ui: fix context gauge for single-model usage (#25738)

* webui: hide loaded model in context gauge at single-model mode

* webui: keep context gauge details open state across reopens

.github/actions/windows-setup-cuda/action.yml

@@ -4,6 +4,10 @@ inputs:
   cuda_version:
     description: "CUDA toolkit version"
     required: true
+  cuda_arch:
+    description: "CUDA target architecture"
+    required: false
+    default: "x64"
 
 runs:
   using: "composite"
@@ -127,3 +131,26 @@ runs:
           echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
           echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
           echo "CUDA_PATH_V13_3=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.3" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
+
+    - name: Install Cuda Toolkit 13.4 for ARM64
+      if: ${{ inputs.cuda_version == '13.4' && inputs.cuda_arch == 'arm64' }}
+      shell: pwsh
+      run: |
+          mkdir -p "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4"
+          choco install unzip -y
+          curl -O "https://packages.nvidia.com/bin-archive/pool/windows-x86_64/5B515474-7E78-11F1-8656-C51E4F4B317F/cccl-windows-x86_64-13.3.4.1.2-archive.zip"
+          curl -O "https://packages.nvidia.com/bin-archive/pool/windows-x86_64/5B515474-7E78-11F1-8656-C51E4F4B317F/cuda_crt-windows-x86_64-13.4.46-archive.zip"
+          curl -O "https://packages.nvidia.com/bin-archive/pool/windows-x86_64/5B515474-7E78-11F1-8656-C51E4F4B317F/cuda_nvcc-windows-x86_64-13.4.46-archive.zip"
+          curl -O "https://packages.nvidia.com/bin-archive/pool/windows-x86_64/5B515474-7E78-11F1-8656-C51E4F4B317F/libnvvm-windows-x86_64-13.4.46-archive.zip"
+          curl -O "https://packages.nvidia.com/bin-archive/pool/windows-arm64/5B515474-7E78-11F1-8656-C51E4F4B317F/cuda_cudart-windows-arm64-13.4.46-archive.zip"
+          curl -O "https://packages.nvidia.com/bin-archive/pool/windows-arm64/5B515474-7E78-11F1-8656-C51E4F4B317F/libcublas-windows-arm64-13.7.0.10-archive.zip"
+          unzip '*.zip' -d "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4"
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\cccl-windows-x86_64-13.3.4.1.2-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\cuda_crt-windows-x86_64-13.4.46-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\cuda_nvcc-windows-x86_64-13.4.46-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\libnvvm-windows-x86_64-13.4.46-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\cuda_cudart-windows-arm64-13.4.46-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\libcublas-windows-arm64-13.7.0.10-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" /E /I /H /Y
+          echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+          echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
+          echo "CUDA_PATH_V13_4=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8

.github/actions/windows-setup-rocm/action.yml

@@ -8,8 +8,26 @@ inputs:
 runs:
   using: "composite"
   steps:
-    - name: Setup ROCm
-      uses: ./.github/actions/install-exe
-      with:
-        url: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-${{ inputs.version }}-Win11-For-HIP.exe
-        args: -install
+    - name: Install ROCm with Wheels
+      shell: pwsh
+      run: |
+        $ErrorActionPreference = "Stop"
+        write-host "Setting up Python virtual environment"
+
+        # Create the venv directly at the cache location to avoid relocation issues
+        New-Item -Path "C:\TheRock\build" -ItemType Directory -Force | Out-Null
+        python -m venv C:\TheRock\build\.venv
+        & C:\TheRock\build\.venv\Scripts\Activate.ps1
+
+        write-host "Upgrading pip"
+        python -m pip install --upgrade pip
+
+        write-host "Installing ROCm wheels for multi-arch support"
+        # Install ROCm wheels for multi-arch support (this may take several minutes)
+        python -m pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "rocm[libraries,devel]==${{ inputs.version }}"
+
+        # Pre-expand the devel tree so it is included in the cache
+        write-host "Initializing ROCm devel tree"
+        rocm-sdk init
+        if ($LASTEXITCODE -ne 0) { throw "rocm-sdk init failed with exit code $LASTEXITCODE" }
+        write-host "Completed ROCm wheel installation to C:\TheRock\build"

.github/workflows/build-cache.yml

@@ -123,8 +123,8 @@ jobs:
     runs-on: windows-2022
 
     env:
-      # Make sure this is in sync with build.yml
-      HIPSDK_INSTALLER_VERSION: "26.Q1"
+      # Make sure this is in sync with release.yml and build-cuda-windows.yml
+      ROCM_VERSION: "7.14.0"
 
     steps:
       - name: Clone
@@ -135,11 +135,11 @@ jobs:
         uses: actions/cache@v5
         id: cache-rocm
         with:
-          path: C:\Program Files\AMD\ROCm
-          key: cache-gha-rocm-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ runner.os }}
+          path: C:\TheRock\build
+          key: rocm-wheels-${{ env.ROCM_VERSION }}-multi-arch-${{ runner.os }}
 
       - name: Setup ROCm
         if: steps.cache-rocm.outputs.cache-hit != 'true'
         uses: ./.github/actions/windows-setup-rocm
         with:
-          version: ${{ env.HIPSDK_INSTALLER_VERSION }}
+          version: ${{ env.ROCM_VERSION }}

.github/workflows/build-cuda-windows.yml

@@ -83,7 +83,7 @@ jobs:
 
     env:
       # Make sure this is in sync with build-cache.yml
-      HIPSDK_INSTALLER_VERSION: "26.Q1"
+      ROCM_VERSION: "7.14.0"
 
     strategy:
       matrix:
@@ -97,36 +97,53 @@ jobs:
         id: checkout
         uses: actions/checkout@v6
 
-      - name: Grab rocWMMA package
-        id: grab_rocwmma
-        run: |
-          curl -o rocwmma.deb "https://repo.radeon.com/rocm/apt/7.2.1/pool/main/r/rocwmma-dev/rocwmma-dev_2.2.0.70201-81~24.04_amd64.deb"
-          7z x rocwmma.deb
-          7z x data.tar
-
-      - name: Use ROCm Installation Cache
+      - name: Cache ROCm Installation
         uses: actions/cache@v5
         id: cache-rocm
         with:
-          path: C:\Program Files\AMD\ROCm
-          key: cache-gha-rocm-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ runner.os }}
+          path: C:\TheRock\build
+          key: rocm-wheels-${{ env.ROCM_VERSION }}-multi-arch-${{ runner.os }}
 
       - name: Setup ROCm
         if: steps.cache-rocm.outputs.cache-hit != 'true'
         uses: ./.github/actions/windows-setup-rocm
         with:
-          version: ${{ env.HIPSDK_INSTALLER_VERSION }}
+          version: ${{ env.ROCM_VERSION }}
+
+      - name: Setup ROCm Environment
+        run: |
+          $ErrorActionPreference = "Stop"
+
+          # Activate venv from cache or fresh install
+          & C:\TheRock\build\.venv\Scripts\Activate.ps1
+
+          # Expand the devel tree (idempotent; no-op if already done during install)
+          rocm-sdk init
+          if ($LASTEXITCODE -ne 0) { throw "rocm-sdk init failed with exit code $LASTEXITCODE" }
+
+          # Get ROCm installation paths using the rocm-sdk CLI tool
+          $rocmPath = (rocm-sdk path --root)
+          if (-not $rocmPath) { throw "rocm-sdk path --root returned empty - devel package may not be installed" }
+          $rocmPath = $rocmPath.Trim()
+          $cmakePath = (rocm-sdk path --cmake).Trim()
+          $binPath = (rocm-sdk path --bin).Trim()
+          write-host "ROCm root: $rocmPath"
+
+          echo "HIP_PATH=$rocmPath" >> $env:GITHUB_ENV
+          echo "CMAKE_PREFIX_PATH=$cmakePath" >> $env:GITHUB_ENV
+          echo "HIP_DEVICE_LIB_PATH=$rocmPath\lib\llvm\amdgcn\bitcode" >> $env:GITHUB_ENV
+          echo "HIP_PLATFORM=amd" >> $env:GITHUB_ENV
+          echo "LLVM_PATH=$rocmPath\lib\llvm" >> $env:GITHUB_ENV
+          echo "$binPath" >> $env:GITHUB_PATH
+
+          # Keep venv in PATH for subsequent steps
+          echo "C:\TheRock\build\.venv\Scripts" >> $env:GITHUB_PATH
 
       - name: Verify ROCm
         id: verify
         run: |
-          # Find and test ROCm installation
-          $clangPath = Get-ChildItem 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | Select-Object -First 1
-          if (-not $clangPath) {
-            Write-Error "ROCm installation not found"
-            exit 1
-          }
-          & $clangPath.FullName --version
+          # Test the ROCm clang shipped in the installed wheel
+          & "${env:HIP_PATH}\lib\llvm\bin\clang.exe" --version
 
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.21
@@ -134,28 +151,27 @@ jobs:
           # TODO: this build does not match the build in release.yml, so we use a different cache key
           #       ideally, the builds should match, similar to the CUDA build above so that we would be able
           #       to populate the ccache for the release with manual runs of this workflow
-          #key: release-windows-2022-x64-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ matrix.name }}
-          key: cuda-windows-2022-x64-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ matrix.name }}
+          #key: release-windows-2022-x64-hip-${{ env.ROCM_VERSION }}-${{ matrix.name }}
+          key: cuda-windows-2022-x64-hip-${{ env.ROCM_VERSION }}-${{ matrix.name }}
 
       - name: Build
         id: cmake_build
         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('\', '/'))/opt/rocm-7.2.1/include/" `
+            -DCMAKE_PREFIX_PATH="${env:HIP_PATH}" `
+            -DCMAKE_C_COMPILER="${env:HIP_PATH}\lib\llvm\bin\clang.exe" `
+            -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\lib\llvm\bin\clang++.exe" `
+            -DCMAKE_HIP_COMPILER="${env:HIP_PATH}\lib\llvm\bin\clang.exe" `
             -DCMAKE_BUILD_TYPE=Release `
             -DLLAMA_BUILD_BORINGSSL=ON `
-            -DROCM_DIR="${env:HIP_PATH}" `
+            -DHIP_PATH="${env:HIP_PATH}" `
             -DGGML_HIP=ON `
-            -DGPU_TARGETS="gfx1100"  `
+            -DGPU_TARGETS="gfx1100" `
             -DGGML_RPC=ON
           cmake --build build -j ${env:NUMBER_OF_PROCESSORS}
 
       - name: ccache-clear
         uses: ./.github/actions/ccache-clear
         with:
-          #key: release-windows-2022-x64-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ matrix.name }}
-          key: cuda-windows-2022-x64-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ matrix.name }}
+          #key: release-windows-2022-x64-hip-${{ env.ROCM_VERSION }}-${{ matrix.name }}
+          key: cuda-windows-2022-x64-hip-${{ env.ROCM_VERSION }}-${{ matrix.name }}

.github/workflows/release.yml

@@ -748,6 +748,132 @@ jobs:
           path: llama-bin-win-cpu-${{ matrix.arch }}.zip
           name: llama-bin-win-cpu-${{ matrix.arch }}.zip
 
+  windows-rocm:
+    runs-on: windows-2022
+
+    strategy:
+      matrix:
+        include:
+          - ROCM_VERSION: "7.14.0"
+            gpu_targets: "gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1103;gfx1150;gfx1151;gfx1152;gfx1153;gfx1200;gfx1201"
+            build: x64
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: windows-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}
+          evict-old-files: 1d
+
+      - name: Cache ROCm Installation
+        id: cache-rocm
+        uses: actions/cache@v5
+        with:
+          path: C:\TheRock\build
+          key: rocm-wheels-${{ matrix.ROCM_VERSION }}-multi-arch-${{ runner.os }}
+
+      - name: Setup ROCm
+        if: steps.cache-rocm.outputs.cache-hit != 'true'
+        uses: ./.github/actions/windows-setup-rocm
+        with:
+          version: ${{ matrix.ROCM_VERSION }}
+
+      - name: Setup ROCm Environment
+        run: |
+          $ErrorActionPreference = "Stop"
+
+          # Activate venv from cache or fresh install
+          & C:\TheRock\build\.venv\Scripts\Activate.ps1
+
+          # Expand the devel tree (idempotent; no-op if already done during install)
+          rocm-sdk init
+          if ($LASTEXITCODE -ne 0) { throw "rocm-sdk init failed with exit code $LASTEXITCODE" }
+
+          # Get ROCm installation paths using the rocm-sdk CLI tool
+          $rocmPath = (rocm-sdk path --root)
+          if (-not $rocmPath) { throw "rocm-sdk path --root returned empty - devel package may not be installed" }
+          $rocmPath = $rocmPath.Trim()
+          $cmakePath = (rocm-sdk path --cmake).Trim()
+          $binPath = (rocm-sdk path --bin).Trim()
+          write-host "ROCm root: $rocmPath"
+          write-host "CMake path: $cmakePath"
+          write-host "Bin path: $binPath"
+
+          echo "HIP_PATH=$rocmPath" >> $env:GITHUB_ENV
+          echo "CMAKE_PREFIX_PATH=$cmakePath" >> $env:GITHUB_ENV
+          echo "HIP_DEVICE_LIB_PATH=$rocmPath\lib\llvm\amdgcn\bitcode" >> $env:GITHUB_ENV
+          echo "HIP_PLATFORM=amd" >> $env:GITHUB_ENV
+          echo "LLVM_PATH=$rocmPath\lib\llvm" >> $env:GITHUB_ENV
+          echo "$binPath" >> $env:GITHUB_PATH
+
+          # Keep venv in PATH for subsequent steps
+          echo "C:\TheRock\build\.venv\Scripts" >> $env:GITHUB_PATH
+
+      - name: Build
+        run: |
+          mkdir build
+          cd build
+          cmake .. `
+            -G "Unix Makefiles" `
+            -DCMAKE_PREFIX_PATH="${env:HIP_PATH}" `
+            -DCMAKE_BUILD_TYPE=Release `
+            -DGGML_BACKEND_DL=ON `
+            -DGGML_NATIVE=OFF `
+            -DGGML_CPU=ON `
+            -DGGML_CPU_ALL_VARIANTS=ON `
+            -DGGML_HIP=ON `
+            -DCMAKE_C_COMPILER="${env:HIP_PATH}\lib\llvm\bin\clang.exe" `
+            -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\lib\llvm\bin\clang++.exe" `
+            -DCMAKE_C_FLAGS="-Wno-error=incompatible-pointer-types" `
+            -DCMAKE_HIP_COMPILER="${env:HIP_PATH}\lib\llvm\bin\clang.exe" `
+            -DHIP_PATH="${env:HIP_PATH}" `
+            -DGGML_HIP_ROCWMMA_FATTN=ON `
+            -DAMDGPU_TARGETS="${{ matrix.gpu_targets }}"
+          cmake --build . --config Release --parallel ${env:NUMBER_OF_PROCESSORS}
+
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: windows-rocm-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}
+
+      - name: Verify HIP backend was built
+        run: |
+          $hipDll = Get-ChildItem -Path build\bin -Filter "ggml-hip*.dll" -ErrorAction SilentlyContinue
+          if (-not $hipDll) {
+            Write-Host "##[error]ggml-hip*.dll was NOT produced. The HIP backend silently failed to build."
+            Write-Host "Contents of build\bin:"
+            Get-ChildItem build\bin | Format-Table -AutoSize
+            exit 1
+          }
+          Write-Host "HIP backend artifact found:"
+          $hipDll | Format-Table FullName, Length -AutoSize
+
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name
+
+      - name: Get ROCm short version
+        run: |
+          $rocmVersionShort = ('${{ matrix.ROCM_VERSION }}'.Split('.')[0..1] -join '.')
+          echo "ROCM_VERSION_SHORT=$rocmVersionShort" >> $env:GITHUB_ENV
+
+      - name: Pack artifacts
+        run: |
+          cp "LICENSE" "build\bin\"
+          7z a -snl llama-bin-win-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.zip .\build\bin\*
+
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v6
+        with:
+          path: llama-bin-win-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.zip
+          name: llama-bin-win-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.zip
+
   windows:
     needs: [check-release]
     if: ${{ needs.check-release.outputs.should_release == 'true' }}
@@ -848,6 +974,7 @@ jobs:
           name: llama-bin-win-${{ matrix.backend }}-${{ matrix.arch }}.zip
 
   windows-cuda:
+    name: windows-cuda (${{ matrix.cuda }}, ${{ matrix.arch }})
     needs: [check-release]
     if: ${{ needs.check-release.outputs.should_release == 'true' }}
 
@@ -858,7 +985,16 @@ jobs:
 
     strategy:
       matrix:
-        cuda: ['12.4', '13.3']
+        include:
+          - cuda: '12.4'
+            arch: x64
+            defines: '-DGGML_CUDA_CUB_3DOT2=ON'
+          - cuda: '13.3'
+            arch: x64
+            defines: ''
+          - cuda: '13.4'
+            arch: arm64
+            defines: '-DCMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc-cuda.cmake'
 
     steps:
       - name: Clone
@@ -876,6 +1012,7 @@ jobs:
         uses: ./.github/actions/windows-setup-cuda
         with:
           cuda_version: ${{ matrix.cuda }}
+          cuda_arch: ${{ matrix.arch }}
 
       - name: Install Ninja
         id: install_ninja
@@ -885,54 +1022,62 @@ jobs:
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.21
         with:
-          key: release-windows-2022-x64-cuda-${{ matrix.cuda }}
+          key: release-windows-2022-${{ matrix.arch }}-cuda-${{ matrix.cuda }}
 
       - name: Build
         id: cmake_build
         shell: cmd
         # TODO: Remove GGML_CUDA_CUB_3DOT2 flag once CCCL 3.2 is bundled within CTK and that CTK version is used in this project
         run: |
-          call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" x64
+          call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" ${{ matrix.arch == 'x64' && 'x64' || 'amd64_arm64' }}
           cmake -S . -B build -G "Ninja Multi-Config" ^
             -DGGML_BACKEND_DL=ON ^
             -DGGML_NATIVE=OFF ^
             -DGGML_CPU=OFF ^
             -DGGML_CUDA=ON ^
-            -DLLAMA_BUILD_BORINGSSL=ON ^
-            -DGGML_CUDA_CUB_3DOT2=ON
+            -DLLAMA_BUILD_BORINGSSL=ON ${{ matrix.defines }}
           set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1
           cmake --build build --config Release -j %NINJA_JOBS% --target ggml-cuda
 
       - name: ccache-clear
         uses: ./.github/actions/ccache-clear
         with:
-          key: release-windows-2022-x64-cuda-${{ matrix.cuda }}
+          key: release-windows-2022-${{ matrix.arch }}-cuda-${{ matrix.cuda }}
 
       - name: Pack artifacts
         id: pack_artifacts
         run: |
-          7z a -snl llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip .\build\bin\Release\ggml-cuda.dll
+          7z a -snl llama-bin-win-cuda-${{ matrix.cuda }}-${{ matrix.arch }}.zip .\build\bin\Release\ggml-cuda.dll
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
         with:
-          path: llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip
-          name: llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip
+          path: llama-bin-win-cuda-${{ matrix.cuda }}-${{ matrix.arch }}.zip
+          name: llama-bin-win-cuda-${{ matrix.cuda }}-${{ matrix.arch }}.zip
 
-      - name: Copy and pack Cuda runtime
+      - name: Copy and pack Cuda runtime (x64)
+        if: ${{ matrix.arch == 'x64' }}
         run: |
           echo "Cuda install location: ${{ env.CUDA_PATH }}"
           $dst='.\build\bin\cudart\'
           robocopy "${{env.CUDA_PATH}}\bin" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll
           robocopy "${{env.CUDA_PATH}}\lib" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll
           robocopy "${{env.CUDA_PATH}}\bin\x64" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll
-          7z a cudart-llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip $dst\*
+          7z a cudart-llama-bin-win-cuda-${{ matrix.cuda }}-${{ matrix.arch }}.zip $dst\*
+
+      - name: Copy and pack Cuda runtime (ARM64)
+        if: ${{ matrix.arch == 'arm64' }}
+        run: |
+          echo "Cuda install location: ${{ env.CUDA_PATH }}"
+          $dst='.\build\bin\cudart\'
+          robocopy "${{env.CUDA_PATH}}\bin\arm64" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll
+          7z a cudart-llama-bin-win-cuda-${{ matrix.cuda }}-${{ matrix.arch }}.zip $dst\*
 
       - name: Upload Cuda runtime
         uses: actions/upload-artifact@v6
         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-${{ matrix.cuda }}-${{ matrix.arch }}.zip
+          name: cudart-llama-bin-win-cuda-${{ matrix.cuda }}-${{ matrix.arch }}.zip
 
   windows-sycl:
     needs: [check-release]
@@ -1149,8 +1294,8 @@ jobs:
     strategy:
       matrix:
         include:
-          - ROCM_VERSION: "7.2.1"
-            gpu_targets: "gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1151;gfx1150;gfx1200;gfx1201"
+          - ROCM_VERSION: "7.14.0"
+            gpu_targets: "gfx908;gfx90a;gfx942;gfx950;gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1150;gfx1151;gfx1152;gfx1200;gfx1201"
             build: 'x64'
 
     steps:
@@ -1182,38 +1327,36 @@ jobs:
         run: |
           sudo apt install -y build-essential git cmake wget
 
-      - name: Setup Legacy ROCm
-        if: matrix.ROCM_VERSION == '7.2.1'
-        id: legacy_env
-        run: |
-          sudo mkdir --parents --mode=0755 /etc/apt/keyrings
-          wget https://repo.radeon.com/rocm/rocm.gpg.key -O - | \
-            gpg --dearmor | sudo tee /etc/apt/keyrings/rocm.gpg > /dev/null
-
-          sudo tee /etc/apt/sources.list.d/rocm.list << EOF
-          deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/${{ matrix.ROCM_VERSION }} jammy main
-          EOF
-
-          sudo tee /etc/apt/preferences.d/rocm-pin-600 << EOF
-          Package: *
-          Pin: release o=repo.radeon.com
-          Pin-Priority: 600
-          EOF
-
-          sudo apt update
-          sudo apt-get install -y libssl-dev rocm-hip-sdk
-
-      - name: Setup TheRock
-        if: matrix.ROCM_VERSION != '7.2.1'
+      - name: Setup TheRock with Wheels
         id: therock_env
         run: |
-          wget https://repo.amd.com/rocm/tarball/therock-dist-linux-gfx1151-${{ matrix.ROCM_VERSION }}.tar.gz
-          mkdir install
-          tar -xf *.tar.gz -C install
-          export ROCM_PATH=$(pwd)/install
-          echo ROCM_PATH=$ROCM_PATH >> $GITHUB_ENV
-          echo PATH=$PATH:$ROCM_PATH/bin >> $GITHUB_ENV
-          echo LD_LIBRARY_PATH=$ROCM_PATH/lib:$ROCM_PATH/llvm/lib:$ROCM_PATH/lib/rocprofiler-systems >> $GITHUB_ENV
+          # Create Python virtual environment
+          python3 -m venv .venv
+          source .venv/bin/activate
+
+          # Install ROCm wheels for build
+          # libraries = HIP runtime and CMake configs needed for linking
+          # devel = compilers, headers, static libs
+          python -m pip install --upgrade pip
+          python -m pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "rocm[libraries,devel]==${{ matrix.ROCM_VERSION }}"
+
+          # Get ROCm installation paths using the rocm-sdk CLI tool
+          ROCM_PATH=$(rocm-sdk path --root)
+          CMAKE_PATH=$(rocm-sdk path --cmake)
+          BIN_PATH=$(rocm-sdk path --bin)
+          echo "ROCM_PATH=$ROCM_PATH"
+          echo "CMAKE_PATH=$CMAKE_PATH"
+          echo "BIN_PATH=$BIN_PATH"
+
+          # Set environment variables
+          echo "ROCM_PATH=$ROCM_PATH" >> $GITHUB_ENV
+          echo "CMAKE_PREFIX_PATH=$CMAKE_PATH" >> $GITHUB_ENV
+          echo "HIP_PATH=$ROCM_PATH" >> $GITHUB_ENV
+          echo "PATH=$BIN_PATH:${PATH}" >> $GITHUB_ENV
+          echo "LD_LIBRARY_PATH=$ROCM_PATH/lib:${LD_LIBRARY_PATH:-}" >> $GITHUB_ENV
+
+          # Keep venv activated for subsequent steps
+          echo "$(pwd)/.venv/bin" >> $GITHUB_PATH
 
       - name: Build with native CMake HIP support
         id: cmake_build
@@ -1257,129 +1400,6 @@ jobs:
           path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz
           name: llama-bin-ubuntu-rocm-${{ env.ROCM_VERSION_SHORT }}-${{ matrix.build }}.tar.gz
 
-  windows-hip:
-    needs: [check-release, get-version]
-    if: ${{ needs.check-release.outputs.should_release == 'true' }}
-
-    runs-on: windows-2022
-
-    permissions:
-      actions: write
-
-    env:
-      HIPSDK_INSTALLER_VERSION: "26.Q1"
-
-    strategy:
-      matrix:
-        include:
-          - name: "radeon"
-            gpu_targets: "gfx1150;gfx1151;gfx1200;gfx1201;gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032"
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v6
-        with:
-          node-version: "24"
-          cache: "npm"
-          cache-dependency-path: "tools/ui/package-lock.json"
-
-      - name: Grab rocWMMA package
-        id: grab_rocwmma
-        run: |
-          curl -o rocwmma.deb "https://repo.radeon.com/rocm/apt/7.2.1/pool/main/r/rocwmma-dev/rocwmma-dev_2.2.0.70201-81~24.04_amd64.deb"
-          7z x rocwmma.deb
-          7z x data.tar
-
-      - name: Cache ROCm Installation
-        id: cache-rocm
-        uses: actions/cache@v5
-        with:
-          path: C:\Program Files\AMD\ROCm
-          key: cache-gha-rocm-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ runner.os }}
-
-      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          key: release-windows-2022-x64-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ matrix.name }}
-
-      - name: Install ROCm
-        if: steps.cache-rocm.outputs.cache-hit != 'true'
-        id: depends
-        run: |
-          $ErrorActionPreference = "Stop"
-          write-host "Downloading AMD HIP SDK Installer"
-          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-${{ env.HIPSDK_INSTALLER_VERSION }}-Win11-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
-          write-host "Installing AMD HIP SDK"
-          $proc = Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -PassThru
-          $completed = $proc.WaitForExit(600000)
-          if (-not $completed) {
-              Write-Error "ROCm installation timed out after 10 minutes. Killing the process"
-              $proc.Kill()
-              exit 1
-          }
-          if ($proc.ExitCode -ne 0) {
-              Write-Error "ROCm installation failed with exit code $($proc.ExitCode)"
-              exit 1
-          }
-          write-host "Completed AMD HIP SDK installation"
-
-      - name: Verify ROCm
-        id: verify
-        run: |
-          # Find and test ROCm installation
-          $clangPath = Get-ChildItem 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | Select-Object -First 1
-          if (-not $clangPath) {
-            Write-Error "ROCm installation not found"
-            exit 1
-          }
-          & $clangPath.FullName --version
-
-      - name: Build
-        id: cmake_build
-        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('\', '/'))/opt/rocm-7.2.1/include/ -Wno-ignored-attributes -Wno-nested-anon-types" `
-            -DCMAKE_BUILD_TYPE=Release `
-            -DGGML_BACKEND_DL=ON `
-            -DGGML_NATIVE=OFF `
-            -DGGML_CPU=OFF `
-            -DGPU_TARGETS="${{ matrix.gpu_targets }}" `
-            -DGGML_HIP=ON `
-            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }} `
-            -DLLAMA_BUILD_BORINGSSL=ON
-          cmake --build build --target ggml-hip -j ${env:NUMBER_OF_PROCESSORS}
-          md "build\bin\rocblas\library\"
-          md "build\bin\hipblaslt\library"
-          cp "${env:HIP_PATH}\bin\libhipblas.dll" "build\bin\"
-          cp "${env:HIP_PATH}\bin\libhipblaslt.dll" "build\bin\"
-          cp "${env:HIP_PATH}\bin\rocblas.dll" "build\bin\"
-          cp "${env:HIP_PATH}\bin\rocblas\library\*" "build\bin\rocblas\library\"
-          cp "${env:HIP_PATH}\bin\hipblaslt\library\*" "build\bin\hipblaslt\library\"
-
-      - name: ccache-clear
-        uses: ./.github/actions/ccache-clear
-        with:
-          key: release-windows-2022-x64-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ matrix.name }}
-
-      - name: Pack artifacts
-        id: pack_artifacts
-        run: |
-          7z a -snl llama-bin-win-hip-${{ matrix.name }}-x64.zip .\build\bin\*
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v6
-        with:
-          path: llama-bin-win-hip-${{ matrix.name }}-x64.zip
-          name: llama-bin-win-hip-${{ matrix.name }}-x64.zip
-
   ios-xcode:
     needs: [check-release, get-version]
     if: ${{ needs.check-release.outputs.should_release == 'true' }}
@@ -1553,7 +1573,7 @@ jobs:
       - windows-cpu
       - windows-cuda
       #- windows-sycl
-      - windows-hip
+      - windows-rocm
       - windows-openvino
       - ubuntu-22-rocm
       - ubuntu-cpu
@@ -1665,7 +1685,7 @@ jobs:
             - [Ubuntu s390x (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-s390x.tar.gz)
             - [Ubuntu x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.tar.gz)
             - [Ubuntu arm64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-arm64.tar.gz)
-            - [Ubuntu x64 (ROCm 7.2)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-7.2-x64.tar.gz)
+            - [Ubuntu x64 (ROCm 7.14)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-rocm-7.14-x64.tar.gz)
             - [Ubuntu x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ needs.ubuntu-24-openvino.outputs.openvino_version }}-x64.tar.gz)
             - [Ubuntu x64 (SYCL FP32)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-fp32-x64.tar.gz)
             - [Ubuntu x64 (SYCL FP16)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-sycl-fp16-x64.tar.gz)
@@ -1679,10 +1699,11 @@ jobs:
             - [Windows arm64 (OpenCL Adreno)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-opencl-adreno-arm64.zip)
             - [Windows x64 (CUDA 12)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-12.4-x64.zip) - [CUDA 12.4 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-12.4-x64.zip)
             - [Windows x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-13.3-x64.zip) - [CUDA 13.3 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-13.3-x64.zip)
+            - [Windows arm64 (CUDA 13) (preview)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-13.4-arm64.zip) - [CUDA 13.4 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-13.4-arm64.zip)
             - [Windows x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-vulkan-x64.zip)
             - [Windows x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-openvino-${{ needs.windows-openvino.outputs.openvino_version }}-x64.zip)
             - [Windows x64 (SYCL)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip)
-            - [Windows x64 (HIP)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-hip-radeon-x64.zip)
+            - [Windows x64 (ROCm 7.14)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-rocm-7.14-x64.zip)
 
             **openEuler:**
             - [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23705)

.github/workflows/server-sanitize.yml

@@ -25,6 +25,12 @@ on:
       'tools/server/**.*'
     ]
 
+  pull_request:
+    types: [opened, synchronize, reopened]
+    paths: [
+      '.github/workflows/server-sanitize.yml'
+    ]
+
 env:
   LLAMA_ARG_LOG_COLORS: 1
   LLAMA_ARG_LOG_PREFIX: 1
@@ -90,23 +96,27 @@ jobs:
 
       - name: Python setup
         id: setup_python
-        uses: actions/setup-python@v6
-        with:
-          python-version: '3.11'
-          pip-install: -r tools/server/tests/requirements.txt
+        uses: actions/setup-python@v7
+
+      - name: Install Python dependencies
+        run: |
+          python3 -m venv .venv
+          .venv/bin/pip install -r tools/server/tests/requirements.txt
 
       - name: Tests
         id: server_integration_tests
         if: ${{ (!matrix.disabled_on_pr || !github.event.pull_request) }}
         run: |
+          source .venv/bin/activate
           cd tools/server/tests
           export ${{ matrix.extra_args }}
-          pytest -v -x -m "not slow"
+          ./tests.sh
 
       - name: Slow tests
         id: server_integration_tests_slow
         if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
         run: |
+          source .venv/bin/activate
           cd tools/server/tests
           export ${{ matrix.extra_args }}
-          SLOW_TESTS=1 pytest -v -x
+          SLOW_TESTS=1 ./tests.sh

.github/workflows/server-self-hosted.yml

@@ -72,7 +72,7 @@ jobs:
         run: |
           cd tools/server/tests
           source venv/bin/activate
-          pytest -v -x -m "not slow"
+          ./tests.sh
 
       - name: Tests (GPUx1, backend-sampling)
         id: server_integration_tests_backend_sampling
@@ -81,7 +81,7 @@ jobs:
           cd tools/server/tests
           source venv/bin/activate
           export LLAMA_ARG_BACKEND_SAMPLING=1
-          pytest -v -x -m "not slow"
+          ./tests.sh
 
       - name: Tests (GPUx2)
         id: server_integration_tests_gpu2
@@ -90,7 +90,7 @@ jobs:
           cd tools/server/tests
           source venv/bin/activate
           export GGML_METAL_DEVICES=2
-          pytest -v -x -m "not slow"
+          ./tests.sh
 
       - name: Tests (GPUx2, backend-sampling)
         id: server_integration_tests_gpu2_backend_sampling
@@ -99,7 +99,7 @@ jobs:
           cd tools/server/tests
           source venv/bin/activate
           export GGML_METAL_DEVICES=2 LLAMA_ARG_BACKEND_SAMPLING=1
-          pytest -v -x -m "not slow"
+          ./tests.sh
 
   server-cuda:
     runs-on: [self-hosted, llama-server, Linux, NVIDIA]
@@ -132,7 +132,7 @@ jobs:
         run: |
           cd tools/server/tests
           source venv/bin/activate
-          pytest -v -x -m "not slow"
+          ./tests.sh
 
       - name: Tests (GPUx1, backend-sampling)
         id: server_integration_tests_backend_sampling
@@ -141,7 +141,7 @@ jobs:
           cd tools/server/tests
           source venv/bin/activate
           export LLAMA_ARG_BACKEND_SAMPLING=1
-          pytest -v -x -m "not slow"
+          ./tests.sh
 
       - name: Tests (GPUx2)
         id: server_integration_tests_gpu2
@@ -150,7 +150,7 @@ jobs:
           cd tools/server/tests
           source venv/bin/activate
           export GGML_CUDA_DEVICES=2
-          pytest -v -x -m "not slow"
+          ./tests.sh
 
       - name: Tests (GPUx2, backend-sampling)
         id: server_integration_tests_gpu2_backend_sampling
@@ -159,7 +159,7 @@ jobs:
           cd tools/server/tests
           source venv/bin/activate
           export GGML_CUDA_DEVICES=2 LLAMA_ARG_BACKEND_SAMPLING=1
-          pytest -v -x -m "not slow"
+          ./tests.sh
 
   server-kleidiai:
     runs-on: ah-ubuntu_22_04-c8g_8x
@@ -219,4 +219,4 @@ jobs:
         run: |
           cd tools/server/tests
           source venv/bin/activate
-          pytest -v -x -m "not slow"
+          ./tests.sh

.github/workflows/server.yml

@@ -104,21 +104,21 @@ jobs:
         id: server_integration_tests
         run: |
           cd tools/server/tests
-          pytest -v -x -m "not slow"
+          ./tests.sh
 
       - name: Slow tests
         id: server_integration_tests_slow
         if: ${{ github.event.schedule || github.event.inputs.slow_tests == 'true' }}
         run: |
           cd tools/server/tests
-          SLOW_TESTS=1 pytest -v -x
+          SLOW_TESTS=1 ./tests.sh
 
       - name: Tests (Backend sampling)
         id: server_integration_tests_backend_sampling
         run: |
           cd tools/server/tests
           export LLAMA_ARG_BACKEND_SAMPLING=1
-          pytest -v -x -m "not slow"
+          ./tests.sh
 
       - name: Slow tests (Backend sampling)
         id: server_integration_tests_slow_backend_sampling
@@ -126,7 +126,7 @@ jobs:
         run: |
           cd tools/server/tests
           export LLAMA_ARG_BACKEND_SAMPLING=1
-          SLOW_TESTS=1 pytest -v -x
+          SLOW_TESTS=1 ./tests.sh
 
   windows:
     runs-on: windows-2025
@@ -167,15 +167,17 @@ jobs:
 
       - name: Tests
         id: server_integration_tests
+        shell: bash
         run: |
           cd tools/server/tests
-          $env:PYTHONIOENCODING = ":replace"
-          pytest -v -x -m "not slow"
+          export PYTHONIOENCODING=":replace"
+          ./tests.sh
 
       - name: Slow tests
         id: server_integration_tests_slow
         if: ${{ github.event.schedule || github.event.inputs.slow_tests == 'true' }}
+        shell: bash
         run: |
           cd tools/server/tests
-          $env:SLOW_TESTS = "1"
-          pytest -v -x
+          export SLOW_TESTS="1"
+          ./tests.sh

ci/run.sh

@@ -49,6 +49,14 @@ mkdir -p "$2"
 OUT=$(realpath "$1")
 MNT=$(realpath "$2")
 
