ui: npm run format for new upstream formatting rules

ui: use arrow function in fetch interceptor
ui: (demo) access server remotely via webrtc
2026-08-23 14:08:11 +02:00 · 2026-08-16 09:08:43 +02:00 · 2026-08-16 09:07:08 +02:00 · 2026-08-16 08:59:59 +02:00
549 changed files with 38132 additions and 25002 deletions
@@ -1,18 +1,18 @@
-ARG OPENVINO_VERSION_MAJOR=2026.3
-ARG OPENVINO_VERSION_FULL=2026.3.0.22451.bd8d6542e3c
+ARG OPENVINO_VERSION_MAJOR=2026.2.1
+ARG OPENVINO_VERSION_FULL=2026.2.1.21919.ede283a88e3
 ARG UBUNTU_VERSION=24.04

 # Intel GPU driver versions. https://github.com/intel/compute-runtime/releases
-ARG IGC_VERSION=v2.38.2
-ARG IGC_VERSION_FULL=2_2.38.2+22051
-ARG COMPUTE_RUNTIME_VERSION=26.27.39122.11
-ARG COMPUTE_RUNTIME_VERSION_FULL=26.27.39122.11-0
+ARG IGC_VERSION=v2.36.3
+ARG IGC_VERSION_FULL=2_2.36.3+21719
+ARG COMPUTE_RUNTIME_VERSION=26.22.38646.4
+ARG COMPUTE_RUNTIME_VERSION_FULL=26.22.38646.4-0
 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=v1.33.0
+ARG NPU_DRIVER_FULL=v1.33.0.20260529-26625960453
+ARG LIBZE1_VERSION=1.27.0-1~24.04~ppa2

 # Optional proxy build arguments
 ARG http_proxy=
@@ -170,7 +170,7 @@ 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 "$DEB" https://snapshot.ppa.launchpadcontent.net/kobuk-team/intel-graphics/ubuntu/20260324T100000Z/pool/main/l/level-zero-loader/libze1_${LIBZE1_VERSION}_amd64.deb; \
    fi; \
    mkdir /tmp/npu/ && cd /tmp/npu/ && tar -xf "$TGZ" && cp "$DEB" .; \
    apt-get update; \
@@ -1,88 +1,22 @@
-# note: place this as the last step of the job, so the new cache is saved by "Post ccache" right after the old one is cleared
 name: "ccache-clear"
-description: "Delete GitHub Actions caches matching a key prefix, oldest first"
+description: "Delete all GitHub Actions caches matching a key prefix"
 inputs:
  key:
    description: "Cache key prefix to match and delete"
    required: true
-  older:
-    description: "Only delete caches created more than this long ago (e.g. 90m, 1h, 1d). By default all matching caches are deleted"
-    required: false
-    default: ""
-  min:
-    description: "Stop deleting if fewer than this many caches would remain (e.g. 1). By default there is no minimum"
-    required: false
-    default: "0"
-  dry-run:
-    description: "Only print the caches that would be deleted, without deleting them"
-    required: false
-    default: "false"

 runs:
  using: "composite"
  steps:
    - name: Clear caches
      shell: bash
-      env:
-        CLEAR_KEY: ${{ inputs.key }}
-        CLEAR_OLDER: ${{ inputs.older }}
-        CLEAR_MIN: ${{ inputs.min }}
-        CLEAR_DRY_RUN: ${{ inputs.dry-run }}
      run: |
-        # Convert a duration (e.g. 90m, 1h, 1d, plain seconds) to seconds
-        to_seconds() {
-          local val="$1"
-          [[ "$val" =~ ^[0-9]+$ ]] && { echo "$val"; return 0; }
-          local num="${val%?}" unit="${val: -1}" mult
-          [[ "$num" =~ ^[0-9]+$ ]] || return 1
-          case "$unit" in
-            s) mult=1 ;;
-            m) mult=60 ;;
-            h) mult=3600 ;;
-            d) mult=86400 ;;
-            *) return 1 ;;
-          esac
-          echo $((num * mult))
-        }
-
-        [[ "$CLEAR_MIN" =~ ^[0-9]+$ ]] || { echo "Invalid min value: $CLEAR_MIN" >&2; exit 1; }
-        [[ "$CLEAR_DRY_RUN" =~ ^(true|false)$ ]] || { echo "Invalid dry-run value: $CLEAR_DRY_RUN" >&2; exit 1; }
-
-        CACHES=$(gh cache list --key "ccache-$CLEAR_KEY" --json id,key,createdAt --jq '.[] | [.createdAt, .id, .key] | @tsv' 2>/dev/null | LC_ALL=C sort)
+        CACHES=$(gh cache list --key "ccache-${{ inputs.key }}" --json id,key --jq '.[] | "\(.id) \(.key)"' 2>/dev/null)
        if [ -z "$CACHES" ]; then
-          echo "No caches found with key prefix: $CLEAR_KEY"
+          echo "No caches found with key prefix: ${{ inputs.key }}"
          exit 0
        fi
-
-        TOTAL=$(( $(wc -l <<< "$CACHES") ))
-
-        echo "Found $TOTAL cache(s) with key prefix: $CLEAR_KEY (oldest first):"
-        while IFS=$'\t' read -r CREATED ID KEY; do
-          printf '  %s  %s  %s\n' "$CREATED" "$ID" "$KEY"
-        done <<< "$CACHES"
-
-        CUTOFF=""
-        if [ -n "$CLEAR_OLDER" ]; then
-          OLDER_SECONDS=$(to_seconds "$CLEAR_OLDER") || { echo "Invalid older value: $CLEAR_OLDER (expected e.g. 90m, 1h, 1d)" >&2; exit 1; }
-          CUTOFF=$(( $(date +%s) - OLDER_SECONDS ))
-        fi
-
-        # Caches are sorted oldest first
-        DELETED=0
-        while IFS=$'\t' read -r CREATED ID KEY; do
-          if [ -n "$CUTOFF" ] && [ "$(date -d "$CREATED" +%s)" -ge "$CUTOFF" ]; then
-            echo "Rest are not older than $CLEAR_OLDER, stopping"
-            break
-          fi
-          if [ $((TOTAL - DELETED - 1)) -lt "$CLEAR_MIN" ]; then
-            echo "Keeping at least $CLEAR_MIN cache(s), stopping"
-            break
-          fi
-          if [ "$CLEAR_DRY_RUN" = "true" ]; then
-            echo "Would delete cache: $ID ($KEY)"
-          else
-            echo "Deleting cache: $ID ($KEY)"
-            gh cache delete "$ID"
-          fi
-          DELETED=$((DELETED + 1))
+        while read -r id key; do
+          echo "Deleting cache: $id ($key)"
+          gh cache delete "$id"
        done <<< "$CACHES"
@@ -0,0 +1,20 @@
+name: "Linux - Setup Vulkan SDK"
+description: "Setup Vulkan SDK for Linux"
+inputs:
+  path:
+    description: "Installation path"
+    required: true
+  version:
+    description: "Vulkan SDK version"
+    required: true
+
+runs:
+  using: "composite"
+  steps:
+    - name: Setup Vulkan SDK
+      id: setup
+      uses: ./.github/actions/unarchive-tar
+      with:
+        url: https://sdk.lunarg.com/sdk/download/${{ inputs.version }}/linux/vulkan_sdk.tar.xz
+        path: ${{ inputs.path }}
+        strip: 1
@@ -6,7 +6,8 @@ inputs:
    required: true
  cuda_arch:
    description: "CUDA target architecture"
-    required: true
+    required: false
+    default: "x64"

 runs:
  using: "composite"
@@ -10,6 +10,33 @@ concurrency:
  cancel-in-progress: true

 jobs:
+  ubuntu-24-vulkan-cache:
+    runs-on: ubuntu-24.04
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Get latest Vulkan SDK version
+        id: vulkan_sdk_version
+        run: |
+          echo "VULKAN_SDK_VERSION=$(curl https://vulkan.lunarg.com/sdk/latest/linux.txt)" >> "$GITHUB_ENV"
+
+      - name: Setup Cache
+        uses: actions/cache@v5
+        id: cache-sdk
+        with:
+          path: ./vulkan_sdk
+          key: cache-gha-vulkan-sdk-${{ env.VULKAN_SDK_VERSION }}-${{ runner.os }}
+
+      - name: Setup Vulkan SDK
+        if: steps.cache-sdk.outputs.cache-hit != 'true'
+        uses: ./.github/actions/linux-setup-vulkan
+        with:
+          path: ./vulkan_sdk
+          version: ${{ env.VULKAN_SDK_VERSION }}
+
  #ubuntu-24-spacemit-cache:
  #  runs-on: ubuntu-24.04

@@ -40,9 +67,9 @@ jobs:
    runs-on: ubuntu-24.04

    env:
-      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.3"
-      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
+      # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"

    steps:
      - name: Clone
@@ -69,8 +96,8 @@ jobs:

    env:
      # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.3"
-      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"

    steps:
      - name: Clone
@@ -27,26 +27,30 @@ jobs:
          cmake --install build --prefix "$PREFIX" --config Release

          export LLAMA_CONFIG="$PREFIX"/lib/cmake/llama/llama-config.cmake
-          build_commit=$(git rev-parse --short HEAD | xargs)
-          build_number=$(git rev-list --count HEAD | xargs)
+          tclsh <<'EOF'
+          set build(commit)  [string trim [exec git rev-parse --short HEAD]]
+          set build(number)  [string trim [exec git rev-list  --count HEAD]]

-          major=$(grep -oE "set\(LLAMA_VERSION_MAJOR[[:space:]]+[0-9]+" CMakeLists.txt | grep -oE "[0-9]+$")
-          minor=$(grep -oE "set\(LLAMA_VERSION_MINOR[[:space:]]+[0-9]+" CMakeLists.txt | grep -oE "[0-9]+$")
-          patch=$(grep -oE "set\(LLAMA_VERSION_PATCH[[:space:]]+[0-9]+" CMakeLists.txt | grep -oE "[0-9]+$")
-          build_version="$major.$minor.$patch"
+          set cmakelists [read [open "CMakeLists.txt" r]]
+          regexp {set\(LLAMA_VERSION_MAJOR\s+(\d+)\)} $cmakelists -> major
+          regexp {set\(LLAMA_VERSION_MINOR\s+(\d+)\)} $cmakelists -> minor
+          regexp {set\(LLAMA_VERSION_PATCH\s+(\d+)\)} $cmakelists -> patch
+          set build(version) "$major.$minor.$patch"

-          checks=("set\(LLAMA_VERSION[[:space:]]+$build_version\)"
-                  "set\(LLAMA_BUILD_COMMIT[[:space:]]+$build_commit\)"
-                  "set\(LLAMA_BUILD_NUMBER[[:space:]]+$build_number\)")
+          set llamaconfig [read [open "$env(LLAMA_CONFIG)" r]]
+          set checks [list "set\\(LLAMA_VERSION     \\s+$build(version)\\)" \
+                           "set\\(LLAMA_BUILD_COMMIT\\s+$build(commit)\\)" \
+                           "set\\(LLAMA_BUILD_NUMBER\\s+$build(number)\\)"]

-          for check in "${checks[@]}"; do
-              if ! grep -qE "$check" "$LLAMA_CONFIG"; then
-                  echo "Checking llama-config.cmake version... \"$check\" failed!"
+          puts -nonewline "Checking llama-config.cmake version... "
+          foreach check $checks {
+              if {![regexp -expanded -- $check $llamaconfig]} {
+                  puts "\"$check\" failed!"
                  exit 1
-              fi
-          done
-
-          echo "Checking llama-config.cmake version... success."
+              }
+          }
+          puts "success."
+          EOF

          cd examples/simple-cmake-pkg
          cmake -S . -B build -DCMAKE_PREFIX_PATH="$PREFIX"/lib/cmake
@@ -21,7 +21,6 @@ on:
    paths: [
      '.github/workflows/build-cpu.yml',
      '.github/workflows/build-cmake-pkg.yml',
-      'ggml/src/ggml-rpc/**',
      '**/CMakeLists.txt',
      '**/.cmake',
      '**/*.h',
@@ -97,7 +96,8 @@ jobs:
          cmake -B build \
            -DGGML_NATIVE=OFF \
            -DLLAMA_FATAL_WARNINGS=ON \
-            -DGGML_RPC=ON
+            -DGGML_RPC=ON \
+            -DGGML_NATIVE=OFF
          time cmake --build build --config Release -j $(nproc)

      - name: Test
@@ -117,38 +117,29 @@ jobs:
          ./bin/llama-convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf
          ./bin/llama-completion -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256

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

    env:
      OPENBLAS_VERSION: 0.3.23
      SDE_VERSION: 9.33.0-2024-01-07
+      VULKAN_VERSION: 1.4.357.0

    strategy:
      matrix:
        include:
          - build: 'x64-cpu-static'
            arch: 'x64'
-            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DGGML_OPENMP_FETCH=ON -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF'
+            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF'
          - build: 'x64-openblas'
            arch: 'x64'
            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_OPENMP=OFF -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"'
+          - build: 'x64-vulkan'
+            arch: 'x64'
+            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_VULKAN=ON'
          - build: 'arm64'
            arch: 'arm64'
-            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DGGML_OPENMP_FETCH=ON -DLLAMA_BUILD_SERVER=ON'
+            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON'

    steps:
      - name: Clone
@@ -176,6 +167,15 @@ jobs:
          $lib =  $(join-path $msvc 'bin\Hostx64\x64\lib.exe')
          & $lib /machine:x64 "/def:${env:RUNNER_TEMP}/openblas/lib/libopenblas.def" "/out:${env:RUNNER_TEMP}/openblas/lib/openblas.lib" /name:openblas.dll

+      - name: Install Vulkan SDK
+        id: get_vulkan
+        if: ${{ matrix.build == 'x64-vulkan' }}
+        run: |
+          curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/vulkansdk-windows-X64-${env:VULKAN_VERSION}.exe"
+          & "$env:RUNNER_TEMP\VulkanSDK-Installer.exe" --accept-licenses --default-answer --confirm-command install
+          Add-Content $env:GITHUB_ENV "VULKAN_SDK=C:\VulkanSDK\${env:VULKAN_VERSION}"
+          Add-Content $env:GITHUB_PATH "C:\VulkanSDK\${env:VULKAN_VERSION}\bin"
+
      - name: Install Ninja
        id: install_ninja
        run: |
@@ -22,7 +22,6 @@ env:

 jobs:
  cuda:
-    name: windows-cuda (${{ matrix.cuda }}, ${{ matrix.arch }})
    runs-on: windows-2022

    permissions:
@@ -30,16 +29,7 @@ jobs:

    strategy:
      matrix:
-        include:
-          - cuda: '12.4'
-            arch: x64
-            defines: '-DGGML_CUDA_CUB_3DOT2=ON'
-          - cuda: '13.3'
-            arch: x64
-            defines: ''
-          - cuda: '13.4'
-            arch: arm64
-            defines: '-DCMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc-cuda.cmake'
+        cuda: ['12.4', '13.3']

    steps:
      - name: Clone
@@ -49,13 +39,12 @@ jobs:
      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
        with:
-          key: release-windows-2022-${{ matrix.arch }}-cuda-${{ matrix.cuda }}
+          key: release-windows-2022-x64-cuda-${{ matrix.cuda }}

      - name: Install Cuda Toolkit
        uses: ./.github/actions/windows-setup-cuda
        with:
          cuda_version: ${{ matrix.cuda }}
-          cuda_arch: ${{ matrix.arch }}

      - name: Install Ninja
        id: install_ninja
@@ -65,21 +54,26 @@ jobs:
      - name: Build
        id: cmake_build
        shell: cmd
+        # TODO: Remove GGML_CUDA_CUB_3DOT2 flag once CCCL 3.2 is bundled within CTK and that CTK version is used in this project
        run: |
-          call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" ${{ matrix.arch == 'x64' && 'x64' || 'amd64_arm64' }}
+          call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" x64
          cmake -S . -B build -G "Ninja Multi-Config" ^
-            -DGGML_BACKEND_DL=ON ^
+            -DLLAMA_BUILD_SERVER=ON ^
+            -DLLAMA_BUILD_BORINGSSL=ON ^
            -DGGML_NATIVE=OFF ^
-            -DGGML_CPU=OFF ^
+            -DGGML_BACKEND_DL=ON ^
+            -DGGML_CPU_ALL_VARIANTS=ON ^
            -DGGML_CUDA=ON ^
-            -DLLAMA_BUILD_BORINGSSL=ON ${{ matrix.defines }}
+            -DGGML_RPC=ON ^
+            -DGGML_CUDA_CUB_3DOT2=ON
          set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1
-          cmake --build build --config Release -j %NINJA_JOBS% --target ggml-cuda
+          cmake --build build --config Release -j %NINJA_JOBS% -t ggml
+          cmake --build build --config Release

      - name: ccache-clear
        uses: ./.github/actions/ccache-clear
        with:
-          key: release-windows-2022-${{ matrix.arch }}-cuda-${{ matrix.cuda }}
+          key: release-windows-2022-x64-cuda-${{ matrix.cuda }}

  hip:
    runs-on: windows-2022
@@ -39,8 +39,8 @@ jobs:

    env:
      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.3"
-      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"

    steps:
      - name: Clone
@@ -81,7 +81,7 @@ jobs:
        # TODO: fix and re-enable the `test-llama-archs` test below
        run: |
          cd ${{ github.workspace }}
-          ctest --test-dir build/ReleaseOV -L main -E "test-llama-archs|test-recurrent-state-rollback-nemotron-h" --verbose --timeout 2000
+          ctest --test-dir build/ReleaseOV -L main -E "test-llama-archs" --verbose --timeout 2000

      - name: Test (GPU)
        id: cmake_test_gpu
@@ -89,15 +89,15 @@ jobs:
        run: |
          cd ${{ github.workspace }}
          export GGML_OPENVINO_DEVICE=GPU
-          ctest --test-dir build/ReleaseOV -L main -E "test-llama-archs|test-recurrent-state-rollback-nemotron-h" --verbose --timeout 3000
+          ctest --test-dir build/ReleaseOV -L main -E "test-llama-archs" --verbose --timeout 3000

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

    env:
      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.3"
-      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"

    steps:
      - name: Clone
@@ -166,4 +166,4 @@ jobs:
          call "%OPENVINO_ROOT%\setupvars.bat"

          cd build
-          ctest --test-dir ReleaseOV -L main -E "test-llama-archs|test-recurrent-state-rollback-nemotron-h" -C Release --verbose --timeout 3000
+          ctest --test-dir ReleaseOV -L main -E "test-llama-archs" -C Release --verbose --timeout 3000
@@ -0,0 +1,66 @@
+name: CI (rpc)
+
+on:
+  workflow_dispatch: # allows manual triggering
+  push:
+    branches:
+      - master
+    paths: [
+      '.github/workflows/build-rpc.yml',
+      '**/CMakeLists.txt',
+      '**/.cmake',
+      '**/*.h',
+      '**/*.hpp',
+      '**/*.c',
+      '**/*.cpp'
+    ]
+
+  pull_request:
+    types: [opened, synchronize, reopened]
+    paths: [
+      '.github/workflows/build-rpc.yml',
+      'ggml/src/ggml-rpc/**'
+    ]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
+env:
+  GGML_NLOOP: 3
+  GGML_N_THREADS: 1
+  LLAMA_ARG_LOG_COLORS: 1
+  LLAMA_ARG_LOG_PREFIX: 1
+  LLAMA_ARG_LOG_TIMESTAMPS: 1
+
+jobs:
+  ubuntu-24-rpc:
+    runs-on: ${{ 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}
+
+    continue-on-error: true
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt-get update
+          sudo apt-get install build-essential libssl-dev ninja-build
+
+      - name: Build
+        id: cmake_build
+        run: |
+          cmake -B build \
+            -G "Ninja" \
+            -DCMAKE_BUILD_TYPE=Release \
+            -DGGML_RPC=ON
+          time cmake --build build --config Release -j $(nproc)
+
+      - name: Test
+        id: cmake_test
+        run: |
+          cd build
+          ctest -L main --verbose
@@ -288,8 +288,8 @@ jobs:

    env:
      # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.3"
-      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"

    steps:
      - name: Clone
@@ -93,13 +93,19 @@ jobs:
        run: |
          echo "VULKAN_SDK_VERSION=$(curl https://vulkan.lunarg.com/sdk/latest/linux.txt)" >> "$GITHUB_ENV"

-      - name: Setup Vulkan SDK
-        id: setup
-        uses: ./.github/actions/unarchive-tar
+      - name: Use Vulkan SDK Cache
+        uses: actions/cache@v5
+        id: cache-sdk
        with:
-          url: https://sdk.lunarg.com/sdk/download/${{ env.VULKAN_SDK_VERSION }}/linux/vulkan_sdk.tar.xz
          path: ./vulkan_sdk
-          strip: 1
+          key: cache-gha-vulkan-sdk-${{ env.VULKAN_SDK_VERSION }}-${{ runner.os }}
+
+      - name: Setup Vulkan SDK
+        if: steps.cache-sdk.outputs.cache-hit != 'true'
+        uses: ./.github/actions/linux-setup-vulkan
+        with:
+          path: ./vulkan_sdk
+          version: ${{ env.VULKAN_SDK_VERSION }}

      - name: ccache
        uses: ggml-org/ccache-action@v1.2.21
@@ -127,56 +133,3 @@ jobs:
          # This is using llvmpipe and runs slower than other backends
          # test-backend-ops is too slow on llvmpipe, skip it
          ctest -L main -E test-backend-ops --verbose --timeout 900
-
-  windows:
-    runs-on: windows-2025
-
-    env:
-      VULKAN_VERSION: 1.4.357.0
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: cpu-windows-2025-x64-vulkan
-          variant: ccache
-          evict-old-files: 1d
-          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
-
-      - name: Install Vulkan SDK
-        id: get_vulkan
-        run: |
-          curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/vulkansdk-windows-X64-${env:VULKAN_VERSION}.exe"
-          & "$env:RUNNER_TEMP\VulkanSDK-Installer.exe" --accept-licenses --default-answer --confirm-command install
-          Add-Content $env:GITHUB_ENV "VULKAN_SDK=C:\VulkanSDK\${env:VULKAN_VERSION}"
-          Add-Content $env:GITHUB_PATH "C:\VulkanSDK\${env:VULKAN_VERSION}\bin"
-
-      - name: Install Ninja
-        id: install_ninja
-        run: |
-          choco install ninja
-
-      - name: Build
-        id: cmake_build
-        run: |
-          cmake -S . -B build -G "Ninja Multi-Config" `
-            -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake `
-            -DCMAKE_BUILD_TYPE=Release `
-            -DGGML_NATIVE=OFF `
-            -DLLAMA_BUILD_SERVER=ON `
-            -DGGML_RPC=ON `
-            -DGGML_BACKEND_DL=ON `
-            -DGGML_CPU_ALL_VARIANTS=ON `
-            -DGGML_VULKAN=ON `
-            -DLLAMA_BUILD_BORINGSSL=ON
-          cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS}
-
-      - name: Test
-        id: cmake_test
-        run: |
-          cd build
-          ctest -L main -C Release --verbose --timeout 900
@@ -44,7 +44,6 @@ jobs:
        uses: actions/checkout@v6
        with:
          fetch-depth: 0
-          ssh-key: ${{ secrets.DEPLOY_KEY_RELEASE }}

      - name: Determine source tag name
        id: srctag
@@ -394,11 +393,6 @@ jobs:
    name: Create shared tags from digests
    needs: [prepare_matrices, push_to_registry, create_tag]
    runs-on: ubuntu-24.04
-    permissions:
-      contents: read
-      packages: write
-      id-token: write
-      attestations: write
    strategy:
      fail-fast: false
      matrix:
@@ -433,7 +427,6 @@ jobs:
          password: ${{ secrets.GITHUB_TOKEN }}

      - name: Create tags from digests
-        id: create_tags
        shell: bash
        run: |
          set -euo pipefail
@@ -445,7 +438,6 @@ jobs:
          SRC_TAG="${{ needs.create_tag.outputs.source_tag }}"
          BUILD_DATE="${{ steps.build_date.outputs.date }}"
          COMMIT_SHA="${{ steps.checkout.outputs.commit }}"
-          echo "image_repo=${IMAGE_REPO}" >> "$GITHUB_OUTPUT"
          TAGS="${{ matrix.config.tag }}"
          ARCHES="${{ matrix.config.arches }}"
          DIGEST_GLOB="/tmp/digests/*.tsv"
@@ -512,16 +504,6 @@ jobs:

              echo "Creating ${merged_versioned_tag} from ${refs[*]}"
              docker buildx imagetools create "${annotations[@]}" --tag "${merged_versioned_tag}" "${refs[@]}"
-
-              if [[ "$tag_name" == "${TAGS%% *}" ]]; then
-                  local digest
-                  digest="$(docker buildx imagetools inspect "${merged_versioned_tag}" --format '{{.Manifest.Digest}}')"
-                  if [[ ! "$digest" =~ ^sha256:[0-9a-f]{64}$ ]]; then
-                      echo "Invalid digest for ${merged_versioned_tag}: ${digest}" >&2
-                      exit 1
-                  fi
-                  echo "${image_type}_digest=${digest}" >> "$GITHUB_OUTPUT"
-              fi
          }

          for tag in $TAGS; do
@@ -545,24 +527,3 @@ jobs:
          done
        env:
          GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}'
-
-      - name: Attest full image
-        if: ${{ matrix.config.full }}
-        uses: actions/attest@v4
-        with:
-          subject-name: ${{ steps.create_tags.outputs.image_repo }}
-          subject-digest: ${{ steps.create_tags.outputs.full_digest }}
-
-      - name: Attest light image
-        if: ${{ matrix.config.light }}
-        uses: actions/attest@v4
-        with:
-          subject-name: ${{ steps.create_tags.outputs.image_repo }}
-          subject-digest: ${{ steps.create_tags.outputs.light_digest }}
-
-      - name: Attest server image
-        if: ${{ matrix.config.server }}
-        uses: actions/attest@v4
-        with:
-          subject-name: ${{ steps.create_tags.outputs.image_repo }}
-          subject-digest: ${{ steps.create_tags.outputs.server_digest }}
@@ -3,11 +3,6 @@ name: Make Release
 on:
  workflow_dispatch:
    inputs:
-      commit:
-        description: 'Commit SHA to release (empty = branch HEAD)'
-        required: false
-        default: ''
-        type: string
      dry_run:
        description: 'Dry run - validate without creating the tag'
        required: true
@@ -27,17 +22,12 @@ jobs:
    steps:
      - name: Checkout
        uses: actions/checkout@v6
-        with:
-          ssh-key: ${{ secrets.DEPLOY_KEY_RELEASE }}
-          ref: ${{ inputs.commit != '' && inputs.commit || github.ref_name }}
-          fetch-depth: 0

