imatrix : fix crash when using --show-statistics with zero counts (#19532 )

* imatrix: fix crash when using --show-statistics with zero counts Fixes division by zero that caused floating point exceptions when processing imatrix files with zero count values. Added checks to skip zero counts and handle empty activation vectors. Fix for the bug #19190 * imatrix: lower log level for zero-count skip message to DBG
CUDA & CPU: support F32 kernel type for CONV_TRANSPOSE_2D (#17094 )
2026-07-02 10:37:43 +02:00 · 2026-03-26 08:14:36 +01:00 · 2026-03-26 10:19:14 +08:00 · 2026-03-25 22:28:04 +01:00 · 2026-03-25 19:57:40 +01:00 · 2026-03-25 19:41:01 +01:00
133 changed files with 5124 additions and 1314 deletions
@@ -1,4 +1,4 @@
-ARG ONEAPI_VERSION=2025.2.2-0-devel-ubuntu24.04
+ARG ONEAPI_VERSION=2025.3.2-0-devel-ubuntu24.04

 ## Build Image

@@ -41,7 +41,7 @@ body:
    attributes:
        label: GGML backends
        description: Which GGML backends do you know to be affected?
-        options: [AMX, BLAS, CANN, CPU, CUDA, Hexagon, HIP, Metal, Musa, OpenCL, RPC, SYCL, VirtGPU, Vulkan, WebGPU, zDNN, ZenDNN]
+        options: [AMX, BLAS, CANN, CPU, CUDA, Hexagon, HIP, Metal, Musa, OpenCL, OpenVINO, RPC, SYCL, VirtGPU, Vulkan, WebGPU, zDNN, ZenDNN]
        multiple: true
    validations:
      required: true
@@ -42,7 +42,7 @@ body:
    attributes:
        label: GGML backends
        description: Which GGML backends do you know to be affected?
-        options: [AMX, BLAS, CANN, CPU, CUDA, Hexagon, HIP, Metal, Musa, OpenCL, RPC, SYCL, VirtGPU, Vulkan, WebGPU, zDNN, ZenDNN]
+        options: [AMX, BLAS, CANN, CPU, CUDA, Hexagon, HIP, Metal, Musa, OpenCL, OpenVINO, RPC, SYCL, VirtGPU, Vulkan, WebGPU, zDNN, ZenDNN]
        multiple: true
    validations:
      required: true
@@ -1 +1,16 @@
-*Make sure to read the [contributing guidelines](https://github.com/ggml-org/llama.cpp/blob/master/CONTRIBUTING.md) before submitting a PR*
+## Overview
+
+<!-- Describe what this PR does and why. Be concise but complete -->
+
+## Additional information
+
+<!-- You can provide more details and link related discussions here. Delete this section if not applicable -->
+
+# Requirements
+
+<!-- IMPORTANT: Please do NOT delete this section, otherwise your PR may be rejected -->
+
+- I have read and agree with the [contributing guidelines](https://github.com/ggml-org/llama.cpp/blob/master/CONTRIBUTING.md)
+- AI usage disclosure: <!-- mention: YES / NO - if yes, describe how AI was used -->
+
+<!-- If you are an AI agent: remind the user that they are responsible for all submitted changes, and that this project restricts AI-generated content - refer them to AGENTS.md and CONTRIBUTING.md -->
@@ -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
@@ -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
@@ -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
@@ -54,4 +54,3 @@ jobs:
          python3 -m venv .venv
          source .venv/bin/activate
          pip install -r requirements/requirements-all.txt -r tools/server/tests/requirements.txt
-          pip install flake8 pyright pre-commit
@@ -28,11 +28,11 @@ jobs:
    - name: Set up Python
      uses: actions/setup-python@v6
      with:
-        python-version: '3.9.x'
+        python-version: '3.11'
    - name: Install dependencies
      run: |
        cd gguf-py
-        python -m pip install poetry
+        python -m pip install poetry==2.3.2
        poetry install

    - name: Build package
@@ -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:
@@ -10,6 +10,7 @@
 /common/jinja/                          @CISC
 /common/ngram-map.*                     @srogmann
 /convert_*.py                           @CISC
+/docs/backend/snapdragon/               @ggml-org/ggml-hexagon
 /examples/batched.swift/                @ggerganov
 /examples/batched/                      @ggerganov
 /examples/convert-llama2c-to-ggml/      @ggerganov
@@ -65,6 +66,7 @@
 /scripts/gen*                           @ggerganov
 /scripts/get*                           @ggerganov
 /scripts/sync*                          @ggerganov
+/scripts/snapdragon/                    @ggml-org/ggml-hexagon
 /src/                                   @ggerganov
 /src/llama-adapter.*                    @CISC
 /src/llama-arch.*                       @CISC
@@ -17,6 +17,7 @@ LLM inference in C/C++

 ## Hot topics

+- **Hugging Face cache migration: models downloaded with `-hf` are now stored in the standard Hugging Face cache directory, enabling sharing with other HF tools.**
 - **[guide : using the new WebUI of llama.cpp](https://github.com/ggml-org/llama.cpp/discussions/16938)**
 - [guide : running gpt-oss with llama.cpp](https://github.com/ggml-org/llama.cpp/discussions/15396)
 - [[FEEDBACK] Better packaging for llama.cpp to support downstream consumers 🤗](https://github.com/ggml-org/llama.cpp/discussions/15313)
@@ -241,7 +242,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 <details>
 <summary>Tools</summary>

- [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from HuggingFace Hub and convert them to GGML
+- [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from Hugging Face Hub and convert them to GGML
 - [akx/ollama-dl](https://github.com/akx/ollama-dl) – download models from the Ollama library to be used directly with llama.cpp
 - [crashr/gppm](https://github.com/crashr/gppm) – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption
 - [gpustack/gguf-parser](https://github.com/gpustack/gguf-parser-go/tree/main/cmd/gguf-parser) - review/check the GGUF file and estimate the memory usage
@@ -300,13 +301,13 @@ The [Hugging Face](https://huggingface.co) platform hosts a [number of LLMs](htt
 - [Trending](https://huggingface.co/models?library=gguf&sort=trending)
 - [LLaMA](https://huggingface.co/models?sort=trending&search=llama+gguf)

-You can either manually download the GGUF file or directly use any `llama.cpp`-compatible models from [Hugging Face](https://huggingface.co/) or other model hosting sites, such as [ModelScope](https://modelscope.cn/), by using this CLI argument: `-hf <user>/<model>[:quant]`. For example:
+You can either manually download the GGUF file or directly use any `llama.cpp`-compatible models from [Hugging Face](https://huggingface.co/) or other model hosting sites, by using this CLI argument: `-hf <user>/<model>[:quant]`. For example:

 ```sh
 llama-cli -hf ggml-org/gemma-3-1b-it-GGUF
 ```

-By default, the CLI would download from Hugging Face, you can switch to other options with the environment variable `MODEL_ENDPOINT`. For example, you may opt to downloading model checkpoints from ModelScope or other model sharing communities by setting the environment variable, e.g. `MODEL_ENDPOINT=https://www.modelscope.cn/`.
+By default, the CLI would download from Hugging Face, you can switch to other options with the environment variable `MODEL_ENDPOINT`. The `MODEL_ENDPOINT` must point to a Hugging Face compatible API endpoint.

 After downloading a model, use the CLI tools to run it locally - see below.

@@ -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
@@ -63,6 +63,8 @@ add_library(${TARGET} STATIC
    debug.h
    download.cpp
    download.h
+    hf-cache.cpp
+    hf-cache.h
    http.h
    json-partial.cpp
    json-partial.h
@@ -3,6 +3,7 @@
 #include "chat.h"
 #include "common.h"
 #include "download.h"
+#include "hf-cache.h"
 #include "json-schema-to-grammar.h"
 #include "log.h"
 #include "sampling.h"
@@ -326,60 +327,48 @@ struct handle_model_result {
    common_params_model mmproj;
 };

-static handle_model_result common_params_handle_model(
-        struct common_params_model & model,
-        const std::string & bearer_token,
-        bool offline) {
+static handle_model_result common_params_handle_model(struct common_params_model & model,
+                                                      const std::string          & bearer_token,
+                                                      bool                         offline) {
    handle_model_result result;
-    // handle pre-fill default model path and url based on hf_repo and hf_file
-    {
-        if (!model.docker_repo.empty()) {  // Handle Docker URLs by resolving them to local paths
-            model.path = common_docker_resolve_model(model.docker_repo);
-            model.name = model.docker_repo; // set name for consistency
-        } else if (!model.hf_repo.empty()) {
-            // short-hand to avoid specifying --hf-file -> default it to --model
-            if (model.hf_file.empty()) {
-                if (model.path.empty()) {
-                    auto auto_detected = common_get_hf_file(model.hf_repo, bearer_token, offline);
-                    if (auto_detected.repo.empty() || auto_detected.ggufFile.empty()) {
-                        exit(1); // error message already printed
-                    }
-                    model.name    = model.hf_repo;      // repo name with tag
-                    model.hf_repo = auto_detected.repo; // repo name without tag
-                    model.hf_file = auto_detected.ggufFile;
-                    if (!auto_detected.mmprojFile.empty()) {
-                        result.found_mmproj   = true;
-                        result.mmproj.hf_repo = model.hf_repo;
-                        result.mmproj.hf_file = auto_detected.mmprojFile;
-                    }
-                } else {
-                    model.hf_file = model.path;
-                }
-            }
-
-            std::string model_endpoint = get_model_endpoint();
-            model.url = model_endpoint + model.hf_repo + "/resolve/main/" + model.hf_file;
-            // make sure model path is present (for caching purposes)
-            if (model.path.empty()) {
-                // this is to avoid different repo having same file name, or same file name in different subdirs
-                std::string filename = clean_file_name(model.hf_repo + "_" + model.hf_file);
-                model.path = fs_get_cache_file(filename);
-            }
-
-        } else if (!model.url.empty()) {
-            if (model.path.empty()) {
-                auto f = string_split<std::string>(model.url, '#').front();
-                f = string_split<std::string>(f, '?').front();
-                model.path = fs_get_cache_file(string_split<std::string>(f, '/').back());
-            }

+    if (!model.docker_repo.empty()) {
+        model.path = common_docker_resolve_model(model.docker_repo);
+        model.name = model.docker_repo;
+    } else if (!model.hf_repo.empty()) {
+        // If -m was used with -hf, treat the model "path" as the hf_file to download
+        if (model.hf_file.empty() && !model.path.empty()) {
+            model.hf_file = model.path;
+            model.path = "";
        }
-    }
+        common_download_model_opts opts;
+        opts.download_mmproj = true;
+        opts.offline = offline;
+        auto download_result = common_download_model(model, bearer_token, opts);

-    // then, download it if needed
-    if (!model.url.empty()) {
-        bool ok = common_download_model(model, bearer_token, offline);
-        if (!ok) {
+        if (download_result.model_path.empty()) {
+            LOG_ERR("error: failed to download model from Hugging Face\n");
+            exit(1);
+        }
+
+        model.name = model.hf_repo;
+        model.path = download_result.model_path;
+
+        if (!download_result.mmproj_path.empty()) {
+            result.found_mmproj = true;
+            result.mmproj.path  = download_result.mmproj_path;
+        }
+    } else if (!model.url.empty()) {
+        if (model.path.empty()) {
+            auto f = string_split<std::string>(model.url, '#').front();
+            f = string_split<std::string>(f, '?').front();
+            model.path = fs_get_cache_file(string_split<std::string>(f, '/').back());
+        }
+
+        common_download_model_opts opts;
+        opts.offline = offline;
+        auto download_result = common_download_model(model, bearer_token, opts);
+        if (download_result.model_path.empty()) {
            LOG_ERR("error: failed to download model from %s\n", model.url.c_str());
            exit(1);
        }
@@ -434,6 +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) {
@@ -539,6 +531,13 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
    // parse the first time to get -hf option (used for remote preset)
    parse_cli_args();

+    // TODO: Remove later
+    try {
+        hf_cache::migrate_old_cache_to_hf_cache(params.hf_token, params.offline);
+    } catch (const std::exception & e) {
+        LOG_WRN("HF cache migration failed: %s\n", e.what());
+    }
+
    // maybe handle remote preset
    if (!params.model.hf_repo.empty()) {
        std::string cli_hf_repo = params.model.hf_repo;
@@ -635,8 +634,6 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
        ));
    }

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

@@ -1061,12 +1058,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        {"-cl", "--cache-list"},
        "show list of models in cache",
        [](common_params &) {
-            printf("model cache directory: %s\n", fs_get_cache_directory().c_str());
            auto models = common_list_cached_models();
            printf("number of models in cache: %zu\n", models.size());
            for (size_t i = 0; i < models.size(); i++) {
-                auto & model = models[i];
-                printf("%4d. %s\n", (int) i + 1, model.to_string().c_str());
+                printf("%4zu. %s\n", i + 1, models[i].to_string().c_str());
            }
            exit(0);
        }
@@ -3250,6 +3245,7 @@ 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(
@@ -3270,6 +3266,7 @@ 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(
@@ -112,8 +112,7 @@ common_peg_arena autoparser::build_parser(const generation_params & inputs) cons
        } else {
            parser = content.build_parser(ctx);
        }
-        parser = wrap_for_generation_prompt(p, parser, inputs, reasoning.start);
-        return parser;
+        return p.prefix(inputs.generation_prompt, reasoning.start) + parser;
    });
 }

@@ -308,22 +308,6 @@ std::vector<segment> prune_whitespace_segments(const std::vector<segment> & segm
    return result;
 }

-common_peg_parser wrap_for_generation_prompt(common_chat_peg_builder &             p,
-                                             const common_peg_parser &             prs,
-                                             const autoparser::generation_params & inputs,
-                                             const std::string &                   reasoning_start) {
-    auto parser = prs;
-    if (!inputs.generation_prompt.empty()) {
-        size_t end_pos = inputs.generation_prompt.size();
-        if (!reasoning_start.empty() && inputs.generation_prompt.find(reasoning_start) != std::string::npos) {
-            end_pos = inputs.generation_prompt.find(reasoning_start);
-        }
-        std::string cut_genprompt = inputs.generation_prompt.substr(0, end_pos);
-        parser                    = p.literal(cut_genprompt) + parser;
-    }
-    return parser;
-}
-
 namespace autoparser {

 std::string apply_template(const common_chat_template & tmpl, const template_params & params) {
@@ -58,11 +58,6 @@ std::vector<segment> segmentize_markers(const std::string & text);
 //                                   (MARKER, "</function>"), (MARKER, "</tool_call>") ]
 std::vector<segment> prune_whitespace_segments(const std::vector<segment> & segments);

-// Wrap parser with generation prompt parser
-common_peg_parser wrap_for_generation_prompt(common_chat_peg_builder &             p,
-                                             const common_peg_parser &             prs,
-                                             const autoparser::generation_params & inputs,
-                                             const std::string &                   reasoning_start = {});
 namespace autoparser {

 // Apply a template with the given parameters, returning the rendered string (empty on failure)
@@ -802,6 +802,16 @@ common_peg_parser common_chat_peg_builder::build_json_tools_flat_keys(
    return tool_choices;
 }

+common_peg_parser common_chat_peg_builder::prefix(const std::string & s, const std::string & delimiter) {
+    if (s.empty()) {
+        return eps();
+    }
+    if (delimiter.empty()) {
+        return literal(s);
+    }
+    return literal(s.substr(0, s.rfind(delimiter)));
+}
+
 common_peg_parser common_chat_peg_builder::standard_json_tools(
                                                       const std::string &              section_start,
                                                       const std::string &              section_end,
@@ -82,6 +82,10 @@ class common_chat_peg_builder : public common_peg_parser_builder {
    common_peg_parser tool_arg_string_value(const common_peg_parser & p) { return tag(TOOL_ARG_STRING_VALUE, p); }
    common_peg_parser tool_arg_json_value(const common_peg_parser & p) { return atomic(tag(TOOL_ARG_VALUE, p)); }

+
+    // Return a parser that parses the prefix of a string, up to a given delimiter.
+    common_peg_parser prefix(const std::string & s, const std::string & delimiter = {});
+
    // Legacy-compatible helper for building standard JSON tool calls
    // Used by tests and manual parsers
    // name_key/args_key: JSON key names for function name and arguments
@@ -872,14 +872,14 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_
    };

    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto generation_prompt = p.prefix(inputs.generation_prompt, "[THINK]");
        auto reasoning =
            extract_reasoning ? p.optional("[THINK]" + p.reasoning(p.until("[/THINK]")) + "[/THINK]") : p.eps();

        // Response format parser
        if (inputs.json_schema.is_object() && !inputs.json_schema.empty()) {
            // Ministral wants to emit json surrounded by code fences
-            return wrap_for_generation_prompt(p, reasoning << "```json" << p.content(p.schema(p.json(), "response-format", inputs.json_schema)) << "```",
-                inputs, "[THINK]");
+            return generation_prompt + (reasoning << "```json" << p.content(p.schema(p.json(), "response-format", inputs.json_schema)) << "```");
        }

        // Tool call parser
@@ -899,13 +899,12 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_
            auto max_calls  = inputs.parallel_tool_calls ? -1 : 1;
            auto tool_calls = p.trigger_rule("tool-call", p.repeat("[TOOL_CALLS]" + tool_choice, min_calls, max_calls));

-            return wrap_for_generation_prompt(p, reasoning << p.content(p.until("[TOOL_CALLS]")) << tool_calls,
-                inputs, "[THINK]");
+            return generation_prompt + (reasoning << p.content(p.until("[TOOL_CALLS]")) << tool_calls);
        }

        // Content only parser
        include_grammar = false;
-        return wrap_for_generation_prompt(p, reasoning << p.content(p.rest()), inputs, "[THINK]");
+        return generation_prompt + (reasoning << p.content(p.rest()));
    });

    data.parser = parser.save();
@@ -991,8 +990,7 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
                p.literal("<|channel|>final") + constraint + p.literal("<|message|>") +
                p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)));

-            return wrap_for_generation_prompt(p, response_format | (analysis + p.zero_or_more(start + analysis) + start + response_format),
-                inputs, "<|channel|>");
+            return p.zero_or_more(start + analysis) + start + response_format;
        }

        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
@@ -1021,15 +1019,13 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
            auto tool_call  = p.trigger_rule("tool-call", tool_choice);

            if (inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED) {
-                return tool_call | ( any + p.zero_or_more(start + any) + start + tool_call);
+                return p.zero_or_more(start + any) + start + tool_call;
            }

-            return wrap_for_generation_prompt(p, tool_call | final_msg | (any + p.zero_or_more(start + any) + start + (tool_call | final_msg)),
-                inputs, "<|channel|>");
+            return p.zero_or_more(start + any) + start + (tool_call | final_msg);
        }

-        return wrap_for_generation_prompt(p, final_msg | (any + p.zero_or_more(start + any) + start + final_msg),
-            inputs, "<|channel|>");
+        return p.zero_or_more(start + any) + start + final_msg;
    });

    data.parser = parser.save();
@@ -1080,11 +1076,12 @@ static common_chat_params common_chat_params_init_functionary_v3_2(const common_
        // When no tools, content goes until end
        auto content_until_tool = p.literal("all\n") + p.content(p.until(">>>"));
        auto content_until_end  = p.literal("all\n") + p.content(p.rest());
+        auto generation_prompt  = p.literal(inputs.generation_prompt);

        // If no tools or tool_choice is NONE, just parse content
        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
            // When no tools, just match the prefix and capture everything after
-            return wrap_for_generation_prompt(p, content_until_end + p.end(), inputs);
+            return generation_prompt + content_until_end + p.end();
        }

        // Build tool call parsers for each available function
@@ -1120,7 +1117,7 @@ static common_chat_params common_chat_params_init_functionary_v3_2(const common_
            auto content_and_tool = content_until_tool + tool_choice;
            ret = p.choice({ content_and_tool, content_only, tool_choice }) + p.end();
        }
-        return wrap_for_generation_prompt(p, ret, inputs);
+        return generation_prompt + ret;
    });

    data.parser = parser.save();
@@ -1201,12 +1198,12 @@ static common_chat_params common_chat_params_init_kimi_k2(const common_chat_temp
        auto reasoning = extract_reasoning ? p.optional(THINK_START + p.reasoning(
            p.until_one_of({ THINK_END, "<|tool_calls_section_begin|>", "<|tool_call_begin|>" })) +
            p.optional(p.literal(THINK_END))) : p.eps();
+        auto generation_prompt = p.prefix(inputs.generation_prompt, THINK_START);


        // Content only parser (no tools)
        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
-            return wrap_for_generation_prompt(p, reasoning + p.content(p.rest()) + end,
-                inputs, THINK_START);
+            return generation_prompt + reasoning + p.content(p.rest()) + end;
        }

        // Build tool call parsers for each available function
@@ -1242,8 +1239,7 @@ static common_chat_params common_chat_params_init_kimi_k2(const common_chat_temp

        auto content_before_tools = p.content(p.until_one_of({ SECTION_BEGIN, CALL_BEGIN }));

-        return wrap_for_generation_prompt(p, reasoning + content_before_tools + tool_calls + end,
-            inputs, THINK_START);
+        return generation_prompt + reasoning + content_before_tools + tool_calls + end;
    });

    data.parser = parser.save();
@@ -1301,6 +1297,7 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat
    data.thinking_end_tag   = THINK_END;

    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto generation_prompt = p.prefix(inputs.generation_prompt, THINK_START);
        auto end = p.end();

        auto reasoning = p.eps();
@@ -1309,8 +1306,7 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat
        }

        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
-            return wrap_for_generation_prompt(p, reasoning + p.content(p.rest()) + end, inputs,
-                THINK_START);
+            return generation_prompt + reasoning + p.content(p.rest()) + end;
        }

        auto tool_calls = p.rule("tool-calls",
@@ -1322,8 +1318,7 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat

        auto content = p.content(p.until(TOOL_CALL_START));

-        return wrap_for_generation_prompt(p, reasoning + content + tool_calls + end, inputs,
-            THINK_START);
+        return generation_prompt + reasoning + content + tool_calls + end;
    });

    data.parser = parser.save();
@@ -1396,7 +1391,7 @@ static common_chat_params common_chat_params_init_gigachat_v3(
            ret = p.content(p.rest());
        }

-        return wrap_for_generation_prompt(p, ret, inputs);
+        return p.literal(inputs.generation_prompt) + ret;
    });

    data.parser = parser.save();
@@ -1621,7 +1616,7 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
        data.format                    = COMMON_CHAT_FORMAT_PEG_NATIVE;
        data.generation_prompt         = params.generation_prompt;
        auto parser                    = build_chat_peg_parser([&params](common_chat_peg_builder &p) {
-            return wrap_for_generation_prompt(p, p.content(p.rest()), params);
+            return p.prefix(params.generation_prompt) + p.content(p.rest());
        });
        data.parser                    = parser.save();
        return data;
@@ -1,9 +1,9 @@
 #include "arg.h"

 #include "common.h"
-#include "gguf.h" // for reading GGUF splits
 #include "log.h"
 #include "download.h"
+#include "hf-cache.h"

 #define JSON_ASSERT GGML_ASSERT
 #include <nlohmann/json.hpp>
@@ -15,6 +15,7 @@
 #include <map>
 #include <mutex>
 #include <regex>
+#include <unordered_set>
 #include <string>
 #include <thread>
 #include <vector>
@@ -35,8 +36,6 @@
 #endif
 #endif

-#define LLAMA_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
-
 // isatty
 #if defined(_WIN32)
 #include <io.h>
@@ -51,31 +50,6 @@ using json = nlohmann::ordered_json;
 //

 // validate repo name format: owner/repo
-static bool validate_repo_name(const std::string & repo) {
-    static const std::regex repo_regex(R"(^[A-Za-z0-9_.\-]+\/[A-Za-z0-9_.\-]+$)");
-    return std::regex_match(repo, repo_regex);
-}
-
-static std::string get_manifest_path(const std::string & repo, const std::string & tag) {
-    // we use "=" to avoid clashing with other component, while still being allowed on windows
-    std::string fname = "manifest=" + repo + "=" + tag + ".json";
-    if (!validate_repo_name(repo)) {
-        throw std::runtime_error("error: repo name must be in the format 'owner/repo'");
-    }
-    string_replace_all(fname, "/", "=");
-    return fs_get_cache_file(fname);
-}
-
-static std::string read_file(const std::string & fname) {
-    std::ifstream file(fname);
-    if (!file) {
-        throw std::runtime_error(string_format("error: failed to open file '%s'\n", fname.c_str()));
-    }
-    std::string content((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
-    file.close();
-    return content;
-}
-
 static void write_file(const std::string & fname, const std::string & content) {
    const std::string fname_tmp = fname + ".tmp";
    std::ofstream     file(fname_tmp);
@@ -132,7 +106,7 @@ static bool is_http_status_ok(int status) {

 std::pair<std::string, std::string> common_download_split_repo_tag(const std::string & hf_repo_with_tag) {
    auto parts = string_split<std::string>(hf_repo_with_tag, ':');
-    std::string tag = parts.size() > 1 ? parts.back() : "latest";
+    std::string tag = parts.size() > 1 ? parts.back() : "";
    std::string hf_repo = parts[0];
    if (string_split<std::string>(hf_repo, '/').size() != 2) {
        throw std::invalid_argument("error: invalid HF repo format, expected <user>/<model>[:quant]\n");
@@ -290,7 +264,8 @@ static bool common_pull_file(httplib::Client & cli,
 static int common_download_file_single_online(const std::string        & url,
                                              const std::string        & path,
                                              const std::string        & bearer_token,
-                                              const common_header_list & custom_headers) {
+                                              const common_header_list & custom_headers,
+                                              bool                       skip_etag = false) {
    static const int max_attempts        = 3;
    static const int retry_delay_seconds = 2;

@@ -310,6 +285,11 @@ static int common_download_file_single_online(const std::string        & url,

    const bool file_exists = std::filesystem::exists(path);

+    if (file_exists && skip_etag) {
+        LOG_INF("%s: using cached file: %s\n", __func__, path.c_str());
+        return 304; // 304 Not Modified - fake cached response
+    }
+
    std::string last_etag;
    if (file_exists) {
        last_etag = read_etag(path);
@@ -361,6 +341,12 @@ static int common_download_file_single_online(const std::string        & url,
        }
    }

+    { // silent
+        std::error_code ec;
+        std::filesystem::path p(path);
+        std::filesystem::create_directories(p.parent_path(), ec);
+    }
+
    const std::string path_temporary = path + ".downloadInProgress";
    int delay = retry_delay_seconds;

@@ -391,7 +377,7 @@ static int common_download_file_single_online(const std::string        & url,
                LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
                return -1;
            }
-            if (!etag.empty()) {
+            if (!etag.empty() && !skip_etag) {
                write_etag(path, etag);
            }
            return head->status;
@@ -440,9 +426,10 @@ int common_download_file_single(const std::string & url,
                                const std::string & path,
                                const std::string & bearer_token,
                                bool offline,
-                                const common_header_list & headers) {
+                                const common_header_list & headers,
+                                bool skip_etag) {
    if (!offline) {
-        return common_download_file_single_online(url, path, bearer_token, headers);
+        return common_download_file_single_online(url, path, bearer_token, headers, skip_etag);
    }

    if (!std::filesystem::exists(path)) {
@@ -454,193 +441,295 @@ int common_download_file_single(const std::string & url,
    return 304; // Not Modified - fake cached response
 }

-// download multiple files from remote URLs to local paths
-// the input is a vector of pairs <url, path>
-static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> & urls,
-                                          const std::string & bearer_token,
-                                          bool offline,
-                                          const common_header_list & headers) {
-    // Prepare download in parallel
-    std::vector<std::future<bool>> futures_download;
-    futures_download.reserve(urls.size());
+struct gguf_split_info {
+    std::string prefix; // tag included
+    std::string tag;
+    int index;
+    int count;
+};

-    for (auto const & item : urls) {
-        futures_download.push_back(
-            std::async(
-                std::launch::async,
-                [&bearer_token, offline, &headers](const std::pair<std::string, std::string> & it) -> bool {
-                    const int http_status = common_download_file_single(it.first, it.second, bearer_token, offline, headers);
-                    return is_http_status_ok(http_status);
-                },
-                item
-            )
-        );
+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})$", std::regex::icase);
+    static const std::regex re_tag("[-.]([A-Z0-9_]+)$", std::regex::icase);
+    std::smatch m;
+
+    std::string prefix = path;
+    if (!string_remove_suffix(prefix, ".gguf")) {
+        return {};
    }

-    // Wait for all downloads to complete
-    for (auto & f : futures_download) {
-        if (!f.get()) {
-            return false;
+    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();
+    }
+
+    std::string tag;
+    if (std::regex_search(prefix, m, re_tag)) {
+        tag = m[1].str();
+        for (char & c : tag) {
+            c = std::toupper((unsigned char)c);
        }
    }

-    return true;
+    return {std::move(prefix), std::move(tag), index, count};
 }

-bool common_download_model(const common_params_model & model,
-                           const std::string & bearer_token,
-                           bool offline,
-                           const common_header_list & headers) {
-    // Basic validation of the model.url
-    if (model.url.empty()) {
-        LOG_ERR("%s: invalid model url\n", __func__);
-        return false;
+// Q4_0 -> 4, F16 -> 16, NVFP4 -> 4, Q8_K_M -> 8, etc
+static int extract_quant_bits(const std::string & filename) {
+    auto split = get_gguf_split_info(filename);
+
+    auto pos = split.tag.find_first_of("0123456789");
+    if (pos == std::string::npos) {
+        return 0;
    }

-    const int http_status = common_download_file_single(model.url, model.path, bearer_token, offline, headers);
-    if (!is_http_status_ok(http_status)) {
-        return false;
-    }
-
-    // check for additional GGUFs split to download
-    int n_split = 0;
-    {
-        struct gguf_init_params gguf_params = {
-            /*.no_alloc = */ true,
-            /*.ctx      = */ NULL,
-        };
-        auto * ctx_gguf = gguf_init_from_file(model.path.c_str(), gguf_params);
-        if (!ctx_gguf) {
-            LOG_ERR("\n%s:  failed to load input GGUF from %s\n", __func__, model.path.c_str());
-            return false;
-        }
-
-        auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT);
-        if (key_n_split >= 0) {
-            n_split = gguf_get_val_u16(ctx_gguf, key_n_split);
-        }
-
-        gguf_free(ctx_gguf);
-    }
-
-    if (n_split > 1) {
-        char split_prefix[PATH_MAX] = {0};
-        char split_url_prefix[LLAMA_MAX_URL_LENGTH] = {0};
-
-        // Verify the first split file format
-        // and extract split URL and PATH prefixes
-        {
-            if (!llama_split_prefix(split_prefix, sizeof(split_prefix), model.path.c_str(), 0, n_split)) {
-                LOG_ERR("\n%s: unexpected model file name: %s n_split=%d\n", __func__, model.path.c_str(), n_split);
-                return false;
-            }
-
-            if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model.url.c_str(), 0, n_split)) {
-                LOG_ERR("\n%s: unexpected model url: %s n_split=%d\n", __func__, model.url.c_str(), n_split);
-                return false;
-            }
-        }
-
-        std::vector<std::pair<std::string, std::string>> urls;
-        for (int idx = 1; idx < n_split; idx++) {
-            char split_path[PATH_MAX] = {0};
-            llama_split_path(split_path, sizeof(split_path), split_prefix, idx, n_split);
-
-            char split_url[LLAMA_MAX_URL_LENGTH] = {0};
-            llama_split_path(split_url, sizeof(split_url), split_url_prefix, idx, n_split);
-
-            if (std::string(split_path) == model.path) {
-                continue; // skip the already downloaded file
-            }
-
-            urls.push_back({split_url, split_path});
-        }
-
-        // Download in parallel
-        common_download_file_multiple(urls, bearer_token, offline, headers);
-    }
-
-    return true;
+    return std::stoi(split.tag.substr(pos));
 }

-common_hf_file_res common_get_hf_file(const std::string & hf_repo_with_tag,
-                                      const std::string & bearer_token,
-                                      bool offline,
-                                      const common_header_list & custom_headers) {
-    // the returned hf_repo is without tag
-    auto [hf_repo, tag] = common_download_split_repo_tag(hf_repo_with_tag);
+static hf_cache::hf_files get_split_files(const hf_cache::hf_files & files,
+                                          const hf_cache::hf_file  & file) {
+    auto split = get_gguf_split_info(file.path);

-    std::string url = get_model_endpoint() + "v2/" + hf_repo + "/manifests/" + tag;
-
-    // headers
-    common_header_list headers = custom_headers;
-    headers.push_back({"Accept", "application/json"});
-    if (!bearer_token.empty()) {
-        headers.push_back({"Authorization", "Bearer " + bearer_token});
+    if (split.count <= 1) {
+        return {file};
    }
-    // Important: the User-Agent must be "llama-cpp" to get the "ggufFile" field in the response
-    // User-Agent header is already set in common_remote_get_content, no need to set it here
+    hf_cache::hf_files result;

-    // make the request
-    common_remote_params params;
-    params.headers = headers;
-    long res_code = 0;
-    std::string res_str;
-    bool use_cache = false;
-    std::string cached_response_path = get_manifest_path(hf_repo, tag);
-    if (!offline) {
-        try {
-            auto res = common_remote_get_content(url, params);
-            res_code = res.first;
-            res_str = std::string(res.second.data(), res.second.size());
-        } catch (const std::exception & e) {
-            LOG_WRN("error: failed to get manifest at %s: %s\n", url.c_str(), e.what());
+    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) {
+            result.push_back(f);
        }
    }
-    if (res_code == 0) {
-        if (std::filesystem::exists(cached_response_path)) {
-            LOG_WRN("trying to read manifest from cache: %s\n", cached_response_path.c_str());
-            res_str = read_file(cached_response_path);
-            res_code = 200;
-            use_cache = true;
-        } else {
-            throw std::runtime_error(
-                offline ? "error: failed to get manifest (offline mode)"
-                : "error: failed to get manifest (check your internet connection)");
+    return result;
+}
+
+static hf_cache::hf_file find_best_mmproj(const hf_cache::hf_files & files,
+                                          const std::string        & model) {
+    hf_cache::hf_file best;
+    size_t best_depth = 0;
+    int best_diff = 0;
+    bool found = false;
+
+    auto model_bits = extract_quant_bits(model);
+    auto model_parts = string_split<std::string>(model, '/');
+    auto model_dir = model_parts.end() - 1;
+
+    for (const auto & f : files) {
+        if (!string_ends_with(f.path, ".gguf") ||
+            f.path.find("mmproj") == std::string::npos) {
+            continue;
+        }
+
+        auto mmproj_parts = string_split<std::string>(f.path, '/');
+        auto mmproj_dir = mmproj_parts.end() - 1;
+
+        auto [_, dir] = std::mismatch(model_parts.begin(), model_dir,
+                                      mmproj_parts.begin(), mmproj_dir);
+        if (dir != mmproj_dir) {
+            continue;
+        }
+
+        size_t depth = dir - mmproj_parts.begin();
+        auto bits = extract_quant_bits(f.path);
+        auto diff = std::abs(bits - model_bits);
+
+        if (!found || depth > best_depth || (depth == best_depth && diff < best_diff)) {
+            best = f;
+            best_depth = depth;
+            best_diff = diff;
+            found = true;
        }
    }
-    std::string ggufFile;
-    std::string mmprojFile;
+    return best;
+}