+# gpu-rocm self-hosted runner can't upload logs to blob; keep each run's logs in
+# their own dir keyed by the GitHub run id so an Actions run URL maps to its logs.
+if [ -n "${GG_BUILD_ROCM}" ] && [ -n "${GITHUB_RUN_ID}" ]; then
+    OUT="$OUT/run-${GITHUB_RUN_ID}-${GITHUB_RUN_ATTEMPT:-1}"
+    mkdir -p "$OUT"
+    echo "ci results dir: $OUT"
+fi
+
 rm -f $OUT/*.log
 rm -f $OUT/*.exit
 rm -f $OUT/*.md

cmake/arm64-windows-msvc-cuda.cmake

@@ -0,0 +1,26 @@
+# Used to cross-compile ggml-cuda for Windows ARM64 on an x64 Windows host.
+set( CMAKE_SYSTEM_NAME Windows )
+set( CMAKE_SYSTEM_PROCESSOR arm64 )
+
+if ( DEFINED CUDAToolkit_ROOT )
+    file( TO_CMAKE_PATH "${CUDAToolkit_ROOT}" CUDA_ROOT )
+elseif ( DEFINED ENV{CUDA_PATH} )
+    file( TO_CMAKE_PATH "$ENV{CUDA_PATH}" CUDA_ROOT )
+else()
+    message( FATAL_ERROR "Set CUDAToolkit_ROOT or CUDA_PATH to a Windows CUDA Toolkit with ARM64 target libraries" )
+endif()
+
+if ( DEFINED ENV{VCToolsInstallDir} )
+    file( TO_CMAKE_PATH "$ENV{VCToolsInstallDir}" MSVC_TOOLS_ROOT )
+    set( CMAKE_CUDA_HOST_COMPILER "${MSVC_TOOLS_ROOT}/bin/Hostx64/arm64/cl.exe" CACHE FILEPATH "" )
+endif()
+
+set( CMAKE_CUDA_COMPILER "${CUDA_ROOT}/bin/nvcc.exe" CACHE FILEPATH "" )
+set( CMAKE_CUDA_FLAGS_INIT "-target-dir=arm64" )
+
+# FindCUDAToolkit selects lib/x64 from the host architecture on Windows.
+set( CUDA_CUDART              "${CUDA_ROOT}/lib/arm64/cudart.lib"   CACHE FILEPATH "" )
+set( CUDA_cudart_LIBRARY      "${CUDA_ROOT}/lib/arm64/cudart.lib"   CACHE FILEPATH "" )
+set( CUDA_cublas_LIBRARY      "${CUDA_ROOT}/lib/arm64/cublas.lib"   CACHE FILEPATH "" )
+set( CUDA_cublasLt_LIBRARY    "${CUDA_ROOT}/lib/arm64/cublasLt.lib" CACHE FILEPATH "" )
+set( CUDA_cuda_driver_LIBRARY "${CUDA_ROOT}/lib/arm64/cuda.lib"     CACHE FILEPATH "" )

common/arg.cpp

@@ -2605,14 +2605,16 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_env("LLAMA_ARG_DIO"));
     add_opt(common_arg(
         {"-lm", "--load-mode"}, "MODE",
-        "model loading mode (default: mmap)\n"
+        "model loading mode (default: auto)\n"
+        "- auto: mmap, unless a device does not support it\n"
         "- none: no special loading mode\n"
         "- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)\n"
         "- mlock: force system to keep model in RAM rather than swapping or compressing\n"
         "- mmap+mlock: mmap + force system to keep model in RAM rather than swapping or compressing\n"
         "- dio: use DirectIO if available\n",
         [](common_params & params, const std::string & value) {
-            /**/ if (value == "none")       { params.load_mode = LLAMA_LOAD_MODE_NONE;       }
+            /**/ if (value == "auto")       { params.load_mode = LLAMA_LOAD_MODE_AUTO;       }
+            else if (value == "none")       { params.load_mode = LLAMA_LOAD_MODE_NONE;       }
             else if (value == "mmap")       { params.load_mode = LLAMA_LOAD_MODE_MMAP;       }
             else if (value == "mlock")      { params.load_mode = LLAMA_LOAD_MODE_MLOCK;      }
             else if (value == "mmap+mlock") { params.load_mode = LLAMA_LOAD_MODE_MMAP_MLOCK; }
@@ -3312,8 +3314,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--tools-runtime"}, "OPTION",
         "experimental: run tools in a separate runtime environment (default: none, use host environment)\n"
         "available options:\n"
-        "  'docker:<image>': spin up a new Docker container and reuse it for all invocations, clean up on server exit\n"
-        "  'docker-container:<id>': use an existing Docker container by ID, won't stop on server exit\n",
+        "  'docker:<image>', 'podman:<image>': spin up a new container and reuse it for all invocations, clean up on server exit\n"
+        "  'docker-container:<id>', 'podman-container:<id>': use an existing container by ID, won't stop on server exit\n"
+        "  'ssh:<target>': run tools on a remote POSIX host over SSH, key-based auth and a trusted host key are required\n",
         [](common_params & params, const std::string & value) {
             params.server_tools_runtime = value;
         }

common/chat.cpp

@@ -3086,6 +3086,151 @@ static common_chat_params common_chat_params_init_minicpm5(const common_chat_tem
     return data;
 }
 
+// An assistant turn is rendered as one or more messages, each
+// "<|start|>assistant to=<recipient><|message|>{content}{END}" where END is
+// <|eom|> (more messages follow) or <|eot|> (end of turn):
+//   - chain-of-thought: to=self, terminated by <|eom|>
+//   - final answer:     to=user, terminated by <|eot|>
+// The generation prompt is just "<|start|>assistant"; the model emits its own
+// " to=...<|message|>".
+static common_chat_params common_chat_params_init_muse_glimmer(const common_chat_template &          tmpl,
+                                                               const autoparser::generation_params & inputs) {
+    common_chat_params data;
+
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt = "<|start|>assistant";
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.supports_thinking = true;
+
+    data.preserved_tokens = {
+        "<|start|>", "<|message|>", "<|eom|>", "<|eot|>",
+        // ATEM tool-call markup emitted on " to=<tool>" turns.
+        "<atem:function_calls>", "<atem:invoke", "<atem:parameter", "</atem:parameter>",
+        "</atem:invoke>", "</atem:function_calls>",
+    };
+
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_ASSISTANT, "<|start|>assistant" },
+        { COMMON_CHAT_ROLE_USER,      "<|start|>user"      },
+        { COMMON_CHAT_ROLE_SYSTEM,    "<|start|>system"    },
+        { COMMON_CHAT_ROLE_TOOL,      "<|start|>tool"      },
+    };
+
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = "<|start|>assistant to=self<|message|>" + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += "<|eom|><|start|>assistant to=user<|message|>" + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
+    auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+
+    auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
+    // Constrained grammar whenever tools are offered.
+    auto include_grammar = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto start = p.rule("start", p.literal("<|start|>assistant"));
+
+        if (!extract_reasoning && !include_grammar) {
+            return start + p.content(p.rest());
+        }
+
+        if (extract_reasoning) {
+            p.rule("analysis", p.literal(" to=self<|message|>") + p.reasoning(p.until("<|eom|>")) + p.literal("<|eom|>"));
+        } else {
+            p.rule("analysis", p.literal(" to=self<|message|>") + p.content(p.until("<|eom|>")) + p.literal("<|eom|>"));
+        }
+        auto analysis = p.ref("analysis");
+
+        auto recipient  = p.optional(p.literal(" to=user"));
+        auto final_msg  = p.rule("final", recipient + p.literal("<|message|>") + p.content(p.until("<|eot|>")));
+
+        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
+            auto string_value = p.ac(
+                p.tool_arg_string_value(p.until("</atem:parameter>")) + p.tool_arg_close(p.literal("</atem:parameter>")),
+                "</atem:parameter>");
+
+            auto tool_choice = p.choice();
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto &      function = tool.at("function");
+                const std::string name     = function.at("name");
+                auto              params   = function.contains("parameters") ? function.at("parameters") : json::object();
+
+                auto args = p.eps();
+                if (params.contains("properties") && params.at("properties").is_object() && !params.at("properties").empty()) {
+                    auto schema_info = common_schema_info();
+                    schema_info.resolve_refs(params);
+
+                    auto arg_choice = p.choice();
+                    for (const auto & [prop_name, prop_schema] : params.at("properties").items()) {
+                        auto value_parser = p.eps();
+                        if (schema_info.resolves_to_string(prop_schema)) {
+                            value_parser = string_value;
+                        } else {
+                            value_parser = p.tool_arg_json_value(
+                                    p.schema(p.json(), "tool-" + name + "-arg-" + prop_name + "-schema", prop_schema, false))
+                                + p.tool_arg_close(p.literal("</atem:parameter>"));
+                        }
+
+                        auto arg_rule = p.tool_arg(
+                            p.tool_arg_open(p.literal("<atem:parameter name=\"") + p.tool_arg_name(p.literal(prop_name)) + p.literal("\">")) +
+                            value_parser);
+
+                        arg_choice |= arg_rule;
+                    }
+                    args = p.zero_or_more(arg_choice + p.space());
+                }
+
+                auto tool_parser = p.tool(
+                    p.tool_open(p.literal(" to=") + p.until("<|message|>") +
+                                p.literal("<|message|><atem:function_calls>") + p.space() +
+                                p.literal("<atem:invoke name=\"") + p.tool_name(p.literal(name)) + p.literal("\">") + p.space())
+                    << p.tool_args(args)
+                    << p.tool_close(p.literal("</atem:invoke>") + p.space() + p.literal("</atem:function_calls>")));
+
+                tool_choice |= p.rule("tool-" + name, tool_parser);
+            });
+
+            auto tool_calls = inputs.parallel_tool_calls
+                ? p.trigger_rule("tool-call", tool_choice + p.zero_or_more(p.literal("<|eom|>") + start + tool_choice))
+                : p.trigger_rule("tool-call", tool_choice);
+
+
+            if (inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED) {
+                return p.zero_or_more(start + analysis) + start + tool_calls;
+            }
+            return p.zero_or_more(start + analysis) + start + (tool_calls | final_msg);
+        }
+
+        return p.zero_or_more(start + analysis) + start + final_msg;
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
+        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto         schema   = function.contains("parameters") ? function.at("parameters") : json::object();
+                builder.resolve_refs(schema);
+            });
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+        data.grammar_triggers = {
+            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN,
+              "<\\|start\\|>assistant( to=(?!self<\\|message\\|>)(?!user<\\|message\\|>)[^<]*?<\\|message\\|>)" },
+        };
+    }
+
+    return data;
+}
+
 static json common_chat_extra_context() {
     json ctx = json::object();
     std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
@@ -3114,6 +3259,12 @@ std::optional<common_chat_params> common_chat_try_specialized_template(
         return common_chat_params_init_gpt_oss(tmpl, params);
     }
 
+    // Muse Glimmer format using " to=<recipient>" recipients and <|eom|>/<|eot|> message terminators.
+    if (src.find("<atem:function_calls>") != std::string::npos && src.find("<|eom|>") != std::string::npos) {
+        LOG_DBG("Using specialized template: Muse Glimmer\n");
+        return common_chat_params_init_muse_glimmer(tmpl, params);
+    }
+
     // Functionary v3.2 - uses recipient-based format with >>>recipient\n{content}
     // Detection: template has ">>>all" for content and ">>>" prefix for tool calls
     if (src.find(">>>all") != std::string::npos && src.find(">>>${recipient}") != std::string::npos) {

common/common.cpp

@@ -1639,6 +1639,7 @@ struct llama_context_params common_context_params_to_llama(const common_params &
     cparams.n_seq_max         = params.n_parallel;
     cparams.n_rs_seq          = params.speculative.need_n_rs_seq();
     cparams.n_outputs_max     = std::max(params.n_outputs_max, 0);
+    cparams.n_outputs_max_per_seq = std::max(params.n_outputs_max_per_seq, 0);
     cparams.n_batch           = params.n_batch;
     cparams.n_ubatch          = params.n_ubatch;
     cparams.n_threads         = params.cpuparams.n_threads;

common/common.h

@@ -447,6 +447,7 @@ struct common_params {
     int32_t n_parallel            =     1; // number of parallel sequences to decode
     int32_t n_sequences           =     1; // number of sequences to decode
     int32_t n_outputs_max         =     0; // max outputs in a batch (0 = n_batch)
+    int32_t n_outputs_max_per_seq =     1; // max outputs per sequence
     int32_t grp_attn_n            =     1; // group-attention factor
     int32_t grp_attn_w            =   512; // group-attention width
     int32_t n_print               =    -1; // print token count every n tokens (-1 = disabled)
@@ -472,7 +473,7 @@ struct common_params {
     std::vector<size_t> fit_params_target = std::vector<size_t>(llama_max_devices(), 1024 * 1024*1024);
 
     enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
-    enum llama_load_mode  load_mode  = LLAMA_LOAD_MODE_MMAP; // how to load the model
+    enum llama_load_mode  load_mode  = LLAMA_LOAD_MODE_AUTO; // how to load the model
 
     common_cpu_params cpuparams;
     common_cpu_params cpuparams_batch;

common/llguidance.cpp

@@ -116,6 +116,8 @@ static llama_sampler_i llama_sampler_llg_i = {
     /* .backend_accept    = */ NULL,
     /* .backend_apply     = */ NULL,
     /* .backend_set_input = */ NULL,
+    /* .backend_reset     = */ NULL,
+    /* .copy_state        = */ NULL,
 };
 
 static size_t llama_sampler_llg_tokenize_fn(const void * user_data, const uint8_t * bytes, size_t bytes_len,

common/peg-parser.cpp

@@ -570,23 +570,34 @@ struct parser_executor {
     }
 
     static common_peg_parse_result handle_escape_sequence(common_peg_parse_context & ctx, size_t start, size_t & pos, const char delimiter) {
+        auto save = pos;
+
         ++pos; // consume '\'
         if (pos >= ctx.input.size()) {
             if (!ctx.is_lenient()) {
                 return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start);
             }
+            pos = save; // suppress unmatched '\'
             return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start, pos);
         }
 
         char c = ctx.input[pos];
+
         if (c == delimiter || c == '\\' || c == '/' || c == 'b' || c == 'f' || c == 'n' || c == 'r' || c == 't') {
             ++pos;
             return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start, pos);
-        } else if (c == 'u') {
-            return handle_unicode_escape(ctx, start, pos);
-        } else {
-            return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start);
         }
+
+        if (c == 'u') {
+            auto result = handle_unicode_escape(ctx, start, pos);
+            if (result.need_more_input()) {
+                pos = save; // suppress incomplete sequence
+                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start, pos);
+            }
+            return result;
+        }
+
+        return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start);
     }
 
     static common_peg_parse_result handle_unicode_escape(common_peg_parse_context & ctx, size_t start, size_t & pos) {

common/reasoning-budget.cpp

@@ -217,6 +217,8 @@ static struct llama_sampler_i common_reasoning_budget_i = {
     /* .backend_accept    = */ nullptr,
     /* .backend_apply     = */ nullptr,
     /* .backend_set_input = */ nullptr,
+    /* .backend_reset     = */ nullptr,
+    /* .copy_state        = */ nullptr,
 };
 
 static struct llama_sampler * common_reasoning_budget_clone(const struct llama_sampler * smpl) {

common/sampling.cpp

@@ -518,6 +518,26 @@ struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
     };
 }
 
+void common_sampler_copy(const common_sampler * src, common_sampler * dst) {
+    if (!src || !dst || src == dst) {
+        return;
+    }
+
+    GGML_ASSERT((src->grmr == nullptr) == (dst->grmr == nullptr));
+    GGML_ASSERT((src->rbudget == nullptr) == (dst->rbudget == nullptr));
+
+    llama_sampler_copy(src->grmr,    dst->grmr);
+    llama_sampler_copy(src->rbudget, dst->rbudget);
+    llama_sampler_copy(src->chain,   dst->chain);
+
+    dst->params     = src->params;
+    dst->prev       = src->prev;
+    dst->cur        = src->cur;
+    dst->cur_p      = src->cur_p;
+    dst->cur_p.data = src->cur_p.data ? dst->cur.data() : nullptr; // re-point to dst's buffer
+    dst->t_total_us = src->t_total_us;
+}
+
 void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl) {
     // TODO: measure grammar performance
 

common/sampling.h

@@ -47,6 +47,7 @@ void common_sampler_free(struct common_sampler * gsmpl);
 void                    common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool is_generated);
 void                    common_sampler_reset (struct common_sampler * gsmpl);
 struct common_sampler * common_sampler_clone (struct common_sampler * gsmpl);
+void                    common_sampler_copy  (const struct common_sampler * src, struct common_sampler * dst);
 
 // arguments can be nullptr to skip printing
 void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl);

common/speculative.cpp

@@ -1032,7 +1032,14 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
             return true;
         }
 
-        if (batch_in.token == nullptr || batch_in.embd != nullptr) {
+        // Target prefill may contain token IDs or multimodal embeddings. Both
+        // produce the target-layer features used to seed the draft KV cache, so
+        // skipping the embedding batches leaves a hole in the draft's cache and
+        // the next injection fails to initialize.
+        // TODO: revisit after https://github.com/ggml-org/llama.cpp/pull/24669 is merged
+        const bool has_tokens     = batch_in.token != nullptr;
+        const bool has_embeddings = batch_in.embd  != nullptr;
+        if (has_tokens == has_embeddings) {
             return true;
         }
 
@@ -2292,6 +2299,7 @@ common_params common_base_params_to_speculative(const common_params & params) {
     result.cache_type_k  = params_spec.cache_type_k;
     result.cache_type_v  = params_spec.cache_type_v;
     result.n_outputs_max = params.n_parallel;
+    result.n_outputs_max_per_seq = 1;
 
     return result;
 }
@@ -2377,6 +2385,17 @@ common_speculative_init_result_ptr common_speculative_init_from_params(common_pa
     return std::make_unique<common_speculative_init_result>(params, model_tgt, ctx_tgt);
 }
 
+common_speculative_output_limits common_speculative_get_output_limits(
+        int32_t n_batch, int32_t n_parallel, int32_t n_draft) {
+    const int64_t per_seq = 1 + (int64_t) std::max(0, n_draft);
+    const int64_t total   = (int64_t) n_parallel * per_seq;
+
+    return {
+        /* .total   = */ (int32_t) std::min<int64_t>(n_batch, total),
+        /* .per_seq = */ (int32_t) std::min<int64_t>(n_batch, per_seq),
+    };
+}
+
 // initialization of the speculative decoding system
 //
 common_speculative * common_speculative_init(common_params_speculative & params, uint32_t n_seq) {

common/speculative.h

@@ -25,6 +25,15 @@ int32_t common_speculative_n_max(const common_params_speculative * spec);
 
 common_params common_base_params_to_speculative(const common_params & params);
 
+struct common_speculative_output_limits {
+    int32_t total;
+    int32_t per_seq;
+};
+
+// return the output limits needed for speculative decoding
+common_speculative_output_limits common_speculative_get_output_limits(
+        int32_t n_batch, int32_t n_parallel, int32_t n_draft);
+
 common_speculative * common_speculative_init(common_params_speculative & params, uint32_t n_seq);
 
 void common_speculative_free(common_speculative * spec);

conversion/__init__.py

@@ -183,6 +183,8 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "Olmo3ForCausalLM": "olmo",
     "OlmoForCausalLM": "olmo",
     "OlmoeForCausalLM": "olmo",
+    "MuseGlimmerAssistantModel": "muse_glimmer",
+    "MuseGlimmerForConditionalGeneration": "muse_glimmer",
     "OpenELMForCausalLM": "openelm",
     "OrionForCausalLM": "orion",
     "PLMForCausalLM": "plm",
@@ -298,6 +300,7 @@ MMPROJ_MODEL_MAP: dict[str, str] = {
     "MiniCPMV4_6ForConditionalGeneration": "minicpm",
     "Mistral3ForConditionalGeneration": "llava",
     "NemotronH_Nano_VL_V2": "nemotron",
+    "MuseGlimmerForConditionalGeneration": "muse_glimmer",
     "PaddleOCRVisionModel": "ernie",
     "Phi4ForCausalLMV": "phi",
     "Qwen2AudioForConditionalGeneration": "ultravox",

conversion/muse_glimmer.py

@@ -0,0 +1,179 @@
+from __future__ import annotations
+
+import json
+from typing import Any, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, TextModel, gguf
+
+
+def _unpermute_for_rope(tensor: "Tensor", n_heads: int) -> "Tensor":
+    """Invert transformers' `_permute_for_rope`: HF stores Q/K in rotate_half layout,
+    llama.cpp consumes the interleaved (NORM) layout."""
+    if tensor.ndim == 2:
+        dim1, dim2 = tensor.shape
+        return tensor.view(n_heads, 2, dim1 // n_heads // 2, dim2).transpose(1, 2).reshape(dim1, dim2)
+    if tensor.ndim == 1:
+        (dim1,) = tensor.shape
+        return tensor.view(n_heads, 2, dim1 // n_heads // 2).transpose(1, 2).reshape(dim1)
+    raise ValueError(f"_unpermute_for_rope: unexpected shape {tuple(tensor.shape)}")
+
+
+@ModelBase.register("MuseGlimmerForConditionalGeneration")
+class MuseGlimmerModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.MUSE_GLIMMER
+
+    def norm_shift(self, name: str) -> float:
+        # All four layer norms use 1, the final norm uses 0.
+        return 1.0 if name.endswith("layernorm.weight") else 0.0
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+        from transformers import AutoTokenizer
+        tok = AutoTokenizer.from_pretrained(self.dir_model)
+        eot_id = tok.convert_tokens_to_ids("<|eot|>")
+        if isinstance(eot_id, int) and eot_id >= 0:
+            self.gguf_writer.add_eot_token_id(eot_id)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+
+        self.gguf_writer.add_final_logit_softcapping(hparams["final_logit_softcapping"])
+        self.gguf_writer.add_logit_scale(hparams["output_multiplier"])
+        self.gguf_writer.add_sliding_window(hparams["sliding_window"])
+        self.gguf_writer.add_sliding_window_pattern([t == "sliding_attention" for t in hparams["layer_types"]])
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        shift = self.norm_shift(name)
+        if shift != 0.0:
+            data_torch = data_torch + shift
+
+        # Invert transformers' `_permute_for_rope` on Q/K, we keep ggml's NORM (interleaved) rope
+        if ".self_attn.q_proj." in name:
+            data_torch = _unpermute_for_rope(data_torch, int(self.hparams["num_attention_heads"]))
+        elif ".self_attn.k_proj." in name:
+            data_torch = _unpermute_for_rope(data_torch, int(self.hparams["num_key_value_heads"]))
+
+        # Synthesize QK-norm weights to absorb qk_scale_factor.
+        # MuseGlimmer implementation: scaleless RMSNorm followed by qk_scale_factor..
+        if bid is not None and name.endswith(f"model.layers.{bid}.self_attn.q_proj.weight"):
+            head_dim = self.hparams["head_dim"]
+            q_scale = float(self.hparams["qk_scale_factor"])
+            yield (
+                self.map_tensor_name(f"model.layers.{bid}.self_attn.q_norm.weight"),
+                torch.full((head_dim,), q_scale, dtype=torch.float32),
+            )
+            yield (
+                self.map_tensor_name(f"model.layers.{bid}.self_attn.k_norm.weight"),
+                torch.ones((head_dim,), dtype=torch.float32),
+            )
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("MuseGlimmerForConditionalGeneration")
+class MuseGlimmerVisionModel(MmprojModel):
+    def get_vision_config(self) -> dict[str, Any] | None:
+        c = self.global_config.get("vision_config")
+        if not c:
+            return None
+        # MuseGlimmer actually uses dynamic size, initialize with nominal size
+        image_size = c["pos_emb_height"] * c["patch_size"] * c["merge_size"]
+        return {**c, "image_size": image_size}
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        assert self.hparams_vision is not None
+        c = self.hparams_vision  # enriched vision_config from get_vision_config()
+
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MUSE_GLIMMER)
+        self.gguf_writer.add_vision_attention_layernorm_eps(float(c["layer_norm_eps"]))
+        self.gguf_writer.add_vision_spatial_merge_size(int(c["merge_size"]))
+
+    @classmethod
+    def filter_tensors(cls, item):
+        name, gen = item
+        keep = ("model.vision_tower.", "model.vision_adapter.", "model.vision_projection.")
+        if not any(name.startswith(k) for k in keep):
+            return None
+        return super().filter_tensors((name, gen))
+
+    # 3-layer projector MLP
+    _MM_MLP_MAP = {
+        "model.vision_adapter.fc1": (gguf.MODEL_TENSOR.V_MMPROJ, 0),
+        "model.vision_adapter.fc2": (gguf.MODEL_TENSOR.V_MMPROJ, 1),
+        "model.vision_projection":  (gguf.MODEL_TENSOR.V_MMPROJ, 2),
+    }
+
+    def modify_tensors(self, data_torch, name, bid):
+        assert self.hparams_vision is not None
+        if ".attn.q_proj." in name or ".attn.k_proj." in name:
+            n_heads = int(self.hparams_vision["num_attention_heads"])
+            data_torch = _unpermute_for_rope(data_torch, n_heads)
+        # Lay out the pt=2 temporal slabs of the patch embedding as a conv2d for build_inp()
+        if name.endswith("patch_embedder.patch_embedding.weight"):
+            n_embd = data_torch.shape[0]
+            pt = int(self.hparams_vision["patch_temporal"])
+            ps = int(self.hparams_vision["patch_size"])
+            data_torch = data_torch.view(n_embd, pt, 3, ps, ps).sum(dim=1)  # (n_embd, 3, ps, ps)
+        stem, _, suffix = name.rpartition(".")
+        if stem in self._MM_MLP_MAP:
+            tensor_key, idx = self._MM_MLP_MAP[stem]
+            yield (self.format_tensor_name(tensor_key, bid=idx, suffix="." + suffix), data_torch)
+            return
+        yield (self.map_tensor_name(name), data_torch)
+
+
+@ModelBase.register("MuseGlimmerAssistantModel")
+class MuseGlimmerAssistantModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.DFLASH
+
+    def set_vocab(self):
+        if self.target_model_dir is None:
+            raise ValueError(
+                "MuseGlimmerAssistant (DFlash drafter) requires --target-model-dir pointing to the "
+                "target MuseGlimmer HF directory"
+            )
+
+        original_dir = self.dir_model
+        self.dir_model = self.target_model_dir
+
+        from . import get_model_class
+        with open(self.target_model_dir / "config.json", "r", encoding="utf-8") as f:
+            target_arch = json.load(f)["architectures"][0]
+        target_cls = get_model_class(target_arch)
+        if target_cls is not type(self):
+            target_cls.set_vocab(self)  # ty: ignore[unresolved-attribute]
+        else:
+            super().set_vocab()
+
+        self.dir_model = original_dir
+
+        mask_token_id = self.hparams.get("mask_token_id")
+        if mask_token_id is not None:
+            self.gguf_writer.add_mask_token_id(int(mask_token_id))
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        h = self.hparams
+
+        self.gguf_writer.add_block_size(int(h["block_size"]))
+
+        # dflash.target_layers[k] refers to the inputs going into the ith layer, which come from the (i-1)th layer's output.
+        # The transformers configuration refers to the outputs being recorded.
+        self.gguf_writer.add_target_layers([int(x) + 1 for x in h["target_layer_ids"]])
+
+        if h.get("sliding_window") and h.get("layer_types"):
+            self.gguf_writer.add_sliding_window(int(h["sliding_window"]))
+            self.gguf_writer.add_sliding_window_pattern([t == "sliding_attention" for t in h["layer_types"]])
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # DFlash defaults to NEOX (rotate_half) rope, matching transformers HF layout for Q/K, QK-norms
+        # no permutation needed.
+        yield (self.map_tensor_name(name), data_torch)

conversion/nemotron.py

@@ -197,6 +197,7 @@ class NemotronHModel(GraniteHybridModel):
     """Hybrid mamba2/attention model from NVIDIA"""
     model_arch = gguf.MODEL_ARCH.NEMOTRON_H
     is_moe: bool = False
+    supports_mtp_export = True
 
     def __init__(self, *args, **kwargs):
         # We have to determine the correct model architecture (MoE vs non-MoE) before
@@ -236,6 +237,25 @@ class NemotronHModel(GraniteHybridModel):
             self._ssm_layers = [i for i, val in enumerate(pattern) if val == "mamba"]
             self._mlp_layers = [i for i, val in enumerate(pattern) if val == "moe"]
 
+        # `--no-mtp` drops it entirely; `--mtp` exports only the MTP head
+        self._mtp_bid: int | None = None
+        if self.is_moe and not self.no_mtp:
+            n_nextn = self.hparams.get("num_nextn_predict_layers", 0) or 0
+            if n_nextn > 0:
+                assert n_nextn == 1, (
+                    "NemotronH MTP conversion currently supports num_nextn_predict_layers == 1"
+                )
+                self._mtp_bid = self.block_count
+                self.block_count += 1
+                # The folded MTP block carries both an attention sub-layer and a
+                # MoE sub-layer, so register it as both so the per-layer metadata arrays cover it
+                self._attn_layers.append(self._mtp_bid)
+                self._mlp_layers.append(self._mtp_bid)
+                self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+        if self.mtp_only and self._mtp_bid is None:
+            raise ValueError("--mtp was requested, but this model does not contain a supported MTP head")
+
     def get_attn_layers(self):
         pattern = self.hparams.get("hybrid_override_pattern") or self.hparams.get("layers_block_type")
         if pattern is None:
@@ -246,6 +266,36 @@ class NemotronHModel(GraniteHybridModel):
 
         return [i for i, val in enumerate(pattern) if val == "attention"]
 
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+        if name.startswith("mtp."):
+            # --no-mtp: drop the MTP head entirely
+            if cls.no_mtp:
+                return None
+        elif cls.mtp_only:
+            # --mtp: export the MTP head plus the tensors it shares with the target model
+            keep = name in (
+                "backbone.embeddings.weight",
+                "backbone.norm_f.weight",
+                "lm_head.weight",
+            )
+            if not keep:
+                return None
+        return super().filter_tensors((name, gen))
+
+    def prepare_metadata(self, vocab_only: bool):
+        from_dir = self.fname_out.is_dir()
+        super().prepare_metadata(vocab_only=vocab_only)
+
+        if not self.mtp_only or not from_dir:
+            return
+        output_type: str = self.ftype.name.partition("_")[2]
+        fname_default: str = gguf.naming_convention(
+            self.metadata.name, self.metadata.basename, self.metadata.finetune,
+            self.metadata.version, size_label=None, output_type=output_type, model_type=None)
+        self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf"
+
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
 
@@ -284,6 +334,10 @@ class NemotronHModel(GraniteHybridModel):
             if (latent_size := self.hparams.get("moe_latent_size")) is not None:
                 self.gguf_writer.add_moe_latent_size(latent_size)
 
+        # MTP head: number of trailing NextN blocks
+        if self._mtp_bid is not None:
+            self.gguf_writer.add_nextn_predict_layers(self.hparams["num_nextn_predict_layers"])
+
     def set_vocab(self):
         # The NemotronH config uses pattern characters (e.g. '-') that may not
         # be supported by the installed transformers version. AutoTokenizer
@@ -350,15 +404,24 @@ class NemotronHModel(GraniteHybridModel):
         if not self.is_moe:
             self.gguf_writer.add_add_bos_token(True)
 
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if self.is_moe and bid is not None:
-            # Skip Multi-Token Prediction (MTP) tensors. These are used for
-            # for speculative decoding but we don't include them in this model
-            # conversion. See https://github.com/ggml-org/llama.cpp/pull/18886
-            if name.startswith("mtp."):
-                logger.info(f"gguf: Skipping MTP (Speculative) layer: {name}")
-                return
+    _MTP_SPECIAL_RENAMES = {
+        "mtp.layers.0.enorm.weight":           "model.layers.{bid}.enorm.weight",
+        "mtp.layers.0.hnorm.weight":           "model.layers.{bid}.hnorm.weight",
+        "mtp.layers.0.eh_proj.weight":         "model.layers.{bid}.eh_proj.weight",
+        "mtp.layers.1.norm.weight":            "model.layers.{bid}.post_attention_layernorm.weight",
+        "mtp.layers.1.final_layernorm.weight": "model.layers.{bid}.shared_head.norm.weight",
+    }
 
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        #   mtp.layers.0: NextN input fusion + attention
+        #   mtp.layers.1: MoE + final head norm
+        if self._mtp_bid is not None and name.startswith(("mtp.layers.0.", "mtp.layers.1.")):
+            suffix = name.split(".", 3)[3]
+            bid = self._mtp_bid
+            renamed = self._MTP_SPECIAL_RENAMES.get(name)
+            name = renamed.format(bid=bid) if renamed else f"backbone.layers.{bid}.{suffix}"
+
+        if self.is_moe and bid is not None:
             if name.endswith("mixer.gate.e_score_correction.bias"):
                 yield from ModelBase.modify_tensors(self, data_torch, name, bid)
                 return

docs/speculative.md

@@ -202,6 +202,12 @@ Example Video:
 
 If a draft model is combined with a draftless decoding the draftless decoding has higher precedence.
 
