENSOR.FFN_NORM, bid)
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        if self.rope_parameters["rope_type"] == "proportional":
+            # Keep the unrotated dimensions in place, as in the Gemma4 converter.
+            head_dim = int(self.hparams["global_head_dim"])
+            fraction_value = self.rope_parameters.get("partial_rotary_factor", 0.25)
+            if not isinstance(fraction_value, (int, float)):
+                raise ValueError("Gemma4 DSpark partial_rotary_factor must be numeric")
+            fraction = float(fraction_value)
+            n_rot = int(head_dim * fraction / 2)
+            if not 0 < fraction <= 1 or head_dim * fraction != 2 * n_rot:
+                raise ValueError("Gemma4 DSpark rotary dimension count must be positive and even")
+            factors = torch.tensor([1.0] * n_rot + [1e30] * (head_dim // 2 - n_rot), dtype=torch.float32)
+            yield self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), factors
+
+
@ModelBase.register("Gemma4UnifiedForConditionalGeneration")
@ModelBase.example("hf-tiny-v2/tiny-random-Gemma4UnifiedForConditionalGeneration")
 class Gemma4UnifiedModel(Gemma4Model):
@@ -0,0 +1,79 @@
+from __future__ import annotations
+
+import re
+
+from typing import Iterable, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf
+
+
+@ModelBase.register("HrmTextForCausalLM")
+@ModelBase.example("danish-foundation-models/DFM-Mimir")
+class HrmTextModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.HRM_TEXT
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # training-style configs store the per-stack count in num_hidden_layers,
+        # transformers-style configs keep it in num_layers_per_stack
+        self.layers_per_stack = self.hparams.get("num_layers_per_stack") or self.hparams["num_hidden_layers"]
+        self.h_cycles = self.hparams["H_cycles"]
+        self.l_cycles = self.hparams["L_cycles"]
+
+        # block_count is the expanded cache-slot count; the file only holds
+        # 2 * layers_per_stack physical blocks
+        self.block_count = self.layers_per_stack * self.h_cycles * (self.l_cycles + 1)
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, 2 * self.layers_per_stack)
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        head_dim = self.hparams.get("head_dim") or self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+        self.gguf_writer.add_rope_dimension_count(head_dim)
+        self.gguf_writer.add_embedding_scale(self.hparams["embedding_scale"])
+        self.gguf_writer.add_hrm_layers_per_stack(self.layers_per_stack)
+        self.gguf_writer.add_hrm_h_cycles(self.h_cycles)
+        self.gguf_writer.add_hrm_l_cycles(self.l_cycles)
+        self.gguf_writer.add_hrm_prefix_lm(bool(self.hparams.get("prefix_lm", False)))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name == "model.embed_tokens.weight":
+            yield self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD), data_torch
+            return
+        if name == "lm_head.weight":
+            yield self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch
+            return
+        if name == "model.z_L_init":
+            yield self.format_tensor_name(gguf.MODEL_TENSOR.HRM_Z_L_INIT, suffix=""), data_torch
+            return
+
+        match = re.fullmatch(r"model\.([LH])_module\.layers\.(\d+)\.(.+)", name)
+        if match is None:
+            raise ValueError(f"can not map tensor: {name}")
+
+        stack, layer_s, tensor_name = match.groups()
+        # the L stack occupies blocks [0, layers_per_stack), the H stack follows it
+        layer_idx = int(layer_s) + (self.layers_per_stack if stack == "H" else 0)
+
+        if tensor_name == "attn.gqkv_proj.weight":
+            gate, q, k, v = data_torch.chunk(4, dim=0)
+            yield self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_GATE, layer_idx), gate.contiguous()
+            yield self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, layer_idx), q.contiguous()
+            yield self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, layer_idx), k.contiguous()
+            yield self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, layer_idx), v.contiguous()
+        elif tensor_name == "mlp.gate_up_proj.weight":
+            gate, up = data_torch.chunk(2, dim=0)
+            yield self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, layer_idx), gate.contiguous()
+            yield self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, layer_idx), up.contiguous()
+        else:
+            if tensor_name.startswith("attn."):
+                tensor_name = "self_attn." + tensor_name[len("attn."):]
+            tensor_name = "model.layers.{bid}." + tensor_name
+            yield from super().modify_tensors(data_torch, tensor_name.format(bid=layer_idx), layer_idx)
@@ -159,32 +159,14 @@ class HunYuanMoEModel(TextModel):
 class HunYuanModel(TextModel):
    model_arch = gguf.MODEL_ARCH.HUNYUAN_DENSE

