server: do partial gguf kv read for capability check

.github/workflows/release.yaml

@@ -23,7 +23,7 @@ jobs:
           echo GOFLAGS="'-ldflags=-w -s \"-X=github.com/ollama/ollama/version.Version=${GITHUB_REF_NAME#v}\" \"-X=github.com/ollama/ollama/server.mode=release\"'" >>$GITHUB_OUTPUT
 
   darwin-build:
-    runs-on: macos-13-xlarge
+    runs-on: macos-13
     environment: release
     needs: setup-environment
     strategy:
@@ -54,6 +54,48 @@ jobs:
           name: build-${{ matrix.os }}-${{ matrix.arch }}
           path: dist/*
 
+  darwin-sign:
+    runs-on: macos-13
+    environment: release
+    needs: darwin-build
+    steps:
+      - uses: actions/checkout@v4
+      - run: |
+          echo $MACOS_SIGNING_KEY | base64 --decode > certificate.p12
+          security create-keychain -p password build.keychain
+          security default-keychain -s build.keychain
+          security unlock-keychain -p password build.keychain
+          security import certificate.p12 -k build.keychain -P $MACOS_SIGNING_KEY_PASSWORD -T /usr/bin/codesign
+          security set-key-partition-list -S apple-tool:,apple:,codesign: -s -k password build.keychain
+          security set-keychain-settings -lut 3600 build.keychain
+        env:
+          MACOS_SIGNING_KEY: ${{ secrets.MACOS_SIGNING_KEY }}
+          MACOS_SIGNING_KEY_PASSWORD: ${{ secrets.MACOS_SIGNING_KEY_PASSWORD }}
+      - uses: actions/download-artifact@v4
+        with:
+          name: build-darwin-amd64
+          path: dist/darwin-amd64
+      - uses: actions/download-artifact@v4
+        with:
+          name: build-darwin-arm64
+          path: dist/darwin-arm64
+      - run: |
+          export VERSION=${GITHUB_REF_NAME#v}
+          ./scripts/build_darwin.sh sign macapp
+        env:
+          APPLE_IDENTITY: ${{ secrets.APPLE_IDENTITY }}
+          APPLE_PASSWORD: ${{ secrets.APPLE_PASSWORD }}
+          APPLE_TEAM_ID: ${{ vars.APPLE_TEAM_ID }}
+          APPLE_ID: ${{ vars.APPLE_ID }}
+          SDKROOT: /Applications/Xcode_14.1.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX.sdk
+          DEVELOPER_DIR: /Applications/Xcode_14.1.0.app/Contents/Developer
+      - uses: actions/upload-artifact@v4
+        with:
+          name: dist-darwin
+          path: |
+            dist/Ollama-darwin.zip
+            dist/ollama-darwin.tgz
+
   windows-depends:
     strategy:
       matrix:
@@ -61,18 +103,21 @@ jobs:
         arch: [amd64]
         preset: ['CPU']
         include:
+          - os: windows
+            arch: amd64
+            preset: 'CUDA 11'
+            install: https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda_11.3.1_465.89_win10.exe
+            cuda-version: '11.3'
           - os: windows
             arch: amd64
             preset: 'CUDA 12'
             install: https://developer.download.nvidia.com/compute/cuda/12.8.0/local_installers/cuda_12.8.0_571.96_windows.exe
             cuda-version: '12.8'
-            flags: ''
           - os: windows
             arch: amd64
             preset: 'ROCm 6'
             install: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q4-WinSvr2022-For-HIP.exe
             rocm-version: '6.2'
-            flags: '-DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_C_FLAGS="-parallel-jobs=4 -Wno-ignored-attributes -Wno-deprecated-pragma" -DCMAKE_CXX_FLAGS="-parallel-jobs=4 -Wno-ignored-attributes -Wno-deprecated-pragma"'
     runs-on: ${{ matrix.arch == 'arm64' && format('{0}-{1}', matrix.os, matrix.arch) || matrix.os }}
     environment: release
     env:
@@ -115,9 +160,6 @@ jobs:
           echo "$hipPath\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
           echo "CC=$hipPath\bin\clang.exe" | Out-File -FilePath $env:GITHUB_ENV -Append
           echo "CXX=$hipPath\bin\clang++.exe" | Out-File -FilePath $env:GITHUB_ENV -Append
-          echo "HIPCXX=$hipPath\bin\clang++.exe" | Out-File -FilePath $env:GITHUB_ENV -Append
-          echo "HIP_PLATFORM=amd" | Out-File -FilePath $env:GITHUB_ENV -Append
-          echo "CMAKE_PREFIX_PATH=$hipPath" | Out-File -FilePath $env:GITHUB_ENV -Append
       - if: matrix.preset == 'CPU'
         run: |
           echo "CC=clang.exe" | Out-File -FilePath $env:GITHUB_ENV -Append
@@ -136,9 +178,9 @@ jobs:
           key: ccache-${{ matrix.os }}-${{ matrix.arch }}-${{ matrix.preset }}
       - name: Build target "${{ matrix.preset }}"
         run: |
-          Import-Module 'C:\Program Files\Microsoft Visual Studio\2022\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
-          Enter-VsDevShell -VsInstallPath 'C:\Program Files\Microsoft Visual Studio\2022\Enterprise' -SkipAutomaticLocation  -DevCmdArguments '-arch=x64 -no_logo'
-          cmake --preset "${{ matrix.preset }}" ${{ matrix.flags }}
+          Import-Module 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
+          Enter-VsDevShell -VsInstallPath 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise' -SkipAutomaticLocation  -DevCmdArguments '-arch=x64 -no_logo'
+          cmake --preset "${{ matrix.preset }}"
           cmake --build --parallel --preset "${{ matrix.preset }}"
           cmake --install build --component "${{ startsWith(matrix.preset, 'CUDA ') && 'CUDA' || startsWith(matrix.preset, 'ROCm ') && 'HIP' || 'CPU' }}" --strip --parallel 8
         env:
@@ -188,11 +230,61 @@ jobs:
           go-version-file: go.mod
       - run: |
           go build -o dist/${{ matrix.os }}-${{ matrix.arch }}/ .
+      - if: matrix.arch == 'arm64'
+        run: |
+          Invoke-WebRequest -Uri "https://aka.ms/vs/17/release/vc_redist.arm64.exe" -OutFile "dist\windows-arm64\vc_redist.arm64.exe"
+      - run: |
+          $env:VERSION='${{ github.ref_name }}' -Replace "v(.*)", '$1'
+          & .\scripts\build_windows.ps1 buildApp
+        env:
+          VCToolsRedistDir: stub
       - uses: actions/upload-artifact@v4
         with:
           name: build-${{ matrix.os }}-${{ matrix.arch }}
           path: |
             dist\${{ matrix.os }}-${{ matrix.arch }}\*.exe
+            dist\${{ matrix.os }}-${{ matrix.arch }}-app.exe
+
+  windows-sign:
+    runs-on: windows-2022
+    environment: release
+    needs: [windows-depends, windows-build]
+    steps:
+      - uses: actions/checkout@v4
+      - uses: google-github-actions/auth@v2
+        with:
+          project_id: ollama
+          credentials_json: ${{ secrets.GOOGLE_SIGNING_CREDENTIALS }}
+      - run: |
+          $ErrorActionPreference = "Stop"
+          Invoke-WebRequest -Uri "https://go.microsoft.com/fwlink/p/?LinkId=323507" -OutFile "${{ runner.temp }}\sdksetup.exe"
+          Start-Process "${{ runner.temp }}\sdksetup.exe" -ArgumentList @("/q") -NoNewWindow -Wait
+
+          Invoke-WebRequest -Uri "https://github.com/GoogleCloudPlatform/kms-integrations/releases/download/cng-v1.0/kmscng-1.0-windows-amd64.zip" -OutFile "${{ runner.temp }}\plugin.zip"
+          Expand-Archive -Path "${{ runner.temp }}\plugin.zip" -DestinationPath "${{ runner.temp }}\plugin\"
+          & "${{ runner.temp }}\plugin\*\kmscng.msi" /quiet
+
+          echo "${{ vars.OLLAMA_CERT }}" >ollama_inc.crt
+      - uses: actions/download-artifact@v4
+        with:
+          pattern: build-windows-*
+          path: dist\
+          merge-multiple: true
+      - uses: actions/download-artifact@v4
+        with:
+          pattern: depends-windows-amd64-*
+          path: dist\windows-amd64\
+          merge-multiple: true
+      - run: |
+          & .\scripts\build_windows.ps1 gatherDependencies sign buildInstaller distZip
+        env:
+          KEY_CONTAINER: ${{ vars.KEY_CONTAINER }}
+      - uses: actions/upload-artifact@v4
+        with:
+          name: dist-windows
+          path: |
+            dist\OllamaSetup.exe
+            dist\ollama-windows-*.zip
 
   linux-build:
     strategy:
@@ -225,26 +317,21 @@ jobs:
             CGO_CFLAGS=${{ env.CGO_CFLAGS }}
             CGO_CXXFLAGS=${{ env.CGO_CXXFLAGS }}
           outputs: type=local,dest=dist/${{ matrix.os }}-${{ matrix.arch }}
-          cache-from: type=registry,ref=${{ vars.DOCKER_REPO }}:latest
+          cache-from: type=registry,ref=ollama/ollama:latest
           cache-to: type=inline
       - run: |
           for COMPONENT in bin/* lib/ollama/*; do
             case "$COMPONENT" in
-              bin/ollama)                echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
-              lib/ollama/*.so*)          echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
-              lib/ollama/cuda_sbsa)      echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
-              lib/ollama/cuda_jetpack5)  echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}-jetpack5.tar.in ;;
-              lib/ollama/cuda_jetpack6)  echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}-jetpack6.tar.in ;;
-              lib/ollama/rocm)           echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}-rocm.tar.in ;;
+              bin/ollama)               echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
+              lib/ollama/*.so)          echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
+              lib/ollama/cuda_v11)      echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
+              lib/ollama/cuda_v12)      echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
+              lib/ollama/cuda_jetpack5) echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}-jetpack5.tar.in ;;
+              lib/ollama/cuda_jetpack6) echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}-jetpack6.tar.in ;;
+              lib/ollama/rocm)          echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}-rocm.tar.in ;;
             esac
           done
         working-directory: dist/${{ matrix.os }}-${{ matrix.arch }}
-      - run: |
-          echo "Manifests"
-          for ARCHIVE in dist/${{ matrix.os }}-${{ matrix.arch }}/*.tar.in ; do
-            echo $ARCHIVE
-            cat $ARCHIVE
-          done
       - run: |
           for ARCHIVE in dist/${{ matrix.os }}-${{ matrix.arch }}/*.tar.in; do
             tar c -C dist/${{ matrix.os }}-${{ matrix.arch }} -T $ARCHIVE --owner 0 --group 0 | pigz -9vc >$(basename ${ARCHIVE//.*/}.tgz);
@@ -298,8 +385,8 @@ jobs:
           context: .
           platforms: ${{ matrix.os }}/${{ matrix.arch }}
           build-args: ${{ matrix.build-args }}
-          outputs: type=image,name=${{ vars.DOCKER_REPO }},push-by-digest=true,name-canonical=true,push=true
-          cache-from: type=registry,ref=${{ vars.DOCKER_REPO }}:latest
+          outputs: type=image,name=ollama/ollama,push-by-digest=true,name-canonical=true,push=true
+          cache-from: type=registry,ref=ollama/ollama:latest
           cache-to: type=inline
       - run: |
           mkdir -p ${{ matrix.os }}-${{ matrix.arch }}
@@ -331,7 +418,7 @@ jobs:
             latest=false
             suffix=${{ matrix.suffix }}
           images: |
-            ${{ vars.DOCKER_REPO }}
+            ollama/ollama
           tags: |
             type=ref,enable=true,priority=600,prefix=pr-,event=pr
             type=semver,pattern={{version}}
@@ -341,24 +428,56 @@ jobs:
           path: ${{ runner.temp }}
           merge-multiple: true
       - run: |
-          docker buildx imagetools create $(echo '${{ steps.metadata.outputs.json }}' | jq -cr '.tags | map("-t", .) | join(" ")') $(cat *-${{ matrix.suffix }}.txt | xargs printf '${{ vars.DOCKER_REPO }}@%s ')
-          docker buildx imagetools inspect ${{ vars.DOCKER_REPO }}:${{ steps.metadata.outputs.version }}
+          docker buildx imagetools create $(echo '${{ steps.metadata.outputs.json }}' | jq -cr '.tags | map("-t", .) | join(" ")') $(cat *-${{ matrix.suffix }}.txt | xargs printf 'ollama/ollama@%s ')
+          docker buildx imagetools inspect ollama/ollama:${{ steps.metadata.outputs.version }}
         working-directory: ${{ runner.temp }}
 
   # Trigger downstream release process
   trigger:
     runs-on: ubuntu-latest
     environment: release
-    needs: [darwin-build, windows-build, windows-depends, linux-build]
+    needs: [darwin-build, windows-build, windows-depends]
+    steps:
+      - name: Trigger downstream release process
+        run: |
+          curl -L \
+            -X POST \
+            -H "Accept: application/vnd.github+json" \
+            -H "Authorization: Bearer ${{ secrets.RELEASE_TOKEN }}" \
+            -H "X-GitHub-Api-Version: 2022-11-28" \
+            https://api.github.com/repos/ollama/${{ vars.RELEASE_REPO }}/dispatches \
+            -d "{\"event_type\": \"trigger-workflow\", \"client_payload\": {\"run_id\": \"${GITHUB_RUN_ID}\", \"version\": \"${GITHUB_REF_NAME#v}\"}}"
+
+  # Aggregate all the assets and ship a release
+  release:
+    needs: [darwin-sign, windows-sign, linux-build]
+    runs-on: linux
+    environment: release
     permissions:
       contents: write
     env:
       GH_TOKEN: ${{ github.token }}
     steps:
       - uses: actions/checkout@v4
-      - name: Create or update Release for tag
+      - uses: actions/download-artifact@v4
+        with:
+          name: dist-darwin
+          path: dist
+      - uses: actions/download-artifact@v4
+        with:
+          name: dist-windows
+          path: dist
+      - uses: actions/download-artifact@v4
+        with:
+          pattern: dist-linux-*
+          path: dist
+          merge-multiple: true
+      - run: find . -type f -not -name 'sha256sum.txt' | xargs sha256sum | tee sha256sum.txt
+        working-directory: dist
+      - name: Create or update Release
         run: |
           RELEASE_VERSION="$(echo ${GITHUB_REF_NAME} | cut -f1 -d-)"
+
           echo "Looking for existing release for ${RELEASE_VERSION}"
           OLD_TAG=$(gh release ls --json name,tagName | jq -r ".[] | select(.name == \"${RELEASE_VERSION}\") | .tagName")
           if [ -n "$OLD_TAG" ]; then
@@ -372,12 +491,5 @@ jobs:
               --generate-notes \
               --prerelease
           fi
-      - name: Trigger downstream release process
-        run: |
-          curl -L \
-            -X POST \
-            -H "Accept: application/vnd.github+json" \
-            -H "Authorization: Bearer ${{ secrets.RELEASE_TOKEN }}" \
-            -H "X-GitHub-Api-Version: 2022-11-28" \
-            https://api.github.com/repos/ollama/${{ vars.RELEASE_REPO }}/dispatches \
-            -d "{\"event_type\": \"trigger-workflow\", \"client_payload\": {\"run_id\": \"${GITHUB_RUN_ID}\", \"version\": \"${GITHUB_REF_NAME#v}\", \"origin\": \"${GITHUB_REPOSITORY}\", \"publish\": \"1\"}}"
+          echo "Uploading artifacts for tag ${GITHUB_REF_NAME}"
+          gh release upload ${GITHUB_REF_NAME} dist/* --clobber

.github/workflows/test.yaml

@@ -36,7 +36,7 @@ jobs:
               | xargs python3 -c "import sys; from pathlib import Path; print(any(Path(x).match(glob) for x in sys.argv[1:] for glob in '$*'.split(' ')))"
           }
 
-          echo changed=$(changed 'llama/llama.cpp/**/*' 'ml/backend/ggml/ggml/**/*') | tee -a $GITHUB_OUTPUT
+          echo changed=$(changed 'llama/llama.cpp/**' 'ml/backend/ggml/ggml/**') | tee -a $GITHUB_OUTPUT
 
   linux:
     needs: [changes]
@@ -46,7 +46,7 @@ jobs:
         include:
           - preset: CPU
           - preset: CUDA
-            container: nvidia/cuda:12.8.1-devel-ubuntu22.04
+            container: nvidia/cuda:11.8.0-devel-ubuntu22.04
             flags: '-DCMAKE_CUDA_ARCHITECTURES=87'
           - preset: ROCm
             container: rocm/dev-ubuntu-22.04:6.1.2
@@ -78,11 +78,11 @@ jobs:
         include:
           - preset: CPU
           - preset: CUDA
-            install: https://developer.download.nvidia.com/compute/cuda/12.8.0/local_installers/cuda_12.8.0_571.96_windows.exe
+            install: https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda_11.3.1_465.89_win10.exe
             flags: '-DCMAKE_CUDA_ARCHITECTURES=80'
           - preset: ROCm
             install: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q4-WinSvr2022-For-HIP.exe
-            flags: '-DAMDGPU_TARGETS=gfx1010 -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_C_FLAGS="-parallel-jobs=4 -Wno-ignored-attributes -Wno-deprecated-pragma" -DCMAKE_CXX_FLAGS="-parallel-jobs=4 -Wno-ignored-attributes -Wno-deprecated-pragma"'
+            flags: '-DAMDGPU_TARGETS=gfx1010'
     runs-on: windows
     steps:
       - run: |
@@ -102,7 +102,7 @@ jobs:
           $ErrorActionPreference = "Stop"
           if ("${{ steps.cache-install.outputs.cache-hit }}" -ne 'true') {
             Invoke-WebRequest -Uri "${{ matrix.install }}" -OutFile "install.exe"
-            Start-Process -FilePath .\install.exe -ArgumentList (@("-s", "cudart_12.8", "nvcc_12.8", "cublas_12.8", "cublas_dev_12.8")) -NoNewWindow -Wait
+            Start-Process -FilePath .\install.exe -ArgumentList (@("-s", "cudart_11.3", "nvcc_11.3", "cublas_11.3", "cublas_dev_11.3")) -NoNewWindow -Wait
           }
 