      - 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 }}

      - name: Create release tag
        if: ${{ github.event.inputs.dry_run == 'false' }}
@@ -49,78 +39,8 @@ jobs:
          git push origin "${VERSION}"
          echo "Created and pushed tag ${VERSION}"

-      - name: Generate release description
-        id: desc
-        run: bash scripts/make-release-desc.sh "${{ steps.checks.outputs.version }}"
-        env:
-          GITHUB_REPOSITORY: ${{ github.repository }}
-
-      - name: Create nightly-tag.txt
-        id: nightly_tag_file
-        run: |
-          NIGHTLY_TAG="${{ steps.desc.outputs.nightly_tag }}"
-          if [[ -z "${NIGHTLY_TAG}" ]]; then
-            echo "Warning: no nightly tag found for the release commit - nightly-tag.txt will not be created"
-            echo "create=false" >> "$GITHUB_OUTPUT"
-            exit 0
-          fi
-          echo "${NIGHTLY_TAG}" > nightly-tag.txt
-          echo "create=true" >> "$GITHUB_OUTPUT"
-          echo "nightly-tag.txt:"
-          cat nightly-tag.txt
-
-      - name: Create release
-        id: create_release
-        if: ${{ github.event.inputs.dry_run == 'false' }}
-        uses: ggml-org/action-create-release@v1
-        env:
-          GITHUB_TOKEN: ${{ github.token }}
-        with:
-          tag_name: ${{ steps.checks.outputs.version }}
-          prerelease: false
-          # TODO: enrich the body of the release with more information
-          body: |
-            ## Overview
-
-            New version has been released.
-
-            ${{ steps.desc.outputs.nightly }}
-
-            **Web UI:** the `nightly-tag.txt` asset contains the tag of the corresponding nightly release
-
-            **More info:** [dist : releases and versioning of ggml-org projects](https://github.com/ggml-org/ggml/discussions/1579)
-
-            ## ${{ steps.desc.outputs.changelog_title }}
-
-            ${{ steps.desc.outputs.changelog }}
-
-      - name: Upload nightly-tag.txt
-        if: ${{ github.event.inputs.dry_run == 'false' && steps.nightly_tag_file.outputs.create == 'true' }}
-        uses: actions/github-script@v8
-        with:
-          github-token: ${{secrets.GITHUB_TOKEN}}
-          script: |
-            const fs = require('fs');
-            const release_id = '${{ steps.create_release.outputs.id }}';
-            console.log('uploadReleaseAsset', 'nightly-tag.txt');
-            await github.rest.repos.uploadReleaseAsset({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              release_id: release_id,
-              name: 'nightly-tag.txt',
-              data: await fs.readFileSync('./nightly-tag.txt')
-            });
-
      - name: Dry run summary
        if: ${{ github.event.inputs.dry_run == 'true' }}
        run: |
-          if [[ "${{ steps.checks.outputs.checks_passed }}" == "true" ]]; then
-            echo "Dry run complete - all checks passed."
-            echo "Would have created tag: ${{ steps.checks.outputs.version }}"
-            if [[ -n "${{ steps.desc.outputs.nightly_tag }}" ]]; then
-              echo "Would have uploaded nightly-tag.txt: ${{ steps.desc.outputs.nightly_tag }}"
-            fi
-          else
-            echo "::error::Dry run found release check failures. A release tag would not be created."
-            exit 1
-          fi
+          echo "Dry run complete - all checks passed."
+          echo "Would have created tag: ${{ steps.checks.outputs.version }}"
@@ -145,6 +145,11 @@ jobs:
            ${{ env.CMAKE_ARGS }}
          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-${{ matrix.os }}-${{ matrix.arch }}
+
      - name: Determine tag name
        id: tag
        uses: ./.github/actions/get-tag-name
@@ -161,11 +166,6 @@ jobs:
          path: llama-${{ steps.tag.outputs.name }}-bin-macos-${{ matrix.build }}.tar.gz
          name: llama-bin-macos-${{ matrix.build }}.tar.gz

-      - name: ccache-clear
-        uses: ./.github/actions/ccache-clear
-        with:
-          key: release-${{ matrix.os }}-${{ matrix.arch }}
-
  ubuntu-cpu:
    needs: [check-release, get-version]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}
@@ -231,6 +231,12 @@ jobs:
            ${{ env.CMAKE_ARGS }}
          cmake --build build --config Release -j $(nproc)

+      - name: ccache-clear
+        if: ${{ matrix.build != 's390x' }}
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-${{ matrix.os }}-cpu
+
      - name: Determine tag name
        id: tag
        uses: ./.github/actions/get-tag-name
@@ -247,12 +253,6 @@ jobs:
          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.tar.gz
          name: llama-bin-ubuntu-${{ matrix.build }}.tar.gz

-      - name: ccache-clear
-        if: ${{ matrix.build != 's390x' }}
-        uses: ./.github/actions/ccache-clear
-        with:
-          key: release-${{ matrix.os }}-cpu
-
  ubuntu-vulkan:
    needs: [check-release, get-version]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}
@@ -318,6 +318,11 @@ jobs:
            ${{ env.CMAKE_ARGS }}
          cmake --build build --config Release -j $(nproc)

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-${{ matrix.os }}-vulkan
+
      - name: Determine tag name
        id: tag
        uses: ./.github/actions/get-tag-name
@@ -334,11 +339,6 @@ jobs:
          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-${{ matrix.build }}.tar.gz
          name: llama-bin-ubuntu-vulkan-${{ matrix.build }}.tar.gz

-      - name: ccache-clear
-        uses: ./.github/actions/ccache-clear
-        with:
-          key: release-${{ matrix.os }}-vulkan
-
  android-arm64:
    needs: [check-release, get-version]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}
@@ -446,8 +446,8 @@ 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"
-      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"

    steps:
      - name: Set OpenVINO version output
@@ -512,6 +512,11 @@ jobs:
            ${{ env.CMAKE_ARGS }}
          cmake --build build/ReleaseOV --config Release --parallel

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-ubuntu-24.04-openvino-release-no-preset-v1
+
      - name: Determine tag name
        id: tag
        uses: ./.github/actions/get-tag-name
@@ -546,11 +551,6 @@ jobs:
          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz
          name: llama-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz

-      - name: ccache-clear
-        uses: ./.github/actions/ccache-clear
-        with:
-          key: release-ubuntu-24.04-openvino-release-no-preset-v1
-
  windows-openvino:
    needs: [check-release]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}
@@ -562,8 +562,8 @@ 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"
-      OPENVINO_VERSION_FULL: "2026.3.0.22451.bd8d6542e3c"
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"

    steps:
      - name: Set OpenVINO version output
@@ -637,6 +637,11 @@ jobs:

          cmake --build build\ReleaseOV --config Release -- /m

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-windows-2022-openvino
+
      - name: Determine tag name
        id: tag
        uses: ./.github/actions/get-tag-name
@@ -675,13 +680,7 @@ jobs:
          path: llama-${{ steps.tag.outputs.name }}-bin-win-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.zip
          name: llama-bin-win-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.zip

-      - name: ccache-clear
-        uses: ./.github/actions/ccache-clear
-        with:
-          key: release-windows-2022-openvino
-
  windows-cpu:
-    name: windows-cpu / ${{ matrix.arch }}
    needs: [check-release]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}

@@ -729,13 +728,18 @@ jobs:
            -DGGML_BACKEND_DL=ON ^
            -DGGML_CPU_ALL_VARIANTS=${{ matrix.arch == 'x64' && 'ON' || 'OFF' }} ^
            -DGGML_OPENMP=ON ^
-            -DGGML_OPENMP_FETCH=ON ^
            ${{ env.CMAKE_ARGS }}
          cmake --build build --config Release

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-windows-2025-vs2026-${{ matrix.arch }}-cpu
+
      - name: Pack artifacts
        id: pack_artifacts
        run: |
+          Copy-Item "C:\Program Files\Microsoft Visual Studio\18\Enterprise\VC\Redist\MSVC\14.51.36231\debug_nonredist\${{ matrix.arch }}\Microsoft.VC145.OpenMP.LLVM\libomp140.${{ matrix.arch == 'x64' && 'x86_64' || 'aarch64' }}.dll" .\build\bin\Release\
          7z a -snl llama-bin-win-cpu-${{ matrix.arch }}.zip .\build\bin\Release\*

      - name: Upload artifacts
@@ -744,11 +748,6 @@ jobs:
          path: llama-bin-win-cpu-${{ matrix.arch }}.zip
          name: llama-bin-win-cpu-${{ matrix.arch }}.zip

-      - name: ccache-clear
-        uses: ./.github/actions/ccache-clear
-        with:
-          key: release-windows-2025-vs2026-${{ matrix.arch }}-cpu
-
  windows-rocm:
    needs: [check-release]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}
@@ -774,7 +773,6 @@ jobs:
        with:
          key: windows-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}
          evict-old-files: 1d
-          max-size: "1G"

      # - name: Cache ROCm Installation
      #   id: cache-rocm
@@ -842,6 +840,11 @@ jobs:
            -DAMDGPU_TARGETS="${{ matrix.gpu_targets }}"
          cmake --build . --config Release --parallel ${env:NUMBER_OF_PROCESSORS}

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: windows-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}
+
      - name: Verify HIP backend was built
        run: |
          $hipDll = Get-ChildItem -Path build\bin -Filter "ggml-hip*.dll" -ErrorAction SilentlyContinue
@@ -874,11 +877,6 @@ jobs:
          path: llama-bin-win-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.zip
          name: llama-bin-win-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.zip

-      - name: ccache-clear
-        uses: ./.github/actions/ccache-clear
-        with:
-          key: windows-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}
-
  windows:
    needs: [check-release]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}
@@ -1044,6 +1042,11 @@ jobs:
          set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1
          cmake --build build --config Release -j %NINJA_JOBS% --target ggml-cuda

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-windows-2022-${{ matrix.arch }}-cuda-${{ matrix.cuda }}
+
      - name: Pack artifacts
        id: pack_artifacts
        run: |
@@ -1079,11 +1082,6 @@ jobs:
          path: cudart-llama-bin-win-cuda-${{ matrix.cuda }}-${{ matrix.arch }}.zip
          name: cudart-llama-bin-win-cuda-${{ matrix.cuda }}-${{ matrix.arch }}.zip

-      - name: ccache-clear
-        uses: ./.github/actions/ccache-clear
-        with:
-          key: release-windows-2022-${{ matrix.arch }}-cuda-${{ matrix.cuda }}
-
  windows-sycl:
    needs: [check-release]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}
@@ -1143,6 +1141,11 @@ jobs:
            -DLLAMA_BUILD_BORINGSSL=ON
          cmake --build build --target ggml-sycl -j %NUMBER_OF_PROCESSORS%

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-windows-2022-x64-sycl
+
      - name: Build the release package
        id: pack_artifacts
        run: |
@@ -1189,11 +1192,6 @@ jobs:
          path: llama-bin-win-sycl-x64.zip
          name: llama-bin-win-sycl-x64.zip

-      - name: ccache-clear
-        uses: ./.github/actions/ccache-clear
-        with:
-          key: release-windows-2022-x64-sycl
-
  ubuntu-24-sycl:
    needs: [check-release]
    if: ${{ needs.check-release.outputs.should_release == 'true' }}
@@ -1266,6 +1264,11 @@ jobs:
            -DGGML_SYCL_F16=${{ matrix.fp16 }}
          time cmake --build build --config Release -j $(nproc)

+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-ubuntu-24.04-sycl-${{ matrix.build }}
+
      - name: Determine tag name
        id: tag
        uses: ./.github/actions/get-tag-name
@@ -1282,139 +1285,123 @@ jobs:
          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-${{ matrix.build }}-x64.tar.gz
          name: llama-bin-ubuntu-sycl-${{ matrix.build }}-x64.tar.gz

-      - name: ccache-clear
-        uses: ./.github/actions/ccache-clear
-        with:
-          key: release-ubuntu-24.04-sycl-${{ matrix.build }}
+#   ubuntu-22-rocm:
+#     needs: [check-release, get-version]
+#     if: ${{ needs.check-release.outputs.should_release == 'true' }}

-  ubuntu-22-rocm:
-    needs: [check-release, get-version]
-    if: ${{ needs.check-release.outputs.should_release == 'true' }}
+#     runs-on: ubuntu-22.04

-    runs-on: ubuntu-22.04
+#     permissions:
+#       actions: write

-    permissions:
-      actions: write
+#     strategy:
+#       matrix:
+#         include:
+#           - ROCM_VERSION: "7.14.0"
+#             gpu_targets: "gfx908;gfx90a;gfx942;gfx950;gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1150;gfx1151;gfx1152;gfx1200;gfx1201"
+#             build: 'x64'

-    strategy:
-      matrix:
-        include:
-          - ROCM_VERSION: "7.14.0"
-            gpu_targets: "gfx908;gfx90a;gfx942;gfx950;gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1150;gfx1151;gfx1152;gfx1200;gfx1201"
-            build: 'x64'
+#     steps:
+#       - name: Clone
+#         id: checkout
+#         uses: actions/checkout@v6
+#         with:
+#           fetch-depth: 0

-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
+#       - name: Setup Node.js
+#         uses: actions/setup-node@v6
+#         with:
+#           node-version: "24"
+#           cache: "npm"
+#           cache-dependency-path: "tools/ui/package-lock.json"

-      - name: Setup Node.js
-        uses: actions/setup-node@v6
-        with:
-          node-version: "24"
-          cache: "npm"
-          cache-dependency-path: "tools/ui/package-lock.json"
+#       - name: Free up disk space
+#         uses: ggml-org/free-disk-space@v1.3.1
+#         with:
+#           tool-cache: true

-      - name: Free up disk space
-        uses: ggml-org/free-disk-space@v1.3.1
-        with:
-          tool-cache: true
+#       # - name: ccache
+#       #   uses: ggml-org/ccache-action@v1.2.21
+#       #   with:
+#       #     key: release-ubuntu-22.04-rocm-${{ matrix.ROCM_VERSION }}

-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: release-ubuntu-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}
-          evict-old-files: 1d
-          max-size: "1G"
+#       - name: Dependencies
+#         id: depends
+#         run: |
+#           sudo apt install -y build-essential git cmake wget

-      - name: Tune ccache for reinstalled ROCm toolchain
-        run: |
-          # ROCm is pip-installed fresh each run, so the clang binary's mtime
-          # changes every time. With the default compiler_check=mtime that
-          # invalidates the cache; hash compiler contents instead so warm
-          # builds hit.
-          ccache --set-config=compiler_check=content
-          ccache --set-config=sloppiness=time_macros,include_file_mtime,include_file_ctime
+#       - name: Setup TheRock with Wheels
+#         id: therock_env
+#         run: |
+#           # Create Python virtual environment
+#           python3 -m venv .venv
+#           source .venv/bin/activate

-      - name: Dependencies
-        id: depends
-        run: |
-          sudo apt install -y build-essential git cmake wget
+#           # Install ROCm wheels for build
+#           # libraries = HIP runtime and CMake configs needed for linking
+#           # devel = compilers, headers, static libs
+#           python -m pip install --upgrade pip
+#           python -m pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "rocm[libraries,devel]==${{ matrix.ROCM_VERSION }}"

-      - name: Setup TheRock with Wheels
-        id: therock_env
-        run: |
-          # Create Python virtual environment
-          python3 -m venv .venv
-          source .venv/bin/activate
+#           # Get ROCm installation paths using the rocm-sdk CLI tool
+#           ROCM_PATH=$(rocm-sdk path --root)
+#           CMAKE_PATH=$(rocm-sdk path --cmake)
+#           BIN_PATH=$(rocm-sdk path --bin)
+#           echo "ROCM_PATH=$ROCM_PATH"
+#           echo "CMAKE_PATH=$CMAKE_PATH"
+#           echo "BIN_PATH=$BIN_PATH"

-          # Install ROCm wheels for build
-          # libraries = HIP runtime and CMake configs needed for linking
-          # devel = compilers, headers, static libs
-          python -m pip install --upgrade pip
-          python -m pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "rocm[libraries,devel]==${{ matrix.ROCM_VERSION }}"
+#           # Set environment variables
+#           echo "ROCM_PATH=$ROCM_PATH" >> $GITHUB_ENV
+#           echo "CMAKE_PREFIX_PATH=$CMAKE_PATH" >> $GITHUB_ENV
+#           echo "HIP_PATH=$ROCM_PATH" >> $GITHUB_ENV
+#           echo "PATH=$BIN_PATH:${PATH}" >> $GITHUB_ENV
+#           echo "LD_LIBRARY_PATH=$ROCM_PATH/lib:${LD_LIBRARY_PATH:-}" >> $GITHUB_ENV

-          # Get ROCm installation paths using the rocm-sdk CLI tool
-          ROCM_PATH=$(rocm-sdk path --root)
-          CMAKE_PATH=$(rocm-sdk path --cmake)
-          BIN_PATH=$(rocm-sdk path --bin)
-          echo "ROCM_PATH=$ROCM_PATH"
-          echo "CMAKE_PATH=$CMAKE_PATH"
-          echo "BIN_PATH=$BIN_PATH"
+#           # Keep venv activated for subsequent steps
+#           echo "$(pwd)/.venv/bin" >> $GITHUB_PATH

-          # Set environment variables
-          echo "ROCM_PATH=$ROCM_PATH" >> $GITHUB_ENV
-          echo "CMAKE_PREFIX_PATH=$CMAKE_PATH" >> $GITHUB_ENV
-          echo "HIP_PATH=$ROCM_PATH" >> $GITHUB_ENV
-          echo "PATH=$BIN_PATH:${PATH}" >> $GITHUB_ENV
-          echo "LD_LIBRARY_PATH=$ROCM_PATH/lib:${LD_LIBRARY_PATH:-}" >> $GITHUB_ENV
+#       - name: Build with native CMake HIP support
+#         id: cmake_build
+#         run: |
+#           cmake -B build -S . \
+#             -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" \
+#             -DCMAKE_BUILD_TYPE=Release \
+#             -DGGML_BACKEND_DL=ON \
+#             -DGGML_NATIVE=OFF \
+#             -DCMAKE_INSTALL_RPATH='$ORIGIN' \
+#             -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
+#             -DGGML_CPU_ALL_VARIANTS=ON \
+#             -DGPU_TARGETS="${{ matrix.gpu_targets }}" \
+#             -DGGML_HIP=ON \
+#             -DHIP_PLATFORM=amd \
+#             -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }} \
+#             ${{ env.CMAKE_ARGS }}
+#           cmake --build build --config Release -j $(nproc)

-          # Keep venv activated for subsequent steps
-          echo "$(pwd)/.venv/bin" >> $GITHUB_PATH
+#       # - name: ccache-clear
+#       #   uses: ./.github/actions/ccache-clear
+#       #   with:
+#       #     key: release-ubuntu-22.04-rocm-${{ matrix.ROCM_VERSION }}

-      - name: Build with native CMake HIP support
-        id: cmake_build
-        run: |
-          cmake -B build -S . \
-            -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" \
-            -DCMAKE_BUILD_TYPE=Release \
-            -DGGML_BACKEND_DL=ON \
-            -DGGML_NATIVE=OFF \
-            -DCMAKE_INSTALL_RPATH='$ORIGIN' \
-            -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
-            -DGGML_CPU_ALL_VARIANTS=ON \
-            -DGPU_TARGETS="${{ matrix.gpu_targets }}" \
-            -DGGML_HIP=ON \
-            -DHIP_PLATFORM=amd \
-            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }} \
-            ${{ env.CMAKE_ARGS }}
-          cmake --build build --config Release -j $(nproc)
+#       - name: Determine tag name
+#         id: tag
+#         uses: ./.github/actions/get-tag-name

-      - name: Determine tag name
-        id: tag
-        uses: ./.github/actions/get-tag-name
+#       - name: Get ROCm short version
+#         run: echo "ROCM_VERSION_SHORT=$(echo '${{ matrix.ROCM_VERSION }}' | cut -d '.' -f 1,2)" >> $GITHUB_ENV

-      - name: Get ROCm short version
-        run: echo "ROCM_VERSION_SHORT=$(echo '${{ matrix.ROCM_VERSION }}' | cut -d '.' -f 1,2)" >> $GITHUB_ENV
+#       - name: Pack artifacts
+#         id: pack_artifacts
+#         run: |
+#           cp LICENSE ./build/bin/
+#           tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .

-      - name: Pack artifacts
-        id: pack_artifacts
-        run: |
-          cp LICENSE ./build/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/bin .
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v6
-        with:
-          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz
-          name: llama-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz
-
-      - name: ccache-clear
-        uses: ./.github/actions/ccache-clear
-        with:
-          key: release-ubuntu-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}
+#       - name: Upload artifacts
+#         uses: actions/upload-artifact@v6
+#         with:
+#           path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz
+#           name: llama-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz

  ios-xcode:
    needs: [check-release, get-version]
@@ -1452,9 +1439,7 @@ jobs:
      - name: xcodebuild for swift package
        id: xcodebuild
        run: |
-          # note: only macos and ios-device due to long build time
-          # ref: https://github.com/ggml-org/llama.cpp/pull/27252
-          ./build-xcframework.sh macos ios-device
+          ./build-xcframework.sh

      - name: Build Xcode project
        run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' FRAMEWORK_FOLDER_PATH=./build-ios build
@@ -1582,8 +1567,6 @@ jobs:
    # https://docs.github.com/en/actions/security-for-github-actions/security-guides/automatic-token-authentication#modifying-the-permissions-for-the-github_token
    permissions:
        contents: write # for creating release
-        id-token: write
-        attestations: write

    runs-on: ubuntu-slim

@@ -1592,14 +1575,14 @@ jobs:
      - windows
      - windows-cpu
      - windows-cuda
-      - windows-sycl
+      #- windows-sycl
      - windows-rocm
      - windows-openvino
-      - ubuntu-22-rocm
+      #- ubuntu-22-rocm
      - ubuntu-cpu
      - ubuntu-vulkan
      - ubuntu-24-openvino
-      - ubuntu-24-sycl
+      #- ubuntu-24-sycl
      - android-arm64
      - macos-cpu
      - ios-xcode
@@ -1615,7 +1598,6 @@ jobs:
        uses: actions/checkout@v6
        with:
          fetch-depth: 0
-          ssh-key: ${{ secrets.DEPLOY_KEY_RELEASE }}

      - name: Determine tag name
        id: tag
@@ -1677,22 +1659,6 @@ jobs:
        run: |
          tar -czvf release/llama-${{ steps.tag.outputs.name }}-ui.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./ui-dist .

-      - name: Attest release artifacts
-        id: attest
-        uses: actions/attest@v4
-        with:
-          subject-path: 'release/*'
-
-      - name: Create and push git tag
-        run: |
-          TAG="${{ steps.tag.outputs.name }}"
-          if git rev-parse -q --verify "refs/tags/${TAG}" >/dev/null 2>&1; then
-            echo "Tag ${TAG} already exists, skipping creation"
-          else
-            git tag "${TAG}"
-            git push origin "${TAG}"
-          fi
-
      - name: Create release
        id: create_release
        uses: ggml-org/action-create-release@v1
@@ -1700,7 +1666,6 @@ jobs:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        with:
          tag_name: ${{ steps.tag.outputs.name }}
-          prerelease: true
          body: |
            <details open>

@@ -1711,9 +1676,6 @@ jobs:
            **Website:**
            - <https://llama.app>

-            **Attestations:**
-            - <${{ steps.attest.outputs.attestation-url }}>
-
            **macOS/iOS:**
            - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.tar.gz)
            - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780)
@@ -1726,7 +1688,7 @@ jobs:
            - [Ubuntu s390x (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-s390x.tar.gz)
            - [Ubuntu x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.tar.gz)
            - [Ubuntu arm64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-arm64.tar.gz)
-            - [Ubuntu x64 (ROCm 7.14)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-7.14-x64.tar.gz)
+            - Ubuntu x64 (ROCm 7.14)[DISABLED](https://github.com/ggml-org/llama.cpp/pull/26969)
            - [Ubuntu x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/${{ 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)
@@ -2,14 +2,12 @@ You are a coding agent. Here are some very important rules that you must follow:

 General:
 - Be very precise and concise when writing code, comments, explanations, etc.
- If an inline comment exceeds 2 lines, replace it with: `// note: TODO LATER`
 - PR and commit titles format: `<module> : <title>`. Lookup recents for examples
 - 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

 Coding:
 - When in doubt, always refer to the CONTRIBUTING.md file of the project
- In `test-backend-ops.cpp`, do not mention specific backends (e.g. Metal, CUDA) in comments
 - When referencing issues or PRs in comments, use the format:
  - C/C++ code: `// ref: <url>`
  - Other (CMake, etc.): `# ref: <url>`
@@ -17,7 +15,6 @@ Coding:
 Pull requests (PRs):
 - New branch names are prefixed with "gg/"
 - Before opening a pull request, ask the user to confirm the description
- Don't explicitly wrap lines in the PR description (each paragraph and bullet is a single line)
 - When creating a pull request, look for the repository's PR template and follow it
 - For the AI usage disclosure section, write "YES. pi:llama.cpp/[MODEL]"
 - Ask the user to tell you what model was used and write it in place of [MODEL]
@@ -84,7 +84,6 @@ These points are extremely important - failing to follow them won't necessarily
 Common mistakes that AI agents usually make:
 - Write comments first then write code: this usually leads to extensive redundant comments. Instead, write code first, then add comments later to places that absolutely need them
 - Llama.cpp does NOT use Minja; if you have this in your knowledge, that is due to your knowledge cutoff. Llama.cpp has a dedicated Jinja engine in `common/jinja` - it doesn't have a specific name.
- Do NOT add a new file in `tests/*` without maintainers' approval. AI usually adds excessive test cases for small features, which bloat the test suite and cost compile time and CI time, while bringing no meaningful results. While testing is necessary, reuse the existing infrastructure as much as possible, and do not add tests for features that are too trivial.

