rpc : fix misleading error log (#21184 )

When RPC is running with a remote backend which doesn't have init_tensor function (like CPU and Metal), the server log gets full with error messages saying that init_tensor is being called with null buffer which is incorrect. This patch fixes this.
webui: Fix branching logic on edit message (#21175 )
2026-07-17 01:45:59 +02:00 · 2026-03-30 17:05:11 +03:00 · 2026-03-30 14:40:50 +02:00 · 2026-03-30 17:40:17 +08:00 · 2026-03-30 09:29:15 +02:00 · 2026-03-30 08:59:16 +02:00
207 changed files with 8939 additions and 2439 deletions
@@ -4,7 +4,7 @@

 # Define the CANN base image for easier version updates later
 ARG CHIP_TYPE=910b
-ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.3.rc2-${CHIP_TYPE}-openeuler24.03-py3.11
+ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.5.0-${CHIP_TYPE}-openeuler24.03-py3.11

 # ==============================================================================
 # BUILD STAGE
@@ -1,11 +1,13 @@
-ARG UBUNTU_VERSION=22.04
+ARG UBUNTU_VERSION=24.04

 FROM ubuntu:$UBUNTU_VERSION AS build

 ARG TARGETARCH

 RUN apt-get update && \
-    apt-get install -y build-essential git cmake libssl-dev
+    apt-get install -y gcc-14 g++-14 build-essential git cmake libssl-dev
+
+ENV CC=gcc-14 CXX=g++-14

 WORKDIR /app

@@ -55,8 +57,9 @@ RUN apt-get update \
    git \
    python3 \
    python3-pip \
-    && pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt \
+    python3-wheel \
+    && pip install --break-system-packages --upgrade setuptools \
+    && pip install --break-system-packages -r requirements.txt \
    && apt autoremove -y \
    && apt clean -y \
    && rm -rf /tmp/* /var/tmp/* \
@@ -33,6 +33,23 @@ RUN mkdir -p /app/full \

 FROM intel/deep-learning-essentials:$ONEAPI_VERSION AS base

+ARG IGC_VERSION=v2.30.1
+ARG IGC_VERSION_FULL=2_2.30.1+20950
+ARG COMPUTE_RUNTIME_VERSION=26.09.37435.1
+ARG COMPUTE_RUNTIME_VERSION_FULL=26.09.37435.1-0
+ARG IGDGMM_VERSION=22.9.0
+RUN mkdir /tmp/neo/ && cd /tmp/neo/ \
+  && wget https://github.com/intel/intel-graphics-compiler/releases/download/$IGC_VERSION/intel-igc-core-${IGC_VERSION_FULL}_amd64.deb \
+  && wget https://github.com/intel/intel-graphics-compiler/releases/download/$IGC_VERSION/intel-igc-opencl-${IGC_VERSION_FULL}_amd64.deb \
+  && wget https://github.com/intel/compute-runtime/releases/download/$COMPUTE_RUNTIME_VERSION/intel-ocloc-dbgsym_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.ddeb \
+  && wget https://github.com/intel/compute-runtime/releases/download/$COMPUTE_RUNTIME_VERSION/intel-ocloc_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.deb \
+  && wget https://github.com/intel/compute-runtime/releases/download/$COMPUTE_RUNTIME_VERSION/intel-opencl-icd-dbgsym_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.ddeb \
+  && wget https://github.com/intel/compute-runtime/releases/download/$COMPUTE_RUNTIME_VERSION/intel-opencl-icd_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.deb \
+  && wget https://github.com/intel/compute-runtime/releases/download/$COMPUTE_RUNTIME_VERSION/libigdgmm12_${IGDGMM_VERSION}_amd64.deb \
+  && wget https://github.com/intel/compute-runtime/releases/download/$COMPUTE_RUNTIME_VERSION/libze-intel-gpu1-dbgsym_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.ddeb \
+  && wget https://github.com/intel/compute-runtime/releases/download/$COMPUTE_RUNTIME_VERSION/libze-intel-gpu1_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.deb \
+  && dpkg --install *.deb
+
 RUN apt-get update \
    && apt-get install -y libgomp1 curl\
    && apt autoremove -y \
@@ -1,4 +1,4 @@
-ARG ASCEND_VERSION=8.1.RC1.alpha001-910b-openeuler22.03-py3.10
+ARG ASCEND_VERSION=8.5.0-910b-openeuler22.03-py3.10

 FROM ascendai/cann:$ASCEND_VERSION AS build

@@ -41,6 +41,7 @@
  effectiveStdenv ? if useCuda then cudaPackages.backendStdenv else stdenv,
  enableStatic ? effectiveStdenv.hostPlatform.isStatic,
  precompileMetalShaders ? false,
+  useWebUi ? true,
 }:

 let
@@ -164,6 +165,7 @@ effectiveStdenv.mkDerivation (finalAttrs: {
  cmakeFlags =
    [
      (cmakeBool "LLAMA_BUILD_SERVER" true)
+      (cmakeBool "LLAMA_BUILD_WEBUI" useWebUi)
      (cmakeBool "BUILD_SHARED_LIBS" (!enableStatic))
      (cmakeBool "CMAKE_SKIP_BUILD_RPATH" true)
      (cmakeBool "GGML_NATIVE" false)
@@ -40,13 +40,9 @@ jobs:
    steps:
      - name: Clone
        uses: actions/checkout@v6
-
-      # Disabled due to size (400MB) and always 0 cache hits
-      # - name: ccache
-      #   uses: ggml-org/ccache-action@v1.2.16
-      #   with:
-      #     key: android-build
-      #     evict-old-files: 1d
+        with:
+          fetch-depth: 0
+          lfs: false

      - name: Set up JDK
        uses: actions/setup-java@v5
@@ -55,7 +51,7 @@ jobs:
          distribution: zulu

      - name: Setup Android SDK
-        uses: android-actions/setup-android@v3
+        uses: android-actions/setup-android@9fc6c4e9069bf8d3d10b2204b1fb8f6ef7065407 # v3
        with:
          log-accepted-android-sdk-licenses: false

@@ -66,10 +62,11 @@ jobs:

  android-ndk:
    runs-on: ubuntu-latest
-
-    env:
-      OPENCL_VERSION: 2025.07.22
-
+    container:
+      image: 'ghcr.io/snapdragon-toolchain/arm64-android:v0.3'
+    defaults:
+      run:
+        shell: bash
    strategy:
      matrix:
        include:
@@ -82,59 +79,23 @@ jobs:
      - name: Clone
        id: checkout
        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+          lfs: false

-      - name: Install OpenCL Headers and Libs
-        id: install_opencl
-        if: ${{ matrix.build == 'arm64-snapdragon' }}
-        run: |
-          mkdir opencl
-          curl -L -o opencl/clhpp.tar.gz      https://github.com/KhronosGroup/OpenCL-CLHPP/archive/refs/tags/v${OPENCL_VERSION}.tar.gz
-          curl -L -o opencl/headers.tar.gz    https://github.com/KhronosGroup/OpenCL-Headers/archive/refs/tags/v${OPENCL_VERSION}.tar.gz
-          curl -L -o opencl/icd-loader.tar.gz https://github.com/KhronosGroup/OpenCL-ICD-Loader/archive/refs/tags/v${OPENCL_VERSION}.tar.gz
-          tar -xaf opencl/headers.tar.gz    -C opencl
-          tar -xaf opencl/clhpp.tar.gz      -C opencl
-          tar -xaf opencl/icd-loader.tar.gz -C opencl
-          sudo cp -r opencl/OpenCL-Headers-${OPENCL_VERSION}/CL         ${ANDROID_NDK_ROOT}/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include
-          sudo cp -r opencl/OpenCL-CLHPP-${OPENCL_VERSION}/include/CL/* ${ANDROID_NDK_ROOT}/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include/CL
-          cd opencl/OpenCL-ICD-Loader-${OPENCL_VERSION}
-          cmake -B build -G Ninja -DCMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=${ANDROID_NDK_ROOT}/build/cmake/android.toolchain.cmake -DOPENCL_ICD_LOADER_HEADERS_DIR=${ANDROID_NDK_ROOT}/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=31 -DANDROID_STL=c++_shared
-          cmake --build build
-          sudo cp build/libOpenCL.so ${ANDROID_NDK_ROOT}/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android
-          rm -rf opencl
-
-      - name: Install Hexagon SDK
-        id: install_hexsdk
-        if: ${{ matrix.build == 'arm64-snapdragon' }}
-        env:
-          HEXSDK_VER: 6.4.0.2
-          HEXTLS_VER: 19.0.04
-        run: |
-          curl -L -o hex-sdk.tar.gz https://github.com/snapdragon-toolchain/hexagon-sdk/releases/download/v$HEXSDK_VER/hexagon-sdk-v$HEXSDK_VER-amd64-lnx.tar.xz
-          mkdir hex-sdk
-          tar -xaf hex-sdk.tar.gz -C hex-sdk
-          ls -l hex-sdk
-          sudo mv hex-sdk /opt/hexagon
-          echo "HEXAGON_SDK_ROOT=/opt/hexagon/$HEXSDK_VER"                                     >> "$GITHUB_ENV"
-          echo "HEXAGON_TOOLS_ROOT=/opt/hexagon/$HEXSDK_VER/tools/HEXAGON_Tools/$HEXTLS_VER"   >> "$GITHUB_ENV"
-          echo "DEFAULT_HLOS_ARCH=64"                                                          >> "$GITHUB_ENV"
-          echo "DEFAULT_TOOLS_VARIANT=toolv19"                                                 >> "$GITHUB_ENV"
-          echo "DEFAULT_NO_QURT_INC=0"                                                         >> "$GITHUB_ENV"
-          echo "DEFAULT_DSP_ARCH=v73"                                                          >> "$GITHUB_ENV"
-
-      - name: Update CMake presets
-        id: update_presets
-        if: ${{ matrix.build == 'arm64-snapdragon' }}
-        run: |
-          cp docs/backend/snapdragon/CMakeUserPresets.json .
-
-      - name: Build
-        id: ndk_build
+      - name: Build Llama.CPP for Hexagon Android
+        id: build_llama_cpp_hexagon_android
        run: |
+          if [[ "${{ matrix.build }}" == "arm64-snapdragon" ]]; then
+            cp docs/backend/snapdragon/CMakeUserPresets.json .
+          fi
          cmake ${{ matrix.defines }} -B build
          cmake --build build
          cmake --install build --prefix pkg-adb/llama.cpp

-      - name: Test
-        id: cmake_test
-        run: |
-          echo "FIXME: test on devices"
+      - name: Upload Llama.CPP Hexagon Android Build Artifact
+        if: ${{ always() && steps.build_llama_cpp_hexagon_android.outcome == 'success' }}
+        uses: actions/upload-artifact@v6
+        with:
+          name: llama-cpp-android-${{ matrix.build }}
+          path: pkg-adb/llama.cpp
@@ -63,7 +63,7 @@ jobs:
      - name: Set container image
        id: cann-image
        run: |
-          image="ascendai/cann:${{ matrix.chip_type == '910b' &&  '8.3.rc2-910b-openeuler24.03-py3.11' || '8.3.rc2-310p-openeuler24.03-py3.11' }}"
+          image="ascendai/cann:${{ matrix.chip_type == '910b' &&  '8.5.0-910b-openeuler24.03-py3.11' || '8.5.0-310p-openeuler24.03-py3.11' }}"
          echo "image=${image}" >> "${GITHUB_OUTPUT}"

      - name: Pull container image
@@ -43,7 +43,7 @@ jobs:
      #    save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}

      - name: Setup ${{ matrix.sys }}
-        uses: msys2/setup-msys2@v2
+        uses: msys2/setup-msys2@cafece8e6baf9247cf9b1bf95097b0b983cc558d # v2
        with:
          update: true
          msystem: ${{matrix.sys}}
@@ -141,60 +141,61 @@ jobs:
  #         amd-smi static
  #         GG_BUILD_ROCM=1 GG_BUILD_AMDGPU_TARGETS="gfx1101" bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp

-  ggml-ci-mac-metal:
-    runs-on: [self-hosted, macOS, ARM64]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          GG_BUILD_METAL=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  ggml-ci-mac-webgpu:
-    runs-on: [self-hosted, macOS, ARM64]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Dawn Dependency
-        id: dawn-depends
-        run: |
-          DAWN_VERSION="v2.0.0"
-          DAWN_OWNER="reeselevine"
-          DAWN_REPO="dawn"
-          DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-macos-latest-Release"
-          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
-          curl -L -o artifact.zip \
-            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
-          mkdir dawn
-          unzip artifact.zip
-          tar -xvf ${DAWN_ASSET_NAME}.tar.gz -C dawn --strip-components=1
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          GG_BUILD_WEBGPU=1 GG_BUILD_WEBGPU_DAWN_PREFIX="$GITHUB_WORKSPACE/dawn" \
-            bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-
-  ggml-ci-mac-vulkan:
-    runs-on: [self-hosted, macOS, ARM64]
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Test
-        id: ggml-ci
-        run: |
-          vulkaninfo --summary
-          GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
+  # TODO: sandbox Mac runners
+  #  ggml-ci-mac-metal:
+  #    runs-on: [self-hosted, macOS, ARM64]
+  #
+  #    steps:
+  #      - name: Clone
+  #        id: checkout
+  #        uses: actions/checkout@v6
+  #
+  #      - name: Test
+  #        id: ggml-ci
+  #        run: |
+  #          GG_BUILD_METAL=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
+  #
+  #  ggml-ci-mac-webgpu:
+  #    runs-on: [self-hosted, macOS, ARM64]
+  #
+  #    steps:
+  #      - name: Clone
+  #        id: checkout
+  #        uses: actions/checkout@v6
+  #
+  #      - name: Dawn Dependency
+  #        id: dawn-depends
+  #        run: |
+  #          DAWN_VERSION="v2.0.0"
+  #          DAWN_OWNER="reeselevine"
+  #          DAWN_REPO="dawn"
+  #          DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-macos-latest-Release"
+  #          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
+  #          curl -L -o artifact.zip \
+  #            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
+  #          mkdir dawn
+  #          unzip artifact.zip
+  #          tar -xvf ${DAWN_ASSET_NAME}.tar.gz -C dawn --strip-components=1
+  #
+  #      - name: Test
+  #        id: ggml-ci
+  #        run: |
+  #          GG_BUILD_WEBGPU=1 GG_BUILD_WEBGPU_DAWN_PREFIX="$GITHUB_WORKSPACE/dawn" \
+  #            bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
+  #
+  #  ggml-ci-mac-vulkan:
+  #    runs-on: [self-hosted, macOS, ARM64]
+  #
+  #    steps:
+  #      - name: Clone
+  #        id: checkout
+  #        uses: actions/checkout@v6
+  #
+  #      - name: Test
+  #        id: ggml-ci
+  #        run: |
+  #          vulkaninfo --summary
+  #          GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp

  ggml-ci-linux-intel-vulkan:
    runs-on: [self-hosted, Linux, Intel]
@@ -87,7 +87,7 @@ jobs:
            -DGGML_METAL_EMBED_LIBRARY=OFF \
            -DGGML_METAL_SHADER_DEBUG=ON \
            -DGGML_RPC=ON
-          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
+          time cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
          leaks -atExit -- ./build/bin/test-thread-safety -hf ggml-org/gemma-3-270m-qat-GGUF -ngl 99 -p "$(printf 'hello %.0s' {1..128})" -n 16 -c 512 -ub 32 -np 2 -t 2 -lv 1

      - name: Test
@@ -124,7 +124,7 @@ jobs:
            -DGGML_METAL=OFF \
            -DGGML_RPC=ON \
            -DCMAKE_OSX_DEPLOYMENT_TARGET=13.3
-          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
+          time cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)

      - name: Test
        id: cmake_test
@@ -165,8 +165,8 @@ jobs:
        id: cmake_build
        run: |
          export CMAKE_PREFIX_PATH=dawn
-          cmake -B build -DGGML_WEBGPU=ON -DGGML_METAL=OFF -DGGML_BLAS=OFF
-          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
+          cmake -B build -G "Ninja" -DCMAKE_BUILD_TYPE=Release -DGGML_WEBGPU=ON -DGGML_METAL=OFF -DGGML_BLAS=OFF
+          time cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)

      - name: Test
        id: cmake_test
@@ -231,7 +231,7 @@ jobs:
          cmake -B build \
            -DLLAMA_FATAL_WARNINGS=ON \
            -DGGML_RPC=ON
-          cmake --build build --config Release -j $(nproc)
+          time cmake --build build --config Release -j $(nproc)

      - name: Test
        id: cmake_test
@@ -274,14 +274,16 @@ jobs:
        id: depends
        run: |
          sudo apt-get update
-          sudo apt-get install build-essential libssl-dev
+          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
-          cmake --build build --config Release -j $(nproc)
+          time cmake --build build --config Release -j $(nproc)

      - name: Test
        id: cmake_test
@@ -300,12 +302,13 @@ jobs:
      - name: Dependencies
        id: depends
        run: |
-          sudo apt-get install -y glslc libvulkan-dev libssl-dev
+          sudo apt-get install -y glslc libvulkan-dev libssl-dev ninja-build

      - name: Configure
        id: cmake_configure
        run: |
          cmake -B build \
+            -G "Ninja" \
            -DCMAKE_BUILD_TYPE=RelWithDebInfo \
            -DGGML_BACKEND_DL=ON \
            -DGGML_CPU_ALL_VARIANTS=ON \
@@ -314,7 +317,7 @@ jobs:
      - name: Build
        id: cmake_build
        run: |
-          cmake --build build -j $(nproc)
+          time cmake --build build -j $(nproc)

  ubuntu-24-webgpu:
    runs-on: ubuntu-24.04
@@ -336,7 +339,8 @@ jobs:
        run: |
          sudo add-apt-repository -y ppa:kisak/kisak-mesa
          sudo apt-get update -y
-          sudo apt-get install -y build-essential mesa-vulkan-drivers libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libssl-dev
+          sudo apt-get install -y build-essential mesa-vulkan-drivers \
+            libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libssl-dev

      - name: Get latest Vulkan SDK version
        id: vulkan_sdk_version
@@ -378,7 +382,7 @@ jobs:
          export Dawn_DIR=dawn/lib64/cmake/Dawn
          cmake -B build \
            -DGGML_WEBGPU=ON
-          cmake --build build --config Release -j $(nproc)
+          time cmake --build build --config Release -j $(nproc)

      - name: Test
        id: cmake_test
@@ -415,11 +419,13 @@ jobs:
        run: |
          source emsdk/emsdk_env.sh
          emcmake cmake -B build-wasm \
+            -G "Ninja" \
+            -DCMAKE_BUILD_TYPE=Release \
            -DGGML_WEBGPU=ON \
            -DLLAMA_OPENSSL=OFF \
            -DEMDAWNWEBGPU_DIR=emdawnwebgpu_pkg

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

  ubuntu-22-hip:
    runs-on: ubuntu-22.04
@@ -479,7 +485,7 @@ jobs:
        run: |
          cmake -B build -S . \
            -DGGML_MUSA=ON
-          cmake --build build --config Release -j $(nproc)
+          time cmake --build build --config Release -j $(nproc)

  ubuntu-22-sycl:
    runs-on: ubuntu-22.04
@@ -528,7 +534,7 @@ jobs:
            -DGGML_SYCL=ON \
            -DCMAKE_C_COMPILER=icx \
            -DCMAKE_CXX_COMPILER=icpx
-          cmake --build build --config Release -j $(nproc)
+          time cmake --build build --config Release -j $(nproc)

  ubuntu-22-sycl-fp16:
    runs-on: ubuntu-22.04
@@ -551,7 +557,7 @@ jobs:
        shell: bash
        run: |
          sudo apt update
-          sudo apt install intel-oneapi-compiler-dpcpp-cpp libssl-dev
+          sudo apt install intel-oneapi-compiler-dpcpp-cpp libssl-dev ninja-build

      - name: install oneAPI MKL library
        shell: bash
@@ -574,11 +580,13 @@ jobs:
        run: |
          source /opt/intel/oneapi/setvars.sh
          cmake -B build \
+            -G "Ninja" \
+            -DCMAKE_BUILD_TYPE=Release \
            -DGGML_SYCL=ON \
            -DCMAKE_C_COMPILER=icx \
            -DCMAKE_CXX_COMPILER=icpx \
            -DGGML_SYCL_F16=ON
-          cmake --build build --config Release -j $(nproc)
+          time cmake --build build --config Release -j $(nproc)

  ubuntu-24-openvino:
      name: ubuntu-24-openvino-${{ matrix.openvino_device }}
@@ -648,7 +656,7 @@ jobs:
            cmake -B build/ReleaseOV -G Ninja \
              -DCMAKE_BUILD_TYPE=Release \
              -DGGML_OPENVINO=ON
-            cmake --build build/ReleaseOV --config Release -j $(nproc)
+            time cmake --build build/ReleaseOV --config Release -j $(nproc)

        - name: Test
          id: cmake_test
@@ -1039,7 +1047,7 @@ jobs:
            -DCMAKE_C_COMPILER=riscv64-linux-gnu-gcc-14 \
            -DCMAKE_CXX_COMPILER=riscv64-linux-gnu-g++-14

-          cmake --build build --config Release -j $(nproc)
+          time cmake --build build --config Release -j $(nproc)

      - name: Test
        id: cmake_test
@@ -36,18 +36,16 @@ jobs:
      matrix:
        config:
          # Multi-stage build
-          # Note: the arm64 images are failing, which prevents the amd64 images from being built
-          # https://github.com/ggml-org/llama.cpp/issues/11888
-          #- { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, free_disk_space: false }
-          - { tag: "cpu",    dockerfile: ".devops/cpu.Dockerfile",    platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-22.04" }
-          - { tag: "cuda cuda12", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-22.04", cuda_version: "12.4.0", ubuntu_version: "22.04" }
-          - { tag: "cuda13", dockerfile: ".devops/cuda-new.Dockerfile",  platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-22.04", cuda_version: "13.1.0", ubuntu_version: "24.04" }
-          - { tag: "musa",   dockerfile: ".devops/musa.Dockerfile",   platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-22.04" }
-          - { tag: "intel",  dockerfile: ".devops/intel.Dockerfile",  platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-22.04" }
-          - { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-22.04" }
-          - { tag: "s390x",  dockerfile: ".devops/s390x.Dockerfile",  platforms: "linux/s390x", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-22.04-s390x" }
-          - { tag: "rocm",   dockerfile: ".devops/rocm.Dockerfile",   platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-22.04" }
-          - { tag: "openvino", dockerfile: ".devops/openvino.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-22.04" }
+          - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/arm64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-24.04" }
+          - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-24.04" }
+          - { tag: "cuda cuda12", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-24.04", cuda_version: "12.4.0", ubuntu_version: "22.04" }
+          - { tag: "cuda13", dockerfile: ".devops/cuda-new.Dockerfile",  platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-24.04", cuda_version: "13.1.0", ubuntu_version: "24.04" }
+          - { tag: "musa",   dockerfile: ".devops/musa.Dockerfile",   platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-24.04" }
+          - { tag: "intel",  dockerfile: ".devops/intel.Dockerfile",  platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-24.04" }
+          - { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-24.04" }
+          - { tag: "s390x",  dockerfile: ".devops/s390x.Dockerfile",  platforms: "linux/s390x", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-24.04-s390x" }
+          - { tag: "rocm",   dockerfile: ".devops/rocm.Dockerfile",   platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-24.04" }
+          - { tag: "openvino", dockerfile: ".devops/openvino.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-24.04" }
    steps:
      - name: Check out the repo
        uses: actions/checkout@v6
@@ -56,15 +54,15 @@ jobs:

      - name: Set up QEMU
        if: ${{ matrix.config.tag != 's390x' }}
-        uses: docker/setup-qemu-action@v3
+        uses: docker/setup-qemu-action@c7c53464625b32c7a7e944ae62b3e17d2b600130 # v3
        with:
-          image: tonistiigi/binfmt:qemu-v7.0.0-28
+          image: tonistiigi/binfmt:qemu-v10.2.1

      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3

      - name: Log in to Docker Hub
-        uses: docker/login-action@v3
+        uses: docker/login-action@c94ce9fb468520275223c153574b00df6fe4bcc9 # v3
        with:
          registry: ghcr.io
          username: ${{ github.repository_owner }}
@@ -127,7 +125,7 @@ jobs:

      - name: Build and push Full Docker image (tagged + versioned)
        if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.full == true }}
-        uses: docker/build-push-action@v6
+        uses: docker/build-push-action@10e90e3645eae34f1e60eeb005ba3a3d33f178e8 # v6
        with:
          context: .
          push: true
@@ -152,7 +150,7 @@ jobs:

      - name: Build and push Light Docker image (tagged + versioned)
        if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.light == true }}
-        uses: docker/build-push-action@v6
+        uses: docker/build-push-action@10e90e3645eae34f1e60eeb005ba3a3d33f178e8 # v6
        with:
          context: .
          push: true
@@ -177,7 +175,7 @@ jobs:

      - name: Build and push Server Docker image (tagged + versioned)
        if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.server == true }}
-        uses: docker/build-push-action@v6
+        uses: docker/build-push-action@10e90e3645eae34f1e60eeb005ba3a3d33f178e8 # v6
        with:
          context: .
          push: true
@@ -23,7 +23,7 @@ jobs:
    runs-on: ubuntu-slim
    steps:
      - uses: actions/checkout@v6
-      - uses: editorconfig-checker/action-editorconfig-checker@v2
+      - uses: editorconfig-checker/action-editorconfig-checker@840e866d93b8e032123c23bac69dece044d4d84c # v2.2.0
        with:
          version: v3.0.3
      - run: editorconfig-checker
@@ -38,7 +38,7 @@ jobs:
    - name: Build package
      run: cd gguf-py && poetry build
    - name: Publish package
-      uses: pypa/gh-action-pypi-publish@release/v1
+      uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e # release/v1
      with:
        password: ${{ secrets.PYPI_API_TOKEN }}
        packages-dir: gguf-py/dist
@@ -8,7 +8,8 @@ on:
    paths: [
      '.github/workflows/hip-quality-check.yml',
      '**/*.cu',
-      '**/*.cuh'
+      '**/*.cuh',
+      'scripts/hip/gcn-cdna-vgpr-check.py'
    ]

  pull_request:
@@ -16,7 +17,8 @@ on:
    paths: [
      '.github/workflows/hip-quality-check.yml',
      '**/*.cu',
-      '**/*.cuh'
+      '**/*.cuh',
+      'scripts/hip/gcn-cdna-vgpr-check.py'
    ]

 concurrency:
@@ -31,6 +31,6 @@ jobs:
        with:
          python-version: "3.11"
      - name: flake8 Lint
-        uses: py-actions/flake8@v2
+        uses: py-actions/flake8@84ec6726560b6d5bd68f2a5bed83d62b52bb50ba # v2
        with:
            plugins: "flake8-no-print"
@@ -31,7 +31,7 @@ jobs:
        uses: actions/setup-python@v6
        with:
          python-version: "3.11"
-          pip-install: -r requirements/requirements-all.txt ty==0.0.24
+          pip-install: -r requirements/requirements-all.txt ty==0.0.26
      # - name: Type-check with Pyright
      #   uses: jakebailey/pyright-action@v2
      #   with:
@@ -907,7 +907,7 @@ jobs:
      - name: Set container image
        id: cann-image
        run: |
-          image="ascendai/cann:${{ matrix.chip_type == '910b' &&  '8.3.rc2-910b-openeuler24.03-py3.11' || '8.3.rc2-310p-openeuler24.03-py3.11' }}"
+          image="ascendai/cann:${{ matrix.chip_type == '910b' &&  '8.5.0-910b-openeuler24.03-py3.11' || '8.5.0-310p-openeuler24.03-py3.11' }}"
          echo "image=${image}" >> "${GITHUB_OUTPUT}"

      - name: Pull container image
@@ -108,6 +108,7 @@ option(LLAMA_BUILD_TESTS    "llama: build tests"          ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_TOOLS    "llama: build tools"          ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_EXAMPLES "llama: build examples"       ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_SERVER   "llama: build server example" ${LLAMA_STANDALONE})
+option(LLAMA_BUILD_WEBUI    "llama: build the embedded Web UI for server"  ON)
 option(LLAMA_TOOLS_INSTALL  "llama: install tools"        ${LLAMA_TOOLS_INSTALL_DEFAULT})
 option(LLAMA_TESTS_INSTALL  "llama: install tests"        ON)

@@ -57,6 +57,13 @@ SRC=`pwd`
 CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=${LLAMA_FATAL_WARNINGS:-ON} -DLLAMA_OPENSSL=OFF -DGGML_SCHED_NO_REALLOC=ON"
 CTEST_EXTRA=""

+# Default to use make unless specified for compatibility
+CMAKE_GENERATOR="Unix Makefiles"
+
+if [ ! -z "${GG_BUILD_NINJA}" ]; then
+    CMAKE_GENERATOR="Ninja"
+fi
+
 if [ ! -z ${GG_BUILD_METAL} ]; then
    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON"
 fi
@@ -242,13 +249,13 @@ function gg_run_ctest_debug {

    set -e

-    # Check cmake, make and ctest are installed
+    # Check cmake and ctest are installed
    gg_check_build_requirements

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

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

    set +e
 }
@@ -273,16 +280,16 @@ function gg_run_ctest_release {

    set -e

-    # Check cmake, make and ctest are installed
+    # Check cmake and ctest are installed
    gg_check_build_requirements

-    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j$(nproc)                                    ) 2>&1 | tee -a $OUT/${ci}-make.log
+    (cmake -G "${CMAKE_GENERATOR}" -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time cmake --build . --config Release -j$(nproc)) 2>&1 | tee -a $OUT/${ci}-make.log

    if [ -z ${GG_BUILD_LOW_PERF} ]; then
-        (time ctest --output-on-failure -L 'main|python' ${CTEST_EXTRA}) 2>&1 | tee -a $OUT/${ci}-ctest.log
+        (time ctest -C Release --output-on-failure -L 'main|python' ${CTEST_EXTRA}) 2>&1 | tee -a $OUT/${ci}-ctest.log
    else
-        (time ctest --output-on-failure -L main -E test-opt ${CTEST_EXTRA}) 2>&1 | tee -a $OUT/${ci}-ctest.log
+        (time ctest -C Release --output-on-failure -L main -E test-opt ${CTEST_EXTRA}) 2>&1 | tee -a $OUT/${ci}-ctest.log
    fi

    set +e
@@ -340,7 +347,7 @@ function gg_run_ctest_with_model_debug {
    cd build-ci-debug
    set -e

-    (LLAMACPP_TEST_MODELFILE="$model" time ctest --output-on-failure -L model) 2>&1 | tee -a $OUT/${ci}-ctest.log
+    (LLAMACPP_TEST_MODELFILE="$model" time ctest -C Debug --output-on-failure -L model) 2>&1 | tee -a $OUT/${ci}-ctest.log

    set +e
    cd ..
@@ -353,7 +360,7 @@ function gg_run_ctest_with_model_release {
    cd build-ci-release
    set -e

-    (LLAMACPP_TEST_MODELFILE="$model" time ctest --output-on-failure -L model) 2>&1 | tee -a $OUT/${ci}-ctest.log
+    (LLAMACPP_TEST_MODELFILE="$model" time ctest -C Release --output-on-failure -L model) 2>&1 | tee -a $OUT/${ci}-ctest.log

    # test memory leaks
    #if [[ ! -z ${GG_BUILD_METAL} ]]; then
@@ -407,8 +414,8 @@ function gg_run_qwen3_0_6b {

    set -e

-    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j$(nproc)                                    ) 2>&1 | tee -a $OUT/${ci}-make.log
+    (cmake -G "${CMAKE_GENERATOR}" -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time cmake --build . --config Release -j$(nproc)) 2>&1 | tee -a $OUT/${ci}-make.log

    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf  --outtype f16
    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-bf16.gguf --outtype bf16
@@ -556,8 +563,8 @@ function gg_run_embd_bge_small {

    set -e

-    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j$(nproc)                                    ) 2>&1 | tee -a $OUT/${ci}-make.log
+    (cmake -G "${CMAKE_GENERATOR}" -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time cmake --build . --config Release -j$(nproc)) 2>&1 | tee -a $OUT/${ci}-make.log

    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf

@@ -601,8 +608,8 @@ function gg_run_rerank_tiny {

    set -e

-    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j$(nproc)                                    ) 2>&1 | tee -a $OUT/${ci}-make.log
+    (cmake -G "${CMAKE_GENERATOR}" -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time cmake --build . --config Release -j$(nproc)) 2>&1 | tee -a $OUT/${ci}-make.log

    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf

@@ -652,10 +659,6 @@ function gg_check_build_requirements {
        gg_printf 'cmake not found, please install'
    fi

-    if ! command -v make &> /dev/null; then
-        gg_printf 'make not found, please install'
-    fi
-
    if ! command -v ctest &> /dev/null; then
        gg_printf 'ctest not found, please install'
    fi
@@ -423,6 +423,9 @@ static bool parse_bool_value(const std::string & value) {
 static bool common_params_parse_ex(int argc, char ** argv, common_params_context & ctx_arg) {
    common_params & params = ctx_arg.params;

+    // setup log directly from params.verbosity: see tools/cli/cli.cpp
+    common_log_set_verbosity_thold(params.verbosity);
+
    std::unordered_map<std::string, std::pair<common_arg *, bool>> arg_to_options;
    for (auto & opt : ctx_arg.options) {
        for (const auto & arg : opt.args) {
@@ -631,8 +634,6 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
        ));
    }

-    common_log_set_verbosity_thold(params.verbosity);
-
    return true;
 }

@@ -1078,7 +1079,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        [](common_params & params) {
            params.verbose_prompt = true;
        }
-    ));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL}));
    add_opt(common_arg(
        {"--display-prompt"},
        {"--no-display-prompt"},
@@ -2806,6 +2807,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.port = value;
        }
    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_PORT"));
+    add_opt(common_arg(
+        {"--reuse-port"},
+        string_format("allow multiple sockets to bind to the same port (default: %s)", params.reuse_port ? "enabled" : "disabled"),
+        [](common_params & params) {
+            params.reuse_port = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_REUSE_PORT"));
    add_opt(common_arg(
        {"--path"}, "PATH",
        string_format("path to serve static files from (default: %s)", params.public_path.c_str()),
@@ -2842,6 +2850,15 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.webui_mcp_proxy = value;
        }
    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_MCP_PROXY"));
+    add_opt(common_arg(
+        {"--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, apply_diff",
+        [](common_params & params, const std::string & value) {
+            params.server_tools = parse_csv_row(value);
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TOOLS"));
    add_opt(common_arg(
        {"--webui"},
        {"--no-webui"},
@@ -3244,6 +3261,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        "Set verbosity level to infinity (i.e. log all messages, useful for debugging)",
        [](common_params & params) {
            params.verbosity = INT_MAX;
+            common_log_set_verbosity_thold(INT_MAX);
        }
    ));
    add_opt(common_arg(
@@ -3264,6 +3282,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            "(default: %d)\n", params.verbosity),
        [](common_params & params, int value) {
            params.verbosity = value;
+            common_log_set_verbosity_thold(value);
        }
    ).set_env("LLAMA_LOG_VERBOSITY"));
    add_opt(common_arg(
@@ -65,7 +65,7 @@ common_chat_params peg_generator::generate_parser(const common_chat_template &
        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
            foreach_function(inputs.tools, [&](const json & tool) {
                const auto & function = tool.at("function");
-                auto         schema   = function.at("parameters");
+                auto         schema   = function.contains("parameters") ? function.at("parameters") : json::object();
                builder.resolve_refs(schema);
            });
            parser.build_grammar(builder, data.grammar_lazy);
@@ -221,7 +221,7 @@ common_peg_parser analyze_tools::build_tool_parser_tag_json(parser_build_context
    foreach_function(inputs.tools, [&](const json & tool) {
        const auto & func   = tool.at("function");
        std::string  name   = func.at("name");
-        const auto & schema = func.at("parameters");
+        const auto & schema = func.contains("parameters") ? func.at("parameters") : json::object();

        // Build call_id parser based on position (if supported)
        common_peg_parser call_id_section = p.eps();
@@ -282,19 +282,11 @@ common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_conte
    common_peg_parser tool_choice = p.choice();

    foreach_function(inputs.tools, [&](const json & tool) {
-        const auto & func   = tool.at("function");
-        std::string  name   = func.at("name");
-        const auto & params = func.at("parameters");
-
-        if (!params.contains("properties") || !params.at("properties").is_object()) {
-            return;
-        }
-
-        const auto &          properties = params.at("properties");
+        const auto &          func       = tool.at("function");
+        std::string           name       = func.at("name");
+        const auto &          params     = func.contains("parameters") ? func.at("parameters") : json::object();
+        const auto &          properties = params.contains("properties") ? params.at("properties") : json::object();
        std::set<std::string> required;
-        if (params.contains("required") && params.at("required").is_array()) {
-            params.at("required").get_to(required);
-        }

        // Build parser for each argument, separating required and optional
        std::vector<common_peg_parser> required_parsers;
@@ -311,17 +303,18 @@ common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_conte
                }
            }