           $cudaPath = (Resolve-Path "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\*").path
@@ -120,9 +120,6 @@ jobs:
           echo "$hipPath\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
           echo "CC=$hipPath\bin\clang.exe" | Out-File -FilePath $env:GITHUB_ENV -Append
           echo "CXX=$hipPath\bin\clang++.exe" | Out-File -FilePath $env:GITHUB_ENV -Append
-          echo "HIPCXX=$hipPath\bin\clang++.exe" | Out-File -FilePath $env:GITHUB_ENV -Append
-          echo "HIP_PLATFORM=amd" | Out-File -FilePath $env:GITHUB_ENV -Append
-          echo "CMAKE_PREFIX_PATH=$hipPath" | Out-File -FilePath $env:GITHUB_ENV -Append
       - if: ${{ !cancelled() && steps.cache-install.outputs.cache-hit != 'true' }}
         uses: actions/cache/save@v4
         with:
@@ -136,8 +133,8 @@ jobs:
           path: ${{ github.workspace }}\.ccache
           key: ccache-${{ runner.os }}-${{ runner.arch }}-${{ matrix.preset }}
       - run: |
-          Import-Module 'C:\Program Files\Microsoft Visual Studio\2022\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
-          Enter-VsDevShell -VsInstallPath 'C:\Program Files\Microsoft Visual Studio\2022\Enterprise' -SkipAutomaticLocation  -DevCmdArguments '-arch=x64 -no_logo'
+          Import-Module 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
+          Enter-VsDevShell -VsInstallPath 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise' -SkipAutomaticLocation  -DevCmdArguments '-arch=x64 -no_logo'
           cmake --preset "${{ matrix.preset }}" ${{ matrix.flags }}
           cmake --build --parallel --preset "${{ matrix.preset }}"
         env:

CMakeLists.txt

@@ -78,13 +78,14 @@ if(CMAKE_CUDA_COMPILER)
 
     find_package(CUDAToolkit)
     add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-cuda)
+    set(OLLAMA_CUDA_INSTALL_DIR ${OLLAMA_INSTALL_DIR}/cuda_v${CUDAToolkit_VERSION_MAJOR})
     install(TARGETS ggml-cuda
         RUNTIME_DEPENDENCIES
             DIRECTORIES ${CUDAToolkit_BIN_DIR} ${CUDAToolkit_LIBRARY_DIR}
             PRE_INCLUDE_REGEXES cublas cublasLt cudart
             PRE_EXCLUDE_REGEXES ".*"
-        RUNTIME DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT CUDA
-        LIBRARY DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT CUDA
+        RUNTIME DESTINATION ${OLLAMA_CUDA_INSTALL_DIR} COMPONENT CUDA
+        LIBRARY DESTINATION ${OLLAMA_CUDA_INSTALL_DIR} COMPONENT CUDA
     )
 endif()
 
@@ -115,11 +116,7 @@ if(CMAKE_HIP_COMPILER)
 
         set(OLLAMA_HIP_INSTALL_DIR ${OLLAMA_INSTALL_DIR}/rocm)
         install(TARGETS ggml-hip
-            RUNTIME_DEPENDENCY_SET rocm
-            RUNTIME DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT HIP
-            LIBRARY DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT HIP
-        )
-        install(RUNTIME_DEPENDENCY_SET rocm
+            RUNTIME_DEPENDENCIES
                 DIRECTORIES ${HIP_BIN_INSTALL_DIR} ${HIP_LIB_INSTALL_DIR}
                 PRE_INCLUDE_REGEXES hipblas rocblas amdhip64 rocsolver amd_comgr hsa-runtime64 rocsparse tinfo rocprofiler-register drm drm_amdgpu numa elf
                 PRE_EXCLUDE_REGEXES ".*"

CMakePresets.json

@@ -17,12 +17,20 @@
       "name": "CUDA",
       "inherits": [ "Default" ]
     },
+    {
+      "name": "CUDA 11",
+      "inherits": [ "CUDA" ],
+      "cacheVariables": {
+        "CMAKE_CUDA_ARCHITECTURES": "50;52;53;60;61;70;75;80;86",
+        "CMAKE_CUDA_FLAGS": "-Wno-deprecated-gpu-targets"
+      }
+    },
     {
       "name": "CUDA 12",
       "inherits": [ "CUDA" ],
       "cacheVariables": {
         "CMAKE_CUDA_ARCHITECTURES": "50;60;61;70;75;80;86;87;89;90;90a;120",
-        "CMAKE_CUDA_FLAGS": "-Wno-deprecated-gpu-targets -t 2"
+        "CMAKE_CUDA_FLAGS": "-Wno-deprecated-gpu-targets"
       }
     },
     {
@@ -50,7 +58,6 @@
       "name": "ROCm 6",
       "inherits": [ "ROCm" ],
       "cacheVariables": {
-        "CMAKE_HIP_FLAGS": "-parallel-jobs=4",
         "AMDGPU_TARGETS": "gfx900;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102;gfx1151;gfx1200;gfx1201;gfx906:xnack-;gfx908:xnack-;gfx90a:xnack+;gfx90a:xnack-"
       }
     }
@@ -71,6 +78,11 @@
       "configurePreset": "CUDA",
       "targets": [ "ggml-cuda" ]
     },
+    {
+      "name": "CUDA 11",
+      "inherits": [ "CUDA" ],
+      "configurePreset": "CUDA 11"
+    },
     {
       "name": "CUDA 12",
       "inherits": [ "CUDA" ],

Dockerfile

@@ -7,13 +7,12 @@ ARG JETPACK5VERSION=r35.4.1
 ARG JETPACK6VERSION=r36.4.0
 ARG CMAKEVERSION=3.31.2
 
-# We require gcc v10 minimum.  v10.3 has regressions, so the rockylinux 8.5 AppStream has the latest compatible version
+# CUDA v11 requires gcc v10.  v10.3 has regressions, so the rockylinux 8.5 AppStream has the latest compatible version
 FROM --platform=linux/amd64 rocm/dev-almalinux-8:${ROCMVERSION}-complete AS base-amd64
 RUN yum install -y yum-utils \
     && yum-config-manager --add-repo https://dl.rockylinux.org/vault/rocky/8.5/AppStream/\$basearch/os/ \
     && rpm --import https://dl.rockylinux.org/pub/rocky/RPM-GPG-KEY-Rocky-8 \
     && dnf install -y yum-utils ccache gcc-toolset-10-gcc-10.2.1-8.2.el8 gcc-toolset-10-gcc-c++-10.2.1-8.2.el8 gcc-toolset-10-binutils-2.35-11.el8 \
-    && dnf install -y ccache \
     && yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo
 ENV PATH=/opt/rh/gcc-toolset-10/root/usr/bin:$PATH
 
@@ -39,6 +38,15 @@ RUN --mount=type=cache,target=/root/.ccache \
         && cmake --build --parallel --preset 'CPU' \
         && cmake --install build --component CPU --strip --parallel 8
 
+FROM base AS cuda-11
+ARG CUDA11VERSION=11.3
+RUN dnf install -y cuda-toolkit-${CUDA11VERSION//./-}
+ENV PATH=/usr/local/cuda-11/bin:$PATH
+RUN --mount=type=cache,target=/root/.ccache \
+    cmake --preset 'CUDA 11' \
+        && cmake --build --parallel --preset 'CUDA 11' \
+        && cmake --install build --component CUDA --strip --parallel 8
+
 FROM base AS cuda-12
 ARG CUDA12VERSION=12.8
 RUN dnf install -y cuda-toolkit-${CUDA12VERSION//./-}
@@ -90,21 +98,23 @@ RUN --mount=type=cache,target=/root/.cache/go-build \
     go build -trimpath -buildmode=pie -o /bin/ollama .
 
 FROM --platform=linux/amd64 scratch AS amd64
-COPY --from=cuda-12 dist/lib/ollama /lib/ollama
+COPY --from=cuda-11 dist/lib/ollama/cuda_v11 /lib/ollama/cuda_v11
+COPY --from=cuda-12 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_v12
 
 FROM --platform=linux/arm64 scratch AS arm64
-COPY --from=cuda-12 dist/lib/ollama /lib/ollama/cuda_sbsa
-COPY --from=jetpack-5 dist/lib/ollama /lib/ollama/cuda_jetpack5
-COPY --from=jetpack-6 dist/lib/ollama /lib/ollama/cuda_jetpack6
+COPY --from=cuda-11 dist/lib/ollama/cuda_v11 /lib/ollama/cuda_v11
+COPY --from=cuda-12 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_v12
+COPY --from=jetpack-5 dist/lib/ollama/cuda_v11 /lib/ollama/cuda_jetpack5
+COPY --from=jetpack-6 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_jetpack6
 
 FROM scratch AS rocm
-COPY --from=rocm-6 dist/lib/ollama /lib/ollama
+COPY --from=rocm-6 dist/lib/ollama/rocm /lib/ollama/rocm
 
 FROM ${FLAVOR} AS archive
 COPY --from=cpu dist/lib/ollama /lib/ollama
 COPY --from=build /bin/ollama /bin/ollama
 
-FROM ubuntu:24.04
+FROM ubuntu:20.04
 RUN apt-get update \
     && apt-get install -y ca-certificates \
     && apt-get clean \

README.md

@@ -1,6 +1,6 @@
 <div align="center">
   <a href="https://ollama.com">
-    <img alt="ollama" width="240" src="https://github.com/ollama/ollama/assets/3325447/0d0b44e2-8f4a-4e99-9b52-a5c1c741c8f7">
+    <img alt="ollama" height="200px" src="https://github.com/ollama/ollama/assets/3325447/0d0b44e2-8f4a-4e99-9b52-a5c1c741c8f7">
   </a>
 </div>
 
@@ -10,7 +10,7 @@ Get up and running with large language models.
 
 ### macOS
 
-[Download](https://ollama.com/download/Ollama.dmg)
+[Download](https://ollama.com/download/Ollama-darwin.zip)
 
 ### Windows
 
@@ -40,10 +40,10 @@ The official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama) `olla
 
 ## Quickstart
 
-To run and chat with [Gemma 3](https://ollama.com/library/gemma3):
+To run and chat with [Llama 3.2](https://ollama.com/library/llama3.2):
 
 ```shell
-ollama run gemma3
+ollama run llama3.2
 ```
 
 ## Model library
@@ -360,7 +360,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Tkinter-based client](https://github.com/chyok/ollama-gui) (Python tkinter-based Client for Ollama)
 - [LLMChat](https://github.com/trendy-design/llmchat) (Privacy focused, 100% local, intuitive all-in-one chat interface)
 - [Local Multimodal AI Chat](https://github.com/Leon-Sander/Local-Multimodal-AI-Chat) (Ollama-based LLM Chat with support for multiple features, including PDF RAG, voice chat, image-based interactions, and integration with OpenAI.)
-- [ARGO](https://github.com/xark-argo/argo) (Locally download and run Ollama and Huggingface models with RAG and deep research on Mac/Windows/Linux)
+- [ARGO](https://github.com/xark-argo/argo) (Locally download and run Ollama and Huggingface models with RAG on Mac/Windows/Linux)
 - [OrionChat](https://github.com/EliasPereirah/OrionChat) - OrionChat is a web interface for chatting with different AI providers
 - [G1](https://github.com/bklieger-groq/g1) (Prototype of using prompting strategies to improve the LLM's reasoning through o1-like reasoning chains.)
 - [Web management](https://github.com/lemonit-eric-mao/ollama-web-management) (Web management page)
@@ -407,9 +407,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Lumina](https://github.com/cushydigit/lumina.git) (A lightweight, minimal React.js frontend for interacting with Ollama servers)
 - [Tiny Notepad](https://pypi.org/project/tiny-notepad) (A lightweight, notepad-like interface to chat with ollama available on PyPI)
 - [macLlama (macOS native)](https://github.com/hellotunamayo/macLlama) (A native macOS GUI application for interacting with Ollama models, featuring a chat interface.) 
-- [GPTranslate](https://github.com/philberndt/GPTranslate) (A fast and lightweight, AI powered desktop translation application written with Rust and Tauri. Features real-time translation with OpenAI/Azure/Ollama.)
-- [ollama launcher](https://github.com/NGC13009/ollama-launcher) (A launcher for Ollama, aiming to provide users with convenient functions such as ollama server launching, management, or configuration.)
-- [ai-hub](https://github.com/Aj-Seven/ai-hub) (AI Hub supports multiple models via API keys and Chat support via Ollama API.)
 
 ### Cloud
 
@@ -454,8 +451,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [orca-cli](https://github.com/molbal/orca-cli) Ollama Registry CLI Application - Browse, pull, and download models from Ollama Registry in your terminal.
 - [GGUF-to-Ollama](https://github.com/jonathanhecl/gguf-to-ollama) - Importing GGUF to Ollama made easy (multiplatform)
 - [AWS-Strands-With-Ollama](https://github.com/rapidarchitect/ollama_strands) - AWS Strands Agents with Ollama Examples
-- [ollama-multirun](https://github.com/attogram/ollama-multirun) - A bash shell script to run a single prompt against any or all of your locally installed ollama models, saving the output and performance statistics as easily navigable web pages. ([Demo](https://attogram.github.io/ai_test_zone/))
-- [ollama-bash-toolshed](https://github.com/attogram/ollama-bash-toolshed) - Bash scripts to chat with tool using models. Add new tools to your shed with ease. Runs on Ollama.
 
 ### Apple Vision Pro
 
@@ -594,12 +589,10 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [mcp-llm](https://github.com/sammcj/mcp-llm) (MCP Server to allow LLMs to call other LLMs)
 - [SimpleOllamaUnity](https://github.com/HardCodeDev777/SimpleOllamaUnity) (Unity Engine extension for communicating with Ollama in a few lines of code. Also works at runtime)
 - [UnityCodeLama](https://github.com/HardCodeDev777/UnityCodeLama) (Unity Edtior tool to analyze scripts via Ollama)
-- [NativeMind](https://github.com/NativeMindBrowser/NativeMindExtension) (Private, on-device AI Assistant, no cloud dependencies)
-- [GMAI - Gradle Managed AI](https://gmai.premex.se/) (Gradle plugin for automated Ollama lifecycle management during build phases)
 
 ### Supported backends
 
-- [llama.cpp](https://github.com/ggml-org/llama.cpp) project founded by Georgi Gerganov.
+- [llama.cpp](https://github.com/ggerganov/llama.cpp) project founded by Georgi Gerganov.
 
 ### Observability
 - [Opik](https://www.comet.com/docs/opik/cookbook/ollama) is an open-source platform to debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards. Opik supports native intergration to Ollama.

api/client.go

@@ -222,6 +222,10 @@ func (c *Client) stream(ctx context.Context, method, path string, data any, fn f
 			return fmt.Errorf("unmarshal: %w", err)
 		}
 
+		if errorResponse.Error != "" {
+			return errors.New(errorResponse.Error)
+		}
+
 		if response.StatusCode >= http.StatusBadRequest {
 			return StatusError{
 				StatusCode:   response.StatusCode,
@@ -230,10 +234,6 @@ func (c *Client) stream(ctx context.Context, method, path string, data any, fn f
 			}
 		}
 
-		if errorResponse.Error != "" {
-			return errors.New(errorResponse.Error)
-		}
-
 		if err := fn(bts); err != nil {
 			return err
 		}

api/client_test.go

@@ -89,16 +89,6 @@ func TestClientStream(t *testing.T) {
 			},
 			wantErr: "mid-stream error",
 		},
-		{
-			name: "http status error takes precedence over general error",
-			responses: []any{
-				testError{
-					message:    "custom error message",
-					statusCode: http.StatusInternalServerError,
-				},
-			},
-			wantErr: "500",
-		},
 		{
 			name: "successful stream completion",
 			responses: []any{

api/types.go

@@ -143,7 +143,6 @@ type Message struct {
 	Thinking  string      `json:"thinking,omitempty"`
 	Images    []ImageData `json:"images,omitempty"`
 	ToolCalls []ToolCall  `json:"tool_calls,omitempty"`
-	ToolName  string      `json:"tool_name,omitempty"`
 }
 
 func (m *Message) UnmarshalJSON(b []byte) error {
@@ -458,24 +457,24 @@ type ProcessResponse struct {
 
 // ListModelResponse is a single model description in [ListResponse].
 type ListModelResponse struct {
-	Name       string       `json:"name"`
-	Model      string       `json:"model"`
-	ModifiedAt time.Time    `json:"modified_at"`
-	Size       int64        `json:"size"`
-	Digest     string       `json:"digest"`
-	Details    ModelDetails `json:"details,omitempty"`
+	Name         string             `json:"name"`
+	Model        string             `json:"model"`
+	ModifiedAt   time.Time          `json:"modified_at"`
+	Size         int64              `json:"size"`
+	Digest       string             `json:"digest"`
+	Capabilities []model.Capability `json:"capabilities,omitempty"`
+	Details      ModelDetails       `json:"details,omitempty"`
 }
 