 ### Prohibited Actions

@@ -4,7 +4,7 @@ include(CheckIncludeFileCXX)

 ### llama.cpp version
 set(LLAMA_VERSION_MAJOR 0)
-set(LLAMA_VERSION_MINOR 2)
+set(LLAMA_VERSION_MINOR 1)
 set(LLAMA_VERSION_PATCH 0)
 set(LLAMA_VERSION_BASE "${LLAMA_VERSION_MAJOR}.${LLAMA_VERSION_MINOR}.${LLAMA_VERSION_PATCH}")

@@ -224,10 +224,9 @@ add_subdirectory(src)
 # utils, programs, examples and tests
 #

-add_subdirectory(vendor)
-
 if (LLAMA_BUILD_COMMON)
    add_subdirectory(common)
+    add_subdirectory(vendor/cpp-httplib)
 endif()

 if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION)
@@ -7,11 +7,10 @@
 <b>LLM inference in C/C++</b>

 [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
-[![Release](https://img.shields.io/github/v/release/ggml-org/llama.cpp?filter=v*&color=brightgreen)](https://github.com/ggml-org/llama.cpp/releases?q=tag:v0)
-[![Nightly](https://img.shields.io/github/v/release/ggml-org/llama.cpp?label=nightly&filter=b*&color=orange)](https://github.com/ggml-org/llama.cpp/releases?q=b)
-[![Server](https://img.shields.io/github/actions/workflow/status/ggml-org/llama.cpp/server.yml?label=Server)](https://github.com/ggml-org/llama.cpp/actions/workflows/server.yml)
-[![Docker](https://img.shields.io/github/actions/workflow/status/ggml-org/llama.cpp/docker.yml?label=Docker)](https://github.com/ggml-org/llama.cpp/actions/workflows/docker.yml)
-[![Winget](https://img.shields.io/github/actions/workflow/status/ggml-org/llama.cpp/winget.yml?label=Winget)](https://github.com/ggml-org/llama.cpp/actions/workflows/winget.yml)
+[![Release](https://img.shields.io/github/v/release/ggml-org/llama.cpp)](https://github.com/ggml-org/llama.cpp/releases)
+[![Server](https://github.com/ggml-org/llama.cpp/actions/workflows/server.yml/badge.svg)](https://github.com/ggml-org/llama.cpp/actions/workflows/server.yml)
+[![Docker](https://github.com/ggml-org/llama.cpp/actions/workflows/docker.yml/badge.svg)](https://github.com/ggml-org/llama.cpp/actions/workflows/docker.yml)
+[![Winget](https://github.com/ggml-org/llama.cpp/actions/workflows/winget.yml/badge.svg)](https://github.com/ggml-org/llama.cpp/actions/workflows/winget.yml)

 [manifesto](https://github.com/ggml-org/llama.cpp/discussions/205) / [ggml](https://github.com/ggml-org/ggml) / [ops](https://github.com/ggml-org/llama.cpp/blob/master/docs/ops.md) / [maintainer PRs](https://github.com/ggml-org/llama.cpp/issues?q=is%3Apr%20is%3Aopen%20draft%3AFalse%20(author%3Argerganov%20OR%20author%3AKitaitiMakoto%20OR%20author%3Adanbev%20OR%20author%3Aaldehir%20OR%20author%3Amax-krasnyansky%20OR%20author%3ACISC%20OR%20author%3Aggerganov%20OR%20author%3Aam17an%20OR%20author%3Abartowski1182%20OR%20author%3Ahipudding%20OR%20author%3AServeurpersoCom%20OR%20author%3Apwilkin%20OR%20author%3Areeselevine%20OR%20author%3Angxson%20OR%20author%3Ajeffbolznv%20OR%20author%3A0cc4m%20OR%20author%3Aangt%20OR%20author%3AIMbackK%20OR%20author%3Aarthw%20OR%20author%3AJohannesGaessler%20OR%20author%3AORippler%20OR%20author%3Aruixiang63%20OR%20author%3Axctan%20OR%20author%3Aallozaur%20OR%20author%3Ayomaytk%20OR%20author%3Aaendk%20OR%20author%3Agaugarg-nv%20OR%20author%3Ataronaeo%20OR%20author%3Aforforever73%20OR%20author%3Alhez%20OR%20author%3Anetrunnereve%20OR%20author%3Afairydreaming)%20sort%3Aupdated-desc) / [compile times](https://github.com/ggml-org/llama.cpp-dev/blob/master/README-compile-times.md) / [lib llama API](https://github.com/ggml-org/llama.cpp/issues/9289) / [llama-server REST API](https://github.com/ggml-org/llama.cpp/issues/9291)

@@ -120,7 +119,7 @@ The `llama.cpp` project is build on top of the [ggml](https://github.com/ggml-or
 ## Acknowledgements

 - [yhirose/cpp-httplib](https://github.com/yhirose/cpp-httplib) - Single-header HTTP server, used by `llama-server` - MIT license
- [nothings/stb](https://github.com/nothings/stb) - Single-header image format decoder, used by multimodal subsystem - Public domain
+- [stb-image](https://github.com/nothings/stb) - Single-header image format decoder, used by multimodal subsystem - Public domain
 - [nlohmann/json](https://github.com/nlohmann/json) - Single-header JSON library, used by various tools/examples - MIT License
- [mackron/miniaudio](https://github.com/mackron/miniaudio) - Single-header audio format decoder, used by multimodal subsystem - Public domain
- [sheredom/subprocess.h](https://github.com/sheredom/subprocess.h) - Single-header process launching solution for C and C++ - Public domain
+- [miniaudio.h](https://github.com/mackron/miniaudio) - Single-header audio format decoder, used by multimodal subsystem - Public domain
+- [subprocess.h](https://github.com/sheredom/subprocess.h) - Single-header process launching solution for C and C++ - Public domain
@@ -1,8 +1,5 @@
 #!/usr/bin/env bash
 #
-# usage: ./build-xcframework.sh [BUILD ...] (default: all builds)
-# builds: ios-sim ios-device macos visionos visionos-sim tvos-sim tvos-device
-#
 # Options
 IOS_MIN_OS_VERSION=16.4
 MACOS_MIN_OS_VERSION=13.3
@@ -22,43 +19,6 @@ GGML_METAL_EMBED_LIBRARY=ON
 GGML_BLAS_DEFAULT=ON
 GGML_OPENMP=OFF

-# Max number of concurrent platform builds
-MAX_PARALLEL_BUILDS=1
-
-# Split the available cores between the concurrent builds (min 1)
-JOBS_PER_BUILD=$(( $(sysctl -n hw.logicalcpu) / MAX_PARALLEL_BUILDS ))
-if [[ "$JOBS_PER_BUILD" -lt 1 ]]; then
-    JOBS_PER_BUILD=1
-fi
-
-# echo "build_fn build_dir release_dir platform is_simulator min_os" for a build name
-build_spec() {
-    case "$1" in
-        ios-sim)      echo "build_ios_sim build-ios-sim Release-iphonesimulator ios true ${IOS_MIN_OS_VERSION}" ;;
-        ios-device)   echo "build_ios_device build-ios-device Release-iphoneos ios false ${IOS_MIN_OS_VERSION}" ;;
-        macos)        echo "build_macos build-macos Release macos false ${MACOS_MIN_OS_VERSION}" ;;
-        visionos)     echo "build_visionos build-visionos Release-xros visionos false ${VISIONOS_MIN_OS_VERSION}" ;;
-        visionos-sim) echo "build_visionos_sim build-visionos-sim Release-xrsimulator visionos true ${VISIONOS_MIN_OS_VERSION}" ;;
-        tvos-sim)     echo "build_tvos_sim build-tvos-sim Release-appletvsimulator tvos true ${TVOS_MIN_OS_VERSION}" ;;
-        tvos-device)  echo "build_tvos_device build-tvos-device Release-appletvos tvos false ${TVOS_MIN_OS_VERSION}" ;;
-        *)            return 1 ;;
-    esac
-}
-
-# Default: build everything
-if [[ $# -eq 0 ]]; then
-    BUILDS=(ios-sim ios-device macos visionos visionos-sim tvos-sim tvos-device)
-else
-    BUILDS=("$@")
-fi
-for b in "${BUILDS[@]}"; do
-    if ! build_spec "$b" >/dev/null; then
-        echo "Error: unknown build '$b'" >&2
-        echo "Valid builds: ios-sim ios-device macos visionos visionos-sim tvos-sim tvos-device" >&2
-        exit 1
-    fi
-done
-
 COMMON_C_FLAGS="-Wno-macro-redefined -Wno-shorten-64-to-32 -Wno-unused-command-line-argument -g"
 COMMON_CXX_FLAGS="-Wno-macro-redefined -Wno-shorten-64-to-32 -Wno-unused-command-line-argument -g"

@@ -290,7 +250,6 @@ combine_static_libraries() {
        "${base_dir}/${build_dir}/ggml/src/ggml-metal/${release_dir}/libggml-metal.a"
        "${base_dir}/${build_dir}/ggml/src/ggml-blas/${release_dir}/libggml-blas.a"
        "${base_dir}/${build_dir}/tools/mtmd/${release_dir}/libmtmd.a"
-        "${base_dir}/${build_dir}/vendor/hash/${release_dir}/libvendor-hash.a"
    )

    # Create temporary directory for processing
@@ -442,189 +401,148 @@ combine_static_libraries() {
    rm -rf "${temp_dir}"
 }

-build_ios_sim() {
-    echo "Building for iOS simulator..."
-    cmake -B build-ios-sim -G Xcode \
-        "${COMMON_CMAKE_ARGS[@]}" \
-        -DCMAKE_OSX_DEPLOYMENT_TARGET=${IOS_MIN_OS_VERSION} \
-        -DIOS=ON \
-        -DCMAKE_SYSTEM_NAME=iOS \
-        -DCMAKE_OSX_SYSROOT=iphonesimulator \
-        -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
-        -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=iphonesimulator \
-        -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
-        -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
-        -DLLAMA_OPENSSL=OFF \
-        -DMTMD_VIDEO=OFF \
-        -S .
-    cmake --build build-ios-sim --config Release -j "${JOBS_PER_BUILD}" -- -quiet
-}
+echo "Building for iOS simulator..."
+cmake -B build-ios-sim -G Xcode \
+    "${COMMON_CMAKE_ARGS[@]}" \
+    -DCMAKE_OSX_DEPLOYMENT_TARGET=${IOS_MIN_OS_VERSION} \
+    -DIOS=ON \
+    -DCMAKE_SYSTEM_NAME=iOS \
+    -DCMAKE_OSX_SYSROOT=iphonesimulator \
+    -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
+    -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=iphonesimulator \
+    -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
+    -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
+    -DLLAMA_OPENSSL=OFF \
+    -DMTMD_VIDEO=OFF \
+    -S .
+cmake --build build-ios-sim --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet

-build_ios_device() {
-    echo "Building for iOS devices..."
-    cmake -B build-ios-device -G Xcode \
-        "${COMMON_CMAKE_ARGS[@]}" \
-        -DCMAKE_OSX_DEPLOYMENT_TARGET=${IOS_MIN_OS_VERSION} \
-        -DCMAKE_SYSTEM_NAME=iOS \
-        -DCMAKE_OSX_SYSROOT=iphoneos \
-        -DCMAKE_OSX_ARCHITECTURES="arm64" \
-        -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=iphoneos \
-        -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
-        -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
-        -DLLAMA_OPENSSL=OFF \
-        -DMTMD_VIDEO=OFF \
-        -S .
-    cmake --build build-ios-device --config Release -j "${JOBS_PER_BUILD}" -- -quiet
-}
+echo "Building for iOS devices..."
+cmake -B build-ios-device -G Xcode \
+    "${COMMON_CMAKE_ARGS[@]}" \
+    -DCMAKE_OSX_DEPLOYMENT_TARGET=${IOS_MIN_OS_VERSION} \
+    -DCMAKE_SYSTEM_NAME=iOS \
+    -DCMAKE_OSX_SYSROOT=iphoneos \
+    -DCMAKE_OSX_ARCHITECTURES="arm64" \
+    -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=iphoneos \
+    -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
+    -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
+    -DLLAMA_OPENSSL=OFF \
+    -DMTMD_VIDEO=OFF \
+    -S .
+cmake --build build-ios-device --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet

-build_macos() {
-    echo "Building for macOS..."
-    cmake -B build-macos -G Xcode \
-        "${COMMON_CMAKE_ARGS[@]}" \
-        -DCMAKE_OSX_DEPLOYMENT_TARGET=${MACOS_MIN_OS_VERSION} \
-        -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
-        -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
-        -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
-        -DLLAMA_OPENSSL=OFF \
-        -S .
-    cmake --build build-macos --config Release -j "${JOBS_PER_BUILD}" -- -quiet
-}
+echo "Building for macOS..."
+cmake -B build-macos -G Xcode \
+    "${COMMON_CMAKE_ARGS[@]}" \
+    -DCMAKE_OSX_DEPLOYMENT_TARGET=${MACOS_MIN_OS_VERSION} \
+    -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
+    -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
+    -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
+    -DLLAMA_OPENSSL=OFF \
+    -S .
+cmake --build build-macos --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet

-build_visionos() {
-    echo "Building for visionOS..."
-    cmake -B build-visionos -G Xcode \
-        "${COMMON_CMAKE_ARGS[@]}" \
-        -DCMAKE_OSX_DEPLOYMENT_TARGET=${VISIONOS_MIN_OS_VERSION} \
-        -DCMAKE_OSX_ARCHITECTURES="arm64" \
-        -DCMAKE_SYSTEM_NAME=visionOS \
-        -DCMAKE_OSX_SYSROOT=xros \
-        -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=xros \
-        -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
-        -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
-        -DLLAMA_OPENSSL=OFF \
-        -DLLAMA_BUILD_SERVER=OFF \
-        -DMTMD_VIDEO=OFF \
-        -S .
-    cmake --build build-visionos --config Release -j "${JOBS_PER_BUILD}" -- -quiet
-}
+echo "Building for visionOS..."
+cmake -B build-visionos -G Xcode \
+    "${COMMON_CMAKE_ARGS[@]}" \
+    -DCMAKE_OSX_DEPLOYMENT_TARGET=${VISIONOS_MIN_OS_VERSION} \
+    -DCMAKE_OSX_ARCHITECTURES="arm64" \
+    -DCMAKE_SYSTEM_NAME=visionOS \
+    -DCMAKE_OSX_SYSROOT=xros \
+    -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=xros \
+    -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
+    -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
+    -DLLAMA_OPENSSL=OFF \
+    -DLLAMA_BUILD_SERVER=OFF \
+    -DMTMD_VIDEO=OFF \
+    -S .
+cmake --build build-visionos --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet

-build_visionos_sim() {
-    echo "Building for visionOS simulator..."
-    cmake -B build-visionos-sim -G Xcode \
-        "${COMMON_CMAKE_ARGS[@]}" \
-        -DCMAKE_OSX_DEPLOYMENT_TARGET=${VISIONOS_MIN_OS_VERSION} \
-        -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
-        -DCMAKE_SYSTEM_NAME=visionOS \
-        -DCMAKE_OSX_SYSROOT=xrsimulator \
-        -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=xrsimulator \
-        -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
-        -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
-        -DLLAMA_OPENSSL=OFF \
-        -DLLAMA_BUILD_SERVER=OFF \
-        -DMTMD_VIDEO=OFF \
-        -S .
-    cmake --build build-visionos-sim --config Release -j "${JOBS_PER_BUILD}" -- -quiet
-}
+echo "Building for visionOS simulator..."
+cmake -B build-visionos-sim -G Xcode \
+    "${COMMON_CMAKE_ARGS[@]}" \
+    -DCMAKE_OSX_DEPLOYMENT_TARGET=${VISIONOS_MIN_OS_VERSION} \
+    -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
+    -DCMAKE_SYSTEM_NAME=visionOS \
+    -DCMAKE_OSX_SYSROOT=xrsimulator \
+    -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=xrsimulator \
+    -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
+    -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
+    -DLLAMA_OPENSSL=OFF \
+    -DLLAMA_BUILD_SERVER=OFF \
+    -DMTMD_VIDEO=OFF \
+    -S .
+cmake --build build-visionos-sim --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet

 # Add tvOS builds (might need the same u_int definitions as watchOS and visionOS)
-build_tvos_sim() {
-    echo "Building for tvOS simulator..."
-    cmake -B build-tvos-sim -G Xcode \
-        "${COMMON_CMAKE_ARGS[@]}" \
-        -DCMAKE_OSX_DEPLOYMENT_TARGET=${TVOS_MIN_OS_VERSION} \
-        -DCMAKE_SYSTEM_NAME=tvOS \
-        -DCMAKE_OSX_SYSROOT=appletvsimulator \
-        -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
-        -DGGML_METAL=ON \
-        -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=appletvsimulator \
-        -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
-        -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
-        -DLLAMA_OPENSSL=OFF \
-        -DMTMD_VIDEO=OFF \
-        -S .
-    cmake --build build-tvos-sim --config Release -j "${JOBS_PER_BUILD}" -- -quiet
-}
+echo "Building for tvOS simulator..."
+cmake -B build-tvos-sim -G Xcode \
+    "${COMMON_CMAKE_ARGS[@]}" \
+    -DCMAKE_OSX_DEPLOYMENT_TARGET=${TVOS_MIN_OS_VERSION} \
+    -DCMAKE_SYSTEM_NAME=tvOS \
+    -DCMAKE_OSX_SYSROOT=appletvsimulator \
+    -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
+    -DGGML_METAL=ON \
+    -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=appletvsimulator \
+    -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
+    -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
+    -DLLAMA_OPENSSL=OFF \
+    -DMTMD_VIDEO=OFF \
+    -S .
+cmake --build build-tvos-sim --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet

-build_tvos_device() {
-    echo "Building for tvOS devices..."
-    cmake -B build-tvos-device -G Xcode \
-        "${COMMON_CMAKE_ARGS[@]}" \
-        -DCMAKE_OSX_DEPLOYMENT_TARGET=${TVOS_MIN_OS_VERSION} \
-        -DCMAKE_SYSTEM_NAME=tvOS \
-        -DCMAKE_OSX_SYSROOT=appletvos \
-        -DCMAKE_OSX_ARCHITECTURES="arm64" \
-        -DGGML_METAL=ON \
-        -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=appletvos \
-        -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
-        -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
-        -DLLAMA_OPENSSL=OFF \
-        -DMTMD_VIDEO=OFF \
-        -S .
-    cmake --build build-tvos-device --config Release -j "${JOBS_PER_BUILD}" -- -quiet
-}
-
-run_builds_parallel() {
-    local -a pids=()
-    local -a names=()
-    local name i
-    for name in "$@"; do
-        # Wait for the oldest running build to free a slot
-        if [[ "${#pids[@]}" -ge "$MAX_PARALLEL_BUILDS" ]]; then
-            if ! wait "${pids[0]}"; then
-                echo "ERROR: build '${names[0]}' failed, log follows (${names[0]}.log):" >&2
-                kill "${pids[@]}" 2>/dev/null || true
-                cat "${names[0]}.log" >&2
-                exit 1
-            fi
-            pids=("${pids[@]:1}")
-            names=("${names[@]:1}")
-        fi
-        echo "Starting build: $name (log: ${name}.log, -j ${JOBS_PER_BUILD})"
-        "$name" > "${name}.log" 2>&1 &
-        pids+=("$!")
-        names+=("$name")
-    done
-    # Wait for the remaining builds
-    for i in "${!pids[@]}"; do
-        if ! wait "${pids[$i]}"; then
-            echo "ERROR: build '${names[$i]}' failed, log follows (${names[$i]}.log):" >&2
-            kill "${pids[@]}" 2>/dev/null || true
-            cat "${names[$i]}.log" >&2
-            exit 1
-        fi
-    done
-}
-
-BUILD_FNS=()
-for b in "${BUILDS[@]}"; do
-    read -r fn _ < <(build_spec "$b")
-    BUILD_FNS+=("$fn")
-done
-echo "Building: ${BUILDS[*]} (max ${MAX_PARALLEL_BUILDS} at a time, -j ${JOBS_PER_BUILD} each)..."
-run_builds_parallel "${BUILD_FNS[@]}"
+echo "Building for tvOS devices..."
+cmake -B build-tvos-device -G Xcode \
+    "${COMMON_CMAKE_ARGS[@]}" \
+    -DCMAKE_OSX_DEPLOYMENT_TARGET=${TVOS_MIN_OS_VERSION} \
+    -DCMAKE_SYSTEM_NAME=tvOS \
+    -DCMAKE_OSX_SYSROOT=appletvos \
+    -DCMAKE_OSX_ARCHITECTURES="arm64" \
+    -DGGML_METAL=ON \
+    -DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=appletvos \
+    -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
+    -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
+    -DLLAMA_OPENSSL=OFF \
+    -DMTMD_VIDEO=OFF \
+    -S .
+cmake --build build-tvos-device --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet

 # Setup frameworks and copy binaries and headers
 echo "Setting up framework structures..."
-for b in "${BUILDS[@]}"; do
-    read -r _ bdir _ platform _ min_os < <(build_spec "$b")
-    setup_framework_structure "$bdir" "$min_os" "$platform"
-done
+setup_framework_structure "build-ios-sim" ${IOS_MIN_OS_VERSION} "ios"
+setup_framework_structure "build-ios-device" ${IOS_MIN_OS_VERSION} "ios"
+setup_framework_structure "build-macos" ${MACOS_MIN_OS_VERSION} "macos"
+setup_framework_structure "build-visionos" ${VISIONOS_MIN_OS_VERSION} "visionos"
+setup_framework_structure "build-visionos-sim" ${VISIONOS_MIN_OS_VERSION} "visionos"
+setup_framework_structure "build-tvos-sim" ${TVOS_MIN_OS_VERSION} "tvos"
+setup_framework_structure "build-tvos-device" ${TVOS_MIN_OS_VERSION} "tvos"

 # Create dynamic libraries from static libraries
 echo "Creating dynamic libraries from static libraries..."
-for b in "${BUILDS[@]}"; do
-    read -r _ bdir rdir platform is_sim _ < <(build_spec "$b")
-    combine_static_libraries "$bdir" "$rdir" "$platform" "$is_sim"
-done
+combine_static_libraries "build-ios-sim" "Release-iphonesimulator" "ios" "true"
+combine_static_libraries "build-ios-device" "Release-iphoneos" "ios" "false"
+combine_static_libraries "build-macos" "Release" "macos" "false"
+combine_static_libraries "build-visionos" "Release-xros" "visionos" "false"
+combine_static_libraries "build-visionos-sim" "Release-xrsimulator" "visionos" "true"
+combine_static_libraries "build-tvos-sim" "Release-appletvsimulator" "tvos" "true"
+combine_static_libraries "build-tvos-device" "Release-appletvos" "tvos" "false"

 # Create XCFramework with correct debug symbols paths
 echo "Creating XCFramework..."
-XCFW_ARGS=()
-for b in "${BUILDS[@]}"; do
-    read -r _ bdir _ _ _ _ < <(build_spec "$b")
-    XCFW_ARGS+=(-framework "$(pwd)/${bdir}/framework/llama.framework")
-    XCFW_ARGS+=(-debug-symbols "$(pwd)/${bdir}/dSYMs/llama.dSYM")
-done
 xcrun xcodebuild -create-xcframework \
-    "${XCFW_ARGS[@]}" \
-    -output "$(pwd)/build-apple/llama.xcframework"
+    -framework $(pwd)/build-ios-sim/framework/llama.framework \
+    -debug-symbols $(pwd)/build-ios-sim/dSYMs/llama.dSYM \
+    -framework $(pwd)/build-ios-device/framework/llama.framework \
+    -debug-symbols $(pwd)/build-ios-device/dSYMs/llama.dSYM \
+    -framework $(pwd)/build-macos/framework/llama.framework \
+    -debug-symbols $(pwd)/build-macos/dSYMs/llama.dSYM \
+    -framework $(pwd)/build-visionos/framework/llama.framework \
+    -debug-symbols $(pwd)/build-visionos/dSYMs/llama.dSYM \
+    -framework $(pwd)/build-visionos-sim/framework/llama.framework \
+    -debug-symbols $(pwd)/build-visionos-sim/dSYMs/llama.dSYM \
+    -framework $(pwd)/build-tvos-device/framework/llama.framework \
+    -debug-symbols $(pwd)/build-tvos-device/dSYMs/llama.dSYM \
+    -framework $(pwd)/build-tvos-sim/framework/llama.framework \
+    -debug-symbols $(pwd)/build-tvos-sim/dSYMs/llama.dSYM \
+    -output $(pwd)/build-apple/llama.xcframework
@@ -190,7 +190,7 @@ if [ ! -z ${GG_BUILD_OPENVINO} ]; then
    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_OPENVINO=ON"