-    def _get_eod_token_id(self) -> int | None:
-        """Get the actual end-of-generation token from config (eod_token_id)."""
-        return self.hparams.get("eod_token_id")
-
-    def _get_eot_token_id(self) -> int | None:
-        """Get the end-of-turn token from generation_config.json.
-        This is the first entry in eos_token_id when it's a list."""
-        gen_cfg_path = self.dir_model / "generation_config.json"
-        if gen_cfg_path.is_file():
-            with open(gen_cfg_path, encoding="utf-8") as f:
-                gen_cfg = json.load(f)
-            eos = gen_cfg.get("eos_token_id")
-            if isinstance(eos, list) and len(eos) >= 2:
-                return eos[0]
-        return None
-
-    def _fix_special_tokens(self):
-        """Fix EOS/EOT tokens that are incorrect in upstream configs."""
-        eod_id = self._get_eod_token_id()
-        if eod_id is not None:
-            self.gguf_writer.add_eos_token_id(eod_id)
-        eot_id = self._get_eot_token_id()
-        if eot_id is not None:
-            self.gguf_writer.add_eot_token_id(eot_id)
-
    def set_vocab(self):
+        # Also called by draft models (e.g. DFlash), with dir_model pointing at
+        # the target model.
+        config = ModelBase.load_hparams(self.dir_model, self.is_mistral_format)
+        config = {**config, **config.get("text_config", {})}
+        self.hparams["pad_token_id"] = config.get("pad_token_id")
+        self.hparams["eod_token_id"] = config.get("eod_token_id")
+
        if (self.dir_model / "tokenizer.json").is_file():
            tokens, toktypes, tokpre = self.get_vocab_base()
            self.gguf_writer.add_tokenizer_model("gpt2")
@@ -199,7 +181,6 @@ class HunYuanModel(TextModel):
                token_types = ('bos', 'eos', 'unk', 'sep', 'cls', 'mask')
            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True, special_token_types=token_types)
            special_vocab.add_to_gguf(self.gguf_writer)
-            self._fix_special_tokens()
        else:
            from transformers import AutoTokenizer
            tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
@@ -251,7 +232,18 @@ class HunYuanModel(TextModel):
            # FIX for BOS token: Overwrite incorrect id read from config.json
            if self.hparams['hidden_size'] == 4096:
                self.gguf_writer.add_bos_token_id(127958) # only for 7b dense, fix <|bos|> token
-            self._fix_special_tokens()
+
+        # Fix EOS/EOT tokens that are incorrect in upstream configs.
+        eod_id = self.hparams.get("eod_token_id")
+        if eod_id is not None:
+            self.gguf_writer.add_eos_token_id(eod_id)
+
+        gen_cfg = self.dir_model / "generation_config.json"
+        if gen_cfg.is_file():
+            with open(gen_cfg, encoding="utf-8") as f:
+                eos = json.load(f).get("eos_token_id")
+            if isinstance(eos, list) and len(eos) >= 2:
+                self.gguf_writer.add_eot_token_id(eos[0])

    def set_gguf_parameters(self):
        # Some HunYuanVL variants set num_experts=1 (not real MoE);
@@ -2,15 +2,14 @@ from __future__ import annotations

 import re
 from pathlib import Path
-from typing import Callable, Iterable, Iterator, TYPE_CHECKING
+from typing import Iterable, Iterator, TYPE_CHECKING

-import numpy as np
 import torch

 if TYPE_CHECKING:
    from torch import Tensor

-from .base import LazyTorchTensor, ModelBase, TextModel, gguf, logger
+from .base import ModelBase, TextModel, gguf, logger

 from .kimi_linear import KimiLinearModel

@@ -104,36 +103,6 @@ class KimiK3Model(TextModel):
                "only the routed experts have a repack path"
            )

-    def _mxfp4_expert_tensor(self, loaders: list[tuple[Callable[[], Tensor], Callable[[], Tensor]]]):
-        """
-        One stacked [n_expert, rows, cols] MXFP4 tensor, built lazily.
-
-        gguf_writer holds every added tensor until the final write, so building
-        this eagerly (like the DeepSeek-V4 path does) keeps all ~1.38 TB of
-        experts in memory. lazy means only the tensor being written is resident.
-        """
-        # meta shapes, so this does not read any weights
-        rows, packed_cols = loaders[0][0]().shape
-        n_blocks = (packed_cols * 2) // 32
-        byte_shape = (len(loaders), rows, n_blocks * 17)
-
-        def load(fns: list[tuple[Callable[[], Tensor], Callable[[], Tensor]]]) -> np.ndarray:
-            out = np.empty(byte_shape, dtype=np.uint8)
-            for eid, (packed_fn, scale_fn) in enumerate(fns):
-                out[eid] = self.repack_mxfp4_blocks(
-                    LazyTorchTensor.to_eager(packed_fn()),
-                    LazyTorchTensor.to_eager(scale_fn()),
-                )
-            return out
-
-        # loaders goes through args, not the closure, so that `func` matches
-        # LazyBase's single-argument shape
-        return gguf.LazyNumpyTensor(
-            meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(np.uint8, byte_shape),
-            args=(loaders,),
-            func=load,
-        )
-
    def _write_mxfp4_experts(self) -> None:
        n_experts = self.hparams["num_experts"]

