cont : fix defrag erasing cells that didn't move

ggml-ci
cont : reset shift[i]
2026-04-23 16:37:33 +03:00 · 2025-06-09 20:45:56 +03:00 · 2025-06-09 19:24:25 +03:00 · 2025-06-09 10:53:26 +03:00 · 2025-06-09 05:15:31 +01:00 · 2025-06-09 11:20:06 +08:00
290 changed files with 117208 additions and 10166 deletions
--- a/.devops/intel.Dockerfile
+++ b/.devops/intel.Dockerfile
@@ -1,4 +1,4 @@
-ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04
+ARG ONEAPI_VERSION=2025.1.1-0-devel-ubuntu24.04

 ## Build Image

--- a/.devops/musa.Dockerfile
+++ b/.devops/musa.Dockerfile
@@ -1,10 +1,10 @@
 ARG UBUNTU_VERSION=22.04
 # This needs to generally match the container host's environment.
-ARG MUSA_VERSION=rc3.1.1
+ARG MUSA_VERSION=rc4.0.1
 # Target the MUSA build image
-ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
+ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-mudnn-devel-ubuntu${UBUNTU_VERSION}

-ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
+ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-mudnn-runtime-ubuntu${UBUNTU_VERSION}

 FROM ${BASE_MUSA_DEV_CONTAINER} AS build

@@ -21,21 +21,14 @@ RUN apt-get update && \
    libcurl4-openssl-dev \
    libgomp1

-COPY requirements.txt   requirements.txt
-COPY requirements       requirements
-
-RUN pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt
-
 WORKDIR /app

 COPY . .

-# Use the default MUSA archs if not specified
 RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \
        export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \
    fi && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
+    cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_BUILD_TESTS=OFF ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
    cmake --build build --config Release -j$(nproc)

 RUN mkdir -p /app/lib && \
--- a/.editorconfig
+++ b/.editorconfig
@@ -48,3 +48,7 @@ end_of_line = unset
 charset = unset
 trim_trailing_whitespace = unset
 insert_final_newline = unset
+
+[vendor/miniaudio/miniaudio.h]
+trim_trailing_whitespace = unset
+insert_final_newline = unset
--- a/.github/labeler.yml
+++ b/.github/labeler.yml
@@ -86,3 +86,10 @@ nix:
 embedding:
    - changed-files:
        - any-glob-to-any-file: examples/embedding/
+
+Ascend NPU:
+    - changed-files:
+        - any-glob-to-any-file:
+            - ggml/include/ggml-cann.h
+            - ggml/src/ggml-cann/**
+            - docs/backend/CANN.md
--- a/.github/workflows/build-linux-cross.yml
+++ b/.github/workflows/build-linux-cross.yml
@@ -26,12 +26,12 @@ jobs:
          sudo apt-get install -y --no-install-recommends \
                  build-essential \
                  gcc-14-riscv64-linux-gnu \
-                  g++-14-riscv64-linux-gnu \
-                  libcurl4-openssl-dev:riscv64
+                  g++-14-riscv64-linux-gnu

      - name: Build
        run: |
-          cmake -B build -DCMAKE_BUILD_TYPE=Release \
+          cmake -B build -DLLAMA_CURL=OFF \
+                         -DCMAKE_BUILD_TYPE=Release \
                         -DGGML_OPENMP=OFF \
                         -DLLAMA_BUILD_EXAMPLES=ON \
                         -DLLAMA_BUILD_TOOLS=ON \
@@ -72,12 +72,12 @@ jobs:
                  glslc \
                  gcc-14-riscv64-linux-gnu \
                  g++-14-riscv64-linux-gnu \
-                  libvulkan-dev:riscv64 \
-                  libcurl4-openssl-dev:riscv64
+                  libvulkan-dev:riscv64

      - name: Build
        run: |
-          cmake -B build -DCMAKE_BUILD_TYPE=Release \
+          cmake -B build -DLLAMA_CURL=OFF \
+                         -DCMAKE_BUILD_TYPE=Release \
                         -DGGML_VULKAN=ON \
                         -DGGML_OPENMP=OFF \
                         -DLLAMA_BUILD_EXAMPLES=ON \
@@ -118,12 +118,12 @@ jobs:
                  build-essential \
                  glslc \
                  crossbuild-essential-arm64 \
-                  libvulkan-dev:arm64 \
-                  libcurl4-openssl-dev:arm64
+                  libvulkan-dev:arm64

      - name: Build
        run: |
-          cmake -B build -DCMAKE_BUILD_TYPE=Release \
+          cmake -B build -DLLAMA_CURL=OFF \
+                         -DCMAKE_BUILD_TYPE=Release \
                         -DGGML_VULKAN=ON \
                         -DGGML_OPENMP=OFF \
                         -DLLAMA_BUILD_EXAMPLES=ON \
@@ -163,12 +163,12 @@ jobs:
          sudo apt-get install -y --no-install-recommends \
                  build-essential \
                  gcc-14-powerpc64le-linux-gnu \
-                  g++-14-powerpc64le-linux-gnu \
-                  libcurl4-openssl-dev:ppc64el
+                  g++-14-powerpc64le-linux-gnu

      - name: Build
        run: |
-          cmake -B build -DCMAKE_BUILD_TYPE=Release \
+          cmake -B build -DLLAMA_CURL=OFF \
+                         -DCMAKE_BUILD_TYPE=Release \
                         -DGGML_OPENMP=OFF \
                         -DLLAMA_BUILD_EXAMPLES=ON \
                         -DLLAMA_BUILD_TOOLS=ON \
@@ -209,12 +209,12 @@ jobs:
                  glslc \
                  gcc-14-powerpc64le-linux-gnu \
                  g++-14-powerpc64le-linux-gnu \
-                  libvulkan-dev:ppc64el \
-                  libcurl4-openssl-dev:ppc64el
+                  libvulkan-dev:ppc64el

      - name: Build
        run: |
-          cmake -B build -DCMAKE_BUILD_TYPE=Release \
+          cmake -B build -DLLAMA_CURL=OFF \
+                         -DCMAKE_BUILD_TYPE=Release \
                         -DGGML_VULKAN=ON \
                         -DGGML_OPENMP=OFF \
                         -DLLAMA_BUILD_EXAMPLES=ON \
@@ -231,3 +231,116 @@ jobs:
                         -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH

          cmake --build build --config Release -j $(nproc)
+
+  debian-13-loongarch64-cpu-cross:
+    runs-on: ubuntu-24.04
+    container: debian@sha256:653dfb9f86c3782e8369d5f7d29bb8faba1f4bff9025db46e807fa4c22903671
+
+    steps:
+      - uses: actions/checkout@v4
+      - name: Setup LoongArch
+        run: |
+          rm -f /etc/apt/sources.list.d/*
+          cat << EOF | tee /etc/apt/sources.list.d/debian-ports.list
+          deb http://snapshot.debian.org/archive/debian/20250515T202920Z/ trixie main
+          EOF
+          ( echo 'quiet "true";'; \
+            echo 'APT::Get::Assume-Yes "true";'; \
+            echo 'APT::Install-Recommends "false";'; \
+            echo 'Acquire::Check-Valid-Until "false";'; \
+            echo 'Acquire::Retries "5";'; \
+          ) > /etc/apt/apt.conf.d/99snapshot-repos
+
+          apt-get update
+          apt-get install -y ca-certificates debian-ports-archive-keyring cmake git zip
+          dpkg --add-architecture loong64
+
+          # Add arch-specific repositories for non-amd64 architectures
+          cat << EOF | tee /etc/apt/sources.list.d/loong64-ports.list
+          deb [arch=loong64] http://snapshot.debian.org/archive/debian-ports/20250515T194251Z/ sid main
+          EOF
+
+          apt-get update || true    ;# Prevent failure due to missing URLs.
+
+          apt-get install -y --no-install-recommends \
+                  build-essential \
+                  gcc-14-loongarch64-linux-gnu \
+                  g++-14-loongarch64-linux-gnu
+
+      - name: Build
+        run: |
+          cmake -B build -DLLAMA_CURL=OFF \
+                         -DCMAKE_BUILD_TYPE=Release \
+                         -DGGML_OPENMP=OFF \
+                         -DLLAMA_BUILD_EXAMPLES=ON \
+                         -DLLAMA_BUILD_TOOLS=ON \
+                         -DLLAMA_BUILD_TESTS=OFF \
+                         -DCMAKE_SYSTEM_NAME=Linux \
+                         -DCMAKE_SYSTEM_PROCESSOR=loongarch64 \
+                         -DCMAKE_C_COMPILER=loongarch64-linux-gnu-gcc-14 \
+                         -DCMAKE_CXX_COMPILER=loongarch64-linux-gnu-g++-14 \
+                         -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
+                         -DCMAKE_FIND_ROOT_PATH=/usr/lib/loongarch64-linux-gnu \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH
+
+          cmake --build build --config Release -j $(nproc)
+
+  debian-13-loongarch64-vulkan-cross:
+    runs-on: ubuntu-24.04
+    container: debian@sha256:653dfb9f86c3782e8369d5f7d29bb8faba1f4bff9025db46e807fa4c22903671
+
+    steps:
+      - uses: actions/checkout@v4
+      - name: Setup LoongArch
+        run: |
+          rm -f /etc/apt/sources.list.d/*
+          cat << EOF | tee /etc/apt/sources.list.d/debian-ports.list
+          deb http://snapshot.debian.org/archive/debian/20250515T202920Z/ trixie main
+          EOF
+          ( echo 'quiet "true";'; \
+            echo 'APT::Get::Assume-Yes "true";'; \
+            echo 'APT::Install-Recommends "false";'; \
+            echo 'Acquire::Check-Valid-Until "false";'; \
+            echo 'Acquire::Retries "5";'; \
+          ) > /etc/apt/apt.conf.d/99snapshot-repos
+
+          apt-get update
+          apt-get install -y ca-certificates debian-ports-archive-keyring cmake git zip
+          dpkg --add-architecture loong64
+
+          # Add arch-specific repositories for non-amd64 architectures
+          cat << EOF | tee /etc/apt/sources.list.d/loong64-ports.list
+          deb [arch=loong64] http://snapshot.debian.org/archive/debian-ports/20250515T194251Z/ sid main
+          EOF
+
+          apt-get update || true    ;# Prevent failure due to missing URLs.
+
+          apt-get install -y --no-install-recommends \
+                  build-essential \
+                  glslc \
+                  gcc-14-loongarch64-linux-gnu \
+                  g++-14-loongarch64-linux-gnu \
+                  libvulkan-dev:loong64
+
+      - name: Build
+        run: |
+          cmake -B build -DLLAMA_CURL=OFF \
+                         -DCMAKE_BUILD_TYPE=Release \
+                         -DGGML_VULKAN=ON \
+                         -DGGML_OPENMP=OFF \
+                         -DLLAMA_BUILD_EXAMPLES=ON \
+                         -DLLAMA_BUILD_TOOLS=ON \
+                         -DLLAMA_BUILD_TESTS=OFF \
+                         -DCMAKE_SYSTEM_NAME=Linux \
+                         -DCMAKE_SYSTEM_PROCESSOR=loongarch64 \
+                         -DCMAKE_C_COMPILER=loongarch64-linux-gnu-gcc-14 \
+                         -DCMAKE_CXX_COMPILER=loongarch64-linux-gnu-g++-14 \
+                         -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
+                         -DCMAKE_FIND_ROOT_PATH=/usr/lib/loongarch64-linux-gnu \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH
+
+          cmake --build build --config Release -j $(nproc)
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -351,7 +351,7 @@ jobs:

  ubuntu-22-cmake-musa:
    runs-on: ubuntu-22.04
-    container: mthreads/musa:rc3.1.1-devel-ubuntu22.04
+    container: mthreads/musa:rc4.0.1-mudnn-devel-ubuntu22.04

    steps:
      - name: Clone
@@ -839,12 +839,12 @@ jobs:
              -DGGML_CUDA=ON
            cmake --build build

-  windows-2019-cmake-cuda:
-    runs-on: windows-2019
+  windows-2022-cmake-cuda:
+    runs-on: windows-2022

    strategy:
      matrix:
-        cuda: ['12.4', '11.7']
+        cuda: ['12.4']

    steps:
      - name: Clone
@@ -878,7 +878,7 @@ jobs:
        env:
          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
        run: |
-          call "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Auxiliary\Build\vcvars64.bat"
+          call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" x64
          cmake -S . -B build -G "Ninja Multi-Config" ^
            -DLLAMA_BUILD_SERVER=ON ^
            -DGGML_NATIVE=OFF ^
@@ -899,7 +899,7 @@ jobs:
        shell: bash

    env:
-      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/b380d914-366b-4b77-a74a-05e3c38b3514/intel-oneapi-base-toolkit-2025.0.0.882_offline.exe
+      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/7cd9bba0-7aab-4e30-b3ae-2221006a4a05/intel-oneapi-base-toolkit-2025.1.1.34_offline.exe
      WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel:intel.oneapi.win.dnnl:intel.oneapi.win.tbb.devel
      ONEAPI_ROOT: "C:/Program Files (x86)/Intel/oneAPI"
    steps:
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -1,4 +1,4 @@
-name: Create Release
+name: Release

 on:
  workflow_dispatch: # allows manual triggering
@@ -131,8 +131,9 @@ jobs:
        include:
          - build: 'x64'
            os: ubuntu-22.04
-          - build: 'arm64'
-            os: ubuntu-22.04-arm
+          # GGML_BACKEND_DL and GGML_CPU_ALL_VARIANTS are not currently supported on arm
+          # - build: 'arm64'
+          #   os: ubuntu-22.04-arm

    runs-on: ${{ matrix.os }}

@@ -159,6 +160,9 @@ jobs:
        id: cmake_build
        run: |
          cmake -B build \
+            -DGGML_BACKEND_DL=ON \
+            -DGGML_NATIVE=OFF \
+            -DGGML_CPU_ALL_VARIANTS=ON \
            -DLLAMA_FATAL_WARNINGS=ON \
            ${{ env.CMAKE_ARGS }}
          cmake --build build --config Release -j $(nproc)
@@ -207,6 +211,9 @@ jobs:
        id: cmake_build
        run: |
          cmake -B build \
+            -DGGML_BACKEND_DL=ON \
+            -DGGML_NATIVE=OFF \
+            -DGGML_CPU_ALL_VARIANTS=ON \
            -DGGML_VULKAN=ON \
            ${{ env.CMAKE_ARGS }}
          cmake --build build --config Release -j $(nproc)
@@ -227,6 +234,69 @@ jobs:
          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.zip
          name: llama-bin-ubuntu-vulkan-x64.zip

+  windows-cpu:
+    runs-on: windows-latest
+
+    strategy:
+      matrix:
+        include:
+          - arch: 'x64'
+          - arch: 'arm64'
+
+    steps:
+      - name: Clone
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: windows-latest-cmake-cpu-${{ matrix.arch }}
+          variant: ccache
+          evict-old-files: 1d
+
+      - name: Install Ninja
+        run: |
+          choco install ninja
+
+      - name: libCURL
+        id: get_libcurl
+        uses: ./.github/actions/windows-setup-curl
+        with:
+          architecture: ${{ matrix.arch == 'x64' && 'win64' || 'win64a' }}
+
+      - name: Build
+        shell: cmd
+        env:
+          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
+        run: |
+          call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" ${{ matrix.arch }}
+          cmake -S . -B build -G "Ninja Multi-Config" ^
+            -D CMAKE_TOOLCHAIN_FILE=cmake/${{ matrix.arch }}-windows-llvm.cmake ^
+            -DGGML_NATIVE=OFF ^
+            -DGGML_BACKEND_DL=ON ^
+            -DGGML_CPU_ALL_VARIANTS=${{ matrix.arch == 'x64' && 'ON' || 'OFF' }} ^
+            -DGGML_OPENMP=ON ^
+            -DCURL_LIBRARY="%CURL_PATH%/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="%CURL_PATH%/include" ^
+            ${{ env.CMAKE_ARGS }}
+          cmake --build build --config Release
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        env:
+          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
+        run: |
+          Copy-Item $env:CURL_PATH\bin\libcurl-${{ matrix.arch }}.dll .\build\bin\Release\
+          Copy-Item "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Redist\MSVC\14.42.34433\debug_nonredist\${{ matrix.arch }}\Microsoft.VC143.OpenMP.LLVM\libomp140.${{ matrix.arch == 'x64' && 'x86_64' || 'aarch64' }}.dll" .\build\bin\Release\
+          7z a llama-bin-win-cpu-${{ matrix.arch }}.zip .\build\bin\Release\*
+
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-bin-win-cpu-${{ matrix.arch }}.zip
+          name: llama-bin-win-cpu-${{ matrix.arch }}.zip
+
  windows:
    runs-on: windows-latest

@@ -237,52 +307,30 @@ jobs:
    strategy:
      matrix:
        include:
-          - build: 'cpu-x64'
+          - backend: 'vulkan'
            arch: 'x64'
-            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_OPENMP=OFF'
-          #- build: 'openblas-x64'
-          #  arch: 'x64'
-          #  defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_OPENMP=OFF -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"'
-          - build: 'vulkan-x64'
-            arch: 'x64'
-            defines: '-DGGML_NATIVE=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_VULKAN=ON'
-          - build: 'cpu-arm64'
-            arch: 'arm64'
-            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF'
-          - build: 'opencl-adreno-arm64'
+            defines: '-DGGML_VULKAN=ON'
+            target: 'ggml-vulkan'
+          - backend: 'opencl-adreno'
            arch: 'arm64'
            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" -DGGML_OPENCL=ON -DGGML_OPENCL_USE_ADRENO_KERNELS=ON'
+            target: 'ggml-opencl'

    steps:
      - name: Clone
        id: checkout
        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0

      - name: ccache
        uses: hendrikmuhs/ccache-action@v1.2.16
        with:
-          key: windows-latest-cmake-${{ matrix.build }}
+          key: windows-latest-cmake-${{ matrix.backend }}-${{ matrix.arch }}
          variant: ccache
          evict-old-files: 1d

-      - name: Download OpenBLAS
-        id: get_openblas
-        if: ${{ matrix.build == 'openblas-x64' }}
-        run: |
-          curl.exe -o $env:RUNNER_TEMP/openblas.zip -L "https://github.com/xianyi/OpenBLAS/releases/download/v${env:OPENBLAS_VERSION}/OpenBLAS-${env:OPENBLAS_VERSION}-x64.zip"
-          curl.exe -o $env:RUNNER_TEMP/OpenBLAS.LICENSE.txt -L "https://github.com/xianyi/OpenBLAS/raw/v${env:OPENBLAS_VERSION}/LICENSE"
-          mkdir $env:RUNNER_TEMP/openblas
-          tar.exe -xvf $env:RUNNER_TEMP/openblas.zip -C $env:RUNNER_TEMP/openblas
-          $vcdir = $(vswhere -latest -products * -requires Microsoft.VisualStudio.Component.VC.Tools.x86.x64 -property installationPath)
-          $msvc = $(join-path $vcdir $('VC\Tools\MSVC\'+$(gc -raw $(join-path $vcdir 'VC\Auxiliary\Build\Microsoft.VCToolsVersion.default.txt')).Trim()))
-          $lib =  $(join-path $msvc 'bin\Hostx64\x64\lib.exe')
-          & $lib /machine:x64 "/def:${env:RUNNER_TEMP}/openblas/lib/libopenblas.def" "/out:${env:RUNNER_TEMP}/openblas/lib/openblas.lib" /name:openblas.dll
-
      - name: Install Vulkan SDK
        id: get_vulkan
-        if: ${{ matrix.build == 'vulkan-x64' }}
+        if: ${{ matrix.backend == 'vulkan' }}
        run: |
          curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/VulkanSDK-${env:VULKAN_VERSION}-Installer.exe"
          & "$env:RUNNER_TEMP\VulkanSDK-Installer.exe" --accept-licenses --default-answer --confirm-command install
@@ -296,7 +344,7 @@ jobs:

      - name: Install OpenCL Headers and Libs
        id: install_opencl
-        if: ${{ matrix.build == 'opencl-adreno-arm64' }}
+        if: ${{ matrix.backend == 'opencl-adreno' && matrix.arch == 'arm64' }}
        run: |
          git clone https://github.com/KhronosGroup/OpenCL-Headers
          cd OpenCL-Headers
@@ -314,60 +362,34 @@ jobs:
            -DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release"
          cmake --build build-arm64-release --target install --config release

-      - name: libCURL
-        id: get_libcurl
-        uses: ./.github/actions/windows-setup-curl
-        with:
-          architecture: ${{ matrix.arch == 'x64' && 'win64' || 'win64a' }}
-
      - name: Build
        id: cmake_build
-        env:
-          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
        run: |
-          cmake -S . -B build ${{ matrix.defines }} `
-            -DCURL_LIBRARY="$env:CURL_PATH/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:CURL_PATH/include" `
-            ${{ env.CMAKE_ARGS }}
-          cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS}
-
-      - name: Add libopenblas.dll
-        id: add_libopenblas_dll
-        if: ${{ matrix.build == 'openblas-x64' }}
-        run: |
-          cp $env:RUNNER_TEMP/openblas/bin/libopenblas.dll ./build/bin/Release/openblas.dll
-          cp $env:RUNNER_TEMP/OpenBLAS.LICENSE.txt ./build/bin/Release/OpenBLAS-${env:OPENBLAS_VERSION}.txt
-
-      - name: Determine tag name
-        id: tag
-        uses: ./.github/actions/get-tag-name
+          cmake -S . -B build ${{ matrix.defines }} -DGGML_NATIVE=OFF -DGGML_CPU=OFF -DGGML_BACKEND_DL=ON -DLLAMA_CURL=OFF
+          cmake --build build --config Release --target ${{ matrix.target }}

      - name: Pack artifacts
        id: pack_artifacts
-        env:
-          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
        run: |
-          Copy-Item $env:CURL_PATH\bin\libcurl-${{ matrix.arch }}.dll .\build\bin\Release\
-          7z a llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}.zip .\build\bin\Release\*
+          7z a llama-bin-win-${{ matrix.backend }}-${{ matrix.arch }}.zip .\build\bin\Release\${{ matrix.target }}.dll

      - name: Upload artifacts
        uses: actions/upload-artifact@v4
        with:
-          path: llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}.zip
-          name: llama-bin-win-${{ matrix.build }}.zip
+          path: llama-bin-win-${{ matrix.backend }}-${{ matrix.arch }}.zip
+          name: llama-bin-win-${{ matrix.backend }}-${{ matrix.arch }}.zip

  windows-cuda:
-    runs-on: windows-2019
+    runs-on: windows-2022

    strategy:
      matrix:
-        cuda: ['12.4', '11.7']
+        cuda: ['12.4']

    steps:
      - name: Clone
        id: checkout
        uses: actions/checkout@v4
-        with:
-            fetch-depth: 0

      - name: Install ccache
        uses: hendrikmuhs/ccache-action@v1.2.16
@@ -386,45 +408,30 @@ jobs:
        run: |
          choco install ninja

-      - name: libCURL
-        id: get_libcurl
-        uses: ./.github/actions/windows-setup-curl
-
      - name: Build
        id: cmake_build
        shell: cmd
-        env:
-          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
        run: |
-          call "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Auxiliary\Build\vcvars64.bat"
+          call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" x64
          cmake -S . -B build -G "Ninja Multi-Config" ^
-            -DGGML_NATIVE=OFF ^
            -DGGML_BACKEND_DL=ON ^
-            -DGGML_CPU_ALL_VARIANTS=ON ^
+            -DGGML_NATIVE=OFF ^
+            -DGGML_CPU=OFF ^
            -DGGML_CUDA=ON ^
-            -DCURL_LIBRARY="%CURL_PATH%/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="%CURL_PATH%/include" ^
-            ${{ env.CMAKE_ARGS }}
+            -DLLAMA_CURL=OFF
          set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1
-          cmake --build build --config Release -j %NINJA_JOBS% -t ggml
-          cmake --build build --config Release
-
-      - name: Determine tag name
-        id: tag
-        uses: ./.github/actions/get-tag-name
+          cmake --build build --config Release -j %NINJA_JOBS% --target ggml-cuda

      - name: Pack artifacts
        id: pack_artifacts
-        env:
-          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
        run: |
-          cp $env:CURL_PATH\bin\libcurl-x64.dll .\build\bin\Release\libcurl-x64.dll
-          7z a llama-${{ steps.tag.outputs.name }}-bin-win-cuda${{ matrix.cuda }}-x64.zip .\build\bin\Release\*
+          7z a llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip .\build\bin\Release\ggml-cuda.dll

      - name: Upload artifacts
        uses: actions/upload-artifact@v4
        with:
-          path: llama-${{ steps.tag.outputs.name }}-bin-win-cuda${{ matrix.cuda }}-x64.zip
-          name: llama-bin-win-cuda${{ matrix.cuda }}-x64.zip
+          path: llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip
+          name: llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip

      - name: Copy and pack Cuda runtime
        run: |
@@ -432,13 +439,13 @@ jobs:
          $dst='.\build\bin\cudart\'
          robocopy "${{env.CUDA_PATH}}\bin" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll
          robocopy "${{env.CUDA_PATH}}\lib" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll
-          7z a cudart-llama-bin-win-cuda${{ matrix.cuda }}-x64.zip $dst\*
+          7z a cudart-llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip $dst\*

      - name: Upload Cuda runtime
        uses: actions/upload-artifact@v4
        with:
-          path: cudart-llama-bin-win-cuda${{ matrix.cuda }}-x64.zip
-          name: cudart-llama-bin-win-cuda${{ matrix.cuda }}-x64.zip
+          path: cudart-llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip
+          name: cudart-llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip

  windows-sycl:
    runs-on: windows-latest
@@ -448,15 +455,14 @@ jobs:
        shell: bash

    env:
-      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/b380d914-366b-4b77-a74a-05e3c38b3514/intel-oneapi-base-toolkit-2025.0.0.882_offline.exe
+      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/7cd9bba0-7aab-4e30-b3ae-2221006a4a05/intel-oneapi-base-toolkit-2025.1.1.34_offline.exe
      WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel:intel.oneapi.win.dnnl:intel.oneapi.win.tbb.devel
      ONEAPI_ROOT: "C:/Program Files (x86)/Intel/oneAPI"
+
    steps:
      - name: Clone
        id: checkout
        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0

      - name: ccache
        uses: hendrikmuhs/ccache-action@v1.2.16
@@ -469,15 +475,18 @@ jobs:
        run:  |
          scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL

-      # TODO: add libcurl support ; we will also need to modify win-build-sycl.bat to accept user-specified args
-
      - name: Build
        id: cmake_build
-        run:  examples/sycl/win-build-sycl.bat
-
-      - name: Determine tag name
-        id: tag
-        uses: ./.github/actions/get-tag-name
+        shell: cmd
+        run: |
+          call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
+          cmake -G "Ninja" -B build ^
+            -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx ^
+            -DCMAKE_BUILD_TYPE=Release ^
+            -DGGML_BACKEND_DL=ON -DBUILD_SHARED_LIBS=ON ^
+            -DGGML_CPU=OFF -DGGML_SYCL=ON ^
+            -DLLAMA_CURL=OFF
+          cmake --build build --target ggml-sycl -j

      - name: Build the release package
        id: pack_artifacts
@@ -502,12 +511,12 @@ jobs:
          cp "${{ env.ONEAPI_ROOT }}/tbb/latest/bin/tbb12.dll" ./build/bin

          echo "cp oneAPI running time dll files to ./build/bin done"
-          7z a llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip ./build/bin/*
+          7z a llama-bin-win-sycl-x64.zip ./build/bin/*

      - name: Upload the release package
        uses: actions/upload-artifact@v4
        with:
-          path: llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip
+          path: llama-bin-win-sycl-x64.zip
          name: llama-bin-win-sycl-x64.zip

  windows-hip:
@@ -515,14 +524,14 @@ jobs:

    strategy:
      matrix:
-        gpu_target: [gfx1100, gfx1101, gfx1030]
+        include:
+          - name: "radeon"
+            gpu_targets: "gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032"

    steps:
      - name: Clone
        id: checkout
        uses: actions/checkout@v4
-        with:
-            fetch-depth: 0

      - name: Clone rocWMMA repository
        id: clone_rocwmma
@@ -532,7 +541,7 @@ jobs:
      - name: ccache
        uses: hendrikmuhs/ccache-action@v1.2.16
        with:
-          key: windows-latest-cmake-hip-release
+          key: windows-latest-cmake-hip-${{ matrix.name }}-x64
          evict-old-files: 1d

      - name: Install
@@ -550,50 +559,39 @@ jobs:
        run: |
          & 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version

-      - name: libCURL
-        id: get_libcurl
-        uses: ./.github/actions/windows-setup-curl
-
      - name: Build
        id: cmake_build
-        env:
-          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
        run: |
          $env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path)
          $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}"
          cmake -G "Unix Makefiles" -B build -S . `
            -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" `
            -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" `
-            -DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/rocwmma/library/include/" `
+            -DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/rocwmma/library/include/ -Wno-ignored-attributes -Wno-nested-anon-types" `
            -DCMAKE_BUILD_TYPE=Release `
-            -DAMDGPU_TARGETS=${{ matrix.gpu_target }} `
+            -DGGML_BACKEND_DL=ON `
+            -DGGML_NATIVE=OFF `
+            -DGGML_CPU=OFF `
+            -DAMDGPU_TARGETS="${{ matrix.gpu_targets }}" `
            -DGGML_HIP_ROCWMMA_FATTN=ON `
            -DGGML_HIP=ON `
-            -DCURL_LIBRARY="$env:CURL_PATH/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:CURL_PATH/include" `
-            ${{ env.CMAKE_ARGS }}
-          cmake --build build -j ${env:NUMBER_OF_PROCESSORS}
+            -DLLAMA_CURL=OFF
+          cmake --build build --target ggml-hip -j ${env:NUMBER_OF_PROCESSORS}
          md "build\bin\rocblas\library\"
          cp "${env:HIP_PATH}\bin\hipblas.dll" "build\bin\"
          cp "${env:HIP_PATH}\bin\rocblas.dll" "build\bin\"
          cp "${env:HIP_PATH}\bin\rocblas\library\*" "build\bin\rocblas\library\"

-      - name: Determine tag name
-        id: tag
-        uses: ./.github/actions/get-tag-name
-
      - name: Pack artifacts
        id: pack_artifacts
-        env:
-          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
        run: |
-          cp $env:CURL_PATH\bin\libcurl-x64.dll .\build\bin\libcurl-x64.dll
-          7z a llama-${{ steps.tag.outputs.name }}-bin-win-hip-x64-${{ matrix.gpu_target }}.zip .\build\bin\*
+          7z a llama-bin-win-hip-${{ matrix.name }}-x64.zip .\build\bin\*

      - name: Upload artifacts
        uses: actions/upload-artifact@v4
        with:
-          path: llama-${{ steps.tag.outputs.name }}-bin-win-hip-x64-${{ matrix.gpu_target }}.zip
-          name: llama-bin-win-hip-x64-${{ matrix.gpu_target }}.zip
+          path: llama-bin-win-hip-${{ matrix.name }}-x64.zip
+          name: llama-bin-win-hip-${{ matrix.name }}-x64.zip

  ios-xcode-build:
    runs-on: macos-latest
@@ -655,14 +653,16 @@ jobs:
    runs-on: ubuntu-latest

    needs:
-      - ubuntu-22-cpu
-      - ubuntu-22-vulkan
      - windows
+      - windows-cpu
      - windows-cuda
      - windows-sycl
      - windows-hip
+      - ubuntu-22-cpu
+      - ubuntu-22-vulkan
      - macOS-arm64
      - macOS-x64
+      - ios-xcode-build

    steps:
      - name: Clone
@@ -680,10 +680,43 @@ jobs:
        uses: actions/download-artifact@v4
        with:
          path: ./artifact
+          merge-multiple: true

      - name: Move artifacts
        id: move_artifacts
-        run: mkdir -p ./artifact/release && mv ./artifact/*/*.zip ./artifact/release
+        run: |
+          mkdir -p release
+
+          echo "Adding CPU backend files to existing zips..."
+          for arch in x64 arm64; do
+            cpu_zip="artifact/llama-bin-win-cpu-${arch}.zip"
+            temp_dir=$(mktemp -d)
+            echo "Extracting CPU backend for $arch..."
+            unzip "$cpu_zip" -d "$temp_dir"
+
+            echo "Adding CPU files to $arch zips..."
+            for target_zip in artifact/llama-bin-win-*-${arch}.zip; do
+              if [[ "$target_zip" == "$cpu_zip" ]]; then
+                continue
+              fi
+              echo "Adding CPU backend to $(basename "$target_zip")"
+              realpath_target_zip=$(realpath "$target_zip")
+              (cd "$temp_dir" && zip -r "$realpath_target_zip" .)
+            done
+
+            rm -rf "$temp_dir"
+          done
+
+          echo "Renaming and moving zips to release..."
+          for zip_file in artifact/llama-bin-win-*.zip; do
+            base_name=$(basename "$zip_file" .zip)
+            zip_name="llama-${{ steps.tag.outputs.name }}-${base_name#llama-}.zip"
+            echo "Moving $zip_file to release/$zip_name"
+            mv "$zip_file" "release/$zip_name"
+          done
+
+          echo "Moving other artifacts..."
+          mv -v artifact/*.zip release

      - name: Create release
        id: create_release
@@ -702,7 +735,7 @@ jobs:
            const path = require('path');
            const fs = require('fs');
            const release_id = '${{ steps.create_release.outputs.id }}';
-            for (let file of await fs.readdirSync('./artifact/release')) {
+            for (let file of await fs.readdirSync('./release')) {
              if (path.extname(file) === '.zip') {
                console.log('uploadReleaseAsset', file);
                await github.repos.uploadReleaseAsset({
@@ -710,7 +743,7 @@ jobs:
                  repo: context.repo.repo,
                  release_id: release_id,
                  name: file,
-                  data: await fs.readFileSync(`./artifact/release/${file}`)
+                  data: await fs.readFileSync(`./release/${file}`)
                });
              }
            }
--- a/.github/workflows/server.yml
+++ b/.github/workflows/server.yml
@@ -180,7 +180,7 @@ jobs:


  server-windows:
-    runs-on: windows-2019
+    runs-on: windows-2022

    steps:
      - name: Clone
--- a/.github/workflows/winget.yml
+++ b/.github/workflows/winget.yml
@@ -0,0 +1,42 @@
+name: Update Winget Package
+
+on:
+  workflow_dispatch: # allows manual triggering
+  schedule:
+    - cron: '28 5 * * *' # Update every day at 5:28 UTC
+
+jobs:
+  update:
+    name: Update Winget Package
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Install cargo binstall
+        uses: cargo-bins/cargo-binstall@268643a6b5ea099f5718ee5cd3ff7dc89a5eb49b
+
+      - name: Install komac
+        run: |
+          cargo binstall komac@2.11.2 -y
+
+      - name: Find latest release
+        id: find_latest_release
+        uses: actions/github-script@v6
+        with:
+          script: |
+            const { data: releases } = await github.rest.repos.listReleases({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+            });
+            console.log("Latest release:", releases[0].tag_name);
+            return releases[0].tag_name;
+
+      - name: Update manifest
+        env:
+          VERSION: ${{ steps.find_latest_release.outputs.result }}
+        run: |
+          echo "Updating manifest..."
+          komac update --version ${{ env.VERSION }} \
+            --urls "https://github.com/ggml-org/llama.cpp/releases/download/${{ env.VERSION }}/llama-${{ env.VERSION }}-bin-win-vulkan-x64.zip" \
+            --token ${{ secrets.WINGET_GITHUB_TOKEN }} \
+            --submit \
+            ggml.llamacpp
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -159,6 +159,11 @@ if (NOT TARGET ggml AND NOT LLAMA_USE_SYSTEM_GGML)
    # ... otherwise assume ggml is added by a parent CMakeLists.txt
 endif()

+if (MINGW)
+    # Target Windows 8 for PrefetchVirtualMemory
+    add_compile_definitions(_WIN32_WINNT=${GGML_WIN_VER})
+endif()
+
 #
 # build the library
 #
--- a/README.md
+++ b/README.md
@@ -3,6 +3,7 @@
 ![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png)

 [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
+[![Release](https://img.shields.io/github/v/release/ggml-org/llama.cpp)](https://github.com/ggml-org/llama.cpp/releases)
 [![Server](https://github.com/ggml-org/llama.cpp/actions/workflows/server.yml/badge.svg)](https://github.com/ggml-org/llama.cpp/actions/workflows/server.yml)

 [Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggml-org/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggml-org/llama.cpp/discussions/205) / [ggml](https://github.com/ggml-org/ggml)
@@ -28,6 +29,30 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)

 ----

+## Quick start
+
+Getting started with llama.cpp is straightforward. Here are several ways to install it on your machine:
+
+- Install `llama.cpp` using [brew, nix or winget](docs/install.md)
+- Run with Docker - see our [Docker documentation](docs/docker.md)
+- Download pre-built binaries from the [releases page](https://github.com/ggml-org/llama.cpp/releases)
+- Build from source by cloning this repository - check out [our build guide](docs/build.md)
+
+Once installed, you'll need a model to work with. Head to the [Obtaining and quantizing models](#obtaining-and-quantizing-models) section to learn more.
+
+Example command:
+
+```sh
+# Use a local model file
+llama-cli -m my_model.gguf
+
+# Or download and run a model directly from Hugging Face
+llama-cli -hf ggml-org/gemma-3-1b-it-GGUF
+
+# Launch OpenAI-compatible API server
+llama-server -hf ggml-org/gemma-3-1b-it-GGUF
+```
+
 ## Description

 The main goal of `llama.cpp` is to enable LLM inference with minimal setup and state-of-the-art performance on a wide
@@ -37,7 +62,7 @@ range of hardware - locally and in the cloud.
 - Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
 - AVX, AVX2, AVX512 and AMX support for x86 architectures
 - 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads MTT GPUs via MUSA)
+- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads GPUs via MUSA)
 - Vulkan and SYCL backend support
 - CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity

@@ -130,6 +155,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 <details>
 <summary>Bindings</summary>

+- Python: [ddh0/easy-llama](https://github.com/ddh0/easy-llama)
 - Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
 - Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp)
 - Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp)
@@ -229,6 +255,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo

 </details>

+
 ## Supported backends

 | Backend | Target devices |
@@ -237,7 +264,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 | [BLAS](docs/build.md#blas-build) | All |
 | [BLIS](docs/backend/BLIS.md) | All |
 | [SYCL](docs/backend/SYCL.md) | Intel and Nvidia GPU |
-| [MUSA](docs/build.md#musa) | Moore Threads MTT GPU |
+| [MUSA](docs/build.md#musa) | Moore Threads GPU |
 | [CUDA](docs/build.md#cuda) | Nvidia GPU |
 | [HIP](docs/build.md#hip) | AMD GPU |
 | [Vulkan](docs/build.md#vulkan) | GPU |
@@ -245,16 +272,6 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 | [OpenCL](docs/backend/OPENCL.md) | Adreno GPU |
 | [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All |

-## Building the project
-
-The main product of this project is the `llama` library. Its C-style interface can be found in [include/llama.h](include/llama.h).
-The project also includes many example programs and tools using the `llama` library. The examples range from simple, minimal code snippets to sophisticated sub-projects such as an OpenAI-compatible HTTP server. Possible methods for obtaining the binaries:
-
- Clone this repository and build locally, see [how to build](docs/build.md)
- On MacOS or Linux, install `llama.cpp` via [brew, flox or nix](docs/install.md)
- Use a Docker image, see [documentation for Docker](docs/docker.md)
- Download pre-built binaries from [releases](https://github.com/ggml-org/llama.cpp/releases)
-
 ## Obtaining and quantizing models

 The [Hugging Face](https://huggingface.co) platform hosts a [number of LLMs](https://huggingface.co/models?library=gguf&sort=trending) compatible with `llama.cpp`:
@@ -262,7 +279,11 @@ 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]`.
+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:
+
+```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/`.