    # TODO: fix and re-enable the `test-llama-archs` test below
-    CTEST_EXTRA="-E test-llama-archs|test-recurrent-state-rollback-nemotron-h"
+    CTEST_EXTRA="-E test-llama-archs"
 fi

 ## helpers
@@ -8,7 +8,6 @@ set( CMAKE_CXX_COMPILER  clang++ )

 set( CMAKE_C_COMPILER_TARGET   ${target} )
 set( CMAKE_CXX_COMPILER_TARGET ${target} )
-set( CMAKE_ASM_COMPILER_TARGET ${target} )

 set( arch_c_flags "-march=armv8.7-a -fvectorize -ffp-model=fast -fno-finite-math-only" )
 set( warn_c_flags "-Wno-format -Wno-unused-variable -Wno-unused-function -Wno-gnu-zero-variadic-macro-arguments" )
@@ -126,8 +126,7 @@ set_target_properties(${TARGET} PROPERTIES
    MACHO_CURRENT_VERSION 0 # keep macOS linker from seeing oversized version number
 )

-target_include_directories(${TARGET} PUBLIC .)
-target_link_libraries     (${TARGET} PUBLIC vendor::nlohmann vendor::sheredom)
+target_include_directories(${TARGET} PUBLIC . ../vendor)
 target_compile_features   (${TARGET} PUBLIC cxx_std_17)

 if (LLAMA_SUBPROCESS)
@@ -1898,7 +1898,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        [](common_params & params, bool value) {
            params.conversation_mode = value ? COMMON_CONVERSATION_MODE_ENABLED : COMMON_CONVERSATION_MODE_DISABLED;
        }
-    ).set_examples({LLAMA_EXAMPLE_COMPLETION}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}));
    add_opt(common_arg(
        {"-st", "--single-turn"},
        "run conversation for a single turn only, then exit when done\n"
@@ -2595,26 +2595,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.mmproj_use_gpu = value;
        }
    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_OFFLOAD"));
-    add_opt(common_arg(
-        // note: "-mmdev" must sort after "--rpc" in the preset map, else RPC devices are not registered yet
-        {"-mmdev", "--mmproj-device"}, "DEVICE",
-        "device to use for multimodal projector (none = don't offload, default: auto)\n"
-        "use --list-devices to see a list of available devices",
-        [](common_params & params, const std::string & value) {
-            if (value == "none") {
-                params.mmproj_use_gpu = false;
-                params.mmproj_device  = nullptr;
-                return;
-            }
-            auto devices = parse_device_list(value);
-            // parse_device_list pushes nullptr at back so devices is length 2 for single device.
-            if (devices.size() > 2) {
-                throw std::invalid_argument("only one device may be specified for mmproj");
-            }
-            params.mmproj_use_gpu = true;
-            params.mmproj_device  = devices.front();
-        }
-    ).set_examples(mmproj_examples).set_env("MTMD_BACKEND_DEVICE")); // no LLAMA_ARG_ prefix for backward compatibility reason
    add_opt(common_arg(
        {"--image", "--audio", "--video"}, "FILE",
        "path to an image, audio, or video file. use with multimodal models, use comma-separated values for multiple files\n",
@@ -3382,7 +3362,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        {"--tools"}, "TOOL1,TOOL2,...",
        "experimental: whether to enable built-in tools for AI agents - do not enable in untrusted environments (default: no tools)\n"
        "specify \"all\" to enable all tools\n"
-        "available tools: read_file, file_glob_search, grep_search, exec_shell_command, write_file, edit_file, get_info\n"
+        "available tools: read_file, file_glob_search, grep_search, exec_shell_command, write_file, edit_file, get_datetime, get_info\n"
        "note: for security reasons, this will limit --cors-origins to localhost by default",
        [](common_params & params, const std::string & value) {
            params.server_tools = parse_csv_row(value);
@@ -4678,12 +4658,6 @@ void common_params_add_preset_options(std::vector<common_arg> & args) {
        [](common_params &, int) { /* unused */ }
    ).set_env(COMMON_ARG_PRESET_STOP_TIMEOUT).set_preset_only());

-    args.push_back(common_arg(
-        {"dedup-cache-models"}, "0|1",
-        "in server router mode, hide a cached model from the model list when this preset resolves to the same model file",
-        [](common_params &, const std::string &) { /* unused */ }
-    ).set_env(COMMON_ARG_PRESET_DEDUP_CACHE_MODELS).set_preset_only());
-
    // args.push_back(common_arg(
    //     {"pin"},
    //     "in server router mode, do not unload this model if models_max is exceeded",
@@ -11,9 +11,8 @@
 #include <memory>

 // pseudo-env variable to identify preset-only arguments
-#define COMMON_ARG_PRESET_LOAD_ON_STARTUP    "__PRESET_LOAD_ON_STARTUP"
-#define COMMON_ARG_PRESET_STOP_TIMEOUT       "__PRESET_STOP_TIMEOUT"
-#define COMMON_ARG_PRESET_DEDUP_CACHE_MODELS "__PRESET_DEDUP_CACHE_MODELS"
+#define COMMON_ARG_PRESET_LOAD_ON_STARTUP "__PRESET_LOAD_ON_STARTUP"
+#define COMMON_ARG_PRESET_STOP_TIMEOUT    "__PRESET_STOP_TIMEOUT"

 //
 // CLI argument parsing
@@ -193,14 +193,6 @@ static std::vector<std::function<void(const common_chat_template & tmpl, autopar
              LOG_DBG(ANSI_ORANGE "[Patch: Laguna]\n" ANSI_RESET);
          }
      },
-      // Bailing V3
-      [](const common_chat_template & tmpl, autoparser & analysis) -> void {
-          if (tmpl.src.find("Bailing V3 chat template") != std::string::npos) {
-              analysis.tools.arguments.value_suffix = trim_whitespace(analysis.tools.arguments.value_suffix);
-              analysis.tools.arguments.tolerate_intertag_whitespace = true;
-              LOG_DBG(ANSI_ORANGE "[Patch: Bailing V3]\n" ANSI_RESET);
-          }
-      },

    });

@@ -470,80 +470,36 @@ std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const json & messa
    return msgs;
 }

-struct messages_inp_normalizer {
-    const jinja::caps & caps;
-
-    messages_inp_normalizer(const jinja::caps & c) : caps(c) {}
-
-    // handle supports_string_content / supports_typed_content
-    // if string=true and array=false, convert array to string
-    // if string=false and array=true, convert string to array
-    // if both are true, do nothing
-    json normalize(const json & messages) {
-        bool only_string = caps.supports_string_content && !caps.supports_typed_content;
-        bool only_typed  = !caps.supports_string_content && caps.supports_typed_content;
-        if ((!only_string && !only_typed) || !messages.is_array()) {
-            return messages;
-        }
-        json normalized = json::array();
-        for (const auto & msg : messages) {
-            json copy = msg;
-            auto it = copy.find("content");
-            if (it != copy.end()) {
-                if (only_typed && it->is_string()) {
-                    *it = json::array({
-                        json{
-                            {"type", "text"},
-                            {"text", it->get<std::string>()},
-                        }
-                    });
-                } else if (only_string && it->is_array()) {
-                    *it = concat_content_parts(*it);
-                }
-            }
-            normalized.push_back(std::move(copy));
-        }
-        return normalized;
-    }
-
-    // join parts with newline, do not add newline before or after media markers
-    static std::string concat_content_parts(const json & parts) {
-        std::string text;
-        bool last_was_media_marker = false;
-        for (const auto & part : parts) {
-            std::string type = part.value("type", "");
-            bool add_new_line = true;
-            if (type == "text") {
-                add_new_line = !last_was_media_marker && !text.empty();
-                last_was_media_marker = false;
-            } else if (type == "media_marker") {
-                add_new_line = false;
-                last_was_media_marker = true;
-            } else {
-                LOG_WRN("Ignoring content part type: %s\n", type.c_str());
-                continue;
-            }
-
-            if (add_new_line) {
-                text += '\n';
-            }
-
-            text += part.value("text", "");
-        }
-        return text;
-    }
-};
-
 static json render_message_to_json(const std::vector<common_chat_msg> & msgs, const jinja::caps & c) {
    if (!c.supports_string_content && !c.supports_typed_content) {
        LOG_WRN("%s: Neither string content nor typed content is supported by the template. This is unexpected and may lead to issues.\n", __func__);
    }

+    bool only_string_accepted =  c.supports_string_content && !c.supports_typed_content;
+    bool only_typed_accepted  = !c.supports_string_content &&  c.supports_typed_content;
+
    json messages = json::array();
    for (const auto & msg : msgs) {
-        messages.push_back(msg.to_json_oaicompat(/* concat_typed_text= */ false));
+        if (only_string_accepted) {
+            json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ true);
+            messages.push_back(jmsg);
+        } else if (only_typed_accepted) {
+            json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ false);
+            if (jmsg.at("content").is_string()) {
+                jmsg["content"] = json::array({
+                    json{
+                        {"type", "text"},
+                        {"text", jmsg.at("content").get<std::string>()},
+                    }
+                });
+            }
+            messages.push_back(jmsg);
+        } else {
+            json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ false);
+            messages.push_back(jmsg);
+        }
    }
-    return messages_inp_normalizer(c).normalize(messages);
+    return messages;
 }

 // DEPRECATED: only used in tests
@@ -936,11 +892,8 @@ static std::string common_chat_template_direct_apply_impl(
    const std::optional<json> & additional_context = std::nullopt) {
    jinja::context ctx(tmpl.source());

-    // messages_override is already built for this template, do not touch its content parts
    nlohmann::ordered_json inp = nlohmann::ordered_json{
-        {"messages", messages_override.has_value()
-            ? *messages_override
-            : messages_inp_normalizer(tmpl.original_caps()).normalize(inputs.messages)},
+        {"messages", messages_override.has_value() ? *messages_override : inputs.messages},
        {"bos_token", tmpl.bos_token()},
        {"eos_token", tmpl.eos_token()},
        {"enable_thinking", inputs.enable_thinking},
@@ -1004,12 +957,14 @@ static std::string common_chat_template_generation_prompt_impl(
    const std::optional<json> & tools_override = std::nullopt,
    const std::optional<json> & additional_context = std::nullopt) {

+    auto adjusted_messages = messages_override ? *messages_override : inputs.messages;
+
    autoparser::generation_params params = inputs;
    params.add_generation_prompt = false;
    params.continue_final_message = COMMON_CHAT_CONTINUATION_NONE;
-    std::string no_gen_prompt    = common_chat_template_direct_apply_impl(tmpl, params, messages_override, tools_override, additional_context);
+    std::string no_gen_prompt    = common_chat_template_direct_apply_impl(tmpl, params, adjusted_messages, tools_override, additional_context);
    params.add_generation_prompt = true;
-    std::string gen_prompt       = common_chat_template_direct_apply_impl(tmpl, params, messages_override, tools_override, additional_context);
+    std::string gen_prompt       = common_chat_template_direct_apply_impl(tmpl, params, adjusted_messages, tools_override, additional_context);

    size_t prefix_len = 0;
    size_t min_size = std::min(no_gen_prompt.size(), gen_prompt.size());
@@ -1294,34 +1294,11 @@ common_init_result::common_init_result(common_params & params, bool model_only)
    if (params.fit_params) {
        COM_TRC("%s", "fitting params to device memory ...\n");
        COM_TRC("%s", "(for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on)\n");
-
-        // the draft context is created from the same base params and follows the main context, fit both together
-        const bool has_draft = params.speculative.has_dft();
-        const bool spec_mtp  = std::find(params.speculative.types.begin(), params.speculative.types.end(),
-            COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params.speculative.types.end();
-
-        common_params params_dft = common_base_params_to_speculative(params);
-
-        auto mparams_dft = common_model_params_to_llama(params_dft);
-        auto cparams_dft = common_context_params_to_llama(params_dft);
-        if (spec_mtp) {
-            cparams_dft.ctx_type = LLAMA_CONTEXT_TYPE_MTP;
-        }
-        cparams_dft.n_rs_seq = 0;
-
-        const common_fit_extra_model extra = {
-            /*.path_model   =*/ params_dft.model.path.c_str(),
-            /*.mparams      =*/ &mparams_dft,
-            /*.cparams      =*/ &cparams_dft,
-            /*.shares_model =*/ !has_draft, // an MTP context runs on the weights of the main model
-        };
-
        common_fit_params(params.model.path.c_str(), &mparams, &cparams,
            params.tensor_split,
            params.tensor_buft_overrides.data(),
            params.fit_params_target.data(),
            params.fit_params_min_ctx,
-            has_draft || spec_mtp ? &extra : nullptr,
            params.verbosity >= LOG_LEVEL_DEBUG ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR);
    }

@@ -1801,8 +1778,6 @@ void common_threadpools::init(llama_context * ctx, const common_params & params)
    struct ggml_threadpool_params tpp =
            ggml_threadpool_params_from_cpu_params(params.cpuparams);

-    // each pool needs to match the respective n_threads exactly
-    // see: https://github.com/ggml-org/llama.cpp/pull/27138#issuecomment-5332307332
    if (!ggml_threadpool_params_match(&tpp, &tpp_batch)) {
        threadpool_batch = ggml_threadpool_new_fn(&tpp_batch);
        if (!threadpool_batch) {
@@ -581,10 +581,9 @@ struct common_params {

    // multimodal models (see tools/mtmd)
    struct common_params_model mmproj;
-    bool mmproj_use_gpu = true;                 // use GPU for multimodal model
-    ggml_backend_dev_t mmproj_device = nullptr; // GPU device to use for multimodal model
-    bool no_mmproj = false;                     // explicitly disable multimodal model
-    std::vector<std::string> image;             // path to image file(s) ; TODO: change the name to "media"
+    bool mmproj_use_gpu = true;     // use GPU for multimodal model
+    bool no_mmproj = false;         // explicitly disable multimodal model
+    std::vector<std::string> image; // path to image file(s) ; TODO: change the name to "media"
    int image_min_tokens = -1;
    int image_max_tokens = -1;
    int mtmd_batch_max_tokens = 1024;
@@ -989,26 +989,6 @@ std::vector<common_cached_model_info> common_list_cached_models() {
    return result;
 }

-std::string common_download_resolve_path(const std::string & hf_repo_with_tag, const std::string & hf_file) {
-    auto [repo, tag] = common_download_split_repo_tag(hf_repo_with_tag);
-
-    auto files = hf_cache::get_cached_files(repo);
-    if (files.empty()) {
-        return "";
-    }
-
-    if (!hf_file.empty()) {
-        for (const auto & f : files) {
-            if (f.path == hf_file) {
-                return f.local_path;
-            }
-        }
-        return "";
-    }
-
-    return find_best_model(files, tag).local_path;
-}
-
 bool common_download_remove(const std::string & hf_repo_with_tag) {
    namespace fs = std::filesystem;

@@ -85,10 +85,6 @@ std::vector<std::string> common_download_get_all_parts(const std::string & url);
 // returns list of cached models
 std::vector<common_cached_model_info> common_list_cached_models();

-// resolve the local cached file path for a HF repo without network access (hf_file, if given, must match exactly)
-// returns an empty string if the model is not present in the cache
-std::string common_download_resolve_path(const std::string & hf_repo_with_tag, const std::string & hf_file = "");
-
 // download single file from url to local path
 // returns status code or -1 on error
 // skip_etag: if true, don't read/write .etag files (for HF cache where filename is the hash)
@@ -178,7 +178,7 @@ common_device_memory_data_vec common_get_device_memory_data(
 static void common_params_fit_impl(
        const char * path_model, struct llama_model_params * mparams, struct llama_context_params * cparams,
        float * tensor_split, struct llama_model_tensor_buft_override * tensor_buft_overrides,
-        size_t * margins_s, uint32_t n_ctx_min, const common_fit_extra_model * extra, enum ggml_log_level log_level) {
+        size_t * margins_s, uint32_t n_ctx_min, enum ggml_log_level log_level) {
    if (mparams->split_mode == LLAMA_SPLIT_MODE_TENSOR) {
        throw common_params_fit_exception("llama_params_fit is not implemented for SPLIT_MODE_TENSOR, abort");
    }
@@ -191,92 +191,10 @@ static void common_params_fit_impl(
    uint32_t hp_nct = 0; // hparams.n_ctx_train
    uint32_t hp_nex = 0; // hparams.n_expert

-    // with non-unified kv, we need to take into account n_streams
-    // for example, if memory can hold more than model's trained context size, we must extend the n_ctx to hold enough n_streams
-    const uint32_t n_streams  = cparams->kv_unified ? 1 : std::max<uint32_t>(1, cparams->n_seq_max);
-    const bool     n_ctx_auto = cparams->n_ctx == 0;
-
-    dmds_t   dmds_extra;       // memory of the extra model, laid out on the devices of the main model
-    uint32_t n_ctx_extra = 0;  // context that memory was measured at
-
-    // the extra model competes for the same memory as the main model, add it to every measurement
-    // its memory is measured again whenever the context it follows changes
-    auto add_extra_memory = [&](dmds_t & dmds) {
-        if (extra == nullptr) {
-            return;
-        }
-
-        if (dmds_extra.empty() || n_ctx_extra != cparams->n_ctx) {
-            std::vector<ggml_backend_dev_t> devs_extra;
-            uint32_t ngl_extra = 0;
-            uint32_t nct_extra = 0;
-            uint32_t nex_extra = 0;
-
-            extra->cparams->n_ctx = cparams->n_ctx;
-
-            LOG_TRC("%s: getting device memory data for the extra model at a context size of %" PRIu32 ":\n",
-                __func__, cparams->n_ctx);
-
-            dmds_t measured;
-            try {
-                measured = common_get_device_memory_data_impl(
-                    extra->path_model, extra->mparams, extra->cparams, devs_extra, ngl_extra, nct_extra, nex_extra, log_level);
-            } catch (const std::runtime_error & e) {
-                // the extra model is optional, fit the main model alone rather than giving up
-                LOG_WRN("%s: failed to measure the memory of the extra model, fitting without it: %s\n", __func__, e.what());
-                dmds_extra = dmds_t(devs.size() + 1);
-                n_ctx_extra = cparams->n_ctx;
-                return;
-            }
-
-            dmds_extra = dmds_t(devs.size() + 1);
-            dmds_extra.back().mb = measured.back().mb;
-            for (size_t je = 0; je < devs_extra.size(); je++) {
-                for (size_t id = 0; id < devs.size(); id++) {
-                    if (devs_extra[je] == devs[id]) {
-                        dmds_extra[id].mb.model   += measured[je].mb.model;
-                        dmds_extra[id].mb.context += measured[je].mb.context;
-                        dmds_extra[id].mb.compute += measured[je].mb.compute;
-                        break;
-                    }
-                }
-            }
-            if (extra->shares_model) {
-                for (llama_device_memory_data & dmd : dmds_extra) {
-                    dmd.mb.model = 0;
-                }
-            }
-
-            n_ctx_extra = cparams->n_ctx;
-        }
-
-        for (size_t id = 0; id < dmds.size(); id++) {
-            dmds[id].mb.model   += dmds_extra[id].mb.model;
-            dmds[id].mb.context += dmds_extra[id].mb.context;
-            dmds[id].mb.compute += dmds_extra[id].mb.compute;
-        }
-    };
-
    // step 1: get data for default parameters and check whether any changes are necessary in the first place

    LOG_TRC("%s: getting device memory data for initial parameters:\n", __func__);
-    dmds_t dmds_full = common_get_device_memory_data_impl(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
-
-    // saturate instead of overflowing, this also preserves the UINT32_MAX sentinel of n_ctx_min:
-    const uint32_t n_ctx_max       = (uint32_t) std::min<uint64_t>(uint64_t(hp_nct)    * n_streams, UINT32_MAX);
-    const uint32_t n_ctx_min_total = (uint32_t) std::min<uint64_t>(uint64_t(n_ctx_min) * n_streams, UINT32_MAX);
-
-    // llama_context would use only hp_nct in total for n_ctx == 0, resolve the context before measuring anything else:
-    if (n_ctx_auto) {
-        cparams->n_ctx = n_ctx_max;
-        if (n_streams > 1) {
-            LOG_TRC("%s: context size unset and KV cache not unified -> using %" PRIu32 " for %" PRIu32 " sequences:\n",
-                __func__, n_ctx_max, n_streams);
-            dmds_full = common_get_device_memory_data_impl(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
-        }
-    }
-    add_extra_memory(dmds_full);
-
+    const dmds_t dmds_full = common_get_device_memory_data_impl(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
    const size_t nd = devs.size(); // number of devices

    std::vector<int64_t> margins; // this function uses int64_t rather than size_t for memory sizes to more conveniently handle deficits
@@ -389,8 +307,8 @@ static void common_params_fit_impl(
                    "%s: cannot meet free memory targets on all devices, need to use %" PRId64 " MiB less in total\n",
                    __func__, -global_surplus/MiB);
            }
-            if (n_ctx_auto) {
-                if (n_ctx_max > n_ctx_min_total) {
+            if (cparams->n_ctx == 0) {
+                if (hp_nct > n_ctx_min) {
                    int64_t sum_used_target = sum_free;
                    if (nd == 0) {
                        sum_used_target -= margins[0];
@@ -410,9 +328,8 @@ static void common_params_fit_impl(
                    }

                    int64_t sum_projected_used_min_ctx = 0;
-                    cparams->n_ctx = n_ctx_min_total;
-                    dmds_t dmds_min_ctx = common_get_device_memory_data_impl(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
-                    add_extra_memory(dmds_min_ctx);
+                    cparams->n_ctx = n_ctx_min;
+                    const dmds_t dmds_min_ctx = common_get_device_memory_data_impl(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
                    if (nd == 0) {
                        sum_projected_used_min_ctx = dmds_min_ctx.back().mb.total();
                    } else {
@@ -422,16 +339,14 @@ static void common_params_fit_impl(
                    }
                    if (sum_used_target > sum_projected_used_min_ctx) {
                        // linear interpolation between minimum and maximum context size:
-                        cparams->n_ctx += (n_ctx_max - n_ctx_min_total) * (sum_used_target - sum_projected_used_min_ctx)
+                        cparams->n_ctx += (hp_nct - n_ctx_min) * (sum_used_target - sum_projected_used_min_ctx)
                            / (sum_projected_used - sum_projected_used_min_ctx);
-                        // round down context for CUDA backend, keep it divisible by the number of streams:
-                        const uint32_t align = 256 * n_streams;
-                        cparams->n_ctx = std::max(cparams->n_ctx - cparams->n_ctx % align, n_ctx_min_total);
+                        cparams->n_ctx = std::max(cparams->n_ctx - cparams->n_ctx % 256, n_ctx_min); // round down context for CUDA backend

-                        const int64_t bytes_per_ctx = (sum_projected_used - sum_projected_used_min_ctx) / (n_ctx_max - n_ctx_min_total);
-                        const int64_t memory_reduction = (n_ctx_max - cparams->n_ctx) * bytes_per_ctx;
+                        const int64_t bytes_per_ctx = (sum_projected_used - sum_projected_used_min_ctx) / (hp_nct - n_ctx_min);
+                        const int64_t memory_reduction = (hp_nct - cparams->n_ctx) * bytes_per_ctx;
                        LOG_TRC("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
-                            __func__, n_ctx_max, cparams->n_ctx, memory_reduction/MiB);
+                            __func__, hp_nct, cparams->n_ctx, memory_reduction/MiB);
                        if (nd <= 1) {
                            LOG_TRC("%s: entire model can be fit by reducing context\n", __func__);
                            return;
@@ -440,14 +355,14 @@ static void common_params_fit_impl(
                    } else {
                        const int64_t memory_reduction = sum_projected_used - sum_projected_used_min_ctx;
                        LOG_TRC("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
-                            __func__, n_ctx_max, cparams->n_ctx, memory_reduction/MiB);
+                            __func__, hp_nct, cparams->n_ctx, memory_reduction/MiB);
                    }
                } else {
                    if (n_ctx_min == UINT32_MAX) {
-                        LOG_TRC("%s: user has requested full context size of %" PRIu32 " -> no change\n", __func__, n_ctx_max);
+                        LOG_TRC("%s: user has requested full context size of %" PRIu32 " -> no change\n", __func__, hp_nct);
                    } else {
                        LOG_TRC("%s: default model context size is %" PRIu32 " which is <= the min. context size of %" PRIu32 " -> no change\n",
-                            __func__, n_ctx_max, n_ctx_min_total);
+                            __func__, hp_nct, n_ctx_min);
                    }
                }
            } else {
@@ -592,9 +507,8 @@ static void common_params_fit_impl(
        llama_model_params mparams_copy = *mparams;
        set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, mparams_copy);

-        dmds_t dmd_nl = common_get_device_memory_data_impl(
+        const dmds_t dmd_nl = common_get_device_memory_data_impl(
            path_model, &mparams_copy, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
-        add_extra_memory(dmd_nl);

        LOG_TRC("%s: memory for test allocation by device:\n", func_name);
        for (size_t id = 0; id < nd; id++) {
@@ -621,9 +535,8 @@ static void common_params_fit_impl(
        mparams->tensor_buft_overrides = tensor_buft_overrides;