@@ -0,0 +1,87 @@
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING, cast
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import LazyTorchTensor, ModelBase, TextModel, gguf
+
+
+@ModelBase.register("MapleForCausalLM")
+@ModelBase.example("deepgrove/maple-preview")
+class MapleModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.MAPLE
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+
+        assert hparams["hidden_act"] == "silu"
+        assert hparams.get("num_shared_experts", 0) == 0
+        assert hparams.get("norm_topk_prob", True)
+        assert hparams.get("nope_on_global_attention", False)
+
+        head_dim = hparams.get("head_dim", hparams["hidden_size"] // hparams["num_attention_heads"])
+        partial_rotary_factor = self.rope_parameters.get("partial_rotary_factor", 1.0)
+
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+        self.gguf_writer.add_rope_dimension_count(int(head_dim * partial_rotary_factor))
+        self.gguf_writer.add_sliding_window(hparams["sliding_window"])
+        self.gguf_writer.add_sliding_window_pattern([layer_type == "sliding_attention" for layer_type in hparams["layer_types"]])
+        self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
+        # the reference clamps the MoE SwiGLU gate/up at 7.0 (modeling_maple.py)
+        self.gguf_writer.add_swiglu_clamp_exp([7.0] * self.block_count)
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    @staticmethod
+    def _stack_experts(tensors: list[Tensor]) -> Tensor:
+        shape = (len(tensors), *tensors[0].shape)
+        dtype = tensors[0].dtype
+        meta = LazyTorchTensor.meta_with_dtype_and_shape(dtype, shape)
+
+        # tensors goes through args, not the closure, so that `func` matches
+        # LazyBase's single-argument shape
+        def stack(ts: list[Tensor]) -> Tensor:
+            result = torch.empty(shape, dtype=dtype)
+            for expert_id, tensor in enumerate(ts):
+                result[expert_id].copy_(LazyTorchTensor.to_eager(tensor))
+            ts.clear()
+            return result
+
+        return cast(torch.Tensor, LazyTorchTensor(meta=meta, args=(tensors,), func=stack))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if "mlp.experts" in name:
+            n_experts = self.hparams["num_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                for weight_name in ("down_proj", "gate_proj", "up_proj"):
+                    tensors = []
+
+                    for expert_id in range(n_experts):
+                        expert_name = f"model.layers.{bid}.mlp.experts.{expert_id}.{weight_name}.weight"
+                        tensors.append(self._experts[bid].pop(expert_name))
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{weight_name}.weight"
+                    yield from super().modify_tensors(self._stack_experts(tensors), merged_name, bid)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            experts = [name for layer in self._experts for name in layer]
+            if experts:
+                raise ValueError(f"Unprocessed experts: {experts}")
@@ -10,13 +10,14 @@ import torch
 if TYPE_CHECKING:
    from torch import Tensor

-from .base import MmprojModel, ModelBase, TextModel, gguf
+from .base import MmprojModel, ModelBase, TextModel, gguf, logger


@ModelBase.register("MiMoV2FlashForCausalLM", "MiMoV2ForCausalLM")
@ModelBase.example("XiaomiMiMo/MiMo-V2.5")
 class MimoV2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.MIMO2
+    supports_mtp_export = True

    # MiMo V2-Flash, V2.5 and V2.5-Pro all ship 3 trained MTP layers under model.mtp.layers.{0,1,2}.
    # The HF config does not expose the count, so it's hardcoded to match the count found in the safetensors.
@@ -25,6 +26,8 @@ class MimoV2Model(TextModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

+        if self.no_mtp:
+            self._n_nextn = 0
        self.block_count = self.hparams["num_hidden_layers"] + self._n_nextn
        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)

@@ -101,7 +104,7 @@ class MimoV2Model(TextModel):
        qkv_overrides: dict[str, tuple[Callable, Callable, int]] = {}
        qc = self.hparams.get("quantization_config")
        if isinstance(qc, dict) and qc.get("quant_method") == "fp8":
-            pat = re.compile(r"^model\.layers\.(\d+)\.self_attn\.qkv_proj\.weight_scale_inv$")
+            pat = re.compile(r"^model\.(mtp\.)?layers\.(\d+)\.self_attn\.qkv_proj\.weight_scale_inv$")
            for name in list(self.model_tensors.keys()):
                m = pat.match(name)
                if not m:
@@ -109,10 +112,13 @@ class MimoV2Model(TextModel):
                weight_name = name.removesuffix("_scale_inv")
                if weight_name not in self.model_tensors:
                    continue
+                bid = int(m.group(2))
+                if m.group(1) is not None:
+                    bid += self.hparams["num_hidden_layers"]
                qkv_overrides[weight_name] = (
                    self.model_tensors[weight_name],
                    self.model_tensors[name],
-                    int(m.group(1)),
+                    bid,
                )

        super().dequant_model()
@@ -165,7 +171,86 @@ class MimoV2Model(TextModel):
        if v_scale is not None:
            self.gguf_writer.add_attn_value_scale(float(v_scale))

-        self.gguf_writer.add_nextn_predict_layers(self._n_nextn)
+        if self._n_nextn > 0:
+            self.gguf_writer.add_nextn_predict_layers(self._n_nextn)
+
+    _MXFP4_EXPERT_RE = re.compile(
+        r"^model\.layers\.(\d+)\.mlp\.experts\.(\d+)\.(gate|up|down)_proj\.weight$"
+    )
+    _MXFP4_PROJ = {
+        "gate": gguf.MODEL_TENSOR.FFN_GATE_EXP,
+        "up":   gguf.MODEL_TENSOR.FFN_UP_EXP,
+        "down": gguf.MODEL_TENSOR.FFN_DOWN_EXP,
+    }
+
+    def _is_mxfp4_packed(self) -> bool:
+        quant_config = self.hparams.get("quantization_config") or {}
+        if quant_config.get("store_dtype") != "mxfp4":
+            return False
+        # repack_mxfp4_blocks assumes ggml's 32-element group
+        block_size = quant_config.get("mxfp4_block_size", 32)
+        if block_size != 32:
+            raise NotImplementedError(
+                f"MXFP4 block size {block_size} is not ggml's QK_MXFP4 (32)")
+        return True
+
+    def _write_mxfp4_experts(self) -> None:
+        n_experts = self.hparams["n_routed_experts"]
+
+        # the FP8 half uses `weight_scale_inv` and is left to dequant_model
+        stray = [n for n in self.model_tensors
+                 if n.endswith(".weight_scale") and not self._MXFP4_EXPERT_RE.match(n.removesuffix("_scale"))]
+        if stray:
+            raise NotImplementedError(
+                f"{len(stray)} MXFP4 tensor(s) outside the routed experts, e.g. {stray[0]!r}; "
+                "only the routed experts have a repack path"
+            )
+
+        # (bid, proj) -> {expert id: (weight name, scale name)}
+        groups: dict[tuple[int, str], dict[int, tuple[str, str]]] = {}
+        for name in self.model_tensors:
+            m = self._MXFP4_EXPERT_RE.match(name)
+            if m is None:
+                continue
+            bid, eid, proj = int(m.group(1)), int(m.group(2)), m.group(3)
+            scale_name = name + "_scale"
+            if scale_name not in self.model_tensors:
+                raise KeyError(f"missing {scale_name} for {name}")
+            groups.setdefault((bid, proj), {})[eid] = (name, scale_name)
+
+        consumed: list[str] = []
+        for (bid, proj), experts in sorted(groups.items()):
+            missing = [e for e in range(n_experts) if e not in experts]
+            if missing or len(experts) != n_experts:
+                raise KeyError(
+                    f"layer {bid} {proj}_proj: {len(experts)} of {n_experts} experts present"
+                    + (f", first missing is {missing[0]}" if missing else "")
+                )
+
+            loaders = []
+            for eid in range(n_experts):
+                weight_name, scale_name = experts[eid]
+                loaders.append((self.model_tensors[weight_name], self.model_tensors[scale_name]))
+                consumed += [weight_name, scale_name]
+
+            data = self._mxfp4_expert_tensor(loaders)
+            new_name = self.format_tensor_name(self._MXFP4_PROJ[proj], bid)
+            shape = gguf.quant_shape_from_byte_shape(data.shape, gguf.GGMLQuantizationType.MXFP4)
+            logger.info(
+                f"{new_name}: repacked {n_experts} experts to MXFP4, "
+                f"shape = {{{', '.join(str(n) for n in reversed(shape))}}}"
+            )
+            self.gguf_writer.add_tensor(new_name, data, raw_dtype=gguf.GGMLQuantizationType.MXFP4)
+
+        for name in consumed:
+            del self.model_tensors[name]
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        # not a generator on purpose: base.py chains this with get_tensors(), so the
+        # tensors used here must be removed from model_tensors before that starts
+        if self._is_mxfp4_packed():
+            self._write_mxfp4_experts()
+        return ()