@@ -580,3 +601,4 @@ $ echo "source ~/.llama-completion.bash" >> ~/.bashrc
 - [minja](https://github.com/google/minja) - Minimal Jinja parser in C++, used by various tools/examples - MIT License
 - [linenoise.cpp](./tools/run/linenoise.cpp/linenoise.cpp) - C++ library that provides readline-like line editing capabilities, used by `llama-run` - BSD 2-Clause License
 - [curl](https://curl.se/) - Client-side URL transfer library, used by various tools/examples - [CURL License](https://curl.se/docs/copyright.html)
+- [miniaudio.h](https://github.com/mackron/miniaudio) - Single-header audio format decoder, used by multimodal subsystem - Public domain
--- a/ci/README.md
+++ b/ci/README.md
@@ -54,7 +54,7 @@ docker run --privileged -it \
    -v $HOME/llama.cpp/ci-cache:/ci-cache \
    -v $HOME/llama.cpp/ci-results:/ci-results \
    -v $PWD:/ws -w /ws \
-    mthreads/musa:rc3.1.1-devel-ubuntu22.04
+    mthreads/musa:rc4.0.1-mudnn-devel-ubuntu22.04
 ```

 Inside the container, execute the following commands:
--- a/ci/run.sh
+++ b/ci/run.sh
@@ -46,7 +46,20 @@ if [ ! -z ${GG_BUILD_METAL} ]; then
 fi

 if [ ! -z ${GG_BUILD_CUDA} ]; then
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=native"
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_CUDA=ON"
+
+    if command -v nvidia-smi >/dev/null 2>&1; then
+        CUDA_ARCH=$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader,nounits 2>/dev/null | head -1 | tr -d '.')
+        if [[ -n "$CUDA_ARCH" && "$CUDA_ARCH" =~ ^[0-9]+$ ]]; then
+            CMAKE_EXTRA="${CMAKE_EXTRA} -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCH}"
+        else
+            echo "Warning: Using fallback CUDA architectures"
+            CMAKE_EXTRA="${CMAKE_EXTRA} -DCMAKE_CUDA_ARCHITECTURES=61;70;75;80;86;89"
+        fi
+    else
+        echo "Error: nvidia-smi not found, cannot build with CUDA"
+        exit 1
+    fi
 fi

 if [ ! -z ${GG_BUILD_SYCL} ]; then
--- a/common/CMakeLists.txt
+++ b/common/CMakeLists.txt
@@ -58,19 +58,20 @@ add_library(${TARGET} STATIC
    arg.cpp
    arg.h
    base64.hpp
+    chat-parser.cpp
+    chat-parser.h
    chat.cpp
    chat.h
    common.cpp
    common.h
    console.cpp
    console.h
+    json-partial.cpp
+    json-partial.h
    json-schema-to-grammar.cpp
-    json.hpp
    llguidance.cpp
    log.cpp
    log.h
-    minja/chat-template.hpp
-    minja/minja.hpp
    ngram-cache.cpp
    ngram-cache.h
    regex-partial.cpp
@@ -143,7 +144,7 @@ if (LLAMA_LLGUIDANCE)
    set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} llguidance ${LLGUIDANCE_PLATFORM_LIBS})
 endif ()

-target_include_directories(${TARGET} PUBLIC .)
+target_include_directories(${TARGET} PUBLIC . ../vendor)
 target_compile_features   (${TARGET} PUBLIC cxx_std_17)
 target_link_libraries     (${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads)

--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -1,10 +1,11 @@
-#include "gguf.h" // for reading GGUF splits
 #include "arg.h"

+#include "chat.h"
 #include "common.h"
+#include "gguf.h" // for reading GGUF splits
+#include "json-schema-to-grammar.h"
 #include "log.h"
 #include "sampling.h"
-#include "chat.h"

 // fix problem with std::min and std::max
 #if defined(_WIN32)
@@ -15,6 +16,9 @@
 #include <windows.h>
 #endif

+#define JSON_ASSERT GGML_ASSERT
+#include <nlohmann/json.hpp>
+
 #include <algorithm>
 #include <climits>
 #include <cstdarg>
@@ -34,12 +38,10 @@
 #include <future>
 #endif

-#include "json-schema-to-grammar.h"
-
 using json = nlohmann::ordered_json;

 std::initializer_list<enum llama_example> mmproj_examples = {
-    LLAMA_EXAMPLE_LLAVA,
+    LLAMA_EXAMPLE_MTMD,
    LLAMA_EXAMPLE_SERVER,
 };

@@ -242,7 +244,56 @@ static bool curl_perform_with_retry(const std::string & url, CURL * curl, int ma
 }

 // download one single file from remote URL to local path
-static bool common_download_file_single(const std::string & url, const std::string & path, const std::string & bearer_token) {
+static bool common_download_file_single(const std::string & url, const std::string & path, const std::string & bearer_token, bool offline) {
+    // Check if the file already exists locally
+    auto file_exists = std::filesystem::exists(path);
+
+    // If the file exists, check its JSON metadata companion file.
+    std::string metadata_path = path + ".json";
+    nlohmann::json metadata; // TODO @ngxson : get rid of this json, use regex instead
+    std::string etag;
+    std::string last_modified;
+
+    if (file_exists) {
+        if (offline) {
+            LOG_INF("%s: using cached file (offline mode): %s\n", __func__, path.c_str());
+            return true; // skip verification/downloading
+        }
+        // Try and read the JSON metadata file (note: stream autoclosed upon exiting this block).
+        std::ifstream metadata_in(metadata_path);
+        if (metadata_in.good()) {
+            try {
+                metadata_in >> metadata;
+                LOG_DBG("%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
+                if (metadata.contains("etag") && metadata.at("etag").is_string()) {
+                    etag = metadata.at("etag");
+                }
+                if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) {
+                    last_modified = metadata.at("lastModified");
+                }
+            } catch (const nlohmann::json::exception & e) {
+                LOG_ERR("%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
+            }
+        }
+        // if we cannot open the metadata file, we assume that the downloaded file is not valid (etag and last-modified are left empty, so we will download it again)
+    } else {
+        if (offline) {
+            LOG_ERR("%s: required file is not available in cache (offline mode): %s\n", __func__, path.c_str());
+            return false;
+        }
+        LOG_INF("%s: no previous model file found %s\n", __func__, path.c_str());
+    }
+
+    // Send a HEAD request to retrieve the etag and last-modified headers
+    struct common_load_model_from_url_headers {
+        std::string etag;
+        std::string last_modified;
+    };
+
+    common_load_model_from_url_headers headers;
+    bool head_request_ok = false;
+    bool should_download = !file_exists; // by default, we should download if the file does not exist
+
    // Initialize libcurl
    curl_ptr       curl(curl_easy_init(), &curl_easy_cleanup);
    curl_slist_ptr http_headers;
@@ -269,91 +320,47 @@ static bool common_download_file_single(const std::string & url, const std::stri
    curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
 #endif

-    // Check if the file already exists locally
-    auto file_exists = std::filesystem::exists(path);
+    typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
+    auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
+        common_load_model_from_url_headers * headers = (common_load_model_from_url_headers *) userdata;

-    // If the file exists, check its JSON metadata companion file.
-    std::string metadata_path = path + ".json";
-    nlohmann::json metadata; // TODO @ngxson : get rid of this json, use regex instead
-    std::string etag;
-    std::string last_modified;
+        static std::regex header_regex("([^:]+): (.*)\r\n");
+        static std::regex etag_regex("ETag", std::regex_constants::icase);
+        static std::regex last_modified_regex("Last-Modified", std::regex_constants::icase);

-    if (file_exists) {
-        // Try and read the JSON metadata file (note: stream autoclosed upon exiting this block).
-        std::ifstream metadata_in(metadata_path);
-        if (metadata_in.good()) {
-            try {
-                metadata_in >> metadata;
-                LOG_DBG("%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
-                if (metadata.contains("etag") && metadata.at("etag").is_string()) {
-                    etag = metadata.at("etag");
-                }
-                if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) {
-                    last_modified = metadata.at("lastModified");
-                }
-            } catch (const nlohmann::json::exception & e) {
-                LOG_ERR("%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
+        std::string header(buffer, n_items);
+        std::smatch match;
+        if (std::regex_match(header, match, header_regex)) {
+            const std::string & key = match[1];
+            const std::string & value = match[2];
+            if (std::regex_match(key, match, etag_regex)) {
+                headers->etag = value;
+            } else if (std::regex_match(key, match, last_modified_regex)) {
+                headers->last_modified = value;
            }
        }
-        // if we cannot open the metadata file, we assume that the downloaded file is not valid (etag and last-modified are left empty, so we will download it again)
-    } else {
-        LOG_INF("%s: no previous model file found %s\n", __func__, path.c_str());
-    }
-
-    // Send a HEAD request to retrieve the etag and last-modified headers
-    struct common_load_model_from_url_headers {
-        std::string etag;
-        std::string last_modified;
+        return n_items;
    };

-    common_load_model_from_url_headers headers;
-    bool head_request_ok = false;
-    bool should_download = !file_exists; // by default, we should download if the file does not exist
+    curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 1L); // will trigger the HEAD verb
+    curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress
+    curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
+    curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);

-    // get ETag to see if the remote file has changed
-    {
-        typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
-        auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
-            common_load_model_from_url_headers * headers = (common_load_model_from_url_headers *) userdata;
+    // we only allow retrying once for HEAD requests
+    // this is for the use case of using running offline (no internet), retrying can be annoying
+    bool was_perform_successful = curl_perform_with_retry(url, curl.get(), 1, 0, "HEAD");
+    if (!was_perform_successful) {
+        head_request_ok = false;
+    }

-            static std::regex header_regex("([^:]+): (.*)\r\n");
-            static std::regex etag_regex("ETag", std::regex_constants::icase);
-            static std::regex last_modified_regex("Last-Modified", std::regex_constants::icase);
-
-            std::string header(buffer, n_items);
-            std::smatch match;
-            if (std::regex_match(header, match, header_regex)) {
-                const std::string & key = match[1];
-                const std::string & value = match[2];
-                if (std::regex_match(key, match, etag_regex)) {
-                    headers->etag = value;
-                } else if (std::regex_match(key, match, last_modified_regex)) {
-                    headers->last_modified = value;
-                }
-            }
-            return n_items;
-        };
-
-        curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 1L); // will trigger the HEAD verb
-        curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress
-        curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
-        curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
-
-        // we only allow retrying once for HEAD requests
-        // this is for the use case of using running offline (no internet), retrying can be annoying
-        bool was_perform_successful = curl_perform_with_retry(url, curl.get(), 1, 0, "HEAD");
-        if (!was_perform_successful) {
-            head_request_ok = false;
-        }
-
-        long http_code = 0;
-        curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
-        if (http_code == 200) {
-            head_request_ok = true;
-        } else {
-            LOG_WRN("%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
-            head_request_ok = false;
-        }
+    long http_code = 0;
+    curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
+    if (http_code == 200) {
+        head_request_ok = true;
+    } else {
+        LOG_WRN("%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
+        head_request_ok = false;
    }

    // if head_request_ok is false, we don't have the etag or last-modified headers
@@ -460,12 +467,12 @@ static bool common_download_file_single(const std::string & url, const std::stri

 // 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) {
+static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> & urls, const std::string & bearer_token, bool offline) {
    // Prepare download in parallel
    std::vector<std::future<bool>> futures_download;
    for (auto const & item : urls) {
-        futures_download.push_back(std::async(std::launch::async, [bearer_token](const std::pair<std::string, std::string> & it) -> bool {
-            return common_download_file_single(it.first, it.second, bearer_token);
+        futures_download.push_back(std::async(std::launch::async, [bearer_token, offline](const std::pair<std::string, std::string> & it) -> bool {
+            return common_download_file_single(it.first, it.second, bearer_token, offline);
        }, item));
    }

@@ -481,14 +488,15 @@ static bool common_download_file_multiple(const std::vector<std::pair<std::strin

 static bool common_download_model(
        const common_params_model & model,
-        const std::string & bearer_token) {
+        const std::string & bearer_token,
+        bool offline) {
    // Basic validation of the model.url
    if (model.url.empty()) {
        LOG_ERR("%s: invalid model url\n", __func__);
        return false;
    }

-    if (!common_download_file_single(model.url, model.path, bearer_token)) {
+    if (!common_download_file_single(model.url, model.path, bearer_token, offline)) {
        return false;
    }

@@ -547,7 +555,7 @@ static bool common_download_model(
        }

        // Download in parallel
-        common_download_file_multiple(urls, bearer_token);
+        common_download_file_multiple(urls, bearer_token, offline);
    }

    return true;
@@ -608,7 +616,7 @@ std::pair<long, std::vector<char>> common_remote_get_content(const std::string &
 *
 * 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.
 */
-static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_with_tag, const std::string & bearer_token) {
+static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_with_tag, const std::string & bearer_token, bool offline) {
    auto parts = string_split<std::string>(hf_repo_with_tag, ':');
    std::string tag = parts.size() > 1 ? parts.back() : "latest";
    std::string hf_repo = parts[0];
@@ -638,20 +646,25 @@ static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_
    long res_code = 0;
    std::string res_str;
    bool use_cache = false;
-    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: %s\n", e.what());
-        LOG_WRN("try reading from cache\n");
-        // try to read from cache
+    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());
+        }
+    }
+    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;
-        } catch (const std::exception & e) {
-            throw std::runtime_error("error: failed to get manifest (check your internet connection)");
+        } else {
+            throw std::runtime_error(
+                offline ? "error: failed to get manifest (offline mode)"
+                : "error: failed to get manifest (check your internet connection)");
        }
    }
    std::string ggufFile;
@@ -698,24 +711,25 @@ bool common_has_curl() {
    return false;
 }

-static bool common_download_file_single(const std::string &, const std::string &, const std::string &) {
+static bool common_download_file_single(const std::string &, const std::string &, const std::string &, bool) {
    LOG_ERR("error: built without CURL, cannot download model from internet\n");
    return false;
 }

-static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> &, const std::string &) {
+static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> &, const std::string &, bool) {
    LOG_ERR("error: built without CURL, cannot download model from the internet\n");
    return false;
 }

 static bool common_download_model(
        const common_params_model &,
-        const std::string &) {
+        const std::string &,
+        bool) {
    LOG_ERR("error: built without CURL, cannot download model from the internet\n");
    return false;
 }

-static struct common_hf_file_res common_get_hf_file(const std::string &, const std::string &) {
+static struct common_hf_file_res common_get_hf_file(const std::string &, const std::string &, bool) {
    LOG_ERR("error: built without CURL, cannot download model from the internet\n");
    return {};
 }
@@ -742,7 +756,8 @@ struct handle_model_result {
 static handle_model_result common_params_handle_model(
        struct common_params_model & model,
        const std::string & bearer_token,
-        const std::string & model_path_default) {
+        const std::string & model_path_default,
+        bool offline) {
    handle_model_result result;
    // handle pre-fill default model path and url based on hf_repo and hf_file
    {
@@ -750,7 +765,7 @@ static handle_model_result common_params_handle_model(
            // 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);
+                    auto auto_detected = common_get_hf_file(model.hf_repo, bearer_token, offline);
                    if (auto_detected.repo.empty() || auto_detected.ggufFile.empty()) {
                        exit(1); // built without CURL, error message already printed
                    }
@@ -791,7 +806,7 @@ static handle_model_result common_params_handle_model(

    // then, download it if needed
    if (!model.url.empty()) {
-        bool ok = common_download_model(model, bearer_token);
+        bool ok = common_download_model(model, bearer_token, offline);
        if (!ok) {
            LOG_ERR("error: failed to download model from %s\n", model.url.c_str());
            exit(1);
@@ -934,7 +949,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context

    // handle model and download
    {
-        auto res = common_params_handle_model(params.model, params.hf_token, DEFAULT_MODEL_PATH);
+        auto res = common_params_handle_model(params.model, params.hf_token, DEFAULT_MODEL_PATH, params.offline);
        if (params.no_mmproj) {
            params.mmproj = {};
        } else if (res.found_mmproj && params.mmproj.path.empty() && params.mmproj.url.empty()) {
@@ -944,12 +959,12 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
        // only download mmproj if the current example is using it
        for (auto & ex : mmproj_examples) {
            if (ctx_arg.ex == ex) {
-                common_params_handle_model(params.mmproj,    params.hf_token, "");
+                common_params_handle_model(params.mmproj,    params.hf_token, "", params.offline);
                break;
            }
        }
-        common_params_handle_model(params.speculative.model, params.hf_token, "");
-        common_params_handle_model(params.vocoder.model,     params.hf_token, "");
+        common_params_handle_model(params.speculative.model, params.hf_token, "", params.offline);
+        common_params_handle_model(params.vocoder.model,     params.hf_token, "", params.offline);
    }

    if (params.escape) {
@@ -1333,9 +1348,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    ));
    add_opt(common_arg(
        {"--prio"}, "N",
-        string_format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams.priority),
+        string_format("set process/thread priority : low(-1), normal(0), medium(1), high(2), realtime(3) (default: %d)\n", params.cpuparams.priority),
        [](common_params & params, int prio) {
-            if (prio < 0 || prio > 3) {
+            if (prio < GGML_SCHED_PRIO_LOW || prio > GGML_SCHED_PRIO_REALTIME) {
                throw std::invalid_argument("invalid value");
            }
            params.cpuparams.priority = (enum ggml_sched_priority) prio;
@@ -1445,6 +1460,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.n_keep = value;
        }
    ));
+    add_opt(common_arg(
+        {"--swa-full"},
+        string_format("use full-size SWA cache (default: %s)\n"
+            "[(more info)](https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)", params.swa_full ? "true" : "false"),
+        [](common_params & params) {
+            params.swa_full = true;
+        }
+    ).set_env("LLAMA_ARG_SWA_FULL"));
    add_opt(common_arg(
        {"--no-context-shift"},
        string_format("disables context shift on infinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"),
@@ -1670,7 +1693,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        [](common_params & params) {
            params.warmup = false;
        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_EMBEDDING}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL}));
    add_opt(common_arg(
        {"--spm-infill"},
        string_format(
@@ -2057,13 +2080,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.grp_attn_w = value;
        }
    ).set_env("LLAMA_ARG_GRP_ATTN_W").set_examples({LLAMA_EXAMPLE_MAIN}));
-    add_opt(common_arg(
-        {"-dkvc", "--dump-kv-cache"},
-        "verbose print of the KV cache",
-        [](common_params & params) {
-            params.dump_kv_cache = true;
-        }
-    ));
    add_opt(common_arg(
        {"-nkvo", "--no-kv-offload"},
        "disable KV offload",
@@ -2232,12 +2248,12 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        }
    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_NO_MMPROJ_OFFLOAD"));
    add_opt(common_arg(
-        {"--image"}, "FILE",
-        "path to an image file. use with multimodal models. Specify multiple times for batching",
+        {"--image", "--audio"}, "FILE",
+        "path to an image or audio file. use with multimodal models, can be repeated if you have multiple files\n",
        [](common_params & params, const std::string & value) {
            params.image.emplace_back(value);
        }
-    ).set_examples({LLAMA_EXAMPLE_LLAVA}));
+    ).set_examples({LLAMA_EXAMPLE_MTMD}));
    if (llama_supports_rpc()) {
        add_opt(common_arg(
            {"--rpc"}, "SERVERS",
@@ -2847,15 +2863,25 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_JINJA"));
    add_opt(common_arg(
        {"--reasoning-format"}, "FORMAT",
-        "reasoning format (default: deepseek; allowed values: deepseek, none)\n"
-        "controls whether thought tags are extracted from the response, and in which format they're returned. 'none' leaves thoughts unparsed in `message.content`, 'deepseek' puts them in `message.reasoning_content` (for DeepSeek R1 & Command R7B only).\n"
-        "only supported for non-streamed responses",
+        "controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:\n"
+        "- none: leaves thoughts unparsed in `message.content`\n"
+        "- deepseek: puts thoughts in `message.reasoning_content` (except in streaming mode, which behaves as `none`)\n"
+        "(default: deepseek)",
        [](common_params & params, const std::string & value) {
            /**/ if (value == "deepseek") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; }
+            else if (value == "deepseek-legacy") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY; }
            else if (value == "none") {     params.reasoning_format = COMMON_REASONING_FORMAT_NONE; }
-            else { std::invalid_argument("invalid value"); }
+            else { throw std::invalid_argument("invalid value"); }
        }
    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_THINK"));
+    add_opt(common_arg(
+        {"--reasoning-budget"}, "N",
+        "controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)",
+        [](common_params & params, int value) {
+            if (value != 0 && value != -1) { throw std::invalid_argument("invalid value"); }
+            params.reasoning_budget = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_THINK_BUDGET"));
    add_opt(common_arg(
        {"--chat-template"}, "JINJA_TEMPLATE",
        string_format(
@@ -2867,7 +2893,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        [](common_params & params, const std::string & value) {
            params.chat_template = value;
        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_LLAVA}).set_env("LLAMA_ARG_CHAT_TEMPLATE"));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_CHAT_TEMPLATE"));
    add_opt(common_arg(
        {"--chat-template-file"}, "JINJA_TEMPLATE_FILE",
        string_format(
@@ -2880,6 +2906,16 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.chat_template = read_file(value);
        }
    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE"));
+    add_opt(common_arg(
+        {"--no-prefill-assistant"},
+        string_format(
+            "whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)\n"
+            "when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled\n"
+        ),
+        [](common_params & params) {
+            params.prefill_assistant = false;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_PREFILL_ASSISTANT"));
    add_opt(common_arg(
        {"-sps", "--slot-prompt-similarity"}, "SIMILARITY",
        string_format("how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity),
@@ -2944,7 +2980,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        [](common_params & params, const std::string & value) {
            /**/ if (value == "jsonl") { params.batched_bench_output_jsonl = true; }
            else if (value == "md") { params.batched_bench_output_jsonl = false; }
-            else { std::invalid_argument("invalid value"); }
+            else { throw std::invalid_argument("invalid value"); }
        }
    ).set_examples({LLAMA_EXAMPLE_BENCH}));
    add_opt(common_arg(
@@ -2976,6 +3012,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            common_log_set_verbosity_thold(INT_MAX);
        }
    ));
+    add_opt(common_arg(
+        {"--offline"},
+        "Offline mode: forces use of cache, prevents network access",
+        [](common_params & params) {
+            params.offline = true;
+        }
+    ).set_env("LLAMA_OFFLINE"));
    add_opt(common_arg(
        {"-lv", "--verbosity", "--log-verbosity"}, "N",
        "Set the verbosity threshold. Messages with a higher verbosity will be ignored.",
--- a/common/chat-parser.cpp
+++ b/common/chat-parser.cpp
@@ -0,0 +1,380 @@
+#include "chat-parser.h"
+#include "common.h"
+#include "log.h"
+#include "regex-partial.h"
+
+#include <optional>
+#include <stdexcept>
+#include <string>
+#include <vector>
+
+using json = nlohmann::ordered_json;
+
+common_chat_msg_parser::common_chat_msg_parser(const std::string & input, bool is_partial, const common_chat_syntax & syntax)
+    : input_(input), is_partial_(is_partial), syntax_(syntax)
+{
+    result_.role = "assistant";
+
+    while (true) {
+        std::string id = std::to_string(std::rand());
+        if (input.find(id) == std::string::npos) {
+            healing_marker_ = id;
+            break;
+        }
+    }
+}
+
+std::string common_chat_msg_parser::str(const common_string_range & rng) const {
+    GGML_ASSERT(rng.begin <= rng.end);
+    return input_.substr(rng.begin, rng.end - rng.begin);
+}
+
+void common_chat_msg_parser::add_content(const std::string &content) {
+    result_.content += content;
+}
+
+void common_chat_msg_parser::add_reasoning_content(const std::string &reasoning_content) {
+    result_.reasoning_content += reasoning_content;
+}
+
+bool common_chat_msg_parser::add_tool_call(const std::string & name, const std::string & id, const std::string & arguments) {
+    if (name.empty()) {
+        return false;
+    }
+
+    common_chat_tool_call tool_call;
+    tool_call.name = name;
+    tool_call.arguments = arguments;
+    tool_call.id = id;
+
+    // LOG_DBG("Tool call arguments:\n\traw: %s\n\tresult: %s\n", arguments.c_str(), tool_call.arguments.c_str());
+    result_.tool_calls.emplace_back(tool_call);
+    return true;
+}
+bool common_chat_msg_parser::add_tool_call(const json & tool_call) {
+    std::string name = tool_call.contains("name") ? tool_call.at("name") : "";
+    std::string id = tool_call.contains("id") ? tool_call.at("id") : "";
+    std::string arguments = tool_call.contains("arguments") ? tool_call.at("arguments") : "";
+    return add_tool_call(name, id, arguments);
+}
+
+bool common_chat_msg_parser::add_tool_calls(const json & arr) {
+    for (const auto & item : arr) {
+        if (!add_tool_call(item)) {
+            return false;
+        }
+    }
+    return true;
+}
+void common_chat_msg_parser::finish() {
+    if (!is_partial_ && pos_ != input_.size()) {
+        throw std::runtime_error("Unexpected content at end of input");// + input_.substr(pos_));
+    }
+}
+
+bool common_chat_msg_parser::consume_spaces() {
+    const auto length = input_.size();
+    auto consumed = false;
+    while (pos_ < length && std::isspace(input_[pos_])) {
+        ++pos_;
+        consumed = true;
+    }
+    return consumed;
+}
+
+bool common_chat_msg_parser::try_consume_literal(const std::string & literal) {
+    auto pos = pos_;
+    for (auto i = 0u; i < literal.size(); ++i) {
+        if (pos >= input_.size()) {
+            return false;
+        }
+        if (input_[pos] != literal[i]) {
+            return false;
+        }
+        ++pos;
+    }
+    pos_ = pos;
+    return true;
+}
+
+std::optional<common_chat_msg_parser::find_regex_result>  common_chat_msg_parser::try_find_literal(const std::string & literal) {
+    auto idx = input_.find(literal, pos_);
+    if (idx != std::string::npos) {
+        find_regex_result res;
+        res.prelude = input_.substr(pos_, idx - pos_);
+        auto end = idx + literal.size();
+        res.groups.emplace_back(common_string_range{idx, end});
+        move_to(end);
+        return res;
+    }
+    if (is_partial_) {
+        idx = string_find_partial_stop(input_, literal);
+        if (idx != std::string::npos && idx >= pos_) {
+            find_regex_result res;
+            res.prelude = input_.substr(pos_, idx - pos_);
+            auto end = input_.size();
+            res.groups.emplace_back(common_string_range{idx, end});
+            move_to(end);
+            return res;
+        }
+    }
+    return std::nullopt;
+}
+
+void common_chat_msg_parser::consume_literal(const std::string & literal) {
+    if (!try_consume_literal(literal)) {
+        throw common_chat_msg_partial_exception(literal);
+    }
+}
+
+bool common_chat_msg_parser::try_parse_reasoning(const std::string & start_think, const std::string & end_think) {
+    auto handle_reasoning = [&](const std::string & reasoning, bool closed) {
+        auto stripped_reasoning = string_strip(reasoning);
+        if (stripped_reasoning.empty()) {
+            return;
+        }
+        if (syntax_.reasoning_in_content) {
+            add_content(syntax_.reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK ? "<think>" : start_think);
+            add_content(stripped_reasoning);
+            if (closed) {
+                add_content(syntax_.reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK ? "</think>" : end_think);
+            }
+        } else {
+            add_reasoning_content(stripped_reasoning);
+        }
+    };
+    if (syntax_.reasoning_format != COMMON_REASONING_FORMAT_NONE) {
+        if (syntax_.thinking_forced_open || try_consume_literal(start_think)) {
+            if (auto res = try_find_literal(end_think)) {
+                handle_reasoning(res->prelude, /* closed */ true);
+                consume_spaces();
+                return true;
+            }
+            auto rest = consume_rest();
+            if (!rest.empty()) {
+                handle_reasoning(rest, /* closed */ !is_partial());
+            }
+            // Allow unclosed thinking tags, for now (https://github.com/ggml-org/llama.cpp/issues/13812, https://github.com/ggml-org/llama.cpp/issues/13877)
+            // if (!syntax_.thinking_forced_open) {
+            //     throw common_chat_msg_partial_exception(end_think);
+            // }
+            return true;
+        }
+    }
+    return false;
+}
+
+std::string common_chat_msg_parser::consume_rest() {
+    auto rest = input_.substr(pos_);
+    pos_ = input_.size();
+    return rest;
+}
+
+// Tries to find the regex, consumes it (pos right after it) and gives the prelude (right before it) and the groups to the callback.
+std::optional<common_chat_msg_parser::find_regex_result> common_chat_msg_parser::try_find_regex(const common_regex & regex, size_t from, bool add_prelude_to_content) {
+    auto m = regex.search(input_, from == std::string::npos ? pos_ : from);
+    if (m.type == COMMON_REGEX_MATCH_TYPE_NONE) {
+        return std::nullopt;
+    }
+    auto prelude = input_.substr(pos_, m.groups[0].begin - pos_);
+    pos_ = m.groups[0].end;
+
+    if (add_prelude_to_content) {
+        add_content(prelude);
+    }
+    if (m.type == COMMON_REGEX_MATCH_TYPE_PARTIAL) {
+        if (is_partial()) {
+            throw common_chat_msg_partial_exception(regex.str());
+        }
+        return std::nullopt;
+    }
+    return find_regex_result{prelude, m.groups};
+}
+
+common_chat_msg_parser::find_regex_result common_chat_msg_parser::consume_regex(const common_regex & regex) {
+    if (auto result = try_consume_regex(regex)) {
+        return *result;
+    }
+    throw common_chat_msg_partial_exception(regex.str());
+}
+
+std::optional<common_chat_msg_parser::find_regex_result> common_chat_msg_parser::try_consume_regex(const common_regex & regex) {
+    auto m = regex.search(input_, pos_);
+    if (m.type == COMMON_REGEX_MATCH_TYPE_NONE) {
+        return std::nullopt;
+    }
+    if (m.type == COMMON_REGEX_MATCH_TYPE_PARTIAL) {
+        if (is_partial()) {
+            throw common_chat_msg_partial_exception(regex.str());
+        }
+        return std::nullopt;
+    }
+    if (m.groups[0].begin != pos_) {
+        // Didn't match at the current position.
+        return std::nullopt;
+    }
+    pos_ = m.groups[0].end;
+
+    return find_regex_result {
+        /* .prelude = */ "",
+        m.groups,
+    };
+}
+
+std::optional<common_json> common_chat_msg_parser::try_consume_json() {
+    auto it = input_.cbegin() + pos_;
+    const auto end = input_.cend();
+    common_json result;
+    if (!common_json_parse(it, end, healing_marker_, result)) {
+        return std::nullopt;
+    }
+    pos_ = std::distance(input_.cbegin(), it);
+    if (result.healing_marker.marker.empty()) {
+        // No healing marker, just return the parsed json
+        return result;
+    }
+    if (!is_partial()) {
+        throw common_chat_msg_partial_exception("JSON");
+    }
+    return result;
+}
+
+common_json common_chat_msg_parser::consume_json() {
+    if (auto result = try_consume_json()) {
+        return *result;
+    }
+    throw common_chat_msg_partial_exception("JSON");
+}
+
+common_chat_msg_parser::consume_json_result common_chat_msg_parser::consume_json_with_dumped_args(
+    const std::vector<std::vector<std::string>> & args_paths,
+    const std::vector<std::vector<std::string>> & content_paths
+) {
+    if (auto result = try_consume_json_with_dumped_args(args_paths, content_paths)) {
+        return *result;
+    }
+    throw common_chat_msg_partial_exception("JSON");
+}
+
+std::optional<common_chat_msg_parser::consume_json_result> common_chat_msg_parser::try_consume_json_with_dumped_args(
+    const std::vector<std::vector<std::string>> & args_paths,
+    const std::vector<std::vector<std::string>> & content_paths
+) {
+    auto partial = try_consume_json();
+    if (!partial) {
+        return std::nullopt;
+    }
+    auto is_arguments_path = [&](const std::vector<std::string> & path) {
+        return std::find(args_paths.begin(), args_paths.end(), path) != args_paths.end();
+    };
+    auto is_content_path = [&](const std::vector<std::string> & path) {
+        return std::find(content_paths.begin(), content_paths.end(), path) != content_paths.end();
+    };
+
+    if (partial->healing_marker.marker.empty()) {
+        if (args_paths.empty()) {
+            // No arguments to dump, and JSON was parsed fully.
+            return consume_json_result {
+                partial->json,
+                /* .is_partial = */ false,
+            };
+        }
+        if (is_arguments_path({})) {
+            // Entire JSON is the arguments and was parsed fully.
+            return consume_json_result {
+                partial->json.dump(),
+                /* .is_partial = */ false,
+            };
+        }
+    }
+
+    LOG_DBG("Parsed partial JSON: %s (json_healing_marker: %s)\n", partial->json.dump().c_str(), partial->healing_marker.json_dump_marker.c_str());
+
+    auto found_healing_marker = false;
+    std::vector<std::string> path;
+    std::function<json(const json &)> remove_unsupported_healings_and_dump_args = [&](const json & j) -> json {
+        if (is_arguments_path(path)) {
+            auto arguments = j.dump();
+            if (is_partial() && !partial->healing_marker.marker.empty()) {
+                auto idx = arguments.find(partial->healing_marker.json_dump_marker);
+                if (idx != std::string::npos) {
+                    arguments.resize(idx);
+                    found_healing_marker = true;
+                }
+                if (arguments == "\"") {
+                    // This happens because of completing `:"$magic` after `"arguments"`
+                    arguments = "";
+                }
+            }
+            return arguments;
+        }
+        if (is_content_path(path)) {
+            if (!j.is_string()) {
+                throw std::runtime_error("Content path must be a string");
+            }
+            std::string str = j;
+            auto idx = str.find(partial->healing_marker.marker); // not using json_dump_marker as we're inside a string
+            if (idx != std::string::npos) {
+                str.resize(idx);
+                found_healing_marker = true;
+            }
+            return str;
+        }
+        if (j.is_object()) {
+            auto obj = json::object();
+            for (const auto & p : j.items()) {
+                const auto & key = p.key();
+                const auto & value = p.value();
+                const std::string key_str = key; // NOLINT
+                auto idx = key_str.find(healing_marker_);
+                if (idx != std::string::npos) {
+                    found_healing_marker = true;
+                    break;
+                }
+                path.push_back(key_str);
+                if (value.is_string()) {
+                    const std::string value_str = value;
+                    if (value_str.find(healing_marker_) != std::string::npos) {
+                        found_healing_marker = true;
+                        if (is_content_path(path)) {
+                            if (partial->healing_marker.marker == partial->healing_marker.json_dump_marker) {
+                                // The healing occurred inside the string: good. Otherwise we just ditch the entire key/value pair.
+                                obj[key] = remove_unsupported_healings_and_dump_args(value);
+                            }
+                        }
+                        break;
+                    }
+                    obj[key] = value;
+                } else {
+                    obj[key] = remove_unsupported_healings_and_dump_args(value);
+                }
+                path.pop_back();
+            }
+            return obj;
+        }
+        if (j.is_array()) {
+            auto arr = json::array();
+            for (const auto & value : j) {
+                if (value.is_string()) {
+                    std::string str = value;
+                    auto idx = str.find(healing_marker_);
+                    if (idx != std::string::npos) {
+                        // Don't heal array values that aren't in the arguments.
+                        found_healing_marker = true;
+                        break;
+                    }
+                }
+                arr.push_back(remove_unsupported_healings_and_dump_args(value));
+            }
+            return arr;
+        }
+        return j;
+    };
+
+    auto cleaned = remove_unsupported_healings_and_dump_args(partial->json);
+    LOG_DBG("Cleaned up JSON %s to %s (json_healing_marker : '%s')\n", partial->json.dump().c_str(), cleaned.dump().c_str(), partial->healing_marker.json_dump_marker.c_str());
+    return consume_json_result {
+        cleaned,
+        /* .is_partial = */ found_healing_marker,
+    };
+}
--- a/common/chat-parser.h
+++ b/common/chat-parser.h
@@ -0,0 +1,118 @@
+#pragma once
+
+#include "chat.h"
+#include "json-partial.h"
+#include "regex-partial.h"
+
+#include <nlohmann/json.hpp>
+
+#include <optional>
+#include <string>
+#include <vector>
+
+class common_chat_msg_partial_exception : public std::runtime_error {
+  public:
+    common_chat_msg_partial_exception(const std::string & message) : std::runtime_error(message) {}
+};
+
+class common_chat_msg_parser {
+    std::string input_;
+    bool is_partial_;
+    common_chat_syntax syntax_;
+    std::string healing_marker_;
+
+    size_t pos_ = 0;
+    common_chat_msg result_;
+
+  public:
+    common_chat_msg_parser(const std::string & input, bool is_partial, const common_chat_syntax & syntax);
+    const std::string & input() const { return input_; }
+    size_t pos() const { return pos_; }
+    const std::string & healing_marker() const { return healing_marker_; }
+    const bool & is_partial() const { return is_partial_; }
+    const common_chat_msg & result() const { return result_; }
+    const common_chat_syntax & syntax() const { return syntax_; }
+
+    void move_to(size_t pos) {
+        if (pos > input_.size()) {
+            throw std::runtime_error("Invalid position!");
+        }
+        pos_ = pos;
+    }
+    void move_back(size_t n) {
+        if (pos_ < n) {
+            throw std::runtime_error("Can't move back that far!");
+        }
+        pos_ -= n;
+    }
+
+    // Get the substring of the input at the given range
+    std::string str(const common_string_range & rng) const;
+
+    // Appends to the result.content field
+    void add_content(const std::string & content);
+
+    // Appends to the result.reasoning_content field
+    void add_reasoning_content(const std::string & reasoning_content);
+
+    // Adds a tool call to the result. If the tool call is too incomplete (e.g. name empty), it won't add anything.
+    bool add_tool_call(const std::string & name, const std::string & id, const std::string & arguments);
+
+    // Adds a tool call using the "name", "id" and "arguments" fields of the json object
+    bool add_tool_call(const nlohmann::ordered_json & tool_call);
+
+    // Adds an array of tool calls using their "name", "id" and "arguments" fields.
+    bool add_tool_calls(const nlohmann::ordered_json & arr);
+
+    void finish();
+
+    bool consume_spaces();
+
+    void consume_literal(const std::string & literal);
+
+    bool try_parse_reasoning(const std::string & start_think, const std::string & end_think);
+
+    std::string consume_rest();
+
+    struct find_regex_result {
+        std::string prelude;
+        std::vector<common_string_range> groups;
+    };
+
+    std::optional<find_regex_result> try_find_regex(const common_regex & regex, size_t from = std::string::npos, bool add_prelude_to_content = true);
+
+    bool try_consume_literal(const std::string & literal);
+
+    std::optional<find_regex_result> try_find_literal(const std::string & literal);
+
+    find_regex_result consume_regex(const common_regex & regex);
+
+    std::optional<find_regex_result> try_consume_regex(const common_regex & regex);
+
+    std::optional<common_json> try_consume_json();
+    common_json consume_json();
+
+    struct consume_json_result {
+        nlohmann::ordered_json value;
+        bool is_partial;
+    };
+
+    /*
+        Consume (possibly partial) json and converts specific subtrees to (possibly truncated) JSON strings.
+
+        By default, object keys can't be truncated, nor can string values (their corresponding key is removed,
+        e.g. `{"foo": "bar", "baz": "b` -> `{"foo": "bar"}`
+
+        But one can allow subpaths to be kept truncated, and possibly json-dumped to truncated json strings
+        - with `content_paths={{"foo"}}` -> `{"foo": "b` -> {"foo": "b"}`
+        - with `args_paths={{"foo"}}` -> `{"foo": {"b` -> `{"foo": "{b"}`
+    */
+    consume_json_result consume_json_with_dumped_args(
+        const std::vector<std::vector<std::string>> & args_paths = {},
+        const std::vector<std::vector<std::string>> & content_paths = {}
+    );
+    std::optional<consume_json_result> try_consume_json_with_dumped_args(
+        const std::vector<std::vector<std::string>> & args_paths = {},
+        const std::vector<std::vector<std::string>> & content_paths = {}
+    );
+};
--- a/common/chat.cpp
+++ b/common/chat.cpp
--- a/common/chat.h
+++ b/common/chat.h
@@ -3,6 +3,7 @@
 #pragma once

 #include "common.h"
+#include <functional>
 #include <chrono>
 #include <string>
 #include <vector>
@@ -13,11 +14,19 @@ struct common_chat_tool_call {
    std::string name;
    std::string arguments;
    std::string id;
+
+    bool operator==(const common_chat_tool_call & other) const {
+        return name == other.name && arguments == other.arguments && id == other.id;
+    }
 };

 struct common_chat_msg_content_part {
    std::string type;
    std::string text;
+
+    bool operator==(const common_chat_msg_content_part & other) const {
+        return type == other.type && text == other.text;
+    }
 };

 struct common_chat_msg {
@@ -28,6 +37,51 @@ struct common_chat_msg {
    std::string reasoning_content;
    std::string tool_name;
    std::string tool_call_id;
+
+    template <class T> T to_json_oaicompat() const;
+
+    bool empty() const {
+        return content.empty() && content_parts.empty() && tool_calls.empty() && reasoning_content.empty() && tool_name.empty() && tool_call_id.empty();
+    }
+    void ensure_tool_call_ids_set(std::vector<std::string> & ids_cache, const std::function<std::string()> & gen_tool_call_id) {
+        for (auto i = 0u; i < tool_calls.size(); i++) {
+            if (ids_cache.size() <= i) {
+                auto id = tool_calls[i].id;
+                if (id.empty()) {
+                    id = gen_tool_call_id();
+                }
+                ids_cache.push_back(id);
+            }
+            tool_calls[i].id = ids_cache[i];
+        }
+    }
+    bool operator==(const common_chat_msg & other) const {
+        return role == other.role
+            && content == other.content
+            && content_parts == other.content_parts
+            && tool_calls == other.tool_calls
+            && reasoning_content == other.reasoning_content
+            && tool_name == other.tool_name
+            && tool_call_id == other.tool_call_id;
+    }
+    bool operator!=(const common_chat_msg & other) const {
+        return !(*this == other);
+    }
+};
+
+struct common_chat_msg_diff {
+    std::string reasoning_content_delta;
+    std::string content_delta;
+    size_t tool_call_index = std::string::npos;
+    common_chat_tool_call tool_call_delta;
+
+    static std::vector<common_chat_msg_diff> compute_diffs(const common_chat_msg & previous_msg, const common_chat_msg & new_msg);
+
+    bool operator==(const common_chat_msg_diff & other) const {
+        return content_delta == other.content_delta
+        && tool_call_index == other.tool_call_index
+        && tool_call_delta == other.tool_call_delta;
+    }
 };

 struct common_chat_tool {
@@ -49,14 +103,11 @@ enum common_chat_format {
    COMMON_CHAT_FORMAT_LLAMA_3_X,
    COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS,
    COMMON_CHAT_FORMAT_DEEPSEEK_R1,
-    COMMON_CHAT_FORMAT_DEEPSEEK_R1_EXTRACT_REASONING,
    COMMON_CHAT_FORMAT_FIREFUNCTION_V2,
    COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2,
    COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1,
    COMMON_CHAT_FORMAT_HERMES_2_PRO,
-    COMMON_CHAT_FORMAT_HERMES_2_PRO_EXTRACT_REASONING,
    COMMON_CHAT_FORMAT_COMMAND_R7B,
-    COMMON_CHAT_FORMAT_COMMAND_R7B_EXTRACT_REASONING,

    COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats
 };
@@ -71,7 +122,8 @@ struct common_chat_templates_inputs {
    std::vector<common_chat_tool> tools;
    common_chat_tool_choice tool_choice = COMMON_CHAT_TOOL_CHOICE_AUTO;
    bool parallel_tool_calls = false;
-    bool extract_reasoning     = true;
+    common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_NONE;
+    bool enable_thinking = true;
    std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
 };

@@ -80,11 +132,21 @@ struct common_chat_params {
    std::string                         prompt;
    std::string                         grammar;
    bool                                grammar_lazy = false;
+    bool                                thinking_forced_open = false;
    std::vector<common_grammar_trigger> grammar_triggers;
    std::vector<std::string>            preserved_tokens;
    std::vector<std::string>            additional_stops;
 };

+struct common_chat_syntax {
+    common_chat_format       format                = COMMON_CHAT_FORMAT_CONTENT_ONLY;
+    common_reasoning_format  reasoning_format      = COMMON_REASONING_FORMAT_NONE;
+    // Whether reasoning_content should be inlined in the content (e.g. for reasoning_format=deepseek in stream mode)
+    bool                     reasoning_in_content  = false;
+    bool                     thinking_forced_open  = false;
+    bool                     parse_tool_calls      = true;
+};
+
 // Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
 bool common_chat_verify_template(const std::string & tmpl, bool use_jinja);

@@ -121,8 +183,9 @@ std::string common_chat_format_example(
    const struct common_chat_templates * tmpls,
    bool use_jinja);

-std::string               common_chat_format_name(common_chat_format format);
-common_chat_msg           common_chat_parse(      const std::string & input, common_chat_format format);
+const char*               common_chat_format_name(common_chat_format format);
+const char*               common_reasoning_format_name(common_reasoning_format format);
+common_chat_msg           common_chat_parse(const std::string & input, bool is_partial, const common_chat_syntax & syntax);

 common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice);

@@ -135,3 +198,5 @@ template <class T> T common_chat_msgs_to_json_oaicompat(const std::vector<common
 // T can be std::string containing JSON or nlohmann::ordered_json
 template <class T> std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const T & tools);
 template <class T> T common_chat_tools_to_json_oaicompat(const std::vector<common_chat_tool> & tools);
+
+template <class T> T common_chat_msg_diff_to_json_oaicompat(const common_chat_msg_diff & diff);
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -203,6 +203,7 @@ bool set_process_priority(enum ggml_sched_priority prio) {

    DWORD p = NORMAL_PRIORITY_CLASS;
    switch (prio) {
+        case GGML_SCHED_PRIO_LOW:      p = BELOW_NORMAL_PRIORITY_CLASS; break;
        case GGML_SCHED_PRIO_NORMAL:   p = NORMAL_PRIORITY_CLASS;       break;
        case GGML_SCHED_PRIO_MEDIUM:   p = ABOVE_NORMAL_PRIORITY_CLASS; break;
        case GGML_SCHED_PRIO_HIGH:     p = HIGH_PRIORITY_CLASS;         break;
@@ -228,6 +229,7 @@ bool set_process_priority(enum ggml_sched_priority prio) {

    int p = 0;
    switch (prio) {
+        case GGML_SCHED_PRIO_LOW:      p =  5;  break;
        case GGML_SCHED_PRIO_NORMAL:   p =  0;  break;
        case GGML_SCHED_PRIO_MEDIUM:   p = -5;  break;
        case GGML_SCHED_PRIO_HIGH:     p = -10; break;
@@ -849,7 +851,7 @@ std::string fs_get_cache_directory() {
    if (getenv("LLAMA_CACHE")) {
        cache_directory = std::getenv("LLAMA_CACHE");
    } else {
-#if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX)
+#if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX) || defined(__OpenBSD__)
        if (std::getenv("XDG_CACHE_HOME")) {
            cache_directory = std::getenv("XDG_CACHE_HOME");
        } else {
@@ -903,13 +905,16 @@ struct common_init_result common_init_from_params(common_params & params) {
            ok = false;
        }

-        if (llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
-            LOG_WRN("%s: warning: vocab does not have an EOS token, reranking will not work\n", __func__);
-            ok = false;
-        }
+        bool has_eos = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL;
+        bool has_sep = llama_vocab_sep(vocab) != LLAMA_TOKEN_NULL;

-        if (llama_vocab_sep(vocab) == LLAMA_TOKEN_NULL) {
-            LOG_WRN("%s: warning: vocab does not have a  SEP token, reranking will not work\n", __func__);
+        if (!has_eos && !has_sep) {
+            LOG_WRN("%s: warning: vocab does not have an EOS token or SEP token, reranking will not work\n", __func__);
+            ok = false;
+        } else if (!has_eos) {
+            LOG_WRN("%s: warning: vocab does not have an EOS token, using SEP token as fallback\n", __func__);
+        } else if (!has_sep) {
+            LOG_WRN("%s: warning: vocab does not have a SEP token, reranking will not work\n", __func__);
            ok = false;
        }

@@ -929,7 +934,7 @@ struct common_init_result common_init_from_params(common_params & params) {
        return iparams;
    }

-    if (params.ctx_shift && !llama_kv_self_can_shift(lctx)) {
+    if (params.ctx_shift && !llama_memory_can_shift(llama_get_memory(lctx))) {
        LOG_WRN("%s: KV cache shifting is not supported for this context, disabling KV cache shifting\n", __func__);
        params.ctx_shift = false;
    }
@@ -1036,7 +1041,7 @@ struct common_init_result common_init_from_params(common_params & params) {
        if (llama_model_has_decoder(model)) {
            llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch)));
        }
-        llama_kv_self_clear(lctx);
+        llama_memory_clear(llama_get_memory(lctx), true);
        llama_synchronize(lctx);
        llama_perf_context_reset(lctx);
        llama_set_warmup(lctx, false);
@@ -1102,6 +1107,9 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
        mparams.tensor_buft_overrides = params.tensor_buft_overrides.data();
    }

+    mparams.progress_callback           = params.load_progress_callback;
+    mparams.progress_callback_user_data = params.load_progress_callback_user_data;
+
    return mparams;
 }

@@ -1133,6 +1141,7 @@ struct llama_context_params common_context_params_to_llama(const common_params &
    cparams.flash_attn        = params.flash_attn;
    cparams.no_perf           = params.no_perf;
    cparams.op_offload        = !params.no_op_offload;
+    cparams.swa_full          = params.swa_full;

    if (params.reranking) {
        cparams.embeddings    = true;
@@ -1325,81 +1334,6 @@ std::string common_detokenize(const struct llama_vocab * vocab, const std::vecto
    return text;
 }

-//
-// KV cache utils
-//
-
-void common_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) {
-    static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+";
-
-    printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d",
-        view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
-
-    llama_kv_cache_view_cell * c_curr = view.cells;
-    llama_seq_id * cs_curr = view.cells_sequences;
-
-    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
-        if (i % row_size == 0) {
-            printf("\n%5d: ", i);
-        }
-        int seq_count = 0;
-        for (int j = 0; j < view.n_seq_max; j++) {
-            if (cs_curr[j] >= 0) { seq_count++; }
-        }
-        putchar(slot_chars[std::min(sizeof(slot_chars) - 2, size_t(seq_count))]);
-    }
-
-    printf("\n=== Done dumping\n");
-}
-
-void common_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) {
-    static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
-
-    printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n",
-        view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
-
-    std::unordered_map<llama_seq_id, size_t> seqs;
-    llama_kv_cache_view_cell * c_curr = view.cells;
-    llama_seq_id * cs_curr = view.cells_sequences;
-
-    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
-        for (int j = 0; j < view.n_seq_max; j++) {
-            if (cs_curr[j] < 0) { continue; }
-            if (seqs.find(cs_curr[j]) == seqs.end()) {
-                if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
-                const size_t sz = seqs.size();
-                seqs[cs_curr[j]] = sz;
-            }
-        }
-        if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
-    }
-
-    printf("=== Sequence legend: ");
-    for (const auto & it : seqs) {
-        printf("%zu=%d, ", it.second, it.first);
-    }
-    printf("'+'=other sequence ids");
-
-    c_curr = view.cells;
-    cs_curr = view.cells_sequences;
-    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
-        if (i % row_size == 0) {
-            printf("\n%5d: ", i);
-        }
-        for (int j = 0; j < view.n_seq_max; j++) {
-            if (cs_curr[j] >= 0) {
-                const auto & it = seqs.find(cs_curr[j]);
-                putchar(it != seqs.end() ? int(slot_chars[it->second]) : '+');
-            } else {
-                putchar('.');
-            }
-        }
-        putchar(' ');
-    }
-
-    printf("\n=== Done dumping\n");
-}
-
 //
 // Embedding utils
 //
--- a/common/common.h
+++ b/common/common.h
@@ -76,7 +76,7 @@ enum llama_example {
    LLAMA_EXAMPLE_SERVER,
    LLAMA_EXAMPLE_CVECTOR_GENERATOR,
    LLAMA_EXAMPLE_EXPORT_LORA,
-    LLAMA_EXAMPLE_LLAVA,
+    LLAMA_EXAMPLE_MTMD,
    LLAMA_EXAMPLE_LOOKUP,
    LLAMA_EXAMPLE_PARALLEL,
    LLAMA_EXAMPLE_TTS,
@@ -115,7 +115,7 @@ enum common_grammar_trigger_type {
    COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN,
    COMMON_GRAMMAR_TRIGGER_TYPE_WORD,
    COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN,
-    COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_START,
+    COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL,
 };

 struct common_grammar_trigger {
@@ -215,7 +215,8 @@ struct common_params_vocoder {

 enum common_reasoning_format {
    COMMON_REASONING_FORMAT_NONE,
-    COMMON_REASONING_FORMAT_DEEPSEEK, // Extract thinking tag contents and return as `message.reasoning_content`
+    COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY, // Extract thinking tag contents and return as `message.reasoning_content`, or leave inline in <think> tags in stream mode
+    COMMON_REASONING_FORMAT_DEEPSEEK,        // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas.
 };

 struct common_params {
@@ -291,6 +292,7 @@ struct common_params {
    int32_t verbosity                  = 0;
    int32_t control_vector_layer_start = -1; // layer range for control vector
    int32_t control_vector_layer_end   = -1; // layer range for control vector
+    bool    offline                    = false;

    int32_t ppl_stride      = 0;     // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
    int32_t ppl_output_type = 0;     // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
@@ -323,13 +325,13 @@ struct common_params {
    bool flash_attn        = false; // flash attention
    bool no_perf           = false; // disable performance metrics
    bool ctx_shift         = true;  // context shift on inifinite text generation
+    bool swa_full          = false; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)

    bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
    bool use_mmap          = true;  // use mmap for faster loads
    bool use_mlock         = false; // use mlock to keep model in memory
    bool verbose_prompt    = false; // print prompt tokens before generation
    bool display_prompt    = true;  // print prompt before generation
-    bool dump_kv_cache     = false; // dump the KV cache contents for debugging purposes
    bool no_kv_offload     = false; // disable KV offloading
    bool warmup            = true;  // warmup run
    bool check_tensors     = false; // validate tensor data
@@ -368,6 +370,8 @@ struct common_params {
    bool use_jinja = false;                                                                                 // NOLINT
    bool enable_chat_template = true;
    common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
+    int reasoning_budget = -1;
+    bool prefill_assistant = true;                                                                          // if true, any trailing assistant message will be prefilled into the response

    std::vector<std::string> api_keys;

@@ -427,6 +431,11 @@ struct common_params {

    // common params
    std::string out_file; // output filename for all example programs
+    // optional callback for model loading progress and cancellation:
+    // called with a progress value between 0.0 and 1.0.
+    // return false from callback to abort model loading or true to continue
+    llama_progress_callback load_progress_callback = NULL;
+    void *                  load_progress_callback_user_data = NULL;
 };

 // call once at the start of a program if it uses libcommon
@@ -615,16 +624,6 @@ std::string common_detokenize(
        const std::vector<llama_token> & tokens,
                                  bool   special = true);

-//
-// KV cache utils
-//
-
-// Dump the KV cache view with the number of sequences per cell.
-void common_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size = 80);
-
-// Dump the KV cache view showing individual sequences in each cell (long output).
-void common_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size = 40);
-
 //
 // Embedding utils
 //
--- a/common/json-partial.cpp
+++ b/common/json-partial.cpp
@@ -0,0 +1,256 @@
+#include "json-partial.h"
+
+#include "log.h"
+
+#include <nlohmann/json.hpp>
+
+#include <string>
+
+using json = nlohmann::ordered_json;
+
+enum common_json_stack_element_type {
+    COMMON_JSON_STACK_ELEMENT_OBJECT,
+    COMMON_JSON_STACK_ELEMENT_KEY,
+    COMMON_JSON_STACK_ELEMENT_ARRAY,
+};
+
+struct common_json_stack_element {
+    common_json_stack_element_type type;
+    std::string key;
+};
+
+bool common_json_parse(
+    const std::string & input,
+    const std::string & healing_marker,
+    common_json & out)
+{
+    std::string::const_iterator it = input.begin();
+    const auto end = input.end();
+    return common_json_parse(it, end, healing_marker, out);
+}
+
+bool common_json_parse(
+    std::string::const_iterator & it,
+    const std::string::const_iterator & end,
+    const std::string & healing_marker,
+    common_json & out)
+{
+    // // https://json.nlohmann.me/features/parsing/sax_interface/
+    struct json_error_locator : public nlohmann::json_sax<json> {
+        std::size_t position;
+        bool found_error;
+        std::string last_token;
+        std::string exception_message;
+        std::vector<common_json_stack_element> stack;
+
+        json_error_locator() : position(0), found_error(false) {}
+
+        bool parse_error(std::size_t position, const std::string & last_token, const json::exception & ex) override { // NOLINT
+            this->position = position - 1;
+            this->found_error = true;
+            this->last_token = last_token;
+            this->exception_message = ex.what();
+            return false;
+        }
+        void close_value() {
+            if (!stack.empty() && (stack.back().type == COMMON_JSON_STACK_ELEMENT_KEY)) {
+                stack.pop_back();
+            }
+        }
+        bool null() override { // NOLINT
+            close_value();
+            return true;
+        }
+        bool boolean(bool) override { // NOLINT
+            close_value();
+            return true;
+        }
+        bool number_integer(number_integer_t) override { // NOLINT
+            close_value();
+            return true;
+        }
+        bool number_unsigned(number_unsigned_t) override { // NOLINT
+            close_value();
+            return true;
+        }
+        bool number_float(number_float_t, const string_t &) override { // NOLINT
+            close_value();
+            return true;
+        }
+        bool string(string_t &) override { // NOLINT
+            close_value();
+            return true;
+        }
+        bool binary(binary_t &) override { // NOLINT
+            close_value();
+            return true;
+        }
+        bool start_object(std::size_t) override { // NOLINT
+            stack.push_back({COMMON_JSON_STACK_ELEMENT_OBJECT, ""});
+            return true;
+        }
+        bool end_object() override {
+            GGML_ASSERT(!stack.empty() && stack.back().type == COMMON_JSON_STACK_ELEMENT_OBJECT);
+            stack.pop_back();
+            close_value();
+            return true;
+        }
+        bool key(string_t & key) override { // NOLINT
+            stack.push_back({COMMON_JSON_STACK_ELEMENT_KEY, key});
+            return true;
+        }
+        bool start_array(std::size_t) override { // NOLINT
+            stack.push_back({COMMON_JSON_STACK_ELEMENT_ARRAY, ""});
+            return true;
+        }
+        bool end_array() override {
+            GGML_ASSERT(!stack.empty() && stack.back().type == COMMON_JSON_STACK_ELEMENT_ARRAY);
+            stack.pop_back();
+            close_value();
+            return true;
+        }
+    };
+    json_error_locator err_loc;
+    auto start = it;
+    json::sax_parse(it, end, &err_loc);
+
+    if (err_loc.found_error) {
+        it = start;
+        auto temptative_end = it + err_loc.position;
+        // LOG_DBG("Error at position %zu (is_end = %s): %s\n", err_loc.position, temptative_end == end ? "true" : "false", err_loc.exception_message.c_str());
+
+        auto input = std::string(it, temptative_end);
+        try {
+            out.json = json::parse(input);
+            // out.json = json::parse(it, temptative_end);
+            it = temptative_end;
+            return true;
+        } catch (const std::exception & ex) {
+            // No, needs healing.
+            LOG_DBG("Failed to parse up to error: %s: <<<%s>>>\n", ex.what(), std::string(it, temptative_end).c_str());
+        }
+        auto can_parse = [](const std::string & str) {
+            try {
+                auto _ = json::parse(str); // NOLINT
+                return true;
+            } catch (const std::exception &) {
+                return false;
+            }
+        };
+        if (!healing_marker.empty() && !err_loc.stack.empty()) {
+            std::string str(it, temptative_end);
+            auto last_non_sp_pos = str.find_last_not_of(" \n\r\t");
+            if (last_non_sp_pos == std::string::npos) {
+                throw std::runtime_error("Cannot heal a truncated JSON that stopped in an unknown location");
+            }
+            auto last_non_sp_char = str[last_non_sp_pos];
+            // Used to detect stops on a number, which may not be complete.
+            auto was_maybe_number = [&]() {
+                if (!str.empty() && std::isspace(str.back())) {
+                    return false;
+                }
+                return std::isdigit(last_non_sp_char) ||
+                    last_non_sp_char == '.' ||
+                    last_non_sp_char == 'e' ||
+                    last_non_sp_char == 'E' ||
+                    last_non_sp_char == '-';
+            };
+
+            std::string closing;
+            for (size_t i = err_loc.stack.size(); i > 0; i--) {
+                auto & el = err_loc.stack[i - 1];
+                if (el.type == COMMON_JSON_STACK_ELEMENT_OBJECT) {
+                    closing += "}";
+                } else if (el.type == COMMON_JSON_STACK_ELEMENT_ARRAY) {
+                    closing += "]";
+                } else if (el.type != COMMON_JSON_STACK_ELEMENT_KEY) {
+                    throw std::runtime_error("Unexpected stack element type");
+                }
+            }
+
+            const auto & magic_seed = out.healing_marker.marker = healing_marker;//"$llama.cpp.json$";
+
+            if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_KEY) {
+                // We're inside an object value
+                if (last_non_sp_char == ':' && can_parse(str + "1" + closing)) {
+                    // Was about to create an object value
+                    str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
+                } else if (can_parse(str + ": 1" + closing)) {
+                    str += (out.healing_marker.json_dump_marker = ":\"" + magic_seed) + "\"" + closing;
+                } else if (last_non_sp_char == '{' && can_parse(str + closing)) {
+                    // Was about to create an object
+                    str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\": 1" + closing;
+                } else if (can_parse(str + "\"" + closing)) {
+                    // Was inside an object value string
+                    str += (out.healing_marker.json_dump_marker = magic_seed) + "\"" + closing;
+                } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"" + closing)) {
+                    // Was inside an object value string after an escape
+                    str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"" + closing;
+                } else {
+                    // find last :
+                    auto last_pos = str.find_last_of(':');
+                    if (last_pos == std::string::npos) {
+                        throw std::runtime_error("Cannot heal a truncated JSON that stopped in an unknown location");
+                    }
+                    // Cutting back to opening : for object value
+                    str = str.substr(0, last_pos + 1) + (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
+                }
+            } else if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_ARRAY) {
+                if ((last_non_sp_char == ',' || last_non_sp_char == '[') && can_parse(str + "1" + closing)) {
+                    // Was about to create an array value
+                    str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
+                } else if (can_parse(str + "\"" + closing)) {
+                    // Was inside an array value string
+                    str += (out.healing_marker.json_dump_marker = magic_seed) + "\"" + closing;
+                } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"" + closing)) {
+                    // Was inside an array value string after an escape
+                    str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"" + closing;
+                } else if (!was_maybe_number() && can_parse(str + ", 1" + closing)) {
+                    // Had just finished a value
+                    str += (out.healing_marker.json_dump_marker = ",\"" + magic_seed) + "\"" + closing;
+                } else {
+                    auto last_pos = str.find_last_of("[,");
+                    if (last_pos == std::string::npos) {
+                        throw std::runtime_error("Cannot heal a truncated JSON array stopped in an unknown location");
+                    }
+                    // Cutting back to last [ or , for array value
+                    str = str.substr(0, last_pos + 1) + (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
+                }
+            } else if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_OBJECT) {
+                if ((last_non_sp_char == '{' && can_parse(str + closing)) ||
+                        (last_non_sp_char == ',' && can_parse(str + "\"\": 1" + closing))) {
+                    // Was about to create an object key+value
+                    str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\": 1" + closing;
+                } else if (!was_maybe_number() && can_parse(str + ",\"\": 1" + closing)) {
+                    // Was about to create an object key+value
+                    str += (out.healing_marker.json_dump_marker = ",\"" + magic_seed) + "\": 1" + closing;
+                } else if (can_parse(str + "\": 1" + closing)) {
+                    // Was inside an object key string