        LOG_TRC("%s: getting device memory data with all MoE tensors moved to system memory:\n", __func__);
-        dmds_t dmds_cpu_moe = common_get_device_memory_data_impl(
+        const dmds_t dmds_cpu_moe = common_get_device_memory_data_impl(
            path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
-        add_extra_memory(dmds_cpu_moe);

        for (size_t id = 0; id < nd; id++) {
            global_surplus_cpu_moe += dmds_cpu_moe[id].free;
@@ -883,12 +796,11 @@ enum common_params_fit_status common_fit_params(
        llama_model_tensor_buft_override * tensor_buft_overrides,
        size_t * margins,
        uint32_t n_ctx_min,
-        const common_fit_extra_model * extra,
        ggml_log_level log_level) {
    const int64_t t0_us = llama_time_us();
    common_params_fit_status status = COMMON_PARAMS_FIT_STATUS_SUCCESS;
    try {
-        common_params_fit_impl(path_model, mparams, cparams, tensor_split, tensor_buft_overrides, margins, n_ctx_min, extra, log_level);
+        common_params_fit_impl(path_model, mparams, cparams, tensor_split, tensor_buft_overrides, margins, n_ctx_min, log_level);
        LOG_TRC("%s: successfully fit params to free device memory\n", __func__);
    } catch (const common_params_fit_exception & e) {
        LOG_WRN("%s: failed to fit params to free device memory: %s\n", __func__, e.what());
@@ -11,16 +11,6 @@ enum common_params_fit_status {
    COMMON_PARAMS_FIT_STATUS_ERROR   = 2, // a hard error occurred, e.g. because no model could be found at the specified path
 };

-// a second model that shares the devices of the main model, e.g. a draft model
-//   - its context follows the context of the main model, so its memory is measured again whenever that context changes
-//   - shares_model tells the fit that the weights are already counted in the main model, as for an MTP context
-struct common_fit_extra_model {
-    const char * path_model;
-    llama_model_params * mparams;
-    llama_context_params * cparams;
-    bool shares_model;
-};
-
 // fits mparams and cparams to free device memory (assumes system memory is unlimited)
 //   - returns true if the parameters could be successfully modified to fit device memory
 //   - this function is NOT thread safe because it modifies the global llama logger state
@@ -34,7 +24,6 @@ common_params_fit_status common_fit_params(
   llama_model_tensor_buft_override * tensor_buft_overrides, // writable buffer for overrides, needs at least llama_max_tensor_buft_overrides elements
                             size_t * margins,               // margins of memory to leave per device in bytes
                           uint32_t   n_ctx_min,             // minimum context size to set when trying to reduce memory use
-      const common_fit_extra_model * extra,                  // model to fit alongside the main one, nullptr if there is none
                     ggml_log_level   log_level);            // minimum log level to print during fitting, lower levels go to debug log

 // print estimated memory to stdout
@@ -23,7 +23,7 @@ void caps_apply_preserve_reasoning(jinja::context & ctx, bool enabled) {
    ctx.set_val("preserve_thinking",         mk_val<value_bool>(enabled));
    ctx.set_val("clear_thinking",            mk_val<value_bool>(!enabled));
    ctx.set_val("truncate_history_thinking", mk_val<value_bool>(!enabled));
-    ctx.set_val("drop_thinking",             mk_val<value_bool>(!enabled));
+    ctx.set_val("drop_thinking",            mk_val<value_bool>(!enabled));
 }

 void caps_apply_reasoning_effort(jinja::context & ctx, const std::string & effort) {
@@ -117,8 +117,6 @@ caps caps_get(jinja::program & prog) {

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

-    static const std::string content_marker = "STRING_MARKER";
-
    // case: typed content support
    caps_try_execute(
        prog,
@@ -127,26 +125,22 @@ caps caps_get(jinja::program & prog) {
            return json::array({
                {
                    {"role", "user"},
-                    {"content", content_marker}
+                    {"content", "content"}
                }
            });
        },
        nullptr, // ctx_fn
        nullptr, // tools_fn
-        [&](context &, bool success, value & messages, value &, const std::string & rendered) {
+        [&](context &, bool success, value & messages, value &, const std::string &) {
            auto & content = messages->at(0)->at("content");
            caps_print_stats(content, "messages[0].content");
-            bool used_as_array = has_op(content, "selectattr") || has_op(content, "array_access");
-            if (used_as_array) {
+            if (has_op(content, "selectattr") || has_op(content, "array_access")) {
                // accessed as an array
                result.supports_typed_content = true;
            }
            if (!success) {
                // failed to execute with content as string
                result.supports_string_content = false;
-            } else if (used_as_array && rendered.find(content_marker) == std::string::npos) {
-                // edge case: string may be accessed for checking, but does not appear in the output
-                result.supports_string_content = false;
            }
        }
    );
@@ -278,9 +278,7 @@ static std::unordered_map<char, std::string> GRAMMAR_LITERAL_ESCAPES = {
    {'\r', "\\r"}, {'\n', "\\n"}, {'"', "\\\""}, {'-', "\\-"}, {']', "\\]"}, {'\\', "\\\\"}
 };

-static const int MAX_PATTERN_DEPTH = 100;
-
-static std::unordered_set<char> NON_LITERAL_SET = {'|', '.', '(', ')', '[', ']', '{', '}', '*', '+', '?', '^', '$'};
+static std::unordered_set<char> NON_LITERAL_SET = {'|', '.', '(', ')', '[', ']', '{', '}', '*', '+', '?'};
 static std::unordered_set<char> ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = {'^', '$', '.', '[', ']', '(', ')', '|', '{', '}', '*', '+', '?'};

 static std::string replacePattern(const std::string & input, const std::regex & regex, const std::function<std::string(const std::smatch  &)> & replacement) {
@@ -311,32 +309,6 @@ static std::string format_literal(const std::string & literal) {

 std::string gbnf_format_literal(const std::string & literal) { return format_literal(literal); }

-static size_t gbnf_escape_length(const std::string & pattern, size_t pos) {
-    if (pos + 1 >= pattern.length() || pattern[pos] != '\\') {
-        return 0;
-    }
-    size_t n_hex = 0;
-    switch (pattern[pos + 1]) {
-        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 ']':
-            return 2;
-        default:
-            return 0;
-    }
-    if (pos + 2 + n_hex > pattern.length()) {
-        return 0;
-    }
-    for (size_t i = pos + 2; i < pos + 2 + n_hex; i++) {
-        char h = pattern[i];
-        if (!((h >= '0' && h <= '9') || (h >= 'a' && h <= 'f') || (h >= 'A' && h <= 'F'))) {
-            return 0;
-        }
-    }
-    return 2 + n_hex;
-}
-
 class common_schema_converter {
 private:
    friend class common_schema_info;
@@ -373,42 +345,16 @@ private:
        return string_join(rules, " | ");
    }

-    // thrown when the pattern is a valid regex with no grammar equivalent
-    struct unsupported_pattern : public std::runtime_error {
-        using std::runtime_error::runtime_error;
-    };
-
-    // thrown when the pattern is not a valid regex
-    struct invalid_pattern : public std::runtime_error {
-        using std::runtime_error::runtime_error;
-    };
-
    std::string _visit_pattern(const std::string & pattern, const std::string & name) {
-        auto rules_snapshot = _rules;
-        try {
-            return _pattern_to_rule(pattern, name);
-        } catch (const unsupported_pattern & err) {
-            // revert rules
-            _rules = std::move(rules_snapshot);
-            _warnings.push_back("pattern " + pattern + " is not supported (" + err.what() + "), accepting any string");
-            return _add_rule(name, _add_primitive("string", PRIMITIVE_RULES.at("string")));
-        } catch (const invalid_pattern & err) {
-            _rules = std::move(rules_snapshot);
-            _errors.push_back("Invalid pattern " + pattern + ": " + err.what());
+        if (!(pattern.front() == '^' && pattern.back() == '$')) {
+            _errors.push_back("Pattern must start with '^' and end with '$'");
            return "";
        }
-    }
-
-    std::string _pattern_to_rule(const std::string & pattern, const std::string & name) {
-        if (pattern.length() < 2 || pattern.front() != '^' || pattern.back() != '$') {
-            throw unsupported_pattern("not anchored with '^' and '$'");
-        }
        std::string sub_pattern = pattern.substr(1, pattern.length() - 2);
        std::unordered_map<std::string, std::string> sub_rule_ids;

        size_t i = 0;
        size_t length = sub_pattern.length();
-        int paren_depth = 0;

        using literal_or_rule = std::pair<std::string, bool>;
        auto to_rule = [&](const literal_or_rule & ls) {
@@ -417,6 +363,7 @@ private:
            return is_literal ? "\"" + s + "\"" : s;
        };
        std::function<literal_or_rule()> transform = [&]() -> literal_or_rule {
+            size_t start = i;
            std::vector<literal_or_rule> seq;

            auto get_dot = [&]() {
@@ -473,42 +420,43 @@ private:
                        if (i + 1 < length && sub_pattern[i + 1] == ':') {
                            i += 2; // skip "?:" for non-capturing group, treat as regular group
                        } else {
-                            // lookaround, named group, inline flags, ...
-                            throw unsupported_pattern("unsupported group syntax");
+                            // lookahead/lookbehind (?=, ?!, ?<=, ?<!) - not supported
+                            _warnings.push_back("Unsupported pattern syntax");
+                            // skip to matching ')' to avoid UB on empty seq
+                            int depth = 1;
+                            while (i < length && depth > 0) {
+                                if (sub_pattern[i] == '\\' && i + 1 < length) {
+                                    i += 2; // skip escaped character
+                                } else {
+                                    if (sub_pattern[i] == '(') depth++;
+                                    else if (sub_pattern[i] == ')') depth--;
+                                    i++;
+                                }
+                            }
+                            continue;
                        }
                    }
-                    paren_depth++;
-                    if (paren_depth > MAX_PATTERN_DEPTH) {
-                        throw unsupported_pattern("pattern nesting too deep");
-                    }
                    seq.emplace_back("(" + to_rule(transform()) + ")", false);
                } else if (c == ')') {
                    i++;
-                    if (paren_depth == 0) {
-                        throw invalid_pattern("unbalanced parentheses");
+                    if (start > 0 && sub_pattern[start - 1] != '(' && (start < 2 || sub_pattern[start - 2] != '?' || sub_pattern[start - 1] != ':')) {
+                        _errors.push_back("Unbalanced parentheses");
                    }
-                    paren_depth--;
                    return join_seq();
-                } else if (c == '^' || c == '$') {
-                    throw unsupported_pattern("anchor inside the pattern");
                } else if (c == '[') {
                    std::string square_brackets = std::string(1, c);
                    i++;
                    while (i < length && sub_pattern[i] != ']') {
                        if (sub_pattern[i] == '\\') {
-                            auto escape_length = gbnf_escape_length(sub_pattern, i);
-                            if (escape_length == 0) {
-                                throw unsupported_pattern("unsupported escape in character class: " + sub_pattern.substr(i, 2));
-                            }
-                            square_brackets += sub_pattern.substr(i, escape_length);
-                            i += escape_length;
+                            square_brackets += sub_pattern.substr(i, 2);
+                            i += 2;
                        } else {
                            square_brackets += sub_pattern[i];
                            i++;
                        }
                    }
                    if (i >= length) {
-                        throw invalid_pattern("unterminated character class");
+                        _errors.push_back("Unbalanced square brackets");
                    }
                    square_brackets += ']';
                    i++;
@@ -517,9 +465,6 @@ private:
                    seq.emplace_back("|", false);
                    i++;
                } else if (c == '*' || c == '+' || c == '?') {
-                    if (seq.empty()) {
-                        throw invalid_pattern("nothing to repeat");
-                    }
                    seq.back() = std::make_pair(to_rule(seq.back()) + c, false);
                    i++;
                } else if (c == '{') {
@@ -530,19 +475,18 @@ private:
                        i++;
                    }
                    if (i >= length) {
-                        throw unsupported_pattern("unterminated curly brackets");
+                        _errors.push_back("Unbalanced curly brackets");
                    }
                    curly_brackets += '}';
                    i++;
                    auto nums = string_split(curly_brackets.substr(1, curly_brackets.length() - 2), ",");
                    int min_times = 0;
                    int max_times = std::numeric_limits<int>::max();
-                    if (nums.size() != 1 && nums.size() != 2) {
-                        throw unsupported_pattern("wrong number of values in curly brackets");
-                    }
                    try {
                        if (nums.size() == 1) {
                            min_times = max_times = std::stoi(nums[0]);
+                        } else if (nums.size() != 2) {
+                            _errors.push_back("Wrong number of values in curly brackets");
                        } else {
                            if (!nums[0].empty()) {
                                min_times = std::stoi(nums[0]);
@@ -551,11 +495,9 @@ private:
                                max_times = std::stoi(nums[1]);
                            }
                        }
-                    } catch (const std::logic_error &) {
-                        throw unsupported_pattern("invalid number in curly brackets");
-                    }
-                    if (seq.empty()) {
-                        throw invalid_pattern("nothing to repeat");
+                    } catch (const std::invalid_argument & e) {
+                        _errors.push_back("Invalid number in curly brackets");
+                        return std::make_pair("", false);
                    }
                    auto &last = seq.back();
                    auto &sub = last.first;
@@ -581,22 +523,15 @@ private:
                        return NON_LITERAL_SET.find(c) != NON_LITERAL_SET.end();
                    };
                    while (i < length) {
-                        if (sub_pattern[i] == '\\') {
-                            if (i == length - 1) {
-                                throw invalid_pattern("trailing backslash");
-                            }
+                        if (sub_pattern[i] == '\\' && i < length - 1) {
                            char next = sub_pattern[i + 1];
                            if (ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS.find(next) != ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS.end()) {
                                i++;
                                literal += sub_pattern[i];
                                i++;
                            } else {
-                                auto escape_length = gbnf_escape_length(sub_pattern, i);
-                                if (escape_length == 0) {
-                                    throw unsupported_pattern("unsupported escape: " + sub_pattern.substr(i, 2));
-                                }
-                                literal += sub_pattern.substr(i, escape_length);
-                                i += escape_length;
+                                literal += sub_pattern.substr(i, 2);
+                                i += 2;
                            }
                        } else if (sub_pattern[i] == '"') {
                            literal += "\\\"";
@@ -609,21 +544,14 @@ private:
                            break;
                        }
                    }
-                    if (literal.empty()) { // nothing was consumed, ex. a stray ']' or '}'
-                        throw unsupported_pattern(std::string("unsupported character: ") + c);
+                    if (!literal.empty()) {
+                        seq.emplace_back(literal, true);
                    }
-                    seq.emplace_back(literal, true);
                }
            }
            return join_seq();
        };
-
-        auto rule = to_rule(transform());
-        if (paren_depth != 0) {
-            throw invalid_pattern("unbalanced parentheses");
-        }
-
-        return _add_rule(name, "\"\\\"\" (" + rule + ") \"\\\"\"");
+        return _add_rule(name, "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\"");
    }

    /*
@@ -926,9 +926,6 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
    // draft-dspark: the draft carries a Markov head and uses an anchor-first block layout
    const bool is_dspark;

-    // dspark speculators
-    bool sample_from_anchor = true;
-
    const int32_t * target_layer_ids   = nullptr; // model_dft's extract layer indices
    uint32_t        target_layer_ids_n = 0;

@@ -963,20 +960,16 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
            if (llama_model_meta_val_str(model_dft, "dflash.block_size", buf, sizeof(buf)) >= 0) {
                block_size = std::atoi(buf);
            }
-            if (llama_model_meta_val_str(model_dft, "dflash.sample_from_anchor", buf, sizeof(buf)) >= 0) {
-                sample_from_anchor = std::strcmp(buf, "true") == 0;
-            }
        }
        mask_token_id = llama_vocab_mask(llama_model_get_vocab(model_dft));

        LOG_INF("%s: adding speculative implementation '%s'\n", __func__, common_speculative_type_to_str(type).c_str());
        LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%.2f\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min);
-        LOG_INF("%s: - block_size=%d, mask_token_id=%d, n_extract=%u, sample_from_anchor=%s\n", __func__,
-                block_size, mask_token_id, target_layer_ids_n, sample_from_anchor ? "true" : "false");
+        LOG_INF("%s: - block_size=%d, mask_token_id=%d, n_extract=%u\n", __func__, block_size, mask_token_id, target_layer_ids_n);

        // DFlash input is [id_last, <mask> * (block_size-1)]: in-place denoising yields at most
-        // block_size-1 draft tokens, anchor-first DSpark yields a full block_size draft tokens
-        const int32_t n_draft_max = is_dspark && sample_from_anchor ? block_size : block_size - 1;
+        // block_size-1 draft tokens, DSpark yield a full block_size draft tokens
+        const int32_t n_draft_max = is_dspark ? block_size : block_size - 1;
        if (this->params.n_max > n_draft_max || this->params.n_min > n_draft_max) {
            LOG_WRN("%s: requested draft size (n_max=%d, n_min=%d) exceeds the trained block size %d -- clamping to %d\n",
                    __func__, this->params.n_max, this->params.n_min, block_size, n_draft_max);
@@ -1182,7 +1175,7 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {

            const int32_t n_draft = params.n_max;

-            const int32_t n_block_tokens = n_draft + (is_dspark && sample_from_anchor ? 0 : 1);
+            const int32_t n_block_tokens = n_draft + (is_dspark ? 0 : 1);
            i_block_beg[seq_id] = batch.n_tokens;
            n_block    [seq_id] = n_block_tokens;
            for (int32_t i = 0; i < n_block_tokens; ++i) {
@@ -1215,11 +1208,11 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
            auto & result = *dp.result;

            if (is_dspark) {
-                // DSpark: read from the first draft slot, truncate below the confidence threshold
+                // DSpark predicts the next token from position 0 and optionally truncates
+                // at the first position below the confidence threshold.
                const float * conf = params.p_min > 0.0f ? llama_get_embeddings_nextn(ctx_dft) : nullptr;
-                // bonus-anchor drafts read the mask positions only, like DFlash
-                const int32_t i_draft_beg = sample_from_anchor ? 0 : 1;
-                for (int32_t i = i_draft_beg; i < n_block_tokens; ++i) {
+
+                for (int32_t i = 0; i < n_block_tokens; ++i) {
                    const int32_t idx = beg + i;

                    if (conf && conf[(size_t) idx * n_embd_dec] < params.p_min) {
@@ -2322,9 +2315,6 @@ common_params common_base_params_to_speculative(const common_params & params) {
    const auto & params_spec = params.speculative.draft;
    common_params result = params;

-    result.embedding    = false;
-    result.pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED;
-
    if (has_draft) {
        result.devices               = params_spec.devices;
        result.model                 = params_spec.mparams;
@@ -2388,9 +2378,6 @@ common_speculative_init_result::common_speculative_init_result(
        cparams.ctx_type = LLAMA_CONTEXT_TYPE_MTP;
    }

-    // the draft context holds as many tokens per sequence as the target context
-    cparams.n_ctx = llama_n_ctx(ctx_tgt);
-
    // note: for small models maybe we can set this to the maximum possible draft from all speculative types
    //       the extra memory for small models is likely negligible?
    cparams.n_rs_seq  = 0;
@@ -2655,10 +2642,6 @@ void common_speculative_draft(common_speculative * spec) {
        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) dparams.size(); ++seq_id) {
            auto & dp = dparams[seq_id];

-            if (!dp.drafting) {
-                continue;
-            }
-
            auto & result = *dp.result;

            // a new draft has been sampled
@@ -27,7 +27,6 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "BaichuanForCausalLM": "baichuan",
    "BailingMoeForCausalLM": "bailingmoe",
    "BailingMoeV2ForCausalLM": "bailingmoe",
-    "BailingMoeV3ForCausalLM": "bailingmoe3",
    "BambaForCausalLM": "granite",
    "BertForMaskedLM": "bert",
    "BertForSequenceClassification": "bert",
@@ -55,19 +54,12 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "DeepseekV32ForCausalLM": "deepseek",
    "DFlashDraftModel": "qwen",
    "Qwen3DSparkModel": "qwen",
-    "DSparkDraftModel": "qwen",
-    "DSparkSpeculator": "qwen",
-    "Lfm2DSparkDraftModel": "qwen",
-    "LingDSparkModel": "qwen",
    "DeepseekV4ForCausalLM": "deepseek",
    "DeepseekV4DSparkModel": "deepseek",
    "DistilBertForMaskedLM": "bert",
    "DistilBertForSequenceClassification": "bert",
    "DistilBertModel": "bert",
    "Dots1ForCausalLM": "dots1",
-    "Dots3NoteForCausalLM": "dots3",
-    "Dots3NoteForConditionalGeneration": "dots3",
-    "Dots3NoteTextForCausalLM": "dots3",
    "DotsOCRForCausalLM": "qwen",
    "DreamModel": "dream",
    "Ernie4_5ForCausalLM": "ernie",
@@ -114,8 +106,6 @@ TEXT_MODEL_MAP: dict[str, str] = {
    "GraniteSwitchForCausalLM": "granite",
    "GraniteSpeechForConditionalGeneration": "granite",
    "GraniteSpeechPlusForConditionalGeneration": "granite",
-    "GraniteSWAForCausalLM": "granite",
-    "GraniteMoeSWAForCausalLM": "granite",
    "Grok1ForCausalLM": "grok",
    "GrokForCausalLM": "grok",
    "GroveMoeForCausalLM": "grovemoe",
@@ -283,8 +273,6 @@ MMPROJ_MODEL_MAP: dict[str, str] = {
    "CogVLMForCausalLM": "cogvlm",
    "DeepseekOCR2ForCausalLM": "deepseek",
    "DeepseekOCRForCausalLM": "deepseek",
-    "Dots3NoteForCausalLM": "dots3",
-    "Dots3NoteForConditionalGeneration": "dots3",
    "DotsOCRForCausalLM": "dotsocr",
    "Exaone4_5_ForConditionalGeneration": "exaone",
    "Gemma3ForConditionalGeneration": "gemma",
@@ -13,7 +13,6 @@ from .llama import LlamaModel


@ModelBase.register("AfmoeForCausalLM")
-@ModelBase.example("arcee-ai/Trinity-Large-Thinking")
 class AfmoeModel(LlamaModel):
    model_arch = gguf.MODEL_ARCH.AFMOE

@@ -16,7 +16,6 @@ from .llama import LlamaModel


@ModelBase.register("ArcticForCausalLM")
-@ModelBase.example("Snowflake/snowflake-arctic-instruct")
 class ArcticModel(TextModel):
    model_arch = gguf.MODEL_ARCH.ARCTIC

@@ -9,7 +9,6 @@ from .base import ModelBase, TextModel, gguf, logger


@ModelBase.register("BaichuanForCausalLM", "BaiChuanForCausalLM")
-@ModelBase.example("baichuan-inc/Baichuan2-7B-Chat", "baichuan-inc/Baichuan-7B")
 class BaichuanModel(TextModel):
    model_arch = gguf.MODEL_ARCH.BAICHUAN

@@ -11,7 +11,6 @@ from .base import ModelBase, TextModel, gguf


@ModelBase.register("BailingMoeForCausalLM")
-@ModelBase.example("inclusionAI/Ling-lite")
 class BailingMoeModel(TextModel):
    model_arch = gguf.MODEL_ARCH.BAILINGMOE

@@ -109,7 +108,6 @@ class BailingMoeModel(TextModel):


@ModelBase.register("BailingMoeV2ForCausalLM")
-@ModelBase.example("inclusionAI/Ling-mini-2.0")
 class BailingMoeV2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.BAILINGMOE2

@@ -191,7 +189,6 @@ class BailingMoeV2Model(TextModel):