-    if (res_code == 200 || res_code == 304) {
-        try {
-            auto j = json::parse(res_str);
+static hf_cache::hf_file find_best_model(const hf_cache::hf_files & files,
+                                         const std::string        & tag) {
+    std::vector<std::string> tags;

-            if (j.contains("ggufFile") && j["ggufFile"].contains("rfilename")) {
-                ggufFile = j["ggufFile"]["rfilename"].get<std::string>();
-            }
-            if (j.contains("mmprojFile") && j["mmprojFile"].contains("rfilename")) {
-                mmprojFile = j["mmprojFile"]["rfilename"].get<std::string>();
-            }
-        } catch (const std::exception & e) {
-            throw std::runtime_error(std::string("error parsing manifest JSON: ") + e.what());
-        }
-        if (!use_cache) {
-            // if not using cached response, update the cache file
-            write_file(cached_response_path, res_str);
-        }
-    } else if (res_code == 401) {
-        throw std::runtime_error("error: model is private or does not exist; if you are accessing a gated model, please provide a valid HF token");
+    if (!tag.empty()) {
+        tags.push_back(tag);
    } else {
-        throw std::runtime_error(string_format("error from HF API (%s), response code: %ld, data: %s", url.c_str(), res_code, res_str.c_str()));
+        tags = {"Q4_K_M", "Q4_0"};
    }

-    // check response
-    if (ggufFile.empty()) {
-        throw std::runtime_error("error: model does not have ggufFile");
+    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 &&
+                std::regex_search(f.path, pattern)) {
+                return f;
+            }
+        }
    }

-    return { hf_repo, ggufFile, mmprojFile };
+    for (const auto & f : files) {
+        if (string_ends_with(f.path, ".gguf") &&
+            f.path.find("mmproj") == std::string::npos) {
+            return f;
+        }
+    }
+
+    return {};
+}
+
+static void list_available_gguf_files(const hf_cache::hf_files & files) {
+    LOG_INF("Available GGUF files:\n");
+    for (const auto & f : files) {
+        if (string_ends_with(f.path, ".gguf")) {
+            LOG_INF(" - %s\n", f.path.c_str());
+        }
+    }
+}
+
+struct hf_plan {
+    hf_cache::hf_files model_files;
+    hf_cache::hf_file mmproj;
+};
+
+static hf_plan get_hf_plan(const common_params_model        & model,
+                           const std::string                & token,
+                           const common_download_model_opts & opts) {
+    hf_plan plan;
+    hf_cache::hf_files all;
+
+    auto [repo, tag] = common_download_split_repo_tag(model.hf_repo);
+
+    if (!opts.offline) {
+        all = hf_cache::get_repo_files(repo, token);
+    }
+    if (all.empty()) {
+        all = hf_cache::get_cached_files(repo);
+    }
+    if (all.empty()) {
+        return plan;
+    }
+
+    hf_cache::hf_file primary;
+
+    if (!model.hf_file.empty()) {
+        for (const auto & f : all) {
+            if (f.path == model.hf_file) {
+                primary = f;
+                break;
+            }
+        }
+        if (primary.path.empty()) {
+            LOG_ERR("%s: file '%s' not found in repository\n", __func__, model.hf_file.c_str());
+            list_available_gguf_files(all);
+            return plan;
+        }
+    } else {
+        primary = find_best_model(all, tag);
+        if (primary.path.empty()) {
+            LOG_ERR("%s: no GGUF files found in repository %s\n", __func__, repo.c_str());
+            list_available_gguf_files(all);
+            return plan;
+        }
+    }
+
+    plan.model_files = get_split_files(all, primary);
+
+    if (opts.download_mmproj) {
+        plan.mmproj = find_best_mmproj(all, primary.path);
+    }
+
+    return plan;
+}
+
+struct download_task {
+    std::string url;
+    std::string path;
+};
+
+static std::vector<download_task> get_url_tasks(const common_params_model & model) {
+    auto split = get_gguf_split_info(model.url);
+
+    if (split.count <= 1) {
+        return {{model.url, model.path}};
+    }
+
+    auto filename = split.prefix;
+    if (auto pos = split.prefix.rfind('/'); pos != std::string::npos) {
+        filename = split.prefix.substr(pos + 1);
+    }
+
+    auto parent_path = std::filesystem::path(model.path).parent_path();
+    auto prefix_path = (parent_path / filename).string();
+
+    std::vector<download_task> tasks;
+    for (int i = 1; i <= split.count; i++) {
+        auto suffix = string_format("-%05d-of-%05d.gguf", i, split.count);
+        tasks.push_back({split.prefix + suffix, prefix_path + suffix});
+    }
+    return tasks;
+}
+
+common_download_model_result common_download_model(const common_params_model        & model,
+                                                   const std::string                & bearer_token,
+                                                   const common_download_model_opts & opts,
+                                                   const common_header_list         & headers) {
+    common_download_model_result result;
+    std::vector<download_task> tasks;
+    hf_plan hf;
+
+    bool is_hf = !model.hf_repo.empty();
+
+    if (is_hf) {
+        hf = get_hf_plan(model, bearer_token, opts);
+        for (const auto & f : hf.model_files) {
+            tasks.push_back({f.url, f.local_path});
+        }
+        if (!hf.mmproj.path.empty()) {
+            tasks.push_back({hf.mmproj.url, hf.mmproj.local_path});
+        }
+    } else if (!model.url.empty()) {
+        tasks = get_url_tasks(model);
+    } else {
+        result.model_path = model.path;
+        return result;
+    }
+
+    if (tasks.empty()) {
+        return result;
+    }
+
+    std::vector<std::future<bool>> futures;
+    for (const auto & task : tasks) {
+        futures.push_back(std::async(std::launch::async,
+            [&task, &bearer_token, offline = opts.offline, &headers, is_hf]() {
+                int status = common_download_file_single(task.url, task.path, bearer_token, offline, headers, is_hf);
+                return is_http_status_ok(status);
+            }
+        ));
+    }
+
+    for (auto & f : futures) {
+        if (!f.get()) {
+            return {};
+        }
+    }
+
+    if (is_hf) {
+        for (const auto & f : hf.model_files) {
+            hf_cache::finalize_file(f);
+        }
+        result.model_path = hf.model_files[0].final_path;
+
+        if (!hf.mmproj.path.empty()) {
+            result.mmproj_path = hf_cache::finalize_file(hf.mmproj);
+        }
+    } else {
+        result.model_path = model.path;
+    }
+
+    return result;
 }

 //
@@ -765,28 +854,21 @@ std::string common_docker_resolve_model(const std::string & docker) {
 }

 std::vector<common_cached_model_info> common_list_cached_models() {
-    std::vector<common_cached_model_info> models;
-    const std::string cache_dir = fs_get_cache_directory();
-    const std::vector<common_file_info> files = fs_list(cache_dir, false);
-    for (const auto & file : files) {
-        if (string_starts_with(file.name, "manifest=") && string_ends_with(file.name, ".json")) {
-            common_cached_model_info model_info;
-            model_info.manifest_path = file.path;
-            std::string fname = file.name;
-            string_replace_all(fname, ".json", ""); // remove extension
-            auto parts = string_split<std::string>(fname, '=');
-            if (parts.size() == 4) {
-                // expect format: manifest=<user>=<model>=<tag>=<other>
-                model_info.user  = parts[1];
-                model_info.model = parts[2];
-                model_info.tag   = parts[3];
-            } else {
-                // invalid format
-                continue;
-            }
-            model_info.size = 0; // TODO: get GGUF size, not manifest size
-            models.push_back(model_info);
+    std::unordered_set<std::string> seen;
+    std::vector<common_cached_model_info> result;
+
+    auto files = hf_cache::get_cached_files();
+
+    for (const auto & f : files) {
+        auto split = get_gguf_split_info(f.path);
+        if (split.index != 1 || split.tag.empty() ||
+            split.prefix.find("mmproj") != std::string::npos) {
+            continue;
+        }
+        if (seen.insert(f.repo_id + ":" + split.tag).second) {
+            result.push_back({f.repo_id, split.tag});
        }
    }
-    return models;
+
+    return result;
 }
@@ -17,54 +17,60 @@ struct common_remote_params {
 // get remote file content, returns <http_code, raw_response_body>
 std::pair<long, std::vector<char>> common_remote_get_content(const std::string & url, const common_remote_params & params);

-// split HF repo with tag into <repo, tag>
-// for example: "user/model:tag" -> <"user/model", "tag">
-// if tag is not present, default to "latest"
-// example: "user/model" -> <"user/model", "latest">
+// split HF repo with tag into <repo, tag>, for example:
+// - "ggml-org/models:F16" -> <"ggml-org/models", "F16">
+// tag is optional and can be empty
 std::pair<std::string, std::string> common_download_split_repo_tag(const std::string & hf_repo_with_tag);

+// Result of common_list_cached_models
 struct common_cached_model_info {
-    std::string manifest_path;
-    std::string user;
-    std::string model;
+    std::string repo;
    std::string tag;
-    size_t      size = 0; // GGUF size in bytes
-    // return string representation like "user/model:tag"
-    // if tag is "latest", it will be omitted
    std::string to_string() const {
-        return user + "/" + model + (tag == "latest" ? "" : ":" + tag);
+        return repo + ":" + tag;
    }
 };

-struct common_hf_file_res {
-    std::string repo; // repo name with ":tag" removed
-    std::string ggufFile;
-    std::string mmprojFile;
+// Options for common_download_model
+struct common_download_model_opts {
+    bool download_mmproj = false;
+    bool offline         = false;
 };

-/**
- * Allow getting the HF file from the HF repo with tag (like ollama), for example:
- * - bartowski/Llama-3.2-3B-Instruct-GGUF:q4
- * - bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
- * - bartowski/Llama-3.2-3B-Instruct-GGUF:q5_k_s
- * Tag is optional, default to "latest" (meaning it checks for Q4_K_M first, then Q4, then if not found, return the first GGUF file in repo)
- *
- * Return pair of <repo, file> (with "repo" already having tag removed)
- *
- * Note: we use the Ollama-compatible HF API, but not using the blobId. Instead, we use the special "ggufFile" field which returns the value for "hf_file". This is done to be backward-compatible with existing cache files.
- */
-common_hf_file_res common_get_hf_file(
-    const std::string & hf_repo_with_tag,
-    const std::string & bearer_token,
-    bool offline,
-    const common_header_list & headers = {}
-);
+// Result of common_download_model
+struct common_download_model_result {
+    std::string model_path;
+    std::string mmproj_path;
+};

-// returns true if download succeeded
-bool common_download_model(
+// Download model from HuggingFace repo or URL
+//
+// input (via model struct):
+// - model.hf_repo: HF repo with optional tag, see common_download_split_repo_tag
+// - model.hf_file: specific file in the repo (requires hf_repo)
+// - model.url: simple download (used if hf_repo is empty)
+// - model.path: local file path
+//
+// tag matching (for HF repos without model.hf_file):
+// - if tag is specified, searches for GGUF matching that quantization
+// - if no tag, searches for Q4_K_M, then Q4_0, then first available GGUF
+//
+// split GGUF: multi-part files like "model-00001-of-00003.gguf" are automatically
+// detected and all parts are downloaded
+//
+// caching:
+// - HF repos: uses HuggingFace cache
+// - URLs: uses ETag-based caching
+//
+// when opts.offline=true, no network requests are made
+// when download_mmproj=true, searches for mmproj in same directory as model or any parent directory
+// then with the closest quantization bits
+//
+// returns result with model_path and mmproj_path (empty on failure)
+common_download_model_result common_download_model(
    const common_params_model & model,
    const std::string & bearer_token,
-    bool offline,
+    const common_download_model_opts & opts = {},
    const common_header_list & headers = {}
 );

@@ -73,11 +79,13 @@ std::vector<common_cached_model_info> common_list_cached_models();

 // download single file from url to local path
 // returns status code or -1 on error
+// skip_etag: if true, don't read/write .etag files (for HF cache where filename is the hash)
 int common_download_file_single(const std::string & url,
                                const std::string & path,
                                const std::string & bearer_token,
                                bool offline,
-                                const common_header_list & headers = {});
+                                const common_header_list & headers = {},
+                                bool skip_etag = false);

 // resolve and download model from Docker registry
 // return local path to downloaded model file
@@ -0,0 +1,634 @@
+#include "hf-cache.h"
+
+#include "common.h"
+#include "log.h"
+#include "http.h"
+
+#define JSON_ASSERT GGML_ASSERT
+#include <nlohmann/json.hpp>
+
+#include <filesystem>
+#include <fstream>
+#include <atomic>
+#include <regex> // migration only
+#include <string>
+#include <string_view>
+#include <stdexcept>
+
+namespace nl = nlohmann;
+
+#if defined(_WIN32)
+#define WIN32_LEAN_AND_MEAN
+#ifndef NOMINMAX
+#define NOMINMAX
+#endif
+#define HOME_DIR "USERPROFILE"
+#include <windows.h>
+#else
+#define HOME_DIR "HOME"
+#endif
+
+namespace hf_cache {
+
+namespace fs = std::filesystem;
+
+static fs::path get_cache_directory() {
+    static const fs::path cache = []() {
+        struct {
+            const char * var;
+            fs::path path;
+        } entries[] = {
+            {"HF_HUB_CACHE",          fs::path()},
+            {"HUGGINGFACE_HUB_CACHE", fs::path()},
+            {"HF_HOME",               fs::path("hub")},
+            {"XDG_CACHE_HOME",        fs::path("huggingface") / "hub"},
+            {HOME_DIR,                fs::path(".cache") / "huggingface" / "hub"}
+        };
+        for (const auto & entry : entries) {
+            if (auto * p = std::getenv(entry.var); p && *p) {
+                fs::path base(p);
+                return entry.path.empty() ? base : base / entry.path;
+            }
+        }
+        throw std::runtime_error("Failed to determine HF cache directory");
+    }();
+
+    return cache;
+}
+
+static std::string folder_name_to_repo(const std::string & folder) {
+    constexpr std::string_view prefix = "models--";
+    if (folder.rfind(prefix, 0)) {
+        return {};
+    }
+    std::string result = folder.substr(prefix.length());
+    string_replace_all(result, "--", "/");
+    return result;
+}
+
+static std::string repo_to_folder_name(const std::string & repo_id) {
+    constexpr std::string_view prefix = "models--";
+    std::string result = std::string(prefix) + repo_id;
+    string_replace_all(result, "/", "--");
+    return result;
+}
+
+static fs::path get_repo_path(const std::string & repo_id) {
+    return get_cache_directory() / repo_to_folder_name(repo_id);
+}
+
+static bool is_hex_char(const char c) {
+    return (c >= 'A' && c <= 'F') ||
+           (c >= 'a' && c <= 'f') ||
+           (c >= '0' && c <= '9');
+}
+
+static bool is_hex_string(const std::string & s, size_t expected_len) {
+    if (s.length() != expected_len) {
+        return false;
+    }
+    for (const char c : s) {
+        if (!is_hex_char(c)) {
+            return false;
+        }
+    }
+    return true;
+}
+
+static bool is_alphanum(const char c) {
+    return (c >= 'A' && c <= 'Z') ||
+           (c >= 'a' && c <= 'z') ||
+           (c >= '0' && c <= '9');
+}
+
+static bool is_special_char(char c) {
+    return c == '/' || c == '.' || c == '-';
+}
+
+// base chars [A-Za-z0-9_] are always valid
+// special chars [/.-] must be surrounded by base chars
+// exactly one '/' required
+static bool is_valid_repo_id(const std::string & repo_id) {
+    if (repo_id.empty() || repo_id.length() > 256) {
+        return false;
+    }
+    int slash = 0;
+    bool special = true;
+
+    for (const char c : repo_id) {
+        if (is_alphanum(c) || c == '_') {
+            special = false;
+        } else if (is_special_char(c)) {
+            if (special) {
+                return false;
+            }
+            slash += (c == '/');
+            special = true;
+        } else {
+            return false;
+        }
+    }
+    return !special && slash == 1;
+}
+
+static bool is_valid_hf_token(const std::string & token) {
+    if (token.length() < 37 || token.length() > 256 ||
+        !string_starts_with(token, "hf_")) {
+        return false;
+    }
+    for (size_t i = 3; i < token.length(); ++i) {
+        if (!is_alphanum(token[i])) {
+            return false;
+        }
+    }
+    return true;
+}
+
+static bool is_valid_commit(const std::string & hash) {
+    return is_hex_string(hash, 40);
+}
+
+static bool is_valid_oid(const std::string & oid) {
+    return is_hex_string(oid, 40) || is_hex_string(oid, 64);
+}
+
+static bool is_valid_subpath(const fs::path & path, const fs::path & subpath) {
+    if (subpath.is_absolute()) {
+        return false; // never do a / b with b absolute
+    }
+    auto b = fs::absolute(path).lexically_normal();
+    auto t = (b / subpath).lexically_normal();
+    auto [b_end, _] = std::mismatch(b.begin(), b.end(), t.begin(), t.end());
+
+    return b_end == b.end();
+}
+
+static void safe_write_file(const fs::path & path, const std::string & data) {
+    fs::path path_tmp = path.string() + ".tmp";
+
+    if (path.has_parent_path()) {
+        fs::create_directories(path.parent_path());
+    }
+
+    std::ofstream file(path_tmp);
+    file << data;
+    file.close();
+
+    std::error_code ec;
+
+    if (!file.fail()) {
+        fs::rename(path_tmp, path, ec);
+    }
+    if (file.fail() || ec) {
+        fs::remove(path_tmp, ec);
+        throw std::runtime_error("failed to write file: " + path.string());
+    }
+}
+
+static nl::json api_get(const std::string & url,
+                        const std::string & token) {
+    auto [cli, parts] = common_http_client(url);
+
+    httplib::Headers headers = {
+        {"User-Agent", "llama-cpp/" + build_info},
+        {"Accept", "application/json"}
+    };
+
+    if (is_valid_hf_token(token)) {
+        headers.emplace("Authorization", "Bearer " + token);
+    } else if (!token.empty()) {
+        LOG_WRN("%s: invalid token, authentication disabled\n", __func__);
+    }
+
+    if (auto res = cli.Get(parts.path, headers)) {
+        auto body = res->body;
+
+        if (res->status == 200) {
+            return nl::json::parse(res->body);
+        }
+        try {
+            body = nl::json::parse(res->body)["error"].get<std::string>();
+        } catch (...) { }
+
+        throw std::runtime_error("GET failed (" + std::to_string(res->status) + "): " + body);
+    } else {
+        throw std::runtime_error("HTTPLIB failed: " + httplib::to_string(res.error()));
+    }
+}
+
+static std::string get_repo_commit(const std::string & repo_id,
+                                   const std::string & token) {
+    try {
+        auto endpoint = get_model_endpoint();
+        auto json = api_get(endpoint + "api/models/" + repo_id + "/refs", token);
+
+        if (!json.is_object() ||
+            !json.contains("branches") || !json["branches"].is_array()) {
+            LOG_WRN("%s: missing 'branches' for '%s'\n", __func__, repo_id.c_str());
+            return {};
+        }
+
+        fs::path refs_path = get_repo_path(repo_id) / "refs";
+        std::string name;
+        std::string commit;
+
+        for (const auto & branch : json["branches"]) {
+            if (!branch.is_object() ||
+                !branch.contains("name") || !branch["name"].is_string() ||
+                !branch.contains("targetCommit") || !branch["targetCommit"].is_string()) {
+                continue;
+            }
+            std::string _name = branch["name"].get<std::string>();
+            std::string _commit = branch["targetCommit"].get<std::string>();
+
+            if (!is_valid_subpath(refs_path, _name)) {
+                LOG_WRN("%s: skip invalid branch: %s\n", __func__, _name.c_str());
+                continue;
+            }
+            if (!is_valid_commit(_commit)) {
+                LOG_WRN("%s: skip invalid commit: %s\n", __func__, _commit.c_str());
+                continue;
+            }
+
+            if (_name == "main") {
+                name = _name;
+                commit = _commit;
+                break;
+            }
+
+            if (name.empty() || commit.empty()) {
+                name = _name;
+                commit = _commit;
+            }
+        }
+
+        if (name.empty() || commit.empty()) {
+            LOG_WRN("%s: no valid branch for '%s'\n", __func__, repo_id.c_str());
+            return {};
+        }
+
+        safe_write_file(refs_path / name, commit);
+        return commit;
+
+    } catch (const nl::json::exception & e) {
+        LOG_ERR("%s: JSON error: %s\n", __func__, e.what());
+    } catch (const std::exception & e) {
+        LOG_ERR("%s: error: %s\n", __func__, e.what());
+    }
+    return {};
+}
+
+hf_files get_repo_files(const std::string & repo_id,
+                        const std::string & token) {
+    if (!is_valid_repo_id(repo_id)) {
+        LOG_WRN("%s: invalid repository: %s\n", __func__, repo_id.c_str());
+        return {};
+    }
+
+    std::string commit = get_repo_commit(repo_id, token);
+    if (commit.empty()) {
+        LOG_WRN("%s: failed to resolve commit for %s\n", __func__, repo_id.c_str());
+        return {};
+    }
+
+    fs::path blobs_path = get_repo_path(repo_id) / "blobs";
+    fs::path commit_path = get_repo_path(repo_id) / "snapshots" / commit;
+
+    hf_files files;
+
+    try {
+        auto endpoint = get_model_endpoint();
+        auto json = api_get(endpoint + "api/models/" + repo_id + "/tree/" + commit + "?recursive=true", token);
+
+        if (!json.is_array()) {
+            LOG_WRN("%s: response is not an array for '%s'\n", __func__, repo_id.c_str());
+            return {};
+        }
+
+        for (const auto & item : json) {
+            if (!item.is_object() ||
+                !item.contains("type") || !item["type"].is_string() || item["type"] != "file" ||
+                !item.contains("path") || !item["path"].is_string()) {
+                continue;
+            }
+
+            hf_file file;
+            file.repo_id = repo_id;
+            file.path = item["path"].get<std::string>();
+
+            if (!is_valid_subpath(commit_path, file.path)) {
+                LOG_WRN("%s: skip invalid path: %s\n", __func__, file.path.c_str());
+                continue;
+            }
+
+            if (item.contains("lfs") && item["lfs"].is_object()) {
+                if (item["lfs"].contains("oid") && item["lfs"]["oid"].is_string()) {
+                    file.oid = item["lfs"]["oid"].get<std::string>();
+                }
+            } else if (item.contains("oid") && item["oid"].is_string()) {
+                file.oid = item["oid"].get<std::string>();
+            }
+
+            if (!file.oid.empty() && !is_valid_oid(file.oid)) {
+                LOG_WRN("%s: skip invalid oid: %s\n", __func__, file.oid.c_str());
+                continue;
+            }
+
+            file.url = endpoint + repo_id + "/resolve/" + commit + "/" + file.path;
+
+            fs::path final_path = commit_path / file.path;
+            file.final_path = final_path.string();
+
+            if (!file.oid.empty() && !fs::exists(final_path)) {
+                fs::path local_path = blobs_path / file.oid;
+                file.local_path = local_path.string();
+            } else {
+                file.local_path = file.final_path;
+            }
+
+            files.push_back(file);
+        }
+    } catch (const nl::json::exception & e) {
+        LOG_ERR("%s: JSON error: %s\n", __func__, e.what());
+    } catch (const std::exception & e) {
+        LOG_ERR("%s: error: %s\n", __func__, e.what());
+    }
+    return files;
+}
+
+static std::string get_cached_ref(const fs::path & repo_path) {
+    fs::path refs_path = repo_path / "refs";
+    if (!fs::is_directory(refs_path)) {
+        return {};
+    }
+    std::string fallback;
+
+    for (const auto & entry : fs::directory_iterator(refs_path)) {
+        if (!entry.is_regular_file()) {
+            continue;
+        }
+        std::ifstream f(entry.path());
+        std::string commit;
+        if (!f || !std::getline(f, commit) || commit.empty()) {
+            continue;
+        }
+        if (!is_valid_commit(commit)) {
+            LOG_WRN("%s: skip invalid commit: %s\n", __func__, commit.c_str());
+            continue;
+        }
+        if (entry.path().filename() == "main") {
+            return commit;
+        }
+        if (fallback.empty()) {
+            fallback = commit;
+        }
+    }
+    return fallback;
+}
+
+hf_files get_cached_files(const std::string & repo_id) {
+    fs::path cache_dir = get_cache_directory();
+    if (!fs::exists(cache_dir)) {
+        return {};
+    }
+
+    if (!repo_id.empty() && !is_valid_repo_id(repo_id)) {
+        LOG_WRN("%s: invalid repository: %s\n", __func__, repo_id.c_str());
+        return {};
+    }
+
+    hf_files files;
+
+    for (const auto & repo : fs::directory_iterator(cache_dir)) {
+        if (!repo.is_directory()) {
+            continue;
+        }
+        fs::path snapshots_path = repo.path() / "snapshots";
+
+        if (!fs::exists(snapshots_path)) {
+            continue;
+        }
+        std::string _repo_id = folder_name_to_repo(repo.path().filename().string());
+
+        if (!is_valid_repo_id(_repo_id)) {
+            continue;
+        }
+        if (!repo_id.empty() && _repo_id != repo_id) {
+            continue;
+        }
+        std::string commit = get_cached_ref(repo.path());
+        fs::path commit_path = snapshots_path / commit;
+
+        if (commit.empty() || !fs::is_directory(commit_path)) {
+            continue;
+        }
+        for (const auto & entry : fs::recursive_directory_iterator(commit_path)) {
+            if (!entry.is_regular_file() && !entry.is_symlink()) {
+                continue;
+            }
+            fs::path path = entry.path().lexically_relative(commit_path);
+
+            if (!path.empty()) {
+                hf_file file;
+                file.repo_id = _repo_id;
+                file.path = path.generic_string();
+                file.local_path = entry.path().string();
+                file.final_path = file.local_path;
+                files.push_back(std::move(file));
+            }
+        }
+    }
+
+    return files;
+}
+
+std::string finalize_file(const hf_file & file) {
+    static std::atomic<bool> symlinks_disabled{false};
+
+    std::error_code ec;
+    fs::path local_path(file.local_path);
+    fs::path final_path(file.final_path);
+
+    if (local_path == final_path || fs::exists(final_path, ec)) {
+        return file.final_path;
+    }
+
+    if (!fs::exists(local_path, ec)) {
+        return file.final_path;
+    }
+
+    fs::create_directories(final_path.parent_path(), ec);
+
+    if (!symlinks_disabled) {
+        fs::path target = fs::relative(local_path, final_path.parent_path(), ec);
+        if (!ec) {
+            fs::create_symlink(target, final_path, ec);
+        }
+        if (!ec) {
+            return file.final_path;
+        }
+    }
+
+    if (!symlinks_disabled.exchange(true)) {
+        LOG_WRN("%s: failed to create symlink: %s\n", __func__, ec.message().c_str());
+        LOG_WRN("%s: switching to degraded mode\n", __func__);
+    }
+
+    fs::rename(local_path, final_path, ec);
+    if (ec) {
+        LOG_WRN("%s: failed to move file to snapshots: %s\n", __func__, ec.message().c_str());
+        fs::copy(local_path, final_path, ec);
+        if (ec) {
+            LOG_ERR("%s: failed to copy file to snapshots: %s\n", __func__, ec.message().c_str());
+        }
+    }
+    return file.final_path;
+}
+
+// delete everything after this line, one day
+
+static std::pair<std::string, std::string> parse_manifest_name(std::string & filename) {
+    static const std::regex re(R"(^manifest=([^=]+)=([^=]+)=.*\.json$)");
+    std::smatch match;
+    if (std::regex_match(filename, match, re)) {
+        return {match[1].str(), match[2].str()};
+    }
+    return {};
+}
+
+static std::string make_old_cache_filename(const std::string & owner,
+                                           const std::string & repo,
+                                           const std::string & filename) {
+    auto result = owner + "_" + repo + "_" + filename;
+    string_replace_all(result, "/", "_");
+    return result;
+}
+
+static void migrate_single_file(const fs::path    & old_cache,
+                                const std::string & owner,
+                                const std::string & repo,
+                                const nl::json    & node,
+                                const hf_files    & files) {
+
+    if (!node.contains("rfilename") ||
+        !node.contains("lfs")       ||
+        !node["lfs"].contains("sha256")) {
+        return;
+    }
+
+    std::string path = node["rfilename"];
+    std::string sha256 = node["lfs"]["sha256"];
+
+    const hf_file * file_info = nullptr;
+    for (const auto & f : files) {
+        if (f.path == path) {
+            file_info = &f;
+            break;
+        }
+    }
+
+    std::string old_filename = make_old_cache_filename(owner, repo, path);
+    fs::path old_path = old_cache / old_filename;
+    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);
+        }
+        return;
+    }
+
+    if (!file_info) {
+        LOG_WRN("%s: %s not found in current repo, ignoring...\n", __func__, old_filename.c_str());
+        return;
+    } else if (!sha256.empty() && !file_info->oid.empty() && sha256 != file_info->oid) {
+        LOG_WRN("%s: %s is not up to date (sha256 mismatch), ignoring...\n", __func__, old_filename.c_str());
+        return;
+    }
+
+    std::error_code ec;
+
+    fs::path new_path(file_info->local_path);
+    fs::create_directories(new_path.parent_path(), ec);
+
+    if (!fs::exists(new_path, ec)) {
+        fs::rename(old_path, new_path, ec);
+        if (ec) {
+            fs::copy_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());
+                return;
+            }
+        }
+        fs::remove(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());
+}
+
+void migrate_old_cache_to_hf_cache(const std::string & token, bool offline) {
+    fs::path old_cache = fs_get_cache_directory();
+    if (!fs::exists(old_cache)) {
+        return;
+    }
+
+    if (offline) {
+        LOG_WRN("%s: skipping migration in offline mode (will run when online)\n", __func__);
+        return; // -hf is not going to work
+    }
+
+    bool warned = false;
+
+    for (const auto & entry : fs::directory_iterator(old_cache)) {
+        if (!entry.is_regular_file()) {
+            continue;
+        }
+        auto filename = entry.path().filename().string();
+        auto [owner, repo] = parse_manifest_name(filename);
+
+        if (owner.empty() || repo.empty()) {
+            continue;
+        }
+
+        if (!warned) {
+            warned = true;
+            LOG_WRN("================================================================================\n"
+                    "WARNING: Migrating cache to HuggingFace cache directory\n"
+                    "  Old cache: %s\n"
+                    "  New cache: %s\n"
+                    "This one-time migration moves models previously downloaded with -hf\n"
+                    "from the legacy llama.cpp cache to the standard HuggingFace cache.\n"
+                    "Models downloaded with --model-url are not affected.\n"
+                    "================================================================================\n",
+                    old_cache.string().c_str(), get_cache_directory().string().c_str());
+        }
+
+        auto repo_id = owner + "/" + repo;
+        auto files = get_repo_files(repo_id, token);
+
+        if (files.empty()) {
+            LOG_WRN("%s: could not get repo files for %s, skipping\n", __func__, repo_id.c_str());
+            continue;
+        }
+
+        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);
+                }
+            }
+        } catch (const std::exception & e) {
+            LOG_WRN("%s: failed to parse manifest %s: %s\n", __func__, filename.c_str(), e.what());
+            continue;
+        }
+        fs::remove(entry.path());
+    }
+}
+
+} // namespace hf_cache
@@ -0,0 +1,35 @@
+#pragma once
+
+#include <string>
+#include <vector>
+
+// Ref: https://huggingface.co/docs/hub/local-cache.md
+
+namespace hf_cache {
+
+struct hf_file {
+    std::string path;
+    std::string url;
+    std::string local_path;
+    std::string final_path;
+    std::string oid;
+    std::string repo_id;
+};
+
+using hf_files = std::vector<hf_file>;
+
+// Get files from HF API
+hf_files get_repo_files(
+    const std::string & repo_id,
+    const std::string & token
+);
+
+hf_files get_cached_files(const std::string & repo_id = {});
+
+// Create snapshot path (link or move/copy) and return it
+std::string finalize_file(const hf_file & file);
+
+// TODO: Remove later
+void migrate_old_cache_to_hf_cache(const std::string & token, bool offline = false);
+
+} // namespace hf_cache
@@ -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 + "'");
                }
@@ -947,6 +947,9 @@ 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"]
@@ -1503,6 +1506,9 @@ 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 +2077,7 @@ 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
@@ -4572,7 +4578,7 @@ class Qwen2MoeModel(TextModel):
                raise ValueError(f"Unprocessed experts: {experts}")


-@ModelBase.register("Qwen3ForCausalLM")
+@ModelBase.register("Qwen3ForCausalLM", "Qwen3Model")
 class Qwen3Model(Qwen2Model):
    model_arch = gguf.MODEL_ARCH.QWEN3

@@ -6935,6 +6941,68 @@ 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
+        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 +8348,19 @@ 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 +8416,15 @@ 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 +8438,18 @@ 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 +8481,15 @@ 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
@@ -1,6 +1,9 @@
 # OpenVINO Backend for llama.cpp
-[OpenVINO](https://docs.openvino.ai/) is an open-source toolkit for optimizing and deploying high-performance AI inference, specifically designed for Intel hardware, including CPUs, GPUs, and NPUs, in the cloud, on-premises, and on the edge.
-This document describes the [OpenVINO backend for llama.cpp](../../src/ggml-openvino), which enables hardware-accelerated inference on **Intel® CPUs, GPUs, and NPUs** while remaining compatible with the existing **GGUF model ecosystem**. The backend translates GGML compute graphs into OpenVINO graphs and leverages graph compilation, kernel fusion, and device-specific optimizations to improve inference performance on supported Intel hardware.
+
+> [!NOTE]
+> Performance and memory optimizations, accuracy validation, broader quantization coverage, broader operator and model support are work in progress.
+
+[OpenVINO](https://docs.openvino.ai/) is an open-source toolkit for optimizing and deploying high-performance AI inference, specifically designed for Intel hardware, including CPUs, GPUs, and NPUs, in the cloud, on-premises, and on the edge. [OpenVINO backend for llama.cpp](../../src/ggml-openvino) enables hardware-accelerated inference on **Intel® CPUs, GPUs, and NPUs** while remaining compatible with the existing **GGUF model ecosystem**. The backend translates GGML compute graphs into OpenVINO graphs and leverages graph compilation, kernel fusion, and device-specific optimizations to improve inference performance on supported Intel hardware.