    _experts: list[dict[str, Tensor]] | None = None

@@ -173,11 +258,32 @@ class MimoV2Model(TextModel):
    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
        name, gen = item

+        is_mtp = name.startswith("model.mtp.layers.")
+        if is_mtp and cls.no_mtp:
+            return None
+        if cls.mtp_only and not is_mtp and name not in (
+            "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight",
+        ):
+            return None
+
        if "attention_sink" in name and not name.endswith(".weight"):
            name += ".weight"

        return super().filter_tensors((name, gen))

+    def prepare_metadata(self, vocab_only: bool):
+        from_dir = self.fname_out.is_dir()
+        super().prepare_metadata(vocab_only=vocab_only)
+
+        if not self.mtp_only or not from_dir:
+            return
+
+        output_type: str = self.ftype.name.partition("_")[2]
+        fname_default: str = gguf.naming_convention(
+            self.metadata.name, self.metadata.basename, self.metadata.finetune,
+            self.metadata.version, size_label=None, output_type=output_type, model_type=None)
+        self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf"
+
    def modify_tensors(self, data_torch, name, bid):
        # Remap MTP/NextN tensors to additional layer slots so the standard tensor map handles them.
        # HF: model.mtp.layers.{i}.foo  ->  model.layers.{n_layer_text + i}.foo
@@ -192,7 +298,7 @@ class MimoV2Model(TextModel):
            bid = new_bid

        # process the experts separately
-        if name.find("mlp.experts") != -1:
+        if ".mlp.experts." in name and name.endswith(".weight"):
            n_experts = self.hparams["n_routed_experts"]
            assert bid is not None

@@ -229,6 +335,10 @@ class MimoV2Model(TextModel):
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")

+        if self._is_mxfp4_packed():
+            self._is_mxfp4 = True
+            self.ftype = gguf.LlamaFileType.MOSTLY_MXFP4_MOE
+

@ModelBase.register("MiMoV2ForCausalLM")
@ModelBase.example("XiaomiMiMo/MiMo-V2.5")
@@ -382,6 +492,8 @@ class MiMoV2VisionAudioModel(MmprojModel):
            "_codebook.inited",
        )
        for name, tensor in state_dict.items():
+            if name.startswith("decoder."):
+                continue
            if name.endswith(skip_suffixes):
                continue
            if m := codebook_re.match(name):
@@ -424,12 +424,6 @@ class NemotronHModel(GraniteHybridModel):
        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
        special_vocab.add_to_gguf(self.gguf_writer)

-        # The tokenizer _does_ add a BOS token (via post_processor type
-        # TemplateProcessing) but does not set add_bos_token to true in the
-        # config, so we need to explicitly override it here.
-        if not self.is_moe:
-            self.gguf_writer.add_add_bos_token(True)
-
    _MTP_SPECIAL_RENAMES = {
        "mtp.layers.0.enorm.weight":           "model.layers.{bid}.enorm.weight",
        "mtp.layers.0.hnorm.weight":           "model.layers.{bid}.hnorm.weight",
@@ -154,6 +154,21 @@ class Plamo2Model(TextModel):
 class Plamo3Model(TextModel):
    model_arch = gguf.MODEL_ARCH.PLAMO3

+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # PLaMo-3 builds rope_parameters from flat config keys at runtime; mirror the YaRN settings for GGUF.
+        rope_scaling_factor = self.hparams.get("rope_scaling_factor", 1)
+        if rope_scaling_factor != 1 and "rope_type" not in self.rope_parameters:
+            self.rope_parameters.update({
+                "rope_type": "yarn",
+                "factor": float(rope_scaling_factor),
+                "original_max_position_embeddings": int(self.hparams["initial_context_length"]),
+                "beta_fast": 32.0,
+                "beta_slow": 1.0,
+                "truncate": False,
+            })
+
    def set_vocab(self):
        self._set_vocab_plamo()