+                    str += (out.healing_marker.json_dump_marker = magic_seed) + "\": 1" + closing;
+                } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\": 1" + closing)) {
+                    // Was inside an object key string after an escape
+                    str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\": 1" + closing;
+                } else {
+                    auto last_pos = str.find_last_of(':');
+                    if (last_pos == std::string::npos) {
+                        throw std::runtime_error("Cannot heal a truncated JSON object stopped in an unknown location");
+                    }
+                    // fprintf(stderr, "Cutting back to last : for object key+value\n");
+                    str = str.substr(0, last_pos + 1) + (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
+                }
+            } else {
+                throw std::runtime_error("Cannot heal a truncated JSON object stopped in an unknown location");
+            }
+            // fprintf(stderr, "HEALED:\nSTRING <<<\n%s\n>>>\n\nmagic_cut: <<<\n%s\n>>>\n\n", str.c_str(), out.healing_marker.json_dump_marker.c_str());
+            out.json = json::parse(str);
+            it = temptative_end;
+            return true;
+        }
+        // TODO: handle unclosed top-level primitive if the stack was empty but we got an error (e.g. "tru", "\"", etc...)
+        // fprintf(stderr, "Closing: TODO\n");
+        return false;
+    }
+    out.json = json::parse(it, end);
+    it = end;
+    return true;
+}
--- a/common/json-partial.h
+++ b/common/json-partial.h
@@ -0,0 +1,38 @@
+#pragma once
+
+#include <nlohmann/json.hpp>
+
+// Healing marker (empty if the JSON was fully parsed / wasn't healed).
+struct common_healing_marker {
+    // Raw marker.
+    std::string marker;
+
+    // Cutting the `common_json.json.dump()` string at the (only) occurrence of this marker should yield the original partial JSON string (modulo spaces / if it had the same dump format).
+    std::string json_dump_marker;
+};
+
+// Represents a parsed JSON object, with its optional healing marker (a JSON dump fragment that can be used to find the position of healing in the JSON dump string)
+struct common_json {
+    nlohmann::ordered_json json;
+
+    common_healing_marker healing_marker;
+};
+
+// Parse the JSON string, healing (closing) any partial JSON if `healing_marker` is not empty.
+//
+// Healing completes partial JSON strings by adding a (possibly modified) healing marker, then whatever is needed to close the JSON.
+// This allows to parse the resulting healed JSON string, yet be able to cut it again if needed at the healing marker.
+// (this is used when parsing JSON outputs from the models, then crafting partial JSONs for the partial tool calls in OAI format).
+//
+// For instance, parsing `{` with a healing marker `foo` will produce a healed JSON `{"foo":1}`, w/ json_dump_marker = `"foo"` (which can be used to break the JSON again).
+bool common_json_parse(
+    const std::string & input,
+    const std::string & healing_marker,
+    common_json & out);
+
+// Parse the JSON string (see overload above), but advancing an iterator to the end of the input when the (potentially partial) parsing succeeds.
+bool common_json_parse(
+    std::string::const_iterator & it,
+    const std::string::const_iterator & end,
+    const std::string & healing_marker,
+    common_json & out);
--- a/common/json-schema-to-grammar.cpp
+++ b/common/json-schema-to-grammar.cpp
@@ -1,8 +1,9 @@
 #include "json-schema-to-grammar.h"
 #include "common.h"

+#include <nlohmann/json.hpp>
+
 #include <algorithm>
-#include <fstream>
 #include <map>
 #include <regex>
 #include <sstream>
--- a/common/json-schema-to-grammar.h
+++ b/common/json-schema-to-grammar.h
@@ -1,9 +1,9 @@
 #pragma once

-#include "ggml.h"
-// Change JSON_ASSERT from assert() to GGML_ASSERT:
-#define JSON_ASSERT GGML_ASSERT
-#include "json.hpp"
+#include <nlohmann/json_fwd.hpp>
+
+#include <functional>
+#include <string>

 std::string json_schema_to_grammar(const nlohmann::ordered_json & schema,
                                   bool force_gbnf = false);
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -161,7 +161,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
        GGML_ABORT("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
 #endif // LLAMA_USE_LLGUIDANCE
    } else {
-        std::vector<std::string> patterns_at_start;
+        std::vector<std::string> trigger_patterns;
        std::vector<std::string> patterns_anywhere;
        std::vector<llama_token> trigger_tokens;
        for (const auto & trigger : params.grammar_triggers) {
@@ -173,10 +173,13 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                    break;
                }
                case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN:
-                case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_START:
                {
-                    const auto & pattern = trigger.value;
-                    (trigger.type == COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_START ? patterns_at_start : patterns_anywhere).push_back(pattern);
+                    patterns_anywhere.push_back(trigger.value);
+                    break;
+                }
+                case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL:
+                {
+                    trigger_patterns.push_back(trigger.value);
                    break;
                }
                case COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN:
@@ -190,10 +193,6 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
            }
        }

-        std::vector<std::string> trigger_patterns;
-        if (!patterns_at_start.empty()) {
-            trigger_patterns.push_back("^(" + string_join(patterns_at_start, "|") + ")[\\s\\S]*");
-        }
        if (!patterns_anywhere.empty()) {
            trigger_patterns.push_back("^[\\s\\S]*?(" + string_join(patterns_anywhere, "|") + ")[\\s\\S]*");
        }
--- a/common/speculative.cpp
+++ b/common/speculative.cpp
@@ -144,6 +144,8 @@ llama_tokens common_speculative_gen_draft(
    auto & smpl   = spec->smpl;
    auto & prompt = spec->prompt;

+    auto * mem = llama_get_memory(ctx);
+
    int reuse_i = 0;
    int reuse_n = 0;

@@ -173,7 +175,7 @@ llama_tokens common_speculative_gen_draft(
    result.reserve(params.n_draft);

    if (reuse_n == 0) {
-        llama_kv_self_clear(ctx);
+        llama_memory_clear(mem, false);

        prompt.clear();
    } else {
@@ -192,14 +194,14 @@ llama_tokens common_speculative_gen_draft(
        }

        if (reuse_i > 0) {
-            llama_kv_self_seq_rm (ctx, 0, 0, reuse_i);
-            llama_kv_self_seq_add(ctx, 0, reuse_i, -1, -reuse_i);
+            llama_memory_seq_rm (mem, 0, 0, reuse_i);
+            llama_memory_seq_add(mem, 0, reuse_i, -1, -reuse_i);

            prompt.erase(prompt.begin(), prompt.begin() + reuse_i);
        }

        if (reuse_n < (int) prompt.size()) {
-            llama_kv_self_seq_rm (ctx, 0, reuse_n, -1);
+            llama_memory_seq_rm (mem, 0, reuse_n, -1);

            prompt.erase(prompt.begin() + reuse_n, prompt.end());
        }
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -45,7 +45,7 @@ class SentencePieceTokenTypes(IntEnum):

 class ModelType(IntEnum):
    TEXT = 1
-    VISION = 2
+    MMPROJ = 2


 AnyModel = TypeVar("AnyModel", bound="type[ModelBase]")
@@ -54,7 +54,7 @@ AnyModel = TypeVar("AnyModel", bound="type[ModelBase]")
 class ModelBase:
    _model_classes: dict[ModelType, dict[str, type[ModelBase]]] = {
        ModelType.TEXT: {},
-        ModelType.VISION: {},
+        ModelType.MMPROJ: {},
    }

    dir_model: Path
@@ -88,7 +88,7 @@ class ModelBase:
                 small_first_shard: bool = False, hparams: dict[str, Any] | None = None, remote_hf_model_id: str | None = None):
        if type(self) is ModelBase or \
                type(self) is TextModel or \
-                type(self) is VisionModel:
+                type(self) is MmprojModel:
            raise TypeError(f"{type(self).__name__!r} should not be directly instantiated")

        self.dir_model = dir_model
@@ -308,6 +308,8 @@ class ModelBase:
                            gguf.MODEL_TENSOR.TIME_MIX_LERP_FUSED,
                            gguf.MODEL_TENSOR.POSNET_NORM1,
                            gguf.MODEL_TENSOR.POSNET_NORM2,
+                            gguf.MODEL_TENSOR.V_ENC_EMBD_POS,
+                            gguf.MODEL_TENSOR.A_ENC_EMBD_POS,
                        )
                    )
                    or not new_name.endswith(".weight")
@@ -421,23 +423,26 @@ class ModelBase:
        try:
            # for security reason, we don't allow loading remote code by default
            # if a model need remote code, we will fallback to config.json
-            return AutoConfig.from_pretrained(dir_model, trust_remote_code=False).to_dict()
+            config = AutoConfig.from_pretrained(dir_model, trust_remote_code=False).to_dict()
        except Exception as e:
            logger.warning(f"Failed to load model config from {dir_model}: {e}")
            logger.warning("Trying to load config.json instead")
            with open(dir_model / "config.json", "r", encoding="utf-8") as f:
                config = json.load(f)
-                if "llm_config" in config:
-                    # rename for InternVL
-                    config["text_config"] = config["llm_config"]
-                return config
+        if "llm_config" in config:
+            # rename for InternVL
+            config["text_config"] = config["llm_config"]
+        if "thinker_config" in config:
+            # rename for Qwen2.5-Omni
+            config["text_config"] = config["thinker_config"]["text_config"]
+        return config

    @classmethod
    def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]:
        assert names

        def func(modelcls: AnyModel) -> AnyModel:
-            model_type = ModelType.VISION if modelcls.model_arch == gguf.MODEL_ARCH.CLIP_VISION else ModelType.TEXT
+            model_type = ModelType.MMPROJ if modelcls.model_arch == gguf.MODEL_ARCH.MMPROJ else ModelType.TEXT
            for name in names:
                cls._model_classes[model_type][name] = modelcls
            return modelcls
@@ -518,15 +523,15 @@ class TextModel(ModelBase):
            self.gguf_writer.add_context_length(n_ctx)
            logger.info(f"gguf: context length = {n_ctx}")

-        if (n_embd := self.find_hparam(["hidden_size", "n_embd"], optional=True)) is not None:
+        if (n_embd := self.find_hparam(["hidden_size", "n_embd", "dim"], optional=True)) is not None:
            self.gguf_writer.add_embedding_length(n_embd)
            logger.info(f"gguf: embedding length = {n_embd}")

-        if (n_ff := self.find_hparam(["intermediate_size", "n_inner"], optional=True)) is not None:
+        if (n_ff := self.find_hparam(["intermediate_size", "n_inner", "hidden_dim"], optional=True)) is not None:
            self.gguf_writer.add_feed_forward_length(n_ff)
            logger.info(f"gguf: feed forward length = {n_ff}")

-        if (n_head := self.find_hparam(["num_attention_heads", "n_head"], optional=True)) is not None:
+        if (n_head := self.find_hparam(["num_attention_heads", "n_head", "n_heads"], optional=True)) is not None:
            self.gguf_writer.add_head_count(n_head)
            logger.info(f"gguf: head count = {n_head}")

@@ -669,12 +674,12 @@ class TextModel(ModelBase):
        if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed":
            # ref: https://huggingface.co/tiiuae/falcon-7b
            res = "falcon"
-        if chkhsh == "9d032fcbd5501f4a38150912590928bfb36091efb5df11b8e2124b0390e3fb1e":
-            # ref: https://huggingface.co/tiiuae/Falcon3-7B-Base
-            res = "falcon3"
        if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
            # ref: https://huggingface.co/BAAI/bge-small-en-v1.5
            res = "bert-bge"
+        if chkhsh == "9d032fcbd5501f4a38150912590928bfb36091efb5df11b8e2124b0390e3fb1e":
+            # ref: https://huggingface.co/tiiuae/Falcon3-7B-Base
+            res = "falcon3"
        if chkhsh == "8e62295832751ca1e8f92f2226f403dea30dc5165e448b5bfa05af5340c64ec7":
            # ref: https://huggingface.co/BAAI/bge-large-zh-v1.5
            res = "bert-bge-large"
@@ -726,9 +731,6 @@ class TextModel(ModelBase):
        if chkhsh == "7967bfa498ade6b757b064f31e964dddbb80f8f9a4d68d4ba7998fcf281c531a":
            # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-code
            res = "jina-v2-code"
-        if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b" or chkhsh == "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516":
-            # ref: https://huggingface.co/THUDM/glm-4-9b-chat
-            res = "chatglm-bpe"
        if chkhsh == "7fc505bd3104ca1083b150b17d088b59534ede9bde81f0dd2090967d7fe52cee":
            # ref: https://huggingface.co/LumiOpen/Viking-7B
            res = "viking"
@@ -759,9 +761,6 @@ class TextModel(ModelBase):
        if chkhsh == "60824e3c0d9401f89943cbb2fff727f0e2d4c545ba4df2d6e4f09a6db0f5b450":
            # ref: https://huggingface.co/facebook/chameleon-7b
            res = "chameleon"
-        if chkhsh == "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35":
-            # ref: https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0
-            res = "minerva-7b"
        if chkhsh == "8b5a93ed704057481f240da0be7e7dca721d7f8f4755263b6807227a2cbeae65":
            # ref: https://huggingface.co/sentence-transformers/stsb-roberta-base
            res = "roberta-bpe"
@@ -792,15 +791,24 @@ class TextModel(ModelBase):
        if chkhsh == "d353350c764d8c3b39c763113960e4fb4919bea5fbf208a0e3b22e8469dc7406":
            # ref: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct
            res = "llama4"
-        if chkhsh == "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2":
-            # ref: https://huggingface.co/THUDM/glm-4-9b-hf
-            res = "glm4"
        if chkhsh == "0e9433cbbb161f89e264eb32e8e64bfe69e834973ffca5d41d3948a604a3e2a3":
            # ref: https://huggingface.co/mistral-community/pixtral-12b
            res = "pixtral"
        if chkhsh == "d5f1dd6f980fec569fb218a81a7658ac45fc56b38c5a0adeb1c232fbe04ef5ec":
            # ref: https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base
            res = "seed-coder"
+        if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b":
+            # ref: https://huggingface.co/THUDM/glm-4-9b-chat
+            res = "chatglm-bpe"
+        if chkhsh == "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516":
+            # ref: https://huggingface.co/THUDM/glm-4-9b-chat
+            res = "chatglm-bpe"
+        if chkhsh == "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2":
+            # ref: https://huggingface.co/THUDM/glm-4-9b-hf
+            res = "glm4"
+        if chkhsh == "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35":
+            # ref: https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0
+            res = "minerva-7b"

        if res is None:
            logger.warning("\n")
@@ -1039,6 +1047,10 @@ class TextModel(ModelBase):
        special_vocab.chat_template = "rwkv-world"
        # hack: Add '\n\n' as the EOT token to make it chat normally
        special_vocab._set_special_token("eot", 261)
+        # hack: Override these as they have already been set (incorrectly)
+        special_vocab.special_token_ids["bos"] = 0
+        special_vocab.special_token_ids["eos"] = 0
+
        special_vocab.add_to_gguf(self.gguf_writer)

    def _set_vocab_builtin(self, model_name: Literal["gpt-neox", "llama-spm"], vocab_size: int):
@@ -1113,60 +1125,116 @@ class TextModel(ModelBase):
            self.gguf_writer.add_pooling_type(pooling_type)


-class VisionModel(ModelBase):
-    model_type = ModelType.VISION
-    model_arch = gguf.MODEL_ARCH.CLIP_VISION
+class MmprojModel(ModelBase):
+    model_type = ModelType.MMPROJ
+    model_arch = gguf.MODEL_ARCH.MMPROJ
    preprocessor_config: dict[str, Any]
    global_config: dict[str, Any]

+    n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth"]
+
+    has_vision_encoder: bool = True # by default
+    has_audio_encoder: bool = False
+
+    # for models having multiple encoders, we need to separate their hparams
+    hparams_vision: dict[str, Any] | None = None
+    hparams_audio: dict[str, Any] | None = None
+
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

-        if self.model_arch != gguf.MODEL_ARCH.CLIP_VISION:
-            raise TypeError("VisionModel must be subclassed with model_arch = gguf.MODEL_ARCH.CLIP_VISION")
+        if self.model_arch != gguf.MODEL_ARCH.MMPROJ:
+            raise TypeError("MmprojModel must be subclassed with model_arch = gguf.MODEL_ARCH.MMPROJ")

        # get n_embd of the text model
        if "text_config" not in self.hparams:
            self.hparams["text_config"] = {}
+        if "audio_config" not in self.hparams:
+            self.hparams["audio_config"] = {}
        text_config = {**self.hparams, **self.hparams["text_config"]}
        self.n_embd_text = text_config.get("hidden_size", text_config.get("n_embd", 0))
        assert self.n_embd_text > 0, "n_embd not found in hparams"

-        if "vision_config" not in self.hparams:
-            raise ValueError("vision_config not found in hparams")
        # move vision config to the top level, while preserving the original hparams in global_config
-        self.global_config = self.hparams
-        self.hparams = self.hparams["vision_config"]
+        import copy
+        self.global_config = copy.deepcopy(self.hparams)
+        self.hparams_vision = self.get_vision_config()
+        self.hparams_audio = self.get_audio_config()

-        self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth"])
-        self.tensor_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.CLIP_VISION, self.block_count)
+        if self.hparams_vision is None and self.hparams_audio is None:
+            raise ValueError("vision_config / audio_config not found in hparams")
+
+        # for compat with vision-only models
+        self.hparams = self.hparams_vision or self.hparams_audio or self.hparams
+
+        # TODO @ngxson : this is a hack to support both vision and audio encoders
+        have_multiple_encoders = self.has_audio_encoder and self.has_vision_encoder
+        self.block_count = 128 if have_multiple_encoders else self.find_hparam(self.n_block_keys, True)
+        self.tensor_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.MMPROJ, self.block_count)

        # load preprocessor config
        with open(self.dir_model / "preprocessor_config.json", "r", encoding="utf-8") as f:
            self.preprocessor_config = json.load(f)

+    def get_vision_config(self) -> dict[str, Any] | None:
+        return self.global_config.get("vision_config")
+
+    def get_audio_config(self) -> dict[str, Any] | None:
+        return self.global_config.get("audio_config")
+
    def set_type(self):
-        self.gguf_writer.add_type(gguf.GGUFType.CLIP_VISION)
+        self.gguf_writer.add_type(gguf.GGUFType.MMPROJ)

    def set_gguf_parameters(self):
        self.gguf_writer.add_file_type(self.ftype)
-        self.gguf_writer.add_vision_projection_dim(self.n_embd_text)
-        self.gguf_writer.add_vision_has_vision_encoder(True)

-        # vision config
-        self.gguf_writer.add_vision_image_size(self.find_hparam(["image_size"]))
-        self.gguf_writer.add_vision_patch_size(self.find_hparam(["patch_size"]))
-        self.gguf_writer.add_vision_embedding_length(self.find_hparam(["hidden_size"]))
-        self.gguf_writer.add_vision_feed_forward_length(self.find_hparam(["intermediate_size"]))
-        self.gguf_writer.add_vision_block_count(self.block_count)
-        self.gguf_writer.add_vision_head_count(self.find_hparam(["num_attention_heads"]))
+        if self.has_vision_encoder:
+            self.gguf_writer.add_clip_has_vision_encoder(True)
+            self.gguf_writer.add_vision_projection_dim(self.n_embd_text)

-        # preprocessor config
-        self.gguf_writer.add_vision_image_mean(self.preprocessor_config["image_mean"])
-        self.gguf_writer.add_vision_image_std(self.preprocessor_config["image_std"])
+            # vision config
+            self.gguf_writer.add_vision_image_size(self.find_vparam(["image_size"]))
+            self.gguf_writer.add_vision_patch_size(self.find_vparam(["patch_size"]))
+            self.gguf_writer.add_vision_embedding_length(self.find_vparam(["hidden_size"]))
+            self.gguf_writer.add_vision_feed_forward_length(self.find_vparam(["intermediate_size"]))
+            self.gguf_writer.add_vision_block_count(self.find_vparam(self.n_block_keys))
+            self.gguf_writer.add_vision_head_count(self.find_vparam(["num_attention_heads"]))
+
+            # preprocessor config
+            self.gguf_writer.add_vision_image_mean(self.preprocessor_config["image_mean"])
+            self.gguf_writer.add_vision_image_std(self.preprocessor_config["image_std"])
+
+        if self.has_audio_encoder:
+            self.gguf_writer.add_clip_has_audio_encoder(True)
+            self.gguf_writer.add_audio_projection_dim(self.n_embd_text)
+
+            # audio config
+            self.gguf_writer.add_audio_embedding_length(self.find_aparam(["hidden_size"]))
+            self.gguf_writer.add_audio_feed_forward_length(self.find_aparam(["intermediate_size"]))
+            self.gguf_writer.add_audio_block_count(self.find_aparam(self.n_block_keys))
+            self.gguf_writer.add_audio_head_count(self.find_aparam(["num_attention_heads"]))
+
+        if not self.has_vision_encoder and not self.has_audio_encoder:
+            raise ValueError("MmprojModel must have either vision or audio encoder")

    def write_vocab(self):
-        raise ValueError("VisionModel does not support vocab writing")
+        raise ValueError("MmprojModel does not support vocab writing")
+
+    def find_vparam(self, keys: Iterable[str], optional: bool = False) -> Any:
+        assert self.hparams_vision is not None
+        return self._find_param(self.hparams_vision, keys, optional)
+
+    def find_aparam(self, keys: Iterable[str], optional: bool = False) -> Any:
+        assert self.hparams_audio is not None
+        return self._find_param(self.hparams_audio, keys, optional)
+
+    def _find_param(self, obj: dict[str, Any], keys: Iterable[str], optional: bool = False) -> Any:
+        key = next((k for k in keys if k in obj), None)
+        if key is not None:
+            return obj[key]
+        if optional:
+            return None
+        raise KeyError(f"could not find any of: {keys}")


@ModelBase.register("GPTNeoXForCausalLM")
@@ -1780,7 +1848,8 @@ class StableLMModel(TextModel):
    "MistralForCausalLM",
    "MixtralForCausalLM",
    "VLlama3ForCausalLM",
-    "LlavaForConditionalGeneration")
+    "LlavaForConditionalGeneration",
+    "LlamaModel")
 class LlamaModel(TextModel):
    model_arch = gguf.MODEL_ARCH.LLAMA
    undo_permute = True
@@ -1860,6 +1929,8 @@ class LlamaModel(TextModel):

        if is_vision_tensor:
            return [] # skip vision tensors
+        elif self.hf_arch == "LlamaModel":
+            name = "model." + name
        elif name.startswith("model.text_model"):
            name = name.replace("text_model.", "") # for SmolVLM
        elif name.startswith("language_model."):
@@ -1950,7 +2021,7 @@ class LlamaModel(TextModel):
    "LlavaForConditionalGeneration", # pixtral
    "Mistral3ForConditionalGeneration", # mistral small 3.1
 )
-class LlavaVisionModel(VisionModel):
+class LlavaVisionModel(MmprojModel):
    img_break_tok_id = -1

    def __init__(self, *args, **kwargs):
@@ -1976,7 +2047,7 @@ class LlavaVisionModel(VisionModel):
        super().set_gguf_parameters()
        hparams = self.hparams
        if hparams["model_type"] == "pixtral":
-            self.gguf_writer.add_vision_projector_type(gguf.VisionProjectorType.PIXTRAL)
+            self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.PIXTRAL)
            self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])

            # hidden_act
@@ -2015,7 +2086,7 @@ class LlavaVisionModel(VisionModel):


@ModelBase.register("Idefics3ForConditionalGeneration", "SmolVLMForConditionalGeneration")
-class SmolVLMModel(VisionModel):
+class SmolVLMModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if self.hparams["model_type"] == "smolvlm_vision":
@@ -2027,7 +2098,7 @@ class SmolVLMModel(VisionModel):

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
-        self.gguf_writer.add_vision_projector_type(gguf.VisionProjectorType.IDEFICS3)
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.IDEFICS3)
        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-5))
        self.gguf_writer.add_vision_projector_scale_factor(self.global_config.get("scale_factor", 2))
        self.gguf_writer.add_vision_use_gelu(True)
@@ -2092,6 +2163,29 @@ class Llama4Model(LlamaModel):
        return super().modify_tensors(data_torch, name, bid)


+@ModelBase.register("Llama4ForConditionalGeneration")
+class Llama4VisionModel(MmprojModel):
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LLAMA4)
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams["norm_eps"])
+        self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / self.hparams["pixel_shuffle_ratio"]))
+        assert self.hparams["hidden_act"] == "gelu"
+        self.gguf_writer.add_vision_use_gelu(True)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid # unused
+        if "multi_modal_projector" in name or "vision_model" in name:
+            # process vision tensors
+            if "positional_embedding_vlm" in name and ".weight" not in name:
+                name += ".weight"
+            if "multi_modal_projector.linear_1" in name:
+                # despite the name with number postfix, this is a single fully connected layer
+                return [(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_MMPROJ_FC], data_torch)]
+            return [(self.map_tensor_name(name), data_torch)]
+        return []
+
+
@ModelBase.register("Mistral3ForConditionalGeneration")
 class Mistral3Model(LlamaModel):
    model_arch = gguf.MODEL_ARCH.LLAMA
@@ -2594,7 +2688,7 @@ class QwenModel(TextModel):
        self.gguf_writer.add_file_type(self.ftype)


-@ModelBase.register("Qwen2Model", "Qwen2ForCausalLM")
+@ModelBase.register("Qwen2Model", "Qwen2ForCausalLM", "Qwen2AudioForConditionalGeneration")
 class Qwen2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.QWEN2

@@ -2618,13 +2712,19 @@ class Qwen2Model(TextModel):
            name = f"model.{name}"  # map to Qwen2ForCausalLM tensors
        if "language_model." in name:
            name = name.replace("language_model.", "") # for InternVL
-        if name.startswith("mlp") or name.startswith("vision_model"):
-            # skip visual tensors
+        if name.startswith("mlp") or name.startswith("multi_modal_projector") \
+                or name.startswith("vision_model") or name.startswith("audio_tower"):
+            # skip vision and audio tensors
            return []
        yield from super().modify_tensors(data_torch, name, bid)


-@ModelBase.register("Qwen2VLForConditionalGeneration", "Qwen2_5_VLForConditionalGeneration")
+@ModelBase.register(
+    "Qwen2VLModel",
+    "Qwen2VLForConditionalGeneration",
+    "Qwen2_5_VLForConditionalGeneration",
+    "Qwen2_5OmniModel",
+)
 class Qwen2VLModel(TextModel):
    model_arch = gguf.MODEL_ARCH.QWEN2VL

@@ -2642,31 +2742,40 @@ class Qwen2VLModel(TextModel):

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        del bid  # unused
-        if name.startswith("visual."):
-            # skip visual tensors
+        if name.startswith("thinker."):
+            name = name.replace("thinker.", "")
+        if name.startswith("visual") or name.startswith("audio") or \
+                name.startswith("talker") or name.startswith("token2wav"):
+            # skip multimodal tensors
            return []
        return [(self.map_tensor_name(name), data_torch)]


-@ModelBase.register("Qwen2VLForConditionalGeneration", "Qwen2_5_VLForConditionalGeneration")
-class Qwen2VLVisionModel(VisionModel):
+@ModelBase.register("Qwen2VLModel", "Qwen2VLForConditionalGeneration", "Qwen2_5_VLForConditionalGeneration")
+class Qwen2VLVisionModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
-        self.hparams["image_size"] = self.hparams.get("image_size", 560)
+        assert self.hparams_vision is not None
+        self.hparams_vision["image_size"] = self.hparams_vision.get("image_size", 560)
        # rename config.json values
-        self.hparams["num_attention_heads"] = self.hparams.get("num_heads")
-        self.hparams["num_hidden_layers"] = self.hparams.get("depth")
-        if "embed_dim" in self.hparams: # qwen2vl
-            self.hparams["intermediate_size"] = self.hparams.get("hidden_size")
-            self.hparams["hidden_size"] = self.hparams.get("embed_dim")
+        self.hparams_vision["num_attention_heads"] = self.hparams_vision.get("num_heads")
+        self.hparams_vision["num_hidden_layers"] = self.hparams_vision.get("depth")
+        if "embed_dim" in self.hparams_vision: # qwen2vl
+            self.hparams_vision["intermediate_size"] = self.hparams_vision.get("hidden_size")
+            self.hparams_vision["hidden_size"] = self.hparams_vision.get("embed_dim")

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
-        hparams = self.hparams
-        if self.global_config['model_type'] == 'qwen2_vl':
-            self.gguf_writer.add_vision_projector_type(gguf.VisionProjectorType.QWEN2VL)
-        elif self.global_config['model_type'] == 'qwen2_5_vl':
-            self.gguf_writer.add_vision_projector_type(gguf.VisionProjectorType.QWEN25VL)
+        assert self.hparams_vision is not None
+        hparams = self.hparams_vision
+        model_type = self.global_config['model_type']
+        if model_type == 'qwen2_vl':
+            self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN2VL)
+        elif model_type == 'qwen2_5_vl' or model_type == 'qwen2_5_omni':
+            if model_type == 'qwen2_5_omni':
+                self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN25O)
+            else:
+                self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN25VL)
            self.gguf_writer.add_vision_use_silu(True)
            # find n_wa_pattern (window attention pattern)
            fullatt_block_indexes = hparams.get("fullatt_block_indexes")
@@ -2724,12 +2833,72 @@ class Qwen2VLVisionModel(VisionModel):
        return [] # skip other tensors


+@ModelBase.register("Qwen2_5OmniModel")
+class Qwen25OmniModel(Qwen2VLVisionModel):
+    has_vision_encoder = True
+    has_audio_encoder = True
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_audio is not None
+        self.hparams_audio["hidden_size"] = self.hparams_audio["d_model"]
+        self.hparams_audio["intermediate_size"] = self.hparams_audio["encoder_ffn_dim"]
+        self.hparams_audio["num_attention_heads"] = self.hparams_audio["encoder_attention_heads"]
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        assert self.hparams_audio is not None
+        self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["num_mel_bins"])
+        self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams_audio.get("layer_norm_eps", 1e-5))
+
+    def get_vision_config(self) -> dict[str, Any] | None:
+        return self.global_config["thinker_config"].get("vision_config")
+
+    def get_audio_config(self) -> dict[str, Any] | None:
+        return self.global_config["thinker_config"].get("audio_config")
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        # SinusoidsPositionEmbedding
+        assert self.hparams_audio is not None
+        max_timescale = 10000
+        length = 1500
+        channels = self.hparams_audio["hidden_size"]
+        log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
+        inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2).float())
+        scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
+        pos_embd = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1).to(dtype=torch.float32)
+        yield ("audio_tower.embed_positions.weight", pos_embd)
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        del bid, new_name, n_dims  # unused
+        if ".conv" in name and ".weight" in name:
+            return gguf.GGMLQuantizationType.F16
+        return False
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("thinker."):
+            name = name.replace("thinker.", "")
+
+        if name.startswith("audio_tower"):
+            # process audio tensors
+            if "conv1.bias" in name or "conv2.bias" in name:
+                # transpose conv1 and conv2 bias
+                data_torch = data_torch.unsqueeze(-1)
+            if "audio_bos_eos_token" in name:
+                # this tensor is left unused in transformers code
+                # https://github.com/huggingface/transformers/blob/6e3063422c4b1c014aa60c32b9254fd2902f0f28/src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py#L1809
+                return []
+            return [(self.map_tensor_name(name), data_torch)]
+
+        return super().modify_tensors(data_torch, name, bid)
+
+
@ModelBase.register("InternVisionModel")
-class InternVisionModel(VisionModel):
+class InternVisionModel(MmprojModel):
    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams
-        self.gguf_writer.add_vision_projector_type(gguf.VisionProjectorType.INTERNVL)
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.INTERNVL)
        self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])
        # hidden_act
        if hparams["hidden_act"] == "silu":
@@ -3520,7 +3689,7 @@ class InternLM3Model(TextModel):
        return [(self.map_tensor_name(name), data_torch)]


-@ModelBase.register("BertModel", "BertForMaskedLM", "CamembertModel")
+@ModelBase.register("BertModel", "BertForMaskedLM", "CamembertModel", "BertForSequenceClassification")
 class BertModel(TextModel):
    model_arch = gguf.MODEL_ARCH.BERT

@@ -3528,11 +3697,20 @@ class BertModel(TextModel):
        super().__init__(*args, **kwargs)
        self.vocab_size = None

+        if cls_out_labels := self.hparams.get("id2label"):
+            if len(cls_out_labels) == 2 and cls_out_labels[0] == "LABEL_0":
+                # Remove dummy labels added by AutoConfig
+                cls_out_labels = None
+        self.cls_out_labels = cls_out_labels
+
    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        self.gguf_writer.add_causal_attention(False)
        self._try_set_pooling_type()

+        if self.cls_out_labels:
+            self.gguf_writer.add_classifier_output_labels([v for k, v in sorted(self.cls_out_labels.items())])
+
    def set_vocab(self):
        tokens, toktypes, tokpre = self.get_vocab_base()
        self.vocab_size = len(tokens)
@@ -3583,6 +3761,14 @@ class BertModel(TextModel):
        if name.startswith("cls.seq_relationship"):
            return []

+        if self.cls_out_labels:
+            # For BertForSequenceClassification (direct projection layer)
+            if name == "classifier.weight":
+                name = "classifier.out_proj.weight"
+
+            if name == "classifier.bias":
+                name = "classifier.out_proj.bias"
+
        return [(self.map_tensor_name(name), data_torch)]

    def _xlmroberta_tokenizer_init(self) -> None:
@@ -3602,62 +3788,111 @@ class BertModel(TextModel):
        from sentencepiece import sentencepiece_model_pb2 as model

        tokenizer_path = self.dir_model / 'sentencepiece.bpe.model'
+
+        tokenizer_json = {}
+        tokenizer_config_json = {}
        if not tokenizer_path.is_file():
-            raise FileNotFoundError(f"File not found: {tokenizer_path}")
+            tokenizer_path = self.dir_model / 'tokenizer.json'
+            tokenizer_config_path = self.dir_model / 'tokenizer_config.json'

-        sentencepiece_model = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue]
-        sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
-        assert sentencepiece_model.trainer_spec.model_type == 1  # UNIGRAM
+            if not tokenizer_path.is_file():
+                raise FileNotFoundError(f"File not found: {tokenizer_path}")

-        add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
-        remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces
-        precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap
+            from base64 import b64decode
+            from transformers import AutoTokenizer
+            tokenizer = AutoTokenizer.from_pretrained(self.dir_model)

-        tokenizer = SentencePieceProcessor()
-        tokenizer.LoadFromFile(str(tokenizer_path))
+            with open(tokenizer_path, "r", encoding="utf-8") as fp:
+                tokenizer_json = json.load(fp)

-        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
+            if tokenizer_config_path.is_file():
+                with open(tokenizer_config_path, "r", encoding="utf-8") as fp:
+                    tokenizer_config_json = json.load(fp)
+
+            add_prefix = tokenizer.add_prefix_space
+            remove_whitespaces = tokenizer.clean_up_tokenization_spaces
+            precompiled_charsmap = b64decode(tokenizer_json["normalizer"]["precompiled_charsmap"])
+
+            vocab_size = max(self.hparams.get("vocab_size", 0), tokenizer.vocab_size)
+        else:
+            sentencepiece_model = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue]
+            sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
+            assert sentencepiece_model.trainer_spec.model_type == 1  # UNIGRAM
+
+            add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
+            remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces
+            precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap
+
+            tokenizer = SentencePieceProcessor()
+            tokenizer.LoadFromFile(str(tokenizer_path))
+
+            vocab_size = max(self.hparams.get("vocab_size", 0), tokenizer.vocab_size())

        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
        scores: list[float] = [-10000.0] * vocab_size
        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size

-        for token_id in range(tokenizer.vocab_size()):
-            piece = tokenizer.IdToPiece(token_id)
-            text = piece.encode("utf-8")
-            score = tokenizer.GetScore(token_id)
+        if isinstance(tokenizer, SentencePieceProcessor):
+            for token_id in range(tokenizer.vocab_size()):
+                piece = tokenizer.IdToPiece(token_id)
+                text = piece.encode("utf-8")
+                score = tokenizer.GetScore(token_id)

-            toktype = SentencePieceTokenTypes.NORMAL
-            if tokenizer.IsUnknown(token_id):
-                toktype = SentencePieceTokenTypes.UNKNOWN
-            elif tokenizer.IsControl(token_id):
-                toktype = SentencePieceTokenTypes.CONTROL
-            elif tokenizer.IsUnused(token_id):
-                toktype = SentencePieceTokenTypes.UNUSED
-            elif tokenizer.IsByte(token_id):
-                toktype = SentencePieceTokenTypes.BYTE
+                toktype = SentencePieceTokenTypes.NORMAL
+                if tokenizer.IsUnknown(token_id):
+                    toktype = SentencePieceTokenTypes.UNKNOWN
+                elif tokenizer.IsControl(token_id):
+                    toktype = SentencePieceTokenTypes.CONTROL
+                elif tokenizer.IsUnused(token_id):
+                    toktype = SentencePieceTokenTypes.UNUSED
+                elif tokenizer.IsByte(token_id):
+                    toktype = SentencePieceTokenTypes.BYTE

-            tokens[token_id] = text
-            scores[token_id] = score
-            toktypes[token_id] = toktype
+                tokens[token_id] = text
+                scores[token_id] = score
+                toktypes[token_id] = toktype
+        else:
+            added_vocab = tokenizer.get_added_vocab()
+            unk_token = tokenizer_config_json.get("unk_token")
+            unk_token_id = added_vocab.get(unk_token, tokenizer_json["model"].get("unk_id", 3))

-        if vocab_size > len(tokens):
-            pad_count = vocab_size - len(tokens)
-            logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
-            for i in range(1, pad_count + 1):
-                tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
-                scores.append(-1000.0)
-                toktypes.append(SentencePieceTokenTypes.UNUSED)
+            for token_id in range(tokenizer.vocab_size):
+                piece = tokenizer._convert_id_to_token(token_id)
+                if (piece := tokenizer._convert_id_to_token(token_id)) is not None:
+                    text = piece.encode("utf-8")
+                    score = tokenizer_json["model"]["vocab"][token_id][1]

-        # realign tokens (see HF tokenizer code)
-        tokens = [b'<s>', b'<pad>', b'</s>', b'<unk>'] + tokens[3:-1]
-        scores = [0.0, 0.0, 0.0, 0.0] + scores[3:-1]
-        toktypes = [
-            SentencePieceTokenTypes.CONTROL,
-            SentencePieceTokenTypes.CONTROL,
-            SentencePieceTokenTypes.CONTROL,
-            SentencePieceTokenTypes.UNKNOWN,
-        ] + toktypes[3:-1]
+                    toktype = SentencePieceTokenTypes.NORMAL
+                    if token_id == unk_token_id:
+                        toktype = SentencePieceTokenTypes.UNKNOWN
+                    elif token_id in tokenizer.all_special_ids:
+                        toktype = SentencePieceTokenTypes.CONTROL
+                    elif token_id in added_vocab.values():
+                        toktype = SentencePieceTokenTypes.USER_DEFINED
+                    # No reliable way to detect this, but jina doesn't have any
+                    # elif tokenizer.IsByte(token_id):
+                    #     toktype = SentencePieceTokenTypes.BYTE
+
+                    tokens[token_id] = text
+                    scores[token_id] = score
+                    toktypes[token_id] = toktype
+
+        if isinstance(tokenizer, SentencePieceProcessor):
+            # realign tokens (see HF tokenizer code)
+            tokens = [b'<s>', b'<pad>', b'</s>', b'<unk>'] + tokens[3:-1]
+            scores = [0.0, 0.0, 0.0, 0.0] + scores[3:-1]
+            toktypes = [
+                SentencePieceTokenTypes.CONTROL,
+                SentencePieceTokenTypes.CONTROL,
+                SentencePieceTokenTypes.CONTROL,
+                SentencePieceTokenTypes.UNKNOWN,
+            ] + toktypes[3:-1]
+
+            if self.model_arch == gguf.MODEL_ARCH.NOMIC_BERT_MOE:
+                # Add mask token missing from sentencepiece.bpe.model
+                tokens[250001] = b'<mask>'
+                scores[250001] = 0.0
+                toktypes[250001] = SentencePieceTokenTypes.CONTROL

        self.gguf_writer.add_tokenizer_model("t5")
        self.gguf_writer.add_tokenizer_pre("default")
@@ -3677,7 +3912,27 @@ class BertModel(TextModel):
        self.gguf_writer.add_add_eos_token(True)


-@ModelBase.register("RobertaModel")
+@ModelBase.register("DistilBertModel", "DistilBertForMaskedLM", "DistilBertForSequenceClassification")
+class DistilBertModel(BertModel):
+    model_arch = gguf.MODEL_ARCH.BERT
+
+    def set_gguf_parameters(self):
+        self.gguf_writer.add_layer_norm_eps(1e-12)
+        logger.info("gguf: layer norm epsilon = 1e-12")
+        super().set_gguf_parameters()
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("distilbert."):
+            name = name[11:]
+
+        # These layers act as MLM head, so we don't need them
+        if name.startswith("vocab_"):
+            return []
+
+        return super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("RobertaModel", "RobertaForSequenceClassification")
 class RobertaModel(BertModel):
    model_arch = gguf.MODEL_ARCH.BERT

@@ -3987,11 +4242,11 @@ class Gemma3Model(TextModel):


@ModelBase.register("Gemma3ForConditionalGeneration")
-class Gemma3VisionModel(VisionModel):
+class Gemma3VisionModel(MmprojModel):
    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        hparams = self.hparams
-        self.gguf_writer.add_vision_projector_type(gguf.VisionProjectorType.GEMMA3)
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.GEMMA3)
        # 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)
@@ -5938,6 +6193,65 @@ class ChameleonModel(TextModel):
        return data_torch


+@ModelBase.register("UltravoxModel")
+class UltravoxModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.LLAMA # dummy
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        raise NotImplementedError("Ultravox does not have text decoder. Instead, it uses Llama or other models for text. If you want to get the audio encoder, please use --mmproj argument")
+
+
+@ModelBase.register("Qwen2AudioForConditionalGeneration")
+class WhisperEncoderModel(MmprojModel):
+    has_vision_encoder = False # no vision encoder
+    has_audio_encoder = True
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.hparams["hidden_size"] = self.hparams["d_model"]
+        self.hparams["intermediate_size"] = self.hparams["encoder_ffn_dim"]
+        self.hparams["num_attention_heads"] = self.hparams["encoder_attention_heads"]
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN2A)
+        self.gguf_writer.add_audio_num_mel_bins(self.hparams["num_mel_bins"])
+        self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-5))
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        del bid, new_name, n_dims  # unused
+        if ".conv" in name and ".weight" in name:
+            return gguf.GGMLQuantizationType.F16
+        return False
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        if name.startswith("language_model."):
+            # skip language model tensors
+            return []
+
+        # prevent clash naming with vision tensors
+        if name.startswith("multi_modal_projector"):
+            name = "audio." + name
+
+        if "conv1.bias" in name or "conv2.bias" in name:
+            # transpose conv1 and conv2 bias
+            data_torch = data_torch.unsqueeze(-1)
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+
+@ModelBase.register("UltravoxModel")
+class UltravoxWhisperEncoderModel(WhisperEncoderModel):
+    has_vision_encoder = False # no vision encoder
+    has_audio_encoder = True
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_audio_stack_factor(self.global_config["stack_factor"])
+
 ###### CONVERSION LOGIC ######


@@ -6113,13 +6427,15 @@ def split_str_to_n_bytes(split_str: str) -> int:


 def get_model_architecture(hparams: dict[str, Any], model_type: ModelType) -> str:
+    # TODO @ngxson : this won't work correctly if the model has both audio & vision encoders
+    # maybe we should fallback to text model's arch in that case, since not many models have both
    text_config = hparams.get("text_config", {})
    vision_config = hparams.get("vision_config", {})
    arch = hparams["architectures"][0]
    # if "architectures" is found in the sub-config, use that instead
    if model_type == ModelType.TEXT and text_config.get("architectures") is not None:
        arch = text_config["architectures"][0]
-    elif model_type == ModelType.VISION and vision_config.get("architectures") is not None:
+    elif model_type == ModelType.MMPROJ and vision_config.get("architectures") is not None:
        arch = vision_config["architectures"][0]
    return arch

@@ -6182,7 +6498,7 @@ def main() -> None:

    with torch.inference_mode():
        output_type = ftype_map[args.outtype]
-        model_type = ModelType.VISION if args.mmproj else ModelType.TEXT
+        model_type = ModelType.MMPROJ if args.mmproj else ModelType.TEXT
        hparams = ModelBase.load_hparams(dir_model)
        model_architecture = get_model_architecture(hparams, model_type)
        logger.info(f"Model architecture: {model_architecture}")
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -1,28 +1,6 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-

-# This script downloads the tokenizer models of the specified models from Huggingface and
-# generates the get_vocab_base_pre() function for convert_hf_to_gguf.py
-#
-# This is necessary in order to analyze the type of pre-tokenizer used by the model and
-# provide the necessary information to llama.cpp via the GGUF header in order to implement
-# the same pre-tokenizer.
-#
-# ref: https://github.com/ggml-org/llama.cpp/pull/6920
-#
-# Instructions:
-#
-# - Add a new model to the "models" list
-# - Run the script with your huggingface token:
-#
-#   python3 convert_hf_to_gguf_update.py <huggingface_token>
-#
-# - The convert_hf_to_gguf.py script will have had its get_vocab_base_pre() function updated
-# - Update llama.cpp with the new pre-tokenizer if necessary
-#
-# TODO: generate tokenizer tests for llama.cpp
-#
-
 import logging
 import os
 import pathlib
@@ -32,6 +10,7 @@ import requests
 import sys
 import json
 import shutil
+import argparse

 from hashlib import sha256
 from enum import IntEnum, auto
@@ -41,6 +20,11 @@ logging.basicConfig(level=logging.DEBUG)
 logger = logging.getLogger("convert_hf_to_gguf_update")
 sess = requests.Session()

+convert_py_pth = pathlib.Path("convert_hf_to_gguf.py")
+convert_py = convert_py_pth.read_text(encoding="utf-8")
+hf_token_pth = pathlib.Path.home() / ".cache" / "huggingface" / "token"
+hf_token = hf_token_pth.read_text(encoding="utf-8").strip() if hf_token_pth.exists() else None
+

 class TOKENIZER_TYPE(IntEnum):
    SPM = auto()
@@ -49,20 +33,49 @@ class TOKENIZER_TYPE(IntEnum):
    UGM = auto()


+DOC_STRING = """
+This script downloads the tokenizer models of the specified models from Huggingface and
+generates the get_vocab_base_pre() function for convert_hf_to_gguf.py
+
+/!\\ It is intended to be used by contributors and is not meant to be run by end users
+
+This is necessary in order to analyze the type of pre-tokenizer used by the model and
+provide the necessary information to llama.cpp via the GGUF header in order to implement
+the same pre-tokenizer.
+
+ref: https://github.com/ggml-org/llama.cpp/pull/6920
+
+Instructions:
+
+- Add a new model to the "models" list
+- Run the script with your huggingface token
+    By default, token will be read from ~/.cache/huggingface/token
+- The convert_hf_to_gguf.py script will have had its get_vocab_base_pre() function updated
+- Update llama.cpp with the new pre-tokenizer if necessary
+"""
+# TODO: generate tokenizer tests for llama.cpp
+
+parser = argparse.ArgumentParser(description=DOC_STRING, formatter_class=argparse.RawTextHelpFormatter)
+parser.add_argument(
+    "--full", action="store_true",
+    help="download full list of models - make sure you have access to all of them",
+)
+parser.add_argument(
+    "hf_token",
+    help="optional HF token",
+    nargs="?",
+)
+args = parser.parse_args()
+hf_token = args.hf_token if args.hf_token is not None else hf_token
+
+if hf_token is None:
+    logger.error("HF token is required. Please provide it as an argument or set it in ~/.cache/huggingface/token")
+    sys.exit(1)
+
 # TODO: this string has to exercise as much pre-tokenizer functionality as possible
 #       will be updated with time - contributions welcome
 CHK_TXT = '\n \n\n \n\n\n \t \t\t \t\n  \n   \n    \n     \n🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````\"\"\"\"......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL'

-if len(sys.argv) == 2:
-    token = sys.argv[1]
-    if not token.startswith("hf_"):
-        logger.info("Huggingface token seems invalid")
-        logger.info("Usage: python convert_hf_to_gguf_update.py <huggingface_token>")
-        sys.exit(1)
-else:
-    logger.info("Usage: python convert_hf_to_gguf_update.py <huggingface_token>")
-    sys.exit(1)
-
 # TODO: add models here, base models preferred
 models = [
    {"name": "llama-spm",        "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", },
@@ -103,7 +116,6 @@ models = [
    {"name": "exaone",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct", },
    {"name": "phi-2",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/microsoft/phi-2", },
    {"name": "chameleon",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/facebook/chameleon-7b", },
-    {"name": "minerva-7b",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", },
    {"name": "roberta-bpe",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"},
    {"name": "gigachat",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"},
    {"name": "megrez",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Infinigence/Megrez-3B-Instruct"},
@@ -114,11 +126,19 @@ models = [
    {"name": "trillion",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/trillionlabs/Trillion-7B-preview", },
    {"name": "bailingmoe",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/Ling-lite", },
    {"name": "llama4",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct", },
-    {"name": "glm4",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-hf", },
    {"name": "pixtral",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistral-community/pixtral-12b", },
    {"name": "seed-coder",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base", },
 ]

+# some models are known to be broken upstream, so we will skip them as exceptions
+pre_computed_hashes = [
+    # chatglm-bpe has 2 hashes, why?
+    {"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b"},
+    {"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516"},
+    {"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-hf", "chkhsh": "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2"},
+    {"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", "chkhsh": "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35"},
+]
+

 def download_file_with_auth(url, token, save_path):
    headers = {"Authorization": f"Bearer {token}"}
@@ -169,9 +189,29 @@ def download_model(model):
            if os.path.isfile(save_path):
                logger.info(f"{name}: File {save_path} already exists - skipping")
                continue
-            download_file_with_auth(f"{repo}/resolve/main/{file}", token, save_path)
+            download_file_with_auth(f"{repo}/resolve/main/{file}", hf_token, save_path)


+# get list of existing models and chkhsh from the convert_hf_to_gguf.py file
+# returns mapping res --> chkhsh
+def get_existing_models(convert_py):
+    pattern = r'if chkhsh == "([a-f0-9]{64})":\s*\n\s*.*\s*res = "([^"]+)"'
+    matches = re.findall(pattern, convert_py)
+    output = {}
+    for chkhsh, res in matches:
+        output[res] = chkhsh
+    return output
+
+
+existing_models = {}
+all_models = models.copy()
+if not args.full:
+    # Filter out models that already exist in convert_hf_to_gguf.py
+    existing_models = get_existing_models(convert_py)
+    all_models = models.copy()
+    models = [model for model in all_models if model["name"] not in existing_models]
+
+logging.info(f"Downloading {len(models)} models...")
 for model in models:
    try:
        download_model(model)
@@ -182,9 +222,10 @@ for model in models:
 # generate the source code for the convert_hf_to_gguf.py:get_vocab_base_pre() function:

 src_ifs = ""
-for model in models:
+for model in [*all_models, *pre_computed_hashes]:
    name = model["name"]
    tokt = model["tokt"]
+    chkhsh = model.get("chkhsh")

    if tokt == TOKENIZER_TYPE.SPM or tokt == TOKENIZER_TYPE.UGM:
        continue
@@ -195,35 +236,44 @@ for model in models:
        continue

    # create the tokenizer
-    try:
-        if name == "t5":
-            tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
-        else:
-            tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
-    except OSError as e:
-        logger.error(f"Error loading tokenizer for model {name}. The model may not exist or is not accessible with the provided token. Error: {e}")
-        continue  # Skip to the next model if the tokenizer can't be loaded
+    if chkhsh is not None:
+        # if the model has a pre-computed hash, use it
+        logger.info(f"Using pre-computed hash for model {name}: {chkhsh}")
+    elif name in existing_models:
+        # if the model already exists in convert_hf_to_gguf.py, skip compute hash
+        chkhsh = existing_models[name]
+    else:
+        # otherwise, compute the hash of the tokenizer
+        try:
+            logger.info(f"Loading tokenizer from {f'models/tokenizers/{name}'}...")
+            if name == "t5":
+                tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
+            else:
+                tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
+        except OSError as e:
+            logger.error(f"Error loading tokenizer for model {name}. The model may not exist or is not accessible with the provided token. Error: {e}")
+            continue  # Skip to the next model if the tokenizer can't be loaded

-    chktok = tokenizer.encode(CHK_TXT)
-    chkhsh = sha256(str(chktok).encode()).hexdigest()
+        chktok = tokenizer.encode(CHK_TXT)
+        chkhsh = sha256(str(chktok).encode()).hexdigest()

-    logger.info(f"model: {name}")
-    logger.info(f"tokt: {tokt}")
-    logger.info(f"repo: {model['repo']}")
-    logger.info(f"chktok: {chktok}")
-    logger.info(f"chkhsh: {chkhsh}")
+        logger.info(f"model: {name}")
+        logger.info(f"tokt: {tokt}")
+        logger.info(f"repo: {model['repo']}")
+        logger.info(f"chktok: {chktok}")
+        logger.info(f"chkhsh: {chkhsh}")

-    # print the "pre_tokenizer" content from the tokenizer.json
-    with open(f"models/tokenizers/{name}/tokenizer.json", "r", encoding="utf-8") as f:
-        cfg = json.load(f)
-        normalizer = cfg["normalizer"]
-        logger.info("normalizer: " + json.dumps(normalizer, indent=4))
-        pre_tokenizer = cfg["pre_tokenizer"]
-        logger.info("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4))
-        if "ignore_merges" in cfg["model"]:
-            logger.info("ignore_merges: " + json.dumps(cfg["model"]["ignore_merges"], indent=4))
+        # print the "pre_tokenizer" content from the tokenizer.json
+        with open(f"models/tokenizers/{name}/tokenizer.json", "r", encoding="utf-8") as f:
+            cfg = json.load(f)
+            normalizer = cfg["normalizer"]
+            logger.info("normalizer: " + json.dumps(normalizer, indent=4))
+            pre_tokenizer = cfg["pre_tokenizer"]
+            logger.info("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4))
+            if "ignore_merges" in cfg["model"]:
+                logger.info("ignore_merges: " + json.dumps(cfg["model"]["ignore_merges"], indent=4))

-    logger.info("")
+        logger.info("")

    src_ifs += f"        if chkhsh == \"{chkhsh}\":\n"
    src_ifs += f"            # ref: {model['repo']}\n"
@@ -271,8 +321,6 @@ src_func = f"""
        return res
 """

-convert_py_pth = pathlib.Path("convert_hf_to_gguf.py")
-convert_py = convert_py_pth.read_text(encoding="utf-8")
 convert_py = re.sub(
    r"(# Marker: Start get_vocab_base_pre)(.+?)( +# Marker: End get_vocab_base_pre)",
    lambda m: m.group(1) + src_func + m.group(3),
@@ -288,7 +336,7 @@ logger.info("+++ convert_hf_to_gguf.py was updated")

 tests = [
    "ied 4 ½ months",
-    "Führer",
+    "Äpfel",
    "",
    " ",
    "  ",
@@ -367,6 +415,10 @@ for model in models:
        logger.error(f"Failed to load tokenizer for model {name}. Error: {e}")
        continue  # Skip this model and continue with the next one in the loop

+    if not os.path.exists(f"models/ggml-vocab-{name}.gguf"):
+        logger.info(f"Skip vocab files for model {name}, no GGUF file found")
+        continue
+
    with open(f"models/ggml-vocab-{name}.gguf.inp", "w", encoding="utf-8") as f:
        for text in tests:
            f.write(f"{text}")
--- a/docs/backend/CANN.md
+++ b/docs/backend/CANN.md
@@ -56,60 +56,82 @@ The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the abi

 ## Model Supports

-| Model Name                  | FP16  | Q8_0 | Q4_0 |
+| Model Name                  | FP16  | Q4_0 | Q8_0 |
 |:----------------------------|:-----:|:----:|:----:|
-| AquilaChat2-7B              |   √   |   √  |   √  |
-| Baichuan-7b                 |   √   |   √  |   √  |
-| Baichuan2-7B-Chat           |   √   |   √  |   √  |
-| bitnet_b1_58-large          |   √   |   √  |   √  |
-| bloom-560m                  |   √   |   x  |   √  |
-| bloomz-alpaca-560m          |   √   |   x  |   √  |
-| c4ai-command-r-35B-v01      |   x   |   x  |   x  |
-| chatglm3-6B                 |   x   |   x  |   x  |
-| chinese-alpaca-2-1.3b       |   √   |   √  |   √  |
-| CodeShell-7B                |   √   |   √  |   √  |
-| deepseek-ai_deepseek-coder-1.3B-base | x |   x  |   x  |
-| deepseek-ai_DeepSeek-V2-Lite | x   |   x  |   x   |
-| deepseek-coder-6.7B-instruct | x   |   x  |   x   |
-| DeepSeek-V2-Lite-64x1.5B    |   x   |   x  |   x  |
-| falcon-7b-instruct          |   √   |   √  |   √  |
-| flan-t5-large               |   √   |   √  |   √  |
-| gemma-2-9b-it               |   √   |   √  |   √  |
-| glm-4-9B                    |   x   |   x  |   x  |
-| gpt2                        |   √   |   √  |   √  |
-| Gpt2-163M                   |   √   |   √  |   √  |
-| granite-3B-code-instruct    |   √   |   √  |   √  |
+| Llama-2                     |   √   |   √  |   √  |
+| Llama-3                     |   √   |   √  |   √  |
+| Mistral-7B                  |   √   |   √  |   √  |
+| Mistral MOE                 |   √   |   √  |   √  |
+| DBRX                        |   -   |   -  |   -  |
+| Falcon                      |   √   |   √  |   √  |
+| Chinese LLaMA/Alpaca        |   √   |   √  |   √  |
+| Vigogne(French)             |   √   |   √  |   √  |
+| BERT                        |   x   |   x  |   x  |
+| Koala                       |   √   |   √  |   √  |
+| Baichuan                    |   √   |   √  |   √  |
+| Aquila 1 & 2                |   √   |   √  |   √  |
+| Starcoder models            |   √   |   √  |   √  |
+| Refact                      |   √   |   √  |   √  |
+| MPT                         |   √   |   √  |   √  |
+| Bloom                       |   √   |   √  |   √  |
+| Yi models                   |   √   |   √  |   √  |
+| stablelm models             |   √   |   √  |   √  |
+| DeepSeek models             |   x   |   x  |   x  |
+| Qwen models                 |   √   |   √  |   √  |
+| PLaMo-13B                   |   √   |   √  |   √  |
+| Phi models                  |   √   |   √  |   √  |
+| PhiMoE                      |   √   |   √  |   √  |
+| GPT-2                       |   √   |   √  |   √  |
+| Orion                       |   √   |   √  |   √  |
+| InternlLM2                  |   √   |   √  |   √  |
+| CodeShell                   |   √   |   √  |   √  |
+| Gemma                       |   √   |   √  |   √  |
+| Mamba                       |   √   |   √  |   √  |
+| Xverse                      |   √   |   √  |   √  |
+| command-r models            |   √   |   √  |   √  |
+| Grok-1                      |   -   |   -  |   -  |
+| SEA-LION                    |   √   |   √  |   √  |
 | GritLM-7B                   |   √   |   √  |   √  |
-| internlm2_5-7b-chat         |   √   |   √  |   √  |
-| koala-7B-HF                 |   √   |   √  |   √  |
-| Llama-2-7b-chat-hf          |   √   |   √  |   √  |
-| Llama-3-Smaug-8B            |   √   |   √  |   √  |
-| Llama2-Chinese-7b-Chat      |   √   |   √  |   √  |
-| Llama3-8B                   |   √   |   √  |   √  |
-| Llama3-8b-chinese           |   √   |   √  |   √  |
-| mamba-130m-hf               |   √   |   √  |   √  |
-| Mistral-7B-Instruct-v0.2    |   √   |   √  |   √  |
-| Mixtral-8x7B-Instruct-v0.1  |   x   |   √  |   √  |
-| mpt-7B                      |   √   |   √  |   √  |
-| OLMo-1B-hf                  |   √   |   √  |   √  |
-| OpenELM-3B-Instruct         |   √   |   √  |   √  |
-| Orion-14b-base              |   √   |   √  |   √  |
-| phi1                        |   x   |   x  |   x  |
-| phi2                        |   x   |   x  |   x  |
-| Phi-3-mini-4k-instruct      |   √   |   √  |   √  |
-| plamo-13b                   |   √   |   √  |   √  |
-| pythia-70M                  |   x   |   x  |   x  |
-| Qwen-7B                     |   √   |   √  |   √  |
-| Qwen2-1.5B-Instruct         |   √   |   x  |   √  |
-| Refact-1_6B-fim             |   √   |   √  |   √  |
-| SmolLM-135M                 |   √   |   √  |   √  |
-| stablelm-zephyr             |   x   |   x  |   x  |
-| stablelm-2-zephyr-1_6b      |   x   |   x  |   x  |
-| starcoderbase-1b            |   √   |   √  |   √  |
-| starcoder2-3b               |   √   |   √  |   √  |
-| vigogne-7b-chat             |   √   |   √  |   √  |
-| xverse-7b-chat              |   √   |   √  |   √  |
-| Yi-6b-Chat                  |   √   |   √  |   √  |
+| OLMo                        |   √   |   √  |   √  |
+| OLMo 2                      |   √   |   √  |   √  |
+| OLMoE                       |   √   |   √  |   √  |
+| Granite models              |   √   |   √  |   √  |
+| GPT-NeoX                    |   √   |   √  |   √  |
+| Pythia                      |   √   |   √  |   √  |
+| Snowflake-Arctic MoE        |   -   |   -  |   -  |
+| Smaug                       |   √   |   √  |   √  |
+| Poro 34B                    |   √   |   √  |   √  |
+| Bitnet b1.58 models         |   √   |   x  |   x  |
+| Flan-T5                     |   √   |   √  |   √  |
+| Open Elm models             |   x   |   √  |   √  |
+| chatGLM3-6B + ChatGLM4-9b +  GLMEdge-1.5b + GLMEdge-4b    |   √   |   √  |   √  |
+| GLM-4-0414                  |   √   |   √  |   √  |
+| SmolLM                      |   √   |   √  |   √  |
+| EXAONE-3.0-7.8B-Instruct    |   √   |   √  |   √  |
+| FalconMamba Models          |   √   |   √  |   √  |
+| Jais Models                 |   -   |   x  |   x  |
+| Bielik-11B-v2.3             |   √   |   √  |   √  |
+| RWKV-6                      |   -   |   √  |   √  |
+| QRWKV-6                     |   √   |   √  |   √  |
+| GigaChat-20B-A3B            |   x   |   x  |   x  |
+| Trillion-7B-preview         |   √   |   √  |   √  |
+| Ling models                 |   √   |   √  |   √  |
+
+
+**Multimodal**
+| Model Name                  | FP16  | Q4_0 | Q8_0 |
+|:----------------------------|:-----:|:----:|:----:|
+| LLaVA 1.5 models, LLaVA 1.6 models      |   x   |   x  |   x  |
+|  BakLLaVA                   |   √   |   √  |   √  |
+|  Obsidian                   |   √   |   -  |   -  |
+|  ShareGPT4V                 |   x   |   -  |   -  |
+|  MobileVLM 1.7B/3B models   |   -   |   -  |   -  |
+|  Yi-VL                      |   -   |   -  |   -  |
+|  Mini CPM                   |   √   |   √  |   √  |
+|  Moondream                  |   √   |   √  |   √  |
+|  Bunny                      |   √   |   -  |   -  |
+|  GLM-EDGE                   |   √   |   √  |   √  |
+|  Qwen2-VL                   |   √   |   √  |   √  |



@@ -258,6 +280,15 @@ cmake --build build --config release
 ### **GitHub contribution**:
 Please add the **[CANN]** prefix/tag in issues/PRs titles to help the CANN-team check/address them without delay.

+## Updates
+### Basic Flash Attention Support
+The basic FA kernel with aclnnops has been added in aclnn_ops.cpp.
+Currently, the FA only supports the cases with FP16 KV tensors and NO logit softcap.
+Since the aclnn interface for flash attention cannot support the logit softcap, we will only update the quantized version in the future.
+
+Authors from Peking University: Bizhao Shi (bshi@pku.edu.cn), Yuxin Yang (yxyang@pku.edu.cn), Ruiyang Ma (ruiyang@stu.pku.edu.cn), and Guojie Luo (gluo@pku.edu.cn).
+
+We would like to thank Tuo Dai, Shanni Li, and all of the project maintainers from Huawei Technologies Co., Ltd for their help during the code development and pull request.

 ## TODO
 - Support more models and data types.
--- a/docs/backend/SYCL.md
+++ b/docs/backend/SYCL.md
@@ -17,25 +17,25 @@

 **SYCL** is a high-level parallel programming model designed to improve developers productivity writing code across various hardware accelerators such as CPUs, GPUs, and FPGAs. It is a single-source language designed for heterogeneous computing and based on standard C++17.

-**oneAPI** is an open ecosystem and a standard-based specification, supporting multiple architectures including but not limited to intel CPUs, GPUs and FPGAs. The key components of the oneAPI ecosystem include:
+**oneAPI** is an open ecosystem and a standard-based specification, supporting multiple architectures including but not limited to Intel CPUs, GPUs and FPGAs. The key components of the oneAPI ecosystem include:

 - **DPCPP** *(Data Parallel C++)*: The primary oneAPI SYCL implementation, which includes the icpx/icx Compilers.
 - **oneAPI Libraries**: A set of highly optimized libraries targeting multiple domains *(e.g. Intel oneMKL, oneMath and oneDNN)*.
- **oneAPI LevelZero**: A high performance low level interface for fine-grained control over intel iGPUs and dGPUs.
+- **oneAPI LevelZero**: A high performance low level interface for fine-grained control over Intel iGPUs and dGPUs.
 - **Nvidia & AMD Plugins**: These are plugins extending oneAPI's DPCPP support to SYCL on Nvidia and AMD GPU targets.

 ### Llama.cpp + SYCL

-The llama.cpp SYCL backend is designed to support **Intel GPU** firstly. Based on the cross-platform feature of SYCL, it also supports other vendor GPUs: Nvidia and AMD.
+The llama.cpp SYCL backend is primarily designed for **Intel GPUs**.
+SYCL cross-platform capabilities enable support for Nvidia GPUs as well, with limited support for AMD.

 ## Recommended Release

-The SYCL backend would be broken by some PRs due to no online CI.
-
-The following release is verified with good quality:
+The following releases are verified and recommended:

 |Commit ID|Tag|Release|Verified  Platform| Update date|
 |-|-|-|-|-|
+|24e86cae7219b0f3ede1d5abdf5bf3ad515cccb8|b5377 |[llama-b5377-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b5377/llama-b5377-bin-win-sycl-x64.zip) |ArcB580/Linux/oneAPI 2025.1<br>LNL Arc GPU/Windows 11/oneAPI 2025.1.1|2025-05-15|
 |3bcd40b3c593d14261fb2abfabad3c0fb5b9e318|b4040 |[llama-b4040-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b4040/llama-b4040-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1<br>MTL Arc GPU/Windows 11/oneAPI 2024.1| 2024-11-19|
 |fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1<br>MTL Arc GPU/Windows 11/oneAPI 2024.1||

@@ -106,15 +106,14 @@ SYCL backend supports Intel GPU Family:
 |-------------------------------|---------|---------------------------------------|
 | Intel Data Center Max Series  | Support | Max 1550, 1100                        |
 | Intel Data Center Flex Series | Support | Flex 170                              |
-| Intel Arc Series              | Support | Arc 770, 730M, Arc A750               |
-| Intel built-in Arc GPU        | Support | built-in Arc GPU in Meteor Lake, Arrow Lake    |
-| Intel iGPU                    | Support | iGPU in 13700k,iGPU in 13400, i5-1250P, i7-1260P, i7-1165G7 |
+| Intel Arc Series              | Support | Arc 770, 730M, Arc A750, B580         |
+| Intel built-in Arc GPU        | Support | built-in Arc GPU in Meteor Lake, Arrow Lake, Lunar Lake |
+| Intel iGPU                    | Support | iGPU in 13700k, 13400, i5-1250P, i7-1260P, i7-1165G7  |

 *Notes:*

 - **Memory**
  - The device memory is a limitation when running a large model. The loaded model size, *`llm_load_tensors: buffer_size`*, is displayed in the log when running `./bin/llama-cli`.
-
  - Please make sure the GPU shared memory from the host is large enough to account for the model's size. For e.g. the *llama-2-7b.Q4_0* requires at least 8.0GB for integrated GPU and 4.0GB for discrete GPU.

 - **Execution Unit (EU)**
@@ -138,9 +137,11 @@ Note: AMD GPU support is highly experimental and is incompatible with F16.
 Additionally, it only supports GPUs with a sub_group_size (warp size) of 32.

 ## Docker
-The docker build option is currently limited to *intel GPU* targets.
+
+The docker build option is currently limited to *Intel GPU* targets.

 ### Build image
+
 ```sh
 # Using FP16
 docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" --target light -f .devops/intel.Dockerfile .
@@ -148,9 +149,10 @@ docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" --target light -f