-            auto arg = p.tool_arg(
-                p.tool_arg_open(arguments.name_prefix + p.tool_arg_name(p.literal(param_name)) +
-                                arguments.name_suffix) +
-                arguments.value_prefix +
-                (type == "string" ? p.tool_arg_string_value(p.schema(p.until(arguments.value_suffix),
-                                                                     "tool-" + name + "-arg-" + param_name + "-schema",
-                                                                     param_schema, true)) :
-                                    p.tool_arg_json_value(p.schema(
-                                        p.json(), "tool-" + name + "-arg-" + param_name + "-schema", param_schema, false)) +
-                                        p.space()) +
-                p.tool_arg_close(p.literal(arguments.value_suffix)));
+            auto arg =
+                p.tool_arg(p.tool_arg_open(arguments.name_prefix + p.tool_arg_name(p.literal(param_name)) +
+                                           arguments.name_suffix) +
+                           arguments.value_prefix +
+                           (type == "string" ?
+                                p.tool_arg_string_value(p.schema(p.until(arguments.value_suffix),
+                                                                 "tool-" + name + "-arg-" + param_name + "-schema",
+                                                                 param_schema, true)) :
+                                p.tool_arg_json_value(p.schema(
+                                    p.json(), "tool-" + name + "-arg-" + param_name + "-schema", param_schema, false)) +
+                                    p.space()) +
+                           p.tool_arg_close(p.literal(arguments.value_suffix)));

            auto named_arg = p.rule("tool-" + name + "-arg-" + param_name, arg);
            if (is_required) {
@@ -287,7 +287,7 @@ void analyze_reasoning::compare_reasoning_presence() {
            return p.literal(reasoning_content) + p.space() + p.optional(p.tag("post", (p.marker() + p.space())) + p.rest());
        });
        auto parser_wrapped = build_tagged_peg_parser([&](common_peg_parser_builder &p) {
-            return p.tag("pre", p.marker()) + p.space() + p.literal(reasoning_content) + p.space() + p.tag("post", (p.marker() + p.space())) + p.rest();
+            return p.tag("pre", p.marker() + p.space()) + p.literal(reasoning_content) + p.space() + p.tag("post", (p.marker() + p.space())) + p.rest();
        });
        // try the more aggressive parse first, if it fails, fall back to the delimiter one
        auto result = parser_wrapped.parse_anywhere_and_extract(comparison->output_B);
@@ -297,7 +297,7 @@ void analyze_reasoning::compare_reasoning_presence() {
        if (result.result.success()) {
            if (!result.tags["pre"].empty() && !result.tags["post"].empty()) {
                mode = reasoning_mode::TAG_BASED;
-                start = trim_whitespace(result.tags["pre"]);
+                start = trim_leading_whitespace(result.tags["pre"]);
                end   = trim_trailing_whitespace(result.tags["post"]);
            } else if (!result.tags["post"].empty()) {
                mode = reasoning_mode::TAG_BASED;
@@ -333,7 +333,7 @@ void analyze_reasoning::compare_thinking_enabled() {
    if (left_trimmed.empty() && !diff.right.empty()) {
        if (!right_trimmed.empty() && string_ends_with(comparison->output_B, right_trimmed)) {
            if (start.empty()) {
-                start = right_trimmed;
+                start = trim_leading_whitespace(diff.right);
                mode  = reasoning_mode::TAG_BASED;
            }
        }
@@ -344,7 +344,7 @@ void analyze_reasoning::compare_thinking_enabled() {
                if (seg.size() >= 2 && seg[seg.size() - 1].value == left_trimmed && seg[seg.size() - 2].type == segment_type::MARKER) {
                    start = seg[seg.size() - 2].value;
                }
-                end = left_trimmed;
+                end = trim_trailing_whitespace(diff.left);
                mode = reasoning_mode::TAG_BASED;
            }
        }
@@ -363,15 +363,23 @@ void analyze_reasoning::compare_thinking_enabled() {
            size_t len = std::min(base.size(), anchor_len);
            std::string anchor = base.substr(base.size() - len);
            auto pos = extended.rfind(anchor);
-            if (pos == std::string::npos || pos + len >= extended.size()) continue;
+            if (pos == std::string::npos || pos + len >= extended.size()) {
+                continue;
+            }

            std::string extra = trim_whitespace(extended.substr(pos + len));
-            if (extra.empty()) continue;
+            if (extra.empty()) {
+                continue;
+            }

            auto seg = prune_whitespace_segments(segmentize_markers(extra));
            if (seg.size() == 2 && seg[0].type == segment_type::MARKER && seg[1].type == segment_type::MARKER) {
-                if (start.empty()) start = seg[0].value;
-                if (end.empty())   end   = seg[1].value;
+                if (start.empty()) {
+                    start = seg[0].value;
+                }
+                if (end.empty()) {
+                    end   = seg[1].value;
+                }
                mode = reasoning_mode::TAG_BASED;
                break;
            }
@@ -423,7 +431,7 @@ void analyze_reasoning::compare_reasoning_scope() {
        LOG_DBG(ANSI_ORANGE "%s: Detected TOOLS_ONLY reasoning mode\n" ANSI_RESET, __func__);

        auto parser_wrapped = build_tagged_peg_parser([&](common_peg_parser_builder &p) {
-            return p.tag("pre", p.marker()) + p.space() + p.literal(reasoning_content) + p.space() + p.tag("post", (p.marker() + p.space()));
+            return p.tag("pre", p.marker() + p.space()) + p.literal(reasoning_content) + p.space() + p.tag("post", (p.marker() + p.space()));
        });
        auto result = parser_wrapped.parse_anywhere_and_extract(comparison->output_B);
        if (result.result.success()) {
@@ -516,7 +524,7 @@ analyze_content::analyze_content(const common_chat_template & tmpl, const analyz
        // Take the more promising diff
        std::string pure_content = rdiff.length() > diff_tools.left.length() ? rdiff : diff_tools.left;
        auto parser_wrapped = build_tagged_peg_parser([&](common_peg_parser_builder &p) {
-            return p.tag("pre", p.marker()) + p.space() + p.literal(response) + p.space() + p.tag("post", (p.marker() + p.space())) + p.rest();
+            return p.tag("pre", p.marker() + p.space()) + p.literal(response) + p.space() + p.tag("post", (p.marker() + p.space())) + p.rest();
        });
        auto result = parser_wrapped.parse_anywhere_and_extract(pure_content);
        start = result.tags["pre"];
@@ -971,6 +971,7 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
    auto has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
    auto has_response_format = !inputs.json_schema.is_null() && inputs.json_schema.is_object();
    auto include_grammar     = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
+    auto extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;

    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
        auto start           = p.rule("start", p.literal("<|start|>assistant"));
@@ -979,7 +980,13 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
        auto channel         = p.literal("<|channel|>") + (p.literal("commentary") | p.literal("analysis"));
        auto constrain_type  = p.chars("[A-Za-z0-9_-]", 1, -1);

-        auto analysis = p.rule("analysis", p.literal("<|channel|>analysis<|message|>") + p.reasoning(content) + end);
+        if (extract_reasoning) {
+            p.rule("analysis", p.literal("<|channel|>analysis<|message|>") + p.reasoning(content) + end);
+        } else {
+            p.rule("analysis", p.content(p.literal("<|channel|>analysis<|message|>") + content + end));
+        }
+
+        auto analysis = p.ref("analysis");
        auto preamble = p.rule("preamble", p.literal("<|channel|>commentary<|message|>") + p.content(content) + end);
        auto final_msg = p.rule("final", p.literal("<|channel|>final<|message|>") + p.content(content));
        auto any = p.rule("any", preamble | analysis);
@@ -656,6 +656,97 @@ bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_over
    return true;
 }

+static inline bool glob_class_match(const char c, const char * pattern, const char * class_end) {
+    const char * class_start = pattern;
+    bool negated = false;
+
+    if (*class_start == '!') {
+        negated = true;
+        class_start++;
+    }
+
+    // If first character after negation is ']' or '-', treat it as literal
+    if (*class_start == ']' || *class_start == '-') {
+        if (class_start < class_end && *class_start == c) {
+            return !negated;
+        }
+        class_start++;
+    }
+
+    bool matched = false;
+
+    while (class_start < class_end) {
+        if (class_start + 2 < class_end && class_start[1] == '-' && class_start[2] != ']') {
+            char start_char = *class_start;
+            char end_char = class_start[2];
+            if (c >= start_char && c <= end_char) {
+                matched = true;
+                break;
+            }
+            class_start += 3;
+        } else {
+            if (*class_start == c) {
+                matched = true;
+                break;
+            }
+            class_start++;
+        }
+    }
+
+    return negated ? !matched : matched;
+}
+
+// simple glob: * matches non-/ chars, ** matches anything including /, [] matches character class
+static inline bool glob_match(const char * pattern, const char * str) {
+    if (*pattern == '\0') {
+        return *str == '\0';
+    }
+    if (pattern[0] == '*' && pattern[1] == '*') {
+        const char * p = pattern + 2;
+        if (glob_match(p, str)) return true;
+        if (*str != '\0') return glob_match(pattern, str + 1);
+        return false;
+    }
+    if (*pattern == '*') {
+        const char * p = pattern + 1;
+        for (; *str != '\0' && *str != '/'; str++) {
+            if (glob_match(p, str)) return true;
+        }
+        return glob_match(p, str);
+    }
+    if (*pattern == '?' && *str != '\0' && *str != '/') {
+        return glob_match(pattern + 1, str + 1);
+    }
+    if (*pattern == '[') {
+        const char * class_end = pattern + 1;
+        // If first character after '[' is ']' or '-', treat it as literal
+        if (*class_end == ']' || *class_end == '-') {
+            class_end++;
+        }
+        while (*class_end != '\0' && *class_end != ']') {
+            class_end++;
+        }
+        if (*class_end == ']') {
+            if (*str == '\0') return false;
+            bool matched = glob_class_match(*str, pattern + 1, class_end);
+            return matched && glob_match(class_end + 1, str + 1);
+        } else {
+            if (*str == '[') {
+                return glob_match(pattern + 1, str + 1);
+            }
+            return false;
+        }
+    }
+    if (*pattern == *str) {
+        return glob_match(pattern + 1, str + 1);
+    }
+    return false;
+}
+
+bool glob_match(const std::string & pattern, const std::string & str) {
+    return glob_match(pattern.c_str(), str.c_str());
+}
+
 //
 // Filesystem utils
 //
@@ -573,6 +573,7 @@ struct common_params {

    // server params
    int32_t port                = 8080;          // server listens on this network port
+    bool    reuse_port          = false;         // allow multiple sockets to bind to the same port
    int32_t timeout_read        = 600;           // http read timeout in seconds
    int32_t timeout_write       = timeout_read;  // http write timeout in seconds
    int32_t n_threads_http      = -1;    // number of threads to process HTTP requests (TODO: support threadpool)
@@ -613,6 +614,9 @@ struct common_params {
    bool endpoint_props   = false; // only control POST requests, not GET
    bool endpoint_metrics = false;

+    // enable built-in tools
+    std::vector<std::string> server_tools;
+
    // router server configs
    std::string models_dir    = ""; // directory containing models for the router server
    std::string models_preset = ""; // directory containing model presets for the router server
@@ -790,6 +794,8 @@ std::string string_from(const std::vector<int> & values);
 std::string string_from(const struct llama_context * ctx, const std::vector<llama_token> & tokens);
 std::string string_from(const struct llama_context * ctx, const struct llama_batch & batch);

+bool glob_match(const std::string & pattern, const std::string & str);
+
 //
 // Filesystem utils
 //
@@ -454,7 +454,9 @@ static gguf_split_info get_gguf_split_info(const std::string & path) {
    std::smatch m;

    std::string prefix = path;
-    string_remove_suffix(prefix, ".gguf");
+    if (!string_remove_suffix(prefix, ".gguf")) {
+        return {};
+    }

    int index = 1;
    int count = 1;
@@ -546,6 +548,20 @@ static hf_cache::hf_file find_best_mmproj(const hf_cache::hf_files & files,
    return best;
 }

+static bool gguf_filename_is_model(const std::string & filepath) {
+    if (!string_ends_with(filepath, ".gguf")) {
+        return false;
+    }
+
+    std::string filename = filepath;
+    if (auto pos = filename.rfind('/'); pos != std::string::npos) {
+        filename = filename.substr(pos + 1);
+    }
+
+    return filename.find("mmproj")  == std::string::npos &&
+           filename.find("imatrix") == std::string::npos;
+}
+
 static hf_cache::hf_file find_best_model(const hf_cache::hf_files & files,
                                         const std::string        & tag) {
    std::vector<std::string> tags;
@@ -559,8 +575,7 @@ static hf_cache::hf_file find_best_model(const hf_cache::hf_files & files,
    for (const auto & t : tags) {
        std::regex pattern(t + "[.-]", std::regex::icase);
        for (const auto & f : files) {
-            if (string_ends_with(f.path, ".gguf") &&
-                f.path.find("mmproj") == std::string::npos &&
+            if (gguf_filename_is_model(f.path) &&
                std::regex_search(f.path, pattern)) {
                return f;
            }
@@ -568,8 +583,7 @@ static hf_cache::hf_file find_best_model(const hf_cache::hf_files & files,
    }

    for (const auto & f : files) {
-        if (string_ends_with(f.path, ".gguf") &&
-            f.path.find("mmproj") == std::string::npos) {
+        if (gguf_filename_is_model(f.path)) {
            return f;
        }
    }
@@ -26,6 +26,8 @@ namespace nl = nlohmann;
 #include <windows.h>
 #else
 #define HOME_DIR "HOME"
+#include <unistd.h>
+#include <pwd.h>
 #endif

 namespace hf_cache {
@@ -38,6 +40,7 @@ static fs::path get_cache_directory() {
            const char * var;
            fs::path path;
        } entries[] = {
+            {"LLAMA_CACHE",           fs::path()},
            {"HF_HUB_CACHE",          fs::path()},
            {"HUGGINGFACE_HUB_CACHE", fs::path()},
            {"HF_HOME",               fs::path("hub")},
@@ -50,6 +53,13 @@ static fs::path get_cache_directory() {
                return entry.path.empty() ? base : base / entry.path;
            }
        }
+#ifndef _WIN32
+        const struct passwd * pw = getpwuid(getuid());
+
+        if (pw->pw_dir && *pw->pw_dir) {
+            return fs::path(pw->pw_dir) / ".cache" / "huggingface" / "hub";
+        }
+#endif
        throw std::runtime_error("Failed to determine HF cache directory");
    }();

@@ -325,9 +335,15 @@ hf_files get_repo_files(const std::string & repo_id,
                if (item["lfs"].contains("oid") && item["lfs"]["oid"].is_string()) {
                    file.oid = item["lfs"]["oid"].get<std::string>();
                }
+                if (item["lfs"].contains("size") && item["lfs"]["size"].is_number()) {
+                    file.size = item["lfs"]["size"].get<size_t>();
+                }
            } else if (item.contains("oid") && item["oid"].is_string()) {
                file.oid = item["oid"].get<std::string>();
            }
+            if (file.size == 0 && item.contains("size") && item["size"].is_number()) {
+                file.size = item["size"].get<size_t>();
+            }

            if (!file.oid.empty() && !is_valid_oid(file.oid)) {
                LOG_WRN("%s: skip invalid oid: %s\n", __func__, file.oid.c_str());
@@ -487,6 +503,34 @@ std::string finalize_file(const hf_file & file) {

 // delete everything after this line, one day

+// copied from download.cpp without the tag part
+struct gguf_split_info {
+    std::string prefix; // tag included
+    int index;
+    int count;
+};
+
+static gguf_split_info get_gguf_split_info(const std::string & path) {
+    static const std::regex re_split("^(.+)-([0-9]{5})-of-([0-9]{5})$", std::regex::icase);
+    std::smatch m;
+
+    std::string prefix = path;
+    if (!string_remove_suffix(prefix, ".gguf")) {
+        return {};
+    }
+
+    int index = 1;
+    int count = 1;
+
+    if (std::regex_match(prefix, m, re_split)) {
+        index = std::stoi(m[2].str());
+        count = std::stoi(m[3].str());
+        prefix = m[1].str();
+    }
+
+    return {std::move(prefix), index, count};
+}
+
 static std::pair<std::string, std::string> parse_manifest_name(std::string & filename) {
    static const std::regex re(R"(^manifest=([^=]+)=([^=]+)=.*\.json$)");
    std::smatch match;
@@ -504,25 +548,30 @@ static std::string make_old_cache_filename(const std::string & owner,
    return result;
 }

-static bool migrate_single_file(const fs::path    & old_cache,
-                                const std::string & owner,
-                                const std::string & repo,
-                                const nl::json    & node,
-                                const hf_files    & files) {
+struct migrate_file {
+    std::string path;
+    std::string sha256;
+    size_t size;
+    fs::path old_path;
+    fs::path etag_path;
+    const hf_file * file;
+};

-    if (!node.contains("rfilename") ||
-        !node.contains("lfs")       ||
-        !node["lfs"].contains("sha256")) {
-        return false;
-    }
+using migrate_files = std::vector<migrate_file>;

-    std::string path = node["rfilename"];
-    std::string sha256 = node["lfs"]["sha256"];
+static bool collect_file(const fs::path    & old_cache,
+                         const std::string & owner,
+                         const std::string & repo,
+                         const std::string & path,
+                         const std::string & sha256,
+                         const hf_files    & files,
+                         migrate_files     & to_migrate) {
+
+    const hf_file * file = nullptr;

-    const hf_file * file_info = nullptr;
    for (const auto & f : files) {
        if (f.path == path) {
-            file_info = &f;
+            file = &f;
            break;
        }
    }
@@ -532,50 +581,104 @@ static bool migrate_single_file(const fs::path    & old_cache,
    fs::path etag_path = old_path.string() + ".etag";

    if (!fs::exists(old_path)) {
-        if (fs::exists(etag_path)) {
-            LOG_WRN("%s: %s is orphan, deleting...\n", __func__, etag_path.string().c_str());
-            fs::remove(etag_path);
+        if (file && fs::exists(file->final_path)) {
+            return true;
        }
+        LOG_WRN("%s: %s not found in old cache or HF cache\n", __func__, old_filename.c_str());
        return false;
    }

-    bool delete_old_path = false;
-
-    if (!file_info) {
-        LOG_WRN("%s: %s not found in current repo, deleting...\n", __func__, old_filename.c_str());
-        delete_old_path = true;
-    } else if (!sha256.empty() && !file_info->oid.empty() && sha256 != file_info->oid) {
-        LOG_WRN("%s: %s is not up to date (sha256 mismatch), deleting...\n", __func__, old_filename.c_str());
-        delete_old_path = true;
+    if (!file) {
+        LOG_WRN("%s: %s not found in current repo\n", __func__, old_filename.c_str());
+        return false;
    }

-    std::error_code ec;
+    if (!sha256.empty() && !file->oid.empty() && sha256 != file->oid) {
+        LOG_WRN("%s: %s is not up to date (sha256 mismatch)\n", __func__, old_filename.c_str());
+        return false;
+    }

-    if (delete_old_path) {
-        fs::remove(old_path, ec);
-        fs::remove(etag_path, ec);
+    if (file->size > 0) {
+        size_t size = fs::file_size(old_path);
+        if (size != file->size) {
+            LOG_WRN("%s: %s has wrong size %zu (expected %zu)\n", __func__, old_filename.c_str(), size, file->size);
+            return false;
+        }
+    }
+
+    to_migrate.push_back({path, sha256, file->size, old_path, etag_path, file});
+    return true;
+}
+
+static bool collect_files(const fs::path    & old_cache,
+                          const std::string & owner,
+                          const std::string & repo,
+                          const nl::json    & node,
+                          const hf_files    & files,
+                          migrate_files     & to_migrate) {
+
+    if (!node.contains("rfilename") ||
+        !node.contains("lfs")       ||
+        !node["lfs"].contains("sha256")) {
        return true;
    }

-    fs::path new_path(file_info->local_path);
+    std::string path = node["rfilename"];
+    std::string sha256 = node["lfs"]["sha256"];
+
+    auto split = get_gguf_split_info(path);
+
+    if (split.count <= 1) {
+        return collect_file(old_cache, owner, repo, path, sha256, files, to_migrate);
+    }
+
+    std::vector<std::pair<std::string, std::string>> splits;
+
+    for (const auto & f : files) {
+        auto split_f = get_gguf_split_info(f.path);
+        if (split_f.count == split.count && split_f.prefix == split.prefix) {
+            // sadly the manifest only provides the sha256 of the first file (index == 1)
+            // the rest will be verified using the size...
+            std::string f_sha256 = (split_f.index == 1) ? sha256 : "";
+            splits.emplace_back(f.path, f_sha256);
+        }
+    }
+
+    if ((int)splits.size() != split.count) {
+        LOG_WRN("%s: expected %d split files but found %d in repo\n", __func__, split.count, (int)splits.size());
+        return false;
+    }
+
+    for (const auto & [f_path, f_sha256] : splits) {
+        if (!collect_file(old_cache, owner, repo, f_path, f_sha256, files, to_migrate)) {
+            return false;
+        }
+    }
+
+    return true;
+}
+
+static bool migrate_file(const migrate_file & file) {
+    std::error_code ec;
+
+    fs::path new_path(file.file->local_path);
    fs::create_directories(new_path.parent_path(), ec);

    if (!fs::exists(new_path, ec)) {
-        fs::rename(old_path, new_path, ec);
+        fs::rename(file.old_path, new_path, ec);
        if (ec) {
-            fs::copy_file(old_path, new_path, ec);
+            fs::copy_file(file.old_path, new_path, ec);
            if (ec) {
-                LOG_WRN("%s: failed to move/copy %s: %s\n", __func__, old_path.string().c_str(), ec.message().c_str());
+                LOG_ERR("%s: failed to move/copy %s: %s\n", __func__, file.old_path.string().c_str(), ec.message().c_str());
                return false;
            }
        }
-        fs::remove(old_path, ec);
+        fs::remove(file.old_path, ec);
    }
-    fs::remove(etag_path, ec);
-
-    std::string filename = finalize_file(*file_info);
-    LOG_INF("%s: migrated %s -> %s\n", __func__, old_filename.c_str(), filename.c_str());
+    fs::remove(file.etag_path, ec);

+    std::string filename = finalize_file(*file.file);
+    LOG_INF("%s: migrated %s -> %s\n", __func__, file.old_path.filename().string().c_str(), filename.c_str());
    return true;
 }

@@ -624,19 +727,43 @@ void migrate_old_cache_to_hf_cache(const std::string & token, bool offline) {
            continue;
        }

+        migrate_files to_migrate;
+        bool ok = true;
+
        try {
            std::ifstream manifest(entry.path());
            auto json = nl::json::parse(manifest);
-
            for (const char * key : {"ggufFile", "mmprojFile"}) {
                if (json.contains(key)) {
-                    migrate_single_file(old_cache, owner, repo, json[key], files);
+                    if (!collect_files(old_cache, owner, repo, json[key], files, to_migrate)) {
+                        ok = false;
+                        break;
+                    }
                }
            }
        } catch (const std::exception & e) {
            LOG_WRN("%s: failed to parse manifest %s: %s\n", __func__, filename.c_str(), e.what());
            continue;
        }
+
+        if (!ok) {
+            LOG_WRN("%s: migration skipped: one or more files failed validation\n", __func__);
+            continue;
+        }
+
+        for (const auto & file : to_migrate) {
+            if (!migrate_file(file)) {
+                ok = false;
+                break;
+            }
+        }
+
+        if (!ok) {
+            LOG_WRN("%s: migration failed: could not migrate all files\n", __func__);
+            continue;
+        }
+
+        LOG_INF("%s: migration complete, deleting manifest: %s\n", __func__, entry.path().string().c_str());
        fs::remove(entry.path());
    }
 }
@@ -14,6 +14,7 @@ struct hf_file {
    std::string final_path;
    std::string oid;
    std::string repo_id;
+    size_t size = 0; // only for the migration
 };

 using hf_files = std::vector<hf_file>;
@@ -667,8 +667,9 @@ value macro_statement::execute_impl(context & ctx) {
                if (is_stmt<identifier>(this->args[i])) {
                    // normal parameter
                    std::string param_name = cast_stmt<identifier>(this->args[i])->val;
-                    JJ_DEBUG("  Binding parameter '%s' to argument of type %s", param_name.c_str(), args.get_pos(i)->type().c_str());
-                    macro_ctx.set_val(param_name, args.get_pos(i));
+                    value param_value = args.get_kwarg_or_pos(param_name, i);
+                    JJ_DEBUG("  Binding parameter '%s' to argument of type %s", param_name.c_str(), param_value->type().c_str());
+                    macro_ctx.set_val(param_name, param_value);
                } else if (is_stmt<keyword_argument_expression>(this->args[i])) {
                    // default argument used as normal parameter
                    auto kwarg = cast_stmt<keyword_argument_expression>(this->args[i]);
@@ -676,8 +677,9 @@ value macro_statement::execute_impl(context & ctx) {
                        throw std::runtime_error("Keyword argument key must be an identifier in macro '" + name + "'");
                    }
                    std::string param_name = cast_stmt<identifier>(kwarg->key)->val;
-                    JJ_DEBUG("  Binding parameter '%s' to argument of type %s", param_name.c_str(), args.get_pos(i)->type().c_str());
-                    macro_ctx.set_val(param_name, args.get_pos(i));
+                    value param_value = args.get_kwarg_or_pos(param_name, i);
+                    JJ_DEBUG("  Binding parameter '%s' to argument of type %s", param_name.c_str(), param_value->type().c_str());
+                    macro_ctx.set_val(param_name, param_value);
                } else {
                    throw std::runtime_error("Invalid parameter type in macro '" + name + "'");
                }
@@ -416,15 +416,30 @@ private:
                    i++;
                } else if (c == '(') {
                    i++;
-                    if (i < length) {
-                        if (sub_pattern[i] == '?') {
+                    if (i < length && sub_pattern[i] == '?') {
+                        if (i + 1 < length && sub_pattern[i + 1] == ':') {
+                            i += 2; // skip "?:" for non-capturing group, treat as regular group
+                        } else {
+                            // 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;
                        }
                    }
                    seq.emplace_back("(" + to_rule(transform()) + ")", false);
                } else if (c == ')') {
                    i++;
-                    if (start > 0 && sub_pattern[start - 1] != '(') {
+                    if (start > 0 && sub_pattern[start - 1] != '(' && (start < 2 || sub_pattern[start - 2] != '?' || sub_pattern[start - 1] != ':')) {
                        _errors.push_back("Unbalanced parentheses");
                    }
                    return join_seq();
@@ -115,9 +115,11 @@ static void common_reasoning_budget_accept(struct llama_sampler * smpl, llama_to
            break;
        }
        case REASONING_BUDGET_FORCING:
-            // force_pos is advanced in apply(), not here.
-            // This ensures the first forced token isn't skipped when the sampler
-            // is initialized directly in FORCING state (e.g. COUNTING + budget=0)
+            ctx->force_pos++;
+            if (ctx->force_pos >= ctx->forced_tokens.size()) {
+                ctx->state = REASONING_BUDGET_DONE;
+                LOG_INF("reasoning-budget: forced sequence complete, done\n");
+            }
            break;
        case REASONING_BUDGET_DONE:
            break;
@@ -144,14 +146,6 @@ static void common_reasoning_budget_apply(struct llama_sampler * smpl, llama_tok
            cur_p->data[i].logit = -INFINITY;
        }
    }
-
-    // advance to next forced token (done here rather than in accept so that
-    // the first forced token isn't skipped when starting in FORCING state)
-    ctx->force_pos++;
-    if (ctx->force_pos >= ctx->forced_tokens.size()) {
-        ctx->state = REASONING_BUDGET_DONE;
-        LOG_INF("reasoning-budget: forced sequence complete, done\n");
-    }
 }

 static void common_reasoning_budget_reset(struct llama_sampler * smpl) {
@@ -261,3 +255,10 @@ struct llama_sampler * common_reasoning_budget_init(
        common_reasoning_budget_state    initial_state) {
    return common_reasoning_budget_init_state(vocab, start_tokens, end_tokens, forced_tokens, budget, initial_state);
 }
+
+common_reasoning_budget_state common_reasoning_budget_get_state(const struct llama_sampler * smpl) {
+    if (!smpl) {
+        return REASONING_BUDGET_IDLE;
+    }
+    return ((const common_reasoning_budget_ctx *)smpl->ctx)->state;
+}
@@ -51,3 +51,5 @@ struct llama_sampler * common_reasoning_budget_init(
        const std::vector<llama_token> & forced_tokens,
        int32_t                          budget,
        common_reasoning_budget_state    initial_state);
+
+common_reasoning_budget_state common_reasoning_budget_get_state(const struct llama_sampler * smpl);
@@ -7,6 +7,7 @@

 #include <algorithm>
 #include <cctype>
+#include <climits>
 #include <cmath>
 #include <cstring>
 #include <unordered_map>
@@ -109,6 +110,7 @@ struct common_sampler {
    common_params_sampling params;

    struct llama_sampler * grmr;
+    struct llama_sampler * rbudget;
    struct llama_sampler * chain;

    ring_buffer<llama_token> prev;
@@ -188,6 +190,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
    lparams.no_perf = params.no_perf;

    llama_sampler * grmr = nullptr;
+    llama_sampler * rbudget = nullptr;
    llama_sampler * chain = llama_sampler_chain_init(lparams);

    std::vector<llama_sampler *> samplers;
@@ -270,7 +273,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
            }
        }

-        if (grmr) {
+        if (grmr && !params.grammar_lazy) {
            try {
                for (const auto & token : prefill_tokens) {
                    llama_sampler_accept(grmr, token);
@@ -284,15 +287,15 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
        }
    }

-    // reasoning budget sampler — added first so it can force tokens before other samplers
-    if (params.reasoning_budget_tokens >= 0 && !params.reasoning_budget_forced.empty()) {
-        samplers.push_back(common_reasoning_budget_init(
+    // reasoning budget sampler
+    if (!params.reasoning_budget_start.empty() && !params.reasoning_budget_end.empty()) {
+        rbudget = common_reasoning_budget_init(
            vocab,
            params.reasoning_budget_start,
            params.reasoning_budget_end,
            params.reasoning_budget_forced,
-            params.reasoning_budget_tokens,
-            prefill_tokens));
+            params.reasoning_budget_tokens < 0 ? INT_MAX : params.reasoning_budget_tokens,
+            prefill_tokens);
    }

    if (params.has_logit_bias()) {
@@ -383,6 +386,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
    auto * result = new common_sampler {
        /* .params  = */ params,
        /* .grmr    = */ grmr,
+        /* .rbudget = */ rbudget,
        /* .chain   = */ chain,
        /* .prev    = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
        /* .cur     = */ {},
@@ -398,11 +402,27 @@ void common_sampler_free(struct common_sampler * gsmpl) {
    }

    llama_sampler_free(gsmpl->grmr);
+    llama_sampler_free(gsmpl->rbudget);
    llama_sampler_free(gsmpl->chain);

    delete gsmpl;
 }

+static bool grammar_should_apply(struct common_sampler * gsmpl) {
+    if (!gsmpl->grmr) {
+        return false;
+    }
+    if (!gsmpl->rbudget) {
+        return true;
+    }
+    if (gsmpl->params.grammar_lazy) {
+        // if grammar is lazy, only apply when reasoning budget is not active
+        const auto state = common_reasoning_budget_get_state(gsmpl->rbudget);
+        return state == REASONING_BUDGET_IDLE || state == REASONING_BUDGET_DONE;
+    }
+    return true;
+}
+
 void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) {
    if (!gsmpl) {
        return;
@@ -410,6 +430,11 @@ void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, boo

    const auto tm = gsmpl->tm();

+    // grammar_should_apply() checks the reasoning budget state, so calculate this before we accept
+    accept_grammar = accept_grammar && grammar_should_apply(gsmpl);
+
+    llama_sampler_accept(gsmpl->rbudget, token);
+
    if (gsmpl->grmr && accept_grammar) {
        llama_sampler_accept(gsmpl->grmr, token);
    }
@@ -431,6 +456,7 @@ struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
    return new common_sampler {
        /* .params  = */ gsmpl->params,
        /* .grmr    = */ llama_sampler_clone(gsmpl->grmr),
+        /* .rbudget = */ llama_sampler_clone(gsmpl->rbudget),
        /* .chain   = */ llama_sampler_clone(gsmpl->chain),
        /* .prev    = */ gsmpl->prev,
        /* .cur     = */ gsmpl->cur,
@@ -500,6 +526,7 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
    llama_token id = LLAMA_TOKEN_NULL;

    auto & grmr  = gsmpl->grmr;
+    auto & rbudget = gsmpl->rbudget;
    auto & chain = gsmpl->chain;
    auto & cur_p = gsmpl->cur_p; // initialized by set_logits

@@ -511,7 +538,8 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
        if (id != LLAMA_TOKEN_NULL) {
            LOG_DBG("%s: Backend sampler selected token: '%d'. Will not run any CPU samplers\n", __func__, id);

-            GGML_ASSERT(!gsmpl->grmr && "using grammar in combination with backend sampling is not supported");
+            GGML_ASSERT(!gsmpl->grmr    && "using grammar in combination with backend sampling is not supported");
+            GGML_ASSERT(!gsmpl->rbudget && "using reasoning budget in combination with backend sampling is not supported");

            // TODO: simplify
            gsmpl->cur.resize(1);
@@ -524,7 +552,10 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co

    gsmpl->set_logits(ctx, idx);

-    if (grammar_first) {
+    // apply reasoning budget first
+    llama_sampler_apply(rbudget, &cur_p);
+
+    if (grammar_first && grammar_should_apply(gsmpl)) {
        llama_sampler_apply(grmr, &cur_p);
    }

@@ -532,7 +563,7 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co

    id = cur_p.data[cur_p.selected].id;

-    if (grammar_first) {
+    if (grammar_first || !grammar_should_apply(gsmpl)) {
        return id;
    }

@@ -553,7 +584,12 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
    // if the token is not valid, sample again, but first apply the grammar sampler and then the sampling chain
    gsmpl->set_logits(ctx, idx);

-    llama_sampler_apply(grmr,  &cur_p);
+    llama_sampler_apply(rbudget,  &cur_p);
+
+    if (grammar_should_apply(gsmpl)) {
+        llama_sampler_apply(grmr,  &cur_p);
+    }
+
    llama_sampler_apply(chain, &cur_p);

    GGML_ASSERT(cur_p.selected != -1 && "no selected token during sampling - check your sampling configuration");
@@ -31,10 +31,10 @@ import gguf
 from gguf.vocab import MistralTokenizerType, MistralVocab

 try:
-    from mistral_common.tokens.tokenizers.base import TokenizerVersion # type: ignore[import-not-found]
-    from mistral_common.tokens.tokenizers.multimodal import DATASET_MEAN as _MISTRAL_COMMON_DATASET_MEAN, DATASET_STD as _MISTRAL_COMMON_DATASET_STD # type: ignore[import-not-found]
-    from mistral_common.tokens.tokenizers.tekken import Tekkenizer # type: ignore[import-not-found]
-    from mistral_common.tokens.tokenizers.sentencepiece import ( # type: ignore[import-not-found]
+    from mistral_common.tokens.tokenizers.base import TokenizerVersion # type: ignore[import-not-found, ty:unresolved-import]
+    from mistral_common.tokens.tokenizers.multimodal import DATASET_MEAN as _MISTRAL_COMMON_DATASET_MEAN, DATASET_STD as _MISTRAL_COMMON_DATASET_STD # type: ignore[import-not-found, ty:unresolved-import]
+    from mistral_common.tokens.tokenizers.tekken import Tekkenizer # type: ignore[import-not-found, ty:unresolved-import]
+    from mistral_common.tokens.tokenizers.sentencepiece import ( # type: ignore[import-not-found, ty:unresolved-import]
        SentencePieceTokenizer,
    )

@@ -486,7 +486,7 @@ class ModelBase:
            elif quant_method == "modelopt":
                # Mixed-precision ModelOpt models: NVFP4 tensors are handled by
                # _generate_nvfp4_tensors; FP8 tensors have 1D weight_scale and
-                # are dequantized here. input_scale tensors are unused.
+                # are dequantized here. k/v scale tensors are unused.
                for name in self.model_tensors.keys():
                    if name.endswith(".weight_scale"):
                        weight_name = name.removesuffix("_scale")
@@ -494,7 +494,7 @@ class ModelBase:
                        s = self.model_tensors[name]
                        self.model_tensors[weight_name] = lambda w=w, s=s: dequant_simple(w(), s(), None)
                        tensors_to_remove.append(name)
-                    if name.endswith((".input_scale", ".k_scale", ".v_scale")):
+                    if name.endswith((".k_scale", ".v_scale")):
                        tensors_to_remove.append(name)
            elif quant_method is not None:
                raise NotImplementedError(f"Quant method is not yet supported: {quant_method!r}")
@@ -542,7 +542,6 @@ class ModelBase:
        raise NotImplementedError("set_gguf_parameters() must be implemented in subclasses")

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-
        new_name = self.map_tensor_name(name)

        # Handle gate/up expert tensor fusion if enabled
@@ -607,7 +606,12 @@ class ModelBase:
    def _nvfp4_scale2_is_trivial(scale2: Tensor) -> bool:
        return scale2.numel() <= 1 and abs(float(scale2.float().sum()) - 1.0) < 1e-6

-    def _repack_nvfp4(self, new_name: str, weight: Tensor, scale: Tensor, scale2: Tensor):
+    def _repack_nvfp4(self, name: str, weight: Tensor, scale: Tensor, scale2: Tensor, input_scale: Tensor):
+        if "language_model." in name:
+            name = name.replace("language_model.", "")
+
+        new_name = self.map_tensor_name(name)
+
        raw, shape = self._nvfp4_pack(weight, scale)
        logger.info(f"Repacked {new_name} with shape {shape} and quantization NVFP4")
        self.gguf_writer.add_tensor(new_name, raw, raw_dtype=gguf.GGMLQuantizationType.NVFP4)
@@ -619,10 +623,18 @@ class ModelBase:
            logger.info(f"  + {scale_name} (per-tensor NVFP4 scale2, shape [{scale2_f32.size}])")
            self.gguf_writer.add_tensor(scale_name, scale2_f32)