 // ProcessModelResponse is a single model description in [ProcessResponse].
 type ProcessModelResponse struct {
-	Name          string       `json:"name"`
-	Model         string       `json:"model"`
-	Size          int64        `json:"size"`
-	Digest        string       `json:"digest"`
-	Details       ModelDetails `json:"details,omitempty"`
-	ExpiresAt     time.Time    `json:"expires_at"`
-	SizeVRAM      int64        `json:"size_vram"`
-	ContextLength int          `json:"context_length"`
+	Name      string       `json:"name"`
+	Model     string       `json:"model"`
+	Size      int64        `json:"size"`
+	Digest    string       `json:"digest"`
+	Details   ModelDetails `json:"details,omitempty"`
+	ExpiresAt time.Time    `json:"expires_at"`
+	SizeVRAM  int64        `json:"size_vram"`
 }
 
 type TokenResponse struct {

benchmark/server_benchmark_test.go

@@ -0,0 +1,178 @@
+package benchmark
+
+import (
+	"context"
+	"flag"
+	"fmt"
+	"testing"
+	"time"
+
+	"github.com/ollama/ollama/api"
+)
+
+// Command line flags
+var modelFlag string
+
+func init() {
+	flag.StringVar(&modelFlag, "m", "", "Name of the model to benchmark")
+	flag.Lookup("m").DefValue = "model"
+}
+
+// modelName returns the model name from flags, failing the test if not set
+func modelName(b *testing.B) string {
+	if modelFlag == "" {
+		b.Fatal("Error: -m flag is required for benchmark tests")
+	}
+	return modelFlag
+}
+
+type TestCase struct {
+	name      string
+	prompt    string
+	maxTokens int
+}
+
+// runGenerateBenchmark contains the common generate and metrics logic
+func runGenerateBenchmark(b *testing.B, ctx context.Context, client *api.Client, req *api.GenerateRequest) {
+	start := time.Now()
+	var ttft time.Duration
+	var metrics api.Metrics
+
+	err := client.Generate(ctx, req, func(resp api.GenerateResponse) error {
+		if ttft == 0 && resp.Response != "" {
+			ttft = time.Since(start)
+		}
+		if resp.Done {
+			metrics = resp.Metrics
+		}
+		return nil
+	})
+
+	// Report custom metrics as part of the benchmark results
+	b.ReportMetric(float64(ttft.Milliseconds()), "ttft_ms")
+	b.ReportMetric(float64(metrics.LoadDuration.Milliseconds()), "load_ms")
+
+	// Token throughput metrics
+	promptThroughput := float64(metrics.PromptEvalCount) / metrics.PromptEvalDuration.Seconds()
+	genThroughput := float64(metrics.EvalCount) / metrics.EvalDuration.Seconds()
+	b.ReportMetric(promptThroughput, "prompt_tok/s")
+	b.ReportMetric(genThroughput, "gen_tok/s")
+
+	// Token counts
+	b.ReportMetric(float64(metrics.PromptEvalCount), "prompt_tokens")
+	b.ReportMetric(float64(metrics.EvalCount), "gen_tokens")
+	if err != nil {
+		b.Fatal(err)
+	}
+}
+
+// BenchmarkColdStart runs benchmarks with model loading from cold state
+func BenchmarkColdStart(b *testing.B) {
+	client := setup(b)
+	tests := []TestCase{
+		{"short_prompt", "Write a long story", 100},
+		{"medium_prompt", "Write a detailed economic analysis", 500},
+		{"long_prompt", "Write a comprehensive AI research paper", 1000},
+	}
+	m := modelName(b)
+
+	for _, tt := range tests {
+		b.Run(fmt.Sprintf("%s/cold/%s", m, tt.name), func(b *testing.B) {
+			ctx := b.Context()
+
+			// Set number of tokens as our throughput metric
+			b.SetBytes(int64(tt.maxTokens))
+
+			for b.Loop() {
+				b.StopTimer()
+				// Ensure model is unloaded before each iteration
+				unload(client, m, b)
+				b.StartTimer()
+
+				req := &api.GenerateRequest{
+					Model:   m,
+					Prompt:  tt.prompt,
+					Options: map[string]any{"num_predict": tt.maxTokens, "temperature": 0.1},
+				}
+
+				runGenerateBenchmark(b, ctx, client, req)
+			}
+		})
+	}
+}
+
+// BenchmarkWarmStart runs benchmarks with pre-loaded model
+func BenchmarkWarmStart(b *testing.B) {
+	client := setup(b)
+	tests := []TestCase{
+		{"short_prompt", "Write a long story", 100},
+		{"medium_prompt", "Write a detailed economic analysis", 500},
+		{"long_prompt", "Write a comprehensive AI research paper", 1000},
+	}
+	m := modelName(b)
+
+	for _, tt := range tests {
+		b.Run(fmt.Sprintf("%s/warm/%s", m, tt.name), func(b *testing.B) {
+			ctx := b.Context()
+
+			// Pre-warm the model
+			warmup(client, m, tt.prompt, b)
+
+			// Set number of tokens as our throughput metric
+			b.SetBytes(int64(tt.maxTokens))
+
+			for b.Loop() {
+				req := &api.GenerateRequest{
+					Model:   m,
+					Prompt:  tt.prompt,
+					Options: map[string]any{"num_predict": tt.maxTokens, "temperature": 0.1},
+				}
+
+				runGenerateBenchmark(b, ctx, client, req)
+			}
+		})
+	}
+}
+
+// setup verifies server and model availability
+func setup(b *testing.B) *api.Client {
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		b.Fatal(err)
+	}
+	if _, err := client.Show(b.Context(), &api.ShowRequest{Model: modelName(b)}); err != nil {
+		b.Fatalf("Model unavailable: %v", err)
+	}
+
+	return client
+}
+
+// warmup ensures the model is loaded and warmed up
+func warmup(client *api.Client, model string, prompt string, b *testing.B) {
+	for range 3 {
+		err := client.Generate(
+			context.Background(),
+			&api.GenerateRequest{
+				Model:   model,
+				Prompt:  prompt,
+				Options: map[string]any{"num_predict": 50, "temperature": 0.1},
+			},
+			func(api.GenerateResponse) error { return nil },
+		)
+		if err != nil {
+			b.Logf("Error during model warm-up: %v", err)
+		}
+	}
+}
+
+// unload forces model unloading using KeepAlive: 0 parameter
+func unload(client *api.Client, model string, b *testing.B) {
+	req := &api.GenerateRequest{
+		Model:     model,
+		KeepAlive: &api.Duration{Duration: 0},
+	}
+	if err := client.Generate(context.Background(), req, func(api.GenerateResponse) error { return nil }); err != nil {
+		b.Logf("Unload error: %v", err)
+	}
+	time.Sleep(1 * time.Second)
+}

cmd/cmd.go

@@ -583,13 +583,12 @@ func ListRunningHandler(cmd *cobra.Command, args []string) error {
 			} else {
 				until = format.HumanTime(m.ExpiresAt, "Never")
 			}
-			ctxStr := strconv.Itoa(m.ContextLength)
-			data = append(data, []string{m.Name, m.Digest[:12], format.HumanBytes(m.Size), procStr, ctxStr, until})
+			data = append(data, []string{m.Name, m.Digest[:12], format.HumanBytes(m.Size), procStr, until})
 		}
 	}
 
 	table := tablewriter.NewWriter(os.Stdout)
-	table.SetHeader([]string{"NAME", "ID", "SIZE", "PROCESSOR", "CONTEXT", "UNTIL"})
+	table.SetHeader([]string{"NAME", "ID", "SIZE", "PROCESSOR", "UNTIL"})
 	table.SetHeaderAlignment(tablewriter.ALIGN_LEFT)
 	table.SetAlignment(tablewriter.ALIGN_LEFT)
 	table.SetHeaderLine(false)
@@ -1080,11 +1079,10 @@ func chat(cmd *cobra.Command, opts runOptions) (*api.Message, error) {
 	var state *displayResponseState = &displayResponseState{}
 	var latest api.ChatResponse
 	var fullResponse strings.Builder
+	var role string
 	var thinkTagOpened bool = false
 	var thinkTagClosed bool = false
 
-	role := "assistant"
-
 	fn := func(response api.ChatResponse) error {
 		if response.Message.Content != "" || !opts.HideThinking {
 			p.StopAndClear()
@@ -1418,13 +1416,13 @@ func NewCLI() *cobra.Command {
 
 	createCmd := &cobra.Command{
 		Use:     "create MODEL",
-		Short:   "Create a model",
+		Short:   "Create a model from a Modelfile",
 		Args:    cobra.ExactArgs(1),
 		PreRunE: checkServerHeartbeat,
 		RunE:    CreateHandler,
 	}
 
-	createCmd.Flags().StringP("file", "f", "", "Name of the Modelfile (default \"Modelfile\")")
+	createCmd.Flags().StringP("file", "f", "", "Name of the Modelfile (default \"Modelfile\"")
 	createCmd.Flags().StringP("quantize", "q", "", "Quantize model to this level (e.g. q4_K_M)")
 
 	showCmd := &cobra.Command{

cmd/interactive.go

@@ -385,21 +385,18 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 				case "modelfile":
 					fmt.Println(resp.Modelfile)
 				case "parameters":
-					fmt.Println("Model defined parameters:")
 					if resp.Parameters == "" {
-						fmt.Println("  No additional parameters were specified for this model.")
+						fmt.Println("No parameters were specified for this model.")
 					} else {
-						for _, l := range strings.Split(resp.Parameters, "\n") {
-							fmt.Printf("  %s\n", l)
+						if len(opts.Options) > 0 {
+							fmt.Println("User defined parameters:")
+							for k, v := range opts.Options {
+								fmt.Printf("%-*s %v\n", 30, k, v)
+							}
+							fmt.Println()
 						}
-					}
-					fmt.Println()
-					if len(opts.Options) > 0 {
-						fmt.Println("User defined parameters:")
-						for k, v := range opts.Options {
-							fmt.Printf("  %-*s %v\n", 30, k, v)
-						}
-						fmt.Println()
+						fmt.Println("Model defined parameters:")
+						fmt.Println(resp.Parameters)
 					}
 				case "system":
 					switch {

cmd/start_darwin.go

@@ -5,7 +5,7 @@ import (
 	"errors"
 	"os"
 	"os/exec"
-	"regexp"
+	"strings"
 
 	"github.com/ollama/ollama/api"
 )
@@ -19,12 +19,11 @@ func startApp(ctx context.Context, client *api.Client) error {
 	if err != nil {
 		return err
 	}
-	r := regexp.MustCompile(`^.*/Ollama\s?\d*.app`)
-	m := r.FindStringSubmatch(link)
-	if len(m) != 1 {
+	if !strings.Contains(link, "Ollama.app") {
 		return errors.New("could not find ollama app")
 	}
-	if err := exec.Command("/usr/bin/open", "-j", "-a", m[0], "--args", "--fast-startup").Run(); err != nil {
+	path := strings.Split(link, "Ollama.app")
+	if err := exec.Command("/usr/bin/open", "-j", "-a", path[0]+"Ollama.app").Run(); err != nil {
 		return err
 	}
 	return waitForServer(ctx, client)

cmd/start_windows.go

@@ -47,7 +47,7 @@ func startApp(ctx context.Context, client *api.Client) error {
 	}
 
 	cmd_path := "c:\\Windows\\system32\\cmd.exe"
-	cmd := exec.Command(cmd_path, "/c", appExe, "--hide", "--fast-startup")
+	cmd := exec.Command(cmd_path, "/c", appExe, "hidden")
 	cmd.SysProcAttr = &syscall.SysProcAttr{CreationFlags: 0x08000000, HideWindow: true}
 
 	cmd.Stdin = strings.NewReader("")

convert/convert.go

@@ -190,8 +190,6 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
 		conv = &gemma2Model{}
 	case "Gemma3ForCausalLM", "Gemma3ForConditionalGeneration":
 		conv = &gemma3Model{Architecture: p.Architectures[0]}
-	case "Gemma3nForConditionalGeneration":
-		conv = &gemma3nModel{}
 	case "Phi3ForCausalLM":
 		conv = &phi3Model{}
 	case "Qwen2ForCausalLM":

convert/convert_gemma3n.go

@@ -1,165 +0,0 @@
-package convert
-
-import (
-	"slices"
-	"strings"
-
-	"github.com/ollama/ollama/fs/ggml"
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-	"gonum.org/v1/gonum/stat/distuv"
-)
-
-type gemma3nModel struct {
-	ModelParameters
-
-	TextModel struct {
-		ActivationSparsityPattern []float32 `json:"activation_sparsity_pattern"`
-		AltupActiveIdx            uint32    `json:"altup_active_idx"`
-		AltupCoefClip             float32   `json:"altup_coef_clip"`
-		AltupCorrectScale         bool      `json:"altup_correct_scale"`
-		AltupLRMultiplier         float32   `json:"altup_lr_multiplier"`
-		AltupNumInputs            uint32    `json:"altup_num_inputs"`
-		HeadDim                   uint32    `json:"head_dim"`
-		HiddenSize                uint32    `json:"hidden_size"`
-		HiddenSizePerLayerInput   uint32    `json:"hidden_size_per_layer_input"`
-		IntermediateSize          uint32    `json:"intermediate_size"`
-		MaxPositionEmbeddings     uint32    `json:"max_position_embeddings"`
-		NumAttentionHeads         uint32    `json:"num_attention_heads"`
-		NumHiddenLayers           uint32    `json:"num_hidden_layers"`
-		NumKeyValueHeads          uint32    `json:"num_key_value_heads"`
-		NumKVSharedLayers         uint32    `json:"num_kv_shared_layers"`
-		RMSNormEPS                float32   `json:"rms_norm_eps"`
-		RopeLocalBaseFreq         float32   `json:"rope_local_base_freq"`
-		RopeTheta                 float32   `json:"rope_theta"`
-		SlidingWindow             uint32    `json:"sliding_window"`
-		LayerTypes                []string  `json:"layer_types"`
-	} `json:"text_config"`
-	VisionModel struct{} `json:"vision_config"`
-}
-
-func (m *gemma3nModel) KV(t *Tokenizer) ggml.KV {
-	kv := m.ModelParameters.KV(t)
-	kv["general.architecture"] = "gemma3n"
-	kv["gemma3n.activation_sparsity_scale"] = slices.Collect(func(yield func(float32) bool) {
-		norm := distuv.Normal{Mu: 0, Sigma: 1}
-		for _, v := range m.TextModel.ActivationSparsityPattern {
-			if !yield(float32(norm.Quantile(float64(v)))) {
-				break
-			}
-		}
-	})
-	kv["gemma3n.altup.active_idx"] = m.TextModel.AltupActiveIdx
-	kv["gemma3n.altup.correct_scale"] = m.TextModel.AltupCorrectScale
-	kv["gemma3n.altup.lr_multiplier"] = m.TextModel.AltupLRMultiplier
-	kv["gemma3n.altup.num_inputs"] = m.TextModel.AltupNumInputs
-	kv["gemma3n.attention.head_count_kv"] = m.TextModel.NumKeyValueHeads
-	kv["gemma3n.attention.head_count"] = m.TextModel.NumAttentionHeads
-	kv["gemma3n.attention.layer_norm_rms_epsilon"] = m.TextModel.RMSNormEPS
-	kv["gemma3n.attention.sliding_window"] = m.TextModel.SlidingWindow
-	kv["gemma3n.attention.sliding_window_pattern"] = slices.Collect(func(yield func(bool) bool) {
-		for _, t := range m.TextModel.LayerTypes {
-			if !yield(t == "sliding_attention") {
-				break
-			}
-		}
-	})
-	kv["gemma3n.attention.shared_kv_layers"] = m.TextModel.NumKVSharedLayers
-	kv["gemma3n.block_count"] = m.TextModel.NumHiddenLayers
-	kv["gemma3n.context_length"] = m.TextModel.MaxPositionEmbeddings
-	kv["gemma3n.embedding_length_per_layer_input"] = m.TextModel.HiddenSizePerLayerInput
-	kv["gemma3n.embedding_length"] = m.TextModel.HiddenSize
-	kv["gemma3n.feed_forward_length"] = m.TextModel.IntermediateSize
-	kv["gemma3n.head_dim"] = m.TextModel.HeadDim
-	kv["gemma3n.rope.freq_base_local"] = m.TextModel.RopeLocalBaseFreq
-	kv["gemma3n.rope.freq_base"] = m.TextModel.RopeTheta
-	return kv
-}
-
-func (m *gemma3nModel) Tensors(ts []Tensor) []*ggml.Tensor {
-	out, ts := mergeTensors(ts,
-		merge{"altup_proj.*.weight", "altup_proj.weight"},
-		merge{"altup_unembd_proj.*.weight", "altup_unembd_proj.weight"},
-	)
-
-	for _, t := range ts {
-		switch {
-		case strings.Contains(t.Name(), "audio_tower"),
-			strings.Contains(t.Name(), "embed_audio"),
-			strings.Contains(t.Name(), "vision_tower"),
-			strings.Contains(t.Name(), "embed_vision"):
-			// TODO: handle audio and vision towers
-			continue
-		case strings.Contains(t.Name(), "altup_predict_coef"),
-			strings.Contains(t.Name(), "altup_correct_coef"):
-			if m.TextModel.AltupCoefClip > 0 {
-				t.SetRepacker(func(name string, data []float32, shape []uint64) (_ []float32, err error) {
-					dims := make([]int, len(shape))
-					for i := range shape {
-						dims[i] = int(shape[i])
-					}
-
-					var t tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
-
-					t, err = tensor.Clamp(t, -m.TextModel.AltupCoefClip, m.TextModel.AltupCoefClip)
-					if err != nil {
-						return nil, err
-					}
-
-					if err := t.Reshape(t.Shape().TotalSize()); err != nil {
-						return nil, err
-					}
-
-					return native.VectorF32(t.(*tensor.Dense))
-				})
-			}
-		}
-
-		out = append(out, &ggml.Tensor{
-			Name:     t.Name(),
-			Kind:     t.Kind(),
-			Shape:    t.Shape(),
-			WriterTo: t,
-		})
-	}
-
-	return out
-}
-
-func (m *gemma3nModel) Replacements() []string {
-	return []string{
-		"model.language_model.embed_tokens_per_layer", "per_layer_token_embd",
-		"model.language_model.embed_tokens", "token_embd",
-		"model.language_model.per_layer_model_projection", "per_layer_model_proj",
-		"model.language_model.per_layer_projection_norm", "per_layer_proj_norm", "model.language_model.altup_projections", "altup_proj",
-		"model.language_model.altup_unembed_projections", "altup_unembd_proj",
-		"model.language_model.norm", "output_norm",
-		"model.language_model.layers", "blk",
-
-		"input_layernorm", "attn_norm",
-		"self_attn.q_proj", "attn_q",
-		"self_attn.q_norm", "attn_q_norm",
-		"self_attn.k_proj", "attn_k",
-		"self_attn.k_norm", "attn_k_norm",
-		"self_attn.v_proj", "attn_v",
-		"self_attn.o_proj", "attn_output",
-		"post_attention_layernorm", "post_attention_norm",
-		"pre_feedforward_layernorm", "ffn_norm",
-		"mlp.gate_proj", "ffn_gate",
-		"mlp.up_proj", "ffn_up",
-		"mlp.down_proj", "ffn_down",
-		"post_feedforward_layernorm", "post_ffw_norm",
-		"per_layer_input_gate", "inp_gate",
-		"per_layer_projection", "proj",
-		"post_per_layer_input_norm", "post_norm",
-		"altup.", "altup_",
-		"modality_router", "router",
-		"prediction_coefs", "predict_coef",
-		"correction_coefs", "correct_coef",
-		"correct_output_scale", "correct_scale.weight",
-		"laurel.", "laurel_",
-		"linear_left", "l",
-		"linear_right", "r",
-		"post_laurel_norm", "post_norm",
-	}
-}

convert/convert_mixtral.go

@@ -2,6 +2,9 @@ package convert
 
 import (
 	"fmt"
+	"io"
+	"slices"
+	"strings"
 