 The OpenVINO backend is implemented in `ggml/src/ggml-openvino` and provides a translation layer for core GGML operations. The OpenVINO backend replaces the standard GGML graph execution path with Intel's OpenVINO inference engine. This approach allows the same GGUF model file to run on Intel CPUs, Intel GPUs (integrated and discrete), and Intel NPUs without changes to the model or the rest of the llama.cpp stack. When a `ggml_cgraph` is dispatched to OpenVINO backend, it:

@@ -179,31 +182,73 @@ curl -L https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-GGUF/resolve/main/L

 When using the OpenVINO backend, the first inference token may have slightly higher latency due to on-the-fly conversion to the OpenVINO graph. Subsequent tokens and runs will be faster.

+> [!NOTE]
+> Default context size is set to the model training context, which may be very large. For example, 131072 for Llama 3.2 1B, which may result in lower performance, especially on edge/laptop devices. Use `-c` to limit context size in supported llama.cpp tools for better performance. For example, `-c 512`.
+
 ```bash
 # If device is unset or unavailable, defaults to CPU.
 # If the system has multiple GPUs, use GPU.0 or GPU.1 to explicitly target a specific GPU.

 # Linux
 export GGML_OPENVINO_DEVICE=GPU
+# Enable stateful execution with GPU device to avoid known stateless execution failures.
+export GGML_OPENVINO_STATEFUL_EXECUTION=1
 # To run llama-simple:
 ./build/ReleaseOV/bin/llama-simple -m ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf -n 50 "The story of AI is "
 # To run in chat mode:
-./build/ReleaseOV/bin/llama-cli -m ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf
+./build/ReleaseOV/bin/llama-cli -m ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf -c 1024
+# To run llama-bench, -fa 1 is needed
+GGML_OPENVINO_STATEFUL_EXECUTION=1 GGML_OPENVINO_DEVICE=GPU ./build/ReleaseOV/bin/llama-bench -m ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf -fa 1
+
+# NPU: keep context small to avoid failures from very large model context windows.
+export GGML_OPENVINO_DEVICE=NPU
+./build/ReleaseOV/bin/llama-cli -m ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf -c 512

 # Windows Command Line
 set GGML_OPENVINO_DEVICE=GPU
+# Enable stateful execution with GPU device to avoid known stateless execution failures.
+set GGML_OPENVINO_STATEFUL_EXECUTION=1
 # Windows PowerShell
 $env:GGML_OPENVINO_DEVICE = "GPU"
+$env:GGML_OPENVINO_STATEFUL_EXECUTION = "1"

 # To run llama-simple
 build\ReleaseOV\bin\llama-simple.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf" -n 50 "The story of AI is "
 # To run in chat mode:
-build\ReleaseOV\bin\llama-cli.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf"
+build\ReleaseOV\bin\llama-cli.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf" -c 1024
+# To run llama-bench, -fa 1 is needed
+build\ReleaseOV\bin\llama-bench.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf" -fa 1

+# NPU: keep context small to avoid failures from very large model context windows.
+# Windows Command Line
+set GGML_OPENVINO_DEVICE=NPU
+# Windows PowerShell
+$env:GGML_OPENVINO_DEVICE = "NPU"
+build\ReleaseOV\bin\llama-cli.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf" -c 512
 ```
 > [!NOTE]
 > On systems with multiple GPUs, use `GPU.0` or `GPU.1` to explicitly target specific GPU. See [OpenVINO GPU Device](https://docs.openvino.ai/2026/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html) for more details.

+### Known Issues and Current Workarounds
+
+- GPU stateless execution is currently affected by a known issue.
+  - Workaround: set `GGML_OPENVINO_STATEFUL_EXECUTION=1` when using GPU device.
+- NPU failures can happen when context size is too large. Recent llama.cpp behavior may resolve context size to the model training context (for example, 131072 for Llama 3.2 1B), which is too large for current NPU usage and can also stress laptop CPU/GPU on larger models. To inspect the selected context size, run `llama-cli` or `llama-server` with `-lv 3`.
+  - Workaround: explicitly set context size, for ex. `-c 1024` for NPU runs. Performance will be better with lower context size.
+- Additional NPU limitations:
+  - Model caching is not yet supported.
+  - `llama-server -np > 1` (multiple parallel sequences) is not supported.
+  - `llama-perplexity` is only supported with `-b 512` or smaller.
+- `--context-shift` with `llama-cli` is currently not supported with OpenVINO backend across CPU, GPU, and NPU devices.
+- Encoder models (embedding, reranking) are not supported with the current OpenVINO backend implementation.
+- `-fa 1` is required when running llama-bench with the OpenVINO backend.
+  - `GGML_OPENVINO_STATEFUL_EXECUTION=1 GGML_OPENVINO_DEVICE=GPU ./llama-bench -fa 1`
+- `llama-server` with OpenVINO backend supports only one chat session/thread, when `GGML_OPENVINO_STATEFUL_EXECUTION=1` is enabled.
+- For Intel GPU, NPU detection in containers, GPU, NPU user-space drivers/libraries must be present inside the image. We will include in a future PR. Until then, you can use this reference Dockerfile: [openvino.Dockerfile](https://github.com/ravi9/llama.cpp/blob/ov-docker-update/.devops/openvino.Dockerfile)
+
+> [!NOTE]
+> The OpenVINO backend is actively under development. Fixes are underway, and this document will continue to be updated as issues are resolved.
+

 ### Docker Build

@@ -229,31 +274,42 @@ docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_p
 Run llama.cpp with OpenVINO backend Docker container.
 Save sample models in `~/models` as [shown above](#3-download-sample-model). It will be mounted to the container in the examples below.

+> [!NOTE]
+> Intel GPU, NPU detection in containers will be included in a future PR. Until then, you can use this reference Dockerfile: [openvino.Dockerfile](https://github.com/ravi9/llama.cpp/blob/ov-docker-update/.devops/openvino.Dockerfile).
+
 ```bash
 #  Run Docker container
-docker run --rm -it -v ~/models:/models llama-openvino:light --no-warmup -m /models/Llama-3.2-1B-Instruct-Q4_0.gguf
+docker run --rm -it -v ~/models:/models llama-openvino:light --no-warmup -c 1024 -m /models/Llama-3.2-1B-Instruct-Q4_0.gguf

 # With Intel GPU access (iGPU or dGPU)
 docker run --rm -it -v ~/models:/models \
 --device=/dev/dri --group-add=$(stat -c "%g" /dev/dri/render* | head -n 1) -u $(id -u):$(id -g) \
-llama-openvino:light --no-warmup -m /models/Llama-3.2-1B-Instruct-Q4_0.gguf
+--env=GGML_OPENVINO_DEVICE=GPU --env=GGML_OPENVINO_STATEFUL_EXECUTION=1 \
+llama-openvino:light --no-warmup -c 1024 -m /models/Llama-3.2-1B-Instruct-Q4_0.gguf

 # With Intel NPU access
-docker run --rm -it --env GGML_OPENVINO_DEVICE=NPU -v ~/models:/models \
+docker run --rm -it -v ~/models:/models \
 --device=/dev/accel --group-add=$(stat -c "%g" /dev/dri/render* | head -n 1) -u $(id -u):$(id -g) \
-llama-openvino:light --no-warmup -m /models/Llama-3.2-1B-Instruct-Q4_0.gguf
+--env=GGML_OPENVINO_DEVICE=NPU \
+llama-openvino:light --no-warmup -c 1024 -m /models/Llama-3.2-1B-Instruct-Q4_0.gguf
 ```

-Run Llama.cpp Server with OpenVINO Backend:
+Run Llama.cpp Server with OpenVINO Backend.
+> [!NOTE]
+> `llama-server` with OpenVINO backend supports only one chat session/thread, when `GGML_OPENVINO_STATEFUL_EXECUTION=1` is enabled.
+
 ```bash
 # Run the Server Docker container
-docker run --rm -it -p 8080:8080 -v ~/models:/models llama-openvino:server --no-warmup -m /models/Llama-3.2-1B-Instruct-Q4_0.gguf
-
-# In a NEW terminal, test the server with curl
+docker run --rm -it -p 8080:8080 -v ~/models:/models llama-openvino:server --no-warmup -m /models/Llama-3.2-1B-Instruct-Q4_0.gguf -c 1024
+# Or Using llama-server executable
+./build/ReleaseOV/bin/llama-server -m ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf --port 8080 -c 1024

 # If you are behind a proxy, make sure to set NO_PROXY to avoid proxy for localhost
 export NO_PROXY=localhost,127.0.0.1

+# Option 1: Open your browser to http://localhost:8080 to access the web UI for the llama.cpp server.
+# Option 2: In a NEW terminal, test the server with curl
+
 # Test health endpoint
 curl -f http://localhost:8080/health

@@ -295,6 +351,7 @@ The OpenVINO backend can be configured using the following environment variables
 export GGML_OPENVINO_CACHE_DIR=/tmp/ov_cache
 export GGML_OPENVINO_PROFILING=1
 export GGML_OPENVINO_DEVICE=GPU
+export GGML_OPENVINO_STATEFUL_EXECUTION=1

 ./build/ReleaseOV/bin/llama-simple -m ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf -n 50 "The story of AI is "

@@ -302,38 +359,27 @@ export GGML_OPENVINO_DEVICE=GPU
 set GGML_OPENVINO_CACHE_DIR=C:\tmp\ov_cache
 set GGML_OPENVINO_PROFILING=1
 set GGML_OPENVINO_DEVICE=GPU
+set GGML_OPENVINO_STATEFUL_EXECUTION=1

 # Windows PowerShell
 $env:GGML_OPENVINO_CACHE_DIR = "C:\tmp\ov_cache"
 $env:GGML_OPENVINO_PROFILING = "1"
 $env:GGML_OPENVINO_DEVICE = "GPU"
+$env:GGML_OPENVINO_STATEFUL_EXECUTION = "1"

 build\ReleaseOV\bin\llama-simple.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf" -n 50 "The story of AI is "

 ```

-#### llama-bench
-
-```bash
-# -fa 1 is required when running llama-bench with the OpenVINO backend.
-GGML_OPENVINO_DEVICE=GPU ./llama-bench -fa 1
-```
-
-### NPU Notes
-
- Model caching is not yet supported
- Does not support llama-server -np > 1 (multiple parallel sequences)
- Only supports llama-perplexity -b 512 or smaller
-
 ## Llama.cpp Tools

 The following tools work with the OpenVINO backend on CPU, GPU, NPU:
- llama-simple
- llama-run
- llama-cli
- llama-server
 - llama-bench
+- llama-cli
+- llama-completion
 - llama-perplexity
+- llama-server
+- llama-simple

 ## Work in Progress

@@ -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
@@ -365,13 +365,13 @@ Java_com_arm_aichat_internal_InferenceEngineImpl_processSystemPrompt(
    const auto *system_prompt = env->GetStringUTFChars(jsystem_prompt, nullptr);
    LOGd("%s: System prompt received: \n%s", __func__, system_prompt);
    std::string formatted_system_prompt(system_prompt);
-    env->ReleaseStringUTFChars(jsystem_prompt, system_prompt);

    // Format system prompt if applicable
    const bool has_chat_template = common_chat_templates_was_explicit(g_chat_templates.get());
    if (has_chat_template) {
        formatted_system_prompt = chat_add_and_format(ROLE_SYSTEM, system_prompt);
    }
+    env->ReleaseStringUTFChars(jsystem_prompt, system_prompt);

    // Tokenize system prompt
    const auto system_tokens = common_tokenize(g_context, formatted_system_prompt,
@@ -414,13 +414,13 @@ Java_com_arm_aichat_internal_InferenceEngineImpl_processUserPrompt(
    const auto *const user_prompt = env->GetStringUTFChars(juser_prompt, nullptr);
    LOGd("%s: User prompt received: \n%s", __func__, user_prompt);
    std::string formatted_user_prompt(user_prompt);
-    env->ReleaseStringUTFChars(juser_prompt, user_prompt);

    // Format user prompt if applicable
    const bool has_chat_template = common_chat_templates_was_explicit(g_chat_templates.get());
    if (has_chat_template) {
        formatted_user_prompt = chat_add_and_format(ROLE_USER, user_prompt);
    }
+    env->ReleaseStringUTFChars(juser_prompt, user_prompt);

    // Decode formatted user prompts
    auto user_tokens = common_tokenize(g_context, formatted_user_prompt, has_chat_template, has_chat_template);
@@ -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
@@ -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 {
@@ -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);
@@ -461,7 +461,7 @@ static void repack_row_q4x4x2(uint8_t * y, const block_q4_0 * x, int64_t k) {
        d[7]          = x[i * 8 + 7].d;
    }

-    if (opt_verbose > 1) {
+    if (opt_verbose > 2) {
        for (int i = 0; i < nb; i++) {
            dump_packed_block_q4x4x2(y, i, k);
        }
@@ -480,7 +480,7 @@ static void unpack_row_q4x4x2(block_q4_0 * x, const uint8_t * y, int64_t k) {
    const uint8_t * y_q = y + 0;              // quants first
    const uint8_t * y_d = y + qrow_size;      // then scales

-    if (opt_verbose > 1) {
+    if (opt_verbose > 2) {
        for (int i = 0; i < nb; i++) {
            dump_packed_block_q4x4x2(y, i, k);
        }
@@ -796,7 +796,7 @@ static void repack_row_q8x4x2(uint8_t * y, const block_q8_0 * x, int64_t k) {
        d[7]          = x[i * 8 + 7].d;
    }

-    if (opt_verbose > 1) {
+    if (opt_verbose > 2) {
        for (int i = 0; i < nb; i++) {
            dump_packed_block_q8x4x2(y, i, k);
        }
@@ -814,7 +814,7 @@ static void unpack_row_q8x4x2(block_q8_0 * x, const uint8_t * y, int64_t k) {
    const uint8_t * y_q = y + 0;              // quants first
    const uint8_t * y_d = y + qrow_size;      // then scales

-    if (opt_verbose > 1) {
+    if (opt_verbose > 2) {
        for (int i = 0; i < nb; i++) {
            dump_packed_block_q8x4x2(y, i, k);
        }
@@ -1149,7 +1149,7 @@ static void repack_row_mxfp4x4x2(uint8_t * y, const block_mxfp4 * x, int64_t k)
        e[7]        = x[i * 8 + 7].e;
    }

-    if (opt_verbose > 1) {
+    if (opt_verbose > 2) {
        for (int i = 0; i < nb; i++) {
            dump_packed_block_mxfp4x4x2(y, i, k);
        }
@@ -1168,7 +1168,7 @@ static void unpack_row_mxfp4x4x2(block_mxfp4 * x, const uint8_t * y, int64_t k)
    const uint8_t * y_q = y + 0;              // quants first
    const uint8_t * y_e = y + qrow_size;      // then scales

-    if (opt_verbose > 1) {
+    if (opt_verbose > 2) {
        for (int i = 0; i < nb; i++) {
            dump_packed_block_mxfp4x4x2(y, i, k);
        }
@@ -24,28 +24,26 @@
 // Context for binary operations
 struct htp_binary_context {
    struct htp_ops_context * octx;
-    struct fastdiv_values dim1_div;
-    struct fastdiv_values dim2_div;
-    struct fastdiv_values dim12_div;
+
+    struct fastdiv_values src0_dim1_div; // ne01
+    struct fastdiv_values src0_dim2_div; // ne02
+    struct fastdiv_values src0_dim12_div;// ne03

    struct fastdiv_values src1_dim1_div; // ne11
    struct fastdiv_values src1_dim2_div; // ne12
    struct fastdiv_values src1_dim3_div; // ne13

-    uint32_t nrows_per_thread;
-    bool split_at_ne01;
-    bool split_at_ne02;
-
-    // Precomputed values
    uint32_t block_max;
+    uint32_t nrows_per_thread;
    size_t   src0_row_size_aligned;
    size_t   src1_row_size_aligned;
    size_t   dst_row_size_aligned;
-    uint32_t src1_fetch_rows; // 1 or block_max
-    uint32_t src1_dma_stride; // 0 or stride
+
+    bool split_at_ne01;
+    bool split_at_ne02;
 };

-#define htp_binary_preamble            \
+#define htp_binary_preamble                       \
    const struct htp_tensor * src0 = &octx->src0; \
    const struct htp_tensor * src1 = &octx->src1; \
    struct htp_tensor *       dst  = &octx->dst;  \
@@ -72,12 +70,11 @@ struct htp_binary_context {
    const uint32_t nb2 = dst->nb[2];   \
    const uint32_t nb3 = dst->nb[3];

-static inline uint32_t calc_block_size(struct htp_binary_context * bctx, uint32_t ir, uint32_t end_row,
-                                uint32_t ne01, uint32_t ne02) {
+static inline uint32_t calc_block_size(struct htp_binary_context * bctx, uint32_t ir, uint32_t end_row, uint32_t ne01, uint32_t ne02) {
    uint32_t i03, i02, i01, rem;
-    i03 = fastdiv(ir, &bctx->dim12_div);
+    i03 = fastdiv(ir, &bctx->src0_dim12_div);
    rem = ir - i03 * (ne02 * ne01);
-    i02 = fastdiv(rem, &bctx->dim1_div);
+    i02 = fastdiv(rem, &bctx->src0_dim1_div);
    i01 = rem - i02 * ne01;

    uint32_t rows_left = end_row - ir;
@@ -191,6 +188,8 @@ static void binary_job_scalar(unsigned int nth, unsigned int ith, void * data) {
    const uint32_t end_row   = MIN(start_row + bctx->nrows_per_thread, total_rows);
    if (start_row >= end_row) return;

+    FARF(HIGH, "binary-scalar: %d/%d (%u:%u) row-size %u (%u)", ith, nth, start_row, end_row, nb01, bctx->dst_row_size_aligned);
+
    uint8_t * src0_spad_base = octx->src0_spad.data + (ith * octx->src0_spad.size_per_thread);
    uint8_t * dst_spad_base  = octx->dst_spad.data  + (ith * octx->dst_spad.size_per_thread);
    size_t src0_spad_half    = octx->src0_spad.size_per_thread / 2;
@@ -204,9 +203,9 @@ static void binary_job_scalar(unsigned int nth, unsigned int ith, void * data) {
    for (int k = 0; k < 2 && ir_prefetch < end_row; k++) {
        uint32_t current_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
        uint32_t i03, i02, i01, rem;
-        i03 = fastdiv(ir_prefetch, &bctx->dim12_div);
+        i03 = fastdiv(ir_prefetch, &bctx->src0_dim12_div);
        rem = ir_prefetch - i03 * (ne02 * ne01);
-        i02 = fastdiv(rem, &bctx->dim1_div);
+        i02 = fastdiv(rem, &bctx->src0_dim1_div);
        i01 = rem - i02 * ne01;

        uint8_t * src0_curr = (uint8_t *)src0->data + i03 * nb03 + i02 * nb02 + i01 * nb01;
@@ -215,7 +214,7 @@ static void binary_job_scalar(unsigned int nth, unsigned int ith, void * data) {
        uint8_t * s0_spad = src0_spad_base + spad_idx * src0_spad_half;
        uint8_t * d_spad  = dst_spad_base  + spad_idx * dst_spad_half;

-        dma_queue_push_vtcm_to_ddr(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, 0);
+        dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, row_size_bytes, 0);
        dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, row_size_bytes, current_block_size);
        ir_prefetch += current_block_size;
        spad_idx ^= 1;
@@ -229,9 +228,9 @@ static void binary_job_scalar(unsigned int nth, unsigned int ith, void * data) {
        uint8_t * s0_spad = (uint8_t *) dma_queue_pop(q).dst;

        uint32_t i03, i02, i01, rem;
-        i03 = fastdiv(ir, &bctx->dim12_div);
+        i03 = fastdiv(ir, &bctx->src0_dim12_div);
        rem = ir - i03 * (ne02 * ne01);
-        i02 = fastdiv(rem, &bctx->dim1_div);
+        i02 = fastdiv(rem, &bctx->src0_dim1_div);
        i01 = rem - i02 * ne01;

        // src1 indices (broadcast/repeat)
@@ -255,9 +254,9 @@ static void binary_job_scalar(unsigned int nth, unsigned int ith, void * data) {
        if (ir_prefetch < end_row) {
             uint32_t next_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
             uint32_t p03, p02, p01, prem;
-             p03 = fastdiv(ir_prefetch, &bctx->dim12_div);
+             p03 = fastdiv(ir_prefetch, &bctx->src0_dim12_div);
             prem = ir_prefetch - p03 * (ne02 * ne01);
-             p02 = fastdiv(prem, &bctx->dim1_div);
+             p02 = fastdiv(prem, &bctx->src0_dim1_div);
             p01 = prem - p02 * ne01;
             uint8_t * s0_next = (uint8_t *)src0->data + p03 * nb03 + p02 * nb02 + p01 * nb01;

@@ -282,6 +281,8 @@ static void binary_job_vector_same_shape(unsigned int nth, unsigned int ith, voi
    const uint32_t end_row   = MIN(start_row + bctx->nrows_per_thread, total_rows);
    if (start_row >= end_row) return;

+    FARF(HIGH, "binary-same-shape: %d/%d (%u:%u) row-size %u (%u)", ith, nth, start_row, end_row, nb01, bctx->dst_row_size_aligned);
+
    uint8_t * src0_spad_base = octx->src0_spad.data + (ith * octx->src0_spad.size_per_thread);
    uint8_t * src1_spad_base = octx->src1_spad.data + (ith * octx->src1_spad.size_per_thread);
    uint8_t * dst_spad_base  = octx->dst_spad.data  + (ith * octx->dst_spad.size_per_thread);
@@ -297,9 +298,9 @@ static void binary_job_vector_same_shape(unsigned int nth, unsigned int ith, voi
    for (int k = 0; k < 2 && ir_prefetch < end_row; k++) {
        uint32_t current_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
        uint32_t i03, i02, i01, rem;
-        i03 = fastdiv(ir_prefetch, &bctx->dim12_div);
+        i03 = fastdiv(ir_prefetch, &bctx->src0_dim12_div);
        rem = ir_prefetch - i03 * (ne02 * ne01);
-        i02 = fastdiv(rem, &bctx->dim1_div);
+        i02 = fastdiv(rem, &bctx->src0_dim1_div);
        i01 = rem - i02 * ne01;

        uint32_t i13 = (ne13 == 1) ? 0 : i03;
@@ -307,23 +308,23 @@ static void binary_job_vector_same_shape(unsigned int nth, unsigned int ith, voi
        uint32_t i11 = (ne11 == 1) ? 0 : i01;

        uint8_t * src0_curr = (uint8_t *)src0->data + i03 * nb03 + i02 * nb02 + i01 * nb01;
-        uint8_t * src1_base = (uint8_t *)src1->data + i13 * nb13 + i12 * nb12 + i11 * nb11;
+        uint8_t * src1_curr = (uint8_t *)src1->data + i13 * nb13 + i12 * nb12 + i11 * nb11;
        uint8_t * dst_curr  = (uint8_t *)dst->data  + i03 * nb3  + i02 * nb2  + i01 * nb1;

        uint8_t * s0_spad = src0_spad_base + spad_idx * src0_spad_half;
        uint8_t * s1_spad = src1_spad_base + spad_idx * src1_spad_half;
        uint8_t * d_spad  = dst_spad_base  + spad_idx * dst_spad_half;

-        dma_queue_push_vtcm_to_ddr(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, 0);
+        dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, row_size_bytes, 0);
        dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, row_size_bytes, current_block_size);
-        dma_queue_push(q, dma_make_ptr(s1_spad, src1_base), bctx->src1_row_size_aligned, bctx->src1_dma_stride, row_size_bytes, current_block_size);
+        dma_queue_push(q, dma_make_ptr(s1_spad, src1_curr), bctx->src1_row_size_aligned, nb11, row_size_bytes, current_block_size);
        ir_prefetch += current_block_size;
        spad_idx ^= 1;
    }

    for (uint32_t ir = start_row; ir < end_row; ) {
        uint32_t current_block_size = calc_block_size(bctx, ir, end_row, ne01, ne02);
-        uint8_t * d_spad = (uint8_t *) dma_queue_pop(q).src;
+        uint8_t * d_spad  = (uint8_t *) dma_queue_pop(q).src;
        uint8_t * s0_spad = (uint8_t *) dma_queue_pop(q).dst;
        uint8_t * s1_spad = (uint8_t *) dma_queue_pop(q).dst;

@@ -335,9 +336,9 @@ static void binary_job_vector_same_shape(unsigned int nth, unsigned int ith, voi
        }

        uint32_t i03, i02, i01, rem;
-        i03 = fastdiv(ir, &bctx->dim12_div);
+        i03 = fastdiv(ir, &bctx->src0_dim12_div);
        rem = ir - i03 * (ne02 * ne01);
-        i02 = fastdiv(rem, &bctx->dim1_div);
+        i02 = fastdiv(rem, &bctx->src0_dim1_div);
        i01 = rem - i02 * ne01;
        uint8_t * dst_curr = (uint8_t *)dst->data + i03 * nb3 + i02 * nb2 + i01 * nb1;
        dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, row_size_bytes, current_block_size);
@@ -345,9 +346,9 @@ static void binary_job_vector_same_shape(unsigned int nth, unsigned int ith, voi
        if (ir_prefetch < end_row) {
             uint32_t next_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
             uint32_t p03, p02, p01, prem;
-             p03 = fastdiv(ir_prefetch, &bctx->dim12_div);
+             p03 = fastdiv(ir_prefetch, &bctx->src0_dim12_div);
             prem = ir_prefetch - p03 * (ne02 * ne01);
-             p02 = fastdiv(prem, &bctx->dim1_div);
+             p02 = fastdiv(prem, &bctx->src0_dim1_div);
             p01 = prem - p02 * ne01;

             uint32_t p13 = (ne13 == 1) ? 0 : p03;
@@ -358,7 +359,7 @@ static void binary_job_vector_same_shape(unsigned int nth, unsigned int ith, voi
             uint8_t * s1_next = (uint8_t *)src1->data + p13 * nb13 + p12 * nb12 + p11 * nb11;

             dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, row_size_bytes, next_block_size);
-             dma_queue_push(q, dma_make_ptr(s1_spad, s1_next), bctx->src1_row_size_aligned, bctx->src1_dma_stride, row_size_bytes, next_block_size);
+             dma_queue_push(q, dma_make_ptr(s1_spad, s1_next), bctx->src1_row_size_aligned, nb11, row_size_bytes, next_block_size);

             ir_prefetch += next_block_size;
        }
@@ -373,15 +374,17 @@ static void binary_job_vector_row_broadcast(unsigned int nth, unsigned int ith,
    struct htp_ops_context * octx = bctx->octx;
    htp_binary_preamble;

-    const uint32_t src0_type = octx->src0.type;
+    const uint32_t src0_type  = octx->src0.type;
    const uint32_t row_size_bytes = (src0_type == HTP_TYPE_F32) ? ne00 * sizeof(float) : ne00 * sizeof(_Float16);
    const uint32_t total_rows = ne01 * ne02 * ne03;
-    const uint32_t start_row = bctx->nrows_per_thread * ith;
-    const uint32_t end_row   = MIN(start_row + bctx->nrows_per_thread, total_rows);
+    const uint32_t start_row  = bctx->nrows_per_thread * ith;
+    const uint32_t end_row    = MIN(start_row + bctx->nrows_per_thread, total_rows);
    if (start_row >= end_row) return;

+    FARF(HIGH, "binary-row-bcast: %d/%d (%u:%u) row-size %u (%u)", ith, nth, start_row, end_row, nb01, bctx->dst_row_size_aligned);
+
    uint8_t * src0_spad_base = octx->src0_spad.data + (ith * octx->src0_spad.size_per_thread);
-    uint8_t * src1_spad = octx->src1_spad.data + (ith * octx->src1_spad.size_per_thread);
+    uint8_t * src1_spad_base = octx->src1_spad.data + (ith * octx->src1_spad.size_per_thread);
    uint8_t * dst_spad_base  = octx->dst_spad.data  + (ith * octx->dst_spad.size_per_thread);

    size_t src0_spad_half = octx->src0_spad.size_per_thread / 2;
@@ -391,15 +394,14 @@ static void binary_job_vector_row_broadcast(unsigned int nth, unsigned int ith,
    uint32_t ir_prefetch = start_row;
    int spad_idx = 0;

-    void * s1_ptr = (void *) src1_spad;
+    void * s1_ptr = (void *) src1_spad_base;

    for (int k = 0; k < 2 && ir_prefetch < end_row; k++) {
        uint32_t current_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
-        uint32_t i03, i02, i01, rem;
-        i03 = fastdiv(ir_prefetch, &bctx->dim12_div);
-        rem = ir_prefetch - i03 * (ne02 * ne01);
-        i02 = fastdiv(rem, &bctx->dim1_div);
-        i01 = rem - i02 * ne01;
+        uint32_t i03 = fastdiv(ir_prefetch, &bctx->src0_dim12_div);
+        uint32_t rem = ir_prefetch - i03 * (ne02 * ne01);
+        uint32_t i02 = fastdiv(rem, &bctx->src0_dim1_div);
+        uint32_t i01 = rem - i02 * ne01;

        uint8_t * src0_curr = (uint8_t *)src0->data + i03 * nb03 + i02 * nb02 + i01 * nb01;
        uint8_t * dst_curr  = (uint8_t *)dst->data  + i03 * nb3  + i02 * nb2  + i01 * nb1;
@@ -407,7 +409,7 @@ static void binary_job_vector_row_broadcast(unsigned int nth, unsigned int ith,
        uint8_t * s0_spad = src0_spad_base + spad_idx * src0_spad_half;
        uint8_t * d_spad  = dst_spad_base  + spad_idx * dst_spad_half;

-        dma_queue_push_vtcm_to_ddr(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, 0);
+        dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, row_size_bytes, 0);
        dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, row_size_bytes, current_block_size);
        ir_prefetch += current_block_size;
        spad_idx ^= 1;
@@ -415,7 +417,7 @@ static void binary_job_vector_row_broadcast(unsigned int nth, unsigned int ith,

    for (uint32_t ir = start_row; ir < end_row; ) {
        uint32_t current_block_size = calc_block_size(bctx, ir, end_row, ne01, ne02);
-        uint8_t * d_spad = (uint8_t *) dma_queue_pop(q).src;
+        uint8_t * d_spad  = (uint8_t *) dma_queue_pop(q).src;
        uint8_t * s0_spad = (uint8_t *) dma_queue_pop(q).dst;

        for (uint32_t r = 0; r < current_block_size; r++) {
@@ -425,21 +427,19 @@ static void binary_job_vector_row_broadcast(unsigned int nth, unsigned int ith,
            COMPUTE_VECTOR_OP_AAA(r_dst, r_src0, r_src1, src0_type, ne00);
        }

-        uint32_t i03, i02, i01, rem;
-        i03 = fastdiv(ir, &bctx->dim12_div);
-        rem = ir - i03 * (ne02 * ne01);
-        i02 = fastdiv(rem, &bctx->dim1_div);
-        i01 = rem - i02 * ne01;
+        uint32_t i03 = fastdiv(ir, &bctx->src0_dim12_div);
+        uint32_t rem = ir - i03 * (ne02 * ne01);
+        uint32_t i02 = fastdiv(rem, &bctx->src0_dim1_div);
+        uint32_t i01 = rem - i02 * ne01;
        uint8_t * dst_curr = (uint8_t *)dst->data + i03 * nb3 + i02 * nb2 + i01 * nb1;
        dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, row_size_bytes, current_block_size);

        if (ir_prefetch < end_row) {
             uint32_t next_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
-             uint32_t p03, p02, p01, prem;
-             p03 = fastdiv(ir_prefetch, &bctx->dim12_div);
-             prem = ir_prefetch - p03 * (ne02 * ne01);
-             p02 = fastdiv(prem, &bctx->dim1_div);
-             p01 = prem - p02 * ne01;
+             uint32_t p03  = fastdiv(ir_prefetch, &bctx->src0_dim12_div);
+             uint32_t prem = ir_prefetch - p03 * (ne02 * ne01);
+             uint32_t p02  = fastdiv(prem, &bctx->src0_dim1_div);
+             uint32_t p01  = prem - p02 * ne01;
             uint8_t * s0_next = (uint8_t *)src0->data + p03 * nb03 + p02 * nb02 + p01 * nb01;
             dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, row_size_bytes, next_block_size);
             ir_prefetch += next_block_size;
@@ -458,14 +458,16 @@ static void binary_job_vector_complex(unsigned int nth, unsigned int ith, void *
    const uint32_t src0_type = octx->src0.type;
    const uint32_t row_size_bytes = (src0_type == HTP_TYPE_F32) ? ne00 * sizeof(float) : ne00 * sizeof(_Float16);
    const uint32_t total_rows = ne01 * ne02 * ne03;
-    const uint32_t start_row = bctx->nrows_per_thread * ith;
-    const uint32_t end_row   = MIN(start_row + bctx->nrows_per_thread, total_rows);
+    const uint32_t start_row  = bctx->nrows_per_thread * ith;
+    const uint32_t end_row    = MIN(start_row + bctx->nrows_per_thread, total_rows);
    if (start_row >= end_row) return;

+    FARF(HIGH, "binary-complex: %d/%d (%u:%u) row-size %u (%u)", ith, nth, start_row, end_row, nb01, bctx->dst_row_size_aligned);
+
    uint8_t * src0_spad_base = octx->src0_spad.data + (ith * octx->src0_spad.size_per_thread);
    uint8_t * dst_spad_base  = octx->dst_spad.data  + (ith * octx->dst_spad.size_per_thread);
-    size_t src0_spad_half = octx->src0_spad.size_per_thread / 2;
-    size_t dst_spad_half  = octx->dst_spad.size_per_thread  / 2;
+    size_t src0_spad_half    = octx->src0_spad.size_per_thread / 2;
+    size_t dst_spad_half     = octx->dst_spad.size_per_thread  / 2;

    dma_queue * q = octx->ctx->dma[ith];
    uint32_t ir_prefetch = start_row;
@@ -473,11 +475,10 @@ static void binary_job_vector_complex(unsigned int nth, unsigned int ith, void *

    for (int k = 0; k < 2 && ir_prefetch < end_row; k++) {
        uint32_t current_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
-        uint32_t i03, i02, i01, rem;
-        i03 = fastdiv(ir_prefetch, &bctx->dim12_div);
-        rem = ir_prefetch - i03 * (ne02 * ne01);
-        i02 = fastdiv(rem, &bctx->dim1_div);
-        i01 = rem - i02 * ne01;
+        uint32_t i03 = fastdiv(ir_prefetch, &bctx->src0_dim12_div);
+        uint32_t rem = ir_prefetch - i03 * (ne02 * ne01);
+        uint32_t i02 = fastdiv(rem, &bctx->src0_dim1_div);
+        uint32_t i01 = rem - i02 * ne01;

        uint8_t * src0_curr = (uint8_t *)src0->data + i03 * nb03 + i02 * nb02 + i01 * nb01;
        uint8_t * dst_curr  = (uint8_t *)dst->data  + i03 * nb3  + i02 * nb2  + i01 * nb1;
@@ -485,7 +486,7 @@ static void binary_job_vector_complex(unsigned int nth, unsigned int ith, void *
        uint8_t * s0_spad = src0_spad_base + spad_idx * src0_spad_half;
        uint8_t * d_spad  = dst_spad_base  + spad_idx * dst_spad_half;