+        # Emit per-tensor input_scale as a separate F32 tensor when non-trivial
+        if not self._nvfp4_scale2_is_trivial(input_scale):
+            input_scale_f32 = input_scale.float().numpy().flatten()
+            input_scale_name = new_name.replace(".weight", ".input_scale")
+            logger.info(f"  + {input_scale_name} (per-tensor NVFP4 input_scale, shape [{input_scale_f32.size}])")
+            self.gguf_writer.add_tensor(input_scale_name, input_scale_f32)
+
    def _generate_nvfp4_tensors(self):
        # Per-layer expert merging to avoid holding all experts in memory
        expert_blocks: dict[tuple[int, str], list[tuple[int, np.ndarray]]] = {}
        expert_scales: dict[tuple[int, str], list[tuple[int, float]]] = {}
+        expert_input_scales: dict[tuple[int, str], list[tuple[int, float]]] = {}
        expert_shapes: dict[tuple[int, str], list[int]] = {}
        n_experts = self.find_hparam(["num_local_experts", "num_experts"], optional=True) or 0
        consumed: list[str] = []
@@ -632,6 +644,7 @@ class ModelBase:
                continue
            scale_name = name.replace(".weight", ".weight_scale")
            scale2_name = name.replace(".weight", ".weight_scale_2")
+            input_scale_name = name.replace(".weight", ".input_scale")
            if scale_name not in self.model_tensors:
                continue
            # Force eager materialization of lazy tensors
@@ -643,11 +656,14 @@ class ModelBase:
                continue

            scale2 = LazyTorchTensor.to_eager(self.model_tensors.get(scale2_name, lambda: torch.tensor(1.0))())
+            input_scale = LazyTorchTensor.to_eager(self.model_tensors.get(input_scale_name, lambda: torch.tensor(1.0))())

            # Mark tensors for removal from model_tensors (already written to gguf)
            consumed.extend([name, scale_name])
            if scale2_name in self.model_tensors:
                consumed.append(scale2_name)
+            if input_scale_name in self.model_tensors:
+                consumed.append(input_scale_name)

            # Check if this is a per-expert tensor
            m = re.search(r'\.experts\.(\d+)\.(gate_proj|up_proj|down_proj)\.weight$', name)
@@ -663,34 +679,37 @@ class ModelBase:
                if key not in expert_blocks:
                    expert_blocks[key] = []
                    expert_scales[key] = []
+                    expert_input_scales[key] = []
                    expert_shapes[key] = shape
                expert_blocks[key].append((expert_id, raw.copy()))
                # Collect per-expert scale2 (scalar per expert)
                expert_scales[key].append((expert_id, float(scale2.float().sum())))
+                # Collect per-expert input_scale (scalar per expert)
+                expert_input_scales[key].append((expert_id, float(input_scale.float().sum())))

                # Flush when all experts for this (layer, proj) are collected
                if n_experts > 0 and len(expert_blocks[key]) >= n_experts:
-                    self._flush_nvfp4_experts(key, expert_blocks, expert_scales, expert_shapes, bid, proj_type)
+                    self._flush_nvfp4_experts(key, expert_blocks, expert_scales, expert_input_scales, expert_shapes, bid, proj_type)
            else:
-                new_name = self.map_tensor_name(name)
-                self._repack_nvfp4(new_name, weight, scale, scale2)
+                self._repack_nvfp4(name, weight, scale, scale2, input_scale)

        # Flush any remaining experts (fallback if n_experts was unknown)
        for (bid, proj_type) in list(expert_blocks.keys()):
-            self._flush_nvfp4_experts((bid, proj_type), expert_blocks, expert_scales, expert_shapes, bid, proj_type)
+            self._flush_nvfp4_experts((bid, proj_type), expert_blocks, expert_scales, expert_input_scales, expert_shapes, bid, proj_type)

        # Remove consumed tensors so get_tensors/modify_tensors won't see them
        for name in consumed:
            self.model_tensors.pop(name, None)

-        # Remove unused auxiliary tensors (input_scale, k_scale, v_scale)
+        # Remove any remaining unused auxiliary tensors
        for name in list(self.model_tensors.keys()):
-            if name.endswith((".input_scale", ".k_scale", ".v_scale")):
+            if name.endswith((".k_scale", ".v_scale")):
                del self.model_tensors[name]

-    def _flush_nvfp4_experts(self, key, expert_blocks, expert_scales, expert_shapes, bid, proj_type):
+    def _flush_nvfp4_experts(self, key, expert_blocks, expert_scales, expert_input_scales, expert_shapes, bid, proj_type):
        experts = expert_blocks.pop(key)
        scales = expert_scales.pop(key)
+        input_scales = expert_input_scales.pop(key)
        shape = expert_shapes.pop(key)

        experts.sort(key=lambda x: x[0])
@@ -708,6 +727,14 @@ class ModelBase:
            logger.info(f"  + {scale_name} (per-expert NVFP4 scale2, shape [{len(scales)}])")
            self.gguf_writer.add_tensor(scale_name, scale_vals)

+        # Emit per-expert input_scale tensor if any expert has non-trivial input_scale
+        input_scales.sort(key=lambda x: x[0])
+        input_scale_vals = np.array([s[1] for s in input_scales], dtype=np.float32)
+        if not np.allclose(input_scale_vals, 1.0, atol=1e-6):
+            input_scale_name = new_name.replace(".weight", ".input_scale")
+            logger.info(f"  + {input_scale_name} (per-expert NVFP4 input_scale, shape [{len(input_scales)}])")
+            self.gguf_writer.add_tensor(input_scale_name, input_scale_vals)
+
        del experts, merged

    def prepare_tensors(self):
@@ -947,6 +974,9 @@ class ModelBase:
        if "thinker_config" in config:
            # rename for Qwen2.5-Omni
            config["text_config"] = config["thinker_config"]["text_config"]
+        if "language_config" in config:
+            # rename for DeepSeekOCR
+            config["text_config"] = config["language_config"]
        if "lfm" in config:
            # rename for LFM2-Audio
            config["text_config"] = config["lfm"]
@@ -1308,6 +1338,9 @@ class TextModel(ModelBase):
        if chkhsh == "b3d1dd861f1d4c5c0d2569ce36baf3f90fe8a102db3de50dd71ff860d91be3df":
            # ref: https://huggingface.co/aari1995/German_Semantic_V3
            res = "jina-v2-de"
+        if chkhsh == "0fe1cf6eda062318a1af7270f3331a85c539a01778ff948e24388e949c5282f4":
+            # ref: https://huggingface.co/evilfreelancer/ruGPT3XL
+            res = "gpt-2"
        if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
            # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
            res = "llama-bpe"
@@ -1503,6 +1536,9 @@ class TextModel(ModelBase):
        if chkhsh == "e4d54df1ebc1f2b91acd986c5b51aa50837d5faf7c7398e73c1f9e9ee5d19869":
            # ref: https://huggingface.co/kakaocorp/kanana-2-30b-a3b-instruct-2601
            res = "kanana2"
+        if chkhsh == "862f827721df956049dff5ca81a57f29e575280bc622e290d3bf4e35eca29015":
+            # ref: https://huggingface.co/codefuse-ai/F2LLM-v2-4B
+            res = "f2llmv2"

        if res is None:
            logger.warning("\n")
@@ -2071,7 +2107,7 @@ class MmprojModel(ModelBase):
    preprocessor_config: dict[str, Any]
    global_config: dict[str, Any]

-    n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth", "encoder_layers", "vt_num_hidden_layers"]
+    n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth", "layers", "encoder_layers", "vt_num_hidden_layers"]

    has_vision_encoder: bool = True # by default
    has_audio_encoder: bool = False
@@ -5005,6 +5041,97 @@ class _LinearAttentionVReorderBase(Qwen3NextModel):
        perm[dim], perm[dim + 1] = perm[dim + 1], perm[dim]
        return tensor.permute(*perm).contiguous().reshape(*shape)

+    def _transform_nvfp4_weight(self, name: str, weight: Tensor, scale: Tensor) -> tuple[Tensor, Tensor]:
+        if not name.endswith((
+            ".linear_attn.in_proj_qkv.weight",
+            ".linear_attn.in_proj_z.weight",
+            ".linear_attn.in_proj_a.weight",
+            ".linear_attn.in_proj_b.weight",
+            ".linear_attn.out_proj.weight",
+        )):
+            return weight, scale
+
+        num_k_heads = self.hparams["linear_num_key_heads"]
+        num_v_heads = self.hparams["linear_num_value_heads"]
+        head_k_dim = self.hparams["linear_key_head_dim"]
+        head_v_dim = self.hparams["linear_value_head_dim"]
+        num_v_per_k = num_v_heads // num_k_heads
+
+        def unpack_nibbles(qs: Tensor) -> Tensor:
+            lo = torch.bitwise_and(qs, 0x0F)
+            hi = torch.bitwise_right_shift(qs, 4)
+            return torch.stack((lo, hi), dim=-1).reshape(*qs.shape[:-1], qs.shape[-1] * 2)
+
+        def pack_nibbles(codes: Tensor) -> Tensor:
+            codes = codes.reshape(*codes.shape[:-1], codes.shape[-1] // 2, 2)
+            lo = torch.bitwise_and(codes[..., 0], 0x0F)
+            hi = torch.bitwise_left_shift(torch.bitwise_and(codes[..., 1], 0x0F), 4)
+            return torch.bitwise_or(lo, hi).contiguous()
+
+        def apply_col_perm(qs: Tensor, scales: Tensor, col_perm: Tensor) -> tuple[Tensor, Tensor]:
+            assert qs.ndim >= 2
+            assert scales.ndim >= 2
+
+            k = qs.shape[-1] * 2
+            assert col_perm.numel() == k
+            assert k % 16 == 0
+
+            group_cols = col_perm.reshape(-1, 16)
+            group_starts = group_cols[:, 0]
+            expected = group_starts.unsqueeze(1) + torch.arange(16, dtype=col_perm.dtype)
+            assert torch.equal(group_cols, expected)
+            assert torch.all(group_starts % 16 == 0)
+
+            group_perm = (group_starts // 16).to(dtype=torch.long)
+            expected_groups = torch.arange(scales.shape[-1], dtype=torch.long)
+            assert group_perm.numel() == scales.shape[-1]
+            assert torch.equal(torch.sort(group_perm).values, expected_groups)
+
+            codes = unpack_nibbles(qs)
+            codes = codes.index_select(-1, col_perm.to(device=qs.device, dtype=torch.long))
+            qs = pack_nibbles(codes)
+            scales = scales.index_select(-1, group_perm.to(device=scales.device))
+            return qs, scales
+
+        def reorder_rows(qs: Tensor, scales: Tensor, head_dim: int) -> tuple[Tensor, Tensor]:
+            row_perm = self._reorder_v_heads(
+                torch.arange(num_v_heads * head_dim, dtype=torch.long).unsqueeze(-1),
+                0, num_k_heads, num_v_per_k, head_dim,
+            ).squeeze(-1)
+            return (
+                qs.index_select(0, row_perm.to(device=qs.device)),
+                scales.index_select(0, row_perm.to(device=scales.device)),
+            )
+
+        if name.endswith(".linear_attn.in_proj_qkv.weight"):
+            q_dim = head_k_dim * num_k_heads
+            k_dim = head_k_dim * num_k_heads
+            q = weight[:q_dim]
+            k = weight[q_dim:q_dim + k_dim]
+            v = weight[q_dim + k_dim:]
+            q_scale = scale[:q_dim]
+            k_scale = scale[q_dim:q_dim + k_dim]
+            v_scale = scale[q_dim + k_dim:]
+            v, v_scale = reorder_rows(v, v_scale, head_v_dim)
+            return torch.cat([q, k, v], dim=0), torch.cat([q_scale, k_scale, v_scale], dim=0)
+
+        if name.endswith(".linear_attn.in_proj_z.weight"):
+            weight, scale = reorder_rows(weight, scale, head_v_dim)
+        elif name.endswith((".linear_attn.in_proj_a.weight", ".linear_attn.in_proj_b.weight")):
+            weight, scale = reorder_rows(weight, scale, 1)
+        elif name.endswith(".linear_attn.out_proj.weight"):
+            col_perm = self._reorder_v_heads(
+                torch.arange(num_v_heads * head_v_dim, dtype=torch.long).unsqueeze(0),
+                1, num_k_heads, num_v_per_k, head_v_dim,
+            ).squeeze(0)
+            weight, scale = apply_col_perm(weight, scale, col_perm)
+
+        return weight, scale
+
+    def _repack_nvfp4(self, name: str, weight: Tensor, scale: Tensor, scale2: Tensor, input_scale: Tensor):
+        weight, scale = self._transform_nvfp4_weight(name, weight, scale)
+        super()._repack_nvfp4(name, weight, scale, scale2, input_scale)
+
    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        num_k_heads = self.hparams.get("linear_num_key_heads", 0)
        num_v_heads = self.hparams.get("linear_num_value_heads", 0)
@@ -5094,6 +5221,47 @@ class GPT2Model(TextModel):
        yield from super().modify_tensors(data_torch, new_name, bid)


+@ModelBase.register("RuGPT3XLForCausalLM")
+class RuGPT3XLModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.GPT2
+
+    _qkv_parts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # Fuse separate Q, K, V projections into a single QKV tensor
+        if ".self_attn.q_proj." in name or ".self_attn.k_proj." in name or ".self_attn.v_proj." in name:
+            suffix = "weight" if name.endswith(".weight") else "bias"
+            part = "q" if ".q_proj." in name else ("k" if ".k_proj." in name else "v")
+            key = f"{part}.{suffix}"
+
+            assert bid is not None
+            if self._qkv_parts is None:
+                self._qkv_parts = [{} for _ in range(self.block_count)]
+            self._qkv_parts[bid][key] = data_torch
+
+            q_key, k_key, v_key = f"q.{suffix}", f"k.{suffix}", f"v.{suffix}"
+            if all(k in self._qkv_parts[bid] for k in [q_key, k_key, v_key]):
+                q = self._qkv_parts[bid].pop(q_key)
+                k = self._qkv_parts[bid].pop(k_key)
+                v = self._qkv_parts[bid].pop(v_key)
+                data_torch = torch.cat([q, k, v], dim=0)
+                name = self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_QKV, bid, f".{suffix}")
+                logger.debug(f"Fused Q/K/V {suffix} for layer {bid} -> {name}")
+            else:
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._qkv_parts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            parts = [f"({i}){k}" for i, d in enumerate(self._qkv_parts) for k in d.keys()]
+            if len(parts) > 0:
+                raise ValueError(f"Unprocessed Q/K/V parts: {parts}")
+
+
@ModelBase.register("PhiForCausalLM")
 class Phi2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.PHI2
@@ -6935,6 +7103,70 @@ class ConformerAudioModel(MmprojModel):
        yield from super().modify_tensors(data_torch, name, bid)


+@ModelBase.register("DeepseekOCRForCausalLM")
+class DeepseekOCRVisionModel(MmprojModel):
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.DEEPSEEKOCR)
+        # default values below are taken from HF tranformers code
+        self.gguf_writer.add_vision_attention_layernorm_eps(hparams.get("layer_norm_eps", 1e-6))
+        self.gguf_writer.add_vision_use_gelu(True)
+        # calculate proj_scale_factor (used by tinygemma3 test model)
+        image_seq_length = self.preprocessor_config.get("image_seq_length", 256)
+        n_per_side = int(image_seq_length ** 0.5)
+        image_size = self.hparams["image_size"]
+        patch_size = self.hparams["patch_size"]
+        proj_scale_factor = (image_size // patch_size) // n_per_side
+        if proj_scale_factor > 0 and proj_scale_factor != 4:
+            # we only need to write this if it's not the default value
+            # in this case, we are converting a test model
+            self.gguf_writer.add_vision_projector_scale_factor(proj_scale_factor)
+        # @bluebread: there's no window_size in config but just add it here anyway
+        self.gguf_writer.add_vision_window_size(self.hparams.get("window_size", 14))
+
+        # SAM configuration
+        sam_hparams = hparams['sam']
+        self.gguf_writer.add_vision_sam_layers_count(sam_hparams['layers'])
+        self.gguf_writer.add_vision_sam_embedding_length(sam_hparams['width'])
+        self.gguf_writer.add_vision_sam_head_count(sam_hparams['heads'])
+
+    def get_vision_config(self) -> dict[str, Any]:
+        vision_config: dict[str, Any] | None = self.global_config.get("vision_config")
+
+        if not vision_config:
+            raise ValueError("DeepseekOCR model requires 'vision_config' in the model configuration, but it was not found")
+
+        vision_config['sam'] = vision_config['width']['sam_vit_b']
+        vision_config.update(vision_config['width']['clip-l-14-224'])
+        vision_config['hidden_size'] = vision_config['width']
+        vision_config['num_heads'] = vision_config['heads']
+        vision_config['intermediate_size'] = vision_config['heads'] * 4
+
+        return vision_config
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        if ".embeddings." in name or 'pos_embed' in name:
+            return gguf.GGMLQuantizationType.F32
+        if ".rel_pos_h" in name or '.rel_pos_w' in name:
+            return gguf.GGMLQuantizationType.F32
+        if ".neck." in name or ".net_" in name:
+            return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # Only process vision-related tensors, skip language model tensors
+        # Vision components: sam_model, vision_model, projector, image_newline, view_seperator
+        # Language model components to skip: lm_head, embed_tokens, layers, norm
+        if name.startswith(("lm_head.", "model.embed_tokens.", "model.layers.", "model.norm.")):
+            return
+
+        if name.endswith("pos_embed") or name.endswith("rel_pos_h") or name.endswith("rel_pos_w"):
+            name += ".weight"
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
@ModelBase.register("Gemma3nForConditionalGeneration")
 class Gemma3nVisionAudioModel(ConformerAudioModel):
    has_audio_encoder = True
@@ -8280,6 +8512,19 @@ class DeepseekV2Model(TextModel):

    merge_expert = True

+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        hparams: dict = ModelBase.load_hparams(self.dir_model, is_mistral_format=False)
+        self.origin_hf_arch = hparams.get('architectures', [None])[0]
+
+        # special handling for Deepseek OCR
+        if self.origin_hf_arch == "DeepseekOCRForCausalLM":
+            self.model_arch = gguf.MODEL_ARCH.DEEPSEEK2OCR
+            self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[self.model_arch]
+            self.gguf_writer.add_architecture()
+            # default jinja template
+            self.gguf_writer.add_chat_template("{% for m in messages %}{{m['content']}}{% endfor %}")
+
    def set_vocab(self):
        try:
            self._set_vocab_gpt2()
@@ -8335,9 +8580,15 @@ class DeepseekV2Model(TextModel):
            raise NotImplementedError(f"Deepseek pre-tokenizer {tokpre!r} is not supported yet!")

    def set_gguf_parameters(self):
+        is_ocr = (self.model_arch == gguf.MODEL_ARCH.DEEPSEEK2OCR)

-        # note: deepseek2 using MLA converts into MQA (ie: GQA with 1 group)
-        self.hparams["num_key_value_heads"] = 1
+        if is_ocr:
+            self.hparams['rope_theta'] = self.hparams.get('rope_theta', 10000.0)
+        else:
+            # note: deepseek2 using MLA converts into MQA (ie: GQA with 1 group)
+            self.hparams["num_key_value_heads"] = 1
+
+        self.hparams['rms_norm_eps'] = self.hparams.get('rms_norm_eps', 1e-6)

        super().set_gguf_parameters()
        hparams = self.hparams
@@ -8351,16 +8602,18 @@ class DeepseekV2Model(TextModel):
            # Default: if no MoE, all layers are dense; if MoE, none are dense
            first_k_dense_replace = hparams["num_hidden_layers"] if not has_moe else 0
        self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace)
+        kv_lora_rank = hparams.get("kv_lora_rank", 512)
        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
        if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None:
            self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"])
-        self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"])

        # note: deepseek2 using MLA converts into MQA with larger heads, then decompresses to MHA
-        self.gguf_writer.add_key_length(hparams["kv_lora_rank"] + hparams["qk_rope_head_dim"])
-        self.gguf_writer.add_value_length(hparams["kv_lora_rank"])
-        self.gguf_writer.add_key_length_mla(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
-        self.gguf_writer.add_value_length_mla(hparams["v_head_dim"])
+        if not is_ocr:
+            self.gguf_writer.add_kv_lora_rank(kv_lora_rank)
+            self.gguf_writer.add_key_length(kv_lora_rank + hparams["qk_rope_head_dim"])
+            self.gguf_writer.add_value_length(kv_lora_rank)
+            self.gguf_writer.add_key_length_mla(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
+            self.gguf_writer.add_value_length_mla(hparams["v_head_dim"])

        # MoE parameters (required by C++ code for DEEPSEEK2 arch)
        # For non-MoE models like Youtu, use intermediate_size as expert_feed_forward_length
@@ -8392,8 +8645,15 @@ class DeepseekV2Model(TextModel):
    _experts: list[dict[str, Tensor]] | None = None

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # skip vision tensors and remove "language_model." for Kimi-VL and Kimi-K2.5
-        if "vision_tower" in name or "multi_modal_projector" in name or "mm_projector" in name:
+        # skip vision tensors and remove "language_model." for Kimi-VL and Kimi-K2.5, and DeepSeek-OCR
+        if ("vision_tower" in name
+                or "multi_modal_projector" in name
+                or "mm_projector" in name
+                or "vision_model" in name
+                or "image_newline" in name
+                or "model.projector" in name
+                or "sam_model" in name
+                or "view_seperator" in name):
            return
        if name.startswith("siglip2.") or name.startswith("merger."):
            return
@@ -154,6 +154,7 @@ models = [
    {"name": "qwen35",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen3.5-9B-Instruct", },
    {"name": "joyai-llm",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jdopensource/JoyAI-LLM-Flash", },
    {"name": "kanana2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/kakaocorp/kanana-2-30b-a3b-instruct-2601", },
+    {"name": "f2llmv2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/codefuse-ai/F2LLM-v2-4B", },
 ]

 # some models are known to be broken upstream, so we will skip them as exceptions
@@ -177,6 +178,7 @@ pre_computed_hashes = [
    {"name": "grok-2",    "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/alvarobartt/grok-2-tokenizer", "chkhsh": "66b8d4e19ab16c3bfd89bce5d785fb7e0155e8648708a1f42077cb9fe002c273"},
    # jina-v2-de variants
    {"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/aari1995/German_Semantic_V3", "chkhsh": "b3d1dd861f1d4c5c0d2569ce36baf3f90fe8a102db3de50dd71ff860d91be3df"},
+    {"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/evilfreelancer/ruGPT3XL", "chkhsh": "0fe1cf6eda062318a1af7270f3331a85c539a01778ff948e24388e949c5282f4"},
 ]


@@ -42,12 +42,22 @@ The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the abi

 ### Ascend NPU

-**Verified devices**
+You can retrieve your Ascend device IDs using the following command:

-| Ascend NPU                    | Status  |
-|:-----------------------------:|:-------:|
-| Atlas 300T A2                 | Support |
-| Atlas 300I Duo                | Support |
+```sh
+lspci -n | grep -Eo '19e5:d[0-9a-f]{3}' | cut -d: -f2
+```
+
+**Devices**
+
+| Device Id | Product Series | Product Models | Chip Model | Verified Status |
+|:---------:|----------------|----------------|:----------:|:---------------:|
+|    d803   | Atlas A3 Train |                |    910C    |                 |
+|    d803   | Atlas A3 Infer |                |    910C    |                 |
+|    d802   | Atlas A2 Train |                |    910B    |                 |
+|    d802   | Atlas A2 Infer | Atlas 300I A2  |    910B    |     Support     |
+|    d801   | Atlas Train    |                |     910    |                 |
+|    d500   | Atlas Infer    | Atlas 300I Duo |    310P    |     Support     |

 *Notes:*

@@ -57,6 +67,9 @@ The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the abi

 ## Model Supports

+<details>
+<summary>Text-only</summary>
+
 | Model Name                  | FP16  | Q4_0 | Q8_0 |
 |:----------------------------|:-----:|:----:|:----:|
 | Llama-2                     |   √   |   √  |   √  |
@@ -118,8 +131,11 @@ The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the abi
 | Trillion-7B-preview         |   √   |   √  |   √  |
 | Ling models                 |   √   |   √  |   √  |

+</details>
+
+<details>
+<summary>Multimodal</summary>

-**Multimodal**
 | Model Name                  | FP16  | Q4_0 | Q8_0 |
 |:----------------------------|:-----:|:----:|:----:|
 | LLaVA 1.5 models, LLaVA 1.6 models      |   x   |   x  |   x  |
@@ -134,15 +150,22 @@ The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the abi
 |  GLM-EDGE                   |   √   |   √  |   √  |
 |  Qwen2-VL                   |   √   |   √  |   √  |

+</details>
+


 ## DataType Supports

-| DataType               | Status  |
-|:----------------------:|:-------:|
-| FP16                   | Support |
-| Q8_0                   | Support |
-| Q4_0                   | Support |
+| DataType               | 910B    | 310P    |
+|:----------------------:|:-------:|:-------:|
+| FP16                   | Support | Support |
+| Q8_0                   | Support | Partial |
+| Q4_0                   | Support | Partial |
+| BF16                   | Support |         |
+
+> **310P note**
+> - `Q8_0`: data transform / buffer path is implemented, and `GET_ROWS` is supported, but quantized `MUL_MAT` / `MUL_MAT_ID` are not supported.
+> - `Q4_0`: data transform / buffer path is implemented, but quantized `MUL_MAT` / `MUL_MAT_ID` are not supported.

 ## Docker

@@ -160,7 +183,20 @@ npu-smi info

 # Select the cards that you want to use, make sure these cards are not used by someone.
 # Following using cards of device0.
-docker run --name llamacpp --device /dev/davinci0  --device /dev/davinci_manager --device /dev/devmm_svm --device /dev/hisi_hdc -v /usr/local/dcmi:/usr/local/dcmi -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info -v /PATH_TO_YOUR_MODELS/:/app/models -it llama-cpp-cann -m /app/models/MODEL_PATH -ngl 32 -p "Building a website can be done in 10 simple steps:"
+docker run --name llamacpp \
+  --device /dev/davinci0 \
+  --device /dev/davinci_manager \
+  --device /dev/devmm_svm \
+  --device /dev/hisi_hdc \
+  -v /usr/local/dcmi:/usr/local/dcmi \
+  -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
+  -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
+  -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
+  -v /PATH_TO_YOUR_MODELS/:/app/models \
+  -it llama-cpp-cann \
+  -m /app/models/MODEL_PATH \
+  -ngl 32 \
+  -p "Building a website can be done in 10 simple steps:"
 ```

 *Notes:*
@@ -171,69 +207,57 @@ docker run --name llamacpp --device /dev/davinci0  --device /dev/davinci_manager

 ### I. Setup Environment

-1. **Install Ascend Driver and firmware**
+1. **Configure Ascend user and group**

    ```sh
-    # create driver running user.
-    sudo groupadd -g HwHiAiUser
+    sudo groupadd HwHiAiUser
    sudo useradd -g HwHiAiUser -d /home/HwHiAiUser -m HwHiAiUser -s /bin/bash
    sudo usermod -aG HwHiAiUser $USER
-
-    # download driver from https://www.hiascend.com/hardware/firmware-drivers/community according to your system
-    # and install driver.
-    sudo sh Ascend-hdk-910b-npu-driver_x.x.x_linux-{arch}.run --full --install-for-all
    ```

-    Once installed, run `npu-smi info` to check whether driver is installed successfully.
+2. **Install dependencies**
+
+    **Ubuntu/Debian:**
    ```sh
-    +-------------------------------------------------------------------------------------------+
-    | npu-smi 24.1.rc2               Version: 24.1.rc2                                          |
-    +----------------------+---------------+----------------------------------------------------+
-    | NPU   Name           | Health        | Power(W)    Temp(C)           Hugepages-Usage(page)|
-    | Chip                 | Bus-Id        | AICore(%)   Memory-Usage(MB)  HBM-Usage(MB)        |
-    +======================+===============+====================================================+
-    | 2     xxx            | OK            | 64.4        51                15   / 15            |
-    | 0                    | 0000:01:00.0  | 0           1873 / 15077      0    / 32768         |
-    +======================+===============+====================================================+
-    | 5     xxx            | OK            | 64.0        52                15   / 15            |
-    | 0                    | 0000:81:00.0  | 0           1874 / 15077      0    / 32768         |
-    +======================+===============+====================================================+
-    | No running processes found in NPU 2                                                       |
-    +======================+===============+====================================================+
-    | No running processes found in NPU 5                                                       |
-    +======================+===============+====================================================+
+    sudo apt-get update
+    sudo apt-get install -y gcc python3 python3-pip linux-headers-$(uname -r)
    ```

-2. **Install Ascend Firmware**
+    **RHEL/CentOS:**
    ```sh
-    # download driver from https://www.hiascend.com/hardware/firmware-drivers/community according to your system
-    # and install driver.
-    sudo sh Ascend-hdk-910b-npu-firmware_x.x.x.x.X.run --full
+    sudo yum makecache
+    sudo yum install -y gcc python3 python3-pip kernel-headers-$(uname -r) kernel-devel-$(uname -r)
    ```
-    If the following message appears, firmware is installed successfully.
+
+3. **Install CANN (driver + toolkit)**
+
+    > The `Ascend-cann` package includes both the driver and toolkit.
+    > `$ARCH` can be `x86_64` or `aarch64`, `$CHIP` can be `910b` or `310p`.
+
    ```sh
-    Firmware package installed successfully!
+    wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%208.5.T63/Ascend-cann_8.5.0_linux-$ARCH.run
+    sudo bash ./Ascend-cann_8.5.0_linux-$ARCH.run --install
+
+    wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%208.5.T63/Ascend-cann-$CHIP-ops_8.5.0_linux-$ARCH.run
+    sudo bash ./Ascend-cann-$CHIP-ops_8.5.0_linux-$ARCH.run --install
    ```

+4. **Verify installation**

-3. **Install CANN toolkit and kernels**
-
-    CANN toolkit and kernels can be obtained from the official [CANN Toolkit](https://www.hiascend.com/zh/developer/download/community/result?module=cann) page.
-
-    Please download the corresponding version that satified your system. The minimum version required is 8.0.RC2.alpha002 and here is the install command.
    ```sh
-    pip3 install attrs numpy decorator sympy cffi pyyaml pathlib2 psutil protobuf scipy requests absl-py wheel typing_extensions
-    sh Ascend-cann-toolkit_8.0.RC2.alpha002_linux-aarch64.run --install
-    sh Ascend-cann-kernels-910b_8.0.RC2.alpha002_linux.run --install
+    npu-smi info
    ```

-    Set Ascend Variables:
+    If device information is displayed correctly, the driver is functioning properly.
+
    ```sh
-    echo "source ~/Ascend/ascend-toolkit/set_env.sh" >> ~/.bashrc
-    source ~/.bashrc
+    # Set environment variables (adjust path if needed)
+    source /usr/local/Ascend/cann/set_env.sh
+
+    python3 -c "import acl; print(acl.get_soc_name())"
    ```

-Upon a successful installation, CANN is enabled for the available ascend devices.
+    If the command outputs the chip model, the installation was successful.

 ### II. Build llama.cpp

@@ -31,6 +31,13 @@ llama-server -m gemma-3-4b-it-Q4_K_M.gguf --mmproj mmproj-gemma-3-4b-it-Q4_K_M.g
 llama-server -hf ggml-org/gemma-3-4b-it-GGUF --no-mmproj-offload
 ```

+> [!IMPORTANT]
+>
+> OCR models are trained with specific prompt and input structure, please refer to these discussions for more info:
+> - PaddleOCR-VL: https://github.com/ggml-org/llama.cpp/pull/18825
+> - GLM-OCR: https://github.com/ggml-org/llama.cpp/pull/19677
+> - Deepseek-OCR: https://github.com/ggml-org/llama.cpp/pull/17400
+
 ## Pre-quantized models

 These are ready-to-use models, most of them come with `Q4_K_M` quantization by default. They can be found at the Hugging Face page of the ggml-org: https://huggingface.co/collections/ggml-org/multimodal-ggufs-68244e01ff1f39e5bebeeedc
@@ -7,7 +7,7 @@ import os

 # Add utils directory to path for direct script execution
 sys.path.insert(0, str(Path(__file__).parent.parent / "utils"))
-from common import get_model_name_from_env_path, compare_tokens, exit_with_warning  # type: ignore[import-not-found]
+from common import get_model_name_from_env_path, compare_tokens, exit_with_warning  # type: ignore[import-not-found, ty:unresolved-import]

 def quick_logits_check(pytorch_file, llamacpp_file):
    """Lightweight sanity check before NMSE"""
@@ -5,7 +5,7 @@ import sys
 import os
 import argparse
 from pathlib import Path
-from common import get_model_name_from_env_path  # type: ignore[import-not-found]
+from common import get_model_name_from_env_path  # type: ignore[import-not-found, ty:unresolved-import]

 def calculate_nmse(reference, test):
    mse = np.mean((test - reference) ** 2)
@@ -2,7 +2,7 @@

 import argparse
 import sys
-from common import compare_tokens  # type: ignore[import-not-found]
+from common import compare_tokens  # type: ignore[import-not-found, ty:unresolved-import]


 def parse_arguments():
@@ -7,7 +7,7 @@ import importlib
 from pathlib import Path

 from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM, AutoModel
-from common import compare_tokens, exit_with_warning  # type: ignore[import-not-found]
+from common import compare_tokens, exit_with_warning  # type: ignore[import-not-found, ty:unresolved-import]

 unreleased_model_name = os.getenv('UNRELEASED_MODEL_NAME')

@@ -20,4 +20,4 @@ cmake .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA
 #cmake --build . --config Release --target llama-bench

 #build all binary
-cmake --build . --config Release -j -v
+cmake --build . --config Release -j$((($(nproc)+1)/2)) -v
@@ -23,9 +23,9 @@ if [ $# -gt 0 ]; then
    GGML_SYCL_DEVICE=$1
    echo "use $GGML_SYCL_DEVICE as main GPU"
    #use signle GPU only
-    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} -mg $GGML_SYCL_DEVICE -sm none ${LOAD_MODE}
+    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 200 -e -ngl ${NGL} -s 0 -c ${CONTEXT} -mg $GGML_SYCL_DEVICE -sm none ${LOAD_MODE}

 else
    #use multiple GPUs with same max compute units
-    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} ${LOAD_MODE}
+    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 200 -e -ngl ${NGL} -s 0 -c ${CONTEXT} ${LOAD_MODE}
 fi
@@ -77,6 +77,7 @@ extern "C" {
    };

    GGML_API struct gguf_context * gguf_init_empty(void);
+    GGML_API struct gguf_context * gguf_init_from_file_ptr(FILE * file, struct gguf_init_params params);
    GGML_API struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params);
    //GGML_API struct gguf_context * gguf_init_from_buffer(..);

@@ -189,6 +190,7 @@ extern "C" {
    //

    // write the entire context to a binary file
+    GGML_API bool gguf_write_to_file_ptr(const struct gguf_context * ctx, FILE * file, bool only_meta);
    GGML_API bool gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta);

    // get the size in bytes of the meta data (header, kv pairs, tensor info) including padding
@@ -460,6 +460,10 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
        endif()
        if(NOT GGML_CPU_ALL_VARIANTS)
            set(MARCH_STR "rv64gc")
+            if (GGML_RVV)
+                string(APPEND MARCH_STR "v")
+            endif()
+
            if (GGML_RV_ZFH)
                string(APPEND MARCH_STR "_zfh")
            endif()
@@ -467,7 +471,6 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
            if (GGML_XTHEADVECTOR)
                string(APPEND MARCH_STR "_xtheadvector")
            elseif (GGML_RVV)
-                string(APPEND MARCH_STR "_v")
                if (GGML_RV_ZVFH)
                    string(APPEND MARCH_STR "_zvfh")
                endif()
@@ -475,12 +478,14 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
                    string(APPEND MARCH_STR "_zvfbfwma")
                endif()
            endif()
+
            if (GGML_RV_ZICBOP)
                string(APPEND MARCH_STR "_zicbop")
            endif()
            if (GGML_RV_ZIHINTPAUSE)
                string(APPEND MARCH_STR "_zihintpause")
            endif()
+
            list(APPEND ARCH_FLAGS "-march=${MARCH_STR}" -mabi=lp64d)
        else()
            # Begin with the lowest baseline
@@ -2871,8 +2871,12 @@ struct ggml_cplan ggml_graph_plan(
                        const int64_t ne11 = node->src[1]->ne[1]; // H
                        const int64_t ne12 = node->src[1]->ne[2]; // Channels In

-                        cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02*ne03;
-                        cur += sizeof(ggml_fp16_t)*ne10*ne11*ne12;
+                        GGML_ASSERT(node->src[0]->type == GGML_TYPE_F16 || node->src[0]->type == GGML_TYPE_F32);
+                        GGML_ASSERT(node->src[1]->type == GGML_TYPE_F32);
+
+                        cur += ggml_type_size(node->src[0]->type) * ne00 * ne01 * ne02 * ne03;
+                        cur += ggml_type_size(node->src[0]->type) * ne10 * ne11 * ne12;
+
                    } break;
                case GGML_OP_TOP_K:
                    {
@@ -6923,16 +6923,15 @@ void ggml_compute_forward_conv_3d(
    ggml_compute_forward_conv_3d_impl(params, src0, src1, dst, src0->type);
 }

-// ggml_compute_forward_conv_transpose_2d
-
-void ggml_compute_forward_conv_transpose_2d(
-        const ggml_compute_params * params,
-              ggml_tensor * dst) {
+template <typename kernel_t>
+static void ggml_compute_forward_conv_transpose_2d_impl(
+    const ggml_compute_params * params,
+          ggml_tensor * dst) {

    const ggml_tensor * src0 = dst->src[0];
    const ggml_tensor * src1 = dst->src[1];

-    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_F32);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);
    GGML_ASSERT( dst->type == GGML_TYPE_F32);

@@ -6943,7 +6942,7 @@ void ggml_compute_forward_conv_transpose_2d(

    const int nk = ne00*ne01*ne02*ne03;