@@ -10,7 +10,7 @@ import torch
 if TYPE_CHECKING:
    from torch import Tensor

-from .base import LazyTorchTensor, ModelBase, TextModel, gguf, logger
+from .base import LazyTorchTensor, ModelBase, ModelType, TextModel, get_model_architecture, gguf, logger


@ModelBase.register("QWenLMHeadModel")
@@ -666,7 +666,7 @@ class DFlashModel(Qwen3Model):
        from . import get_model_class
        with open(self.target_model_dir / "config.json", "r", encoding="utf-8") as f:
            target_hparams = json.load(f)
-            target_arch = target_hparams["architectures"][0]
+        target_arch = get_model_architecture(target_hparams, ModelType.TEXT)
        target_cls = get_model_class(target_arch)

        if target_cls is not type(self):
@@ -711,7 +711,7 @@ class DFlashModel(Qwen3Model):
        if embedding_scale is not None:
            self.gguf_writer.add_embedding_scale(float(embedding_scale))

-        target_layer_ids = dflash_config.get("target_layer_ids", [])
+        target_layer_ids = dflash_config.get("target_layer_ids", self.hparams.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)
@@ -719,8 +719,9 @@ class DFlashModel(Qwen3Model):
        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")
        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]
+        if use_sliding_window and sliding_window:
+            is_swa = ([True] * self.block_count if dflash_config.get("use_swa", False)
+                      else [lt == "sliding_attention" for lt in layer_types or []])
            self.gguf_writer.add_sliding_window(sliding_window)
            self.gguf_writer.add_sliding_window_pattern(is_swa)

@@ -840,13 +841,6 @@ class DSparkModel(DFlashModel):
            return None
        return super().filter_tensors(item)

-    _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.d2t":
            self._d2t = data_torch
@@ -855,12 +849,6 @@ class DSparkModel(DFlashModel):
        if self._n_vocab_draft == self.hparams["vocab_size"] and name.endswith("lm_head.weight"):
            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)
-
        yield from super().modify_tensors(data_torch, name, bid)

    def prepare_tensors(self):
@@ -163,6 +163,7 @@ models = [
    {"name": "granite-embed-multi-311m", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ibm-granite/granite-embedding-311m-multilingual-r2", },
    {"name": "mellum2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/JetBrains/Mellum2-12B-A2.5B-Base"},
    {"name": "laguna",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/poolside/Laguna-XS.2", },
+    {"name": "ufakzeka",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ufakai/ufakzeka-1", },
 ]

 # some models are known to be broken upstream, so we will skip them as exceptions
@@ -191,6 +192,10 @@ 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"},
+    # hrm-text (DFM Mimir) is SPM-style BPE: normalizer maps ' ' -> '▁', merges
+    # over the whole text (fix_mistral_regex inserts a tekken regex that is a
+    # no-op here); the gemma4 pre (escape ws, split on newlines only) matches it.
+    {"name": "gemma4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/danish-foundation-models/DFM-Mimir", "chkhsh": "846deafc5b0fa786186fa4ae6c7b49903cf2f1d1895bdb80b9120d60be135252"},
    {"name": "spark2_5", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/XHToken/Spark-X2.5-1.7B", "chkhsh": "0a766d034107bc736a3f2dc4968fd62e54a3570f1454443e0c5a4cc6bd7941ed"},
 ]

@@ -12,6 +12,8 @@ The OpenVINO backend is implemented in `ggml/src/ggml-openvino` and provides a t
 - Compiles and caches the model for the target device.
 - Binds GGML tensor memory to OpenVINO inference tensors and runs inference.

+For guidance on contributing to the OpenVINO backend, see the [OpenVINO Backend Contributing Guide](https://github.com/ravi9/llamacpp-ov-dev-guide/blob/main/contributing-llamacpp-ov.md).
+
 ## Contents

 - [Supported Devices](#supported-devices)
@@ -96,7 +98,7 @@ 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 Graphics Compiler 2.41.5 | Intel OpenCL GPU Driver 26.31.39395.13-0 | Intel NPU Driver 1.38.0.
 - See [Known Limitations](#known-limitations) for context on observed failures.

 | Model | CPU (SL / SF) | GPU (SL / SF) | NPU (SL) |
@@ -117,9 +119,9 @@ Although, the validated models below were tested with `llama-cli` using the `Q4_
 | [lmstudio-community/Qwen3.5-9B-Q4_K_M](https://huggingface.co/lmstudio-community/Qwen3.5-9B-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/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/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) | ✓ / ✓ | ✓ / ✓ | ✓ |
@@ -132,9 +134,9 @@ Although, the validated models below were tested with `llama-cli` using the `Q4_
 | [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) | ✓ / ✓ | ✓ / ✓ | ✓ |
 |  |  |  |  |
-| [ibm-granite/granite-4.0-350m-Q4_K_M](https://huggingface.co/ibm-granite/granite-4.0-350m-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) | ✓ / ✓ | ✓ / ✓ | ✓ |
-| [ibm-granite/granite-4.0-1b-Q4_K_M](https://huggingface.co/ibm-granite/granite-4.0-1b-GGUF) | ✓ / ✓ | ✗ / ✗ | ✗ |
+| [ibm-granite/granite-4.0-1b-Q4_K_M](https://huggingface.co/ibm-granite/granite-4.0-1b-GGUF) | ✓ / ✓ | ✓ / ✓ | ✗ |
 | [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) | ✓ / ✓ | ✓ / ✓ | ✓ |
@@ -242,8 +244,8 @@ 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="2026.4"
+OPENVINO_VERSION_FULL="2026.4.0.22959.99c81491cc3"

 SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
 OPENVINO_INSTALL_DIR="/opt/intel/openvino_${OPENVINO_VERSION_MAJOR}"
@@ -340,7 +342,7 @@ 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 `2026.4` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release.