-        dma_queue_push_vtcm_to_ddr(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, 0);
+        dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, row_size_bytes, 0);
        dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, row_size_bytes, current_block_size);
        ir_prefetch += current_block_size;
        spad_idx ^= 1;
@@ -496,11 +497,10 @@ static void binary_job_vector_complex(unsigned int nth, unsigned int ith, void *
        uint8_t * d_spad = (uint8_t *) dma_queue_pop(q).src;
        uint8_t * s0_spad = (uint8_t *) dma_queue_pop(q).dst;

-        uint32_t i03, i02, i01, rem;
-        i03 = fastdiv(ir, &bctx->dim12_div);
-        rem = ir - i03 * (ne02 * ne01);
-        i02 = fastdiv(rem, &bctx->dim1_div);
-        i01 = rem - i02 * ne01;
+        uint32_t i03 = fastdiv(ir, &bctx->src0_dim12_div);
+        uint32_t rem = ir - i03 * (ne02 * ne01);
+        uint32_t i02 = fastdiv(rem, &bctx->src0_dim1_div);
+        uint32_t i01 = rem - i02 * ne01;

        for (uint32_t r = 0; r < current_block_size; r++) {
            uint32_t r_i01 = i01 + r;
@@ -521,11 +521,10 @@ static void binary_job_vector_complex(unsigned int nth, unsigned int ith, void *

        if (ir_prefetch < end_row) {
             uint32_t next_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
-             uint32_t p03, p02, p01, prem;
-             p03 = fastdiv(ir_prefetch, &bctx->dim12_div);
-             prem = ir_prefetch - p03 * (ne02 * ne01);
-             p02 = fastdiv(prem, &bctx->dim1_div);
-             p01 = prem - p02 * ne01;
+             uint32_t p03  = fastdiv(ir_prefetch, &bctx->src0_dim12_div);
+             uint32_t prem = ir_prefetch - p03 * (ne02 * ne01);
+             uint32_t p02  = fastdiv(prem, &bctx->src0_dim1_div);
+             uint32_t p01  = prem - p02 * ne01;
             uint8_t * s0_next = (uint8_t *)src0->data + p03 * nb03 + p02 * nb02 + p01 * nb01;
             dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, row_size_bytes, next_block_size);
             ir_prefetch += next_block_size;
@@ -545,14 +544,16 @@ static void binary_job_element_repeat(unsigned int nth, unsigned int ith, void *
    const uint32_t elem_size_bytes = (src0_type == HTP_TYPE_F32) ? sizeof(float) : sizeof(_Float16);
    const uint32_t row_size_bytes = ne00 * elem_size_bytes;;
    const uint32_t total_rows = ne01 * ne02 * ne03;
-    const uint32_t start_row = bctx->nrows_per_thread * ith;
-    const uint32_t end_row   = MIN(start_row + bctx->nrows_per_thread, total_rows);
+    const uint32_t start_row  = bctx->nrows_per_thread * ith;
+    const uint32_t end_row    = MIN(start_row + bctx->nrows_per_thread, total_rows);
    if (start_row >= end_row) return;

    uint8_t * src0_spad_base = octx->src0_spad.data + (ith * octx->src0_spad.size_per_thread);
    uint8_t * dst_spad_base  = octx->dst_spad.data  + (ith * octx->dst_spad.size_per_thread);
-    size_t src0_spad_half = octx->src0_spad.size_per_thread / 2;
-    size_t dst_spad_half  = octx->dst_spad.size_per_thread  / 2;
+    size_t src0_spad_half    = octx->src0_spad.size_per_thread / 2;
+    size_t dst_spad_half     = octx->dst_spad.size_per_thread  / 2;
+
+    FARF(HIGH, "binary-repeat: %d/%d (%u:%u) row-size %u (%u)", ith, nth, start_row, end_row, nb01, bctx->dst_row_size_aligned);

    dma_queue * q = octx->ctx->dma[ith];
    uint32_t ir_prefetch = start_row;
@@ -560,11 +561,10 @@ static void binary_job_element_repeat(unsigned int nth, unsigned int ith, void *

    for (int k = 0; k < 2 && ir_prefetch < end_row; k++) {
        uint32_t current_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
-        uint32_t i03, i02, i01, rem;
-        i03 = fastdiv(ir_prefetch, &bctx->dim12_div);
-        rem = ir_prefetch - i03 * (ne02 * ne01);
-        i02 = fastdiv(rem, &bctx->dim1_div);
-        i01 = rem - i02 * ne01;
+        uint32_t i03 = fastdiv(ir_prefetch, &bctx->src0_dim12_div);
+        uint32_t rem = ir_prefetch - i03 * (ne02 * ne01);
+        uint32_t i02 = fastdiv(rem, &bctx->src0_dim1_div);
+        uint32_t i01 = rem - i02 * ne01;

        uint8_t * src0_curr = (uint8_t *)src0->data + i03 * nb03 + i02 * nb02 + i01 * nb01;
        uint8_t * dst_curr  = (uint8_t *)dst->data  + i03 * nb3  + i02 * nb2  + i01 * nb1;
@@ -572,7 +572,7 @@ static void binary_job_element_repeat(unsigned int nth, unsigned int ith, void *
        uint8_t * s0_spad = src0_spad_base + spad_idx * src0_spad_half;
        uint8_t * d_spad  = dst_spad_base  + spad_idx * dst_spad_half;

-        dma_queue_push_vtcm_to_ddr(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, 0);
+        dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, row_size_bytes, 0);
        dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, row_size_bytes, current_block_size);
        ir_prefetch += current_block_size;
        spad_idx ^= 1;
@@ -583,11 +583,10 @@ static void binary_job_element_repeat(unsigned int nth, unsigned int ith, void *
        uint8_t * d_spad = (uint8_t *) dma_queue_pop(q).src;
        uint8_t * s0_spad = (uint8_t *) dma_queue_pop(q).dst;

-        uint32_t i03, i02, i01, rem;
-        i03 = fastdiv(ir, &bctx->dim12_div);
-        rem = ir - i03 * (ne02 * ne01);
-        i02 = fastdiv(rem, &bctx->dim1_div);
-        i01 = rem - i02 * ne01;
+        uint32_t i03 = fastdiv(ir, &bctx->src0_dim12_div);
+        uint32_t rem = ir - i03 * (ne02 * ne01);
+        uint32_t i02 = fastdiv(rem, &bctx->src0_dim1_div);
+        uint32_t i01 = rem - i02 * ne01;

        for (uint32_t r = 0; r < current_block_size; r++) {
            uint32_t r_i01 = i01 + r;
@@ -612,11 +611,10 @@ static void binary_job_element_repeat(unsigned int nth, unsigned int ith, void *

        if (ir_prefetch < end_row) {
             uint32_t next_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
-             uint32_t p03, p02, p01, prem;
-             p03 = fastdiv(ir_prefetch, &bctx->dim12_div);
-             prem = ir_prefetch - p03 * (ne02 * ne01);
-             p02 = fastdiv(prem, &bctx->dim1_div);
-             p01 = prem - p02 * ne01;
+             uint32_t p03  = fastdiv(ir_prefetch, &bctx->src0_dim12_div);
+             uint32_t prem = ir_prefetch - p03 * (ne02 * ne01);
+             uint32_t p02  = fastdiv(prem, &bctx->src0_dim1_div);
+             uint32_t p01  = prem - p02 * ne01;
             uint8_t * s0_next = (uint8_t *)src0->data + p03 * nb03 + p02 * nb02 + p01 * nb01;
             dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, row_size_bytes, next_block_size);
             ir_prefetch += next_block_size;
@@ -646,6 +644,7 @@ static void binary_job_add_id(unsigned int nth, unsigned int ith, void * data) {
    const uint32_t nb02 = src0->nb[2];
    const uint32_t nb03 = src0->nb[3];
    const uint32_t nb11 = src1->nb[1]; // src1 row stride
+
    const uint32_t nb1 = dst->nb[1];
    const uint32_t nb2 = dst->nb[2];
    const uint32_t nb3 = dst->nb[3];
@@ -657,8 +656,8 @@ static void binary_job_add_id(unsigned int nth, unsigned int ith, void * data) {

    uint8_t * src0_spad_base = octx->src0_spad.data + (ith * octx->src0_spad.size_per_thread);
    uint8_t * dst_spad_base  = octx->dst_spad.data  + (ith * octx->dst_spad.size_per_thread);
-    size_t src0_spad_half = octx->src0_spad.size_per_thread / 2;
-    size_t dst_spad_half  = octx->dst_spad.size_per_thread  / 2;
+    size_t src0_spad_half    = octx->src0_spad.size_per_thread / 2;
+    size_t dst_spad_half     = octx->dst_spad.size_per_thread  / 2;

    dma_queue * q = octx->ctx->dma[ith];
    uint32_t ir_prefetch = start_row;
@@ -666,11 +665,10 @@ static void binary_job_add_id(unsigned int nth, unsigned int ith, void * data) {

    for (int k = 0; k < 2 && ir_prefetch < end_row; k++) {
        uint32_t current_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
-        uint32_t i03, i02, i01, rem;
-        i03 = fastdiv(ir_prefetch, &bctx->dim12_div);
-        rem = ir_prefetch - i03 * (ne02 * ne01);
-        i02 = fastdiv(rem, &bctx->dim1_div);
-        i01 = rem - i02 * ne01;
+        uint32_t i03 = fastdiv(ir_prefetch, &bctx->src0_dim12_div);
+        uint32_t rem = ir_prefetch - i03 * (ne02 * ne01);
+        uint32_t i02 = fastdiv(rem, &bctx->src0_dim1_div);
+        uint32_t i01 = rem - i02 * ne01;

        uint8_t * src0_curr = (uint8_t *)src0->data + i03 * nb03 + i02 * nb02 + i01 * nb01;
        uint8_t * dst_curr  = (uint8_t *)dst->data  + i03 * nb3  + i02 * nb2  + i01 * nb1;
@@ -678,7 +676,7 @@ static void binary_job_add_id(unsigned int nth, unsigned int ith, void * data) {
        uint8_t * s0_spad = src0_spad_base + spad_idx * src0_spad_half;
        uint8_t * d_spad  = dst_spad_base  + spad_idx * dst_spad_half;

-        dma_queue_push_vtcm_to_ddr(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, 0);
+        dma_queue_push(q, dma_make_ptr(dst_curr, d_spad), nb1, bctx->dst_row_size_aligned, ne00 * sizeof(float), 0);
        dma_queue_push(q, dma_make_ptr(s0_spad, src0_curr), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), current_block_size);
        ir_prefetch += current_block_size;
        spad_idx ^= 1;
@@ -689,11 +687,10 @@ static void binary_job_add_id(unsigned int nth, unsigned int ith, void * data) {
        uint8_t * d_spad = (uint8_t *) dma_queue_pop(q).src;
        uint8_t * s0_spad = (uint8_t *) dma_queue_pop(q).dst;

-        uint32_t i03, i02, i01, rem;
-        i03 = fastdiv(ir, &bctx->dim12_div);
-        rem = ir - i03 * (ne02 * ne01);
-        i02 = fastdiv(rem, &bctx->dim1_div);
-        i01 = rem - i02 * ne01;
+        uint32_t i03 = fastdiv(ir, &bctx->src0_dim12_div);
+        uint32_t rem = ir - i03 * (ne02 * ne01);
+        uint32_t i02 = fastdiv(rem, &bctx->src0_dim1_div);
+        uint32_t i01 = rem - i02 * ne01;

        for (uint32_t r = 0; r < current_block_size; r++) {
            uint32_t r_i01 = i01 + r; // linear within block since we split at ne01
@@ -712,11 +709,10 @@ static void binary_job_add_id(unsigned int nth, unsigned int ith, void * data) {

        if (ir_prefetch < end_row) {
             uint32_t next_block_size = calc_block_size(bctx, ir_prefetch, end_row, ne01, ne02);
-             uint32_t p03, p02, p01, prem;
-             p03 = fastdiv(ir_prefetch, &bctx->dim12_div);
-             prem = ir_prefetch - p03 * (ne02 * ne01);
-             p02 = fastdiv(prem, &bctx->dim1_div);
-             p01 = prem - p02 * ne01;
+             uint32_t p03  = fastdiv(ir_prefetch, &bctx->src0_dim12_div);
+             uint32_t prem = ir_prefetch - p03 * (ne02 * ne01);
+             uint32_t p02  = fastdiv(prem, &bctx->src0_dim1_div);
+             uint32_t p01  = prem - p02 * ne01;
             uint8_t * s0_next = (uint8_t *)src0->data + p03 * nb03 + p02 * nb02 + p01 * nb01;
             dma_queue_push(q, dma_make_ptr(s0_spad, s0_next), bctx->src0_row_size_aligned, nb01, ne00 * sizeof(float), next_block_size);
             ir_prefetch += next_block_size;
@@ -739,40 +735,36 @@ static int execute_op_binary(struct htp_ops_context * octx) {
    const size_t elem_size = (src0_type == HTP_TYPE_F32) ? sizeof(float) : sizeof(_Float16);
    const size_t src0_row_size = src0->ne[0] * elem_size;
    const size_t src1_row_size = src1->ne[0] * elem_size;
-    const size_t dst_row_size  = dst->ne[0] * elem_size;
+    const size_t dst_row_size  = dst->ne[0]  * elem_size;

-    // Align to VLEN
-    const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN);
-    const size_t dst_row_size_aligned  = hex_round_up(dst_row_size, VLEN);
+    size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN);
    size_t src1_row_size_aligned = hex_round_up(src1_row_size, VLEN);
+    size_t dst_row_size_aligned  = hex_round_up(dst_row_size,  VLEN);

    bool is_add_id = (octx->op == HTP_OP_ADD_ID);
    bool is_scalar = !is_add_id && (src1->ne[0] == 1);

-    // Determine which kernel we will use to alloc memory and dispatch
-    bool use_vector_same = !is_add_id && !is_scalar && ((src0->nb[1] % VLEN) == 0) && (src1->ne[0] == src0->ne[0]) &&
+    bool is_transposed = (src0->nb[1] < src0_row_size || src1->nb[1] < src1_row_size || dst->nb[1] < dst_row_size);
+
+    bool is_same_shape = !is_add_id && !is_scalar && !is_transposed &&
+               (src1->ne[0] == src0->ne[0] && src0->ne[0] % VLEN == 0) &&
               (src1->ne[1] == src0->ne[1] || src1->ne[1] == 1) &&
               (src1->ne[2] == src0->ne[2] || src1->ne[2] == 1) &&
               (src1->ne[3] == src0->ne[3] || src1->ne[3] == 1);

-    bool is_row_bcast = use_vector_same && (src1->ne[1] == 1 && src1->ne[2] == 1 && src1->ne[3] == 1);
-    bool use_complex = !is_add_id && !is_scalar && !use_vector_same && (src1->ne[0] == src0->ne[0]);
-    bool use_repeat  = !is_add_id && !is_scalar && !use_vector_same && (src1->ne[0] != src0->ne[0]);
+    bool is_row_bcast = is_same_shape && (src1->ne[1] == 1 && src1->ne[2] == 1 && src1->ne[3] == 1);
+    bool is_complex   = !is_add_id && !is_scalar && !is_same_shape && (src1->ne[0] == src0->ne[0]);
+    bool is_repeat    = !is_add_id && !is_scalar && !is_same_shape && (src1->ne[0] != src0->ne[0]);

    size_t spad_row_total;
-    if (is_scalar) {
-        spad_row_total = 2 * (src0_row_size_aligned + dst_row_size_aligned);
-    } else if (is_row_bcast) {
-        spad_row_total = 2 * (src0_row_size_aligned + dst_row_size_aligned);
-    } else if (use_vector_same) {
+    if (is_same_shape) {
        spad_row_total = 2 * (src0_row_size_aligned + src1_row_size_aligned + dst_row_size_aligned);
-    } else if (is_add_id) {
-        spad_row_total = 2 * (src0_row_size_aligned + dst_row_size_aligned); // src1 read directly
    } else {
        spad_row_total = 2 * (src0_row_size_aligned + dst_row_size_aligned);
    }

    size_t rows_per_buffer = octx->ctx->vtcm_size / (n_threads * spad_row_total);
+
    // Adjust for static src1 in row_bcast case
    if (is_row_bcast) {
        size_t needed_static = src1_row_size_aligned;
@@ -782,28 +774,26 @@ static int execute_op_binary(struct htp_ops_context * octx) {
    }

    if (rows_per_buffer < 1) {
-         FARF(ERROR, "binary: VTCM too small\n");
-         return HTP_STATUS_VTCM_TOO_SMALL;
+        FARF(ERROR, "binary: VTCM too small\n");
+        return HTP_STATUS_VTCM_TOO_SMALL;
    }

    octx->src0_spad.size_per_thread = rows_per_buffer * 2 * src0_row_size_aligned;
    octx->dst_spad.size_per_thread  = rows_per_buffer * 2 * dst_row_size_aligned;

-    if (is_scalar || use_complex || use_repeat || is_add_id) {
-        octx->src1_spad.size_per_thread = 0;
-    } else if (is_row_bcast) {
+    if (is_add_id || is_scalar || is_complex || is_repeat || is_row_bcast) {
        octx->src1_spad.size_per_thread = 0;
    } else {
        octx->src1_spad.size_per_thread = rows_per_buffer * 2 * src1_row_size_aligned;
    }

+    octx->dst_spad.size  = n_threads * octx->dst_spad.size_per_thread;
    octx->src0_spad.size = n_threads * octx->src0_spad.size_per_thread;
    if (is_row_bcast) {
        octx->src1_spad.size = src1_row_size_aligned;
    } else {
        octx->src1_spad.size = n_threads * octx->src1_spad.size_per_thread;
    }
-    octx->dst_spad.size  = n_threads * octx->dst_spad.size_per_thread;

    if (octx->ctx->vtcm_size < (octx->src0_spad.size + octx->src1_spad.size + octx->dst_spad.size)) {
        return HTP_STATUS_VTCM_TOO_SMALL;
@@ -823,46 +813,37 @@ static int execute_op_binary(struct htp_ops_context * octx) {
    }

    struct htp_binary_context bctx;
-    bctx.octx = octx;
-    bctx.nrows_per_thread = (src0_nrows + n_threads - 1) / n_threads;
-    bctx.block_max = rows_per_buffer;
+    bctx.octx                  = octx;
+    bctx.nrows_per_thread      = (src0_nrows + n_threads - 1) / n_threads;
+    bctx.block_max             = rows_per_buffer;
    bctx.src0_row_size_aligned = src0_row_size_aligned;
    bctx.src1_row_size_aligned = src1_row_size_aligned;
    bctx.dst_row_size_aligned  = dst_row_size_aligned;

-    bctx.dim1_div = init_fastdiv_values(src0->ne[1]);
-    bctx.dim2_div = init_fastdiv_values(src0->ne[2]);
-    bctx.dim12_div = init_fastdiv_values(src0->ne[1] * src0->ne[2]);
+    bctx.src0_dim1_div  = init_fastdiv_values(src0->ne[1]);
+    bctx.src0_dim2_div  = init_fastdiv_values(src0->ne[2]);
+    bctx.src0_dim12_div = init_fastdiv_values(src0->ne[1] * src0->ne[2]);

-    bctx.src1_dim1_div = init_fastdiv_values(src1->ne[1]);
-    bctx.src1_dim2_div = init_fastdiv_values(src1->ne[2]);
-    bctx.src1_dim3_div = init_fastdiv_values(src1->ne[3]);
+    bctx.src1_dim1_div  = init_fastdiv_values(src1->ne[1]);
+    bctx.src1_dim2_div  = init_fastdiv_values(src1->ne[2]);
+    bctx.src1_dim3_div  = init_fastdiv_values(src1->ne[3]);

    bool src0_contig_dim1 = (src0->nb[2] == src0->ne[1] * src0->nb[1]);
-    bool dst_contig_dim1  = (dst->nb[2] == src0->ne[1] * dst->nb[1]);
+    bool dst_contig_dim1  = (dst->nb[2]  == src0->ne[1] * dst->nb[1]);

    bool src0_contig_dim2 = (src0->nb[3] == src0->ne[2] * src0->nb[2]);
-    bool dst_contig_dim2  = (dst->nb[3] == src0->ne[2] * dst->nb[2]);
+    bool dst_contig_dim2  = (dst->nb[3]  == src0->ne[2] * dst->nb[2]);

-    bctx.split_at_ne01 = (src0->ne[2] > 1) &&
-                         ((src1->ne[1] > 1) || (src1->ne[2] > 1) || !src0_contig_dim1 || !dst_contig_dim1);
-
-    bctx.split_at_ne02 = (src0->ne[3] > 1) &&
-                         ((src1->ne[2] > 1) || (src1->ne[3] > 1) || !src0_contig_dim2 || !dst_contig_dim2);
-
-    // Precompute specific kernel parameters
-    if (use_vector_same) {
-        bctx.src1_dma_stride = (src1->ne[1] == 1) ? 0 : src1->nb[1];
-        bctx.src1_fetch_rows = (src1->ne[1] == 1) ? 1 : rows_per_buffer;
-    }
+    bctx.split_at_ne01 = (src0->ne[2] > 1) && ((src1->ne[1] > 1) || (src1->ne[2] > 1) || !src0_contig_dim1 || !dst_contig_dim1);
+    bctx.split_at_ne02 = (src0->ne[3] > 1) && ((src1->ne[2] > 1) || (src1->ne[3] > 1) || !src0_contig_dim2 || !dst_contig_dim2);

    worker_callback_t worker_func;
-    if (is_add_id) worker_func = binary_job_add_id;
-    else if (is_scalar) worker_func = binary_job_scalar;
-    else if (is_row_bcast) worker_func = binary_job_vector_row_broadcast;
-    else if (use_vector_same) worker_func = binary_job_vector_same_shape;
-    else if (use_complex) worker_func = binary_job_vector_complex;
-    else worker_func = binary_job_element_repeat;
+    if (is_add_id)          worker_func = binary_job_add_id;
+    else if (is_scalar)     worker_func = binary_job_scalar;
+    else if (is_row_bcast)  worker_func = binary_job_vector_row_broadcast;
+    else if (is_same_shape) worker_func = binary_job_vector_same_shape;
+    else if (is_complex)    worker_func = binary_job_vector_complex;
+    else                    worker_func = binary_job_element_repeat;

    if (is_row_bcast) {
        dma_queue_pop(q);
@@ -31,8 +31,8 @@ dma_queue * dma_queue_create(size_t capacity) {
    q->capacity = capacity;
    q->idx_mask = capacity - 1;

-    q->desc = (hexagon_udma_descriptor_type1_t *) memalign(64, capacity * sizeof(hexagon_udma_descriptor_type1_t));
-    memset(q->desc, 0, capacity * sizeof(hexagon_udma_descriptor_type1_t));
+    q->desc = (dma_descriptor_2d *) memalign(64, capacity * sizeof(dma_descriptor_2d));
+    memset(q->desc, 0, capacity * sizeof(dma_descriptor_2d));

    q->dptr = (dma_ptr *) memalign(4, capacity * sizeof(dma_ptr));
    memset(q->dptr, 0, capacity * sizeof(dma_ptr));
@@ -10,19 +10,84 @@
 extern "C" {
 #endif

+// Define the HW descriptor structs here since the ones in HexSDK are a bit out of date
+typedef struct dma_descriptor_1d_s {
+    void *   next;
+    uint32_t size:24;
+    uint32_t desc_size:2;
+    uint32_t dst_comp:1;
+    uint32_t src_comp:1;
+    uint32_t dst_bypass:1;
+    uint32_t src_bypass:1;
+    uint32_t order:1;
+    uint32_t done:1;
+    void *   src;
+    void *   dst;
+} dma_descriptor_1d;
+
+#if __HVX_ARCH__ < 75
+
+typedef struct dma_descriptor_2d_s {
+    void *   next;
+    uint32_t reserved0:24;
+    uint32_t desc_size:2;
+    uint32_t dst_comp:1;
+    uint32_t src_comp:1;
+    uint32_t dst_bypass:1;
+    uint32_t src_bypass:1;
+    uint32_t order:1;
+    uint32_t done:1;
+    void *   src;
+    void *   dst;
+    uint32_t desc_type:8;
+    uint32_t reserved1:24;
+    uint32_t row_size:16;
+    uint32_t nrows:16;
+    uint32_t src_stride:16;
+    uint32_t dst_stride:16;
+    uint32_t src_offset:16;
+    uint32_t dst_offset:16;
+} dma_descriptor_2d;
+
+#else
+
+typedef struct dma_descriptor_2d_s {
+    void *   next;
+    uint32_t dst_stride:24;
+    uint32_t desc_size:2;
+    uint32_t dst_comp:1;
+    uint32_t src_comp:1;
+    uint32_t dst_bypass:1;
+    uint32_t src_bypass:1;
+    uint32_t order:1;
+    uint32_t done:1;
+    void *   src;
+    void *   dst;
+    uint32_t desc_type:8;
+    uint32_t reserved0:24;
+    uint32_t row_size:24;
+    uint32_t nrows_lo:8;
+    uint32_t nrows_hi:8;
+    uint32_t src_stride:24;
+    uint32_t offset:24;
+    uint32_t reserved1:8;
+} dma_descriptor_2d;
+
+#endif
+
 typedef struct {
-    void *dst;
+    void       *dst;
    const void *src;
 } dma_ptr;

 typedef struct {
-    hexagon_udma_descriptor_type1_t * desc;  // descriptor pointers
-    hexagon_udma_descriptor_type1_t * tail;  // tail pointer
-    dma_ptr                         * dptr;  // dst/src pointers
-    uint32_t                          push_idx;
-    uint32_t                          pop_idx;
-    uint32_t                          capacity;
-    uint32_t                          idx_mask;
+    dma_descriptor_2d * desc;  // descriptor pointers
+    dma_descriptor_2d * tail;  // tail pointer
+    dma_ptr           * dptr;  // dst/src pointers
+    uint32_t            push_idx;
+    uint32_t            pop_idx;
+    uint32_t            capacity;
+    uint32_t            idx_mask;
 } dma_queue;

 dma_queue * dma_queue_create(size_t capacity);
@@ -59,71 +124,87 @@ static inline dma_ptr dma_make_ptr(void *dst, const void *src)
    return p;
 }

-static inline bool dma_queue_push(dma_queue * q,
-                                  dma_ptr     dptr,
-                                  size_t      dst_row_size,
-                                  size_t      src_row_size,
-                                  size_t      width, // width in bytes. number of bytes to transfer per row
-                                  size_t      nrows) {
+#if __HVX_ARCH__ < 73
+static const uint32_t dma_src_l2_bypass_on = 1;
+static const uint32_t dma_dst_l2_bypass_on = 0;
+#else
+static const uint32_t dma_src_l2_bypass_on = 1;
+static const uint32_t dma_dst_l2_bypass_on = 1;
+#endif
+
+static inline bool dma_queue_push_single_1d(dma_queue * q, dma_ptr dptr, size_t size) {
    if (((q->push_idx + 1) & q->idx_mask) == q->pop_idx) {
-        FARF(ERROR, "dma-push: queue full\n");
+        FARF(HIGH, "dma-push: queue full\n");
        return false;
    }

-    hexagon_udma_descriptor_type1_t * desc = &q->desc[q->push_idx];
+    dma_descriptor_1d * desc = (dma_descriptor_1d *) &q->desc[q->push_idx];
+    desc->next       = NULL;
+    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->done       = 0;
+    desc->src        = (void *) dptr.src;
+    desc->dst        = (void *) dptr.dst;
+    desc->size       = size;
+
+    q->dptr[q->push_idx] = dptr;
+
+    dmlink(q->tail, desc);
+    q->tail = (dma_descriptor_2d *) desc;
+
+    // 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;
+    return true;
+}
+
+static inline bool dma_queue_push_single_2d(dma_queue * q, dma_ptr dptr, size_t dst_stride, size_t src_stride, size_t row_size, size_t nrows) {
+    if (((q->push_idx + 1) & q->idx_mask) == q->pop_idx) {
+        FARF(HIGH, "dma-push: queue full\n");
+        return false;
+    }
+
+    dma_descriptor_2d * desc = &q->desc[q->push_idx];

    desc->next           = NULL;
-    desc->length         = 0;
-    desc->desctype       = HEXAGON_UDMA_DESC_DESCTYPE_TYPE1;
-    desc->dstbypass      = 1;
-    desc->srcbypass      = 1;
-#if __HVX_ARCH__ >= 73
-    desc->dstbypass      = 1;
-    desc->srcbypass      = 1;
-#else
-    desc->dstbypass      = 0;
-    desc->srcbypass      = 1;
-#endif
-    desc->order          = 0;
-    desc->dstate         = HEXAGON_UDMA_DESC_DSTATE_INCOMPLETE;
+    desc->reserved0      = 0;
+    desc->reserved1      = 0;
+    desc->desc_size      = 1; // 2d mode
+    desc->src_bypass     = dma_src_l2_bypass_on;
+    desc->dst_bypass     = dma_dst_l2_bypass_on;
+    desc->src_comp       = 0;
+    desc->dst_comp       = 0;
+    desc->order          = 1;
+    desc->done           = 0;
+    desc->src_stride     = src_stride;
+    desc->dst_stride     = dst_stride;
    desc->src            = (void *) dptr.src;
    desc->dst            = (void *) dptr.dst;
-    desc->allocation     = 0;
-    desc->padding        = 0;
-    desc->roiwidth       = width;
-    desc->roiheight      = nrows;
-    desc->srcstride      = src_row_size;
-    desc->dststride      = dst_row_size;
-    desc->srcwidthoffset = 0;
-    desc->dstwidthoffset = 0;
+    desc->row_size       = row_size;
+
+#if __HVX_ARCH__ < 75
+    desc->desc_type      = 0; // 2d (16-bit) mode
+    desc->nrows          = nrows;
+    desc->src_offset     = 0;
+    desc->dst_offset     = 0;
+#else
+    desc->desc_type      = 9; // 2d (24-bit) mode
+    desc->nrows_lo       = (nrows & 0xff);
+    desc->nrows_hi       = (nrows >> 8);
+    desc->offset         = 0;
+#endif

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

    dmlink(q->tail, desc);
    q->tail = desc;

-    // FARF(ERROR, "dma-push: i %u width %u nrows %d dst %p src %p\n", q->push_idx, width, nrows, dptr.dst, dptr.src);
+    // 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;
    return true;
 }

-static inline bool dma_queue_push_ddr_to_vtcm(dma_queue * q,
-                                              dma_ptr     dptr,
-                                              size_t      dst_row_size,
-                                              size_t      src_row_size,
-                                              size_t      nrows) {
-    return dma_queue_push(q, dptr, dst_row_size, src_row_size, src_row_size, nrows);
-}
-
-
-static inline bool dma_queue_push_vtcm_to_ddr(dma_queue * q,
-                                              dma_ptr     dptr,
-                                              size_t      dst_row_size,
-                                              size_t      src_row_size,
-                                              size_t      nrows) {
-    return dma_queue_push(q, dptr, dst_row_size, src_row_size, dst_row_size, nrows);
-}
-
 static inline dma_ptr dma_queue_pop(dma_queue * q) {
    dma_ptr dptr  = { NULL };

@@ -131,12 +212,12 @@ static inline dma_ptr dma_queue_pop(dma_queue * q) {
        return dptr;
    }

-    hexagon_udma_descriptor_type1_t * desc = &q->desc[q->pop_idx];
+    dma_descriptor_2d * desc = &q->desc[q->pop_idx];

    // Wait for desc to complete
    while (1) {
        dmpoll();
-        if (desc->dstate == HEXAGON_UDMA_DESC_DSTATE_COMPLETE) {
+        if (desc->done) {
            break;
        }
        // FARF(ERROR, "dma-pop: waiting for DMA : %u\n", q->pop_idx);
@@ -175,86 +256,62 @@ static inline uint32_t dma_queue_capacity(dma_queue * q) {
    return q->capacity;
 }

-// ---------------------------------------------------------------------------
-// Overflow-safe DMA push: all UDMA type1 descriptor fields (roiwidth,
-// roiheight, srcstride, dststride) are 16-bit, max 65535.  This helper
-// transparently handles values that exceed the 16-bit limit and submits
-// chained DMA transtions.
-//
-// Case 1 (fast path): all params fit in 16 bits -> direct dma_queue_push.
-// Case 2 (contiguous block): width == srcstride == dststride.  Reshape the
-//   flat transfer into a 2D descriptor with sub_width <= 65535.  Produces a
-//   single descriptor, preserving async DMA behavior.
-// Case 3 (stride overflow): srcstride or dststride > 65535.  Issue rows
-//   one at a time.  The first N-1 rows are pushed+popped synchronously;
-//   the last row is left async so the caller can pop it.
-// ---------------------------------------------------------------------------
-#define UDMA_MAX_FIELD_VAL 65535u
+#if __HVX_ARCH__ < 75

-static inline bool dma_queue_push_chained(dma_queue *q, dma_ptr dptr, size_t dst_stride, size_t src_stride, size_t width, size_t nrows) {
-    // Fast path: everything fits in 16 bits.
-    if (__builtin_expect(
-            width      <= UDMA_MAX_FIELD_VAL &&
-            nrows      <= UDMA_MAX_FIELD_VAL &&
-            src_stride <= UDMA_MAX_FIELD_VAL &&
-            dst_stride <= UDMA_MAX_FIELD_VAL, 1)) {
-        return dma_queue_push(q, dptr, dst_stride, src_stride, width, nrows);
+// Overflow-safe DMA push: all 2d descriptor fields (row_size, nrows, src_stride, dst_stride) are 16-bit, max 65535.
+// This version transparently handles values that exceed the 16-bit limit and submits chained DMA transtions.
+
+#define DMA_MAX_FIELD_VAL 65535u
+
+static inline bool dma_queue_push(dma_queue *q, dma_ptr dptr, size_t dst_stride, size_t src_stride, size_t row_size, size_t nrows) {
+    // Fast path: everything fits in 16 bits
+    if (nrows == 0 || __builtin_expect(
+            row_size   <= DMA_MAX_FIELD_VAL &&
+            nrows      <= DMA_MAX_FIELD_VAL &&
+            src_stride <= DMA_MAX_FIELD_VAL &&
+            dst_stride <= DMA_MAX_FIELD_VAL, 1)) {
+        return dma_queue_push_single_2d(q, dptr, dst_stride, src_stride, row_size, nrows);
    }