-    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb00 == ggml_type_size(src0->type));
    GGML_ASSERT(nb10 == sizeof(float));

    if (ith == 0) {
@@ -6951,12 +6950,12 @@ void ggml_compute_forward_conv_transpose_2d(

        // permute kernel data (src0) from (Kw x Kh x Cout x Cin) to (Cin x Kw x Kh x Cout)
        {
-            ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0;
+            kernel_t * const wdata = (kernel_t *) params->wdata + 0;

            for (int64_t i03 = 0; i03 < ne03; i03++) {
                for (int64_t i02 = 0; i02 < ne02; i02++) {
-                    const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i03*nb03 + i02*nb02);
-                    ggml_fp16_t * dst_data = wdata + i02*ne01*ne00*ne03;
+                    const kernel_t * const src = (kernel_t *)((char *) src0->data + i03*nb03 + i02*nb02);
+                    kernel_t * dst_data = wdata + i02*ne01*ne00*ne03;
                    for (int64_t i01 = 0; i01 < ne01; i01++) {
                        for (int64_t i00 = 0; i00 < ne00; i00++) {
                            dst_data[i01*ne00*ne03 + i00*ne03 + i03] = src[i01 * ne00 + i00];
@@ -6968,13 +6967,17 @@ void ggml_compute_forward_conv_transpose_2d(

        // permute source data (src1) from (Sw x Sh x Cin) to (Cin x Sw x Sh)
        {
-            ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk;
+            kernel_t * const wdata = (kernel_t *) params->wdata + nk;
            for (int i12 = 0; i12 < ne12; i12++) {
                for (int i11 = 0; i11 < ne11; i11++) {
                    const float * const src = (float *)((char *) src1->data + i12*nb12 + i11*nb11);
-                    ggml_fp16_t * dst_data = wdata + i11*ne10*ne12;
+                    kernel_t * dst_data = wdata + i11*ne10*ne12;
                    for (int i10 = 0; i10 < ne10; i10++) {
-                        dst_data[i10*ne12 + i12] = GGML_CPU_FP32_TO_FP16(src[i10]);
+                        if constexpr (std::is_same_v<kernel_t, ggml_fp16_t>) {
+                            dst_data[i10*ne12 + i12] = GGML_CPU_FP32_TO_FP16(src[i10]);
+                        } else {
+                            dst_data[i10*ne12 + i12] = src[i10];
+                        }
                    }
                }
            }
@@ -6996,21 +6999,27 @@ void ggml_compute_forward_conv_transpose_2d(
    const int ip0 = dp*ith;
    const int ip1 = MIN(ip0 + dp, np);

-    ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0;
-    ggml_fp16_t * const wdata_src = wdata + nk;
+    kernel_t * const wdata = (kernel_t *) params->wdata + 0;
+    kernel_t * const wdata_src = wdata + nk;

    for (int i2 = ip0; i2 < ip1; i2++) { // Cout
        float * dst_data = (float *)((char *) dst->data + i2*nb2);
-        ggml_fp16_t * wdata_kernel = wdata + i2*ne01*ne00*ne03;
+        kernel_t * wdata_kernel = wdata + i2*ne01*ne00*ne03;
        for (int i11 = 0; i11 < ne11; i11++) {
            for (int i10 = 0; i10 < ne10; i10++) {
                const int i1n = i11*ne10*ne12 + i10*ne12;
                for (int i01 = 0; i01 < ne01; i01++) {
                    for (int i00 = 0; i00 < ne00; i00++) {
                        float v = 0;
-                        ggml_vec_dot_f16(ne03, &v, 0,
-                                wdata_src + i1n, 0,
-                                wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1);
+                        if constexpr (std::is_same_v<kernel_t, ggml_fp16_t>) {
+                            ggml_vec_dot_f16(ne03, &v, 0,
+                                    wdata_src + i1n, 0,
+                                    wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1);
+                        } else {
+                            ggml_vec_dot_f32(ne03, &v, 0,
+                                    wdata_src + i1n, 0,
+                                    wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1);
+                        }
                        dst_data[(i11*stride + i01)*ne0 + i10*stride + i00] += v;
                    }
                }
@@ -7019,6 +7028,28 @@ void ggml_compute_forward_conv_transpose_2d(
    }
 }

+void ggml_compute_forward_conv_transpose_2d(
+        const ggml_compute_params * params,
+              ggml_tensor * dst) {
+
+    const ggml_tensor * src0 = dst->src[0];
+
+    switch (src0->type) {
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_conv_transpose_2d_impl<ggml_fp16_t>(params, dst);
+            } break;
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_conv_transpose_2d_impl<float>(params, dst);
+            } break;
+        default:
+            {
+                GGML_ABORT("fatal error");
+            }
+    }
+}
+
 // ggml_compute_forward_conv_2d_dw

 struct ggml_conv_2d_dw_params {
@@ -799,6 +799,22 @@ static __device__ __forceinline__ float ggml_cuda_e8m0_to_fp32(uint8_t x) {
 #endif // CUDART_VERSION >= 12050
 }

+static __device__ __forceinline__ float ggml_cuda_ue4m3_to_fp32(uint8_t x) {
+#ifdef FP8_AVAILABLE
+    const uint32_t bits = x * (x != 0x7F && x != 0xFF); // Convert NaN to 0.0f to match CPU implementation.
+#if defined(GGML_USE_HIP) && defined(CDNA3)
+    // ROCm dose not support fp8 in software on devices with fp8 hardware,
+    // but CDNA3 supports only e4m3_fnuz (no inf).
+    const __hip_fp8_e4m3_fnuz xf = *reinterpret_cast<const __hip_fp8_e4m3_fnuz *>(&bits);
+#else
+    const __nv_fp8_e4m3 xf = *reinterpret_cast<const __nv_fp8_e4m3 *>(&bits);
+#endif // defined(GGML_USE_HIP) && defined(GGML_USE_HIP)
+    return static_cast<float>(xf) / 2;
+#else
+    NO_DEVICE_CODE;
+#endif // FP8_AVAILABLE
+}
+
 __device__ __forceinline__ uint8_t ggml_cuda_float_to_fp4_e2m1(float x, float e) {
    const uint8_t sign_bit = (x < 0.0f) << 3;
    float         ax       = fabsf(x) * e;
@@ -931,6 +947,13 @@ struct ggml_cuda_type_traits<GGML_TYPE_MXFP4> {
    static constexpr int qi = QI_MXFP4;
 };

+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_NVFP4> {
+    static constexpr int qk = QK_NVFP4;
+    static constexpr int qr = QR_NVFP4;
+    static constexpr int qi = QI_NVFP4;
+};
+
 template<>
 struct ggml_cuda_type_traits<GGML_TYPE_Q2_K> {
    static constexpr int qk = QK_K;
@@ -1,12 +1,20 @@
-#include <algorithm>
-
 #include "conv2d-transpose.cuh"
-#include "ggml.h"
+#include "convert.cuh"

-__global__ void conv2d_transpose_kernel(const float * __restrict__ input, const half * __restrict__ kernel,
-                                        float * __restrict__ output, const int in_w, const int in_h, const int out_w,
-                                        const int out_h, const int kernel_w, const int kernel_h, const int stride,
-                                        const int c_in, const int c_out, const int batches) {
+template <typename kernel_t>
+static __global__ void conv2d_transpose_kernel(const float * __restrict__ input,
+                                               const kernel_t * __restrict__ kernel,
+                                               float * __restrict__ output,
+                                               const int in_w,
+                                               const int in_h,
+                                               const int out_w,
+                                               const int out_h,
+                                               const int kernel_w,
+                                               const int kernel_h,
+                                               const int stride,
+                                               const int c_in,
+                                               const int c_out,
+                                               const int batches) {
    const int global_idx = blockIdx.x * blockDim.x + threadIdx.x;

    const int total_elements = out_w * out_h * c_out * batches;
@@ -26,24 +34,32 @@ __global__ void conv2d_transpose_kernel(const float * __restrict__ input, const
    for (int c_in_idx = 0; c_in_idx < c_in; c_in_idx++) {
        for (int kh = 0; kh < kernel_h; ++kh) {
            int in_y = out_y_idx - kh;
-            if (in_y < 0 || in_y % stride) continue;
+            if (in_y < 0 || in_y % stride) {
+                continue;
+            }
            in_y /= stride;
-            if (in_y >= in_h) continue;
+            if (in_y >= in_h) {
+                continue;
+            }

            for (int kw = 0; kw < kernel_w; ++kw) {
                int in_x = out_x_idx - kw;
-                if (in_x < 0 || in_x % stride) continue;
+                if (in_x < 0 || in_x % stride) {
+                    continue;
+                }
                in_x /= stride;
-                if (in_x >= in_w) continue;
+                if (in_x >= in_w) {
+                    continue;
+                }

                const int input_idx = (in_w * in_h * c_in) * n_idx + (in_w * in_h) * c_in_idx + (in_w) *in_y + in_x;
                const int kernel_idx =
                    (kernel_h * kernel_w * c_out) * c_in_idx + (kernel_h * kernel_w) * c_idx + (kernel_w) *kh + kw;

-                float input_val = input[input_idx];
-                half  kern_val  = kernel[kernel_idx];
+                float    input_val = input[input_idx];
+                kernel_t kern_val  = kernel[kernel_idx];

-                accumulator += input_val * (float) kern_val;
+                accumulator += input_val * ggml_cuda_cast<float>(kern_val);
            }
        }
    }
@@ -56,11 +72,12 @@ void ggml_cuda_conv_2d_transpose_p0(ggml_backend_cuda_context & ctx, ggml_tensor
    const ggml_tensor * kernel = dst->src[0];
    const ggml_tensor * input  = dst->src[1];

-    GGML_ASSERT(kernel->type == GGML_TYPE_F16 && input->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(kernel->type == GGML_TYPE_F16 || kernel->type == GGML_TYPE_F32);
+    GGML_ASSERT(input->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);

    const float * input_data  = (const float *) input->data;
    float *       output_data = (float *) dst->data;
-    const half * kernel_data = (const half *) kernel->data;
+    const void *  kernel_data = kernel->data;

    const int input_w      = input->ne[0];
    const int input_h      = input->ne[1];
@@ -82,10 +99,17 @@ void ggml_cuda_conv_2d_transpose_p0(ggml_backend_cuda_context & ctx, ggml_tensor
    GGML_ASSERT(ggml_is_contiguous(kernel));
    GGML_ASSERT(ggml_is_contiguous(dst));

-    const int total  = (output_w * output_h * channels_out * batches);
+    const int total  = output_w * output_h * channels_out * batches;
    const int blocks = (total + CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE - 1) / CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE;

-    conv2d_transpose_kernel<<<blocks, CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE, 0, st>>>(
-        input_data, kernel_data, output_data, input_w, input_h, output_w, output_h, kernel_w, kernel_h, stride,
-        channels_in, channels_out, batches);
+    if (kernel->type == GGML_TYPE_F16) {
+        conv2d_transpose_kernel<half><<<blocks, CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE, 0, st>>>(
+            input_data, (const half *) kernel_data, output_data, input_w, input_h, output_w, output_h, kernel_w,
+            kernel_h, stride, channels_in, channels_out, batches);
+
+    } else {
+        conv2d_transpose_kernel<float><<<blocks, CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE, 0, st>>>(
+            input_data, (const float *) kernel_data, output_data, input_w, input_h, output_w, output_h, kernel_w,
+            kernel_h, stride, channels_in, channels_out, batches);
+    }
 }
@@ -1,4 +1,5 @@
 #include "common.cuh"

 #define CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE 256
+
 void ggml_cuda_conv_2d_transpose_p0(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
@@ -617,6 +617,45 @@ static void dequantize_row_mxfp4_cuda(const void * vx, dst_t * y, const int64_t
    dequantize_block_mxfp4<<<nb, 32, 0, stream>>>(vx, y);
 }

+template <typename dst_t>
+static __global__ void dequantize_block_nvfp4(
+        const void * __restrict__ vx,
+        dst_t * __restrict__ yy,
+        const int64_t ne) {
+    const int64_t i = blockIdx.x;
+    const int     tid = threadIdx.x;
+
+    const int64_t base = i * QK_NVFP4;
+    if (base >= ne) {
+        return;
+    }
+
+    const block_nvfp4 * x = (const block_nvfp4 *) vx;
+    const block_nvfp4 & xb = x[i];
+
+    const int sub = tid / (QK_NVFP4_SUB / 2);
+    const int j = tid % (QK_NVFP4_SUB / 2);
+
+    const float d = ggml_cuda_ue4m3_to_fp32(xb.d[sub]);
+    const uint8_t q = xb.qs[sub * (QK_NVFP4_SUB / 2) + j];
+
+    const int64_t y0 = base + sub * QK_NVFP4_SUB + j;
+    const int64_t y1 = y0 + QK_NVFP4_SUB / 2;
+
+    yy[y0] = ggml_cuda_cast<dst_t>(d * kvalues_mxfp4[q & 0x0F]);
+    yy[y1] = ggml_cuda_cast<dst_t>(d * kvalues_mxfp4[q >> 4]);
+}
+
+template <typename dst_t>
+static void dequantize_row_nvfp4_cuda(
+        const void * vx,
+        dst_t * y,
+        const int64_t k,
+        cudaStream_t stream) {
+    GGML_ASSERT(k % QK_NVFP4 == 0);
+    const int nb = k / QK_NVFP4;
+    dequantize_block_nvfp4<<<nb, 32, 0, stream>>>(vx, y, k);
+}
 template <typename src_t, typename dst_t>
 static __global__ void convert_unary(
        const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t ne00, const int64_t ne01,
@@ -715,6 +754,8 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
            return dequantize_row_iq3_s_cuda;
        case GGML_TYPE_MXFP4:
            return dequantize_row_mxfp4_cuda;
+        case GGML_TYPE_NVFP4:
+            return dequantize_row_nvfp4_cuda;
        case GGML_TYPE_F32:
            return convert_unary_cont_cuda<float>;
        case GGML_TYPE_BF16:
@@ -766,6 +807,8 @@ to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
            return dequantize_row_iq3_s_cuda;
        case GGML_TYPE_MXFP4:
            return dequantize_row_mxfp4_cuda;
+        case GGML_TYPE_NVFP4:
+            return dequantize_row_nvfp4_cuda;
        case GGML_TYPE_F16:
            return convert_unary_cont_cuda<half>;
        case GGML_TYPE_BF16:
@@ -1297,7 +1297,12 @@ static void ggml_cuda_op_mul_mat_cublas(
    const bool supports_bf16 = GGML_CUDA_CC_IS_NVIDIA(cc) || GGML_CUDA_CC_IS_AMD(cc) ||
        (GGML_CUDA_CC_IS_MTHREADS(cc) && cc >= GGML_CUDA_CC_QY2);

-    const bool use_fp16 = (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT;
+    const bool use_fp16 =
+        src0->type != GGML_TYPE_NVFP4 &&
+        (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
+        ggml_is_contiguous(src0) &&
+        row_diff == src0->ne[1] &&
+        dst->op_params[0] == GGML_PREC_DEFAULT;

    if (supports_bf16 && src0->type == GGML_TYPE_BF16 && ggml_is_contiguous(src0) && row_diff == src0->ne[1]) {
        ggml_cuda_pool_alloc<nv_bfloat16> src1_as_bf16(ctx.pool(id));
@@ -2338,7 +2343,8 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
        static_assert(MMVQ_MAX_BATCH_SIZE == MMVF_MAX_BATCH_SIZE);
        if (ne2 <= MMVQ_MAX_BATCH_SIZE) {
            if (ggml_is_quantized(src0->type)) {
-                if (ne2 <= MMVQ_MMID_MAX_BATCH_SIZE) {
+                const int mmvq_mmid_max = get_mmvq_mmid_max_batch(src0->type, cc);
+                if (ne2 <= mmvq_mmid_max) {
                    ggml_cuda_mul_mat_vec_q(ctx, src0, src1, ids, dst);
                    return;
                }
@@ -2941,14 +2947,18 @@ static bool ggml_cuda_graph_check_compability(ggml_cgraph * cgraph) {
        }

        // [TAG_MUL_MAT_ID_CUDA_GRAPHS]
-        if (node->op == GGML_OP_MUL_MAT_ID && (!ggml_is_quantized(node->src[0]->type) || node->ne[2] > MMVQ_MMID_MAX_BATCH_SIZE)) {
-            // under these conditions, the mul_mat_id operation will need to synchronize the stream, so we cannot use CUDA graphs
-            // TODO: figure out a way to enable for larger batch sizes, without hurting performance
-            // ref: https://github.com/ggml-org/llama.cpp/pull/18958
-            use_cuda_graph = false;
+        if (node->op == GGML_OP_MUL_MAT_ID) {
+            const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
+            const int mmvq_mmid_max = get_mmvq_mmid_max_batch(node->src[0]->type, cc);
+            if (!ggml_is_quantized(node->src[0]->type) || node->ne[2] > mmvq_mmid_max) {
+                // under these conditions, the mul_mat_id operation will need to synchronize the stream, so we cannot use CUDA graphs
+                // TODO: figure out a way to enable for larger batch sizes, without hurting performance
+                // ref: https://github.com/ggml-org/llama.cpp/pull/18958
+                use_cuda_graph = false;
 #ifndef NDEBUG
-            GGML_LOG_DEBUG("%s: disabling CUDA graphs due to unsupported node type\n", __func__);
+                GGML_LOG_DEBUG("%s: disabling CUDA graphs due to unsupported node type\n", __func__);
 #endif
+            }
        }

        if (!use_cuda_graph) {
@@ -4781,6 +4791,9 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                    case GGML_TYPE_Q5_1:
                    case GGML_TYPE_Q8_0:
                    case GGML_TYPE_MXFP4:
+#ifdef FP8_AVAILABLE
+                    case GGML_TYPE_NVFP4:
+#endif // FP8_AVAILABLE
                    case GGML_TYPE_Q2_K:
                    case GGML_TYPE_Q3_K:
                    case GGML_TYPE_Q4_K:
@@ -15,6 +15,7 @@ static constexpr __device__ vec_dot_q_cuda_t get_vec_dot_q_cuda(ggml_type type)
        case GGML_TYPE_Q5_1:    return vec_dot_q5_1_q8_1;
        case GGML_TYPE_Q8_0:    return vec_dot_q8_0_q8_1;
        case GGML_TYPE_MXFP4:   return vec_dot_mxfp4_q8_1;
+        case GGML_TYPE_NVFP4:   return vec_dot_nvfp4_q8_1;
        case GGML_TYPE_Q2_K:    return vec_dot_q2_K_q8_1;
        case GGML_TYPE_Q3_K:    return vec_dot_q3_K_q8_1;
        case GGML_TYPE_Q4_K:    return vec_dot_q4_K_q8_1;
@@ -41,6 +42,7 @@ static constexpr __host__ __device__ int get_vdr_mmvq(ggml_type type) {
        case GGML_TYPE_Q5_1:    return VDR_Q5_1_Q8_1_MMVQ;
        case GGML_TYPE_Q8_0:    return VDR_Q8_0_Q8_1_MMVQ;
        case GGML_TYPE_MXFP4:   return VDR_MXFP4_Q8_1_MMVQ;
+        case GGML_TYPE_NVFP4:   return VDR_NVFP4_Q8_1_MMVQ;
        case GGML_TYPE_Q2_K:    return VDR_Q2_K_Q8_1_MMVQ;
        case GGML_TYPE_Q3_K:    return VDR_Q3_K_Q8_1_MMVQ;
        case GGML_TYPE_Q4_K:    return VDR_Q4_K_Q8_1_MMVQ;
@@ -95,6 +97,194 @@ static __host__ mmvq_parameter_table_id get_device_table_id(int cc) {
    return MMVQ_PARAMETERS_GENERIC;
 }

+// Per-architecture maximum batch size for which MMVQ should be used for MUL_MAT_ID.
+// Returns a value <= MMVQ_MAX_BATCH_SIZE. Default is MMVQ_MAX_BATCH_SIZE.
+// Check https://github.com/ggml-org/llama.cpp/pull/20905#issuecomment-4145835627 for details
+
+static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_pascal_older(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_IQ1_S:   return 6;
+        case GGML_TYPE_IQ1_M:   return 6;
+        case GGML_TYPE_IQ2_S:   return 4;
+        case GGML_TYPE_IQ2_XS:  return 5;
+        case GGML_TYPE_IQ2_XXS: return 5;
+        case GGML_TYPE_IQ3_S:   return 4;
+        case GGML_TYPE_IQ3_XXS: return 4;
+        case GGML_TYPE_IQ4_NL:  return 6;
+        case GGML_TYPE_IQ4_XS:  return 5;
+        case GGML_TYPE_MXFP4:   return 4;
+        case GGML_TYPE_Q2_K:    return 4;
+        case GGML_TYPE_Q3_K:    return 4;
+        case GGML_TYPE_Q4_0:    return 6;
+        case GGML_TYPE_Q4_1:    return 6;
+        case GGML_TYPE_Q4_K:    return 5;
+        case GGML_TYPE_Q5_0:    return 6;
+        case GGML_TYPE_Q5_1:    return 6;
+        case GGML_TYPE_Q5_K:    return 5;
+        case GGML_TYPE_Q6_K:    return 4;
+        case GGML_TYPE_Q8_0:    return 4;
+        default:                return MMVQ_MAX_BATCH_SIZE;
+    }
+}
+
+static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_turing_plus(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_IQ2_S:   return 7;
+        case GGML_TYPE_IQ3_S:   return 6;
+        case GGML_TYPE_IQ3_XXS: return 7;
+        case GGML_TYPE_MXFP4:   return 7;
+        case GGML_TYPE_Q2_K:    return 7;
+        case GGML_TYPE_Q3_K:    return 5;
+        default:                return MMVQ_MAX_BATCH_SIZE;
+    }
+}
+
+static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_gcn(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_IQ1_S:   return 5;
+        case GGML_TYPE_IQ1_M:   return 5;
+        case GGML_TYPE_IQ2_S:   return 4;
+        case GGML_TYPE_IQ2_XS:  return 4;
+        case GGML_TYPE_IQ2_XXS: return 4;
+        case GGML_TYPE_IQ3_S:   return 4;
+        case GGML_TYPE_IQ3_XXS: return 4;
+        case GGML_TYPE_IQ4_NL:  return 6;
+        case GGML_TYPE_IQ4_XS:  return 4;
+        case GGML_TYPE_Q2_K:    return 4;
+        case GGML_TYPE_Q3_K:    return 4;
+        case GGML_TYPE_Q4_0:    return 5;
+        case GGML_TYPE_Q4_1:    return 5;
+        case GGML_TYPE_Q4_K:    return 4;
+        case GGML_TYPE_Q5_K:    return 4;
+        case GGML_TYPE_Q6_K:    return 4;
+        case GGML_TYPE_Q8_0:    return 4;
+        default:                return MMVQ_MAX_BATCH_SIZE;
+    }
+}
+
+static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_cdna(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_IQ2_S:   return 5;
+        case GGML_TYPE_IQ2_XS:  return 5;
+        case GGML_TYPE_IQ2_XXS: return 5;
+        case GGML_TYPE_IQ3_S:   return 4;
+        case GGML_TYPE_IQ3_XXS: return 5;
+        default:                return MMVQ_MAX_BATCH_SIZE;
+    }
+}
+
+static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_rdna1_rdna2(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_IQ2_S:   return 4;
+        case GGML_TYPE_IQ2_XS:  return 4;
+        case GGML_TYPE_IQ2_XXS: return 4;
+        case GGML_TYPE_IQ3_S:   return 4;
+        case GGML_TYPE_IQ3_XXS: return 4;
+        case GGML_TYPE_Q2_K:    return 7;
+        case GGML_TYPE_Q3_K:    return 4;
+        case GGML_TYPE_Q4_K:    return 5;
+        case GGML_TYPE_Q5_K:    return 6;
+        case GGML_TYPE_Q6_K:    return 5;
+        default:                return MMVQ_MAX_BATCH_SIZE;
+    }
+}
+
+static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_rdna3(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_IQ1_S:   return 6;
+        case GGML_TYPE_IQ1_M:   return 6;
+        case GGML_TYPE_IQ2_S:   return 4;
+        case GGML_TYPE_IQ2_XS:  return 4;
+        case GGML_TYPE_IQ2_XXS: return 4;
+        case GGML_TYPE_IQ3_S:   return 4;
+        case GGML_TYPE_IQ3_XXS: return 4;
+        case GGML_TYPE_IQ4_NL:  return 6;
+        case GGML_TYPE_IQ4_XS:  return 6;
+        case GGML_TYPE_Q4_K:    return 4;
+        case GGML_TYPE_Q5_K:    return 4;
+        case GGML_TYPE_Q6_K:    return 4;
+        default:                return MMVQ_MAX_BATCH_SIZE;
+    }
+}
+
+static constexpr __host__ __device__ int get_mmvq_mmid_max_batch_rdna4(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_IQ1_S:   return 7;
+        case GGML_TYPE_IQ1_M:   return 7;
+        case GGML_TYPE_IQ2_S:   return 4;
+        case GGML_TYPE_IQ2_XS:  return 4;
+        case GGML_TYPE_IQ2_XXS: return 4;
+        case GGML_TYPE_IQ3_S:   return 4;
+        case GGML_TYPE_IQ3_XXS: return 4;
+        case GGML_TYPE_IQ4_NL:  return 7;
+        case GGML_TYPE_IQ4_XS:  return 5;
+        case GGML_TYPE_MXFP4:   return 5;
+        case GGML_TYPE_Q3_K:    return 4;
+        case GGML_TYPE_Q4_0:    return 7;
+        case GGML_TYPE_Q4_1:    return 7;
+        case GGML_TYPE_Q4_K:    return 4;
+        case GGML_TYPE_Q5_0:    return 7;
+        case GGML_TYPE_Q5_1:    return 7;
+        case GGML_TYPE_Q5_K:    return 5;
+        case GGML_TYPE_Q6_K:    return 5;
+        case GGML_TYPE_Q8_0:    return 7;
+        default:                return MMVQ_MAX_BATCH_SIZE;
+    }
+}
+
+// Host function: returns the max batch size for the current arch+type at runtime.
+int get_mmvq_mmid_max_batch(ggml_type type, int cc) {
+    // NVIDIA: Volta, Ada Lovelace, and Blackwell always use MMVQ for MUL_MAT_ID.
+    if (cc == GGML_CUDA_CC_VOLTA || cc >= GGML_CUDA_CC_ADA_LOVELACE) {
+        return MMVQ_MAX_BATCH_SIZE;
+    }
+    if (cc >= GGML_CUDA_CC_TURING) {
+        return get_mmvq_mmid_max_batch_turing_plus(type);
+    }
+    if (GGML_CUDA_CC_IS_NVIDIA(cc)) {
+        return get_mmvq_mmid_max_batch_pascal_older(type);
+    }
+    // AMD
+    if (GGML_CUDA_CC_IS_RDNA4(cc)) {
+        return get_mmvq_mmid_max_batch_rdna4(type);
+    }
+    if (GGML_CUDA_CC_IS_RDNA3(cc)) {
+        return get_mmvq_mmid_max_batch_rdna3(type);
+    }
+    if (GGML_CUDA_CC_IS_RDNA1(cc) || GGML_CUDA_CC_IS_RDNA2(cc)) {
+        return get_mmvq_mmid_max_batch_rdna1_rdna2(type);
+    }
+    if (GGML_CUDA_CC_IS_CDNA(cc)) {
+        return get_mmvq_mmid_max_batch_cdna(type);
+    }
+    if (GGML_CUDA_CC_IS_GCN(cc)) {
+        return get_mmvq_mmid_max_batch_gcn(type);
+    }
+    return MMVQ_MAX_BATCH_SIZE;
+}
+
+// Device constexpr: returns the max batch size for the current arch+type at compile time.
+template <ggml_type type>
+static constexpr __device__ int get_mmvq_mmid_max_batch_for_device() {
+#if defined(RDNA4)
+    return get_mmvq_mmid_max_batch_rdna4(type);
+#elif defined(RDNA3)
+    return get_mmvq_mmid_max_batch_rdna3(type);
+#elif defined(RDNA2) || defined(RDNA1)
+    return get_mmvq_mmid_max_batch_rdna1_rdna2(type);
+#elif defined(CDNA)
+    return get_mmvq_mmid_max_batch_cdna(type);
+#elif defined(GCN)
+    return get_mmvq_mmid_max_batch_gcn(type);
+#elif defined(__CUDA_ARCH__) && (__CUDA_ARCH__ == GGML_CUDA_CC_VOLTA || __CUDA_ARCH__ >= GGML_CUDA_CC_ADA_LOVELACE)
+    return MMVQ_MAX_BATCH_SIZE;
+#elif defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
+    return get_mmvq_mmid_max_batch_turing_plus(type);
+#else
+    return get_mmvq_mmid_max_batch_pascal_older(type);
+#endif
+}
+
 static constexpr __host__ __device__ int calc_nwarps(ggml_type type, int ncols_dst, mmvq_parameter_table_id table_id) {
    if (table_id == MMVQ_PARAMETERS_GENERIC) {
        switch (ncols_dst) {
@@ -193,7 +383,7 @@ static constexpr __host__ __device__ int calc_rows_per_block(int ncols_dst, int
    return 1;
 }

-template <ggml_type type, int ncols_dst, bool has_fusion, bool is_multi_token_id = false, bool small_k = false>
+template <ggml_type type, int ncols_dst, bool has_fusion, bool small_k = false>
 __launch_bounds__(calc_nwarps(type, ncols_dst, get_device_table_id())*ggml_cuda_get_physical_warp_size(), 1)
 static __global__ void mul_mat_vec_q(
        const void * __restrict__ vx, const void * __restrict__ vy, const int32_t * __restrict__ ids, const ggml_cuda_mm_fusion_args_device fusion, float * __restrict__ dst,
@@ -220,22 +410,13 @@ static __global__ void mul_mat_vec_q(

    const uint32_t channel_dst = blockIdx.y;

-    uint32_t token_idx = 0;
    uint32_t channel_x;
    uint32_t channel_y;
    uint32_t sample_dst;

-    if constexpr (is_multi_token_id) {
-        // Multi-token MUL_MAT_ID path, adding these in the normal path causes a perf regression for n_tokens=1 case
-        token_idx  = blockIdx.z;
-        channel_x  = ids[channel_dst + token_idx * ids_stride];
-        channel_y  = fastmodulo(channel_dst, nchannels_y);
-        sample_dst = 0;
-    } else {
-        channel_x  = ncols_dst == 1 && ids ? ids[channel_dst]                     : fastdiv(channel_dst, channel_ratio);
-        channel_y  = ncols_dst == 1 && ids ? fastmodulo(channel_dst, nchannels_y) : channel_dst;
-        sample_dst = blockIdx.z;
-    }
+    channel_x  = ncols_dst == 1 && ids ? ids[channel_dst]                     : fastdiv(channel_dst, channel_ratio);
+    channel_y  = ncols_dst == 1 && ids ? fastmodulo(channel_dst, nchannels_y) : channel_dst;
+    sample_dst = blockIdx.z;

    const uint32_t sample_x    = fastdiv(sample_dst, sample_ratio);
    const uint32_t sample_y    = sample_dst;
@@ -292,9 +473,6 @@ static __global__ void mul_mat_vec_q(
    float tmp_gate[ncols_dst][rows_per_cuda_block] = {{0.0f}};

    const block_q8_1 * y = ((const block_q8_1 *) vy) + sample_y*stride_sample_y + channel_y*stride_channel_y;
-    if constexpr (is_multi_token_id) {
-        y += token_idx*stride_col_y;
-    }
    const int kbx_offset = sample_x*stride_sample_x + channel_x*stride_channel_x + row0*stride_row_x;

    for (int kbx = tid / (qi/vdr); kbx < blocks_per_row_x; kbx += blocks_per_iter) {
@@ -348,10 +526,6 @@ static __global__ void mul_mat_vec_q(

    dst += sample_dst*stride_sample_dst + channel_dst*stride_channel_dst + row0;

-    if constexpr (is_multi_token_id) {
-        dst += token_idx*stride_col_dst;
-    }
-
    // sum up partial sums and write back result
 #pragma unroll
    for (int j = 0; j < ncols_dst; ++j) {
@@ -411,6 +585,69 @@ static __global__ void mul_mat_vec_q(
    }
 }

+// Dedicated MoE multi-token kernel.
+// Grid: (ceil(nrows_x / c_rows_per_block), nchannels_dst)
+// Block: (warp_size, ncols_dst) - each warp handles one token independently.
+// No shared memory reduction needed since each warp works alone.
+template <ggml_type type, int c_rows_per_block>
+__launch_bounds__(get_mmvq_mmid_max_batch_for_device<type>()*ggml_cuda_get_physical_warp_size(), 1)
+static __global__ void mul_mat_vec_q_moe(
+        const void * __restrict__ vx, const void * __restrict__ vy, const int32_t * __restrict__ ids,
+        float * __restrict__ dst,
+        const uint32_t ncols_x, const uint3 nchannels_y, const uint32_t nrows_x,
+        const uint32_t stride_row_x, const uint32_t stride_col_y, const uint32_t stride_col_dst,
+        const uint32_t stride_channel_x, const uint32_t stride_channel_y, const uint32_t stride_channel_dst,
+        const uint32_t ncols_dst, const uint32_t ids_stride) {
+
+    constexpr int qk  = ggml_cuda_type_traits<type>::qk;
+    constexpr int qi  = ggml_cuda_type_traits<type>::qi;
+    constexpr int vdr = get_vdr_mmvq(type);
+    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
+
+    constexpr vec_dot_q_cuda_t vec_dot_q_cuda = get_vec_dot_q_cuda(type);
+
+    const uint32_t token_idx   = threadIdx.y;
+    const int      row0        = c_rows_per_block*blockIdx.x;
+    const int      blocks_per_row_x = ncols_x / qk;
+    constexpr int  blocks_per_iter  = vdr * warp_size / qi;
+
+    const uint32_t channel_dst = blockIdx.y;
+
+    if (token_idx >= ncols_dst) {
+        return;
+    }
+
+    const uint32_t channel_x = ids[channel_dst + token_idx * ids_stride];
+    const uint32_t channel_y = fastmodulo(channel_dst, nchannels_y);
+
+    const block_q8_1 * y = ((const block_q8_1 *) vy) + channel_y*stride_channel_y + token_idx*stride_col_y;
+    const int kbx_offset  = channel_x*stride_channel_x + row0*stride_row_x;
+
+    // partial sum for each thread
+    float tmp[c_rows_per_block] = {0.0f};
+
+    for (int kbx = threadIdx.x / (qi/vdr); kbx < blocks_per_row_x; kbx += blocks_per_iter) {
+        const int kby = kbx * (qk/QK8_1);
+        const int kqs = vdr * (threadIdx.x % (qi/vdr));
+
+#pragma unroll
+        for (int i = 0; i < c_rows_per_block; ++i) {
+            tmp[i] += vec_dot_q_cuda(vx, &y[kby], kbx_offset + i*stride_row_x + kbx, kqs);
+        }
+    }
+
+    // Warp-level reduction only - no shared memory needed
+#pragma unroll
+    for (int i = 0; i < c_rows_per_block; ++i) {
+        tmp[i] = warp_reduce_sum<warp_size>(tmp[i]);
+    }
+
+    // Write results
+    if (threadIdx.x < c_rows_per_block && (c_rows_per_block == 1 || uint32_t(row0 + threadIdx.x) < nrows_x)) {
+        dst[channel_dst*stride_channel_dst + token_idx*stride_col_dst + row0 + threadIdx.x] = tmp[threadIdx.x];
+    }
+}
+
 template<ggml_type type>
 static std::pair<dim3, dim3> calc_launch_params(
        const int ncols_dst, const int nrows_x, const int nchannels_dst, const int nsamples_or_ntokens,
@@ -423,7 +660,7 @@ static std::pair<dim3, dim3> calc_launch_params(
    return {block_nums, block_dims};
 }

-template<ggml_type type, int c_ncols_dst, bool is_multi_token_id = false, bool small_k = false>
+template<ggml_type type, int c_ncols_dst, bool small_k = false>
 static void mul_mat_vec_q_switch_fusion(
        const void * vx, const void * vy, const int32_t * ids, const ggml_cuda_mm_fusion_args_device fusion, float * dst,
        const uint32_t ncols_x, const uint3 nchannels_y, const uint32_t stride_row_x, const uint32_t stride_col_y,
@@ -436,7 +673,7 @@ static void mul_mat_vec_q_switch_fusion(
    const bool has_fusion = fusion.gate != nullptr || fusion.x_bias != nullptr || fusion.gate_bias != nullptr;
    if constexpr (c_ncols_dst == 1) {
        if (has_fusion) {
-            mul_mat_vec_q<type, c_ncols_dst, true, is_multi_token_id, small_k><<<block_nums, block_dims, nbytes_shared, stream>>>
+            mul_mat_vec_q<type, c_ncols_dst, true, small_k><<<block_nums, block_dims, nbytes_shared, stream>>>
                (vx, vy, ids, fusion, dst, ncols_x, nchannels_y, stride_row_x, stride_col_y, stride_col_dst,
                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride);
@@ -446,12 +683,33 @@ static void mul_mat_vec_q_switch_fusion(

    GGML_ASSERT(!has_fusion && "fusion only supported for ncols_dst=1");

-    mul_mat_vec_q<type, c_ncols_dst, false, is_multi_token_id, small_k><<<block_nums, block_dims, nbytes_shared, stream>>>
+    mul_mat_vec_q<type, c_ncols_dst, false, small_k><<<block_nums, block_dims, nbytes_shared, stream>>>
        (vx, vy, ids, fusion, dst, ncols_x, nchannels_y, stride_row_x, stride_col_y, stride_col_dst,
        channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
        sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride);
 }