+### Backend Sampling
+
+Use `--backend-sampling` to run supported target-model samplers on the model backend. Draft-model sampling uses the backend by default and can be controlled with `--spec-draft-backend-sampling` and `--no-spec-draft-backend-sampling`.
+
+Unsupported samplers and device layouts fall back to CPU sampling. Tensor split mode does not support backend sampling. A fixed seed produces repeatable random draws, but stochastic CPU and backend sampling can still select different tokens because floating-point operations can differ between implementations and devices. Use greedy sampling when exact output matching is required.
+
 ### General Speculative Parameters
 
 ```

examples/lookup/lookup.cpp

@@ -3,9 +3,11 @@
 #include "common.h"
 #include "ngram-cache.h"
 #include "sampling.h"
+#include "speculative.h"
 #include "log.h"
 #include "llama.h"
 
+#include <algorithm>
 #include <clocale>
 #include <cstdint>
 #include <cstdio>
@@ -27,6 +29,10 @@ int main(int argc, char ** argv){
     // max. number of additional tokens to draft if match is found
     const int n_draft = params.speculative.draft.n_max;
 
+    const auto output_limits = common_speculative_get_output_limits(params.n_batch, params.n_parallel, n_draft);
+    params.n_outputs_max = output_limits.total;
+    params.n_outputs_max_per_seq = output_limits.per_seq;
+
     // init llama.cpp
     llama_backend_init();
     llama_numa_init(params.numa);

examples/model-conversion/scripts/causal/run-casual-gen-embeddings-org.py

@@ -2,12 +2,15 @@
 
 import argparse
 import os
+import sys
 import importlib
 import torch
 import numpy as np
 
 from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM
-from pathlib import Path
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
+from utils.common import save_output_data
 
 unreleased_model_name = os.getenv('UNRELEASED_MODEL_NAME')
 
@@ -54,6 +57,7 @@ print(f"Model name: {model_name}")
 
 prompt = "Hello world today"
 input_ids = tokenizer(prompt, return_tensors="pt").input_ids  # ty: ignore[call-non-callable]
+token_ids = input_ids[0].cpu().tolist()
 print(f"Input tokens: {input_ids}")
 print(f"Input text: {repr(prompt)}")
 print(f"Tokenized: {tokenizer.convert_ids_to_tokens(input_ids[0])}")  # ty: ignore[unresolved-attribute]
@@ -74,21 +78,8 @@ with torch.no_grad():
     print(f"Hidden dimension: {token_embeddings.shape[-1]}")
     print(f"Number of tokens: {token_embeddings.shape[0]}")
 
-    # Save raw token embeddings
-    data_dir = Path("data")
-    data_dir.mkdir(exist_ok=True)
-    bin_filename = data_dir / f"pytorch-{model_name}-embeddings.bin"
-    txt_filename = data_dir / f"pytorch-{model_name}-embeddings.txt"
-
-    # Save all token embeddings as binary
     print(token_embeddings)
-    token_embeddings.astype(np.float32).tofile(bin_filename)
-
-    # Save as text for inspection
-    with open(txt_filename, "w") as f:
-        for i, embedding in enumerate(token_embeddings):
-            for j, val in enumerate(embedding):
-                f.write(f"{i} {j} {val:.6f}\n")
+    save_output_data(token_embeddings, token_ids, prompt, model_name, type_suffix="-embeddings")
 
     # Print embeddings per token in the requested format
     print("\nToken embeddings:")
@@ -110,5 +101,3 @@ with torch.no_grad():
     for i, token in enumerate(tokens):
         print(f"  Token {i}: {repr(token)}")
 
-    print(f"Saved bin logits to: {bin_filename}")
-    print(f"Saved txt logist to: {txt_filename}")

examples/speculative-simple/speculative-simple.cpp

@@ -5,6 +5,7 @@
 #include "log.h"
 #include "llama.h"
 
+#include <algorithm>
 #include <clocale>
 #include <cstdio>
 #include <cstring>
@@ -29,6 +30,11 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
+    const auto output_limits = common_speculative_get_output_limits(
+            params.n_batch, params.n_parallel, common_speculative_n_max(&params.speculative));
+    params.n_outputs_max = output_limits.total;
+    params.n_outputs_max_per_seq = output_limits.per_seq;
+
     // init llama.cpp
     llama_backend_init();
     llama_numa_init(params.numa);
@@ -55,6 +61,9 @@ int main(int argc, char ** argv) {
 
         auto params_dft = params;
 
+        params_dft.n_outputs_max = params.n_parallel;
+        params_dft.n_outputs_max_per_seq = 1;
+
         params_dft.devices      = params_spec.devices;
         params_dft.model        = params_spec.mparams;
         params_dft.n_gpu_layers = params_spec.n_gpu_layers;

examples/speculative/speculative.cpp

@@ -1,6 +1,7 @@
 #include "arg.h"
 #include "common.h"
 #include "sampling.h"
+#include "speculative.h"
 #include "log.h"
 #include "llama.h"
 
@@ -57,6 +58,11 @@ int main(int argc, char ** argv) {
     // max number of parallel drafting sequences (i.e. tree branches)
     const int n_seq_dft = params.n_parallel;
 
+    const auto output_limits = common_speculative_get_output_limits(
+            params.n_batch, params.n_parallel, params.speculative.draft.n_max);
+    params.n_outputs_max = output_limits.total;
+    params.n_outputs_max_per_seq = output_limits.per_seq;
+
     // probability threshold for splitting a draft branch (only for n_seq_dft > 1)
     const float p_draft_split = params.speculative.draft.p_split;
 
@@ -83,6 +89,8 @@ int main(int argc, char ** argv) {
     params.devices = params.speculative.draft.devices;
     params.model = params.speculative.draft.mparams;
     params.n_gpu_layers = params.speculative.draft.n_gpu_layers;
+    params.n_outputs_max = params.n_parallel;
+    params.n_outputs_max_per_seq = 1;
     if (params.speculative.draft.cpuparams.n_threads > 0) {
         params.cpuparams.n_threads = params.speculative.draft.cpuparams.n_threads;
     }

ggml/include/ggml-backend.h

@@ -154,6 +154,8 @@ extern "C" {
         bool buffer_from_host_ptr;
         // event synchronization
         bool events;
+        // mmap is supported for loading
+        bool mmap_support;
     };
 
     // all the device properties

ggml/src/ggml-backend-meta.cpp

@@ -132,6 +132,7 @@ static void ggml_backend_meta_device_get_props(ggml_backend_dev_t dev, ggml_back
         /* .host_buffer           = */ false, // Not implemented.
         /* .buffer_from_host_ptr  = */ false, // Not implemented.
         /* .events                = */ false, // Not implemented.
+        /* .mmap_support          = */ true,
     };
     for (ggml_backend_dev_t simple_dev : meta_dev_ctx->simple_devs) {
         ggml_backend_dev_props tmp_props;
@@ -140,6 +141,7 @@ static void ggml_backend_meta_device_get_props(ggml_backend_dev_t dev, ggml_back
         props->caps.host_buffer          = props->caps.host_buffer          && tmp_props.caps.host_buffer;
         props->caps.buffer_from_host_ptr = props->caps.buffer_from_host_ptr && tmp_props.caps.buffer_from_host_ptr;
         props->caps.events               = props->caps.events               && tmp_props.caps.events;
+        props->caps.mmap_support         = props->caps.mmap_support         && tmp_props.caps.mmap_support;
     }
 }
 

ggml/src/ggml-blas/ggml-blas.cpp

@@ -367,6 +367,7 @@ static void ggml_backend_blas_device_get_props(ggml_backend_dev_t dev, struct gg
         /* .host_buffer           = */ false,
         /* .buffer_from_host_ptr  = */ true,
         /* .events                = */ false,
+        /* .mmap_support          = */ true,
     };
 }
 

ggml/src/ggml-cann/ggml-cann.cpp

@@ -2815,6 +2815,7 @@ static void ggml_backend_cann_device_get_props(ggml_backend_dev_t dev, ggml_back
         /* .host_buffer           = */ host_buffer,
         /* .buffer_from_host_ptr  = */ false,
         /* .events                = */ true,
+        /* .mmap_support          = */ true,
     };
 }
 

ggml/src/ggml-cpu/ggml-cpu.c

@@ -2608,7 +2608,7 @@ static bool ggml_thread_apply_priority(int32_t prio) {
     return true;
 }
 
-#elif defined(__gnu_linux__)
+#elif defined(__linux__)
 // TODO: this may not work on BSD, to be verified
 
 static bool ggml_thread_apply_affinity(const bool * mask) {

ggml/src/ggml-cpu/ggml-cpu.cpp

@@ -397,6 +397,7 @@ static void ggml_backend_cpu_device_get_props(ggml_backend_dev_t dev, struct ggm
         /* .host_buffer           = */ false,
         /* .buffer_from_host_ptr  = */ true,
         /* .events                = */ false,
+        /* .mmap_support          = */ true,
     };
 }
 

ggml/src/ggml-cuda/ggml-cuda.cu

@@ -4801,6 +4801,7 @@ static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_back
         /* .host_buffer           = */ host_buffer,
         /* .buffer_from_host_ptr  = */ false,
         /* .events                = */ events,
+        /* .mmap_support          = */ props->type != GGML_BACKEND_DEVICE_TYPE_IGPU,
     };
 }
 
@@ -5185,7 +5186,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
             return max_bias == 0.0f;
         }
         case GGML_OP_ROLL:
-            if(op->src[0]->type == GGML_TYPE_F32) {
+            if(op->src[0]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0])) {
                 return true;
             }
             return false;

ggml/src/ggml-et/ggml-et.cpp

@@ -1646,6 +1646,7 @@ static void ggml_backend_et_device_get_props(ggml_backend_dev_t dev, struct ggml
         /* .host_buffer           = */ false,
         /* .buffer_from_host_ptr  = */ false,
         /* .events                = */ false,
+        /* .mmap_support          = */ true,
     };
 }
 

ggml/src/ggml-hexagon/ggml-hexagon.cpp

@@ -3930,6 +3930,7 @@ static void ggml_backend_hexagon_device_get_props(ggml_backend_dev_t dev, struct
         /* .host_buffer           = */ (bool) opt_hostbuf,
         /* .buffer_from_host_ptr  = */ false,
         /* .events                = */ false,
+        /* .mmap_support          = */ false,
     };
 }
 

ggml/src/ggml-metal/ggml-metal-device.m

@@ -1268,8 +1268,9 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
         case GGML_OP_ARGSORT:
         case GGML_OP_TOP_K:
         case GGML_OP_ARANGE:
-        case GGML_OP_ROLL:
             return true;
+        case GGML_OP_ROLL:
+            return ggml_is_contiguous(op->src[0]);
         case GGML_OP_FLASH_ATTN_EXT:
             // for new head sizes, add checks here
             if (op->src[0]->ne[0] != 32 &&

ggml/src/ggml-metal/ggml-metal.cpp

@@ -681,6 +681,7 @@ static void ggml_backend_metal_device_get_props(ggml_backend_dev_t dev, ggml_bac
         /* .host_buffer          = */ false,
         /* .buffer_from_host_ptr = */ true,
         /* .events               = */ true,
+        /* .mmap_support         = */ true,
     };
 }
 

ggml/src/ggml-opencl/ggml-opencl.cpp

@@ -73,6 +73,7 @@ typedef const void * (*get_adreno_bin_kernel_func_t)(
 //------------------------------------------------------------------------------
 
 bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor);
+
 static bool ggml_cl_is_q4_0_soa(const ggml_tensor * tensor);
 static bool ggml_cl_is_q8_0_soa(const ggml_tensor * tensor);
 static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
@@ -4629,6 +4630,23 @@ static std::string ggml_opencl_fa_compile_opts(ggml_backend_opencl_context * bac
     if (backend_ctx->adreno_gen == ADRENO_GPU_GEN::X1E) {
         opts += " -D FA_C8_NO_SG_PIN";
     }
+    // Transposed K tile in local memory: the KV rows the QK loop walks together become
+    // adjacent, so a group of them is ONE 128-bit local read instead of several narrow
+    // ones. The QK loop is LDS-read-issue-bound (a wrong-math probe that kept every FMA/dp4a
+    // but removed the LDS reads ran the kernel ~40% faster), so this is worth up to +26% on
+    // fa=1 prefill. Output is bit-identical -- only the layout moves.
+    //
+    // DK <= 128 only. At DK=256 (gemma-3-4b) it measures 1-2% NEGATIVE and reproduces across
+    // rounds; padding the row stride does not recover it, so the cause is not a simple bank
+    // conflict and the wider tile does not want this layout.
+    //
+    // Default on within that gate; GGML_OPENCL_FA_K_LDS_T=0 restores the row-major tile.
+    {
+        const char * e = getenv("GGML_OPENCL_FA_K_LDS_T");
+        if ((e == nullptr || e[0] != '0') && cfg->dk <= 128) {
+            opts += " -D FA_K_LDS_T";
+        }
+    }
     return opts;
 }
 
@@ -10777,6 +10795,7 @@ static void ggml_backend_opencl_device_get_props(ggml_backend_dev_t dev, struct
         /* .host_buffer           = */ false,
         /* .buffer_from_host_ptr  = */ false,
         /* .events                = */ false,
+        /* .mmap_support          = */ false,
     };
 }
 

ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl

@@ -211,7 +211,30 @@ __kernel void FA_TILE_NAME(
 
     float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
 
+#ifdef FA_K_LDS_T
+    // K tile transposed: [dk vec][kv row] instead of [kv row][dk vec].
+    //
+    // The QK loop walks 2 or 4 KV rows at a time against the same dk element. Row-major
+    // those are DK_VEC half4s apart, so each is its own 64-bit local read. Transposed they
+    // are adjacent, so a pair is one 128-bit read -- half the LDS issues for the same bytes,
+    // no extra registers, arithmetic untouched.
+    //
+    // This kernel looked like it should be FMA-bound (a half4 mad does ~4 ALU ops per LDS
+    // read, unlike the 1:1 of the dp4a loop), but it is NOT: a wrong-math probe that kept
+    // every FMA and removed the LDS reads ran it 38.6% faster (18.92 -> 11.62 ms/op).
+    // Explicitly 16-byte aligned: FA_LK_PAIR below reads two adjacent half4 as one float4,
+    // and the element type only obliges the compiler to align this array to 8. The indices
+    // are even so the offset is a multiple of 16, but the base has to be too, and relying
+    // on the compiler to over-align it is relying on luck.
+    __local KV_DATA_TYPE4 l_k[DK_VEC][BLOCK_N] __attribute__((aligned(16)));
+#define FA_LK(ROW, C) l_k[C][ROW]
+    // Two adjacent KV rows as one 128-bit local read (half4 pair == 16 B). j is even and
+    // BLOCK_N is even, so &l_k[c][j] is 16 B past a 16 B-aligned base.
+#define FA_LK_PAIR(C, J) as_half8(*(__local const float4 *)(&l_k[C][J]))
+#else
     __local KV_DATA_TYPE4 l_k[BLOCK_N][DK_VEC];
+#define FA_LK(ROW, C) l_k[ROW][C]
+#endif
     __local KV_DATA_TYPE4 l_v[BLOCK_N][DV_VEC];
 
 #if N_SPLIT > 1 && !defined(HAS_SUBGROUP_SHUFFLE)
@@ -254,17 +277,17 @@ __kernel void FA_TILE_NAME(
 #ifdef FA_K_IMG
                 if (use_kv_pad) {
                     const ulong k_row_offset = batch_idx * k_tile_nb3 + head_kv_idx * k_tile_nb2 + k_row_idx * k_nb1;
-                    l_k[row][col] = ((__global KV_DATA_TYPE4*)(k_tile_base + k_row_offset))[col];
+                    FA_LK(row, col) = ((__global KV_DATA_TYPE4*)(k_tile_base + k_row_offset))[col];
                 } else {
                     const int k_row_px = batch_idx * k_pitch_px_batch + head_kv_idx * k_pitch_px_head + k_row_idx * k_pitch_px_row;
-                    l_k[row][col] = read_imageh(k_img, k_row_px + col);
+                    FA_LK(row, col) = read_imageh(k_img, k_row_px + col);
                 }
 #else
                 const ulong k_row_offset = batch_idx * k_tile_nb3 + head_kv_idx * k_tile_nb2 + k_row_idx * k_nb1;
-                l_k[row][col] = ((__global KV_DATA_TYPE4*)(k_tile_base + k_row_offset))[col];
+                FA_LK(row, col) = ((__global KV_DATA_TYPE4*)(k_tile_base + k_row_offset))[col];
 #endif
             } else {
-                l_k[row][col] = (KV_DATA_TYPE4)(0.0h);
+                FA_LK(row, col) = (KV_DATA_TYPE4)(0.0h);
             }
         }
         for (int i = tid; i < BLOCK_N * DV_VEC; i += WG_SIZE) {
@@ -292,8 +315,15 @@ __kernel void FA_TILE_NAME(
                 FA_UNROLL
                 for (int k = 0; k < SPLIT_DK_VEC; k++) {
                     const ACC_TYPE4 qk = q_priv[k];
+#if defined(FA_K_LDS_T)
+                    // 2 KV rows adjacent in the transposed tile: one 128-bit local read.
+                    const half8 kk = FA_LK_PAIR(dk_off + k, j);
+                    ACC_TYPE4 dot0 = qk * CONVERT_KV_ACC4(kk.lo);
+                    ACC_TYPE4 dot1 = qk * CONVERT_KV_ACC4(kk.hi);
+#else
                     ACC_TYPE4 dot0 = qk * CONVERT_KV_ACC4(l_k[j  ][dk_off + k]);
                     ACC_TYPE4 dot1 = qk * CONVERT_KV_ACC4(l_k[j+1][dk_off + k]);
+#endif
                     partial0 += dot0.s0 + dot0.s1 + dot0.s2 + dot0.s3;
                     partial1 += dot1.s0 + dot1.s1 + dot1.s2 + dot1.s3;
                 }
@@ -359,7 +389,7 @@ __kernel void FA_TILE_NAME(
             ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
             FA_UNROLL
             for (int k = 0; k < SPLIT_DK_VEC; k++) {
-                dot_acc = mad(q_priv[k], CONVERT_KV_ACC4(l_k[j][dk_off + k]), dot_acc);
+                dot_acc = mad(q_priv[k], CONVERT_KV_ACC4(FA_LK(j, dk_off + k)), dot_acc);
             }
             local_partial[j][tid] =
                 dot_acc.s0 + dot_acc.s1 + dot_acc.s2 + dot_acc.s3;
@@ -452,10 +482,21 @@ __kernel void FA_TILE_NAME(
                 FA_UNROLL
                 for (int k = 0; k < DK_VEC; k++) {
                     const ACC_TYPE4 qk = q_priv[k];
+#if defined(FA_K_LDS_T)
+                    // 4 KV rows adjacent in the transposed tile: two 128-bit local reads
+                    // instead of four 64-bit ones.
+                    const half8 kk01 = FA_LK_PAIR(k, j);
+                    const half8 kk23 = FA_LK_PAIR(k, j + 2);
+                    dot_acc0 = mad(qk, CONVERT_KV_ACC4(kk01.lo), dot_acc0);
+                    dot_acc1 = mad(qk, CONVERT_KV_ACC4(kk01.hi), dot_acc1);
+                    dot_acc2 = mad(qk, CONVERT_KV_ACC4(kk23.lo), dot_acc2);
+                    dot_acc3 = mad(qk, CONVERT_KV_ACC4(kk23.hi), dot_acc3);
+#else
                     dot_acc0 = mad(qk, CONVERT_KV_ACC4(l_k[j][k]),   dot_acc0);
                     dot_acc1 = mad(qk, CONVERT_KV_ACC4(l_k[j+1][k]), dot_acc1);
                     dot_acc2 = mad(qk, CONVERT_KV_ACC4(l_k[j+2][k]), dot_acc2);
                     dot_acc3 = mad(qk, CONVERT_KV_ACC4(l_k[j+3][k]), dot_acc3);
+#endif
                 }
                 ACC_TYPE s0 = (dot_acc0.s0 + dot_acc0.s1 + dot_acc0.s2 + dot_acc0.s3) * scale;
                 ACC_TYPE s1 = (dot_acc1.s0 + dot_acc1.s1 + dot_acc1.s2 + dot_acc1.s3) * scale;

ggml/src/ggml-opencl/kernels/flash_attn_f32_q4_0.cl

@@ -1631,8 +1631,25 @@ __kernel void flash_attn_f32_q4_0(
     float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
 
 #ifdef FA_HAVE_INT_DOT
+// Accessors so the staging code is layout-agnostic.
+#ifdef FA_K_LDS_T
+#define FA_K_PACKED(ROW, IDX) l_k_packed[IDX][ROW]
+#define FA_K_SCALE(ROW, BLK)  l_k_scale[BLK][ROW]
+#else
+#define FA_K_PACKED(ROW, IDX) l_k_packed[ROW][IDX]
+#define FA_K_SCALE(ROW, BLK)  l_k_scale[ROW][BLK]
+#endif
+
+#ifdef FA_K_LDS_T
+    // K tile transposed: the 4 KV rows the QK loop walks together become adjacent, so each
+    // (block, group) step is ONE 128-bit local read instead of four 32-bit ones. The QK
+    // loop is LDS-read-issue-bound.
+    __local uint  l_k_packed[DK_Q4_BLOCKS_PREFILL * 8][BLOCK_N];
+    __local float l_k_scale [DK_Q4_BLOCKS_PREFILL][BLOCK_N];
+#else
     __local uint  l_k_packed[BLOCK_N][DK_Q4_BLOCKS_PREFILL * 8];
     __local float l_k_scale [BLOCK_N][DK_Q4_BLOCKS_PREFILL];
+#endif
 #else
     __local half4 l_k[BLOCK_N][DK_VEC];
 #endif
@@ -1660,17 +1677,17 @@ __kernel void flash_attn_f32_q4_0(
                     const global char * blk_ptr = k_base + k_row_off + blk * Q4_0_BLOCK_SIZE;
                     const float df = (float) vload_half(0, (const global half *) blk_ptr);
                     const global uchar * qs = (const global uchar *)(blk_ptr + 2);
-                    l_k_scale[row][blk] = df;
+                    FA_K_SCALE(row, blk) = df;
                     uint k_packed[8];
                     pack_q4_0_nibbles(qs, k_packed);
                     #pragma unroll
                     for (int j = 0; j < 8; ++j) {
-                        l_k_packed[row][blk * 8 + j] = k_packed[j];
+                        FA_K_PACKED(row, blk * 8 + j) = k_packed[j];
                     }
                 } else {
-                    l_k_scale[row][blk] = 0.0f;
+                    FA_K_SCALE(row, blk) = 0.0f;
                     #pragma unroll
-                    for (int j = 0; j < 8; ++j) l_k_packed[row][blk * 8 + j] = 0u;
+                    for (int j = 0; j < 8; ++j) FA_K_PACKED(row, blk * 8 + j) = 0u;
                 }
             }
 #else
@@ -1760,6 +1777,19 @@ __kernel void flash_attn_f32_q4_0(
                 for (int b_local = 0; b_local < SPLIT_DK_Q4_BLOCKS; ++b_local) {
                     const int b = k_blk_base + b_local;
                     int sum0 = 0, sum1 = 0, sum2 = 0, sum3 = 0;
+#ifdef FA_K_LDS_T
+                    // 4 KV rows are adjacent in the transposed tile: one 128-bit local
+                    // read per (block, group) instead of four 32-bit ones.
+                    #pragma unroll
+                    for (int g = 0; g < 8; ++g) {
+                        const uint  qp  = q_packed_pf[b_local * 8 + g];
+                        const uint4 kq4 = vload4(0, &l_k_packed[b * 8 + g][j]);
+                        sum0 = dot_acc_sat_4x8packed_ss_int(qp, kq4.s0, sum0);
+                        sum1 = dot_acc_sat_4x8packed_ss_int(qp, kq4.s1, sum1);
+                        sum2 = dot_acc_sat_4x8packed_ss_int(qp, kq4.s2, sum2);
+                        sum3 = dot_acc_sat_4x8packed_ss_int(qp, kq4.s3, sum3);
+                    }
+#else
                     #pragma unroll
                     for (int g = 0; g < 8; ++g) {
                         const uint qp = q_packed_pf[b_local * 8 + g];
@@ -1768,12 +1798,21 @@ __kernel void flash_attn_f32_q4_0(
                         sum2 = dot_acc_sat_4x8packed_ss_int(qp, l_k_packed[j+2][b * 8 + g], sum2);
                         sum3 = dot_acc_sat_4x8packed_ss_int(qp, l_k_packed[j+3][b * 8 + g], sum3);
                     }
+#endif
                     const float qd    = q_d_pf[b_local];
                     const int   q_sum = q_sum_pf[b_local];
+#ifdef FA_K_LDS_T
+                    const float4 ks4 = vload4(0, &l_k_scale[b][j]);
+                    s0 += (float)(sum0 - 8 * q_sum) * qd * ks4.s0;
+                    s1 += (float)(sum1 - 8 * q_sum) * qd * ks4.s1;
+                    s2 += (float)(sum2 - 8 * q_sum) * qd * ks4.s2;
+                    s3 += (float)(sum3 - 8 * q_sum) * qd * ks4.s3;
+#else
                     s0 += (float)(sum0 - 8 * q_sum) * qd * l_k_scale[j  ][b];
                     s1 += (float)(sum1 - 8 * q_sum) * qd * l_k_scale[j+1][b];
                     s2 += (float)(sum2 - 8 * q_sum) * qd * l_k_scale[j+2][b];
                     s3 += (float)(sum3 - 8 * q_sum) * qd * l_k_scale[j+3][b];
+#endif
                 }
 #else
                 ACC_TYPE4 dot_acc0 = (ACC_TYPE4)(0.0f);

ggml/src/ggml-opencl/kernels/flash_attn_f32_q8_0.cl

@@ -1393,8 +1393,31 @@ __kernel void flash_attn_f32_q8_0(
     float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
 
 #ifdef FA_HAVE_INT_DOT
+// Accessors so the staging code is layout-agnostic.
+#ifdef FA_K_LDS_T
+#define FA_K_PACKED(ROW, IDX) l_k_packed[IDX][ROW]
+#define FA_K_SCALE(ROW, BLK)  l_k_scale[BLK][ROW]
+#else
+#define FA_K_PACKED(ROW, IDX) l_k_packed[ROW][IDX]
+#define FA_K_SCALE(ROW, BLK)  l_k_scale[ROW][BLK]
+#endif
+
+#ifdef FA_K_LDS_T
+    // K tile transposed: [block*8 + g][kv row] instead of [kv row][block*8 + g].
+    //
+    // The QK loop walks 4 KV rows at a time against the same (b, g), so in the original
+    // layout those 4 values are BLOCK_N*8 uints apart and cost 4 separate 32-bit local
+    // reads. Transposed they are adjacent, so they are one 128-bit read -- 4x fewer LDS
+    // issues for the same bytes and no extra registers. That matters because the QK loop
+    // is LDS-read-issue-bound: a wrong-math probe that kept every dp4a but cut the LDS
+    // reads ran the whole kernel 41% faster (18.51 -> 10.91 ms/op), and deleting QK
+    // outright only reached 10.88 -- i.e. essentially ALL of QK's cost is these reads.
+    __local uint  l_k_packed[DK_Q8_BLOCKS_PREFILL * 8][BLOCK_N];
+    __local float l_k_scale [DK_Q8_BLOCKS_PREFILL][BLOCK_N];
+#else
     __local uint  l_k_packed[BLOCK_N][DK_Q8_BLOCKS_PREFILL * 8];
     __local float l_k_scale [BLOCK_N][DK_Q8_BLOCKS_PREFILL];
+#endif
 #else
     __local half4 l_k[BLOCK_N][DK_VEC];
 #endif
@@ -1427,7 +1450,7 @@ __kernel void flash_attn_f32_q8_0(
                     const global char * blk_ptr = k_base + k_row_off + blk * Q8_0_BLOCK_SIZE;
                     const float df = (float) vload_half(0, (const global half *) blk_ptr);
                     const global uchar * qs = (const global uchar *)(blk_ptr + 2);
-                    l_k_scale[row][blk] = df;
+                    FA_K_SCALE(row, blk) = df;
                     #pragma unroll
                     for (int j = 0; j < 8; ++j) {
                         uint k_packed =
@@ -1435,12 +1458,12 @@ __kernel void flash_attn_f32_q8_0(
                              ((uint) qs[j*4 + 1]) <<  8 |
                              ((uint) qs[j*4 + 2]) << 16 |
                              ((uint) qs[j*4 + 3]) << 24;
-                        l_k_packed[row][blk * 8 + j] = k_packed;
+                        FA_K_PACKED(row, blk * 8 + j) = k_packed;
                     }
                 } else {
-                    l_k_scale[row][blk] = 0.0f;
+                    FA_K_SCALE(row, blk) = 0.0f;
                     #pragma unroll
-                    for (int j = 0; j < 8; ++j) l_k_packed[row][blk * 8 + j] = 0u;
+                    for (int j = 0; j < 8; ++j) FA_K_PACKED(row, blk * 8 + j) = 0u;
                 }
             }
 #else
@@ -1556,6 +1579,19 @@ __kernel void flash_attn_f32_q8_0(
                 for (int b_local = 0; b_local < SPLIT_DK_Q8_BLOCKS; ++b_local) {
                     const int b = k_blk_base + b_local;
                     int sum0 = 0, sum1 = 0, sum2 = 0, sum3 = 0;
+#if defined(FA_K_LDS_T)
+                    // The 4 KV rows are adjacent in the transposed tile, so each (b, g)
+                    // step is ONE 128-bit local read instead of four 32-bit ones.
+                    #pragma unroll
+                    for (int g = 0; g < 8; ++g) {
+                        const uint qp = q_packed_pf[b_local * 8 + g];
+                        const uint4 kq4 = vload4(0, &l_k_packed[b * 8 + g][j]);
+                        sum0 = dot_acc_sat_4x8packed_ss_int(qp, kq4.s0, sum0);
+                        sum1 = dot_acc_sat_4x8packed_ss_int(qp, kq4.s1, sum1);
+                        sum2 = dot_acc_sat_4x8packed_ss_int(qp, kq4.s2, sum2);
+                        sum3 = dot_acc_sat_4x8packed_ss_int(qp, kq4.s3, sum3);
+                    }
+#else
                     #pragma unroll
                     for (int g = 0; g < 8; ++g) {
                         const uint qp = q_packed_pf[b_local * 8 + g];
@@ -1564,11 +1600,20 @@ __kernel void flash_attn_f32_q8_0(
                         sum2 = dot_acc_sat_4x8packed_ss_int(qp, l_k_packed[j+2][b * 8 + g], sum2);
                         sum3 = dot_acc_sat_4x8packed_ss_int(qp, l_k_packed[j+3][b * 8 + g], sum3);
                     }
+#endif
                     const float qd = q_d_pf[b_local];
+#ifdef FA_K_LDS_T
+                    const float4 ks4 = vload4(0, &l_k_scale[b][j]);
+                    s0 += (float)sum0 * qd * ks4.s0;
+                    s1 += (float)sum1 * qd * ks4.s1;
+                    s2 += (float)sum2 * qd * ks4.s2;
+                    s3 += (float)sum3 * qd * ks4.s3;
+#else
                     s0 += (float)sum0 * qd * l_k_scale[j  ][b];
                     s1 += (float)sum1 * qd * l_k_scale[j+1][b];
                     s2 += (float)sum2 * qd * l_k_scale[j+2][b];
                     s3 += (float)sum3 * qd * l_k_scale[j+3][b];
+#endif
                 }
 #else
                 ACC_TYPE4 dot_acc0 = (ACC_TYPE4)(0.0f);

ggml/src/ggml-openvino/ggml-openvino.cpp

@@ -763,6 +763,7 @@ static void ggml_backend_openvino_device_get_props(ggml_backend_dev_t dev, ggml_
         /* .host_buffer           = */ false,
         /* .buffer_from_host_ptr  = */ false,
         /* .events                = */ false,
+        /* .mmap_support          = */ true,
     };
 }
 

ggml/src/ggml-rpc/ggml-rpc.cpp

@@ -1881,6 +1881,7 @@ static void ggml_backend_rpc_device_get_props(ggml_backend_dev_t dev, struct ggm
         /* .host_buffer           = */ false,
         /* .buffer_from_host_ptr  = */ false,
         /* .events                = */ false,
+        /* .mmap_support          = */ true,
     };
 }
 

ggml/src/ggml-sycl/ggml-sycl.cpp

@@ -5649,6 +5649,7 @@ static void ggml_backend_sycl_device_get_props(ggml_backend_dev_t dev, ggml_back
         /* .host_buffer           = */ host_buffer,
         /* .buffer_from_host_ptr  = */ false,
         /* .events                = */ events,
+        /* .mmap_support          = */ true,
     };
 }
 

ggml/src/ggml-virtgpu/backend/backend-dispatched-device.cpp

@@ -111,6 +111,7 @@ uint32_t backend_device_get_props(apir_encoder * enc, apir_decoder * dec, virgl_
     apir_encode_bool_t(enc, &props.caps.host_buffer);
     apir_encode_bool_t(enc, &props.caps.buffer_from_host_ptr);
     apir_encode_bool_t(enc, &props.caps.events);
+    apir_encode_bool_t(enc, &props.caps.mmap_support);
 
     return 0;
 }

ggml/src/ggml-virtgpu/backend/shared/api_remoting.h

@@ -7,7 +7,7 @@
 #include <cstdint>
 
 #define APIR_PROTOCOL_MAJOR 0
-#define APIR_PROTOCOL_MINOR 1
+#define APIR_PROTOCOL_MINOR 2
 
 #define APIR_HANDSHAKE_MAGIC 0xab1e
 

ggml/src/ggml-virtgpu/ggml-backend-buffer-type.cpp

@@ -11,9 +11,9 @@ static ggml_backend_buffer_t ggml_backend_remoting_buffer_type_alloc_buffer(ggml
 
     context->gpu = gpu;
 
-    bool async__unused, host_buffer__unused, events__unused;
+    bool async__unused, host_buffer__unused, events__unused, mmap_support__unused;
     bool buffer_from_host_ptr;
-    apir_device_get_props(gpu, &async__unused, &host_buffer__unused, &buffer_from_host_ptr, &events__unused);
+    apir_device_get_props(gpu, &async__unused, &host_buffer__unused, &buffer_from_host_ptr, &events__unused, &mmap_support__unused);
 
     if (buffer_from_host_ptr) {
         context->apir_context = apir_device_buffer_from_ptr(gpu, size, size);

ggml/src/ggml-virtgpu/ggml-backend-device.cpp

@@ -65,7 +65,7 @@ static void ggml_backend_remoting_device_get_props(ggml_backend_dev_t dev, ggml_
 
     virtgpu * gpu = DEV_TO_GPU(dev);
     apir_device_get_props(gpu, &props->caps.async, &props->caps.host_buffer, &props->caps.buffer_from_host_ptr,
-                          &props->caps.events);
+                          &props->caps.events, &props->caps.mmap_support);
 
     props->caps.buffer_from_host_ptr = false;
     props->caps.async                = false;

ggml/src/ggml-virtgpu/virtgpu-forward-device.cpp

@@ -144,7 +144,8 @@ void apir_device_get_props(virtgpu * gpu,
                            bool *    async,
                            bool *    host_buffer,
                            bool *    buffer_from_host_ptr,
-                           bool *    events) {
+                           bool *    events,
+                           bool *    mmap_support) {
     apir_encoder *        encoder;
     apir_decoder *        decoder;
     ApirForwardReturnCode ret;
@@ -157,6 +158,7 @@ void apir_device_get_props(virtgpu * gpu,
     apir_decode_bool_t(decoder, host_buffer);
     apir_decode_bool_t(decoder, buffer_from_host_ptr);
     apir_decode_bool_t(decoder, events);
+    apir_decode_bool_t(decoder, mmap_support);
 
     remote_call_finish(gpu, encoder, decoder);
 

ggml/src/ggml-virtgpu/virtgpu-forward.gen.h

@@ -13,7 +13,8 @@ void                           apir_device_get_props(struct virtgpu * gpu,
                                                      bool *           async,
                                                      bool *           host_buffer,
                                                      bool *           buffer_from_host_ptr,
-                                                     bool *           events);
+                                                     bool *           events,
+                                                     bool *           mmap_support);
 apir_buffer_context_t          apir_device_buffer_from_ptr(struct virtgpu * gpu, size_t size, size_t max_tensor_size);
 
 /* buffer-type */

ggml/src/ggml-vulkan/ggml-vulkan.cpp

@@ -17891,6 +17891,7 @@ static void ggml_backend_vk_device_get_props(ggml_backend_dev_t dev, struct ggml
         /* .host_buffer           = */ true,
         /* .buffer_from_host_ptr  = */ false,
         /* .events                = */ true,
+        /* .mmap_support          = */ !ctx->is_integrated_gpu,
     };
 }
 

ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp

@@ -2815,11 +2815,25 @@ class ggml_webgpu_shader_lib {
         key.common.v_direct &= decisions.use_sg_matrix && key.common.v_type == GGML_TYPE_F16;
         key.use_sg_matrix = decisions.use_sg_matrix;
 
-        const uint32_t max_kv_tile = ggml_webgpu_flash_attn_max_kv_tile(
+        uint32_t max_kv_tile = ggml_webgpu_flash_attn_max_kv_tile(
             context.wg_mem_limit_bytes, decisions.q_tile, decisions.use_sg_matrix ? context.sg_mat_n : 1u,
             key.common.head_dim_qk, key.common.head_dim_v, key.common.has_mask,
             key.common.k_direct || key.common.v_direct);
-        GGML_ASSERT(max_kv_tile > 0);
+
+        // WorkGroup storage size isn't enough for some params with subgroup matrices path (ref. https://github.com/ggml-org/llama.cpp/pull/26566)
+        if (max_kv_tile == 0) {
+            GGML_ASSERT(decisions.use_sg_matrix);
+            // switch to flash_attn_reg_tile path
+            decisions.use_sg_matrix = false;
+            decisions.q_tile        = GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE;
+            key.common.k_direct     = false;
+            key.common.v_direct     = false;
+            key.use_sg_matrix       = false;
+            max_kv_tile             = ggml_webgpu_flash_attn_max_kv_tile(
+                context.wg_mem_limit_bytes, decisions.q_tile, 1u, key.common.head_dim_qk, key.common.head_dim_v,
+                key.common.has_mask, key.common.k_direct || key.common.v_direct);
+            GGML_ASSERT(max_kv_tile > 0);
+        }
 
         decisions.kv_tile = decisions.use_sg_matrix ?
                                 std::min(max_kv_tile, context.sg_mat_n * GGML_WEBGPU_FLASH_ATTN_PREFERRED_KV_SG_TILES) :
@@ -2993,6 +3007,10 @@ class ggml_webgpu_shader_lib {
                 defines.push_back("SRC_F16");
                 variant += "_f16";
                 break;
+            case GGML_TYPE_I32:
+                defines.push_back("SRC_I32");
+                variant += "_i32";
+                break;
             default:
                 GGML_ABORT("Unsupported src type for cpy shader");
         }

ggml/src/ggml-webgpu/ggml-webgpu.cpp

@@ -3942,6 +3942,7 @@ static void ggml_backend_webgpu_device_get_props(ggml_backend_dev_t dev, struct
         /* .host_buffer           = */ false,
         /* .buffer_from_host_ptr  = */ false,
         /* .events                = */ false,
+        /* .mmap_support          = */ true,
     };
 }
 
@@ -4283,9 +4284,8 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
             break;
         case GGML_OP_CPY:
         case GGML_OP_CONT:
-            supports_op = ((op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) &&
-                           (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) ||
-                          (op->type == GGML_TYPE_I32 && src0->type == GGML_TYPE_F32);
+            supports_op = (op->type == GGML_TYPE_F16 || op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_I32) &&
+                          (src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_I32);
             break;
         case GGML_OP_SET:
             supports_op = src0->type == src1->type && src0->type == op->type &&

ggml/src/ggml-webgpu/wgsl-shaders/cpy.wgsl

@@ -4,6 +4,8 @@ enable f16;
 #define SRC_TYPE f32
 #elif defined(SRC_F16)
 #define SRC_TYPE f16
+#elif defined(SRC_I32)
+#define SRC_TYPE i32
 #endif
 
 #ifdef DST_F32

ggml/src/ggml-zdnn/ggml-zdnn.cpp

@@ -487,7 +487,8 @@ static void ggml_backend_zdnn_device_get_props(ggml_backend_dev_t dev, ggml_back
         /* .async                = */ false,
         /* .host_buffer          = */ false,
         /* .buffer_from_host_ptr = */ false,
-        /* .events               = */ false
+        /* .events               = */ false,
+        /* .mmap_support         = */ true,
     };
 }
 

ggml/src/ggml-zendnn/ggml-zendnn.cpp

@@ -654,7 +654,8 @@ static void ggml_backend_zendnn_device_get_props(ggml_backend_dev_t dev, struct