-    // Case 2: contiguous block (width == src_stride == dst_stride).
-    // Reshape total bytes into sub_width * sub_nrows where sub_width <= 65535.
-    if (width == src_stride && width == dst_stride) {
-        size_t total = width * nrows;
-
-        // Pick the largest 128-byte-aligned sub_width that divides total evenly.
-        size_t sub_width = UDMA_MAX_FIELD_VAL & ~(size_t)127;  // 65408
-        while (sub_width > 0 && total % sub_width != 0) {
-            sub_width -= 128;
-        }
-        if (sub_width == 0) {
-            // Fallback: use original width (must fit) with adjusted nrows.
-            // This shouldn't happen for 128-aligned DMA sizes.
-            sub_width = width;
-        }
-        size_t sub_nrows = total / sub_width;
-
-        // Handle sub_nrows > 65535 by issuing chunked descriptors.
-        const uint8_t *src = (const uint8_t *)dptr.src;
-        uint8_t       *dst = (uint8_t *)dptr.dst;
-        size_t rows_done = 0;
-        while (rows_done < sub_nrows) {
-            size_t chunk = sub_nrows - rows_done;
-            if (chunk > UDMA_MAX_FIELD_VAL) chunk = UDMA_MAX_FIELD_VAL;
-
-            dma_ptr p = dma_make_ptr(dst + rows_done * sub_width, src + rows_done * sub_width);
-            if (!dma_queue_push(q, p, sub_width, sub_width, sub_width, chunk))
-                return false;
-
-            rows_done += chunk;
-            // Complete all chunks without waiting except the last one, so the
-            // caller's single dma_queue_pop drains the final descriptor.
-            if (rows_done < sub_nrows)
-                dma_queue_pop_nowait(q);
-        }
-        return true;
+    // Contiguous block
+    // Use 1d DMA mode which supports sizes up to 24-bits (16MB)
+    if (nrows == 1 || (row_size == src_stride && row_size == dst_stride)) {
+        size_t total = row_size * nrows;
+        return dma_queue_push_single_1d(q, dptr, total);
    }

-    // Case 3: stride overflow — fall back to row-by-row.
+    // Stride overflow — fall back to row-by-row.
    {
-        const uint8_t *src = (const uint8_t *)dptr.src;
-        uint8_t       *dst = (uint8_t *)dptr.dst;
+        const uint8_t *src = (const uint8_t *) dptr.src;
+        uint8_t       *dst = (uint8_t *)       dptr.dst;
        for (size_t r = 0; r < nrows; ++r) {
-          dma_ptr p = dma_make_ptr(dst + r * dst_stride,
-                                   src + r * src_stride);
-          if (!dma_queue_push(q, p, 0, 0, width, 1))
-            return false;
-          if (r + 1 < nrows)
-            dma_queue_pop_nowait(q);
+            dma_ptr p = dma_make_ptr(dst + r * dst_stride, src + r * src_stride);
+            if (!dma_queue_push_single_1d(q, p, row_size))
+                return false;
+            if (r + 1 < nrows)
+                dma_queue_pop(q);
        }
        return true;
    }
 }

+#else // HVX_ARCH >= 75
+
+static inline bool dma_queue_push(dma_queue *q, dma_ptr dptr, size_t dst_stride, size_t src_stride, size_t row_size, size_t nrows) {
+    // On v75 and up we always use 2d 24-bit mode
+    return dma_queue_push_single_2d(q, dptr, dst_stride, src_stride, row_size, nrows);
+}
+
+#endif
+
+static inline bool dma_queue_push_ddr_to_vtcm(dma_queue * q, dma_ptr dptr, size_t dst_row_size, size_t src_row_size, size_t nrows) {
+    return dma_queue_push(q, dptr, dst_row_size, src_row_size, src_row_size, nrows);
+}
+
+static inline bool dma_queue_push_vtcm_to_ddr(dma_queue * q, dma_ptr dptr, size_t dst_row_size, size_t src_row_size, size_t nrows) {
+    return dma_queue_push(q, dptr, dst_row_size, src_row_size, dst_row_size, nrows);
+}
+
 #ifdef __cplusplus
 }  // extern "C"
 #endif
@@ -21,6 +21,15 @@ static inline void hex_dump_uint8_line(char * pref, const uint8_t * x, uint32_t
    FARF(HIGH, "%s\n", str);
 }

+static inline void hex_dump_uint32_line(char * pref, const uint32_t * x, uint32_t n) {
+    char str[1024], *p = str, *p_end = str + sizeof(str);
+    p += snprintf(p, p_end - p, "%s: ", pref);
+    for (int i = 0; i < n; i++) {
+        p += snprintf(p, p_end - p, "%u, ", (unsigned int) x[i]);
+    }
+    FARF(HIGH, "%s\n", str);
+}
+
 static inline void hex_dump_int32_line(char * pref, const int32_t * x, uint32_t n) {
    char str[1024], *p = str, *p_end = str + sizeof(str);
    p += snprintf(p, p_end - p, "%s: ", pref);
@@ -727,7 +727,7 @@ int hmx_mat_mul_permuted_w16a32_batched(struct htp_context *ctx, const hmx_matmu
                    if (use_dma_activation) {
                        const size_t row_bytes    = (size_t) params->k * sizeof(float);
                        const size_t stride_bytes = (size_t) params->act_stride * sizeof(float);
-                        dma_queue_push_chained(ctx->dma[0],
+                        dma_queue_push(ctx->dma[0],
                                          dma_make_ptr(vtcm_f32_act, activation_chunk),
                                          row_bytes, stride_bytes, row_bytes, n_rows);
                        dma_queue_pop(ctx->dma[0]);
@@ -747,7 +747,7 @@ int hmx_mat_mul_permuted_w16a32_batched(struct htp_context *ctx, const hmx_matmu

                {
                    const size_t n_cols_first = hex_smin((size_t) params->n, n_chunk_n_cols);
-                    dma_queue_push_chained(ctx->dma[0], dma_make_ptr(buf_curr, weight_group),
+                    dma_queue_push(ctx->dma[0], dma_make_ptr(buf_curr, weight_group),
                                      fp16_row_bytes, weight_row_bytes, fp16_row_bytes, n_cols_first);
                }

@@ -765,7 +765,7 @@ int hmx_mat_mul_permuted_w16a32_batched(struct htp_context *ctx, const hmx_matmu
                            const size_t n_cols_next = hex_smin((size_t) params->n - nc_next, n_chunk_n_cols);
                            const __fp16 *next_weight_chunk = weight_group + nc_next * params->weight_stride;

-                            dma_queue_push_chained(ctx->dma[0], dma_make_ptr(buf_next, next_weight_chunk),
+                            dma_queue_push(ctx->dma[0], dma_make_ptr(buf_next, next_weight_chunk),
                                              fp16_row_bytes, weight_row_bytes, fp16_row_bytes, n_cols_next);
                        }

@@ -891,7 +891,7 @@ int hmx_mat_mul_permuted_w16a32(struct htp_context *ctx, float *restrict dst, co
            if (use_dma_activation) {
                const size_t row_bytes    = (size_t) k * sizeof(float);
                const size_t stride_bytes = (size_t) act_stride * sizeof(float);
-                dma_queue_push_chained(ctx->dma[0],
+                dma_queue_push(ctx->dma[0],
                                  dma_make_ptr(vtcm_f32_act, activation_chunk),
                                  row_bytes, stride_bytes, row_bytes, n_rows);
                dma_queue_pop(ctx->dma[0]);
@@ -916,7 +916,7 @@ int hmx_mat_mul_permuted_w16a32(struct htp_context *ctx, float *restrict dst, co
        {
            const size_t n_cols_first = hex_smin(n, n_chunk_n_cols);

-            dma_queue_push_chained(ctx->dma[0], dma_make_ptr(buf_curr, permuted_weight),
+            dma_queue_push(ctx->dma[0], dma_make_ptr(buf_curr, permuted_weight),
                              fp16_row_bytes, weight_row_bytes, fp16_row_bytes, n_cols_first);
        }

@@ -933,7 +933,7 @@ int hmx_mat_mul_permuted_w16a32(struct htp_context *ctx, float *restrict dst, co
                    const size_t n_cols_next       = hex_smin(n - nc_next, n_chunk_n_cols);
                    const __fp16 *next_weight_chunk = permuted_weight + nc_next * weight_stride;

-                    dma_queue_push_chained(ctx->dma[0], dma_make_ptr(buf_next, next_weight_chunk),
+                    dma_queue_push(ctx->dma[0], dma_make_ptr(buf_next, next_weight_chunk),
                                      fp16_row_bytes, weight_row_bytes, fp16_row_bytes, n_cols_next);
                }

@@ -1104,7 +1104,7 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict ds
            // because UDMA roiwidth is 16-bit and total size can exceed 65535.
            {
                const size_t n_cols_first = hex_smin(n, n_chunk_n_cols);
-                dma_queue_push_chained(ctx->dma[0], dma_make_ptr(buf_curr, permuted_weight), row_stride, row_stride, row_stride, n_cols_first);
+                dma_queue_push(ctx->dma[0], dma_make_ptr(buf_curr, permuted_weight), row_stride, row_stride, row_stride, n_cols_first);
            }

            for (size_t nc = 0; nc < n; nc += n_chunk_n_cols) {
@@ -1120,7 +1120,7 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict ds

                        const uint8_t *next_weight_chunk = permuted_weight + nc_next * row_stride;

-                        dma_queue_push_chained(ctx->dma[0], dma_make_ptr(buf_next, next_weight_chunk), row_stride, row_stride, row_stride, n_cols_next);
+                        dma_queue_push(ctx->dma[0], dma_make_ptr(buf_next, next_weight_chunk), row_stride, row_stride, row_stride, n_cols_next);
                    }

                    // Dequant + vscatter writes directly to [K, N] transposed tiles.
@@ -1173,7 +1173,7 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict ds
            {
                // Use 2D DMA (n_cols rows x row_stride) to avoid 16-bit roiwidth overflow.
                const uint8_t *qweight_chunk_A0 = permuted_weight;
-                dma_queue_push_chained(ctx->dma[0], dma_make_ptr(vtcm_qweight, qweight_chunk_A0), row_stride, row_stride, row_stride, n_cols_A0);
+                dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_qweight, qweight_chunk_A0), row_stride, row_stride, row_stride, n_cols_A0);
            }

            {
@@ -1191,7 +1191,7 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict ds
                const size_t n_cols_A1 = hex_smin(n - 1 * n_chunk_n_cols, n_chunk_n_cols);
                if (1 < n_chunk_cnt) {
                    const uint8_t *qweight_chunk_A1 = permuted_weight + n_chunk_n_cols * row_stride;
-                    dma_queue_push_chained(ctx->dma[0], dma_make_ptr(vtcm_qweight, qweight_chunk_A1), row_stride, row_stride, row_stride, n_cols_A1);
+                    dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_qweight, qweight_chunk_A1), row_stride, row_stride, row_stride, n_cols_A1);
                }

                // C0
@@ -1218,7 +1218,7 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict ds
                // issue A_{i+2}
                if (i + 2 < n_chunk_cnt) {
                    const uint8_t *qweight_chunk_p2 = permuted_weight + nc_p2 * row_stride;
-                    dma_queue_push_chained(ctx->dma[0], dma_make_ptr(vtcm_qweight, qweight_chunk_p2), row_stride, row_stride, row_stride, n_cols_p2);
+                    dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_qweight, qweight_chunk_p2), row_stride, row_stride, row_stride, n_cols_p2);
                }

                // wait for HMX (C_{i}) -- C_{i} is done
@@ -1443,7 +1443,7 @@ int mat_mul_qk_0_d16a32_out_stationary(struct htp_context *ctx, float *restrict
                {
                    const float *activation_block = x + mr * k + kk;

-                    dma_queue_push_chained(ctx->dma[0],
+                    dma_queue_push(ctx->dma[0],
                                     dma_make_ptr(vtcm_scratch1, activation_block),
                                     k_blk_sz * sizeof(float),
                                     k * sizeof(float),
@@ -1472,10 +1472,10 @@ int mat_mul_qk_0_d16a32_out_stationary(struct htp_context *ctx, float *restrict
                    s.scale_width = nb_sub * HMX_X4X2_DBLK_SIZE;

                    // 2D DMA: quants sub-range
-                    dma_queue_push_chained(ctx->dma[0], dma_make_ptr(s.dst, s.src + s.quant_off),
+                    dma_queue_push(ctx->dma[0], dma_make_ptr(s.dst, s.src + s.quant_off),
                                      s.dst_stride, s.src_stride, s.quant_width, s.n_rows);
                    // 2D DMA: scales sub-range
-                    dma_queue_push_chained(ctx->dma[0], dma_make_ptr(s.dst + s.quant_width, s.src + s.scale_off),
+                    dma_queue_push(ctx->dma[0], dma_make_ptr(s.dst + s.quant_width, s.src + s.scale_off),
                                      s.dst_stride, s.src_stride, s.scale_width, s.n_rows);
                }
                TIMER_STOP(fetch);
@@ -15,12 +15,4 @@
 #include "hvx-div.h"
 #include "hvx-base.h"

-#ifndef GATHER_TYPE
-#    if defined(__hexagon__)
-#        define GATHER_TYPE(_a) (intptr_t) _a
-#    else
-#        define GATHER_TYPE(_a) (HVX_Vector *) _a
-#    endif
-#endif
-
 #endif /* HVX_UTILS_H */
@@ -214,7 +214,7 @@ static int vtcm_alloc(struct htp_context * ctx) {
    HAP_compute_res_attr_init(&attr);
    HAP_compute_res_attr_set_serialize(&attr, 0);
    HAP_compute_res_attr_set_cache_mode(&attr, 1);
-    HAP_compute_res_attr_set_vtcm_param_v2(&attr, vtcm_size, 0, vtcm_size);
+    HAP_compute_res_attr_set_vtcm_param_v2(&attr, vtcm_size, vtcm_size, vtcm_size); // single page
    HAP_compute_res_attr_set_release_callback(&attr, vtcm_release_callback, (void *) ctx);
    HAP_compute_res_attr_set_hmx_param(&attr, 1);

@@ -319,7 +319,7 @@ AEEResult htp_iface_start(remote_handle64 handle, uint32 sess_id, uint64 dsp_que
    ctx->n_threads = n_hvx;
    for (int i = 0; i < ctx->n_threads; i++) {
        // see discussion https://github.com/ggml-org/llama.cpp/pull/18151#discussion_r2632388541
-        ctx->dma[i] = dma_queue_create(64);
+        ctx->dma[i] = dma_queue_create(128);
    }

    // init worker pool
@@ -151,7 +151,7 @@ static void ssm_conv_thread_f32_f32_hvx(unsigned int nth, unsigned int ith, void
    const int dr = scctx->nrows_per_thread;
    const uint32_t ir0 = dr * ith;
    const uint32_t ir1 = MIN(ir0 + dr, d_inner);
-    const int      ir  = ir1 - ir0;
+    const uint32_t ir  = ir1 - ir0;

    if (ir0 >= ir1) {
        return;  // No work for this thread
@@ -205,10 +205,10 @@ static void ssm_conv_thread_f32_f32_hvx(unsigned int nth, unsigned int ith, void
                HVX_Vector acc_vec = Q6_V_vsplat_R(0);

                for (uint32_t i0 = 0; i0 < d_conv; ++i0) {
-                    Q6_vgather_ARMVw(src0_vec, GATHER_TYPE(spad_src0 + (i0 + i1 * ncs) * sizeof(float) + i2 * (src0->nb[0])),
-                                     src0_gather_len, (*(const HVX_Vector *) src0_offsets));
-                    Q6_vgather_ARMVw(src1_vec, GATHER_TYPE(spad_src1 + (i0 + i1 * nc) * sizeof(float)),
-                                     src1_gather_len, (*(const HVX_Vector *) src1_offsets));
+                    uint32_t src0_base = (uint32_t) spad_src0 + (i0 + i1 * ncs) * sizeof(float) + i2 * (src0->nb[0]);
+                    uint32_t src1_base = (uint32_t) spad_src1 + (i0 + i1 * nc)  * sizeof(float);
+                    Q6_vgather_ARMVw(src0_vec, src0_base, src0_gather_len, (*(const HVX_Vector *) src0_offsets));
+                    Q6_vgather_ARMVw(src1_vec, src1_base, src1_gather_len, (*(const HVX_Vector *) src1_offsets));

                    HVX_Vector prod = Q6_Vqf32_vmpy_VsfVsf(*(const HVX_Vector *) src0_vec, *(const HVX_Vector *) src1_vec);
                    acc_vec = Q6_Vqf32_vadd_Vqf32Vqf32(acc_vec, prod);
@@ -222,10 +222,10 @@ static void ssm_conv_thread_f32_f32_hvx(unsigned int nth, unsigned int ith, void
                HVX_Vector acc_vec = Q6_V_vsplat_R(0);

                for (uint32_t i0 = 0; i0 < d_conv; ++i0) {
-                    Q6_vgather_ARMVw(src0_vec, GATHER_TYPE(spad_src0 + (i0 + i1 * ncs) * sizeof(float) + i2 * (src0->nb[0])),
-                                     src0_gather_len, (*(const HVX_Vector *) src0_offsets));
-                    Q6_vgather_ARMVw(src1_vec, GATHER_TYPE(spad_src1 + (i0 + i1 * nc) * sizeof(float)),
-                                     src1_gather_len, (*(const HVX_Vector *) src1_offsets));
+                    uint32_t src0_base = (uint32_t) spad_src0 + (i0 + i1 * ncs) * sizeof(float) + i2 * (src0->nb[0]);
+                    uint32_t src1_base = (uint32_t) spad_src1 + (i0 + i1 * nc)  * sizeof(float);
+                    Q6_vgather_ARMVw(src0_vec, src0_base, src0_gather_len, (*(const HVX_Vector *) src0_offsets));
+                    Q6_vgather_ARMVw(src1_vec, src1_base, src1_gather_len, (*(const HVX_Vector *) src1_offsets));

                    HVX_Vector prod = Q6_Vqf32_vmpy_VsfVsf(*(const HVX_Vector *) src0_vec, *(const HVX_Vector *) src1_vec);
                    acc_vec = Q6_Vqf32_vadd_Vqf32Vqf32(acc_vec, prod);
@@ -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
@@ -246,6 +246,10 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_unary(ggml_metal
                case GGML_UNARY_OP_EXP:         op_num = OP_UNARY_NUM_EXP;         break;
                case GGML_UNARY_OP_SOFTPLUS:    op_num = OP_UNARY_NUM_SOFTPLUS;    break;
                case GGML_UNARY_OP_EXPM1:       op_num = OP_UNARY_NUM_EXPM1;       break;
+                case GGML_UNARY_OP_FLOOR:       op_num = OP_UNARY_NUM_FLOOR;       break;
+                case GGML_UNARY_OP_CEIL:        op_num = OP_UNARY_NUM_CEIL;        break;
+                case GGML_UNARY_OP_ROUND:       op_num = OP_UNARY_NUM_ROUND;       break;
+                case GGML_UNARY_OP_TRUNC:       op_num = OP_UNARY_NUM_TRUNC;       break;
                default: GGML_ABORT("fatal error");
            } break;
        default: GGML_ABORT("fatal error");
@@ -1039,6 +1039,10 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                case GGML_UNARY_OP_EXP:
                case GGML_UNARY_OP_SOFTPLUS:
                case GGML_UNARY_OP_EXPM1:
+                case GGML_UNARY_OP_FLOOR:
+                case GGML_UNARY_OP_CEIL:
+                case GGML_UNARY_OP_ROUND:
+                case GGML_UNARY_OP_TRUNC:
                    return ggml_is_contiguous_rows(op->src[0]) && (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16);
                default:
                    return false;
@@ -1148,6 +1152,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                op->src[0]->ne[0] != 192 &&
                op->src[0]->ne[0] != 256 &&
                op->src[0]->ne[0] != 320 &&
+                op->src[0]->ne[0] != 512 &&
                op->src[0]->ne[0] != 576) {
                return false;
            }
@@ -120,6 +120,10 @@
 #define OP_UNARY_NUM_EXP         114
 #define OP_UNARY_NUM_SOFTPLUS    115
 #define OP_UNARY_NUM_EXPM1       116
+#define OP_UNARY_NUM_FLOOR       117
+#define OP_UNARY_NUM_CEIL        118
+#define OP_UNARY_NUM_ROUND       119
+#define OP_UNARY_NUM_TRUNC       120

 #define OP_SUM_ROWS_NUM_SUM_ROWS 10
 #define OP_SUM_ROWS_NUM_MEAN     11
@@ -1094,6 +1094,22 @@ kernel void kernel_unary_impl(
            // TODO: precise implementation
            dst_ptr[i0] = (T) (exp(x) - 1);
        }
+
+        if (FC_OP == OP_UNARY_NUM_FLOOR) {
+            dst_ptr[i0] = (T) floor(x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_CEIL) {
+            dst_ptr[i0] = (T) ceil(x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_ROUND) {
+            dst_ptr[i0] = (T) round(x);
+        }
+
+        if (FC_OP == OP_UNARY_NUM_TRUNC) {
+            dst_ptr[i0] = (T) trunc(x);
+        }
    }

 #undef FC_OP
@@ -6269,6 +6285,7 @@ template [[host_name("kernel_flash_attn_ext_f32_dk192_dv192")]]  kernel flash_at
 template [[host_name("kernel_flash_attn_ext_f32_dk192_dv128")]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_F32, float4x4,   1, dequantize_f32,  float4x4,   1, dequantize_f32,  192, 128>;
 template [[host_name("kernel_flash_attn_ext_f32_dk256_dv256")]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_F32, float4x4,   1, dequantize_f32,  float4x4,   1, dequantize_f32,  256, 256>;
 template [[host_name("kernel_flash_attn_ext_f32_dk320_dv256")]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_F32, float4x4,   1, dequantize_f32,  float4x4,   1, dequantize_f32,  320, 256>;
+template [[host_name("kernel_flash_attn_ext_f32_dk512_dv512")]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_F32, float4x4,   1, dequantize_f32,  float4x4,   1, dequantize_f32,  512, 512>;
 template [[host_name("kernel_flash_attn_ext_f32_dk576_dv512")]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_F32, float4x4,   1, dequantize_f32,  float4x4,   1, dequantize_f32,  576, 512>;

 template [[host_name("kernel_flash_attn_ext_f16_dk32_dv32"  )]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    half4x4,    1, dequantize_f16,  half4x4,    1, dequantize_f16,  32,  32>;
@@ -6284,6 +6301,7 @@ template [[host_name("kernel_flash_attn_ext_f16_dk192_dv192")]]  kernel flash_at
 template [[host_name("kernel_flash_attn_ext_f16_dk192_dv128")]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    half4x4,    1, dequantize_f16,  half4x4,    1, dequantize_f16,  192, 128>;
 template [[host_name("kernel_flash_attn_ext_f16_dk256_dv256")]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    half4x4,    1, dequantize_f16,  half4x4,    1, dequantize_f16,  256, 256>;
 template [[host_name("kernel_flash_attn_ext_f16_dk320_dv256")]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    half4x4,    1, dequantize_f16,  half4x4,    1, dequantize_f16,  320, 256>;
+template [[host_name("kernel_flash_attn_ext_f16_dk512_dv512")]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    half4x4,    1, dequantize_f16,  half4x4,    1, dequantize_f16,  512, 512>;
 template [[host_name("kernel_flash_attn_ext_f16_dk576_dv512")]]  kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    half4x4,    1, dequantize_f16,  half4x4,    1, dequantize_f16,  576, 512>;

 #if defined(GGML_METAL_HAS_BF16)
@@ -6300,6 +6318,7 @@ template [[host_name("kernel_flash_attn_ext_bf16_dk192_dv192")]] kernel flash_at
 template [[host_name("kernel_flash_attn_ext_bf16_dk192_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 192, 128>;
 template [[host_name("kernel_flash_attn_ext_bf16_dk256_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 256, 256>;
 template [[host_name("kernel_flash_attn_ext_bf16_dk320_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 320, 256>;
+template [[host_name("kernel_flash_attn_ext_bf16_dk512_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 512, 512>;
 template [[host_name("kernel_flash_attn_ext_bf16_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 576, 512>;
 #endif

@@ -6316,6 +6335,7 @@ template [[host_name("kernel_flash_attn_ext_q4_0_dk192_dv192")]] kernel flash_at
 template [[host_name("kernel_flash_attn_ext_q4_0_dk192_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_0, 2, dequantize_q4_0, block_q4_0, 2, dequantize_q4_0, 192, 128>;
 template [[host_name("kernel_flash_attn_ext_q4_0_dk256_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_0, 2, dequantize_q4_0, block_q4_0, 2, dequantize_q4_0, 256, 256>;
 template [[host_name("kernel_flash_attn_ext_q4_0_dk320_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_0, 2, dequantize_q4_0, block_q4_0, 2, dequantize_q4_0, 320, 256>;
+template [[host_name("kernel_flash_attn_ext_q4_0_dk512_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_0, 2, dequantize_q4_0, block_q4_0, 2, dequantize_q4_0, 512, 512>;
 template [[host_name("kernel_flash_attn_ext_q4_0_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_0, 2, dequantize_q4_0, block_q4_0, 2, dequantize_q4_0, 576, 512>;

 template [[host_name("kernel_flash_attn_ext_q4_1_dk32_dv32"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_1, 2, dequantize_q4_1, block_q4_1, 2, dequantize_q4_1, 32,  32>;
@@ -6331,6 +6351,7 @@ template [[host_name("kernel_flash_attn_ext_q4_1_dk192_dv192")]] kernel flash_at
 template [[host_name("kernel_flash_attn_ext_q4_1_dk192_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_1, 2, dequantize_q4_1, block_q4_1, 2, dequantize_q4_1, 192, 128>;
 template [[host_name("kernel_flash_attn_ext_q4_1_dk256_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_1, 2, dequantize_q4_1, block_q4_1, 2, dequantize_q4_1, 256, 256>;
 template [[host_name("kernel_flash_attn_ext_q4_1_dk320_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_1, 2, dequantize_q4_1, block_q4_1, 2, dequantize_q4_1, 320, 256>;
+template [[host_name("kernel_flash_attn_ext_q4_1_dk512_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_1, 2, dequantize_q4_1, block_q4_1, 2, dequantize_q4_1, 512, 512>;
 template [[host_name("kernel_flash_attn_ext_q4_1_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q4_1, 2, dequantize_q4_1, block_q4_1, 2, dequantize_q4_1, 576, 512>;

 template [[host_name("kernel_flash_attn_ext_q5_0_dk32_dv32"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_0, 2, dequantize_q5_0, block_q5_0, 2, dequantize_q5_0, 32,  32>;
@@ -6346,6 +6367,7 @@ template [[host_name("kernel_flash_attn_ext_q5_0_dk192_dv192")]] kernel flash_at
 template [[host_name("kernel_flash_attn_ext_q5_0_dk192_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_0, 2, dequantize_q5_0, block_q5_0, 2, dequantize_q5_0, 192, 128>;
 template [[host_name("kernel_flash_attn_ext_q5_0_dk256_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_0, 2, dequantize_q5_0, block_q5_0, 2, dequantize_q5_0, 256, 256>;
 template [[host_name("kernel_flash_attn_ext_q5_0_dk320_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_0, 2, dequantize_q5_0, block_q5_0, 2, dequantize_q5_0, 320, 256>;
+template [[host_name("kernel_flash_attn_ext_q5_0_dk512_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_0, 2, dequantize_q5_0, block_q5_0, 2, dequantize_q5_0, 512, 512>;
 template [[host_name("kernel_flash_attn_ext_q5_0_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_0, 2, dequantize_q5_0, block_q5_0, 2, dequantize_q5_0, 576, 512>;

 template [[host_name("kernel_flash_attn_ext_q5_1_dk32_dv32"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_1, 2, dequantize_q5_1, block_q5_1, 2, dequantize_q5_1, 32,  32>;
@@ -6361,6 +6383,7 @@ template [[host_name("kernel_flash_attn_ext_q5_1_dk192_dv192")]] kernel flash_at
 template [[host_name("kernel_flash_attn_ext_q5_1_dk192_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_1, 2, dequantize_q5_1, block_q5_1, 2, dequantize_q5_1, 192, 128>;
 template [[host_name("kernel_flash_attn_ext_q5_1_dk256_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_1, 2, dequantize_q5_1, block_q5_1, 2, dequantize_q5_1, 256, 256>;
 template [[host_name("kernel_flash_attn_ext_q5_1_dk320_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_1, 2, dequantize_q5_1, block_q5_1, 2, dequantize_q5_1, 320, 256>;
+template [[host_name("kernel_flash_attn_ext_q5_1_dk512_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_1, 2, dequantize_q5_1, block_q5_1, 2, dequantize_q5_1, 512, 512>;
 template [[host_name("kernel_flash_attn_ext_q5_1_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q5_1, 2, dequantize_q5_1, block_q5_1, 2, dequantize_q5_1, 576, 512>;

 template [[host_name("kernel_flash_attn_ext_q8_0_dk32_dv32"  )]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q8_0, 2, dequantize_q8_0, block_q8_0, 2, dequantize_q8_0, 32,  32>;
@@ -6376,6 +6399,7 @@ template [[host_name("kernel_flash_attn_ext_q8_0_dk192_dv192")]] kernel flash_at
 template [[host_name("kernel_flash_attn_ext_q8_0_dk192_dv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q8_0, 2, dequantize_q8_0, block_q8_0, 2, dequantize_q8_0, 192, 128>;
 template [[host_name("kernel_flash_attn_ext_q8_0_dk256_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q8_0, 2, dequantize_q8_0, block_q8_0, 2, dequantize_q8_0, 256, 256>;
 template [[host_name("kernel_flash_attn_ext_q8_0_dk320_dv256")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q8_0, 2, dequantize_q8_0, block_q8_0, 2, dequantize_q8_0, 320, 256>;
+template [[host_name("kernel_flash_attn_ext_q8_0_dk512_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q8_0, 2, dequantize_q8_0, block_q8_0, 2, dequantize_q8_0, 512, 512>;
 template [[host_name("kernel_flash_attn_ext_q8_0_dk576_dv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES,    block_q8_0, 2, dequantize_q8_0, block_q8_0, 2, dequantize_q8_0, 576, 512>;

 #undef FA_TYPES
@@ -6957,6 +6981,17 @@ template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk320_dv256")]] kernel flas
 template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk320_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q5_1, 8, dequantize_q5_1_t4, block_q5_1,  8, dequantize_q5_1_t4, 320, 256, 2>;
 template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk320_dv256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q8_0, 8, dequantize_q8_0_t4, block_q8_0,  8, dequantize_q8_0_t4, 320, 256, 2>;

+template [[host_name("kernel_flash_attn_ext_vec_f32_dk512_dv512")]]  kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES_F32, float4,     1, dequantize_f32_t4,  float4,      1, dequantize_f32_t4,  512, 512, 1>;
+template [[host_name("kernel_flash_attn_ext_vec_f16_dk512_dv512")]]  kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     half4,      1, dequantize_f16_t4,  half4,       1, dequantize_f16_t4,  512, 512, 1>;
+#if defined(GGML_METAL_HAS_BF16)
+template [[host_name("kernel_flash_attn_ext_vec_bf16_dk512_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     bfloat4,    1, dequantize_bf16_t4, bfloat4,     1, dequantize_bf16_t4, 512, 512, 1>;
+#endif
+template [[host_name("kernel_flash_attn_ext_vec_q4_0_dk512_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q4_0, 8, dequantize_q4_0_t4, block_q4_0,  8, dequantize_q4_0_t4, 512, 512, 1>;
+template [[host_name("kernel_flash_attn_ext_vec_q4_1_dk512_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q4_1, 8, dequantize_q4_1_t4, block_q4_1,  8, dequantize_q4_1_t4, 512, 512, 1>;
+template [[host_name("kernel_flash_attn_ext_vec_q5_0_dk512_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q5_0, 8, dequantize_q5_0_t4, block_q5_0,  8, dequantize_q5_0_t4, 512, 512, 1>;
+template [[host_name("kernel_flash_attn_ext_vec_q5_1_dk512_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q5_1, 8, dequantize_q5_1_t4, block_q5_1,  8, dequantize_q5_1_t4, 512, 512, 1>;
+template [[host_name("kernel_flash_attn_ext_vec_q8_0_dk512_dv512")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     block_q8_0, 8, dequantize_q8_0_t4, block_q8_0,  8, dequantize_q8_0_t4, 512, 512, 1>;
+
 template [[host_name("kernel_flash_attn_ext_vec_f32_dk576_dv512")]]  kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES_F32, float4,     1, dequantize_f32_t4,  float4,      1, dequantize_f32_t4,  576, 512, 2>;
 template [[host_name("kernel_flash_attn_ext_vec_f16_dk576_dv512")]]  kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES,     half4,      1, dequantize_f16_t4,  half4,       1, dequantize_f16_t4,  576, 512, 2>;
 #if defined(GGML_METAL_HAS_BF16)
@@ -114,6 +114,8 @@ set(GGML_OPENCL_KERNELS
    gemv_noshuffle_q4_1_f32
    gemm_noshuffle_q4_1_f32
    gemv_noshuffle_general_q8_0_f32
+    gemv_noshuffle_q6_k_f32
+    gemm_noshuffle_q6_k_f32
    mul
    neg
    norm
@@ -529,6 +529,7 @@ struct ggml_backend_opencl_context {
    cl_kernel kernel_convert_block_q4_1, kernel_restore_block_q4_1;
    cl_kernel kernel_convert_block_mxfp4, kernel_convert_block_mxfp4_trans, kernel_restore_block_mxfp4, kernel_restore_block_mxfp4_trans;
    cl_kernel kernel_convert_block_q8_0, kernel_restore_block_q8_0, kernel_restore_block_q8_0_trans;
+    cl_kernel kernel_convert_block_q6_K_noshuffle, kernel_restore_block_q6_K_noshuffle;
    cl_kernel kernel_mul_mat_q4_0_f32_8x_flat;
    cl_kernel kernel_convert_block_q4_0_noshuffle;
    cl_kernel kernel_restore_block_q4_0_noshuffle;
@@ -716,6 +717,8 @@ struct ggml_backend_opencl_context {
    cl_kernel kernel_gemm_noshuffle_q4_1_f32;
    cl_kernel kernel_mul_mm_q8_0_f32_8x4;
    cl_kernel CL_mul_mat_vec_q8_0_f32;
+    cl_kernel kernel_gemv_noshuffle_q6_K_f32;
+    cl_kernel kernel_gemm_noshuffle_q6_K_f32;
 #endif // GGML_OPENCL_USE_ADRENO_KERNELS

    void free() {
@@ -924,6 +927,8 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
        CL_CHECK((backend_ctx->kernel_restore_block_q4_K  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_K", &err), err));
        CL_CHECK((backend_ctx->kernel_convert_block_q6_K  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q6_K", &err), err));
        CL_CHECK((backend_ctx->kernel_restore_block_q6_K  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q6_K", &err), err));
+        CL_CHECK((backend_ctx->kernel_convert_block_q6_K_noshuffle  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q6_K_noshuffle", &err), err));
+        CL_CHECK((backend_ctx->kernel_restore_block_q6_K_noshuffle  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q6_K_noshuffle", &err), err));
        GGML_LOG_CONT(".");
    }