+template <ggml_type type>
+static void mul_mat_vec_q_moe_launch(
+        const void * vx, const void * vy, const int32_t * ids, float * dst,
+        const uint32_t ncols_x, const uint3 nchannels_y, const uint32_t nrows_x,
+        const uint32_t stride_row_x, const uint32_t stride_col_y, const uint32_t stride_col_dst,
+        const uint32_t stride_channel_x, const uint32_t stride_channel_y, const uint32_t stride_channel_dst,
+        const uint32_t ncols_dst, const uint32_t ids_stride,
+        const int warp_size, const int nchannels_dst, cudaStream_t stream) {
+
+    constexpr int rows_per_block = 2; // 2 gives best perf based on tuning
+    const int64_t nblocks_rows = (nrows_x + rows_per_block - 1) / rows_per_block;
+    const dim3 block_nums(nblocks_rows, nchannels_dst);
+    const dim3 block_dims(warp_size, ncols_dst);
+
+    mul_mat_vec_q_moe<type, rows_per_block><<<block_nums, block_dims, 0, stream>>>(
+        vx, vy, ids, dst, ncols_x, nchannels_y, nrows_x,
+        stride_row_x, stride_col_y, stride_col_dst,
+        stride_channel_x, stride_channel_y, stride_channel_dst,
+        ncols_dst, ids_stride);
+}
+
 template <ggml_type type>
 static void mul_mat_vec_q_switch_ncols_dst(
        const void * vx, const void * vy, const int32_t * ids, const ggml_cuda_mm_fusion_args_device fusion, float * dst,
@@ -470,20 +728,62 @@ static void mul_mat_vec_q_switch_ncols_dst(
    const uint3 sample_ratio_fd  = init_fastdiv_values(nsamples_dst  / nsamples_x);

    const int device = ggml_cuda_get_device();
+    const int                     cc        = ggml_cuda_info().devices[device].cc;
    const int warp_size = ggml_cuda_info().devices[device].warp_size;
-    const mmvq_parameter_table_id table_id = get_device_table_id(ggml_cuda_info().devices[device].cc);
+    const mmvq_parameter_table_id table_id  = get_device_table_id(cc);

    const bool has_fusion = fusion.gate != nullptr || fusion.x_bias != nullptr || fusion.gate_bias != nullptr;
    const bool has_ids = ids != nullptr;

+    const auto should_use_small_k = [&](int c_ncols_dst) {
+        // When K is small, increase rows_per_block to match nwarps so each warp has more work to do
+        // Trigger when the full thread block covers all K blocks in a single loop iteration and few threads remain idle.
+        constexpr int qk                    = ggml_cuda_type_traits<type>::qk;
+        constexpr int qi                    = ggml_cuda_type_traits<type>::qi;
+        constexpr int vdr                   = get_vdr_mmvq(type);
+        const int     blocks_per_row_x      = ncols_x / qk;
+        const int     blocks_per_iter_1warp = vdr * warp_size / qi;
+        const int     nwarps                = calc_nwarps(type, c_ncols_dst, table_id);
+        bool          use                   = nwarps > 1 && blocks_per_row_x < nwarps * blocks_per_iter_1warp;
+
+        constexpr std::array<ggml_type, 2> iq_slow_turing = {
+            GGML_TYPE_IQ3_XXS,
+            GGML_TYPE_IQ3_S,
+        };
+        constexpr std::array<ggml_type, 8> iq_slow_other = {
+            GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M,   GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS,
+            GGML_TYPE_IQ2_S, GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ3_S,   GGML_TYPE_IQ4_XS,
+        };
+        constexpr std::array<ggml_type, 3> slow_pascal = {
+            GGML_TYPE_IQ3_S,
+            GGML_TYPE_Q2_K,
+            GGML_TYPE_Q3_K,
+        };
+
+        const bool is_nvidia_turing_plus  = GGML_CUDA_CC_IS_NVIDIA(cc) && cc >= GGML_CUDA_CC_TURING;
+        const bool is_nvidia_pascal_older = GGML_CUDA_CC_IS_NVIDIA(cc) && cc < GGML_CUDA_CC_VOLTA;
+
+        if (is_nvidia_turing_plus) {
+            if (ncols_dst == 1 &&
+                    std::find(iq_slow_turing.begin(), iq_slow_turing.end(), type) != iq_slow_turing.end()) {
+                use = false;
+            }
+        } else if ((ncols_dst == 1 && std::find(iq_slow_other.begin(), iq_slow_other.end(), type) != iq_slow_other.end()) ||
+                (is_nvidia_pascal_older && std::find(slow_pascal.begin(), slow_pascal.end(), type) != slow_pascal.end()) ||
+                GGML_CUDA_CC_IS_RDNA(cc)) {
+            use = false;
+        }
+
+        return use;
+    };
+
    if (has_ids && ncols_dst > 1) {
-        // Multi-token MUL_MAT_ID path only - single-token goes through regular path below
-        constexpr int c_ncols_dst = 1;
-        std::pair<dim3, dim3> dims = calc_launch_params<type>(c_ncols_dst, nrows_x, nchannels_dst, ncols_dst, warp_size, table_id);
-        mul_mat_vec_q_switch_fusion<type, c_ncols_dst, true>(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
-             channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
-             sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
-             dims.first, dims.second, 0, ids_stride, stream);
+        // Multi-token MUL_MAT_ID path - dedicated MoE kernel
+        mul_mat_vec_q_moe_launch<type>(
+            vx, vy, ids, dst, ncols_x, nchannels_y_fd, nrows_x,
+            stride_row_x, stride_col_y, stride_col_dst,
+            stride_channel_x, stride_channel_y, stride_channel_dst,
+            ncols_dst, ids_stride, warp_size, nchannels_dst, stream);
        return;
    }

@@ -491,31 +791,24 @@ static void mul_mat_vec_q_switch_ncols_dst(
        case 1: {
            constexpr int c_ncols_dst = 1;

-            // When K is small, increase rows_per_block to match nwarps so each warp has more work to do
-            // Trigger when the full thread block covers all K blocks in a single loop iteration and few threads remain idle.
-            constexpr int qk  = ggml_cuda_type_traits<type>::qk;
-            constexpr int qi  = ggml_cuda_type_traits<type>::qi;
-            constexpr int vdr = get_vdr_mmvq(type);
-            const int blocks_per_row_x = ncols_x / qk;
-            const int blocks_per_iter_1warp = vdr * warp_size / qi;
-            const int nwarps = calc_nwarps(type, c_ncols_dst, table_id);
-            const bool use_small_k = nwarps > 1 && blocks_per_row_x < nwarps * blocks_per_iter_1warp;
+            bool use_small_k = should_use_small_k(c_ncols_dst);
+
            if (use_small_k) {
-                std::pair<dim3, dim3> dims = calc_launch_params<type>(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst,
-                                                                    warp_size, table_id, true);
-                mul_mat_vec_q_switch_fusion<type, c_ncols_dst, false, true>(
+                std::pair<dim3, dim3> dims = calc_launch_params<type>(c_ncols_dst, nrows_x, nchannels_dst,
+                                                                        nsamples_dst, warp_size, table_id, true);
+                mul_mat_vec_q_switch_fusion<type, c_ncols_dst, true>(
                    vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
-                    channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
-                    sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
-                    dims.first, dims.second, 0, ids_stride, stream);
+                    channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio_fd,
+                    stride_sample_x, stride_sample_y, stride_sample_dst, dims.first, dims.second, 0, ids_stride,
+                    stream);
            } else {
-                std::pair<dim3, dim3> dims = calc_launch_params<type>(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst,
-                                                                    warp_size, table_id);
+                std::pair<dim3, dim3> dims = calc_launch_params<type>(c_ncols_dst, nrows_x, nchannels_dst,
+                                                                        nsamples_dst, warp_size, table_id);
                mul_mat_vec_q_switch_fusion<type, c_ncols_dst>(
                    vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
-                    channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
-                    sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
-                    dims.first, dims.second, 0, ids_stride, stream);
+                    channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio_fd,
+                    stride_sample_x, stride_sample_y, stride_sample_dst, dims.first, dims.second, 0, ids_stride,
+                    stream);
            }
        } break;
        case 2: {
@@ -626,6 +919,12 @@ static void mul_mat_vec_q_switch_type(
                 nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
            break;
+        case GGML_TYPE_NVFP4:
+            mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_NVFP4>
+                (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+                 nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
+            break;
        case GGML_TYPE_Q2_K:
            mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q2_K>
                (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
@@ -1,7 +1,10 @@
 #include "common.cuh"

 #define MMVQ_MAX_BATCH_SIZE 8 // Max. batch size for which to use MMVQ kernels.
-#define MMVQ_MMID_MAX_BATCH_SIZE 4 // Max. batch size for which to use MMVQ kernels for MUL_MAT_ID
+
+// Returns the maximum batch size for which MMVQ should be used for MUL_MAT_ID,
+// based on the quantization type and GPU architecture (compute capability).
+int get_mmvq_mmid_max_batch(ggml_type type, int cc);

 void ggml_cuda_mul_mat_vec_q(ggml_backend_cuda_context & ctx,
    const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst, const ggml_cuda_mm_fusion_args_host * fusion = nullptr);
@@ -322,6 +322,38 @@ static __device__ __forceinline__ float vec_dot_mxfp4_q8_1(
    return d * sumi;
 }

+#define VDR_NVFP4_Q8_1_MMVQ 4
+#define VDR_NVFP4_Q8_1_MMQ  8
+
+static __device__ __forceinline__ float vec_dot_nvfp4_q8_1(
+                                        const void * __restrict__ vbq,
+                                        const block_q8_1 * __restrict__ bq8_1,
+                                        const int32_t & kbx,
+                                        const int32_t & iqs) {
+
+    const block_nvfp4 * bq4 = (const block_nvfp4 *) vbq + kbx;
+    float sum = 0.0f;
+#pragma unroll
+    for (int i = 0; i < VDR_NVFP4_Q8_1_MMVQ/2; i++) {
+        const int32_t iqs0 = iqs + 2*i;
+        const int32_t iqs1 = iqs0 + 1;
+        const int32_t is = iqs0 >> 1;
+        const int2 v0 = get_int_from_table_16(get_int_b4(bq4->qs, iqs0), kvalues_mxfp4);
+        const int2 v1 = get_int_from_table_16(get_int_b4(bq4->qs, iqs1), kvalues_mxfp4);
+        const block_q8_1 * bq8 = bq8_1 + (is >> 1);
+        const int32_t i8 = ((is & 1) << 2);
+
+        int sumi = ggml_cuda_dp4a(v0.x, get_int_b4(bq8->qs, i8 + 0), 0);
+        sumi = ggml_cuda_dp4a(v0.y, get_int_b4(bq8->qs, i8 + 2), sumi);
+        sumi = ggml_cuda_dp4a(v1.x, get_int_b4(bq8->qs, i8 + 1), sumi);
+        sumi = ggml_cuda_dp4a(v1.y, get_int_b4(bq8->qs, i8 + 3), sumi);
+
+        const float d = ggml_cuda_ue4m3_to_fp32(bq4->d[is]) * __low2float(bq8->ds);
+        sum += d * float(sumi);
+    }
+
+    return sum;
+}
 #define VDR_Q2_K_Q8_1_MMVQ 1
 #define VDR_Q2_K_Q8_1_MMQ  4

@@ -6,9 +6,10 @@
 #include <cuda_bf16.h>
 #include <cuda_fp16.h>

-#if CUDART_VERSION >= 12050
+#if CUDART_VERSION >= 11080
 #include <cuda_fp8.h>
-#endif // CUDART_VERSION >= 12050
+#define FP8_AVAILABLE
+#endif // CUDART_VERSION >= 11080

 #if CUDART_VERSION >= 12080
 #include <cuda_fp4.h>
@@ -235,6 +235,12 @@
 typedef __hip_bfloat16 nv_bfloat16;
 typedef __hip_bfloat162 nv_bfloat162;

+#if HIP_VERSION >= 60200000
+#include <hip/hip_fp8.h>
+typedef __hip_fp8_e4m3 __nv_fp8_e4m3;
+#define FP8_AVAILABLE
+#endif // HIP_VERSION >= 60200000
+
 typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
 typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4)));
 static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
@@ -1406,6 +1406,13 @@ static void ggml_backend_hexagon_buffer_set_tensor(ggml_backend_buffer_t buffer,
            repack_q8_0_q8x4x2(tensor, data, size);
            break;

+        case GGML_TYPE_IQ4_NL:
+            GGML_ASSERT(offset == 0);
+            GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
+            // IQ4_NL has identical block layout to Q4_0 (ggml_half d + uint8_t qs[16])
+            repack_q4_0_q4x4x2(tensor, data, size);
+            break;
+
        case GGML_TYPE_MXFP4:
            GGML_ASSERT(offset == 0);
            GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
@@ -1442,6 +1449,12 @@ static void ggml_backend_hexagon_buffer_get_tensor(ggml_backend_buffer_t buffer,
            repack_q8x4x2_q8_0(data, tensor, size);
            break;

+        case GGML_TYPE_IQ4_NL:
+            GGML_ASSERT(offset == 0);
+            GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
+            repack_q4x4x2_q4_0(data, tensor, size);
+            break;
+
        case GGML_TYPE_MXFP4:
            GGML_ASSERT(offset == 0);
            GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
@@ -1819,6 +1832,7 @@ static bool ggml_hexagon_supported_mul_mat(const struct ggml_hexagon_session * s
    switch (src0->type) {
        case GGML_TYPE_Q4_0:
        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_IQ4_NL:
        case GGML_TYPE_MXFP4:
            if (src0->ne[0] % 32) {
                return false;
@@ -1868,6 +1882,7 @@ static bool ggml_hexagon_supported_mul_mat_id(const struct ggml_hexagon_session
    switch (src0->type) {
        case GGML_TYPE_Q4_0:
        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_IQ4_NL:
        case GGML_TYPE_MXFP4:
            if ((src0->ne[0] % 32)) {
                return false;
@@ -2596,8 +2611,26 @@ static void ggml_backend_hexagon_free(ggml_backend_t backend) {
    delete backend;
 }

+// Map weight type to its activation quantization family.
+// Types in the same family produce identical Q8 formats in VTCM and can
+// safely share quantized activation data via SKIP_QUANTIZE.
+// When adding a new quantized type, assign it the correct family here.
+static inline int act_quant_family(enum ggml_type wtype) {
+    switch (wtype) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_MXFP4:
+            return 1;  // Q8x4x2
+        default:
+            return 0;  // unknown / not quantized
+    }
+}
+
 static inline bool op_reuse_src1(const ggml_tensor * op1, const ggml_tensor * op0) {
-    return (op0 && op0->src[1] == op1->src[1] && ggml_is_quantized(op0->src[0]->type));
+    return (op0 && op0->src[1] == op1->src[1] &&
+            act_quant_family(op0->src[0]->type) == act_quant_family(op1->src[0]->type) &&
+            act_quant_family(op0->src[0]->type) != 0);
 }

 static inline bool is_compute_op(ggml_tensor *node)
@@ -3364,6 +3397,8 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) {
                  "please update hexagon_type to match ggml_type");
    static_assert((unsigned int) HTP_TYPE_MXFP4 == (unsigned int) GGML_TYPE_MXFP4,
                  "please update hexagon_type to match ggml_type");
+    static_assert((unsigned int) HTP_TYPE_IQ4_NL == (unsigned int) GGML_TYPE_IQ4_NL,
+                  "please update hexagon_type to match ggml_type");

    const char * str_experimental = getenv("GGML_HEXAGON_EXPERIMENTAL");
    const char * str_verbose = getenv("GGML_HEXAGON_VERBOSE");
@@ -346,6 +346,9 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *

    const HVX_Vector logit_cap = hvx_vec_splat_f32(factx->logit_softcap);

+    dma_cache m_cache;
+    dma_cache_init(&m_cache, spad_m, factx->size_m_block, DMA_CACHE_MAX_SIZE);
+
    for (uint32_t ir = ir0; ir < ir1; ++ir) {
        const uint32_t iq3 = fastdiv(ir, &factx->src0_div21);
        const uint32_t iq2 = fastdiv(ir - iq3*neq2*neq1, &factx->src0_div1);
@@ -389,9 +392,8 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
            // Mask
            if (mask) {
                const uint8_t * m_src = (const uint8_t *) (mp_base + ic_start);
-                uint8_t * m_dst = spad_m + (ib % 2) * factx->size_m_block;
                // Mask is 1D contiguous for this row
-                dma_queue_push(dma, dma_make_ptr(m_dst, m_src), current_block_size * 2, current_block_size * 2, current_block_size * 2, 1);
+                dma_cache_push(dma, &m_cache, m_src, current_block_size * 2, current_block_size * 2, current_block_size * 2, 1);
            }

            // FARF(HIGH, "fa %u: prefetch KVM: ir %u ib %u iq1 %u iq2 %u iq3 %u : size_k_row %u size_v_row %u bs %u: usec %u",
@@ -554,7 +556,7 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
                // Mask
                if (mask) {
                    const uint8_t * m_src = (const uint8_t *) (mp_base + next_ic_start);
-                    dma_queue_push(dma, dma_make_ptr(m_base, m_src), next_block_size * 2, next_block_size * 2, next_block_size * 2, 1);
+                    dma_cache_push(dma, &m_cache, m_src, next_block_size * 2, next_block_size * 2, next_block_size * 2, 1);
                }

                // FARF(HIGH, "fa %u: prefetch KVM: ir %u ib %u : iq1 %u iq2 %u iq3 %u : size_k_row %u size_v_row %u bs %u: usec %u",
@@ -684,7 +686,7 @@ int op_flash_attn_ext(struct htp_ops_context * octx) {
    octx->src0_spad.size_per_thread = size_q_block * 1;
    octx->src1_spad.size_per_thread = factx.size_k_block * 2;
    octx->src2_spad.size_per_thread = factx.size_v_block * 2;
-    octx->src3_spad.size_per_thread = mask ? factx.size_m_block * 2 : 0;
+    octx->src3_spad.size_per_thread = mask ? factx.size_m_block * DMA_CACHE_MAX_SIZE : 0;
    octx->dst_spad.size_per_thread  = size_vkq_acc;

    octx->src0_spad.size = octx->src0_spad.size_per_thread * octx->n_threads;
@@ -705,6 +707,8 @@ int op_flash_attn_ext(struct htp_ops_context * octx) {
    octx->src3_spad.data = octx->src2_spad.data + octx->src2_spad.size;
    octx->dst_spad.data  = octx->src3_spad.data + octx->src3_spad.size;

+    // FARF(ERROR, "fa: qrows-per-thread %u", factx.qrows_per_thread);
+
    if (!(octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) {
        worker_pool_run_func(octx->ctx->worker_pool, flash_attn_ext_f16_thread, &factx, octx->n_threads);
    }
@@ -143,7 +143,7 @@ static inline bool dma_queue_push_single_1d(dma_queue * q, dma_ptr dptr, size_t
    desc->desc_size  = 0; // 1D mode
    desc->src_bypass = dma_src_l2_bypass_on;
    desc->dst_bypass = dma_dst_l2_bypass_on;
-    desc->order      = 1;
+    desc->order      = 0;
    desc->done       = 0;
    desc->src        = (void *) dptr.src;
    desc->dst        = (void *) dptr.dst;
@@ -151,8 +151,12 @@ static inline bool dma_queue_push_single_1d(dma_queue * q, dma_ptr dptr, size_t

    q->dptr[q->push_idx] = dptr;

-    dmlink(q->tail, desc);
-    q->tail = (dma_descriptor_2d *) desc;
+    if (size) {
+        dmlink(q->tail, desc);
+        q->tail = (dma_descriptor_2d *) desc;
+    } else {
+        desc->done = 1;
+    }

    // FARF(ERROR, "dma-push: i %u row-size %u nrows %d dst %p src %p\n", q->push_idx, row_size, nrows, dptr.dst, dptr.src);
    q->push_idx = (q->push_idx + 1) & q->idx_mask;
@@ -175,7 +179,7 @@ static inline bool dma_queue_push_single_2d(dma_queue * q, dma_ptr dptr, size_t
    desc->dst_bypass     = dma_dst_l2_bypass_on;
    desc->src_comp       = 0;
    desc->dst_comp       = 0;
-    desc->order          = 1;
+    desc->order          = 0;
    desc->done           = 0;
    desc->src_stride     = src_stride;
    desc->dst_stride     = dst_stride;
@@ -197,8 +201,12 @@ static inline bool dma_queue_push_single_2d(dma_queue * q, dma_ptr dptr, size_t

    q->dptr[q->push_idx] = dptr;

-    dmlink(q->tail, desc);
-    q->tail = desc;
+    if (nrows) {
+        dmlink(q->tail, desc);
+        q->tail = desc;
+    } else {
+        desc->done = 1;
+    }

    // FARF(ERROR, "dma-push: i %u row-size %u nrows %d dst %p src %p\n", q->push_idx, row_size, nrows, dptr.dst, dptr.src);
    q->push_idx = (q->push_idx + 1) & q->idx_mask;
@@ -215,12 +223,9 @@ static inline dma_ptr dma_queue_pop(dma_queue * q) {
    dma_descriptor_2d * desc = &q->desc[q->pop_idx];

    // Wait for desc to complete
-    while (1) {
-        dmpoll();
-        if (desc->done) {
-            break;
-        }
+    while (!desc->done) {
        // FARF(ERROR, "dma-pop: waiting for DMA : %u\n", q->pop_idx);
+        dmpoll();
    }

    dptr = q->dptr[q->pop_idx];
@@ -312,6 +317,54 @@ static inline bool dma_queue_push_vtcm_to_ddr(dma_queue * q, dma_ptr dptr, size_
    return dma_queue_push(q, dptr, dst_row_size, src_row_size, dst_row_size, nrows);
 }

+#define DMA_CACHE_MAX_SIZE 64U
+
+typedef struct {
+    uint8_t *base;
+    uint32_t line_size;
+    uint32_t capacity;
+    uint32_t src[DMA_CACHE_MAX_SIZE];
+    uint16_t age[DMA_CACHE_MAX_SIZE];
+} dma_cache;
+
+static inline void dma_cache_init(dma_cache *c, uint8_t *base, uint32_t line_size, uint32_t capacity)
+{
+    c->capacity  = (capacity > DMA_CACHE_MAX_SIZE) ? DMA_CACHE_MAX_SIZE : capacity;
+    c->base      = base;
+    c->line_size = line_size;
+
+    for (unsigned i=0; i < c->capacity; i++) {
+        c->src[i] = 0;
+        c->age[i] = 0;
+    }
+}
+
+static inline bool dma_cache_push(dma_queue *q, dma_cache *c, const uint8_t * src, uint32_t dst_stride, uint32_t src_stride, uint32_t row_size, uint32_t nrows)
+{
+    uint32_t o_idx = 0;
+    uint16_t o_age = 0;
+    uint8_t *  dst = 0;
+
+    for (unsigned i=0; i < c->capacity; i++) {
+        if (c->src[i] == (uint32_t) src) {
+            c->age[i] = 0;
+            dst = c->base + (i * c->line_size); nrows = 0; // dummy dma
+            // FARF(ERROR, "dma-cache: found %p", src);
+        } else {
+            c->age[i]++;
+            if (c->age[i] > o_age) { o_age = c->age[i]; o_idx = i; }
+        }
+    }
+    if (!dst) {
+        // FARF(ERROR, "dma-cache: replacing #%u : age %u %p -> %p", o_idx, c->age[o_idx], (void *) c->src[o_idx], src);
+        c->age[o_idx] = 0;
+        c->src[o_idx] = (uint32_t) src;
+        dst = c->base + o_idx * c->line_size; // normal nrows dma
+    }
+
+    return dma_queue_push(q, dma_make_ptr(dst, src), dst_stride, src_stride, row_size, nrows);
+}
+
 #ifdef __cplusplus
 }  // extern "C"
 #endif
@@ -30,6 +30,12 @@ static const __fp16 q4_0_to_fp16_lut[64] __attribute__((aligned(VLEN))) = {
    -8, 0, -7, 0, -6, 0, -5, 0, -4, 0, -3, 0, -2, 0, -1, 0, 0, 0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0,
 };

+// MXFP4 dequantization LUT: maps 4-bit index to fp16 mantissa value
+// kvalues: 0, 0.5, 1, 1.5, 2, 3, 4, 6, 0, -0.5, -1, -1.5, -2, -3, -4, -6
+static const __fp16 mxfp4_to_fp16_lut[64] __attribute__((aligned(VLEN))) = {
+    0, 0, 0.5, 0, 1, 0, 1.5, 0, 2, 0, 3, 0, 4, 0, 6, 0, 0, 0, -0.5, 0, -1, 0, -1.5, 0, -2, 0, -3, 0, -4, 0, -6, 0,
+};
+
 static const __fp16 iq4_nl_to_fp16_lut[64] __attribute__((aligned(VLEN))) = {
    -127, 0, -104, 0, -83, 0, -65, 0, -49, 0, -35, 0, -22, 0, -10, 0,
    1,    0, 13,   0, 25,  0, 38,  0, 53,  0, 69,  0, 89,  0, 113, 0,
@@ -46,7 +52,8 @@ static const int32_t weight_transpose_scatter_offsets[32] __attribute__((aligned

 // Scales per x4x2 logical block: 8 × sizeof(__fp16) = 16 bytes
 #define HMX_X4X2_SCALES_PER_BLK  8
-#define HMX_X4X2_DBLK_SIZE       16  // 8 * 2 bytes
+#define HMX_X4X2_DBLK_SIZE       16  // 8 * 2 bytes (fp16 scales for Q4_0/Q8_0/IQ4_NL)
+#define HMX_X4X2_MXFP4_EBLK_SIZE 8   // 8 * 1 byte  (E8M0 scales for MXFP4)

 static inline void swap_ptr(void **p1, void **p2) {
    void *t = *p1;
@@ -78,9 +85,11 @@ static inline size_t get_x4x2_row_stride(int weight_type, int k) {
    switch (weight_type) {
        case HTP_TYPE_Q4_0:
        case HTP_TYPE_IQ4_NL:
-            return (size_t)nb * (QK_Q4_0x4x2 / 2 + HMX_X4X2_DBLK_SIZE);  // 144 * nb
+            return (size_t) nb * (QK_Q4_0x4x2 / 2 + HMX_X4X2_DBLK_SIZE);         // 144 * nb
        case HTP_TYPE_Q8_0:
-            return (size_t)nb * (QK_Q8_0x4x2 + HMX_X4X2_DBLK_SIZE);      // 272 * nb
+            return (size_t) nb * (QK_Q8_0x4x2 + HMX_X4X2_DBLK_SIZE);             // 272 * nb
+        case HTP_TYPE_MXFP4:
+            return (size_t) nb * (QK_MXFP4x4x2 / 2 + HMX_X4X2_MXFP4_EBLK_SIZE);  // 136 * nb
        default:
            return 0;
    }
@@ -284,6 +293,87 @@ static inline HVX_Vector dequantize_x4x2_q8_0_group_hvx(
    return Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(v_hf, v_scales));
 }

+// --- MXFP4 E8M0 scale conversion and dequantization ---
+//
+// HVX batch-convert 8 E8M0 bytes (one x4x2 block's scales) to __fp16[8] on stack.
+// Scalar loads from the stack array execute on the scalar pipeline, in parallel
+// with HVX vlut16/vmpy/vscatter — freeing HVX slots in the hot loop.
+// Arithmetic: fp16_bits = clamp(e - 112, 0, 30) << 10
+// e=0..112 -> 0 (underflow), e=113..142 -> valid fp16, e>=143 -> clamped to 2^15.
+
+typedef struct {
+    __fp16 v[8] __attribute__((aligned(16)));
+} mxfp4_scales_t;
+
+static inline mxfp4_scales_t mxfp4_convert_scales(const uint8_t * e8m0_8) {
+    mxfp4_scales_t s;
+    HVX_Vector     v  = hvx_vmemu(e8m0_8);
+    HVX_Vector     vh = Q6_V_lo_W(Q6_Wuh_vunpack_Vub(v));
+    vh                = Q6_Vh_vsub_VhVh(vh, Q6_Vh_vsplat_R(112));
+    vh                = Q6_Vh_vmax_VhVh(vh, Q6_V_vzero());
+    vh                = Q6_Vh_vmin_VhVh(vh, Q6_Vh_vsplat_R(30));
+    vh                = Q6_Vh_vasl_VhR(vh, 10);
+    hvx_vec_store_u(s.v, 16, vh);
+    return s;
+}
+
+static inline HVX_Vector mxfp4_extract_splat(mxfp4_scales_t scales, int idx) {
+    return hvx_vec_splat_f16(scales.v[idx]);
+}
+
+// Dequantize one x4x2 MXFP4 group (32 elements from 32 packed bytes) -> 32 FP16.
+static inline HVX_Vector dequantize_x4x2_mxfp4_group_hvx(const uint8_t *  packed_32,
+                                                         bool             upper_nibbles,
+                                                         int              sub_blk,
+                                                         const HVX_Vector vlut_cvt,
+                                                         mxfp4_scales_t   scales) {
+    HVX_Vector       vq       = hvx_vmemu(packed_32);
+    const HVX_Vector mask_h4  = Q6_Vb_vsplat_R(0x0F);
+    HVX_Vector       v_quants = upper_nibbles ? Q6_Vub_vlsr_VubR(vq, 4) : vq;
+    v_quants                  = Q6_V_vand_VV(v_quants, mask_h4);
+
+    HVX_Vector v_sc = mxfp4_extract_splat(scales, sub_blk);
+
+    v_quants            = Q6_Vb_vshuff_Vb(v_quants);
+    HVX_VectorPair vp   = Q6_Wh_vlut16_VbVhR(v_quants, vlut_cvt, 0);
+    HVX_Vector     v_hf = Q6_V_lo_W(vp);
+
+    return Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(v_hf, v_sc));
+}
+
+// Batch-dequantize 4 contiguous x4x2 MXFP4 groups (4x32 = 128 packed bytes).
+static inline void dequantize_x4x2_mxfp4_x4groups_hvx(const uint8_t *  packed_128,
+                                                      bool             upper_nibbles,
+                                                      int              sub_blk_base,
+                                                      const HVX_Vector vlut_cvt,
+                                                      mxfp4_scales_t   scales,
+                                                      HVX_Vector       out[4]) {
+    HVX_Vector       vq       = hvx_vmemu(packed_128);
+    const HVX_Vector mask_h4  = Q6_Vb_vsplat_R(0x0F);
+    HVX_Vector       v_quants = upper_nibbles ? Q6_Vub_vlsr_VubR(vq, 4) : vq;
+    v_quants                  = Q6_V_vand_VV(v_quants, mask_h4);
+
+    v_quants = Q6_Vb_vshuff_Vb(v_quants);
+
+    HVX_VectorPair vp   = Q6_Wh_vlut16_VbVhR(v_quants, vlut_cvt, 0);
+    HVX_Vector     v_lo = Q6_V_lo_W(vp);
+    HVX_Vector     v_hi = Q6_V_hi_W(vp);
+
+    HVX_VectorPred q64    = Q6_Q_vsetq_R(64);
+    HVX_Vector     v_sc01 = Q6_V_vmux_QVV(q64, mxfp4_extract_splat(scales, sub_blk_base + 0),
+                                          mxfp4_extract_splat(scales, sub_blk_base + 1));
+    HVX_Vector     v_sc23 = Q6_V_vmux_QVV(q64, mxfp4_extract_splat(scales, sub_blk_base + 2),
+                                          mxfp4_extract_splat(scales, sub_blk_base + 3));
+
+    v_lo = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(v_lo, v_sc01));
+    v_hi = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(v_hi, v_sc23));
+
+    out[0] = v_lo;
+    out[1] = Q6_V_vror_VR(v_lo, 64);
+    out[2] = v_hi;
+    out[3] = Q6_V_vror_VR(v_hi, 64);
+}
+
 // Dequantize a tile range from x4x2 weight data (already in VTCM) to tile-major FP16.
 // Input:  vtcm_src has n_cols rows of x4x2 data, each row_stride bytes.
 // Output: vtcm_dst in tile-major FP16 layout.
@@ -295,11 +385,11 @@ static void dequantize_x4x2_weight_to_fp16_tiles_task(
        int start_tile, int end_tile) {

    const int n_k_tiles = k_block / HMX_FP16_TILE_N_COLS;
-    const bool is_q4 = (weight_type == HTP_TYPE_Q4_0 || weight_type == HTP_TYPE_IQ4_NL);
-    const int qrow_size = is_q4 ? (k_block / 2) : k_block;
+    const int qrow_size = (weight_type == HTP_TYPE_Q8_0) ? k_block : (k_block / 2);

-    const HVX_Vector vlut_cvt = (weight_type == HTP_TYPE_IQ4_NL)
-        ? hvx_vmem(iq4_nl_to_fp16_lut) : hvx_vmem(q4_0_to_fp16_lut);
+    const HVX_Vector vlut_cvt = (weight_type == HTP_TYPE_IQ4_NL) ? hvx_vmem(iq4_nl_to_fp16_lut) :
+                                (weight_type == HTP_TYPE_MXFP4)  ? hvx_vmem(mxfp4_to_fp16_lut) :
+                                                                   hvx_vmem(q4_0_to_fp16_lut);

    // vscatter setup: write dequantized K-values directly to transposed [K][N] tile positions.
    // Each int32 element holds a K-row-pair (2 adjacent fp16 values).  word[i] at offset i*128
@@ -312,8 +402,9 @@ static void dequantize_x4x2_weight_to_fp16_tiles_task(
        int ct = t / n_k_tiles;  // column tile index
        int kt = t % n_k_tiles;  // K tile index

-        // --- Batch-4 fast path for Q4: process 4 contiguous K-tiles with one vlut16 per row ---
-        if (is_q4 && (kt % 4 == 0) && (t + 4 <= end_tile) && ((t + 3) / n_k_tiles == ct)) {
+        // --- Batch-4 fast path for Q4_0/IQ4_NL: process 4 contiguous K-tiles with one vlut16 per row ---
+        if ((weight_type == HTP_TYPE_Q4_0 || weight_type == HTP_TYPE_IQ4_NL) && (kt % 4 == 0) && (t + 4 <= end_tile) &&
+            ((t + 3) / n_k_tiles == ct)) {
            int blk_idx      = (kt * 32) / QK_Q4_0x4x2;
            int sub_blk_base = ((kt * 32) % QK_Q4_0x4x2) / 32;  // 0 or 4
            bool upper       = (sub_blk_base >= 4);
@@ -351,10 +442,60 @@ static void dequantize_x4x2_weight_to_fp16_tiles_task(
            continue;
        }

+        // --- Batch-4 fast path for MXFP4: same nibble layout but E8M0 scales ---
+        if (weight_type == HTP_TYPE_MXFP4 && (kt % 4 == 0) && (t + 4 <= end_tile) && ((t + 3) / n_k_tiles == ct)) {
+            int  blk_idx      = (kt * 32) / QK_MXFP4x4x2;
+            int  sub_blk_base = ((kt * 32) % QK_MXFP4x4x2) / 32;                 // 0 or 4
+            bool upper        = (sub_blk_base >= 4);
+            int  packed_off   = blk_idx * (QK_MXFP4x4x2 / 2);                    // 128 contiguous packed bytes
+            int  e8m0_blk_off = qrow_size + blk_idx * HMX_X4X2_MXFP4_EBLK_SIZE;  // all 8 E8M0 scales
+
+            __fp16 * tile_bases[4];
+            for (int g = 0; g < 4; g++) {
+                tile_bases[g] = vtcm_dst + (t + g) * HMX_FP16_TILE_N_ELMS;
+            }
+
+            HVX_Vector v_off = v_scat_base;
+            for (int r = 0; r < HMX_FP16_TILE_N_ROWS; r += 2) {
+                int             row0 = ct * HMX_FP16_TILE_N_COLS + r;
+                int             row1 = row0 + 1;
+                const uint8_t * r0   = vtcm_src + row0 * row_stride;
+                const uint8_t * r1   = vtcm_src + row1 * row_stride;
+
+                // Batch-convert all 8 E8M0 scales once per row (stays in HVX register)
+                mxfp4_scales_t r0_e8 = mxfp4_convert_scales(r0 + e8m0_blk_off);
+
+                HVX_Vector v0[4], v1[4];
+                dequantize_x4x2_mxfp4_x4groups_hvx(r0 + packed_off, upper, sub_blk_base, vlut_cvt, r0_e8, v0);
+                if (row1 < n_cols) {
+                    mxfp4_scales_t r1_e8 = mxfp4_convert_scales(r1 + e8m0_blk_off);
+                    dequantize_x4x2_mxfp4_x4groups_hvx(r1 + packed_off, upper, sub_blk_base, vlut_cvt, r1_e8, v1);
+                } else {
+                    v1[0] = v1[1] = v1[2] = v1[3] = Q6_V_vzero();
+                }
+
+                for (int g = 0; g < 4; g++) {
+                    Q6_vscatter_QRMVwV(q_mask64, (size_t) tile_bases[g], HMX_FP16_TILE_SIZE - 1, v_off, v0[g]);
+                }
+                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
+                for (int g = 0; g < 4; g++) {
+                    Q6_vscatter_QRMVwV(q_mask64, (size_t) tile_bases[g], HMX_FP16_TILE_SIZE - 1, v_off, v1[g]);
+                }
+                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
+            }
+
+            for (int g = 0; g < 4; g++) {
+                (void) *(volatile HVX_Vector *) (tile_bases[g]);
+            }
+
+            t += 4;
+            continue;
+        }
+
        // --- Single-tile fallback ---
        __fp16 *tile_base = vtcm_dst + t * HMX_FP16_TILE_N_ELMS;