 *Notes*:

-To build in default FP32 *(Slower than FP16 alternative)*, you can remove the `--build-arg="GGML_SYCL_F16=ON"` argument from the previous command.
+To build in default FP32 *(Slower than FP16 alternative)*, set `--build-arg="GGML_SYCL_F16=OFF"` in the previous command.

 You can also use the `.devops/llama-server-intel.Dockerfile`, which builds the *"server"* alternative.
+Check the [documentation for Docker](../docker.md) to see the available images.

 ### Run container

@@ -250,7 +252,7 @@ sycl-ls

 - **Intel GPU**

-When targeting an intel GPU, the user should expect one or more level-zero devices among the available SYCL devices. Please make sure that at least one GPU is present, for instance [`level_zero:gpu`] in the sample output below:
+When targeting an intel GPU, the user should expect one or more devices among the available SYCL devices. Please make sure that at least one GPU is present via `sycl-ls`, for instance `[level_zero:gpu]` in the sample output below:

 ```
 [opencl:acc][opencl:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2  [2023.16.10.0.17_160000]
@@ -282,7 +284,7 @@ For AMD GPUs we should expect at least one SYCL-HIP device [`hip:gpu`]:

 #### Intel GPU

-```
+```sh
 ./examples/sycl/build.sh
 ```

@@ -351,7 +353,7 @@ cmake --build build --config Release -j -v

 #### Retrieve and prepare model

-You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example.
+You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model preparation, or download an already quantized model like [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) or [Meta-Llama-3-8B-Instruct-Q4_0.gguf](https://huggingface.co/aptha/Meta-Llama-3-8B-Instruct-Q4_0-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-Q4_0.gguf).

 ##### Check device

@@ -398,11 +400,15 @@ Choose one of following methods to run.

 ```sh
 ./examples/sycl/run-llama2.sh 0
+# OR
+./examples/sycl/run-llama3.sh 0
 ```
 - Use multiple devices:

 ```sh
 ./examples/sycl/run-llama2.sh
+# OR
+./examples/sycl/run-llama3.sh
 ```

 2. Command line
@@ -425,13 +431,13 @@ Examples:
 - Use device 0:

 ```sh
-ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm none -mg 0
+ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm none -mg 0
 ```

 - Use multiple devices:

 ```sh
-ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm layer
+ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm layer
 ```

 *Notes:*
@@ -452,7 +458,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512

 1. Install GPU driver

-Intel GPU drivers instructions guide and download page can be found here: [Get intel GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html).
+Intel GPU drivers instructions guide and download page can be found here: [Get Intel GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html).

 2. Install Visual Studio

@@ -629,7 +635,7 @@ Once it is completed, final results will be in **build/Release/bin**

 #### Retrieve and prepare model

-You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example.
+You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model preparation, or download an already quantized model like [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) or [Meta-Llama-3-8B-Instruct-Q4_0.gguf](https://huggingface.co/aptha/Meta-Llama-3-8B-Instruct-Q4_0-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-Q4_0.gguf).

 ##### Check device

@@ -648,7 +654,7 @@ Similar to the native `sycl-ls`, available SYCL devices can be queried as follow
 build\bin\llama-ls-sycl-device.exe
 ```

-This command will only display the selected backend that is supported by SYCL. The default backend is level_zero. For example, in a system with 2 *intel GPU* it would look like the following:
+This command will only display the selected backend that is supported by SYCL. The default backend is level_zero. For example, in a system with 2 *Intel GPU* it would look like the following:
 ```
 found 2 SYCL devices:
 |  |                  |                                             |Compute   |Max compute|Max work|Max sub|               |
@@ -658,13 +664,14 @@ found 2 SYCL devices:
 | 1|[level_zero:gpu:1]|                    Intel(R) UHD Graphics 770|       1.3|         32|     512|     32|    53651849216|

 ```
+
 #### Choose level-zero devices

 |Chosen Device ID|Setting|
 |-|-|
-|0|`set ONEAPI_DEVICE_SELECTOR="level_zero:1"` or no action|
+|0|Default option. You may also want to `set ONEAPI_DEVICE_SELECTOR="level_zero:0"`|
 |1|`set ONEAPI_DEVICE_SELECTOR="level_zero:1"`|
-|0 & 1|`set ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"`|
+|0 & 1|`set ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"` or `set ONEAPI_DEVICE_SELECTOR="level_zero:*"`|

 #### Execute

@@ -673,7 +680,13 @@ Choose one of following methods to run.
 1. Script

 ```
-examples\sycl\win-run-llama2.bat
+examples\sycl\win-run-llama-2.bat
+```
+
+or
+
+```
+examples\sycl\win-run-llama-3.bat
 ```

 2. Command line
@@ -697,13 +710,13 @@ Examples:
 - Use device 0:

 ```
-build\bin\llama-cli.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm none -mg 0
+build\bin\llama-cli.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm none -mg 0
 ```

 - Use multiple devices:

 ```
-build\bin\llama-cli.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm layer
+build\bin\llama-cli.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm layer
 ```


@@ -714,7 +727,9 @@ Note:
 ```sh
 detect 1 SYCL GPUs: [0] with top Max compute units:512
 ```
+
 Or
+
 ```sh
 use 1 SYCL GPUs: [0] with Max compute units:512
 ```
@@ -726,15 +741,17 @@ use 1 SYCL GPUs: [0] with Max compute units:512

 | Name               | Value                                 | Function                                    |
 |--------------------|---------------------------------------|---------------------------------------------|
-| GGML_SYCL          | ON (mandatory)                        | Enable build with SYCL code path.<br>FP32 path - recommended for better perforemance than FP16 on quantized model|
+| GGML_SYCL          | ON (mandatory)                        | Enable build with SYCL code path.           |
 | GGML_SYCL_TARGET   | INTEL *(default)* \| NVIDIA \| AMD    | Set the SYCL target device type.            |
 | GGML_SYCL_DEVICE_ARCH | Optional (except for AMD)             | Set the SYCL device architecture, optional except for AMD. Setting the device architecture can improve the performance. See the table [--offload-arch](https://github.com/intel/llvm/blob/sycl/sycl/doc/design/OffloadDesign.md#--offload-arch) for a list of valid architectures. |
-| GGML_SYCL_F16      | OFF *(default)* \|ON *(optional)*     | Enable FP16 build with SYCL code path.      |
+| GGML_SYCL_F16      | OFF *(default)* \|ON *(optional)*     | Enable FP16 build with SYCL code path. (1.) |
 | GGML_SYCL_GRAPH    | ON *(default)* \|OFF *(Optional)*     | Enable build with [SYCL Graph extension](https://github.com/intel/llvm/blob/sycl/sycl/doc/extensions/experimental/sycl_ext_oneapi_graph.asciidoc). |
 | GGML_SYCL_DNN      | ON *(default)* \|OFF *(Optional)*     | Enable build with oneDNN.                   |
 | CMAKE_C_COMPILER   | `icx` *(Linux)*, `icx/cl` *(Windows)* | Set `icx` compiler for SYCL code path.      |
 | CMAKE_CXX_COMPILER | `icpx` *(Linux)*, `icx` *(Windows)*   | Set `icpx/icx` compiler for SYCL code path. |

+1. FP16 is recommended for better prompt processing performance on quantized models. Performance is equivalent in text generation but set `GGML_SYCL_F16=OFF` if you are experiencing issues with FP16 builds.
+
 #### Runtime

 | Name              | Value            | Function                                                                                                                  |
@@ -752,7 +769,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512

 ## Q&A

- Error:  `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`.
+- Error:  `error while loading shared libraries: libsycl.so: cannot open shared object file: No such file or directory`.

  - Potential cause: Unavailable oneAPI installation or not set ENV variables.
  - Solution: Install *oneAPI base toolkit* and enable its ENV through: `source /opt/intel/oneapi/setvars.sh`.
@@ -781,18 +798,18 @@ use 1 SYCL GPUs: [0] with Max compute units:512

  It's same for other projects including llama.cpp SYCL backend.

- Meet issue: `Native API failed. Native API returns: -6 (PI_ERROR_OUT_OF_HOST_MEMORY) -6 (PI_ERROR_OUT_OF_HOST_MEMORY) -999 (UNKNOWN PI error)` or `failed to allocate SYCL0 buffer`
+- `Native API failed. Native API returns: 39 (UR_RESULT_ERROR_OUT_OF_DEVICE_MEMORY)`, `ggml_backend_sycl_buffer_type_alloc_buffer: can't allocate 3503030272 Bytes of memory on device`, or `failed to allocate SYCL0 buffer`

-  Device Memory is not enough.
+  You are running out of Device Memory.

  |Reason|Solution|
  |-|-|
-  |Default Context is too big. It leads to more memory usage.|Set `-c 8192` or smaller value.|
-  |Model is big and require more memory than device's.|Choose smaller quantized model, like Q5 -> Q4;<br>Use more than one devices to load model.|
+  | The default context is too big. It leads to excessive memory usage.|Set `-c 8192` or a smaller value.|
+  | The model is too big and requires more memory than what is available.|Choose a smaller model or change to a smaller quantization, like Q5 -> Q4;<br>Alternatively, use more than one device to load model.|

 ### **GitHub contribution**:
-Please add the **[SYCL]** prefix/tag in issues/PRs titles to help the SYCL-team check/address them without delay.
+Please add the `SYCL :` prefix/tag in issues/PRs titles to help the SYCL contributors to check/address them without delay.

 ## TODO

- NA
+- Review ZES_ENABLE_SYSMAN: https://github.com/intel/compute-runtime/blob/master/programmers-guide/SYSMAN.md#support-and-limitations
--- a/docs/build.md
+++ b/docs/build.md
@@ -1,5 +1,9 @@
 # Build llama.cpp locally

+The main product of this project is the `llama` library. Its C-style interface can be found in [include/llama.h](include/llama.h).
+
+The project also includes many example programs and tools using the `llama` library. The examples range from simple, minimal code snippets to sophisticated sub-projects such as an OpenAI-compatible HTTP server.
+
 **To get the Code:**

 ```bash
@@ -63,6 +67,7 @@ cmake --build build --config Release
      cmake --preset x64-windows-llvm-release
      cmake --build build-x64-windows-llvm-release
      ```
+- Curl usage is enabled by default and can be turned off with `-DLLAMA_CURL=OFF`. Otherwise you need to install development libraries for libcurl.

 ## BLAS Build

--- a/docs/docker.md
+++ b/docs/docker.md
@@ -22,6 +22,9 @@ Additionally, there the following images, similar to the above:
 - `ghcr.io/ggml-org/llama.cpp:full-musa`: Same as `full` but compiled with MUSA support. (platforms: `linux/amd64`)
 - `ghcr.io/ggml-org/llama.cpp:light-musa`: Same as `light` but compiled with MUSA support. (platforms: `linux/amd64`)
 - `ghcr.io/ggml-org/llama.cpp:server-musa`: Same as `server` but compiled with MUSA support. (platforms: `linux/amd64`)
+- `ghcr.io/ggml-org/llama.cpp:full-intel`: Same as `full` but compiled with SYCL support. (platforms: `linux/amd64`)
+- `ghcr.io/ggml-org/llama.cpp:light-intel`: Same as `light` but compiled with SYCL support. (platforms: `linux/amd64`)
+- `ghcr.io/ggml-org/llama.cpp:server-intel`: Same as `server` but compiled with SYCL support. (platforms: `linux/amd64`)

 The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](../.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](../.github/workflows/docker.yml). If you need different settings (for example, a different CUDA, ROCm or MUSA library, you'll need to build the images locally for now).

@@ -104,7 +107,7 @@ You may want to pass in some different `ARGS`, depending on the MUSA environment

 The defaults are:

- `MUSA_VERSION` set to `rc3.1.1`
+- `MUSA_VERSION` set to `rc4.0.1`

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

--- a/docs/function-calling.md
+++ b/docs/function-calling.md
@@ -2,7 +2,6 @@

 [chat.h](../common/chat.h) (https://github.com/ggml-org/llama.cpp/pull/9639) adds support for [OpenAI-style function calling](https://platform.openai.com/docs/guides/function-calling) and is used in:
 - `llama-server` when started w/ `--jinja` flag
- `llama-cli` (WIP: https://github.com/ggml-org/llama.cpp/pull/11556)

 ## Universal support w/ Native & Generic handlers

@@ -325,36 +324,65 @@ To get the official template from original HuggingFace repos, you can use [scrip
 > [!TIP]
 > If there is no official `tool_use` Jinja template, you may want to set `--chat-template chatml` to use a default that works with many models (YMMV!), or write your own (e.g. we provide a custom [llama-cpp-deepseek-r1.jinja](../models/templates/llama-cpp-deepseek-r1.jinja) for DeepSeek R1 distills)

+> [!CAUTION]
+> Beware of extreme KV quantizations (e.g. `-ctk q4_0`), they can substantially degrade the model's tool calling performance.
+
 Test in CLI (or with any library / software that can use OpenAI-compatible API backends):

 ```bash
 curl http://localhost:8080/v1/chat/completions -d '{
-"model": "gpt-3.5-turbo",
-"tools": [
-    {
-    "type":"function",
-    "function":{
-        "name":"python",
-        "description":"Runs code in an ipython interpreter and returns the result of the execution after 60 seconds.",
-        "parameters":{
-        "type":"object",
-        "properties":{
-            "code":{
-            "type":"string",
-            "description":"The code to run in the ipython interpreter."
+    "model": "gpt-3.5-turbo",
+    "tools": [
+        {
+        "type":"function",
+        "function":{
+            "name":"python",
+            "description":"Runs code in an ipython interpreter and returns the result of the execution after 60 seconds.",
+            "parameters":{
+            "type":"object",
+            "properties":{
+                "code":{
+                "type":"string",
+                "description":"The code to run in the ipython interpreter."
+                }
+            },
+            "required":["code"]
            }
-        },
-        "required":["code"]
        }
-    }
-    }
-],
-"messages": [
-    {
-    "role": "user",
-    "content": "Print a hello world message with python."
-    }
-]
+        }
+    ],
+    "messages": [
+        {
+        "role": "user",
+        "content": "Print a hello world message with python."
+        }
+    ]
+}'
+
+
+curl http://localhost:8080/v1/chat/completions -d '{
+    "model": "gpt-3.5-turbo",
+    "messages": [
+        {"role": "system", "content": "You are a chatbot that uses tools/functions. Dont overthink things."},
+        {"role": "user", "content": "What is the weather in Istanbul?"}
+    ],
+    "tools": [{
+        "type":"function",
+        "function":{
+            "name":"get_current_weather",
+            "description":"Get the current weather in a given location",
+            "parameters":{
+                "type":"object",
+                "properties":{
+                    "location":{
+                        "type":"string",
+                        "description":"The city and country/state, e.g. `San Francisco, CA`, or `Paris, France`"
+                    }
+                },
+                "required":["location"]
+            }
+        }
+    }]
 }'
 ```

--- a/docs/install.md
+++ b/docs/install.md
@@ -1,28 +1,42 @@
 # Install pre-built version of llama.cpp

-## Homebrew
+| Install via | Windows | Mac | Linux |
+|-------------|---------|-----|-------|
+| Winget      | ✅      |      |      |
+| Homebrew    |         | ✅   | ✅   |
+| MacPorts    |         | ✅   |      |
+| Nix         |         | ✅   | ✅   |

-On Mac and Linux, the homebrew package manager can be used via
+## Winget (Windows)
+
+```sh
+winget install llama.cpp
+```
+
+The package is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggml-org/llama.cpp/issues/8188
+
+## Homebrew (Mac and Linux)

 ```sh
 brew install llama.cpp
 ```
+
 The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggml-org/llama.cpp/discussions/7668

-## MacPorts
+## MacPorts (Mac)

 ```sh
 sudo port install llama.cpp
 ```
-see also: https://ports.macports.org/port/llama.cpp/details/

-## Nix
+See also: https://ports.macports.org/port/llama.cpp/details/

-On Mac and Linux, the Nix package manager can be used via
+## Nix (Mac and Linux)

 ```sh
 nix profile install nixpkgs#llama-cpp
 ```
+
 For flake enabled installs.

 Or
@@ -34,13 +48,3 @@ nix-env --file '<nixpkgs>' --install --attr llama-cpp
 For non-flake enabled installs.

 This expression is automatically updated within the [nixpkgs repo](https://github.com/NixOS/nixpkgs/blob/nixos-24.05/pkgs/by-name/ll/llama-cpp/package.nix#L164).
-
-## Flox
-
-On Mac and Linux, Flox can be used to install llama.cpp within a Flox environment via
-
-```sh
-flox install llama-cpp
-```
-
-Flox follows the nixpkgs build of llama.cpp.
--- a/docs/multimodal.md
+++ b/docs/multimodal.md
@@ -4,7 +4,9 @@ llama.cpp supports multimodal input via `libmtmd`. Currently, there are 2 tools
 - [llama-mtmd-cli](../tools/mtmd/README.md)
 - [llama-server](../tools/server/README.md) via OpenAI-compatible `/chat/completions` API

-To enable it, can use use one of the 2 methods below:
+Currently, we support **image** and **audio** input. Audio is highly experimental and may have reduced quality.
+
+To enable it, you can use one of the 2 methods below:

 - Use `-hf` option with a supported model (see a list of pre-quantized model below)
    - To load a model using `-hf` while disabling multimodal, use `--no-mmproj`
@@ -31,12 +33,14 @@ llama-server -hf ggml-org/gemma-3-4b-it-GGUF --no-mmproj-offload

 ## 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/ggml-org
+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

 Replaces the `(tool_name)` with the name of binary you want to use. For example, `llama-mtmd-cli` or `llama-server`

 NOTE: some models may require large context window, for example: `-c 8192`

+**Vision models**:
+
 ```sh
 # Gemma 3
 (tool_name) -hf ggml-org/gemma-3-4b-it-GGUF
@@ -74,4 +78,32 @@ NOTE: some models may require large context window, for example: `-c 8192`
 (tool_name) -hf ggml-org/InternVL3-2B-Instruct-GGUF
 (tool_name) -hf ggml-org/InternVL3-8B-Instruct-GGUF
 (tool_name) -hf ggml-org/InternVL3-14B-Instruct-GGUF
+
+# Llama 4 Scout
+(tool_name) -hf ggml-org/Llama-4-Scout-17B-16E-Instruct-GGUF
+
+# Moondream2 20250414 version
+(tool_name) -hf ggml-org/moondream2-20250414-GGUF
+
+```
+
+**Audio models**:
+
+```sh
+# Ultravox 0.5
+(tool_name) -hf ggml-org/ultravox-v0_5-llama-3_2-1b-GGUF
+(tool_name) -hf ggml-org/ultravox-v0_5-llama-3_1-8b-GGUF
+
+# Qwen2-Audio and SeaLLM-Audio
+# note: no pre-quantized GGUF this model, as they have very poor result
+# ref: https://github.com/ggml-org/llama.cpp/pull/13760
+```
+
+**Mixed modalities**:
+
+```sh
+# Qwen2.5 Omni
+# Capabilities: audio input, vision input
+(tool_name) -hf ggml-org/Qwen2.5-Omni-3B-GGUF
+(tool_name) -hf ggml-org/Qwen2.5-Omni-7B-GGUF
 ```
--- a/examples/batched.swift/Sources/main.swift
+++ b/examples/batched.swift/Sources/main.swift
@@ -116,7 +116,7 @@ if llama_decode(context, batch) != 0 {
 }

 for i in 1 ..< n_parallel {
-    llama_kv_self_seq_cp(context, 0, Int32(i), 0, batch.n_tokens)
+    llama_memory_seq_cp(llama_get_memory(context), 0, Int32(i), 0, batch.n_tokens)
 }

 if n_parallel > 1 {
--- a/examples/embedding/embedding.cpp
+++ b/examples/embedding/embedding.cpp
@@ -37,12 +37,12 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
    const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);

    // clear previous kv_cache values (irrelevant for embeddings)
-    llama_kv_self_clear(ctx);
+    llama_memory_clear(llama_get_memory(ctx), true);

    // run model
    LOG_INF("%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
-    if (llama_encode(ctx, batch) < 0) {
-        LOG_ERR("%s : failed to encode\n", __func__);
+    if (llama_decode(ctx, batch) < 0) {
+        LOG_ERR("%s : failed to process\n", __func__);
    }

    for (int i = 0; i < batch.n_tokens; i++) {
@@ -236,9 +236,24 @@ int main(int argc, char ** argv) {
                LOG("\n");
            }
        } else if (pooling_type == LLAMA_POOLING_TYPE_RANK) {
+            const uint32_t n_cls_out = llama_model_n_cls_out(model);
+            std::vector<std::string> cls_out_labels;
+
+            for (uint32_t i = 0; i < n_cls_out; i++) {
+                const char * label = llama_model_cls_label(model, i);
+                const std::string label_i(label == nullptr ? "" : label);
+                cls_out_labels.emplace_back(label_i.empty() ? std::to_string(i) : label_i);
+            }
+
            for (int j = 0; j < n_embd_count; j++) {
-                // NOTE: if you change this log - update the tests in ci/run.sh
-                LOG("rerank score %d: %8.3f\n", j, emb[j * n_embd]);
+                for (uint32_t i = 0; i < n_cls_out; i++) {
+                    // NOTE: if you change this log - update the tests in ci/run.sh
+                    if (n_cls_out == 1) {
+                        LOG("rerank score %d: %8.3f\n", j, emb[j * n_embd]);
+                    } else {
+                        LOG("rerank score %d: %8.3f [%s]\n", j, emb[j * n_embd + i], cls_out_labels[i].c_str());
+                    }
+                }
            }
        } else {
            // print the first part of the embeddings or for a single prompt, the full embedding
--- a/examples/gritlm/gritlm.cpp
+++ b/examples/gritlm/gritlm.cpp
@@ -45,7 +45,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
        }

        // clear previous kv_cache values (irrelevant for embeddings)
-        llama_kv_self_clear(ctx);
+        llama_memory_clear(llama_get_memory(ctx), true);
        llama_set_embeddings(ctx, true);
        llama_set_causal_attn(ctx, false);

@@ -102,7 +102,7 @@ static std::string generate(llama_context * ctx, llama_sampler * smpl, const std

    llama_token eos_token = llama_vocab_eos(vocab);

-    llama_kv_self_clear(ctx);
+    llama_memory_clear(llama_get_memory(ctx), true);
    llama_set_embeddings(ctx, false);
    llama_set_causal_attn(ctx, true);

--- a/examples/llama.android/llama/src/main/cpp/llama-android.cpp
+++ b/examples/llama.android/llama/src/main/cpp/llama-android.cpp
@@ -194,7 +194,7 @@ Java_android_llama_cpp_LLamaAndroid_bench_1model(
        }

        batch->logits[batch->n_tokens - 1] = true;
-        llama_kv_self_clear(context);
+        llama_memory_clear(llama_get_memory(context), false);

        const auto t_pp_start = ggml_time_us();
        if (llama_decode(context, *batch) != 0) {
@@ -206,7 +206,7 @@ Java_android_llama_cpp_LLamaAndroid_bench_1model(

        LOGi("Benchmark text generation (tg)");

-        llama_kv_self_clear(context);
+        llama_memory_clear(llama_get_memory(context), false);
        const auto t_tg_start = ggml_time_us();
        for (i = 0; i < tg; i++) {

@@ -223,7 +223,7 @@ Java_android_llama_cpp_LLamaAndroid_bench_1model(

        const auto t_tg_end = ggml_time_us();

-        llama_kv_self_clear(context);
+        llama_memory_clear(llama_get_memory(context), false);

        const auto t_pp = double(t_pp_end - t_pp_start) / 1000000.0;
        const auto t_tg = double(t_tg_end - t_tg_start) / 1000000.0;
@@ -448,5 +448,5 @@ Java_android_llama_cpp_LLamaAndroid_completion_1loop(
 extern "C"
 JNIEXPORT void JNICALL
 Java_android_llama_cpp_LLamaAndroid_kv_1cache_1clear(JNIEnv *, jobject, jlong context) {
-    llama_kv_self_clear(reinterpret_cast<llama_context *>(context));
+    llama_memory_clear(llama_get_memory(reinterpret_cast<llama_context *>(context)), true);
 }
--- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
+++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
@@ -210,7 +210,7 @@ actor LlamaContext {
            }
            batch.logits[Int(batch.n_tokens) - 1] = 1 // true

-            llama_kv_self_clear(context)
+            llama_memory_clear(llama_get_memory(context), false)

            let t_pp_start = DispatchTime.now().uptimeNanoseconds / 1000;

@@ -223,7 +223,7 @@ actor LlamaContext {

            // bench text generation

-            llama_kv_self_clear(context)
+            llama_memory_clear(llama_get_memory(context), false)

            let t_tg_start = DispatchTime.now().uptimeNanoseconds / 1000;

@@ -242,7 +242,7 @@ actor LlamaContext {

            let t_tg_end = DispatchTime.now().uptimeNanoseconds / 1000;

-            llama_kv_self_clear(context)
+            llama_memory_clear(llama_get_memory(context), false)

            let t_pp = Double(t_pp_end - t_pp_start) / 1000000.0
            let t_tg = Double(t_tg_end - t_tg_start) / 1000000.0
@@ -292,7 +292,7 @@ actor LlamaContext {
    func clear() {
        tokens_list.removeAll()
        temporary_invalid_cchars.removeAll()
-        llama_kv_self_clear(context)
+        llama_memory_clear(llama_get_memory(context), true)
    }

    private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
--- a/examples/lookahead/lookahead.cpp
+++ b/examples/lookahead/lookahead.cpp
@@ -50,8 +50,6 @@ int main(int argc, char ** argv) {
    const int N = 5;  // n-gram size
    const int G = 15; // max verification n-grams

-    const bool dump_kv_cache = params.dump_kv_cache;
-
    // init llama.cpp
    llama_backend_init();
    llama_numa_init(params.numa);
@@ -62,6 +60,8 @@ int main(int argc, char ** argv) {
    llama_model * model = llama_init.model.get();
    llama_context * ctx = llama_init.context.get();

+    auto * mem = llama_get_memory(ctx);
+
    const llama_vocab * vocab = llama_model_get_vocab(model);

    // Tokenize the prompt
@@ -96,7 +96,7 @@ int main(int argc, char ** argv) {
    llama_decode(ctx, llama_batch_get_one(&inp.back(),           1));

    for (int s = 1; s < W + G + 1; ++s) {
-        llama_kv_self_seq_cp(ctx, 0, s, -1, -1);
+        llama_memory_seq_cp(mem, 0, s, -1, -1);
    }

    const auto t_enc_end = ggml_time_us();
@@ -152,9 +152,6 @@ int main(int argc, char ** argv) {
    // here we keep adding new n-grams as we go
    ngram_container ngrams_observed(llama_vocab_n_tokens(vocab), N, G);

-    // debug
-    struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, W + G + 1);
-
    const auto t_dec_start = ggml_time_us();

    // sample first token
@@ -172,12 +169,6 @@ int main(int argc, char ** argv) {
    }

    while (true) {
-        // debug
-        if (dump_kv_cache) {
-            llama_kv_cache_view_update(ctx, &kvc_view);
-            common_kv_cache_dump_view_seqs(kvc_view, 40);
-        }
-
        // build the mask from https://lmsys.org/blog/2023-11-21-lookahead-decoding/
        //
        // Example for W = 5, N = 4, G = 2:
@@ -438,17 +429,17 @@ int main(int argc, char ** argv) {

        // KV cache management
        // if no verification token matched, we simply remove all cells from this batch -> no fragmentation
-        llama_kv_self_seq_rm(ctx, -1, n_past, -1);
+        llama_memory_seq_rm(mem, -1, n_past, -1);

        if (seq_id_best != 0) {
            // if a verification token matched, we keep the best sequence and remove the rest
            // this leads to some KV cache fragmentation
-            llama_kv_self_seq_keep(ctx, seq_id_best);
-            llama_kv_self_seq_cp  (ctx, seq_id_best, 0, -1, -1);
-            llama_kv_self_seq_rm  (ctx, seq_id_best,    -1, -1);
+            llama_memory_seq_keep(mem, seq_id_best);
+            llama_memory_seq_cp  (mem, seq_id_best, 0, -1, -1);
+            llama_memory_seq_rm  (mem, seq_id_best,    -1, -1);

            for (int s = 1; s < W + G + 1; ++s) {
-                llama_kv_self_seq_cp(ctx, 0, s, -1, -1);
+                llama_memory_seq_cp(mem, 0, s, -1, -1);
            }
        }
    }
@@ -473,8 +464,6 @@ int main(int argc, char ** argv) {

    common_sampler_free(smpl);

-    llama_kv_cache_view_free(&kvc_view);
-
    llama_batch_free(batch);

    llama_backend_free();
--- a/examples/lookup/lookup.cpp
+++ b/examples/lookup/lookup.cpp
@@ -24,8 +24,6 @@ int main(int argc, char ** argv){
    // max. number of additional tokens to draft if match is found
    const int n_draft = params.speculative.n_max;

-    const bool dump_kv_cache = params.dump_kv_cache;
-
    // init llama.cpp
    llama_backend_init();
    llama_numa_init(params.numa);
@@ -110,18 +108,9 @@ int main(int argc, char ** argv){

    llama_batch batch_tgt = llama_batch_init(params.n_ctx, 0, 1);

-    // debug
-    struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, 1);
-
    const auto t_dec_start = ggml_time_us();

    while (true) {
-        // debug
-        if (dump_kv_cache) {
-            llama_kv_cache_view_update(ctx, &kvc_view);
-            common_kv_cache_dump_view_seqs(kvc_view, 40);
-        }
-
        // print current draft sequence
        LOG_DBG("drafted %s\n", string_from(ctx, draft).c_str());

@@ -192,7 +181,7 @@ int main(int argc, char ** argv){

        // KV cache management
        // clean the cache of draft tokens that weren't accepted
-        llama_kv_self_seq_rm(ctx, 0, n_past, -1);
+        llama_memory_seq_rm(llama_get_memory(ctx), 0, n_past, -1);

        common_batch_clear(batch_tgt);
        common_batch_add(batch_tgt, draft[0], n_past, { 0 }, true);
--- a/examples/parallel/README.md
+++ b/examples/parallel/README.md
@@ -4,7 +4,7 @@ Simplified simulation of serving incoming requests in parallel

 ## Example

-Generate 128 client requests (`-ns 128`), simulating 8 concurrent clients (`-np 8`). The system prompt is shared (`-pps`), meaning that it is computed once at the start. The client requests consist of 10 junk questions (`-j 10`) followed by the actual question.
+Generate 128 client requests (`-ns 128`), simulating 8 concurrent clients (`-np 8`). The system prompt is shared (`-pps`), meaning that it is computed once at the start. The client requests consist of up to 10 junk questions (`--junk 10`) followed by the actual question.

 ```bash
 llama-parallel -m model.gguf -np 8 -ns 128 --top-k 1 -pps --junk 10 -c 16384
--- a/examples/parallel/parallel.cpp
+++ b/examples/parallel/parallel.cpp
@@ -158,7 +158,7 @@ int main(int argc, char ** argv) {
    common_params params;

    params.n_predict = 128;
-    params.n_junk = 0;
+    params.n_junk = 1;

    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PARALLEL)) {
        return 1;
@@ -178,13 +178,11 @@ int main(int argc, char ** argv) {
    // insert new requests as soon as the previous one is done
    const bool cont_batching = params.cont_batching;

-    const bool dump_kv_cache = params.dump_kv_cache;
-
    // is the system prompt shared in the cache
    const bool is_sp_shared = params.is_pp_shared;

    // extra text to insert in each client's prompt in order to make it larger
-    const int32_t n_junk = params.n_junk;
+    const int32_t n_junk = std::max(1, params.n_junk);

    // init llama.cpp
    llama_backend_init();
@@ -196,6 +194,8 @@ int main(int argc, char ** argv) {
    llama_model * model = llama_init.model.get();
    llama_context * ctx = llama_init.context.get();

+    auto * mem = llama_get_memory(ctx);
+
    const llama_vocab * vocab = llama_model_get_vocab(model);

    // load the prompts from an external file if there are any
@@ -241,8 +241,6 @@ int main(int argc, char ** argv) {
    int32_t n_total_gen    = 0;
    int32_t n_cache_miss   = 0;

-    struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, n_clients);
-
    const auto t_main_start = ggml_time_us();

    LOG_INF("%s: Simulating parallel requests from clients:\n", __func__);
@@ -263,7 +261,7 @@ int main(int argc, char ** argv) {

        // assign the system KV cache to all parallel sequences
        for (int32_t i = 1; i <= n_clients; ++i) {
-            llama_kv_self_seq_cp(ctx, 0, i, -1, -1);
+            llama_memory_seq_cp(mem, 0, i, -1, -1);
        }

        LOG_INF("\n");
@@ -272,11 +270,6 @@ int main(int argc, char ** argv) {
    LOG_INF("Processing requests ...\n\n");

    while (true) {
-        if (dump_kv_cache) {
-            llama_kv_cache_view_update(ctx, &kvc_view);
-            common_kv_cache_dump_view_seqs(kvc_view, 40);
-        }
-
        common_batch_clear(batch);

        // decode any currently ongoing sequences
@@ -295,9 +288,9 @@ int main(int argc, char ** argv) {
        if (batch.n_tokens == 0) {
            // all sequences have ended - clear the entire KV cache
            for (int i = 1; i <= n_clients; ++i) {
-                llama_kv_self_seq_rm(ctx, i, -1, -1);
+                llama_memory_seq_rm(mem, i, -1, -1);
                // but keep the system prompt
-                llama_kv_self_seq_cp(ctx, 0, i, -1, -1);
+                llama_memory_seq_cp(mem, 0, i, -1, -1);
            }

            LOG_INF("%s: clearing the KV cache\n", __func__);
@@ -324,7 +317,10 @@ int main(int argc, char ** argv) {
                    } else {
                        client.prompt += k_system;
                    }
-                    for (int i = 0; i < n_junk; ++i) {
+
+                    const int n_junk_cur = rand() % n_junk;
+
+                    for (int i = 0; i < n_junk_cur; ++i) {
                        const int r = rand() % k_questions.size();
                        client.prompt += "User:\n" + k_questions[r] + "\nAssistant:\n " + k_answers[r] + "\n";
                    }
@@ -349,7 +345,7 @@ int main(int argc, char ** argv) {
                    client.n_decoded = 0;
                    client.i_batch   = batch.n_tokens - 1;

-                    LOG_INF("\033[31mClient %3d, seq %4d, started decoding ...\033[0m\n", client.id, client.seq_id);
+                    LOG_INF("\033[31mClient %3d, seq %4d, junk = %4d, started decoding ...\033[0m\n", client.id, client.seq_id, n_junk_cur);

                    g_seq_id += 1;

@@ -368,7 +364,9 @@ int main(int argc, char ** argv) {
        // process in chunks of params.n_batch
        int32_t n_batch = params.n_batch;

-        for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
+        int32_t i_next = 0;
+
+        for (int32_t i = 0; i < batch.n_tokens; i = i_next) {
            // experiment: process in powers of 2
            //if (i + n_batch > (int32_t) batch.n_tokens && n_batch > 32) {
            //    n_batch /= 2;
@@ -376,7 +374,7 @@ int main(int argc, char ** argv) {
            //    continue;
            //}

-            const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
+            const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i);

            llama_batch batch_view = {
                n_tokens,
@@ -396,19 +394,24 @@ int main(int argc, char ** argv) {
                    return 1;
                }

-                LOG_ERR("%s : failed to decode the batch, retrying with n_batch = %d\n", __func__, n_batch / 2);
+                LOG_WRN("%s : failed to decode the batch, retrying with n_batch = %d\n", __func__, n_batch / 2);

                n_cache_miss += 1;

                // retry with half the batch size to try to find a free slot in the KV cache
                n_batch /= 2;
-                i -= n_batch;

                continue;
            }

            LOG_DBG("%s : decoded batch of %d tokens\n", __func__, n_tokens);

+            // move the head of the batch forward with the number of tokens we just processed
+            i_next = i + n_tokens;
+
+            // on successful decode, restore the original batch size
+            n_batch = params.n_batch;
+
            for (auto & client : clients) {
                if (client.i_batch < (int) i || client.i_batch >= (int) (i + n_tokens)) {
                    continue;
@@ -446,8 +449,8 @@ int main(int argc, char ** argv) {
                    }

                    // delete only the generated part of the sequence, i.e. keep the system prompt in the cache
-                    llama_kv_self_seq_rm(ctx,    client.id + 1, -1, -1);
-                    llama_kv_self_seq_cp(ctx, 0, client.id + 1, -1, -1);
+                    llama_memory_seq_rm(mem,    client.id + 1, -1, -1);
+                    llama_memory_seq_cp(mem, 0, client.id + 1, -1, -1);

                    const auto t_main_end = ggml_time_us();

--- a/examples/passkey/passkey.cpp
+++ b/examples/passkey/passkey.cpp
@@ -126,6 +126,8 @@ int main(int argc, char ** argv) {

    int n_past = 0;

+    auto * mem = llama_get_memory(ctx);
+
    // fill the KV cache
    for (int i = 0; i < n_ctx; i += n_batch) {
        if (i > 0 && n_grp > 1) {
@@ -133,11 +135,10 @@ int main(int argc, char ** argv) {
            const int ib = i/n_batch - 1;
            const int bd = n_batch_grp*(n_grp - 1);

-            llama_kv_self_seq_add (ctx, 0, n_past - n_batch,         n_past,         ib*bd);
-            llama_kv_self_seq_div (ctx, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp);
-            llama_kv_self_update  (ctx);
+            llama_memory_seq_add(mem, 0, n_past - n_batch,         n_past,         ib*bd);
+            llama_memory_seq_div(mem, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp);

-            n_past = llama_kv_self_seq_pos_max(ctx, 0) + 1;
+            n_past = llama_memory_seq_pos_max(mem, 0) + 1;
        }

        common_batch_clear(batch);
@@ -167,12 +168,10 @@ int main(int argc, char ** argv) {

        LOG_INF("%s: shifting KV cache with %d\n", __func__, n_discard);

-        llama_kv_self_seq_rm (ctx, 0, n_keep            , n_keep + n_discard);
-        llama_kv_self_seq_add(ctx, 0, n_keep + n_discard, n_ctx,  -n_discard);
-      //llama_kv_self_defrag (ctx);
-        llama_kv_self_update (ctx);
+        llama_memory_seq_rm (mem, 0, n_keep            , n_keep + n_discard);
+        llama_memory_seq_add(mem, 0, n_keep + n_discard, n_ctx,  -n_discard);

-        n_past = llama_kv_self_seq_pos_max(ctx, 0) + 1;
+        n_past = llama_memory_seq_pos_max(mem, 0) + 1;

        common_batch_clear(batch);

@@ -198,12 +197,10 @@ int main(int argc, char ** argv) {
        if (n_discard > 0) {
            LOG_INF("%s: shifting KV cache with %d to free space for the answer\n", __func__, n_discard);

-            llama_kv_self_seq_rm (ctx, 0, n_keep            , n_keep + n_discard);
-            llama_kv_self_seq_add(ctx, 0, n_keep + n_discard, n_ctx,  -n_discard);
-          //llama_kv_self_defrag (ctx);
-            llama_kv_self_update (ctx);
+            llama_memory_seq_rm (mem, 0, n_keep            , n_keep + n_discard);
+            llama_memory_seq_add(mem, 0, n_keep + n_discard, n_ctx,  -n_discard);

-            n_past = llama_kv_self_seq_pos_max(ctx, 0) + 1;
+            n_past = llama_memory_seq_pos_max(mem, 0) + 1;
        }
    }

--- a/examples/retrieval/retrieval.cpp
+++ b/examples/retrieval/retrieval.cpp
@@ -81,14 +81,14 @@ static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & toke
    }
 }

-static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) {
+static void batch_process(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) {
    // clear previous kv_cache values (irrelevant for embeddings)
-    llama_kv_self_clear(ctx);
+    llama_memory_clear(llama_get_memory(ctx), false);

    // run model
    LOG_INF("%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
    if (llama_decode(ctx, batch) < 0) {
-        LOG_ERR("%s : failed to decode\n", __func__);
+        LOG_ERR("%s : failed to process\n", __func__);
    }

    for (int i = 0; i < batch.n_tokens; i++) {
@@ -233,7 +233,7 @@ int main(int argc, char ** argv) {
        // encode if at capacity
        if (batch.n_tokens + n_toks > n_batch) {
            float * out = emb + p * n_embd;
-            batch_decode(ctx, batch, out, s, n_embd);
+            batch_process(ctx, batch, out, s, n_embd);
            common_batch_clear(batch);
            p += s;
            s = 0;
@@ -246,7 +246,7 @@ int main(int argc, char ** argv) {

    // final batch
    float * out = emb + p * n_embd;
-    batch_decode(ctx, batch, out, s, n_embd);
+    batch_process(ctx, batch, out, s, n_embd);

    // save embeddings to chunks
    for (int i = 0; i < n_chunks; i++) {
@@ -267,7 +267,7 @@ int main(int argc, char ** argv) {
        batch_add_seq(query_batch, query_tokens, 0);

        std::vector<float> query_emb(n_embd, 0);
-        batch_decode(ctx, query_batch, query_emb.data(), 1, n_embd);
+        batch_process(ctx, query_batch, query_emb.data(), 1, n_embd);

        common_batch_clear(query_batch);

--- a/examples/save-load-state/save-load-state.cpp
+++ b/examples/save-load-state/save-load-state.cpp
@@ -196,7 +196,7 @@ int main(int argc, char ** argv) {
        fprintf(stderr, "%s : seq 0 copied, %zd bytes\n", __func__, ncopy);

        // erase whole kv
-        llama_kv_self_clear(ctx3);
+        llama_memory_clear(llama_get_memory(ctx3), true);
        fprintf(stderr, "%s : kv cache cleared\n", __func__);

        // restore kv into seq 1
--- a/examples/simple-chat/simple-chat.cpp
+++ b/examples/simple-chat/simple-chat.cpp
@@ -98,7 +98,7 @@ int main(int argc, char ** argv) {
    auto generate = [&](const std::string & prompt) {
        std::string response;

-        const bool is_first = llama_kv_self_used_cells(ctx) == 0;
+        const bool is_first = llama_memory_seq_pos_max(llama_get_memory(ctx), 0) == 0;

        // tokenize the prompt
        const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, is_first, true);
@@ -113,7 +113,7 @@ int main(int argc, char ** argv) {
        while (true) {
            // check if we have enough space in the context to evaluate this batch
            int n_ctx = llama_n_ctx(ctx);
-            int n_ctx_used = llama_kv_self_used_cells(ctx);
+            int n_ctx_used = llama_memory_seq_pos_max(llama_get_memory(ctx), 0);
            if (n_ctx_used + batch.n_tokens > n_ctx) {
                printf("\033[0m\n");
                fprintf(stderr, "context size exceeded\n");
--- a/examples/simple/simple.cpp
+++ b/examples/simple/simple.cpp
@@ -84,13 +84,13 @@ int main(int argc, char ** argv) {
    model_params.n_gpu_layers = ngl;

    llama_model * model = llama_model_load_from_file(model_path.c_str(), model_params);
-    const llama_vocab * vocab = llama_model_get_vocab(model);

    if (model == NULL) {
        fprintf(stderr , "%s: error: unable to load model\n" , __func__);
        return 1;
    }

+    const llama_vocab * vocab = llama_model_get_vocab(model);
    // tokenize the prompt

    // find the number of tokens in the prompt
--- a/examples/speculative-simple/speculative-simple.cpp
+++ b/examples/speculative-simple/speculative-simple.cpp
@@ -217,7 +217,7 @@ int main(int argc, char ** argv) {
        {
            LOG_DBG("clear kv cache from any extra tokens, n_past = %d\n", n_past);

-            llama_kv_self_seq_rm(ctx_tgt, 0, n_past, -1);
+            llama_memory_seq_rm(llama_get_memory(ctx_tgt), 0, n_past, -1);
        }

        if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
--- a/examples/speculative/speculative.cpp
+++ b/examples/speculative/speculative.cpp
@@ -142,6 +142,8 @@ int main(int argc, char ** argv) {
        }
    }

+    auto * mem_tgt = llama_get_memory(ctx_tgt);
+    auto * mem_dft = llama_get_memory(ctx_dft);

    // Tokenize the prompt
    std::vector<llama_token> inp;
@@ -420,14 +422,14 @@ int main(int argc, char ** argv) {
            {
                LOG_DBG("keeping sequence %d, n_past_tgt = %d, n_past_dft = %d\n", s_keep, n_past_tgt, n_past_dft);

-                llama_kv_self_seq_keep(ctx_dft, s_keep);
-                llama_kv_self_seq_cp  (ctx_dft, s_keep, 0, -1, -1);
-                llama_kv_self_seq_keep(ctx_dft, 0);
+                llama_memory_seq_keep(mem_dft, s_keep);
+                llama_memory_seq_cp  (mem_dft, s_keep, 0, -1, -1);
+                llama_memory_seq_keep(mem_dft, 0);

-                llama_kv_self_seq_rm  (ctx_tgt, s_keep, n_past_tgt, -1);
-                llama_kv_self_seq_keep(ctx_tgt, s_keep);
-                llama_kv_self_seq_cp  (ctx_tgt, s_keep, 0, -1, -1);
-                llama_kv_self_seq_keep(ctx_tgt, 0);
+                llama_memory_seq_rm  (mem_tgt, s_keep, n_past_tgt, -1);
+                llama_memory_seq_keep(mem_tgt, s_keep);
+                llama_memory_seq_cp  (mem_tgt, s_keep, 0, -1, -1);
+                llama_memory_seq_keep(mem_tgt, 0);
            }

            for (int s = 0; s < n_seq_dft; ++s) {
@@ -444,7 +446,7 @@ int main(int argc, char ** argv) {
            common_batch_clear(batch_dft);
            common_batch_add  (batch_dft, token_id, n_past_dft, { 0 }, true);

-            llama_kv_self_seq_rm(ctx_dft, 0, n_past_dft, -1);
+            llama_memory_seq_rm(mem_dft, 0, n_past_dft, -1);
            // LOG_DBG("dft batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_dft, batch_dft).c_str());
            llama_decode(ctx_dft, batch_dft);

@@ -503,8 +505,8 @@ int main(int argc, char ** argv) {
                    if (n_seq_cur < n_seq_dft && cur_p->data[f].p > p_draft_split) {
                        LOG_DBG("splitting seq %3d into %3d\n", s, n_seq_cur);

-                        llama_kv_self_seq_rm(ctx_dft,    n_seq_cur, -1, -1);
-                        llama_kv_self_seq_cp(ctx_dft, s, n_seq_cur, -1, -1);
+                        llama_memory_seq_rm(mem_dft,    n_seq_cur, -1, -1);
+                        llama_memory_seq_cp(mem_dft, s, n_seq_cur, -1, -1);

                        // all previous tokens from this branch are now also part of the new branch
                        for (int t = 0; t < batch_tgt.n_tokens; ++t) {
@@ -585,9 +587,9 @@ int main(int argc, char ** argv) {

        // evaluate the target model on the drafted tokens
        {
-            llama_kv_self_seq_keep(ctx_tgt, 0);
+            llama_memory_seq_keep(mem_tgt, 0);
            for (int s = 1; s < n_seq_dft; ++s) {
-                llama_kv_self_seq_cp(ctx_tgt, 0, s, -1, -1);
+                llama_memory_seq_cp(mem_tgt, 0, s, -1, -1);
            }

            // LOG_DBG("target batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_tgt, batch_tgt).c_str());
--- a/examples/sycl/run-llama2.sh
+++ b/examples/sycl/run-llama2.sh
@@ -12,16 +12,16 @@ source /opt/intel/oneapi/setvars.sh

 INPUT_PROMPT="Building a website can be done in 10 simple steps:\nStep 1:"
 MODEL_FILE=models/llama-2-7b.Q4_0.gguf
-NGL=33
-CONEXT=4096
+NGL=99
+CONTEXT=4096

 if [ $# -gt 0 ]; then
    GGML_SYCL_DEVICE=$1
    echo "use $GGML_SYCL_DEVICE as main GPU"
    #use signle GPU only
-    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m ${MODEL_FILE} -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONEXT} -mg $GGML_SYCL_DEVICE -sm none
+    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m ${MODEL_FILE} -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} -mg $GGML_SYCL_DEVICE -sm none

 else
    #use multiple GPUs with same max compute units
-    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m ${MODEL_FILE} -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONEXT}
+    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m ${MODEL_FILE} -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT}
 fi
--- a/examples/sycl/run-llama3.sh
+++ b/examples/sycl/run-llama3.sh
@@ -0,0 +1,28 @@
+#!/bin/bash
+
+#  MIT license
+#  Copyright (C) 2025 Intel Corporation
+#  SPDX-License-Identifier: MIT
+
+# If you want more control, DPC++ Allows selecting a specific device through the
+# following environment variable
+#export ONEAPI_DEVICE_SELECTOR="level_zero:0"
+source /opt/intel/oneapi/setvars.sh
+
+#export GGML_SYCL_DEBUG=1
+
+#ZES_ENABLE_SYSMAN=1, Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory. Recommended to use when --split-mode = layer.
+
+INPUT_PROMPT="Building a website can be done in 10 simple steps:\nStep 1:"
+MODEL_FILE=models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf
+NGL=99 # Layers offloaded to the GPU. If the device runs out of memory, reduce this value according to the model you are using.
+CONTEXT=4096
+
+if [ $# -gt 0 ]; then
+    GGML_SYCL_DEVICE=$1
+    echo "Using $GGML_SYCL_DEVICE as the main GPU"
+    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m ${MODEL_FILE} -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -c ${CONTEXT} -mg $GGML_SYCL_DEVICE -sm none
+else
+    #use multiple GPUs with same max compute units
+    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m ${MODEL_FILE} -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -c ${CONTEXT}
+fi
--- a/examples/sycl/win-run-llama2.bat
+++ b/examples/sycl/win-run-llama2.bat
@@ -6,4 +6,4 @@ set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force


-.\build\bin\llama-cli.exe -m models\llama-2-7b.Q4_0.gguf -p %INPUT2% -n 400 -e -ngl 33 -s 0
+.\build\bin\llama-cli.exe -m models\llama-2-7b.Q4_0.gguf -p %INPUT2% -n 400 -e -ngl 99 -s 0
--- a/examples/sycl/win-run-llama3.bat
+++ b/examples/sycl/win-run-llama3.bat
@@ -0,0 +1,9 @@
+::  MIT license
+::  Copyright (C) 2024 Intel Corporation
+::  SPDX-License-Identifier: MIT
+
+set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
+@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
+
+
+.\build\bin\llama-cli.exe -m models\Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf -p %INPUT2% -n 400 -e -ngl 99
--- a/examples/training/README.md
+++ b/examples/training/README.md
@@ -10,8 +10,8 @@ Proof of concept:

 ``` sh
 export model_name=llama_3.2-1b && export quantization=f32
-./build/bin/finetune --file wikitext-2-raw/wiki.test.raw -ngl 999 --model models/${model_name}-${quantization}.gguf -c 512 -b 512 -ub 512
-./build/bin/perplexity --file wikitext-2-raw/wiki.test.raw -ngl 999 --model finetuned-model.gguf
+./build/bin/llama-finetune --file wikitext-2-raw/wiki.test.raw -ngl 999 --model models/${model_name}-${quantization}.gguf -c 512 -b 512 -ub 512
+./build/bin/llama-perplexity --file wikitext-2-raw/wiki.test.raw -ngl 999 --model finetuned-model.gguf
 ```

 The perplexity value of the finetuned model should be lower after training on the test set for 2 epochs.
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -129,6 +129,7 @@ option(GGML_LASX             "ggml: enable lasx"             ON)
 option(GGML_LSX              "ggml: enable lsx"              ON)
 option(GGML_RVV              "ggml: enable rvv"              ON)
 option(GGML_RV_ZFH           "ggml: enable riscv zfh"        OFF)
+option(GGML_XTHEADVECTOR     "ggml: enable xtheadvector"     OFF)
 option(GGML_VXE              "ggml: enable vxe"              ON)

 option(GGML_CPU_ALL_VARIANTS "ggml: build all variants of the CPU backend (requires GGML_BACKEND_DL)" OFF)
@@ -136,7 +137,7 @@ set(GGML_CPU_ARM_ARCH        "" CACHE STRING "ggml: CPU architecture for ARM")
 set(GGML_CPU_POWERPC_CPUTYPE "" CACHE STRING "ggml: CPU type for PowerPC")


-if (WIN32)
+if (MINGW)
    set(GGML_WIN_VER "0x602" CACHE STRING   "ggml: Windows version")
 endif()

@@ -176,7 +177,6 @@ option(GGML_VULKAN_CHECK_RESULTS            "ggml: run Vulkan op checks"
 option(GGML_VULKAN_DEBUG                    "ggml: enable Vulkan debug output"                OFF)
 option(GGML_VULKAN_MEMORY_DEBUG             "ggml: enable Vulkan memory debug output"         OFF)
 option(GGML_VULKAN_SHADER_DEBUG_INFO        "ggml: enable Vulkan shader debug info"           OFF)
-option(GGML_VULKAN_PERF                     "ggml: enable Vulkan perf output"                 OFF)
 option(GGML_VULKAN_VALIDATE                 "ggml: enable Vulkan validation"                  OFF)
 option(GGML_VULKAN_RUN_TESTS                "ggml: run Vulkan tests"                          OFF)
 option(GGML_KOMPUTE                         "ggml: use Kompute"                               OFF)
--- a/ggml/cmake/common.cmake
+++ b/ggml/cmake/common.cmake
@@ -24,3 +24,28 @@ function(ggml_get_flags CCID CCVER)
    set(GF_C_FLAGS   ${C_FLAGS}   PARENT_SCOPE)
    set(GF_CXX_FLAGS ${CXX_FLAGS} PARENT_SCOPE)
 endfunction()
+
+function(ggml_get_system_arch)
+    if (CMAKE_OSX_ARCHITECTURES      STREQUAL "arm64" OR
+        CMAKE_GENERATOR_PLATFORM_LWR STREQUAL "arm64" OR
+        (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
+            CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm.*|ARM64)$"))
+        set(GGML_SYSTEM_ARCH "ARM" PARENT_SCOPE)
+    elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR
+            CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR
+            (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
+            CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64|amd64)$"))
+        set(GGML_SYSTEM_ARCH "x86" PARENT_SCOPE)
+    elseif ("${CMAKE_SYSTEM_PROCESSOR} " STREQUAL "ppc64le " OR
+            "${CMAKE_SYSTEM_PROCESSOR} " STREQUAL "powerpc ")
+        set(GGML_SYSTEM_ARCH "PowerPC" PARENT_SCOPE)
+    elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64")
+        set(GGML_SYSTEM_ARCH "loongarch64"  PARENT_SCOPE)
+    elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "riscv64")
+        set(GGML_SYSTEM_ARCH "riscv64" PARENT_SCOPE)
+    elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "s390x")
+        set(GGML_SYSTEM_ARCH "s390x" PARENT_SCOPE)
+    else()
+        set(GGML_SYSTEM_ARCH "UNKNOWN" PARENT_SCOPE)
+    endif()
+endfunction()
--- a/ggml/include/ggml-opt.h
+++ b/ggml/include/ggml-opt.h
@@ -128,6 +128,8 @@ extern "C" {
    // set gradients to zero, initilize loss, and optionally reset the optimizer
    GGML_API void ggml_opt_reset(ggml_opt_context_t opt_ctx, bool optimizer);

+    GGML_API bool ggml_opt_static_graphs(ggml_opt_context_t opt_ctx); // whether the graphs are allocated_statically
+
    // get underlying tensors that store data
    // if not using static graphs these pointers become invalid with the next call to ggml_opt_alloc
    GGML_API struct ggml_tensor * ggml_opt_inputs(  ggml_opt_context_t opt_ctx); // forward graph input tensor
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -536,6 +536,7 @@ extern "C" {
        GGML_UNARY_OP_HARDSWISH,
        GGML_UNARY_OP_HARDSIGMOID,
        GGML_UNARY_OP_EXP,
+        GGML_UNARY_OP_GELU_ERF,

        GGML_UNARY_OP_COUNT,
    };
@@ -934,6 +935,15 @@ extern "C" {
            struct ggml_tensor  * a,
            struct ggml_tensor  * b);

+    // repeat a to the specified shape
+    GGML_API struct ggml_tensor * ggml_repeat_4d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+                       int64_t    ne0,
+                       int64_t    ne1,
+                       int64_t    ne2,
+                       int64_t    ne3);
+
    // sums repetitions in a into shape of b
    GGML_API struct ggml_tensor * ggml_repeat_back(
            struct ggml_context * ctx,
@@ -1024,6 +1034,16 @@ extern "C" {
            struct ggml_context * ctx,
            struct ggml_tensor  * a);

+    // GELU using erf (error function) when possible
+    // some backends may fallback to approximation based on Abramowitz and Stegun formula
+    GGML_API struct ggml_tensor * ggml_gelu_erf(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_gelu_erf_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
    GGML_API struct ggml_tensor * ggml_gelu_quick(
            struct ggml_context * ctx,
            struct ggml_tensor  * a);
@@ -2075,9 +2095,6 @@ extern "C" {
    GGML_API struct ggml_tensor * ggml_graph_get_grad    (const struct ggml_cgraph * cgraph, const struct ggml_tensor * node);
    GGML_API struct ggml_tensor * ggml_graph_get_grad_acc(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node);

-    GGML_API void                 ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname);
-    GGML_API struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval);
-
    // print info and performance information for the graph
    GGML_API void ggml_graph_print(const struct ggml_cgraph * cgraph);

@@ -2161,6 +2178,7 @@ extern "C" {

    // scheduling priorities
    enum ggml_sched_priority {
+        GGML_SCHED_PRIO_LOW = -1,
        GGML_SCHED_PRIO_NORMAL,
        GGML_SCHED_PRIO_MEDIUM,
        GGML_SCHED_PRIO_HIGH,
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -109,6 +109,8 @@ if (MSVC)
 else ()
    set(CMAKE_GENERATOR_PLATFORM_LWR "")
 endif ()
+ggml_get_system_arch()
+message(STATUS "GGML_SYSTEM_ARCH: ${GGML_SYSTEM_ARCH}")

 if (NOT MSVC)
    if (GGML_STATIC)
@@ -123,7 +125,6 @@ if (NOT MSVC)
 endif()

 if (MINGW)
-    # Target Windows 8 for PrefetchVirtualMemory
    add_compile_definitions(_WIN32_WINNT=${GGML_WIN_VER})
 endif()

@@ -194,6 +195,7 @@ add_library(ggml-base
            ../include/ggml-opt.h
            ../include/gguf.h
            ggml.c
+            ggml.cpp
            ggml-alloc.c
            ggml-backend.cpp
            ggml-opt.cpp
@@ -224,6 +226,7 @@ function(ggml_add_backend_library backend)
        set_target_properties(${backend} PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_RUNTIME_OUTPUT_DIRECTORY})
        target_compile_definitions(${backend} PRIVATE GGML_BACKEND_DL)
        add_dependencies(ggml ${backend})
+        install(TARGETS ${backend} LIBRARY DESTINATION ${CMAKE_INSTALL_BINDIR})
    else()
        add_library(${backend} ${ARGN})
        target_link_libraries(ggml PUBLIC ${backend})
@@ -287,16 +290,20 @@ if (GGML_CPU_ALL_VARIANTS)
    if (NOT GGML_BACKEND_DL)
        message(FATAL_ERROR "GGML_CPU_ALL_VARIANTS requires GGML_BACKEND_DL")
    endif()
-    ggml_add_cpu_backend_variant(x64)
-    ggml_add_cpu_backend_variant(sse42        SSE42)
-    ggml_add_cpu_backend_variant(sandybridge  SSE42 AVX)
-    ggml_add_cpu_backend_variant(haswell      SSE42 AVX F16C AVX2 BMI2 FMA)
-    ggml_add_cpu_backend_variant(skylakex     SSE42 AVX F16C AVX2 BMI2 FMA AVX512)
-    ggml_add_cpu_backend_variant(icelake      SSE42 AVX F16C AVX2 BMI2 FMA AVX512 AVX512_VBMI AVX512_VNNI)
-    ggml_add_cpu_backend_variant(alderlake    SSE42 AVX F16C AVX2 BMI2 FMA AVX_VNNI)
-    if (NOT MSVC)
-        # MSVC doesn't support AMX
-        ggml_add_cpu_backend_variant(sapphirerapids SSE42 AVX F16C AVX2 BMI2 FMA AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16 AMX_TILE AMX_INT8)
+    if (GGML_SYSTEM_ARCH STREQUAL "x86")
+        ggml_add_cpu_backend_variant(x64)
+        ggml_add_cpu_backend_variant(sse42        SSE42)
+        ggml_add_cpu_backend_variant(sandybridge  SSE42 AVX)
+        ggml_add_cpu_backend_variant(haswell      SSE42 AVX F16C AVX2 BMI2 FMA)
+        ggml_add_cpu_backend_variant(skylakex     SSE42 AVX F16C AVX2 BMI2 FMA AVX512)
+        ggml_add_cpu_backend_variant(icelake      SSE42 AVX F16C AVX2 BMI2 FMA AVX512 AVX512_VBMI AVX512_VNNI)
+        ggml_add_cpu_backend_variant(alderlake    SSE42 AVX F16C AVX2 BMI2 FMA AVX_VNNI)
+        if (NOT MSVC)
+            # MSVC doesn't support AMX
+            ggml_add_cpu_backend_variant(sapphirerapids SSE42 AVX F16C AVX2 BMI2 FMA AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16 AMX_TILE AMX_INT8)
+        endif()
+    else()
+        message(FATAL_ERROR "GGML_CPU_ALL_VARIANTS not yet supported on ${GGML_SYSTEM_ARCH}")
    endif()
 elseif (GGML_CPU)
    ggml_add_cpu_backend_variant_impl("")
--- a/ggml/src/ggml-backend.cpp
+++ b/ggml/src/ggml-backend.cpp
@@ -1340,7 +1340,10 @@ static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
    // allocate graph
    if (backend_ids_changed || !ggml_gallocr_alloc_graph(sched->galloc, &sched->graph)) {
        // the re-allocation may cause the split inputs to be moved to a different address
-        ggml_backend_sched_synchronize(sched);
+        // synchronize without ggml_backend_sched_synchronize to avoid changing cur_copy
+        for (int i = 0; i < sched->n_backends; i++) {
+            ggml_backend_synchronize(sched->backends[i]);
+        }
 #ifndef NDEBUG
        GGML_LOG_DEBUG("%s: failed to allocate graph, reserving (backend_ids_changed = %d)\n", __func__, backend_ids_changed);
 #endif
@@ -1564,7 +1567,6 @@ bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgra

    ggml_backend_sched_split_graph(sched, graph);

-
    if (!ggml_backend_sched_alloc_splits(sched)) {
        return false;
    }
@@ -1598,6 +1600,12 @@ void ggml_backend_sched_synchronize(ggml_backend_sched_t sched) {
    for (int i = 0; i < sched->n_backends; i++) {
        ggml_backend_synchronize(sched->backends[i]);
    }
+    if (!sched->is_alloc) {
+        // if the graph is not already allocated, always use copy 0 after a synchronization
+        // this ensures that during generation the same copy is used every time,
+        // which avoids changes in the graph that could cause CUDA or other graphs to be disabled
+        sched->cur_copy = 0;
+    }
 }

 void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) {
--- a/ggml/src/ggml-blas/CMakeLists.txt
+++ b/ggml/src/ggml-blas/CMakeLists.txt
@@ -81,7 +81,7 @@ if (BLAS_FOUND)
    target_link_libraries     (ggml-blas PRIVATE ${BLAS_LIBRARIES})
    target_include_directories(ggml-blas PRIVATE ${BLAS_INCLUDE_DIRS})
 else()
-    message(ERROR "BLAS not found, please refer to "
-                  "https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors"
-                  " to set correct GGML_BLAS_VENDOR")
+    message(FATAL_ERROR "BLAS not found, please refer to "
+                        "https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors"
+                        " to set correct GGML_BLAS_VENDOR")
 endif()
--- a/ggml/src/ggml-cann/CMakeLists.txt
+++ b/ggml/src/ggml-cann/CMakeLists.txt
@@ -30,6 +30,7 @@ string(TOLOWER ${SOC_TYPE} SOC_VERSION) # SOC_VERSION need lower
 string(REGEX MATCH "[0-9]+[a-zA-Z]" SOC_TYPE_MAJOR_SN "${SOC_VERSION}")
 set(SOC_TYPE_COMPILE_OPTION "ASCEND_${SOC_TYPE_MAJOR_SN}")
 string(TOUPPER ${SOC_TYPE_COMPILE_OPTION} SOC_TYPE_COMPILE_OPTION)
+message(STATUS "CANN: SOC_VERSION =  ${SOC_VERSION}")

 if (CANN_INSTALL_DIR)
    # Only Support Linux.
--- a/ggml/src/ggml-cann/Doxyfile
+++ b/ggml/src/ggml-cann/Doxyfile
--- a/ggml/src/ggml-cann/acl_tensor.cpp
+++ b/ggml/src/ggml-cann/acl_tensor.cpp
@@ -31,6 +31,8 @@ aclDataType ggml_cann_type_mapping(ggml_type type) {
            return ACL_FLOAT;
        case GGML_TYPE_F16:
            return ACL_FLOAT16;
+        case GGML_TYPE_BF16:
+            return ACL_BF16;
        case GGML_TYPE_I8:
            return ACL_INT8;
        case GGML_TYPE_I16:
--- a/ggml/src/ggml-cann/acl_tensor.h
+++ b/ggml/src/ggml-cann/acl_tensor.h
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -65,6 +65,8 @@
 #include <aclnnop/aclnn_eq_tensor.h>
 #include <aclnnop/aclnn_gt_scalar.h>
 #include <aclnnop/aclnn_pow.h>
+#include <aclnnop/aclnn_grouped_matmul_v2.h>
+#include <aclnnop/aclnn_fused_infer_attention_score_v2.h>
 #include <float.h>

 #include <cmath>
@@ -73,11 +75,13 @@
 #include <vector>

 #include "ggml-impl.h"
+#include "ggml.h"

 #define GGML_COMMON_DECL_C

 #include "../ggml-common.h"

+
 void bcast_shape(ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst, aclTensor ** acl_src0,
                 aclTensor ** acl_src1, aclTensor ** acl_dst) {
    GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_can_repeat(src1, src0));
@@ -2587,3 +2591,603 @@ void ggml_cann_step(ggml_backend_cann_context& ctx, ggml_tensor* dst){

    ggml_cann_release_resources(ctx, acl_src, acl_dst, alpha);
 }
+
+/**
+ * @brief Performs expert-specific matrix multiplication (MoE) with
+ * floating-point precision using the CANN backend.
+ *
+ * This function executes a matrix multiplication operation tailored for
+ * Mixture of Experts (MoE) models, where the input tensor is multiplied
+ * with expert-specific weight matrices. It uses the CANN backend for
+ * efficient computation and stores the result in the destination tensor `dst`.
+ * The operation may leverage identity-based optimizations or routing masks
+ * as part of sparse expert selection.
+ *
+ * @param ctx The context for executing CANN backend operations.
+ * @param dst The destination tensor where the MoE multiplication result
+ * will be stored.
+ *
+ * @note This function assumes floating-point data types and is designed for
+ * MoE architectures, possibly involving sparse expert routing.
+ */
+static void ggml_cann_mul_mat_id_fp(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    //dst   [M, K, N, 1]
+    ggml_tensor * src0 = dst->src[0];  //src0	[D, M, A, 1]
+    ggml_tensor * src1 = dst->src[1];  //src1	[D, B, N, 1], B = K or B = 1
+    ggml_tensor * ids  = dst->src[2];  //ids	[K, N]
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    // copy index from npu to cpu
+    int64_t n_as = ne02; // A
+    int64_t n_ids = ids->ne[0]; // K
+
+    std::vector<char> ids_host(ggml_nbytes(ids));
+    ggml_cann_async_memcpy(ctx, ids_host.data(), ids->data, ggml_nbytes(ids),
+        ACL_MEMCPY_DEVICE_TO_HOST);
+    ACL_CHECK(aclrtSynchronizeStream(ctx.stream()));
+
+    char * src0_original = (char *) src0->data;
+    char * src1_original = (char *) src1->data;
+    char * dst_original  = (char *)  dst->data;
+    size_t ori_src0_nb[4] = {nb00, nb01, nb02, nb03};
+
+    // src0 is F16, src1 is F32, dst is F32
+    ggml_cann_pool_alloc src0_cast_allocator;
+    if (src0->type == GGML_TYPE_F16) {
+        src0_cast_allocator.alloc(ctx.pool(), sizeof(float) * ggml_nelements(src0));
+        void* src0_cast_buf = src0_cast_allocator.get();
+
+        size_t cast_nb[GGML_MAX_DIMS];
+        cast_nb[0] = sizeof(float_t);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            cast_nb[i] = cast_nb[i - 1] * src0->ne[i - 1];
+        }
+
+        aclTensor* acl_src0_f16 = ggml_cann_create_tensor(src0);
+        aclTensor* acl_cast = ggml_cann_create_tensor(src0_cast_buf,
+            ACL_FLOAT, sizeof(float), src0->ne, cast_nb, 4);
+        GGML_CANN_CALL_ACLNN_OP(ctx, Cast, acl_src0_f16, ACL_FLOAT, acl_cast);
+        ggml_cann_release_resources(ctx, acl_cast, acl_src0_f16);
+
+        src0_original = (char *) src0_cast_buf;
+        memcpy(ori_src0_nb, cast_nb, sizeof(ori_src0_nb));
+    }
+
+    std::vector<aclTensor*> src0_tensor_vec;
+    std::vector<aclTensor*> src1_tensor_vec;
+    std::vector<aclTensor*> dst_tensor_vec;
+    for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) {
+        for (int64_t id = 0; id < n_ids; id++) {
+            // src0_row [M, D] -> weight && permute
+            int64_t src0_ne[2] = {ne01, ne00};
+            size_t src0_nb[2] = {ori_src0_nb[1], ori_src0_nb[0]};
+            // src1_row [D, 1] -> input
+            int64_t src1_ne[2] = {ne10, 1};
+            size_t src1_nb[2] = {nb10, nb11};
+            // dst_row [M, 1] -> out
+            int64_t dst_ne[2] = {ne0, 1};
+            size_t dst_nb[2] = {nb0, nb1};
+
+            // expert index
+            int32_t i02 = *(int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]);
+            GGML_ASSERT(i02 >= 0 && i02 < n_as);
+
+            // If B = 1 (broadcast), always use 0; otherwise, use id.
+            int64_t i11 = (ne11 == 1 ? 0 : id);
+            int64_t i12 = iid1;
+
+            int64_t i1 = id;
+            int64_t i2 = i12;
+
+            void* src0_tmp_ptr = src0_original + i02*ori_src0_nb[2];
+            void* src1_tmp_ptr = src1_original + i11*nb11 + i12*nb12;
+            void* dst_tmp_ptr  = dst_original  + i1*nb1   + i2*nb2;
+
+            aclTensor* acl_src0 = ggml_cann_create_tensor(src0_tmp_ptr,
+                ACL_FLOAT, sizeof(float),
+                src0_ne, src0_nb, 2);
+            aclTensor* acl_src1 = ggml_cann_create_tensor(src1_tmp_ptr,
+                ACL_FLOAT, sizeof(float),
+                src1_ne, src1_nb, 2);
+            aclTensor* acl_dst = ggml_cann_create_tensor(dst_tmp_ptr,
+                ACL_FLOAT, sizeof(float),
+                dst_ne, dst_nb, 2);
+
+            src0_tensor_vec.push_back(acl_src0);
+            src1_tensor_vec.push_back(acl_src1);
+            dst_tensor_vec.push_back(acl_dst);
+        }
+    }
+
+    size_t GROUP_SIZE = 128;
+    // GroupedMatmulV2 required tensor_list.size < 128
+    for (size_t i = 0; i < src0_tensor_vec.size(); i += GROUP_SIZE) {
+        // split and call GroupedMatmulV2
+        size_t end = std::min(i + GROUP_SIZE, src0_tensor_vec.size());
+        std::vector<aclTensor*> src0_tensor_vec_split(src0_tensor_vec.begin() + i, src0_tensor_vec.begin() + end);
+        std::vector<aclTensor*> src1_tensor_vec_split(src1_tensor_vec.begin() + i, src1_tensor_vec.begin() + end);
+        std::vector<aclTensor*> dst_tensor_vec_split(dst_tensor_vec.begin() + i, dst_tensor_vec.begin() + end);
+
+        aclTensorList* src0_tensor_list = aclCreateTensorList(src0_tensor_vec_split.data(), src0_tensor_vec_split.size());
+        aclTensorList* src1_tensor_list = aclCreateTensorList(src1_tensor_vec_split.data(), src1_tensor_vec_split.size());
+        aclTensorList* dst_tensor_list = aclCreateTensorList(dst_tensor_vec_split.data(), dst_tensor_vec_split.size());
+
+        GGML_CANN_CALL_ACLNN_OP(ctx, GroupedMatmulV2, src1_tensor_list, src0_tensor_list,
+            nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, 0, -1, dst_tensor_list);
+
+        ggml_cann_release_resources(ctx, src0_tensor_list, src1_tensor_list, dst_tensor_list);
+    }
+    return;
+}
+
+/**
+ * @brief Performs expert-specific matrix multiplication (MoE) with
+ * quantized precision using the CANN backend.
+ *
+ * This function executes a matrix multiplication operation tailored for
+ * Mixture of Experts (MoE) models, where the input tensor is multiplied
+ * with expert-specific quantized weight matrices. It leverages the CANN
+ * backend to perform efficient low-precision computations and stores the
+ * quantized result in the destination tensor `dst`.
+ *
+ * Quantization techniques reduce memory footprint and improve performance
+ * by using lower-bit representations (e.g., int8) instead of floating-point.
+ * This function is designed to work with such formats and may incorporate
+ * optimizations like identity-based fast paths or routing masks for sparse
+ * expert selection.
+ *
+ * @param ctx The context for executing CANN backend operations.
+ * @param dst The destination tensor where the quantized MoE multiplication result
+ * will be stored.
+ *
+ * @note This function assumes quantized data types and is designed for
+ * MoE architectures with potential sparse expert routing.
+ */
+static void ggml_cann_mul_mat_id_quant(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    // TODO: Use aclnnGroupedMatMul
+    //dst   [M, K, N, 1]
+    ggml_tensor * src0 = dst->src[0];  //src0	[D, M, A, 1]
+    ggml_tensor * src1 = dst->src[1];  //src1	[D, B, N, 1], B = K or B = 1
+    ggml_tensor * ids  = dst->src[2];  //ids	[K, N]
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    // copy index from npu to cpu
+    int64_t n_as = ne02; // A
+    int64_t n_ids = ids->ne[0]; // K
+
+    std::vector<char> ids_host(ggml_nbytes(ids));
+    ggml_cann_async_memcpy(ctx, ids_host.data(), ids->data, ggml_nbytes(ids),
+        ACL_MEMCPY_DEVICE_TO_HOST);
+    ACL_CHECK(aclrtSynchronizeStream(ctx.stream()));
+
+    char * src0_original = (char *) src0->data;
+    char * src1_original = (char *) src1->data;
+    char * dst_original  = (char *)  dst->data;
+
+    ggml_tensor src0_row = *src0;
+    ggml_tensor src1_row = *src1;
+    ggml_tensor dst_row = *dst;
+
+    const enum ggml_type type = dst->src[0]->type;
+    float weight_elem_size;
+    if (type == GGML_TYPE_Q4_0) {
+        weight_elem_size = float(sizeof(uint8_t)) / 2;
+    } else if (type == GGML_TYPE_Q8_0) {
+        weight_elem_size = float(sizeof(uint8_t));
+    } else {
+        GGML_ABORT("MUL_MAT_ID only support quant type Q4_0 and Q8_0 ");
+    }
+
+    // src0_row [D, M, 1, 1] weight without permute
+    src0_row.ne[2] = 1;
+    src0_row.ne[3] = 1;
+    src0_row.nb[0] = weight_elem_size;
+    src0_row.nb[1] = weight_elem_size * ne00;
+    src0_row.nb[2] = weight_elem_size * ne00;
+    src0_row.nb[3] = weight_elem_size * ne00;
+    size_t weight_stride = ne00 * ne01 * weight_elem_size;
+    size_t weight_size = weight_stride * ne02 * ne03;
+
+    // scale [D, M, 1, 1] -> scale && permute
+    size_t scale_elem_size = sizeof(uint16_t);
+    size_t scale_stride = src0->ne[1] * src0->ne[0] / QK8_0 * scale_elem_size;
+
+    // src1_row [D, 1, 1, 1] -> input
+    src1_row.ne[1] = 1;
+    src1_row.ne[2] = 1;
+    src1_row.ne[3] = 1;
+    src1_row.nb[2] = nb11;
+    src1_row.nb[3] = nb11;
+
+    // dst_row [M, 1, 1, 1] -> out
+    dst_row.ne[1] = 1;
+    dst_row.ne[2] = 1;
+    dst_row.ne[3] = 1;
+    dst_row.nb[2] = nb1;
+    dst_row.nb[3] = nb1;
+
+    //create weight for one row
+    ggml_cann_pool_alloc weight_allocator(ctx.pool());
+    void* weight_buffer = weight_allocator.alloc(nb02);
+    for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) {
+        for (int64_t id = 0; id < n_ids; id++) {
+            // expert index
+            int32_t i02 = *(int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]);
+            GGML_ASSERT(i02 >= 0 && i02 < n_as);
+
+            // If B = 1 (broadcast), always use 0; otherwise, use id.
+            int64_t i11 = (ne11 == 1 ? 0 : id);
+            int64_t i12 = iid1;
+
+            int64_t i1 = id;
+            int64_t i2 = i12;
+
+            void* src0_tmp_ptr = src0_original + i02*weight_stride;
+            void* scale_tmp_ptr = src0_original + weight_size + i02*scale_stride;
+            void* src1_tmp_ptr = src1_original + i11*nb11 + i12*nb12;
+            void* dst_tmp_ptr  = dst_original  + i1*nb1   + i2*nb2;
+
+            // mem cpy
+            ggml_cann_async_memcpy(ctx, weight_buffer, src0_tmp_ptr, weight_stride,
+                ACL_MEMCPY_DEVICE_TO_DEVICE);
+            void* scale_buffer = (char*)weight_buffer + weight_stride;
+            ggml_cann_async_memcpy(ctx, scale_buffer, scale_tmp_ptr, scale_stride,
+                ACL_MEMCPY_DEVICE_TO_DEVICE);
+
+            src0_row.data = weight_buffer;
+            src1_row.data = src1_tmp_ptr;
+            dst_row.data = dst_tmp_ptr;
+            dst_row.src[0] = &src0_row;
+            dst_row.src[1] = &src1_row;
+
+            ggml_cann_mul_mat(ctx, &dst_row);
+        }
+    }
+    return;
+}
+
+void ggml_cann_mul_mat_id(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    const enum ggml_type type = dst->src[0]->type;
+    switch (type) {
+        case GGML_TYPE_F32:
+        case GGML_TYPE_F16:
+            ggml_cann_mul_mat_id_fp(ctx, dst);
+            break;
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q8_0:
+            ggml_cann_mul_mat_id_quant(ctx, dst);
+            break;
+        default:
+            GGML_ABORT("Unsupported type for mul_mat_id");
+            break;
+    }
+}
+
+void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst){
+
+    ggml_tensor* src0 = dst->src[0]; // q, fp32
+    ggml_tensor* src1 = dst->src[1]; // k, fp16
+    ggml_tensor* src2 = dst->src[2]; // v, fp16
+    ggml_tensor* src3 = dst->src[3]; // mask, fp16
+
+    float maxBias = 0.0f;
+    float scaleValue = 1.0f;
+    float logitSoftcap = 0.0f;
+    memcpy(&scaleValue,    (float*)dst->op_params + 0, sizeof(float));
+    memcpy(&maxBias,       (float*)dst->op_params + 1, sizeof(float));
+    memcpy(&logitSoftcap,  (float*)dst->op_params + 2, sizeof(float));
+
+    if(logitSoftcap == 0.0f){
+        size_t faElemSize = sizeof(uint16_t);
+        auto   faDataType = ACL_FLOAT16; //ACL_BF16;
+
+        aclTensor* acl_src0_f16_tensor = nullptr;
+        aclTensor* acl_src1_f16_tensor = nullptr;
+        aclTensor* acl_src2_f16_tensor = nullptr;
+        aclTensor* acl_dst_f16_tensor  = nullptr;
+
+        // Step 1: cast the src0 (Query) to fp16 if needed
+        ggml_cann_pool_alloc src0_f16_allocator(ctx.pool());
+        void* src0_f16_buffer = nullptr;
+
+        if(ggml_cann_type_mapping(src0->type) != faDataType){
+            aclTensor* acl_src0_f32_tensor = ggml_cann_create_tensor(src0);
+            src0_f16_buffer = src0_f16_allocator.alloc(
+                                    ggml_nelements(src0) * faElemSize);
+
+            int64_t* src0_f16_ne = src0->ne;
+            size_t   src0_f16_nb[GGML_MAX_DIMS];
+            src0_f16_nb[0] = sizeof(uint16_t);
+            for(int i = 1; i < GGML_MAX_DIMS; ++i){
+                src0_f16_nb[i] = src0_f16_nb[i - 1] * src0_f16_ne[i - 1];
+            }
+
+            acl_src0_f16_tensor = ggml_cann_create_tensor(
+                src0_f16_buffer, faDataType, faElemSize,
+                src0_f16_ne, src0_f16_nb, GGML_MAX_DIMS
+            );
+            aclnn_cast(ctx, acl_src0_f32_tensor, acl_src0_f16_tensor, faDataType);
+            ggml_cann_release_resources(ctx, acl_src0_f32_tensor);
+        }else{
+            acl_src0_f16_tensor = ggml_cann_create_tensor(src0);
+        }
+
+        // Step 2: create the acl tensors for src1 (Key), src2 (Value),
+        //         and the direct output from FusedInferAttention
+
+        acl_src1_f16_tensor = ggml_cann_create_tensor(src1);
+        acl_src2_f16_tensor = ggml_cann_create_tensor(src2);
+
+        ggml_cann_pool_alloc out_f16_allocator(ctx.pool());
+        void* out_f16_buffer = out_f16_allocator.alloc(
+                                    ggml_nelements(dst) * faElemSize);
+
+        int64_t* out_f16_ne = src0->ne;
+        size_t out_f16_nb[GGML_MAX_DIMS];
+        out_f16_nb[0] = faElemSize;
+        for(int i = 1; i < GGML_MAX_DIMS; ++i){
+            out_f16_nb[i] = out_f16_nb[i - 1] * out_f16_ne[i - 1];
+        }
+
+        acl_dst_f16_tensor = ggml_cann_create_tensor(
+            out_f16_buffer, faDataType, faElemSize,
+            out_f16_ne, out_f16_nb, GGML_MAX_DIMS
+        );
+
+        // Step 3: create the PSEShift tensor if needed
+        //         this tensor is considered as mask (f16) in the llama.cpp
+
+        aclTensor* bcast_pse_tensor = nullptr;
+        int64_t bcast_pse_ne[GGML_MAX_DIMS];
+        size_t bcast_pse_nb[GGML_MAX_DIMS];
+        ggml_cann_pool_alloc bcast_pse_allocator(ctx.pool());
+        void* bcast_pse_buffer = nullptr;
+
+        if(src3 != nullptr){
+            bcast_pse_buffer = bcast_pse_allocator.alloc(
+                            ggml_nelements(src3) * src0->ne[2] * sizeof(uint16_t));
+
+            if(src0->ne[1] > 1){
+                // Case 1: broadcast pse for prefill stage with multiple head
+                aclTensor* acl_mask_f16_tensor = ggml_cann_create_tensor(src3);
+                bcast_pse_ne[0] = src3->ne[0];
+                bcast_pse_ne[1] = src3->ne[1];
+                bcast_pse_ne[2] = src0->ne[2];
+                bcast_pse_ne[3] = src3->ne[3];
+
+                bcast_pse_nb[0] = sizeof(uint16_t);
+                for(int i = 1; i < GGML_MAX_DIMS; ++i){
+                    bcast_pse_nb[i] = bcast_pse_nb[i - 1] * bcast_pse_ne[i - 1];
+                }
+
+                bcast_pse_tensor = ggml_cann_create_tensor(
+                    bcast_pse_buffer, ACL_FLOAT16, sizeof(uint16_t),
+                    bcast_pse_ne, bcast_pse_nb, GGML_MAX_DIMS);
+
+                int64_t repeats[] = {1, src0->ne[2], 1, 1};
+                aclnn_repeat(ctx, acl_mask_f16_tensor, bcast_pse_tensor, repeats);
+
+                ggml_cann_release_resources(ctx, acl_mask_f16_tensor);
+            }else{
+                // Case 2: trunc the first row and broadcast pse for decode stage with multiple head
+                int64_t trunc_pse_ne[GGML_MAX_DIMS] = {src3->ne[0], src0->ne[1], src3->ne[2], src3->ne[3]};
+                size_t* trunc_pse_nb = src3->nb;
+
+                aclTensor* acl_mask_f16_trunc_tensor = ggml_cann_create_tensor(
+                    src3->data, ACL_FLOAT16, sizeof(uint16_t),
+                    trunc_pse_ne, trunc_pse_nb, GGML_MAX_DIMS);
+
+                bcast_pse_ne[0] = src3->ne[0];
+                bcast_pse_ne[1] = src0->ne[1];
+                bcast_pse_ne[2] = src0->ne[2];
+                bcast_pse_ne[3] = src3->ne[3];
+
+                bcast_pse_nb[0] = sizeof(uint16_t);
+                for(int i = 1; i < GGML_MAX_DIMS; ++i){
+                    bcast_pse_nb[i] = bcast_pse_nb[i - 1] * bcast_pse_ne[i - 1];
+                }
+
+                bcast_pse_tensor = ggml_cann_create_tensor(
+                    bcast_pse_buffer, ACL_FLOAT16, sizeof(uint16_t),
+                    bcast_pse_ne, bcast_pse_nb, GGML_MAX_DIMS);
+
+                int64_t repeats[] = {1, src0->ne[2], 1, 1};
+                aclnn_repeat(ctx, acl_mask_f16_trunc_tensor, bcast_pse_tensor, repeats);
+
+                ggml_cann_release_resources(ctx, acl_mask_f16_trunc_tensor);
+            }
+
+            // Compute the slope if needed. Derived from ggml_cann_softmax().
+            if(maxBias != 0.0f){
+                // alibi
+                const int64_t ne2_ne3 = src0->ne[2] * src0->ne[3];
+                const int64_t n_head = src0->ne[2];
+                const int n_heads_log2_floor = 1u << (uint32_t)floor(log2(n_head));
+                float m0 = powf(2.0f, -(maxBias) / n_heads_log2_floor);
+                float m1 = powf(2.0f, -(maxBias / 2.0f) / n_heads_log2_floor);
+                // init arange
+                ggml_cann_pool_alloc arange_allocator(ctx.pool(),
+                                                    ne2_ne3 * faElemSize);
+                void* tmp_arange_buffer = arange_allocator.get();
+
+                // arange1: [1, ..., n_heads_log2_floor+1)
+                float start = 1;
+                float stop = n_heads_log2_floor + 1;
+                float step = 1;
+                int64_t n_elements_arange = n_heads_log2_floor;
+
+                int64_t tmp_arange1_ne[] = {n_heads_log2_floor};
+                size_t tmp_arange1_nb[] = {faElemSize};
+                aclTensor* tmp_arange1_tensor = ggml_cann_create_tensor(
+                    tmp_arange_buffer, faDataType, faElemSize,
+                    tmp_arange1_ne, tmp_arange1_nb,
+                    GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+
+                aclnn_arange(ctx, tmp_arange1_tensor, start, stop, step, n_elements_arange);
+
+                aclTensor* tmp_arange2_tensor = nullptr;
+                if (n_heads_log2_floor < ne2_ne3) {
+                    // arange2: [1, ..., 2 * (k - n_heads_log2_floor) + 1)
+                    start = 1;
+                    stop = 2 * (ne2_ne3 - n_heads_log2_floor) + 1;
+                    step = 2;
+                    n_elements_arange = ne2_ne3 - n_heads_log2_floor;
+                    int64_t tmp_arange2_ne[] = {ne2_ne3 - n_heads_log2_floor};
+                    size_t tmp_arange2_nb[] = {faElemSize};
+
+                    aclTensor* tmp_arange2_tensor = ggml_cann_create_tensor(
+                        (char*)tmp_arange_buffer +
+                            n_heads_log2_floor * faElemSize,
+                        faDataType, faElemSize,
+                        tmp_arange2_ne, tmp_arange2_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+                    aclnn_arange(ctx, tmp_arange2_tensor, start, stop, step,
+                                n_elements_arange);
+                }
+
+                // init mk_base
+                ggml_cann_pool_alloc mk_base_allocator(ctx.pool(),
+                                                    ne2_ne3 * faElemSize);
+                void* tmp_mk_base_buffer = mk_base_allocator.get();
+                int64_t tmp_mk_base1_ne[] = {n_heads_log2_floor};
+                size_t tmp_mk_base1_nb[] = {faElemSize};
+                aclTensor* tmp_mk_base1_tensor = ggml_cann_create_tensor(
+                    tmp_mk_base_buffer, faDataType, faElemSize,
+                    tmp_mk_base1_ne, tmp_mk_base1_nb,
+                    GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+
+                aclnn_fill_scalar(ctx, m0, tmp_mk_base1_tensor);
+
+                aclTensor* tmp_mk_base2_tensor = nullptr;
+                if (n_heads_log2_floor < ne2_ne3) {
+                    int64_t tmp_mk_base2_ne[] = {ne2_ne3 - n_heads_log2_floor};
+                    size_t tmp_mk_base2_nb[] = {faElemSize};
+                    aclTensor* tmp_mk_base2_tensor = ggml_cann_create_tensor(
+                        (char*)tmp_mk_base_buffer +
+                            n_heads_log2_floor * faElemSize,
+                        faDataType, faElemSize,
+                        tmp_mk_base2_ne, tmp_mk_base2_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+                    aclnn_fill_scalar(ctx, m1, tmp_mk_base2_tensor);
+                }
+
+                // init mk
+                int64_t tmp_mk_base_ne[] = {ne2_ne3};
+                size_t tmp_mk_base_nb[] = {faElemSize};
+                aclTensor* tmp_mk_base_tensor = ggml_cann_create_tensor(
+                    tmp_mk_base_buffer, faDataType, faElemSize,
+                    tmp_mk_base_ne, tmp_mk_base_nb,
+                    GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+                aclTensor* tmp_arange_tensor = ggml_cann_create_tensor(
+                    tmp_arange_buffer, faDataType, faElemSize,
+                    tmp_mk_base_ne, tmp_mk_base_nb,
+                    GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+                aclnn_pow_tensor_tensor(ctx, tmp_mk_base_tensor, tmp_arange_tensor);
+
+                // reshape mk
+                int64_t tmp_mk_ne[] = {1, 1, src0->ne[2], src0->ne[3]};
+                size_t tmp_mk_nb[GGML_MAX_DIMS];
+                tmp_mk_nb[0] = faElemSize;
+                for (int i = 1; i < GGML_MAX_DIMS; i++) {
+                    tmp_mk_nb[i] = tmp_mk_nb[i - 1] * tmp_mk_ne[i - 1];
+                }
+                aclTensor* tmp_mk_tensor = ggml_cann_create_tensor(
+                    tmp_mk_base_buffer, faDataType, faElemSize,
+                    tmp_mk_ne, tmp_mk_nb, GGML_MAX_DIMS,
+                    ACL_FORMAT_ND);
+                GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMul, bcast_pse_tensor, tmp_mk_tensor);
+
+                ggml_cann_release_resources(ctx, tmp_arange1_tensor, tmp_arange2_tensor,
+                    tmp_mk_base1_tensor, tmp_mk_base2_tensor, tmp_mk_base_tensor,
+                    tmp_arange_tensor, tmp_mk_tensor);
+            }
+        }
+
+        // Step 4: set the inputs for FusedInferAttention.
+        int kvTensorNum = 1;
+        aclTensor* acl_q_tensor = acl_src0_f16_tensor;
+        aclTensor* acl_k_tensors[] = {acl_src1_f16_tensor};
+        aclTensor* acl_v_tensors[] = {acl_src2_f16_tensor};
+        auto acl_k_tensor_list = aclCreateTensorList(acl_k_tensors, kvTensorNum);
+        auto acl_v_tensor_list = aclCreateTensorList(acl_v_tensors, kvTensorNum);
+
+        int64_t numHeads = src0->ne[2]; // N
+        int64_t numKeyValueHeads = src1->ne[2];
+        // double  scaleValue = 1 / sqrt(src0->ne[0]); // 1/sqrt(d)
+        int64_t preTokens = 65535;
+        int64_t nextTokens = 65535;
+        char layout[5] = {'B', 'N', 'S', 'D', 0};
+        int64_t sparseMode = 0;
+        int64_t innerPrecise = (src0->ne[1] == 1) ? 0 : 2;
+        int64_t blockSize = 0;
+        int64_t antiquantMode = 0;
+        bool softmaxLseFlag = false;
+        int64_t keyAntiquantMode = 0;
+        int64_t valueAntiquantMode = 0;
+
+        // Step 5: launch the FusedInferAttentionScoreV2 kernel.
+        // Refer to https://gitee.com/ascend/cann-ops-adv/blob/master/docs/FusedInferAttentionScoreV2.md
+
+        GGML_CANN_CALL_ACLNN_OP(ctx, FusedInferAttentionScoreV2,
+            acl_q_tensor, acl_k_tensor_list, acl_v_tensor_list, // q, k, v
+            bcast_pse_tensor, nullptr, // pse, mask
+            nullptr, nullptr, // actSeqLen, actSeqLenkv
+            nullptr, nullptr, // deqScale1, quantScale1
+            nullptr, nullptr, nullptr, // deqScale2, quantScale2, quantOffset2
+            nullptr, nullptr, // antiquantScale, antiquantOffset
+            nullptr, // blockTable
+            nullptr, nullptr, // qPadSize, kvPadSize
+            nullptr, nullptr, // kAntiquantScale, kAntiQuantOffset
+            nullptr, nullptr, // vAntiquantScale, vAntiQuantOffset
+            nullptr, nullptr, nullptr, // kSharedPrefix, vSharedPrefix, actSharedLen
+            numHeads, scaleValue, // heads, scaleValue
+            preTokens, nextTokens, // preTokens, nextTokens
+            layout, // inputLayout
+            numKeyValueHeads, // numKVHeads
+            sparseMode, innerPrecise, // sparseMode, innerPrecise
+            blockSize, antiquantMode, // blockSize, antiquantMode
+            softmaxLseFlag, // softmaxLseFlag
+            keyAntiquantMode, valueAntiquantMode, // keyAntiqMode, valueAntiqMode
+            acl_dst_f16_tensor, // attentionOut
+            nullptr // softmaxLse
+        );
+
+        // Step 6: post-processing, permute and cast to f32
+
+        int64_t new_dim[] = {0, 2, 1, 3};
+        aclTensor* acl_dst_tensor = ggml_cann_create_tensor(dst);
+
+        if(ggml_cann_type_mapping(dst->type) != faDataType){
+            ggml_cann_pool_alloc perm_out_f16_allocator(ctx.pool());
+            perm_out_f16_allocator.alloc(ggml_nelements(dst) * faElemSize);
+            void* perm_out_f16_buffer = perm_out_f16_allocator.get();
+
+            int64_t* perm_out_f16_ne = dst->ne;
+            size_t  perm_out_f16_nb[GGML_MAX_DIMS];
+            perm_out_f16_nb[0] = faElemSize;
+            for(int i = 1; i < GGML_MAX_DIMS; ++i){
+                perm_out_f16_nb[i] = perm_out_f16_nb[i - 1] * perm_out_f16_ne[i - 1];
+            }
+            aclTensor* acl_perm_out_f16_tensor = ggml_cann_create_tensor(
+                perm_out_f16_buffer, faDataType, faElemSize,
+                perm_out_f16_ne, perm_out_f16_nb, GGML_MAX_DIMS);
+            aclnn_permute(ctx, acl_dst_f16_tensor, acl_perm_out_f16_tensor, new_dim, GGML_MAX_DIMS);
+            aclnn_cast(ctx,
+                acl_perm_out_f16_tensor, acl_dst_tensor, ggml_cann_type_mapping(dst->type));
+            ggml_cann_release_resources(ctx, acl_perm_out_f16_tensor);
+        }else{
+            // only need to permute
+            aclnn_permute(ctx, acl_dst_f16_tensor, acl_dst_tensor, new_dim, GGML_MAX_DIMS);
+        }
+        ggml_cann_release_resources(ctx, acl_src0_f16_tensor,
+                                         acl_src1_f16_tensor,
+                                         acl_src2_f16_tensor,
+                                         acl_dst_f16_tensor,
+                                         acl_dst_tensor);
+        if(src3 != nullptr){
+            ggml_cann_release_resources(ctx, bcast_pse_tensor);
+        }
+    }else{
+        GGML_ABORT("Function is not implemented.");
+    }
+}
--- a/ggml/src/ggml-cann/aclnn_ops.h
+++ b/ggml/src/ggml-cann/aclnn_ops.h
@@ -714,6 +714,21 @@ void ggml_cann_count_equal(ggml_backend_cann_context& ctx, ggml_tensor* dst);
 */
 void ggml_cann_step(ggml_backend_cann_context& ctx, ggml_tensor* dst);

+/**
+ * @brief   Performs the Flash Attention extended operator using the CANN backend.
+ *
+ * @details This function implements the memory-efficient Flash Attention algorithm
+ *          for computing scaled dot-product attention with hardware acceleration.
+ *          The result is stored in the destination tensor `dst`.
+ *
+ *          This operation is accelerated using the CANN backend to improve runtime performance.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the result will be stored.
+ *            dst->op is expected to be `GGML_OP_FLASH_ATTN_EXT`.
+ */
+void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
 /*
 * @brief A generic wrapper for ACL resources with custom deleter support.
 */
@@ -978,6 +993,33 @@ inline void ggml_cann_async_memset(ggml_backend_cann_context & ctx, void * buffe
    }
 }

+/**
+ * @brief   Performs sparse expert-based matrix multiplication using the CANN backend.
+ *
+ * @details This function implements a MoE-style batched matrix multiplication, where each input token
+ *          is routed to one or more experts, and each expert corresponds to a specific [D, M] weight matrix
+ *          in the source tensor `src0`. The routing indices are provided via the `ids` tensor.
+ *
+ *          For each token (from `src1`), the function selects the corresponding expert(s) as specified by `ids`,
+ *          performs the matrix multiplication with the selected expert's weight submatrix (from `src0`),
+ *          and stores the results in `dst`. This operation is optimized and executed on the CANN backend.
+ *
+ *          Dimensions:
+ *              - src0: [D, M, A, 1], where A is the number of experts
+ *              - src1: [D, B, N, 1], where N is batch size and B is the slot count per sample
+ *              - ids : [K, N],       where K is the number of experts each token is routed to
+ *              - dst : [M, K, N, 1], output tensor storing the result of expert × token multiplication
+ *
+ *          The function handles two main modes:
+ *              - If `ne12 == 1`, a simpler per-token loop is used.
+ *              - TODO: If `ne12 > 1`, grouped multiplication and memory copying is used for efficiency.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the expert-weighted token outputs are stored.
+ *            Expected to be of shape [M, K, N, 1].
+ */
+void ggml_cann_mul_mat_id(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
 /**
 * @brief Applies a element-wise operation to two input tensors using the CANN
 * backend.
--- a/ggml/src/ggml-cann/common.h
+++ b/ggml/src/ggml-cann/common.h
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -36,6 +36,7 @@
 #include "ggml-backend-impl.h"
 #include "ggml-cann/aclnn_ops.h"
 #include "ggml-cann/common.h"
+#include "ggml.h"

 #define GGML_COMMON_DECL_C

@@ -1672,7 +1673,8 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx,
            ggml_cann_mul_mat(ctx, dst);
            break;
        case GGML_OP_MUL_MAT_ID:
-            return false;
+            ggml_cann_mul_mat_id(ctx, dst);
+            break;
        case GGML_OP_SCALE:
            ggml_cann_scale(ctx, dst);
            break;
@@ -1747,6 +1749,9 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx,
        case GGML_OP_COUNT_EQUAL:
            ggml_cann_count_equal(ctx, dst);
            break;
+        case GGML_OP_FLASH_ATTN_EXT:
+            ggml_cann_flash_attn_ext(ctx, dst);
+            break;
        default:
            return false;
    }
@@ -2030,7 +2035,22 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
            }
        }
        case GGML_OP_MUL_MAT_ID:
-            return false;
+            switch (op->src[0]->type) {
+                case GGML_TYPE_F16:
+                case GGML_TYPE_F32:
+                    return true;
+                case GGML_TYPE_Q8_0:
+                case GGML_TYPE_Q4_0:
+#ifdef ASCEND_310P
+                    // Q4 && Q8 per group is not suppor on 310p device
+                    return false;
+#endif
+                    // only support contiguous for quantized types.
+                    return ggml_is_contiguous(op->src[0]) &&
+                            ggml_is_contiguous(op->src[1]);
+                default:
+                    return false;
+            }
        // embedding
        case GGML_OP_GET_ROWS: {
            switch (op->src[0]->type) {
@@ -2161,6 +2181,38 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
        case GGML_OP_PAD_REFLECT_1D:
        case GGML_OP_COUNT_EQUAL:
            return true;
+        case GGML_OP_FLASH_ATTN_EXT:{
+            // derived from [ggml-cuda.cu]
+            if(op->src[1]->type != GGML_TYPE_F16 || op->src[2]->type != GGML_TYPE_F16){
+                return false;
+            }
+            if(op->src[1]->type != GGML_TYPE_F16 && op->src[1]->type != GGML_TYPE_F32 && op->src[1]->type != GGML_TYPE_BF16){
+                return false;
+            }
+            if(op->type != GGML_TYPE_F16 && op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_BF16){
+                return false;
+            }
+            if (op->src[1]->ne[0] != op->src[2]->ne[0]) {
+                // different head sizes of K and V are not supported yet
+                return false;
+            }
+            if (op->src[0]->ne[0] == 192) {
+                return false;
+            }
+            if (op->src[0]->ne[0] == 576) {
+                // DeepSeek MLA
+                return false;
+            }
+            if (op->src[0]->ne[3] != 1) {
+                return false;
+            }
+            float logitSoftcap = 0.0f;
+            memcpy(&logitSoftcap,  (float*)op->op_params + 2, sizeof(float));
+            if(logitSoftcap != 0.0f) {
+                return false;
+            }
+            return true;
+        }
        default:
            return false;
    }
--- a/ggml/src/ggml-cpu/CMakeLists.txt
+++ b/ggml/src/ggml-cpu/CMakeLists.txt
@@ -82,13 +82,8 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
        target_link_libraries(${GGML_CPU_NAME} PUBLIC memkind)