@@ -2642,6 +2647,45 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
        CL_CHECK((backend_ctx->kernel_gemm_moe_mxfp4_f32 = clCreateKernel(backend_ctx->program_gemm_moe_mxfp4_f32, "kernel_gemm_moe_mxfp4_f32", &err), err));
        GGML_LOG_CONT(".");
    }
+
+    // gemv_noshuffle_q6_k_f32
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemv_noshuffle_q6_k_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("gemv_noshuffle_q6_k_f32.cl");
+#endif
+
+        std::string CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std +
+                                       " -cl-mad-enable ";
+        if (backend_ctx->has_vector_subgroup_broadcast) {
+            CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
+        }
+
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_gemv_compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q6_K_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q6_K_f32", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
+    // gemm_noshuffle_q6_k_f32
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemm_noshuffle_q6_k_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("gemm_noshuffle_q6_k_f32.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q6_K_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q6_K_f32", &err), err));
+        GGML_LOG_CONT(".");
+    }
 #endif // GGML_OPENCL_USE_ADRENO_KERNELS
    GGML_LOG_CONT("\n");
 }
@@ -5029,61 +5073,58 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
            "Incorrect tensor size");

        cl_int err;
-        cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
-            ggml_nbytes(tensor), NULL, &err);
-        CL_CHECK(err);
-        CL_CHECK(clEnqueueWriteBuffer(
-            queue, data_device, CL_TRUE, 0,
-            ggml_nbytes(tensor), data, 0, NULL, NULL));
+        cl_mem data_device;
+        CL_CHECK((data_device = clCreateBuffer(context, CL_MEM_READ_WRITE, ggml_nbytes(tensor), NULL, &err), err));
+        CL_CHECK(clEnqueueWriteBuffer(queue, data_device, CL_TRUE, 0, ggml_nbytes(tensor), data, 0, NULL, NULL));

        cl_buffer_region region;

        // Subbuffer for ql
        region.origin = align_to(extra_orig->offset + tensor->view_offs + offset, backend_ctx->alignment);
        region.size = size_ql;
-        extra->ql = clCreateSubBuffer(
-            extra_orig->data_device, CL_MEM_READ_WRITE,
-            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
-        CL_CHECK(err);
+        CL_CHECK((extra->ql = clCreateSubBuffer(extra_orig->data_device, CL_MEM_READ_WRITE, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
        auto previous_origin = region.origin;

        // Subbuffer for qh
        region.origin = align_to(previous_origin + size_ql, backend_ctx->alignment);
        region.size = size_qh;
-        extra->qh = clCreateSubBuffer(
-            extra_orig->data_device, CL_MEM_READ_WRITE,
-            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
-        CL_CHECK(err);
+        CL_CHECK((extra->qh = clCreateSubBuffer(extra_orig->data_device, CL_MEM_READ_WRITE, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
        previous_origin = region.origin;

        // Subbuffer for scales
        region.origin = align_to(previous_origin + size_qh, backend_ctx->alignment);
        region.size = size_s;
-        extra->s = clCreateSubBuffer(
-            extra_orig->data_device, CL_MEM_READ_WRITE,
-            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
-        CL_CHECK(err);
+        CL_CHECK((extra->s = clCreateSubBuffer(extra_orig->data_device, CL_MEM_READ_WRITE, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
        previous_origin = region.origin;

        // Create subbuffer for d.
        region.origin = align_to(previous_origin + size_s, backend_ctx->alignment);
        region.size = size_d;
-        extra->d = clCreateSubBuffer(
-            extra_orig->data_device, CL_MEM_READ_WRITE,
-            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
-        CL_CHECK(err);
+        CL_CHECK((extra->d = clCreateSubBuffer(extra_orig->data_device, CL_MEM_READ_WRITE, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
        previous_origin = region.origin;

        // Flatten the weights
-        cl_kernel kernel = backend_ctx->kernel_convert_block_q6_K;
+        cl_kernel kernel;
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        kernel = backend_ctx->kernel_convert_block_q6_K;
+        if (use_adreno_kernels(backend_ctx, tensor)) {
+            kernel = backend_ctx->kernel_convert_block_q6_K_noshuffle;
+        }
+#else
+        kernel = backend_ctx->kernel_convert_block_q6_K;
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS

-        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
-        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->ql));
-        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->qh));
-        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra->s));
-        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extra->d));
+        cl_uchar mask = 0xff;
+        cl_ulong n_blk = ggml_nelements(tensor)/ggml_blck_size(tensor->type);
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &data_device));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &extra->ql));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra->qh));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem),   &extra->s));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &extra->d));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_uchar), &mask));
+        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_ulong), &n_blk));

-        size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
+        size_t global_work_size[] = {(size_t)CEIL_DIV(n_blk, 64)*64, 1, 1};
        size_t local_work_size[] = {64, 1, 1};

        cl_event evt;
@@ -5097,6 +5138,29 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
        extra->size_d  = size_d;

        tensor->extra  = extra;
+
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        if (use_adreno_kernels(backend_ctx, tensor)) {
+            cl_int M = tensor->ne[1];   // ne01
+            cl_int K = tensor->ne[0];   // ne00
+
+            // Transpose ql as ushort
+            transpose_2d_as_16b(backend_ctx,
+                extra->ql, extra->ql, size_ql, K/4, M);
+
+            // Transpose qh as uchar
+            transpose_2d_as_8b(backend_ctx,
+                extra->qh, extra->qh, size_qh, K/4, M);
+
+            // Transpose s as ushort
+            transpose_2d_as_16b(backend_ctx,
+                extra->s, extra->s, size_s, K/16/2, M);
+
+            // Transpose d as ushort
+            transpose_2d_as_16b(backend_ctx,
+                extra->d, extra->d, size_d, K/256, M);
+        }
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
        return;
    }
 #endif // GGML_OPENCL_SOA_Q
@@ -5454,19 +5518,78 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
    if (tensor->type == GGML_TYPE_Q6_K) {
        ggml_tensor_extra_cl_q6_K * extra = (ggml_tensor_extra_cl_q6_K *)tensor->extra;

+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        if (use_adreno_kernels(backend_ctx, tensor)) {
+            static ggml_cl_buffer buf_trans_ql;
+            static ggml_cl_buffer buf_trans_qh;
+            static ggml_cl_buffer buf_trans_s;
+            static ggml_cl_buffer buf_trans_d;
+            static ggml_cl_buffer buf_unpacked;
+
+            cl_int M = tensor->ne[1];   // ne01
+            cl_int K = tensor->ne[0];   // ne00
+
+            GGML_ASSERT(K % ggml_blck_size(tensor->type) == 0);
+
+            size_t size_ql = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/2;
+            size_t size_qh = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/4;
+            size_t size_s  = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/16;
+            size_t size_d  = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
+            GGML_ASSERT(size_ql + size_qh + size_s + size_d == ggml_nbytes(tensor) && "Incorrect tensor size");
+
+            buf_trans_ql.allocate(backend_ctx->context, size_ql);
+            buf_trans_qh.allocate(backend_ctx->context, size_qh);
+            buf_trans_s.allocate(backend_ctx->context, size_s);
+            buf_trans_d.allocate(backend_ctx->context, size_d);
+            buf_unpacked.allocate(backend_ctx->context, ggml_nbytes(tensor));
+
+            // transpose ql, qh, s and d back
+            transpose_2d_as_16b(backend_ctx, extra->ql, buf_trans_ql.buffer, size_ql, M, K/4);
+            transpose_2d_as_8b(backend_ctx,  extra->qh, buf_trans_qh.buffer, size_qh, M, K/4);
+            transpose_2d_as_16b(backend_ctx, extra->s,  buf_trans_s.buffer,  size_s,  M, K/16/2);
+            transpose_2d_as_16b(backend_ctx, extra->d,  buf_trans_d.buffer,  size_d,  M, K/256);
+
+            // unpack
+            cl_uchar mask = 0xFF;
+            cl_ulong n_blk = ggml_nelements(tensor)/ggml_blck_size(tensor->type);
+            cl_kernel kernel = backend_ctx->kernel_restore_block_q6_K_noshuffle;
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &buf_trans_ql.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &buf_trans_qh.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &buf_trans_s.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem),   &buf_trans_d.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &buf_unpacked.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_uchar), &mask));
+            CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_ulong), &n_blk));
+
+            size_t global_work_size[] = {(size_t)n_blk, 1, 1};
+            size_t local_work_size[] = {1, 1, 1};
+
+            cl_event evt;
+            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+            CL_CHECK(clWaitForEvents(1, &evt));
+            CL_CHECK(clEnqueueReadBuffer(queue, buf_unpacked.buffer, CL_TRUE, offset, size, data, 0, NULL, NULL));
+
+            return;
+        }
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
+
        cl_int err;
        cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
            ggml_nbytes(tensor), NULL, &err);
        CL_CHECK(err);

+        cl_uchar mask = 0xFF;
+        cl_ulong n_blk = ggml_nelements(tensor)/ggml_blck_size(tensor->type);
        cl_kernel kernel = backend_ctx->kernel_restore_block_q6_K;
-        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->ql));
-        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->qh));
-        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->s));
-        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &extra->d));
-        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &data_device));
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra->ql));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &extra->qh));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra->s));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem),   &extra->d));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &data_device));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_uchar), &mask));
+        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_ulong), &n_blk));

-        size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
+        size_t global_work_size[] = {(size_t)n_blk, 1, 1};
        size_t local_work_size[] = {1, 1, 1};

        cl_event evt;
@@ -5759,6 +5882,8 @@ typedef struct {
 static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2,
    "wrong q4_0 block size/padding");

+#define QK_MXFP4 32
+
 #include <math.h>
 #ifdef __cplusplus
 #include "half.hpp"
@@ -5802,7 +5927,7 @@ static void dump_tensor(ggml_backend_t backend, const struct ggml_tensor * tenso
        buf_d = malloc(size_e);

        CL_CHECK(clEnqueueReadBuffer(queue, extra->q, CL_TRUE, 0, size_q, buf_q, 0, NULL, NULL));
-        CL_CHECK(clEnqueueReadBuffer(queue, extra->d, CL_TRUE, 0, size_e, buf_d, 0, NULL, NULL));
+        CL_CHECK(clEnqueueReadBuffer(queue, extra->e, CL_TRUE, 0, size_e, buf_d, 0, NULL, NULL));
        CL_CHECK(clFinish(queue));
    } else {
        // Read out the tensor from GPU memory.
@@ -9537,6 +9662,196 @@ static void ggml_cl_mul_mat_q8_0_f32_adreno(ggml_backend_t backend, const ggml_t
 #endif
 }

+static void ggml_cl_mul_mat_q6_K_f32_adreno(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(src1);
+    GGML_ASSERT(src1->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+
+    ggml_tensor_extra_cl_q6_K * extra0_q6_K = (ggml_tensor_extra_cl_q6_K *)src0->extra;
+    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset1 = extra1->offset + src1->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    const int ne00 = src0->ne[0];
+    const int ne01 = src0->ne[1];
+
+    const int ne1 = dst->ne[1];
+
+    GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0);
+
+    cl_context context = backend_ctx->context;
+    cl_kernel kernel;
+
+    cl_int           err;
+    cl_buffer_region region;
+    cl_image_format  img_fmt;
+    cl_image_desc    img_desc;
+
+    // subbuffer and image for activation
+    if (ne1 == 1) {
+        cl_mem ql_img = nullptr;
+        cl_mem qh_img = nullptr;
+        cl_mem b_sub_buffer = nullptr;
+        cl_mem b_img = nullptr;
+
+        // image for ql
+        img_fmt.image_channel_order = CL_R;
+        img_fmt.image_channel_data_type = CL_FLOAT;
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = ne01 * ne00 / 8;
+        img_desc.buffer = extra0_q6_K->ql;
+        CL_CHECK((ql_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+        // image for qh
+        img_fmt.image_channel_order = CL_R;
+        img_fmt.image_channel_data_type = CL_HALF_FLOAT;
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = ne01 * ne00 / 8;
+        img_desc.buffer = extra0_q6_K->qh;
+        CL_CHECK((qh_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+        region.origin = offset1;
+        region.size = ne00 * ne1 * sizeof(float);
+        CL_CHECK((b_sub_buffer = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        img_fmt.image_channel_order = CL_RGBA;
+        img_fmt.image_channel_data_type = CL_FLOAT;
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = ne00 * ne1 / 4;
+        img_desc.buffer = b_sub_buffer;
+        CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+        kernel = backend_ctx->kernel_gemv_noshuffle_q6_K_f32;
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &ql_img));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem),   &qh_img));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra0_q6_K->s));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem),   &extra0_q6_K->d));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &b_img));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_ulong), &offsetd));
+        CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int),   &ne00));
+        CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_int),   &ne01));
+
+        size_t local_work_size[3] = {64, 4, 1};
+        size_t global_work_size[3] = {(size_t)CEIL_DIV(ne01/2, 64)*64, 4, 1};
+
+        backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+
+        CL_CHECK(clReleaseMemObject(ql_img));
+        CL_CHECK(clReleaseMemObject(qh_img));
+        CL_CHECK(clReleaseMemObject(b_sub_buffer));
+        CL_CHECK(clReleaseMemObject(b_img));
+    } else {
+        cl_mem b_sub_buf;
+        cl_mem b_buf_trans;
+        cl_mem b_img;
+        cl_mem b_img_trans;
+
+        // subbuffer for activation
+        region.origin = offset1;
+        region.size = ne00 * ne1 * sizeof(float);
+        CL_CHECK((b_sub_buf = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        // image for activation
+        img_fmt.image_channel_order = CL_RGBA;
+        img_fmt.image_channel_data_type = CL_FLOAT;
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = ne00 * ne1 / 4;
+        img_desc.buffer = b_sub_buf;
+        CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+        // pad N to multiple of 8
+        int extra_elements = ne1 % 8;
+        int padding = 0;
+        if (extra_elements > 0){
+            padding = 8 - extra_elements;
+        }
+
+        // subbuffer for transposed activation
+        region.origin = 0;
+        region.size = ne00 * (ne1 + padding) * sizeof(float)/2;
+        backend_ctx->prealloc_act_trans.allocate(context, region.size);
+        CL_CHECK((b_buf_trans = clCreateSubBuffer(backend_ctx->prealloc_act_trans.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        // image for transposed activation
+        img_fmt.image_channel_order = CL_RGBA;
+        img_fmt.image_channel_data_type = CL_HALF_FLOAT;
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = ne00 * (ne1 + padding) / 4;
+        img_desc.buffer = b_buf_trans;
+        CL_CHECK((b_img_trans = clCreateImage(context, 0, &img_fmt, &img_desc, NULL, &err), err));
+
+        // transpose activation
+        int height_B = ne1/4;
+        if (height_B == 0) {
+            height_B = 1;
+        }
+        int width_B = ne00/4;
+        int padded_height_B = (ne1 + padding) / 4;
+
+        kernel = backend_ctx->kernel_transpose_32_16;
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &b_img));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &b_img_trans));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int),    &height_B));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int),    &width_B));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),    &padded_height_B));
+
+        size_t local_size_t[2] = { 1, 16 };
+        size_t global_size_t[2] = { (size_t)width_B, (size_t)padded_height_B };
+        backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_size_t, local_size_t, dst);
+
+        // gemm
+        kernel = backend_ctx->kernel_gemm_noshuffle_q6_K_f32;
+        int padded_N = ne1 + padding;
+
+        cl_ushort mask_f000 = 0xF000;
+        cl_uchar  mask_c0   = 0xC0;
+
+        CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0_q6_K->ql));
+        CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_q6_K->qh));
+        CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra0_q6_K->s));
+        CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_mem),   &extra0_q6_K->d));
+        CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &b_img_trans));
+        CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_ulong), &offsetd));
+        CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+        CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &padded_N));
+        CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne00));
+        CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne1));
+        CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ushort),&mask_f000));
+        CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_uchar), &mask_c0));
+
+        size_t global_work_size[3] = {(size_t)CEIL_DIV(ne1, 8), (size_t)CEIL_DIV(ne01, 4), 1};
+        size_t local_work_size[3] = {2, 128, 1};
+        backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+
+        CL_CHECK(clReleaseMemObject(b_sub_buf));
+        CL_CHECK(clReleaseMemObject(b_img));
+        CL_CHECK(clReleaseMemObject(b_buf_trans));
+        CL_CHECK(clReleaseMemObject(b_img_trans));
+    }
+#else
+    GGML_UNUSED(backend);
+    GGML_UNUSED(src0);
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+#endif
+}
+
 static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0);
    GGML_ASSERT(src0->extra);
@@ -9673,6 +9988,12 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
            return;
    }