 	"github.com/ollama/ollama/fs/ggml"
 )
@@ -27,38 +30,65 @@ func (p *mixtralModel) KV(t *Tokenizer) ggml.KV {
 }
 
 func (p *mixtralModel) Tensors(ts []Tensor) []*ggml.Tensor {
-	merges := make([]merge, 0, p.NumHiddenLayers*6)
-	for i := range p.NumHiddenLayers {
-		merges = append(merges, merge{
-			fmt.Sprintf("blk.%d.*.w1.weight", i),
-			fmt.Sprintf("blk.%d.ffn_gate_exps.weight", i),
-		}, merge{
-			fmt.Sprintf("blk.%d.*.w1.bias", i),
-			fmt.Sprintf("blk.%d.ffn_gate_exps.bias", i),
-		}, merge{
-			fmt.Sprintf("blk.%d.*.w2.weight", i),
-			fmt.Sprintf("blk.%d.ffn_up_exps.weight", i),
-		}, merge{
-			fmt.Sprintf("blk.%d.*.w2.bias", i),
-			fmt.Sprintf("blk.%d.ffn_up_exps.bias", i),
-		}, merge{
-			fmt.Sprintf("blk.%d.*.w3.weight", i),
-			fmt.Sprintf("blk.%d.ffn_down_exps.weight", i),
-		}, merge{
-			fmt.Sprintf("blk.%d.*.w3.bias", i),
-			fmt.Sprintf("blk.%d.ffn_down_exps.bias", i),
+	oldnew := []string{
+		"model.layers", "blk",
+		"w1", "ffn_gate_exps",
+		"w2", "ffn_down_exps",
+		"w3", "ffn_up_exps",
+	}
+
+	for i := range p.NumLocalExperts {
+		oldnew = append(oldnew, fmt.Sprintf(".block_sparse_moe.experts.%d.", i), ".")
+	}
+
+	// group experts of the same layer (model.layers.%d) and type (w[123]) into a single tensor
+	namer := strings.NewReplacer(oldnew...)
+	experts := make(map[string]experts)
+
+	// merge experts into a single tensor while removing them from ts
+	ts = slices.DeleteFunc(ts, func(t Tensor) bool {
+		if !strings.Contains(t.Name(), ".block_sparse_moe.experts.") {
+			return false
+		}
+
+		name := namer.Replace(t.Name())
+		experts[name] = append(experts[name], t)
+		return true
+	})
+
+	var out []*ggml.Tensor
+	for n, e := range experts {
+		// TODO(mxyng): sanity check experts
+		out = append(out, &ggml.Tensor{
+			Name:     n,
+			Kind:     e[0].Kind(),
+			Shape:    append([]uint64{uint64(len(e))}, e[0].Shape()...),
+			WriterTo: e,
 		})
 	}
 
-	out, ts := mergeTensors(ts, merges...)
 	return append(out, p.llamaModel.Tensors(ts)...)
 }
 
 func (p *mixtralModel) Replacements() []string {
 	return append(
 		p.llamaModel.Replacements(),
-		"model.layers", "blk",
 		"block_sparse_moe.gate", "ffn_gate_inp",
-		"block_sparse_moe.experts.", ".",
 	)
 }
+
+type experts []Tensor
+
+func (e experts) WriteTo(w io.Writer) (int64, error) {
+	// TODO(mxyng): experts _should_ be numerically sorted by expert but this should check
+	for _, t := range e {
+		// the canonical merged experts tensor stacks all experts along a new, 0 axis,
+		// e.g. `tensor.Stack(0, e[0], e[1:]...)`, which requires allocating temporary buffers
+		// this accomplishes the same thing by writing each expert tensor in sequence
+		if _, err := t.WriteTo(w); err != nil {
+			return 0, err
+		}
+	}
+
+	return 0, nil
+}

convert/convert_qwen25vl.go

@@ -65,17 +65,17 @@ func (q *qwen25VLModel) Tensors(ts []Tensor) []*ggml.Tensor {
 	for _, t := range ts {
 		if strings.Contains(t.Name(), "patch_embed.proj") {
 			for t := range splitDim(t, 2,
-				split{Replacer: strings.NewReplacer("patch_embed.proj", "patch_embd_0")},
-				split{Replacer: strings.NewReplacer("patch_embed.proj", "patch_embd_1")},
+				strings.NewReplacer("patch_embed.proj", "patch_embd_0"),
+				strings.NewReplacer("patch_embed.proj", "patch_embd_1"),
 			) {
 				t.Shape = slices.DeleteFunc(t.Shape, func(i uint64) bool { return i == 1 })
 				out = append(out, t)
 			}
 		} else if strings.Contains(t.Name(), "attn.qkv") {
 			out = append(out, slices.Collect(splitDim(t, 0,
-				split{Replacer: strings.NewReplacer("attn.qkv", "attn_q")},
-				split{Replacer: strings.NewReplacer("attn.qkv", "attn_k")},
-				split{Replacer: strings.NewReplacer("attn.qkv", "attn_v")},
+				strings.NewReplacer("attn.qkv", "attn_q"),
+				strings.NewReplacer("attn.qkv", "attn_k"),
+				strings.NewReplacer("attn.qkv", "attn_v"),
 			))...)
 		} else {
 			out = append(out, &ggml.Tensor{

convert/tensor.go

@@ -1,129 +1,56 @@
 package convert
 
 import (
-	"cmp"
-	"io"
 	"iter"
-	"path"
 	"slices"
 	"strings"
 
+	"github.com/ollama/ollama/fs/ggml"
 	"github.com/pdevine/tensor"
 	"github.com/pdevine/tensor/native"
-
-	"github.com/ollama/ollama/fs/ggml"
 )
 
-type split struct {
-	*strings.Replacer
-	dim int
-
-	// fn is an optional function to apply to the tensor after slicing
-	fn func(tensor.Tensor) (tensor.Tensor, error)
-}
-
 // splitDim splits a tensor along a specified dimension into multiple tensors. The dimension
-// is split evenly based on the number of replacers provided unless a specific count is given.
-func splitDim(t Tensor, dim int, splits ...split) iter.Seq[*ggml.Tensor] {
+// is split evenly based on the number of replacers provided.
+func splitDim(t Tensor, dim int, replacers ...*strings.Replacer) iter.Seq[*ggml.Tensor] {
 	return func(yield func(*ggml.Tensor) bool) {
-		var offset int
-		for _, split := range splits {
-			t := t.Clone()
+		for i, replacer := range replacers {
 			shape := slices.Clone(t.Shape())
-			shape[dim] = cmp.Or(uint64(split.dim), shape[dim]/uint64(len(splits)))
+			shape[dim] = shape[dim] / uint64(len(replacers))
 
 			slice := slices.Repeat([]tensor.Slice{nil}, len(shape))
-			slice[dim] = tensor.S(offset, offset+int(shape[dim]))
-			offset += int(shape[dim])
+			slice[dim] = tensor.S(i*int(shape[dim]), (i+1)*int(shape[dim]))
 
-			t.SetRepacker(func(_ string, data []float32, shape []uint64) ([]float32, error) {
+			tt := t.Clone()
+			tt.SetRepacker(func(_ string, data []float32, shape []uint64) ([]float32, error) {
 				dims := make([]int, len(shape))
 				for i := range shape {
 					dims[i] = int(shape[i])
 				}
 
-				var tt tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
-				tt, err := tt.Slice(slice...)
+				var t tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
+				t, err := t.Slice(slice...)
 				if err != nil {
 					return nil, err
 				}
 
-				tt = tensor.Materialize(tt)
-
-				if split.fn != nil {
-					tt, err = split.fn(tt)
-					if err != nil {
-						return nil, err
-					}
-				}
-
+				t = tensor.Materialize(t)
 				// flatten tensor so it can be written as a vector
-				if err := tt.Reshape(tt.Shape().TotalSize()); err != nil {
+				if err := t.Reshape(t.Shape().TotalSize()); err != nil {
 					return nil, err
 				}
 
-				return native.VectorF32(tt.(*tensor.Dense))
+				return native.VectorF32(t.(*tensor.Dense))
 			})
 
 			if !yield(&ggml.Tensor{
-				Name:     split.Replace(t.Name()),
+				Name:     replacer.Replace(t.Name()),
 				Kind:     t.Kind(),
 				Shape:    shape,
-				WriterTo: t,
+				WriterTo: tt,
 			}) {
 				break
 			}
 		}
 	}
 }
-
-type merge struct {
-	pattern, name string
-}
-
-// mergeTensors merges tensors that match a given pattern into a single tensor.
-func mergeTensors(unmatched []Tensor, merges ...merge) (out []*ggml.Tensor, _ []Tensor) {
-	var matched []Tensor
-	for i := range merges {
-		matched, unmatched = slicesSplitFunc(unmatched, func(t Tensor) bool {
-			matched, _ := path.Match(merges[i].pattern, t.Name())
-			return matched
-		})
-
-		if len(matched) > 0 {
-			out = append(out, &ggml.Tensor{
-				Name:     merges[i].name,
-				Kind:     matched[0].Kind(),
-				Shape:    append([]uint64{uint64(len(matched))}, matched[0].Shape()...),
-				WriterTo: mergeGroup(matched),
-			})
-		}
-	}
-
-	return out, unmatched
-}
-
-// slicesSplitFunc splits a slice into two slices based on a predicate function.
-func slicesSplitFunc[S ~[]E, E comparable](s S, fn func(e E) bool) (matched, unmatched S) {
-	for _, e := range s {
-		if fn(e) {
-			matched = append(matched, e)
-		} else {
-			unmatched = append(unmatched, e)
-		}
-	}
-
-	return matched, unmatched
-}
-
-type mergeGroup []Tensor
-
-func (g mergeGroup) WriteTo(w io.Writer) (int64, error) {
-	for _, t := range g {
-		if _, err := t.WriteTo(w); err != nil {
-			return 0, err
-		}
-	}
-
-	return 0, nil
-}

convert/tensor_test.go

@@ -1,402 +0,0 @@
-package convert
-
-import (
-	"bytes"
-	"encoding/binary"
-	"io"
-	"iter"
-	"slices"
-	"strings"
-	"testing"
-
-	"github.com/google/go-cmp/cmp"
-	"github.com/ollama/ollama/fs/ggml"
-	"github.com/pdevine/tensor"
-)
-
-type fakeTensor struct {
-	name  string
-	shape []uint64
-	data  []float32
-
-	repacker Repacker
-}
-
-func (f fakeTensor) Name() string {
-	return f.name
-}
-
-func (f fakeTensor) Shape() []uint64 {
-	return f.shape
-}
-
-func (f fakeTensor) Kind() uint32 {
-	return 0
-}
-
-func (f *fakeTensor) SetRepacker(fn Repacker) {
-	f.repacker = fn
-}
-
-func (f fakeTensor) Clone() Tensor {
-	return &fakeTensor{
-		name:     f.name,
-		shape:    slices.Clone(f.shape),
-		data:     slices.Clone(f.data),
-		repacker: f.repacker,
-	}
-}
-
-func (f fakeTensor) WriteTo(w io.Writer) (n int64, err error) {
-	data := f.data
-	if f.repacker != nil {
-		data, err = f.repacker(f.name, data, f.shape)
-		if err != nil {
-			return 0, err
-		}
-	}
-
-	if err := binary.Write(w, binary.LittleEndian, data); err != nil {
-		return 0, err
-	}
-
-	return int64(len(data) * 4), nil
-}
-
-func mul(shape []uint64) int {
-	n := 1
-	for _, dim := range shape {
-		n *= int(dim)
-	}
-	return n
-}
-
-func TestSplitDim(t *testing.T) {
-	r := fakeTensor{
-		name:  "a.b",
-		shape: []uint64{3, 4},
-		data:  []float32{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11},
-	}
-
-	t.Run("no split", func(t *testing.T) {
-		for tt := range splitDim(&r, 0, split{Replacer: strings.NewReplacer("a", "x")}) {
-			if tt.Name != "x.b" {
-				t.Fatalf("expected name 'x', got '%s'", tt.Name)
-			}
-
-			if !slices.Equal(tt.Shape, []uint64{3, 4}) {
-				t.Fatalf("expected shape [3, 4], got %v", tt.Shape)
-			}
-
-			var b bytes.Buffer
-			if _, err := tt.WriteTo(&b); err != nil {
-				t.Fatal(err)
-			}
-
-			f32s := make([]float32, mul(tt.Shape))
-			if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
-				t.Fatal(err)
-			}
-
-			if !slices.Equal(f32s, []float32{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}) {
-				t.Fatalf("expected data [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], got %v", f32s)
-			}
-		}
-	})
-
-	t.Run("even split", func(t *testing.T) {
-		next, stop := iter.Pull(splitDim(&r, 1,
-			split{Replacer: strings.NewReplacer("a", "x")},
-			split{Replacer: strings.NewReplacer("b", "y")},
-		))
-		defer stop()
-
-		{
-			tt, ok := next()
-			if !ok {
-				t.Fatal("expected at least one split")
-			}
-
-			if tt.Name != "x.b" {
-				t.Fatal("expected name 'x.b', got", tt.Name)
-			}
-
-			if !slices.Equal(tt.Shape, []uint64{3, 2}) {
-				t.Fatal("expected shape [3, 2], got", tt.Shape)
-			}
-
-			var b bytes.Buffer
-			if _, err := tt.WriteTo(&b); err != nil {
-				t.Fatal(err)
-			}
-
-			f32s := make([]float32, mul(tt.Shape))
-			if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
-				t.Fatal(err)
-			}
-
-			if !slices.Equal(f32s, []float32{0, 1, 4, 5, 8, 9}) {
-				t.Fatal("expected data [0, 1, 4, 5, 8, 9], got", f32s)
-			}
-		}
-
-		{
-			tt, ok := next()
-			if !ok {
-				t.Fatal("expected at least one split")
-			}
-
-			if tt.Name != "a.y" {
-				t.Fatal("expected name 'a.y', got", tt.Name)
-			}
-
-			if !slices.Equal(tt.Shape, []uint64{3, 2}) {
-				t.Fatal("expected shape [3, 2], got", tt.Shape)
-			}
-
-			var b bytes.Buffer
-			if _, err := tt.WriteTo(&b); err != nil {
-				t.Fatal(err)
-			}
-
-			f32s := make([]float32, mul(tt.Shape))
-			if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
-				t.Fatal(err)
-			}
-
-			if !slices.Equal(f32s, []float32{2, 3, 6, 7, 10, 11}) {
-				t.Fatal("expected data [2, 3, 6, 7, 10, 11], got", f32s)
-			}
-		}
-	})
-
-	t.Run("uneven split", func(t *testing.T) {
-		next, stop := iter.Pull(splitDim(&r, 0,
-			split{Replacer: strings.NewReplacer("a", "x"), dim: 2},
-			split{Replacer: strings.NewReplacer("b", "y"), dim: 1},
-		))
-		defer stop()
-
-		{
-			tt, ok := next()
-			if !ok {
-				t.Fatal("expected at least one split")
-			}
-
-			if tt.Name != "x.b" {
-				t.Fatal("expected name 'x.b', got", tt.Name)
-			}
-
-			if !slices.Equal(tt.Shape, []uint64{2, 4}) {
-				t.Fatal("expected shape [2, 4], got", tt.Shape)
-			}
-
-			var b bytes.Buffer
-			if _, err := tt.WriteTo(&b); err != nil {
-				t.Fatal(err)
-			}
-
-			f32s := make([]float32, mul(tt.Shape))
-			if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
-				t.Fatal(err)
-			}
-
-			if !slices.Equal(f32s, []float32{0, 1, 2, 3, 4, 5, 6, 7}) {
-				t.Fatal("expected data [0, 1, 2, 3, 4, 5, 6, 7], got", f32s)
-			}
-		}
-
-		{
-			tt, ok := next()
-			if !ok {
-				t.Fatal("expected at least one split")
-			}
-
-			if tt.Name != "a.y" {
-				t.Fatal("expected name 'a.y', got", tt.Name)
-			}
-
-			if !slices.Equal(tt.Shape, []uint64{1, 4}) {
-				t.Fatal("expected shape [1, 4], got", tt.Shape)
-			}
-
-			var b bytes.Buffer
-			if _, err := tt.WriteTo(&b); err != nil {
-				t.Fatal(err)
-			}
-
-			f32s := make([]float32, mul(tt.Shape))
-			if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
-				t.Fatal(err)
-			}
-
-			if !slices.Equal(f32s, []float32{8, 9, 10, 11}) {
-				t.Fatal("expected data [8, 9, 10, 11], got", f32s)
-			}
-		}
-	})
-
-	t.Run("split with transpose", func(t *testing.T) {
-		next, stop := iter.Pull(splitDim(&r, 1,
-			split{Replacer: strings.NewReplacer("a", "x")},
-			split{Replacer: strings.NewReplacer("b", "y"), fn: func(tt tensor.Tensor) (tensor.Tensor, error) {
-				return tensor.Transpose(tt, 1, 0)
-			}},
-		))
-		defer stop()
-
-		{
-			tt, ok := next()
-			if !ok {
-				t.Fatal("expected at least one split")
-			}
-
-			if tt.Name != "x.b" {
-				t.Fatal("expected name 'x.b', got", tt.Name)
-			}
-
-			if !slices.Equal(tt.Shape, []uint64{3, 2}) {
-				t.Fatal("expected shape [3, 2], got", tt.Shape)
-			}
-
-			var b bytes.Buffer
-			if _, err := tt.WriteTo(&b); err != nil {
-				t.Fatal(err)
-			}
-
-			f32s := make([]float32, mul(tt.Shape))
-			if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
-				t.Fatal(err)
-			}
-
-			if !slices.Equal(f32s, []float32{0, 1, 4, 5, 8, 9}) {
-				t.Fatal("expected data [0, 1, 4, 5, 8, 9], got", f32s)
-			}
-		}
-
-		{
-			tt, ok := next()
-			if !ok {
-				t.Fatal("expected at least one split")
-			}
-
-			if tt.Name != "a.y" {
-				t.Fatal("expected name 'a.y', got", tt.Name)
-			}
-
-			if !slices.Equal(tt.Shape, []uint64{3, 2}) {
-				t.Fatal("expected shape [3, 2], got", tt.Shape)
-			}
-
-			var b bytes.Buffer
-			if _, err := tt.WriteTo(&b); err != nil {
-				t.Fatal(err)
-			}
-
-			f32s := make([]float32, mul(tt.Shape))
-			if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
-				t.Fatal(err)
-			}
-
-			if !slices.Equal(f32s, []float32{2, 6, 10, 3, 7, 11}) {
-				t.Fatal("expected data [2, 6, 10, 3, 7, 11], got", f32s)
-			}
-		}
-	})
-}
-
-func TestMerge(t *testing.T) {
-	unmatched := []Tensor{
-		&fakeTensor{
-			name:  "a.0.b",
-			shape: []uint64{5, 2},
-			data:  []float32{10, 11, 12, 13, 14, 15, 16, 17, 18, 19},
-		},
-		&fakeTensor{
-			name:  "a.1.b",
-			shape: []uint64{5, 2},
-			data:  []float32{20, 21, 22, 23, 24, 25, 26, 27, 28, 29},
-		},
-		&fakeTensor{
-			name:  "c.0.d",
-			shape: []uint64{5, 2},
-			data:  []float32{30, 31, 32, 33, 34, 35, 36, 37, 38, 39},
-		},
-		&fakeTensor{
-			name:  "c.1.d",
-			shape: []uint64{5, 2},
-			data:  []float32{40, 41, 42, 43, 44, 45, 46, 47, 48, 49},
-		},
-		&fakeTensor{
-			name:  "e.0.f",
-			shape: []uint64{5, 2},
-			data:  []float32{50, 51, 52, 53, 54, 55, 56, 57, 58, 59},
-		},
-	}
-
-	checkMatched := func(t *testing.T, n int, matched []*ggml.Tensor) {
-		for i := range n {
-			got := matched[i]
-			if diff := cmp.Diff([]uint64{2, 5, 2}, got.Shape); diff != "" {
-				t.Errorf("unexpected (-want +got):\n%s", diff)
-			}
-
-			var b bytes.Buffer
-			if _, err := got.WriteTo(&b); err != nil {
-				t.Fatal(err)
-			}
-
-			f32s := make([]float32, 20)
-			if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
-				t.Fatal(err)
-			}
-
-			offset := 10 + (i * 20)
-			want := make([]float32, 20)
-			for j := range 20 {
-				want[j] = float32(offset + j)
-			}
-
-			if diff := cmp.Diff(want, f32s); diff != "" {
-				t.Errorf("unexpected data (-want +got):\n%s", diff)
-			}
-		}
-	}
-
-	t.Run("single merge", func(t *testing.T) {
-		matched, unmatched := mergeTensors(unmatched, merge{"a.*.b", "a.b"})
-		if len(unmatched) != 3 {
-			t.Error("expected 3 remaining tensors, got", len(unmatched))
-		}
-
-		if len(matched) != 1 {
-			t.Error("expected 1 merged tensor, got", len(matched))
-		}
-
-		checkMatched(t, 1, matched)
-	})
-
-	t.Run("multiple merges", func(t *testing.T) {
-		matched, unmatched := mergeTensors(unmatched, merge{"a.*.b", "a.b"}, merge{"c.*.d", "c.d"})
-		if len(unmatched) != 1 {
-			t.Error("expected 1 remaining tensors, got", len(unmatched))
-		}
-
-		if len(matched) != 2 {
-			t.Error("expected 2 merged tensor, got", len(matched))
-		}
-
-		checkMatched(t, 2, matched)
-	})
-
-	t.Run("no match", func(t *testing.T) {
-		matched, unmatched := mergeTensors(unmatched, merge{"x.*.y", "x.y"})
-		if len(unmatched) != 5 {
-			t.Error("expected 5 remaining tensors, got", len(unmatched))
-		}
-
-		if len(matched) != 0 {
-			t.Error("expected no merged tensors, got", len(matched))
-		}
-	})
-}