 </details>

@@ -370,8 +372,8 @@ 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=2026.4"
+set "OPENVINO_VERSION_FULL=2026.4.0.22959.99c81491cc3"

 set "SCRIPT_DIR=%~dp0"
 set "VCPKG_DIR=C:\vcpkg"
@@ -550,7 +552,7 @@ 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 `2026.4` 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>

@@ -719,10 +721,13 @@ Boolean flags follow a uniform convention: set to a **positive integer** (e.g. `
 | `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. |
+| `GGML_OPENVINO_DISABLE_KV_STATE_RELAYOUT` | Boolean | `0`     | Disable the stateful KV-state sequence-axis relayout (relayout is on by default). It moves the KV state sequence axis from dim 1 to dim 2, so the GPU plugin can append new tokens in place instead of copying the whole state every token, and the reader side no longer transposes the whole accumulated state. Set to `1` to disable. |
 | `GGML_OPENVINO_MANUAL_GQA_ATTN`   | Boolean   | device-based | Tri-state. When **unset**, manual GQA attention is enabled by default on `GPU` and disabled on other devices. Set to a positive integer to force-enable, or `0` to force-disable. |
 | `GGML_OPENVINO_MEMORY_OPTIMIZE`   | Boolean   | `0`        | Umbrella switch for compile-time memory reductions. Enables `GGML_OPENVINO_REDUCE_COMPILE_MEM` and, on GPU, `GGML_OPENVINO_RELEASE_WEIGHTS` unless those fine-grained variables are explicitly set. |
 | `GGML_OPENVINO_REDUCE_COMPILE_MEM`| Boolean   | inherits from `GGML_OPENVINO_MEMORY_OPTIMIZE` | Reduce compile-time host memory use by streaming weight requantization and avoiding extra weight-node materialization where possible. Set explicitly to override the umbrella switch. |
 | `GGML_OPENVINO_RELEASE_WEIGHTS`   | Boolean   | inherits from `GGML_OPENVINO_MEMORY_OPTIMIZE` on GPU | GPU-only. Release host weight buffers after the compiled model cache can reuse the device/plugin copy. Requires stable graph shapes; dynamic workloads that need recompilation should leave this disabled. |
+| `GGML_OPENVINO_SPILL_DIR`         | String    | `not set`  | Directory for a disk-backed weight buffer. When set, the repacked weight buffer is mapped from an unlinked file on this path instead of anonymous memory, so its pages are reclaimable under memory pressure instead of staying pinned, cutting the load-time host memory peak. Must point at real storage; a tmpfs mount (e.g. `/tmp` on many systems) backs it with RAM and makes the peak worse. |
+| `GGML_OPENVINO_REQUANT_KQUANT`    | String    | `not set`  | Requantize Q6_K/Q5_K weights (and matching MoE expert weights) to a 4-bit target instead of the default Q8_0_C, trading accuracy for less memory traffic. One of `q4_sym128` (Q6_K/Q5_K only), `q4_sym128_all` (Q4_K too, drops its per-group zero point), `q4_asym64_all` (Q6_K/Q5_K/Q4_K, keeps a real zero point at group 64), or `native` (no requantization). |
 | `GGML_OPENVINO_PROFILING`         | Boolean   | `0`        | Enable execution-time profiling.                                                                            |
 | `GGML_OPENVINO_DUMP_CGRAPH`       | Boolean   | `0`        | Dump the GGML compute graph to `cgraph_ov.txt`.                                                             |
 | `GGML_OPENVINO_DUMP_IR`           | Boolean   | `0`        | Serialize OpenVINO IR files with timestamps.                                                                |
@@ -811,6 +811,9 @@ User can use the device management in [docs/multi-gpu.md](https://github.com/ggm
 | 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_ESIMD | 0 or 1 (default)| Enable ESIMD kernels when available. |
+| GGML_SYCL_SPARSE_FA | 0 (default) or 1 | Enable Sparse Flash-attention.|
+| GGML_SYCL_SPARSE_FA_DEBUG | 0 (default) or 1 | Enable to debug for Sparse Flash-attention.|
+| GGML_SYCL_SPARSE_FA_MARGIN | [0,..] default:256 | Set the margin value for Sparse Flash-attention.|
 | 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. |
 | GGML_SYCL_USM_SYSTEM | 0 (default) or 1 | Enable experimental support for [USM system allocations](https://github.khronos.org/SYCL_Reference/iface/usm_basic_concept.html#system-allocations) for large GPU buffers. This requires enough host memory for model weights and caches, an Intel Xe2+ GPU such as BMG or newer and supported on Linux only, with CONFIG_DRM_XE_GPUSVM enabled. |
@@ -77,6 +77,8 @@

    { "name": "arm64-android-snapdragon-debug"  , "inherits": [ "base", "arm64-android-snapdragon", "debug" ] },
    { "name": "arm64-android-snapdragon-release", "inherits": [ "base", "arm64-android-snapdragon", "release" ] },
+    { "name": "arm64-android-snapdragon-relwithdebinfo", "inherits": [ "arm64-android-snapdragon-release" ],
+      "cacheVariables": { "GGML_HEXAGON_HTP_BUILD_TYPE": "RelWithDebInfo" } },

    { "name": "arm64-windows-snapdragon-debug"  , "inherits": [ "base", "arm64-windows-snapdragon", "debug" ] },
    { "name": "arm64-windows-snapdragon-release", "inherits": [ "base", "arm64-windows-snapdragon", "release" ] },
@@ -327,6 +327,9 @@ on 4 physical NPUs, or `--devices 'HTP0[0-1:0],HTP1[0-1:1]'` on 2 physical NPUs
 - `GGML_HEXAGON_HOSTBUF=1` (default: 0, disabled)
  Enables allocating host buffers for debugging. By default, host buffers are disabled.