    endif()

-    if (CMAKE_OSX_ARCHITECTURES      STREQUAL "arm64" OR
-        CMAKE_GENERATOR_PLATFORM_LWR STREQUAL "arm64" OR
-        (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
-            CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm.*|ARM64)$"))
-
+    if (GGML_SYSTEM_ARCH STREQUAL "ARM")
        message(STATUS "ARM detected")
-
        if (MSVC AND NOT CMAKE_C_COMPILER_ID STREQUAL "Clang")
            message(FATAL_ERROR "MSVC is not supported for ARM, use clang")
        else()
@@ -170,12 +165,8 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
                endforeach()
            endif()
        endif()
-    elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR
-            (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND
-            CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64|amd64)$"))
-
+    elseif (GGML_SYSTEM_ARCH STREQUAL "x86")
        message(STATUS "x86 detected")
-
        if (MSVC)
            # instruction set detection for MSVC only
            if (GGML_NATIVE)
@@ -299,7 +290,26 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
                endif()
            endif()
        endif()
-    elseif ("${CMAKE_SYSTEM_PROCESSOR} " STREQUAL "ppc64le " OR "${CMAKE_SYSTEM_PROCESSOR} " STREQUAL "powerpc ")
+
+        if (GGML_BACKEND_DL)
+            if (GGML_NATIVE)
+                # the feature check relies on ARCH_DEFINITIONS, but it is not set with GGML_NATIVE
+                message(FATAL_ERROR "GGML_NATIVE is not compatible with GGML_BACKEND_DL, consider using GGML_CPU_ALL_VARIANTS")
+            endif()
+
+            # The feature detection code is compiled as a separate target so that
+            # it can be built without the architecture flags
+            # Since multiple variants of the CPU backend may be included in the same
+            # build, using set_source_files_properties() to set the arch flags is not possible
+            set(GGML_CPU_FEATS_NAME ${GGML_CPU_NAME}-feats)
+            add_library(${GGML_CPU_FEATS_NAME} OBJECT ggml-cpu/cpu-feats-x86.cpp)
+            target_include_directories(${GGML_CPU_FEATS_NAME} PRIVATE . .. ../include)
+            target_compile_definitions(${GGML_CPU_FEATS_NAME} PRIVATE ${ARCH_DEFINITIONS})
+            target_compile_definitions(${GGML_CPU_FEATS_NAME} PRIVATE GGML_BACKEND_DL GGML_BACKEND_BUILD GGML_BACKEND_SHARED)
+            set_target_properties(${GGML_CPU_FEATS_NAME} PROPERTIES POSITION_INDEPENDENT_CODE ON)
+            target_link_libraries(${GGML_CPU_NAME} PRIVATE ${GGML_CPU_FEATS_NAME})
+        endif()
+    elseif (GGML_SYSTEM_ARCH STREQUAL "PowerPC")
        message(STATUS "PowerPC detected")
        if (GGML_NATIVE)
            if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64")
@@ -308,7 +318,8 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
                execute_process(COMMAND bash -c "prtconf |grep 'Implementation' | head -n 1" OUTPUT_VARIABLE POWER10_M)
            endif()

-            string(REGEX MATCHALL "POWER *([0-9]+)" MATCHED_STRING "${POWER10_M}")
+            string(TOUPPER "${POWER10_M}" POWER10_M_UPPER)
+            string(REGEX MATCHALL "POWER *([0-9]+)" MATCHED_STRING "${POWER10_M_UPPER}")
            string(REGEX REPLACE "POWER *([0-9]+)" "\\1" EXTRACTED_NUMBER "${MATCHED_STRING}")

            if (EXTRACTED_NUMBER GREATER_EQUAL 10)
@@ -325,9 +336,8 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
                list(APPEND ARCH_FLAGS -mcpu=${GGML_CPU_POWERPC_CPUTYPE})
            endif()
        endif()
-    elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64")
+    elseif (GGML_SYSTEM_ARCH STREQUAL "loongarch64")
        message(STATUS "loongarch64 detected")
-
        list(APPEND ARCH_FLAGS -march=loongarch64)
        if (GGML_LASX)
            list(APPEND ARCH_FLAGS -mlasx)
@@ -335,16 +345,18 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
        if (GGML_LSX)
            list(APPEND ARCH_FLAGS -mlsx)
        endif()
-    elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "riscv64")
-        message(STATUS "RISC-V detected")
+    elseif (GGML_SYSTEM_ARCH STREQUAL "riscv64")
+        message(STATUS "riscv64 detected")
        if (GGML_RVV)
-            if (GGML_RV_ZFH)
-                list(APPEND ARCH_FLAGS -march=rv64gcv_zfhmin -DGGML_RV_ZFH -mabi=lp64d)
+            if (GGML_XTHEADVECTOR)
+                list(APPEND ARCH_FLAGS -march=rv64gc_xtheadvector -mabi=lp64d)
+            elseif (GGML_RV_ZFH)
+                list(APPEND ARCH_FLAGS -march=rv64gcv_zfhmin -mabi=lp64d)
            else()
                list(APPEND ARCH_FLAGS -march=rv64gcv -mabi=lp64d)
            endif()
        endif()
-    elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "s390x")
+    elseif (GGML_SYSTEM_ARCH STREQUAL "s390x")
        message(STATUS "s390x detected")
        file(READ "/proc/cpuinfo" CPUINFO_CONTENTS)
        string(REGEX REPLACE "machine[ \t\r\n]*=[ \t\r\n]*([0-9]+)" "\\1" S390X_M ${CPUINFO_CONTENTS})
@@ -477,25 +489,6 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
    target_compile_options(${GGML_CPU_NAME} PRIVATE ${ARCH_FLAGS})
    target_compile_definitions(${GGML_CPU_NAME} PRIVATE ${ARCH_DEFINITIONS})

-    if (GGML_BACKEND_DL)
-        if (GGML_NATIVE)
-            # the feature check relies on ARCH_DEFINITIONS, but it is not set with GGML_NATIVE
-            message(FATAL_ERROR "GGML_NATIVE is not compatible with GGML_BACKEND_DL, consider using GGML_CPU_ALL_VARIANTS")
-        endif()
-
-        # The feature detection code is compiled as a separate target so that
-        # it can be built without the architecture flags
-        # Since multiple variants of the CPU backend may be included in the same
-        # build, using set_source_files_properties() to set the arch flags is not possible
-        set(GGML_CPU_FEATS_NAME ${GGML_CPU_NAME}-feats)
-        add_library(${GGML_CPU_FEATS_NAME} OBJECT ggml-cpu/cpu-feats-x86.cpp)
-        target_include_directories(${GGML_CPU_FEATS_NAME} PRIVATE . .. ../include)
-        target_compile_definitions(${GGML_CPU_FEATS_NAME} PRIVATE ${ARCH_DEFINITIONS})
-        target_compile_definitions(${GGML_CPU_FEATS_NAME} PRIVATE GGML_BACKEND_DL GGML_BACKEND_BUILD GGML_BACKEND_SHARED)
-        set_target_properties(${GGML_CPU_FEATS_NAME} PROPERTIES POSITION_INDEPENDENT_CODE ON)
-        target_link_libraries(${GGML_CPU_NAME} PRIVATE ${GGML_CPU_FEATS_NAME})
-    endif()
-
    if (EMSCRIPTEN)
        set_target_properties(${GGML_CPU_NAME} PROPERTIES COMPILE_FLAGS "-msimd128")
    endif()
--- a/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp
+++ b/ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp
@@ -1191,7 +1191,7 @@ static void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, c
        }
    }
    return;
-#elif defined(__riscv_v_intrinsic)
+#elif defined __riscv_v
    if (__riscv_vlenb() >= QK4_0) {
        const size_t vl = QK4_0;

@@ -3783,7 +3783,7 @@ static void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, c
        }
        return;
    }
-#elif defined(__riscv_v_intrinsic)
+#elif defined __riscv_v
    if (__riscv_vlenb() >= QK4_0) {
        const size_t vl = QK4_0;

--- a/ggml/src/ggml-cpu/ggml-cpu-impl.h
+++ b/ggml/src/ggml-cpu/ggml-cpu-impl.h
@@ -320,21 +320,17 @@ inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b)

 #ifdef __wasm_simd128__
 #include <wasm_simd128.h>
-#else
+#endif
+
 #ifdef __POWER9_VECTOR__
 #include <altivec.h>
-#else
+#endif
+
 #if defined(_MSC_VER) || defined(__MINGW32__)
 #include <intrin.h>
-#else
-#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__) || defined(__SSE__)
-#if !defined(__riscv)
+#elif defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__) || defined(__SSE__)
 #include <immintrin.h>
 #endif
-#endif
-#endif
-#endif
-#endif

 #ifdef __riscv_v_intrinsic
 #include <riscv_vector.h>
--- a/ggml/src/ggml-cpu/ggml-cpu-quants.c
+++ b/ggml/src/ggml-cpu/ggml-cpu-quants.c
@@ -883,7 +883,7 @@ void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, i
        _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4);
 #endif
    }
-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)

    size_t vl = QK8_0;

@@ -1221,7 +1221,7 @@ void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, i
        _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4);
 #endif
    }
-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)

    size_t vl = QK8_1;

@@ -2384,7 +2384,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
    }

    sumf = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3);
-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)
    size_t vl = qk / 2;

    for (; ib < nb; ++ib) {
@@ -2774,7 +2774,7 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
    }

    sumf = hsum_float_8(acc) + summs;
-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)
    size_t vl = qk / 2;

    for (; ib < nb; ++ib) {
@@ -3121,7 +3121,7 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
    }

    sumf = hsum_float_8(acc);
-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)
    size_t vl;
    size_t vlenb = __riscv_vlenb();

@@ -3460,7 +3460,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
    }

    sumf = hsum_float_8(acc) + summs;
-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)
    size_t vl;
    size_t vlenb = __riscv_vlenb();

@@ -3897,7 +3897,7 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
    }

    sumf = hsum_float_8(accum);
-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)
    size_t vl = qk;

    for (; ib < nb; ++ib) {
@@ -5100,14 +5100,111 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi

    *s = sumf;

-#elif defined __riscv_v_intrinsic
+#elif defined __riscv_xtheadvector
+
+    float sumf = 0;
+    uint8_t atmp[16];
+
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * q2 = x[i].qs;
+        const  int8_t * q8 = y[i].qs;
+        const uint8_t * sc = x[i].scales;
+        const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
+        uint8_t *patmp = atmp;
+        int vsums;
+        int tmp;
+        __asm__ __volatile__(
+            "th.vsetvli zero, %[vl16], e8, m1\n\t"
+            "th.vmv.v.x v8, zero\n\t"
+            "th.vlb.v v1, (%[sc])\n\t"
+            "th.vand.vi v0, v1, 0xF\n\t"
+            "th.vsrl.vi v1, v1, 4\n\t"
+            "th.vsb.v v0, (%[scale])\n\t"
+            "th.vwaddu.vx v16, v1, zero\n\t"
+            "th.vsetvli zero, %[vl16], e16, m2\n\t"
+            "th.vlh.v v2, (%[bsums])\n\t"
+            "th.vwmul.vv v4, v16, v2\n\t"
+            "th.vsetvli zero, %[vl16], e32, m4\n\t"
+            "th.vredsum.vs v8, v4, v8\n\t"
+            "th.vmv.x.s %[vsums], v8"
+            : [tmp] "=&r" (tmp), [vsums] "=&r" (vsums)
+            : [sc] "r" (sc), [scale] "r" (atmp), [bsums] "r" (y[i].bsums)
+            , [vl16] "r" (16)
+            : "memory"
+            , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
+            , "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15"
+            , "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23"
+            , "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
+        );
+        sumf += dmin * vsums;
+        int isum = 0;
+
+        for (int j = 0; j < QK_K/128; ++j) {
+            __asm__ __volatile__(
+                "th.vsetvli zero, %[vl32], e8, m2\n\t"
+                "th.vlb.v v0, (%[q2])\n\t"
+                "th.vsrl.vi v2, v0, 2\n\t"
+                "th.vsrl.vi v4, v0, 4\n\t"
+                "th.vsrl.vi v6, v0, 6\n\t"
+                "th.vand.vi v0, v0, 0x3\n\t"
+                "th.vand.vi v2, v2, 0x3\n\t"
+                "th.vand.vi v4, v4, 0x3\n\t"
+                "th.vsetvli zero, %[vl128], e8, m8\n\t"
+                "th.vlb.v v8, (%[q8])\n\t"
+                "th.vsetvli zero, %[vl64], e8, m4\n\t"
+                "th.vwmul.vv v16, v0, v8\n\t"
+                "th.vwmul.vv v24, v4, v12\n\t"
+                "th.vsetvli zero, %[vl16], e16, m2\n\t"
+                "th.vmv.v.x v0, zero\n\t"
+                "th.vwredsum.vs v10, v16, v0\n\t"
+                "th.vwredsum.vs v9, v18, v0\n\t"
+                "th.vwredsum.vs v8, v20, v0\n\t"
+                "th.vwredsum.vs v7, v22, v0\n\t"
+                "th.vwredsum.vs v11, v24, v0\n\t"
+                "th.vwredsum.vs v12, v26, v0\n\t"
+                "th.vwredsum.vs v13, v28, v0\n\t"
+                "th.vwredsum.vs v14, v30, v0\n\t"
+                "li %[tmp], 4\n\t"
+                "th.vsetvli zero, %[tmp], e32, m1\n\t"
+                "th.vslideup.vi v10, v9, 1\n\t"
+                "th.vslideup.vi v8, v7, 1\n\t"
+                "th.vslideup.vi v11, v12, 1\n\t"
+                "th.vslideup.vi v13, v14, 1\n\t"
+                "th.vslideup.vi v10, v8, 2\n\t"
+                "th.vslideup.vi v11, v13, 2\n\t"
+                "li %[tmp], 8\n\t"
+                "th.vsetvli zero, %[tmp], e32, m2\n\t"
+                "th.vlbu.v v12, (%[scale])\n\t"
+                "th.vmul.vv v10, v10, v12\n\t"
+                "th.vredsum.vs v0, v10, v0\n\t"
+                "th.vmv.x.s %[tmp], v0\n\t"
+                "add %[isum], %[isum], %[tmp]"
+                : [tmp] "=&r" (tmp), [isum] "+&r" (isum)
+                : [q2] "r" (q2), [scale] "r" (patmp), [q8] "r" (q8)
+                , [vl16] "r" (16), [vl32] "r" (32), [vl64] "r" (64), [vl128] "r" (128)
+                : "memory"
+                , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
+                , "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15"
+                , "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23"
+                , "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
+            );
+            q2 += 32; q8 += 128; patmp += 8;
+        }
+
+        sumf += dall * isum;
+    }
+
+    *s = sumf;
+
+#elif defined __riscv_v
+
+    float sumf = 0;
+    uint8_t atmp[16];

    const int vector_length = __riscv_vlenb() * 8;
-    float sumf = 0;
-
    uint8_t temp_01[32] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                            1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 };
-    uint8_t atmp[16];

    switch (vector_length) {
    case 256:
@@ -6137,14 +6234,141 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi

    *s = sumf;

-#elif defined __riscv_v_intrinsic
+#elif defined __riscv_xtheadvector

-    uint32_t aux[3];
    uint32_t utmp[4];
-
-    const int vector_length = __riscv_vlenb() * 8;
    float sumf = 0;

+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * restrict q3 = x[i].qs;
+        const uint8_t * restrict qh = x[i].hmask;
+        const  int8_t * restrict q8 = y[i].qs;
+
+        int8_t * scale = (int8_t *)utmp;
+        int tmp;
+        __asm__ __volatile__(
+            "li %[tmp], 12\n\t"
+            "th.vsetvli zero, %[tmp], e8, m1\n\t"
+            "th.vlb.v v0, (%[s6b])\n\t"
+            "th.vmv.v.v v2, v0\n\t"
+            "li %[tmp], 2\n\t"
+            "th.vsetvli zero, %[tmp], e64, m1\n\t"
+            "th.vmv.v.x v9, %[sh]\n\t"\
+            "th.vslidedown.vi v1, v0, 1\n\t"
+            "th.vslide1up.vx v8, v9, zero\n\t" // {0, 0, 4, 4}
+            "th.vslideup.vi v0, v2, 1\n\t" // {aux[0], aux[1], aux[0], aux[1]}
+            "li %[tmp], 4\n\t"
+            "th.vsetvli zero, %[tmp], e32, m1\n\t"
+            "th.vid.v v9\n\t"
+            "th.vmv.x.s %[tmp], v1\n\t"
+            "th.vsll.vi v9, v9, 1\n\t" // {0, 2, 4, 6}
+            "th.vmv.v.x v1, %[tmp]\n\t" // {aux[2], aux[2], aux[2], aux[2]}
+            "th.vsrl.vv v4, v1, v9\n\t"
+            "th.vsrl.vv v2, v0, v8\n\t"
+            "th.vand.vx v5, v4, %[kmask1]\n\t"
+            "th.vand.vx v3, v2, %[kmask2]\n\t"
+            "th.vsll.vi v6, v5, 4\n\t"
+            "th.vor.vv v7, v6, v3\n\t"
+            "li %[tmp], 16\n\t"
+            "th.vsetvli zero, %[tmp], e8, m1\n\t"
+            "th.vsub.vx v0, v7, %[c]\n\t"
+            "th.vsb.v v0, (%[scale])"
+            : [tmp] "=&r" (tmp)
+            : [sh] "r" (0x0000000400000004), [s6b] "r" (x[i].scales), [c] "r" (32)
+            , [scale] "r" (scale), [kmask1] "r" (kmask1), [kmask2] "r" (kmask2)
+            : "memory"
+            , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
+            , "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15"
+            , "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23"
+            , "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
+        );
+
+        uint8_t m = 1;
+        int isum = 0;
+        for (int j = 0; j < QK_K; j += 128) {
+            __asm__ __volatile__(
+                // fixme: use v0p7 mask layout directly
+                "th.vsetvli zero, %[vl32], e8, m2\n\t"
+                "th.vlb.v v8, (%[q3])\n\t"
+                "th.vsrl.vi v10, v8, 2\n\t"
+                "th.vsrl.vi v12, v8, 4\n\t"
+                "th.vsrl.vi v14, v8, 6\n\t"
+                "th.vand.vi v8, v8, 3\n\t"
+                "th.vand.vi v10, v10, 3\n\t"
+                "th.vand.vi v12, v12, 3\n\t"
+                "th.vlb.v v2, (%[qh])\n\t"
+                "th.vand.vx v4, v2, %[m]\n\t"
+                "slli %[m], %[m], 1\n\t"
+                "th.vmseq.vx v0, v4, zero\n\t"
+                "th.vadd.vi v8, v8, -4, v0.t\n\t"
+                "th.vand.vx v4, v2, %[m]\n\t"
+                "slli %[m], %[m], 1\n\t"
+                "th.vmseq.vx v0, v4, zero\n\t"
+                "th.vadd.vi v10, v10, -4, v0.t\n\t"
+                "th.vand.vx v4, v2, %[m]\n\t"
+                "slli %[m], %[m], 1\n\t"
+                "th.vmseq.vx v0, v4, zero\n\t"
+                "th.vadd.vi v12, v12, -4, v0.t\n\t"
+                "th.vand.vx v4, v2, %[m]\n\t"
+                "slli %[m], %[m], 1\n\t"
+                "th.vmseq.vx v0, v4, zero\n\t"
+                "th.vadd.vi v14, v14, -4, v0.t\n\t"
+                "th.vsetvli zero, %[vl128], e8, m8\n\t"
+                "th.vlb.v v0, (%[q8])\n\t"
+                "th.vsetvli zero, %[vl64], e8, m4\n\t"
+                "th.vwmul.vv v16, v0, v8\n\t"
+                "th.vwmul.vv v24, v4, v12\n\t"
+                "li %[tmp], 16\n\t"
+                "th.vsetvli zero, %[tmp], e16, m2\n\t"
+                "th.vmv.v.x v0, zero\n\t"
+                "th.vwredsum.vs v10, v16, v0\n\t"
+                "th.vwredsum.vs v9, v18, v0\n\t"
+                "th.vwredsum.vs v8, v20, v0\n\t"
+                "th.vwredsum.vs v7, v22, v0\n\t"
+                "th.vwredsum.vs v11, v24, v0\n\t"
+                "th.vwredsum.vs v12, v26, v0\n\t"
+                "th.vwredsum.vs v13, v28, v0\n\t"
+                "th.vwredsum.vs v14, v30, v0\n\t"
+                "li %[tmp], 4\n\t"
+                "th.vsetvli zero, %[tmp], e32, m1\n\t"
+                "th.vslideup.vi v10, v9, 1\n\t"
+                "th.vslideup.vi v8, v7, 1\n\t"
+                "th.vslideup.vi v11, v12, 1\n\t"
+                "th.vslideup.vi v13, v14, 1\n\t"
+                "th.vslideup.vi v10, v8, 2\n\t"
+                "th.vslideup.vi v11, v13, 2\n\t"
+                "li %[tmp], 8\n\t"
+                "th.vsetvli zero, %[tmp], e32, m2\n\t"
+                "th.vlb.v v12, (%[scale])\n\t"
+                "th.vmul.vv v10, v10, v12\n\t"
+                "th.vredsum.vs v0, v10, v0\n\t"
+                "th.vmv.x.s %[tmp], v0\n\t"
+                "add %[isum], %[isum], %[tmp]"
+                : [tmp] "=&r" (tmp), [m] "+&r" (m), [isum] "+&r" (isum)
+                : [vl128] "r" (128), [vl64] "r" (64), [vl32] "r" (32)
+                , [q3] "r" (q3), [qh] "r" (qh), [scale] "r" (scale), [q8] "r" (q8)
+                : "memory"
+                , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
+                , "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15"
+                , "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23"
+                , "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
+            );
+            q3 += 32;    q8 += 128;   scale += 8;
+        }
+
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        sumf += d * isum;
+    }
+
+    *s = sumf;
+
+#elif defined __riscv_v
+
+    uint32_t utmp[4];
+    float sumf = 0;
+    uint32_t aux[3];
+    const int vector_length = __riscv_vlenb() * 8;
+
    switch (vector_length) {
    case 256:
        for (int i = 0; i < nb; ++i) {
@@ -6331,7 +6555,7 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
                    "vslideup.vi v13, v14, 1\n\t"
                    "vslideup.vi v10, v8, 2\n\t"
                    "vslideup.vi v11, v13, 2\n\t"
-                    "vsetivli zero, 8, e32, m2\n\t"\
+                    "vsetivli zero, 8, e32, m2\n\t"
                    "vle8.v v15, (%[scale])\n\t"
                    "vsext.vf4 v12, v15\n\t"
                    "vmul.vv v10, v10, v12\n\t"
@@ -6771,7 +6995,11 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi

 void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
    assert(n % QK_K == 0);
+#ifdef __ARM_FEATURE_MATMUL_INT8
+    assert((nrc == 2) || (nrc == 1));
+#else
    assert(nrc == 1);
+#endif
    UNUSED(nrc);
    UNUSED(bx);
    UNUSED(by);
@@ -6788,6 +7016,146 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi

    uint32_t utmp[4];

+#if defined(__ARM_FEATURE_MATMUL_INT8)
+    if (nrc == 2) {
+        const block_q4_K * GGML_RESTRICT x0 = x;
+        const block_q4_K * GGML_RESTRICT x1 = (const block_q4_K *) ((const uint8_t *)vx + bx);
+        const block_q8_K * GGML_RESTRICT y0 = y;
+        const block_q8_K * GGML_RESTRICT y1 = (const block_q8_K *) ((const uint8_t *)vy + by);
+
+        const uint8x16_t m4b = vdupq_n_u8(0x0f);
+
+        float32x4_t vfsum = vdupq_n_f32(0.0f);
+
+        for (int i = 0; i < nb; ++i, ++x0, ++x1, ++y0, ++y1) {
+            const uint8_t * GGML_RESTRICT qx0 = x0->qs;
+            const uint8_t * GGML_RESTRICT qx1 = x1->qs;
+            const  int8_t * GGML_RESTRICT qy0 = y0->qs;
+            const  int8_t * GGML_RESTRICT qy1 = y1->qs;
+
+            // decode scales and mins
+            int8_t x0_scales[8], x1_scales[8];
+            int16x8_t x0_mins, x1_mins;
+            {
+                uint32_t scales_mins[3];
+                memcpy(scales_mins, x0->scales, 12);
+                const uint32_t mins_0_3 = scales_mins[1] & kmask1;
+                const uint32_t mins_4_7 = ((scales_mins[2] >> 4) & kmask2) | (((scales_mins[1] >> 6) & kmask3) << 4);
+                const uint32x2_t mins = {mins_0_3, mins_4_7};
+                x0_mins = vreinterpretq_s16_u16(vmovl_u8(vreinterpret_u8_u32(mins)));
+                uint32_t scales[2];
+                scales[0] = scales_mins[0] & kmask1; // scales 0~3
+                scales[1] = (scales_mins[2] & kmask2) | (((scales_mins[0] >> 6) & kmask3) << 4); // scales 4~7
+                memcpy(x0_scales, scales, 8);
+            }
+            {
+                uint32_t scales_mins[3];
+                memcpy(scales_mins, x1->scales, 12);
+                const uint32_t mins_0_3 = scales_mins[1] & kmask1;
+                const uint32_t mins_4_7 = ((scales_mins[2] >> 4) & kmask2) | (((scales_mins[1] >> 6) & kmask3) << 4);
+                const uint32x2_t mins = {mins_0_3, mins_4_7};
+                x1_mins = vreinterpretq_s16_u16(vmovl_u8(vreinterpret_u8_u32(mins)));
+                uint32_t scales[2];
+                scales[0] = scales_mins[0] & kmask1; // scales 0~3
+                scales[1] = (scales_mins[2] & kmask2) | (((scales_mins[0] >> 6) & kmask3) << 4); // scales 4~7
+                memcpy(x1_scales, scales, 8);
+            }
+
+            int32x4_t visum = {0};
+
+            // process 64 data points per iteration, totally 256 data points
+            for (int j = 0; j < QK_K / 64; ++j, qx0 += 32, qx1 += 32, qy0 += 64, qy1 += 64) {
+                const int8x16x4_t vy0 = vld1q_s8_x4(qy0);
+                const int8x16x4_t vy1 = vld1q_s8_x4(qy1);
+
+                int8x16_t vx0[4], vx1[4];
+                {
+                    const uint8x16x2_t vv = vld1q_u8_x2(qx0);
+                    vx0[0] = vreinterpretq_s8_u8(vandq_u8(vv.val[0], m4b));
+                    vx0[1] = vreinterpretq_s8_u8(vandq_u8(vv.val[1], m4b));
+                    vx0[2] = vreinterpretq_s8_u8(vshrq_n_u8(vv.val[0], 4));
+                    vx0[3] = vreinterpretq_s8_u8(vshrq_n_u8(vv.val[1], 4));
+                }
+                {
+                    const uint8x16x2_t vv = vld1q_u8_x2(qx1);
+                    vx1[0] = vreinterpretq_s8_u8(vandq_u8(vv.val[0], m4b));
+                    vx1[1] = vreinterpretq_s8_u8(vandq_u8(vv.val[1], m4b));
+                    vx1[2] = vreinterpretq_s8_u8(vshrq_n_u8(vv.val[0], 4));
+                    vx1[3] = vreinterpretq_s8_u8(vshrq_n_u8(vv.val[1], 4));
+                }
+
+                // process 32 data points (share same block scale) per iteration
+                for (int k = 0; k < 2; ++k) {
+                    const int blk = j * 2 + k;
+                    const int32x4_t block_scale = {
+                        x0_scales[blk],
+                        x0_scales[blk],
+                        x1_scales[blk],
+                        x1_scales[blk],
+                    };
+
+                    int32x4_t vr = {0};
+                    for (int l = 0; l < 2; ++l) {
+                        const int idx = k * 2 + l;
+                        const int64x2_t vx0_s64 = vreinterpretq_s64_s8(vx0[idx]);
+                        const int64x2_t vx1_s64 = vreinterpretq_s64_s8(vx1[idx]);
+                        const int64x2_t vy0_s64 = vreinterpretq_s64_s8(vy0.val[idx]);
+                        const int64x2_t vy1_s64 = vreinterpretq_s64_s8(vy1.val[idx]);
+                        const int8x16_t vx_l = vreinterpretq_s8_s64(vzip1q_s64(vx0_s64, vx1_s64));
+                        const int8x16_t vx_h = vreinterpretq_s8_s64(vzip2q_s64(vx0_s64, vx1_s64));
+                        const int8x16_t vy_l = vreinterpretq_s8_s64(vzip1q_s64(vy0_s64, vy1_s64));
+                        const int8x16_t vy_h = vreinterpretq_s8_s64(vzip2q_s64(vy0_s64, vy1_s64));
+                        vr = vmmlaq_s32(vr, vx_l, vy_l);
+                        vr = vmmlaq_s32(vr, vx_h, vy_h);
+                    }
+                    // apply block scale, will NOT overflow
+                    // block_scale * sum_256(int4*int8) <= 2^(8+8+4+8) = 28 bits
+                    visum = vmlaq_s32(visum, vr, block_scale);
+                }
+            }
+
+            // adjust bias, apply superblock scale
+            {
+                int32_t bias[4];
+                // no obvious uplift from sve sdot-16, just use neon mul add
+                const int16x8_t y0_sums = vpaddq_s16(vld1q_s16(y0->bsums), vld1q_s16(y0->bsums+8));
+                const int16x8_t y1_sums = vpaddq_s16(vld1q_s16(y1->bsums), vld1q_s16(y1->bsums+8));
+                bias[0] = vaddvq_s32(vaddq_s32(vmull_s16(vget_low_s16(y0_sums), vget_low_s16(x0_mins)),
+                                               vmull_s16(vget_high_s16(y0_sums), vget_high_s16(x0_mins))));
+                bias[1] = vaddvq_s32(vaddq_s32(vmull_s16(vget_low_s16(y1_sums), vget_low_s16(x0_mins)),
+                                               vmull_s16(vget_high_s16(y1_sums), vget_high_s16(x0_mins))));
+                bias[2] = vaddvq_s32(vaddq_s32(vmull_s16(vget_low_s16(y0_sums), vget_low_s16(x1_mins)),
+                                               vmull_s16(vget_high_s16(y0_sums), vget_high_s16(x1_mins))));
+                bias[3] = vaddvq_s32(vaddq_s32(vmull_s16(vget_low_s16(y1_sums), vget_low_s16(x1_mins)),
+                                               vmull_s16(vget_high_s16(y1_sums), vget_high_s16(x1_mins))));
+                const float32x4_t dmins = {
+                    GGML_FP16_TO_FP32(x0->dmin) * y0->d,
+                    GGML_FP16_TO_FP32(x0->dmin) * y1->d,
+                    GGML_FP16_TO_FP32(x1->dmin) * y0->d,
+                    GGML_FP16_TO_FP32(x1->dmin) * y1->d,
+                };
+                vfsum = vmlsq_f32(vfsum, vcvtq_f32_s32(vld1q_s32(bias)), dmins);
+
+                const float32x4_t superblock_scale = {
+                    GGML_FP16_TO_FP32(x0->d) * y0->d,
+                    GGML_FP16_TO_FP32(x0->d) * y1->d,
+                    GGML_FP16_TO_FP32(x1->d) * y0->d,
+                    GGML_FP16_TO_FP32(x1->d) * y1->d,
+                };
+                vfsum = vmlaq_f32(vfsum, vcvtq_f32_s32(visum), superblock_scale);
+            }
+        }
+
+        // vfsum = ABCD -> ACBD
+        // AC -> s, BD -> (s+bs)
+        vfsum = vzip1q_f32(vfsum, vextq_f32(vfsum, vfsum, 2));
+        vst1_f32(s,      vget_low_f32 (vfsum));
+        vst1_f32(s + bs, vget_high_f32(vfsum));
+
+        return;
+    }
+#endif
+
 #ifdef __ARM_FEATURE_SVE
    float sumf = 0;
    for (int i = 0; i < nb; ++i) {
@@ -7180,14 +7548,130 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi

    *s = hsum_float_8(acc) + _mm_cvtss_f32(acc_m);

-#elif defined __riscv_v_intrinsic
+#elif defined __riscv_xtheadvector

    const uint8_t * scales = (const uint8_t*)&utmp[0];
    const uint8_t * mins   = (const uint8_t*)&utmp[2];

-    const int vector_length = __riscv_vlenb() * 8;
    float sumf = 0;

+    for (int i = 0; i < nb; ++i) {
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
+
+        int tmp, tmp2, sumi;
+        __asm__ __volatile__(
+            "li %[t1], 12\n\t"
+            "th.vsetvli zero, %[t1], e8, m1\n\t"
+            "th.vlb.v v1, (%[s6b])\n\t" // {aux[0], aux[1], aux[2]}
+            "li %[t1], 4\n\t"
+            "th.vsetvli zero, %[t1], e32, m1\n\t"
+            "th.vslidedown.vi v2, v1, 2\n\t"
+            "th.vmv.v.v v3, v2\n\t"
+            "th.vslideup.vi v2, v3, 1\n\t" // {aux[2], aux[2]}
+            "li %[t1], 2\n\t"
+            "th.vsetvli zero, %[t1], e32, m1\n\t"
+            "th.vmv.v.i v4, 4\n\t"
+            "th.vand.vx v8, v1, %[kmask1]\n\t"
+            "th.vslide1up.vx v5, v4, zero\n\t" // {0, 4}
+            "th.vsrl.vi v6, v1, 6\n\t"
+            "th.vsrl.vv v7, v2, v5\n\t"
+            "th.vand.vx v0, v6, %[kmask3]\n\t"
+            "th.vand.vx v2, v7, %[kmask2]\n\t"
+            "th.vsll.vi v6, v0, 4\n\t"
+            "li %[t2], 8\n\t"
+            "addi %[t1], %[utmp], 4\n\t"
+            "th.vor.vv v1, v6, v2\n\t"
+            "th.vssw.v v8, (%[utmp]), %[t2]\n\t"
+            "th.vssw.v v1, (%[t1]), %[t2]\n\t"
+            "th.vsetvli zero, zero, e32, m2\n\t" // vl == 8
+            "th.vlw.v v2, (%[bsums])\n\t"
+            "th.vsetvli zero, %[t2], e16, m1\n\t"
+            "th.vnsrl.vi v0, v2, 0\n\t"
+            "th.vnsrl.vi v1, v2, 16\n\t"
+            "th.vadd.vv v2, v0, v1\n\t"
+            "th.vlbu.v v4, (%[mins])\n\t"
+            "th.vwmul.vv v6, v4, v2\n\t"
+            "th.vmv.v.x v0, zero\n\t"
+            "th.vsetvli zero, %[t2], e32, m2\n\t"
+            "th.vredsum.vs v0, v6, v0\n\t"
+            "th.vmv.x.s %[sumi], v0"
+            : [t1] "=&r" (tmp), [t2] "=&r" (tmp2), [sumi] "=&r" (sumi)
+            : [bsums] "r" (y[i].bsums), [mins] "r" (mins), [utmp] "r" (utmp)
+            , [s6b] "r" (x[i].scales), [kmask1] "r" (kmask1)
+            , [kmask2] "r" (kmask2), [kmask3] "r" (kmask3)
+            : "memory"
+            , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
+            , "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15"
+            , "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23"
+            , "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
+        );
+        sumf -= dmin * sumi;
+
+        const uint8_t * restrict q4 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        sumi = 0;
+        const uint8_t * scale = scales;
+
+        for (int j = 0; j < QK_K/128; ++j) {
+            int vl128 = 128, vl64 = 64, vl32 = 32;
+            __asm__ __volatile__(
+                "th.vsetvli zero, %[vl128], e8, m8\n\t"
+                "th.vlb.v v8, (%[q8])\n\t"
+                "th.vsetvli zero, %[vl64], e8, m4\n\t"
+                "th.vlb.v v0, (%[q4])\n\t"
+                "th.vsrl.vi v4, v0, 4\n\t"
+                "th.vand.vi v0, v0, 0xF\n\t"
+                "th.vsetvli zero, %[vl32], e8, m2\n\t"
+                "th.vwmul.vv v28, v6, v14\n\t"
+                "th.vwmul.vv v20, v4, v10\n\t"
+                "th.vwmul.vv v24, v2, v12\n\t"
+                "th.vwmul.vv v16, v0, v8\n\t"
+                "li %[tmp], 4\n\t"
+                "th.vsetvli zero, %[tmp], e32, m1\n\t"
+                "th.vlbu.v v1, (%[scale])\n\t"
+                "th.vmv.v.x v0, zero\n\t"
+                "th.vsetvli zero, %[vl32], e16, m4\n\t"
+                "th.vwredsum.vs v6, v24, v0\n\t"
+                "th.vwredsum.vs v7, v28, v0\n\t"
+                "th.vwredsum.vs v4, v16, v0\n\t"
+                "th.vwredsum.vs v5, v20, v0\n\t"
+                "th.vsetvli zero, %[tmp], e32, m1\n\t"
+                "th.vslideup.vi v6, v7, 1\n\t"
+                "th.vslideup.vi v4, v5, 1\n\t"
+                "th.vslideup.vi v4, v6, 2\n\t"
+                "th.vmul.vv v8, v4, v1\n\t"
+                "th.vredsum.vs v0, v8, v0\n\t"
+                "th.vmv.x.s %[tmp], v0\n\t"
+                "add %[sumi], %[sumi], %[tmp]"
+                : [tmp] "=&r" (tmp), [sumi] "+&r" (sumi)
+                : [vl128] "r" (vl128), [vl64] "r" (vl64), [vl32] "r" (vl32)
+                , [q4] "r" (q4), [q8] "r" (q8), [scale] "r" (scale)
+                : "memory"
+                , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
+                , "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15"
+                , "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23"
+                , "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
+            );
+
+            q4 += 64;    q8 += 128;    scale += 4;
+        }
+
+        sumf += d * sumi;
+
+    }
+
+    *s = sumf;
+
+#elif defined __riscv_v
+
+    const uint8_t * scales = (const uint8_t*)&utmp[0];
+    const uint8_t * mins   = (const uint8_t*)&utmp[2];
+
+    float sumf = 0;
+    const int vector_length = __riscv_vlenb() * 8;
+
    switch (vector_length) {
    case 256:
        for (int i = 0; i < nb; ++i) {
@@ -8074,7 +8558,7 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi

    *s = sumf;

-#elif defined __riscv_v_intrinsic
+#elif defined __riscv_v

    const uint8_t * scales = (const uint8_t*)&utmp[0];
    const uint8_t * mins   = (const uint8_t*)&utmp[2];
@@ -9232,11 +9716,92 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
    }
    *s = sumf;

-#elif defined __riscv_v_intrinsic
+#elif defined __riscv_xtheadvector

-    const int vector_length = __riscv_vlenb() * 8;
    float sumf = 0;

+    for (int i = 0; i < nb; ++i) {
+
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+
+        const uint8_t * restrict q6 = x[i].ql;
+        const uint8_t * restrict qh = x[i].qh;
+        const  int8_t * restrict q8 = y[i].qs;
+
+        const int8_t * restrict scale = x[i].scales;
+
+        int sum_t = 0;
+        int t0;
+
+        for (int j = 0; j < QK_K/128; ++j) {
+            __asm__ __volatile__(
+                "th.vsetvli zero, %[vl32], e8, m2\n\t" // vl == 32
+                "th.vlb.v v4, (%[qh])\n\t"
+                "th.vsll.vi v0, v4, 4\n\t"
+                "th.vsll.vi v2, v4, 2\n\t"
+                "th.vsrl.vi v6, v4, 2\n\t"
+                "th.vsetvli zero, %[vl64], e8, m4\n\t" // vl == 64
+                "th.vlb.v v8, (%[q6])\n\t"
+                "th.vsrl.vi v12, v8, 4\n\t"
+                "th.vand.vi v8, v8, 0xF\n\t"
+                "th.vsetvli zero, %[vl128], e8, m8\n\t" // vl == 128
+                "th.vand.vx v0, v0, %[mask]\n\t"
+                "th.vor.vv v8, v8, v0\n\t"
+                "th.vlb.v v0, (%[q8])\n\t"
+                "th.vsub.vx v8, v8, %[vl32]\n\t"
+                "th.vsetvli zero, %[vl64], e8, m4\n\t" // vl == 64
+                "th.vwmul.vv v16, v0, v8\n\t"
+                "th.vwmul.vv v24, v4, v12\n\t"
+                "li %[t0], 16\n\t"
+                "th.vsetvli zero, %[t0], e16, m2\n\t" // vl == 16
+                "th.vmv.v.x v0, zero\n\t"
+                "th.vwredsum.vs v10, v16, v0\n\t"
+                "th.vwredsum.vs v9, v18, v0\n\t"
+                "th.vwredsum.vs v8, v20, v0\n\t"
+                "th.vwredsum.vs v7, v22, v0\n\t"
+                "th.vwredsum.vs v11, v24, v0\n\t"
+                "th.vwredsum.vs v12, v26, v0\n\t"
+                "th.vwredsum.vs v13, v28, v0\n\t"
+                "th.vwredsum.vs v14, v30, v0\n\t"
+                "li %[t0], 4\n\t"
+                "th.vsetvli zero, %[t0], e32, m1\n\t" // vl == 4
+                "th.vslideup.vi v10, v9, 1\n\t"
+                "th.vslideup.vi v8, v7, 1\n\t"
+                "th.vslideup.vi v11, v12, 1\n\t"
+                "th.vslideup.vi v13, v14, 1\n\t"
+                "th.vslideup.vi v10, v8, 2\n\t"
+                "th.vslideup.vi v11, v13, 2\n\t"
+                "li %[t0], 8\n\t"
+                "th.vsetvli zero, %[t0], e32, m2\n\t" // vl == 8
+                "th.vlb.v v4, (%[scale])\n\t"
+                "th.vmul.vv v2, v4, v10\n\t"
+                "th.vredsum.vs v0, v2, v0\n\t"
+                "th.vmv.x.s %[t0], v0\n\t"
+                "add %[sumi], %[sumi], %[t0]"
+                : [sumi] "+&r" (sum_t), [t0] "=&r" (t0)
+                : [qh] "r" (qh), [q6] "r" (q6), [q8] "r" (q8), [scale] "r" (scale)
+                , [vl32] "r" (32), [vl64] "r" (64), [vl128] "r" (128)
+                , [mask] "r" (0x30)
+                : "memory"
+                , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
+                , "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15"
+                , "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23"
+                , "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
+            );
+            q6 += 64;   qh += 32;   q8 += 128;   scale += 8;
+        }
+
+        sumf += d * sum_t;
+
+    }
+
+    *s = sumf;
+
+#elif defined __riscv_v
+
+    float sumf = 0;
+    const int vector_length = __riscv_vlenb() * 8;
+
    switch (vector_length) {
    case 256:
        for (int i = 0; i < nb; ++i) {
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -270,7 +270,11 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = {
        .from_float               = quantize_row_q4_K,
        .vec_dot                  = ggml_vec_dot_q4_K_q8_K,
        .vec_dot_type             = GGML_TYPE_Q8_K,
+#if defined (__ARM_FEATURE_MATMUL_INT8)
+        .nrows                    = 2,
+#else
        .nrows                    = 1,
+#endif
    },
    [GGML_TYPE_Q5_K] = {
        .from_float               = quantize_row_q5_K,
@@ -2202,6 +2206,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
                    } break;

                case GGML_UNARY_OP_GELU:
+                case GGML_UNARY_OP_GELU_ERF:
                case GGML_UNARY_OP_GELU_QUICK:
                case GGML_UNARY_OP_SILU:
                    {
@@ -2413,12 +2418,32 @@ static bool ggml_thread_apply_priority(int32_t prio) {
    // This is up to the applications.
    DWORD p = THREAD_PRIORITY_NORMAL;
    switch (prio) {
+        case GGML_SCHED_PRIO_LOW:      p = THREAD_PRIORITY_BELOW_NORMAL;  break;
        case GGML_SCHED_PRIO_NORMAL:   p = THREAD_PRIORITY_NORMAL;        break;
        case GGML_SCHED_PRIO_MEDIUM:   p = THREAD_PRIORITY_ABOVE_NORMAL;  break;
        case GGML_SCHED_PRIO_HIGH:     p = THREAD_PRIORITY_HIGHEST;       break;
        case GGML_SCHED_PRIO_REALTIME: p = THREAD_PRIORITY_TIME_CRITICAL; break;
    }

+    if (prio != GGML_SCHED_PRIO_LOW) {
+        // Tell Windows that this thread should not be throttled (needs its own CPU core).
+        // Newer Windows 11 versions aggresively park (offline) CPU cores and often place
+        // all our threads onto the first 4 cores which results in terrible performance with
+        // n_threads > 4
+        #if _WIN32_WINNT >= 0x0602
+        THREAD_POWER_THROTTLING_STATE t;
+        ZeroMemory(&t, sizeof(t));
+        t.Version     = THREAD_POWER_THROTTLING_CURRENT_VERSION;
+        t.ControlMask = THREAD_POWER_THROTTLING_EXECUTION_SPEED;
+        t.StateMask   = 0;
+
+        if (!SetThreadInformation(GetCurrentThread(), ThreadPowerThrottling, &t, sizeof(t))) {
+            GGML_LOG_DEBUG("failed to disable thread power throttling %d : (%d)\n", prio, (int) GetLastError());
+            return false;
+        }
+        #endif
+    }
+
    if (prio == GGML_SCHED_PRIO_NORMAL) {
        // Keep inherited policy/priority
        return true;
@@ -2446,6 +2471,8 @@ static bool ggml_thread_apply_priority(int32_t prio) {
    struct sched_param p;
    int32_t policy = SCHED_OTHER;
    switch (prio) {
+        // TODO: there seems to be no way to set lower prio on Apple platforms
+        case GGML_SCHED_PRIO_LOW:      policy = SCHED_OTHER; p.sched_priority = 0;  break;
        case GGML_SCHED_PRIO_NORMAL:   policy = SCHED_OTHER; p.sched_priority = 0;  break;
        case GGML_SCHED_PRIO_MEDIUM:   policy = SCHED_FIFO;  p.sched_priority = 40; break;
        case GGML_SCHED_PRIO_HIGH:     policy = SCHED_FIFO;  p.sched_priority = 80; break;
@@ -2502,6 +2529,7 @@ static bool ggml_thread_apply_priority(int32_t prio) {
    struct sched_param p;
    int32_t policy = SCHED_OTHER;
    switch (prio) {
+        case GGML_SCHED_PRIO_LOW:      policy = SCHED_BATCH; p.sched_priority = 0;  break;
        case GGML_SCHED_PRIO_NORMAL:   policy = SCHED_OTHER; p.sched_priority = 0;  break;
        case GGML_SCHED_PRIO_MEDIUM:   policy = SCHED_FIFO;  p.sched_priority = 40; break;
        case GGML_SCHED_PRIO_HIGH:     policy = SCHED_FIFO;  p.sched_priority = 80; break;
@@ -3483,6 +3511,19 @@ void ggml_cpu_init(void) {
            const uint64_t t_end = ggml_time_us(); UNUSED(t_end);

            GGML_PRINT_DEBUG("%s: GELU, Quick GELU, SILU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0);
+
+#ifdef GGML_USE_OPENMP
+            //if (!getenv("OMP_WAIT_POLICY")) {
+            //    // set the wait policy to active, so that OpenMP threads don't sleep
+            //    putenv("OMP_WAIT_POLICY=active");
+            //}
+
+            if (!getenv("KMP_BLOCKTIME")) {
+                // set the time to wait before sleeping a thread
+                // this is less aggressive than setting the wait policy to active, but should achieve similar results in most cases
+                putenv("KMP_BLOCKTIME=200"); // 200ms
+            }
+#endif
        }

 #if defined(__ARM_ARCH)
--- a/ggml/src/ggml-cpu/ops.cpp
+++ b/ggml/src/ggml-cpu/ops.cpp
@@ -2691,6 +2691,109 @@ static void ggml_compute_forward_gelu(
    }
 }

+// ggml_compute_forward_gelu_erf
+
+static void ggml_compute_forward_gelu_erf_f32(
+        const ggml_compute_params * params,
+        ggml_tensor * dst) {
+
+    const ggml_tensor * src0 = dst->src[0];
+
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nc = src0->ne[0];
+    const int nr = ggml_nrows(src0);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int i1 = ir0; i1 < ir1; i1++) {
+        ggml_vec_gelu_erf_f32(nc,
+                (float *) ((char *) dst->data  + i1*( dst->nb[1])),
+                (float *) ((char *) src0->data + i1*(src0->nb[1])));
+
+#ifndef NDEBUG
+        for (int k = 0; k < nc; k++) {
+            const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k];
+            GGML_UNUSED(x);
+            assert(!isnan(x));
+            assert(!isinf(x));
+        }
+#endif
+    }
+}
+
+static void ggml_compute_forward_gelu_erf_f16(
+    const ggml_compute_params * params,
+    ggml_tensor * dst) {
+
+    const ggml_tensor * src0 = dst->src[0];
+
+    assert(ggml_is_contiguous_1(src0));
+    assert(ggml_is_contiguous_1(dst));
+    assert(ggml_are_same_shape(src0, dst));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nc = src0->ne[0];
+    const int nr = ggml_nrows(src0);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int i1 = ir0; i1 < ir1; i1++) {
+        ggml_vec_gelu_erf_f16(nc,
+                (ggml_fp16_t *) ((char *) dst->data  + i1*( dst->nb[1])),
+                (ggml_fp16_t *) ((char *) src0->data + i1*(src0->nb[1])));
+
+#ifndef NDEBUG
+        for (int k = 0; k < nc; k++) {
+            const ggml_fp16_t x = ((ggml_fp16_t *) ((char *) dst->data + i1*( dst->nb[1])))[k];
+            const float v = GGML_FP16_TO_FP32(x);
+            GGML_UNUSED(v);
+            assert(!isnan(v));
+            assert(!isinf(v));
+        }
+#endif
+    }
+}
+
+static void ggml_compute_forward_gelu_erf(
+        const ggml_compute_params * params,
+        ggml_tensor * dst) {
+
+    const ggml_tensor * src0 = dst->src[0];
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_gelu_erf_f32(params, dst);
+            } break;
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_gelu_erf_f16(params, dst);
+            } break;
+        default:
+            {
+                GGML_ABORT("fatal error");
+            }
+    }
+}
+
 // ggml_compute_forward_gelu_quick

 static void ggml_compute_forward_gelu_quick_f32(
@@ -7530,39 +7633,83 @@ static void ggml_compute_forward_ssm_scan_f32(
    const int ir1 = MIN(ir0 + dr, nr);
    const int ir  = ir1 - ir0;

-    for (int i3 = 0; i3 < n_s; ++i3) {
-        for (int i2 = 0; i2 < n_t; ++i2) {
-            const float * s0 = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); // {d_state, d_inner, n_s}
-            const float * x  = (const float *) ((const char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s}
-            const float * dt = (const float *) ((const char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1]) + i3*(src2->nb[2])); // {d_inner, n_t, n_s}
-            const float * A  = (const float *) ((const char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner}
-            const float * B  = (const float *) ((const char *) src4->data +  i2*(src4->nb[1]) + i3*(src4->nb[2])); // {d_state, n_t, n_s}
-            const float * C  = (const float *) ((const char *) src5->data +  i2*(src5->nb[1]) + i3*(src5->nb[2])); // {d_state, n_t, n_s}
-                  float * y  = (      float *) ((      char *) dst->data  + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s}
-                  float * s  = (      float *) ((      char *) dst->data  + ir0*(src0->nb[1]) + i3*(src0->nb[2]) +     src1->nb[3]);  // {d_state, d_inner, n_s}
+    #ifdef __ARM_FEATURE_SVE
+        for (int i3 = 0; i3 < n_s; ++i3) {
+            for (int i2 = 0; i2 < n_t; ++i2) {
+                const float * s0 = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); // {d_state, d_inner, n_s}
+                const float * x  = (const float *) ((const char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s}
+                const float * dt = (const float *) ((const char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1]) + i3*(src2->nb[2])); // {d_inner, n_t, n_s}
+                const float * A  = (const float *) ((const char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner}
+                const float * B  = (const float *) ((const char *) src4->data +  i2*(src4->nb[1]) + i3*(src4->nb[2])); // {d_state, n_t, n_s}
+                const float * C  = (const float *) ((const char *) src5->data +  i2*(src5->nb[1]) + i3*(src5->nb[2])); // {d_state, n_t, n_s}
+                    float * y  = (      float *) ((      char *) dst->data  + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s}
+                    float * s  = (      float *) ((      char *) dst->data  + ir0*(src0->nb[1]) + i3*(src0->nb[2]) +     src1->nb[3]);  // {d_state, d_inner, n_s}