         /* .async                = */ false,
         /* .host_buffer          = */ false,
         /* .buffer_from_host_ptr = */ true,
-        /* .events               = */ false
+        /* .events               = */ false,
+        /* .mmap_support         = */ true,
     };
 }
 

gguf-py/gguf/constants.py

@@ -509,6 +509,7 @@ class MODEL_ARCH(IntEnum):
     OLMO             = auto()
     OLMO2            = auto()
     OLMOE            = auto()
+    MUSE_GLIMMER     = auto()
     OPENELM          = auto()
     ARCTIC           = auto()
     DEEPSEEK         = auto()
@@ -1181,6 +1182,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.OLMO:             "olmo",
     MODEL_ARCH.OLMO2:            "olmo2",
     MODEL_ARCH.OLMOE:            "olmoe",
+    MODEL_ARCH.MUSE_GLIMMER:     "muse-glimmer",
     MODEL_ARCH.OPENELM:          "openelm",
     MODEL_ARCH.ARCTIC:           "arctic",
     MODEL_ARCH.DEEPSEEK:         "deepseek",
@@ -1562,8 +1564,8 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.V_MM_UP:                   "mm.up",
     MODEL_TENSOR.V_MM_DOWN:                 "mm.down",
     MODEL_TENSOR.V_MM_GATE:                 "mm.gate",
-    MODEL_TENSOR.V_MM_MERGER_FC1:            "mm.merger.fc1",
-    MODEL_TENSOR.V_MM_MERGER_FC2:            "mm.merger.fc2",
+    MODEL_TENSOR.V_MM_MERGER_FC1:           "mm.merger.fc1",
+    MODEL_TENSOR.V_MM_MERGER_FC2:           "mm.merger.fc2",
     MODEL_TENSOR.V_TOK_BOI:                 "v.boi",
     MODEL_TENSOR.V_TOK_EOI:                 "v.eoi",
     MODEL_TENSOR.V_MM_PRE_NORM:             "mm.pre_norm",
@@ -3331,6 +3333,25 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_UP_EXP,
         MODEL_TENSOR.FFN_DOWN_EXP,
     ],
+    MODEL_ARCH.MUSE_GLIMMER: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_GATE,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_POST_NORM,
+        MODEL_TENSOR.FFN_PRE_NORM,
+        MODEL_TENSOR.FFN_POST_NORM,
+    ],
     MODEL_ARCH.OPENELM: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -3846,6 +3867,12 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN_SHEXP,
         MODEL_TENSOR.FFN_UP_SHEXP,
         MODEL_TENSOR.FFN_EXP_PROBS_B,
+        # NextN/MTP (draft head)
+        MODEL_TENSOR.ATTN_POST_NORM,
+        MODEL_TENSOR.NEXTN_EH_PROJ,
+        MODEL_TENSOR.NEXTN_ENORM,
+        MODEL_TENSOR.NEXTN_HNORM,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
     ],
     MODEL_ARCH.EXAONE: [
         MODEL_TENSOR.TOKEN_EMBD,
@@ -5160,6 +5187,7 @@ class VisionProjectorType:
     MIMOVL         = "mimovl"
     MIMO_AUDIO     = "mimo_audio"
     GRANITE4_VISION = "granite4_vision"
+    MUSE_GLIMMER   = "muse-glimmer"
 
 
 # Items here are (block size, type size)

gguf-py/gguf/tensor_mapping.py

@@ -382,7 +382,7 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.ATTN_GATE: (
-            "model.layers.{bid}.self_attn.gate_proj", # afmoe
+            "model.layers.{bid}.self_attn.gate_proj", # afmoe muse-glimmer
             "model.layers.{bid}.linear_attn.in_proj_z",  # qwen3.5
             "model.layers.{bid}.self_attn.g_proj",    # step3.5 head-wise attention gate
         ),
@@ -1298,10 +1298,12 @@ class TensorNameMap:
             "encoder.final_layer_norm", # t5
             "layer_norm",               # neobert
             "model.hidden_norm",        # dflash
+            "encoder.output_norm_enc",  # dflash (transformers MuseGlimmerAssistant)
         ),
 
         MODEL_TENSOR.FC: (
-            "model.fc", # dflash
+            "model.fc",   # dflash
+            "encoder.fc", # dflash (transformers MuseGlimmerAssistant)
         ),
 
         MODEL_TENSOR.DSPARK_MARKOV_W1: (
@@ -1467,6 +1469,7 @@ class TensorNameMap:
             "vision_tower.patch_embed.patchifier.proj", # dots.ocr
             "vision_model.conv1", # Step3-VL
             "model.vision_embedder.patch_dense", # gemma4 unified
+            "model.vision_tower.patch_embedder.patch_embedding", # muse-glimmer
         ),
 
         MODEL_TENSOR.V_ENC_EMBD_NORM: (
@@ -1534,7 +1537,8 @@ class TensorNameMap:
             "siglip2.vision_model.encoder.layers.{bid}.self_attn.q_proj", # youtuvl
             "model.vision_model.transformer.layers.{bid}.self_attn.q_proj", # Deepseek-OCR CLIP, generated
             "vision_model.model.layers.{bid}.self_attn.q_proj.linear", # gemma4
-            "model.qwen2_model.model.model.layers.{bid}.self_attn.q_proj" # Deepseek-OCR-2 qwen2
+            "model.qwen2_model.model.model.layers.{bid}.self_attn.q_proj", # Deepseek-OCR-2 qwen2
+            "model.vision_tower.layers.{bid}.attn.q_proj", # muse-glimmer
         ),
 
         MODEL_TENSOR.V_ENC_ATTN_Q_NORM: (
@@ -1560,7 +1564,8 @@ class TensorNameMap:
             "model.vision_model.transformer.layers.{bid}.self_attn.k_proj", # Deepseek-OCR CLIP, generated
             "siglip2.vision_model.encoder.layers.{bid}.self_attn.k_proj",
             "vision_model.model.layers.{bid}.self_attn.k_proj.linear", # gemma4
-            "model.qwen2_model.model.model.layers.{bid}.self_attn.k_proj" # Deepseek-OCR-2 qwen2
+            "model.qwen2_model.model.model.layers.{bid}.self_attn.k_proj", # Deepseek-OCR-2 qwen2
+            "model.vision_tower.layers.{bid}.attn.k_proj", # muse-glimmer
         ),
 
         MODEL_TENSOR.V_ENC_ATTN_K_NORM: (
@@ -1586,7 +1591,8 @@ class TensorNameMap:
             "siglip2.vision_model.encoder.layers.{bid}.self_attn.v_proj",
             "model.vision_model.transformer.layers.{bid}.self_attn.v_proj", # Deepseek-OCR CLIP, generated
             "vision_model.model.layers.{bid}.self_attn.v_proj.linear", # gemma4
-            "model.qwen2_model.model.model.layers.{bid}.self_attn.v_proj" # Deepseek-OCR-2 qwen2
+            "model.qwen2_model.model.model.layers.{bid}.self_attn.v_proj", # Deepseek-OCR-2 qwen2
+            "model.vision_tower.layers.{bid}.attn.v_proj", # muse-glimmer
         ),
 
         MODEL_TENSOR.V_ENC_INPUT_NORM: (
@@ -1610,6 +1616,7 @@ class TensorNameMap:
             "vision_tower.blocks.{bid}.norm1", # dots.ocr
             "vision_model.transformer.resblocks.{bid}.ln_1", # Step3-VL
             "model.qwen2_model.model.model.layers.{bid}.input_layernorm", # Deepseek-OCR-2 qwen2
+            "model.vision_tower.layers.{bid}.norm1", # muse-glimmer
         ),
 
         MODEL_TENSOR.V_ENC_ATTN_O: (
@@ -1635,6 +1642,7 @@ class TensorNameMap:
             "vision_model.model.layers.{bid}.self_attn.o_proj.linear", # gemma4
             "vision_tower.blocks.{bid}.attn.proj", # dots.ocr
             "vision_model.transformer.resblocks.{bid}.attn.out_proj", # Step3-VL
+            "model.vision_tower.layers.{bid}.attn.proj", # muse-glimmer
         ),
 
         MODEL_TENSOR.V_ENC_ATTN_SINKS: (
@@ -1663,6 +1671,7 @@ class TensorNameMap:
             "vision_tower.blocks.{bid}.norm2", # dots.ocr
             "vision_model.transformer.resblocks.{bid}.ln_2", # Step3-VL
             "model.qwen2_model.model.model.layers.{bid}.post_attention_layernorm", # Deepseek-OCR-2 qwen2
+            "model.vision_tower.layers.{bid}.norm2", # muse-glimmer
         ),
 
         MODEL_TENSOR.V_ENC_FFN_UP: (
@@ -1687,6 +1696,7 @@ class TensorNameMap:
             "vision_model.model.layers.{bid}.mlp.up_proj", # gemma4
             "vision_model.transformer.resblocks.{bid}.mlp.c_fc", # Step3-VL
             "model.qwen2_model.model.model.layers.{bid}.mlp.up_proj", # Deepseek-OCR-2 qwen2
+            "model.vision_tower.layers.{bid}.mlp.fc1", # muse-glimmer
         ),
 
         MODEL_TENSOR.V_ENC_FFN_GATE: (
@@ -1719,6 +1729,7 @@ class TensorNameMap:
             "model.qwen2_model.model.model.layers.{bid}.mlp.down_proj" , # Deepseek-OCR-2 qwen2
             "vision_model.model.layers.{bid}.mlp.down_proj", # gemma4
             "vision_model.transformer.resblocks.{bid}.mlp.c_proj", # Step3-VL
+            "model.vision_tower.layers.{bid}.mlp.fc2", # muse-glimmer
         ),
 
         MODEL_TENSOR.V_ENC_ATTN_POST_NORM: (
@@ -1753,6 +1764,7 @@ class TensorNameMap:
             "model.vision_model.pre_layrnorm", # Deepseek-OCR CLIP
             "vision_tower.patch_embed.patchifier.norm", # dots.ocr
             "vision_model.ln_pre", # Step3-VL
+            "model.vision_tower.ln_pre", # muse-glimmer
         ),
 
         MODEL_TENSOR.V_POST_NORM: (
@@ -1766,6 +1778,7 @@ class TensorNameMap:
             "visual.post_layernorm", # glm4v
             "siglip2.vision_model.post_layernorm",
             "model.qwen2_model.model.model.norm", # Deepseek-OCR-2 qwen2
+            "model.vision_tower.ln_post", # muse-glimmer
         ),
 
         MODEL_TENSOR.V_MM_POST_NORM: (

include/llama.h

@@ -203,11 +203,12 @@ extern "C" {
     };
 
     enum llama_load_mode {
-        LLAMA_LOAD_MODE_NONE       = 0, // no special loading mode
-        LLAMA_LOAD_MODE_MMAP       = 1, // memory map the model
-        LLAMA_LOAD_MODE_MLOCK      = 2, // force system to keep model in RAM rather than swapping or compressing
-        LLAMA_LOAD_MODE_MMAP_MLOCK = 3, // mmap + force system to keep model in RAM rather than swapping or compressing
-        LLAMA_LOAD_MODE_DIRECT_IO  = 4, // use direct I/O if available
+        LLAMA_LOAD_MODE_AUTO       = -1, // auto-detect based on device capabilities
+        LLAMA_LOAD_MODE_NONE       =  0, // no special loading mode
+        LLAMA_LOAD_MODE_MMAP       =  1, // memory map the model
+        LLAMA_LOAD_MODE_MLOCK      =  2, // force system to keep model in RAM rather than swapping or compressing
+        LLAMA_LOAD_MODE_MMAP_MLOCK =  3, // mmap + force system to keep model in RAM rather than swapping or compressing
+        LLAMA_LOAD_MODE_DIRECT_IO  =  4, // use direct I/O if available
     };
 
     LLAMA_API const char * llama_load_mode_name(enum llama_load_mode load_mode);
@@ -348,14 +349,15 @@ extern "C" {
     // NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations
     //       https://github.com/ggml-org/llama.cpp/pull/7544
     struct llama_context_params {
-        uint32_t n_ctx;             // text context, 0 = from model
-        uint32_t n_batch;           // logical maximum batch size that can be submitted to llama_decode
-        uint32_t n_ubatch;          // physical maximum batch size
-        uint32_t n_seq_max;         // max number of sequences (i.e. distinct states for recurrent models)
-        uint32_t n_rs_seq;          // number of recurrent-state snapshots per seq for rollback (0 = no rollback) [EXPERIMENTAL]
-        uint32_t n_outputs_max;     // max outputs in a ubatch (0 = n_batch)
-        int32_t  n_threads;         // number of threads to use for generation
-        int32_t  n_threads_batch;   // number of threads to use for batch processing
+        uint32_t n_ctx;                 // text context, 0 = from model
+        uint32_t n_batch;               // logical maximum batch size that can be submitted to llama_decode
+        uint32_t n_ubatch;              // physical maximum batch size
+        uint32_t n_seq_max;             // max number of sequences (i.e. distinct states for recurrent models)
+        uint32_t n_rs_seq;              // number of recurrent-state snapshots per seq for rollback (0 = no rollback) [EXPERIMENTAL]
+        uint32_t n_outputs_max;         // max outputs in a ubatch (0 = n_batch)
+        uint32_t n_outputs_max_per_seq; // max outputs per sequence (0 = n_outputs_max)
+        int32_t  n_threads;             // number of threads to use for generation
+        int32_t  n_threads_batch;       // number of threads to use for batch processing
 
         enum llama_context_type      ctx_type;          // set the context type (e.g. MTP)
         enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
@@ -1054,6 +1056,9 @@ extern "C" {
     //
 
     // Get the backend sampled token for the ith token.
+    // With multiple outputs, sampler state advances when the token is accepted,
+    // not when it is read through this function.
+    // When accepting multiple outputs, accept a contiguous prefix in output order.
     // Returns LLAMA_TOKEN_NULL if no token was sampled.
     LLAMA_API llama_token llama_get_sampled_token_ith(struct llama_context * ctx, int32_t i);
 
@@ -1270,9 +1275,12 @@ extern "C" {
         // [EXPERIMENTAL]
         // backend sampling interface:
 
-        // return true if the backend supports all ops needed by the sampler
+        // return true if the backend supports all ops needed by the sampler and can handle up to n_outputs_max_per_seq outputs per sequence
         // note: call once per sampler
-        bool (*backend_init)(struct llama_sampler * smpl, ggml_backend_buffer_type_t buft);
+        bool (*backend_init)(
+                struct llama_sampler       * smpl,
+                ggml_backend_buffer_type_t   buft,
+                uint32_t                     n_outputs_max_per_seq);
 
         // call after .backend_apply()
         void (*backend_accept)(
@@ -1290,6 +1298,13 @@ extern "C" {
 
         // called before graph execution to set inputs for the current ubatch
         void (*backend_set_input)(struct llama_sampler * smpl);
+
+        // called before rebuilding a sampling graph to clear any internal sampler state
+        void (*backend_reset)(struct llama_sampler * smpl);
+
+        // copy mutable state from src into dst while keeping dst's references to the current sampling graph
+        // src and dst must have the same type and configuration
+        void (*copy_state)(const struct llama_sampler * src, struct llama_sampler * dst);
     };
 
     struct llama_sampler {
@@ -1310,6 +1325,7 @@ extern "C" {
     LLAMA_API void                   llama_sampler_apply (      struct llama_sampler * smpl, llama_token_data_array * cur_p);
     LLAMA_API void                   llama_sampler_reset (      struct llama_sampler * smpl);
     LLAMA_API struct llama_sampler * llama_sampler_clone (const struct llama_sampler * smpl);
+    LLAMA_API void                   llama_sampler_copy  (const struct llama_sampler * src, struct llama_sampler * dst);
     // important: do not free if the sampler has been added to a llama_sampler_chain (via llama_sampler_chain_add)
     LLAMA_API void                   llama_sampler_free  (      struct llama_sampler * smpl);
 
@@ -1499,6 +1515,7 @@ extern "C" {
     LLAMA_API uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl);
 
     /// @details Sample and accept a token from the idx-th output of the last evaluation
+    // For multiple outputs from one sampler, call this function in output order without gaps.
     //
     // Shorthand for:
     //    const auto * logits = llama_get_logits_ith(ctx, idx);

models/templates/poolside-Laguna-S-2.1.jinja

@@ -1,8 +1,9 @@
 {#- Iteration on laguna_glm_thinking_v8/chat_template.jinja -#}
 {#- No formatting instructions -#}
 {{- "〈|EOS|〉" -}}
-{%- set enable_thinking = enable_thinking | default(false) -%}
+{%- set enable_thinking = enable_thinking | default(true) -%}
 {%- set add_generation_prompt = add_generation_prompt | default(false) -%}
+{%- set preserve_thinking = preserve_thinking | default(false) -%}
 
 {#- ───── header (system message) ───── -#}
 {#- A caller-supplied system message with empty content opts out of the default below, producing no <system> block — used to train without a system message. -#}
@@ -51,7 +52,7 @@
         {%- set reasoning_content = message.reasoning_content -%}
       {%- endif -%}
       {#- Display reasoning content for all messages if enable_thinking -#}
-      {%- if enable_thinking -%}
+      {%- if enable_thinking or preserve_thinking -%}
         {{- '<think>' + reasoning_content + '</think>' -}}
       {%- else -%}
         {{- '</think>' -}}

scripts/fetch_server_test_models.py

@@ -1,105 +0,0 @@
-#!/usr/bin/env python
-'''
-    This script fetches all the models used in the server tests.
-
-    This is useful for slow tests that use larger models, to avoid them timing out on the model downloads.
-
-    It is meant to be run from the root of the repository.
-
-    Example:
-        python scripts/fetch_server_test_models.py
-        ( cd tools/server/tests && ./tests.sh -v -x -m slow )
-'''
-import ast
-import glob
-import logging
-import os
-from typing import Generator
-from pydantic import BaseModel
-from typing import Optional
-import subprocess
-
-
-class HuggingFaceModel(BaseModel):
-    hf_repo: str
-    hf_file: Optional[str] = None
-
-    class Config:
-        frozen = True
-
-
-def collect_hf_model_test_parameters(test_file) -> Generator[HuggingFaceModel, None, None]:
-    try:
-        with open(test_file) as f:
-            tree = ast.parse(f.read())
-    except Exception as e:
-        logging.error(f'collect_hf_model_test_parameters failed on {test_file}: {e}')
-        return
-
-    for node in ast.walk(tree):
-        if isinstance(node, ast.FunctionDef):
-            for dec in node.decorator_list:
-                if isinstance(dec, ast.Call) and isinstance(dec.func, ast.Attribute) and dec.func.attr == 'parametrize':
-                    param_names = ast.literal_eval(dec.args[0]).split(",")
-                    if "hf_repo" not in param_names:
-                        continue
-
-                    raw_param_values = dec.args[1]
-                    if not isinstance(raw_param_values, ast.List):
-                        logging.warning(f'Skipping non-list parametrize entry at {test_file}:{node.lineno}')
-                        continue
-
-                    hf_repo_idx = param_names.index("hf_repo")
-                    hf_file_idx = param_names.index("hf_file") if "hf_file" in param_names else None
-
-                    for t in raw_param_values.elts:
-                        if not isinstance(t, ast.Tuple):
-                            logging.warning(f'Skipping non-tuple parametrize entry at {test_file}:{node.lineno}')
-                            continue
-                        yield HuggingFaceModel(
-                            hf_repo=ast.literal_eval(t.elts[hf_repo_idx]),
-                            hf_file=ast.literal_eval(t.elts[hf_file_idx]) if hf_file_idx is not None else None)
-
-
-if __name__ == '__main__':
-    logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
-
-    models = sorted(list(set([
-        model
-        for test_file in glob.glob('tools/server/tests/unit/test_*.py')
-        for model in collect_hf_model_test_parameters(test_file)
-    ])), key=lambda m: (m.hf_repo, m.hf_file))
-
-    logging.info(f'Found {len(models)} models in parameterized tests:')
-    for m in models:
-        logging.info(f'  - {m.hf_repo} / {m.hf_file}')
-
-    cli_path = os.environ.get(
-        'LLAMA_CLI_BIN_PATH',
-        os.path.join(
-            os.path.dirname(__file__),
-            '../build/bin/Release/llama-cli.exe' if os.name == 'nt' else '../build/bin/llama-cli'))
-
-    for m in models:
-        if '<' in m.hf_repo or (m.hf_file is not None and '<' in m.hf_file):
-            continue
-        if m.hf_file is not None and '-of-' in m.hf_file:
-            logging.warning(f'Skipping model at {m.hf_repo} / {m.hf_file} because it is a split file')
-            continue
-        logging.info(f'Using llama-cli to ensure model {m.hf_repo}/{m.hf_file} was fetched')
-        cmd = [
-            cli_path,
-            '-hfr', m.hf_repo,
-            *([] if m.hf_file is None else ['-hff', m.hf_file]),
-            '-n', '1',
-            '-p', 'Hey',
-            '--no-warmup',
-            '--log-disable',
-            '-st']
-        if m.hf_file != 'tinyllamas/stories260K.gguf' and 'Mistral-Nemo' not in m.hf_repo:
-            cmd += ('-fa', 'on')
-        try:
-            subprocess.check_call(cmd)
-        except subprocess.CalledProcessError:
-            logging.error(f'Failed to fetch model at {m.hf_repo} / {m.hf_file} with command:\n  {" ".join(cmd)}')
-            exit(1)

scripts/hip/gcn-cdna-vgpr-check.py

@@ -60,90 +60,10 @@ def main():
     log_file = sys.argv[1]
     ignored = {
         '_ZL21gated_linear_attn_f32ILi128EEviiiifPKfS1_S1_S1_S1_Pf',
-        '_ZL18flash_attn_ext_f16ILi64ELi64ELi16ELi2ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi80ELi80ELi16ELi2ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi96ELi96ELi16ELi2ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi64ELi64ELi32ELi1ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
         '_ZL13rwkv_wkv7_f32ILi128EEviiiiPKfS1_S1_S1_S1_S1_S1_Pf',
-        '_ZL18flash_attn_ext_f16ILi80ELi80ELi16ELi1ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi112ELi112ELi16ELi2ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi80ELi80ELi32ELi1ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi96ELi96ELi16ELi1ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi16ELi2ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi16ELi2ELb1ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi96ELi96ELi32ELi1ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi112ELi112ELi16ELi1ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi112ELi112ELi32ELi1ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi16ELi1ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi16ELi1ELb1ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi80ELi80ELi2ELi8ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi96ELi96ELi2ELi8ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi112ELi112ELi2ELi8ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi2ELi8ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi2ELi8ELb1ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi112ELi112ELi16ELi4ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi16ELi4ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi16ELi4ELb1ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi32ELi2ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi96ELi96ELi4ELi4ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi112ELi112ELi4ELi4ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi4ELi4ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi4ELi4ELb1ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi80ELi80ELi4ELi8ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi4ELi8ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi96ELi96ELi64ELi1ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi112ELi112ELi64ELi1ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi64ELi1ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi64ELi1ELb1ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi64ELi64ELi8ELi4ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi80ELi80ELi8ELi4ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi96ELi96ELi8ELi4ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi112ELi112ELi8ELi4ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi80ELi80ELi8ELi2ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi8ELi4ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi8ELi4ELb1ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi96ELi96ELi8ELi2ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi112ELi112ELi8ELi2ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi8ELi2ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi8ELi2ELb1ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi112ELi112ELi8ELi8ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi8ELi8ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi8ELi8ELb1ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL24mul_mat_q_stream_k_fixupIL9ggml_type22ELi8ELb1EEvPKiS2_PfPKfiiimimimi',
-        '_ZL9mul_mat_qIL9ggml_type3ELi32ELb0EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL9mul_mat_qIL9ggml_type3ELi48ELb0EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL9mul_mat_qIL9ggml_type20ELi32ELb1EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL9mul_mat_qIL9ggml_type17ELi64ELb0EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL18flash_attn_ext_f16ILi80ELi80ELi4ELi4ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL15flash_attn_tileILi256ELi256ELi32ELi1ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL9mul_mat_qIL9ggml_type19ELi112ELb1EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL9mul_mat_qIL9ggml_type17ELi112ELb1EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL9mul_mat_qIL9ggml_type22ELi112ELb1EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL9mul_mat_qIL9ggml_type19ELi128ELb0EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL9mul_mat_qIL9ggml_type19ELi128ELb1EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL9mul_mat_qIL9ggml_type7ELi112ELb1EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL9mul_mat_qIL9ggml_type3ELi128ELb0EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL9mul_mat_qIL9ggml_type3ELi128ELb1EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL9mul_mat_qIL9ggml_type7ELi128ELb0EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL9mul_mat_qIL9ggml_type7ELi128ELb1EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL9mul_mat_qIL9ggml_type11ELi112ELb0EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL9mul_mat_qIL9ggml_type11ELi112ELb1EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL24mul_mat_q_stream_k_fixupIL9ggml_type11ELi128ELb0EEvPKiS2_PfPKfiiimimimi',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi32ELi1ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL9mul_mat_qIL9ggml_type2ELi112ELb0EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL18flash_attn_ext_f16ILi112ELi112ELi32ELi2ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi112ELi112ELi4ELi8ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi32ELi1ELb1ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi32ELi2ELb1ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi128ELi128ELi4ELi8ELb1ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_f16ILi96ELi96ELi4ELi8ELb0ELb0EEvPKcS1_S1_S1_S1_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS5_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL18flash_attn_ext_vecILi128ELi2EL9ggml_type2ELS0_2ELb0EEvPKcS2_S2_S2_S2_PKiPfP15HIP_vector_typeIfLj2EEffffjfiS6_IjLj3EEiiiiiiiiiiiliiliiiiil',
-        '_ZL9mul_mat_qIL9ggml_type10ELi16ELb1EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL9mul_mat_qIL9ggml_type12ELi128ELb1EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL9mul_mat_qIL9ggml_type40ELi112ELb0EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL9mul_mat_qIL9ggml_type40ELi112ELb1EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL9mul_mat_qIL9ggml_type40ELi128ELb0EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii',
-        '_ZL9mul_mat_qIL9ggml_type40ELi128ELb1EEvPKcPKiS4_S4_PfS5_iiiiiiiiiiiiiiiii'
+        '_ZL12rwkv_wkv_f32ILi128EEviiiiPKfS1_S1_S1_S1_S1_Pf',
+        '_ZL9mul_mat_qIL9ggml_type10ELi64ELb1EEvPKcPKiS4_S4_PfS5_PKf15HIP_vector_typeIjLj3EEiiiiiS9_S9_iiiS9_S9_iiiS9_',
+        '_ZL9mul_mat_qIL9ggml_type42ELi128ELb1EEvPKcPKiS4_S4_PfS5_PKf15HIP_vector_typeIjLj3EEiiiiiS9_S9_iiiS9_S9_iiiS9_',
     }
 
     functions = parse_log_file(log_file)

scripts/sync_vendor.py

@@ -5,7 +5,7 @@ import os
 import sys
 import subprocess
 
-HTTPLIB_VERSION = "refs/tags/v0.52.0"
+HTTPLIB_VERSION = "refs/tags/v0.53.0"
 
 vendor = {
     "https://github.com/nlohmann/json/releases/latest/download/json.hpp":     "vendor/nlohmann/json.hpp",
@@ -21,34 +21,13 @@ vendor = {
     f"https://raw.githubusercontent.com/yhirose/cpp-httplib/{HTTPLIB_VERSION}/split.py":  "split.py",
     f"https://raw.githubusercontent.com/yhirose/cpp-httplib/{HTTPLIB_VERSION}/LICENSE":   "vendor/cpp-httplib/LICENSE",
 
-    "https://raw.githubusercontent.com/sheredom/subprocess.h/8671cee1fc09f11a70ce3782a0ee13177c3aa387/subprocess.h": "vendor/sheredom/subprocess.h",
+    "https://raw.githubusercontent.com/sheredom/subprocess.h/9ce0d701b6fb10f8f8c4445edd31e7c60a1237e3/subprocess.h": "vendor/sheredom/subprocess.h",
 }
 
-# TODO @ngxson : this is temporary, to be removed in the future
-patches = [
-    # https://github.com/sheredom/subprocess.h/pull/102
-    "vendor/sheredom/patch-bsd.patch",
-    # https://github.com/sheredom/subprocess.h/pull/101
-    "vendor/sheredom/patch-windows-quote-backslash.patch",
-    # https://github.com/sheredom/subprocess.h/pull/104
-    # note: must be applied after patch-bsd.patch, they touch adjacent lines
-    "vendor/sheredom/patch-glibc-older-than-2.29.patch",
-]
-
 for url, filename in vendor.items():
     print(f"downloading {url} to {filename}") # noqa: NP100
     urllib.request.urlretrieve(url, filename)
 
-for patch in patches:
-    print(f"applying {patch}") # noqa: NP100
-    try:
-        subprocess.check_call([
-            "git", "apply", "--directory", os.path.dirname(patch), patch
-        ])
-    except Exception as e:
-        print(f"Error: {e}") # noqa: NP100
-        sys.exit(1)
-
 print("Splitting httplib.h...") # noqa: NP100
 try:
     subprocess.check_call([

src/llama-arch.cpp

@@ -71,6 +71,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_OLMO,             "olmo"             },
     { LLM_ARCH_OLMO2,            "olmo2"            },
     { LLM_ARCH_OLMOE,            "olmoe"            },
+    { LLM_ARCH_MUSE_GLIMMER,     "muse-glimmer"     },
     { LLM_ARCH_OPENELM,          "openelm"          },
     { LLM_ARCH_ARCTIC,           "arctic"           },
     { LLM_ARCH_DEEPSEEK,         "deepseek"         },

src/llama-arch.h

@@ -76,6 +76,7 @@ enum llm_arch {
     LLM_ARCH_OLMO,
     LLM_ARCH_OLMO2,
     LLM_ARCH_OLMOE,
+    LLM_ARCH_MUSE_GLIMMER,
     LLM_ARCH_OPENELM,
     LLM_ARCH_ARCTIC,
     LLM_ARCH_DEEPSEEK,

src/llama-context.cpp

@@ -10,6 +10,7 @@
 #include "llama-mmap.h"
 #include "llama-model.h"
 #include "llama-ext.h"
+#include "llama-sampler.h"
 #include "llama.h"
 
 #include <cinttypes>
@@ -159,25 +160,6 @@ llama_context::llama_context(
         }
     }
 
-    // Initialize backend samplers here so they are part of the sampling graph
-    // before the reserve passes run later in this function. This avoids a later
-    // re-reserve when graph nodes change.
-    if (params.samplers != nullptr && params.n_samplers > 0) {
-        for (size_t i = 0; i < params.n_samplers; ++i) {
-            const auto & config = params.samplers[i];
-
-            if (llama_sampler_chain_get(config.sampler, -1) == nullptr) {
-                throw std::runtime_error("the backend samplers must be of type llama_sampler_chain");
-            }
-
-            if (set_sampler(config.seq_id, config.sampler)) {
-                const int n_samplers = llama_sampler_chain_n(config.sampler);
-
-                LLAMA_LOG_INFO("%s: setting backend sampler for seq_id %d (n = %d)\n", __func__, config.seq_id, n_samplers);
-            }
-        }
-    }
-
     auto rope_scaling_type = params.rope_scaling_type;
     if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED) {
         rope_scaling_type = hparams.rope_scaling_type_train;
@@ -265,6 +247,27 @@ llama_context::llama_context(
     cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);
 
     cparams.n_outputs_max = params.n_outputs_max == 0 || llama_model_has_encoder(&model) ? cparams.n_batch : params.n_outputs_max;
+    cparams.n_outputs_max_per_seq = params.n_outputs_max_per_seq == 0 ?
+            cparams.n_outputs_max : std::min(params.n_outputs_max_per_seq, cparams.n_outputs_max);
+
+    // Initialize backend samplers here so they are part of the sampling graph
+    // before the reserve passes run later in this function. This avoids a later
+    // re-reserve when graph nodes change.
+    if (params.samplers != nullptr && params.n_samplers > 0) {
+        for (size_t i = 0; i < params.n_samplers; ++i) {
+            const auto & config = params.samplers[i];
+
+            if (llama_sampler_chain_get(config.sampler, -1) == nullptr) {
+                throw std::runtime_error("the backend samplers must be of type llama_sampler_chain");
+            }
+
+            if (set_sampler(config.seq_id, config.sampler)) {
+                const int n_samplers = llama_sampler_chain_n(config.sampler);
+
+                LLAMA_LOG_INFO("%s: setting backend sampler for seq_id %d (n = %d)\n", __func__, config.seq_id, n_samplers);
+            }
+        }
+    }
 
     cparams.op_offload = params.op_offload;
     cparams.kv_unified = params.kv_unified;
@@ -300,18 +303,19 @@ llama_context::llama_context(
         }
     }
 
-    LLAMA_LOG_INFO("%s: n_seq_max     = %u\n",   __func__, cparams.n_seq_max);
-    LLAMA_LOG_INFO("%s: n_ctx         = %u\n",   __func__, cparams.n_ctx);
-    LLAMA_LOG_INFO("%s: n_ctx_seq     = %u\n",   __func__, cparams.n_ctx_seq);
-    LLAMA_LOG_INFO("%s: n_batch       = %u\n",   __func__, cparams.n_batch);
-    LLAMA_LOG_INFO("%s: n_ubatch      = %u\n",   __func__, cparams.n_ubatch);
-    LLAMA_LOG_INFO("%s: causal_attn   = %d\n",   __func__, cparams.causal_attn);
-    LLAMA_LOG_INFO("%s: flash_attn    = %s\n",   __func__, llama_flash_attn_type_name(params.flash_attn_type));
-    LLAMA_LOG_INFO("%s: kv_unified    = %s\n",   __func__, cparams.kv_unified ? "true" : "false");
-    LLAMA_LOG_INFO("%s: freq_base     = %.1f\n", __func__, cparams.rope_freq_base);
-    LLAMA_LOG_INFO("%s: freq_scale    = %g\n",   __func__, cparams.rope_freq_scale);
-    LLAMA_LOG_INFO("%s: n_rs_seq      = %u\n",   __func__, cparams.n_rs_seq);
-    LLAMA_LOG_INFO("%s: n_outputs_max = %u\n",   __func__, cparams.n_outputs_max);
+    LLAMA_LOG_INFO("%s: n_seq_max             = %u\n",   __func__, cparams.n_seq_max);
+    LLAMA_LOG_INFO("%s: n_ctx                 = %u\n",   __func__, cparams.n_ctx);
+    LLAMA_LOG_INFO("%s: n_ctx_seq             = %u\n",   __func__, cparams.n_ctx_seq);
+    LLAMA_LOG_INFO("%s: n_batch               = %u\n",   __func__, cparams.n_batch);
+    LLAMA_LOG_INFO("%s: n_ubatch              = %u\n",   __func__, cparams.n_ubatch);
+    LLAMA_LOG_INFO("%s: causal_attn           = %d\n",   __func__, cparams.causal_attn);
+    LLAMA_LOG_INFO("%s: flash_attn            = %s\n",   __func__, llama_flash_attn_type_name(params.flash_attn_type));
+    LLAMA_LOG_INFO("%s: kv_unified            = %s\n",   __func__, cparams.kv_unified ? "true" : "false");
+    LLAMA_LOG_INFO("%s: freq_base             = %.1f\n", __func__, cparams.rope_freq_base);
+    LLAMA_LOG_INFO("%s: freq_scale            = %g\n",   __func__, cparams.rope_freq_scale);
+    LLAMA_LOG_INFO("%s: n_rs_seq              = %u\n",   __func__, cparams.n_rs_seq);
+    LLAMA_LOG_INFO("%s: n_outputs_max         = %u\n",   __func__, cparams.n_outputs_max);
+    LLAMA_LOG_INFO("%s: n_outputs_max_per_seq = %u\n",   __func__, cparams.n_outputs_max_per_seq);
 
     if (cparams.n_ctx_seq < hparams.n_ctx_train) {
         LLAMA_LOG_INFO("%s: n_ctx_seq (%u) < n_ctx_train (%u) -- the full capacity of the model will not be utilized\n",
@@ -1231,7 +1235,7 @@ bool llama_context::set_sampler(llama_seq_id seq_id, llama_sampler * sampler) {
     if (sampler && can_offload) {
         auto * buft = ggml_backend_dev_buffer_type(model.dev_output());
 
-        sampler->iface->backend_init(sampler, buft);
+        sampler->iface->backend_init(sampler, buft, cparams.n_outputs_max_per_seq);
 
         sampling.samplers[seq_id] = sampler;
 
@@ -1576,108 +1580,38 @@ int llama_context::encode(const llama_batch & batch_inp) {
     return 0;
 }
 
-static std::map<llama_seq_id, uint32_t> build_seq_to_output_row(const llama_ubatch & ubatch, uint32_t row_offset) {
-    std::map<llama_seq_id, uint32_t> seq_to_row;
-    // how many output tokens we have seen so far for this ubatch.
-    uint32_t local = 0;
-    for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
-        // skip tokens that are not output.
-        if (!ubatch.output[i]) {
-            continue;
-        }
-
-        const llama_seq_id seq_id = ubatch.seq_id[i][0];
-        // row_offset is the number of output tokens before this ubatch.
-        seq_to_row[seq_id] = row_offset + local;
-        ++local;
-    }
-    return seq_to_row;
-}
-
-static void copy_tensor_async_ints(
-    const std::map<llama_seq_id, ggml_tensor*> & tensor_map,
-    const buffer_view<llama_token> & sampled,
-    const std::map<llama_seq_id, uint32_t> & seq_to_row,
-    ggml_backend_sched_t sched) {
-    if (!sampled.has_data()) {
-        return;
-    }
-
-    for (const auto & [seq_id, tensor] : tensor_map) {
-        auto it = seq_to_row.find(seq_id);
-        if (it == seq_to_row.end()) {
-            continue;
-        }
-
-        const uint32_t row = it->second;
-        GGML_ASSERT(row < sampled.size);
-
-        GGML_ASSERT(ggml_is_contiguous(tensor) && "sampled tokens tensor must be contiguous for async copy");
-
-        ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
-        ggml_backend_tensor_get_async(backend, tensor, sampled.data + row, 0, sizeof(sampled.data[row]));
-    }
-}
-
-static void copy_tensor_async_floats(
-    const std::map<llama_seq_id, ggml_tensor*> & tensor_map,
-    const buffer_view<float> & dst,
+template<typename T>
+static void copy_tensor_async_rows(