discover/cuda_common.go

@@ -3,7 +3,6 @@
 package discover
 
 import (
-	"fmt"
 	"log/slog"
 	"os"
 	"regexp"
@@ -56,13 +55,10 @@ func cudaVariant(gpuInfo CudaGPUInfo) string {
 				}
 			}
 		}
-		return "sbsa"
 	}
 
 	// driver 12.0 has problems with the cuda v12 library, so run v11 on those older drivers
 	if gpuInfo.DriverMajor < 12 || (gpuInfo.DriverMajor == 12 && gpuInfo.DriverMinor == 0) {
-		// The detected driver is older than Feb 2023
-		slog.Warn("old CUDA driver detected - please upgrade to a newer driver", "version", fmt.Sprintf("%d.%d", gpuInfo.DriverMajor, gpuInfo.DriverMinor))
 		return "v11"
 	}
 	return "v12"

discover/path.go

@@ -12,7 +12,7 @@ import (
 // '../lib/ollama' on Linux and the executable's directory on macOS
 // note: distribution builds, additional GPU-specific libraries are
 // found in subdirectories of the returned path, such as
-// 'cuda_v12', 'rocm', etc.
+// 'cuda_v11', 'cuda_v12', 'rocm', etc.
 var LibOllamaPath string = func() string {
 	exe, err := os.Executable()
 	if err != nil {

docs/README.md

@@ -4,7 +4,6 @@
 * [Quickstart](../README.md#quickstart)
 * [Examples](./examples.md)
 * [Importing models](./import.md)
-* [MacOS Documentation](./macos.md)
 * [Linux Documentation](./linux.md)
 * [Windows Documentation](./windows.md)
 * [Docker Documentation](./docker.md)

docs/api.md

@@ -500,7 +500,6 @@ The `message` object has the following fields:
 - `thinking`: (for thinking models) the model's thinking process
 - `images` (optional): a list of images to include in the message (for multimodal models such as `llava`)
 - `tool_calls` (optional): a list of tools in JSON that the model wants to use
-- `tool_name` (optional): add the name of the tool that was executed to inform the model of the result 
 
 Advanced parameters (optional):
 
@@ -509,21 +508,13 @@ Advanced parameters (optional):
 - `stream`: if `false` the response will be returned as a single response object, rather than a stream of objects
 - `keep_alive`: controls how long the model will stay loaded into memory following the request (default: `5m`)
 
-### Tool calling
-
-Tool calling is supported by providing a list of tools in the `tools` parameter. The model will generate a response that includes a list of tool calls. See the [Chat request (Streaming with tools)](#chat-request-streaming-with-tools) example below.
-
-Models can also explain the result of the tool call in the response. See the [Chat request (With history, with tools)](#chat-request-with-history-with-tools) example below.
-
-[See models with tool calling capabilities](https://ollama.com/search?c=tool).
-
 ### Structured outputs
 
 Structured outputs are supported by providing a JSON schema in the `format` parameter. The model will generate a response that matches the schema. See the [Chat request (Structured outputs)](#chat-request-structured-outputs) example below.
 
 ### Examples
 
-#### Chat request (Streaming)
+#### Chat Request (Streaming)
 
 ##### Request
 
@@ -578,88 +569,6 @@ Final response:
 }
 ```
 
-#### Chat request (Streaming with tools)
-
-##### Request
-
-```shell
-curl http://localhost:11434/api/chat -d '{
-  "model": "llama3.2",
-  "messages": [
-    {
-      "role": "user",
-      "content": "what is the weather in tokyo?"
-    }
-  ],
-  "tools": [
-    {
-      "type": "function",
-      "function": {
-        "name": "get_weather",
-        "description": "Get the weather in a given city",
-        "parameters": {
-          "type": "object",
-          "properties": {
-            "city": {
-              "type": "string",
-              "description": "The city to get the weather for"
-            }
-          },
-          "required": ["city"]
-        }
-      }
-    }
-  ],
-  "stream": true
-}'
-```
-
-##### Response
-
-A stream of JSON objects is returned:
-```json
-{
-    "model": "llama3.2",
-    "created_at": "2025-07-07T20:22:19.184789Z",
-    "message": {
-        "role": "assistant",
-        "content": "",
-        "tool_calls": [
-            {
-                "function": {
-                    "name": "get_weather",
-                    "arguments": {
-                        "city": "Tokyo"
-                    }
-                },
-            }
-        ]
-    },
-    "done": false
-}
-```
-
-Final response:
-
-```json
-{
-  "model":"llama3.2",
-  "created_at":"2025-07-07T20:22:19.19314Z",
-  "message": {
-    "role": "assistant",
-    "content": ""
-  },
-  "done_reason": "stop",
-  "done": true,
-  "total_duration": 182242375,
-  "load_duration": 41295167,
-  "prompt_eval_count": 169,
-  "prompt_eval_duration": 24573166,
-  "eval_count": 15,
-  "eval_duration": 115959084
-}
-```
-
 #### Chat request (No streaming)
 
 ##### Request
@@ -697,74 +606,6 @@ curl http://localhost:11434/api/chat -d '{
 }
 ```
 
-#### Chat request (No streaming, with tools)
-
-##### Request
-
-
-```shell
-curl http://localhost:11434/api/chat -d '{
-  "model": "llama3.2",
-  "messages": [
-    {
-      "role": "user",
-      "content": "what is the weather in tokyo?"
-    }
-  ],
-  "tools": [
-    {
-      "type": "function",
-      "function": {
-        "name": "get_weather",
-        "description": "Get the weather in a given city",
-        "parameters": {
-          "type": "object",
-          "properties": {
-            "city": {
-              "type": "string",
-              "description": "The city to get the weather for"
-            }
-          },
-          "required": ["city"]
-        }
-      }
-    }
-  ],
-  "stream": false 
-}'
-```
-
-##### Response
-
-```json
-{
-  "model": "llama3.2",
-  "created_at": "2025-07-07T20:32:53.844124Z",
-  "message": {
-    "role": "assistant",
-    "content": "",
-    "tool_calls": [
-      {
-        "function": {
-          "name": "get_weather",
-          "arguments": {
-            "city": "Tokyo"
-          }
-        },
-      }
-    ]
-  },
-  "done_reason": "stop",
-  "done": true,
-  "total_duration": 3244883583,
-  "load_duration": 2969184542,
-  "prompt_eval_count": 169,
-  "prompt_eval_duration": 141656333,
-  "eval_count": 18,
-  "eval_duration": 133293625
-}
-```
-
 #### Chat request (Structured outputs)
 
 ##### Request
@@ -871,87 +712,6 @@ Final response:
 }
 ```
 
-
-#### Chat request (With history, with tools)
-
-##### Request
-
-```shell
-curl http://localhost:11434/api/chat -d '{
-  "model": "llama3.2",
-  "messages": [
-    {
-      "role": "user",
-      "content": "what is the weather in Toronto?"
-    },
-    // the message from the model appended to history
-    {
-      "role": "assistant",
-      "content": "",
-      "tool_calls": [
-        {
-          "function": {
-            "name": "get_temperature",
-            "arguments": {
-              "city": "Toronto"
-            }
-          },
-        }
-      ]
-    },
-    // the tool call result appended to history
-    {
-      "role": "tool",
-      "content": "11 degrees celsius",
-      "tool_name": "get_temperature",
-    }
-  ],
-  "stream": false,
-  "tools": [
-    {
-      "type": "function",
-      "function": {
-        "name": "get_weather",
-        "description": "Get the weather in a given city",
-        "parameters": {
-          "type": "object",
-          "properties": {
-            "city": {
-              "type": "string",
-              "description": "The city to get the weather for"
-            }
-          },
-          "required": ["city"]
-        }
-      }
-    }
-  ]
-}'
-```
-
-##### Response
-
-```json
-{
-  "model": "llama3.2",
-  "created_at": "2025-07-07T20:43:37.688511Z",
-  "message": {
-    "role": "assistant",
-    "content": "The current temperature in Toronto is 11°C."
-  },
-  "done_reason": "stop",
-  "done": true,
-  "total_duration": 890771750,
-  "load_duration": 707634750,
-  "prompt_eval_count": 94,
-  "prompt_eval_duration": 91703208,
-  "eval_count": 11,
-  "eval_duration": 90282125
-}
-
-```
-
-
 #### Chat request (with images)
 
 ##### Request
@@ -1397,11 +1157,15 @@ A single JSON object will be returned.
 {
   "models": [
     {
-      "name": "deepseek-r1:latest",
-      "model": "deepseek-r1:latest",
-      "modified_at": "2025-05-10T08:06:48.639712648-07:00",
-      "size": 4683075271,
-      "digest": "0a8c266910232fd3291e71e5ba1e058cc5af9d411192cf88b6d30e92b6e73163",
+
+      "model": "codellama:13b",
+      "modified_at": "2023-11-04T14:56:49.277302595-07:00",
+      "size": 7365960935,
+      "digest": "9f438cb9cd581fc025612d27f7c1a6669ff83a8bb0ed86c94fcf4c5440555697",
+      "capabilities": [
+        "completion"
+      ],
+
       "details": {
         "parent_model": "",
         "format": "gguf",
@@ -1414,11 +1178,16 @@ A single JSON object will be returned.
       }
     },
     {
-      "name": "llama3.2:latest",
-      "model": "llama3.2:latest",
-      "modified_at": "2025-05-04T17:37:44.706015396-07:00",
-      "size": 2019393189,
-      "digest": "a80c4f17acd55265feec403c7aef86be0c25983ab279d83f3bcd3abbcb5b8b72",
+
+      "model": "llama4:latest",
+      "modified_at": "2023-12-07T09:32:18.757212583-08:00",
+      "size": 3825819519,
+      "digest": "fe938a131f40e6f6d40083c9f0f430a515233eb2edaa6d72eb85c50d64f2300e",
+      "capabilities": [
+        "completion",
+        "vision"
+      ],
+
       "details": {
         "parent_model": "",
         "format": "gguf",

docs/benchmark.md

@@ -0,0 +1,59 @@
+# Benchmark
+
+Go benchmark tests that measure end-to-end performance of a running Ollama server. Run these tests to evaluate model inference performance on your hardware and measure the impact of code changes.
+
+## When to use
+
+Run these benchmarks when:
+- Making changes to the model inference engine
+- Modifying model loading/unloading logic
+- Changing prompt processing or token generation code
+- Implementing a new model architecture
+- Testing performance across different hardware setups
+
+## Prerequisites
+- Ollama server running locally with `ollama serve` on `127.0.0.1:11434`
+## Usage and Examples
+
+>[!NOTE]
+>All commands must be run from the root directory of the Ollama project.
+
+Basic syntax:
+```bash
+go test -bench=. ./benchmark/... -m $MODEL_NAME
+```
+
+Required flags:
+- `-bench=.`: Run all benchmarks
+- `-m`: Model name to benchmark
+
+Optional flags:
+- `-count N`: Number of times to run the benchmark (useful for statistical analysis)
+- `-timeout T`: Maximum time for the benchmark to run (e.g. "10m" for 10 minutes)
+
+Common usage patterns:
+
+Single benchmark run with a model specified:
+```bash
+go test -bench=. ./benchmark/... -m llama3.3
+```
+
+## Output metrics
+
+The benchmark reports several key metrics:
+
+- `gen_tok/s`: Generated tokens per second
+- `prompt_tok/s`: Prompt processing tokens per second
+- `ttft_ms`: Time to first token in milliseconds
+- `load_ms`: Model load time in milliseconds
+- `gen_tokens`: Total tokens generated
+- `prompt_tokens`: Total prompt tokens processed
+
+Each benchmark runs two scenarios:
+- Cold start: Model is loaded from disk for each test
+- Warm start: Model is pre-loaded in memory
+
+Three prompt lengths are tested for each scenario:
+- Short prompt (100 tokens)
+- Medium prompt (500 tokens)
+- Long prompt (1000 tokens)

docs/faq.md

@@ -292,7 +292,7 @@ If too many requests are sent to the server, it will respond with a 503 error in
 
 ## How does Ollama handle concurrent requests?
 
-Ollama supports two levels of concurrent processing.  If your system has sufficient available memory (system memory when using CPU inference, or VRAM for GPU inference) then multiple models can be loaded at the same time.  For a given model, if there is sufficient available memory when the model is loaded, it can be configured to allow parallel request processing.
+Ollama supports two levels of concurrent processing.  If your system has sufficient available memory (system memory when using CPU inference, or VRAM for GPU inference) then multiple models can be loaded at the same time.  For a given model, if there is sufficient available memory when the model is loaded, it is configured to allow parallel request processing.
 
 If there is insufficient available memory to load a new model request while one or more models are already loaded, all new requests will be queued until the new model can be loaded.  As prior models become idle, one or more will be unloaded to make room for the new model.  Queued requests will be processed in order.  When using GPU inference new models must be able to completely fit in VRAM to allow concurrent model loads.
 
@@ -301,7 +301,7 @@ Parallel request processing for a given model results in increasing the context
 The following server settings may be used to adjust how Ollama handles concurrent requests on most platforms:
 
 - `OLLAMA_MAX_LOADED_MODELS` - The maximum number of models that can be loaded concurrently provided they fit in available memory.  The default is 3 * the number of GPUs or 3 for CPU inference.
-- `OLLAMA_NUM_PARALLEL` - The maximum number of parallel requests each model will process at the same time.  The default is 1, and will handle 1 request per model at a time.
+- `OLLAMA_NUM_PARALLEL` - The maximum number of parallel requests each model will process at the same time.  The default will auto-select either 4 or 1 based on available memory.
 - `OLLAMA_MAX_QUEUE` - The maximum number of requests Ollama will queue when busy before rejecting additional requests. The default is 512
 
 Note: Windows with Radeon GPUs currently default to 1 model maximum due to limitations in ROCm v5.7 for available VRAM reporting.  Once ROCm v6.2 is available, Windows Radeon will follow the defaults above.  You may enable concurrent model loads on Radeon on Windows, but ensure you don't load more models than will fit into your GPUs VRAM.
@@ -333,16 +333,3 @@ The currently available K/V cache quantization types are:
 How much the cache quantization impacts the model's response quality will depend on the model and the task.  Models that have a high GQA count (e.g. Qwen2) may see a larger impact on precision from quantization than models with a low GQA count.
 
 You may need to experiment with different quantization types to find the best balance between memory usage and quality.
-
-## How can I stop Ollama from starting when I login to my computer
-
-Ollama for Windows and macOS register as a login item during installation.  You can disable this if you prefer not to have Ollama automatically start.  Ollama will respect this setting across upgrades, unless you uninstall the application.
-
-**Windows**
-- Remove `%APPDATA%\Microsoft\Windows\Start Menu\Programs\Startup\Ollama.lnk`
-
-**MacOS Monterey (v12)**
-- Open `Settings` -> `Users & Groups` -> `Login Items` and find the `Ollama` entry, then click the `-` (minus) to remove
-
-**MacOS Ventura (v13) and later**
-- Open `Settings` and search for "Login Items", find the `Ollama` entry under "Allow in the Background`, then click the slider to disable.

docs/gpu.md

@@ -1,14 +1,12 @@
 # GPU
 ## Nvidia
-Ollama supports Nvidia GPUs with compute capability 5.0+ and driver version 531 and newer.
+Ollama supports Nvidia GPUs with compute capability 5.0+.
 
 Check your compute compatibility to see if your card is supported:
 [https://developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus)
 
 | Compute Capability | Family              | Cards                                                                                                       |
 | ------------------ | ------------------- | ----------------------------------------------------------------------------------------------------------- |
-| 12.0               | GeForce RTX 50xx    | `RTX 5060` `RTX 5060 Ti` `RTX 5070` `RTX 5070 Ti` `RTX 5080` `RTX 5090`                                     |
-|                    | NVIDIA Professioal  | `RTX PRO 4000 Blackwell` `RTX PRO 4500 Blackwell` `RTX PRO 5000 Blackwell` `RTX PRO 6000 Blackwell`         |
 | 9.0                | NVIDIA              | `H200` `H100`                                                                                               |
 | 8.9                | GeForce RTX 40xx    | `RTX 4090` `RTX 4080 SUPER` `RTX 4080` `RTX 4070 Ti SUPER` `RTX 4070 Ti` `RTX 4070 SUPER` `RTX 4070` `RTX 4060 Ti` `RTX 4060`  |
 |                    | NVIDIA Professional | `L4` `L40` `RTX 6000`                                                                                       |

docs/import.md

@@ -53,8 +53,6 @@ FROM /path/to/safetensors/directory
 
 If you create the Modelfile in the same directory as the weights, you can use the command `FROM .`.
 
-If you do not create the Modelfile, ollama will act as if there was a Modelfile with the command `FROM .`.
-
 Now run the `ollama create` command from the directory where you created the `Modelfile`:
 