@ModelBase.register("SarvamMoEForCausalLM", "modeling_sarvam_moe.SarvamMoEForCausalLM")
-@ModelBase.example("sarvamai/sarvam-30b")
 class SarvamMoEModel(BailingMoeV2Model):
    model_arch = gguf.MODEL_ARCH.BAILINGMOE2
    # Sarvam-MoE shares the BailingMoeV2 architecture; only differences:
@@ -1,193 +0,0 @@
-from __future__ import annotations
-
-import re
-
-from typing import Callable, Iterable, TYPE_CHECKING
-
-import torch
-
-if TYPE_CHECKING:
-    from torch import Tensor
-
-from .base import ModelBase, TextModel, gguf
-
-
-@ModelBase.register("BailingMoeV3ForCausalLM")
-@ModelBase.example("inclusionAI/Ling-3.0-tiny", "inclusionAI/Ling-3.0-flash")
-class BailingMoeV3Model(TextModel):
-    model_arch = gguf.MODEL_ARCH.BAILINGMOE3
-    supports_mtp_export = True
-
-    _experts: list[dict[str, Tensor]] | None = None
-    _main_layers: int | None = None
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        nextn_layers = self.hparams.get("num_nextn_predict_layers", 0) or 0
-        if self.no_mtp:
-            nextn_layers = 0
-        self.block_count = self.hparams["num_hidden_layers"] + nextn_layers
-        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
-
-    def index_tensors(self, remote_hf_model_id: str | None = None):
-        type(self)._main_layers = self.hparams["num_hidden_layers"]
-        return super().index_tensors(remote_hf_model_id=remote_hf_model_id)
-
-    def set_vocab(self):
-        self._set_vocab_gpt2()
-
-    def is_full_attention(self, bid: int) -> bool:
-        n_layer = self.hparams["num_hidden_layers"]
-        layer_group_size = self.hparams["layer_group_size"]
-        return bid >= n_layer or (bid + 1) % layer_group_size == 0 or bid >= n_layer // layer_group_size * layer_group_size
-
-    def set_gguf_parameters(self):
-        if not self.hparams.get("no_kda_lora", False):
-            raise ValueError("BailingMoeV3 KDA LoRA projections are not supported")
-        if not self.hparams.get("kda_safe_gate", False):
-            raise ValueError("BailingMoeV3 non-safe KDA gates are not supported")
-        if self.hparams.get("gated_attention_proj_granularity_type") != "head_wise":
-            raise ValueError("BailingMoeV3 requires head-wise attention gates")
-
-        self.hparams["num_key_value_heads"] = 1
-        super().set_gguf_parameters()
-
-        n_head_kv = [1 if self.is_full_attention(il) else 0 for il in range(self.block_count)]
-        self.gguf_writer.add_head_count_kv(n_head_kv)
-
-        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
-        self.gguf_writer.add_ssm_conv_kernel(self.hparams["short_conv_kernel_size"])
-        self.gguf_writer.add_kda_head_dim(self.hparams["head_dim"])
-        self.gguf_writer.add_kda_safe_gate(self.hparams["kda_safe_gate"])
-        self.gguf_writer.add_kda_gate_lower_bound(self.hparams["kda_lower_bound"])
-
-        kv_lora_rank = self.hparams["kv_lora_rank"]
-        qk_nope_head_dim = self.hparams["qk_nope_head_dim"]
-        qk_rope_head_dim = self.hparams["qk_rope_head_dim"]
-        if (q_lora_rank := self.hparams.get("q_lora_rank")) is not None:
-            self.gguf_writer.add_q_lora_rank(q_lora_rank)
-        self.gguf_writer.add_kv_lora_rank(kv_lora_rank)
-        self.gguf_writer.add_rope_dimension_count(qk_rope_head_dim)
-        self.gguf_writer.add_key_length(kv_lora_rank + qk_rope_head_dim)
-        self.gguf_writer.add_key_length_mla(qk_nope_head_dim + qk_rope_head_dim)
-        self.gguf_writer.add_value_length_mla(self.hparams["v_head_dim"])
-
-        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_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"])
-
-        def clamp_limits(key: str) -> list[float] | None:
-            values = self.hparams.get(key)
-            if values is None:
-                return None
-            values = [0.0 if value is None else float(value) for value in values[:self.block_count]]
-            return values + [0.0] * (self.block_count - len(values))
-
-        if (values := clamp_limits("expert_swiglu_limit_list")) is not None:
-            self.gguf_writer.add_swiglu_clamp_exp(values)
-        if (values := clamp_limits("share_expert_swiglu_limit_list")) is not None:
-            self.gguf_writer.add_swiglu_clamp_shexp(values)
-
-        if not self.no_mtp and (nextn_layers := self.hparams.get("num_nextn_predict_layers", 0)):
-            self.gguf_writer.add_nextn_predict_layers(nextn_layers)
-
-    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"
-
-    @classmethod
-    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
-        name, gen = item
-        if name.endswith(".expert_bias"):
-            name += ".bias"
-
-        if cls._main_layers is None:
-            return super().filter_tensors((name, gen))
-
-        m = re.match(r"model\.layers\.(\d+)\.", name)
-        is_mtp = m is not None and int(m.group(1)) >= cls._main_layers
-
-        if is_mtp and cls.no_mtp:
-            return None
-        if cls.mtp_only and not is_mtp and name not in (
-            "model.word_embeddings.weight", "model.norm.weight", "lm_head.weight",
-        ):
-            return None
-
-        return super().filter_tensors((name, gen))
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name.endswith((".q_conv1d.weight", ".k_conv1d.weight", ".v_conv1d.weight")) and data_torch.ndim in (2, 3):
-            d_inner = data_torch.shape[0]
-            d_conv = data_torch.shape[-1]
-            data_torch = data_torch.reshape(1, d_inner, 1, d_conv)
-
-        if name.endswith(".A_log"):
-            data_torch = torch.exp(data_torch).reshape(-1, 1)
-
-        if name.endswith(".dt_bias"):
-            name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
-
-        if name.endswith(".attention.f_proj.weight"):
-            assert bid is not None
-            if self.is_full_attention(bid):
-                raise ValueError(f"unexpected f_proj on full-attention layer {bid}")
-            name = self.format_tensor_name(gguf.MODEL_TENSOR.SSM_F_A, bid)
-
-        if name.endswith(".attention.g_proj.weight"):
-            assert bid is not None
-            tensor = gguf.MODEL_TENSOR.ATTN_GATE if self.is_full_attention(bid) else gguf.MODEL_TENSOR.SSM_G_A
-            name = self.format_tensor_name(tensor, bid)
-
-        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(torch.stack(tensors, dim=0), merged_name, bid)
-            return
-
-        if name.endswith(".attention.kv_b_proj.weight"):
-            assert bid is not None
-            n_head = self.hparams["num_attention_heads"]
-            v_head_dim = self.hparams["v_head_dim"]
-            qk_nope_head_dim = self.hparams["qk_nope_head_dim"]
-            assert data_torch.shape[0] == n_head * (v_head_dim + qk_nope_head_dim)
-            kv_b = data_torch.view(n_head, v_head_dim + qk_nope_head_dim, data_torch.shape[-1])
-            k_b, v_b = torch.split(kv_b, [qk_nope_head_dim, v_head_dim], dim=1)
-            name_k = self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K_B, bid)
-            name_v = self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V_B, bid)
-            yield from super().modify_tensors(k_b.transpose(1, 2), name_k, bid)
-            yield from super().modify_tensors(v_b, name_v, 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}")
@@ -1149,14 +1149,6 @@ class ModelBase:
            return modelcls
        return func

-    @classmethod
-    def example(cls, *hf_repos: str) -> Callable[[AnyModel], AnyModel]:
-        del hf_repos  # unused
-
-        def func(modelcls: AnyModel) -> AnyModel:
-            return modelcls
-        return func
-
    @classmethod
    def print_registered_models(cls):
        for model_type, model_classes in cls._model_classes.items():
@@ -15,7 +15,6 @@ from .base import ModelBase, SentencePieceTokenTypes, TextModel, gguf, logger


@ModelBase.register("BertModel", "BertForMaskedLM", "CamembertModel", "BertForSequenceClassification")
-@ModelBase.example("BAAI/bge-small-en-v1.5", "dangvantuan/sentence-camembert-base")
 class BertModel(TextModel):
    model_arch = gguf.MODEL_ARCH.BERT

@@ -241,7 +240,6 @@ class BertModel(TextModel):


@ModelBase.register("DistilBertModel", "DistilBertForMaskedLM", "DistilBertForSequenceClassification")
-@ModelBase.example("distilbert/distilbert-base-uncased")
 class DistilBertModel(BertModel):
    model_arch = gguf.MODEL_ARCH.BERT

@@ -265,7 +263,6 @@ class DistilBertModel(BertModel):


@ModelBase.register("RobertaModel", "RobertaForSequenceClassification")
-@ModelBase.example("sentence-transformers/stsb-roberta-base")
 class RobertaModel(BertModel):
    model_arch = gguf.MODEL_ARCH.BERT

@@ -315,7 +312,6 @@ class RobertaModel(BertModel):


@ModelBase.register("NomicBertModel")
-@ModelBase.example("nomic-ai/nomic-embed-text-v1.5")
 class NomicBertModel(BertModel):
    model_arch = gguf.MODEL_ARCH.BERT

@@ -404,7 +400,6 @@ class NomicBertModel(BertModel):


@ModelBase.register("NeoBERT", "NeoBERTLMHead", "NeoBERTForSequenceClassification")
-@ModelBase.example("chandar-lab/NeoBERT")
 class NeoBert(BertModel):
    model_arch = gguf.MODEL_ARCH.NEO_BERT

@@ -436,7 +431,6 @@ class NeoBert(BertModel):


@ModelBase.register("EuroBertModel", "JinaEmbeddingsV5Model")
-@ModelBase.example("hf-tiny-v2/tiny-random-EuroBertModel", "jinaai/jina-embeddings-v5-text-nano")
 class EuroBertModel(TextModel):
    model_arch = gguf.MODEL_ARCH.EUROBERT

@@ -465,7 +459,6 @@ class EuroBertModel(TextModel):


@ModelBase.register("XLMRobertaModel", "XLMRobertaForSequenceClassification")
-@ModelBase.example("BAAI/bge-m3")
 class XLMRobertaModel(BertModel):
    model_arch = gguf.MODEL_ARCH.BERT
    _lora_files = {}
@@ -568,7 +561,6 @@ class XLMRobertaModel(BertModel):


@ModelBase.register("JinaBertModel", "JinaBertForMaskedLM")
-@ModelBase.example("jinaai/jina-embeddings-v2-base-en")
 class JinaBertV2Model(BertModel):
    model_arch = gguf.MODEL_ARCH.JINA_BERT_V2

@@ -596,7 +588,6 @@ class JinaBertV2Model(BertModel):


@ModelBase.register("ModernBertModel", "ModernBertForMaskedLM", "ModernBertForSequenceClassification")
-@ModelBase.example("answerdotai/ModernBERT-base")
 class ModernBertModel(BertModel):
    model_arch = gguf.MODEL_ARCH.MODERN_BERT

@@ -9,7 +9,6 @@ from .base import ModelBase, TextModel, gguf


@ModelBase.register("BitnetForCausalLM", "BitNetForCausalLM")
-@ModelBase.example("microsoft/bitnet-b1.58-2B-4T")
 class BitnetModel(TextModel):
    model_arch = gguf.MODEL_ARCH.BITNET

@@ -13,7 +13,6 @@ from .base import ModelBase, TextModel, gguf, logger


@ModelBase.register("BloomForCausalLM", "BloomModel")
-@ModelBase.example("bigscience/bloom-560m")
 class BloomModel(TextModel):
    model_arch = gguf.MODEL_ARCH.BLOOM

@@ -12,8 +12,6 @@ from .llama import LlamaModel

@ModelBase.register("ChameleonForConditionalGeneration")
@ModelBase.register("ChameleonForCausalLM")  # obsolete
-# [TAG_HF_EXAMPLE_GATED] facebook/chameleon-7b is gated
-# [TAG_HF_EXAMPLE_MISSING]
 class ChameleonModel(TextModel):
    model_arch = gguf.MODEL_ARCH.CHAMELEON

@@ -9,7 +9,6 @@ from .base import ModelBase, SentencePieceTokenTypes, TextModel, gguf


@ModelBase.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration")
-@ModelBase.example("THUDM/chatglm3-6b", "zai-org/glm-4-9b-chat-hf")
 class ChatGLMModel(TextModel):
    model_arch = gguf.MODEL_ARCH.CHATGLM

@@ -4,7 +4,6 @@ from .base import ModelBase, TextModel, gguf


@ModelBase.register("CodeShellForCausalLM")
-@ModelBase.example("WisdomShell/CodeShell-7B")
 class CodeShellModel(TextModel):
    model_arch = gguf.MODEL_ARCH.CODESHELL

@@ -11,7 +11,6 @@ from .llama import LlamaModel


@ModelBase.register("CogVLMForCausalLM")
-@ModelBase.example("THUDM/cogvlm2-llama3-chat-19B", "THUDM/cogvlm-chat-hf")
 class CogVLMVisionModel(MmprojModel):

    def set_gguf_parameters(self):
@@ -30,6 +29,5 @@ class CogVLMVisionModel(MmprojModel):


@ModelBase.register("CogVLMForCausalLM")
-@ModelBase.example("THUDM/cogvlm2-llama3-chat-19B", "THUDM/cogvlm-chat-hf")
 class CogVLMModel(LlamaModel):
    model_arch = gguf.MODEL_ARCH.COGVLM
@@ -12,8 +12,6 @@ from .base import ModelBase, TextModel, gguf, logger


@ModelBase.register("CohereForCausalLM")
-# [TAG_HF_EXAMPLE_GATED] CohereLabs/c4ai-command-r-v01 is gated
-# [TAG_HF_EXAMPLE_MISSING]
 class CommandR2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.COMMAND_R

@@ -32,8 +30,6 @@ class CommandR2Model(TextModel):


@ModelBase.register("Cohere2ForCausalLM")
-# [TAG_HF_EXAMPLE_GATED] CohereLabs/c4ai-command-r7b-12-2024 is gated
-@ModelBase.example("hf-tiny-v2/tiny-random-Cohere2ForCausalLM")
 class Cohere2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.COHERE2

@@ -63,7 +59,6 @@ class Cohere2Model(TextModel):


@ModelBase.register("Cohere2MoeForCausalLM")
-@ModelBase.example("CohereLabs/North-Mini-Code-1.0")
 class Cohere2MoeModel(TextModel):
    model_arch = gguf.MODEL_ARCH.COHERE2MOE
    _n_main_layers: int | None = None
@@ -9,7 +9,6 @@ from .base import ModelBase, TextModel, gguf, logger


@ModelBase.register("DbrxForCausalLM")
-@ModelBase.example("alpindale/dbrx-instruct")
 class DbrxModel(TextModel):
    model_arch = gguf.MODEL_ARCH.DBRX

@@ -13,7 +13,6 @@ from .base import ModelBase, TextModel, gguf


@ModelBase.register("DeciLMForCausalLM")
-@ModelBase.example("nvidia/Llama-3_1-Nemotron-51B-Instruct", "Deci/DeciLM-7B")
 class DeciModel(TextModel):
    model_arch = gguf.MODEL_ARCH.DECI

@@ -18,7 +18,6 @@ from .qwen import QwenModel


@ModelBase.register("DeepseekOCRForCausalLM")
-@ModelBase.example("deepseek-ai/DeepSeek-OCR")
 class DeepseekOCRVisionModel(MmprojModel):
    # HF dynamic_preprocess() max_num, which differs per model
    preproc_max_tiles = 9
@@ -101,13 +100,11 @@ class DeepseekOCRVisionModel(MmprojModel):


@ModelBase.register("UnlimitedOCRForCausalLM")
-@ModelBase.example("baidu/Unlimited-OCR")
 class UnlimitedOCRVisionModel(DeepseekOCRVisionModel):
    preproc_max_tiles = 32


@ModelBase.register("DeepseekOCR2ForCausalLM")
-@ModelBase.example("deepseek-ai/DeepSeek-OCR-2")
 class DeepseekOCR2VisionModel(DeepseekOCRVisionModel):
    preproc_max_tiles = 6

@@ -137,7 +134,6 @@ class DeepseekOCR2VisionModel(DeepseekOCRVisionModel):


@ModelBase.register("DeepseekForCausalLM")
-@ModelBase.example("deepseek-ai/deepseek-moe-16b-chat")
 class DeepseekModel(TextModel):
    model_arch = gguf.MODEL_ARCH.DEEPSEEK

@@ -232,7 +228,6 @@ class DeepseekModel(TextModel):
    "YoutuForCausalLM",
    "YoutuVLForConditionalGeneration",
 )
-@ModelBase.example("deepseek-ai/DeepSeek-V2-Lite", "deepseek-ai/DeepSeek-V3")
 class DeepseekV2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.DEEPSEEK2

@@ -462,7 +457,6 @@ class DeepseekV2Model(TextModel):


@ModelBase.register("DeepseekV32ForCausalLM")
-@ModelBase.example("deepseek-ai/DeepSeek-V3.2-Exp")
 class DeepseekV32Model(DeepseekV2Model):
    model_arch = gguf.MODEL_ARCH.DEEPSEEK32
    skip_mtp = False
@@ -523,7 +517,6 @@ class DeepseekV32Model(DeepseekV2Model):


@ModelBase.register("DeepseekV4ForCausalLM")
-@ModelBase.example("deepseek-ai/DeepSeek-V4-Flash-Base")
 class DeepseekV4Model(TextModel):
    model_arch = gguf.MODEL_ARCH.DEEPSEEK4
    supports_mtp_export = True
@@ -918,7 +911,6 @@ class DeepseekV4Model(TextModel):


@ModelBase.register("DeepseekV4DSparkModel")
-@ModelBase.example("deepseek-ai/DeepSeek-V4-Flash-DSpark")
 class DeepseekV4DSparkModel(DeepseekV4Model):
    model_arch = gguf.MODEL_ARCH.DFLASH

@@ -11,7 +11,6 @@ from .qwen import Qwen2MoeModel


@ModelBase.register("Dots1ForCausalLM")
-@ModelBase.example("rednote-hilab/dots.llm1.inst")
 class Dots1Model(Qwen2MoeModel):
    model_arch = gguf.MODEL_ARCH.DOTS1

@@ -1,323 +0,0 @@
-from __future__ import annotations
-
-import math
-import re
-
-import torch
-
-from typing import TYPE_CHECKING, Any, Callable, Iterable
-
-if TYPE_CHECKING:
-    from torch import Tensor
-
-from .base import MmprojModel, ModelBase, gguf
-
-from .deepseek import DeepseekV2Model
-
-
-@ModelBase.register("Dots3NoteForCausalLM", "Dots3NoteForConditionalGeneration", "Dots3NoteTextForCausalLM")
-class Dots3NoteModel(DeepseekV2Model):
-    model_arch = gguf.MODEL_ARCH.DOTS3NOTE
-    skip_mtp = False
-    supports_mtp_export = True
-
-    # trunk layer count, stashed before indexing for filter_tensors (mirrors DeepseekV32Model)
-    _n_main_layers: int | None = None
-
-    def index_tensors(self, remote_hf_model_id: str | None = None):
-        type(self)._n_main_layers = self.hparams["num_hidden_layers"]
-        return super().index_tensors(remote_hf_model_id=remote_hf_model_id)
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        hparams = self.hparams
-
-        # config file doesn't specify MTP block, detect it from model weight
-        self.n_nextn = 1 if "model.mtp.embed_tokens.weight" in self.model_tensors else 0
-        if self.n_nextn:
-            self.block_count += self.n_nextn
-            self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
-
-        self.layer_types = hparams["layer_types"]
-        if len(self.layer_types) < hparams["num_hidden_layers"]:
-            raise ValueError("layer_types is shorter than num_hidden_layers")
-
-        if hparams.get("use_dsa", True) is not True:
-            raise ValueError("dots3-note conversion requires use_dsa=true")
-        if hparams.get("normalization", "RMSNorm") != "RMSNorm" or hparams.get("final_norm", "RMSNorm") != "RMSNorm":
-            raise ValueError("dots3-note conversion only supports RMSNorm")
-        if hparams.get("k_rope_only_layernorm", True) is not True:
-            raise ValueError("dots3-note conversion requires k_rope_only_layernorm=true")
-        if hparams.get("topk_method", "noaux_tc") != "noaux_tc" or hparams.get("scoring_func") != "sigmoid":
-            raise ValueError("dots3-note conversion only supports noaux_tc/sigmoid expert gating")
-        if hparams.get("n_group", 1) != 1 or hparams.get("topk_group", 1) != 1:
-            raise ValueError("dots3-note conversion does not support grouped expert routing")
-        if hparams.get("use_dynamic_rsf", False) or hparams.get("moe_gating_fp32", False):
-            raise ValueError("dots3-note conversion does not support use_dynamic_rsf/moe_gating_fp32")
-        for key in ("attention_gate_type", "swa_attention_gate_type"):
-            if hparams.get(key, "headwise") != "headwise":
-                raise ValueError(f"dots3-note conversion only supports headwise attention gate, got {key}={hparams.get(key)!r}")
-        if hparams["swa_qk_nope_head_dim"] + hparams["swa_qk_rope_head_dim"] != hparams.get("swa_head_dim", 256):
-            raise ValueError("swa_head_dim must equal swa_qk_nope_head_dim + swa_qk_rope_head_dim")
-        if hparams["swa_qk_rope_head_dim"] != hparams["qk_rope_head_dim"]:
-            # both layer kinds share a single rope_dimension_count
-            raise ValueError("swa_qk_rope_head_dim must match qk_rope_head_dim")
-
-        self.apply_lora_rescale = hparams.get("apply_mla_qkv_lora_rescale", False)
-
-    def _is_swa_layer(self, bid: int) -> bool:
-        if bid >= self.hparams["num_hidden_layers"]:
-            # note: the NextN/MTP block uses the sliding-attention MLA
-            return True
-        return self.layer_types[bid] == "sliding_attention"
-
-    def set_vocab(self):
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
-        tokens, toktypes, tokpre = self.get_vocab_base()
-        self.gguf_writer.add_tokenizer_model("gpt2")
-        self.gguf_writer.add_tokenizer_pre(tokpre)
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|endofassistant|>"])  # ty: ignore[unresolved-attribute]
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    @classmethod
-    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
-        if (titem := super().filter_tensors(item)) is None:
-            return None
-        name, gen = titem
-        if name.startswith(("vision_encoder.", "audio_encoder.")):
-            return None
-
-        assert cls._n_main_layers is not None
-        is_mtp = name.startswith("model.mtp.") or \
-            ((m := re.match(r"model\.layers\.(\d+)\.", name)) is not None and int(m.group(1)) >= cls._n_main_layers)
-
-        # --no-mtp: drop the NextN/MTP block; --mtp: keep only that block plus the shared embeddings/norm/lm_head
-        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
-
-        return name, gen
-
-    def set_gguf_parameters(self):
-        hparams = self.hparams
-
-        # head_count is a per-layer array because the two layer kinds have different head counts
-        n_layer = hparams["num_hidden_layers"]
-        hparams["num_attention_heads"] = [
-            hparams["swa_num_attention_heads"] if self._is_swa_layer(il) else hparams["num_attention_heads"]
-            for il in range(self.block_count)
-        ]
-
-        # prevent the base class from emitting key/value_length from the unused head_dim
-        hparams.pop("head_dim", None)
-
-        super().set_gguf_parameters()
-
-        # MLA geometry of the sliding-window layers (rope.freq_base_swa is emitted by the base class)
-        swa_kv_lora_rank = hparams["swa_kv_lora_rank"]
-        self.gguf_writer.add_sliding_window(hparams["sliding_window_size"])
-        self.gguf_writer.add_sliding_window_pattern([self._is_swa_layer(il) for il in range(n_layer)])
-        self.gguf_writer.add_kv_lora_rank_swa(swa_kv_lora_rank)
-        self.gguf_writer.add_key_length_swa(swa_kv_lora_rank + hparams["swa_qk_rope_head_dim"])
-        self.gguf_writer.add_value_length_swa(swa_kv_lora_rank)
-        self.gguf_writer.add_key_length_mla_swa(hparams["swa_qk_nope_head_dim"] + hparams["swa_qk_rope_head_dim"])
-        self.gguf_writer.add_value_length_mla_swa(hparams["swa_v_head_dim"])
-        if hparams["swa_q_lora_rank"] != hparams["q_lora_rank"]:
-            raise ValueError("dots3-note conversion assumes a shared q_lora_rank for both layer kinds")
-
-        if self.n_nextn:
-            self.gguf_writer.add_nextn_predict_layers(self.n_nextn)
-
-        # DSA indexer (full-attention layers only)
-        self.gguf_writer.add_indexer_head_count(hparams["index_n_heads"])
-        self.gguf_writer.add_indexer_key_length(hparams["index_head_dim"])
-        self.gguf_writer.add_indexer_top_k(hparams["index_topk"])
-        self.gguf_writer.add_indexer_types([not self._is_swa_layer(il) for il in range(n_layer)])
-
-    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: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # move the MTP token embedding into the NextN block so the standard nextn mapping picks it up
-        if name == "model.mtp.embed_tokens.weight":
-            name = f"model.layers.{self.hparams['num_hidden_layers']}.embed_tokens.weight"
-            bid = self.hparams["num_hidden_layers"]
-
-        # fold the activation rescale sqrt(n_embd/lora_rank) into the preceding RMSNorm weight
-        # this also covers the indexer wq_b, which reads the same rescaled q_lora activation
-        if self.apply_lora_rescale and bid is not None:
-            if name.endswith("q_a_layernorm.weight"):
-                data_torch = data_torch * math.sqrt(self.hparams["hidden_size"] / self.hparams["q_lora_rank"])
-            elif name.endswith("kv_a_layernorm.weight"):
-                rank = self.hparams["swa_kv_lora_rank"] if self._is_swa_layer(bid) else self.hparams["kv_lora_rank"]
-                data_torch = data_torch * math.sqrt(self.hparams["hidden_size"] / rank)
-
-        # MLA absorption: split kv_b_proj into k_b (transposed) and v_b, per-layer-kind geometry
-        if name.endswith("kv_b_proj.weight"):
-            assert bid is not None
-            if self._is_swa_layer(bid):
-                n_head = self.hparams["swa_num_attention_heads"]
-                qk_nope_head_dim = self.hparams["swa_qk_nope_head_dim"]
-                v_head_dim = self.hparams["swa_v_head_dim"]
-            else:
-                n_head = self.hparams["num_attention_heads"]
-                qk_nope_head_dim = self.hparams["qk_nope_head_dim"]
-                v_head_dim = self.hparams["v_head_dim"]
-            if isinstance(n_head, list):  # set_gguf_parameters turns this into a per-layer array
-                n_head = n_head[bid]
-
-            assert data_torch.shape[0] == n_head * (qk_nope_head_dim + v_head_dim)
-
-            kv_b = data_torch.view(n_head, qk_nope_head_dim + v_head_dim, data_torch.shape[-1])
-            k_b, v_b = kv_b.split([qk_nope_head_dim, v_head_dim], dim=1)
-            k_b = k_b.transpose(1, 2)
-
-            yield from ModelBase.modify_tensors(self, k_b, name.replace("kv_b_proj", "k_b_proj"), bid)
-            yield from ModelBase.modify_tensors(self, v_b, name.replace("kv_b_proj", "v_b_proj"), bid)
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
-@ModelBase.register("Dots3NoteForCausalLM", "Dots3NoteForConditionalGeneration")
-class Dots3NoteMmprojModel(MmprojModel):
-    has_vision_encoder = True
-    has_audio_encoder = True
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        assert self.hparams_vision is not None
-        assert self.hparams_audio is not None
-
-        # preprocessor_config.json nests the image params under vision_config
-        self.preprocessor_config = {**self.preprocessor_config, **self.preprocessor_config.get("vision_config", {})}
-
-        vis = self.hparams_vision
-        # in this config, hidden_size is the adapter output width; embed_dim is the tower width
-        vis["hidden_size"] = vis["embed_dim"]
-        vis["image_size"] = 0  # dynamic resolution
-        self.pyramid = [max(0, n) for n in vis["pyramid_num_routed"]]
-
-        if vis.get("adapter_type") != "patch_merger" or not vis.get("pre_pixel_shuffle"):
-            raise ValueError("dots3-note vision conversion requires adapter_type=patch_merger and pre_pixel_shuffle")
-        if vis.get("router_scoring_func", "sigmoid") != "sigmoid" or vis.get("router_scale", 1.0) != 1.0:
-            raise ValueError("dots3-note vision conversion only supports sigmoid routing with router_scale=1.0")
-        if vis.get("temporal_patch_size", 1) != 1 or vis.get("use_bias") or not vis.get("use_qk_norm"):
-            raise ValueError("unsupported dots3-note vision config variant")
-
-        aud = self.hparams_audio
-        if not aud.get("use_conv2d_stem") or not aud.get("use_rope") or not aud.get("use_rms_norm") or aud.get("use_causal"):
-            raise ValueError("unsupported dots3-note audio config variant")
-        if aud["whisper_config"].get("activation_function") != "swiglu":
-            raise ValueError("dots3-note audio conversion requires the swiglu activation")
-        if aud.get("merge_factor", 1) != 1 or aud.get("chunk_seconds") != 60:
-            raise ValueError("unsupported dots3-note audio chunking config")
-        # the graph hard-codes these rope parameters
-        rope = aud.get("rope_parameters", {})
-        if rope.get("partial_rotary_factor") != 0.5 or rope.get("rope_theta") != 10000.0:
-            raise ValueError("unsupported dots3-note audio rope config")
-
-    def get_audio_config(self) -> dict[str, Any] | None:
-        cfg = self.global_config.get("audio_config")
-        if cfg is not None:
-            # aliases so MmprojModel.find_aparam() / n_block_keys can resolve them
-            whisper = cfg["whisper_config"]
-            cfg["hidden_size"] = whisper["d_model"]
-            cfg["intermediate_size"] = whisper["encoder_ffn_dim"]
-            cfg["num_attention_heads"] = whisper["encoder_attention_heads"]
-            cfg["num_hidden_layers"] = whisper["encoder_layers"]
-        return cfg
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        assert self.hparams_vision is not None
-        assert self.hparams_audio is not None
-
-        self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.DOTS3NOTE_V)
-        self.gguf_writer.add_vision_use_silu(True)
-        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision["rms_norm_eps"])
-        self.gguf_writer.add_vision_spatial_merge_size(self.hparams_vision["spatial_merge_size"])
-        self.gguf_writer.add_vision_min_pixels(self.preprocessor_config["min_pixels"])
-        self.gguf_writer.add_vision_max_pixels(self.preprocessor_config["max_pixels"])
-        # pyramid MoE: per-block routed expert count, 0 = dense block
-        self.gguf_writer.add_vision_expert_count_per_layer(self.pyramid)
-        self.gguf_writer.add_vision_expert_used_count(int(self.hparams_vision["capacity_factor"]))
-
-        self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.DOTS3NOTE_A)
-        self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["whisper_config"]["num_mel_bins"])
-        self.gguf_writer.add_audio_attention_layernorm_eps(1e-6)  # Dots3NoteAudioRMSNorm default
-
-    @classmethod
-    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
-        name, _ = item
-        if not name.startswith(("vision_encoder.", "audio_encoder.")):
-            return None
-        return super().filter_tensors(item)
-
-    _vis_experts: dict[int, dict[str, Tensor]] | None = None
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # router params have no .weight suffix in the checkpoint, but gguf tools expect one
-        if name.endswith((".gate_weight", ".router_bias")):
-            name += ".weight"
-
-        # audio fc1 fuses gate and up for swiglu; split it
-        if ".speech_encoder.layers." in name and ".fc1." in name:
-            gate, up = data_torch.chunk(2, dim=0)
-            yield from super().modify_tensors(gate, name.replace(".fc1.", ".fc1_gate."), bid)
-            yield from super().modify_tensors(up, name.replace(".fc1.", ".fc1_up."), bid)
-            return
-
-        # vision MoE: stack per-expert weights into a single 3D tensor per block
-        if ".mlp.experts." in name:
-            assert bid is not None
-            n_expert = self.pyramid[bid]
-            if self._vis_experts is None:
-                self._vis_experts = {}
-            buf = self._vis_experts.setdefault(bid, {})
-            buf[name] = data_torch
-
-            if len(buf) >= n_expert * 3:
-                for w_name in ("fc1", "fc2", "fc3"):
-                    datas: list[Tensor] = []
-                    for xid in range(n_expert):
-                        ename = f"vision_encoder.blocks.{bid}.mlp.experts.{xid}.{w_name}.weight"
-                        datas.append(buf.pop(ename))
-                    merged = torch.stack(datas, dim=0)
-                    yield from super().modify_tensors(merged, f"vision_encoder.blocks.{bid}.mlp.experts.{w_name}.weight", bid)
-            return
-
-        yield from super().modify_tensors(data_torch, name, bid)
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-        if self._vis_experts is not None:
-            leftover = [k for d in self._vis_experts.values() for k in d.keys()]
-            if leftover:
-                raise ValueError(f"unprocessed vision experts: {leftover}")
-
-    def tensor_force_quant(self, name, new_name, bid, n_dims):
-        # FP32 routing is load-bearing for the vision MoE (near-tied expert scores)
-        if ".ffn_gate_inp." in new_name or ".exp_probs_b." in new_name:
-            return gguf.GGMLQuantizationType.F32
-        if ".conv2d" in new_name or "a.conv_out" in new_name:
-            return gguf.GGMLQuantizationType.F32
-        return super().tensor_force_quant(name, new_name, bid, n_dims)
@@ -9,7 +9,6 @@ from .base import MmprojModel, ModelBase, gguf