+- `GGML_HEXAGON_DMA64=0` (default: enabled on v81+)
+  Disables 64-bit DMA for model weights. Set to `1` to enable it explicitly on a supported architecture.
+
 - `GGML_HEXAGON_VERBOSE=1`
  Enables verbose logging of Ops from the backend. Example output:

@@ -146,6 +146,28 @@ Writing high-performance operators for Hexagon requires following specific guide
  python3 scripts/snapdragon/ggml-hexagon-align-macros.py --fix ggml/src/ggml-hexagon/htp/
  ```

+### Binary Inspection and Spill Analysis
+
+Use [`scripts/snapdragon/ggml-hexagon-inspect.py`](../../../scripts/snapdragon/ggml-hexagon-inspect.py) to audit Hexagon binaries for register
+spills, unexpected float promotions, or disassembly:
+
+- Always verify that compute kernels have zero in-loop vector spills (`--spills --strict`) and no float promotions (`--promotions`).
+- Avoid excessive loop unrolling (`#pragma unroll`), which increases register pressure and causes spills.
+
+```bash
+# Check for vector and scalar register spills
+python3 scripts/snapdragon/ggml-hexagon-inspect.py --spills --strict --func "^compute_"
+
+# Check for float promotions
+python3 scripts/snapdragon/ggml-hexagon-inspect.py --promotions --func "^compute_"
+
+# Disassemble with annotated loops and spill markers
+python3 scripts/snapdragon/ggml-hexagon-inspect.py --disasm compute_same_shape_div_f32
+
+# Resolve crash addresses to function symbols and lines
+python3 scripts/snapdragon/ggml-hexagon-inspect.py --addr2line 0x51a30 0x5ba54
+```
+
 ## Multi-Device Partitioning (mdev)

 Multi-device (mdev) mode enables row-level tensor parallel execution across multiple physical NPU cores or virtual NPU
@@ -282,6 +282,13 @@ Consider setting `CUDA_SCALE_LAUNCH_QUEUES=4x`, which increases the CUDA command
 Override default, speed-optimized compute types for cuBLAS matrix multiplications.
 Legal values: `auto`, `f16`, `fp16`, `bf16`, `f32`, `fp32`.

+#### GGML_CUDA_MMQ_PREC
+
+Override the activation precision that the model requests for NVFP4 and MXFP4 matrix multiplications.
+Currently supported values: `auto`, `q8`, `q4`.
+
+NVFP4 and MXFP4 layers marked as W4A16 request 8-bit activations, so on Blackwell those layers run through the W4A8 path instead of the native W4A4 path. Set `q4` to keep the native W4A4 path for faster prompt processing at the cost of accuracy, or `q8` to use the W4A8 path for every layer, `auto` uses per-tensor prec metadata (this is the same behavior as when the environment variable is not set).
+
 ### Unified Memory

 The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted. In Windows this setting is available in the NVIDIA control panel as `System Memory Fallback`.
@@ -323,11 +330,11 @@ cmake --build build --config Release
 By default, all supported compute capabilities are enabled. To customize this behavior, you can specify the `MUSA_ARCHITECTURES` option in the CMake command:

 ```bash
-cmake -B build -DGGML_MUSA=ON -DMUSA_ARCHITECTURES="21"
+cmake -B build -DGGML_MUSA=ON -DMUSA_ARCHITECTURES="31"
 cmake --build build --config Release
 ```

-This configuration enables only compute capability `2.1` (MTT S80) during compilation, which can help reduce compilation time.
+This configuration enables only compute capability `3.1` (MTT S5000) during compilation, which can help reduce compilation time.

 #### Compilation options

@@ -123,7 +123,7 @@ You may want to pass in some different `ARGS`, depending on the MUSA environment

 The defaults are:

- `MUSA_VERSION` set to `rc4.3.0`
+- the base image is the MUSA 5.2.0 image from the Moore Threads registry

 The resulting images, are essentially the same as the non-MUSA images:

@@ -14,22 +14,22 @@ Legend:

 | Operation | BLAS | CANN | CPU | CUDA | ET | HTP | MTL | OpenCL | SYCL | Vulkan | WebGPU | ZenDNN | zDNN |
 |-----------|------|------|------|------|------|------|------|------|------|------|------|------|------|
-|                              ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              ACC | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                           ARANGE | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             CEIL | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        COL2IM_1D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
 |                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                          CONV_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              COS | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
@@ -44,7 +44,7 @@ Legend:
 |                     DSV4_HC_COMB | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                     DSV4_HC_POST | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                      DSV4_HC_PRE | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                              DUP | ❌ | ✅ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                              DUP | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                            EXPM1 | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
@@ -55,26 +55,30 @@ Legend:
 |                GATED_LINEAR_ATTN | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                      GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                      GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                       GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
-|                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
+|                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
 |                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                           IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                           IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                          L2_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | 🟡 | ❌ | ❌ |
-|                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | 🟡 | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                LIGHTNING_INDEXER | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                              LOG | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                              LOG | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                 MUL_MAT_HADAMARD | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ✅ | 🟡 | 🟡 | ❌ |
+|                 MUL_MAT_HADAMARD | ❌ | ❌ | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | ❌ |
+|                  MUL_MAT_ID_W4A4 | ❌ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                  MUL_MAT_ID_W4A8 | ❌ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                     MUL_MAT_W4A4 | ❌ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                     MUL_MAT_W4A8 | ❌ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ |
 |                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
@@ -85,12 +89,12 @@ Legend:
 |                          POOL_1D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                           REPEAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
 |                      REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                         RMS_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                             ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                             ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             ROPE | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            ROUND | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
@@ -98,7 +102,7 @@ Legend:
 |                        RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              SET | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                         SET_ROWS | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ |
+|                         SET_ROWS | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |                              SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
@@ -108,20 +112,20 @@ Legend:
 |                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             SQRT | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             SQRT | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         SSM_CONV | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
-|                             STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                              SUM | ❌ | 🟡 | ✅ | 🟡 | ❌ | ❌ | 🟡 | ❌ | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
+|                              SUM | ❌ | 🟡 | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |                         SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
 |                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                     SWIGLU_CLAMP | ❌ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                     SWIGLU_CLAMP | ❌ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                            TOP_K | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                            TOP_K | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                              TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                            TRUNC | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
@@ -4588,264 +4588,282 @@
 "CUDA0","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],stride=1,padding=0,dilation=1,cwhn=1","support","1","yes","CUDA"
 "CUDA0","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=0","support","1","yes","CUDA"
 "CUDA0","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=1","support","1","yes","CUDA"
-"CUDA0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","CUDA"
-"CUDA0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","CUDA"
-"CUDA0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","CUDA"
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-"CUDA0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","CUDA"
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+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=1,IC=4,ID=8,IH=8,IW=8,OC=8,KD=1,KH=1,KW=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=8,ID=5,IH=11,IW=9,OC=65,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=5,ID=7,IH=9,IW=13,OC=17,KD=2,KH=3,KW=4,s0=2,s1=1,s2=3,p0=3,p1=2,p2=2,d0=2,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=3,IC=16,ID=3,IH=7,IW=9,OC=33,KD=1,KH=1,KW=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=8,ID=7,IH=5,IW=9,OC=33,KD=3,KH=1,KW=1,s0=1,s1=1,s2=2,p0=0,p1=0,p2=2,d0=1,d1=1,d2=2,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=3,ID=1,IH=2,IW=1,OC=7,KD=1,KH=1,KW=1,s0=1,s1=1,s2=1,p0=3,p1=4,p2=2,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=2,IC=8,ID=5,IH=7,IW=9,OC=17,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=1","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=1,IC=1,ID=2,IH=2,IW=2,OC=1,KD=1,KH=1,KW=0,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=1,IC=1,ID=2,IH=2,IW=2,OC=1,KD=1,KH=0,KW=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
+"CUDA0","CONV_3D","N=1,IC=1,ID=2,IH=2,IW=2,OC=1,KD=0,KH=1,KW=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16,kernel_offset=0","support","1","yes","CUDA"
 "CUDA0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=1,p0=0,d0=1","support","1","yes","CUDA"
 "CUDA0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=2,p0=0,d0=1","support","1","yes","CUDA"
 "CUDA0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=3,p0=0,d0=1","support","1","yes","CUDA"
@@ -5000,14 +5018,14 @@
 "CUDA0","REPEAT_BACK","type=f32,ne=[8,6,4,2],nr=[1,1,1,2],v=1","support","1","yes","CUDA"
 "CUDA0","DUP","type=f32,ne=[10,10,20,1]","support","1","yes","CUDA"
 "CUDA0","DUP","type=f16,ne=[10,10,20,1]","support","1","yes","CUDA"
-"CUDA0","DUP","type=i32,ne=[10,10,20,1]","support","0","no","CUDA"
-"CUDA0","DUP","type=i16,ne=[10,10,20,1]","support","0","no","CUDA"
+"CUDA0","DUP","type=i32,ne=[10,10,20,1]","support","1","yes","CUDA"
+"CUDA0","DUP","type=i16,ne=[10,10,20,1]","support","1","yes","CUDA"
 "CUDA0","DUP","type=f32,ne=[10,10,5,1],permute=[0,2,1,3]","support","1","yes","CUDA"
 "CUDA0","DUP","type=f16,ne=[10,10,5,1],permute=[0,2,1,3]","support","1","yes","CUDA"
 "CUDA0","DUP","type=f32,ne=[10,10,5,1],permute=[1,0,2,3]","support","1","yes","CUDA"
 "CUDA0","DUP","type=f16,ne=[10,10,5,1],permute=[1,0,2,3]","support","1","yes","CUDA"
-"CUDA0","DUP","type=i16,ne=[10,8,3,1],permute=[0,2,1,3]","support","0","no","CUDA"
-"CUDA0","DUP","type=i16,ne=[10,8,3,1],permute=[1,2,0,3]","support","0","no","CUDA"
+"CUDA0","DUP","type=i16,ne=[10,8,3,1],permute=[0,2,1,3]","support","1","yes","CUDA"
+"CUDA0","DUP","type=i16,ne=[10,8,3,1],permute=[1,2,0,3]","support","1","yes","CUDA"
 "CUDA0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=1","support","1","yes","CUDA"
 "CUDA0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=2","support","1","yes","CUDA"
 "CUDA0","SET","type_src=f32,type_dst=f32,ne=[6,5,4,3],dim=3","support","1","yes","CUDA"
--- a/Show More
+++ b/Show More