+    const std::vector<ggml_tensor *> & tensors,
+    const buffer_view<T> & dst,
     size_t stride,
-    std::vector<uint32_t> & counts,
-    const std::map<llama_seq_id, uint32_t> & seq_to_row,
-    ggml_backend_sched_t sched) {
+    uint32_t row_offset,
+    ggml_backend_sched_t sched,
+    std::vector<uint32_t> * counts = nullptr) {
     if (!dst.has_data()) {
         return;
     }
 
-    for (const auto & [seq_id, tensor] : tensor_map) {
-        auto it = seq_to_row.find(seq_id);
-        if (it == seq_to_row.end()) {
+    for (size_t i = 0; i < tensors.size(); ++i) {
+        auto * tensor = tensors[i];
+        if (tensor == nullptr) {
             continue;
         }
 
-        const uint32_t row = it->second;
-        GGML_ASSERT(row < counts.size());
-
-        GGML_ASSERT(ggml_is_contiguous(tensor) && "logits/probs tensor must be contiguous for async copy");
+        const uint32_t row = row_offset + i;
+        const size_t n_elements = ggml_nelements(tensor);
+        GGML_ASSERT(ggml_is_contiguous(tensor) && "sampling tensor must be contiguous for async copy");
+        GGML_ASSERT(n_elements <= stride);
+        GGML_ASSERT((size_t) row * stride + n_elements <= dst.size);
 
         ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
-        float * row_ptr = dst.data + (size_t) row * stride;
+        T * row_ptr = dst.data + (size_t) row * stride;
         ggml_backend_tensor_get_async(backend, tensor, row_ptr, 0, ggml_nbytes(tensor));
 
-        // Update the actual number of logits/probabilities that were written for this row.
-        counts[row] = ggml_nelements(tensor);
-    }
-}
-
-static void copy_tensor_async_candidates(
-    const std::map<llama_seq_id, ggml_tensor*> & tensor_map,
-    const buffer_view<llama_token> & dst,
-    size_t stride,
-    std::vector<uint32_t> & counts,
-    const std::map<llama_seq_id, uint32_t> & seq_to_row,
-    ggml_backend_sched_t sched) {
-    if (!dst.has_data()) {
-        return;
-    }
-
-    for (const auto & [seq_id, tensor] : tensor_map) {
-        auto it = seq_to_row.find(seq_id);
-        if (it == seq_to_row.end()) {
-            continue;
+        if (counts) {
+            GGML_ASSERT(row < counts->size());
+            (*counts)[row] = n_elements;
         }
-
-        const uint32_t row = it->second;
-        GGML_ASSERT(row < counts.size());
-
-        GGML_ASSERT(ggml_is_contiguous(tensor) && "candidates tensor must be contiguous for async copy");
-
-        ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched, tensor);
-        llama_token * row_ptr = dst.data + (size_t) row * stride;
-        ggml_backend_tensor_get_async(backend, tensor, row_ptr, 0, ggml_nbytes(tensor));
-
-        // Update the actual number of candidates that were written.
-        counts[row] = ggml_nelements(tensor);
     }
 }
 
@@ -1726,12 +1660,12 @@ int llama_context::decode(const llama_batch & batch_inp) {
 
     const uint32_t n_seq_max = cparams.kv_unified ? LLAMA_MAX_SEQ : cparams.n_seq_max;
 
-    // TODO: avoid this workaround in the future
-    if (has_samplers && batch_inp.logits) {
+    // embedding contexts output every token even when batch.logits is not set
+    if (has_samplers && (output_all || batch_inp.logits)) {
         std::vector<int32_t> seq_output_count(n_seq_max, 0);
 
         for (int32_t i = 0; i < batch_inp.n_tokens; ++i) {
-            if (batch_inp.logits[i] == 0) {
+            if (!output_all && batch_inp.logits[i] == 0) {
                 continue;
             }
 
@@ -1740,10 +1674,17 @@ int llama_context::decode(const llama_batch & batch_inp) {
             for (int32_t s = 0; s < ns; ++s) {
                 const llama_seq_id seq_id = batch_inp.seq_id ? batch_inp.seq_id[i][s] : 0;
 
+                if (seq_id < 0 || (uint32_t) seq_id >= n_seq_max) {
+                    continue;
+                }
+
                 seq_output_count[seq_id]++;
-                if (seq_output_count[seq_id] > 1) {
-                    LLAMA_LOG_ERROR("%s: backend sampling requires at most one output token per sequence (seq_id %d had %d)\n",
-                            __func__, seq_id, seq_output_count[seq_id]);
+                auto sampler = sampling.samplers.find(seq_id);
+                if (sampler != sampling.samplers.end() &&
+                        seq_output_count[seq_id] > (int32_t) cparams.n_outputs_max_per_seq) {
+                    LLAMA_LOG_ERROR("%s: backend sampling supports at most %u outputs per sequence "
+                            "(seq_id %d had %d)\n", __func__, cparams.n_outputs_max_per_seq,
+                            seq_id, seq_output_count[seq_id]);
                     return -1;
                 }
             }
@@ -1843,6 +1784,11 @@ int llama_context::decode(const llama_batch & batch_inp) {
         return -2;
     };
 
+    // start a new sampling transaction for this logical batch
+    for (const auto & entry : sampling.samplers) {
+        llama_sampler_backend_begin(entry.second);
+    }
+
     int64_t n_outputs_prev = 0;
     int64_t n_tokens_prev  = 0;
 
@@ -2009,17 +1955,14 @@ int llama_context::decode(const llama_batch & batch_inp) {
             }
         }
 
-        // Copy backend sampling output if this ubatch produced any sampling tensors.
-        if (has_samplers && (!res->t_sampled.empty() || !res->t_sampled_probs.empty() || !res->t_sampled_logits.empty())) {
-            const auto seq_to_output_row = build_seq_to_output_row(ubatch, n_outputs_prev);
+        if (has_samplers) {
             const auto stride = n_vocab;
 
             // async copy the sampling data from the backend to the host
-            copy_tensor_async_ints(res->t_sampled, sampling.sampled, seq_to_output_row, sched.get());
-
-            copy_tensor_async_floats    (res->t_sampled_logits, sampling.logits,     stride, sampling.logits_count,     seq_to_output_row, sched.get());
-            copy_tensor_async_floats    (res->t_sampled_probs,  sampling.probs,      stride, sampling.probs_count,      seq_to_output_row, sched.get());
-            copy_tensor_async_candidates(res->t_candidates,     sampling.candidates, stride, sampling.candidates_count, seq_to_output_row, sched.get());
+            copy_tensor_async_rows(res->t_sampled,        sampling.sampled,    1,      n_outputs_prev, sched.get());
+            copy_tensor_async_rows(res->t_sampled_logits, sampling.logits,     stride, n_outputs_prev, sched.get(), &sampling.logits_count);
+            copy_tensor_async_rows(res->t_sampled_probs,  sampling.probs,      stride, n_outputs_prev, sched.get(), &sampling.probs_count);
+            copy_tensor_async_rows(res->t_candidates,     sampling.candidates, stride, n_outputs_prev, sched.get(), &sampling.candidates_count);
         }
 
         n_outputs_prev += n_outputs;
@@ -2349,6 +2292,7 @@ void llama_context::output_reorder() {
 //
 
 uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
+    uint32_t res;
     if (model.arch == LLM_ARCH_QWEN3NEXT ||
         model.arch == LLM_ARCH_KIMI_LINEAR ||
         model.arch == LLM_ARCH_QWEN35 ||
@@ -2357,11 +2301,31 @@ uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
         (model.arch == LLM_ARCH_DFLASH && model.hparams.dsv4_hc_mult > 0) ||
         model.arch == LLM_ARCH_NANBEIGE ||
         model.arch == LLM_ARCH_MINIMAX_M3) {
-        return std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
+        res = std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
+    } else {
+        res = std::max<uint32_t>(1024u, 8u*model.n_tensors());
+        for (const auto & lora : model.loras) {
+            res += lora->get_n_nodes();
+        }
     }
-    uint32_t res = std::max<uint32_t>(1024u, 8u*model.n_tensors());
-    for (const auto & lora : model.loras) {
-        res += lora->get_n_nodes();
+
+    uint32_t n_sampling_nodes = 0;
+    uint32_t n_sampling_nodes_max = 0;
+    for (const auto & [seq_id, sampler] : sampling.samplers) {
+        const uint32_t n_nodes = llama_sampler_backend_n_nodes(sampler);
+        n_sampling_nodes += n_nodes;
+        if (cparams.n_outputs_max_per_seq > 1) {
+            n_sampling_nodes_max = std::max(n_sampling_nodes_max, n_nodes);
+        }
+    }
+
+    const uint32_t n_sampling_outputs_max = std::min<uint64_t>(
+            std::min(n_tokens, cparams.n_outputs_max),
+            (uint64_t) cparams.n_seq_max * cparams.n_outputs_max_per_seq);
+
+    res += n_sampling_nodes;
+    if (n_sampling_outputs_max > 1) {
+        res += (n_sampling_outputs_max - 1) * n_sampling_nodes_max;
     }
     return res;
 }
@@ -2370,6 +2334,63 @@ llm_graph_result * llama_context::get_gf_res_reserve() const {
     return static_cast<llm_graph_result *>(gf_res_reserve.get());
 }
 
+// pack sampler outputs into as few sequences as possible before using sequences without samplers
+static void ubatch_prepare_reserve(
+              llama_ubatch                            & ubatch,
+              uint32_t                                  n_outputs,
+        const std::map<llama_seq_id, llama_sampler *> & samplers,
+              uint32_t                                  n_outputs_max_per_seq) {
+    const uint32_t n_seqs       = ubatch.n_seqs;
+    const uint32_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    for (uint32_t s = 0; s < n_seqs; ++s) {
+        for (uint32_t t = 0; t < n_seq_tokens; ++t) {
+            const uint32_t i = s * n_seq_tokens + t;
+            ubatch.n_seq_id[i] = 1;
+            ubatch.seq_id[i] = &ubatch.seq_id_unq[s];
+        }
+    }
+
+    // sequences with a sampler that fit in this ubatch
+    std::vector<uint32_t> sampler_seqs;
+    std::vector<bool> has_sampler(n_seqs, false);
+    for (const auto & entry : samplers) {
+        const llama_seq_id seq_id = entry.first;
+        if (seq_id < 0 || (uint32_t) seq_id >= n_seqs) {
+            continue;
+        }
+
+        sampler_seqs.push_back(seq_id);
+        has_sampler[seq_id] = true;
+    }
+
+    uint32_t n_outputs_set = 0;
+
+    const uint32_t n_outputs_per_seq = std::min(n_seq_tokens, n_outputs_max_per_seq);
+    for (uint32_t s : sampler_seqs) {
+        if (n_outputs_set >= n_outputs) {
+            break;
+        }
+
+        for (uint32_t t = 0; t < n_outputs_per_seq && n_outputs_set < n_outputs; ++t) {
+            ubatch.output[s * n_seq_tokens + t] = true;
+            ++n_outputs_set;
+        }
+    }
+
+    // use sequences without samplers for any remaining outputs
+    for (uint32_t t = 0; t < n_seq_tokens && n_outputs_set < n_outputs; ++t) {
+        for (uint32_t s = 0; s < n_seqs && n_outputs_set < n_outputs; ++s) {
+            if (has_sampler[s]) {
+                continue;
+            }
+
+            ubatch.output[s * n_seq_tokens + t] = true;
+            ++n_outputs_set;
+        }
+    }
+}
+
 ggml_cgraph * llama_context::graph_reserve(
         uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only, size_t * sizes) {
     LLAMA_LOG_DEBUG("%s: reserving a graph for ubatch with n_tokens = %4u, n_seqs = %2u, n_outputs = %4u\n", __func__, n_tokens, n_seqs, n_outputs);
@@ -2394,14 +2415,7 @@ ggml_cgraph * llama_context::graph_reserve(
     llama_batch_allocr balloc(model.hparams.n_pos_per_embd());
     llama_ubatch ubatch = balloc.ubatch_reserve(n_tokens/n_seqs, n_seqs);
 
-    // set one output token per sequence in order to activate all backend samplers
-    std::vector<llama_seq_id> seq_ids(n_seqs);
-    for (uint32_t i = 0; i < n_seqs; ++i) {
-        seq_ids[i] = i;
-        ubatch.n_seq_id[i] = 1;
-        ubatch.seq_id[i] = &seq_ids[i];
-        ubatch.output[i] = true;
-    }
+    ubatch_prepare_reserve(ubatch, n_outputs, sampling.samplers, cparams.n_outputs_max_per_seq);
 
     auto * res = gf_res_reserve.get();
 
@@ -3488,6 +3502,7 @@ llama_context_params llama_context_default_params() {
         /*.n_seq_max                   =*/ 1,
         /*.n_rs_seq                    =*/ 0,
         /*.n_outputs_max               =*/ 0,
+        /*.n_outputs_max_per_seq       =*/ 1,
         /*.n_threads                   =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
         /*.n_threads_batch             =*/ GGML_DEFAULT_N_THREADS,
         /*.ctx_type                    =*/ LLAMA_CONTEXT_TYPE_DEFAULT,

src/llama-cparams.h

@@ -15,6 +15,7 @@ struct llama_cparams {
     uint32_t n_seq_max;
     uint32_t n_rs_seq;        // number of recurrent-state snapshots per seq for rollback
     uint32_t n_outputs_max;   // max outputs supported by the context
+    uint32_t n_outputs_max_per_seq;
     int32_t  n_threads;       // number of threads to use for generation
     int32_t  n_threads_batch; // number of threads to use for batch processing
 

src/llama-graph.cpp

@@ -4,6 +4,7 @@
 #include "llama-model.h"
 #include "llama-batch.h"
 #include "llama-cparams.h"
+#include "llama-sampler.h"
 
 #include "llama-kv-cache.h"
 #include "llama-kv-cache-iswa.h"
@@ -1353,24 +1354,24 @@ void llm_graph_result::set_outputs(const llm_graph_params & params) {
             }
         }
     }
-    for (auto & [seq_id, t] : t_sampled) {
-        if (t != nullptr) {
-            ggml_set_output(t);
+    for (auto * tensor : t_sampled) {
+        if (tensor != nullptr) {
+            ggml_set_output(tensor);
         }
     }
-    for (auto & [seq_id, t] : t_sampled_probs) {
-        if (t != nullptr) {
-            ggml_set_output(t);
+    for (auto * tensor : t_sampled_probs) {
+        if (tensor != nullptr) {
+            ggml_set_output(tensor);
         }
     }
-    for (auto & [seq_id, t] : t_sampled_logits) {
-        if (t != nullptr) {
-            ggml_set_output(t);
+    for (auto * tensor : t_sampled_logits) {
+        if (tensor != nullptr) {
+            ggml_set_output(tensor);
         }
     }
-    for (auto & [seq_id, t] : t_candidates) {
-        if (t != nullptr) {
-            ggml_set_output(t);
+    for (auto * tensor : t_candidates) {
+        if (tensor != nullptr) {
+            ggml_set_output(tensor);
         }
     }
 }
@@ -3649,77 +3650,102 @@ void llm_graph_context::build_sampling() const {
     auto inp_sampling = std::make_unique<llm_graph_input_sampling>(samplers);
     res->add_input(std::move(inp_sampling));
 
-    std::map<llama_seq_id, int32_t> seq_to_logit_row;
-    int32_t logit_row_idx = 0;
-
-    for (uint32_t i = 0; i < ubatch.n_tokens; i++) {
+    std::map<llama_seq_id, std::vector<uint32_t>> sampling_rows;
+    uint32_t n_rows = 0;
+    for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
         if (ubatch.output[i]) {
-            llama_seq_id seq_id = ubatch.seq_id[i][0];
-            seq_to_logit_row[seq_id] = logit_row_idx;
-            logit_row_idx++;
+            sampling_rows[ubatch.seq_id[i][0]].push_back(n_rows++);
         }
     }
 
+    res->t_sampled.resize(n_rows, nullptr);
+    res->t_sampled_probs.resize(n_rows, nullptr);
+    res->t_sampled_logits.resize(n_rows, nullptr);
+    res->t_candidates.resize(n_rows, nullptr);
+
     // res->t_logits will contain logits for all tokens that want the logits calculated (logits=1 or output=1)
     GGML_ASSERT(res->t_logits != nullptr && "missing t_logits tensor");
 
-    // add a dummy row of logits
-    // this trick makes the graph static, regardless of which samplers are activated
-    // this is important in order to minimize graph reallocations
+    // add a dummy row to keep the single-output graph static regardless of active samplers
+    // multi-output graphs can still vary with the number of output rows
     ggml_tensor * logits_t = ggml_pad(ctx0, res->t_logits, 0, 1, 0, 0);
 
-    for (const auto & [seq_id, sampler] : samplers) {
-        const auto it = seq_to_logit_row.find(seq_id);
-
-        // inactive samplers always work on the first row
-        const auto row_idx = it != seq_to_logit_row.end() ? it->second : 0;
-        const int i_out    = it != seq_to_logit_row.end() ? 1          : 0;
-
-        ggml_tensor * logits_seq = ggml_view_1d(ctx0, logits_t, logits_t->ne[0], row_idx * logits_t->nb[1]);
-        ggml_format_name(logits_seq, "logits_seq_%d", seq_id);
-
-        struct llama_sampler_data data = {
-            /*.logits      =*/ logits_seq,
-            /*.probs       =*/ nullptr,
-            /*.sampled     =*/ nullptr,
-            /*.candidates  =*/ nullptr,
-        };
-
-        assert(sampler->iface->backend_apply);
-        sampler->iface->backend_apply(sampler, ctx0, gf, &data);
-
-        if (data.sampled != nullptr) {
-            res->t_sampled[seq_id] = data.sampled;
-            outs[1] = data.sampled;
-            ggml_build_forward_select(gf, outs.data(), outs.size(), i_out);
-        }
-
-        if (data.probs != nullptr) {
-            res->t_sampled_probs[seq_id] = data.probs;
-            outs[1] = data.probs;
-            ggml_build_forward_select(gf, outs.data(), outs.size(), i_out);
-        }
-
-        if (data.logits != nullptr) {
-            res->t_sampled_logits[seq_id] = data.logits;
-            outs[1] = data.logits;
-            ggml_build_forward_select(gf, outs.data(), outs.size(), i_out);
-        }
-
-        if (data.candidates != nullptr) {
-            res->t_candidates[seq_id] = data.candidates;
-            outs[1] = data.candidates;
-            ggml_build_forward_select(gf, outs.data(), outs.size(), i_out);
+    for (const auto & entry : samplers) {
+        if (entry.second->iface->backend_reset) {
+            entry.second->iface->backend_reset(entry.second);
         }
     }
 
-    // TODO: Call llama_sampler_accept_ggml after all samplers have been applied.
+    static const std::vector<uint32_t> dummy_row = { 0 };
+
+    for (const auto & [seq_id, sampler] : samplers) {
+        const auto it = sampling_rows.find(seq_id);
+
+        // inactive samplers always work on the first row
+        const bool active = it != sampling_rows.end();
+        const auto & rows = active ? it->second : dummy_row;
+        const int i_out   = active ? 1          : 0;
+
+        for (uint32_t i = 0; i < rows.size(); ++i) {
+            ggml_tensor * logits_seq = ggml_view_1d(ctx0, logits_t, logits_t->ne[0], rows[i] * logits_t->nb[1]);
+            ggml_format_name(logits_seq, "logits_seq_%d_%u", seq_id, i);
+
+            struct llama_sampler_data data = {
+                /*.logits       =*/ logits_seq,
+                /*.probs        =*/ nullptr,
+                /*.sampled      =*/ nullptr,
+                /*.candidates   =*/ nullptr,
+            };
+
+            assert(sampler->iface->backend_apply);
+            sampler->iface->backend_apply(sampler, ctx0, gf, &data);
+
+            if (data.sampled != nullptr) {
+                if (active) {
+                    res->t_sampled[rows[i]] = data.sampled;
+                }
+                outs[1] = data.sampled;
+                ggml_build_forward_select(gf, outs.data(), outs.size(), i_out);
+            }
+
+            if (data.probs != nullptr) {
+                if (active) {
+                    res->t_sampled_probs[rows[i]] = data.probs;
+                }
+                outs[1] = data.probs;
+                ggml_build_forward_select(gf, outs.data(), outs.size(), i_out);
+            }
+
+            if (data.logits != nullptr) {
+                if (active) {
+                    res->t_sampled_logits[rows[i]] = data.logits;
+                }
+                outs[1] = data.logits;
+                ggml_build_forward_select(gf, outs.data(), outs.size(), i_out);
+            }
+
+            if (data.candidates != nullptr) {
+                if (active) {
+                    res->t_candidates[rows[i]] = data.candidates;
+                }
+                outs[1] = data.candidates;
+                ggml_build_forward_select(gf, outs.data(), outs.size(), i_out);
+            }
+        }
+    }
+
+    // TODO: Call backend_accept after all samplers have been applied.
     /*
     for (const auto & [seq_id, sampler] : samplers) {
-        if (auto it = res->t_sampled.find(seq_id); it != res->t_sampled.end()) {
-            ggml_tensor * selected_token = it->second;
-            if (selected_token != nullptr) {
-                llama_sampler_accept_ggml(sampler, ctx0, gf, selected_token);
+        const auto it = sampling_rows.find(seq_id);
+        if (it == sampling_rows.end()) {
+            continue;
+        }
+
+        for (uint32_t row : it->second) {
+            ggml_tensor * selected_token = res->t_sampled[row];
+            if (selected_token != nullptr && sampler->iface->backend_accept) {
+                sampler->iface->backend_accept(sampler, ctx0, gf, selected_token);
             }
         }
     }

src/llama-graph.h

@@ -904,10 +904,10 @@ public:
 
     std::vector<ggml_tensor *> t_layer_inp;
 
-    std::map<llama_seq_id, ggml_tensor *> t_sampled_logits;
-    std::map<llama_seq_id, ggml_tensor *> t_candidates;
-    std::map<llama_seq_id, ggml_tensor *> t_sampled;
-    std::map<llama_seq_id, ggml_tensor *> t_sampled_probs;
+    std::vector<ggml_tensor *> t_sampled;
+    std::vector<ggml_tensor *> t_sampled_probs;
+    std::vector<ggml_tensor *> t_sampled_logits;
+    std::vector<ggml_tensor *> t_candidates;
 
     std::vector<llm_graph_input_ptr> inputs;
     std::vector<llm_graph_fused_node> fused_nodes;

src/llama-model-loader.cpp

@@ -543,7 +543,7 @@ llama_model_loader::llama_model_loader(
 
     tensor_buft_overrides = param_tensor_buft_overrides_p;
 
-    this->use_mmap      = load_mode == LLAMA_LOAD_MODE_MMAP || load_mode == LLAMA_LOAD_MODE_MMAP_MLOCK;
+    this->use_mmap      = load_mode == LLAMA_LOAD_MODE_MMAP || load_mode == LLAMA_LOAD_MODE_MMAP_MLOCK || load_mode == LLAMA_LOAD_MODE_AUTO;
     this->use_direct_io = load_mode == LLAMA_LOAD_MODE_DIRECT_IO;
 
     if (!fname.empty()) {

src/llama-model-saver.cpp

@@ -27,6 +27,7 @@ bool llama_model_saver_supports_arch(llm_arch arch) {
         case LLM_ARCH_APERTUS:
         case LLM_ARCH_MIMO2:
         case LLM_ARCH_STEP35:
+        case LLM_ARCH_MUSE_GLIMMER:
         case LLM_ARCH_MELLUM:
         case LLM_ARCH_LAGUNA:
             return false;

src/llama-model.cpp

@@ -40,6 +40,8 @@
 
 static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params & params) {
     switch (arch) {
+        case LLM_ARCH_CLIP:
+            return new llama_model_clip(params);
         case LLM_ARCH_LLAMA:
             return new llama_model_llama(params);
         case LLM_ARCH_LLAMA4:
@@ -174,6 +176,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
             return new llama_model_olmo2(params);
         case LLM_ARCH_OLMOE:
             return new llama_model_olmoe(params);
+        case LLM_ARCH_MUSE_GLIMMER:
+            return new llama_model_muse_glimmer(params);
         case LLM_ARCH_OPENELM:
             return new llama_model_openelm(params);
         case LLM_ARCH_GPTNEOX:
@@ -1267,8 +1271,23 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) {
 
     this->ml = &ml; // to be used by create_tensor() and load_arch_tensors()
 
+    if (ml.use_mmap && params.load_mode == LLAMA_LOAD_MODE_AUTO) {
+        for (const auto & dev : devices) {
+            ggml_backend_dev_props props;
+            ggml_backend_dev_get_props(dev.dev, &props);
+            if (!props.caps.mmap_support) {
+                ml.use_mmap = false;
+                break;
+            }
+        }
+    }
+
+    const char * load_mode_name = params.load_mode == LLAMA_LOAD_MODE_AUTO
+        ? llama_load_mode_name(ml.use_mmap ? LLAMA_LOAD_MODE_MMAP : LLAMA_LOAD_MODE_NONE)
+        : llama_load_mode_name(params.load_mode);
+
     LLAMA_LOG_INFO("%s: loading model tensors, this can take a while... (load_mode = %s)\n",
-        __func__, llama_load_mode_name(params.load_mode));
+        __func__, load_mode_name);
 
     // build a list of buffer types for the CPU and GPU devices
     pimpl->cpu_buft_list = make_cpu_buft_list(devices, params.use_extra_bufts, params.no_host);
@@ -2231,6 +2250,9 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                     params.ctx_type == LLAMA_CONTEXT_TYPE_MTP &&
                     (arch == LLM_ARCH_QWEN3NEXT || arch == LLM_ARCH_QWEN35 || arch == LLM_ARCH_QWEN35MOE);
 
+                const bool mtp_on_hybrid_nemotron =
+                    params.ctx_type == LLAMA_CONTEXT_TYPE_MTP && arch == LLM_ARCH_NEMOTRON_H_MOE;
+
                 if (llm_arch_is_recurrent(arch)) {
                     res = new llama_memory_recurrent(
                             *this,
@@ -2241,7 +2263,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                             cparams.n_seq_max,
                             cparams.n_rs_seq,
                             nullptr);
-                } else if (llm_arch_is_hybrid(arch) && !mtp_on_hybrid_qwen) {
+                } else if (llm_arch_is_hybrid(arch) && !mtp_on_hybrid_qwen && !mtp_on_hybrid_nemotron) {
                     // The main difference between hybrid architectures is the
                     // layer filters, so pick the right one here
                     llama_memory_hybrid::layer_filter_cb filter_attn = nullptr;
@@ -2322,7 +2344,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                         };
                     }
 
-                    if (mtp_on_hybrid_qwen) {
+                    if (mtp_on_hybrid_qwen || mtp_on_hybrid_nemotron) {
                         filter = [&](uint32_t il) { return il >= hparams.n_layer(); };
                     }
 
@@ -2445,7 +2467,7 @@ llama_model_params llama_model_default_params() {
         /*.tensor_buft_overrides       =*/ nullptr,
         /*.n_gpu_layers                =*/ -1,
         /*.split_mode                  =*/ LLAMA_SPLIT_MODE_LAYER,
-        /*.load_mode                   =*/ LLAMA_LOAD_MODE_MMAP,
+        /*.load_mode                   =*/ LLAMA_LOAD_MODE_AUTO,
         /*.main_gpu                    =*/ 0,
         /*.tensor_split                =*/ nullptr,
         /*.progress_callback           =*/ nullptr,
@@ -2594,6 +2616,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_DEEPSEEK2OCR:
         case LLM_ARCH_DEEPSEEK32:
         case LLM_ARCH_DEEPSEEK4:
+        case LLM_ARCH_MUSE_GLIMMER:
         case LLM_ARCH_PLM:
         case LLM_ARCH_CHATGLM:
         case LLM_ARCH_GRANITE:

src/llama-sampler.cpp

@@ -467,9 +467,11 @@ static void llama_sampler_empty_free(struct llama_sampler * smpl) {
 
 static bool llama_sampler_empty_backend_init(
         struct llama_sampler       * smpl,
-        ggml_backend_buffer_type_t   buft) {
+        ggml_backend_buffer_type_t   buft,
+        uint32_t                     n_outputs_max_per_seq) {
     GGML_UNUSED(smpl);
     GGML_UNUSED(buft);
+    GGML_UNUSED(n_outputs_max_per_seq);
 
     return true;
 }
@@ -511,6 +513,8 @@ static struct llama_sampler_i llama_sampler_empty_i = {
     /* .backend_accept    = */ llama_sampler_empty_backend_accept,
     /* .backend_apply     = */ llama_sampler_empty_backend_apply,
     /* .backend_set_input = */ llama_sampler_empty_backend_set_input,
+    /* .backend_reset     = */ nullptr,
+    /* .copy_state        = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_empty(const char * name) {
@@ -551,6 +555,12 @@ struct llama_sampler_backend {
         this->support = support;
     }
 
+    // copy the state that is not tied to the current sampling graph
+    // samplers that hold only immutable configuration can use this as is
+    void copy_state(const llama_sampler_backend & src) {
+        GGML_UNUSED(src);
+    }
+
 private:
     std::string name;
     std::string name_ext;
@@ -559,6 +569,71 @@ private:
     bool support;
 };
 
+// .copy_state for samplers deriving from llama_sampler_backend
+template<typename T>
+static void llama_sampler_backend_copy_state(const struct llama_sampler * src, struct llama_sampler * dst) {
+    ((T *) dst->ctx)->copy_state(*(const T *) src->ctx);
+}
+
+struct llama_sampler_backend_probe {
+    ggml_context_ptr ctx;
+    ggml_cgraph * gf;
+};
+
+static llama_sampler_backend_probe llama_sampler_backend_probe_graph(
+        llama_sampler * sampler,
+        int64_t         n_candidates,
+        uint32_t        max_nodes,
+        bool            with_candidates) {
+    ggml_init_params params = {
+        /*.mem_size   =*/ max_nodes * ggml_tensor_overhead() + ggml_graph_overhead_custom(max_nodes, false),
+        /*.mem_buffer =*/ nullptr,
+        /*.no_alloc   =*/ true,
+    };
+
+    ggml_context_ptr ctx_ptr { ggml_init(params) };
+    if (!ctx_ptr) {
+        throw std::runtime_error(format("failed to create ggml context"));
+    }
+
+    auto * ctx = ctx_ptr.get();
+    auto * gf = ggml_new_graph_custom(ctx, max_nodes, false);
+
+    llama_sampler_data data = {
+        /*.logits       =*/ ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_candidates),
+        /*.probs        =*/ nullptr,
+        /*.sampled      =*/ nullptr,
+        /*.candidates   =*/ with_candidates ? ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_candidates) : nullptr,
+    };
+
+    if (sampler->iface->backend_reset) {
+        sampler->iface->backend_reset(sampler);
+    }
+    sampler->iface->backend_apply(sampler, ctx, gf, &data);
+
+    for (auto * output : { data.logits, data.probs, data.sampled, data.candidates }) {
+        if (output) {
+            ggml_build_forward_expand(gf, output);
+        }
+    }
+
+    if (sampler->iface->backend_reset) {
+        sampler->iface->backend_reset(sampler);
+    }
+
+    return { std::move(ctx_ptr), gf };
+}
+
+static uint32_t llama_sampler_backend_probe_n_nodes(const llama_sampler_backend_probe & probe) {
+    uint32_t n_tensors = 0;
+    for (auto * tensor = ggml_get_first_tensor(probe.ctx.get()); tensor;
+            tensor = ggml_get_next_tensor(probe.ctx.get(), tensor)) {
+        ++n_tensors;
+    }
+
+    return std::max<uint32_t>(ggml_graph_n_nodes(probe.gf), n_tensors);
+}
+
 // check if all ggml ops used by the sampler are supported by the backend
 static bool llama_sampler_backend_support(
         llama_sampler              * smpl,
@@ -569,50 +644,10 @@ static bool llama_sampler_backend_support(
         return true;
     }
 
-    ggml_init_params params = {
-        /*.mem_size   =*/ 128*ggml_tensor_overhead() + ggml_graph_overhead(),
-        /*.mem_buffer =*/ NULL,
-        /*.no_alloc   =*/ true,
-    };
+    auto probe = llama_sampler_backend_probe_graph(smpl, 1024*1024, GGML_DEFAULT_GRAPH_SIZE, true);
 
-    ggml_context_ptr ctx_ptr { ggml_init(params) };
-    if (!ctx_ptr) {
-        throw std::runtime_error(format("failed to create ggml context"));
-    }
-
-    ggml_context * ctx = ctx_ptr.get();
-
-    const int64_t n = 1024*1024;
-
-    llama_sampler_data data = {
-        /*.logits     = */ ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n),
-        /*.probs      = */ nullptr,
-        /*.sampled    = */ nullptr,
-        /*.candidates = */ ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n),
-    };
-
-    ggml_cgraph * gf = ggml_new_graph(ctx);
-
-    smpl->iface->backend_apply(smpl, ctx, gf, &data);
-
-    if (data.logits) {
-        ggml_build_forward_expand(gf, data.logits);
-    }
-
-    if (data.probs) {
-        ggml_build_forward_expand(gf, data.probs);
-    }
-
-    if (data.sampled) {
-        ggml_build_forward_expand(gf, data.sampled);
-    }
-
-    if (data.candidates) {
-        ggml_build_forward_expand(gf, data.candidates);
-    }
-
-    for (int i = 0; i < ggml_graph_n_nodes(gf); i++) {
-        struct ggml_tensor * op = ggml_graph_node(gf, i);
+    for (int i = 0; i < ggml_graph_n_nodes(probe.gf); i++) {
+        struct ggml_tensor * op = ggml_graph_node(probe.gf, i);
 
         if (!ggml_backend_dev_supports_op(device, op)) {
             LLAMA_LOG_WARN("%s: device '%s' does not have support for op %s needed for sampler '%s'\n",
@@ -697,7 +732,8 @@ static void llama_sampler_chain_free(struct llama_sampler * smpl) {
 
 static bool llama_sampler_chain_backend_init(
         struct llama_sampler       * smpl,
-        ggml_backend_buffer_type_t   buft) {
+        ggml_backend_buffer_type_t   buft,
+        uint32_t                     n_outputs_max_per_seq) {
     auto * chain = (llama_sampler_chain *) smpl->ctx;
 
     GGML_ASSERT(chain->is_init == false && "llama_sampler_chain_backend_init() called twice");
@@ -705,26 +741,32 @@ static bool llama_sampler_chain_backend_init(
     chain->is_init = true;
 
     bool res = true;
+    bool backend_prefix = true;
 
     for (auto & smpl : chain->samplers) {
-        bool res_cur = true;
+        bool cur_prefix = backend_prefix;
 
         // to be able to run a sampler on the backend, it has to:
         // - have the .backend_init() API implemented
         // - return true during .backend_init()
-        if (smpl.ptr->iface->backend_init) {
-            if (!smpl.ptr->iface->backend_init(smpl.ptr, buft)) {
-                res_cur = false;
+        // - support the requested per-sequence output limit
+        if (cur_prefix && smpl.ptr->iface->backend_init) {
+            if (!smpl.ptr->iface->backend_init(smpl.ptr, buft, n_outputs_max_per_seq)) {
+                cur_prefix = false;
             }
         } else {
-            res_cur = false;
+            cur_prefix = false;
         }
 
-        smpl.is_backend = res_cur;
+        smpl.is_backend = cur_prefix;
+        backend_prefix = cur_prefix;
 
-        res = res && res_cur;
+        res = res && cur_prefix;
     }
 
+    auto probe = llama_sampler_backend_probe_graph(smpl, 1024*1024, GGML_DEFAULT_GRAPH_SIZE, false);
+    chain->n_nodes = llama_sampler_backend_probe_n_nodes(probe);
+
     return res;
 }
 
@@ -780,6 +822,36 @@ static void llama_sampler_chain_backend_set_input(struct llama_sampler * smpl) {
     }
 }
 
+static void llama_sampler_chain_backend_reset(struct llama_sampler * smpl) {
+    auto * chain = (llama_sampler_chain *) smpl->ctx;
+
+    for (auto & entry : chain->samplers) {
+        if (!entry.is_backend) {
+            break;
+        }
+        if (entry.ptr->iface->backend_reset) {
+            entry.ptr->iface->backend_reset(entry.ptr);
+        }
+    }
+}
+
+static void llama_sampler_chain_copy_state(const struct llama_sampler * src, struct llama_sampler * dst) {
+    const auto * src_chain = (const llama_sampler_chain *) src->ctx;
+    auto * dst_chain = (llama_sampler_chain *) dst->ctx;
+
+    GGML_ASSERT(src_chain->samplers.size() == dst_chain->samplers.size());
+
+    for (size_t i = 0; i < src_chain->samplers.size(); ++i) {
+        llama_sampler_copy(src_chain->samplers[i].ptr, dst_chain->samplers[i].ptr);
+    }
+
+    // note: is_init, n_nodes and is_backend belong to the current sampling graph
+    dst_chain->params      = src_chain->params;
+    dst_chain->cur         = src_chain->cur;
+    dst_chain->t_sample_us = src_chain->t_sample_us;
+    dst_chain->n_sample    = src_chain->n_sample;
+}
+
 static struct llama_sampler_i llama_sampler_chain_i = {
     /* .name              = */ llama_sampler_chain_name,
     /* .accept            = */ llama_sampler_chain_accept,
@@ -791,22 +863,35 @@ static struct llama_sampler_i llama_sampler_chain_i = {
     /* .backend_accept    = */ llama_sampler_chain_backend_accept,
     /* .backend_apply     = */ llama_sampler_chain_backend_apply,
     /* .backend_set_input = */ llama_sampler_chain_backend_set_input,
+    /* .backend_reset     = */ llama_sampler_chain_backend_reset,
+    /* .copy_state        = */ llama_sampler_chain_copy_state,
 };
 
 struct llama_sampler * llama_sampler_chain_init(struct llama_sampler_chain_params params) {
     return llama_sampler_init(
         /* .iface = */ &llama_sampler_chain_i,
         /* .ctx   = */ new llama_sampler_chain {
-            /* .params      = */ params,
-            /* .is_init     = */ false,
-            /* .samplers    = */ {},
-            /* .cur         = */ {},
-            /* .t_sample_us = */ 0,
-            /* .n_sample    = */ 0,
+            /* .params               = */ params,
+            /* .is_init              = */ false,
+            /* .n_nodes              = */ 0,
+            /* .samplers             = */ {},
+            /* .cur                  = */ {},
+            /* .t_sample_us          = */ 0,
+            /* .n_sample             = */ 0,
         }
     );
 }
 
+uint32_t llama_sampler_backend_n_nodes(const llama_sampler * sampler) {
+    GGML_ASSERT(sampler != nullptr);
+    GGML_ASSERT(sampler->iface == &llama_sampler_chain_i);
+
+    const auto * chain = (const llama_sampler_chain *) sampler->ctx;
+    GGML_ASSERT(chain->is_init);
+
+    return chain->n_nodes;
+}
+
 llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_context * ctx, int32_t idx) {
     const llama_token   sampled_token  = llama_get_sampled_token_ith     (ctx, idx);
     const float *       sampled_probs  = llama_get_sampled_probs_ith     (ctx, idx);