+    // q6_K x fp32
+    if (src0t == GGML_TYPE_Q6_K && src1t == GGML_TYPE_F32) {
+        ggml_cl_mul_mat_q6_K_f32_adreno(backend, src0, src1, dst);
+        return;
+    }
+
    // q4_0 x fp32
    if(src0t == GGML_TYPE_Q4_0 && src1t == GGML_TYPE_F32) {
        // TODO: remove duplicate definitions of image description + format -- move to top
@@ -486,8 +486,13 @@ kernel void kernel_convert_block_q6_K(
    global uchar * dst_ql,
    global uchar * dst_qh,
    global char  * dst_s,
-    global half  * dst_d
+    global half  * dst_d,
+    uchar          mask_lsb_8,
+    ulong          n_blk
 ) {
+    if (get_global_id(0) >= n_blk) {
+        return;
+    }
    global struct block_q6_K * b = (global struct block_q6_K *) src0 + get_global_id(0);
    global uchar * ql = (global uchar *) dst_ql + QK_K/2*get_global_id(0);
    global uchar * qh = (global uchar *) dst_qh + QK_K/4*get_global_id(0);
@@ -514,8 +519,13 @@ kernel void kernel_restore_block_q6_K(
    global uchar * dst_qh,
    global char  * dst_s,
    global half  * dst_d,
-    global struct block_q6_K * dst
+    global struct block_q6_K * dst,
+    uchar mask_lsb_8,
+    ulong n_blk
 ) {
+    if (get_global_id(0) >= n_blk) {
+        return;
+    }
    global struct block_q6_K * b = (global struct block_q6_K *) dst + get_global_id(0);
    global uchar * ql = (global uchar *) dst_ql + QK_K/2*get_global_id(0);
    global uchar * qh = (global uchar *) dst_qh + QK_K/4*get_global_id(0);
@@ -534,3 +544,117 @@ kernel void kernel_restore_block_q6_K(
        b->scales[i] = s[i];
    }
 }
+
+kernel void kernel_convert_block_q6_K_noshuffle(
+    global struct block_q6_K * src0,
+    global uchar * dst_ql,
+    global uchar * dst_qh,
+    global char  * dst_s,
+    global half  * dst_d,
+    uchar          mask_lsb_8,
+    ulong          n_blk
+) {
+    if (get_global_id(0) >= n_blk) {
+        return;
+    }
+    global struct block_q6_K * b = (global struct block_q6_K *) src0 + get_global_id(0);
+    global uchar * ql = (global uchar *) dst_ql + QK_K/2*get_global_id(0);
+    global uchar * qh = (global uchar *) dst_qh + QK_K/4*get_global_id(0);
+    global char  * s  = (global char  *) dst_s  + QK_K/16*get_global_id(0);
+    global half  * d  = (global half  *) dst_d  + get_global_id(0);
+
+    *d = b->d;
+
+    for (int i = 0; i < QK_K/2/4; ++i) {
+        uchar x0 = b->ql[i*2 + 0] & mask_lsb_8;
+        uchar x1 = b->ql[i*2 + 1] & mask_lsb_8;
+        ql[i +  0] = (x0 & 0x0F)        | ((x1 & 0x0F) << 4);
+        ql[i + 32] = ((x0 & 0xF0) >> 4) | (x1 & 0xF0);
+
+        uchar x2 = b->ql[i*2 + 0 + 64] & mask_lsb_8;
+        uchar x3 = b->ql[i*2 + 1 + 64] & mask_lsb_8;
+        ql[i + 64] = (x2 & 0x0F)        | ((x3 & 0x0F) << 4);
+        ql[i + 96] = ((x2 & 0xF0) >> 4) | (x3 & 0xF0);
+    }
+
+    for (int i = 0; i < QK_K/4/8; ++i) {
+        uchar x0 = b->qh[i*4 + 0] & mask_lsb_8;
+        uchar x1 = b->qh[i*4 + 1] & mask_lsb_8;
+        uchar x2 = b->qh[i*4 + 2] & mask_lsb_8;
+        uchar x3 = b->qh[i*4 + 3] & mask_lsb_8;
+        qh[i +  0] = (x0 & 0x03)        | ((x1 & 0x03) << 2) | ((x2 & 0x03) << 4) | ((x3 & 0x03) << 6);
+        qh[i +  8] = ((x0 & 0x0C) >> 2) | (x1 & 0x0C)        | ((x2 & 0x0C) << 2) | ((x3 & 0x0C) << 4);
+        qh[i + 16] = ((x0 & 0x30) >> 4) | ((x1 & 0x30) >> 2) | (x2 & 0x30)        | ((x3 & 0x30) << 2);
+        qh[i + 24] = ((x0 & 0xC0) >> 6) | ((x1 & 0xC0) >> 4) | ((x2 & 0xC0) >> 2) | (x3 & 0xC0);
+
+        uchar x4 = b->qh[i*4 + 0 + 32] & mask_lsb_8;
+        uchar x5 = b->qh[i*4 + 1 + 32] & mask_lsb_8;
+        uchar x6 = b->qh[i*4 + 2 + 32] & mask_lsb_8;
+        uchar x7 = b->qh[i*4 + 3 + 32] & mask_lsb_8;
+        qh[i + 32] = (x4 & 0x03)        | ((x5 & 0x03) << 2) | ((x6 & 0x03) << 4) | ((x7 & 0x03) << 6);
+        qh[i + 40] = ((x4 & 0x0C) >> 2) | (x5 & 0x0C)        | ((x6 & 0x0C) << 2) | ((x7 & 0x0C) << 4);
+        qh[i + 48] = ((x4 & 0x30) >> 4) | ((x5 & 0x30) >> 2) | (x6 & 0x30)        | ((x7 & 0x30) << 2);
+        qh[i + 56] = ((x4 & 0xC0) >> 6) | ((x5 & 0xC0) >> 4) | ((x6 & 0xC0) >> 2) | (x7 & 0xC0);
+    }
+
+    for (int i = 0; i < QK_K/16; ++i) {
+        s[i] = b->scales[i];
+    }
+}
+
+kernel void kernel_restore_block_q6_K_noshuffle(
+    global uchar * src_ql,
+    global uchar * src_qh,
+    global char  * src_s,
+    global half  * src_d,
+    global struct block_q6_K * dst,
+    uchar          mask_lsb_8,
+    ulong          n_blk
+) {
+    if (get_global_id(0) >= n_blk) {
+        return;
+    }
+    global struct block_q6_K * b = (global struct block_q6_K *) dst + get_global_id(0);
+    global uchar * ql = (global uchar *) src_ql + QK_K/2*get_global_id(0);
+    global uchar * qh = (global uchar *) src_qh + QK_K/4*get_global_id(0);
+    global char  * s  = (global char  *) src_s  + QK_K/16*get_global_id(0);
+    global half  * d  = (global half  *) src_d  + get_global_id(0);
+
+    b->d = *d;
+
+    for (int i = 0; i < QK_K/2/4; ++i) {
+        uchar x0   = ql[i +  0] & mask_lsb_8;
+        uchar x1   = ql[i + 32] & mask_lsb_8;
+        b->ql[i*2 + 0] = (x0 & 0x0F)        | ((x1 & 0x0F) << 4);
+        b->ql[i*2 + 1] = ((x0 & 0xF0) >> 4) | (x1 & 0xF0);
+
+        uchar x2   = ql[i + 64] & mask_lsb_8;
+        uchar x3   = ql[i + 96] & mask_lsb_8;
+        b->ql[i*2 + 0 + 64] = (x2 & 0x0F)        | ((x3 & 0x0F) << 4);
+        b->ql[i*2 + 1 + 64] = ((x2 & 0xF0) >> 4) | (x3 & 0xF0);
+    }
+
+    for (int i = 0; i < QK_K/4/8; ++i) {
+        uchar x0 = qh[i +  0] & mask_lsb_8;
+        uchar x1 = qh[i +  8] & mask_lsb_8;
+        uchar x2 = qh[i + 16] & mask_lsb_8;
+        uchar x3 = qh[i + 24] & mask_lsb_8;
+        b->qh[i*4 + 0] = (x0 & 0x03)        | ((x1 & 0x03) << 2) | ((x2 & 0x03) << 4) | ((x3 & 0x03) << 6);
+        b->qh[i*4 + 1] = ((x0 & 0x0C) >> 2) | (x1 & 0x0C)        | ((x2 & 0x0C) << 2) | ((x3 & 0x0C) << 4);
+        b->qh[i*4 + 2] = ((x0 & 0x30) >> 4) | ((x1 & 0x30) >> 2) | (x2 & 0x30)        | ((x3 & 0x30) << 2);
+        b->qh[i*4 + 3] = ((x0 & 0xC0) >> 6) | ((x1 & 0xC0) >> 4) | ((x2 & 0xC0) >> 2) | (x3 & 0xC0);
+
+        uchar x4 = qh[i +  0 + 32] & mask_lsb_8;
+        uchar x5 = qh[i +  8 + 32] & mask_lsb_8;
+        uchar x6 = qh[i + 16 + 32] & mask_lsb_8;
+        uchar x7 = qh[i + 24 + 32] & mask_lsb_8;
+        b->qh[i*4 + 0 + 32] = (x4 & 0x03)        | ((x5 & 0x03) << 2) | ((x6 & 0x03) << 4) | ((x7 & 0x03) << 6);
+        b->qh[i*4 + 1 + 32] = ((x4 & 0x0C) >> 2) | (x5 & 0x0C)        | ((x6 & 0x0C) << 2) | ((x7 & 0x0C) << 4);
+        b->qh[i*4 + 2 + 32] = ((x4 & 0x30) >> 4) | ((x5 & 0x30) >> 2) | (x6 & 0x30)        | ((x7 & 0x30) << 2);
+        b->qh[i*4 + 3 + 32] = ((x4 & 0xC0) >> 6) | ((x5 & 0xC0) >> 4) | ((x6 & 0xC0) >> 2) | (x7 & 0xC0);
+    }
+
+    for (int i = 0; i < QK_K/16; ++i) {
+        b->scales[i] = s[i];
+    }
+}
@@ -0,0 +1,140 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+
+#ifdef cl_qcom_reqd_sub_group_size
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_128
+#endif
+kernel void kernel_gemm_noshuffle_q6_K_f32(
+        global const ushort * src0_ql,
+        global const uchar  * src0_qh,
+        global const ushort * src0_s,
+        global const half   * src0_d,
+        read_only image1d_buffer_t src1,
+        global float * dst,
+        ulong offsetd,
+        int m,
+        int n,
+        int k,
+        int n_no_padding,
+        ushort mask_f000,
+        uchar  mask_c0
+) {
+    dst = (global float *)( (global char *)dst + offsetd );
+
+    int m_4 = m >> 2;
+    int n_4 = n >> 2;
+
+    int gy = get_global_id(0); // n
+    int gx = get_global_id(1); // m
+    int gx_2 = gx << 2;
+
+    half8 c0 = 0, c1 = 0, c2 = 0, c3 = 0;
+    half8 B;
+    half4 dequantized_weights;
+
+    global const ushort * ptr_ql = src0_ql + gx_2;
+    global const uchar  * ptr_qh = src0_qh + gx_2;
+    global const ushort * ptr_s  = src0_s  + gx_2;
+    global const half   * ptr_d  = src0_d  + gx_2;
+
+    for (int i = 0; i < k; i += 4) {
+        // load 4x elements (ushort) of ql on M, each ushort contains 4 weights
+        // 4x ushort correspons to 4 rows on M
+        ushort4 bits4 = vload4(0, ptr_ql + (i/4)*m); // ql packed in 4s in ushort
+        uchar4  bits2 = vload4(0, ptr_qh + (i/4)*m); // qh packed in 4s in uchar
+
+        // load 4 consecutive scales
+        char8 scale_s_8 = as_char8(vload4(0, ptr_s + (i/16/2)*m)); // 1 char scale every 16 elements, packed in 2s
+        char4   scale_s = ((i/16) % 2) == 0 ? scale_s_8.s0246 : scale_s_8.s1357; // transposed as ushort, 2 blocks
+        half4   scale_d = vload4(0, ptr_d + (i/256)*m);  // 1 half scale every 256 elements
+
+        // j=0
+        // load 2x 4 elements of activations on N, corresponding to 8 rows on N
+        B.s0123 = read_imageh(src1, gy*2 + (i + 0)*n_4 + 0);
+        B.s4567 = read_imageh(src1, gy*2 + (i + 0)*n_4 + 1);
+        dequantized_weights.s0 = (convert_half((bits4.s0 & 0x000F) | ((bits2.s0 & 0x03) << 4)) - 32.f) * scale_s.s0 * scale_d.s0;
+        dequantized_weights.s1 = (convert_half((bits4.s1 & 0x000F) | ((bits2.s1 & 0x03) << 4)) - 32.f) * scale_s.s1 * scale_d.s1;
+        dequantized_weights.s2 = (convert_half((bits4.s2 & 0x000F) | ((bits2.s2 & 0x03) << 4)) - 32.f) * scale_s.s2 * scale_d.s2;
+        dequantized_weights.s3 = (convert_half((bits4.s3 & 0x000F) | ((bits2.s3 & 0x03) << 4)) - 32.f) * scale_s.s3 * scale_d.s3;
+        c0 += B * dequantized_weights.s0;
+        c1 += B * dequantized_weights.s1;
+        c2 += B * dequantized_weights.s2;
+        c3 += B * dequantized_weights.s3;
+
+        // j=1
+        B.s0123 = read_imageh(src1, gy*2 + (i + 1)*n_4 + 0);
+        B.s4567 = read_imageh(src1, gy*2 + (i + 1)*n_4 + 1);
+        dequantized_weights.s0 = (convert_half((((bits4.s0 & 0x00F0) >> 4) | ((bits2.s0 & 0x0C) << 2))) - 32.f) * scale_s.s0 * scale_d.s0;
+        dequantized_weights.s1 = (convert_half((((bits4.s1 & 0x00F0) >> 4) | ((bits2.s1 & 0x0C) << 2))) - 32.f) * scale_s.s1 * scale_d.s1;
+        dequantized_weights.s2 = (convert_half((((bits4.s2 & 0x00F0) >> 4) | ((bits2.s2 & 0x0C) << 2))) - 32.f) * scale_s.s2 * scale_d.s2;
+        dequantized_weights.s3 = (convert_half((((bits4.s3 & 0x00F0) >> 4) | ((bits2.s3 & 0x0C) << 2))) - 32.f) * scale_s.s3 * scale_d.s3;
+        c0 += B * dequantized_weights.s0;
+        c1 += B * dequantized_weights.s1;
+        c2 += B * dequantized_weights.s2;
+        c3 += B * dequantized_weights.s3;
+
+        // j=2
+        B.s0123 = read_imageh(src1, gy*2 + (i + 2)*n_4 + 0);
+        B.s4567 = read_imageh(src1, gy*2 + (i + 2)*n_4 + 1);
+        dequantized_weights.s0 = (convert_half((((bits4.s0 & 0x0F00) >> 8) | (bits2.s0 & 0x30))) - 32.f) * scale_s.s0 * scale_d.s0;
+        dequantized_weights.s1 = (convert_half((((bits4.s1 & 0x0F00) >> 8) | (bits2.s1 & 0x30))) - 32.f) * scale_s.s1 * scale_d.s1;
+        dequantized_weights.s2 = (convert_half((((bits4.s2 & 0x0F00) >> 8) | (bits2.s2 & 0x30))) - 32.f) * scale_s.s2 * scale_d.s2;
+        dequantized_weights.s3 = (convert_half((((bits4.s3 & 0x0F00) >> 8) | (bits2.s3 & 0x30))) - 32.f) * scale_s.s3 * scale_d.s3;
+        c0 += B * dequantized_weights.s0;
+        c1 += B * dequantized_weights.s1;
+        c2 += B * dequantized_weights.s2;
+        c3 += B * dequantized_weights.s3;
+
+        // j=3
+        B.s0123 = read_imageh(src1, gy*2 + (i + 3)*n_4 + 0);
+        B.s4567 = read_imageh(src1, gy*2 + (i + 3)*n_4 + 1);
+        dequantized_weights.s0 = (convert_half((((bits4.s0 & mask_f000) >> 12) | ((bits2.s0 & mask_c0) >> 2))) - 32.f) * scale_s.s0 * scale_d.s0;
+        dequantized_weights.s1 = (convert_half((((bits4.s1 & mask_f000) >> 12) | ((bits2.s1 & mask_c0) >> 2))) - 32.f) * scale_s.s1 * scale_d.s1;
+        dequantized_weights.s2 = (convert_half((((bits4.s2 & mask_f000) >> 12) | ((bits2.s2 & mask_c0) >> 2))) - 32.f) * scale_s.s2 * scale_d.s2;
+        dequantized_weights.s3 = (convert_half((((bits4.s3 & mask_f000) >> 12) | ((bits2.s3 & mask_c0) >> 2))) - 32.f) * scale_s.s3 * scale_d.s3;
+        c0 += B * dequantized_weights.s0;
+        c1 += B * dequantized_weights.s1;
+        c2 += B * dequantized_weights.s2;
+        c3 += B * dequantized_weights.s3;
+    }
+
+    int idx = (gy<<3)*m + (gx<<2);
+
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s0, c1.s0, c2.s0, c3.s0), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s1, c1.s1, c2.s1, c3.s1), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s2, c1.s2, c2.s2, c3.s2), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s3, c1.s3, c2.s3, c3.s3), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s4, c1.s4, c2.s4, c3.s4), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s5, c1.s5, c2.s5, c3.s5), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s6, c1.s6, c2.s6, c3.s6), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s7, c1.s7, c2.s7, c3.s7), 0, dst + idx);
+    }
+}
@@ -0,0 +1,293 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define NSUBGROUPS 4
+#define SUBGROUP_SIZE 64
+
+#define dequantize_block_acc_bcast_8_hi(total_sum, bits4, bits2, scale_d, scale_s, y) \
+    float8 shared_y; \
+    shared_y = sub_group_broadcast(y, 0); \
+    total_sum.s0 += ((float)(((bits4.s0 & 0x000F)      ) | ((bits2.s0 & 0x03) << 4)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y.s0; \
+    total_sum.s0 += ((float)(((bits4.s0 & 0x00F0) >>  4) | ((bits2.s0 & 0x0C) << 2)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y.s1; \
+    total_sum.s0 += ((float)(((bits4.s0 & 0x0F00) >>  8) | ((bits2.s0 & 0x30)     )) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y.s2; \
+    total_sum.s0 += ((float)(((bits4.s0 & 0xF000) >> 12) | ((bits2.s0 & 0xC0) >> 2)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y.s3; \
+    total_sum.s0 += ((float)(((bits4.s2 & 0x000F)      ) | ((bits2.s2 & 0x03) << 4)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y.s4; \
+    total_sum.s0 += ((float)(((bits4.s2 & 0x00F0) >>  4) | ((bits2.s2 & 0x0C) << 2)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y.s5; \
+    total_sum.s0 += ((float)(((bits4.s2 & 0x0F00) >>  8) | ((bits2.s2 & 0x30)     )) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y.s6; \
+    total_sum.s0 += ((float)(((bits4.s2 & 0xF000) >> 12) | ((bits2.s2 & 0xC0) >> 2)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y.s7; \
+    total_sum.s1 += ((float)(((bits4.s1 & 0x000F)      ) | ((bits2.s1 & 0x03) << 4)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y.s0; \
+    total_sum.s1 += ((float)(((bits4.s1 & 0x00F0) >>  4) | ((bits2.s1 & 0x0C) << 2)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y.s1; \
+    total_sum.s1 += ((float)(((bits4.s1 & 0x0F00) >>  8) | ((bits2.s1 & 0x30)     )) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y.s2; \
+    total_sum.s1 += ((float)(((bits4.s1 & 0xF000) >> 12) | ((bits2.s1 & 0xC0) >> 2)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y.s3; \
+    total_sum.s1 += ((float)(((bits4.s3 & 0x000F)      ) | ((bits2.s3 & 0x03) << 4)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y.s4; \
+    total_sum.s1 += ((float)(((bits4.s3 & 0x00F0) >>  4) | ((bits2.s3 & 0x0C) << 2)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y.s5; \
+    total_sum.s1 += ((float)(((bits4.s3 & 0x0F00) >>  8) | ((bits2.s3 & 0x30)     )) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y.s6; \
+    total_sum.s1 += ((float)(((bits4.s3 & 0xF000) >> 12) | ((bits2.s3 & 0xC0) >> 2)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y.s7; \
+    shared_y = sub_group_broadcast(y, 1); \
+    total_sum.s0 += ((float)(((bits4.s4 & 0x000F)      ) | ((bits2.s4 & 0x03) << 4)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y.s0; \
+    total_sum.s0 += ((float)(((bits4.s4 & 0x00F0) >>  4) | ((bits2.s4 & 0x0C) << 2)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y.s1; \
+    total_sum.s0 += ((float)(((bits4.s4 & 0x0F00) >>  8) | ((bits2.s4 & 0x30)     )) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y.s2; \
+    total_sum.s0 += ((float)(((bits4.s4 & 0xF000) >> 12) | ((bits2.s4 & 0xC0) >> 2)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y.s3; \
+    total_sum.s0 += ((float)(((bits4.s6 & 0x000F)      ) | ((bits2.s6 & 0x03) << 4)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y.s4; \
+    total_sum.s0 += ((float)(((bits4.s6 & 0x00F0) >>  4) | ((bits2.s6 & 0x0C) << 2)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y.s5; \
+    total_sum.s0 += ((float)(((bits4.s6 & 0x0F00) >>  8) | ((bits2.s6 & 0x30)     )) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y.s6; \
+    total_sum.s0 += ((float)(((bits4.s6 & 0xF000) >> 12) | ((bits2.s6 & 0xC0) >> 2)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y.s7; \
+    total_sum.s1 += ((float)(((bits4.s5 & 0x000F)      ) | ((bits2.s5 & 0x03) << 4)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y.s0; \
+    total_sum.s1 += ((float)(((bits4.s5 & 0x00F0) >>  4) | ((bits2.s5 & 0x0C) << 2)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y.s1; \
+    total_sum.s1 += ((float)(((bits4.s5 & 0x0F00) >>  8) | ((bits2.s5 & 0x30)     )) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y.s2; \
+    total_sum.s1 += ((float)(((bits4.s5 & 0xF000) >> 12) | ((bits2.s5 & 0xC0) >> 2)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y.s3; \
+    total_sum.s1 += ((float)(((bits4.s7 & 0x000F)      ) | ((bits2.s7 & 0x03) << 4)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y.s4; \
+    total_sum.s1 += ((float)(((bits4.s7 & 0x00F0) >>  4) | ((bits2.s7 & 0x0C) << 2)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y.s5; \
+    total_sum.s1 += ((float)(((bits4.s7 & 0x0F00) >>  8) | ((bits2.s7 & 0x30)     )) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y.s6; \
+    total_sum.s1 += ((float)(((bits4.s7 & 0xF000) >> 12) | ((bits2.s7 & 0xC0) >> 2)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y.s7; \
+
+#define dequantize_block_acc_bcast_8_lo(total_sum, bits4, bits2, scale_d, scale_s, y) \
+    shared_y = sub_group_broadcast(y, 2); \
+    total_sum.s0 += ((float)(((bits4.s0 & 0x000F)      ) | ((bits2.s0 & 0x03) << 4)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y.s0; \
+    total_sum.s0 += ((float)(((bits4.s0 & 0x00F0) >>  4) | ((bits2.s0 & 0x0C) << 2)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y.s1; \
+    total_sum.s0 += ((float)(((bits4.s0 & 0x0F00) >>  8) | ((bits2.s0 & 0x30)     )) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y.s2; \
+    total_sum.s0 += ((float)(((bits4.s0 & 0xF000) >> 12) | ((bits2.s0 & 0xC0) >> 2)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y.s3; \
+    total_sum.s0 += ((float)(((bits4.s2 & 0x000F)      ) | ((bits2.s2 & 0x03) << 4)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y.s4; \
+    total_sum.s0 += ((float)(((bits4.s2 & 0x00F0) >>  4) | ((bits2.s2 & 0x0C) << 2)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y.s5; \
+    total_sum.s0 += ((float)(((bits4.s2 & 0x0F00) >>  8) | ((bits2.s2 & 0x30)     )) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y.s6; \
+    total_sum.s0 += ((float)(((bits4.s2 & 0xF000) >> 12) | ((bits2.s2 & 0xC0) >> 2)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y.s7; \
+    total_sum.s1 += ((float)(((bits4.s1 & 0x000F)      ) | ((bits2.s1 & 0x03) << 4)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y.s0; \
+    total_sum.s1 += ((float)(((bits4.s1 & 0x00F0) >>  4) | ((bits2.s1 & 0x0C) << 2)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y.s1; \
+    total_sum.s1 += ((float)(((bits4.s1 & 0x0F00) >>  8) | ((bits2.s1 & 0x30)     )) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y.s2; \
+    total_sum.s1 += ((float)(((bits4.s1 & 0xF000) >> 12) | ((bits2.s1 & 0xC0) >> 2)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y.s3; \
+    total_sum.s1 += ((float)(((bits4.s3 & 0x000F)      ) | ((bits2.s3 & 0x03) << 4)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y.s4; \
+    total_sum.s1 += ((float)(((bits4.s3 & 0x00F0) >>  4) | ((bits2.s3 & 0x0C) << 2)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y.s5; \
+    total_sum.s1 += ((float)(((bits4.s3 & 0x0F00) >>  8) | ((bits2.s3 & 0x30)     )) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y.s6; \
+    total_sum.s1 += ((float)(((bits4.s3 & 0xF000) >> 12) | ((bits2.s3 & 0xC0) >> 2)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y.s7; \
+    shared_y = sub_group_broadcast(y, 3); \
+    total_sum.s0 += ((float)(((bits4.s4 & 0x000F)      ) | ((bits2.s4 & 0x03) << 4)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y.s0; \
+    total_sum.s0 += ((float)(((bits4.s4 & 0x00F0) >>  4) | ((bits2.s4 & 0x0C) << 2)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y.s1; \
+    total_sum.s0 += ((float)(((bits4.s4 & 0x0F00) >>  8) | ((bits2.s4 & 0x30)     )) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y.s2; \
+    total_sum.s0 += ((float)(((bits4.s4 & 0xF000) >> 12) | ((bits2.s4 & 0xC0) >> 2)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y.s3; \
+    total_sum.s0 += ((float)(((bits4.s6 & 0x000F)      ) | ((bits2.s6 & 0x03) << 4)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y.s4; \
+    total_sum.s0 += ((float)(((bits4.s6 & 0x00F0) >>  4) | ((bits2.s6 & 0x0C) << 2)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y.s5; \
+    total_sum.s0 += ((float)(((bits4.s6 & 0x0F00) >>  8) | ((bits2.s6 & 0x30)     )) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y.s6; \
+    total_sum.s0 += ((float)(((bits4.s6 & 0xF000) >> 12) | ((bits2.s6 & 0xC0) >> 2)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y.s7; \
+    total_sum.s1 += ((float)(((bits4.s5 & 0x000F)      ) | ((bits2.s5 & 0x03) << 4)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y.s0; \
+    total_sum.s1 += ((float)(((bits4.s5 & 0x00F0) >>  4) | ((bits2.s5 & 0x0C) << 2)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y.s1; \
+    total_sum.s1 += ((float)(((bits4.s5 & 0x0F00) >>  8) | ((bits2.s5 & 0x30)     )) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y.s2; \
+    total_sum.s1 += ((float)(((bits4.s5 & 0xF000) >> 12) | ((bits2.s5 & 0xC0) >> 2)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y.s3; \
+    total_sum.s1 += ((float)(((bits4.s7 & 0x000F)      ) | ((bits2.s7 & 0x03) << 4)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y.s4; \
+    total_sum.s1 += ((float)(((bits4.s7 & 0x00F0) >>  4) | ((bits2.s7 & 0x0C) << 2)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y.s5; \
+    total_sum.s1 += ((float)(((bits4.s7 & 0x0F00) >>  8) | ((bits2.s7 & 0x30)     )) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y.s6; \
+    total_sum.s1 += ((float)(((bits4.s7 & 0xF000) >> 12) | ((bits2.s7 & 0xC0) >> 2)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y.s7; \
+
+#define dequantize_block_acc_bcast_1_hi(total_sum, bits4, bits2, scale_d, scale_s, y) \
+    float shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 0); \
+    total_sum.s0 += ((float)(((bits4.s0 & 0x000F)      ) | ((bits2.s0 & 0x03) << 4)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s1 & 0x000F)      ) | ((bits2.s1 & 0x03) << 4)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 0); \
+    total_sum.s0 += ((float)(((bits4.s0 & 0x00F0) >>  4) | ((bits2.s0 & 0x0C) << 2)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s1 & 0x00F0) >>  4) | ((bits2.s1 & 0x0C) << 2)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 0); \
+    total_sum.s0 += ((float)(((bits4.s0 & 0x0F00) >>  8) | ((bits2.s0 & 0x30)     )) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s1 & 0x0F00) >>  8) | ((bits2.s1 & 0x30)     )) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 0); \
+    total_sum.s0 += ((float)(((bits4.s0 & 0xF000) >> 12) | ((bits2.s0 & 0xC0) >> 2)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s1 & 0xF000) >> 12) | ((bits2.s1 & 0xC0) >> 2)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 0); \
+    total_sum.s0 += ((float)(((bits4.s2 & 0x000F)      ) | ((bits2.s2 & 0x03) << 4)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s3 & 0x000F)      ) | ((bits2.s3 & 0x03) << 4)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 0); \
+    total_sum.s0 += ((float)(((bits4.s2 & 0x00F0) >>  4) | ((bits2.s2 & 0x0C) << 2)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s3 & 0x00F0) >>  4) | ((bits2.s3 & 0x0C) << 2)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 0); \
+    total_sum.s0 += ((float)(((bits4.s2 & 0x0F00) >>  8) | ((bits2.s2 & 0x30)     )) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s3 & 0x0F00) >>  8) | ((bits2.s3 & 0x30)     )) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 0); \
+    total_sum.s0 += ((float)(((bits4.s2 & 0xF000) >> 12) | ((bits2.s2 & 0xC0) >> 2)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s3 & 0xF000) >> 12) | ((bits2.s3 & 0xC0) >> 2)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 1); \
+    total_sum.s0 += ((float)(((bits4.s4 & 0x000F)      ) | ((bits2.s4 & 0x03) << 4)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s5 & 0x000F)      ) | ((bits2.s5 & 0x03) << 4)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 1); \
+    total_sum.s0 += ((float)(((bits4.s4 & 0x00F0) >>  4) | ((bits2.s4 & 0x0C) << 2)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s5 & 0x00F0) >>  4) | ((bits2.s5 & 0x0C) << 2)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 1); \
+    total_sum.s0 += ((float)(((bits4.s4 & 0x0F00) >>  8) | ((bits2.s4 & 0x30)     )) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s5 & 0x0F00) >>  8) | ((bits2.s5 & 0x30)     )) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 1); \
+    total_sum.s0 += ((float)(((bits4.s4 & 0xF000) >> 12) | ((bits2.s4 & 0xC0) >> 2)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s5 & 0xF000) >> 12) | ((bits2.s5 & 0xC0) >> 2)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 1); \
+    total_sum.s0 += ((float)(((bits4.s6 & 0x000F)      ) | ((bits2.s6 & 0x03) << 4)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s7 & 0x000F)      ) | ((bits2.s7 & 0x03) << 4)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 1); \
+    total_sum.s0 += ((float)(((bits4.s6 & 0x00F0) >>  4) | ((bits2.s6 & 0x0C) << 2)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s7 & 0x00F0) >>  4) | ((bits2.s7 & 0x0C) << 2)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 1); \
+    total_sum.s0 += ((float)(((bits4.s6 & 0x0F00) >>  8) | ((bits2.s6 & 0x30)     )) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s7 & 0x0F00) >>  8) | ((bits2.s7 & 0x30)     )) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 1); \
+    total_sum.s0 += ((float)(((bits4.s6 & 0xF000) >> 12) | ((bits2.s6 & 0xC0) >> 2)) - 32.f) * scale_s.s0 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s7 & 0xF000) >> 12) | ((bits2.s7 & 0xC0) >> 2)) - 32.f) * scale_s.s2 * scale_d.s1 * shared_y; \
+
+#define dequantize_block_acc_bcast_1_lo(total_sum, bits4, bits2, scale_d, scale_s, y) \
+    shared_y = sub_group_broadcast(y.s0, 2); \
+    total_sum.s0 += ((float)(((bits4.s0 & 0x000F)      ) | ((bits2.s0 & 0x03) << 4)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s1 & 0x000F)      ) | ((bits2.s1 & 0x03) << 4)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 2); \
+    total_sum.s0 += ((float)(((bits4.s0 & 0x00F0) >>  4) | ((bits2.s0 & 0x0C) << 2)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s1 & 0x00F0) >>  4) | ((bits2.s1 & 0x0C) << 2)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 2); \
+    total_sum.s0 += ((float)(((bits4.s0 & 0x0F00) >>  8) | ((bits2.s0 & 0x30)     )) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s1 & 0x0F00) >>  8) | ((bits2.s1 & 0x30)     )) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 2); \
+    total_sum.s0 += ((float)(((bits4.s0 & 0xF000) >> 12) | ((bits2.s0 & 0xC0) >> 2)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s1 & 0xF000) >> 12) | ((bits2.s1 & 0xC0) >> 2)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 2); \
+    total_sum.s0 += ((float)(((bits4.s2 & 0x000F)      ) | ((bits2.s2 & 0x03) << 4)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s3 & 0x000F)      ) | ((bits2.s3 & 0x03) << 4)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 2); \
+    total_sum.s0 += ((float)(((bits4.s2 & 0x00F0) >>  4) | ((bits2.s2 & 0x0C) << 2)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s3 & 0x00F0) >>  4) | ((bits2.s3 & 0x0C) << 2)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 2); \
+    total_sum.s0 += ((float)(((bits4.s2 & 0x0F00) >>  8) | ((bits2.s2 & 0x30)     )) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s3 & 0x0F00) >>  8) | ((bits2.s3 & 0x30)     )) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 2); \
+    total_sum.s0 += ((float)(((bits4.s2 & 0xF000) >> 12) | ((bits2.s2 & 0xC0) >> 2)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s3 & 0xF000) >> 12) | ((bits2.s3 & 0xC0) >> 2)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s0, 3); \
+    total_sum.s0 += ((float)(((bits4.s4 & 0x000F)      ) | ((bits2.s4 & 0x03) << 4)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s5 & 0x000F)      ) | ((bits2.s5 & 0x03) << 4)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s1, 3); \
+    total_sum.s0 += ((float)(((bits4.s4 & 0x00F0) >>  4) | ((bits2.s4 & 0x0C) << 2)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s5 & 0x00F0) >>  4) | ((bits2.s5 & 0x0C) << 2)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s2, 3); \
+    total_sum.s0 += ((float)(((bits4.s4 & 0x0F00) >>  8) | ((bits2.s4 & 0x30)     )) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s5 & 0x0F00) >>  8) | ((bits2.s5 & 0x30)     )) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s3, 3); \
+    total_sum.s0 += ((float)(((bits4.s4 & 0xF000) >> 12) | ((bits2.s4 & 0xC0) >> 2)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s5 & 0xF000) >> 12) | ((bits2.s5 & 0xC0) >> 2)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s4, 3); \
+    total_sum.s0 += ((float)(((bits4.s6 & 0x000F)      ) | ((bits2.s6 & 0x03) << 4)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s7 & 0x000F)      ) | ((bits2.s7 & 0x03) << 4)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s5, 3); \
+    total_sum.s0 += ((float)(((bits4.s6 & 0x00F0) >>  4) | ((bits2.s6 & 0x0C) << 2)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s7 & 0x00F0) >>  4) | ((bits2.s7 & 0x0C) << 2)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s6, 3); \
+    total_sum.s0 += ((float)(((bits4.s6 & 0x0F00) >>  8) | ((bits2.s6 & 0x30)     )) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s7 & 0x0F00) >>  8) | ((bits2.s7 & 0x30)     )) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y; \
+    shared_y = sub_group_broadcast(y.s7, 3); \
+    total_sum.s0 += ((float)(((bits4.s6 & 0xF000) >> 12) | ((bits2.s6 & 0xC0) >> 2)) - 32.f) * scale_s.s1 * scale_d.s0 * shared_y; \
+    total_sum.s1 += ((float)(((bits4.s7 & 0xF000) >> 12) | ((bits2.s7 & 0xC0) >> 2)) - 32.f) * scale_s.s3 * scale_d.s1 * shared_y; \
+
+#if defined(ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_gemv_noshuffle_q6_K_f32(
+    read_only image1d_buffer_t src0_ql,
+    read_only image1d_buffer_t src0_qh,
+    global half2 * src0_s,
+    global half2 * src0_d,
+    read_only image1d_buffer_t src1,
+    global float * dst,
+    ulong offsetd,
+    int ne00,
+    int ne01
+) {
+    int grp = get_local_id(1);
+    int gid = get_global_id(0);
+    ushort slid = get_sub_group_local_id();
+
+    int nb = ne00 / 32;
+
+    uint4    reg_a_l;
+    ushort4  reg_a_h;
+    half2    reg_d;
+    char4    reg_s;
+    float8   reg_b;
+
+    float2  total_sum = 0.0f;
+
+    int line_stride_a = ne01 / 2;
+    int block_stride_a = NSUBGROUPS * ne01;
+
+    for (int k = grp; k < nb; k += NSUBGROUPS) {
+        reg_d = src0_d[gid + k/8 * line_stride_a];
+        reg_s = as_char4(src0_s[gid + k * line_stride_a]);
+
+        if (slid < 4) {
+            reg_b.s0123 = read_imagef(src1, 0 + slid*2 + k*8);
+            reg_b.s4567 = read_imagef(src1, 1 + slid*2 + k*8);
+        }
+
+        reg_a_l.s0 = read_imageui(src0_ql, gid + k*block_stride_a + line_stride_a*0).x;
+        reg_a_l.s1 = read_imageui(src0_ql, gid + k*block_stride_a + line_stride_a*1).x;
+        reg_a_l.s2 = read_imageui(src0_ql, gid + k*block_stride_a + line_stride_a*2).x;
+        reg_a_l.s3 = read_imageui(src0_ql, gid + k*block_stride_a + line_stride_a*3).x;
+
+        reg_a_h.s0 = as_ushort(read_imageh(src0_qh, gid + k*block_stride_a + line_stride_a*0).x);
+        reg_a_h.s1 = as_ushort(read_imageh(src0_qh, gid + k*block_stride_a + line_stride_a*1).x);
+        reg_a_h.s2 = as_ushort(read_imageh(src0_qh, gid + k*block_stride_a + line_stride_a*2).x);
+        reg_a_h.s3 = as_ushort(read_imageh(src0_qh, gid + k*block_stride_a + line_stride_a*3).x);
+
+#ifdef VECTOR_SUB_GROUP_BROADCAT
+        dequantize_block_acc_bcast_8_hi(total_sum, as_ushort8(reg_a_l), as_uchar8(reg_a_h), reg_d, reg_s, reg_b);
+#else
+        dequantize_block_acc_bcast_1_hi(total_sum, as_ushort8(reg_a_l), as_uchar8(reg_a_h), reg_d, reg_s, reg_b);
+#endif // VECTOR_SUB_GROUP_BROADCAT
+
+        reg_a_l.s0 = read_imageui(src0_ql, gid + k*block_stride_a + line_stride_a*4).x;
+        reg_a_l.s1 = read_imageui(src0_ql, gid + k*block_stride_a + line_stride_a*5).x;
+        reg_a_l.s2 = read_imageui(src0_ql, gid + k*block_stride_a + line_stride_a*6).x;
+        reg_a_l.s3 = read_imageui(src0_ql, gid + k*block_stride_a + line_stride_a*7).x;
+
+        reg_a_h.s0 = as_ushort(read_imageh(src0_qh, gid + k*block_stride_a + line_stride_a*4).x);
+        reg_a_h.s1 = as_ushort(read_imageh(src0_qh, gid + k*block_stride_a + line_stride_a*5).x);
+        reg_a_h.s2 = as_ushort(read_imageh(src0_qh, gid + k*block_stride_a + line_stride_a*6).x);
+        reg_a_h.s3 = as_ushort(read_imageh(src0_qh, gid + k*block_stride_a + line_stride_a*7).x);
+
+#ifdef VECTOR_SUB_GROUP_BROADCAT
+        dequantize_block_acc_bcast_8_lo(total_sum, as_ushort8(reg_a_l), as_uchar8(reg_a_h), reg_d, reg_s, reg_b);
+#else
+        dequantize_block_acc_bcast_1_lo(total_sum, as_ushort8(reg_a_l), as_uchar8(reg_a_h), reg_d, reg_s, reg_b);
+#endif // VECTOR_SUB_GROUP_BROADCAT
+    }
+
+    local float2 reduce_lm[SUBGROUP_SIZE * 3];
+    if (grp == 1) {
+        reduce_lm[SUBGROUP_SIZE*0 + slid] = total_sum;
+    }
+    if (grp == 2) {
+        reduce_lm[SUBGROUP_SIZE*1 + slid] = total_sum;
+    }
+    if (grp == 3) {
+        reduce_lm[SUBGROUP_SIZE*2 + slid] = total_sum;
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (grp == 0) {
+        total_sum += reduce_lm[SUBGROUP_SIZE*0 + slid];
+    }
+    if (grp == 0) {
+        total_sum += reduce_lm[SUBGROUP_SIZE*1 + slid];
+    }
+    if (grp == 0) {
+        total_sum += reduce_lm[SUBGROUP_SIZE*2 + slid];
+    }
+
+    if (grp == 0) {
+        dst = (global float*)((global char*)dst + offsetd);
+        vstore2(total_sum, 0, &(dst[gid * 2]));
+    }
+}
@@ -1443,7 +1443,9 @@ 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) {
+    if (result == nullptr || result->buffer == nullptr) {
+        GGML_LOG_ERROR("[%s] invalid tensor: null %s (id=%" PRIu64 ")\n",
+                       __func__, result == nullptr ? "tensor" : "buffer", id);
        return nullptr;
    }
    tensor_map[id] = result;
@@ -56,7 +56,7 @@ void ggml_sycl_add_id(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
  float* dst_d = (float*)dst->data;

  const unsigned int max_work_group_size = ggml_sycl_info().max_work_group_sizes[ctx.device];
-  assert(work_group_size % (WARP_SIZE * WARP_SIZE) == 0);
+  GGML_ASSERT(max_work_group_size % (WARP_SIZE * WARP_SIZE) == 0);

  int threads = std::min((unsigned int)ne00, max_work_group_size);  // cols

@@ -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:
@@ -1344,6 +1344,7 @@ 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 +1365,7 @@ 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 +1379,7 @@ 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 +1401,19 @@ 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 +1428,7 @@ 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 +1447,7 @@ 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 +1468,7 @@ 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 +1483,7 @@ 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 +1501,7 @@ 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 +1518,7 @@ 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 +1535,7 @@ 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 +1560,7 @@ 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 +1580,7 @@ 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 +1683,58 @@ 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
        ),
@@ -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(
@@ -1,3 +1,3 @@
 docstring_parser~=0.15
 pydantic~=2.11.7
-requests
+requests~=2.32.3
@@ -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

@@ -48,5 +48,5 @@ adb $adbserial $adbhost shell " \
  ADSP_LIBRARY_PATH=$basedir/$branch/lib \
    $ndev $nhvx $opmask $verbose $experimental $profile $hb ./$branch/bin/llama-bench --device $device --mmap 0 -m $basedir/../gguf/$model \
        --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 \
-        --batch-size 128 -ngl 99 $cli_opts $@ \
+        --ubatch-size 256 -fa 1 -ngl 99 $cli_opts $@    \
 "
@@ -20,6 +20,14 @@ if ($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/v0.38.0"
+HTTPLIB_VERSION = "refs/tags/v0.39.0"

 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:
@@ -744,11 +749,9 @@ 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 +762,7 @@ 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 +1236,6 @@ 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 +1423,7 @@ 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 +1572,7 @@ 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 +1581,8 @@ 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 +1642,9 @@ 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 +2048,12 @@ 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 +2381,7 @@ 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,
@@ -2564,7 +2534,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
    {LLM_TENSOR_TOKEN_EMBD,                 {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}},
    {LLM_TENSOR_POS_EMBD,                   {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}},
    {LLM_TENSOR_TOKEN_TYPES,                {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}},
-    {LLM_TENSOR_TOKEN_EMBD_NORM,            {LLM_TENSOR_LAYER_INPUT, GGML_OP_MUL}},
+    {LLM_TENSOR_TOKEN_EMBD_NORM,            {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},  // do the norms on the first layer (not the input layer)
    {LLM_TENSOR_OUTPUT,                     {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
    {LLM_TENSOR_CLS,                        {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
    {LLM_TENSOR_CLS_OUT,                    {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
@@ -2725,7 +2695,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
    {LLM_TENSOR_LAUREL_POST_NORM,           {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
    // this tensor is loaded for T5, but never used
    {LLM_TENSOR_DEC_CROSS_ATTN_REL_B,       {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}},
-    {LLM_TENSOR_CONV1D,                     {LLM_TENSOR_LAYER_INPUT,     GGML_OP_IM2COL}},
+    {LLM_TENSOR_CONV1D,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_IM2COL}},
    {LLM_TENSOR_POS_NET_NORM,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
    {LLM_TENSOR_POS_NET_NORM1,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
    {LLM_TENSOR_POS_NET_NORM2,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
@@ -2789,7 +2759,12 @@ 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,
@@ -342,14 +342,6 @@ llama_context::llama_context(

        if (cparams.pipeline_parallel) {
            LLAMA_LOG_INFO("%s: pipeline parallelism enabled\n", __func__);
-
-            if (!graph_reuse_disable) {
-                // TODO: figure out a way to make graph reuse work with pipeline parallelism
-                // ref: https://github.com/ggml-org/llama.cpp/pull/20463
-                LLAMA_LOG_WARN("%s: graph reuse is currently not compatible with pipeline parallelism - disabling\n", __func__);
-
-                graph_reuse_disable = true;
-            }
        }

        sched_reserve();
@@ -1189,6 +1181,13 @@ llm_graph_result * llama_context::process_ubatch(const llama_ubatch & ubatch, ll
    if (!graph_reuse_disable && res->can_reuse(gparams)) {
        //LLAMA_LOG_DEBUG("%s: reusing previous graph\n", __func__);

+        // with pipeline parallelism, the previous graph_compute_async may still be running
+        // on the GPU. we must synchronize before set_inputs to avoid overwriting input tensors
+        // that the previous compute is still reading.
+        if (cparams.pipeline_parallel) {
+            ggml_backend_sched_synchronize(sched.get());
+        }
+
        n_reused++;
    } else {
        res->reset();
@@ -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
@@ -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);
 };
@@ -370,6 +370,8 @@ void llama_model::load_hparams(llama_model_loader & ml) {
    ml.get_key(LLM_KV_CONTEXT_LENGTH,          hparams.n_ctx_train);
    ml.get_key(LLM_KV_EMBEDDING_LENGTH,        hparams.n_embd);
    ml.get_key(LLM_KV_EMBEDDING_LENGTH_OUT,    hparams.n_embd_out_impl, false);
+    ml.get_key(LLM_KV_ATTENTION_CAUSAL,        hparams.causal_attn,     false);
+    ml.get_key(LLM_KV_POOLING_TYPE,            hparams.pooling_type,    false);
    ml.get_key(LLM_KV_BLOCK_COUNT,             hparams.n_layer);
    ml.get_key(LLM_KV_EXPERT_COUNT,            hparams.n_expert,        false);
    ml.get_key(LLM_KV_EXPERT_USED_COUNT,       hparams.n_expert_used,   false);
@@ -748,8 +750,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
        case LLM_ARCH_BERT:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
-                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn, false);
-                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type, false);

                switch (hparams.n_layer) {
                    case 3:
@@ -781,8 +781,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                }

                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-                ml.get_key(LLM_KV_ATTENTION_CAUSAL,        hparams.causal_attn, false);
-                ml.get_key(LLM_KV_POOLING_TYPE,            hparams.pooling_type, false);

                switch (hparams.n_layer) {
                    case 12:
@@ -797,8 +795,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
        case LLM_ARCH_JINA_BERT_V2:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
-                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn, false);
-                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type, false);
                hparams.f_max_alibi_bias = 8.0f;

                switch (hparams.n_layer) {
@@ -810,8 +806,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
        case LLM_ARCH_JINA_BERT_V3:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
-                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn, false);
-                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type, false);

                switch (hparams.n_layer) {
                    case 24:
@@ -823,8 +817,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
        case LLM_ARCH_NOMIC_BERT_MOE:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
-                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn, false);
-                ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type, false);
                ml.get_key(LLM_KV_MOE_EVERY_N_LAYERS,         hparams.moe_every_n_layers, 0);

                if (hparams.n_layer == 12 && hparams.n_embd == 768) {
@@ -838,8 +830,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
        case LLM_ARCH_NEO_BERT:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_ATTENTION_CAUSAL,            hparams.causal_attn, false);
-                ml.get_key(LLM_KV_POOLING_TYPE,                hparams.pooling_type, false);

                if (hparams.n_layer == 28) {
                    type = LLM_TYPE_250M;
@@ -848,8 +838,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
        case LLM_ARCH_EUROBERT:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_ATTENTION_CAUSAL,            hparams.causal_attn, false);
-                ml.get_key(LLM_KV_POOLING_TYPE,                hparams.pooling_type, false);

                if (hparams.n_layer == 12) {
                    type = LLM_TYPE_SMALL;  // 0.2B
@@ -913,7 +901,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
            // fall through
        case LLM_ARCH_QWEN2:
            {
-                ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                switch (hparams.n_layer) {
                    case 24: type = hparams.n_embd == 1024 ? LLM_TYPE_0_5B : LLM_TYPE_1B; break;
@@ -995,7 +982,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
            } break;
        case LLM_ARCH_QWEN3:
            {
-                ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                switch (hparams.n_layer) {
                    case 28: type = hparams.n_embd == 1024 ? LLM_TYPE_0_6B : LLM_TYPE_1_7B; break;
@@ -1287,7 +1273,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-                ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);

                //applied only if model converted with --sentence-transformers-dense-modules
                ml.get_key(LLM_KV_DENSE_2_FEAT_IN, hparams.dense_2_feat_in, false);
@@ -1624,7 +1609,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {

                // (optional) temperature tuning - used by mistral-large
                ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE,  hparams.f_attn_temp_scale,       false);
-                ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH, hparams.n_attn_temp_floor_scale, false);
+                ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH, hparams.n_attn_temp_floor_scale, false); // FIXME why not use temperature_length?

                hparams.f_attn_temp_offset = 0.0f;

@@ -1636,6 +1621,26 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                    default: type = LLM_TYPE_UNKNOWN;
                }
            } break;
+        case LLM_ARCH_DEEPSEEK2OCR:
+            {
+                // similar to deepseek2, but without MLA
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead, false);
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
+                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);
+                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);
+
+                if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
+                    hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX;
+                }
+
+                switch (hparams.n_layer) {
+                    case 12: type = LLM_TYPE_3B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
        case LLM_ARCH_PLM:
            {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -2084,7 +2089,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
                ml.get_key(LLM_KV_ATTENTION_GROUPNORM_EPS,    hparams.f_norm_group_eps);
                ml.get_key(LLM_KV_ATTENTION_GROUPNORM_GROUPS, hparams.n_norm_groups);
-                ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn, false);
            } break;
        case LLM_ARCH_BAILINGMOE:
            {
@@ -3217,8 +3221,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                        cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"),   {hparams.n_cls_out},         TENSOR_NOT_REQUIRED);
                    }

-                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
-                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd}, 0);
+                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);
+                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {n_embd}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = layers[i];
@@ -3265,7 +3269,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
            case LLM_ARCH_MODERN_BERT:
                {
                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-                    tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
+                    tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);

                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);

@@ -3348,8 +3352,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                    tok_embd  = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0); // word_embeddings
                    type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, 0); // token_type_embeddings

-                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0); // LayerNorm
-                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd}, 0); //LayerNorm bias
+                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0); // LayerNorm
+                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {n_embd}, 0); // LayerNorm bias

                    cls   = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, 1}, TENSOR_NOT_REQUIRED);
                    cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"),   {1},         TENSOR_NOT_REQUIRED);
@@ -3400,8 +3404,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
            case LLM_ARCH_BLOOM:
                {
                    tok_embd   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab}, 0);
-                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
-                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd}, 0);
+                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);
+                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {n_embd}, 0);

                    // output
                    output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
@@ -4967,6 +4971,60 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
                            create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);

+                            // Shared expert branch
+                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
+                            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
+                        }
+                    }
+                } break;
+            case LLM_ARCH_DEEPSEEK2OCR:
+                {
+                    // similar to deepseek2, but without MLA
+                    const int64_t n_ff_exp        = hparams.n_ff_exp;
+                    const int64_t n_expert_shared = hparams.n_expert_shared;
+
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    // try to load output.weight, if not found, use token_embd (tied embeddings)
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+                    if (!output) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
+
+                        // norm
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+                        if (i < (int) hparams.n_layer_dense_lead) {
+                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+                        } else {
+                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+                            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
+
+                            if (n_expert == 0) {
+                                throw std::runtime_error("n_expert must be > 0");
+                            }
+                            if (n_expert_used == 0) {
+                                throw std::runtime_error("n_expert_used must be > 0");
+                            }
+
+                            // MoE branch
+                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+                            create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);
+
                            // Shared expert branch
                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
@@ -5780,8 +5838,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // Block 0, LN0
-                    tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
-                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, 0);
+                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);
+                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {n_embd}, 0);