-            // use the output as the source for the next token-wise iterations
-            if (i2 > 0) { s0 = s; }
+                // use the output as the source for the next token-wise iterations
+                if (i2 > 0) { s0 = s; }

-            // d_inner
-            for (int i1 = 0; i1 < ir; ++i1) {
-                // ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78
-                float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1];
-                float x_dt = x[i1] * dt_soft_plus;
-                float sumf = 0.0f;
-                // d_state
-                for (int i0 = 0; i0 < nc; ++i0) {
-                    int i = i0 + i1*nc;
-                    // state = prev_state * dA + dB * x
-                    float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt);
-                    // y = rowwise_dotprod(state, C)
-                    sumf += state * C[i0];
-                    s[i] = state;
+                // d_inner
+                for (int i1 = 0; i1 < ir; ++i1) {
+                    float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1];
+                    float x_dt = x[i1] * dt_soft_plus;
+                    svfloat32_t vx_dt = GGML_F32_VEC_SET1(x_dt);
+                    svfloat32_t vdt_soft_plus = GGML_F32_VEC_SET1(dt_soft_plus);
+                    svfloat32_t r1_vector = GGML_F32_VEC_ZERO;
+
+                    for (int64_t k = 0; k < nc; k += svcntw()) {
+                        svfloat32_t vA = GGML_F32_VEC_LOAD(&A[i1*nc + k]);
+                        svfloat32_t vB = GGML_F32_VEC_LOAD(&B[k]);
+                        svfloat32_t vC = GGML_F32_VEC_LOAD(&C[k]);
+                        svfloat32_t vs0 = GGML_F32_VEC_LOAD(&s0[i1*nc + k]);
+
+                        svfloat32_t t1 = GGML_F32_VEC_MUL(vdt_soft_plus, vA);
+                        t1 = exp_ps_sve(svptrue_b32(), t1);
+                        svfloat32_t t2 = GGML_F32_VEC_MUL(vx_dt, vB);
+
+                        vs0 = GGML_F32_VEC_FMA(vs0, t1, t2);
+                        r1_vector = GGML_F32_VEC_ADD(GGML_F32_VEC_MUL(vs0, vC), r1_vector);
+
+                        GGML_F32_VEC_STORE(&s[i1*nc + k], vs0);
+                    }
+                    y[i1] = GGML_F32xt_REDUCE_ONE(r1_vector);
                }
-                y[i1] = sumf;
            }
        }
-    }
+    #else
+        for (int i3 = 0; i3 < n_s; ++i3) {
+            for (int i2 = 0; i2 < n_t; ++i2) {
+                const float * s0 = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); // {d_state, d_inner, n_s}
+                const float * x  = (const float *) ((const char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s}
+                const float * dt = (const float *) ((const char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1]) + i3*(src2->nb[2])); // {d_inner, n_t, n_s}
+                const float * A  = (const float *) ((const char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner}
+                const float * B  = (const float *) ((const char *) src4->data +  i2*(src4->nb[1]) + i3*(src4->nb[2])); // {d_state, n_t, n_s}
+                const float * C  = (const float *) ((const char *) src5->data +  i2*(src5->nb[1]) + i3*(src5->nb[2])); // {d_state, n_t, n_s}
+                    float * y  = (      float *) ((      char *) dst->data  + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s}
+                    float * s  = (      float *) ((      char *) dst->data  + ir0*(src0->nb[1]) + i3*(src0->nb[2]) +     src1->nb[3]);  // {d_state, d_inner, n_s}
+
+                // use the output as the source for the next token-wise iterations
+                if (i2 > 0) { s0 = s; }
+
+                // d_inner
+                for (int i1 = 0; i1 < ir; ++i1) {
+                    // ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78
+                    float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1];
+                    float x_dt = x[i1] * dt_soft_plus;
+                    float sumf = 0.0f;
+                    // d_state
+                    for (int i0 = 0; i0 < nc; ++i0) {
+                        int i = i0 + i1*nc;
+                        // state = prev_state * dA + dB * x
+                        float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt);
+                        // y = rowwise_dotprod(state, C)
+                        sumf += state * C[i0];
+                        s[i] = state;
+                    }
+                    y[i1] = sumf;
+                }
+            }
+        }
+    #endif
 }

 void ggml_compute_forward_ssm_scan(
@@ -7749,6 +7896,10 @@ void ggml_compute_forward_unary(
            {
                ggml_compute_forward_gelu(params, dst);
            } break;
+        case GGML_UNARY_OP_GELU_ERF:
+            {
+                ggml_compute_forward_gelu_erf(params, dst);
+            } break;
        case GGML_UNARY_OP_GELU_QUICK:
            {
                ggml_compute_forward_gelu_quick(params, dst);
@@ -7963,6 +8114,14 @@ static void ggml_compute_forward_rwkv_wkv6_f32(
        #define GGML_F32X_MUL GGML_F32x16_MUL
        #define GGML_F32X_FMA GGML_F32x16_FMA
        #define WKV_VECTOR_SIZE 16
+    #elif defined(__ARM_FEATURE_SVE) && defined(__aarch64__)
+        #define GGML_F32X GGML_F32xt
+        #define GGML_F32X_SET1 GGML_F32xt_SET1
+        #define GGML_F32X_LOAD GGML_F32xt_LOAD
+        #define GGML_F32X_STORE GGML_F32xt_STORE
+        #define GGML_F32X_MUL GGML_F32xt_MUL
+        #define GGML_F32X_FMA GGML_F32xt_FMA
+        #define WKV_VECTOR_SIZE 8
    #elif defined(__ARM_NEON) && defined(__aarch64__)
        #define GGML_F32X GGML_F32x4
        #define GGML_F32X_SET1 GGML_F32x4_SET1
@@ -7974,7 +8133,13 @@ static void ggml_compute_forward_rwkv_wkv6_f32(
    #endif

    #ifdef WKV_VECTOR_SIZE
-        const int64_t vec_count = head_size / WKV_VECTOR_SIZE;
+        int wkv_vector_size;
+        #if defined(__ARM_FEATURE_SVE)
+            wkv_vector_size = svcntw();
+        #else
+            wkv_vector_size = WKV_VECTOR_SIZE;
+        #endif
+        const int64_t vec_count = head_size / wkv_vector_size;

        for (int64_t t = 0; t < T; t++) {
            size_t t_offset = t * t_stride;
@@ -8004,7 +8169,7 @@ static void ggml_compute_forward_rwkv_wkv6_f32(
                    GGML_F32X time_decay_vec = GGML_F32X_SET1(time_decay_val);

                    for (int64_t j = 0; j < vec_count; j++) {
-                        size_t base_j = j * WKV_VECTOR_SIZE;
+                        size_t base_j = j * wkv_vector_size;
                        size_t t_h_j_offset = t_h_offset + base_j;
                        size_t h_2d_i_j_offset = h_2d_i_offset + base_j;

@@ -8029,7 +8194,7 @@ static void ggml_compute_forward_rwkv_wkv6_f32(
                    }

                    // Handle remaining elements, this will not be used.
-                    for (int64_t j = vec_count * WKV_VECTOR_SIZE; j < head_size; j++) {
+                    for (int64_t j = vec_count * wkv_vector_size; j < head_size; j++) {
                        size_t t_h_j_offset = t_h_offset + j;
                        size_t h_2d_i_j_offset = h_2d_i_offset + j;
                        float v_val = v[t_h_j_offset];
@@ -8165,6 +8330,14 @@ static void ggml_compute_forward_gla_f32(
        #define GGML_F32X_MUL GGML_F32x16_MUL
        #define GGML_F32X_FMA GGML_F32x16_FMA
        #define GLA_VECTOR_SIZE 16
+    #elif defined(__ARM_FEATURE_SVE) && defined(__aarch64__)
+        #define GGML_F32X GGML_F32xt
+        #define GGML_F32X_SET1 GGML_F32xt_SET1
+        #define GGML_F32X_LOAD GGML_F32xt_LOAD
+        #define GGML_F32X_STORE GGML_F32xt_STORE
+        #define GGML_F32X_MUL GGML_F32xt_MUL
+        #define GGML_F32X_FMA GGML_F32xt_FMA
+        #define GLA_VECTOR_SIZE 8
    #elif defined(__ARM_NEON) && defined(__aarch64__)
        #define GGML_F32X GGML_F32x4
        #define GGML_F32X_SET1 GGML_F32x4_SET1
@@ -8176,7 +8349,13 @@ static void ggml_compute_forward_gla_f32(
    #endif

    #ifdef GLA_VECTOR_SIZE
-        const int64_t vec_count = head_size / GLA_VECTOR_SIZE;
+        int gla_vector_size;
+        #if defined(__ARM_FEATURE_SVE)
+            gla_vector_size = svcntw();
+        #else
+            gla_vector_size = GLA_VECTOR_SIZE;
+        #endif
+        const int64_t vec_count = head_size / gla_vector_size;

        for (int64_t t = 0; t < T; t++) {
            size_t t_offset = t * t_stride;
@@ -8203,7 +8382,7 @@ static void ggml_compute_forward_gla_f32(
                    GGML_F32X g_vec = GGML_F32X_SET1(g_val);

                    for (int64_t j = 0; j < vec_count; j++) {
-                        size_t base_j = j * GLA_VECTOR_SIZE;
+                        size_t base_j = j * gla_vector_size;
                        size_t t_h_j_offset = t_h_offset + base_j;
                        size_t h_2d_i_j_offset = h_2d_i_offset + base_j;

@@ -8227,7 +8406,7 @@ static void ggml_compute_forward_gla_f32(
                    }

                    // Handle remaining elements, this will not be used.
-                    for (int64_t j = vec_count * GLA_VECTOR_SIZE; j < head_size; j++) {
+                    for (int64_t j = vec_count * gla_vector_size; j < head_size; j++) {
                        size_t t_h_j_offset = t_h_offset + j;
                        size_t h_2d_i_j_offset = h_2d_i_offset + j;
                        float v_val = v[t_h_j_offset];
@@ -8336,83 +8515,126 @@ static void ggml_compute_forward_rwkv_wkv7_f32(
    int64_t h_stride_2d = head_size * head_size;

    #if defined(GGML_SIMD)
-        for (int64_t t = 0; t < T; t++) {
-            int64_t t_offset = t * t_stride;
-            int64_t state_offset = head_size * C * (t / (T / n_seqs));
-            float * state_cur = state + state_offset;
-            float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[6]->data + state_offset;
+        #if defined(__ARM_FEATURE_SVE)
+            // scalar Route to scalar implementation       //TODO: Write SVE code
+            for (int64_t t = 0; t < T; t++) {
+                int64_t t_offset = t * t_stride;
+                int64_t state_offset = head_size * C * (t / (T / n_seqs));
+                float * state_cur = state + state_offset;
+                float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[6]->data + state_offset;

-            for (int64_t h = h_start; h < h_end; h++) {
-                int64_t h_offset = h * h_stride;
-                int64_t t_h_offset = t_offset + h_offset;
-                int64_t h_2d_offset = h * h_stride_2d;
+                for (int64_t h = h_start; h < h_end; h++) {
+                    int64_t h_offset = h * h_stride;
+                    int64_t t_h_offset = t_offset + h_offset;
+                    int64_t h_2d_offset = h * h_stride_2d;

-                for (int64_t ii = 0; ii < head_size; ii++) {
-                    int64_t t_h_i_offset = t_h_offset + ii;
-                    int64_t h_2d_i_offset = h_2d_offset + ii * h_stride;
+                    for (int64_t i = 0; i < head_size; i++) {
+                        int64_t t_h_i_offset = t_h_offset + i;
+                        int64_t h_2d_i_offset = h_2d_offset + i * h_stride;

-                    GGML_F32_VEC v_vec = GGML_F32_VEC_SET1(v[t_h_i_offset]);
+                        float v_val = v[t_h_i_offset];

-                    float sa = 0;
-                    {
-                        GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO };
-                        GGML_F32_VEC ax[GGML_F32_ARR];
-                        GGML_F32_VEC ay[GGML_F32_ARR];
-                        for (int64_t j = 0; j < head_size; j += GGML_F32_STEP) {
-                            for (int64_t kk = 0; kk < GGML_F32_ARR; kk++) {
-                                ax[kk] = GGML_F32_VEC_LOAD(&a[t_h_offset + j + kk * GGML_F32_EPR]);
-                                ay[kk] = GGML_F32_VEC_LOAD(&state_prev[h_2d_i_offset + j + kk * GGML_F32_EPR]);
-                                sum[kk] = GGML_F32_VEC_FMA(sum[kk], ax[kk], ay[kk]);
-                            }
+                        float sa = 0, result = 0;
+                        for (int64_t j = 0; j < head_size; j++) {
+                            sa += a[t_h_offset + j] * state_prev[h_2d_i_offset + j];
                        }
-                        GGML_F32_VEC_REDUCE(sa, sum);
-                    }

-                    GGML_F32_VEC sa_vec = GGML_F32_VEC_SET1(sa);
+                        for (int64_t j = 0; j < head_size; j++) {
+                            int64_t t_h_j_offset = t_h_offset + j;
+                            int64_t h_2d_i_j_offset = h_2d_i_offset + j;

-                    int64_t j = 0;
-                    GGML_F32_VEC result_vec[GGML_F32_ARR] = { GGML_F32_VEC_ZERO };
-                    for (; j < head_size; j += GGML_F32_STEP) {
-                        for (int64_t kk = 0; kk < GGML_F32_ARR; kk++) {
-                            int64_t t_h_j_offset = t_h_offset + j + kk * GGML_F32_EPR;
-                            int64_t h_2d_i_j_offset = h_2d_i_offset + j + kk * GGML_F32_EPR;
-
-                            GGML_F32_VEC r_vec = GGML_F32_VEC_LOAD(&r[t_h_j_offset]);
-                            GGML_F32_VEC w_vec = GGML_F32_VEC_LOAD(&w[t_h_j_offset]);
-                            GGML_F32_VEC k_vec = GGML_F32_VEC_LOAD(&k[t_h_j_offset]);
-                            GGML_F32_VEC b_vec = GGML_F32_VEC_LOAD(&b[t_h_j_offset]);
-
-                            k_vec = GGML_F32_VEC_MUL(v_vec, k_vec);
-
-                            GGML_F32_VEC state_vec = GGML_F32_VEC_LOAD(&state_prev[h_2d_i_j_offset]);
-                            // kv + s * decay + sa * b
-                            state_vec = GGML_F32_VEC_FMA(k_vec, state_vec, w_vec);
-                            state_vec = GGML_F32_VEC_FMA(state_vec, sa_vec, b_vec);
-                            GGML_F32_VEC_STORE(&state_cur[h_2d_i_j_offset], state_vec);
-
-                            result_vec[kk] = GGML_F32_VEC_FMA(result_vec[kk], state_vec, r_vec);
+                            float r_val = r[t_h_j_offset];
+                            float w_val = w[t_h_j_offset];
+                            float k_val = k[t_h_j_offset];
+                            float b_val = b[t_h_j_offset];
+                            float kv_val = v_val * k_val;
+                            float prev_state_val = state_prev[h_2d_i_j_offset];
+                            state_cur[h_2d_i_j_offset] = prev_state_val * w_val + kv_val + sa * b_val;
+                            result += state_cur[h_2d_i_j_offset] * r_val;
                        }
-                    }
-                    GGML_F32_VEC_REDUCE(dst_data[t_h_i_offset], result_vec);
-
-                    // There shouldn't be left-overs though.
-                    for (; j < head_size; j++) {
-                        int64_t t_h_j_offset = t_h_offset + j;
-                        int64_t h_2d_i_j_offset = h_2d_i_offset + j;
-
-                        float r_val = r[t_h_j_offset];
-                        float w_val = w[t_h_j_offset];
-                        float k_val = k[t_h_j_offset];
-                        float b_val = b[t_h_j_offset];
-                        float kv_val = v[t_h_i_offset] * k_val;
-
-                        float prev_state_val = state_prev[h_2d_i_j_offset];
-                        state_cur[h_2d_i_j_offset] = prev_state_val * w_val + kv_val + sa * b_val;
-                        dst_data[t_h_i_offset] += state_cur[h_2d_i_j_offset] * r_val;
+                        dst_data[t_h_i_offset] = result;
                    }
                }
            }
-        }
+        #else
+            for (int64_t t = 0; t < T; t++) {
+                int64_t t_offset = t * t_stride;
+                int64_t state_offset = head_size * C * (t / (T / n_seqs));
+                float * state_cur = state + state_offset;
+                float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[6]->data + state_offset;
+
+                for (int64_t h = h_start; h < h_end; h++) {
+                    int64_t h_offset = h * h_stride;
+                    int64_t t_h_offset = t_offset + h_offset;
+                    int64_t h_2d_offset = h * h_stride_2d;
+
+                    for (int64_t ii = 0; ii < head_size; ii++) {
+                        int64_t t_h_i_offset = t_h_offset + ii;
+                        int64_t h_2d_i_offset = h_2d_offset + ii * h_stride;
+
+                        GGML_F32_VEC v_vec = GGML_F32_VEC_SET1(v[t_h_i_offset]);
+
+                        float sa = 0;
+                        {
+                            GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO };
+                            GGML_F32_VEC ax[GGML_F32_ARR];
+                            GGML_F32_VEC ay[GGML_F32_ARR];
+                            for (int64_t j = 0; j < head_size; j += GGML_F32_STEP) {
+                                for (int64_t kk = 0; kk < GGML_F32_ARR; kk++) {
+                                    ax[kk] = GGML_F32_VEC_LOAD(&a[t_h_offset + j + kk * GGML_F32_EPR]);
+                                    ay[kk] = GGML_F32_VEC_LOAD(&state_prev[h_2d_i_offset + j + kk * GGML_F32_EPR]);
+                                    sum[kk] = GGML_F32_VEC_FMA(sum[kk], ax[kk], ay[kk]);
+                                }
+                            }
+                            GGML_F32_VEC_REDUCE(sa, sum);
+                        }
+
+                        GGML_F32_VEC sa_vec = GGML_F32_VEC_SET1(sa);
+
+                        int64_t j = 0;
+                        GGML_F32_VEC result_vec[GGML_F32_ARR] = { GGML_F32_VEC_ZERO };
+                        for (; j < head_size; j += GGML_F32_STEP) {
+                            for (int64_t kk = 0; kk < GGML_F32_ARR; kk++) {
+                                int64_t t_h_j_offset = t_h_offset + j + kk * GGML_F32_EPR;
+                                int64_t h_2d_i_j_offset = h_2d_i_offset + j + kk * GGML_F32_EPR;
+
+                                GGML_F32_VEC r_vec = GGML_F32_VEC_LOAD(&r[t_h_j_offset]);
+                                GGML_F32_VEC w_vec = GGML_F32_VEC_LOAD(&w[t_h_j_offset]);
+                                GGML_F32_VEC k_vec = GGML_F32_VEC_LOAD(&k[t_h_j_offset]);
+                                GGML_F32_VEC b_vec = GGML_F32_VEC_LOAD(&b[t_h_j_offset]);
+
+                                k_vec = GGML_F32_VEC_MUL(v_vec, k_vec);
+
+                                GGML_F32_VEC state_vec = GGML_F32_VEC_LOAD(&state_prev[h_2d_i_j_offset]);
+                                // kv + s * decay + sa * b
+                                state_vec = GGML_F32_VEC_FMA(k_vec, state_vec, w_vec);
+                                state_vec = GGML_F32_VEC_FMA(state_vec, sa_vec, b_vec);
+                                GGML_F32_VEC_STORE(&state_cur[h_2d_i_j_offset], state_vec);
+
+                                result_vec[kk] = GGML_F32_VEC_FMA(result_vec[kk], state_vec, r_vec);
+                            }
+                        }
+                        GGML_F32_VEC_REDUCE(dst_data[t_h_i_offset], result_vec);
+
+                        // There shouldn't be left-overs though.
+                        for (; j < head_size; j++) {
+                            int64_t t_h_j_offset = t_h_offset + j;
+                            int64_t h_2d_i_j_offset = h_2d_i_offset + j;
+
+                            float r_val = r[t_h_j_offset];
+                            float w_val = w[t_h_j_offset];
+                            float k_val = k[t_h_j_offset];
+                            float b_val = b[t_h_j_offset];
+                            float kv_val = v[t_h_i_offset] * k_val;
+
+                            float prev_state_val = state_prev[h_2d_i_j_offset];
+                            state_cur[h_2d_i_j_offset] = prev_state_val * w_val + kv_val + sa * b_val;
+                            dst_data[t_h_i_offset] += state_cur[h_2d_i_j_offset] * r_val;
+                        }
+                    }
+                }
+            }
+        #endif
    #else
        for (int64_t t = 0; t < T; t++) {
            int64_t t_offset = t * t_stride;
--- a/ggml/src/ggml-cpu/simd-mappings.h
+++ b/ggml/src/ggml-cpu/simd-mappings.h
@@ -17,7 +17,123 @@
 //   number of elements to fit in a single register
 //

-#if defined(__ARM_NEON) && defined(__ARM_FEATURE_FMA)
+#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_FMA)
+
+#define GGML_SIMD
+
+// F32 SVE
+#define GGML_F32_EPR 8
+#define DEFAULT_PG svptrue_b32()
+
+#define GGML_F32xt                        svfloat32_t
+#define GGML_F32xt_ZERO                   svdup_n_f32(0.0f)
+#define GGML_F32xt_SET1(x)                svdup_n_f32(x)
+#define GGML_F32xt_LOAD_IMPL(pg, a, ...)  svld1_f32(pg, a)
+#define GGML_F32xt_LOAD(...)              GGML_F32xt_LOAD_IMPL(DEFAULT_PG, __VA_ARGS__)
+#define GGML_F32xt_STORE_IMPL(pg,a,b)     svst1_f32(pg, a, b)
+#define GGML_F32xt_STORE(...)             GGML_F32xt_STORE_IMPL(DEFAULT_PG, __VA_ARGS__)
+#define GGML_F32xt_FMA_IMPL(pg, a, b, c)  svmad_f32_m(pg, a, b, c)
+#define GGML_F32xt_FMA(...)               GGML_F32xt_FMA_IMPL(DEFAULT_PG, __VA_ARGS__)
+#define GGML_F32xt_ADD_IMPL(pg, a, b)     svadd_f32_m(pg, a, b)
+#define GGML_F32xt_ADD(...)               GGML_F32xt_ADD_IMPL(DEFAULT_PG, __VA_ARGS__)
+#define GGML_F32xt_MUL_IMPL(pg, a, b)     svmul_f32_m(pg, a, b)
+#define GGML_F32xt_MUL(...)               GGML_F32xt_MUL_IMPL(DEFAULT_PG, __VA_ARGS__)
+#define GGML_F32xt_REDUCE_ONE_IMPL(pg, a) svaddv(pg, a)
+#define GGML_F32xt_REDUCE_ONE(...)        GGML_F32xt_REDUCE_ONE_IMPL(DEFAULT_PG, __VA_ARGS__)
+#define GGML_F32xt_REDUCE_IMPL(pg, res, sum1, sum2, sum3, sum4, sum5, sum6, sum7, sum8)  \
+{                                                      \
+    sum1 = svadd_f32_m(DEFAULT_PG, sum1, sum2);        \
+    sum3 = svadd_f32_m(DEFAULT_PG, sum3, sum4);        \
+    sum5 = svadd_f32_m(DEFAULT_PG, sum5, sum6);        \
+    sum7 = svadd_f32_m(DEFAULT_PG, sum7, sum8);        \
+    sum1 = svadd_f32_m(DEFAULT_PG, sum1, sum3);        \
+    sum5 = svadd_f32_m(DEFAULT_PG, sum5, sum7);        \
+    sum1 = svadd_f32_m(DEFAULT_PG, sum1, sum5);        \
+    (res) = (ggml_float) GGML_F32xt_REDUCE_ONE(sum1);  \
+}
+#define GGML_F32xt_REDUCE(...) GGML_F32xt_REDUCE_IMPL(DEFAULT_PG, __VA_ARGS__)
+
+#define GGML_F32_VEC        GGML_F32xt
+#define GGML_F32_VEC_ZERO   GGML_F32xt_ZERO
+#define GGML_F32_VEC_SET1   GGML_F32xt_SET1
+#define GGML_F32_VEC_LOAD   GGML_F32xt_LOAD
+#define GGML_F32_VEC_STORE  GGML_F32xt_STORE
+#define GGML_F32_VEC_FMA    GGML_F32xt_FMA
+#define GGML_F32_VEC_ADD    GGML_F32xt_ADD
+#define GGML_F32_VEC_MUL    GGML_F32xt_MUL
+#define GGML_F32_VEC_REDUCE GGML_F32xt_REDUCE
+
+// F16 NEON
+
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+    #define GGML_F16_STEP 32
+    #define GGML_F16_EPR  8
+
+    #define GGML_F16x8              float16x8_t
+    #define GGML_F16x8_ZERO         vdupq_n_f16(0.0f)
+    #define GGML_F16x8_SET1(x)      vdupq_n_f16(x)
+    #define GGML_F16x8_LOAD(x)      vld1q_f16((const __fp16 *)(x))
+    #define GGML_F16x8_STORE        vst1q_f16
+    #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c)
+    #define GGML_F16x8_ADD          vaddq_f16
+    #define GGML_F16x8_MUL          vmulq_f16
+    #define GGML_F16x8_REDUCE(res, x)                               \
+    do {                                                            \
+        int offset = GGML_F16_ARR >> 1;                             \
+        for (int i = 0; i < offset; ++i) {                          \
+            (x)[i] = vaddq_f16((x)[i], (x)[offset+i]);              \
+        }                                                           \
+        offset >>= 1;                                               \
+        for (int i = 0; i < offset; ++i) {                          \
+            (x)[i] = vaddq_f16((x)[i], (x)[offset+i]);              \
+        }                                                           \
+        offset >>= 1;                                               \
+        for (int i = 0; i < offset; ++i) {                          \
+            (x)[i] = vaddq_f16((x)[i], (x)[offset+i]);              \
+        }                                                           \
+        const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 ((x)[0])); \
+        const float32x4_t t1 = vcvt_f32_f16(vget_high_f16((x)[0])); \
+        (res) = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1));         \
+    } while (0)
+
+    #define GGML_F16_VEC                GGML_F16x8
+    #define GGML_F16_VEC_ZERO           GGML_F16x8_ZERO
+    #define GGML_F16_VEC_SET1           GGML_F16x8_SET1
+    #define GGML_F16_VEC_LOAD(p, i)     GGML_F16x8_LOAD(p)
+    #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE((__fp16 *)(p), (r)[i])
+    #define GGML_F16_VEC_FMA            GGML_F16x8_FMA
+    #define GGML_F16_VEC_ADD            GGML_F16x8_ADD
+    #define GGML_F16_VEC_MUL            GGML_F16x8_MUL
+    #define GGML_F16_VEC_REDUCE         GGML_F16x8_REDUCE
+#else
+    // if FP16 vector arithmetic is not supported, we use FP32 instead
+    // and take advantage of the vcvt_ functions to convert to/from FP16
+
+    #define GGML_F16_STEP 16
+    #define GGML_F16_EPR  4
+
+    #define GGML_F32Cx4              float32x4_t
+    #define GGML_F32Cx4_ZERO         vdupq_n_f32(0.0f)
+    #define GGML_F32Cx4_SET1(x)      vdupq_n_f32(x)
+    #define GGML_F32Cx4_LOAD(x)      vcvt_f32_f16(vld1_f16((const __fp16 *)(x)))
+    #define GGML_F32Cx4_STORE(x, y)  vst1_f16(x, vcvt_f16_f32(y))
+    #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c)
+    #define GGML_F32Cx4_ADD          vaddq_f32
+    #define GGML_F32Cx4_MUL          vmulq_f32
+    #define GGML_F32Cx4_REDUCE       GGML_F32x4_REDUCE
+
+    #define GGML_F16_VEC                GGML_F32Cx4
+    #define GGML_F16_VEC_ZERO           GGML_F32Cx4_ZERO
+    #define GGML_F16_VEC_SET1           GGML_F32Cx4_SET1
+    #define GGML_F16_VEC_LOAD(p, i)     GGML_F32Cx4_LOAD(p)
+    #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE((__fp16 *)(p), r[i])
+    #define GGML_F16_VEC_FMA            GGML_F32Cx4_FMA
+    #define GGML_F16_VEC_ADD            GGML_F32Cx4_ADD
+    #define GGML_F16_VEC_MUL            GGML_F32Cx4_MUL
+    #define GGML_F16_VEC_REDUCE         GGML_F32Cx4_REDUCE
+#endif
+
+#elif defined(__ARM_NEON) && defined(__ARM_FEATURE_FMA)

 #define GGML_SIMD

--- a/ggml/src/ggml-cpu/vec.cpp
+++ b/ggml/src/ggml-cpu/vec.cpp
@@ -17,29 +17,98 @@ void ggml_vec_dot_f32(int n, float * GGML_RESTRICT s, size_t bs, const float * G

 #if defined(GGML_SIMD)
    float sumf = 0.0f;
-    const int np = (n & ~(GGML_F32_STEP - 1));

-    GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO };
+    #if defined(__ARM_FEATURE_SVE)
+        const int sve_register_length = ggml_cpu_get_sve_cnt() * 8;
+        const int ggml_f32_epr = sve_register_length / 32;//8;//svcntw(); // SVE128:4, SVE256:8, SVE512:16
+        const int ggml_f32_step = 8 * ggml_f32_epr; // choose 8 SVE registers

-    GGML_F32_VEC ax[GGML_F32_ARR];
-    GGML_F32_VEC ay[GGML_F32_ARR];
+        const int np = (n & ~(ggml_f32_step - 1));
+        svfloat32_t sum1 = svdup_n_f32(0.0f);
+        svfloat32_t sum2 = svdup_n_f32(0.0f);
+        svfloat32_t sum3 = svdup_n_f32(0.0f);
+        svfloat32_t sum4 = svdup_n_f32(0.0f);
+        svfloat32_t sum5 = svdup_n_f32(0.0f);
+        svfloat32_t sum6 = svdup_n_f32(0.0f);
+        svfloat32_t sum7 = svdup_n_f32(0.0f);
+        svfloat32_t sum8 = svdup_n_f32(0.0f);
+        svfloat32_t ax1,ax2,ax3,ax4,ax5,ax6,ax7,ax8;
+        svfloat32_t ay1,ay2,ay3,ay4,ay5,ay6,ay7,ay8;
+        for (int i = 0; i < np; i += ggml_f32_step) {
+            ax1 = GGML_F32_VEC_LOAD(x + i);
+            ay1 = GGML_F32_VEC_LOAD(y + i);
+            sum1 = GGML_F32_VEC_FMA(ax1, ay1, sum1);

-    for (int i = 0; i < np; i += GGML_F32_STEP) {
-        for (int j = 0; j < GGML_F32_ARR; j++) {
-            ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR);
-            ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
+            ax2 = GGML_F32_VEC_LOAD(x + i + 1*ggml_f32_epr);
+            ay2 = GGML_F32_VEC_LOAD(y + i + 1*ggml_f32_epr);
+            sum2 = GGML_F32_VEC_FMA(ax2, ay2, sum2);

-            sum[j] = GGML_F32_VEC_FMA(sum[j], ax[j], ay[j]);
+            ax3 = GGML_F32_VEC_LOAD(x + i + 2*ggml_f32_epr);
+            ay3 = GGML_F32_VEC_LOAD(y + i + 2*ggml_f32_epr);
+            sum3 = GGML_F32_VEC_FMA(ax3, ay3, sum3);
+
+            ax4 = GGML_F32_VEC_LOAD(x + i + 3*ggml_f32_epr);
+            ay4 = GGML_F32_VEC_LOAD(y + i + 3*ggml_f32_epr);
+            sum4 = GGML_F32_VEC_FMA(ax4, ay4, sum4);
+
+            ax5 = GGML_F32_VEC_LOAD(x + i + 4*ggml_f32_epr);
+            ay5 = GGML_F32_VEC_LOAD(y + i + 4*ggml_f32_epr);
+            sum5 = GGML_F32_VEC_FMA(ax5, ay5, sum5);
+
+            ax6 = GGML_F32_VEC_LOAD(x + i + 5*ggml_f32_epr);
+            ay6 = GGML_F32_VEC_LOAD(y + i + 5*ggml_f32_epr);
+            sum6 = GGML_F32_VEC_FMA(ax6, ay6, sum6);
+
+            ax7 = GGML_F32_VEC_LOAD(x + i + 6*ggml_f32_epr);
+            ay7 = GGML_F32_VEC_LOAD(y + i + 6*ggml_f32_epr);
+            sum7 = GGML_F32_VEC_FMA(ax7, ay7, sum7);
+
+            ax8 = GGML_F32_VEC_LOAD(x + i + 7*ggml_f32_epr);
+            ay8 = GGML_F32_VEC_LOAD(y + i + 7*ggml_f32_epr);
+            sum8 = GGML_F32_VEC_FMA(ax8, ay8, sum8);
        }
-    }
+        // leftovers
+        // Since 8 unrolls are done in above loop, leftovers lie in range [0, ggml_f32_step] which is handled in below loop
+        const int np2 = (n & ~(ggml_f32_epr - 1));
+        for (int i = np; i < np2; i += ggml_f32_epr) {
+            ax1 = GGML_F32_VEC_LOAD(x + i);
+            ay1 = GGML_F32_VEC_LOAD(y + i);
+            sum1 = GGML_F32_VEC_FMA(ax1, ay1, sum1);
+        }
+        // maximum number of leftover elements will be less that ggml_f32_epr. Apply predicated svmad on available elements only
+        if (np2 < n) {
+            svbool_t pg = svwhilelt_b32(np2, n);
+            ax1 = svld1_f32(pg, x + np2);
+            ay1 = svld1_f32(pg, y + np2);
+            sum1 = svmad_f32_m(pg, ax1, ay1, sum1);
+        }
+        // reduce sum1,sum2 to sum1
+        GGML_F32_VEC_REDUCE(sumf, sum1, sum2, sum3, sum4, sum5, sum6, sum7, sum8);
+    #else
+        const int np = (n & ~(GGML_F32_STEP - 1));

-    // reduce sum0..sum3 to sum0
-    GGML_F32_VEC_REDUCE(sumf, sum);
+        GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO };

-    // leftovers
-    for (int i = np; i < n; ++i) {
-        sumf += x[i]*y[i];
-    }
+        GGML_F32_VEC ax[GGML_F32_ARR];
+        GGML_F32_VEC ay[GGML_F32_ARR];
+
+        for (int i = 0; i < np; i += GGML_F32_STEP) {
+            for (int j = 0; j < GGML_F32_ARR; j++) {
+                ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR);
+                ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
+
+                sum[j] = GGML_F32_VEC_FMA(sum[j], ax[j], ay[j]);
+            }
+        }
+
+        // reduce sum0..sum3 to sum0
+        GGML_F32_VEC_REDUCE(sumf, sum);
+
+        // leftovers
+        for (int i = np; i < n; ++i) {
+            sumf += x[i]*y[i];
+        }
+    #endif
 #else
    // scalar
    ggml_float sumf = 0.0;
--- a/ggml/src/ggml-cpu/vec.h
+++ b/ggml/src/ggml-cpu/vec.h
@@ -5,6 +5,7 @@
 #include "ggml-impl.h"
 #include "simd-mappings.h"
 #include "ggml.h"
+#include "ggml-cpu.h"

 #if defined(GGML_USE_ACCELERATE)
 #include <Accelerate/Accelerate.h>
@@ -148,27 +149,108 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * GG

 inline static void ggml_vec_mad_f32(const int n, float * GGML_RESTRICT y, const float * GGML_RESTRICT x, const float v) {
 #if defined(GGML_SIMD)
-    const int np = (n & ~(GGML_F32_STEP - 1));
+    #if defined(__ARM_FEATURE_SVE)

-    GGML_F32_VEC vx = GGML_F32_VEC_SET1(v);
+        const int sve_register_length = ggml_cpu_get_sve_cnt() * 8;
+        const int ggml_f32_epr = sve_register_length / 32;//8;//svcntw(); // SVE128:4, SVE256:8, SVE512:16
+        const int ggml_f32_step = 8 * ggml_f32_epr; // choose 8 SVE registers
+        GGML_F32_VEC vx = GGML_F32_VEC_SET1(v);

-    GGML_F32_VEC ax[GGML_F32_ARR];
-    GGML_F32_VEC ay[GGML_F32_ARR];
+        const int np = (n & ~(ggml_f32_step - 1));
+        svfloat32_t ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8;
+        svfloat32_t ay1, ay2, ay3, ay4, ay5, ay6, ay7, ay8;
+        for (int i = 0; i < np; i += ggml_f32_step) {

-    for (int i = 0; i < np; i += GGML_F32_STEP) {
-        for (int j = 0; j < GGML_F32_ARR; j++) {
-            ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR);
-            ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
-            ay[j] = GGML_F32_VEC_FMA(ay[j], ax[j], vx);
+            ax1 = GGML_F32_VEC_LOAD(x + i);
+            ay1 = GGML_F32_VEC_LOAD(y + i);
+            ay1 = GGML_F32_VEC_FMA(ax1, vx, ay1);

-            GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]);
+            GGML_F32_VEC_STORE(y + i, ay1);
+
+            ax2 = GGML_F32_VEC_LOAD(x + i + 1*ggml_f32_epr);
+            ay2 = GGML_F32_VEC_LOAD(y + i + 1*ggml_f32_epr);
+            ay2 = GGML_F32_VEC_FMA(ax2, vx, ay2);
+
+            GGML_F32_VEC_STORE(y + i + 1*ggml_f32_epr, ay2);
+
+            ax3 = GGML_F32_VEC_LOAD(x + i + 2*ggml_f32_epr);
+            ay3 = GGML_F32_VEC_LOAD(y + i + 2*ggml_f32_epr);
+            ay3 = GGML_F32_VEC_FMA(ax3, vx, ay3);
+
+            GGML_F32_VEC_STORE(y + i + 2*ggml_f32_epr, ay3);
+
+            ax4 = GGML_F32_VEC_LOAD(x + i + 3*ggml_f32_epr);
+            ay4 = GGML_F32_VEC_LOAD(y + i + 3*ggml_f32_epr);
+            ay4 = GGML_F32_VEC_FMA(ax4, vx, ay4);
+
+            GGML_F32_VEC_STORE(y + i + 3*ggml_f32_epr, ay4);
+
+            ax5 = GGML_F32_VEC_LOAD(x + i + 4*ggml_f32_epr);
+            ay5 = GGML_F32_VEC_LOAD(y + i + 4*ggml_f32_epr);
+            ay5 = GGML_F32_VEC_FMA(ax5, vx, ay5);
+
+            GGML_F32_VEC_STORE(y + i + 4*ggml_f32_epr, ay5);
+
+            ax6 = GGML_F32_VEC_LOAD(x + i + 5*ggml_f32_epr);
+            ay6 = GGML_F32_VEC_LOAD(y + i + 5*ggml_f32_epr);
+            ay6 = GGML_F32_VEC_FMA(ax6, vx, ay6);
+
+            GGML_F32_VEC_STORE(y + i + 5*ggml_f32_epr, ay6);
+
+            ax7 = GGML_F32_VEC_LOAD(x + i + 6*ggml_f32_epr);
+            ay7 = GGML_F32_VEC_LOAD(y + i + 6*ggml_f32_epr);
+            ay7 = GGML_F32_VEC_FMA(ax7, vx, ay7);
+
+            GGML_F32_VEC_STORE(y + i + 6*ggml_f32_epr, ay7);
+
+            ax8 = GGML_F32_VEC_LOAD(x + i + 7*ggml_f32_epr);
+            ay8 = GGML_F32_VEC_LOAD(y + i + 7*ggml_f32_epr);
+            ay8 = GGML_F32_VEC_FMA(ax8, vx, ay8);
+
+            GGML_F32_VEC_STORE(y + i + 7*ggml_f32_epr, ay8);
        }
-    }
+        // leftovers
+        // Since 8 unrolls are done in above loop, leftovers lie in range [0, ggml_f32_step] which is handled in below loop
+        const int np2 = (n & ~(ggml_f32_epr - 1));
+        for (int i = np; i < np2; i += ggml_f32_epr) {
+            ax1 = GGML_F32_VEC_LOAD(x + i);
+            ay1 = GGML_F32_VEC_LOAD(y + i);
+            ay1 = GGML_F32_VEC_FMA(ax1, vx, ay1);

-    // leftovers
-    for (int i = np; i < n; ++i) {
-        y[i] += x[i]*v;
-    }
+            GGML_F32_VEC_STORE(y + i, ay1);
+        }
+        // maximum number of leftover elements will be less that ggml_f32_epr. Apply predicated svmad on available elements only
+        if (np2 < n) {
+            svbool_t pg =svwhilelt_b32(np2, n);
+            ax1 = svld1_f32(pg, x + np2);
+            ay1 = svld1_f32(pg, y + np2);
+            ay1 = svmad_f32_m(pg, ax1, vx, ay1);
+
+            svst1_f32(pg, y + np2, ay1);
+        }
+    #else
+        const int np = (n & ~(GGML_F32_STEP - 1));
+
+        GGML_F32_VEC vx = GGML_F32_VEC_SET1(v);
+
+        GGML_F32_VEC ax[GGML_F32_ARR];
+        GGML_F32_VEC ay[GGML_F32_ARR];
+
+        for (int i = 0; i < np; i += GGML_F32_STEP) {
+            for (int j = 0; j < GGML_F32_ARR; j++) {
+                ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR);
+                ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
+                ay[j] = GGML_F32_VEC_FMA(ay[j], ax[j], vx);
+
+                GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]);
+            }
+        }
+
+        // leftovers
+        for (int i = np; i < n; ++i) {
+            y[i] += x[i]*v;
+        }
+    #endif
 #else
    // scalar
    for (int i = 0; i < n; ++i) {
@@ -220,36 +302,45 @@ inline static void ggml_vec_mad_f32_unroll(const int n, const int xs, const int
    }

 #if defined(GGML_SIMD)
-    const int np = (n & ~(GGML_F32_STEP - 1));
-
-    GGML_F32_VEC vx[GGML_VEC_MAD_UNROLL];
-
-    for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) {
-        vx[k] = GGML_F32_VEC_SET1(v[k][0]);
-    }
-
-    GGML_F32_VEC ax[GGML_VEC_MAD_UNROLL][GGML_F32_ARR];
-    GGML_F32_VEC ay[GGML_F32_ARR];
-
-    for (int i = 0; i < np; i += GGML_F32_STEP) {
-        for (int j = 0; j < GGML_F32_ARR; j++) {
-            ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
-
-            for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) {
-                ax[k][j] = GGML_F32_VEC_LOAD(x[k] + i + j*GGML_F32_EPR);
-                ay[j] = GGML_F32_VEC_FMA(ay[j], ax[k][j], vx[k]);
+    #if defined(__ARM_FEATURE_SVE)
+        // scalar Route to scalar implementation       //TODO: Write SVE code
+        for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) {
+            for (int i = 0; i < n; ++i) {
+                y[i] += x[k][i]*v[k][0];
            }
-
-            GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]);
        }
-    }
+    #else
+        const int np = (n & ~(GGML_F32_STEP - 1));

-    // leftovers
-    for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) {
-        for (int i = np; i < n; ++i) {
-            y[i] += x[k][i]*v[k][0];
+        GGML_F32_VEC vx[GGML_VEC_MAD_UNROLL];
+
+        for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) {
+            vx[k] = GGML_F32_VEC_SET1(v[k][0]);
        }
-    }
+
+        GGML_F32_VEC ax[GGML_VEC_MAD_UNROLL][GGML_F32_ARR];
+        GGML_F32_VEC ay[GGML_F32_ARR];
+
+        for (int i = 0; i < np; i += GGML_F32_STEP) {
+            for (int j = 0; j < GGML_F32_ARR; j++) {
+                ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
+
+                for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) {
+                    ax[k][j] = GGML_F32_VEC_LOAD(x[k] + i + j*GGML_F32_EPR);
+                    ay[j] = GGML_F32_VEC_FMA(ay[j], ax[k][j], vx[k]);
+                }
+
+                GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]);
+            }
+        }
+
+        // leftovers
+        for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) {
+            for (int i = np; i < n; ++i) {
+                y[i] += x[k][i]*v[k][0];
+            }
+        }
+    #endif
 #else
    // scalar
    for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) {
@@ -265,25 +356,53 @@ inline static void ggml_vec_scale_f32(const int n, float * y, const float   v) {
 #if defined(GGML_USE_ACCELERATE)
    vDSP_vsmul(y, 1, &v, y, 1, n);
 #elif defined(GGML_SIMD)
-    const int np = (n & ~(GGML_F32_STEP - 1));
+    #if defined(__ARM_FEATURE_SVE)
+        const int sve_register_length = ggml_cpu_get_sve_cnt() * 8;
+        const int ggml_f32_epr = sve_register_length / 32;//8;//svcntw(); // SVE128:4, SVE256:8, SVE512:16
+        const int ggml_f32_step = 2 * ggml_f32_epr;

-    GGML_F32_VEC vx = GGML_F32_VEC_SET1(v);
+        GGML_F32_VEC vx = GGML_F32_VEC_SET1(v);
+        const int np = (n & ~(ggml_f32_step - 1));
+        svfloat32_t ay1;
+        svfloat32_t ay2;
+        for (int i = 0; i < np; i += ggml_f32_step) {
+            ay1 = GGML_F32_VEC_LOAD(y + i);
+            ay1 = GGML_F32_VEC_MUL(ay1, vx);
+            GGML_F32_VEC_STORE(y + i, ay1);

-    GGML_F32_VEC ay[GGML_F32_ARR];
-
-    for (int i = 0; i < np; i += GGML_F32_STEP) {
-        for (int j = 0; j < GGML_F32_ARR; j++) {
-            ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
-            ay[j] = GGML_F32_VEC_MUL(ay[j], vx);
-
-            GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]);
+            ay2 = GGML_F32_VEC_LOAD(y + i + 1*ggml_f32_epr);
+            ay2 = GGML_F32_VEC_MUL(ay2, vx);
+            GGML_F32_VEC_STORE(y + i + 1*ggml_f32_epr, ay2);
        }
-    }
+        // leftovers
+        // maximum number of leftover elements will be less that ggml_f32_epr. Apply predicated svmad on available elements only
+        if (np < n) {
+            svbool_t pg = svwhilelt_b32(np, n);
+            ay1 = svld1_f32(pg, y + np);
+            ay1 = svmul_f32_m(pg, ay1, vx);
+            svst1_f32(pg, y + np, ay1);
+        }
+    #else
+        const int np = (n & ~(GGML_F32_STEP - 1));

-    // leftovers
-    for (int i = np; i < n; ++i) {
-        y[i] *= v;
-    }
+        GGML_F32_VEC vx = GGML_F32_VEC_SET1(v);
+
+        GGML_F32_VEC ay[GGML_F32_ARR];
+
+        for (int i = 0; i < np; i += GGML_F32_STEP) {
+            for (int j = 0; j < GGML_F32_ARR; j++) {
+                ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
+                ay[j] = GGML_F32_VEC_MUL(ay[j], vx);
+
+                GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]);
+            }
+        }
+
+        // leftovers
+        for (int i = np; i < n; ++i) {
+            y[i] *= v;
+        }
+    #endif
 #else
    // scalar
    for (int i = 0; i < n; ++i) {
@@ -428,6 +547,7 @@ inline static void ggml_vec_exp_f16 (const int n, ggml_fp16_t * y, const ggml_fp
 static const float GELU_COEF_A     = 0.044715f;
 static const float GELU_QUICK_COEF = -1.702f;
 static const float SQRT_2_OVER_PI  = 0.79788456080286535587989211986876f;
+static const float SQRT_2_INV      = 0.70710678118654752440084436210484f;

 inline static float ggml_gelu_f32(float x) {
    return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
@@ -440,6 +560,14 @@ inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp
    }
 }

+inline static void ggml_vec_gelu_erf_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
+    for (int i = 0; i < n; ++i) {
+        float xi = GGML_FP16_TO_FP32(x[i]);
+        float res = 0.5f*xi*(1.0f + erff(xi*SQRT_2_INV));
+        y[i] = GGML_FP32_TO_FP16(res);
+    }
+}
+
 #ifdef GGML_GELU_FP16
 inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) {
    uint16_t t;
@@ -463,6 +591,13 @@ inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) {
 }
 #endif

+inline static void ggml_vec_gelu_erf_f32(const int n, float * y, const float * x) {
+    for (int i = 0; i < n; ++i) {
+        float xi = x[i];
+        y[i] = 0.5f*xi*(1.0f + erff(xi*SQRT_2_INV));
+    }
+}
+
 inline static float ggml_gelu_quick_f32(float x) {
    return x*(1.0f/(1.0f+expf(GELU_QUICK_COEF*x)));
 }
@@ -512,6 +647,42 @@ inline static ggml_fp16_t ggml_silu_f16(ggml_fp16_t x) {
 #error "ref: https://github.com/ggml-org/llama.cpp/pull/7154#issuecomment-2143844461"
 #endif

+/* Below function was borrowed from the GitHub repository:
+https://github.com/openvinotoolkit/openvino/blob/master/src/plugins/intel_cpu/src/nodes/kernels/scaled_attn/common.hpp */
+#if defined(__ARM_FEATURE_SVE) && defined(__aarch64__)
+    inline static svfloat32_t exp_ps_sve(svbool_t pg, svfloat32_t src) {
+        // Constants
+        const svfloat32_t log2_e = svdup_n_f32(1.4426950409f);
+        const svfloat32_t ln2 = svdup_n_f32(0.6931473921f);
+        const svfloat32_t half_ln2_sq = svdup_n_f32(0.2413862043f);
+        const svuint32_t not_mask17 = svdup_n_u32(~((1u << 17) - 1));
+        const svfloat32_t one = svdup_n_f32(1.0f);
+        const svfloat32_t inactive1 = svdup_n_f32(0.0f);
+        const svint32_t inactive2 = svdup_n_s32(0);
+
+        // Algorithm starts here
+        svfloat32_t t0 = svmul_f32_m(pg, src, log2_e);  // y = x * log2(e)
+        svfloat32_t t1 = svrintm_f32_m(inactive1, pg, t0);         // rount to int (float)
+        svint32_t t2 = svcvt_s32_f32_m(inactive2, pg, t1);         // n
+
+        t1 = svsub_f32_m(pg, t0, t1);   // a = y - floor(y)
+        t1 = svadd_f32_m(pg, t1, one);  // b = a + 1
+
+        svuint32_t t3 = svlsr_n_u32_m(pg, svreinterpret_u32_f32(t1), 17);  // v = b >> 17 (u32)
+        svfloat32_t t4 = svexpa_f32(t3);                                   // c = fexpa(v)
+        t4 = svscale_f32_m(pg, t4, t2);                                    // fexpa(v) * 2^(n)
+
+        // and_(t2.d, t1.d, not_mask17.d)
+        svfloat32_t t5 = svreinterpret_f32_u32(svand_u32_m(pg, svreinterpret_u32_f32(t1), not_mask17));
+        t5 = svsub_f32_m(pg, t1, t5);                // z
+        t0 = svmla_f32_m(pg, ln2, t5, half_ln2_sq);  // ln2 + half_ln2_sq * z
+        t0 = svmla_f32_m(pg, one, t5, t0);           // 1 + (ln2 * z) + (half_ln2_sq * z * z)
+        t0 = svmul_f32_m(pg, t0, t4);                // Final result
+
+        return t0;
+    }
+#endif
+
 #if defined(__ARM_NEON) && defined(__aarch64__)

 // adapted from arm limited optimized routine
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -168,7 +168,7 @@ void ggml_cuda_error(const char * stmt, const char * func, const char * file, in

 #define CUBLAS_CHECK(err) CUDA_CHECK_GEN(err, CUBLAS_STATUS_SUCCESS, cublas_get_error_str)

-#if !defined(GGML_USE_HIP)
+#if !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM)
 static const char * cu_get_error_str(CUresult err) {
    const char * err_str;
    cuGetErrorString(err, &err_str);
@@ -635,6 +635,7 @@ struct ggml_cuda_device_info {
        int     nsm;                // number of streaming multiprocessors
        size_t  smpb;               // max. shared memory per block
        size_t  smpbo;              // max. shared memory per block (with opt-in)
+        bool    integrated;         // Device is integrated as opposed to discrete
        bool    vmm;                // virtual memory support
        size_t  vmm_granularity;    // granularity of virtual memory
        size_t  total_vram;
--- a/ggml/src/ggml-cuda/cpy.cu
+++ b/ggml/src/ggml-cuda/cpy.cu
@@ -1,5 +1,8 @@
 #include "cpy.cuh"
 #include "dequantize.cuh"
+#ifdef GGML_USE_MUSA
+#include "ggml-musa/mudnn.cuh"
+#endif // GGML_USE_MUSA

 typedef void (*cpy_kernel_t)(const char * cx, char * cdst);

@@ -597,7 +600,14 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
 #endif
    if (src0->type == src1->type && ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
        GGML_ASSERT(ggml_nbytes(src0) == ggml_nbytes(src1));
-        CUDA_CHECK(cudaMemcpyAsync(src1_ddc, src0_ddc, ggml_nbytes(src0), cudaMemcpyDeviceToDevice, main_stream));
+#ifdef GGML_USE_MUSA
+        if (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16) {
+            CUDA_CHECK(mudnnMemcpyAsync(ctx, src1, src0));
+        } else
+#endif // GGML_USE_MUSA
+        {
+            CUDA_CHECK(cudaMemcpyAsync(src1_ddc, src0_ddc, ggml_nbytes(src0), cudaMemcpyDeviceToDevice, main_stream));
+        }
    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
        ggml_cpy_f32_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_BF16) {
--- a/ggml/src/ggml-cuda/fattn-common.cuh
+++ b/ggml/src/ggml-cuda/fattn-common.cuh
@@ -623,8 +623,8 @@ static __global__ void flash_attn_combine_results(
    __builtin_assume(tid < D);

    extern __shared__ float2 meta[];
-    if (tid < 2*parallel_blocks) {