-        if (is_q4) {
+        if (weight_type == HTP_TYPE_Q4_0 || weight_type == HTP_TYPE_IQ4_NL) {
            int blk_idx  = (kt * 32) / QK_Q4_0x4x2;
            int sub_blk  = ((kt * 32) % QK_Q4_0x4x2) / 32;
            bool upper   = (sub_blk >= 4);
@@ -382,6 +523,39 @@ static void dequantize_x4x2_weight_to_fp16_tiles_task(
                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
            }
            (void) *(volatile HVX_Vector *)(tile_base);
+        } else if (weight_type == HTP_TYPE_MXFP4) {
+            int  blk_idx      = (kt * 32) / QK_MXFP4x4x2;
+            int  sub_blk      = ((kt * 32) % QK_MXFP4x4x2) / 32;
+            bool upper        = (sub_blk >= 4);
+            int  byte_off     = blk_idx * (QK_MXFP4x4x2 / 2) + (upper ? (sub_blk - 4) : sub_blk) * 32;
+            int  e8m0_blk_off = qrow_size + blk_idx * HMX_X4X2_MXFP4_EBLK_SIZE;
+
+            HVX_Vector v_off = v_scat_base;
+            for (int r = 0; r < HMX_FP16_TILE_N_ROWS; r += 2) {
+                int row0 = ct * HMX_FP16_TILE_N_COLS + r;
+                int row1 = row0 + 1;
+
+                const uint8_t * r0 = vtcm_src + row0 * row_stride;
+                const uint8_t * r1 = vtcm_src + row1 * row_stride;
+
+                // Batch-convert all 8 E8M0 scales once per row (stays in HVX register)
+                mxfp4_scales_t r0_e8 = mxfp4_convert_scales(r0 + e8m0_blk_off);
+
+                HVX_Vector v0 = dequantize_x4x2_mxfp4_group_hvx(r0 + byte_off, upper, sub_blk, vlut_cvt, r0_e8);
+                HVX_Vector v1;
+                if (row1 < n_cols) {
+                    mxfp4_scales_t r1_e8 = mxfp4_convert_scales(r1 + e8m0_blk_off);
+                    v1 = dequantize_x4x2_mxfp4_group_hvx(r1 + byte_off, upper, sub_blk, vlut_cvt, r1_e8);
+                } else {
+                    v1 = Q6_V_vzero();
+                }
+
+                Q6_vscatter_QRMVwV(q_mask64, (size_t) tile_base, HMX_FP16_TILE_SIZE - 1, v_off, v0);
+                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
+                Q6_vscatter_QRMVwV(q_mask64, (size_t) tile_base, HMX_FP16_TILE_SIZE - 1, v_off, v1);
+                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
+            }
+            (void) *(volatile HVX_Vector *) (tile_base);
        } else {
            // Q8_0
            int blk_idx  = (kt * 32) / QK_Q8_0x4x2;
@@ -1455,21 +1629,24 @@ int mat_mul_qk_0_d16a32_out_stationary(struct htp_context *ctx, float *restrict
                {
                    qweight_fetch_task_state_t s;

-                    const bool is_q4 = (weight_type == HTP_TYPE_Q4_0 || weight_type == HTP_TYPE_IQ4_NL);
                    const int blk_start = kk / QK_Q4_0x4x2;
                    const int nb_sub = (k_blk_sz + QK_Q4_0x4x2 - 1) / QK_Q4_0x4x2;
-                    const int full_qrow = is_q4 ? (k / 2) : k;
+                    const int    full_qrow      = (weight_type == HTP_TYPE_Q8_0) ? k : (k / 2);
                    const size_t sub_row_stride = get_x4x2_row_stride(weight_type, k_blk_sz);
+                    const int    scale_blk_size =
+                        (weight_type == HTP_TYPE_MXFP4) ? HMX_X4X2_MXFP4_EBLK_SIZE : HMX_X4X2_DBLK_SIZE;

                    s.dst         = vtcm_scratch0;
                    s.src         = w + nc * row_stride;
                    s.n_rows      = n_blk_sz;
                    s.src_stride  = row_stride;
                    s.dst_stride  = sub_row_stride;
-                    s.quant_off   = is_q4 ? (blk_start * (QK_Q4_0x4x2 / 2)) : (blk_start * QK_Q8_0x4x2);
-                    s.quant_width = is_q4 ? (nb_sub    * (QK_Q4_0x4x2 / 2)) : (nb_sub * QK_Q8_0x4x2);
-                    s.scale_off   = full_qrow + blk_start * HMX_X4X2_DBLK_SIZE;
-                    s.scale_width = nb_sub * HMX_X4X2_DBLK_SIZE;
+                    s.quant_off =
+                        (weight_type == HTP_TYPE_Q8_0) ? (blk_start * QK_Q8_0x4x2) : (blk_start * (QK_Q4_0x4x2 / 2));
+                    s.quant_width =
+                        (weight_type == HTP_TYPE_Q8_0) ? (nb_sub * QK_Q8_0x4x2) : (nb_sub * (QK_Q4_0x4x2 / 2));
+                    s.scale_off   = full_qrow + blk_start * scale_blk_size;
+                    s.scale_width = nb_sub * scale_blk_size;

                    // 2D DMA: quants sub-range
                    dma_queue_push(ctx->dma[0], dma_make_ptr(s.dst, s.src + s.quant_off),
@@ -31,6 +31,12 @@ struct htp_context {

    uint32_t opmask;

+    // Cached src1 spad position from the last quantize pass.
+    // When SKIP_QUANTIZE is set the Q8 activation data is already in VTCM
+    // at this address; the matmul must read from here instead of recomputing
+    // the offset (which depends on the current op's src0 size).
+    uint8_t * prev_src1_spad;
+
    // HMX acceleration fields (v73+, enabled by compile-time HTP_HAS_HMX)
 #ifdef HTP_HAS_HMX
    int        hmx_enabled;       // Runtime flag: HMX initialisation succeeded
@@ -1114,14 +1114,12 @@ static void proc_hmx_matmul_req(struct htp_context *     ctx,
        return;
    }

-    // HMX only supports F16, Q4_0, Q8_0, IQ4_NL weights.
-    // Other types (e.g. MXFP4) fall back to HVX.
+    // HMX supports F16, Q4_0, Q8_0, IQ4_NL, MXFP4 weights.
+    // Other types fall back to HVX.
    {
        uint32_t wtype = req->src0.type;
-        if (wtype != HTP_TYPE_F16  &&
-            wtype != HTP_TYPE_Q4_0 &&
-            wtype != HTP_TYPE_Q8_0 &&
-            wtype != HTP_TYPE_IQ4_NL) {
+        if (wtype != HTP_TYPE_F16 && wtype != HTP_TYPE_Q4_0 && wtype != HTP_TYPE_Q8_0 && wtype != HTP_TYPE_IQ4_NL &&
+            wtype != HTP_TYPE_MXFP4) {
            proc_matmul_req(ctx, req, bufs, n_bufs);
            return;
        }
@@ -60,6 +60,16 @@ static const uint8_t __attribute__((aligned(128))) expand_x32_e8m0[128] = {
    0x00, 0x00, 0x09, 0x08, 0x00, 0x00, 0x22, 0x20, 0x24, 0x20, 0x21, 0x22, 0x20, 0x20,
 };

+// IQ4_NL dequantization LUT: maps 4-bit index (0-15) to int8 kvalue
+// kvalues: -127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113
+static const uint8_t __attribute__((aligned(VLEN))) kvalues_iq4nl_lut[] = {
+    0x81, 0, 0x98, 0, 0xAD, 0, 0xBF, 0, 0xCF, 0, 0xDD, 0, 0xEA, 0, 0xF6, 0, 0x01, 0, 0x0D, 0, 0x19, 0, 0x26, 0,
+    0x35, 0, 0x45, 0, 0x59, 0, 0x71, 0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0,
+    0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0,
+    0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0,
+    0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0, 0,    0,
+};
+
 static const uint8_t __attribute__((aligned(VLEN))) kvalues_mxfp4_lut[] = {
    0,    0, 1,    0, 2,    0, 3, 0, 4, 0, 6, 0, 8, 0, 12, 0, 0, 0, 0xff, 0, 0xfe, 0, 0xfd, 0, 0xfc, 0,
    0xfa, 0, 0xf8, 0, 0xf4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,  0, 0, 0, 0,    0, 0,    0, 0,    0, 0,    0,
@@ -68,6 +78,73 @@ static const uint8_t __attribute__((aligned(VLEN))) kvalues_mxfp4_lut[] = {
    0,    0, 0,    0, 0,    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,  0, 0, 0, 0,    0, 0,    0, 0,    0,
 };

+static inline HVX_Vector_x8 hvx_vec_load_iq4nlx4x8_full(const uint8_t * restrict ptr) {
+    const HVX_Vector * restrict vptr = (const HVX_Vector *) ptr;
+
+    HVX_Vector v0_1 = vptr[0];  // first 256 elements (128 bytes)
+    HVX_Vector v2_3 = vptr[1];  // ...
+    HVX_Vector v4_5 = vptr[2];  // ...
+    HVX_Vector v6_7 = vptr[3];  // ...
+
+    const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
+    const HVX_Vector lut     = *(const HVX_Vector *) kvalues_iq4nl_lut;
+
+    HVX_Vector v0 = Q6_V_vand_VV(v0_1, mask_h4);  // & 0x0F
+    HVX_Vector v1 = Q6_Vub_vlsr_VubR(v0_1, 4);    // >> 4
+    HVX_Vector v2 = Q6_V_vand_VV(v2_3, mask_h4);  // & 0x0F
+    HVX_Vector v3 = Q6_Vub_vlsr_VubR(v2_3, 4);    // >> 4
+    HVX_Vector v4 = Q6_V_vand_VV(v4_5, mask_h4);  // & 0x0F
+    HVX_Vector v5 = Q6_Vub_vlsr_VubR(v4_5, 4);    // >> 4
+    HVX_Vector v6 = Q6_V_vand_VV(v6_7, mask_h4);  // & 0x0F
+    HVX_Vector v7 = Q6_Vub_vlsr_VubR(v6_7, 4);    // >> 4
+
+    v0 = Q6_Vb_vlut32_VbVbI(v0, lut, 0);
+    v1 = Q6_Vb_vlut32_VbVbI(v1, lut, 0);
+    v2 = Q6_Vb_vlut32_VbVbI(v2, lut, 0);
+    v3 = Q6_Vb_vlut32_VbVbI(v3, lut, 0);
+    v4 = Q6_Vb_vlut32_VbVbI(v4, lut, 0);
+    v5 = Q6_Vb_vlut32_VbVbI(v5, lut, 0);
+    v6 = Q6_Vb_vlut32_VbVbI(v6, lut, 0);
+    v7 = Q6_Vb_vlut32_VbVbI(v7, lut, 0);
+
+    HVX_Vector_x8 r = { v0, v1, v2, v3, v4, v5, v6, v7 };
+    return r;
+}
+
+static inline HVX_Vector_x8 hvx_vec_load_iq4nlx4x8_partial(const uint8_t * restrict ptr, uint32_t n) {
+    const HVX_Vector * restrict vptr = (const HVX_Vector *) ptr;
+
+    const uint32_t qk   = QK_Q4_0x4x2;  // 256
+    const uint32_t nb   = n / qk;
+    const uint32_t nloe = n % qk;
+
+    const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
+    const HVX_Vector lut     = *(const HVX_Vector *) kvalues_iq4nl_lut;
+
+    HVX_Vector_x8 r;
+    uint32_t      i = 0;
+
+    #pragma unroll(2)
+    for (i = 0; i < nb; i++) {
+        HVX_Vector v   = vptr[i];                   // 256 elements (128 bytes)
+        HVX_Vector v0  = Q6_V_vand_VV(v, mask_h4);  // & 0x0F : first  128 elements
+        HVX_Vector v1  = Q6_Vub_vlsr_VubR(v, 4);    // >> 4   : second 128 elements
+        r.v[i * 2 + 0] = Q6_Vb_vlut32_VbVbI(v0, lut, 0);
+        r.v[i * 2 + 1] = Q6_Vb_vlut32_VbVbI(v1, lut, 0);
+    }
+
+    if (nloe) {
+        HVX_Vector     v      = vptr[i];                      // 256 elements (128 bytes)
+        HVX_Vector     v0     = Q6_V_vand_VV(v, mask_h4);     // & 0x0F : even 128 elements
+        HVX_Vector     v1     = Q6_Vub_vlsr_VubR(v, 4);       // >> 4   : odd  128 elements
+        HVX_VectorPair v0_1_p = Q6_W_vshuff_VVR(v1, v0, -1);  // zip even:odd:...
+        r.v[i * 2 + 0]        = Q6_Vb_vlut32_VbVbI(Q6_V_lo_W(v0_1_p), lut, 0);
+        r.v[i * 2 + 1]        = Q6_Vb_vlut32_VbVbI(Q6_V_hi_W(v0_1_p), lut, 0);
+    }
+
+    return r;
+}
+
 // q4x4x2 and q8x4x2 are the flat q4/8_0 formats where all quants are stored first followed by all scales

 static inline size_t q8x4x2_row_size(uint32_t ne) {
@@ -921,6 +998,293 @@ static void vec_dot_q8x4x2_q8x4x2_2x2(const int n, float * restrict s0, float *
    hvx_vec_store_u(&s1[0], 8, r0_r1_c1_sum);  // row0,col1 row1,col1
 }

+// ======== IQ4_NL x Q8_0 vec_dot kernels ========
+// Same structure as Q4_0 vec_dot but uses IQ4_NL LUT-based load (4-bit index -> int8 kvalue).
+// Scale format is identical to Q4_0 (fp16 scales).
+
+static void vec_dot_iq4nlx4x2_q8x4x2_1x1(const int n,
+                                         float * restrict s0,
+                                         const void * restrict vx0,
+                                         const void * restrict vy0) {
+    assert(n % 32 == 0);
+    assert((unsigned long) vx0 % 128 == 0);
+    assert((unsigned long) vy0 % 128 == 0);
+
+    const uint32_t qk = QK_Q4_0x4x2 * 4;
+
+    const uint32_t x_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
+    const uint32_t x_qblk_size = qk / 2;                                      // int4
+    const uint32_t x_qrow_size = n / 2;                                       // int4 (not padded)
+
+    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
+    const uint32_t y_qblk_size = qk;                                          // int8
+    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
+
+    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0 + 0);            // quants first
+    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0 + x_qrow_size);  // then scales
+
+    const uint8_t * restrict y_q = ((const uint8_t *) vy0 + 0);               // quants first
+    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);     // then scales
+
+    HVX_Vector r0_sum = Q6_V_vzero();
+
+    const uint32_t nb   = n / qk;
+    const uint32_t nloe = n % qk;
+
+    uint32_t i = 0;
+    for (; i < nb; i++) {
+        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(y_q + i * y_qblk_size);
+        HVX_Vector_x8 r0_q = hvx_vec_load_iq4nlx4x8_full(r0_x_q + i * x_qblk_size);
+
+        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q));
+
+        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size));
+        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
+
+        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
+
+        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
+
+        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
+    }
+
+    if (nloe) {
+        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(y_q + i * y_qblk_size, nloe);
+        HVX_Vector_x8 r0_q = hvx_vec_load_iq4nlx4x8_partial(r0_x_q + i * x_qblk_size, nloe);
+
+        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy_q, nloe));
+
+        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size));
+        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
+
+        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
+
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
+        r0_dd                = Q6_V_vand_QV(bmask, r0_dd);
+        r0_ia                = Q6_V_vand_QV(bmask, r0_ia);
+
+        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
+
+        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
+    }
+
+    r0_sum = hvx_vec_reduce_sum_f32(r0_sum);
+
+    hvx_vec_store_u(s0, 4, r0_sum);
+}
+
+static void vec_dot_iq4nlx4x2_q8x4x2_2x1(const int n,
+                                         float * restrict s0,
+                                         const void * restrict vx0,
+                                         const void * restrict vx1,
+                                         const void * restrict vy0) {
+    assert(n % 32 == 0);
+    assert((unsigned long) vx0 % 128 == 0);
+    assert((unsigned long) vx1 % 128 == 0);
+    assert((unsigned long) vy0 % 128 == 0);
+
+    const uint32_t qk = QK_Q4_0x4x2 * 4;
+
+    const uint32_t x_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
+    const uint32_t x_qblk_size = qk / 2;                                      // int4
+    const uint32_t x_qrow_size = n / 2;                                       // int4 (not padded)
+
+    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
+    const uint32_t y_qblk_size = qk;                                          // int8
+    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
+
+    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;            // quants first
+    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;  // then scales
+    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;            // quants first
+    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;  // then scales
+
+    const uint8_t * restrict y_q = ((const uint8_t *) vy0 + 0);               // quants first
+    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);     // then scales
+
+    HVX_Vector r0_sum = Q6_V_vzero();
+    HVX_Vector r1_sum = Q6_V_vzero();
+
+    const uint32_t nb   = n / qk;
+    const uint32_t nloe = n % qk;
+
+    uint32_t i = 0;
+    for (; i < nb; i++) {
+        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(y_q + i * y_qblk_size);
+        HVX_Vector_x8 r0_q = hvx_vec_load_iq4nlx4x8_full(r0_x_q + i * x_qblk_size);
+        HVX_Vector_x8 r1_q = hvx_vec_load_iq4nlx4x8_full(r1_x_q + i * x_qblk_size);
+
+        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q));
+        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy_q));
+
+        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size));
+        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
+        HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
+
+        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
+        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy_d)));
+
+        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
+        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
+
+        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
+        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum));
+    }
+
+    if (nloe) {
+        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(y_q + i * y_qblk_size, nloe);
+        HVX_Vector_x8 r0_q = hvx_vec_load_iq4nlx4x8_partial(r0_x_q + i * x_qblk_size, nloe);
+        HVX_Vector_x8 r1_q = hvx_vec_load_iq4nlx4x8_partial(r1_x_q + i * x_qblk_size, nloe);
+
+        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy_q, nloe));
+        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy_q, nloe));
+
+        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size));
+        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
+        HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
+
+        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
+        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy_d)));
+
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
+        r0_dd                = Q6_V_vand_QV(bmask, r0_dd);
+        r1_dd                = Q6_V_vand_QV(bmask, r1_dd);
+        r0_ia                = Q6_V_vand_QV(bmask, r0_ia);
+        r1_ia                = Q6_V_vand_QV(bmask, r1_ia);
+
+        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
+        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
+
+        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
+        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum));
+    }
+
+    HVX_Vector rsum = hvx_vec_reduce_sum_f32x2(r0_sum, r1_sum);
+    hvx_vec_store_u(s0, 8, rsum);
+}
+
+static void vec_dot_iq4nlx4x2_q8x4x2_2x2(const int n,
+                                         float * restrict s0,
+                                         float * restrict s1,
+                                         const void * restrict vx0,
+                                         const void * restrict vx1,
+                                         const void * restrict vy0,
+                                         const void * restrict vy1) {
+    assert(n % 32 == 0);
+    assert((unsigned long) vx0 % 128 == 0);
+    assert((unsigned long) vx1 % 128 == 0);
+    assert((unsigned long) vy0 % 128 == 0);
+    assert((unsigned long) vy1 % 128 == 0);
+
+    const uint32_t qk = QK_Q4_0x4x2 * 4;
+
+    const uint32_t x_dblk_size = 8 * 4 * 2;  // 32x __fp16
+    const uint32_t x_qblk_size = qk / 2;     // int4
+    const uint32_t x_qrow_size = n / 2;      // int4 (not padded)
+
+    const uint32_t y_dblk_size = 8 * 4 * 2;  // 32x __fp16
+    const uint32_t y_qblk_size = qk;         // int8
+    const uint32_t y_qrow_size = n;          // int8 (not padded)
+
+    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;
+    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;
+    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;
+    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;
+
+    const uint8_t * restrict y0_q = ((const uint8_t *) vy0) + 0;
+    const uint8_t * restrict y0_d = ((const uint8_t *) vy0) + y_qrow_size;
+    const uint8_t * restrict y1_q = ((const uint8_t *) vy1) + 0;
+    const uint8_t * restrict y1_d = ((const uint8_t *) vy1) + y_qrow_size;
+
+    HVX_Vector r0_c0_sum = Q6_V_vzero();
+    HVX_Vector r0_c1_sum = Q6_V_vzero();
+    HVX_Vector r1_c0_sum = Q6_V_vzero();
+    HVX_Vector r1_c1_sum = Q6_V_vzero();
+
+    const uint32_t nb   = n / qk;
+    const uint32_t nloe = n % qk;
+
+    uint32_t i = 0;
+    for (; i < nb; i++) {
+        HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8_full(y0_q + i * y_qblk_size);
+        HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8_full(y1_q + i * y_qblk_size);
+        HVX_Vector_x8 r0_q  = hvx_vec_load_iq4nlx4x8_full(r0_x_q + i * x_qblk_size);
+        HVX_Vector_x8 r1_q  = hvx_vec_load_iq4nlx4x8_full(r1_x_q + i * x_qblk_size);
+
+        HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy0_q));
+        HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy1_q));
+        HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy0_q));
+        HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy1_q));
+
+        HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y0_d + i * y_dblk_size));
+        HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y1_d + i * y_dblk_size));
+        HVX_Vector r0_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
+        HVX_Vector r1_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
+
+        HVX_Vector r0_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy0_d)));
+        HVX_Vector r0_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy1_d)));
+        HVX_Vector r1_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy0_d)));
+        HVX_Vector r1_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy1_d)));
+
+        HVX_Vector r0_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c0_ia, r0_c0_dd);
+        HVX_Vector r0_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c1_ia, r0_c1_dd);
+        HVX_Vector r1_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c0_ia, r1_c0_dd);
+        HVX_Vector r1_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c1_ia, r1_c1_dd);
+
+        r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_fa, r0_c0_sum));
+        r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_fa, r0_c1_sum));
+        r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_fa, r1_c0_sum));
+        r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_fa, r1_c1_sum));
+    }
+
+    if (nloe) {
+        HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8_partial(y0_q + i * y_qblk_size, nloe);
+        HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8_partial(y1_q + i * y_qblk_size, nloe);
+        HVX_Vector_x8 r0_q  = hvx_vec_load_iq4nlx4x8_partial(r0_x_q + i * x_qblk_size, nloe);
+        HVX_Vector_x8 r1_q  = hvx_vec_load_iq4nlx4x8_partial(r1_x_q + i * x_qblk_size, nloe);
+
+        HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy0_q, nloe));
+        HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy1_q, nloe));
+        HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy0_q, nloe));
+        HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy1_q, nloe));
+
+        HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y0_d + i * y_dblk_size));
+        HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y1_d + i * y_dblk_size));
+        HVX_Vector r0_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
+        HVX_Vector r1_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
+
+        HVX_Vector r0_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy0_d)));
+        HVX_Vector r0_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy1_d)));
+        HVX_Vector r1_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy0_d)));
+        HVX_Vector r1_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy1_d)));
+
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
+        r0_c0_dd             = Q6_V_vand_QV(bmask, r0_c0_dd);
+        r0_c1_dd             = Q6_V_vand_QV(bmask, r0_c1_dd);
+        r1_c0_dd             = Q6_V_vand_QV(bmask, r1_c0_dd);
+        r1_c1_dd             = Q6_V_vand_QV(bmask, r1_c1_dd);
+        r0_c0_ia             = Q6_V_vand_QV(bmask, r0_c0_ia);
+        r0_c1_ia             = Q6_V_vand_QV(bmask, r0_c1_ia);
+        r1_c0_ia             = Q6_V_vand_QV(bmask, r1_c0_ia);
+        r1_c1_ia             = Q6_V_vand_QV(bmask, r1_c1_ia);
+
+        HVX_Vector r0_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c0_ia, r0_c0_dd);
+        HVX_Vector r0_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c1_ia, r0_c1_dd);
+        HVX_Vector r1_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c0_ia, r1_c0_dd);
+        HVX_Vector r1_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c1_ia, r1_c1_dd);
+
+        r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_fa, r0_c0_sum));
+        r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_fa, r0_c1_sum));
+        r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_fa, r1_c0_sum));
+        r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_fa, r1_c1_sum));
+    }
+
+    HVX_Vector r0_r1_c0_sum = hvx_vec_reduce_sum_f32x2(r0_c0_sum, r1_c0_sum);
+    HVX_Vector r0_r1_c1_sum = hvx_vec_reduce_sum_f32x2(r0_c1_sum, r1_c1_sum);
+
+    hvx_vec_store_u(&s0[0], 8, r0_r1_c0_sum);
+    hvx_vec_store_u(&s1[0], 8, r0_r1_c1_sum);
+}
+
 static void vec_dot_mxfp4x4x2_q8x4x2_1x1(const int n, float * restrict s0, const void * restrict vx0, const void * restrict vy0) {
    assert(n % 32 == 0);  // min sub-block size
    assert((unsigned long) vx0 % 128 == 0);
@@ -2393,6 +2757,12 @@ static int htp_mminit_vec_dot(struct htp_matmul_context * mmctx, enum htp_data_t
            mmctx->vec_dot_2x1 = vec_dot_q8x4x2_q8x4x2_2x1;
            mmctx->vec_dot_2x2 = vec_dot_q8x4x2_q8x4x2_2x2;
            return 0;
+        case HTP_TYPE_IQ4_NL:
+            mmctx->type        = "iq4nlx4x2-f32";
+            mmctx->vec_dot_1x1 = vec_dot_iq4nlx4x2_q8x4x2_1x1;
+            mmctx->vec_dot_2x1 = vec_dot_iq4nlx4x2_q8x4x2_2x1;
+            mmctx->vec_dot_2x2 = vec_dot_iq4nlx4x2_q8x4x2_2x2;
+            return 0;
        case HTP_TYPE_MXFP4:
            mmctx->type        = "mxfp4x4x2-f32";
            mmctx->vec_dot_1x1 = vec_dot_mxfp4x4x2_q8x4x2_1x1;
@@ -2556,6 +2926,13 @@ int op_matmul(struct htp_ops_context * octx) {
        const uint32_t n_quant_jobs  = MIN(src1_nrows, octx->n_threads);
        mmctx->src1_nrows_per_thread = (src1_nrows + n_quant_jobs - 1) / n_quant_jobs;
        worker_pool_run_func(octx->ctx->worker_pool, quant_job_func, mmctx, n_quant_jobs);
+        // Cache where src1 was written so subsequent SKIP_QUANTIZE ops can find it
+        octx->ctx->prev_src1_spad = octx->src1_spad.data;
+    } else {
+        // SKIP_QUANTIZE: Q8 data lives at the address written by the previous
+        // quantize pass.  The current op may have a different src0 size (e.g.
+        // IQ4_NL vs MXFP4), so src1_spad.data computed above could be wrong.
+        octx->src1_spad.data = octx->ctx->prev_src1_spad;
    }

    if (!(octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) {
@@ -2659,6 +3036,9 @@ int op_matmul_id(struct htp_ops_context * octx) {
        const uint32_t n_quant_jobs = MIN(src1_nrows, octx->n_threads);
        mmctx->src1_nrows_per_thread = (src1_nrows + n_quant_jobs - 1) / n_quant_jobs;
        worker_pool_run_func(octx->ctx->worker_pool, quant_job_func, mmctx, n_quant_jobs);
+        octx->ctx->prev_src1_spad = octx->src1_spad.data;
+    } else {
+        octx->src1_spad.data = octx->ctx->prev_src1_spad;
    }

    if (!(octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) {
@@ -333,8 +333,8 @@ static void rope_job_f32(unsigned int nth, unsigned int ith, void * data) {
                    //         (unsigned) HAP_perf_qtimer_count_to_us(HAP_perf_get_qtimer_count() - rctx->t_start));
                }

-                // Skip DMA transactions from prev block (if any)
-                // No need to wait for these since the DMA is setup for in-order processing
+                // Skip output DMA transactions from prev block (if any)
+                // No need to wait for those here since we're explicitly waiting for the latest prefecthes below.
                for (uint32_t d=0; d < dma_depth; d++) { dma_queue_pop_nowait(dma_queue); }

                // Compute loop
@@ -773,6 +773,5 @@ inline bool ggml_check_edges(const struct ggml_cgraph *                cgraph,

 // expose GGUF internals for test code
 GGML_API size_t gguf_type_size(enum gguf_type type);
-GGML_API struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_params params);
 GGML_API void gguf_write_to_buf(const struct gguf_context * ctx, std::vector<int8_t> & buf, bool only_meta);
 #endif // __cplusplus
@@ -690,7 +690,7 @@ ggml_metal_device_t ggml_metal_device_init(int device) {
                    "    auto tB = B.slice((int)tgid.x, 0); \n"
                    " \n"
                    "    matmul2d< \n"
-                    "        matmul2d_descriptor(8, 8, dynamic_extent), \n"
+                    "        matmul2d_descriptor(16, 16, dynamic_extent), \n"
                    "        execution_simdgroups<4>> mm; \n"
                    " \n"
                    "    auto cT = mm.get_destination_cooperative_tensor<decltype(tA), decltype(tB), float>(); \n"
@@ -740,7 +740,7 @@ ggml_metal_device_t ggml_metal_device_init(int device) {
                    "    auto tB = B.slice((int)tgid.x, 0); \n"
                    " \n"
                    "    matmul2d< \n"
-                    "        matmul2d_descriptor(8, 8, dynamic_extent), \n"
+                    "        matmul2d_descriptor(16, 16, dynamic_extent), \n"
                    "        execution_simdgroups<4>> mm; \n"
                    " \n"
                    "    auto cT = mm.get_destination_cooperative_tensor<decltype(tA), decltype(tB), float>(); \n"
@@ -394,6 +394,9 @@ struct ggml_backend_opencl_context {
    bool fp16_support;
    bool has_vector_subgroup_broadcast;
    bool disable_fusion;
+
+    bool adreno_has_large_buffer;
+    bool adreno_use_large_buffer;
    ggml_cl_compiler_version adreno_cl_compiler_version;

    int adreno_wave_size;
@@ -787,6 +790,10 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
                               " -cl-mad-enable -cl-unsafe-math-optimizations"
                               " -cl-finite-math-only -cl-fast-relaxed-math";

+    if (backend_ctx->adreno_use_large_buffer) {
+        compile_opts += " -qcom-enable-large-buffer ";
+    }
+
    GGML_LOG_INFO("ggml_opencl: loading OpenCL kernels");

    // add
@@ -3020,6 +3027,8 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
    // Check if ext_buffer contains cl_khr_fp16
    backend_ctx->fp16_support = strstr(ext_buffer, "cl_khr_fp16") != NULL;
    GGML_LOG_INFO("ggml_opencl: device FP16 support: %s\n", backend_ctx->fp16_support ? "true" : "false");
+    // check Adreno large buffer support
+    backend_ctx->adreno_has_large_buffer = strstr(ext_buffer, "cl_qcom_large_buffer") != NULL;

    // fp16 is required
    if (!backend_ctx->fp16_support) {
@@ -3086,6 +3095,18 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
    GGML_LOG_INFO("ggml_opencl: using kernels optimized for Adreno (GGML_OPENCL_USE_ADRENO_KERNELS)\n");
 #endif // GGML_OPENCL_USE_ADRENO_KERNELS

+    // determine whether to use large buffer for Adreno
+    backend_ctx->adreno_use_large_buffer = getenv("GGML_OPENCL_ADRENO_USE_LARGE_BUFFER") != nullptr &&
+                                           backend_ctx->gpu_family == GPU_FAMILY::ADRENO;
+    if (backend_ctx->adreno_use_large_buffer) {
+        if (!backend_ctx->adreno_has_large_buffer) {
+            GGML_LOG_INFO("ggml_opencl: Adreno large buffer requested but not supported by driver, will use regular buffer\n");
+            backend_ctx->adreno_use_large_buffer = false;
+        } else {
+            GGML_LOG_INFO("ggml_opencl: Adreno large buffer enabled\n");
+        }
+    }
+
    cl_int err;

    // A local ref of cl_context for convenience
@@ -5660,6 +5681,11 @@ static ggml_backend_buffer_t ggml_backend_opencl_buffer_type_alloc_buffer(ggml_b

    cl_int err;
    cl_mem mem = clCreateBuffer(backend_ctx->context, CL_MEM_READ_WRITE, size, NULL, &err);
+    if (err != CL_SUCCESS && backend_ctx->adreno_use_large_buffer) {
+        cl_mem_properties props[] = { 0x41A6 /* CL_LARGE_BUFFER_QCOM */, 1, 0 };
+        mem = clCreateBufferWithProperties(backend_ctx->context, props, CL_MEM_READ_WRITE, size, NULL, &err);
+    }
+
    if (err != CL_SUCCESS) {
        GGML_LOG_INFO("%s: failed to allocate %.2f MiB\n", __func__, size / 1024.0 / 1024.0);
        return nullptr;
@@ -589,8 +589,10 @@ static rpc_tensor serialize_tensor(const ggml_tensor * tensor) {
        ggml_backend_buffer_t buffer = tensor->buffer;
        ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context;
        result.buffer = ctx != nullptr ? ctx->remote_ptr : 0;
+        result.data = reinterpret_cast<uint64_t>(tensor->data);
    } else {
        result.buffer = 0;
+        result.data   = 0;
    }
    for (uint32_t i = 0; i < GGML_MAX_DIMS; i++) {
        result.ne[i] = tensor->ne[i];
@@ -606,7 +608,6 @@ static rpc_tensor serialize_tensor(const ggml_tensor * tensor) {
    }
    result.view_src = reinterpret_cast<uint64_t>(tensor->view_src);
    result.view_offs = tensor->view_offs;
-    result.data = reinterpret_cast<uint64_t>(tensor->data);

    // Avoid sending uninitialized data over the wire
    memset(result.name, 0, sizeof(result.name));
@@ -1339,7 +1340,9 @@ bool rpc_server::init_tensor(const rpc_msg_init_tensor_req & request) {
    if (buffer && buffer->iface.init_tensor) {
        buffer->iface.init_tensor(buffer, tensor);
    } else {
-        GGML_LOG_ERROR("Null buffer for tensor passed to init_tensor function\n");
+        if (!buffer) {
+            GGML_LOG_ERROR("Tensor with null buffer passed to init_tensor function\n");
+        }
    }

    if (tensor->extra != nullptr) {
@@ -1443,9 +1446,11 @@ ggml_tensor * rpc_server::create_node(uint64_t id,
    const rpc_tensor * tensor = it_ptr->second;

    struct ggml_tensor * result = deserialize_tensor(ctx, tensor);
-    if (result == nullptr || result->buffer == nullptr) {
-        GGML_LOG_ERROR("[%s] invalid tensor: null %s (id=%" PRIu64 ")\n",
-                       __func__, result == nullptr ? "tensor" : "buffer", id);
+    if (result == nullptr) {
+        return nullptr;
+    }
+    if (result->buffer == nullptr && result->data != nullptr) {
+        GGML_LOG_ERROR("[%s] invalid data ptr", __func__);
        return nullptr;
    }
    tensor_map[id] = result;
@@ -1112,6 +1112,16 @@ struct vk_op_glu_push_constants {
    uint32_t mode;  // 0: default, 1: swapped, 2: split
    float alpha; // for swiglu_oai
    float limit;
+    uint32_t nb01;
+    uint32_t nb02;
+    uint32_t nb03;
+    uint32_t ne01;
+    uint32_t ne02;
+    uint32_t nb11;
+    uint32_t nb12;
+    uint32_t nb13;
+    uint32_t ne11;
+    uint32_t ne12;
 };

 struct vk_op_unary_push_constants {
@@ -5044,7 +5054,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
        } else {
            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities});
        }
-        vk::DeviceCreateInfo device_create_info;
+        vk::DeviceCreateInfo device_create_info{};
        std::vector<const char *> device_extensions;
        vk::PhysicalDeviceFeatures device_features = device->physical_device.getFeatures();

@@ -5413,12 +5423,10 @@ static vk_device ggml_vk_get_device(size_t idx) {
 #endif
        device->name = GGML_VK_NAME + std::to_string(idx);

-        device_create_info = {
-            vk::DeviceCreateFlags(),
-            device_queue_create_infos,
-            {},
-            device_extensions
-        };
+        device_create_info
+            .setFlags(vk::DeviceCreateFlags())
+            .setQueueCreateInfos(device_queue_create_infos)
+            .setPEnabledExtensionNames(device_extensions);
        device_create_info.setPNext(&device_features2);
        device->device = device->physical_device.createDevice(device_create_info);

@@ -11048,8 +11056,6 @@ static void ggml_vk_glu(ggml_backend_vk_context * ctx, vk_context& subctx, const
    const float alpha = op_params_f[2];
    const float limit = op_params_f[3];

-    GGML_ASSERT(ggml_is_contiguous(src0));
-
    if (!split) {
        GGML_ASSERT(src0->ne[0] / 2 == dst->ne[0]);
    } else {
@@ -11067,7 +11073,17 @@ static void ggml_vk_glu(ggml_backend_vk_context * ctx, vk_context& subctx, const
            (uint32_t)dst->ne[0],
            mode,
            alpha,
-            limit
+            limit,
+            (uint32_t)(src0->nb[1] / src0->nb[0]),
+            (uint32_t)(src0->nb[2] / src0->nb[0]),
+            (uint32_t)(src0->nb[3] / src0->nb[0]),
+            (uint32_t)src0->ne[1],
+            (uint32_t)src0->ne[2],
+            (uint32_t)(dst->nb[1] / dst->nb[0]),
+            (uint32_t)(dst->nb[2] / dst->nb[0]),
+            (uint32_t)(dst->nb[3] / dst->nb[0]),
+            (uint32_t)dst->ne[1],
+            (uint32_t)dst->ne[2]
        });
 }

@@ -15217,8 +15233,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                case GGML_GLU_OP_SWIGLU_OAI:
                case GGML_GLU_OP_GEGLU_ERF:
                case GGML_GLU_OP_GEGLU_QUICK:
-                    return ggml_is_contiguous(op->src[0]) &&
-                           (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
+                    return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
                           (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) &&
                           (op->src[0]->type == op->type);
                default:
@@ -16,4 +16,14 @@ layout (push_constant) uniform parameter
    uint mode;
    float alpha;
    float limit;
+    uint nb01;
+    uint nb02;
+    uint nb03;
+    uint ne01;
+    uint ne02;
+    uint nb11;
+    uint nb12;
+    uint nb13;
+    uint ne11;
+    uint ne12;
 } p;
@@ -8,22 +8,32 @@ void main() {
    const uint row = i / p.ne20;
    const uint col = i - row * p.ne20;