@@ -816,6 +901,7 @@ llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_conte
     // If a backend sampler has already sampled a token, return it.
     if (sampled_token != LLAMA_TOKEN_NULL) {
         LLAMA_LOG_DEBUG("%s: Backend sampler selected token for idx %d. Skipping CPU samplers\n", __func__, idx);
+        llama_sampler_accept(smpl, sampled_token);
         return sampled_token;
     }
 
@@ -975,8 +1061,10 @@ static void llama_sampler_greedy_apply(struct llama_sampler * /*smpl*/, llama_to
 
 static bool llama_sampler_greedy_backend_init(
         struct llama_sampler       * smpl,
-        ggml_backend_buffer_type_t   buft) {
+        ggml_backend_buffer_type_t   buft,
+        uint32_t                     n_outputs_max_per_seq) {
     auto * sctx = (llama_sampler_greedy *) smpl->ctx;
+    GGML_UNUSED(n_outputs_max_per_seq);
 
     const bool res = llama_sampler_backend_support(smpl, buft);
 
@@ -1012,6 +1100,8 @@ static struct llama_sampler_i llama_sampler_greedy_i = {
     /* .backend_accept    = */ nullptr,
     /* .backend_apply     = */ llama_sampler_greedy_backend_apply,
     /* .backend_set_input = */ nullptr,
+    /* .backend_reset     = */ nullptr,
+    /* .copy_state        = */ llama_sampler_backend_copy_state<llama_sampler_greedy>,
 };
 
 struct llama_sampler * llama_sampler_init_greedy() {
@@ -1031,7 +1121,25 @@ struct llama_sampler_dist : public llama_sampler_backend {
 
     std::mt19937 rng;
 
-    ggml_tensor * inp_uniform;
+    // TODO: refactor + fix naming
+    //       https://github.com/ggml-org/llama.cpp/pull/25532/changes#r3749906719
+    // use a temporary RNG for multi-output sampling so rejected tokens do not advance rng
+    bool backend_transactional;
+    std::mt19937 rng_backend;
+    size_t n_backend_draws_generated;
+    size_t n_backend_draws_committed;
+
+    // inputs for the current sampling graph
+    std::vector<ggml_tensor *> inp_uniforms;
+
+    void copy_state(const llama_sampler_dist & src) {
+        // note: inp_uniforms and backend_transactional belong to the current sampling graph
+        seed_cur                  = src.seed_cur;
+        rng                       = src.rng;
+        rng_backend               = src.rng_backend;
+        n_backend_draws_generated = src.n_backend_draws_generated;
+        n_backend_draws_committed = src.n_backend_draws_committed;
+    }
 };
 
 static const char * llama_sampler_dist_name(const struct llama_sampler * smpl) {
@@ -1050,7 +1158,11 @@ static void llama_sampler_dist_apply(struct llama_sampler * smpl, llama_token_da
 
     cur_p->selected = 0;
 
+    std::uniform_real_distribution<double> dist(0.0f, 1.0f);
+
     if (cur_p->size == 1) {
+        // keep the RNG state aligned with backend sampling, which draws once per output
+        dist(ctx->rng);
         cur_p->data[0].p = 1.0f;
         return;
     }
@@ -1075,7 +1187,6 @@ static void llama_sampler_dist_apply(struct llama_sampler * smpl, llama_token_da
     // sample from the obtained probabilities and normalize the probs in a single pass
     // this is ~3x faster on Mac with full gpt-oss vocab than the version below
     //
-    std::uniform_real_distribution<double> dist(0.0f, 1.0f);
     const double rnd = dist(ctx->rng);
 
           double sum_run = 0.0f;
@@ -1115,6 +1226,9 @@ static void llama_sampler_dist_reset(struct llama_sampler * smpl) {
     auto * ctx = (llama_sampler_dist *) smpl->ctx;
     ctx->seed_cur = get_rng_seed(ctx->seed);
     ctx->rng.seed(ctx->seed_cur);
+    ctx->rng_backend = ctx->rng;
+    ctx->n_backend_draws_generated = 0;
+    ctx->n_backend_draws_committed = 0;
 }
 
 static struct llama_sampler * llama_sampler_dist_clone(const struct llama_sampler * smpl) {
@@ -1125,7 +1239,12 @@ static struct llama_sampler * llama_sampler_dist_clone(const struct llama_sample
     {
         auto * result_ctx = (llama_sampler_dist *) result->ctx;
 
-        result_ctx->rng = ctx->rng;
+        result_ctx->seed_cur                  = ctx->seed_cur;
+        result_ctx->rng                       = ctx->rng;
+        result_ctx->backend_transactional     = ctx->backend_transactional;
+        result_ctx->rng_backend               = ctx->rng_backend;
+        result_ctx->n_backend_draws_generated = ctx->n_backend_draws_generated;
+        result_ctx->n_backend_draws_committed = ctx->n_backend_draws_committed;
     }
 
     return result;
@@ -1137,12 +1256,17 @@ static void llama_sampler_dist_free(struct llama_sampler * smpl) {
 
 static bool llama_sampler_dist_backend_init(
         struct llama_sampler       * smpl,
-        ggml_backend_buffer_type_t   buft) {
+        ggml_backend_buffer_type_t   buft,
+        uint32_t                     n_outputs_max_per_seq) {
     auto * sctx = (llama_sampler_dist *) smpl->ctx;
 
     const bool res = llama_sampler_backend_support(smpl, buft);
 
     sctx->init(res);
+    sctx->backend_transactional = n_outputs_max_per_seq > 1;
+    sctx->rng_backend = sctx->rng;
+    sctx->n_backend_draws_generated = 0;
+    sctx->n_backend_draws_committed = 0;
 
     return res;
 }
@@ -1156,9 +1280,10 @@ static void llama_sampler_dist_backend_apply(
 
     auto * sctx = (llama_sampler_dist *) smpl->ctx;
 
-    sctx->inp_uniform = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
-    ggml_set_name (sctx->inp_uniform, "uniform");
-    ggml_set_input(sctx->inp_uniform);
+    ggml_tensor * inp_uniform = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
+    ggml_format_name(inp_uniform, "uniform_%zu", sctx->inp_uniforms.size());
+    ggml_set_input(inp_uniform);
+    sctx->inp_uniforms.push_back(inp_uniform);
 
     // flatten
     struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits));
@@ -1174,7 +1299,7 @@ static void llama_sampler_dist_backend_apply(
     // Recall that each entry in cumsum is the cumulative probability up to that
     // index so values stay negative while the cumulative total is below the
     // random value, and become zero/positive once the threshold is crossed.
-    struct ggml_tensor * diff = ggml_sub(ctx, cumsum, sctx->inp_uniform);
+    struct ggml_tensor * diff = ggml_sub(ctx, cumsum, inp_uniform);
     ggml_set_name(diff, "dist_cumsum");
 
     // The ggml_step function produces a tensor where entries are 1 if the
@@ -1189,6 +1314,9 @@ static void llama_sampler_dist_backend_apply(
     struct ggml_tensor * idxf = ggml_sum(ctx, mask);
     ggml_set_name(idxf, "dist_index_f32");
 
+    // Clamp to prevent out-of-bounds access when computing the index.
+    idxf = ggml_clamp(ctx, idxf, 1.0f, mask->ne[0]);
+
     // Use ggml_scale_bias to scale the index value by -1 and then add the size
     // of the mask to that value so we get the correct index ((-1 * idxf) + n).
     struct ggml_tensor * idx = ggml_cast(ctx, ggml_scale_bias(ctx, idxf, -1.0f, mask->ne[0]), GGML_TYPE_I32);
@@ -1210,22 +1338,52 @@ static void llama_sampler_dist_backend_apply(
 static void llama_sampler_dist_backend_set_input(struct llama_sampler * smpl) {
     auto * sctx = (llama_sampler_dist *) smpl->ctx;
 
-    GGML_ASSERT(sctx->inp_uniform != nullptr);
+    GGML_ASSERT(!sctx->inp_uniforms.empty());
 
     // We sample in double precision and cast to float to match rnd numbers of
-    // llama_dampler_dist which uses double precision (sampling from
+    // llama_sampler_dist which uses double precision (sampling from
     // std::uniform_real_distribution<double> and
     // std::uniform_real_distribution<float> with same rng will produce
     // different sequences).
     std::uniform_real_distribution<double> dist(0.0f, 1.0f);
-    const float rnd = dist(sctx->rng);
 
-    ggml_backend_tensor_set(sctx->inp_uniform, &rnd, 0, sizeof(float));
+    auto & rng = sctx->backend_transactional ? sctx->rng_backend : sctx->rng;
+
+    for (auto * inp_uniform : sctx->inp_uniforms) {
+        GGML_ASSERT(inp_uniform != nullptr);
+
+        const float rnd = dist(rng);
+        ggml_backend_tensor_set(inp_uniform, &rnd, 0, sizeof(float));
+
+        if (sctx->backend_transactional) {
+            ++sctx->n_backend_draws_generated;
+        }
+    }
+}
+
+static void llama_sampler_dist_backend_reset(struct llama_sampler * smpl) {
+    auto * sctx = (llama_sampler_dist *) smpl->ctx;
+    sctx->inp_uniforms.clear();
+}
+
+static void llama_sampler_dist_accept(struct llama_sampler * smpl, llama_token token) {
+    GGML_UNUSED(token);
+
+    auto * sctx = (llama_sampler_dist *) smpl->ctx;
+
+    if (!sctx->backend_transactional ||
+            sctx->n_backend_draws_committed >= sctx->n_backend_draws_generated) {
+        return;
+    }
+
+    std::uniform_real_distribution<double> dist(0.0f, 1.0f);
+    dist(sctx->rng);
+    ++sctx->n_backend_draws_committed;
 }
 
 static struct llama_sampler_i llama_sampler_dist_i = {
     /* .name              = */ llama_sampler_dist_name,
-    /* .accept            = */ nullptr,
+    /* .accept            = */ llama_sampler_dist_accept,
     /* .apply             = */ llama_sampler_dist_apply,
     /* .reset             = */ llama_sampler_dist_reset,
     /* .clone             = */ llama_sampler_dist_clone,
@@ -1234,6 +1392,8 @@ static struct llama_sampler_i llama_sampler_dist_i = {
     /* .backend_accept    = */ nullptr,
     /* .backend_apply     = */ llama_sampler_dist_backend_apply,
     /* .backend_set_input = */ llama_sampler_dist_backend_set_input,
+    /* .backend_reset     = */ llama_sampler_dist_backend_reset,
+    /* .copy_state        = */ llama_sampler_backend_copy_state<llama_sampler_dist>,
 };
 
 struct llama_sampler * llama_sampler_init_dist(uint32_t seed) {
@@ -1242,14 +1402,39 @@ struct llama_sampler * llama_sampler_init_dist(uint32_t seed) {
         /* .iface = */ &llama_sampler_dist_i,
         /* .ctx   = */ new llama_sampler_dist {
             ("dist"),
-            /* .seed        = */ seed,
-            /* .seed_cur    = */ seed_cur,
-            /* .rng         = */ std::mt19937(seed_cur),
-            /* .inp_uniform = */ nullptr,
+            /* .seed                      = */ seed,
+            /* .seed_cur                  = */ seed_cur,
+            /* .rng                       = */ std::mt19937(seed_cur),
+            /* .backend_transactional     = */ false,
+            /* .rng_backend               = */ std::mt19937(seed_cur),
+            /* .n_backend_draws_generated = */ 0,
+            /* .n_backend_draws_committed = */ 0,
+            /* .inp_uniforms              = */ {},
         }
     );
 }
 
+void llama_sampler_backend_begin(llama_sampler * sampler) {
+    GGML_ASSERT(sampler != nullptr);
+
+    if (sampler->iface == &llama_sampler_chain_i) {
+        auto * chain = (llama_sampler_chain *) sampler->ctx;
+        for (auto & entry : chain->samplers) {
+            if (!entry.is_backend) {
+                break;
+            }
+            llama_sampler_backend_begin(entry.ptr);
+        }
+    } else if (sampler->iface == &llama_sampler_dist_i) {
+        auto * ctx = (llama_sampler_dist *) sampler->ctx;
+        if (ctx->backend_transactional) {
+            ctx->rng_backend = ctx->rng;
+            ctx->n_backend_draws_generated = 0;
+            ctx->n_backend_draws_committed = 0;
+        }
+    }
+}
+
 // top-k
 
 struct llama_sampler_top_k : public llama_sampler_backend {
@@ -1277,8 +1462,10 @@ static void llama_sampler_top_k_free(struct llama_sampler * smpl) {
 
 static bool llama_sampler_top_k_backend_init(
         struct llama_sampler       * smpl,
-        ggml_backend_buffer_type_t   buft) {
+        ggml_backend_buffer_type_t   buft,
+        uint32_t                     n_outputs_max_per_seq) {
     auto * sctx = (llama_sampler_top_k *) smpl->ctx;
+    GGML_UNUSED(n_outputs_max_per_seq);
 
     const bool res = llama_sampler_backend_support(smpl, buft);
 
@@ -1325,6 +1512,8 @@ static struct llama_sampler_i llama_sampler_top_k_i = {
     /* .backend_accept    = */ nullptr,
     /* .backend_apply     = */ llama_sampler_top_k_backend_apply,
     /* .backend_set_input = */ nullptr,
+    /* .backend_reset     = */ nullptr,
+    /* .copy_state        = */ llama_sampler_backend_copy_state<llama_sampler_top_k>,
 };
 
 struct llama_sampler * llama_sampler_init_top_k(int32_t k) {
@@ -1423,8 +1612,10 @@ static void llama_sampler_top_p_free(struct llama_sampler * smpl) {
 
 static bool llama_sampler_top_p_backend_init(
         struct llama_sampler       * smpl,
-        ggml_backend_buffer_type_t   buft) {
+        ggml_backend_buffer_type_t   buft,
+        uint32_t                     n_outputs_max_per_seq) {
     auto * sctx = (llama_sampler_top_p *) smpl->ctx;
+    GGML_UNUSED(n_outputs_max_per_seq);
 
     const bool res = llama_sampler_backend_support(smpl, buft);
 
@@ -1521,6 +1712,8 @@ static struct llama_sampler_i llama_sampler_top_p_i = {
     /* .backend_accept    = */ nullptr,
     /* .backend_apply     = */ llama_sampler_top_p_backend_apply,
     /* .backend_set_input = */ nullptr,
+    /* .backend_reset     = */ nullptr,
+    /* .copy_state        = */ llama_sampler_backend_copy_state<llama_sampler_top_p>,
 };
 
 struct llama_sampler * llama_sampler_init_top_p(float p, size_t min_keep) {
@@ -1618,8 +1811,10 @@ static void llama_sampler_min_p_free(struct llama_sampler * smpl) {
 
 static bool llama_sampler_min_p_backend_init(
         struct llama_sampler       * smpl,
-        ggml_backend_buffer_type_t   buft) {
+        ggml_backend_buffer_type_t   buft,
+        uint32_t                     n_outputs_max_per_seq) {
     auto * sctx = (llama_sampler_min_p *) smpl->ctx;
+    GGML_UNUSED(n_outputs_max_per_seq);
 
     const bool res = llama_sampler_backend_support(smpl, buft);
 
@@ -1680,6 +1875,8 @@ static struct llama_sampler_i llama_sampler_min_p_i = {
     /* .backend_accept    = */ nullptr,
     /* .backend_apply     = */ llama_sampler_min_p_backend_apply,
     /* .backend_set_input = */ nullptr,
+    /* .backend_reset     = */ nullptr,
+    /* .copy_state        = */ llama_sampler_backend_copy_state<llama_sampler_min_p>,
 };
 
 struct llama_sampler * llama_sampler_init_min_p(float p, size_t min_keep) {
@@ -1790,6 +1987,8 @@ static struct llama_sampler_i llama_sampler_typical_i = {
     /* .backend_accept    = */ nullptr,
     /* .backend_apply     = */ nullptr,
     /* .backend_set_input = */ nullptr,
+    /* .backend_reset     = */ nullptr,
+    /* .copy_state        = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_typical(float p, size_t min_keep) {
@@ -1866,8 +2065,10 @@ static void llama_sampler_backend_temp_sampling(
 
 static bool llama_sampler_temp_backend_init(
         struct llama_sampler       * smpl,
-        ggml_backend_buffer_type_t   buft) {
+        ggml_backend_buffer_type_t   buft,
+        uint32_t                     n_outputs_max_per_seq) {
     auto * sctx = (llama_sampler_temp *) smpl->ctx;
+    GGML_UNUSED(n_outputs_max_per_seq);
 
     const bool res = llama_sampler_backend_support(smpl, buft);
 
@@ -1896,6 +2097,8 @@ static struct llama_sampler_i llama_sampler_temp_i = {
     /* .backend_accept    = */ nullptr,
     /* .backend_apply     = */ llama_sampler_temp_backend_apply,
     /* .backend_set_input = */ nullptr,
+    /* .backend_reset     = */ nullptr,
+    /* .copy_state        = */ llama_sampler_backend_copy_state<llama_sampler_temp>,
 };
 
 struct llama_sampler * llama_sampler_init_temp(float temp) {
@@ -2009,8 +2212,10 @@ static void llama_sampler_temp_ext_free(struct llama_sampler * smpl) {
 
 static bool llama_sampler_temp_ext_backend_init(
         struct llama_sampler       * smpl,
-        ggml_backend_buffer_type_t   buft) {
+        ggml_backend_buffer_type_t   buft,
+        uint32_t                     n_outputs_max_per_seq) {
     auto * sctx = (llama_sampler_temp_ext *) smpl->ctx;
+    GGML_UNUSED(n_outputs_max_per_seq);
 
     const bool res = llama_sampler_backend_support(smpl, buft);
 
@@ -2095,6 +2300,8 @@ static struct llama_sampler_i llama_sampler_temp_ext_i = {
     /* .backend_accept    = */ nullptr,
     /* .backend_apply     = */ llama_sampler_temp_ext_backend_apply,
     /* .backend_set_input = */ nullptr,
+    /* .backend_reset     = */ nullptr,
+    /* .copy_state        = */ llama_sampler_backend_copy_state<llama_sampler_temp_ext>,
 };
 
 struct llama_sampler * llama_sampler_init_temp_ext(float temp, float delta, float exponent) {
@@ -2202,6 +2409,8 @@ static struct llama_sampler_i llama_sampler_xtc_i = {
     /* .backend_accept    = */ nullptr,
     /* .backend_apply     = */ nullptr,
     /* .backend_set_input = */ nullptr,
+    /* .backend_reset     = */ nullptr,
+    /* .copy_state        = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_xtc(float p, float t, size_t min_keep, uint32_t seed) {
@@ -2290,7 +2499,7 @@ static struct llama_sampler * llama_sampler_mirostat_clone(const struct llama_sa
 
     // copy the state
     {
-        auto * result_ctx = (llama_sampler_mirostat *) smpl->ctx;
+        auto * result_ctx = (llama_sampler_mirostat *) result->ctx;
 
         result_ctx->mu  = ctx->mu;
         result_ctx->rng = ctx->rng;
@@ -2321,6 +2530,8 @@ static struct llama_sampler_i llama_sampler_mirostat_i = {
     /* .backend_accept    = */ nullptr,
     /* .backend_apply     = */ nullptr,
     /* .backend_set_input = */ nullptr,
+    /* .backend_reset     = */ nullptr,
+    /* .copy_state        = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_mirostat(int32_t n_vocab, uint32_t seed, float tau, float eta, int32_t m) {
@@ -2425,6 +2636,8 @@ static struct llama_sampler_i llama_sampler_mirostat_v2_i = {
     /* .backend_accept    = */ nullptr,
     /* .backend_apply     = */ nullptr,
     /* .backend_set_input = */ nullptr,
+    /* .backend_reset     = */ nullptr,
+    /* .copy_state        = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_mirostat_v2(uint32_t seed, float tau, float eta) {
@@ -2546,6 +2759,8 @@ static struct llama_sampler_i llama_sampler_grammar_i = {
     /* .backend_accept    = */ nullptr,
     /* .backend_apply     = */ nullptr,
     /* .backend_set_input = */ nullptr,
+    /* .backend_reset     = */ nullptr,
+    /* .copy_state        = */ nullptr,
 };
 
 static struct llama_sampler * llama_sampler_init_grammar_impl(
@@ -2661,6 +2876,12 @@ struct llama_sampler_penalties : public llama_sampler_backend {
     std::vector<int32_t> host_token_ids;
     std::vector<int32_t> host_counts;
 
+    void copy_state(const llama_sampler_penalties & src) {
+        // note: inp_token_ids/inp_counts belong to the current sampling graph
+        prev        = src.prev;
+        token_count = src.token_count;
+    }
+
     static bool is_disabled(
             int32_t penalty_last_n,
             float   penalty_repeat,
@@ -2790,9 +3011,15 @@ static void llama_sampler_penalties_free(struct llama_sampler * smpl) {
 
 static bool llama_sampler_penalties_backend_init(
         struct llama_sampler       * smpl,
-        ggml_backend_buffer_type_t   buft) {
+        ggml_backend_buffer_type_t   buft,
+        uint32_t                     n_outputs_max_per_seq) {
     auto * sctx = (llama_sampler_penalties *) smpl->ctx;
 
+    if (n_outputs_max_per_seq > 1) {
+        sctx->init(false);
+        return false;
+    }
+
     const bool res = llama_sampler_backend_support(smpl, buft);
 
     sctx->init(res);
@@ -2952,6 +3179,12 @@ static void llama_sampler_penalties_backend_set_input(struct llama_sampler * smp
     ggml_backend_tensor_set(sctx->inp_counts,    sctx->host_counts.data(),    0, sctx->n_max * sizeof(int32_t));
 }
 
+static void llama_sampler_penalties_backend_reset(struct llama_sampler * smpl) {
+    auto * sctx = (llama_sampler_penalties *) smpl->ctx;
+    sctx->inp_token_ids = nullptr;
+    sctx->inp_counts    = nullptr;
+}
+
 static struct llama_sampler_i llama_sampler_penalties_i = {
     /* .name              = */ llama_sampler_penalties_name,
     /* .accept            = */ llama_sampler_penalties_accept,
@@ -2963,6 +3196,8 @@ static struct llama_sampler_i llama_sampler_penalties_i = {
     /* .backend_accept    = */ nullptr,
     /* .backend_apply     = */ llama_sampler_penalties_backend_apply,
     /* .backend_set_input = */ llama_sampler_penalties_backend_set_input,
+    /* .backend_reset     = */ llama_sampler_penalties_backend_reset,
+    /* .copy_state        = */ llama_sampler_backend_copy_state<llama_sampler_penalties>,
 };
 
 struct llama_sampler * llama_sampler_init_penalties(
@@ -3058,6 +3293,8 @@ static struct llama_sampler_i llama_sampler_top_n_sigma_i = {
     /* .backend_accept    = */ nullptr,
     /* .backend_apply     = */ nullptr,
     /* .backend_set_input = */ nullptr,
+    /* .backend_reset     = */ nullptr,
+    /* .copy_state        = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_top_n_sigma(float n) {
@@ -3395,6 +3632,8 @@ static struct llama_sampler_i llama_sampler_dry_i = {
     /* .backend_accept    = */ nullptr,
     /* .backend_apply     = */ nullptr,
     /* .backend_set_input = */ nullptr,
+    /* .backend_reset     = */ nullptr,
+    /* .copy_state        = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_dry(const struct llama_vocab * vocab, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const char** seq_breakers, size_t num_breakers) {
@@ -3614,6 +3853,8 @@ static struct llama_sampler_i llama_sampler_adaptive_p_i = {
     /* .backend_accept    = */ nullptr,
     /* .backend_apply     = */ nullptr,
     /* .backend_set_input = */ nullptr,
+    /* .backend_reset     = */ nullptr,
+    /* .copy_state        = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_adaptive_p(
@@ -3715,13 +3956,17 @@ static void llama_sampler_logit_bias_backend_apply(
 
     const size_t n = sctx->logit_bias.size();
 
-    sctx->inp_logit_bias = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, n);
-    ggml_set_name(sctx->inp_logit_bias, "logit_bias");
-    ggml_set_input(sctx->inp_logit_bias);
+    if (sctx->inp_logit_bias == nullptr) {
+        GGML_ASSERT(sctx->inp_logit_idxs == nullptr);
 
-    sctx->inp_logit_idxs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n);
-    ggml_set_name(sctx->inp_logit_idxs, "logit_idxs");
-    ggml_set_input(sctx->inp_logit_idxs);
+        sctx->inp_logit_bias = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, n);
+        ggml_set_name(sctx->inp_logit_bias, "logit_bias");
+        ggml_set_input(sctx->inp_logit_bias);
+
+        sctx->inp_logit_idxs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n);
+        ggml_set_name(sctx->inp_logit_idxs, "logit_idxs");
+        ggml_set_input(sctx->inp_logit_idxs);
+    }
 
     ggml_tensor * cur = ggml_fill(ctx, data->logits, 0.0f);
 
@@ -3756,10 +4001,18 @@ static void llama_sampler_logit_bias_backend_set_input(struct llama_sampler * sm
     ggml_backend_tensor_set(sctx->inp_logit_idxs, data_logit_idxs.data(), 0, ggml_nbytes(sctx->inp_logit_idxs));
 }
 
+static void llama_sampler_logit_bias_backend_reset(struct llama_sampler * smpl) {
+    auto * sctx = (llama_sampler_logit_bias *) smpl->ctx;
+    sctx->inp_logit_bias = nullptr;
+    sctx->inp_logit_idxs = nullptr;
+}
+
 static bool llama_sampler_logit_bias_backend_init(
         struct llama_sampler       * smpl,
-        ggml_backend_buffer_type_t   buft) {
+        ggml_backend_buffer_type_t   buft,
+        uint32_t                     n_outputs_max_per_seq) {
     GGML_UNUSED(buft);
+    GGML_UNUSED(n_outputs_max_per_seq);
 
     auto * sctx = (llama_sampler_logit_bias *) smpl->ctx;
 
@@ -3783,6 +4036,8 @@ static struct llama_sampler_i llama_sampler_logit_bias_i = {
     /* .backend_accept    = */ nullptr,
     /* .backend_apply     = */ llama_sampler_logit_bias_backend_apply,
     /* .backend_set_input = */ llama_sampler_logit_bias_backend_set_input,
+    /* .backend_reset     = */ llama_sampler_logit_bias_backend_reset,
+    /* .copy_state        = */ llama_sampler_backend_copy_state<llama_sampler_logit_bias>,
 };
 
 struct llama_sampler * llama_sampler_init_logit_bias(
@@ -4022,10 +4277,12 @@ static struct llama_sampler_i llama_sampler_infill_i = {
     /* .reset             = */ nullptr,
     /* .clone             = */ llama_sampler_infill_clone,
     /* .free              = */ llama_sampler_infill_free,
-    /* .backend_apply     = */ nullptr,
-    /* .backend_accept    = */ nullptr,
-    /* .backend_set_input = */ nullptr,
     /* .backend_init      = */ nullptr,
+    /* .backend_accept    = */ nullptr,
+    /* .backend_apply     = */ nullptr,
+    /* .backend_set_input = */ nullptr,
+    /* .backend_reset     = */ nullptr,
+    /* .copy_state        = */ nullptr,
 };
 
 struct llama_sampler * llama_sampler_init_infill(const struct llama_vocab * vocab) {
@@ -4039,6 +4296,32 @@ struct llama_sampler * llama_sampler_init_infill(const struct llama_vocab * voca
     );
 }
 
+void llama_sampler_copy(const struct llama_sampler * src, struct llama_sampler * dst) {
+    if (!src || !dst || src == dst) {
+        return;
+    }
+
+    GGML_ASSERT(src->iface == dst->iface && "llama_sampler_copy: cannot copy between different sampler types");
+
+    if (dst->iface->copy_state) {
+        dst->iface->copy_state(src, dst);
+        return;
+    }
+
+    // build a temporary sampler carrying src's current state
+    llama_sampler * tmp = llama_sampler_clone(src);
+
+    // free dst's old state (frees dst->ctx, including children for a chain)
+    if (dst->iface->free) {
+        dst->iface->free(dst);
+    }
+
+    // transplant tmp's state into dst, then destroy the (now empty) temp shell
+    dst->ctx = tmp->ctx;
+    tmp->ctx = nullptr;
+    delete tmp;
+}
+
 // utils
 
 uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl) {

src/llama-sampler.h

@@ -15,6 +15,8 @@ struct llama_sampler_chain {
     // has .backend_init() been called?
     bool is_init = false;
 
+    uint32_t n_nodes = 0;
+
     struct info {
         bool is_backend;
 
@@ -33,6 +35,9 @@ struct llama_sampler_chain {
     mutable int32_t n_sample;
 };
 
+uint32_t llama_sampler_backend_n_nodes(const llama_sampler * sampler);
+void llama_sampler_backend_begin(llama_sampler * sampler);
+
 struct llama_sampler * llama_sampler_init_dry_testing(
         float   dry_multiplier,
         float   dry_base,

src/llama.cpp

@@ -48,6 +48,8 @@ const char * llama_flash_attn_type_name(enum llama_flash_attn_type flash_attn_ty
 
 const char * llama_load_mode_name(enum llama_load_mode load_mode) {
     switch (load_mode) {
+        case LLAMA_LOAD_MODE_AUTO:
+            return "auto";
         case LLAMA_LOAD_MODE_NONE:
             return "none";
         case LLAMA_LOAD_MODE_MMAP:
@@ -63,11 +65,12 @@ const char * llama_load_mode_name(enum llama_load_mode load_mode) {
 }
 
 enum llama_load_mode llama_load_mode_from_str(const char * str) {
-    if (std::strcmp(str, "none") == 0)       { return LLAMA_LOAD_MODE_NONE;       }
-    if (std::strcmp(str, "mmap") == 0)       { return LLAMA_LOAD_MODE_MMAP;       }
-    if (std::strcmp(str, "mlock") == 0)      { return LLAMA_LOAD_MODE_MLOCK;      }
+    if (std::strcmp(str, "auto")       == 0) { return LLAMA_LOAD_MODE_AUTO;       }
+    if (std::strcmp(str, "none")       == 0) { return LLAMA_LOAD_MODE_NONE;       }
+    if (std::strcmp(str, "mmap")       == 0) { return LLAMA_LOAD_MODE_MMAP;       }
+    if (std::strcmp(str, "mlock")      == 0) { return LLAMA_LOAD_MODE_MLOCK;      }
     if (std::strcmp(str, "mmap+mlock") == 0) { return LLAMA_LOAD_MODE_MMAP_MLOCK; }
-    if (std::strcmp(str, "dio") == 0)        { return LLAMA_LOAD_MODE_DIRECT_IO;  }
+    if (std::strcmp(str, "dio")        == 0) { return LLAMA_LOAD_MODE_DIRECT_IO;  }
     throw std::invalid_argument(std::string("unknown load mode: ") + str);
 }
 

src/models/clip.cpp

@@ -0,0 +1,18 @@
+#include "models.h"
+
+// Stub to allow llama-quantize to open mmproj GGUFs
+
+[[noreturn]]
+void llama_model_clip::load_arch_hparams(llama_model_loader &) {
+    GGML_ABORT("CLIP is a quant-only stub; load_arch_hparams should not be called");
+}
+
+[[noreturn]]
+void llama_model_clip::load_arch_tensors(llama_model_loader &) {
+    GGML_ABORT("CLIP is a quant-only stub; load_arch_tensors should not be called");
+}
+
+[[noreturn]]
+std::unique_ptr<llm_graph_context> llama_model_clip::build_arch_graph(const llm_graph_params &) const {
+    GGML_ABORT("CLIP has no inference graph via llama_model dispatch; runtime lives in tools/mtmd/clip.cpp");
+}

src/models/exaone4.cpp

@@ -1,6 +1,9 @@
 #include "models.h"
 
 void llama_model_exaone4::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
+    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer");
+
     if (hparams.n_layer() == 64) {    // 32B
         hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
         hparams.n_swa = 4096;
@@ -15,9 +18,6 @@ void llama_model_exaone4::load_arch_hparams(llama_model_loader & ml) {
 
     ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa, false);
     ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
-    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS,        hparams.n_layer_nextn, false);
-
-    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer");
 
     switch (hparams.n_layer()) {
         case 30: type = LLM_TYPE_1_2B; break;

src/models/models.h

@@ -386,6 +386,22 @@ struct llama_model_bloom : public llama_model_base {
 };
 
 
+// Quant-only stub for mmproj GGUFs
+// none of these are ever called, they only exist to satisfy the llama_model_base interface
+struct llama_model_clip : public llama_model_base {
+    llama_model_clip(const struct llama_model_params & params) : llama_model_base(params) {}
+
+    [[noreturn]]
+    void load_arch_hparams(llama_model_loader & ml) override;
+
+    [[noreturn]]
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    [[noreturn]]
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
 struct llama_model_mpt : public llama_model_base {
     llama_model_mpt(const struct llama_model_params & params) : llama_model_base(params) {}
     void load_arch_hparams(llama_model_loader & ml) override;
@@ -1028,6 +1044,19 @@ struct llama_model_olmoe : public llama_model_base {
 };
 
 
+struct llama_model_muse_glimmer : public llama_model_base {
+    llama_model_muse_glimmer(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
 struct llama_model_openelm : public llama_model_base {
     llama_model_openelm(const struct llama_model_params & params) : llama_model_base(params) {}
     void load_arch_hparams(llama_model_loader & ml) override;
@@ -1461,6 +1490,10 @@ struct llama_model_nemotron_h_moe : public llama_model_nemotron_h {
 
     using graph = llama_model_nemotron_h::graph;
 
+    struct graph_mtp : public llm_graph_context {
+        graph_mtp(const llama_model & model, const llm_graph_params & params);
+    };
+
     std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 

src/models/muse-glimmer.cpp

@@ -0,0 +1,208 @@
+#include "models.h"
+
+void llama_model_muse_glimmer::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa);
+    ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING,     hparams.f_final_logit_softcapping, false);
+    ml.get_key(LLM_KV_LOGIT_SCALE,                 hparams.f_logit_scale);
+
+    hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
+    ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
+
+    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+    uint32_t swa_period = 4;
+    if (ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false)) {
+        hparams.set_swa_pattern(swa_period);
+    } else {
+        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer());
+    }
+
+    switch (hparams.n_layer()) {
+        case 52: type = LLM_TYPE_30B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_muse_glimmer::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd    = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0);
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        // Pre/post-attention norms (Muse Glimmer's `weight + 1` applied at conversion time).
+        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), {n_embd}, 0);
+        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
+
+        // Q/K/V/O projections.
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        // QK-norm. Weights are synthesized at conversion time to absorb `qk_scale_factor`.
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+
+        // Attention output gate: sigmoid(gate) * attn_out before o_proj (same as afmoe).
+        layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+
+        // Pre/post-FFN norms (FFN_PRE_NORM is aliased to LLM_TENSOR_FFN_NORM).
+        layer.ffn_norm      = create_tensor(tn(LLM_TENSOR_FFN_NORM,      "weight", i), {n_embd}, 0);
+        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
+
+        // Dense FFN (unlike afmoe, no MoE branches).
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+    }
+}
+
+llama_model_muse_glimmer::graph::graph(const llama_model & model, const llm_graph_params & params)
+    : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v();
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+
+    // Different to f_norm_rms_eps for post-attn / post-FFN norms
+    const float post_norm_eps = 1e-8f;
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+    inpL = build_norm(inpL, nullptr, nullptr, LLM_NORM_RMS, -1);
+    cb(inpL, "embd_norm", -1);
+
+    ggml_tensor * inp_pos = build_inp_pos();
+    auto * inp_attn = build_attn_inp_kv_iswa();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
+
+    for (int il = 0; il < n_layer; ++il) {
+        // expose per-layer residual for speculative drafts (see LLM_KV_TARGET_LAYERS).
+        res->t_layer_inp[il] = inpL;
+
+        const float freq_base_l  = model.get_rope_freq_base (cparams, il);
+        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
+
+        ggml_tensor * inpSA = inpL;
+
+        // RoPE runs on the SWA layers, NoPE on full ones.
+        const bool use_rope = hparams.is_swa(il);
+
+        // pre-attention norm (weight+1 folded at conversion time)
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention: attention output gate around SDPA (afmoe.cpp:147-191)
+        {
+            ggml_tensor * attn_inp = cur;  // save input for gate computation
+
+            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
+                    n_embd_head, n_head, n_head_kv, il);
+
+            // gate = wqkv_gate @ attn_inp (from pre-attn hidden state)
+            ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, attn_inp);
+            cb(gate, "attn_gate_proj", il);
+
+            // QK-norm. attn_q_norm weight was synthesized at conversion to broadcast
+            // qk_scale_factor across head_dim; attn_k_norm is identity (ones).
+            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+            cb(Qcur, "Qcur_normed", il);
+            cb(Kcur, "Kcur_normed", il);
+
+            if (use_rope) {
+                Qcur = ggml_rope_ext(
+                        ctx0, Qcur, inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
+                cb(Qcur, "Qcur_rope", il);
+
+                Kcur = ggml_rope_ext(
+                        ctx0, Kcur, inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
+                cb(Kcur, "Kcur_rope", il);
+            }
+
+            // SDPA. wo is deferred; the gate goes between attn_out and o_proj.
+            cur = build_attn(inp_attn,
+                    NULL, NULL, NULL,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+            cb(cur, "attn_out", il);
+
+            gate = ggml_sigmoid(ctx0, gate);
+            cb(gate, "attn_gate_sig", il);
+            cur = ggml_mul(ctx0, cur, gate);
+            cb(cur, "attn_gated", il);
+
+            cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s);
+            cb(cur, "attn_o_proj", il);
+        }
+
+        cur = ggml_rms_norm(ctx0, cur, post_norm_eps);
+        cur = ggml_mul(ctx0, cur, model.layers[il].attn_post_norm);
+        cb(cur, "attn_post_norm", il);
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // pre-FFN norm
+        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        // SwiGLU dense FFN
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up,   NULL, NULL,
+                model.layers[il].ffn_gate, NULL, NULL,
+                model.layers[il].ffn_down, NULL, NULL,
+                NULL,
+                LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(cur, "ffn_out", il);
+
+        cur = ggml_rms_norm(ctx0, cur, post_norm_eps);
+        cur = ggml_mul(ctx0, cur, model.layers[il].ffn_post_norm);
+        cb(cur, "ffn_post_norm", il);
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    // final norm
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head, followed by output multiplier
+    cur = build_lora_mm(model.output, cur, model.output_s);
+    cur = ggml_scale(ctx0, cur, hparams.f_logit_scale);
+