-        ((float *) meta)[threadIdx.x] = ((const float *)VKQ_meta) [blockIdx.z*(2*parallel_blocks) + tid];
+    for (int i = tid; i < 2*parallel_blocks; i += D) {
+        ((float *) meta)[i] = ((const float *)VKQ_meta) [blockIdx.z*(2*parallel_blocks) + i];
    }

    __syncthreads();
--- a/ggml/src/ggml-cuda/fattn-mma-f16.cuh
+++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
@@ -652,9 +652,12 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
        float KQ_max_scale[cols_per_thread];
 #pragma unroll
        for (int col = 0; col < cols_per_thread; ++col) {
-            KQ_max_scale[col] = expf(KQ_max[col] - KQ_max_new[col]);
+            const float KQ_max_diff = KQ_max[col] - KQ_max_new[col];
+            KQ_max_scale[col] = expf(KQ_max_diff);
            KQ_max[col] = KQ_max_new[col];

+            *((uint32_t *) &KQ_max_scale[col]) *= KQ_max_diff >= SOFTMAX_FTZ_THRESHOLD;
+
            // Scale previous KQ_rowsum to account for a potential increase in KQ_max:
            KQ_rowsum[col] = KQ_max_scale[col]*KQ_rowsum[col] + KQ_rowsum_add[col];
        }
@@ -772,7 +775,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
    GGML_UNUSED(stride_mask); GGML_UNUSED(jt); GGML_UNUSED(tile_K);
    GGML_UNUSED(tile_V); GGML_UNUSED(tile_mask); GGML_UNUSED(Q_B);
    GGML_UNUSED(VKQ_C); GGML_UNUSED(KQ_max); GGML_UNUSED(KQ_rowsum);
-    GGML_UNUSED(kb0);
+    GGML_UNUSED(kb0); GGML_UNUSED(tile_Q);
    NO_DEVICE_CODE;
 #endif // NEW_MMA_AVAILABLE
 }
@@ -1246,7 +1249,7 @@ static __global__ void flash_attn_ext_f16(
        NO_DEVICE_CODE;
        return;
    }
-#endif __CUDA_ARCH__ == GGML_CUDA_CC_TURING
+#endif // __CUDA_ARCH__ == GGML_CUDA_CC_TURING

    static_assert(!mla || DKQ >= DV, "MLA needs DKQ >= DV");

--- a/ggml/src/ggml-cuda/fattn-vec-f16.cuh
+++ b/ggml/src/ggml-cuda/fattn-vec-f16.cuh
@@ -2,9 +2,9 @@
 #include "fattn-common.cuh"

 template<int D, int ncols, ggml_type type_K, ggml_type type_V, bool use_logit_softcap> // D == head size
-#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+#ifndef GGML_USE_HIP
 __launch_bounds__(D, 1)
-#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+#endif // GGML_USE_HIP
 static __global__ void flash_attn_vec_ext_f16(
        const char * __restrict__ Q,
        const char * __restrict__ K,
@@ -48,6 +48,12 @@ static __global__ void flash_attn_vec_ext_f16(
        NO_DEVICE_CODE;
        return;
    }
+#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+    if (ncols > 1) {
+        NO_DEVICE_CODE;
+        return;
+    }
+#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)

    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.

@@ -91,6 +97,13 @@ static __global__ void flash_attn_vec_ext_f16(
            kqsum_shared[j][threadIdx.x] = 0.0f;
        }
    }
+
+    __shared__ half maskh_shared[ncols*D];
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        maskh_shared[j*D + tid] = 0.0f;
+    }
+
    __syncthreads();

    // Convert Q to half2 (f16 K) or q8_1 (quantized K) and store in registers:
@@ -175,6 +188,36 @@ static __global__ void flash_attn_vec_ext_f16(
    for (int k_VKQ_0 = blockIdx.y*D; k_VKQ_0 < ne11; k_VKQ_0 += gridDim.y*D) {
        // Calculate KQ tile and keep track of new maximum KQ values:

+        if (mask) {
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+                maskh_shared[j*D + tid] = slopeh*maskh[j*ne11 + k_VKQ_0 + tid];
+            }
+
+            __syncthreads();
+
+            // When using multiple parallel sequences in llama.cpp, some KV slices can be fully masked out.
+            // In such cases, skip the KV slice.
+            // On AMD __all_sync would not work correctly because it assumes a warp size of 64.
+#ifndef GGML_USE_HIP
+            bool skip = true;
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+#pragma unroll
+                for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
+                    const int i = i0 + threadIdx.x;
+
+                    const float2 tmp = __half22float2(((const half2 *) maskh_shared)[j*(D/2) + i]);
+                    skip = skip && isinf(tmp.x) && isinf(tmp.y);
+                }
+            }
+            if (__all_sync(0xFFFFFFFF, skip)) {
+                __syncthreads();
+                continue;
+            }
+#endif // GGML_USE_HIP
+        }
+
        // For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression,
        // see https://github.com/ggerganov/llama.cpp/pull/7061 .
        // Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable).
@@ -202,7 +245,7 @@ static __global__ void flash_attn_vec_ext_f16(
                    sum = logit_softcap*tanhf(sum);
                }

-                sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f);
+                sum += maskh_shared[j*D + i_KQ];

                if (ncols == 1) {
                    kqmax_new        = ggml_cuda_hmax(kqmax_new,        sum);
@@ -335,7 +378,9 @@ void ggml_cuda_flash_attn_ext_vec_f16_case(ggml_backend_cuda_context & ctx, ggml
    float logit_softcap;
    memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));

-    if (Q->ne[1] == 1) {
+    const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
+
+    if (Q->ne[1] == 1 || GGML_CUDA_CC_IS_NVIDIA(cc)) {
        constexpr int cols_per_block = 1;
        if (logit_softcap == 0.0f) {
            constexpr bool use_logit_softcap = false;
--- a/ggml/src/ggml-cuda/fattn-vec-f32.cuh
+++ b/ggml/src/ggml-cuda/fattn-vec-f32.cuh
@@ -2,9 +2,9 @@
 #include "fattn-common.cuh"

 template<int D, int ncols, ggml_type type_K, ggml_type type_V, bool use_logit_softcap> // D == head size
-#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+#ifndef GGML_USE_HIP
 __launch_bounds__(D, 1)
-#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+#endif // GGML_USE_HIP
 static __global__ void flash_attn_vec_ext_f32(
        const char * __restrict__ Q,
        const char * __restrict__ K,
@@ -60,6 +60,12 @@ static __global__ void flash_attn_vec_ext_f32(
        NO_DEVICE_CODE;
        return;
    }
+#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+    if (ncols > 1) {
+        NO_DEVICE_CODE;
+        return;
+    }
+#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)

    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.

@@ -104,6 +110,13 @@ static __global__ void flash_attn_vec_ext_f32(
            kqsum_shared[j][threadIdx.x] = 0.0f;
        }
    }
+
+    __shared__ float maskf_shared[ncols*D];
+#pragma unroll
+    for (int j = 0; j < ncols; ++j) {
+        maskf_shared[j*D + tid] = 0.0f;
+    }
+
    __syncthreads();

    // Convert Q to float2 (f16 K) or q8_1 (quantized K) and store in registers:
@@ -181,6 +194,35 @@ static __global__ void flash_attn_vec_ext_f32(
    for (int k_VKQ_0 = blockIdx.y*D; k_VKQ_0 < ne11; k_VKQ_0 += gridDim.y*D) {
        // Calculate KQ tile and keep track of new maximum KQ values:

+        if (mask) {
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+                maskf_shared[j*D + tid] = slope*__half2float(maskh[j*ne11 + k_VKQ_0 + tid]);
+            }
+
+            __syncthreads();
+
+            // When using multiple parallel sequences in llama.cpp, some KV slices can be fully masked out.
+            // In such cases, skip the KV slice.
+            // On AMD __all_sync would not work correctly because it assumes a warp size of 64.
+#ifndef GGML_USE_HIP
+            bool skip = true;
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+#pragma unroll
+                for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
+                    const int i = i0 + threadIdx.x;
+
+                    skip = skip && isinf(maskf_shared[j*D + i]);
+                }
+            }
+            if (__all_sync(0xFFFFFFFF, skip)) {
+                __syncthreads();
+                continue;
+            }
+#endif // GGML_USE_HIP
+        }
+
        float kqmax_new_arr[ncols];
 #pragma unroll
        for (int j = 0; j < ncols; ++j) {
@@ -204,7 +246,7 @@ static __global__ void flash_attn_vec_ext_f32(
                    sum = logit_softcap*tanhf(sum);
                }

-                sum += mask ? slope*__half2float(maskh[j*ne11 + k_VKQ_0 + i_KQ]) : 0.0f;
+                sum += maskf_shared[j*D + i_KQ];

                kqmax_new_arr[j] = fmaxf(kqmax_new_arr[j], sum);

@@ -326,7 +368,9 @@ void ggml_cuda_flash_attn_ext_vec_f32_case(ggml_backend_cuda_context & ctx, ggml
    float logit_softcap;
    memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));

-    if (Q->ne[1] == 1) {
+    const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
+
+    if (Q->ne[1] == 1 || GGML_CUDA_CC_IS_NVIDIA(cc)) {
        constexpr int cols_per_block = 1;
        if (logit_softcap == 0.0f) {
            constexpr bool use_logit_softcap = false;
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -243,10 +243,10 @@ static ggml_cuda_device_info ggml_cuda_init() {

        info.default_tensor_split[id] = total_vram;
        total_vram += prop.totalGlobalMem;
-
-        info.devices[id].nsm       = prop.multiProcessorCount;
-        info.devices[id].smpb      = prop.sharedMemPerBlock;
-        info.devices[id].warp_size = prop.warpSize;
+        info.devices[id].integrated = prop.integrated;
+        info.devices[id].nsm        = prop.multiProcessorCount;
+        info.devices[id].smpb       = prop.sharedMemPerBlock;
+        info.devices[id].warp_size  = prop.warpSize;
 #if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
        info.devices[id].smpbo = prop.sharedMemPerBlock;

@@ -1065,6 +1065,10 @@ static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_
    GGML_UNUSED(buft);
 }

+static bool ggml_backend_buft_is_cuda_host(ggml_backend_buffer_type_t buft) {
+    return buft->iface.get_name == ggml_backend_cuda_host_buffer_type_name;
+}
+
 static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
    CUDA_CHECK(cudaFreeHost(buffer->context));
 }
@@ -1140,7 +1144,6 @@ typedef void (*ggml_cuda_op_mul_mat_t)(
 static cudaError_t ggml_cuda_cpy_tensor_2d(
    void * dst, const struct ggml_tensor * src, int64_t i3, int64_t i2, int64_t i1_low, int64_t i1_high, cudaStream_t stream) {

-    GGML_ASSERT(ggml_backend_buffer_is_cuda(src->buffer));
    const char * src_ptr = (const char *) src->data;
    char       * dst_ptr = (char       *) dst;

@@ -1423,8 +1426,6 @@ static void ggml_cuda_op_mul_mat(
    const int64_t nb2 = dst->nb[2];
    const int64_t nb3 = dst->nb[3];

-    GGML_ASSERT(ggml_backend_buffer_is_cuda(dst->buffer));
-    GGML_ASSERT(ggml_backend_buffer_is_cuda(src1->buffer));
    ggml_backend_cuda_buffer_context * src1_ctx = (ggml_backend_cuda_buffer_context *) src1->buffer->context;
    ggml_backend_cuda_buffer_context * dst_ctx  = (ggml_backend_cuda_buffer_context *) dst->buffer->context;

@@ -1746,7 +1747,7 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
    GGML_ASSERT(!ggml_is_transposed(src0));
    GGML_ASSERT(!ggml_is_transposed(src1));

-    GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer));
+    GGML_ASSERT(!ggml_backend_buft_is_cuda_split(src0->buffer->buft));
    GGML_ASSERT(src0->type == GGML_TYPE_F16);

    // Byte offsets and tensor dimensions are currently used in an inconsistent way for dst.
@@ -2192,6 +2193,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
                case GGML_UNARY_OP_SILU:
                    ggml_cuda_op_silu(ctx, dst);
                    break;
+                case GGML_UNARY_OP_GELU_ERF:
+                    ggml_cuda_op_gelu_erf(ctx, dst);
+                    break;
                case GGML_UNARY_OP_GELU_QUICK:
                    ggml_cuda_op_gelu_quick(ctx, dst);
                    break;
@@ -2638,6 +2642,8 @@ static void update_cuda_graph_executable(ggml_backend_cuda_context * cuda_ctx) {

 static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph,
    bool & graph_evaluated_or_captured, bool & use_cuda_graph, bool & cuda_graph_update_required) {
+    // flag used to determine whether it is an integrated_gpu
+    const bool integrated = ggml_cuda_info().devices[cuda_ctx->device].integrated;

    while (!graph_evaluated_or_captured) {
        // Only perform the graph execution if CUDA graphs are not enabled, or we are capturing the graph.
@@ -2656,7 +2662,7 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
                    if (node->src[j] != nullptr) {
                        assert(node->src[j]->buffer);
                        assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) ||
-                               ggml_backend_buft_is_cuda_split(node->src[j]->buffer->buft));
+                               ggml_backend_buft_is_cuda_split(node->src[j]->buffer->buft) || (integrated && ggml_backend_buft_is_cuda_host(node->src[j]->buffer->buft)));
                    }
                }
 #endif
@@ -2977,6 +2983,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                case GGML_UNARY_OP_SIGMOID:
                case GGML_UNARY_OP_HARDSIGMOID:
                case GGML_UNARY_OP_HARDSWISH:
+                case GGML_UNARY_OP_GELU_ERF:
                case GGML_UNARY_OP_GELU_QUICK:
                case GGML_UNARY_OP_TANH:
                case GGML_UNARY_OP_EXP:
@@ -2990,9 +2997,12 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
            {
                struct ggml_tensor * a = op->src[0];
                struct ggml_tensor * b = op->src[1];
-                // for small weight matrices the active device can end up without any rows, don't use row split in those cases
-                // this avoids some edge cases (and the performance would not be good anyways)
                if (a->buffer && ggml_backend_buft_is_cuda_split(a->buffer->buft)) {
+                    if (a->ne[2] > 1 || a->ne[3] > 1) {
+                        return false;
+                    }
+                    // for small weight matrices the active device can end up without any rows, don't use row split in those cases
+                    // this avoids some edge cases (and the performance would not be good anyways)
                    ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) a->buffer->buft->context;
                    int64_t row_low;
                    int64_t row_high;
@@ -3259,7 +3269,9 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
 }

 static bool ggml_backend_cuda_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
-    return (ggml_backend_buft_is_cuda(buft) || ggml_backend_buft_is_cuda_split(buft)) && buft->device == dev;
+    ggml_backend_cuda_device_context * dev_ctx = (ggml_backend_cuda_device_context *) dev->context;
+    const bool integrated = ggml_cuda_info().devices[dev_ctx->device].integrated;
+    return (((ggml_backend_buft_is_cuda(buft) || ggml_backend_buft_is_cuda_split(buft)) && buft->device == dev) || (integrated && ggml_backend_buft_is_cuda_host(buft)));
 }

 static int64_t get_op_batch_size(const ggml_tensor * op) {
--- a/ggml/src/ggml-cuda/unary.cu
+++ b/ggml/src/ggml-cuda/unary.cu
@@ -23,6 +23,12 @@ static __device__ __forceinline__ float op_gelu(float x) {
    return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
 }

+static __device__ __forceinline__ float op_gelu_erf(float x) {
+    const float SQRT_2_INV = 0.70710678118654752440084436210484f;
+
+    return 0.5f*x*(1.0f + erff(x*SQRT_2_INV));
+}
+
 static __device__ __forceinline__ float op_gelu_quick(float x) {
    const float GELU_QUICK_COEF = -1.702f;

@@ -134,6 +140,10 @@ void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
    ggml_cuda_op_unary<op_gelu>(ctx, dst);
 }

+void ggml_cuda_op_gelu_erf(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_cuda_op_unary<op_gelu_erf>(ctx, dst);
+}
+
 void ggml_cuda_op_gelu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
    ggml_cuda_op_unary<op_gelu_quick>(ctx, dst);
 }
--- a/ggml/src/ggml-cuda/unary.cuh
+++ b/ggml/src/ggml-cuda/unary.cuh
@@ -30,6 +30,8 @@ void ggml_cuda_op_silu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

 void ggml_cuda_op_silu_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

+void ggml_cuda_op_gelu_erf(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
 void ggml_cuda_op_gelu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

 void ggml_cuda_op_tanh(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
--- a/ggml/src/ggml-impl.h
+++ b/ggml/src/ggml-impl.h
@@ -32,6 +32,8 @@
 extern "C" {
 #endif

+void ggml_print_backtrace(void);
+
 #ifndef MIN
 #    define MIN(a, b) ((a) < (b) ? (a) : (b))
 #endif
@@ -386,7 +388,7 @@ GGML_API void ggml_aligned_free(void * ptr, size_t size);
        return r;
    }

-#elif defined(__riscv) && defined(GGML_RV_ZFH)
+#elif defined(__riscv) && defined(__riscv_zfhmin)

    static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
        float f;
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -149,6 +149,8 @@ enum ggml_metal_kernel_type {
    GGML_METAL_KERNEL_TYPE_SIGMOID,
    GGML_METAL_KERNEL_TYPE_GELU,
    GGML_METAL_KERNEL_TYPE_GELU_4,
+    GGML_METAL_KERNEL_TYPE_GELU_ERF,
+    GGML_METAL_KERNEL_TYPE_GELU_ERF_4,
    GGML_METAL_KERNEL_TYPE_GELU_QUICK,
    GGML_METAL_KERNEL_TYPE_GELU_QUICK_4,
    GGML_METAL_KERNEL_TYPE_SILU,
@@ -1103,6 +1105,8 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SIGMOID,                         sigmoid,                         true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU,                            gelu,                            true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_4,                          gelu_4,                          true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_ERF,                        gelu_erf,                        true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_ERF_4,                      gelu_erf_4,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK,                      gelu_quick,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK_4,                    gelu_quick_4,                    true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU,                            silu,                            true);
@@ -1613,6 +1617,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
                case GGML_UNARY_OP_RELU:
                case GGML_UNARY_OP_SIGMOID:
                case GGML_UNARY_OP_GELU:
+                case GGML_UNARY_OP_GELU_ERF:
                case GGML_UNARY_OP_GELU_QUICK:
                case GGML_UNARY_OP_SILU:
                case GGML_UNARY_OP_ELU:
@@ -2251,6 +2256,25 @@ static bool ggml_metal_encode_node(

                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                } break;
+                case GGML_UNARY_OP_GELU_ERF:
+                {
+                    int64_t n = ggml_nelements(dst);
+
+                    id<MTLComputePipelineState> pipeline = nil;
+
+                    if (n % 4 == 0) {
+                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_ERF_4].pipeline;
+                        n /= 4;
+                    } else {
+                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_ERF].pipeline;
+                    }
+
+                    [encoder setComputePipelineState:pipeline];
+                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+
+                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                } break;
                case GGML_UNARY_OP_GELU_QUICK:
                {
                    int64_t n = ggml_nelements(dst);
@@ -4742,6 +4766,8 @@ static bool ggml_metal_encode_node(
                    GGML_ASSERT(nqptg  % 8  == 0);
                    GGML_ASSERT(ncpsg  % 32 == 0);

+                    const int is_q = ggml_is_quantized(src1->type) ? 1 : 0;
+
                    // 2*(2*ncpsg + nqptg)*(nsg)
                    // ncpsg soft_max values + ncpsg mask values + a diagonal scaling matrix (in float)
                    //
@@ -4749,7 +4775,7 @@ static bool ggml_metal_encode_node(
                    // the shared memory needed for the simdgroups to load the KV cache
                    // each thread loads (dequantizes) 16 head elements, there are 32 threads in th SG
                    //
-#define FATTN_SMEM(nsg) (GGML_PAD((nqptg*(ne00 + 2*(2*ncpsg + nqptg)*(nsg)) + 16*32*(nsg))*(sizeof(float)/2), 16))
+#define FATTN_SMEM(nsg) (GGML_PAD((nqptg*(2*ne00 + 2*(2*ncpsg + nqptg)*(nsg)) + is_q*(16*32*(nsg)))*(sizeof(float)/2), 16))

                    int64_t nsgmax = 2;

@@ -4786,9 +4812,9 @@ static bool ggml_metal_encode_node(
                    // and store the soft_max values and the mask
                    //
                    // ne00*(nsg)
-                    // each simdgroup has a full f16 head vector in shared mem to accumulate results
+                    // each simdgroup has a full f32 head vector in shared mem to accumulate results
                    //
-#define FATTN_SMEM(nsg) (GGML_PAD((nqptg*(GGML_PAD(ne00, 128) + 4*ncpsg*(nsg)) + ne20*(nsg))*(sizeof(float)/2), 16))
+#define FATTN_SMEM(nsg) (GGML_PAD((nqptg*(GGML_PAD(ne00, 128) + 4*ncpsg*(nsg)) + 2*ne20*(nsg))*(sizeof(float)/2), 16))

                    int64_t nsgmax = 2;
                    while (true) {
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -856,6 +856,7 @@ kernel void kernel_tanh(
 constant float GELU_COEF_A     = 0.044715f;
 constant float GELU_QUICK_COEF = -1.702f;
 constant float SQRT_2_OVER_PI  = 0.79788456080286535587989211986876f;
+constant float SQRT_2_INV      = 0.70710678118654752440084436210484f;

 kernel void kernel_gelu(
    device const float * src0,
@@ -897,6 +898,42 @@ kernel void kernel_gelu_quick_4(
    dst[tpig] = x*(1.0f/(1.0f+exp(GELU_QUICK_COEF*x)));
 }

+// based on Abramowitz and Stegun formula 7.1.26 or similar Hastings' approximation
+// ref: https://www.johndcook.com/blog/python_erf/
+constant float p_erf  = 0.3275911f;
+constant float a1_erf = 0.254829592f;
+constant float a2_erf = -0.284496736f;
+constant float a3_erf = 1.421413741f;
+constant float a4_erf = -1.453152027f;
+constant float a5_erf = 1.061405429f;
+
+template<typename T>
+T erf_approx(T x) {
+    T sign_x = sign(x);
+    x = fabs(x);
+    T t = 1.0f / (1.0f + p_erf * x);
+    T y = 1.0f - (((((a5_erf * t + a4_erf) * t) + a3_erf) * t + a2_erf) * t + a1_erf) * t * exp(-x * x);
+    return sign_x * y;
+}
+
+kernel void kernel_gelu_erf(
+    device const float * src0,
+    device       float * dst,
+    uint tpig[[thread_position_in_grid]]) {
+    device const float & x = src0[tpig];
+
+    dst[tpig] = 0.5f*x*(1.0f+erf_approx<float>(x*SQRT_2_INV));
+}
+
+kernel void kernel_gelu_erf_4(
+    device const float4 * src0,
+    device       float4 * dst,
+    uint tpig[[thread_position_in_grid]]) {
+    device const float4 & x = src0[tpig];
+
+    dst[tpig] = 0.5f*x*(1.0f+erf_approx<float4>(x*SQRT_2_INV));
+}
+
 kernel void kernel_silu(
        device const float * src0,
        device       float * dst,
@@ -3255,7 +3292,7 @@ template<
    typename kd4x4_t, // key type in device memory
    short nl_k,
    void (*deq_k)(device const kd4x4_t *, short, thread k4x4_t &),
-    typename vd4x4_t, // key type in device memory
+    typename vd4x4_t, // value type in device memory
    short nl_v,
    void (*deq_v)(device const vd4x4_t *, short, thread v4x4_t &),
    short DK,        // K head size
@@ -3291,14 +3328,14 @@ kernel void kernel_flash_attn_ext(
    constexpr short NW  = N_SIMDWIDTH;
    constexpr short SH  = (2*C + Q); // shared memory per simdgroup (s_t == float)

-    const short TS = nsg*SH;   // shared memory size per query in (s_t == float)
-    const short T  = DK + 2*TS; // shared memory size per query in (half)
+    const short TS = nsg*SH;      // shared memory size per query in (s_t == float)
+    const short T  = 2*DK + 2*TS; // shared memory size per query in (half)

-    threadgroup q_t  * sq  = (threadgroup q_t  *) (shmem_f16 +              0*DK); // holds the query data
-    threadgroup q4_t * sq4 = (threadgroup q4_t *) (shmem_f16 +              0*DK); // same as above but in q4_t
-    threadgroup o_t  * so  = (threadgroup o_t  *) (shmem_f16 +              0*DK); // reuse query data for accumulation
-    threadgroup o4_t * so4 = (threadgroup o4_t *) (shmem_f16 +              0*DK); // same as above but in o4_t
-    threadgroup s_t  * ss  = (threadgroup s_t  *) (shmem_f16 + 2*sgitg*SH + Q*DK); // scratch buffer for attention, mask and diagonal matrix
+    threadgroup q_t  * sq  = (threadgroup q_t  *) (shmem_f16 +                0*DK); // holds the query data
+    threadgroup q4_t * sq4 = (threadgroup q4_t *) (shmem_f16 +                0*DK); // same as above but in q4_t
+    threadgroup o_t  * so  = (threadgroup o_t  *) (shmem_f16 +                0*DK); // reuse query data for accumulation
+    threadgroup o4_t * so4 = (threadgroup o4_t *) (shmem_f16 +                0*DK); // same as above but in o4_t
+    threadgroup s_t  * ss  = (threadgroup s_t  *) (shmem_f16 + 2*sgitg*SH + 2*Q*DK); // scratch buffer for attention, mask and diagonal matrix

    threadgroup k_t    * sk    = (threadgroup k_t    *) (shmem_f16 + sgitg*(4*16*KV) + Q*T); // scratch buffer to load K in shared memory
    threadgroup k4x4_t * sk4x4 = (threadgroup k4x4_t *) (shmem_f16 + sgitg*(4*16*KV) + Q*T); // same as above but in k4x4_t
@@ -3317,7 +3354,7 @@ kernel void kernel_flash_attn_ext(
            if (iq1 + j < args.ne01) {
                sq4[j*DK4 + i] = (q4_t) q4[i];
            } else {
-                sq4[j*DK4 + i] = (q4_t) 0.0f;
+                sq4[j*DK4 + i] = 0;
            }
        }
    }
@@ -3597,9 +3634,6 @@ kernel void kernel_flash_attn_ext(

    // reduce the warps sequentially
    for (ushort sg = 1; sg < nsg; ++sg) {
-        float S = { 0.0f };
-        float M = { -__FLT_MAX__/2 };
-
        threadgroup_barrier(mem_flags::mem_threadgroup);

        // each simdgroup stores its output to shared memory, reusing sq
@@ -3620,12 +3654,12 @@ kernel void kernel_flash_attn_ext(
                const float M0 = ss[j*TS +         1];
                const float M1 = ss[j*TS + sg*SH + 1];

-                M = max(M0, M1);
+                const float M = max(M0, M1);

                const float ms0 = exp(M0 - M);
                const float ms1 = exp(M1 - M);

-                S = S0*ms0 + S1*ms1;
+                const float S = S0*ms0 + S1*ms1;

                if (tiisg == 0) {
                    ss[j*TS + 0] = S;
@@ -3664,16 +3698,18 @@ kernel void kernel_flash_attn_ext(
        }
    }

-    device float4 * dst4 = (device float4 *) dst;
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    threadgroup s_t * sf = (threadgroup s_t *) (shmem_f16 + 2*Q*DK);

    // final rescale with 1/S and store to global memory
-    if (sgitg == 0) {
-        for (short j = 0; j < Q && iq1 + j < args.ne01; ++j) {
-            const float S = ss[j*TS + 0];
+    for (short j = sgitg; j < Q && iq1 + j < args.ne01; j += nsg) {
+        const float S = 1.0f/sf[j*TS + 0];

-            for (short i = tiisg; i < DV4; i += NW) {
-                dst4[((uint64_t)iq3*args.ne2*args.ne1 + iq2 + (uint64_t)(iq1 + j)*args.ne1)*DV4 + i] = (float4) so4[j*DV4 + i]/S;
-            }
+        device float4 * dst4 = (device float4 *) dst + ((uint64_t)iq3*args.ne2*args.ne1 + iq2 + (uint64_t)(iq1 + j)*args.ne1)*DV4;
+
+        for (short i = tiisg; i < DV4; i += NW) {
+            dst4[i] = (float4) so4[j*DV4 + i]*S;
        }
    }
 }
@@ -3682,12 +3718,22 @@ kernel void kernel_flash_attn_ext(
 //       template to be able to explore different combinations
 //
 #define FA_TYPES \
-    half,  half4,   simdgroup_half8x8,  \
-    half,  half4x4, simdgroup_half8x8,  \
-    half,  half4x4, simdgroup_half8x8,  \
-    float,          simdgroup_float8x8, \
-    float,          simdgroup_float8x8, \
-    half,  half4,   simdgroup_half8x8
+    float,  float4,    simdgroup_float8x8, \
+    half,   half4x4,   simdgroup_half8x8,  \
+    half,   half4x4,   simdgroup_half8x8,  \
+    float,             simdgroup_float8x8, \
+    float,             simdgroup_float8x8, \
+    float,  float4,    simdgroup_float8x8
+    //half,   half4,     simdgroup_half8x8
+
+#define FA_TYPES_BF \
+    bfloat, bfloat4,   simdgroup_bfloat8x8, \
+    bfloat, bfloat4x4, simdgroup_bfloat8x8, \
+    bfloat, bfloat4x4, simdgroup_bfloat8x8, \
+    float,             simdgroup_float8x8,  \
+    float,             simdgroup_float8x8,  \
+    float,  float4,    simdgroup_float8x8
+    //half,   half4,     simdgroup_half8x8

 typedef decltype(kernel_flash_attn_ext<FA_TYPES, half4x4, 1, dequantize_f16, half4x4, 1, dequantize_f16, 64, 64>) flash_attn_ext_t;

@@ -3702,15 +3748,15 @@ template [[host_name("kernel_flash_attn_ext_f16_h256")]]         kernel flash_at
 template [[host_name("kernel_flash_attn_ext_f16_hk576_hv512")]]  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_USE_BF16)
-template [[host_name("kernel_flash_attn_ext_bf16_h64" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 64,  64>;
-template [[host_name("kernel_flash_attn_ext_bf16_h80" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 80,  80>;
-template [[host_name("kernel_flash_attn_ext_bf16_h96" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 96,  96>;
-template [[host_name("kernel_flash_attn_ext_bf16_h112")]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 112, 112>;
-template [[host_name("kernel_flash_attn_ext_bf16_h128")]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 128, 128>;
-template [[host_name("kernel_flash_attn_ext_bf16_h192")]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 192, 192>;
-template [[host_name("kernel_flash_attn_ext_bf16_hk192_hv128")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 192, 128>;
-template [[host_name("kernel_flash_attn_ext_bf16_h256")]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 256, 256>;
-template [[host_name("kernel_flash_attn_ext_bf16_hk576_hv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 576, 512>;
+template [[host_name("kernel_flash_attn_ext_bf16_h64" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 64,  64>;
+template [[host_name("kernel_flash_attn_ext_bf16_h80" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 80,  80>;
+template [[host_name("kernel_flash_attn_ext_bf16_h96" )]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 96,  96>;
+template [[host_name("kernel_flash_attn_ext_bf16_h112")]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 112, 112>;
+template [[host_name("kernel_flash_attn_ext_bf16_h128")]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 128, 128>;
+template [[host_name("kernel_flash_attn_ext_bf16_h192")]]        kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 192, 192>;
+template [[host_name("kernel_flash_attn_ext_bf16_hk192_hv128")]] 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_h256")]]        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_hk576_hv512")]] kernel flash_attn_ext_t kernel_flash_attn_ext<FA_TYPES_BF, bfloat4x4,  1, dequantize_bf16, bfloat4x4,  1, dequantize_bf16, 576, 512>;
 #endif

 template [[host_name("kernel_flash_attn_ext_q4_0_h64" )]]        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, 64,  64>;
@@ -3764,6 +3810,7 @@ template [[host_name("kernel_flash_attn_ext_q8_0_h256")]]        kernel flash_at
 template [[host_name("kernel_flash_attn_ext_q8_0_hk576_hv512")]] 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
+#undef FA_TYPES_BF

 template<
    typename q4_t,  // query types in shared memory
@@ -3776,7 +3823,7 @@ template<
    typename kd4_t, // key type in device memory
    short nl_k,
    void (*deq_k_t4)(device const kd4_t *, short, thread k4_t &),
-    typename vd4_t, // key type in device memory
+    typename vd4_t, // value type in device memory
    short nl_v,
    void (*deq_v_t4)(device const vd4_t *, short, thread v4_t &),
    short DK,       // K head size
@@ -3810,12 +3857,12 @@ kernel void kernel_flash_attn_ext_vec(

    const short T = DK + nsg*SH; // shared memory size per query in (half)

-  //threadgroup q_t   * sq  = (threadgroup q_t   *) (shmem_f16 +                  0*DK); // holds the query data
-    threadgroup q4_t  * sq4 = (threadgroup q4_t  *) (shmem_f16 +                  0*DK); // same as above but in q4_t
-    threadgroup s_t   * ss  = (threadgroup s_t   *) (shmem_f16 + sgitg*SH       + Q*DK); // scratch buffer for attention
-    threadgroup s4_t  * ss4 = (threadgroup s4_t  *) (shmem_f16 + sgitg*SH       + Q*DK); // same as above but in s4_t
-    threadgroup float * sm  = (threadgroup float *) (shmem_f16 + sgitg*SH + 2*C + Q*DK); // scratch buffer for mask
-    threadgroup o4_t  * sr4 = (threadgroup o4_t  *) (shmem_f16 + sgitg*DV       + Q*T);  // scratch buffer for the results
+  //threadgroup q_t   * sq  = (threadgroup q_t   *) (shmem_f16 +                    0*DK); // holds the query data
+    threadgroup q4_t  * sq4 = (threadgroup q4_t  *) (shmem_f16 +                    0*DK); // same as above but in q4_t
+    threadgroup s_t   * ss  = (threadgroup s_t   *) (shmem_f16 +   sgitg*SH       + Q*DK); // scratch buffer for attention
+    threadgroup s4_t  * ss4 = (threadgroup s4_t  *) (shmem_f16 +   sgitg*SH       + Q*DK); // same as above but in s4_t
+    threadgroup float * sm  = (threadgroup float *) (shmem_f16 +   sgitg*SH + 2*C + Q*DK); // scratch buffer for mask
+    threadgroup o4_t  * sr4 = (threadgroup o4_t  *) (shmem_f16 + 2*sgitg*DV       + Q*T);  // scratch buffer for the results

    // store the result for all queries in local memory (the O matrix from the paper)
    o4_t lo[DV4/NL];
@@ -4120,7 +4167,7 @@ kernel void kernel_flash_attn_ext_vec(
           half4,  \
    float,         \
    float, float4, \
-           half4
+           float4

 typedef decltype(kernel_flash_attn_ext_vec<FA_TYPES, half4, 1, dequantize_f16_t4, half4, 1, dequantize_f16_t4, 128, 128, 4>) flash_attn_ext_vec_t;

--- a/ggml/src/ggml-musa/CMakeLists.txt
+++ b/ggml/src/ggml-musa/CMakeLists.txt
@@ -27,12 +27,15 @@ if (MUSAToolkit_FOUND)

    file(GLOB   GGML_HEADERS_MUSA "../ggml-cuda/*.cuh")
    list(APPEND GGML_HEADERS_MUSA "../../include/ggml-cuda.h")
+    list(APPEND GGML_HEADERS_MUSA "../ggml-musa/mudnn.cuh")

    file(GLOB   GGML_SOURCES_MUSA "../ggml-cuda/*.cu")
    file(GLOB   SRCS "../ggml-cuda/template-instances/fattn-mma*.cu")
    list(APPEND GGML_SOURCES_MUSA ${SRCS})
    file(GLOB   SRCS "../ggml-cuda/template-instances/mmq*.cu")
    list(APPEND GGML_SOURCES_MUSA ${SRCS})
+    file(GLOB   SRCS "../ggml-musa/*.cu")
+    list(APPEND GGML_SOURCES_MUSA ${SRCS})

    if (GGML_CUDA_FA_ALL_QUANTS)
        file(GLOB   SRCS "../ggml-cuda/template-instances/fattn-vec*.cu")
@@ -62,7 +65,9 @@ if (MUSAToolkit_FOUND)
                            )

    # TODO: do not use CUDA definitions for MUSA
-    target_compile_definitions(ggml PUBLIC GGML_USE_CUDA)
+    if (NOT GGML_BACKEND_DL)
+        target_compile_definitions(ggml PUBLIC GGML_USE_CUDA)
+    endif()

    add_compile_definitions(GGML_USE_MUSA)
    add_compile_definitions(GGML_CUDA_PEER_MAX_BATCH_SIZE=${GGML_CUDA_PEER_MAX_BATCH_SIZE})
@@ -92,9 +97,10 @@ if (MUSAToolkit_FOUND)
    endif()

    if (GGML_STATIC)
+        # TODO: mudnn has not provided static libraries yet
        target_link_libraries(ggml-musa PRIVATE MUSA::musart_static MUSA::mublas_static)
    else()
-        target_link_libraries(ggml-musa PRIVATE MUSA::musart MUSA::mublas)
+        target_link_libraries(ggml-musa PRIVATE MUSA::musart MUSA::mublas mudnn)
    endif()

    if (GGML_CUDA_NO_VMM)
--- a/ggml/src/ggml-musa/mudnn.cu
+++ b/ggml/src/ggml-musa/mudnn.cu
@@ -0,0 +1,112 @@
+#include <mutex>
+#include <mudnn.h>
+
+#include "mudnn.cuh"
+
+namespace mudnn = musa::dnn;
+
+// Returns a human-readable error string for mudnn::Status
+const char* mudnnGetErrorString(mudnn::Status err) {
+    switch (err) {
+        case mudnn::Status::SUCCESS:
+            return "Success";
+        case mudnn::Status::INVALID_PARAMETER:
+            return "Invalid parameter";
+        case mudnn::Status::NOT_INITIALIZED:
+            return "Not initialized";
+        case mudnn::Status::ALLOC_FAILED:
+            return "Allocation failed";
+        case mudnn::Status::NOT_SUPPORTED:
+            return "Not supported";
+        case mudnn::Status::INTERNAL_ERROR:
+            return "Internal error";
+        case mudnn::Status::ARCH_MISMATCH:
+            return "Architecture mismatch";
+        case mudnn::Status::EXECUTION_FAILED:
+            return "Execution failed";
+        default:
+            return "Unknown mudnn status";
+    }
+}
+
+// Error checking macro for MUDNN calls
+#define MUDNN_CHECK(err) CUDA_CHECK_GEN(err, mudnn::Status::SUCCESS, mudnnGetErrorString)
+
+namespace {
+    // Thread-safe cache for mudnn::Handle objects per device
+    std::unordered_map<int, std::unique_ptr<mudnn::Handle>> handle_cache;
+    std::mutex handle_cache_mutex;
+
+    mudnn::Handle* get_cached_handle(int device_id) {
+        std::lock_guard<std::mutex> lock(handle_cache_mutex);
+        auto it = handle_cache.find(device_id);
+        if (it != handle_cache.end()) {
+            return it->second.get();
+        }
+        auto handle = std::make_unique<mudnn::Handle>(device_id);
+        mudnn::Handle* handle_ptr = handle.get();
+        handle_cache[device_id] = std::move(handle);
+        return handle_ptr;
+    }
+}
+
+// Extracts dimensions and strides from a ggml_tensor
+int get_ggml_dims_and_strides(const ggml_tensor* tensor,
+                              std::vector<int64_t>& dims,
+                              std::vector<int64_t>& strides) {
+    const int ndims = ggml_n_dims(tensor);
+    const size_t element_size = ggml_element_size(tensor);
+
+    dims.resize(ndims);
+    strides.resize(ndims);
+
+    for (int i = 0; i < ndims; ++i) {
+        dims[i] = tensor->ne[i];
+        strides[i] = tensor->nb[i] / static_cast<int64_t>(element_size);
+    }
+    return ndims;
+}
+
+// Converts ggml_type to mudnn::Tensor::Type
+mudnn::Tensor::Type ggml_type_to_mudnn_type(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_F32:
+            return mudnn::Tensor::Type::FLOAT;
+        case GGML_TYPE_F16:
+            return mudnn::Tensor::Type::HALF;
+
+        // TODO: Add support for other types
+
+        default:
+            MUDNN_CHECK(mudnn::Status::NOT_SUPPORTED);
+    }
+
+    return mudnn::Tensor::Type::FLOAT; // Default fallback
+}
+
+// Asynchronous memory copy using mudnn::Unary::IDENTITY
+musaError_t mudnnMemcpyAsync(ggml_backend_cuda_context& ctx, const ggml_tensor* dst, const ggml_tensor* src) {
+    mudnn::Tensor tensor_dst, tensor_src;
+
+    MUDNN_CHECK(tensor_dst.SetType(ggml_type_to_mudnn_type(dst->type)));
+    MUDNN_CHECK(tensor_src.SetType(ggml_type_to_mudnn_type(src->type)));
+
+    std::vector<int64_t> dims, strides;
+    const int ndims = get_ggml_dims_and_strides(src, dims, strides);
+
+    MUDNN_CHECK(tensor_dst.SetNdInfo(ndims, dims.data(), strides.data()));
+    MUDNN_CHECK(tensor_src.SetNdInfo(ndims, dims.data(), strides.data()));
+    MUDNN_CHECK(tensor_dst.SetAddr(dst->data));
+    MUDNN_CHECK(tensor_src.SetAddr(src->data));
+
+    mudnn::Unary op;
+    MUDNN_CHECK(op.SetMode(mudnn::Unary::Mode::IDENTITY));
+    MUDNN_CHECK(op.SetAlpha(0.0f));
+    MUDNN_CHECK(op.SetBeta(0.0f));
+
+    mudnn::Handle* handle = get_cached_handle(ctx.device);
+    MUDNN_CHECK(handle->SetStream(ctx.stream()));
+    MUDNN_CHECK(op.Run(*handle, tensor_dst, tensor_src));
+
+    return musaSuccess;
+}
--- a/ggml/src/ggml-musa/mudnn.cuh
+++ b/ggml/src/ggml-musa/mudnn.cuh
@@ -0,0 +1,12 @@
+#pragma once
+
+#include "../include/ggml.h"
+#include "../ggml-cuda/common.cuh"
+
+// Asynchronously copies data from src tensor to dst tensor using the provided context.
+// Returns a musaError_t indicating success or failure.
+musaError_t mudnnMemcpyAsync(
+    ggml_backend_cuda_context &ctx,
+    const ggml_tensor *dst,
+    const ggml_tensor *src
+);
--- a/ggml/src/ggml-opencl/CMakeLists.txt
+++ b/ggml/src/ggml-opencl/CMakeLists.txt
@@ -55,14 +55,17 @@ endfunction()

 set(GGML_OPENCL_KERNELS
    add
+    argsort
    clamp
    cpy
    cvt
    diag_mask_inf
+    div
    gelu
    gemv_noshuffle_general
    gemv_noshuffle
    get_rows
+    group_norm
    im2col_f32
    im2col_f16
    mul_mat_Ab_Bi_8x4
@@ -83,12 +86,21 @@ set(GGML_OPENCL_KERNELS
    rms_norm
    rope
    scale
+    sigmoid
    silu
    softmax_4_f32
    softmax_4_f16
    softmax_f32
    softmax_f16
+    sub
+    sum_rows
    transpose
+    concat
+    tsembd
+    upscale
+    tanh
+    pad
+    repeat
 )

 foreach (K ${GGML_OPENCL_KERNELS})
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
--- a/ggml/src/ggml-opencl/kernels/argsort.cl
+++ b/ggml/src/ggml-opencl/kernels/argsort.cl
@@ -0,0 +1,86 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#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 SWAP(x, y, T) { T tmp = (x); (x) = (y); (y) = tmp; }
+
+enum ggml_sort_order {
+    GGML_SORT_ORDER_ASC,
+    GGML_SORT_ORDER_DESC,
+};
+
+kernel void kernel_argsort_f32_i32(
+    global float * src0,
+    ulong          offset0,
+    global int   * dst,
+    ulong          offsetd,
+    const int      ne00,
+    const int      ne00_pad,
+    const int      order,
+    local int    * dst_row
+) {
+    // bitonic sort
+    int col = get_local_id(0);
+    int row = get_group_id(1);
+
+    if (col >= ne00_pad) {
+        return;
+    }
+
+    src0 = (global char  *)((global char *)src0 + offset0);
+    dst  = (global float *)((global char *)dst  + offsetd);
+
+    global float * x_row = src0 + row * ne00;
+
+    // initialize indices
+    dst_row[col] = col;
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    for (int k = 2; k <= ne00_pad; k *= 2) {
+        for (int j = k / 2; j > 0; j /= 2) {
+            int ixj = col ^ j;
+            if (ixj > col) {
+                if ((col & k) == 0) {
+                    if (dst_row[col] >= ne00 ||
+                        (dst_row[ixj] < ne00 && (order == GGML_SORT_ORDER_ASC ?
+                            x_row[dst_row[col]] > x_row[dst_row[ixj]] :
+                            x_row[dst_row[col]] < x_row[dst_row[ixj]]))
+                    ) {
+                        SWAP(dst_row[col], dst_row[ixj], int);
+                    }
+                } else {
+                    if (dst_row[ixj] >= ne00 ||
+                        (dst_row[col] < ne00 && (order == GGML_SORT_ORDER_ASC ?
+                            x_row[dst_row[col]] < x_row[dst_row[ixj]] :
+                            x_row[dst_row[col]] > x_row[dst_row[ixj]]))
+                    ) {
+                        SWAP(dst_row[col], dst_row[ixj], int);
+                    }
+                }
+            }
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
+    }
+
+    // copy the result to dst without the padding
+    if (col < ne00) {
+        dst[row * ne00 + col] = dst_row[col];
+    }
+}
--- a/ggml/src/ggml-opencl/kernels/concat.cl
+++ b/ggml/src/ggml-opencl/kernels/concat.cl
@@ -0,0 +1,109 @@
+kernel void kernel_concat_f32_contiguous(
+    global const char * p_src0, ulong off_src0,
+    global const char * p_src1, ulong off_src1,
+    global char * p_dst, ulong off_dst,
+    int d_ne00, int d_ne01, int d_ne02, // src0->ne[0..2] for the slice
+    int d_ne10, int d_ne11, int d_ne12, // src1->ne[0..2] for the slice (d_ne1X must match d_ne0X on non-concat axes)
+    int d_ne0,  int d_ne1,  int d_ne2,  // dst->ne[0..2] for the slice
+    int dim
+) {
+    global const float * src0 = (global const float*)((global char*)p_src0 + off_src0);
+    global const float * src1 = (global const float*)((global char*)p_src1 + off_src1);
+    global float * dst        = (global float*)((global char*)p_dst + off_dst);
+
+    int i0 = get_global_id(0); // Index along dst's 0th dimension
+    int i1 = get_global_id(1); // Index along dst's 1st dimension
+    int i2 = get_global_id(2); // Index along dst's 2nd dimension
+
+    if (i0 >= d_ne0 || i1 >= d_ne1 || i2 >= d_ne2) {
+        return;
+    }
+
+    ulong dst_idx = (ulong)i2 * d_ne0 * d_ne1 + (ulong)i1 * d_ne0 + i0;
+    ulong src_idx;
+
+    if (dim == 0) {
+        if (i0 < d_ne00) { // Data from src0
+            src_idx = (ulong)i2 * d_ne00 * d_ne01 + (ulong)i1 * d_ne00 + i0;
+            dst[dst_idx] = src0[src_idx];
+        } else { // Data from src1
+            src_idx = (ulong)i2 * d_ne10 * d_ne11 + (ulong)i1 * d_ne10 + (i0 - d_ne00);
+            dst[dst_idx] = src1[src_idx];
+        }
+    } else if (dim == 1) {
+        if (i1 < d_ne01) { // Data from src0
+            src_idx = (ulong)i2 * d_ne00 * d_ne01 + (ulong)i1 * d_ne00 + i0;
+            dst[dst_idx] = src0[src_idx];
+        } else { // Data from src1
+            src_idx = (ulong)i2 * d_ne10 * d_ne11 + (ulong)(i1 - d_ne01) * d_ne10 + i0;
+            dst[dst_idx] = src1[src_idx];
+        }
+    } else if (dim == 2) {
+        if (i2 < d_ne02) { // Data from src0
+            src_idx = (ulong)i2 * d_ne00 * d_ne01 + (ulong)i1 * d_ne00 + i0;
+            dst[dst_idx] = src0[src_idx];
+        } else { // Data from src1
+
+            src_idx = (ulong)(i2 - d_ne02) * d_ne10 * d_ne11 + (ulong)i1 * d_ne10 + i0;
+            dst[dst_idx] = src1[src_idx];
+        }
+    }
+}
+
+kernel void kernel_concat_f32_non_contiguous(
+    global const char * p_src0, ulong off_src0,
+    global const char * p_src1, ulong off_src1,
+    global char * p_dst, ulong off_dst,
+
+    long ne00, long ne01, long ne02, long ne03,
+    ulong nb00, ulong nb01, ulong nb02, ulong nb03,
+
+    ulong nb10, ulong nb11, ulong nb12, ulong nb13, // Strides for src1
+
+    long d_ne0, long d_ne1, long d_ne2, long d_ne3,
+    ulong d_nb0, ulong d_nb1, ulong d_nb2, ulong d_nb3,
+    int dim
+) {
+    global const char * src0_base = p_src0 + off_src0;
+    global const char * src1_base = p_src1 + off_src1;
+    global char * dst_base        = p_dst + off_dst;
+
+    long current_i1 = get_global_id(0); // Index for dst_dim_1
+    long current_i2 = get_global_id(1); // Index for dst_dim_2
+    long current_i3 = get_global_id(2); // Index for dst_dim_3
+
+    if (current_i1 >= d_ne1 || current_i2 >= d_ne2 || current_i3 >= d_ne3) {
+        return;
+    }
+
+    global const float * x_val_ptr;
+    global float * y_val_ptr;
+
+    for (long current_i0 = 0; current_i0 < d_ne0; ++current_i0) {
+        bool use_src0;
+        long s_i0 = current_i0, s_i1 = current_i1, s_i2 = current_i2, s_i3 = current_i3;
+
+        if (dim == 0) {
+            use_src0 = (current_i0 < ne00);
+            if (!use_src0) { s_i0 = current_i0 - ne00; }
+        } else if (dim == 1) {
+            use_src0 = (current_i1 < ne01);
+            if (!use_src0) { s_i1 = current_i1 - ne01; }
+        } else if (dim == 2) {
+            use_src0 = (current_i2 < ne02);
+            if (!use_src0) { s_i2 = current_i2 - ne02; }
+        } else { // dim == 3
+            use_src0 = (current_i3 < ne03);
+            if (!use_src0) { s_i3 = current_i3 - ne03; }
+        }
+
+        if (use_src0) {
+            x_val_ptr = (global const float *)(src0_base + (ulong)s_i3*nb03 + (ulong)s_i2*nb02 + (ulong)s_i1*nb01 + (ulong)s_i0*nb00);
+        } else {
+            x_val_ptr = (global const float *)(src1_base + (ulong)s_i3*nb13 + (ulong)s_i2*nb12 + (ulong)s_i1*nb11 + (ulong)s_i0*nb10);
+        }
+
+        y_val_ptr = (global float *)(dst_base + (ulong)current_i3*d_nb3 + (ulong)current_i2*d_nb2 + (ulong)current_i1*d_nb1 + (ulong)current_i0*d_nb0);
+        *y_val_ptr = *x_val_ptr;
+    }
+}
--- a/Show More
+++ b/Show More