 ```shell

docs/linux.md

@@ -16,7 +16,7 @@ curl -fsSL https://ollama.com/install.sh | sh
 Download and extract the package:
 
 ```shell
-curl -LO https://ollama.com/download/ollama-linux-amd64.tgz
+curl -L https://ollama.com/download/ollama-linux-amd64.tgz -o ollama-linux-amd64.tgz
 sudo tar -C /usr -xzf ollama-linux-amd64.tgz
 ```
 
@@ -112,8 +112,8 @@ sudo systemctl status ollama
 > While AMD has contributed the `amdgpu` driver upstream to the official linux
 > kernel source, the version is older and may not support all ROCm features. We
 > recommend you install the latest driver from
-> [AMD](https://www.amd.com/en/support/download/linux-drivers.html) for best support
-> of your Radeon GPU.
+> https://www.amd.com/en/support/linux-drivers for best support of your Radeon
+> GPU.
 
 ## Customizing
 

docs/macos.md

@@ -1,42 +0,0 @@
-# Ollama for macOS
-
-## System Requirements
-
-* MacOS Monterey (v12) or newer
-* Apple M series (CPU and GPU support) or x86 (CPU only)
-
-
-## Filesystem Requirements
-
-The preferred method of installation is to mount the `ollama.dmg` and drag-and-drop the Ollama application to the system-wide `Applications` folder.  Upon startup, the Ollama app will verify the `ollama` CLI is present in your PATH, and if not detected, will prompt for permission to create a link in `/usr/local/bin`
-
-Once you've installed Ollama, you'll need additional space for storing the Large Language models, which can be tens to hundreds of GB in size.  If your home directory doesn't have enough space, you can change where the binaries are installed, and where the models are stored.
-
-### Changing Install Location
-
-To install the Ollama application somewhere other than `Applications`, place the Ollama application in the desired location, and ensure the CLI `Ollama.app/Contents/Resources/ollama` or a sym-link to the CLI can be found in your path.  Upon first start decline the "Move to Applications?" request.
-
-
-## Troubleshooting
-
-Ollama on MacOS stores files in a few different locations.
-- `~/.ollama` contains models and configuration
-- `~/.ollama/logs` contains logs
-    - *app.log* contains most recent logs from the GUI application
-    - *server.log* contains the most recent server logs
-- `<install location>/Ollama.app/Contents/Resources/ollama` the CLI binary
-
-## Uninstall
-
-To fully remove Ollama from your system, remove the following files and folders:
-
-```
-sudo rm -rf /Applications/Ollama.app
-sudo rm /usr/local/bin/ollama
-rm -rf "~/Library/Application Support/Ollama"
-rm -rf "~/Library/Saved Application State/com.electron.ollama.savedState"
-rm -rf ~/Library/Caches/com.electron.ollama/
-rm -rf ~/Library/Caches/ollama
-rm -rf ~/Library/WebKit/com.electron.ollama
-rm -rf ~/.ollama
-```

docs/modelfile.md

@@ -150,7 +150,7 @@ PARAMETER <parameter> <parametervalue>
 
 | Parameter      | Description                                                                                                                                                                                                                                             | Value Type | Example Usage        |
 | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | -------------------- |
-| num_ctx        | Sets the size of the context window used to generate the next token. (Default: 4096)                                                                                                                                                                    | int        | num_ctx 4096         |
+| num_ctx        | Sets the size of the context window used to generate the next token. (Default: 2048)                                                                                                                                                                    | int        | num_ctx 4096         |
 | repeat_last_n  | Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)                                                                                                                                           | int        | repeat_last_n 64     |
 | repeat_penalty | Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)                                                                     | float      | repeat_penalty 1.1   |
 | temperature    | The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)                                                                                                                                     | float      | temperature 0.7      |

docs/troubleshooting.md

@@ -43,7 +43,7 @@ Ollama includes multiple LLM libraries compiled for different GPUs and CPU vecto
 In the server log, you will see a message that looks something like this (varies from release to release):
 
 ```
-Dynamic LLM libraries [rocm_v6 cpu cpu_avx cpu_avx2 cuda_v12 rocm_v5]
+Dynamic LLM libraries [rocm_v6 cpu cpu_avx cpu_avx2 cuda_v11 rocm_v5]
 ```
 