+    // Final logit tanh softcap (from gemma3.cpp).
+    if (hparams.f_final_logit_softcapping) {
+        cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_final_logit_softcapping);
+        cur = ggml_tanh(ctx0, cur);
+        cur = ggml_scale(ctx0, cur, hparams.f_final_logit_softcapping);
+    }
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
+
+std::unique_ptr<llm_graph_context> llama_model_muse_glimmer::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}

src/models/nemotron-h-moe.cpp

@@ -1,6 +1,156 @@
 #include "models.h"
 
 std::unique_ptr<llm_graph_context> llama_model_nemotron_h_moe::build_arch_graph(const llm_graph_params & params) const {
+    if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
+        return std::make_unique<graph_mtp>(*this, params);
+    }
     return std::make_unique<graph>(*this, params);
 }
 
+// MTP draft head for Nemotron-H MoE
+llama_model_nemotron_h_moe::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)
+    : llm_graph_context(params) {
+    GGML_ASSERT(hparams.n_layer_nextn == 1 && "NEMOTRON_H_MOE MTP currently supports a single MTP block");
+
+    const int64_t n_embd_head = hparams.n_embd_head_v();
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+
+    const int il = hparams.n_layer();
+    const auto & layer = model.layers[il];
+
+    GGML_ASSERT(layer.nextn.eh_proj && layer.nextn.enorm && layer.nextn.hnorm);
+    GGML_ASSERT(layer.ffn_gate_inp);
+
+    // token embedding weights
+    ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
+    GGML_ASSERT(tok_embd_w != nullptr && "NEMOTRON_H_MOE MTP requires token embeddings");
+
+    auto inp = std::make_unique<llm_graph_input_embd_h>(hparams.n_embd);
+
+    inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    ggml_set_input(inp->tokens);
+
+    inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens);
+    ggml_set_input(inp->embd);
+
+    ggml_tensor * tok_embd;
+    if (ubatch.token) {
+        tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
+    } else {
+        tok_embd = inp->embd;
+    }
+    cb(tok_embd, "mtp_tok_embd", il);
+
+    inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
+    ggml_set_input(inp->h);
+    ggml_set_name(inp->h, "mtp_h_input");
+
+    ggml_tensor * h_embd = inp->h;
+
+    res->add_input(std::move(inp));
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    // attention fills KV over all tokens, but the MoE is position-wise: gather output rows before
+    // it to save FFN compute (unless unmasked embeddings_nextn needs the full-length hidden state)
+    const bool emit_h_nextn    = cparams.embeddings_nextn;
+    const bool crop_before_ffn = inp_out_ids && (!emit_h_nextn || cparams.embeddings_nextn_masked);
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);
+    cb(h_norm, "mtp_hnorm", il);
+    ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il);
+    cb(e_norm, "mtp_enorm", il);
+
+    ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0);
+    cb(concat, "mtp_concat", il);
+
+    ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s);
+    cb(cur, "mtp_eh_proj", il);
+
+    // dense NoPE attention sub-layer (mtp.layers.0)
+    ggml_tensor * inpSA = cur;
+    cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
+    cb(cur, "mtp_attn_norm", il);
+
+    {
+        auto [Qcur, Kcur, Vcur] = build_qkv(layer, cur, n_embd_head, hparams.n_head(il), hparams.n_head_kv(il), il);
+        const float kq_scale = hparams.f_attention_scale == 0.0f
+            ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+        cur = build_attn(inp_attn, layer.wo, layer.wo_b, layer.wo_s,
+                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+        cb(cur, "mtp_attn_out", il);
+    }
+
+    cur = ggml_add(ctx0, cur, inpSA);
+    cb(cur, "mtp_attn_residual", il);
+
+    // gather the output rows here so the MoE FFN below only runs on the positions we keep
+    if (crop_before_ffn) {
+        cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+    }
+
+    // MoE FFN sub-layer (mtp.layers.1)
+    ggml_tensor * ffn_residual = cur;
+    cur = build_norm(cur, layer.attn_post_norm, nullptr, LLM_NORM_RMS, il);
+    cb(cur, "mtp_attn_post_norm", il);
+
+    {
+        ggml_tensor * router_logits = build_lora_mm(layer.ffn_gate_inp, cur);
+        cb(router_logits, "mtp_ffn_moe_logits", il);
+
+        ggml_tensor * moe_out =
+            build_moe_ffn(cur,
+                layer.ffn_gate_inp,
+                layer.ffn_up_exps,
+                nullptr, // no gate
+                layer.ffn_down_exps,
+                layer.ffn_exp_probs_b,
+                n_expert, n_expert_used,
+                LLM_FFN_RELU_SQR, hparams.expert_weights_norm,
+                hparams.expert_weights_scale,
+                LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID,
+                il,
+                router_logits, nullptr,
+                layer.ffn_up_exps_s,
+                nullptr, // no gate
+                layer.ffn_down_exps_s);
+        cb(moe_out, "mtp_ffn_moe_out", il);
+
+        ggml_tensor * ffn_shexp = build_ffn(cur,
+                layer.ffn_up_shexp,   NULL, layer.ffn_up_shexp_s,
+                NULL,                 NULL, NULL,
+                layer.ffn_down_shexp, NULL, layer.ffn_down_shexp_s,
+                NULL,
+                LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);
+        cb(ffn_shexp, "mtp_ffn_shexp", il);
+
+        cur = ggml_add(ctx0, moe_out, ffn_shexp);
+        cb(cur, "mtp_ffn_out", il);
+    }
+
+    cur = ggml_add(ctx0, cur, ffn_residual);
+    cb(cur, "mtp_post_ffn", il);
+
+    // final head norm: the MTP head has its own LayerNorm
+    GGML_ASSERT(layer.nextn.shared_head_norm && "NEMOTRON_H_MOE MTP: missing final head norm");
+    cur = build_norm(cur, layer.nextn.shared_head_norm, nullptr, LLM_NORM, -1);
+
+    cb(cur, "h_nextn", -1);
+    res->t_h_nextn = cur;
+
+    if (!crop_before_ffn && inp_out_ids) {
+        cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+    }
+
+    // LM head
+    ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output;
+    ggml_tensor * head_s = layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : model.output_s;
+    GGML_ASSERT(head_w != nullptr && "NEMOTRON_H_MOE MTP requires an output projection");
+    cur = build_lora_mm(head_w, cur, head_s);
+    cb(cur, "result_output", -1);
+
+    res->t_logits = cur;
+    ggml_build_forward_expand(gf, cur);
+}

src/models/nemotron-h.cpp

@@ -7,13 +7,18 @@ void llama_model_nemotron_h::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
     ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
 
+    // NextN/MTP: optional draft head appended as extra trailing block(s)
+    ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
+    GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_all");
+
     // A layer is recurrent IFF the n_head_kv value is set to 0 and
-    // the n_ff value is set to 0
-    for (uint32_t i = 0; i < hparams.n_layer(); ++i) {
-        hparams.is_recr_impl[i] = (hparams.n_head_kv(i) == 0 && hparams.n_ff(i) == 0);
+    // the n_ff value is set to 0. Appended MTP blocks are dense (non-recurrent)
+    for (uint32_t i = 0; i < hparams.n_layer_all; ++i) {
+        hparams.is_recr_impl[i] = i < hparams.n_layer() && hparams.n_head_kv(i) == 0 && hparams.n_ff(i) == 0;
     }
 
     ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,     hparams.f_norm_eps); // MTP head final_layernorm
 
     ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp,        false);
     ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp,      false);
@@ -30,9 +35,13 @@ void llama_model_nemotron_h::load_arch_hparams(llama_model_loader & ml) {
     }
 }
 
-void llama_model_nemotron_h::load_arch_tensors(llama_model_loader &) {
+void llama_model_nemotron_h::load_arch_tensors(llama_model_loader & ml) {
     LLAMA_LOAD_LOCALS;
 
+    const bool mtp_only    = hparams.n_layer_nextn > 0 && ml.get_weight("blk.0.attn_norm.weight") == nullptr;
+    const int  trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
+    const int  mtp_flags   = !ml.load_mtp ? TENSOR_SKIP : 0;
+
     // mamba2 Mixer SSM params
     // NOTE: int64_t for tensor dimensions
     const int64_t d_conv     = hparams.ssm_d_conv;
@@ -60,61 +69,94 @@ void llama_model_nemotron_h::load_arch_tensors(llama_model_loader &) {
         auto & layer = layers[i];
 
         // all blocks use the attn norm
-        layer.attn_norm  = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+        layer.attn_norm  = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, trunk_flags);
 
         if (hparams.is_recr(i)) {
             // ssm layers
-            layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, d_in_proj}, 0);
+            layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, d_in_proj}, trunk_flags);
 
-            layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner + 2*n_group*d_state}, 0);
+            layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner + 2*n_group*d_state}, trunk_flags);
             layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner + 2*n_group*d_state}, TENSOR_NOT_REQUIRED);
 
-            layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {n_ssm_head}, 0);
+            layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {n_ssm_head}, trunk_flags);
 
             // no "weight" suffix for these
-            layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_ssm_head}, 0);
-            layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, n_ssm_head}, 0);
+            layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_ssm_head}, trunk_flags);
+            layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, n_ssm_head}, trunk_flags);
 
-            layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {d_inner / n_group, n_group}, 0);
+            layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {d_inner / n_group, n_group}, trunk_flags);
 
             // out_proj
-            layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);
+            layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, trunk_flags);
         } else if (hparams.n_ff(i) == 0) {
             // attention layers (with optional bias)
             const int64_t n_head_i = hparams.n_head(i);
             const int64_t n_embd_k_gqa_i = hparams.n_embd_k_gqa(i);
             const int64_t n_embd_v_gqa_i = hparams.n_embd_v_gqa(i);
-            create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head_i, n_embd_k_gqa_i, n_embd_v_gqa_i, 0);
-            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head_i, n_embd}, 0);
+            create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head_i, n_embd_k_gqa_i, n_embd_v_gqa_i, trunk_flags);
+            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head_i, n_embd}, trunk_flags);
             layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
         }  else {
             if (n_expert != 0) {
                 const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
                 const int64_t n_ff_shexp = hparams.n_ff_shexp;
 
-                layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert}, 0);
-                layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert         }, 0);
+                layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert}, trunk_flags);
+                layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert         }, trunk_flags);
 
                 // MoE branch
                 layer.ffn_latent_down = create_tensor(tn(LLM_TENSOR_FFN_LATENT_DOWN, "weight", i), {n_embd, moe_n_embd}, TENSOR_NOT_REQUIRED);
                 layer.ffn_latent_up   = create_tensor(tn(LLM_TENSOR_FFN_LATENT_UP,   "weight", i), {moe_n_embd, n_embd}, TENSOR_NOT_REQUIRED);
 
-                layer.ffn_down_exps   = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   moe_n_embd, n_expert}, 0);
-                layer.ffn_up_exps     = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {moe_n_embd, n_ff_exp, n_expert}, 0);
+                layer.ffn_down_exps   = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   moe_n_embd, n_expert}, trunk_flags);
+                layer.ffn_up_exps     = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {moe_n_embd, n_ff_exp, n_expert}, trunk_flags);
 
                 // Shared expert branch
-                layer.ffn_down_shexp  = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
-                layer.ffn_up_shexp    = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp}, 0);
+                layer.ffn_down_shexp  = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, trunk_flags);
+                layer.ffn_up_shexp    = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp}, trunk_flags);
 
             } else {
                 // mlp layers
-                layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  hparams.n_ff(i), n_embd}, 0);
-                layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   hparams.n_ff(i)}, 0);
+                layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  hparams.n_ff(i), n_embd}, trunk_flags);
+                layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   hparams.n_ff(i)}, trunk_flags);
                 layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
                 layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias",   i), {hparams.n_ff(i)}, TENSOR_NOT_REQUIRED);
             }
         }
     }
+
+    // NextN/MTP draft head: each predict layer folds an attention sub-layer and a MoE
+    // sub-layer into a single trailing block
+    for (int i = n_layer; i < n_layer_all; ++i) {
+        auto & layer = layers[i];
+
+        const int64_t n_head_i       = hparams.n_head(i);
+        const int64_t n_embd_k_gqa_i = hparams.n_embd_k_gqa(i);
+        const int64_t n_embd_v_gqa_i = hparams.n_embd_v_gqa(i);
+        const int64_t n_ff_exp       = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+        const int64_t n_ff_shexp     = hparams.n_ff_shexp;
+
+        // NextN input-fusion tensors
+        layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM,            "weight", i), {n_embd}, mtp_flags);
+        layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM,            "weight", i), {n_embd}, mtp_flags);
+        layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ,          "weight", i), {2*n_embd, n_embd}, mtp_flags);
+        layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), {n_embd}, mtp_flags);
+
+        // attention sub-layer
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, mtp_flags);
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head_i, n_embd_k_gqa_i, n_embd_v_gqa_i, mtp_flags);
+        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head_i, n_embd}, mtp_flags);
+        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias",   i), {n_embd}, mtp_flags | TENSOR_NOT_REQUIRED);
+
+        // MoE sub-layer
+        layer.attn_post_norm  = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM,  "weight", i), {n_embd}, mtp_flags);
+        layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,    "weight", i), {n_embd, n_expert}, mtp_flags);
+        layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias",   i), {n_expert}, mtp_flags);
+        layer.ffn_down_exps   = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS,   "weight", i), {n_ff_exp,   moe_n_embd, n_expert}, mtp_flags);
+        layer.ffn_up_exps     = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,     "weight", i), {moe_n_embd, n_ff_exp,   n_expert}, mtp_flags);
+        layer.ffn_down_shexp  = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP,  "weight", i), {n_ff_shexp, n_embd}, mtp_flags);
+        layer.ffn_up_shexp    = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,    "weight", i), {n_embd, n_ff_shexp}, mtp_flags);
+    }
 }
 
 std::unique_ptr<llm_graph_context> llama_model_nemotron_h::build_arch_graph(const llm_graph_params & params) const {
@@ -153,7 +195,7 @@ llama_model_nemotron_h::graph::graph(const llama_model & model, const llm_graph_
             cur = build_ffn_layer(cur, model, il);
         }
 
-        if (il == n_layer - 1 && inp_out_ids) {
+        if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
             cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
             inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
         }
@@ -170,6 +212,14 @@ llama_model_nemotron_h::graph::graph(const llama_model & model, const llm_graph_
 
     cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
 
+    // seed for the MTP/NextN draft head
+    cb(cur, "h_nextn", -1);
+    res->t_h_nextn = cur;
+
+    if (!cparams.embeddings_nextn_masked && inp_out_ids) {
+        cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+    }
+
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 

tests/peg-parser/test-json-parser.cpp

@@ -77,6 +77,30 @@ void test_json_parser(testing &t) {
         t.assert_equal("result_is_need_more_input", true, result.need_more_input());
     });
 
+    // Test need_more_input() parsing - incomplete escape sequence in a string value
+    t.test("need_more_input() parsing - incomplete escape sequence", [](testing &t) {
+        auto json = build_peg_parser([](common_peg_parser_builder & p) { return p.json(); });
+
+        std::vector<std::string> inputs {
+            R"({"text": "hello\)",     // dangling backslash
+            R"({"text": "hello\u)",    // incomplete unicode escape sequence
+            R"({"text": "hello\u00)",
+        };
+
+        for (const auto & input : inputs) {
+            t.test(input, [&](testing &t) {
+                common_peg_parse_context ctx(input, COMMON_PEG_PARSE_FLAG_LENIENT);
+
+                auto result = json.parse(ctx);
+
+                t.assert_equal("result_is_need_more_input", true, result.need_more_input());
+
+                // the incomplete escape sequence is not part of the partial value
+                t.assert_equal("result_end", input.find('\\'), result.end);
+            });
+        }
+    });
+
     t.test("object member", [](testing &t) {
         auto parser = build_peg_parser([](common_peg_parser_builder & p) {
             return p.json_member("name", "\"" + p.chars("[a-z]") + "\"");

tests/test-arg-parser.cpp

@@ -2,7 +2,9 @@
 #include "common.h"
 #include "download.h"
 #include "llama.h"
+#include "speculative.h"
 
+#include <limits>
 #include <string>
 #include <vector>
 #include <sstream>
@@ -14,6 +16,34 @@
 static void test(void) {
     common_params params;
 
+    auto assert_output_limits = [](int32_t n_batch, int32_t n_parallel, int32_t n_draft,
+                                   int32_t total, int32_t per_seq) {
+        const auto limits = common_speculative_get_output_limits(n_batch, n_parallel, n_draft);
+        assert(limits.total == total);
+        assert(limits.per_seq == per_seq);
+    };
+
+    assert_output_limits(16, 2,  3, 8, 4);
+    assert_output_limits(16, 2, -1, 2, 1);
+    assert_output_limits( 6, 2,  3, 6, 4);
+    assert_output_limits( 2, 1,  3, 2, 2);
+    assert_output_limits(
+            std::numeric_limits<int32_t>::max(),
+            std::numeric_limits<int32_t>::max(),
+            std::numeric_limits<int32_t>::max(),
+            std::numeric_limits<int32_t>::max(),
+            std::numeric_limits<int32_t>::max());
+
+    {
+        common_params base;
+        base.n_parallel = 4;
+        base.n_outputs_max_per_seq = 8;
+
+        const auto draft = common_base_params_to_speculative(base);
+        assert(draft.n_outputs_max == 4);
+        assert(draft.n_outputs_max_per_seq == 1);
+    }
+
     printf("test-arg-parser: make sure there is no duplicated arguments in any examples\n\n");
     for (int ex = 0; ex < LLAMA_EXAMPLE_COUNT; ex++) {
         try {

tests/test-backend-ops.cpp

@@ -6712,19 +6712,26 @@ struct test_roll : public test_case {
     const int shift1;
     const int shift3;
     const int shift4;
+    const bool permute;
 
     std::string vars() override {
-        return VARS_TO_STR4(shift0, shift1, shift3, shift4);
+        return VARS_TO_STR5(shift0, shift1, shift3, shift4, permute);
     }
 
-    test_roll(int shift0 = 3, int shift1 = -2, int shift3 = 1, int shift4 = -1)
-        : shift0(shift0), shift1(shift1), shift3(shift3), shift4(shift4) {}
+    test_roll(int shift0 = 3, int shift1 = -2, int shift3 = 1, int shift4 = -1, bool permute = false)
+        : shift0(shift0), shift1(shift1), shift3(shift3), shift4(shift4), permute(permute) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         int64_t ne[4] = {10, 5, 4, 3};
         ggml_tensor * a = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
         ggml_set_name(a, "a");
 
+        if (permute) {
+            // ggml_roll only requires nb[0] == type size, so a permuted src is valid
+            a = ggml_permute(ctx, a, 0, 2, 1, 3);
+            ggml_set_name(a, "a_permuted");
+        }
+
         ggml_tensor * out = ggml_roll(ctx, a, shift0, shift1, shift3, shift4);
         ggml_set_name(out, "out");
 
@@ -9459,6 +9466,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_pad_reflect_1d());
     test_cases.emplace_back(new test_pad_reflect_1d(GGML_TYPE_F32, {3000, 384, 4, 1}));
     test_cases.emplace_back(new test_roll());
+    test_cases.emplace_back(new test_roll(3, -2, 1, -1, true));
     test_cases.emplace_back(new test_arange());
     test_cases.emplace_back(new test_arange(GGML_TYPE_F32, 0.0f, 1048576.0f, 1.0f));
     test_cases.emplace_back(new test_timestep_embedding());

tests/test-backend-sampler.cpp

@@ -14,6 +14,7 @@
 #include <fstream>
 #include <functional>
 #include <map>
+#include <random>
 #include <string>
 #include <unordered_map>
 #include <unordered_set>
@@ -80,7 +81,13 @@ struct test_context {
     std::unordered_map<llama_seq_id, int32_t> seq_positions;
     std::unordered_map<llama_seq_id, int32_t> last_batch_info;
 
-    test_context(const test_params & params, std::vector<llama_sampler_seq_config> & configs, int32_t n_seq_max = -1) {
+    test_context(
+            const test_params & params,
+            std::vector<llama_sampler_seq_config> & configs,
+            int32_t n_seq_max = -1,
+            uint32_t n_outputs_max = 0,
+            uint32_t n_ubatch = 0,
+            uint32_t n_outputs_max_per_seq = 1) {
         auto * model = params.model.get();
 
         GGML_ASSERT(model);
@@ -89,6 +96,11 @@ struct test_context {
         llama_context_params cparams = llama_context_default_params();
         cparams.n_ctx = 512;
         cparams.n_batch = 512;
+        if (n_ubatch > 0) {
+            cparams.n_ubatch = n_ubatch;
+        }
+        cparams.n_outputs_max = n_outputs_max;
+        cparams.n_outputs_max_per_seq = n_outputs_max_per_seq;
         cparams.samplers = configs.data();
         cparams.n_samplers = configs.size();
         cparams.kv_unified = true;
@@ -262,6 +274,66 @@ struct test_context {
     }
 };
 
+struct test_single_output_backend_sampler {
+    bool backend_initialized = false;
+    uint32_t backend_outputs_max_per_seq = 0;
+    int backend_apply_count = 0;
+    int apply_count = 0;
+};
+
+static const char * test_single_output_backend_sampler_name(const llama_sampler * /*smpl*/) {
+    return "single-output-backend";
+}
+
+static void test_single_output_backend_sampler_apply(
+        llama_sampler * smpl, llama_token_data_array * /*cur_p*/) {
+    auto * ctx = (test_single_output_backend_sampler *) smpl->ctx;
+    ctx->apply_count++;
+}
+
+static void test_single_output_backend_sampler_free(llama_sampler * smpl) {
+    delete (test_single_output_backend_sampler *) smpl->ctx;
+}
+
+static bool test_single_output_backend_sampler_backend_init(
+        llama_sampler * smpl, ggml_backend_buffer_type_t /*buft*/, uint32_t n_outputs_max_per_seq) {
+    auto * ctx = (test_single_output_backend_sampler *) smpl->ctx;
+    ctx->backend_outputs_max_per_seq = n_outputs_max_per_seq;
+    if (n_outputs_max_per_seq > 1) {
+        return false;
+    }
+    ctx->backend_initialized = true;
+    return true;
+}
+
+static void test_single_output_backend_sampler_backend_apply(
+        llama_sampler * smpl, ggml_context * /*ctx*/, ggml_cgraph * /*gf*/, llama_sampler_data * /*data*/) {
+    auto * ctx = (test_single_output_backend_sampler *) smpl->ctx;
+    ctx->backend_apply_count++;
+}
+
+static llama_sampler_i test_single_output_backend_sampler_i = {
+    /* .name              = */ test_single_output_backend_sampler_name,
+    /* .accept            = */ nullptr,
+    /* .apply             = */ test_single_output_backend_sampler_apply,
+    /* .reset             = */ nullptr,
+    /* .clone             = */ nullptr,
+    /* .free              = */ test_single_output_backend_sampler_free,
+    /* .backend_init      = */ test_single_output_backend_sampler_backend_init,
+    /* .backend_accept    = */ nullptr,
+    /* .backend_apply     = */ test_single_output_backend_sampler_backend_apply,
+    /* .backend_set_input = */ nullptr,
+    /* .backend_reset     = */ nullptr,
+    /* .copy_state        = */ nullptr,
+};
+
+static llama_sampler * test_single_output_backend_sampler_init(
+        test_single_output_backend_sampler ** sampler_ctx) {
+    auto * ctx = new test_single_output_backend_sampler;
+    *sampler_ctx = ctx;
+    return llama_sampler_init(&test_single_output_backend_sampler_i, ctx);
+}
+
 static void test_backend_greedy_sampling(const test_params & params) {
     const int seq_id = 0;
 
@@ -661,7 +733,7 @@ static void test_backend_multi_sequence_sampling(const test_params & params) {
 }
 
 static void test_backend_dist_sampling(const test_params & params) {
-    const int seq_id = 189;
+    const int seq_id = 0;
     const int32_t seed = 88;
 
     struct llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
@@ -1527,43 +1599,398 @@ static void test_backend_cpu_mixed_batch(const test_params & params) {
     printf("backend-cpu mixed batch test PASSED\n");
 }
 
-static void test_backend_max_outputs(const test_params & params) {
-    const int seq_id = 0;
-    const int32_t seed = 88;
+static void test_backend_multi_output_limit(const test_params & params) {
+    const llama_seq_id seq_id = 0;
 
-    llama_sampler_chain_params backend_chain_params = llama_sampler_chain_default_params();
-    llama_sampler_ptr backend_sampler_chain(llama_sampler_chain_init(backend_chain_params));
-    llama_sampler_chain_add(backend_sampler_chain.get(), llama_sampler_init_dist(seed));
-    std::vector<llama_sampler_seq_config> backend_sampler_configs = {{ seq_id, backend_sampler_chain.get() }};
+    llama_sampler_ptr chain(llama_sampler_chain_init(llama_sampler_chain_default_params()));
+    llama_sampler_chain_add(chain.get(), llama_sampler_init_dist(88));
+    std::vector<llama_sampler_seq_config> configs = {{ seq_id, chain.get() }};
+    test_context test_ctx(params, configs, 1, 3, 0, 2);
 
-    test_context test_ctx(params, backend_sampler_configs);
-
-    llama_batch batch = llama_batch_init(512, 0, 1);
-    std::string prompt = "Hello";
-
-    std::vector<llama_token> tokens;
-    tokens.push_back(llama_vocab_bos(test_ctx.vocab));
-
-    std::vector<llama_token> prompt_tokens(32);
-    int n_tokens = llama_tokenize(test_ctx.vocab, prompt.c_str(), prompt.length(),
-                                   prompt_tokens.data(), prompt_tokens.size(),
-                                   false, false);
-    for (int i = 0; i < n_tokens; i++) {
-        tokens.push_back(prompt_tokens[i]);
+    llama_batch batch = llama_batch_init(3, 0, 1);
+    for (int i = 0; i < 3; ++i) {
+        common_batch_add(batch, llama_vocab_bos(test_ctx.vocab), i, { seq_id }, true);
     }
 
-    for (size_t i = 0; i < tokens.size(); i++) {
-        // set all tokens as output to trigger error
-        common_batch_add(batch, tokens[i], i, { seq_id }, true);
-    }
-
-    printf(">>> test_max_outputs expected error start:\n");
+    printf(">>> test_backend_multi_output_limit expected error start:\n");
     const int ret = llama_decode(test_ctx.ctx.get(), batch);
-    GGML_ASSERT(ret != 0 && "llama_decode should not succeed multiple outputs per sequence");
-    printf("<<< test_max_outputs expected error end.\n");
+    GGML_ASSERT(ret != 0 && "llama_decode should reject outputs above the per-sequence limit");
+    printf("<<< test_backend_multi_output_limit expected error end.\n");
+
     llama_batch_free(batch);
 
-    printf("backend max outputs test PASSED\n");
+    printf("backend multi-output limit test PASSED\n");
+}
+
+static void test_backend_multi_sequence_multi_output_dist(const test_params & params) {
+    const llama_vocab * vocab = llama_model_get_vocab(params.model.get());
+    const int32_t n_vocab = llama_vocab_n_tokens(vocab);
+    const uint32_t seeds[] = { 88, 1337 };
+    // reduce the chance that swapped random inputs select the same token
+    const float temp = 10.0f;
+
+    llama_sampler_ptr chain_0(llama_sampler_chain_init(llama_sampler_chain_default_params()));
+    llama_sampler_ptr chain_1(llama_sampler_chain_init(llama_sampler_chain_default_params()));
+    llama_sampler_chain_add(chain_0.get(), llama_sampler_init_temp(temp));
+    llama_sampler_chain_add(chain_0.get(), llama_sampler_init_dist(seeds[0]));
+    llama_sampler_chain_add(chain_1.get(), llama_sampler_init_temp(temp));
+    llama_sampler_chain_add(chain_1.get(), llama_sampler_init_dist(seeds[1]));
+    std::vector<llama_sampler_seq_config> configs = {
+        { 0, chain_0.get() },
+        { 1, chain_1.get() },
+    };
+    test_context test_ctx(params, configs, 2, 4, 0, 2);
+
+    std::vector<llama_sampler_seq_config> reference_configs;
+    test_context reference_ctx(params, reference_configs, 2, 4);
+
+    const llama_token seq_tokens[2][2] = {
+        { llama_vocab_bos(vocab), llama_vocab_eos(vocab) },
+        { llama_vocab_eos(vocab), llama_vocab_bos(vocab) },
+    };
+
+    llama_batch batch = llama_batch_init(4, 0, 1);
+    for (int pos = 0; pos < 2; ++pos) {
+        common_batch_add(batch, seq_tokens[0][pos], pos, { 0 }, true);
+        common_batch_add(batch, seq_tokens[1][pos], pos, { 1 }, true);
+    }
+
+    GGML_ASSERT(llama_decode(test_ctx.ctx.get(), batch) == 0);
+    GGML_ASSERT(llama_decode(reference_ctx.ctx.get(), batch) == 0);
+
+    std::mt19937 reference_rngs[] = {
+        std::mt19937(seeds[0]),
+        std::mt19937(seeds[1]),
+    };
+    std::uniform_real_distribution<double> reference_dist(0.0, 1.0);
+
+    for (int i = 0; i < batch.n_tokens; ++i) {
+        const llama_seq_id seq_id = batch.seq_id[i][0];
+        GGML_ASSERT(seq_id == 0 || seq_id == 1);
+
+        llama_sampler * chain = seq_id == 0 ? chain_0.get() : chain_1.get();
+        const llama_token backend_token = llama_sampler_sample(chain, test_ctx.ctx.get(), i);
+        const float * sampled_logits = llama_get_sampled_logits_ith(test_ctx.ctx.get(), i);
+        const float * sampled_probs = llama_get_sampled_probs_ith(test_ctx.ctx.get(), i);
+        const uint32_t n_logits = llama_get_sampled_logits_count_ith(test_ctx.ctx.get(), i);
+        const uint32_t n_probs = llama_get_sampled_probs_count_ith(test_ctx.ctx.get(), i);
+        const float * reference_logits = llama_get_logits_ith(reference_ctx.ctx.get(), i);
+
+        GGML_ASSERT(backend_token >= 0 && backend_token < n_vocab);
+        GGML_ASSERT(sampled_logits != nullptr);
+        GGML_ASSERT(sampled_probs != nullptr);
+        GGML_ASSERT(reference_logits != nullptr);
+        GGML_ASSERT(n_logits == (uint32_t) n_vocab);
+        GGML_ASSERT(n_probs == (uint32_t) n_vocab);
+
+        float prob_sum = 0.0f;
+        float cumsum_before = 0.0f;
+        for (llama_token token = 0; token < n_vocab; ++token) {
+            const float expected_logit = reference_logits[token] / temp;
+            const float tolerance = 1e-4f * std::max(1.0f, std::fabs(expected_logit));
+            GGML_ASSERT(std::fabs(sampled_logits[token] - expected_logit) <= tolerance);
+            GGML_ASSERT(std::isfinite(sampled_probs[token]));
+            GGML_ASSERT(sampled_probs[token] >= 0.0f);
+
+            prob_sum += sampled_probs[token];
+            if (token < backend_token) {
+                cumsum_before += sampled_probs[token];
+            }
+        }
+
+        GGML_ASSERT(std::fabs(prob_sum - 1.0f) <= 1e-3f);
+
+        const float rnd = reference_dist(reference_rngs[seq_id]);
+        const float cumsum_sampled = cumsum_before + sampled_probs[backend_token];
+        GGML_ASSERT(rnd >= cumsum_before - 1e-4f);
+        GGML_ASSERT(rnd <= cumsum_sampled + 1e-4f);
+    }
+
+    llama_batch_free(batch);
+
+    printf("backend multi-sequence multi-output dist test PASSED\n");
+}
+
+static void test_backend_multi_output_dist_transaction(const test_params & params) {
+    const llama_seq_id seq_id = 0;
+    const uint32_t seed = 95;
+    const llama_vocab * vocab = llama_model_get_vocab(params.model.get());
+
+    llama_sampler_ptr chain(llama_sampler_chain_init(llama_sampler_chain_default_params()));
+    llama_sampler_chain_add(chain.get(), llama_sampler_init_temp(10.0f));
+    llama_sampler_chain_add(chain.get(), llama_sampler_init_dist(seed));
+    std::vector<llama_sampler_seq_config> configs = {{ seq_id, chain.get() }};
+    test_context test_ctx(params, configs, 1, 3, 2, 3);
+
+    auto verify_random = [&](int32_t row, float rnd, bool accept = true) {
+        const llama_token token = accept ?
+            llama_sampler_sample(chain.get(), test_ctx.ctx.get(), row) :
+            llama_get_sampled_token_ith(test_ctx.ctx.get(), row);
+        const float * probs = llama_get_sampled_probs_ith(test_ctx.ctx.get(), row);
+
+        GGML_ASSERT(token >= 0 && token < llama_vocab_n_tokens(vocab));
+        GGML_ASSERT(probs != nullptr);
+
+        float cumsum_before = 0.0f;
+        for (llama_token i = 0; i < token; ++i) {
+            cumsum_before += probs[i];
+        }
+
+        const float cumsum_sampled = cumsum_before + probs[token];
+        GGML_ASSERT(rnd >= cumsum_before - 1e-4f);
+        GGML_ASSERT(rnd <= cumsum_sampled + 1e-4f);
+    };
+
+    std::mt19937 rng(seed);
+    std::uniform_real_distribution<double> dist(0.0, 1.0);
+    float randoms[3];
+    for (float & rnd : randoms) {
+        rnd = dist(rng);
+    }
+
+    int32_t pos = 0;
+    auto decode = [&]() {
+        llama_batch batch = llama_batch_init(3, 0, 1);
+        for (int32_t i = 0; i < 3; ++i) {
+            common_batch_add(batch, llama_vocab_bos(vocab), pos++, { seq_id }, true);
+        }
+        GGML_ASSERT(llama_decode(test_ctx.ctx.get(), batch) == 0);
+        return batch;
+    };
+
+    llama_batch batch = decode();
+    verify_random(0, randoms[0], false);
+    llama_batch_free(batch);
+
+    batch = decode();
+    verify_random(0, randoms[0]);
+    verify_random(1, randoms[1]);
+    llama_batch_free(batch);
+
+    batch = decode();
+    llama_sampler_ptr saved(llama_sampler_clone(chain.get()));
+    verify_random(0, randoms[2]);
+    llama_batch_free(batch);
+
+    llama_sampler_copy(saved.get(), chain.get());
+
+    batch = decode();
+    verify_random(0, randoms[2]);
+    llama_batch_free(batch);
+
+    printf("backend multi-output dist transaction test PASSED\n");
+}
+
+static void test_backend_multi_output_sampling_chain(const test_params & params) {
+    const llama_seq_id seq_id = 0;
+    const uint32_t seed = 88;
+    const float p = 0.9f;
+    const float temp = 0.8f;
+    const float cdf_epsilon = 1e-4f;
+    const llama_vocab * vocab = llama_model_get_vocab(params.model.get());
+    const int32_t n_vocab = llama_vocab_n_tokens(vocab);
+    const uint32_t k = std::min<uint32_t>(512, n_vocab);
+    const llama_logit_bias bias = { llama_vocab_bos(vocab), -0.1f };
+
+    auto make_filter_chain = [&]() {
+        llama_sampler_ptr result(llama_sampler_chain_init(llama_sampler_chain_default_params()));
+        llama_sampler_chain_add(result.get(), llama_sampler_init_logit_bias(n_vocab, 1, &bias));
+        llama_sampler_chain_add(result.get(), llama_sampler_init_top_k(k));
+        llama_sampler_chain_add(result.get(), llama_sampler_init_top_p(p, 1));
+        llama_sampler_chain_add(result.get(), llama_sampler_init_min_p(0.01f, 1));
+        llama_sampler_chain_add(result.get(), llama_sampler_init_temp(temp));
+        return result;
+    };
+
+    llama_sampler_ptr chain = make_filter_chain();
+    llama_sampler_chain_add(chain.get(), llama_sampler_init_dist(seed));
+    std::vector<llama_sampler_seq_config> configs = {{ seq_id, chain.get() }};
+    test_context test_ctx(params, configs, 1, 2, 2, 2);
+
+    std::vector<llama_sampler_seq_config> reference_configs;