+    const uint i3 = row / (p.ne01 * p.ne02);
+    const uint i2 = (row % (p.ne01 * p.ne02)) / p.ne01;
+    const uint i1 = row % p.ne01;
+    const uint src_idx = i3 * p.nb03 + i2 * p.nb02 + i1 * p.nb01 + col;
+
+    const uint dst_i3 = row / (p.ne11 * p.ne12);
+    const uint dst_i2 = (row % (p.ne11 * p.ne12)) / p.ne11;
+    const uint dst_i1 = row % p.ne11;
+    const uint dst_idx = dst_i3 * p.nb13 + dst_i2 * p.nb12 + dst_i1 * p.nb11 + col;
+
    if (p.mode == 0) {
        // Default
        const uint offset = p.ne00 / 2;
-        const uint idx = row * p.ne00 + col;
+        const uint idx = src_idx;

-        data_d[row * offset + col] = D_TYPE(op(float(data_a[idx]), float(data_a[idx + offset])));
+        data_d[dst_idx] = D_TYPE(op(float(data_a[idx]), float(data_a[idx + offset])));
    } else if (p.mode == 1) {
        // Swapped
        const uint offset = p.ne00 / 2;
-        const uint idx = row * p.ne00 + col;
+        const uint idx = src_idx;

-        data_d[row * offset + col] = D_TYPE(op(float(data_a[idx + offset]), float(data_a[idx])));
+        data_d[dst_idx] = D_TYPE(op(float(data_a[idx + offset]), float(data_a[idx])));
    } else {
        // Split
-        const uint idx = row * p.ne00 + col;
+        const uint idx = src_idx;

-        data_d[idx] = D_TYPE(op(float(data_a[idx]), float(data_b[idx])));
+        data_d[dst_idx] = D_TYPE(op(float(data_a[idx]), float(data_b[idx])));
    }
 }
@@ -4962,6 +4962,7 @@ static struct ggml_tensor * ggml_interpolate_impl(
    GGML_ASSERT((mode & 0xFF) < GGML_SCALE_MODE_COUNT);
    // TODO: implement antialias for modes other than bilinear
    GGML_ASSERT(!(mode & GGML_SCALE_FLAG_ANTIALIAS) || (mode & 0xFF) == GGML_SCALE_MODE_BILINEAR);
+    GGML_ASSERT(a->type == GGML_TYPE_F32);

    struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, ne0, ne1, ne2, ne3);

@@ -5307,6 +5308,7 @@ struct ggml_tensor * ggml_flash_attn_ext(
    GGML_ASSERT(q->ne[3] == v->ne[3]);

    if (mask) {
+        GGML_ASSERT(mask->type == GGML_TYPE_F16);
        GGML_ASSERT(ggml_is_contiguous(mask));
        //GGML_ASSERT(ggml_can_repeat_rows(mask, qk));

@@ -394,7 +394,11 @@ bool gguf_read_emplace_helper(const struct gguf_reader & gr, std::vector<struct
    return true;
 }

-struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_params params) {
+struct gguf_context * gguf_init_from_file_ptr(FILE * file, struct gguf_init_params params) {
+    if (!file) {
+        return nullptr;
+    }
+
    const struct gguf_reader gr(file);
    struct gguf_context * ctx = new gguf_context;

@@ -848,7 +852,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
        return nullptr;
    }

-    struct gguf_context * result = gguf_init_from_file_impl(file, params);
+    struct gguf_context * result = gguf_init_from_file_ptr(file, params);
    fclose(file);
    return result;
 }
@@ -1508,6 +1512,19 @@ void gguf_write_to_buf(const struct gguf_context * ctx, std::vector<int8_t> & bu
    gguf_write_out(ctx, gw, only_meta);
 }

+bool gguf_write_to_file_ptr(const struct gguf_context * ctx, FILE * file, bool only_meta) {
+    GGML_ASSERT(file);
+
+    try {
+        gguf_writer_file gw(file);
+        gguf_write_out(ctx, gw, only_meta);
+    } catch (const std::runtime_error& ex) {
+        GGML_LOG_ERROR("%s: failed to write GGUF data: %s\n", __func__, ex.what());
+        return false;
+    }
+    return true;
+}
+
 bool gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta) {
    FILE * file = ggml_fopen(fname, "wb");

@@ -1516,17 +1533,13 @@ bool gguf_write_to_file(const struct gguf_context * ctx, const char * fname, boo
        return false;
    }

-    try {
-        gguf_writer_file gw(file);
-        gguf_write_out(ctx, gw, only_meta);
-    } catch (const std::runtime_error& ex) {
-        GGML_LOG_ERROR("%s: failed to write GGUF data into '%s': %s\n", __func__, fname, ex.what());
-        fclose(file);
-        return false;
+    const bool success = gguf_write_to_file_ptr(ctx, file, only_meta);
+    if (!success) {
+        GGML_LOG_ERROR("%s: failed to write GGUF data into '%s'\n", __func__, fname);
    }

    fclose(file);
-    return true;
+    return success;
 }

 size_t gguf_get_meta_size(const struct gguf_context * ctx) {
@@ -326,6 +326,11 @@ class Keys:
        class Projector:
            SCALE_FACTOR    = "clip.vision.projector.scale_factor"

+        class SAM:
+            BLOCK_COUNT         = "clip.vision.sam.block_count"
+            EMBEDDING_LENGTH    = "clip.vision.sam.embedding_length"
+            HEAD_COUNT          = "clip.vision.sam.head_count"
+
    class ClipAudio:
        PROJECTOR_TYPE      = "clip.audio.projector_type" # for mixed modality models
        NUM_MEL_BINS        = "clip.audio.num_mel_bins"
@@ -434,6 +439,7 @@ class MODEL_ARCH(IntEnum):
    ARCTIC           = auto()
    DEEPSEEK         = auto()
    DEEPSEEK2        = auto()
+    DEEPSEEK2OCR     = auto()
    CHATGLM          = auto()
    GLM4             = auto()
    GLM4_MOE         = auto()
@@ -755,6 +761,22 @@ class MODEL_TENSOR(IntEnum):
    V_MM_GATE            = auto() # cogvlm
    V_TOK_BOI            = auto() # cogvlm
    V_TOK_EOI            = auto() # cogvlm
+    V_SAM_POS_EMBD       = auto() # Deepseek-OCR
+    V_SAM_PATCH_EMBD     = auto() # Deepseek-OCR
+    V_SAM_PRE_NORM       = auto() # Deepseek-OCR
+    V_SAM_POST_NORM      = auto() # Deepseek-OCR
+    V_SAM_ATTN_POS_H     = auto() # Deepseek-OCR
+    V_SAM_ATTN_POS_W     = auto() # Deepseek-OCR
+    V_SAM_ATTN_QKV       = auto() # Deepseek-OCR
+    V_SAM_ATTN_OUT       = auto() # Deepseek-OCR
+    V_SAM_MLP_LIN_1      = auto() # Deepseek-OCR
+    V_SAM_MLP_LIN_2      = auto() # Deepseek-OCR
+    V_SAM_NECK           = auto() # Deepseek-OCR
+    V_SAM_NET_2          = auto() # Deepseek-OCR
+    V_SAM_NET_3          = auto() # Deepseek-OCR
+    V_ENC_EMBD_IMGNL     = auto() # Deepseek-OCR
+    V_ENC_EMBD_VSEP      = auto() # Deepseek-OCR
+
    # audio (mtmd)
    A_ENC_EMBD_POS        = auto()
    A_ENC_EMBD_NORM       = auto()
@@ -880,6 +902,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
    MODEL_ARCH.ARCTIC:           "arctic",
    MODEL_ARCH.DEEPSEEK:         "deepseek",
    MODEL_ARCH.DEEPSEEK2:        "deepseek2",
+    MODEL_ARCH.DEEPSEEK2OCR:     "deepseek2-ocr",
    MODEL_ARCH.CHATGLM:          "chatglm",
    MODEL_ARCH.GLM4:             "glm4",
    MODEL_ARCH.GLM4_MOE:         "glm4moe",
@@ -1199,6 +1222,22 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
    MODEL_TENSOR.V_MM_GATE:                 "mm.gate",
    MODEL_TENSOR.V_TOK_BOI:                 "v.boi",
    MODEL_TENSOR.V_TOK_EOI:                 "v.eoi",
+    # DeepSeek-OCR SAM
+    MODEL_TENSOR.V_SAM_POS_EMBD:            "v.sam.pos_embd",
+    MODEL_TENSOR.V_SAM_PATCH_EMBD:          "v.sam.patch_embd",
+    MODEL_TENSOR.V_SAM_PRE_NORM:            "v.sam.blk.{bid}.pre_ln",
+    MODEL_TENSOR.V_SAM_POST_NORM:           "v.sam.blk.{bid}.post_ln",
+    MODEL_TENSOR.V_SAM_ATTN_POS_H:          "v.sam.blk.{bid}.attn.pos_h",
+    MODEL_TENSOR.V_SAM_ATTN_POS_W:          "v.sam.blk.{bid}.attn.pos_w",
+    MODEL_TENSOR.V_SAM_ATTN_QKV:            "v.sam.blk.{bid}.attn.qkv",
+    MODEL_TENSOR.V_SAM_ATTN_OUT:            "v.sam.blk.{bid}.attn.out",
+    MODEL_TENSOR.V_SAM_MLP_LIN_1:           "v.sam.blk.{bid}.mlp.lin1",
+    MODEL_TENSOR.V_SAM_MLP_LIN_2:           "v.sam.blk.{bid}.mlp.lin2",
+    MODEL_TENSOR.V_SAM_NECK:                "v.sam.neck.{bid}",
+    MODEL_TENSOR.V_SAM_NET_2:               "v.sam.net_2",
+    MODEL_TENSOR.V_SAM_NET_3:               "v.sam.net_3",
+    MODEL_TENSOR.V_ENC_EMBD_IMGNL:          "v.image_newline", # Deepseek-OCR
+    MODEL_TENSOR.V_ENC_EMBD_VSEP:           "v.view_seperator", # Deepseek-OCR
    # audio (mtmd)
    # note: all audio tensor names must use prefix "a." or "mm.a."
    MODEL_TENSOR.A_ENC_EMBD_POS:            "a.position_embd",
@@ -1265,6 +1304,8 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        MODEL_TENSOR.V_ENC_EMBD_PATCH,
        MODEL_TENSOR.V_ENC_EMBD_NORM,
        MODEL_TENSOR.V_ENC_EMBD_POS,
+        MODEL_TENSOR.V_ENC_EMBD_IMGNL,
+        MODEL_TENSOR.V_ENC_EMBD_VSEP,
        MODEL_TENSOR.V_ENC_INPUT_NORM,
        MODEL_TENSOR.V_ENC_ATTN_QKV,
        MODEL_TENSOR.V_ENC_ATTN_Q,
@@ -1317,6 +1358,19 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        MODEL_TENSOR.V_MM_GATE,
        MODEL_TENSOR.V_TOK_BOI,
        MODEL_TENSOR.V_TOK_EOI,
+        MODEL_TENSOR.V_SAM_POS_EMBD,
+        MODEL_TENSOR.V_SAM_PATCH_EMBD,
+        MODEL_TENSOR.V_SAM_PRE_NORM,
+        MODEL_TENSOR.V_SAM_POST_NORM,
+        MODEL_TENSOR.V_SAM_ATTN_POS_H,
+        MODEL_TENSOR.V_SAM_ATTN_POS_W,
+        MODEL_TENSOR.V_SAM_ATTN_QKV,
+        MODEL_TENSOR.V_SAM_ATTN_OUT,
+        MODEL_TENSOR.V_SAM_MLP_LIN_1,
+        MODEL_TENSOR.V_SAM_MLP_LIN_2,
+        MODEL_TENSOR.V_SAM_NECK,
+        MODEL_TENSOR.V_SAM_NET_2,
+        MODEL_TENSOR.V_SAM_NET_3,
        # audio
        MODEL_TENSOR.A_ENC_EMBD_POS,
        MODEL_TENSOR.A_ENC_EMBD_NORM,
@@ -2612,7 +2666,41 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        MODEL_TENSOR.ATTN_Q_B,
        MODEL_TENSOR.ATTN_KV_A_MQA,
        MODEL_TENSOR.ATTN_KV_B,
+        MODEL_TENSOR.ATTN_K,
        MODEL_TENSOR.ATTN_K_B,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_V_B,
+        MODEL_TENSOR.ATTN_Q_A_NORM,
+        MODEL_TENSOR.ATTN_KV_A_NORM,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.FFN_EXP_PROBS_B,
+    ],
+    MODEL_ARCH.DEEPSEEK2OCR: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_A,
+        MODEL_TENSOR.ATTN_Q_B,
+        MODEL_TENSOR.ATTN_KV_A_MQA,
+        MODEL_TENSOR.ATTN_KV_B,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_B,
+        MODEL_TENSOR.ATTN_V,
        MODEL_TENSOR.ATTN_V_B,
        MODEL_TENSOR.ATTN_Q_A_NORM,
        MODEL_TENSOR.ATTN_KV_A_NORM,
@@ -3741,6 +3829,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        MODEL_TENSOR.ROPE_FREQS,
        MODEL_TENSOR.ATTN_ROT_EMBD,
    ],
+    MODEL_ARCH.DEEPSEEK2OCR: [
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+    ],
    MODEL_ARCH.CHATGLM: [
        MODEL_TENSOR.ROPE_FREQS,
    ],
@@ -3938,6 +4030,7 @@ class VisionProjectorType:
    LIGHTONOCR = "lightonocr"
    COGVLM = "cogvlm"
    JANUS_PRO = "janus_pro"
+    DEEPSEEKOCR = "deepseekocr"
    LFM2A = "lfm2a" # audio
    MUSIC_FLAMINGO = "musicflamingo" # audio
    GLM4V = "glm4v"
@@ -1218,6 +1218,15 @@ class GGUFWriter:
    def add_vision_window_size(self, value: int) -> None:
        self.add_uint32(Keys.ClipVision.WINDOW_SIZE, value)

+    def add_vision_sam_layers_count(self, value: int) -> None:
+        self.add_uint32(Keys.ClipVision.SAM.BLOCK_COUNT, value)
+
+    def add_vision_sam_embedding_length(self, value: int) -> None:
+        self.add_uint32(Keys.ClipVision.SAM.EMBEDDING_LENGTH, value)
+
+    def add_vision_sam_head_count(self, value: int) -> None:
+        self.add_uint32(Keys.ClipVision.SAM.HEAD_COUNT, value)
+
    # audio models

    def add_clip_audio_projector_type(self, value: str) -> None:
@@ -63,6 +63,7 @@ class TensorNameMap:
            "transformer.wpe",                 # gpt2
            "embeddings.position_embeddings",  # bert
            "wpe",                             # gpt2
+            "model.embed_positions",           # rugpt3xl
        ),

        # Output
@@ -1344,6 +1345,7 @@ class TensorNameMap:
        MODEL_TENSOR.V_MMPROJ_FC: (
            "model.connector.modality_projection.proj", # SmolVLM
            "model.vision.linear_proj.linear_proj", # cogvlm
+            "model.projector.layers", # Deepseek-OCR
            "visual.merger.proj", # glm4v
        ),

@@ -1364,6 +1366,7 @@ class TensorNameMap:
            "vision_model.class_embedding", # llama 4
            "model.vision.patch_embedding.cls_embedding", # cogvlm
            "vision_model.radio_model.model.patch_generator.cls_token.token", # Nemotron Nano v2 VL
+            "model.vision_model.embeddings.class_embedding", # Deepseek-OCR
        ),

        MODEL_TENSOR.V_ENC_EMBD_PATCH: (
@@ -1377,6 +1380,7 @@ class TensorNameMap:
            "visual.patch_embed.proj", # qwen2vl
            "vision_tower.patch_embed.proj", # kimi-vl
            "model.vision.patch_embedding.proj", # cogvlm
+            "model.vision_model.embeddings.patch_embedding", # Deepseek-OCR CLIP
            "siglip2.vision_model.embeddings.patch_embedding",
            "vision_model.radio_model.model.patch_generator.embedder", # Nemotron Nano v2 VL
        ),
@@ -1398,10 +1402,19 @@ class TensorNameMap:
            "vision_model.radio_model.model.patch_generator.pos_embed", # Nemotron Nano v2 VL
        ),

+        MODEL_TENSOR.V_ENC_EMBD_IMGNL: (
+            "model.image_newline",  # Deepseek-OCR
+        ),
+
+        MODEL_TENSOR.V_ENC_EMBD_VSEP: (
+            "model.view_seperator",  # Deepseek-OCR
+        ),
+
        MODEL_TENSOR.V_ENC_ATTN_QKV: (
            "visual.blocks.{bid}.attn.qkv", # qwen3vl
            "model.vision.transformer.layers.{bid}.attention.query_key_value", # cogvlm
-            "vision_tower.encoder.blocks.{bid}.wqkv", # Kimi-K2.5
+            "model.vision_model.transformer.layers.{bid}.self_attn.qkv_proj", # Deepseek-OCR CLIP
+            "vision_tower.encoder.blocks.{bid}.wqkv" # Kimi-K2.5
            "vision_model.radio_model.model.blocks.{bid}.attn.qkv", # Nemotron Nano v2 VL
        ),

@@ -1416,6 +1429,7 @@ class TensorNameMap:
            "visual.blocks.{bid}.attn.q", # qwen2vl, generated
            "vision_tower.encoder.blocks.{bid}.wq", # kimi-vl, generated
            "siglip2.vision_model.encoder.layers.{bid}.self_attn.q_proj", # youtuvl
+            "model.vision_model.transformer.layers.{bid}.self_attn.q_proj", # Deepseek-OCR CLIP, generated
        ),

        MODEL_TENSOR.V_ENC_ATTN_Q_NORM: (
@@ -1434,6 +1448,7 @@ class TensorNameMap:
            "vision_encoder.transformer.layers.{bid}.attention.wk", # pixtral
            "visual.blocks.{bid}.attn.k", # qwen2vl, generated
            "vision_tower.encoder.blocks.{bid}.wk", # kimi-vl, generated
+            "model.vision_model.transformer.layers.{bid}.self_attn.k_proj", # Deepseek-OCR CLIP, generated
            "siglip2.vision_model.encoder.layers.{bid}.self_attn.k_proj",
        ),

@@ -1454,6 +1469,7 @@ class TensorNameMap:
            "visual.blocks.{bid}.attn.v", # qwen2vl, generated
            "vision_tower.encoder.blocks.{bid}.wv", # kimi-vl, generated
            "siglip2.vision_model.encoder.layers.{bid}.self_attn.v_proj",
+            "model.vision_model.transformer.layers.{bid}.self_attn.v_proj", # Deepseek-OCR CLIP, generated
        ),

        MODEL_TENSOR.V_ENC_INPUT_NORM: (
@@ -1468,6 +1484,7 @@ class TensorNameMap:
            "visual.blocks.{bid}.norm1", # qwen2vl
            "vision_tower.encoder.blocks.{bid}.norm0", # kimi-vl (norm0/norm1)
            "model.vision.transformer.layers.{bid}.input_layernorm", # cogvlm
+            "model.vision_model.transformer.layers.{bid}.layer_norm1", # Deepseek-OCR CLIP
            "siglip2.vision_model.encoder.layers.{bid}.layer_norm1",
            "vision_model.radio_model.model.blocks.{bid}.norm1", # Nemotron Nano v2 VL
        ),
@@ -1485,6 +1502,7 @@ class TensorNameMap:
            "visual.blocks.{bid}.attn.proj", # qwen2vl
            "vision_tower.encoder.blocks.{bid}.wo", # kimi-vl
            "model.vision.transformer.layers.{bid}.attention.dense", # cogvlm
+            "model.vision_model.transformer.layers.{bid}.self_attn.out_proj", # Deepseek-OCR CLIP
            "siglip2.vision_model.encoder.layers.{bid}.self_attn.out_proj", # youtuvl
            "vision_model.radio_model.model.blocks.{bid}.attn.proj", # Nemotron Nano v2 VL
        ),
@@ -1501,6 +1519,7 @@ class TensorNameMap:
            "visual.blocks.{bid}.norm2", # qwen2vl
            "vision_tower.encoder.blocks.{bid}.norm1", # kimi-vl (norm0/norm1)
            "model.vision.transformer.layers.{bid}.post_attention_layernorm", # cogvlm
+            "model.vision_model.transformer.layers.{bid}.layer_norm2", # Deepseek-OCR CLIP
            "siglip2.vision_model.encoder.layers.{bid}.layer_norm2",
            "vision_model.radio_model.model.blocks.{bid}.norm2", # Nemotron Nano v2 VL
        ),
@@ -1517,6 +1536,7 @@ class TensorNameMap:
            "visual.blocks.{bid}.mlp.up_proj", # qwen2.5vl
            "visual.blocks.{bid}.mlp.linear_fc1", # qwen3vl
            "vision_tower.encoder.blocks.{bid}.mlp.fc0", # kimi-vl (fc0/fc1)
+            "model.vision_model.transformer.layers.{bid}.mlp.fc1", # Deepseek-OCR CLIP
            "model.vision.transformer.layers.{bid}.mlp.fc1", # cogvlm
            "siglip2.vision_model.encoder.layers.{bid}.mlp.fc1",
            "vision_model.radio_model.model.blocks.{bid}.mlp.fc1", # Nemotron Nano v2 VL
@@ -1541,6 +1561,7 @@ class TensorNameMap:
            "visual.blocks.{bid}.mlp.linear_fc2", # qwen3vl
            "vision_tower.encoder.blocks.{bid}.mlp.fc1", # kimi-vl (fc0/fc1)
            "model.vision.transformer.layers.{bid}.mlp.fc2", # cogvlm
+            "model.vision_model.transformer.layers.{bid}.mlp.fc2", # Deepseek-OCR CLIP
            "siglip2.vision_model.encoder.layers.{bid}.mlp.fc2",
            "vision_model.radio_model.model.blocks.{bid}.mlp.fc2", # Nemotron Nano v2 VL
        ),
@@ -1560,6 +1581,7 @@ class TensorNameMap:
            "vision_tower.ln_pre", # pixtral-hf
            "vision_encoder.ln_pre", # pixtral
            "vision_model.layernorm_pre", # llama4
+            "model.vision_model.pre_layrnorm", # Deepseek-OCR CLIP
        ),

        MODEL_TENSOR.V_POST_NORM: (
@@ -1662,6 +1684,58 @@ class TensorNameMap:
            "model.visual.deepstack_merger_list.{bid}.linear_fc2", # deepstack in qwen3vl
        ),

+        MODEL_TENSOR.V_SAM_POS_EMBD: (
+            "model.sam_model.pos_embed",
+        ),
+
+        MODEL_TENSOR.V_SAM_PATCH_EMBD: (
+            "model.sam_model.patch_embed.proj",
+        ),
+
+        MODEL_TENSOR.V_SAM_PRE_NORM: (
+            "model.sam_model.blocks.{bid}.norm1", # deepstack in qwen3vl
+        ),
+
+        MODEL_TENSOR.V_SAM_POST_NORM: (
+            "model.sam_model.blocks.{bid}.norm2", # deepstack in qwen3vl
+        ),
+
+        MODEL_TENSOR.V_SAM_ATTN_POS_H: (
+            "model.sam_model.blocks.{bid}.attn.rel_pos_h",
+        ),
+
+        MODEL_TENSOR.V_SAM_ATTN_POS_W: (
+            "model.sam_model.blocks.{bid}.attn.rel_pos_w",
+        ),
+
+        MODEL_TENSOR.V_SAM_ATTN_QKV: (
+            "model.sam_model.blocks.{bid}.attn.qkv",
+        ),
+
+        MODEL_TENSOR.V_SAM_ATTN_OUT: (
+            "model.sam_model.blocks.{bid}.attn.proj",
+        ),
+
+        MODEL_TENSOR.V_SAM_MLP_LIN_1: (
+            "model.sam_model.blocks.{bid}.mlp.lin1",
+        ),
+
+        MODEL_TENSOR.V_SAM_MLP_LIN_2: (
+            "model.sam_model.blocks.{bid}.mlp.lin2",
+        ),
+
+        MODEL_TENSOR.V_SAM_NECK: (
+            "model.sam_model.neck.{bid}",
+        ),
+
+        MODEL_TENSOR.V_SAM_NET_2: (
+            "model.sam_model.net_2",
+        ),
+
+        MODEL_TENSOR.V_SAM_NET_3: (
+            "model.sam_model.net_3",
+        ),
+
        MODEL_TENSOR.V_MM_POST_FC_NORM: (
            "model.vision.linear_proj.norm1", # cogvlm
        ),
@@ -14,12 +14,12 @@ except ImportError:
    SentencePieceProcessor: Any = None

 try:
-    from mistral_common.tokens.tokenizers.mistral import MistralTokenizer # type: ignore[import-not-found]
-    from mistral_common.tokens.tokenizers.tekken import Tekkenizer # type: ignore[import-not-found]
-    from mistral_common.tokens.tokenizers.utils import ( # type: ignore[import-not-found]
+    from mistral_common.tokens.tokenizers.mistral import MistralTokenizer # type: ignore[import-not-found, ty:unresolved-import]
+    from mistral_common.tokens.tokenizers.tekken import Tekkenizer # type: ignore[import-not-found, ty:unresolved-import]
+    from mistral_common.tokens.tokenizers.utils import ( # type: ignore[import-not-found, ty:unresolved-import]
        _filter_valid_tokenizer_files,
    )
-    from mistral_common.tokens.tokenizers.sentencepiece import ( # type: ignore[import-not-found]
+    from mistral_common.tokens.tokenizers.sentencepiece import ( # type: ignore[import-not-found, ty:unresolved-import]
        SentencePieceTokenizer,
    )
 except ImportError:
@@ -32,7 +32,7 @@ else:
    _mistral_common_installed = True

 try:
-    from mistral_common.tokens.tokenizers.utils import ( # type: ignore[import-not-found]
+    from mistral_common.tokens.tokenizers.utils import ( # type: ignore[import-not-found, ty:unresolved-import]
        get_one_valid_tokenizer_file,
    )
 except ImportError:
@@ -465,6 +465,11 @@ extern "C" {
                             const char * path_model,
              struct llama_model_params   params);

+    // Load a model from an open FILE pointer
+    LLAMA_API struct llama_model * llama_model_load_from_file_ptr(
+                                   FILE * file,
+              struct llama_model_params   params);
+
    // Load a model from multiple splits (support custom naming scheme)
    // The paths must be in the correct order
    LLAMA_API struct llama_model * llama_model_load_from_splits(
@@ -0,0 +1,154 @@
+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- macro render_content(content, do_vision_count, is_system_content=false) %}
+    {%- if content is string %}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping %}
+        {%- for item in content %}
+            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain images.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Picture ' ~ image_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}
+            {%- elif 'video' in item or item.type == 'video' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain videos.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Video ' ~ video_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}
+            {%- elif 'text' in item %}
+                {{- item.text }}
+            {%- else %}
+                {{- raise_exception('Unexpected item type in content.') }}
+            {%- endif %}
+        {%- endfor %}
+    {%- elif content is none or content is undefined %}
+        {{- '' }}
+    {%- else %}
+        {{- raise_exception('Unexpected content type.') }}
+    {%- endif %}
+{%- endmacro %}
+{%- if not messages %}
+    {{- raise_exception('No messages provided.') }}
+{%- endif %}
+{%- if tools and tools is iterable and tools is not mapping %}
+    {{- '<|im_start|>system\n' }}
+    {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- '\n\n' + content }}
+        {%- endif %}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {{- '<|im_start|>system\n' + content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" %}
+        {%- set content = render_content(message.content, false)|trim %}
+        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
+            {%- set ns.multi_step_tool = false %}
+            {%- set ns.last_query_index = index %}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if ns.multi_step_tool %}
+    {{- raise_exception('No user query found in messages.') }}
+{%- endif %}
+{%- for message in messages %}
+    {%- set content = render_content(message.content, true)|trim %}
+    {%- if message.role == "system" %}
+        {%- if not loop.first %}
+            {{- raise_exception('System message must be at the beginning.') }}
+        {%- endif %}
+    {%- elif message.role == "user" %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- set reasoning_content = reasoning_content|trim %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if tool_call.function is defined %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {%- if loop.first %}
+                    {%- if content|trim %}
+                        {{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- else %}
+                        {{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- endif %}
+                {%- else %}
+                    {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                {%- endif %}
+                {%- if tool_call.arguments is defined %}
+                    {%- for args_name, args_value in tool_call.arguments|items %}
+                        {{- '<parameter=' + args_name + '>\n' }}
+                        {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
+                        {{- args_value }}
+                        {{- '\n</parameter>\n' }}
+                    {%- endfor %}
+                {%- endif %}
+                {{- '</function>\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- raise_exception('Unexpected message role.') }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- else %}
+        {{- '<think>\n' }}
+    {%- endif %}
+{%- endif %}
@@ -147,7 +147,7 @@ ranges_nfd: list[tuple[int, int, int]] = [(0, 0, 0)]  # start, last, nfd
 for codepoint, norm in table_nfd:
    start = ranges_nfd[-1][0]
    if ranges_nfd[-1] != (start, codepoint - 1, norm):
-        ranges_nfd.append(None)  # type: ignore[arg-type]  # dummy, will be replaced below
+        ranges_nfd.append((0, 0, 0))  # dummy, will be replaced below
        start = codepoint
    ranges_nfd[-1] = (start, codepoint, norm)

@@ -2,37 +2,51 @@

 import sys
 from collections import defaultdict
+import re


 def parse_log_file(filepath):
-    """Parse log file and extract function VGPR usage."""
-    import re
-
    functions = defaultdict(lambda: {'vgprs': 0, 'spill': 0, 'location': ''})
+    func_stack = []

    try:
        with open(filepath, 'r') as f:
-            content = f.read()
-            # Find all function entries with VGPR usage including location
-            pattern = r'([^:]+:\d+):.*?Function Name: (\S+).*?VGPRs: (\d+).*?VGPRs Spill: (\d+)'
-            matches = re.findall(pattern, content, re.DOTALL)
+            for line in f:
+                # Match function name lines
+                func_match = re.search(r'remark: ([^:]+):(\d+):\d+: Function Name: (\S+)', line)
+                if func_match:
+                    location = func_match.group(1) + ':' + func_match.group(2)
+                    func_name = func_match.group(3)
+                    # Extract just the filename and line number
+                    parts = location.split('/')
+                    short_location = parts[-1] if len(parts) > 0 else location
+                    functions[func_name]['location'] = short_location
+                    # Push function onto stack with its location
+                    func_stack.append({'name': func_name, 'location': location})
+                    continue

-            for location, func_name, vgprs, spill in matches:
-                functions[func_name]['vgprs'] = int(vgprs)
-                functions[func_name]['spill'] = int(spill)
-                # Extract just the filename and line number
-                parts = location.split('/')
-                if len(parts) > 0:
-                    short_location = parts[-1]  # Get last part (filename)
-                    # Check if there's a line number after filename
-                    if ':' in short_location:
-                        functions[func_name]['location'] = short_location
-                    else:
-                        functions[func_name]['location'] = location
-                else:
-                    functions[func_name]['location'] = location
+                # Match VGPR usage lines (only if we have functions in stack)
+                vgpr_match = re.search(r'remark: ([^:]+):(\d+):\d+:\s+VGPRs: (\d+)', line)
+                if vgpr_match:
+                    location = vgpr_match.group(1) + ':' + vgpr_match.group(2)
+                    # Find the most recent function with matching location
+                    for i in range(len(func_stack) - 1, -1, -1):
+                        if func_stack[i]['location'] == location:
+                            functions[func_stack[i]['name']]['vgprs'] = int(vgpr_match.group(3))
+                            break
+                    continue
+
+                spill_match = re.search(r'remark: ([^:]+):(\d+):\d+:\s+VGPRs Spill: (\d+)', line)
+                if spill_match:
+                    location = spill_match.group(1) + ':' + spill_match.group(2)
+                    # Find the most recent function with matching location
+                    for i in range(len(func_stack) - 1, -1, -1):
+                        if func_stack[i]['location'] == location:
+                            functions[func_stack[i]['name']]['spill'] = int(spill_match.group(3))
+                            break
+                    continue
    except FileNotFoundError:
-        print(f"Error: File {filepath} not found", file=sys.stderr) # noqa: NP100
+        print(f"Error: File {filepath} not found", file=sys.stderr)  # noqa: NP100
        sys.exit(1)

    return functions
@@ -40,7 +54,7 @@ def parse_log_file(filepath):

 def main():
    if len(sys.argv) < 2:
-        print("Usage: ./vgpr_check.py <log_file>", file=sys.stderr) # noqa: NP100
+        print("Usage: ./vgpr_check.py <log_file>", file=sys.stderr)  # noqa: NP100
        sys.exit(1)

    log_file = sys.argv[1]
@@ -123,6 +137,9 @@ def main():
        '_ZL18flash_attn_ext_f16ILi128ELi128ELi32ELi2ELb1ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
        '_ZL18flash_attn_ext_f16ILi128ELi128ELi4ELi8ELb1ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
        '_ZL18flash_attn_ext_f16ILi96ELi96ELi4ELi8ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
+        '_ZL18flash_attn_ext_vecILi128ELi2EL9ggml_type2ELS0_2ELb0EEvPKcS2_S2_S2_S2_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS6_IjLj3EEiiiiiiiiiiiliiliiiiil',
+        '_ZL9mul_mat_qIL9ggml_type10ELi16ELb1EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
+        '_ZL9mul_mat_qIL9ggml_type12ELi128ELb1EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii'
    }

    functions = parse_log_file(log_file)
@@ -134,7 +151,7 @@ def main():
        total_vgprs = int(data['vgprs']) + int(data['spill'])
        if total_vgprs > 256 and func_name in ignored and func_name not in printed_ignored:
            location = data.get('location', log_file)
-            print(f"{location}: {func_name} - Total VGPRs: {total_vgprs} ({data['vgprs']} + {data['spill']}) [IGNORED]") # noqa: NP100
+            print(f"{location}: {func_name} - Total VGPRs: {total_vgprs} ({data['vgprs']} + {data['spill']}) [IGNORED]")  # noqa: NP100
            printed_ignored.add(func_name)

    # Then print new functions with issues in red
@@ -146,7 +163,7 @@ def main():
            # Print in red if not ignored
            color_code = "\033[91m" if func_name not in ignored else ""
            reset_code = "\033[0m" if func_name not in ignored else ""
-            print(f"{color_code}{location}: {func_name} - Total VGPRs: {total_vgprs} ({data['vgprs']} + {data['spill']}) {status}{reset_code}") # noqa: NP100
+            print(f"{color_code}{location}: {func_name} - Total VGPRs: {total_vgprs} ({data['vgprs']} + {data['spill']}) {status}{reset_code}")  # noqa: NP100
            if func_name not in ignored:
                found_issues = True

@@ -20,6 +20,14 @@ if ($null -ne $env:V) {
    $env:GGML_HEXAGON_VERBOSE=$env:V
 }

+if ($null -ne $env:E) {
+    $env:GGML_HEXAGON_EXPERIMENTAL=$env:E
+}
+
+if ($null -ne $env:PROF) {
+    $env:GGML_HEXAGON_PROFILE=$env:PROF; $env:GGML_HEXAGON_OPSYNC=1
+}
+
 if ($null -ne $env:OPMASK) {
    $env:GGML_HEXAGON_OPMASK=$env:OPMASK
 }
@@ -32,6 +40,10 @@ if ($null -ne $env:NDEV) {
    $env:GGML_HEXAGON_NDEV=$env:NDEV
 }

+if ($null -ne $env:HB) {
+    $env:GGML_HEXAGON_HOSTBUF=$env:HB
+}
+
 $env:ADSP_LIBRARY_PATH="$basedir\lib"

 & "$basedir\bin\llama-bench.exe" `
@@ -44,10 +44,14 @@ if ($null -ne $env:NDEV) {
    $env:GGML_HEXAGON_NDEV=$env:NDEV
 }

+if ($null -ne $env:HB) {
+    $env:GGML_HEXAGON_HOSTBUF=$env:HB
+}
+
 $env:ADSP_LIBRARY_PATH="$basedir\lib"

 & "$basedir\bin\llama-cli.exe" `
    --no-mmap -m $basedir\..\..\gguf\$model `
    --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 `
-    --ctx-size 8192 --ubatch-size 128 -fa on `
+    --ctx-size 8192 --ubatch-size 256 -fa on `
    -ngl 99 --device $device $cli_opts
@@ -44,10 +44,14 @@ if ($null -ne $env:NDEV) {
    $env:GGML_HEXAGON_NDEV=$env:NDEV
 }

+if ($null -ne $env:HB) {
+    $env:GGML_HEXAGON_HOSTBUF=$env:HB
+}
+
 $env:ADSP_LIBRARY_PATH="$basedir\lib"