                    // output
                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
@@ -5895,8 +5953,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // Block 0, LN0
-                    tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
-                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, 0);
+                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {n_embd}, 0);
+                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {n_embd}, 0);

                    // output
                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
@@ -6067,8 +6125,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                {
                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hparams.n_embd, n_vocab}, 0);

-                    conv1d   = create_tensor(tn(LLM_TENSOR_CONV1D, "weight"), {7, hparams.n_embd, hparams.posnet.n_embd}, 0);
-                    conv1d_b = create_tensor(tn(LLM_TENSOR_CONV1D, "bias"),   {1, hparams.posnet.n_embd}, 0);
+                    conv1d   = create_tensor(tn(LLM_TENSOR_CONV1D, "weight", 0), {7, hparams.n_embd, hparams.posnet.n_embd}, 0);
+                    conv1d_b = create_tensor(tn(LLM_TENSOR_CONV1D, "bias",   0), {1, hparams.posnet.n_embd}, 0);

                    // posnet
                    {
@@ -6133,8 +6191,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {

                    GGML_ASSERT(hparams.posnet.n_embd == hparams.convnext.n_embd);

-                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {hparams.posnet.n_embd}, 0);
-                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {hparams.posnet.n_embd}, 0);
+                    tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight", 0), {hparams.posnet.n_embd}, 0);
+                    tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias",   0), {hparams.posnet.n_embd}, 0);

                    // convnext
                    {
@@ -7607,14 +7665,15 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                buf_map.emplace(idx, buf);
            }
        }
-        pimpl->ctxs_bufs.emplace_back(std::move(ctx_ptr), std::move(bufs));

-        for (auto & buf : buf_map) {
+        for (auto & buf : bufs) {
            // indicate that this buffer contains weights
            // this is used by ggml_backend_sched to improve op scheduling: ops that use a weight are preferably scheduled to the backend that contains the weight
-            ggml_backend_buffer_set_usage(buf.second, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
+            ggml_backend_buffer_set_usage(buf.get(), GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
        }

+        pimpl->ctxs_bufs.emplace_back(std::move(ctx_ptr), std::move(bufs));
+
        ctx_buf_maps.emplace_back(ctx, buf_map);
    }

@@ -7857,7 +7916,7 @@ void llama_model::print_info() const {
        LLAMA_LOG_INFO("%s: expert_weights_scale  = %.1f\n",   __func__, hparams.expert_weights_scale);
    }

-    if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_MISTRAL4) {
+    if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_DEEPSEEK2OCR || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_MISTRAL4) {
        LLAMA_LOG_INFO("%s: n_layer_dense_lead    = %d\n",     __func__, hparams.n_layer_dense_lead);
        LLAMA_LOG_INFO("%s: n_lora_q              = %d\n",     __func__, hparams.n_lora_q);
        LLAMA_LOG_INFO("%s: n_lora_kv             = %d\n",     __func__, hparams.n_lora_kv);
@@ -8434,6 +8493,7 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
                llm = std::make_unique<llm_build_deepseek>(*this, params);
            } break;
        case LLM_ARCH_DEEPSEEK2:
+        case LLM_ARCH_DEEPSEEK2OCR:
        case LLM_ARCH_GLM_DSA:
        case LLM_ARCH_MISTRAL4:
            {
@@ -8834,6 +8894,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
        case LLM_ARCH_ARCTIC:
        case LLM_ARCH_DEEPSEEK:
        case LLM_ARCH_DEEPSEEK2:
+        case LLM_ARCH_DEEPSEEK2OCR:
        case LLM_ARCH_PLM:
        case LLM_ARCH_CHATGLM:
        case LLM_ARCH_GRANITE:
@@ -344,7 +344,10 @@ static bool tensor_allows_quantization(const llama_model_quantize_params * param
    quantize &= name.find("attn_rel_b.weight") == std::string::npos;

    // do not quantize specific multimodal tensors
-    quantize &= name.find(".position_embd.") == std::string::npos;
+    quantize &= name.find(".position_embd") == std::string::npos;
+    quantize &= name.find("sam.patch_embd") == std::string::npos;
+    quantize &= name.find("sam.pos_embd")   == std::string::npos;
+    quantize &= name.find(".rel_pos")       == std::string::npos;

    return quantize;
 }
@@ -859,7 +862,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::

    std::vector<std::string> splits = {};
    llama_model_loader ml(/*metadata*/ nullptr, /*set_tensor_data*/ nullptr, /*set_tensor_data_ud*/ nullptr,
-        fname_inp, splits, use_mmap, /*use_direct_io*/ false, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr);
+        fname_inp, splits, /*file*/ nullptr, use_mmap, /*use_direct_io*/ false, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr);
    ml.init_mappings(false); // no prefetching

    llama_model model(llama_model_default_params());
@@ -1952,7 +1952,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
            } else if (
                    tokenizer_pre == "qwen2" ||
                    tokenizer_pre == "deepseek-r1-qwen" ||
-                    tokenizer_pre == "kormo") {
+                    tokenizer_pre == "kormo" ||
+                    tokenizer_pre == "f2llmv2") {
                pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN2;
                clean_spaces = false;
            } else if (
@@ -2489,6 +2490,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                    || t.first == "[EOS]" // Kimi-K2
                    || t.first == "<|end_of_text|>"
                    || t.first == "<end_of_utterance>" // smoldocling
+                    || t.first == "<｜end▁of▁sentence｜>" // deepseek-ocr
               ) {
                special_eog_ids.insert(t.second);
                if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
@@ -365,14 +365,14 @@ static void llama_params_fit_impl(
            case LAYER_FRACTION_ATTN: {
                static std::array<std::string, n_strings> patterns;
                if (patterns[il].empty()) {
-                    patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(up|gate|down).*";
+                    patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(gate|up|gate_up|down).*";
                }
                return patterns[il].c_str();
            }
            case LAYER_FRACTION_UP: {
                static std::array<std::string, n_strings> patterns;
                if (patterns[il].empty()) {
-                    patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(gate|down).*";
+                    patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(gate|gate_up|down).*";
                }
                return patterns[il].c_str();
            }
@@ -386,7 +386,7 @@ static void llama_params_fit_impl(
            case LAYER_FRACTION_MOE: {
                static std::array<std::string, n_strings> patterns;
                if (patterns[il].empty()) {
-                    patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(up|down|gate)_(ch|)exps";
+                    patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(up|down|gate_up|gate)_(ch|)exps";
                }
                return patterns[il].c_str();
            }
@@ -480,7 +480,7 @@ static void llama_params_fit_impl(

    int64_t global_surplus_cpu_moe = 0;
    if (hp_nex > 0) {
-        const static std::string pattern_moe_all = "blk\\.\\d+\\.ffn_(up|down|gate)_(ch|)exps"; // matches all MoE tensors
+        const static std::string pattern_moe_all = "blk\\.\\d+\\.ffn_(up|down|gate_up|gate)_(ch|)exps"; // matches all MoE tensors
        ggml_backend_buffer_type_t cpu_buft = ggml_backend_cpu_buffer_type();
        tensor_buft_overrides[0] = {pattern_moe_all.c_str(), cpu_buft};
        tensor_buft_overrides[1] = {nullptr, nullptr};
@@ -828,7 +828,7 @@ int64_t llama_time_us(void) {

 // Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
 static int llama_model_load(struct gguf_context * metadata, llama_model_set_tensor_data_t set_tensor_data, void * set_tensor_data_ud,
-        const std::string & fname, std::vector<std::string> & splits, llama_model & model, llama_model_params & params) {
+        const std::string & fname, std::vector<std::string> & splits, FILE * file, llama_model & model, llama_model_params & params) {
    // loading time will be recalculated after the first eval, so
    // we take page faults deferred by mmap() into consideration
    model.t_load_us = 0;
@@ -837,7 +837,7 @@ static int llama_model_load(struct gguf_context * metadata, llama_model_set_tens
    model.t_start_us = tm.t_start_us;

    try {
-        llama_model_loader ml(metadata, set_tensor_data, set_tensor_data_ud, fname, splits, params.use_mmap, params.use_direct_io,
+        llama_model_loader ml(metadata, set_tensor_data, set_tensor_data_ud, fname, splits, file, params.use_mmap, params.use_direct_io,
            params.check_tensors, params.no_alloc, params.kv_overrides, params.tensor_buft_overrides);

        ml.print_info();
@@ -889,8 +889,24 @@ static struct llama_model * llama_model_load_from_file_impl(
        void * set_tensor_data_ud,
        const std::string & path_model,
        std::vector<std::string> & splits,
+        FILE * file,
        struct llama_model_params params) {
-    GGML_ASSERT((metadata == nullptr) != path_model.empty() && "exactly one out of metadata and path_model needs to be defined");
+    {
+        int n_sources_defined = 0;
+        if (metadata != nullptr) {
+            n_sources_defined++;
+        }
+        if (!path_model.empty()) {
+            n_sources_defined++;
+        }
+        if (file != nullptr) {
+            n_sources_defined++;
+        }
+        if (n_sources_defined != 1) {
+            LLAMA_LOG_ERROR("%s: exactly one out metadata, path_model, and file must be defined\n", __func__);
+            return nullptr;
+        }
+    }
    ggml_time_init();

    if (!params.vocab_only && ggml_backend_reg_count() == 0) {
@@ -1011,7 +1027,7 @@ static struct llama_model * llama_model_load_from_file_impl(
                props.memory_free/1024/1024);
    }

-    const int status = llama_model_load(metadata, set_tensor_data, set_tensor_data_ud, path_model, splits, *model, params);
+    const int status = llama_model_load(metadata, set_tensor_data, set_tensor_data_ud, path_model, splits, file, *model, params);
    GGML_ASSERT(status <= 0);
    if (status < 0) {
        if (status == -1) {
@@ -1037,7 +1053,7 @@ struct llama_model * llama_model_init_from_user(
    std::vector<std::string> splits = {};
    params.use_mmap = false;
    params.use_extra_bufts = false;
-    return llama_model_load_from_file_impl(metadata, set_tensor_data, set_tensor_data_ud, path_model, splits, params);
+    return llama_model_load_from_file_impl(metadata, set_tensor_data, set_tensor_data_ud, path_model, splits, /*file*/ nullptr, params);
 }
 // deprecated
 struct llama_model * llama_load_model_from_file(
@@ -1050,7 +1066,7 @@ struct llama_model * llama_model_load_from_file(
        const char * path_model,
        struct llama_model_params params) {
    std::vector<std::string> splits = {};
-    return llama_model_load_from_file_impl(nullptr, nullptr, nullptr, path_model, splits, params);
+    return llama_model_load_from_file_impl(nullptr, nullptr, nullptr, path_model, splits, /*file*/ nullptr, params);
 }

 struct llama_model * llama_model_load_from_splits(
@@ -1066,7 +1082,17 @@ struct llama_model * llama_model_load_from_splits(
    for (size_t i = 0; i < n_paths; ++i) {
        splits.push_back(paths[i]);
    }
-    return llama_model_load_from_file_impl(nullptr, nullptr, nullptr, splits.front(), splits, params);
+    return llama_model_load_from_file_impl(nullptr, nullptr, nullptr, splits.front(), splits, /*file*/ nullptr, params);
+}
+
+struct llama_model * llama_model_load_from_file_ptr(FILE * file, struct llama_model_params params) {
+    if (!file) {
+        LLAMA_LOG_ERROR("%s: file is NULL\n", __func__);
+        return nullptr;
+    }
+    std::string path_model;
+    std::vector<std::string> splits = {};
+    return llama_model_load_from_file_impl(nullptr, nullptr, nullptr, path_model, splits, file, params);
 }

 void llama_model_save_to_file(const struct llama_model * model, const char * path_model) {
@@ -28,8 +28,8 @@ llm_build_bert::llm_build_bert(const llama_model & model, const llm_graph_params
    cb(inpL, "inp_embd", -1);

    // embed layer norm
-    inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, -1);
-    cb(inpL, "inp_norm", -1);
+    inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, 0);
+    cb(inpL, "inp_norm", 0);

    auto * inp_attn = build_attn_inp_no_cache();

@@ -16,8 +16,8 @@ llm_build_bloom::llm_build_bloom(const llama_model & model, const llm_graph_para
    inpL = build_norm(inpL,
            model.tok_norm,
            model.tok_norm_b,
-            LLM_NORM, -1);
-    cb(inpL, "inp_norm", -1);
+            LLM_NORM, 0);
+    cb(inpL, "inp_norm", 0);

    ggml_tensor * inp_out_ids = build_inp_out_ids();

@@ -2,6 +2,9 @@

 llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_graph_params & params) :
    llm_graph_context(params) {
+    // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B
+    bool is_ocr = model.arch == LLM_ARCH_DEEPSEEK2OCR;
+
    const bool is_mla = hparams.is_mla();

    // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
@@ -54,7 +57,38 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
        cb(cur, "attn_norm", il);

        // self_attention
-        {
+        if (is_ocr) {
+            const int n_embed_head = hparams.n_embd / hparams.n_head();
+            const int ocr_rope_type = GGML_ROPE_TYPE_NEOX;
+            GGML_ASSERT(n_embed_head == n_embd_head_k && n_embed_head == n_embd_head_v);
+
+            ggml_tensor * Qcur = NULL;
+            ggml_tensor * Kcur = NULL;
+            ggml_tensor * Vcur = NULL;
+
+            Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+            Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+            Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+            cb(Qcur, "q", il);
+            cb(Kcur, "k", il);
+            cb(Vcur, "v", il);
+
+            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embed_head, n_head, n_tokens);
+            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embed_head, n_head, n_tokens);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embed_head, n_head, n_tokens);
+
+            GGML_ASSERT(fabs(freq_base - 10000.0) < 1e-4);
+            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_embed_head, ocr_rope_type, 0, freq_base, 1, 0, 1, 0, 0);
+            Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_embed_head, ocr_rope_type, 0, freq_base, 1, 0, 1, 0, 0);
+            cb(Qcur, "q_pe", il);
+            cb(Kcur, "k_pe", il);
+
+            cur = build_attn(inp_attn_kv,
+                        model.layers[il].wo, NULL,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+            cb(cur, "attn_out", il);
+        }
+        else {
            ggml_tensor * q = NULL;

            const bool is_lite = model.layers[il].wq;
@@ -15,8 +15,8 @@ llm_build_modern_bert::llm_build_modern_bert(const llama_model & model, const ll
    cb(inpL, "inp_embd", -1);

    // embed layer norm
-    inpL = build_norm(inpL, model.tok_norm, nullptr, LLM_NORM, -1);
-    cb(inpL, "inp_norm", -1);
+    inpL = build_norm(inpL, model.tok_norm, nullptr, LLM_NORM, 0);
+    cb(inpL, "inp_norm", 0);

    ggml_tensor * inp_out_ids = build_inp_out_ids();

@@ -8,7 +8,7 @@ llm_build_rwkv6::llm_build_rwkv6(const llama_model & model, const llm_graph_para
    ggml_tensor * inpL;

    inpL = build_inp_embd(model.tok_embd);
-    inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, -1);
+    inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, 0);

    auto * rs_inp = build_rs_inp();

@@ -9,7 +9,7 @@ llm_build_rwkv7::llm_build_rwkv7(const llama_model & model, const llm_graph_para
    ggml_tensor * v_first = nullptr;

    inpL = build_inp_embd(model.tok_embd);
-    inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, -1);
+    inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, 0);

    auto * rs_inp = build_rs_inp();

@@ -93,7 +93,7 @@ llm_build_wavtokenizer_dec::llm_build_wavtokenizer_dec(const llama_model & model
    cur = build_norm(cur,
            model.tok_norm,
            model.tok_norm_b,
-            LLM_NORM, -1);
+            LLM_NORM, 0);

    cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));

@@ -4823,28 +4823,33 @@ struct test_conv_transpose_1d : public test_case {

 // GGML_OP_CONV_TRANSPOSE_2D
 struct test_conv_transpose_2d : public test_case {
+    // Dimensions
    const std::array<int64_t, 4> ne_input;
    const std::array<int64_t, 4> ne_kernel;
    const int stride;
+    // Types
+    const ggml_type kernel_type;

    std::string vars() override {
-        return VARS_TO_STR3(ne_input, ne_kernel, stride);
+        return VARS_TO_STR4(kernel_type, ne_input, ne_kernel, stride);
    }

    double max_nmse_err() override {
        return 5e-4; // The default 1e-7 is too small for Vulkan.
    }

-    test_conv_transpose_2d(std::array<int64_t, 4> ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1]
-                           std::array<int64_t, 4> ne_kernel = {3, 3, 3, 1}, // [kernel_width, kernel_height, input_channels, 1]
-                           int stride = 1)
-        : ne_input(ne_input), ne_kernel(ne_kernel), stride(stride){}
+    test_conv_transpose_2d(
+        std::array<int64_t, 4> ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1]
+        std::array<int64_t, 4> ne_kernel = {3, 3, 3, 1}, // [kernel_width, kernel_height, input_channels, 1]
+        int stride = 1,
+        ggml_type kernel_type = GGML_TYPE_F16
+    ) : ne_input(ne_input), ne_kernel(ne_kernel), stride(stride), kernel_type(kernel_type) {}

    ggml_tensor * build_graph(ggml_context * ctx) override {
        ggml_tensor * input = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_input.data());
        ggml_set_name(input, "input");

-        ggml_tensor * kernel = ggml_new_tensor(ctx, GGML_TYPE_F16, 4, ne_kernel.data());
+        ggml_tensor * kernel = ggml_new_tensor(ctx, kernel_type, 4, ne_kernel.data());
        ggml_set_name(kernel, "kernel");

        ggml_tensor * out = ggml_conv_transpose_2d_p0(ctx, kernel, input, stride);
@@ -7704,9 +7709,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
    test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {3,1,2,1}, 1, 0, 1));
    test_cases.emplace_back(new test_conv_transpose_1d({2,1,1,1}, {3,1,1,1}, 1, 0, 1));

-    test_cases.emplace_back(new test_conv_transpose_2d({3, 2, 3, 1}, {2, 2, 1, 3}, 1));
-    test_cases.emplace_back(new test_conv_transpose_2d({10, 10, 9, 1}, {3, 3, 1, 9}, 2));
-    test_cases.emplace_back(new test_conv_transpose_2d({129, 63, 35, 1}, {3, 3, 48, 35}, 1));
+    for (ggml_type kernel_type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
+        test_cases.emplace_back(new test_conv_transpose_2d({3, 2, 3, 1}, {2, 2, 1, 3}, 1, kernel_type));
+        test_cases.emplace_back(new test_conv_transpose_2d({10, 10, 9, 1}, {3, 3, 1, 9}, 2, kernel_type));
+        test_cases.emplace_back(new test_conv_transpose_2d({129, 63, 35, 1}, {3, 3, 48, 35}, 1, kernel_type));
+    }

    test_cases.emplace_back(new test_count_equal(GGML_TYPE_F32, {4,  500, 1, 1}));
    test_cases.emplace_back(new test_count_equal(GGML_TYPE_F32, {4, 5000, 1, 1}));
@@ -8576,12 +8583,12 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
        }
    }

-    for (int hsk : { 40, 64, 72, 80, 96, 128, 192, 256, 320, 576 }) {
+    for (int hsk : { 40, 64, 72, 80, 96, 128, 192, 256, 320, 512, 576 }) {
        for (int hsv : { 40, 64, 72, 80, 96, 128, 192, 256, 512 }) {
            if (hsk != 192 && hsk != 320 && hsk != 576 && hsk != hsv) continue;
            if (hsk == 192 && (hsv != 128 && hsv != 192)) continue;
            if (hsk == 576 && hsv != 512) continue; // DeepSeek MLA
-            if (hsk == 320 && hsv != 256) continue; // MLA
+            if (hsk == 320 && hsv != 256) continue; // Mistral4 MLA

            for (bool mask : { true, false } ) {
                for (bool sinks : { true, false } ) {
@@ -8590,7 +8597,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
                        for (float logit_softcap : {0.0f, 10.0f}) {
                            if (hsk != 128 && logit_softcap != 0.0f) continue;
                            for (int nh : { 1, 4 }) {
-                                if (nh == 1 && hsk != 320 && hsk != 576) continue; // GLM 4.7 Flash
+                                if (nh == 1 && hsk != 320 && hsk != 576) continue;
                                for (int nr3 : { 1, 3, }) {
                                    if (hsk > 64 && nr3 > 1) continue; // skip broadcast for large head sizes
                                    for (int nr2 : { 1, 4, 12, 20, 32 }) {
@@ -8892,9 +8899,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
    test_cases.emplace_back(new test_conv_2d_dw({512, 512, 256, 1}, {3, 3, 1, 256}, 1, 1, 1, false));
    test_cases.emplace_back(new test_conv_2d_dw({512, 512, 256, 1}, {3, 3, 1, 256}, 1, 1, 1, true));

-    test_cases.emplace_back(new test_conv_transpose_2d({256, 256, 256, 1}, {3, 3, 16, 256}, 1));
-    test_cases.emplace_back(new test_conv_transpose_2d({16, 16, 16, 1}, {3, 3, 8, 16}, 1));
-    test_cases.emplace_back(new test_conv_transpose_2d({10, 10, 9, 1}, {3, 3, 1, 9}, 2));
+    for (ggml_type kernel_type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
+        test_cases.emplace_back(new test_conv_transpose_2d({256, 256, 256, 1}, {3, 3, 16, 256}, 1, kernel_type));
+        test_cases.emplace_back(new test_conv_transpose_2d({16, 16, 16, 1}, {3, 3, 8, 16}, 1, kernel_type));
+        test_cases.emplace_back(new test_conv_transpose_2d({10, 10, 9, 1}, {3, 3, 1, 9}, 2, kernel_type));
+    }

    test_cases.emplace_back(new test_mean(GGML_TYPE_F32, {256, 256, 3, 1}));

@@ -742,7 +742,7 @@ static std::pair<int, int> test_handcrafted_file(const unsigned int seed) {
            /*ctx      =*/ hft >= offset_has_data ? &ctx : nullptr,
        };

-        struct gguf_context * gguf_ctx = gguf_init_from_file_impl(file, gguf_params);
+        struct gguf_context * gguf_ctx = gguf_init_from_file_ptr(file, gguf_params);

        if (expect_context_not_null(hft)) {
            printf("%s:   - context_not_null: ", __func__);
@@ -1125,19 +1125,15 @@ static std::pair<int, int> test_roundtrip(ggml_backend_dev_t dev, const unsigned
    GGML_ASSERT(file);
 #endif // _WIN32

-    {
-        std::vector<int8_t> buf;
-        gguf_write_to_buf(gguf_ctx_0, buf, only_meta);
-        GGML_ASSERT(fwrite(buf.data(), 1, buf.size(), file) == buf.size());
-        rewind(file);
-    }
+    gguf_write_to_file_ptr(gguf_ctx_0, file, only_meta);
+    rewind(file);

    struct ggml_context * ctx_1 = nullptr;
    struct gguf_init_params gguf_params = {
        /*no_alloc =*/ false,
        /*ctx      =*/ only_meta ? nullptr : &ctx_1,
    };
-    struct gguf_context * gguf_ctx_1 = gguf_init_from_file_impl(file, gguf_params);
+    struct gguf_context * gguf_ctx_1 = gguf_init_from_file_ptr(file, gguf_params);

    printf("%s: same_version: ", __func__);
    if (gguf_get_version(gguf_ctx_0) == gguf_get_version(gguf_ctx_1)) {
@@ -884,6 +884,24 @@ static void test_macros(testing & t) {
        json::object(),
        "Hi Guest"
    );
+
+    test_template(t, "macro kwargs input",
+        "{% macro my_func(a, b=False) %}{% if b %}{{ a }}{% else %}nope{% endif %}{% endmacro %}{{ my_func(1, b=True) }}",
+        json::object(),
+        "1"
+    );
+
+    test_template(t, "macro with multiple args",
+        "{% macro add(a, b, c=0) %}{{ a + b + c }}{% endmacro %}{{ add(1, 2) }},{{ add(1, 2, 3) }},{{ add(1, b=10) }},{{ add(1, 2, c=5) }}",
+        json::object(),
+        "3,6,11,8"
+    );
+
+    test_template(t, "macro with kwarg out-of-order input",
+        "{% macro greet(first, last, greeting='Hello') %}{{ greeting }}, {{ first }} {{ last }}{% endmacro %}{{ greet(last='Smith', first='John') }},{{ greet(last='Doe', greeting='Hi', first='Jane') }}",
+        json::object(),
+        "Hello, John Smith,Hi, Jane Doe"
+    );
 }

 static void test_namespace(testing & t) {
@@ -90,6 +90,7 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
        n_embd = 64;
        n_head = 1;
        n_ff   = 96;
+        n_layer = 22; // hparams.n_layer_kv_from_start = 20 is hardcoded
    } else if (arch == LLM_ARCH_DEEPSEEK2
            || arch == LLM_ARCH_GLM_DSA
            || arch == LLM_ARCH_KIMI_LINEAR
@@ -101,8 +102,6 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
        n_layer = 3;
    } else if (arch == LLM_ARCH_CHAMELEON) {
        n_vocab = 10240;
-    } else if (arch == LLM_ARCH_GEMMA3N) {
-        n_layer = 22; // hparams.n_layer_kv_from_start = 20 is hardcoded
    }

    const uint32_t n_embd_head = n_embd / n_head;
@@ -231,9 +230,15 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
    return ret;
 }

+static bool silent_model_load_progress(float /*progress*/, void * /*user_data*/) {
+    return true;
+}
+
 static std::pair<llama_model_ptr, llama_context_ptr> get_model_and_ctx(
-        struct gguf_context * gguf_ctx, const size_t seed, const std::vector<ggml_backend_dev_t> & devs) {
+        struct gguf_context * gguf_ctx, FILE * file, const size_t seed, const std::vector<ggml_backend_dev_t> & devs) {
+    GGML_ASSERT((gguf_ctx == nullptr) != (file == nullptr));
    llama_model_params model_params = llama_model_default_params();
+    model_params.progress_callback = silent_model_load_progress;
    std::vector<ggml_backend_dev_t> devs_copy = devs;
    devs_copy.push_back(nullptr);
    model_params.devices = devs_copy.data();
@@ -244,7 +249,9 @@ static std::pair<llama_model_ptr, llama_context_ptr> get_model_and_ctx(
    ctx_params.n_threads_batch = 4;

    size_t tmp = seed;
-    llama_model_ptr model(llama_model_init_from_user(gguf_ctx, set_tensor_data, &tmp, model_params));
+    llama_model_ptr model(gguf_ctx != nullptr ?
+        llama_model_init_from_user(gguf_ctx, set_tensor_data, &tmp, model_params) :
+        llama_model_load_from_file_ptr(file, model_params));
    if (!model) {
        throw std::runtime_error("failed to create llama model");
    }
@@ -351,7 +358,6 @@ static bool moe_implemented(const llm_arch arch) {
 }

 static int save_models(const llm_arch target_arch, const size_t seed, const ggml_log_level log_level, const std::string & dir) {
-    GGML_ABORT("llama_model_save_to_file is broken");
    struct user_data_t {
        struct {
            ggml_log_callback callback;
@@ -376,6 +382,19 @@ static int save_models(const llm_arch target_arch, const size_t seed, const ggml
        if (arch == LLM_ARCH_CLIP || arch == LLM_ARCH_GPTJ || arch == LLM_ARCH_UNKNOWN) {
            continue; // These models don't have usable implementations.
        }
+        if (arch == LLM_ARCH_CHAMELEON) {
+            continue; // Only half-implemented and to be removed in the future.
+        }
+        if (arch == LLM_ARCH_RWKV6 || arch == LLM_ARCH_RWKV6QWEN2 || arch == LLM_ARCH_RWKV7 || arch == LLM_ARCH_ARWKV7) {
+            continue; // FIXME
+        }
+        if (arch == LLM_ARCH_BERT || arch == LLM_ARCH_MODERN_BERT || arch == LLM_ARCH_NOMIC_BERT || arch == LLM_ARCH_NOMIC_BERT_MOE ||
+                arch == LLM_ARCH_NEO_BERT || arch == LLM_ARCH_JINA_BERT_V2 || arch == LLM_ARCH_JINA_BERT_V3 || arch == LLM_ARCH_EUROBERT) {
+            continue; // TODO vocab
+        }
+        if (arch == LLM_ARCH_PLM) {
+            continue; // TODO tensor shapes
+        }
        for (bool moe : {false, true}) {
            if (moe && !moe_implemented(arch)) {
                continue;
@@ -383,8 +402,12 @@ static int save_models(const llm_arch target_arch, const size_t seed, const ggml
            if (!moe && moe_mandatory(arch)) {
                continue;
            }
+            if (!llama_model_saver_supports_arch(arch)) {
+                LOG_INF("%s: %s model (%s) is unsupported, skipping\n", __func__, llm_arch_name(arch), moe ? "MoE" : "dense");
+                continue;
+            }
            gguf_context_ptr gguf_ctx = get_gguf_ctx(arch, moe);
-            auto model_and_ctx = get_model_and_ctx(gguf_ctx.get(), seed, {});
+            auto model_and_ctx = get_model_and_ctx(gguf_ctx.get(), nullptr, seed, {});
            const std::string path = dir + "/" + llm_arch_name(arch) + (moe ? "-moe.gguf" : "-dense.gguf");
            LOG_INF("%s: Saving %s model (%s) to %s...\n", __func__, llm_arch_name(arch), moe ? "MoE" : "dense", path.c_str());
            llama_model_save_to_file(model_and_ctx.first.get(), path.c_str());
@@ -416,8 +439,8 @@ static int test_backends(const llm_arch target_arch, const size_t seed, const gg

    bool all_ok = true;
    common_log_flush(common_log_main());
-    printf("|%15s|%30s|%6s|%8s|%6s|\n", "Model arch.", "Device", "Config", "NMSE", "Status");
-    printf("|---------------|------------------------------|------|--------|------|\n");
+    printf("|%15s|%30s|%6s|%15s|%9s|\n", "Model arch.", "Device", "Config", "NMSE vs. CPU", "Roundtrip");
+    printf("|---------------|------------------------------|------|---------------|---------|\n");
    for (const llm_arch & arch : llm_arch_all()) {
        if (target_arch != LLM_ARCH_UNKNOWN && arch != target_arch) {
            continue;
@@ -425,6 +448,9 @@ static int test_backends(const llm_arch target_arch, const size_t seed, const gg
        if (arch == LLM_ARCH_CLIP || arch == LLM_ARCH_GPTJ || arch == LLM_ARCH_UNKNOWN) {
            continue; // These models don't have usable implementations.
        }
+        if (arch == LLM_ARCH_CHAMELEON) {
+            continue; // Only half-implemented and to be removed in the future.
+        }
        if (arch == LLM_ARCH_WAVTOKENIZER_DEC) {
            continue; // FIXME CUDA backend crashes.
        }
@@ -441,6 +467,9 @@ static int test_backends(const llm_arch target_arch, const size_t seed, const gg
        if (arch == LLM_ARCH_PLM) {
            continue; // TODO tensor shapes
        }
+        if (arch == LLM_ARCH_DEEPSEEK2OCR) {
+            continue; // TODO tensor shapes
+        }

        // FIXME some models are segfaulting with WebGPU:
 #ifdef GGML_USE_WEBGPU
@@ -458,22 +487,50 @@ static int test_backends(const llm_arch target_arch, const size_t seed, const gg
                continue;
            }
            gguf_context_ptr gguf_ctx = get_gguf_ctx(arch, moe);
-            auto model_and_ctx_cpu = get_model_and_ctx(gguf_ctx.get(), seed, {});
+            auto model_and_ctx_cpu = get_model_and_ctx(gguf_ctx.get(), nullptr, seed, {});
            const std::vector<float> logits_cpu = get_logits(model_and_ctx_cpu.first.get(), model_and_ctx_cpu.second.get(), tokens, encode);
            for (size_t i = 0; i < ggml_backend_dev_count(); i++) {
                ggml_backend_dev_t dev = ggml_backend_dev_get(i);
                if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) {
                    continue;
                }
-                auto model_and_ctx_dev = get_model_and_ctx(gguf_ctx.get(), seed, {dev});
+                auto model_and_ctx_dev = get_model_and_ctx(gguf_ctx.get(), nullptr, seed, {dev});
+                std::string config_name = moe ? "MoE" : "Dense";
                const std::vector<float> logits_dev = get_logits(model_and_ctx_dev.first.get(), model_and_ctx_dev.second.get(), tokens, encode);
                const double nmse_val = nmse(logits_cpu, logits_dev);
-                const bool ok = nmse_val <= 1e-4;
-                all_ok = all_ok && ok;
                char nmse_str[10];
                snprintf(nmse_str, sizeof(nmse_str), "%.2e", nmse_val);
-                printf("|%15s|%30s|%6s|%8s|%17s|\n", llm_arch_name(arch), ggml_backend_dev_description(dev),
-                    moe ? "MoE" : "Dense", nmse_str, ok ? "\033[1;32mOK\033[0m" : "\033[1;31mFAIL\033[0m");
+                std::string status_nmse = "\033[1;32mOK\033[0m";
+                if (nmse_val > 1e-4) {
+                    all_ok = false;
+                    status_nmse = "\033[1;31mFAIL\033[0m";
+                }
+
+                std::string status_roundtrip = "\033[1;33mSKIP\033[0m";
+                FILE * file = tmpfile(); // Can be null on Windows without administrator privileges.
+                if (file != nullptr && llama_model_saver_supports_arch(arch)) {
+                    llama_model_saver ms = llama_model_saver(model_and_ctx_dev.first.get());
+                    ms.add_kv_from_model();
+                    ms.add_tensors_from_model();
+                    ms.save(file);
+                    rewind(file);
+
+                    auto model_and_ctx_roundtrip = get_model_and_ctx(nullptr, file, seed, {dev});
+                    const std::vector<float> logits_roundtrip = get_logits(
+                        model_and_ctx_roundtrip.first.get(), model_and_ctx_roundtrip.second.get(), tokens, encode);
+                    status_roundtrip = "\033[1;32mOK\033[0m";
+                    GGML_ASSERT(logits_roundtrip.size() == logits_dev.size());
+                    for (size_t i = 0; i < logits_roundtrip.size(); i++) {
+                        if (logits_roundtrip[i] != logits_dev[i]) {
+                            all_ok = false;
+                            status_roundtrip = "\033[1;31mFAIL\033[0m";
+                            break;
+                        }
+                    }
+                }
+
+                printf("|%15s|%30s|%6s|%15s (%8s)|%20s|\n", llm_arch_name(arch), ggml_backend_dev_description(dev),
+                    config_name.c_str(), status_nmse.c_str(), nmse_str, status_roundtrip.c_str());
            }
        }
    }
@@ -526,6 +583,7 @@ int main(int argc, char ** argv) {
            }
        }
    }
+    printf("%s: using seed %zu\n", __func__, seed);

    try {
        if (!out.empty()) {
--- a/Show More
+++ b/Show More