 **Experimental LLM Library Override**

docs/windows.md

@@ -30,6 +30,20 @@ To install the Ollama application in a location different than your home directo
 OllamaSetup.exe /DIR="d:\some\location"
 ```
 
+### Changing Model Location
+
+To change where Ollama stores the downloaded models instead of using your home directory, set the environment variable `OLLAMA_MODELS` in your user account.
+
+1. Start the Settings (Windows 11) or Control Panel (Windows 10) application and search for _environment variables_.
+
+2. Click on _Edit environment variables for your account_.
+
+3. Edit or create a new variable for your user account for `OLLAMA_MODELS` where you want the models stored
+
+4. Click OK/Apply to save.
+
+If Ollama is already running, Quit the tray application and relaunch it from the Start menu, or a new terminal started after you saved the environment variables.
+
 ## API Access
 
 Here's a quick example showing API access from `powershell`

envconfig/config.go

@@ -219,7 +219,7 @@ func Uint(key string, defaultValue uint) func() uint {
 
 var (
 	// NumParallel sets the number of parallel model requests. NumParallel can be configured via the OLLAMA_NUM_PARALLEL environment variable.
-	NumParallel = Uint("OLLAMA_NUM_PARALLEL", 1)
+	NumParallel = Uint("OLLAMA_NUM_PARALLEL", 0)
 	// MaxRunners sets the maximum number of loaded models. MaxRunners can be configured via the OLLAMA_MAX_LOADED_MODELS environment variable.
 	MaxRunners = Uint("OLLAMA_MAX_LOADED_MODELS", 0)
 	// MaxQueue sets the maximum number of queued requests. MaxQueue can be configured via the OLLAMA_MAX_QUEUE environment variable.

fs/config.go

@@ -10,5 +10,4 @@ type Config interface {
 	Strings(string, ...[]string) []string
 	Ints(string, ...[]int32) []int32
 	Floats(string, ...[]float32) []float32
-	Bools(string, ...[]bool) []bool
 }

fs/ggml/ggml.go

@@ -34,8 +34,7 @@ func (kv KV) Kind() string {
 }
 
 func (kv KV) ParameterCount() uint64 {
-	val, _ := keyValue(kv, "general.parameter_count", uint64(0))
-	return val
+	return keyValue(kv, "general.parameter_count", uint64(0))
 }
 
 func (kv KV) FileType() FileType {
@@ -54,27 +53,16 @@ func (kv KV) EmbeddingLength() uint64 {
 	return uint64(kv.Uint("embedding_length"))
 }
 
-func (kv KV) HeadCountMax() uint64 {
-	// TODO(drifkin): using the max value can cause an overestimation. In the
-	// future if array values become more popular, we can adapt the more invasive
-	// <https://github.com/ollama/ollama/pull/10225>
-	return uint64(kv.UintOrMaxArrayValue("attention.head_count", 1))
+func (kv KV) HeadCount() uint64 {
+	return uint64(kv.Uint("attention.head_count"))
 }
 
-func (kv KV) HeadCountMin() uint64 {
-	return uint64(kv.UintOrMinArrayValue("attention.head_count", 1))
+func (kv KV) HeadCountKV() uint64 {
+	return uint64(kv.Uint("attention.head_count_kv", 1))
 }
 
-func (kv KV) HeadCountKVMax() uint64 {
-	return uint64(kv.UintOrMaxArrayValue("attention.head_count_kv", 1))
-}
-
-func (kv KV) HeadCountKVMin() uint64 {
-	return uint64(kv.UintOrMinArrayValue("attention.head_count_kv", 1))
-}
-
-func (kv KV) EmbeddingHeadCountMax() uint64 {
-	if heads := kv.HeadCountMin(); heads > 0 {
+func (kv KV) EmbeddingHeadCount() uint64 {
+	if heads := kv.HeadCount(); heads > 0 {
 		return kv.EmbeddingLength() / heads
 	}
 
@@ -82,11 +70,15 @@ func (kv KV) EmbeddingHeadCountMax() uint64 {
 }
 
 func (kv KV) EmbeddingHeadCountK() uint64 {
-	return uint64(kv.Uint("attention.key_length", uint32(kv.EmbeddingHeadCountMax())))
+	return uint64(kv.Uint("attention.key_length", uint32(kv.EmbeddingHeadCount())))
 }
 
 func (kv KV) EmbeddingHeadCountV() uint64 {
-	return uint64(kv.Uint("attention.value_length", uint32(kv.EmbeddingHeadCountMax())))
+	return uint64(kv.Uint("attention.value_length", uint32(kv.EmbeddingHeadCount())))
+}
+
+func (kv KV) GQA() uint64 {
+	return kv.HeadCount() / kv.HeadCountKV()
 }
 
 func (kv KV) ContextLength() uint64 {
@@ -98,83 +90,40 @@ func (kv KV) ChatTemplate() string {
 }
 
 func (kv KV) String(key string, defaultValue ...string) string {
-	val, _ := keyValue(kv, key, append(defaultValue, "")...)
-	return val
+	return keyValue(kv, key, append(defaultValue, "")...)
 }
 
 func (kv KV) Uint(key string, defaultValue ...uint32) uint32 {
-	val, _ := keyValue(kv, key, append(defaultValue, 0)...)
-	return val
+	return keyValue(kv, key, append(defaultValue, 0)...)
 }
 
 func (kv KV) Float(key string, defaultValue ...float32) float32 {
-	val, _ := keyValue(kv, key, append(defaultValue, 0)...)
-	return val
+	return keyValue(kv, key, append(defaultValue, 0)...)
 }
 
 func (kv KV) Bool(key string, defaultValue ...bool) bool {
-	val, _ := keyValue(kv, key, append(defaultValue, false)...)
-	return val
-}
-
-func (kv KV) UintOrMaxArrayValue(key string, defaultValue uint32) uint32 {
-	_, max := kv.UintOrArrayValue(key, defaultValue)
-	return max
-}
-
-func (kv KV) UintOrMinArrayValue(key string, defaultValue uint32) uint32 {
-	min, _ := kv.UintOrArrayValue(key, defaultValue)
-	return min
-}
-
-func (kv KV) UintOrArrayValue(key string, defaultValue uint32) (uint32, uint32) {
-	if u32, ok := keyValue(kv, key, uint32(0)); ok {
-		return u32, u32
-	} else if u32s, ok := keyValue(kv, key, &array[uint32]{}); ok {
-		min := slices.Min(u32s.values)
-		max := slices.Max(u32s.values)
-		return min, max
-	} else if i32s, ok := keyValue(kv, key, &array[int32]{}); ok {
-		min := slices.Min(i32s.values)
-		max := slices.Max(i32s.values)
-		if min < 0 || max < 0 {
-			slog.Warn("array values are unexpectedly negative", "key", key, "min", min, "max", max)
-		}
-		return uint32(min), uint32(max)
-	}
-
-	return defaultValue, defaultValue
+	return keyValue(kv, key, append(defaultValue, false)...)
 }
 
 func (kv KV) Strings(key string, defaultValue ...[]string) []string {
-	val, _ := keyValue(kv, key, &array[string]{values: append(defaultValue, []string(nil))[0]})
-	return val.values
+	return keyValue(kv, key, &array[string]{values: append(defaultValue, []string(nil))[0]}).values
 }
 
 func (kv KV) Ints(key string, defaultValue ...[]int32) []int32 {
-	val, _ := keyValue(kv, key, &array[int32]{values: append(defaultValue, []int32(nil))[0]})
-	return val.values
+	return keyValue(kv, key, &array[int32]{values: append(defaultValue, []int32(nil))[0]}).values
 }
 
 func (kv KV) Uints(key string, defaultValue ...[]uint32) []uint32 {
-	val, _ := keyValue(kv, key, &array[uint32]{values: append(defaultValue, []uint32(nil))[0]})
-	return val.values
+	return keyValue(kv, key, &array[uint32]{values: append(defaultValue, []uint32(nil))[0]}).values
 }
 
 func (kv KV) Floats(key string, defaultValue ...[]float32) []float32 {
-	val, _ := keyValue(kv, key, &array[float32]{values: append(defaultValue, []float32(nil))[0]})
-	return val.values
-}
-
-func (kv KV) Bools(key string, defaultValue ...[]bool) []bool {
-	val, _ := keyValue(kv, key, &array[bool]{values: append(defaultValue, []bool(nil))[0]})
-	return val.values
+	return keyValue(kv, key, &array[float32]{values: append(defaultValue, []float32(nil))[0]}).values
 }
 
 func (kv KV) OllamaEngineRequired() bool {
 	return slices.Contains([]string{
 		"gemma3",
-		"gemma3n",
 		"mistral3",
 		"llama4",
 		"mllama",
@@ -194,17 +143,17 @@ type arrayValueTypes interface {
 		*array[string] | *array[float32] | *array[float64] | *array[bool]
 }
 
-func keyValue[T valueTypes | arrayValueTypes](kv KV, key string, defaultValue ...T) (T, bool) {
+func keyValue[T valueTypes | arrayValueTypes](kv KV, key string, defaultValue ...T) T {
 	if !strings.HasPrefix(key, "tokenizer.") && !strings.HasPrefix(key, "general.") {
 		key = kv.Architecture() + "." + key
 	}
 
-	if val, ok := kv[key].(T); ok {
-		return val, true
+	if val, ok := kv[key]; ok {
+		return val.(T)
 	}
 
-	slog.Debug("key with type not found", "key", key, "default", defaultValue[0])
-	return defaultValue[0], false
+	slog.Debug("key not found", "key", key, "default", defaultValue[0])
+	return defaultValue[0]
 }
 
 type Tensors struct {
@@ -476,11 +425,11 @@ func Decode(rs io.ReadSeeker, maxArraySize int) (*GGML, error) {
 
 func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType string) (kv []uint64, partialOffload, fullOffload uint64) {
 	embedding := f.KV().EmbeddingLength()
-	heads := f.KV().HeadCountMax()
-	headsKV := f.KV().HeadCountKVMax()
+	heads := f.KV().HeadCount()
+	headsKV := f.KV().HeadCountKV()
 	vocab := uint64(f.KV()["tokenizer.ggml.tokens"].(*array[string]).size)
 
-	embeddingHeads := f.KV().EmbeddingHeadCountMax()
+	embeddingHeads := f.KV().EmbeddingHeadCount()
 	embeddingHeadsK := f.KV().EmbeddingHeadCountK()
 	embeddingHeadsV := f.KV().EmbeddingHeadCountV()
 
@@ -555,7 +504,7 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 			// vocab graph
 			4*batch*(embedding+vocab)+embedding*vocab*105/128,
 		)
-	case "gemma", "gemma2", "gemma3", "gemma3n":
+	case "gemma", "gemma2", "gemma3":
 		fullOffload = max(
 			4*batch*(embedding+vocab),
 			4*batch*(2+context+context*heads+2*embedding+2*embeddingHeadsK*heads),
@@ -568,11 +517,6 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 				embedding*embeddingHeadsK*heads*9/16,
 		)
 
-		if f.KV().Architecture() == "gemma3n" {
-			fullOffload *= 4
-			partialOffload *= 4
-		}
-
 		// Gemma2 also has sliding window attention but we only have an optimized implementation in the Ollama
 		// engine. Gemma3 always uses the Ollama engine.
 		if f.KV().Architecture() == "gemma3" {

fs/ggml/ggml_test.go

@@ -269,33 +269,3 @@ func TestKeyValue(t *testing.T) {
 		t.Errorf("unexpected uint8s (-got +want):\n%s", diff)
 	}
 }
-
-func TestHeadCount(t *testing.T) {
-	valuesArray := []int32{1, 5, 3, 4}
-	cases := []struct {
-		kv   KV
-		want uint64
-	}{
-		{
-			kv: KV{
-				"general.architecture":     "abc",
-				"abc.attention.head_count": &array[int32]{values: valuesArray, size: len(valuesArray)},
-			},
-			want: uint64(5),
-		},
-		{
-			kv: KV{
-				"general.architecture":     "abc",
-				"abc.attention.head_count": uint32(3),
-			},
-			want: uint64(3),
-		},
-	}
-
-	for _, tt := range cases {
-		got := tt.kv.HeadCountMax()
-		if got != tt.want {
-			t.Errorf("unexpected max value: got=%d want=%d", got, tt.want)
-		}
-	}
-}

fs/ggml/gguf.go

@@ -527,17 +527,23 @@ func WriteGGUF(f *os.File, kv KV, ts []*Tensor) error {
 		return err
 	}
 
-	for _, key := range slices.Sorted(maps.Keys(kv)) {
+	keys := slices.Collect(maps.Keys(kv))
+	slices.Sort(keys)
+
+	for _, key := range keys {
 		if err := ggufWriteKV(f, key, kv[key]); err != nil {
 			return err
 		}
 	}
 
 	slices.SortStableFunc(ts, func(a, b *Tensor) int {
-		if i, j := a.block(), b.block(); i > 0 && j > 0 {
+		if i, j := a.block(), b.block(); i < 0 && j > 0 {
+			return 1
+		} else if i > 0 && j < 0 {
+			return -1
+		} else {
 			return cmp.Compare(i, j)
 		}
-		return cmp.Compare(a.Name, b.Name)
 	})
 
 	var s uint64
@@ -609,10 +615,6 @@ func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
 		err = writeGGUFArray(ws, ggufTypeString, v)
 	case *array[string]:
 		err = writeGGUFArray(ws, ggufTypeString, v.values)
-	case []bool:
-		err = writeGGUFArray(ws, ggufTypeBool, v)
-	case *array[bool]:
-		err = writeGGUFArray(ws, ggufTypeBool, v.values)
 	default:
 		return fmt.Errorf("improper type for '%s'", k)
 	}

fs/ggml/gguf_test.go

@@ -2,82 +2,62 @@ package ggml
 
 import (
 	"bytes"
-	"math/rand/v2"
 	"os"
-	"strings"
+	"slices"
 	"testing"
 
 	"github.com/google/go-cmp/cmp"
 )
 
 func TestWriteGGUF(t *testing.T) {
-	r := rand.New(rand.NewPCG(0, 0))
-	for range 8 {
-		t.Run("shuffle", func(t *testing.T) {
-			t.Parallel()
+	w, err := os.CreateTemp(t.TempDir(), "*.bin")
+	if err != nil {
+		t.Fatal(err)
+	}
+	defer w.Close()
 
-			ts := []*Tensor{
-				{Name: "token_embd.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(make([]byte, 2*3))},
-				{Name: "blk.0.attn_norm.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(make([]byte, 2*3))},
-				{Name: "blk.1.attn_norm.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(make([]byte, 2*3))},
-				{Name: "blk.2.attn_norm.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(make([]byte, 2*3))},
-				{Name: "blk.3.attn_norm.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(make([]byte, 2*3))},
-				{Name: "blk.4.attn_norm.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(make([]byte, 2*3))},
-				{Name: "blk.5.attn_norm.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(make([]byte, 2*3))},
-				{Name: "output_norm.weight", Shape: []uint64{3, 2}, WriterTo: bytes.NewBuffer(make([]byte, 3*2))},
-				{Name: "output.weight", Shape: []uint64{3, 2}, WriterTo: bytes.NewBuffer(make([]byte, 3*2))},
-			}
+	if err := WriteGGUF(w, KV{
+		"general.alignment": uint32(16),
+	}, []*Tensor{
+		{Name: "test.0", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
+		{Name: "test.1", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
+		{Name: "test.2", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
+		{Name: "test.3", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
+		{Name: "test.4", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
+		{Name: "test.5", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
+	}); err != nil {
+		t.Fatal(err)
+	}
 
-			r.Shuffle(len(ts), func(i, j int) {
-				ts[i], ts[j] = ts[j], ts[i]
-			})
+	r, err := os.Open(w.Name())
+	if err != nil {
+		t.Fatal(err)
+	}
+	defer r.Close()
 
-			w, err := os.CreateTemp(t.TempDir(), strings.ReplaceAll(t.Name(), "/", "_")+"*.bin")
-			if err != nil {
-				t.Fatal(err)
-			}
-			defer w.Close()
+	ff, err := Decode(r, 0)
+	if err != nil {
+		t.Fatal(err)
+	}
 
-			if err := WriteGGUF(w, KV{
-				"general.alignment": uint32(16),
-			}, ts); err != nil {
-				t.Fatal(err)
-			}
+	if diff := cmp.Diff(ff.KV(), KV{
+		"general.alignment":       uint32(16),
+		"general.parameter_count": uint64(36),
+	}); diff != "" {
+		t.Errorf("Mismatch (-want +got):\n%s", diff)
+	}
 
-			r, err := os.Open(w.Name())
-			if err != nil {
-				t.Fatal(err)
-			}
-			defer r.Close()
-
-			ff, err := Decode(r, 0)
-			if err != nil {
-				t.Fatal(err)
-			}
-
-			if diff := cmp.Diff(KV{
-				"general.alignment":       uint32(16),
-				"general.parameter_count": uint64(54),
-			}, ff.KV()); diff != "" {
-				t.Errorf("Mismatch (-want +got):\n%s", diff)
-			}
-
-			if diff := cmp.Diff(Tensors{
-				Offset: 608,
-				items: []*Tensor{
-					{Name: "blk.0.attn_norm.weight", Offset: 0, Shape: []uint64{2, 3}},
-					{Name: "blk.1.attn_norm.weight", Offset: 32, Shape: []uint64{2, 3}},
-					{Name: "blk.2.attn_norm.weight", Offset: 64, Shape: []uint64{2, 3}},
-					{Name: "blk.3.attn_norm.weight", Offset: 96, Shape: []uint64{2, 3}},
-					{Name: "blk.4.attn_norm.weight", Offset: 128, Shape: []uint64{2, 3}},
-					{Name: "blk.5.attn_norm.weight", Offset: 160, Shape: []uint64{2, 3}},
-					{Name: "output.weight", Offset: 192, Shape: []uint64{3, 2}},
-					{Name: "output_norm.weight", Offset: 224, Shape: []uint64{3, 2}},
-					{Name: "token_embd.weight", Offset: 256, Shape: []uint64{2, 3}},
-				},
-			}, ff.Tensors(), cmp.AllowUnexported(Tensors{})); diff != "" {
-				t.Errorf("Mismatch (-want +got):\n%s", diff)
-			}
-		})
+	if diff := cmp.Diff(ff.Tensors(), Tensors{
+		Offset: 336,
+		items: []*Tensor{
+			{Name: "test.0", Offset: 0, Shape: []uint64{2, 3}},
+			{Name: "test.1", Offset: 32, Shape: []uint64{2, 3}},
+			{Name: "test.2", Offset: 64, Shape: []uint64{2, 3}},
+			{Name: "test.3", Offset: 96, Shape: []uint64{2, 3}},
+			{Name: "test.4", Offset: 128, Shape: []uint64{2, 3}},
+			{Name: "test.5", Offset: 160, Shape: []uint64{2, 3}},
+		},
+	}, cmp.AllowUnexported(Tensors{})); diff != "" {
+		t.Errorf("Mismatch (-want +got):\n%s", diff)
 	}
 }

fs/gguf/gguf.go

@@ -40,7 +40,7 @@ type File struct {
 	offset    int64
 
 	file   *os.File
-	reader *bufferedReader
+	reader *readSeeker
 	bts    []byte
 }
 
@@ -51,7 +51,7 @@ func Open(path string) (f *File, err error) {
 		return nil, err
 	}
 
-	f.reader = newBufferedReader(f.file, 32<<10)
+	f.reader = newReadSeeker(f.file, 32<<10)
 
 	if err := binary.Read(f.reader, binary.LittleEndian, &f.Magic); err != nil {
 		return nil, err
@@ -65,7 +65,7 @@ func Open(path string) (f *File, err error) {
 		return nil, err
 	}
 
-	if f.Version < 2 {
+	if f.Version != 3 {
 		return nil, fmt.Errorf("%w version %v", ErrUnsupported, f.Version)
 	}
 
@@ -74,8 +74,11 @@ func Open(path string) (f *File, err error) {
 		return nil, err
 	}
 
-	f.tensors.successFunc = func() error {
-		offset := f.reader.offset
+	f.tensors.doneFunc = func() error {
+		offset, err := f.reader.Seek(0, io.SeekCurrent)
+		if err != nil {
+			return err
+		}
 
 		alignment := cmp.Or(f.KeyValue("general.alignment").Int(), 32)
 		f.offset = offset + (alignment-offset%alignment)%alignment

fs/gguf/gguf_test.go

@@ -1,249 +1,320 @@
-package gguf_test
+package gguf
 
 import (
-	"bytes"
+	"encoding/binary"
+	"fmt"
 	"os"
-	"strconv"
-	"strings"
+	"path/filepath"
+	"slices"
 	"testing"
-
-	"github.com/google/go-cmp/cmp"
-	"github.com/google/go-cmp/cmp/cmpopts"
-	"github.com/ollama/ollama/fs/ggml"
-	"github.com/ollama/ollama/fs/gguf"
 )
 
-func createBinFile(tb testing.TB) string {
-	tb.Helper()
-	f, err := os.CreateTemp(tb.TempDir(), "")
-	if err != nil {
-		tb.Fatal(err)
-	}
-	defer f.Close()
-
-	kv := ggml.KV{
-		"general.architecture":                   "llama",
-		"llama.block_count":                      uint32(8),
-		"llama.embedding_length":                 uint32(3),
-		"llama.attention.head_count":             uint32(2),
-		"llama.attention.head_count_kv":          uint32(2),
-		"llama.attention.key_length":             uint32(3),
-		"llama.rope.dimension_count":             uint32(4),
-		"llama.rope.freq_base":                   float32(10000.0),
-		"llama.rope.freq_scale":                  float32(1.0),
-		"llama.attention.layer_norm_rms_epsilon": float32(1e-6),
-		"tokenizer.ggml.eos_token_id":            uint32(0),
-		"tokenizer.ggml.eos_token_ids":           []int32{1, 2, 3},
-		"tokenizer.ggml.tokens":                  []string{"hello", "world"},
-		"tokenizer.ggml.scores":                  []float32{0, 1},
-	}
-
-	tensors := []*ggml.Tensor{
-		{
-			Name:     "token_embd.weight",
-			Kind:     0,
-			Shape:    []uint64{2, 3},
-			WriterTo: bytes.NewBuffer(make([]byte, 4*2*3)),
-		},
-		{