 & "$basedir\bin\llama-completion.exe" `
    --no-mmap -m $basedir\..\..\gguf\$model `
    --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 `
-    --ctx-size 8192 --batch-size 128 -fa on `
+    --ctx-size 8192 --batch-size 256 -fa on `
    -ngl 99 -no-cnv --device $device $cli_opts
@@ -0,0 +1,74 @@
+#!/usr/bin/env pwsh
+
+# Basedir on device
+$basedir=".\pkg-snapdragon"
+
+$cli_opts=$args
+
+$model="gemma-3-4b-it-Q4_0.gguf"
+if ($null -ne $env:M) {
+    $model=$env:M
+}
+
+$mmproj="mmproj-F16.gguf"
+if ($null -ne $env:MMPROJ) {
+    $mmproj=$env:MMPROJ
+}
+
+$image=""
+if ($null -ne $env:IMG) {
+    $image=$env:IMG
+}
+
+$device="HTP0"
+if ($null -ne $env:D) {
+    $device=$env:D
+}
+
+if ($null -ne $env:V) {
+    $env:GGML_HEXAGON_VERBOSE=$env:V
+}
+
+# Default experimental to 1
+$env:GGML_HEXAGON_EXPERIMENTAL=1
+if ($null -ne $env:E) {
+    $env:GGML_HEXAGON_EXPERIMENTAL=$env:E
+}
+
+if ($null -ne $env:SCHED) {
+    $env:GGML_SCHED_DEBUG=$env:SCHED; $cli_opts="$cli_opts -v"
+}
+
+if ($null -ne $env:PROF) {
+    $env:GGML_HEXAGON_PROFILE=$env:PROF; $env:GGML_HEXAGON_OPSYNC=1
+}
+
+if ($null -ne $env:OPMASK) {
+    $env:GGML_HEXAGON_OPMASK=$env:OPMASK
+}
+
+if ($null -ne $env:NHVX) {
+    $env:GGML_HEXAGON_NHVX=$env:NHVX
+}
+
+if ($null -ne $env:NDEV) {
+    $env:GGML_HEXAGON_NDEV=$env:NDEV
+}
+
+if ($null -ne $env:HB) {
+    $env:GGML_HEXAGON_HOSTBUF=$env:HB
+}
+
+if ($null -ne $env:MTMD_DEVICE) {
+    $env:MTMD_BACKEND_DEVICE=$env:MTMD_DEVICE
+}
+
+$env:ADSP_LIBRARY_PATH="$basedir\lib"
+
+& "$basedir\bin\llama-mtmd-cli.exe" `
+    --no-mmap -m $basedir\..\..\gguf\$model `
+    --mmproj $basedir\..\..\gguf\$mmproj `
+    --image $basedir\..\..\gguf\$image `
+    --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 `
+    --ctx-size 8192 --ubatch-size 256 -fa on `
+    -ngl 99 --device $device -v $cli_opts
@@ -50,6 +50,10 @@ if ($null -ne $env:NDEV) {
    $env:GGML_HEXAGON_NDEV=$env:NDEV
 }

+if ($null -ne $env:HB) {
+    $env:GGML_HEXAGON_HOSTBUF=$env:HB
+}
+
 $env:ADSP_LIBRARY_PATH="$basedir\lib"

 & "$basedir\bin\$tool" `
@@ -5,7 +5,7 @@ import os
 import sys
 import subprocess

-HTTPLIB_VERSION = "refs/tags/v0.39.0"
+HTTPLIB_VERSION = "refs/tags/v0.40.0"

 vendor = {
    "https://github.com/nlohmann/json/releases/latest/download/json.hpp":     "vendor/nlohmann/json.hpp",
@@ -73,6 +73,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
    { LLM_ARCH_ARCTIC,           "arctic"           },
    { LLM_ARCH_DEEPSEEK,         "deepseek"         },
    { LLM_ARCH_DEEPSEEK2,        "deepseek2"        },
+    { LLM_ARCH_DEEPSEEK2OCR,     "deepseek2-ocr"    },
    { LLM_ARCH_CHATGLM,          "chatglm"          },
    { LLM_ARCH_GLM4,             "glm4"             },
    { LLM_ARCH_GLM4_MOE,         "glm4moe"          },
@@ -544,6 +545,10 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
        case LLM_ARCH_CLIP:
            return {};
        case LLM_ARCH_LLAMA:
+        case LLM_ARCH_REFACT:
+        case LLM_ARCH_MINICPM:
+        case LLM_ARCH_GRANITE:
+        case LLM_ARCH_GRANITE_MOE:
        case LLM_ARCH_DECI:
        case LLM_ARCH_MISTRAL3:
        case LLM_ARCH_LLAMA_EMBED:
@@ -552,6 +557,8 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                LLM_TENSOR_OUTPUT_NORM,
                LLM_TENSOR_OUTPUT,
                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ROPE_FACTORS_LONG,
+                LLM_TENSOR_ROPE_FACTORS_SHORT,
                LLM_TENSOR_ATTN_NORM,
                LLM_TENSOR_ATTN_Q,
                LLM_TENSOR_ATTN_K,
@@ -744,11 +751,9 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                LLM_TENSOR_ATTN_Q_NORM,
                LLM_TENSOR_ATTN_K_NORM,
            };
-        case LLM_ARCH_REFACT:
        case LLM_ARCH_QWEN2:
        case LLM_ARCH_QWEN2VL:
        case LLM_ARCH_INTERNLM2:
-        case LLM_ARCH_GRANITE:
        case LLM_ARCH_ERNIE4_5:
        case LLM_ARCH_PADDLEOCR:
        case LLM_ARCH_SMOLLM3:
@@ -759,6 +764,7 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                LLM_TENSOR_TOKEN_EMBD,
                LLM_TENSOR_OUTPUT_NORM,
                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
                LLM_TENSOR_ATTN_NORM,
                LLM_TENSOR_ATTN_Q,
                LLM_TENSOR_ATTN_K,
@@ -1232,29 +1238,6 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                LLM_TENSOR_FFN_DOWN,
                LLM_TENSOR_FFN_UP,
            };
-        case LLM_ARCH_MINICPM:
-            return {
-                LLM_TENSOR_TOKEN_EMBD,
-                LLM_TENSOR_OUTPUT_NORM,
-                LLM_TENSOR_OUTPUT,
-                LLM_TENSOR_ROPE_FREQS,
-                LLM_TENSOR_ROPE_FACTORS_LONG,
-                LLM_TENSOR_ROPE_FACTORS_SHORT,
-                LLM_TENSOR_ATTN_NORM,
-                LLM_TENSOR_ATTN_Q,
-                LLM_TENSOR_ATTN_K,
-                LLM_TENSOR_ATTN_V,
-                LLM_TENSOR_ATTN_OUT,
-                LLM_TENSOR_ATTN_ROT_EMBD,
-                LLM_TENSOR_FFN_GATE_INP,
-                LLM_TENSOR_FFN_NORM,
-                LLM_TENSOR_FFN_GATE,
-                LLM_TENSOR_FFN_DOWN,
-                LLM_TENSOR_FFN_UP,
-                LLM_TENSOR_FFN_GATE_EXP,
-                LLM_TENSOR_FFN_DOWN_EXP,
-                LLM_TENSOR_FFN_UP_EXP,
-            };
        case LLM_ARCH_MINICPM3:
            return {
                LLM_TENSOR_TOKEN_EMBD,
@@ -1442,6 +1425,7 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                LLM_TENSOR_TOKEN_EMBD,
                LLM_TENSOR_OUTPUT,
                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_ROPE_FREQS,
                LLM_TENSOR_ATTN_NORM,
                LLM_TENSOR_ATTN_Q,
                LLM_TENSOR_ATTN_K,
@@ -1590,6 +1574,7 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                LLM_TENSOR_FFN_UP_SHEXP,
            };
        case LLM_ARCH_DEEPSEEK2:
+        case LLM_ARCH_DEEPSEEK2OCR:
        case LLM_ARCH_MISTRAL4:
            return {
                LLM_TENSOR_TOKEN_EMBD,
@@ -1598,6 +1583,8 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                LLM_TENSOR_ATTN_NORM,
                LLM_TENSOR_ATTN_Q_A_NORM,
                LLM_TENSOR_ATTN_KV_A_NORM,
+                LLM_TENSOR_ATTN_K, // deepseek-ocr
+                LLM_TENSOR_ATTN_V, // deepseek-ocr
                LLM_TENSOR_ATTN_Q,
                LLM_TENSOR_ATTN_Q_A,
                LLM_TENSOR_ATTN_Q_B,
@@ -1657,7 +1644,9 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                LLM_TENSOR_ROPE_FREQS,
                LLM_TENSOR_OUTPUT_NORM,
                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_TOKEN_EMBD,
                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_QKV,
                LLM_TENSOR_ATTN_Q,
                LLM_TENSOR_ATTN_K,
                LLM_TENSOR_ATTN_V,
@@ -2061,30 +2050,12 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                LLM_TENSOR_FFN_DOWN,
                LLM_TENSOR_FFN_UP,
            };
-        case LLM_ARCH_GRANITE_MOE:
-            return {
-                LLM_TENSOR_TOKEN_EMBD,
-                LLM_TENSOR_OUTPUT_NORM,
-                LLM_TENSOR_OUTPUT,
-                LLM_TENSOR_ATTN_NORM,
-                LLM_TENSOR_ATTN_Q,
-                LLM_TENSOR_ATTN_K,
-                LLM_TENSOR_ATTN_V,
-                LLM_TENSOR_ATTN_OUT,
-                LLM_TENSOR_FFN_NORM,
-                LLM_TENSOR_FFN_GATE_INP,
-                LLM_TENSOR_FFN_GATE_EXPS,
-                LLM_TENSOR_FFN_DOWN_EXPS,
-                LLM_TENSOR_FFN_UP_EXPS,
-                LLM_TENSOR_FFN_GATE_SHEXP,
-                LLM_TENSOR_FFN_DOWN_SHEXP,
-                LLM_TENSOR_FFN_UP_SHEXP,
-            };
        case LLM_ARCH_GRANITE_HYBRID:
            return {
                LLM_TENSOR_TOKEN_EMBD,
                LLM_TENSOR_OUTPUT_NORM,
                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
                LLM_TENSOR_ATTN_NORM,
                LLM_TENSOR_SSM_IN,
                LLM_TENSOR_SSM_CONV1D,
@@ -2412,6 +2383,7 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                LLM_TENSOR_TOKEN_EMBD,
                LLM_TENSOR_OUTPUT_NORM,
                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
                LLM_TENSOR_ATTN_NORM,
                LLM_TENSOR_ATTN_QKV,
                LLM_TENSOR_ATTN_OUT,
@@ -2789,7 +2761,12 @@ std::string LLM_TN_IMPL::str() const {
    }

    if (model_tensors.find(tensor) == model_tensors.end()) {
-        return LLM_TENSOR_NAMES.at(tensor);
+        const char * name = LLM_TENSOR_NAMES.at(tensor);
+        if (suffix != nullptr || bid != -1 || xid != -1) {
+            LLAMA_LOG_WARN("%s: cannot properly format tensor name %s with suffix=%s bid=%d xid=%d\n",
+                __func__, name, suffix, bid, xid);
+        }
+        return name;
    }

    std::string name = ::format(LLM_TENSOR_NAMES.at(tensor), bid, xid);
@@ -77,6 +77,7 @@ enum llm_arch {
    LLM_ARCH_ARCTIC,
    LLM_ARCH_DEEPSEEK,
    LLM_ARCH_DEEPSEEK2,
+    LLM_ARCH_DEEPSEEK2OCR,
    LLM_ARCH_CHATGLM,
    LLM_ARCH_GLM4,
    LLM_ARCH_GLM4_MOE,
@@ -49,6 +49,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
    { "deepseek",          LLM_CHAT_TEMPLATE_DEEPSEEK          },
    { "deepseek2",         LLM_CHAT_TEMPLATE_DEEPSEEK_2        },
    { "deepseek3",         LLM_CHAT_TEMPLATE_DEEPSEEK_3        },
+    { "deepseek-ocr",      LLM_CHAT_TEMPLATE_DEEPSEEK_OCR      },
    { "command-r",         LLM_CHAT_TEMPLATE_COMMAND_R         },
    { "llama3",            LLM_CHAT_TEMPLATE_LLAMA_3           },
    { "chatglm3",          LLM_CHAT_TEMPLATE_CHATGLM_3         },
@@ -548,6 +549,11 @@ int32_t llm_chat_apply_template(
        if (add_ass) {
            ss << LU8("<｜Assistant｜>");
        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_DEEPSEEK_OCR) {
+        for (auto message : chat) {
+            // no template
+            ss << message->content;
+        }
    } else if (tmpl == LLM_CHAT_TEMPLATE_EXAONE_3) {
        // ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb
        // EXAONE-3.0-7.8B-Instruct
@@ -28,6 +28,7 @@ enum llm_chat_template {
    LLM_CHAT_TEMPLATE_DEEPSEEK,
    LLM_CHAT_TEMPLATE_DEEPSEEK_2,
    LLM_CHAT_TEMPLATE_DEEPSEEK_3,
+    LLM_CHAT_TEMPLATE_DEEPSEEK_OCR,
    LLM_CHAT_TEMPLATE_COMMAND_R,
    LLM_CHAT_TEMPLATE_LLAMA_3,
    LLM_CHAT_TEMPLATE_CHATGLM_3,
@@ -1516,7 +1516,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(

    if (!weight_before_ffn) {
        experts = ggml_mul(ctx0, experts, weights);
-        cb(cur, "ffn_moe_weighted", il);
+        cb(experts, "ffn_moe_weighted", il);
    }

    ggml_tensor * cur_experts[LLAMA_MAX_EXPERTS] = { nullptr };
@@ -1561,7 +1561,6 @@ ggml_tensor * llama_kv_cache::build_rope_shift(
                                // ref: https://github.com/ggml-org/llama.cpp/pull/13870
                                ? LLAMA_ROPE_TYPE_NEOX
                                : hparams.rope_type;
-
    ggml_tensor * tmp;

    if (ggml_is_quantized(cur->type)) {
@@ -86,6 +86,14 @@ struct llama_file::impl {
        seek(0, SEEK_SET);
    }

+    impl(FILE * file) : owns_fp(false) {
+        fp = file;
+        fp_win32 = (HANDLE) _get_osfhandle(_fileno(fp));
+        seek(0, SEEK_END);
+        size = tell();
+        seek(0, SEEK_SET);
+    }
+
    size_t tell() const {
        LARGE_INTEGER li;
        li.QuadPart = 0;
@@ -159,7 +167,7 @@ struct llama_file::impl {
    }

    ~impl() {
-        if (fp) {
+        if (fp && owns_fp) {
            std::fclose(fp);
        }
    }
@@ -209,6 +217,13 @@ struct llama_file::impl {
        seek(0, SEEK_SET);
    }

+    impl(FILE * file) : fname("(file*)"), owns_fp(false) {
+        fp = file;
+        seek(0, SEEK_END);
+        size = tell();
+        seek(0, SEEK_SET);
+    }
+
    size_t tell() const {
        if (fd == -1) {
            long ret = std::ftell(fp);
@@ -353,7 +368,7 @@ struct llama_file::impl {
    ~impl() {
        if (fd != -1) {
            close(fd);
-        } else {
+        } else if (owns_fp) {
            std::fclose(fp);
        }
    }
@@ -369,10 +384,14 @@ struct llama_file::impl {

    FILE * fp{};
    size_t size{};
+    bool owns_fp = true;
 };

 llama_file::llama_file(const char * fname, const char * mode, const bool use_direct_io) :
    pimpl(std::make_unique<impl>(fname, mode, use_direct_io)) {}
+
+llama_file::llama_file(FILE * file) : pimpl(std::make_unique<impl>(file)) {}
+
 llama_file::~llama_file() = default;

 size_t llama_file::tell() const { return pimpl->tell(); }
@@ -15,6 +15,7 @@ using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;

 struct llama_file {
    llama_file(const char * fname, const char * mode, bool use_direct_io = false);
+    llama_file(FILE * file);
    ~llama_file();

    size_t tell() const;
@@ -511,6 +511,7 @@ llama_model_loader::llama_model_loader(
        void * set_tensor_data_ud,
        const std::string & fname,
        std::vector<std::string> & splits,
+        FILE * file,
        bool use_mmap,
        bool use_direct_io,
        bool check_tensors,
@@ -658,6 +659,36 @@ llama_model_loader::llama_model_loader(

            LLAMA_LOG_INFO("%s: additional %d GGUFs metadata loaded.\n",  __func__, n_split - 1);
        }
+    } else if (file != nullptr) {
+        struct ggml_context * ctx = NULL;
+        struct gguf_init_params params = {
+            /*.no_alloc = */ true,
+            /*.ctx      = */ &ctx,
+        };
+
+        metadata_ptr.reset(gguf_init_from_file_ptr(file, params));
+        metadata = metadata_ptr.get();
+        if (metadata == nullptr) {
+            throw std::runtime_error(format("%s: failed to load model from file pointer", __func__));
+        }
+
+        get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false);
+        llm_kv = LLM_KV(llm_arch_from_string(arch_name));
+
+        files.emplace_back(new llama_file(file));
+        contexts.emplace_back(ctx);
+
+        // Save tensors data offset info of the main file.
+        for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
+            std::string tensor_name = std::string(cur->name);
+            // make sure there is no duplicated tensor names
+            if (weights_map.find(tensor_name) != weights_map.end()) {
+                throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", ggml_get_name(cur)));
+            }
+            n_elements += ggml_nelements(cur);
+            n_bytes    += ggml_nbytes(cur);
+            weights_map.emplace(tensor_name, llama_tensor_weight(files.back().get(), 0, metadata, cur));
+        }
    } else {
        get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false);
        llm_kv = LLM_KV(llm_arch_from_string(arch_name));
@@ -669,7 +700,7 @@ llama_model_loader::llama_model_loader(
    fver = (enum llama_fver) gguf_get_version(metadata);

    LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n",
-            __func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(fver));
+            __func__, n_kv, n_tensors, fname.empty() ? "(file*)" : fname.c_str(), llama_file_version_name(fver));

    // determine file type based on the number of tensors for each quantization and print meta data
    // TODO: make optional
@@ -1127,6 +1158,12 @@ struct ggml_tensor * llama_model_loader::create_tensor(
                    if (overrides->buft == ggml_backend_cpu_buffer_type()) {
                        // when overriding to a CPU buffer, consider the extra buffer types
                        buft = select_weight_buft(hparams, t_meta, op, buft_list_cpu);
+                        if (use_mmap) {
+                            static std::once_flag once;
+                            std::call_once(once, [] {
+                                LLAMA_LOG_WARN("llama_model_loader: tensor overrides to CPU are used with mmap enabled - consider using --no-mmap for better performance\n");
+                            });
+                        }
                    } else {
                        buft = overrides->buft;
                    }
@@ -125,6 +125,7 @@ struct llama_model_loader {
        void * set_tensor_data_ud,
        const std::string & fname,
        std::vector<std::string> & splits, // optional, only need if the split does not follow naming scheme
+        FILE * file,
        bool use_mmap,
        bool use_direct_io,
        bool check_tensors,
@@ -1,7 +1,9 @@
 #include "llama-model-saver.h"

+#include "ggml.h"
 #include "gguf.h"

+#include "llama-arch.h"
 #include "llama.h"
 #include "llama-hparams.h"
 #include "llama-model.h"
@@ -10,8 +12,33 @@
 #include <cstdint>
 #include <string>

+bool llama_model_saver_supports_arch(llm_arch arch) {
+    switch (arch) {
+        case LLM_ARCH_QWEN3NEXT:
+        case LLM_ARCH_QWEN35:
+        case LLM_ARCH_QWEN35MOE:
+        case LLM_ARCH_PLAMO3:
+        case LLM_ARCH_GEMMA3:
+        case LLM_ARCH_GEMMA3N:
+        case LLM_ARCH_COHERE2:
+        case LLM_ARCH_OLMO2:
+        case LLM_ARCH_BITNET:
+        case LLM_ARCH_T5:
+        case LLM_ARCH_EXAONE_MOE:
+        case LLM_ARCH_AFMOE:
+        case LLM_ARCH_APERTUS:
+        case LLM_ARCH_MIMO2:
+        case LLM_ARCH_STEP35:
+            return false;
+        default:
+            return true;
+    }
+}
+
 llama_model_saver::llama_model_saver(const struct llama_model * model) :
-    gguf_ctx(gguf_init_empty()), gguf_ctx_owned(true), model(model), llm_kv(model->arch) {}
+        gguf_ctx(gguf_init_empty()), gguf_ctx_owned(true), model(model), llm_kv(model->arch) {
+    GGML_ASSERT(llama_model_saver_supports_arch(model->arch));
+}

 llama_model_saver::llama_model_saver(enum llm_arch arch, struct gguf_context * gguf_ctx) :
        gguf_ctx(gguf_ctx == nullptr ? gguf_init_empty() : gguf_ctx), gguf_ctx_owned(gguf_ctx == nullptr), model(nullptr), llm_kv(arch) {}
@@ -105,7 +132,10 @@ void llama_model_saver::add_tensor(const struct ggml_tensor * tensor) {
        return;
    }
    if (gguf_find_tensor(gguf_ctx, tensor->name) >= 0) {
-        GGML_ASSERT(std::string(tensor->name) == "rope_freqs.weight"); // FIXME
+        const std::string tensor_name = tensor->name;
+        GGML_ASSERT(
+            tensor_name == "rope_freqs.weight" || tensor_name == "rope_factors_long.weight" ||
+            tensor_name == "rope_factors_short.weight"); // FIXME
        return;
    }
    gguf_add_tensor(gguf_ctx, tensor);
@@ -127,6 +157,7 @@ void llama_model_saver::add_kv_from_model() {
            tokens[id] = token_data.text;
            scores[id] = token_data.score;

+            // FIXME should this be treated as flags?
            switch(token_data.attr) {
                case LLAMA_TOKEN_ATTR_UNKNOWN:      token_types[id] = LLAMA_TOKEN_TYPE_UNKNOWN;      break;
                case LLAMA_TOKEN_ATTR_UNUSED:       token_types[id] = LLAMA_TOKEN_TYPE_UNUSED;       break;
@@ -134,6 +165,9 @@ void llama_model_saver::add_kv_from_model() {
                case LLAMA_TOKEN_ATTR_CONTROL:      token_types[id] = LLAMA_TOKEN_TYPE_CONTROL;      break;
                case LLAMA_TOKEN_ATTR_USER_DEFINED: token_types[id] = LLAMA_TOKEN_TYPE_USER_DEFINED; break;
                case LLAMA_TOKEN_ATTR_BYTE:         token_types[id] = LLAMA_TOKEN_TYPE_BYTE;         break;
+                // case LLAMA_TOKEN_ATTR_NORMALIZED:   ???
+                // case LLAMA_TOKEN_ATTR_LSTRIP:       ???
+                // case LLAMA_TOKEN_ATTR_RSTRIP:       ???
                case LLAMA_TOKEN_ATTR_UNDEFINED:
                default:                            token_types[id] = LLAMA_TOKEN_TYPE_UNDEFINED;    break;
            }
@@ -144,6 +178,19 @@ void llama_model_saver::add_kv_from_model() {
    add_kv(LLM_KV_GENERAL_ARCHITECTURE,              model->arch_name());
    // add_kv(LLM_KV_GENERAL_QUANTIZATION_VERSION,      ???);
    // add_kv(LLM_KV_GENERAL_ALIGNMENT,                 ???);
+    // add_kv(LLM_KV_GENERAL_FILE_TYPE,                 ???);
+    // add_kv(LLM_KV_GENERAL_SAMPLING_SEQUENCE,         ???);
+    // add_kv(LLM_KV_GENERAL_SAMPLING_TOP_K,            ???);
+    // add_kv(LLM_KV_GENERAL_SAMPLING_TOP_P,            ???);
+    // add_kv(LLM_KV_GENERAL_SAMPLING_MIN_P,            ???);
+    // add_kv(LLM_KV_GENERAL_SAMPLING_XTC_PROBABILITY,  ???);
+    // add_kv(LLM_KV_GENERAL_SAMPLING_XTC_THRESHOLD,    ???);
+    // add_kv(LLM_KV_GENERAL_SAMPLING_TEMP,             ???);
+    // add_kv(LLM_KV_GENERAL_SAMPLING_PENALTY_LAST_N,   ???);
+    // add_kv(LLM_KV_GENERAL_SAMPLING_PENALTY_REPEAT,   ???);
+    // add_kv(LLM_KV_GENERAL_SAMPLING_MIROSTAT,         ???);
+    // add_kv(LLM_KV_GENERAL_SAMPLING_MIROSTAT_TAU,     ???);
+    // add_kv(LLM_KV_GENERAL_SAMPLING_MIROSTAT_ETA,     ???);
    add_kv(LLM_KV_GENERAL_NAME,                      model->name);
    // add_kv(LLM_KV_GENERAL_AUTHOR,                    ???);
    // add_kv(LLM_KV_GENERAL_VERSION,                   ???);
@@ -163,17 +210,31 @@ void llama_model_saver::add_kv_from_model() {
    add_kv(LLM_KV_LEADING_DENSE_BLOCK_COUNT,         hparams.n_layer_dense_lead);
    add_kv(LLM_KV_FEED_FORWARD_LENGTH,               hparams.n_ff_arr, true);
    add_kv(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp);
-    add_kv(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
+    add_kv(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp);
+    add_kv(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_chexp);
+    add_kv(LLM_KV_SWIGLU_CLAMP_EXP,                  hparams.swiglu_clamp_exp);
+    add_kv(LLM_KV_SWIGLU_CLAMP_SHEXP,                hparams.swiglu_clamp_shexp);
    add_kv(LLM_KV_USE_PARALLEL_RESIDUAL,             hparams.use_par_res);
    // add_kv(LLM_KV_TENSOR_DATA_LAYOUT,                ???);
    add_kv(LLM_KV_EXPERT_COUNT,                      hparams.n_expert);
    add_kv(LLM_KV_EXPERT_USED_COUNT,                 hparams.n_expert_used);
    add_kv(LLM_KV_EXPERT_SHARED_COUNT,               hparams.n_expert_shared);
+    add_kv(LLM_KV_EXPERT_GROUP_COUNT,                hparams.n_expert_groups);
+    add_kv(LLM_KV_EXPERT_GROUP_USED_COUNT,           hparams.n_group_used);
    add_kv(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale);
+    add_kv(LLM_KV_EXPERT_WEIGHTS_NORM,               hparams.expert_weights_norm);
+    add_kv(LLM_KV_EXPERT_GATING_FUNC,                hparams.expert_gating_func);
+    add_kv(LLM_KV_EXPERT_GROUP_SCALE,                hparams.expert_group_scale);
+    add_kv(LLM_KV_EXPERTS_PER_GROUP,                 hparams.n_group_experts);
+    add_kv(LLM_KV_MOE_EVERY_N_LAYERS,                hparams.moe_every_n_layers);
+    add_kv(LLM_KV_NEXTN_PREDICT_LAYERS,              hparams.nextn_predict_layers);
+    add_kv(LLM_KV_NUM_DEEPSTACK_LAYERS,              hparams.n_deepstack_layers);
    add_kv(LLM_KV_POOLING_TYPE,                      uint32_t(hparams.pooling_type));
    add_kv(LLM_KV_LOGIT_SCALE,                       hparams.f_logit_scale);
    add_kv(LLM_KV_DECODER_START_TOKEN_ID,            hparams.dec_start_token_id);
+    add_kv(LLM_KV_DECODER_BLOCK_COUNT,               hparams.dec_n_layer);
    add_kv(LLM_KV_ATTN_LOGIT_SOFTCAPPING,            hparams.f_attn_logit_softcapping);
+    add_kv(LLM_KV_ROUTER_LOGIT_SOFTCAPPING,          hparams.f_router_logit_softcapping);
    add_kv(LLM_KV_FINAL_LOGIT_SOFTCAPPING,           hparams.f_final_logit_softcapping);
    add_kv(LLM_KV_SWIN_NORM,                         hparams.swin_norm);
    add_kv(LLM_KV_RESCALE_EVERY_N_LAYERS,            hparams.rescale_every_n_layers);
@@ -181,6 +242,9 @@ void llama_model_saver::add_kv_from_model() {
    add_kv(LLM_KV_TIME_DECAY_EXTRA_DIM,              hparams.time_decay_extra_dim);
    add_kv(LLM_KV_RESIDUAL_SCALE,                    hparams.f_residual_scale);
    add_kv(LLM_KV_EMBEDDING_SCALE,                   hparams.f_embedding_scale);
+    add_kv(LLM_KV_TOKEN_SHIFT_COUNT,                 hparams.token_shift_count);
+    add_kv(LLM_KV_INTERLEAVE_MOE_LAYER_STEP,         hparams.n_moe_layer_step);
+    // add_kv(LLM_KV_FULL_ATTENTION_INTERVAL,           ???);

    add_kv(LLM_KV_ATTENTION_HEAD_COUNT,              hparams.n_head_arr, true);
    add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV,           hparams.n_head_kv_arr, true);
@@ -188,22 +252,39 @@ void llama_model_saver::add_kv_from_model() {
    add_kv(LLM_KV_ATTENTION_CLAMP_KQV,               hparams.f_clamp_kqv);
    add_kv(LLM_KV_ATTENTION_KEY_LENGTH,              hparams.n_embd_head_k_full);
    add_kv(LLM_KV_ATTENTION_VALUE_LENGTH,            hparams.n_embd_head_v_full);
-    add_kv(LLM_KV_ATTENTION_KEY_LENGTH_SWA,          hparams.n_embd_head_k_swa);
-    add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_SWA,        hparams.n_embd_head_v_swa);
    add_kv(LLM_KV_ATTENTION_LAYERNORM_EPS,           hparams.f_norm_eps);
    add_kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
+    add_kv(LLM_KV_ATTENTION_GROUPNORM_EPS,           hparams.f_norm_group_eps);
+    add_kv(LLM_KV_ATTENTION_GROUPNORM_GROUPS,        hparams.n_norm_groups);
    add_kv(LLM_KV_ATTENTION_CAUSAL,                  hparams.causal_attn);
    add_kv(LLM_KV_ATTENTION_Q_LORA_RANK,             hparams.n_lora_q);
    add_kv(LLM_KV_ATTENTION_KV_LORA_RANK,            hparams.n_lora_kv);
+    add_kv(LLM_KV_ATTENTION_DECAY_LORA_RANK,         hparams.n_lora_decay);
+    add_kv(LLM_KV_ATTENTION_ICLR_LORA_RANK,          hparams.n_lora_iclr);
+    add_kv(LLM_KV_ATTENTION_VALUE_RESIDUAL_MIX_LORA_RANK, hparams.n_lora_value_res_mix);
+    add_kv(LLM_KV_ATTENTION_GATE_LORA_RANK,          hparams.n_lora_gate);
    add_kv(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,  hparams.n_rel_attn_bkts);
    add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW,          hparams.n_swa);
+    // add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN,  ???);
    add_kv(LLM_KV_ATTENTION_SCALE,                   hparams.f_attention_scale);
+    add_kv(LLM_KV_ATTENTION_OUTPUT_SCALE,            hparams.f_attn_out_scale);
+    add_kv(LLM_KV_ATTENTION_TEMPERATURE_LENGTH,      hparams.attn_temp_length);
+    add_kv(LLM_KV_ATTENTION_TEMPERATURE_SCALE,       hparams.f_attn_temp_scale);
+    add_kv(LLM_KV_ATTENTION_KEY_LENGTH_MLA,          hparams.n_embd_head_k_mla_impl);
+    add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_MLA,        hparams.n_embd_head_v_mla_impl);
+    add_kv(LLM_KV_ATTENTION_KEY_LENGTH_SWA,          hparams.n_embd_head_k_swa);
+    add_kv(LLM_KV_ATTENTION_VALUE_LENGTH_SWA,        hparams.n_embd_head_v_swa);
+    add_kv(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT,      hparams.indexer_n_head);
+    add_kv(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH,      hparams.indexer_head_size);
+    add_kv(LLM_KV_ATTENTION_INDEXER_TOP_K,           hparams.indexer_top_k);

    const float rope_scaling_factor = hparams.rope_freq_scale_train == 1.0f ? 0.0f : 1.0f/hparams.rope_freq_scale_train;

    add_kv(LLM_KV_ROPE_DIMENSION_COUNT,              hparams.n_rot_full);
    add_kv(LLM_KV_ROPE_DIMENSION_COUNT_SWA,          hparams.n_rot_swa);
+    add_kv(LLM_KV_ROPE_DIMENSION_SECTIONS,           hparams.rope_sections);
    add_kv(LLM_KV_ROPE_FREQ_BASE,                    hparams.rope_freq_base_train);
+    add_kv(LLM_KV_ROPE_FREQ_BASE_SWA,                hparams.rope_freq_base_train_swa);
    // add_kv(LLM_KV_ROPE_SCALE_LINEAR,                 rope_scaling_factor); // old name
    add_kv(LLM_KV_ROPE_SCALING_TYPE,                 llama_rope_scaling_type_name(hparams.rope_scaling_type_train));
    add_kv(LLM_KV_ROPE_SCALING_FACTOR,               rope_scaling_factor);
@@ -211,6 +292,10 @@ void llama_model_saver::add_kv_from_model() {
    add_kv(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,         hparams.n_ctx_orig_yarn);
    add_kv(LLM_KV_ROPE_SCALING_FINETUNED,            hparams.rope_finetuned);
    add_kv(LLM_KV_ROPE_SCALING_YARN_LOG_MUL,         hparams.rope_yarn_log_mul);
+    add_kv(LLM_KV_ROPE_SCALING_YARN_EXT_FACTOR,      hparams.yarn_ext_factor);
+    add_kv(LLM_KV_ROPE_SCALING_YARN_ATTN_FACTOR,     hparams.yarn_attn_factor);
+    add_kv(LLM_KV_ROPE_SCALING_YARN_BETA_FAST,       hparams.yarn_beta_fast);
+    add_kv(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW,       hparams.yarn_beta_slow);

    // TODO: implement split file support
    // add_kv(LLM_KV_SPLIT_NO,                          ???);
@@ -221,8 +306,11 @@ void llama_model_saver::add_kv_from_model() {
    add_kv(LLM_KV_SSM_CONV_KERNEL,                   hparams.ssm_d_conv);
    add_kv(LLM_KV_SSM_STATE_SIZE,                    hparams.ssm_d_state);
    add_kv(LLM_KV_SSM_TIME_STEP_RANK,                hparams.ssm_dt_rank);
+    add_kv(LLM_KV_SSM_GROUP_COUNT,                   hparams.ssm_n_group);
    add_kv(LLM_KV_SSM_DT_B_C_RMS,                    hparams.ssm_dt_b_c_rms);

+    add_kv(LLM_KV_KDA_HEAD_DIM,                      hparams.n_embd_head_kda);
+
    add_kv(LLM_KV_WKV_HEAD_SIZE,                     hparams.wkv_head_size);

    add_kv(LLM_KV_TOKENIZER_MODEL,                   vocab.get_tokenizer_model());
@@ -260,15 +348,39 @@ void llama_model_saver::add_kv_from_model() {
    // TODO: implement LoRA support
    // add_kv(LLM_KV_ADAPTER_TYPE,                      ???);
    // add_kv(LLM_KV_ADAPTER_LORA_ALPHA,                ???);
+    // add_kv(LLM_KV_ADAPTER_LORA_TASK_NAME,            ???);
+    // add_kv(LLM_KV_ADAPTER_LORA_PROMPT_PREFIX,        ???);
+    // add_kv(LLM_KV_ADAPTER_ALORA_INVOCATION_TOKENS,   ???);
+
+    add_kv(LLM_KV_POSNET_EMBEDDING_LENGTH,           hparams.posnet.n_embd);
+    add_kv(LLM_KV_POSNET_BLOCK_COUNT,                hparams.posnet.n_layer);
+
+    add_kv(LLM_KV_CONVNEXT_EMBEDDING_LENGTH,         hparams.convnext.n_embd);
+    add_kv(LLM_KV_CONVNEXT_BLOCK_COUNT,              hparams.convnext.n_layer);
+
+    add_kv(LLM_KV_CLASSIFIER_OUTPUT_LABELS,          model->classifier_labels);
+
+    add_kv(LLM_KV_SHORTCONV_L_CACHE,                 hparams.n_shortconv_l_cache);
+
+    add_kv(LLM_KV_XIELU_ALPHA_N,                     hparams.xielu_alpha_n);
+    add_kv(LLM_KV_XIELU_ALPHA_P,                     hparams.xielu_alpha_p);
+    add_kv(LLM_KV_XIELU_BETA,                        hparams.xielu_beta);
+    add_kv(LLM_KV_XIELU_EPS,                         hparams.xielu_eps);

    // deprecated
    // add_kv(LLM_KV_TOKENIZER_PREFIX_ID,               ???);
    // add_kv(LLM_KV_TOKENIZER_SUFFIX_ID,               ???);
    // add_kv(LLM_KV_TOKENIZER_MIDDLE_ID,               ???);
+
+    add_kv(LLM_KV_DENSE_2_FEAT_IN,                   hparams.dense_2_feat_in);
+    add_kv(LLM_KV_DENSE_2_FEAT_OUT,                  hparams.dense_2_feat_out);
+    add_kv(LLM_KV_DENSE_3_FEAT_IN,                   hparams.dense_3_feat_in);
+    add_kv(LLM_KV_DENSE_3_FEAT_OUT,                  hparams.dense_3_feat_out);
 }

 void llama_model_saver::add_tensors_from_model() {
-    if (std::string(model->output->name) != std::string(model->tok_embd->name)) {
+    if (model->output != nullptr &&
+            std::string(model->output->name) != std::string(model->tok_embd->name)) {
        add_tensor(model->tok_embd); // some models use the same tensor for tok_embd and output
    }
    add_tensor(model->type_embd);
@@ -297,3 +409,6 @@ void llama_model_saver::save(const std::string & path_model) {
    gguf_write_to_file(gguf_ctx, path_model.c_str(), false);
 }

+void llama_model_saver::save(FILE * file) {
+    gguf_write_to_file_ptr(gguf_ctx, file, false);
+}
@@ -6,6 +6,9 @@

 #include <vector>

+// FIXME temporary function for better error messages
+bool llama_model_saver_supports_arch(llm_arch arch);
+
 struct llama_model_saver {
    struct gguf_context * gguf_ctx = nullptr;
    const bool gguf_ctx_owned;
@@ -37,4 +40,5 @@ struct llama_model_saver {
    void add_tensors_from_model();

    void save(const std::string & path_model);
+    void save(FILE * file);
 };
--- a/Show More
+++ b/Show More