@ModelBase.register("DotsOCRForCausalLM")
-@ModelBase.example("rednote-hilab/dots.ocr")
 class DotsOCRVisionModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
@@ -9,7 +9,6 @@ from .base import ModelBase, TextModel, gguf


@ModelBase.register("DreamModel")
-@ModelBase.example("Dream-org/Dream-v0-Instruct-7B")
 class DreamModel(TextModel):
    model_arch = gguf.MODEL_ARCH.DREAM

@@ -15,7 +15,6 @@ from .base import MmprojModel, ModelBase, TextModel, gguf


@ModelBase.register("Ernie4_5_ForCausalLM", "Ernie4_5ForCausalLM")
-@ModelBase.example("baidu/ERNIE-4.5-0.3B-PT")
 class Ernie4_5Model(TextModel):
    model_arch = gguf.MODEL_ARCH.ERNIE4_5

@@ -74,7 +73,6 @@ class Ernie4_5Model(TextModel):


@ModelBase.register("Ernie4_5_MoeForCausalLM")
-@ModelBase.example("baidu/ERNIE-4.5-21B-A3B-PT")
 class Ernie4_5MoeModel(Ernie4_5Model):
    model_arch = gguf.MODEL_ARCH.ERNIE4_5_MOE
    _experts: list[dict[str, Tensor]] | None = None
@@ -158,13 +156,11 @@ class Ernie4_5MoeModel(Ernie4_5Model):


@ModelBase.register("PaddleOCRVLForConditionalGeneration")
-@ModelBase.example("PaddlePaddle/PaddleOCR-VL")
 class PaddleOCRModel(Ernie4_5Model):
    model_arch = gguf.MODEL_ARCH.PADDLEOCR


@ModelBase.register("PaddleOCRVisionModel")
-@ModelBase.example("PaddlePaddle/PaddleOCR-VL")
 class PaddleOCRVisionModel(MmprojModel):
    # PaddleOCR-VL uses a modified version of Siglip
    min_pixels: int = 0
@@ -15,7 +15,6 @@ from .qwenvl import Qwen2VLVisionModel


@ModelBase.register("ExaoneForCausalLM")
-@ModelBase.example("LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct")
 class ExaoneModel(TextModel):
    model_arch = gguf.MODEL_ARCH.EXAONE

@@ -61,7 +60,6 @@ class ExaoneModel(TextModel):


@ModelBase.register("Exaone4ForCausalLM")
-@ModelBase.example("LGAI-EXAONE/EXAONE-4.0-32B")
 class Exaone4Model(TextModel):
    model_arch = gguf.MODEL_ARCH.EXAONE4

@@ -128,7 +126,6 @@ class Exaone4Model(TextModel):
 # note: transformers >= 5.1 renamed the class to "ExaoneMoeForCausalLM" (lowercase 'e'),
 #       so accept both spellings - LG AI have updated the configs of already-released models
@ModelBase.register("ExaoneMoEForCausalLM", "ExaoneMoeForCausalLM")
-@ModelBase.example("LGAI-EXAONE/K-EXAONE-236B-A23B")
 class ExaoneMoEModel(Exaone4Model):
    model_arch = gguf.MODEL_ARCH.EXAONE_MOE

@@ -217,7 +214,6 @@ class ExaoneMoEModel(Exaone4Model):


@ModelBase.register("Exaone4_5_ForConditionalGeneration")
-@ModelBase.example("LGAI-EXAONE/EXAONE-4.5-33B")
 class Exaone4_5_TextModel(Exaone4Model):
    """Text tower of EXAONE 4.5; Tensors match EXAONE4"""

@@ -271,7 +267,6 @@ class Exaone4_5_TextModel(Exaone4Model):


@ModelBase.register("Exaone4_5_ForConditionalGeneration")
-@ModelBase.example("LGAI-EXAONE/EXAONE-4.5-33B")
 class Exaone4_5VisionModel(Qwen2VLVisionModel):
    """Vision tower for EXAONE 4.5; Qwen2-VL-style ViT (GQA) + patch merger"""

@@ -11,7 +11,6 @@ from .base import ModelBase, TextModel, gguf


@ModelBase.register("FalconForCausalLM", "RWForCausalLM")
-@ModelBase.example("tiiuae/falcon-7b")
 class FalconModel(TextModel):
    model_arch = gguf.MODEL_ARCH.FALCON

@@ -12,7 +12,6 @@ from .mamba import Mamba2Model


@ModelBase.register("FalconH1ForCausalLM")
-@ModelBase.example("tiiuae/Falcon-H1-0.5B-Base")
 class FalconH1Model(Mamba2Model):
    model_arch = gguf.MODEL_ARCH.FALCON_H1

@@ -14,8 +14,6 @@ from .base import MmprojModel, ModelBase, TextModel, gguf, logger


@ModelBase.register("GemmaForCausalLM")
-# [TAG_HF_EXAMPLE_GATED] google/gemma-2b is gated
-@ModelBase.example("trl-internal-testing/tiny-GemmaForCausalLM")
 class GemmaModel(TextModel):
    model_arch = gguf.MODEL_ARCH.GEMMA

@@ -70,8 +68,6 @@ class GemmaModel(TextModel):


@ModelBase.register("Gemma2ForCausalLM")
-# [TAG_HF_EXAMPLE_GATED] google/gemma-2-9b-it is gated
-@ModelBase.example("trl-internal-testing/tiny-Gemma2ForCausalLM")
 class Gemma2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.GEMMA2

@@ -122,8 +118,6 @@ class Gemma2Model(TextModel):


@ModelBase.register("Gemma3ForCausalLM", "Gemma3ForConditionalGeneration")
-# [TAG_HF_EXAMPLE_GATED] google/gemma-3-4b-it is gated
-@ModelBase.example("trl-internal-testing/tiny-Gemma3ForConditionalGeneration", "hf-tiny-v2/tiny-random-Gemma3ForCausalLM")
 class Gemma3Model(TextModel):
    model_arch = gguf.MODEL_ARCH.GEMMA3

@@ -180,8 +174,6 @@ class Gemma3Model(TextModel):


@ModelBase.register("Gemma3TextModel")
-# [TAG_HF_EXAMPLE_GATED] google/embeddinggemma-300m is gated
-@ModelBase.example("hf-tiny-v2/tiny-random-Gemma3TextModel")
 class EmbeddingGemma(Gemma3Model):
    model_arch = gguf.MODEL_ARCH.GEMMA_EMBEDDING
    module_paths = []
@@ -256,8 +248,6 @@ class EmbeddingGemma(Gemma3Model):


@ModelBase.register("Gemma3ForConditionalGeneration")
-# [TAG_HF_EXAMPLE_GATED] google/gemma-3-4b-it is gated
-@ModelBase.example("trl-internal-testing/tiny-Gemma3ForConditionalGeneration")
 class Gemma3VisionModel(MmprojModel):
    def set_gguf_parameters(self):
        super().set_gguf_parameters()
@@ -362,8 +352,6 @@ class ConformerAudioModel(MmprojModel):


@ModelBase.register("Gemma3nForConditionalGeneration")
-# [TAG_HF_EXAMPLE_GATED] google/gemma-3n-E2B-it is gated
-@ModelBase.example("hf-tiny-v2/tiny-random-Gemma3nForConditionalGeneration")
 class Gemma3nVisionAudioModel(ConformerAudioModel):
    has_audio_encoder = True
    has_vision_encoder = True
@@ -483,8 +471,6 @@ class Gemma3nVisionAudioModel(ConformerAudioModel):


@ModelBase.register("Gemma3nForCausalLM", "Gemma3nForConditionalGeneration")
-# [TAG_HF_EXAMPLE_GATED] google/gemma-3n-E2B-it is gated
-@ModelBase.example("hf-tiny-v2/tiny-random-Gemma3nForConditionalGeneration")
 class Gemma3NModel(Gemma3Model):
    model_arch = gguf.MODEL_ARCH.GEMMA3N

@@ -629,7 +615,6 @@ class Gemma3NModel(Gemma3Model):


@ModelBase.register("Gemma4ForConditionalGeneration", "Gemma4ForCausalLM")
-@ModelBase.example("google/gemma-4-31B-it", "google/gemma-4-26B-A4B-it", "google/gemma-4-E2B-it")
 class Gemma4Model(Gemma3Model):
    model_arch = gguf.MODEL_ARCH.GEMMA4

@@ -810,7 +795,6 @@ class Gemma4Model(Gemma3Model):


@ModelBase.register("Gemma4UnifiedForConditionalGeneration")
-@ModelBase.example("hf-tiny-v2/tiny-random-Gemma4UnifiedForConditionalGeneration")
 class Gemma4UnifiedModel(Gemma4Model):
    model_arch = gguf.MODEL_ARCH.GEMMA4

@@ -831,7 +815,6 @@ class Gemma4UnifiedModel(Gemma4Model):


@ModelBase.register("Gemma4AssistantForCausalLM", "Gemma4UnifiedAssistantForCausalLM")
-@ModelBase.example("google/gemma-4-31B-it-assistant", "google/gemma-4-26B-A4B-it-assistant", "google/gemma-4-E2B-it-assistant")
 class Gemma4AssistantModel(Gemma4Model):
    model_arch = gguf.MODEL_ARCH.GEMMA4_ASSISTANT

@@ -852,7 +835,6 @@ class Gemma4AssistantModel(Gemma4Model):


@ModelBase.register("Gemma4ForConditionalGeneration")
-@ModelBase.example("google/gemma-4-31B-it", "google/gemma-4-26B-A4B-it", "google/gemma-4-E2B-it")
 class Gemma4VisionAudioModel(MmprojModel):
    has_audio_encoder = True
    has_vision_encoder = True
@@ -931,7 +913,6 @@ class Gemma4VisionAudioModel(MmprojModel):


@ModelBase.register("Gemma4UnifiedForConditionalGeneration")
-@ModelBase.example("hf-tiny-v2/tiny-random-Gemma4UnifiedForConditionalGeneration")
 class Gemma4UnifiedVisionAudioModel(Gemma4VisionAudioModel):
    has_audio_encoder = True
    has_vision_encoder = True
@@ -15,7 +15,6 @@ from .deepseek import DeepseekV2Model


@ModelBase.register("Glm4ForCausalLM", "Glm4vForConditionalGeneration")
-@ModelBase.example("zai-org/GLM-4-9B-0414")
 class Glm4Model(TextModel):
    model_arch = gguf.MODEL_ARCH.GLM4
    use_mrope = False
@@ -87,7 +86,6 @@ class Glm4Model(TextModel):


@ModelBase.register("GlmOcrForConditionalGeneration")
-@ModelBase.example("zai-org/GLM-OCR")
 class GlmOCRModel(Glm4Model):
    model_arch = gguf.MODEL_ARCH.GLM4
    use_mrope = False
@@ -109,7 +107,6 @@ class GlmOCRModel(Glm4Model):


@ModelBase.register("Glm4MoeForCausalLM", "Glm4vMoeForConditionalGeneration")
-@ModelBase.example("zai-org/GLM-4.5-Air")
 class Glm4MoeModel(TextModel):
    model_arch = gguf.MODEL_ARCH.GLM4_MOE

@@ -207,7 +204,6 @@ class Glm4MoeModel(TextModel):


@ModelBase.register("Glm4MoeLiteForCausalLM")
-@ModelBase.example("zai-org/GLM-4.7-Flash")
 class Glm4MoeLiteModel(DeepseekV2Model):
    model_arch = gguf.MODEL_ARCH.DEEPSEEK2
    skip_mtp = False
@@ -276,7 +272,6 @@ class Glm4MoeLiteModel(DeepseekV2Model):


@ModelBase.register("GlmMoeDsaForCausalLM")
-@ModelBase.example("zai-org/GLM-5.2")
 class GlmMoeDsaModel(DeepseekV2Model):
    model_arch = gguf.MODEL_ARCH.GLM_DSA
    skip_mtp = False
@@ -345,7 +340,6 @@ class GlmMoeDsaModel(DeepseekV2Model):


@ModelBase.register("SolarOpenForCausalLM")
-@ModelBase.example("upstage/Solar-Open-100B")
 class SolarOpenModel(Glm4MoeModel):
    model_arch = gguf.MODEL_ARCH.GLM4_MOE

@@ -11,7 +11,6 @@ from .base import ModelBase, TextModel, gguf, logger


@ModelBase.register("GPT2LMHeadModel")
-@ModelBase.example("openai-community/gpt2")
 class GPT2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.GPT2

@@ -39,7 +38,6 @@ class GPT2Model(TextModel):


@ModelBase.register("RuGPT3XLForCausalLM")
-@ModelBase.example("evilfreelancer/ruGPT3XL")
 class RuGPT3XLModel(TextModel):
    model_arch = gguf.MODEL_ARCH.GPT2

@@ -11,7 +11,6 @@ from .base import ModelBase, TextModel, gguf, logger


@ModelBase.register("GptOssForCausalLM")
-@ModelBase.example("openai/gpt-oss-20b")
 class GptOssModel(TextModel):
    model_arch = gguf.MODEL_ARCH.GPT_OSS

@@ -13,7 +13,6 @@ from .base import ModelBase, TextModel, gguf, logger


@ModelBase.register("GPTNeoXForCausalLM")
-@ModelBase.example("EleutherAI/pythia-70m")
 class GPTNeoXModel(TextModel):
    model_arch = gguf.MODEL_ARCH.GPTNEOX

@@ -15,7 +15,6 @@ from .mamba import Mamba2Model


@ModelBase.register("GraniteForCausalLM")
-@ModelBase.example("ibm-granite/granite-3.3-2b-instruct")
 class GraniteModel(LlamaModel):
    """Conversion for IBM's GraniteForCausalLM"""
    model_arch = gguf.MODEL_ARCH.GRANITE
@@ -74,110 +73,7 @@ class GraniteModel(LlamaModel):
        return super().filter_tensors(item)


-@ModelBase.register("GraniteSWAForCausalLM")
-class GraniteSWAModel(GraniteModel):
-    """Conversion for IBM's GraniteSWAForCausalLM (interleaved sliding window attention)"""
-    model_arch = gguf.MODEL_ARCH.GRANITE_SWA
-
-    @classmethod
-    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
-        name, gen = item
-
-        if name.endswith("sinks"):
-            name += ".weight"
-
-        return super().filter_tensors((name, gen))
-
-    def set_gguf_parameters(self):
-        """GraniteSWA uses Granite parameters plus sliding window configuration."""
-        super().set_gguf_parameters()
-
-        # Add sliding_window from config
-        sliding_window = self.hparams.get("sliding_window", 128)
-        self.gguf_writer.add_sliding_window(sliding_window)
-        logger.info("gguf: (granite_swa) sliding_window = %s", sliding_window)
-
-        # Derive sliding_window_pattern from layer_types
-        if layer_types := self.hparams.get("layer_types"):
-            is_swa = [t == "sliding_attention" for t in layer_types]
-            self.gguf_writer.add_sliding_window_pattern(is_swa)
-            logger.info("gguf: (granite_swa) sliding_window_pattern = %d SWA layers / %d total",
-                        sum(is_swa), len(is_swa))
-        else:
-            # Fall back to period-based pattern: i % 4 != 0
-            # This matches the transformers default pattern
-            n_layers = self.block_count
-            is_swa = [i % 4 != 0 for i in range(n_layers)]
-            self.gguf_writer.add_sliding_window_pattern(is_swa)
-            logger.info("gguf: (granite_swa) sliding_window_pattern (inferred) = %d SWA layers / %d total",
-                        sum(is_swa), n_layers)
-
-        # Add rope_pattern from no_rope_layers
-        if no_rope_layers := self.hparams.get("no_rope_layers"):
-            # Convert 1/0 to bool (1 = use RoPE, 0 = NoPE)
-            rope_pattern = [bool(x) for x in no_rope_layers]
-            self.gguf_writer.add_rope_pattern(rope_pattern)
-            logger.info("gguf: (granite_swa) rope_pattern = %d RoPE layers / %d total",
-                        sum(rope_pattern), len(rope_pattern))
-
-
-@ModelBase.register("GraniteMoeSWAForCausalLM")
-class GraniteMoeSWAModel(GraniteSWAModel):
-    """Conversion for IBM's GraniteMoeSWAForCausalLM (unified dense + MoE with iSWA)"""
-    model_arch = gguf.MODEL_ARCH.GRANITE_SWA
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        if shared_intermediate_size := self.hparams.get("shared_intermediate_size"):
-            self.gguf_writer.add_expert_shared_feed_forward_length(shared_intermediate_size)
-            logger.info("gguf: (granitemoewa) shared_intermediate_size = %s", shared_intermediate_size)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        """Split merged MoE tensors (gate+up) following standard MoE pattern."""
-
-        # Handle expert FFN tensors (merged gate+up) - swash format: experts.gate_up_proj
-        # Kept fused since inference (build_moe_ffn) supports a single gate_up_exps
-        # tensor for the routed experts.
-        if name.endswith("block_sparse_moe.experts.gate_up_proj"):
-            ffn_dim = self.hparams["intermediate_size"]
-            assert data_torch.shape[-2] == 2 * ffn_dim, f"Merged FFN tensor size must be 2 * intermediate_size, got {data_torch.shape[-2]}"
-            yield from ModelBase.modify_tensors(self, data_torch, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_UP_EXP, bid), bid)
-            return
-
-        # Handle expert FFN down projection - swash format: experts.down_proj
-        if name.endswith("block_sparse_moe.experts.down_proj"):
-            yield from ModelBase.modify_tensors(self, data_torch, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_DOWN_EXP, bid), bid)
-            return
-
-        # Handle expert FFN tensors (merged gate+up) - standard granite format: input_linear.weight
-        # Kept fused since inference (build_moe_ffn) supports a single gate_up_exps
-        # tensor for the routed experts.
-        if name.endswith("block_sparse_moe.input_linear.weight"):
-            ffn_dim = self.hparams["intermediate_size"]
-            assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * intermediate_size"
-            yield from ModelBase.modify_tensors(self, data_torch, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_UP_EXP, bid), bid)
-            return
-
-        # Handle shared expert FFN tensors (if present) - kept fused since
-        # inference (build_ffn) supports a single ffn_up_shexp tensor with
-        # LLM_FFN_SWIGLU for the shared expert.
-        if name.endswith("shared_mlp.input_linear.weight"):
-            ffn_dim = self.hparams.get("shared_intermediate_size", self.hparams["intermediate_size"])
-            assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * shared_intermediate_size"
-            yield from ModelBase.modify_tensors(self, data_torch, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_SHEXP, bid), bid)
-            return
-
-        # Handle shared expert output (if present)
-        if name.endswith("shared_mlp.output_linear.weight"):
-            yield from ModelBase.modify_tensors(self, data_torch, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_DOWN_SHEXP, bid), bid)
-            return
-
-        # Pass through to parent for all other tensors (including sinks)
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
@ModelBase.register("GraniteMoeForCausalLM", "GraniteMoeSharedForCausalLM")
-@ModelBase.example("ibm-granite/granite-3.1-3b-a800m-instruct")
 class GraniteMoeModel(GraniteModel):
    """Conversion for IBM's GraniteMoeForCausalLM"""
    model_arch = gguf.MODEL_ARCH.GRANITE_MOE
@@ -228,7 +124,6 @@ class GraniteMoeModel(GraniteModel):


@ModelBase.register("GraniteSwitchForCausalLM")
-@ModelBase.example("ibm-granite/granite-switch-4.1-3b-preview")
 class GraniteSwitchModel(GraniteMoeModel):
    """Dense, all-attention Granite with N per-token embedded LoRA adapters, stacked
    over the adapter dim with a zero adapter at slot 0 (N = num_adapters + 1)."""
@@ -389,7 +284,6 @@ class GraniteSwitchModel(GraniteMoeModel):