+    test_context reference_ctx(params, reference_configs, 1, 2, 2);
+
+    llama_sampler_ptr reference_bias(llama_sampler_init_logit_bias(n_vocab, 1, &bias));
+    llama_sampler_ptr reference_top_k(llama_sampler_init_top_k(k));
+    llama_sampler_ptr reference_top_p(llama_sampler_init_top_p(p, 1));
+    llama_sampler_ptr reference_min_p(llama_sampler_init_min_p(0.01f, 1));
+    llama_sampler_ptr reference_temp(llama_sampler_init_temp(temp));
+    std::vector<llama_token_data> reference_data(n_vocab);
+
+    auto make_batch = [&](int32_t pos) {
+        llama_batch batch = llama_batch_init(2, 0, 1);
+        for (int i = 0; i < 2; ++i) {
+            common_batch_add(batch, llama_vocab_bos(vocab), pos + i, { seq_id }, true);
+        }
+        return batch;
+    };
+
+    llama_batch batch = make_batch(0);
+    GGML_ASSERT(llama_decode(test_ctx.ctx.get(), batch) == 0);
+    GGML_ASSERT(llama_decode(reference_ctx.ctx.get(), batch) == 0);
+
+    for (int i = 0; i < batch.n_tokens; ++i) {
+        const llama_token backend_token = llama_sampler_sample(chain.get(), test_ctx.ctx.get(), i);
+        const float * sampled_logits = llama_get_sampled_logits_ith(test_ctx.ctx.get(), i);
+        const float * sampled_probs = llama_get_sampled_probs_ith(test_ctx.ctx.get(), i);
+        const llama_token * sampled_candidates = llama_get_sampled_candidates_ith(test_ctx.ctx.get(), i);
+        const uint32_t n_logits = llama_get_sampled_logits_count_ith(test_ctx.ctx.get(), i);
+        const uint32_t n_probs = llama_get_sampled_probs_count_ith(test_ctx.ctx.get(), i);
+        const uint32_t n_candidates = llama_get_sampled_candidates_count_ith(test_ctx.ctx.get(), i);
+        const float * reference_logits = llama_get_logits_ith(reference_ctx.ctx.get(), i);
+
+        GGML_ASSERT(backend_token >= 0 && backend_token < n_vocab);
+        GGML_ASSERT(sampled_logits != nullptr);
+        GGML_ASSERT(sampled_probs != nullptr);
+        GGML_ASSERT(sampled_candidates != nullptr);
+        GGML_ASSERT(reference_logits != nullptr);
+        GGML_ASSERT(n_logits == k);
+        GGML_ASSERT(n_probs == n_logits);
+        GGML_ASSERT(n_candidates == n_logits);
+
+        for (llama_token token = 0; token < n_vocab; ++token) {
+            reference_data[token] = { token, reference_logits[token], 0.0f };
+        }
+
+        llama_token_data_array reference = {
+            /* .data     = */ reference_data.data(),
+            /* .size     = */ reference_data.size(),
+            /* .selected = */ LLAMA_TOKEN_NULL,
+            /* .sorted   = */ false,
+        };
+
+        llama_sampler_apply(reference_bias.get(), &reference);
+        llama_sampler_apply(reference_top_k.get(), &reference);
+        llama_sampler_apply(reference_top_p.get(), &reference);
+        GGML_ASSERT(reference.size > 0);
+
+        float cdf = 0.0f;
+        for (size_t j = 0; j < reference.size; ++j) {
+            cdf += reference.data[j].p;
+        }
+        const float cdf_before = cdf - reference.data[reference.size - 1].p;
+        const float boundary_distance = std::min(std::fabs(cdf_before - p), std::fabs(cdf - p));
+
+        llama_sampler_apply(reference_min_p.get(), &reference);
+        llama_sampler_apply(reference_temp.get(), &reference);
+
+        std::unordered_map<llama_token, float> reference_by_id;
+        for (size_t j = 0; j < reference.size; ++j) {
+            reference_by_id.emplace(reference.data[j].id, reference.data[j].logit);
+        }
+        size_t n_backend_only = 0;
+        int32_t sampled_index = -1;
+        float prob_sum = 0.0f;
+
+        for (uint32_t j = 0; j < n_logits; ++j) {
+            GGML_ASSERT(sampled_candidates[j] >= 0 && sampled_candidates[j] < n_vocab);
+            GGML_ASSERT(std::isfinite(sampled_probs[j]));
+            GGML_ASSERT(sampled_probs[j] >= 0.0f);
+            prob_sum += sampled_probs[j];
+
+            if (sampled_candidates[j] == backend_token) {
+                sampled_index = j;
+            }
+            if (!std::isfinite(sampled_logits[j])) {
+                GGML_ASSERT(std::isinf(sampled_logits[j]) && sampled_logits[j] < 0.0f);
+                GGML_ASSERT(sampled_probs[j] == 0.0f);
+                continue;
+            }
+
+            const auto match = reference_by_id.find(sampled_candidates[j]);
+            if (match == reference_by_id.end()) {
+                ++n_backend_only;
+                continue;
+            }
+
+            const float tolerance = 1e-4f * std::max(1.0f, std::fabs(match->second));
+            GGML_ASSERT(std::fabs(sampled_logits[j] - match->second) <= tolerance);
+            reference_by_id.erase(match);
+        }
+
+        const size_t n_reference_only = reference_by_id.size();
+
+        if (n_backend_only != 0 || n_reference_only != 0) {
+            GGML_ASSERT(n_backend_only <= 1);
+            GGML_ASSERT(n_reference_only <= 1);
+            GGML_ASSERT(boundary_distance <= cdf_epsilon);
+        }
+
+        GGML_ASSERT(sampled_index >= 0);
+        GGML_ASSERT(std::isfinite(sampled_logits[sampled_index]));
+        GGML_ASSERT(sampled_probs[sampled_index] > 0.0f);
+        GGML_ASSERT(std::fabs(prob_sum - 1.0f) <= 1e-3f);
+    }
+
+    llama_batch_free(batch);
+
+    batch = make_batch(2);
+    GGML_ASSERT(llama_decode(test_ctx.ctx.get(), batch) == 0);
+    llama_batch_free(batch);
+
+    printf("backend multi-output sampling chain test PASSED\n");
+}
+
+static void test_backend_multi_output_cpu_suffix(const test_params & params) {
+    const llama_seq_id seq_id = 0;
+    const int32_t k = 8;
+    const llama_vocab * vocab = llama_model_get_vocab(params.model.get());
+
+    auto make_chain = [&](test_single_output_backend_sampler ** sampler_ctx) {
+        llama_sampler_ptr result(llama_sampler_chain_init(llama_sampler_chain_default_params()));
+        llama_sampler_chain_add(result.get(), llama_sampler_init_top_k(k));
+        llama_sampler_chain_add(result.get(), test_single_output_backend_sampler_init(sampler_ctx));
+        llama_sampler_chain_add(result.get(), llama_sampler_init_dist(88));
+        return result;
+    };
+
+    {
+        test_single_output_backend_sampler * sampler_ctx = nullptr;
+        llama_sampler_ptr chain = make_chain(&sampler_ctx);
+        std::vector<llama_sampler_seq_config> configs = {{ seq_id, chain.get() }};
+        test_context test_ctx(params, configs, 1, 1, 0, 4);
+
+        llama_batch batch = llama_batch_init(1, 0, 1);
+        common_batch_add(batch, llama_vocab_bos(vocab), 0, { seq_id }, true);
+        GGML_ASSERT(llama_decode(test_ctx.ctx.get(), batch) == 0);
+
+        GGML_ASSERT(sampler_ctx->backend_initialized);
+        GGML_ASSERT(sampler_ctx->backend_outputs_max_per_seq == 1);
+        GGML_ASSERT(sampler_ctx->backend_apply_count > 0);
+        GGML_ASSERT(sampler_ctx->apply_count == 0);
+        GGML_ASSERT(llama_get_sampled_token_ith(test_ctx.ctx.get(), 0) != LLAMA_TOKEN_NULL);
+
+        llama_batch_free(batch);
+    }
+
+    {
+        test_single_output_backend_sampler * sampler_ctx = nullptr;
+        llama_sampler_ptr chain = make_chain(&sampler_ctx);
+        std::vector<llama_sampler_seq_config> configs = {{ seq_id, chain.get() }};
+        test_context test_ctx(params, configs, 1, 2, 0, 0);
+
+        llama_batch batch = llama_batch_init(2, 0, 1);
+        for (int i = 0; i < 2; ++i) {
+            common_batch_add(batch, llama_vocab_bos(vocab), i, { seq_id }, true);
+        }
+        GGML_ASSERT(llama_decode(test_ctx.ctx.get(), batch) == 0);
+
+        GGML_ASSERT(!sampler_ctx->backend_initialized);
+        GGML_ASSERT(sampler_ctx->backend_outputs_max_per_seq == 2);
+        GGML_ASSERT(sampler_ctx->backend_apply_count == 0);
+        for (int i = 0; i < batch.n_tokens; ++i) {
+            GGML_ASSERT(llama_get_sampled_token_ith(test_ctx.ctx.get(), i) == LLAMA_TOKEN_NULL);
+            GGML_ASSERT(llama_get_sampled_logits_count_ith(test_ctx.ctx.get(), i) == (uint32_t) k);
+            GGML_ASSERT(llama_get_sampled_candidates_count_ith(test_ctx.ctx.get(), i) == (uint32_t) k);
+            const llama_token token = llama_sampler_sample(chain.get(), test_ctx.ctx.get(), i);
+            GGML_ASSERT(token >= 0 && token < llama_vocab_n_tokens(vocab));
+        }
+        GGML_ASSERT(sampler_ctx->apply_count == batch.n_tokens);
+
+        llama_batch_free(batch);
+    }
+
+    printf("backend multi-output CPU suffix test PASSED\n");
 }
 
 struct backend_test_case {
@@ -1583,7 +2010,11 @@ static const backend_test_case BACKEND_TESTS[] = {
     { "dist",            test_backend_dist_sampling,           true  },
     { "dist_and_cpu",    test_backend_dist_sampling_and_cpu,   true  },
     { "set_sampler",     test_backend_set_sampler,             true  },
-    { "max_outputs",     test_backend_max_outputs,             true  },
+    { "multi_output_limit",    test_backend_multi_output_limit,      true },
+    { "multi_sequence_multi_output_dist", test_backend_multi_sequence_multi_output_dist, true },
+    { "multi_output_dist_transaction", test_backend_multi_output_dist_transaction, true },
+    { "multi_output_sampling_chain", test_backend_multi_output_sampling_chain, true },
+    { "multi_output_cpu",      test_backend_multi_output_cpu_suffix, true },
     { "mixed",           test_backend_mixed_sampling,          true  },
     { "min_p",           test_backend_min_p_sampling,          true  },
     { "cpu_mixed",       test_backend_cpu_mixed_batch,         true  },
@@ -1674,7 +2105,9 @@ static std::vector<const backend_test_case *> collect_tests_to_run(const std::st
 #ifdef GGML_USE_HIP
             // TODO: remove this when https://github.com/ggml-org/llama.cpp/pull/26592 is merged
             if (test.name == "penalties" || test.name == "set_sampler" ||
-                test.name == "mixed"     || test.name == "top_p") {
+                test.name == "mixed"     || test.name == "top_p"       ||
+                test.name == "multi_output_sampling_chain" ||
+                test.name == "multi_output_cpu") {
                 fprintf(stderr, "Skipping test '%s' on HIP backend (no backend TOP_K support)\n", test.name.c_str());
                 continue;
             }

tests/test-chat-auto-parser.cpp

@@ -63,6 +63,7 @@ static void test_laguna_tool_format(testing & t);
 static void test_laguna_s_analysis(testing & t);
 static void test_laguna_s_reasoning_detection(testing & t);
 static void test_laguna_s_tool_format(testing & t);
+static void test_laguna_s_preserve_reasoning(testing & t);
 static void test_laguna_xs2_analysis(testing & t);
 static void test_laguna_xs2_reasoning_detection(testing & t);
 static void test_laguna_xs2_tool_format(testing & t);
@@ -1451,9 +1452,14 @@ static void test_laguna_s_tool_format(testing & t) {
     analysis.analyze_template(tmpl);
     t.assert_equal("Laguna-S(v8) arg_value_suffix should be '</arg_value>'", "</arg_value>", analysis.tools.arguments.value_suffix);
 }
+static void test_laguna_s_preserve_reasoning(testing & t) {
+    common_chat_template tmpl = load_laguna_s_template(t);
+    t.assert_true("Laguna-S(v8) supports preserving reasoning", tmpl.original_caps().supports_preserve_reasoning);
+}
 static void test_laguna_s_analysis(testing & t) {
     t.test("Laguna-S(v8) reasoning detection", test_laguna_s_reasoning_detection);
     t.test("Laguna-S(v8) tool format", test_laguna_s_tool_format);
+    t.test("Laguna-S(v8) preserve reasoning", test_laguna_s_preserve_reasoning);
 }
 
 static common_chat_template load_laguna_xs2_template(testing & t) {

tests/test-llama-archs.cpp

@@ -192,7 +192,7 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
         ms.add_kv(LLM_KV_ROPE_FREQ_BASE_SWA,              10000.0f);
         // SWA pattern: every 5th layer is full attention (matches E2B layer_types)
         ms.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, uint32_t(5));
-    } else if (arch == LLM_ARCH_COHERE2MOE || arch == LLM_ARCH_MIMO2 || arch == LLM_ARCH_STEP35) {
+    } else if (arch == LLM_ARCH_COHERE2MOE || arch == LLM_ARCH_MIMO2 || arch == LLM_ARCH_STEP35 || arch == LLM_ARCH_MUSE_GLIMMER) {
         std::vector<uint32_t> pattern;
         pattern.reserve(n_layer);
         for (uint32_t il = 0; il < n_layer; il++) {

tests/test-sampling.cpp

@@ -61,6 +61,35 @@ private:
     std::vector<llama_token_data> cur;
 };
 
+static llama_token sample_dist(llama_sampler * sampler, const std::vector<float> & logits) {
+    std::vector<llama_token_data> cur;
+    for (llama_token token_id = 0; token_id < (llama_token) logits.size(); ++token_id) {
+        cur.push_back({ token_id, logits[token_id], 0.0f });
+    }
+
+    llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
+    llama_sampler_apply(sampler, &cur_p);
+    GGML_ASSERT(cur_p.selected >= 0);
+    GGML_ASSERT((size_t) cur_p.selected < cur_p.size);
+    return cur_p.data[cur_p.selected].id;
+}
+
+static void test_dist_singleton_rng() {
+    llama_sampler * singleton = llama_sampler_init_dist(4242);
+    llama_sampler * control   = llama_sampler_init_dist(4242);
+
+    sample_dist(singleton, { 0.0f });
+    sample_dist(control,   { 0.0f, 0.0f });
+
+    const std::vector<float> logits(256, 0.0f);
+    for (int i = 0; i < 4; ++i) {
+        GGML_ASSERT(sample_dist(singleton, logits) == sample_dist(control, logits));
+    }
+
+    llama_sampler_free(singleton);
+    llama_sampler_free(control);
+}
+
 static void test_temp(const std::vector<float> & probs, const std::vector<float> & probs_expected, float temp) {
     sampler_tester tester(probs, probs_expected);
 
@@ -308,6 +337,8 @@ static void test_perf() {
 int main(void) {
     ggml_time_init();
 
+    test_dist_singleton_rng();
+
     test_temp({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 1.0f);
     test_temp({0.1f, 0.2f, 0.3f, 0.4f}, {0.0f, 0.0f, 0.0f, 1.0f}, 0.0f);
 

tools/cli/README.md

@@ -54,10 +54,11 @@
 | `-ctv, --cache-type-v TYPE` | KV cache data type for V<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
 | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
 | `-np, --parallel N` | number of parallel sequences to decode (default: 1)<br/>(env: LLAMA_ARG_N_PARALLEL) |
+| `--rpc SERVERS` | comma-separated list of RPC servers (host:port)<br/>(env: LLAMA_ARG_RPC) |
 | `--mlock` | DEPRECATED in favor of `--load-mode`: force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
 | `--mmap, --no-mmap` | DEPRECATED in favor of `--load-mode`: whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock)<br/>(env: LLAMA_ARG_MMAP) |
 | `-dio, --direct-io, -ndio, --no-direct-io` | DEPRECATED in favor of `--load-mode`: use DirectIO if available<br/>(env: LLAMA_ARG_DIO) |
-| `-lm, --load-mode MODE` | model loading mode (default: mmap)<br/>- none: no special loading mode<br/>- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)<br/>- mlock: force system to keep model in RAM rather than swapping or compressing<br/>- mmap+mlock: mmap + force system to keep model in RAM rather than swapping or compressing<br/>- dio: use DirectIO if available<br/><br/>(env: LLAMA_ARG_LOAD_MODE) |
+| `-lm, --load-mode MODE` | model loading mode (default: auto)<br/>- auto: mmap, unless a device does not support it<br/>- none: no special loading mode<br/>- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)<br/>- mlock: force system to keep model in RAM rather than swapping or compressing<br/>- mmap+mlock: mmap + force system to keep model in RAM rather than swapping or compressing<br/>- dio: use DirectIO if available<br/><br/>(env: LLAMA_ARG_LOAD_MODE) |
 | `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggml-org/llama.cpp/issues/1437<br/>(env: LLAMA_ARG_NUMA) |
 | `-dev, --device <dev1,dev2,..>` | comma-separated list of devices to use for offloading (none = don't offload)<br/>use --list-devices to see a list of available devices<br/>(env: LLAMA_ARG_DEVICE) |
 | `--list-devices` | print list of available devices and exit |
@@ -84,8 +85,6 @@
 | `-dr, --docker-repo [<repo>/]<model>[:quant]` | Docker Hub model repository. repo is optional, default to ai/. quant is optional, default to :latest.<br/>example: gemma3<br/>(default: unused)<br/>(env: LLAMA_ARG_DOCKER_REPO) |
 | `-hf, -hfr, --hf-repo <user>/<model>[:quant]` | Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.<br/>mmproj is also downloaded automatically if available. to disable, add --no-mmproj<br/>example: ggml-org/GLM-4.7-Flash-GGUF:Q4_K_M<br/>(default: unused)<br/>(env: LLAMA_ARG_HF_REPO) |
 | `-hff, --hf-file FILE` | Hugging Face model file. If specified, it will override the quant in --hf-repo (default: unused)<br/>(env: LLAMA_ARG_HF_FILE) |
-| `-hfv, -hfrv, --hf-repo-v <user>/<model>[:quant]` | Hugging Face model repository for the vocoder model (default: unused)<br/>(env: LLAMA_ARG_HF_REPO_V) |
-| `-hffv, --hf-file-v FILE` | Hugging Face model file for the vocoder model (default: unused)<br/>(env: LLAMA_ARG_HF_FILE_V) |
 | `-hft, --hf-token TOKEN` | Hugging Face access token (default: value from HF_TOKEN environment variable)<br/>(env: HF_TOKEN) |
 | `--log-disable` | Log disable |
 | `--log-file FNAME` | Log to file<br/>(env: LLAMA_ARG_LOG_FILE) |

tools/completion/README.md

@@ -137,10 +137,11 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 | `-ctv, --cache-type-v TYPE` | KV cache data type for V<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
 | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
 | `-np, --parallel N` | number of parallel sequences to decode (default: 1)<br/>(env: LLAMA_ARG_N_PARALLEL) |
+| `--rpc SERVERS` | comma-separated list of RPC servers (host:port)<br/>(env: LLAMA_ARG_RPC) |
 | `--mlock` | DEPRECATED in favor of `--load-mode`: force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
 | `--mmap, --no-mmap` | DEPRECATED in favor of `--load-mode`: whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock)<br/>(env: LLAMA_ARG_MMAP) |
 | `-dio, --direct-io, -ndio, --no-direct-io` | DEPRECATED in favor of `--load-mode`: use DirectIO if available<br/>(env: LLAMA_ARG_DIO) |
-| `-lm, --load-mode MODE` | model loading mode (default: mmap)<br/>- none: no special loading mode<br/>- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)<br/>- mlock: force system to keep model in RAM rather than swapping or compressing<br/>- mmap+mlock: mmap + force system to keep model in RAM rather than swapping or compressing<br/>- dio: use DirectIO if available<br/><br/>(env: LLAMA_ARG_LOAD_MODE) |
+| `-lm, --load-mode MODE` | model loading mode (default: auto)<br/>- auto: mmap, unless a device does not support it<br/>- none: no special loading mode<br/>- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)<br/>- mlock: force system to keep model in RAM rather than swapping or compressing<br/>- mmap+mlock: mmap + force system to keep model in RAM rather than swapping or compressing<br/>- dio: use DirectIO if available<br/><br/>(env: LLAMA_ARG_LOAD_MODE) |
 | `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggml-org/llama.cpp/issues/1437<br/>(env: LLAMA_ARG_NUMA) |
 | `-dev, --device <dev1,dev2,..>` | comma-separated list of devices to use for offloading (none = don't offload)<br/>use --list-devices to see a list of available devices<br/>(env: LLAMA_ARG_DEVICE) |
 | `--list-devices` | print list of available devices and exit |
@@ -167,8 +168,6 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 | `-dr, --docker-repo [<repo>/]<model>[:quant]` | Docker Hub model repository. repo is optional, default to ai/. quant is optional, default to :latest.<br/>example: gemma3<br/>(default: unused)<br/>(env: LLAMA_ARG_DOCKER_REPO) |
 | `-hf, -hfr, --hf-repo <user>/<model>[:quant]` | Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.<br/>mmproj is also downloaded automatically if available. to disable, add --no-mmproj<br/>example: ggml-org/GLM-4.7-Flash-GGUF:Q4_K_M<br/>(default: unused)<br/>(env: LLAMA_ARG_HF_REPO) |
 | `-hff, --hf-file FILE` | Hugging Face model file. If specified, it will override the quant in --hf-repo (default: unused)<br/>(env: LLAMA_ARG_HF_FILE) |
-| `-hfv, -hfrv, --hf-repo-v <user>/<model>[:quant]` | Hugging Face model repository for the vocoder model (default: unused)<br/>(env: LLAMA_ARG_HF_REPO_V) |
-| `-hffv, --hf-file-v FILE` | Hugging Face model file for the vocoder model (default: unused)<br/>(env: LLAMA_ARG_HF_FILE_V) |
 | `-hft, --hf-token TOKEN` | Hugging Face access token (default: value from HF_TOKEN environment variable)<br/>(env: HF_TOKEN) |
 | `--log-disable` | Log disable |
 | `--log-file FNAME` | Log to file<br/>(env: LLAMA_ARG_LOG_FILE) |

tools/llama-bench/llama-bench.cpp

@@ -384,7 +384,7 @@ static const cmd_params cmd_params_defaults = {
     /* n_gpu_layers         */ { -1 },
     /* n_cpu_moe            */ { 0 },
     /* split_mode           */ { LLAMA_SPLIT_MODE_LAYER },
-    /* load_mode            */ { LLAMA_LOAD_MODE_MMAP },
+    /* load_mode            */ { LLAMA_LOAD_MODE_AUTO },
     /* main_gpu             */ { 0 },
     /* no_kv_offload        */ { false },
     /* flash_attn           */ { LLAMA_FLASH_ATTN_TYPE_AUTO },
@@ -459,7 +459,7 @@ static void print_usage(int /* argc */, char ** argv) {
     printf("  -nkvo, --no-kv-offload <0|1>                      (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
     printf("  -fa, --flash-attn <on|off|auto>                   (default: %s)\n", join(transform_to_str(cmd_params_defaults.flash_attn, llama_flash_attn_type_name), ",").c_str());
     printf("  -dev, --device <dev0/dev1/...>                    (default: auto)\n");
-    printf("  -lm, --load-mode <none|mmap|mlock|mmap+mlock|dio> (default: %s)\n", join(transform_to_str(cmd_params_defaults.load_mode, llama_load_mode_name), ",").c_str());
+    printf("  -lm, --load-mode <auto|none|mmap|mlock|mmap+mlock|dio> (default: %s)\n", join(transform_to_str(cmd_params_defaults.load_mode, llama_load_mode_name), ",").c_str());
     printf("  -mmp, --mmap <0|1>                                (DEPRECATED IN FAVOUR OF --load-mode)\n");
     printf("  -dio, --direct-io <0|1>                           (DEPRECATED IN FAVOUR OF --load-mode)\n");
     printf("  -embd, --embeddings <0|1>                         (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str());
@@ -764,7 +764,9 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                 std::vector<llama_load_mode> modes;
                 for (const auto & m : p) {
                     llama_load_mode mode;
-                    if (m == "none") {
+                    if (m == "auto") {
+                        mode = LLAMA_LOAD_MODE_AUTO;
+                    } else if (m == "none") {
                         mode = LLAMA_LOAD_MODE_NONE;
                     } else if (m == "mmap") {
                         mode = LLAMA_LOAD_MODE_MMAP;

tools/mtmd/CMakeLists.txt

@@ -43,6 +43,7 @@ add_library(mtmd
             models/kimivl.cpp
             models/kimik25.cpp
             models/nemotron-v2-vl.cpp
+            models/muse-glimmer.cpp
             models/llama4.cpp
             models/llava.cpp
             models/minicpmv.cpp

tools/mtmd/clip-impl.h

@@ -455,6 +455,7 @@ enum projector_type {
     PROJECTOR_TYPE_MIMO_AUDIO,
     PROJECTOR_TYPE_QWEN3TTS_SPKENC,
     PROJECTOR_TYPE_QWEN3TTS_GEN,
+    PROJECTOR_TYPE_MUSE_GLIMMER,
     PROJECTOR_TYPE_UNKNOWN,
 };
 
@@ -514,6 +515,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_PARAKEET,          "parakeet"},
     { PROJECTOR_TYPE_QWEN3TTS_SPKENC,   "qwen3tts_spkenc"},
     { PROJECTOR_TYPE_QWEN3TTS_GEN,      "qwen3tts_gen"},
+    { PROJECTOR_TYPE_MUSE_GLIMMER,      "muse-glimmer"},
 };
 
 static projector_type clip_projector_type_from_string(const std::string & str) {

tools/mtmd/clip-model.h

@@ -109,6 +109,11 @@ struct clip_hparams {
     int32_t downsample_query_side;
     int32_t downsample_window_side;
 
+    // Muse Glimmer vision (per-block sparse-window pattern, learned pos-emb, patch-temporal)
+    // NOTE: these perhaps shouldn't have the architecture prefix
+    int32_t muse_glimmer_patch_temporal = 0;
+    int32_t muse_glimmer_sparse_factor  = 0;
+
     // audio
     int32_t n_mel_bins = 0; // whisper preprocessor
     int32_t proj_stack_factor = 0; // ultravox

tools/mtmd/clip.cpp

@@ -954,6 +954,10 @@ static std::unique_ptr<clip_graph> clip_get_graph_builder(clip_ctx * ctx, const
             {
                 builder = std::make_unique<clip_graph_minimax_m3>(ctx, img);
             } break;
+        case PROJECTOR_TYPE_MUSE_GLIMMER:
+            {
+                builder = std::make_unique<clip_graph_muse_glimmer>(ctx, img);
+            } break;
         case PROJECTOR_TYPE_STEP3VL:
             {
                 builder = std::make_unique<clip_graph_step3vl>(ctx, img);
@@ -1572,6 +1576,17 @@ struct clip_model_loader {
                         hparams.set_limit_image_tokens(8, 576);
                         hparams.set_warmup_n_tokens(16*16);
                     } break;
+                case PROJECTOR_TYPE_MUSE_GLIMMER:
+                    {
+                        hparams.n_merge = 2; // pixel-shuffle downsample after the ViT
+                        hparams.image_resize_algo = RESIZE_ALGO_LANCZOS;
+                        hparams.rope_theta = 10000.0f;
+                        hparams.muse_glimmer_patch_temporal = 2;
+                        hparams.muse_glimmer_sparse_factor  = 4; // 3 sparse layers + 1 global, repeating
+                        get_u32(KEY_SPATIAL_MERGE_SIZE,  hparams.n_merge,             false);
+                        hparams.set_limit_image_tokens(1, 4096);
+                        hparams.set_warmup_n_tokens(32*32);
+                    } break;
                 case PROJECTOR_TYPE_MIMOVL:
                     {
                         hparams.n_merge = 2; // spatial_merge_size
@@ -2317,6 +2332,13 @@ struct clip_model_loader {
                     model.mm_merger_fc2_w = get_tensor(string_format(TN_MM_MERGER_FC2, "weight"));
                     model.mm_merger_fc2_b = get_tensor(string_format(TN_MM_MERGER_FC2, "bias"));
                 } break;
+            case PROJECTOR_TYPE_MUSE_GLIMMER:
+                {
+                    // 3-linear MLP: fc -> erf-GELU -> proj -> erf-GELU -> vision_proj (into LLM residual dim)
+                    model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight"));
+                    model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 1, "weight"));
+                    model.mm_2_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"));
+                } break;
             case PROJECTOR_TYPE_STEP3VL:
                 {
                     model.mm_0_w     = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight"));
@@ -3745,6 +3767,7 @@ int clip_n_output_tokens_x(const clip_ctx * ctx, const clip_image_f32 * img) {
         case PROJECTOR_TYPE_PADDLEOCR:
         case PROJECTOR_TYPE_HUNYUANVL:
         case PROJECTOR_TYPE_YOUTUVL:
+        case PROJECTOR_TYPE_MUSE_GLIMMER:
             return (img->nx() / params.patch_size) / 2;
         case PROJECTOR_TYPE_STEP3VL:
             return img->nx() / (params.patch_size * params.n_merge);
@@ -3770,6 +3793,7 @@ int clip_n_output_tokens_y(const clip_ctx * ctx, const clip_image_f32 * img) {
         case PROJECTOR_TYPE_PADDLEOCR:
         case PROJECTOR_TYPE_HUNYUANVL:
         case PROJECTOR_TYPE_YOUTUVL:
+        case PROJECTOR_TYPE_MUSE_GLIMMER:
             return (img->ny() / params.patch_size) / 2;
         case PROJECTOR_TYPE_STEP3VL:
             return img->ny() / (params.patch_size * params.n_merge);
@@ -3848,6 +3872,7 @@ int clip_n_output_tokens(const clip_ctx * ctx, const clip_image_f32 * img) {
         case PROJECTOR_TYPE_MINIMAX_M3:
         case PROJECTOR_TYPE_GLM4V:
         case PROJECTOR_TYPE_YOUTUVL:
+        case PROJECTOR_TYPE_MUSE_GLIMMER:
             {
                 // dynamic size (2 conv, so double patch size)
                 int x_patch = img->nx() / (params.patch_size * 2);
@@ -4193,6 +4218,70 @@ bool clip_encode(struct clip_ctx * ctx, struct clip_encode_params * params) {
 
     // set input per projector
     switch (ctx->model.proj_type) {
+        case PROJECTOR_TYPE_MUSE_GLIMMER:
+            {
+                const int grid_w = pos_w;            // image_size_width  / patch_size
+                const int grid_h = pos_h;            // image_size_height / patch_size
+                const int n_tok  = grid_w * grid_h;
+                const int pgrid  = (int) std::sqrt((double) ctx->model.position_embeddings->ne[1]); // 32
+                const int f      = hparams.n_merge;  // downsample 2
+
+                // pixel patchify runs inside the graph via build_inp() (ggml_conv_2d);
+                // pos-emb bilinear interp via resize_position_embeddings().
+
+                // --- sparse window grouping (pgrid x pgrid windows) ---
+                const int win = pgrid;
+                const int nwin_h = (grid_h + win - 1) / win;
+                const int nwin_w = (grid_w + win - 1) / win;
+                std::vector<int32_t> sp_perm; sp_perm.reserve(n_tok);
+                std::vector<int>     sp_slens;
+                for (int wy = 0; wy < nwin_h; wy++) {
+                    for (int wx = 0; wx < nwin_w; wx++) {
+                        int cnt = 0;
+                        for (int hh = 0; hh < win; hh++) {
+                            for (int ww = 0; ww < win; ww++) {
+                                const int gy = wy * win + hh;
+                                const int gx = wx * win + ww;
+                                if (gy < grid_h && gx < grid_w) { sp_perm.push_back(gy * grid_w + gx); cnt++; }
+                            }
+                        }
+                        if (cnt > 0) sp_slens.push_back(cnt);
+                    }
+                }
+                std::vector<int32_t> rpos_w(n_tok), rpos_h(n_tok), inv_perm(n_tok);
+                for (int i = 0; i < n_tok; i++) {
+                    const int orig = sp_perm[i];
+                    rpos_w[i] = (orig % grid_w) + 1; // 1-indexed
+                    rpos_h[i] = (orig / grid_w) + 1;
+                    inv_perm[orig] = i;
+                }
+                set_input_i32("muse_glimmer_sp_perm",  sp_perm);
+                set_input_i32("muse_glimmer_inv_perm", inv_perm);
+                set_input_i32("muse_glimmer_pos_w",    rpos_w);
+                set_input_i32("muse_glimmer_pos_h",    rpos_h);
+
+                // block-diagonal window mask (permuted order)
+                std::vector<float> sp_mask((size_t) n_tok * n_tok, -INFINITY);
+                {
+                    int off = 0;
+                    for (int s : sp_slens) {
+                        for (int a = 0; a < s; a++)
+                            for (int b = 0; b < s; b++)
+                                sp_mask[(size_t) (off + a) * n_tok + (off + b)] = 0.0f;
+                        off += s;
+                    }
+                }
+                set_input_f32("muse_glimmer_sp_mask", sp_mask);
+
+                // pixel-shuffle gather (original order): f*f spatial neighbours grouped
+                std::vector<int32_t> dsp; dsp.reserve(n_tok);
+                for (int oy = 0; oy < grid_h / f; oy++)
+                    for (int ox = 0; ox < grid_w / f; ox++)
+                        for (int ry = 0; ry < f; ry++)
+                            for (int rx = 0; rx < f; rx++)
+                                dsp.push_back((oy * f + ry) * grid_w + (ox * f + rx));
+                set_input_i32("muse_glimmer_ds_perm", dsp);
+            } break;
         case PROJECTOR_TYPE_MINICPMV:
             {
                 // inspired from siglip:
@@ -5369,6 +5458,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
             return ctx->model.mm_model_mlp_3_w->ne[1];
         case PROJECTOR_TYPE_MINIMAX_M3:
             return ctx->model.mm_merger_fc2_b->ne[0];
+        case PROJECTOR_TYPE_MUSE_GLIMMER:
+            return ctx->model.mm_2_w->ne[1];
         case PROJECTOR_TYPE_QWEN2VL:
         case PROJECTOR_TYPE_QWEN25VL:
         case PROJECTOR_TYPE_EXAONE4_5:

tools/mtmd/models/models.h

@@ -365,3 +365,8 @@ private:
     ggml_tensor * build_newline_row(ggml_context * ctx0);
     ggml_tensor * append_rowwise_newlines(ggml_context * ctx0, ggml_tensor * tile_output);
 };
+
+struct clip_graph_muse_glimmer : clip_graph {
+    clip_graph_muse_glimmer(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+    ggml_cgraph * build() override;
+};

tools/mtmd/models/muse-glimmer.cpp

@@ -0,0 +1,88 @@
+#include "models.h"
+
+// MuseGlimmer vision encoder: 50-layer ViT with 2D RoPE, sparse block-diagonal
+// window attention (every 4th + last layer global), pixel-shuffle downsample, then
+// adapter MLP + LLM's vision_projection.
+//
+// Several quantities are precomputed on host and fed as named graph inputs (filled in
+// clip.cpp set_input, PROJECTOR_TYPE_MUSE_GLIMMER branch):
+//   muse_glimmer_pos_w/_h [n_tok] i32         : 1-indexed RoPE positions (sparse-permuted order)
+//   muse_glimmer_sp_perm  [n_tok] i32         : window grouping permutation (applied after ln_pre)
+//   muse_glimmer_inv_perm [n_tok] i32         : inverse of sp_perm (applied after blocks)
+//   muse_glimmer_ds_perm  [n_tok] i32         : pixel-shuffle gather (original order)
+//   muse_glimmer_sp_mask  [n_tok, n_tok] f32  : block-diagonal window mask (sparse layers)
+ggml_cgraph * clip_graph_muse_glimmer::build() {
+    const int ds = hparams.n_merge;              // downsample factor (2)
+    const int sf = hparams.muse_glimmer_sparse_factor;   // 4
+    const int n_tok     = n_patches;
+    const int n_out     = (n_patches_x / ds) * (n_patches_y / ds);
+    const float rope_base = hparams.rope_theta;  // 10000
+
+    auto inp_i32 = [&](const char * name, int64_t n) {
+        ggml_tensor * t = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n);
+        ggml_set_name(t, name);
+        ggml_set_input(t);
+        return t;
+    };
+
+    ggml_tensor * pos_w    = inp_i32("muse_glimmer_pos_w",    n_tok);
+    ggml_tensor * pos_h    = inp_i32("muse_glimmer_pos_h",    n_tok);
+    ggml_tensor * sp_perm  = inp_i32("muse_glimmer_sp_perm",  n_tok);
+    ggml_tensor * inv_perm = inp_i32("muse_glimmer_inv_perm", n_tok);
+    ggml_tensor * ds_perm  = inp_i32("muse_glimmer_ds_perm",  n_tok);
+
+    ggml_tensor * sp_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tok, n_tok);
+    ggml_set_name(sp_mask, "muse_glimmer_sp_mask");
+    ggml_set_input(sp_mask);
+
+    // patchify via build_inp (conv2d over raw pixels) + bilinear-resized learned pos-emb
+    ggml_tensor * x = build_inp();                                                     // [n_embd, n_tok, 1]
+    x = ggml_add(ctx0, x, resize_position_embeddings(GGML_SCALE_MODE_BILINEAR));
+    cb(x, "after_posemb", -1);
+
+    // group patches into pgrid x pgrid windows (sparse attention order)
+    x = ggml_get_rows(ctx0, x, sp_perm);
+    cb(x, "after_sp_perm", -1);
+
+    // per-layer mask: sparse layers get sp_mask, global layers (every sf-th and last) get none
+    std::vector<ggml_tensor *> attn_mask_layers(n_layer);
+    for (int il = 0; il < n_layer; ++il) {
+        const bool is_global = (il == n_layer - 1) || ((il + 1) % sf == 0);
+        attn_mask_layers[il] = is_global ? nullptr : sp_mask;
+    }
+
+    // 2D RoPE: first half of head_dim uses width pos, second half uses height pos
+    auto add_pos = [&](ggml_tensor * cur, const clip_layer &) {
+        return build_rope_2d(ctx0, cur, pos_w, pos_h, rope_base, false);
+    };
+
+    build_vit_opts opts;
+    opts.attn_mask_layers = std::move(attn_mask_layers);
+
+    // pre_ln, per-layer transformer, post_ln (all inside build_vit); reference uses exact (erf) GELU
+    x = build_vit(x, n_tok, NORM_TYPE_NORMAL, FFN_GELU_ERF, nullptr, add_pos, opts);
+
+    // un-permute back to original grid order
+    x = ggml_get_rows(ctx0, x, inv_perm);
+    cb(x, "after_inv_perm", -1);
+
+    // pixel-shuffle downsample: gather f*f spatial neighbors then concat channel-outer.
+    // out[c*(ds*ds)+s, o] = x[ds_perm gathered][o*(ds*ds)+s, c]
+    x = ggml_get_rows(ctx0, x, ds_perm);                 // [n_embd, n_tok], grouped
+    x = ggml_reshape_3d(ctx0, x, n_embd, ds * ds, n_out);// [c, s, o]
+    x = ggml_permute(ctx0, x, 1, 0, 2, 3);               // [s, c, o]
+    x = ggml_cont(ctx0, x);
+    x = ggml_reshape_2d(ctx0, x, n_embd * ds * ds, n_out); // [6144, n_out]
+    cb(x, "encoder_out", -1);
+
+    // adapter (6144->4096->4096, exact GELU each) + LLM vision_projection (4096->6656)
+    x = build_mm(model.mm_0_w, x);
+    x = ggml_gelu_erf(ctx0, x);
+    x = build_mm(model.mm_1_w, x);
+    x = ggml_gelu_erf(ctx0, x);
+    x = build_mm(model.mm_2_w, x);                       // [6656, n_out]
+    cb(x, "projected", -1);
+
+    ggml_build_forward_expand(gf, x);
+    return gf;
+}