@ModelBase.register("GraniteMoeHybridForCausalLM", "BambaForCausalLM")
-@ModelBase.example("ibm-granite/granite-4.0-h-tiny", "ibm-ai-platform/Bamba-9B-v2")
 class GraniteHybridModel(Mamba2Model, GraniteMoeModel):
    """GraniteHybrid is a hybrid SSM + Attention model that uses Mamba2 SSM
    layers and optionally uses MoE w/ a shared expert"""
@@ -532,7 +426,6 @@ class GraniteHybridModel(Mamba2Model, GraniteMoeModel):


@ModelBase.register("GraniteSpeechForConditionalGeneration")
-@ModelBase.example("ibm-granite/granite-speech-3.3-2b", "ibm-granite/granite-4.0-1b-speech")
 class GraniteSpeechMmprojModel(MmprojModel):
    has_vision_encoder = False
    has_audio_encoder = True
@@ -616,7 +509,6 @@ class GraniteSpeechMmprojModel(MmprojModel):


@ModelBase.register("GraniteSpeechPlusForConditionalGeneration")
-@ModelBase.example("ibm-granite/granite-speech-4.1-2b-plus")
 class GraniteSpeechPlusMmprojModel(GraniteSpeechMmprojModel):
    """Conversion for GraniteSpeechPlus - extends GraniteSpeech with feature layer concatenation"""
    has_vision_encoder = False
@@ -645,7 +537,6 @@ class GraniteSpeechPlusMmprojModel(GraniteSpeechMmprojModel):


@ModelBase.register("Granite4VisionForConditionalGeneration")
-@ModelBase.example("ibm-granite/granite-4.0-3b-vision")
 class Granite4VisionMmprojModel(MmprojModel):
    has_vision_encoder = True
    has_audio_encoder = False
@@ -13,7 +13,6 @@ from .base import ModelBase, TextModel, gguf, logger


@ModelBase.register("GrokForCausalLM", "Grok1ForCausalLM")
-@ModelBase.example("keyfan/grok-1-hf")
 class GrokModel(TextModel):
    model_arch = gguf.MODEL_ARCH.GROK

@@ -11,7 +11,6 @@ from .base import ModelBase, TextModel, gguf, logger


@ModelBase.register("GroveMoeForCausalLM", "modeling_grove_moe.GroveMoeForCausalLM")
-@ModelBase.example("inclusionAI/GroveMoE-Inst")
 class GroveMoeModel(TextModel):
    model_arch = gguf.MODEL_ARCH.GROVEMOE

@@ -17,7 +17,6 @@ from .qwen import QwenModel


@ModelBase.register("HunYuanMoEV1ForCausalLM")
-@ModelBase.example("tencent/Hunyuan-A13B-Instruct")
 class HunYuanMoEModel(TextModel):
    model_arch = gguf.MODEL_ARCH.HUNYUAN_MOE

@@ -155,7 +154,6 @@ class HunYuanMoEModel(TextModel):


@ModelBase.register("HunYuanDenseV1ForCausalLM")
-@ModelBase.example("tencent/Hunyuan-4B-Instruct")
 class HunYuanModel(TextModel):
    model_arch = gguf.MODEL_ARCH.HUNYUAN_DENSE

@@ -292,7 +290,6 @@ class HunYuanModel(TextModel):


@ModelBase.register("HunYuanVLForConditionalGeneration")
-@ModelBase.example("tencent/HunyuanOCR")
 class HunyuanVLVisionModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
@@ -336,7 +333,6 @@ class HunyuanVLVisionModel(MmprojModel):


@ModelBase.register("HunYuanVLForConditionalGeneration")
-@ModelBase.example("tencent/HunyuanOCR")
 class HunyuanVLTextModel(HunYuanModel):
    model_arch = gguf.MODEL_ARCH.HUNYUAN_VL

@@ -369,7 +365,6 @@ class HunyuanVLTextModel(HunYuanModel):


@ModelBase.register("HYV3ForCausalLM")
-@ModelBase.example("tencent/Hy3")
 class HYV3Model(TextModel):
    model_arch = gguf.MODEL_ARCH.HY_V3
    supports_mtp_export = True
@@ -14,7 +14,6 @@ from .llama import LlamaModel


@ModelBase.register("InternLM2ForCausalLM")
-@ModelBase.example("internlm/internlm2-chat-7b")
 class InternLM2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.INTERNLM2

@@ -171,7 +170,6 @@ class InternLM2Model(TextModel):


@ModelBase.register("InternLM3ForCausalLM")
-@ModelBase.example("internlm/internlm3-8b-instruct")
 class InternLM3Model(TextModel):
    model_arch = gguf.MODEL_ARCH.LLAMA

@@ -9,7 +9,6 @@ from .base import MmprojModel, ModelBase, gguf


@ModelBase.register("InternVisionModel")
-@ModelBase.example("OpenGVLab/InternVL3-2B", "OpenGVLab/InternVL2_5-1B")
 class InternVisionModel(MmprojModel):

    min_dynamic_tiles: int = 0
@@ -11,8 +11,6 @@ from .base import ModelBase, TextModel, gguf


@ModelBase.register("Jais2ForCausalLM")
-# [TAG_HF_EXAMPLE_GATED] inceptionai/Jais-2-8B-Chat is gated
-# [TAG_HF_EXAMPLE_MISSING]
 class Jais2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.JAIS2

@@ -24,7 +22,6 @@ class Jais2Model(TextModel):


@ModelBase.register("JAISLMHeadModel")
-@ModelBase.example("inceptionai/jais-family-590m")
 class JaisModel(TextModel):
    model_arch = gguf.MODEL_ARCH.JAIS

@@ -11,7 +11,6 @@ from .base import ModelBase, TextModel, gguf, logger


@ModelBase.register("JambaForCausalLM")
-@ModelBase.example("ai21labs/Jamba-v0.1")
 class JambaModel(TextModel):
    model_arch = gguf.MODEL_ARCH.JAMBA

@@ -11,7 +11,6 @@ from .llama import LlamaModel


@ModelBase.register("JanusForConditionalGeneration")
-@ModelBase.example("deepseek-community/Janus-Pro-1B")
 class JanusProModel(LlamaModel):
    model_arch = gguf.MODEL_ARCH.LLAMA  # reuse Llama arch

@@ -35,7 +34,6 @@ class JanusProModel(LlamaModel):


@ModelBase.register("JanusForConditionalGeneration")
-@ModelBase.example("deepseek-community/Janus-Pro-1B")
 class JanusProVisionModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
@@ -16,7 +16,6 @@ from .kimi_linear import KimiLinearModel


@ModelBase.register("KimiK3ForConditionalGeneration")
-@ModelBase.example("moonshotai/Kimi-K3")
 class KimiK3Model(TextModel):
    """
    Kimi-K3 text model (KimiLinearForCausalLM under a `language_model.` prefix).
@@ -13,7 +13,6 @@ from .qwen import QwenModel


@ModelBase.register("KimiLinearModel", "KimiLinearForCausalLM")
-@ModelBase.example("moonshotai/Kimi-Linear-48B-A3B-Instruct")
 class KimiLinearModel(TextModel):
    """Kimi-Linear model with hybrid MLA+KDA architecture"""
    model_arch = gguf.MODEL_ARCH.KIMI_LINEAR
@@ -11,7 +11,6 @@ from .base import MmprojModel, ModelBase, gguf


@ModelBase.register("KimiVLForConditionalGeneration")
-@ModelBase.example("moonshotai/Kimi-VL-A3B-Instruct")
 class KimiVLModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
@@ -53,7 +52,6 @@ class KimiVLModel(MmprojModel):


@ModelBase.register("KimiK25ForConditionalGeneration")
-@ModelBase.example("moonshotai/Kimi-K2.5")
 class KimiK25Model(MmprojModel):
    """Kimi-K2.5 with MoonViT3d vision encoder"""

@@ -157,7 +155,6 @@ class KimiK25Model(MmprojModel):


@ModelBase.register("Glm5vForConditionalGeneration")
-# [TAG_HF_EXAMPLE_MISSING]
 class Glm5vModel(KimiK25Model):
    """GLM-5.2-Vision MoonViT3d encoder and projector

@@ -13,7 +13,6 @@ from .base import ModelBase, TextModel, gguf, logger


@ModelBase.register("LagunaForCausalLM")
-@ModelBase.example("poolside/Laguna-XS.2", "poolside/Laguna-S-2.1")
 class LagunaModel(TextModel):
    model_arch = gguf.MODEL_ARCH.LAGUNA
    _experts: list[dict] | None = None
@@ -13,7 +13,6 @@ from .gemma import ConformerAudioModel


@ModelBase.register("Lfm2ForCausalLM", "LFM2ForCausalLM")
-@ModelBase.example("LiquidAI/LFM2-1.2B", "LiquidAI/LFM2.5-350M")
 class LFM2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.LFM2

@@ -66,7 +65,6 @@ class LFM2Model(TextModel):


@ModelBase.register("Lfm2Model", "Lfm2BidirectionalModel")
-@ModelBase.example("LiquidAI/LFM2.5-ColBERT-350M", "LiquidAI/LFM2.5-Embedding-350M")
 class LFM2ColBertModel(LFM2Model):
    model_arch = gguf.MODEL_ARCH.LFM2
    dense_tensor_name = "dense_2"
@@ -95,7 +93,6 @@ class LFM2ColBertModel(LFM2Model):


@ModelBase.register("Lfm2MoeForCausalLM")
-@ModelBase.example("LiquidAI/LFM2-8B-A1B")
 class LFM2MoeModel(TextModel):
    model_arch = gguf.MODEL_ARCH.LFM2MOE

@@ -169,7 +166,6 @@ class LFM2MoeModel(TextModel):


@ModelBase.register("Lfm2VlForConditionalGeneration")
-@ModelBase.example("LiquidAI/LFM2-VL-450M")
 class LFM2VLModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
@@ -204,7 +200,6 @@ class LFM2VLModel(MmprojModel):


@ModelBase.register("Lfm2AudioForConditionalGeneration")
-@ModelBase.example("LiquidAI/LFM2.5-Audio-1.5B", "LiquidAI/LFM2-Audio-1.5B")
 class LFM2AudioModel(ConformerAudioModel):
    has_vision_encoder = False
    has_audio_encoder = True
@@ -243,7 +238,6 @@ class LFM2AudioModel(ConformerAudioModel):


@ModelBase.register("Lfm25AudioTokenizer")
-@ModelBase.example("LiquidAI/LFM2.5-Audio-1.5B")
 class LFM25AudioTokenizer(LFM2Model):
    model_arch = gguf.MODEL_ARCH.LFM2

@@ -11,7 +11,6 @@ from .llava import LlavaVisionModel


@ModelBase.register("LightOnOCRForConditionalGeneration")
-@ModelBase.example("lightonai/LightOnOCR-1B-1025")
 class LightOnOCRVisionModel(LlavaVisionModel):
    is_mistral_format = False
    use_break_tok = False
@@ -11,7 +11,6 @@ from .base import ModelBase, TextModel, gguf


@ModelBase.register("LLaDAModelLM")
-@ModelBase.example("GSAI-ML/LLaDA-8B-Instruct")
 class LLaDAModel(TextModel):
    model_arch = gguf.MODEL_ARCH.LLADA
    undo_permute = True
@@ -115,7 +114,6 @@ class LLaDAModel(TextModel):


@ModelBase.register("LLaDAMoEModel", "LLaDAMoEModelLM")
-@ModelBase.example("inclusionAI/LLaDA-MoE-7B-A1B-Instruct")
 class LLaDAMoEModel(TextModel):
    model_arch = gguf.MODEL_ARCH.LLADA_MOE

@@ -28,8 +28,6 @@ from .base import ModelBase, TextModel, gguf, logger
    "Eagle3DraftModel",
    "IQuestCoderForCausalLM",
    "LlamaModel")
-# [TAG_HF_EXAMPLE_GATED] meta-llama/Llama-3.2-1B-Instruct is gated
-@ModelBase.example("unsloth/Llama-3.2-1B-Instruct", "mistralai/Mistral-7B-Instruct-v0.3", "mistralai/Mixtral-8x7B-Instruct-v0.1")
 class LlamaModel(TextModel):
    model_arch = gguf.MODEL_ARCH.LLAMA
    undo_permute = True
@@ -361,7 +359,6 @@ class LlamaModel(TextModel):


@ModelBase.register("ArceeForCausalLM")
-@ModelBase.example("arcee-ai/AFM-4.5B")
 class ArceeModel(LlamaModel):
    model_arch = gguf.MODEL_ARCH.ARCEE

@@ -374,8 +371,6 @@ class ArceeModel(LlamaModel):
    "Llama4ForConditionalGeneration",
    "Llama4ForCausalLM",
 )
-# [TAG_HF_EXAMPLE_GATED] meta-llama/Llama-4-Scout-17B-16E-Instruct is gated
-@ModelBase.example("unsloth/Llama-4-Scout-17B-16E-Instruct")
 class Llama4Model(LlamaModel):
    model_arch = gguf.MODEL_ARCH.LLAMA4
    undo_permute = False
@@ -417,19 +412,16 @@ class Llama4Model(LlamaModel):


@ModelBase.register("LlamaBidirectionalModel")
-@ModelBase.example("nvidia/llama-embed-nemotron-8b")
 class LlamaEmbedNemotronModel(LlamaModel):
    model_arch = gguf.MODEL_ARCH.LLAMA_EMBED


@ModelBase.register("SmolLM3ForCausalLM")
-@ModelBase.example("HuggingFaceTB/SmolLM3-3B")
 class SmolLM3Model(LlamaModel):
    model_arch = gguf.MODEL_ARCH.SMOLLM3


@ModelBase.register("ApertusForCausalLM")
-@ModelBase.example("swiss-ai/Apertus-8B-Instruct-2509")
 class ApertusModel(LlamaModel):
    model_arch = gguf.MODEL_ARCH.APERTUS
    undo_permute = False
@@ -9,8 +9,6 @@ from .base import MmprojModel, ModelBase, gguf


@ModelBase.register("Llama4ForConditionalGeneration")
-# [TAG_HF_EXAMPLE_GATED] meta-llama/Llama-4-Scout-17B-16E-Instruct is gated
-@ModelBase.example("unsloth/Llama-4-Scout-17B-16E-Instruct")
 class Llama4VisionModel(MmprojModel):
    def set_gguf_parameters(self):
        super().set_gguf_parameters()
@@ -16,7 +16,6 @@ from .llama import LlamaModel
    "LlavaForConditionalGeneration", # pixtral
    "Mistral3ForConditionalGeneration", # mistral small 3.1
 )
-@ModelBase.example("mistral-community/pixtral-12b", "mistralai/Mistral-Small-3.1-24B-Instruct-2503")
 class LlavaVisionModel(MmprojModel):
    img_break_tok_id = -1
    use_break_tok = True
@@ -4,7 +4,6 @@ from .base import ModelBase, TextModel, gguf


@ModelBase.register("MaincoderForCausalLM")
-@ModelBase.example("Maincode/Maincoder-1B")
 class MaincoderModel(TextModel):
    model_arch = gguf.MODEL_ARCH.MAINCODER

@@ -14,7 +14,6 @@ from .base import ModelBase, TextModel, gguf, logger


@ModelBase.register("MambaForCausalLM", "MambaLMHeadModel", "FalconMambaForCausalLM")
-@ModelBase.example("state-spaces/mamba-130m-hf", "tiiuae/falcon-mamba-7b")
 class MambaModel(TextModel):
    model_arch = gguf.MODEL_ARCH.MAMBA

@@ -101,7 +100,6 @@ class MambaModel(TextModel):


@ModelBase.register("Mamba2ForCausalLM")
-@ModelBase.example("mistralai/Mamba-Codestral-7B-v0.1")
 class Mamba2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.MAMBA2

@@ -11,7 +11,6 @@ from .base import ModelBase, TextModel, gguf, logger


@ModelBase.register("MellumForCausalLM")
-@ModelBase.example("JetBrains/Mellum2-12B-A2.5B-Base")
 class MellumModel(TextModel):
    model_arch = gguf.MODEL_ARCH.MELLUM

@@ -14,7 +14,6 @@ from .base import MmprojModel, ModelBase, TextModel, gguf


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

@@ -231,7 +230,6 @@ class MimoV2Model(TextModel):


@ModelBase.register("MiMoV2ForCausalLM")
-@ModelBase.example("XiaomiMiMo/MiMo-V2.5")
 class MiMoV2VisionAudioModel(MmprojModel):
    has_audio_encoder = True

@@ -14,7 +14,6 @@ from .qwen import Qwen3_5TextModel


@ModelBase.register("MiniCPMForCausalLM")
-@ModelBase.example("openbmb/MiniCPM-2B-sft-bf16")
 class MiniCPMModel(TextModel):
    model_arch = gguf.MODEL_ARCH.MINICPM

@@ -62,7 +61,6 @@ class MiniCPMModel(TextModel):


@ModelBase.register("MiniCPM3ForCausalLM")
-@ModelBase.example("openbmb/MiniCPM3-4B")
 class MiniCPM3Model(TextModel):
    model_arch = gguf.MODEL_ARCH.MINICPM3

@@ -119,7 +117,6 @@ class MiniCPM3Model(TextModel):
 # the LM (text mode) and once as the mmproj (vision mode), mirroring the Qwen3-VL setup.

@ModelBase.register("MiniCPMV4_6ForConditionalGeneration")
-@ModelBase.example("openbmb/MiniCPM-V-4_6")
 class MiniCPMV4_6TextModel(Qwen3_5TextModel):
    model_arch = gguf.MODEL_ARCH.QWEN35

@@ -137,7 +134,6 @@ class MiniCPMV4_6TextModel(Qwen3_5TextModel):


@ModelBase.register("MiniCPMV4_6ForConditionalGeneration")
-@ModelBase.example("openbmb/MiniCPM-V-4_6")
 class MiniCPMV4_6VisionModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
@@ -12,7 +12,6 @@ from .base import ModelBase, TextModel, MmprojModel, gguf, logger

@ModelBase.register("MiniMaxText01ForCausalLM")
@ModelBase.register("MiniMaxM1ForCausalLM")
-@ModelBase.example("MiniMaxAI/MiniMax-Text-01", "MiniMaxAI/MiniMax-M1-40k")
 class MiniMaxText01Model(TextModel):
    model_arch = gguf.MODEL_ARCH.MINIMAX01

@@ -120,7 +119,6 @@ class MiniMaxText01Model(TextModel):


@ModelBase.register("MiniMaxM2ForCausalLM")
-@ModelBase.example("MiniMaxAI/MiniMax-M2")
 class MiniMaxM2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.MINIMAXM2
    _experts_cache: dict[int, dict[str, Tensor]] = {}
@@ -165,7 +163,6 @@ class MiniMaxM2Model(TextModel):


@ModelBase.register("MiniMaxM3SparseForCausalLM", "MiniMaxM3SparseForConditionalGeneration")
-@ModelBase.example("MiniMaxAI/MiniMax-M3")
 class MiniMaxM3Model(MiniMaxM2Model):
    model_arch = gguf.MODEL_ARCH.MINIMAXM3

@@ -206,7 +203,6 @@ class MiniMaxM3Model(MiniMaxM2Model):


@ModelBase.register("MiniMaxM3SparseForConditionalGeneration", "MiniMaxM3VLForConditionalGeneration")
-@ModelBase.example("MiniMaxAI/MiniMax-M3")
 class MiniMaxM3VisionModel(MmprojModel):
    @classmethod
    def filter_tensors(cls, item):
@@ -15,7 +15,6 @@ from .llama import LlamaModel
    "Mistral3ForConditionalGeneration",
    "Ministral3ForCausalLM",
 )
-@ModelBase.example("mistralai/Mistral-Small-3.1-24B-Instruct-2503", "hf-tiny-v2/tiny-random-Ministral3ForCausalLM")
 class Mistral3Model(TextModel):
    class Ministral3Model(LlamaModel):
        model_arch = gguf.MODEL_ARCH.MISTRAL3
@@ -9,7 +9,6 @@ from .base import ModelBase, TextModel, gguf


@ModelBase.register("MPTForCausalLM")
-@ModelBase.example("anas-awadalla/mpt-7b")
 class MPTModel(TextModel):
    model_arch = gguf.MODEL_ARCH.MPT

@@ -24,7 +24,6 @@ def _unpermute_for_rope(tensor: "Tensor", n_heads: int) -> "Tensor":


@ModelBase.register("MuseGlimmerForConditionalGeneration")
-@ModelBase.example("meta-models/Muse-Glimmer-30B")
 class MuseGlimmerModel(TextModel):
    model_arch = gguf.MODEL_ARCH.MUSE_GLIMMER

@@ -79,7 +78,6 @@ class MuseGlimmerModel(TextModel):


@ModelBase.register("MuseGlimmerForConditionalGeneration")
-@ModelBase.example("meta-models/Muse-Glimmer-30B")
 class MuseGlimmerVisionModel(MmprojModel):
    def get_vision_config(self) -> dict[str, Any] | None:
        c = self.global_config.get("vision_config")
@@ -133,7 +131,6 @@ class MuseGlimmerVisionModel(MmprojModel):


@ModelBase.register("MuseGlimmerAssistantModel")
-@ModelBase.example("meta-models/Muse-Glimmer-30B-assistant")
 class MuseGlimmerAssistantModel(TextModel):
    model_arch = gguf.MODEL_ARCH.DFLASH

@@ -5,7 +5,6 @@ from .llama import LlamaModel


@ModelBase.register("NanbeigeForCausalLM")
-@ModelBase.example("Nanbeige/Nanbeige4.2-3B")
 class NanbeigeModel(LlamaModel):
    model_arch = gguf.MODEL_ARCH.NANBEIGE
    undo_permute = True
@@ -16,7 +16,6 @@ from .granite import GraniteHybridModel
    "NemotronH_Nano_VL_V2",
    "RADIOModel",
 )
-@ModelBase.example("nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16")
 class NemotronNanoV2VLModel(MmprojModel):
    # ViT-Huge architecture parameters for RADIO v2.5-h
    _vit_hidden_size = 1280
@@ -152,7 +151,6 @@ class NemotronNanoV2VLModel(MmprojModel):


@ModelBase.register("NemotronForCausalLM")
-@ModelBase.example("nvidia/Minitron-4B-Base")
 class NemotronModel(TextModel):
    model_arch = gguf.MODEL_ARCH.NEMOTRON

@@ -195,7 +193,6 @@ class NemotronModel(TextModel):


@ModelBase.register("NemotronHForCausalLM")
-@ModelBase.example("nvidia/Nemotron-H-8B-Base-8K")
 class NemotronHModel(GraniteHybridModel):
    """Hybrid mamba2/attention model from NVIDIA"""
    model_arch = gguf.MODEL_ARCH.NEMOTRON_H
@@ -207,9 +204,7 @@ class NemotronHModel(GraniteHybridModel):
        # calling the parent __init__. This is because the parent constructor
        # uses self.model_arch to build the tensor name map, and all MoE-specific
        # mappings would be missed if it were called with the default non-MoE arch.
-        hparams = kwargs.pop("hparams", None)
-        if hparams is None:
-            hparams = ModelBase.load_hparams(args[0], self.is_mistral_format)
+        hparams = ModelBase.load_hparams(args[0], self.is_mistral_format)
        has_moe_params = (
            "num_experts_per_tok" in hparams
            or (isinstance(hparams.get("llm_config"), dict) and "num_experts_per_tok" in hparams["llm_config"])
@@ -217,11 +212,8 @@ class NemotronHModel(GraniteHybridModel):
        if has_moe_params:
            self.model_arch = gguf.MODEL_ARCH.NEMOTRON_H_MOE
            self.is_moe = True
-        layers_block_type = hparams.get("layers_block_type")
-        if layers_block_type is not None:
-            hparams["num_hidden_layers"] = len(layers_block_type)

-        super().__init__(*args, hparams=hparams, **kwargs)
+        super().__init__(*args, **kwargs)

        # Save the top-level head_dim for later
        self.head_dim = self.hparams.get("head_dim", self.hparams.get("attention_head_dim"))
@@ -14,7 +14,6 @@ from .llama import LlamaModel

@ModelBase.register("OlmoForCausalLM")
@ModelBase.register("OLMoForCausalLM")
-@ModelBase.example("allenai/OLMo-1.7-7B-hf")
 class OlmoModel(TextModel):
    model_arch = gguf.MODEL_ARCH.OLMO

@@ -40,14 +39,12 @@ class OlmoModel(TextModel):


@ModelBase.register("SeedOssForCausalLM")
-@ModelBase.example("ByteDance-Seed/Seed-OSS-36B-Instruct")
 class SeedOssModel(TextModel):
    model_arch = gguf.MODEL_ARCH.SEED_OSS


@ModelBase.register("Olmo2ForCausalLM")
@ModelBase.register("Olmo3ForCausalLM")
-@ModelBase.example("allenai/OLMo-2-1124-7B-Instruct", "allenai/Olmo-3-7B-Instruct")
 class Olmo2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.OLMO2

@@ -70,7 +67,6 @@ class Olmo2Model(TextModel):


@ModelBase.register("OlmoeForCausalLM")
-@ModelBase.example("allenai/OLMoE-1B-7B-0924")
 class OlmoeModel(TextModel):
    model_arch = gguf.MODEL_ARCH.OLMOE

@@ -9,7 +9,6 @@ from .base import ModelBase, TextModel, gguf


@ModelBase.register("OpenELMForCausalLM")
-@ModelBase.example("apple/OpenELM-270M")
 class OpenELMModel(TextModel):
    model_arch = gguf.MODEL_ARCH.OPENELM

--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Pascal	d31babce1e	ui: npm run format for new upstream formatting rules	2026-08-16 09:08:43 +02:00
Pascal	b3fe01f4d3	ui: use arrow function in fetch interceptor	2026-08-16 09:07:08 +02:00
Xuan Son NguyenandPascal	aed7c384f5	ui: (demo) access server remotely via webrtc	2026-08-16 08:59:59 +02:00