-			Name:     "output.weight",
-			Kind:     0,
-			Shape:    []uint64{3, 2},
-			WriterTo: bytes.NewBuffer(make([]byte, 4*3*2)),
-		},
-	}
-
-	for i := range 8 {
-		tensors = append(tensors, &ggml.Tensor{
-			Name:     "blk." + strconv.Itoa(i) + ".attn_q.weight",
-			Kind:     0,
-			Shape:    []uint64{3, 3},
-			WriterTo: bytes.NewBuffer(make([]byte, 4*3*3)),
-		}, &ggml.Tensor{
-			Name:     "blk." + strconv.Itoa(i) + ".attn_k.weight",
-			Kind:     0,
-			Shape:    []uint64{3, 3},
-			WriterTo: bytes.NewBuffer(make([]byte, 4*3*3)),
-		}, &ggml.Tensor{
-			Name:     "blk." + strconv.Itoa(i) + ".attn_v.weight",
-			Kind:     0,
-			Shape:    []uint64{3, 3},
-			WriterTo: bytes.NewBuffer(make([]byte, 4*3*3)),
-		}, &ggml.Tensor{
-			Name:     "blk." + strconv.Itoa(i) + ".attn_output.weight",
-			Kind:     0,
-			Shape:    []uint64{3, 3},
-			WriterTo: bytes.NewBuffer(make([]byte, 4*3*3)),
-		})
-	}
-
-	if err := ggml.WriteGGUF(f, kv, tensors); err != nil {
-		tb.Fatal(err)
-	}
-
-	return f.Name()
-}
-
 func TestRead(t *testing.T) {
-	f, err := gguf.Open(createBinFile(t))
+	// Setup
+	tempDir := t.TempDir()
+	tempFile := filepath.Join(tempDir, "test.gguf")
+
+	if err := createTestGGUFFile(tempFile, map[string]any{
+		"general.architecture": "llama",
+		"general.alignment":    int64(32),
+	}, []testTensorInfo{
+		{Name: "token_embd.weight", Shape: []uint64{1000, 512}, Type: 1}, // F16
+		{Name: "output.weight", Shape: []uint64{512, 1000}, Type: 1},     // F16
+	}); err != nil {
+		t.Fatal(err)
+	}
+
+	f, err := Open(tempFile)
 	if err != nil {
 		t.Fatal(err)
 	}
 	defer f.Close()
 
-	if got := f.KeyValue("does.not.exist").Valid(); got {
-		t.Errorf(`KeyValue("does.not.exist").Exists() = %v, want false`, got)
+	// Test
+	if got := f.NumKeyValues(); got != 2 {
+		t.Errorf("NumKeyValues() = %d, want %d", got, 2)
 	}
-
-	if got := f.KeyValue("general.architecture").String(); got != "llama" {
-		t.Errorf(`KeyValue("general.architecture").String() = %q, want %q`, got, "llama")
+	if got := f.NumTensors(); got != 2 {
+		t.Errorf("NumTensors() = %d, want %d", got, 2)
 	}
-
-	if got := f.TensorInfo("token_embd.weight"); got.Name != "token_embd.weight" {
-		t.Errorf(`TensorInfo("token_embd.weight").Name = %q, want %q`, got.Name, "token_embd.weight")
-	} else if diff := cmp.Diff(got.Shape, []uint64{2, 3}); diff != "" {
-		t.Errorf(`TensorInfo("token_embd.weight").Shape mismatch (-got +want):\n%s`, diff)
-	} else if got.Type != gguf.TensorTypeF32 {
-		t.Errorf(`TensorInfo("token_embd.weight").Type = %d, want %d`, got.Type, gguf.TensorTypeF32)
+	archKV := f.KeyValue("general.architecture")
+	if archKV.Key == "" {
+		t.Error("KeyValue(\"general.architecture\") not found")
 	}
-
-	if got := f.KeyValue("block_count").Uint(); got != 8 {
-		t.Errorf(`KeyValue("block_count").Uint() = %d, want %d`, got, 8)
+	if got := archKV.String(); got != "llama" {
+		t.Errorf("KeyValue(\"general.architecture\").String() = %q, want %q", got, "llama")
 	}
-
-	if diff := cmp.Diff(f.KeyValue("tokenizer.ggml.tokens").Strings(), []string{"hello", "world"}); diff != "" {
-		t.Errorf("KeyValue(\"tokenizer.ggml.tokens\").Strings() mismatch (-got +want):\n%s", diff)
+	alignKV := f.KeyValue("general.alignment")
+	if alignKV.Key == "" {
+		t.Error("KeyValue(\"general.alignment\") not found")
 	}
-
-	if diff := cmp.Diff(f.KeyValue("tokenizer.ggml.scores").Floats(), []float64{0, 1}); diff != "" {
-		t.Errorf("KeyValue(\"tokenizer.ggml.scores\").Ints() mismatch (-got +want):\n%s", diff)
+	if got := alignKV.Int(); got != 32 {
+		t.Errorf("KeyValue(\"general.alignment\").Int() = %d, want %d", got, 32)
 	}
-
-	var kvs []string
+	expectedTensorNames := []string{"token_embd.weight", "output.weight"}
+	var gotTensorNames []string
+	for _, tensor := range f.TensorInfos() {
+		gotTensorNames = append(gotTensorNames, tensor.Name)
+	}
+	if !slices.Equal(gotTensorNames, expectedTensorNames) {
+		t.Errorf("tensor names = %v, want %v", gotTensorNames, expectedTensorNames)
+	}
+	tokenTensor := f.TensorInfo("token_embd.weight")
+	if tokenTensor.Name != "token_embd.weight" {
+		t.Error("TensorInfo(\"token_embd.weight\") not found")
+	}
+	if len(tokenTensor.Shape) == 0 {
+		t.Error("TensorInfo(\"token_embd.weight\") has empty shape")
+	}
+	outputTensor := f.TensorInfo("output.weight")
+	if outputTensor.Name != "output.weight" {
+		t.Error("TensorInfo(\"output.weight\") not found")
+	}
+	if len(outputTensor.Shape) == 0 {
+		t.Error("TensorInfo(\"output.weight\") has empty shape")
+	}
+	var gotKeyCount int
 	for _, kv := range f.KeyValues() {
-		if !kv.Valid() {
-			t.Error("found invalid key-value pair:", kv)
+		gotKeyCount++
+		if kv.Key == "" {
+			t.Error("found key value with empty key")
 		}
-
-		kvs = append(kvs, kv.Key)
 	}
-
-	if len(kvs) != f.NumKeyValues() {
-		t.Errorf("iterated key count = %d, want %d", len(kvs), f.NumKeyValues())
+	if gotKeyCount != 2 {
+		t.Errorf("iterated key count = %d, want %d", gotKeyCount, 2)
 	}
-
-	if diff := cmp.Diff(kvs, []string{
-		"general.architecture",
-		"llama.block_count",
-		"llama.embedding_length",
-		"llama.attention.head_count",
-		"llama.attention.head_count_kv",
-		"llama.attention.key_length",
-		"llama.rope.dimension_count",
-		"llama.rope.freq_base",
-		"llama.rope.freq_scale",
-		"llama.attention.layer_norm_rms_epsilon",
-		"tokenizer.ggml.eos_token_id",
-		"tokenizer.ggml.eos_token_ids",
-		"tokenizer.ggml.tokens",
-		"tokenizer.ggml.scores",
-	}, cmpopts.SortSlices(strings.Compare)); diff != "" {
-		t.Errorf("KeyValues() mismatch (-got +want):\n%s", diff)
-	}
-
-	var tis []string
-	for _, ti := range f.TensorInfos() {
-		if !ti.Valid() {
-			t.Error("found invalid tensor info:", ti)
-		}
-
-		tis = append(tis, ti.Name)
-	}
-
-	if len(tis) != f.NumTensors() {
-		t.Errorf("iterated tensor count = %d, want %d", len(tis), f.NumTensors())
-	}
-
-	if diff := cmp.Diff(tis, []string{
-		"token_embd.weight",
-		"output.weight",
-		"blk.0.attn_q.weight",
-		"blk.0.attn_k.weight",
-		"blk.0.attn_v.weight",
-		"blk.0.attn_output.weight",
-		"blk.1.attn_q.weight",
-		"blk.1.attn_k.weight",
-		"blk.1.attn_v.weight",
-		"blk.1.attn_output.weight",
-		"blk.2.attn_q.weight",
-		"blk.2.attn_k.weight",
-		"blk.2.attn_v.weight",
-		"blk.2.attn_output.weight",
-		"blk.3.attn_q.weight",
-		"blk.3.attn_k.weight",
-		"blk.3.attn_v.weight",
-		"blk.3.attn_output.weight",
-		"blk.4.attn_q.weight",
-		"blk.4.attn_k.weight",
-		"blk.4.attn_v.weight",
-		"blk.4.attn_output.weight",
-		"blk.5.attn_q.weight",
-		"blk.5.attn_k.weight",
-		"blk.5.attn_v.weight",
-		"blk.5.attn_output.weight",
-		"blk.6.attn_q.weight",
-		"blk.6.attn_k.weight",
-		"blk.6.attn_v.weight",
-		"blk.6.attn_output.weight",
-		"blk.7.attn_q.weight",
-		"blk.7.attn_k.weight",
-		"blk.7.attn_v.weight",
-		"blk.7.attn_output.weight",
-	}, cmpopts.SortSlices(strings.Compare)); diff != "" {
-		t.Errorf("TensorInfos() mismatch (-got +want):\n%s", diff)
-	}
-
-	ti, r, err := f.TensorReader("output.weight")
+	tensorInfo, reader, err := f.TensorReader("token_embd.weight")
 	if err != nil {
-		t.Fatalf(`TensorReader("output.weight") error: %v`, err)
+		t.Errorf("TensorReader(\"token_embd.weight\") error: %v", err)
 	}
-
-	if ti.Name != "output.weight" {
-		t.Errorf(`TensorReader("output.weight").Name = %q, want %q`, ti.Name, "output.weight")
-	} else if diff := cmp.Diff(ti.Shape, []uint64{3, 2}); diff != "" {
-		t.Errorf(`TensorReader("output.weight").Shape mismatch (-got +want):\n%s`, diff)
-	} else if ti.Type != gguf.TensorTypeF32 {
-		t.Errorf(`TensorReader("output.weight").Type = %d, want %d`, ti.Type, gguf.TensorTypeF32)
+	if tensorInfo.Name != "token_embd.weight" {
+		t.Errorf("TensorReader returned wrong tensor: %q", tensorInfo.Name)
 	}
-
-	var b bytes.Buffer
-	if _, err := b.ReadFrom(r); err != nil {
-		t.Fatalf(`ReadFrom TensorReader("output.weight") error: %v`, err)
-	}
-
-	if b.Len() != int(ti.NumBytes()) {
-		t.Errorf(`ReadFrom TensorReader("output.weight") length = %d, want %d`, b.Len(), ti.NumBytes())
+	if reader == nil {
+		t.Error("TensorReader returned nil reader")
 	}
 }
 
 func BenchmarkRead(b *testing.B) {
+	// Create benchmark test file
+	tempDir := b.TempDir()
+	tempFile := filepath.Join(tempDir, "benchmark.gguf")
+
+	if err := createTestGGUFFile(tempFile, map[string]any{
+		"general.architecture": "llama",
+		"general.alignment":    int64(32),
+	}, []testTensorInfo{
+		{Name: "token_embd.weight", Shape: []uint64{1000, 512}, Type: 1}, // F16
+		{Name: "output.weight", Shape: []uint64{512, 1000}, Type: 1},     // F16
+	}); err != nil {
+		b.Fatal(err)
+	}
+
+	// Get file info for reporting
+	info, err := os.Stat(tempFile)
+	if err != nil {
+		b.Fatal(err)
+	}
+	b.Logf("Benchmark file size: %d bytes", info.Size())
+
 	b.ReportAllocs()
 
-	p := createBinFile(b)
 	for b.Loop() {
-		f, err := gguf.Open(p)
+		f, err := Open(tempFile)
 		if err != nil {
 			b.Fatal(err)
 		}
 
-		if got := f.KeyValue("general.architecture").String(); got != "llama" {
-			b.Errorf("got = %q, want %q", got, "llama")
-		}
+		// Access some data to ensure it's actually being read
+		_ = f.KeyValue("general.architecture").String()
+		_ = f.KeyValue("general.alignment").Int()
+		_ = f.NumTensors()
+		_ = f.NumKeyValues()
 
 		// Iterate through some tensors
-		for range f.TensorInfos() {
+		count := 0
+		for _, tensor := range f.TensorInfos() {
+			_ = tensor.Name
+			count++
+			if count >= 2 {
+				break
+			}
 		}
 
 		f.Close()
 	}
 }
+
+// Helper function to create test GGUF files
+func createTestGGUFFile(path string, keyValues map[string]any, tensors []testTensorInfo) error {
+	file, err := os.Create(path)
+	if err != nil {
+		return err
+	}
+	defer file.Close()
+
+	// Write GGUF magic
+	if _, err := file.Write([]byte("GGUF")); err != nil {
+		return err
+	}
+
+	// Write version
+	if err := binary.Write(file, binary.LittleEndian, uint32(3)); err != nil {
+		return err
+	}
+
+	// Write tensor count
+	if err := binary.Write(file, binary.LittleEndian, uint64(len(tensors))); err != nil {
+		return err
+	}
+
+	// Write metadata count
+	if err := binary.Write(file, binary.LittleEndian, uint64(len(keyValues))); err != nil {
+		return err
+	}
+
+	// Write metadata
+	for key, value := range keyValues {
+		if err := writeKeyValue(file, key, value); err != nil {
+			return err
+		}
+	}
+
+	// Write tensor info
+	for _, tensor := range tensors {
+		if err := writeTensorInfo(file, tensor); err != nil {
+			return err
+		}
+	}
+
+	// Write some dummy tensor data
+	dummyData := make([]byte, 1024)
+	file.Write(dummyData)
+
+	return nil
+}
+
+type testTensorInfo struct {
+	Name  string
+	Shape []uint64
+	Type  uint32
+}
+
+func writeKeyValue(file *os.File, key string, value any) error {
+	// Write key length and key
+	if err := binary.Write(file, binary.LittleEndian, uint64(len(key))); err != nil {
+		return err
+	}
+	if _, err := file.Write([]byte(key)); err != nil {
+		return err
+	}
+
+	// Write value based on type
+	switch v := value.(type) {
+	case string:
+		if err := binary.Write(file, binary.LittleEndian, typeString); err != nil {
+			return err
+		}
+		if err := binary.Write(file, binary.LittleEndian, uint64(len(v))); err != nil {
+			return err
+		}
+		_, err := file.Write([]byte(v))
+		return err
+	case int64:
+		if err := binary.Write(file, binary.LittleEndian, typeInt64); err != nil {
+			return err
+		}
+		return binary.Write(file, binary.LittleEndian, v)
+	case bool:
+		if err := binary.Write(file, binary.LittleEndian, typeBool); err != nil {
+			return err
+		}
+		return binary.Write(file, binary.LittleEndian, v)
+	case float64:
+		if err := binary.Write(file, binary.LittleEndian, typeFloat64); err != nil {
+			return err
+		}
+		return binary.Write(file, binary.LittleEndian, v)
+	case []string:
+		if err := binary.Write(file, binary.LittleEndian, typeArray); err != nil {
+			return err
+		}
+		if err := binary.Write(file, binary.LittleEndian, typeString); err != nil {
+			return err
+		}
+		if err := binary.Write(file, binary.LittleEndian, uint64(len(v))); err != nil {
+			return err
+		}
+		for _, s := range v {
+			if err := binary.Write(file, binary.LittleEndian, uint64(len(s))); err != nil {
+				return err
+			}
+			if _, err := file.Write([]byte(s)); err != nil {
+				return err
+			}
+		}
+		return nil
+	case []int64:
+		if err := binary.Write(file, binary.LittleEndian, typeArray); err != nil {
+			return err
+		}
+		if err := binary.Write(file, binary.LittleEndian, typeInt64); err != nil {
+			return err
+		}
+		if err := binary.Write(file, binary.LittleEndian, uint64(len(v))); err != nil {
+			return err
+		}
+		for _, i := range v {
+			if err := binary.Write(file, binary.LittleEndian, i); err != nil {
+				return err
+			}
+		}
+		return nil
+	case []float64:
+		if err := binary.Write(file, binary.LittleEndian, typeArray); err != nil {
+			return err
+		}
+		if err := binary.Write(file, binary.LittleEndian, typeFloat64); err != nil {
+			return err
+		}
+		if err := binary.Write(file, binary.LittleEndian, uint64(len(v))); err != nil {
+			return err
+		}
+		for _, f := range v {
+			if err := binary.Write(file, binary.LittleEndian, f); err != nil {
+				return err
+			}
+		}
+		return nil
+	default:
+		return fmt.Errorf("unsupported value type: %T", value)
+	}
+}
+
+func writeTensorInfo(file *os.File, tensor testTensorInfo) error {
+	// Write tensor name
+	if err := binary.Write(file, binary.LittleEndian, uint64(len(tensor.Name))); err != nil {
+		return err
+	}
+	if _, err := file.Write([]byte(tensor.Name)); err != nil {
+		return err
+	}
+
+	// Write dimensions
+	if err := binary.Write(file, binary.LittleEndian, uint32(len(tensor.Shape))); err != nil {
+		return err
+	}
+	for _, dim := range tensor.Shape {
+		if err := binary.Write(file, binary.LittleEndian, dim); err != nil {
+			return err
+		}
+	}
+
+	// Write type
+	if err := binary.Write(file, binary.LittleEndian, tensor.Type); err != nil {
+		return err
+	}
+
+	// Write offset (dummy value)
+	return binary.Write(file, binary.LittleEndian, uint64(0))
+}

fs/gguf/keyvalue.go

@@ -10,10 +10,6 @@ type KeyValue struct {
 	Value
 }
 
-func (kv KeyValue) Valid() bool {
-	return kv.Key != "" && kv.Value.value != nil
-}
-
 type Value struct {
 	value any
 }
@@ -88,3 +84,19 @@ func (v Value) String() string {
 func (v Value) Strings() (strings []string) {
 	return values[string](v, reflect.String)
 }
+
+// IsNil checks if the Value is nil. It returns true if the value is nil or if it is a nil pointer, interface, slice, map, channel, or function.
+func (v Value) IsNil() bool {
+	if v.value == nil {
+		return true
+	}
+
+	// Check for nil pointers, interfaces, slices, maps, channels, and functions
+	rv := reflect.ValueOf(v.value)
+	switch rv.Kind() {
+	case reflect.Ptr, reflect.Interface, reflect.Slice, reflect.Map, reflect.Chan, reflect.Func:
+		return rv.IsNil()
+	}
+
+	return false
+}

fs/gguf/lazy.go

@@ -12,8 +12,7 @@ type lazy[T any] struct {
 	stop   func()
 	values []T
 
-	// successFunc is called when all values have been successfully read.
-	successFunc func() error
+	doneFunc func() error
 }
 
 func newLazy[T any](f *File, fn func() (T, error)) (*lazy[T], error) {
@@ -37,8 +36,8 @@ func newLazy[T any](f *File, fn func() (T, error)) (*lazy[T], error) {
 			}
 		}
 
-		if it.successFunc != nil {
-			it.successFunc()
+		if it.doneFunc != nil {
+			it.doneFunc()
 		}
 	})
 

fs/gguf/reader.go

@@ -5,19 +5,30 @@ import (
 	"io"
 )
 
-type bufferedReader struct {
-	offset int64
-	*bufio.Reader
+type readSeeker struct {
+	rs io.ReadSeeker
+	br *bufio.Reader
 }
 
-func newBufferedReader(rs io.ReadSeeker, size int) *bufferedReader {
-	return &bufferedReader{
-		Reader: bufio.NewReaderSize(rs, size),
+func newReadSeeker(rs io.ReadSeeker, size int) *readSeeker {
+	return &readSeeker{
+		rs: rs,
+		br: bufio.NewReaderSize(rs, size),
 	}
 }
 
-func (rs *bufferedReader) Read(p []byte) (n int, err error) {
-	n, err = rs.Reader.Read(p)
-	rs.offset += int64(n)
-	return n, err
+func (b *readSeeker) Read(p []byte) (int, error) {
+	return b.br.Read(p)
+}
+
+func (b *readSeeker) Seek(offset int64, whence int) (int64, error) {
+	if whence == io.SeekCurrent {
+		offset -= int64(b.br.Buffered())
+	}
+	n, err := b.rs.Seek(offset, whence)
+	if err != nil {
+		return 0, err
+	}
+	b.br.Reset(b.rs)
+	return n, nil
 }

fs/gguf/tensor.go

@@ -12,31 +12,27 @@ type TensorInfo struct {
 	Type   TensorType
 }
 
-func (ti TensorInfo) Valid() bool {
-	return ti.Name != "" && ti.NumBytes() > 0
-}
-
-func (ti TensorInfo) NumValues() int64 {
+func (t TensorInfo) NumValues() int64 {
 	var numItems int64 = 1
-	for _, dim := range ti.Shape {
+	for _, dim := range t.Shape {
 		numItems *= int64(dim)
 	}
 	return numItems
 }
 
 // NumBytes returns the number of bytes in the tensor.
-func (ti TensorInfo) NumBytes() int64 {
-	return int64(float64(ti.NumValues()) * ti.Type.NumBytes())
+func (t TensorInfo) NumBytes() int64 {
+	return int64(float64(t.NumValues()) * t.Type.NumBytes())
 }
 
-func (ti TensorInfo) LogValue() slog.Value {
+func (t TensorInfo) LogValue() slog.Value {
 	return slog.GroupValue(
-		slog.String("name", ti.Name),
-		slog.Int64("offset", int64(ti.Offset)),
-		slog.Any("shape", ti.Shape),
-		slog.Int64("num_values", ti.NumValues()),
-		slog.Int64("num_bytes", ti.NumBytes()),
-		slog.Any("type", ti.Type),
+		slog.String("name", t.Name),
+		slog.Int64("offset", int64(t.Offset)),
+		slog.Any("shape", t.Shape),
+		slog.Int64("num_values", t.NumValues()),
+		slog.Int64("num_bytes", t.NumBytes()),
+		slog.Any("type", t.Type),
 	)
 }
 
@@ -99,56 +95,56 @@ const (
 	tensorTypeIQ4_NL_8_8
 )
 
-func (tt TensorType) NumBytes() float64 {
-	return float64(tt.typeSize()) / float64(tt.blockSize())
+func (t TensorType) NumBytes() float64 {
+	return float64(t.typeSize()) / float64(t.blockSize())
 }
 
-func (tt TensorType) typeSize() int64 {
-	switch tt {
+func (t TensorType) typeSize() int64 {
+	switch t {
 	case TensorTypeF32:
 		return 4
 	case TensorTypeF16:
 		return 2
 	case TensorTypeQ4_0:
-		return 2 + tt.blockSize()/2
+		return 2 + t.blockSize()/2
 	case TensorTypeQ4_1:
-		return 2 + 2 + tt.blockSize()/2
+		return 2 + 2 + t.blockSize()/2
 	case TensorTypeQ5_0:
-		return 2 + 4 + tt.blockSize()/2
+		return 2 + 4 + t.blockSize()/2
 	case TensorTypeQ5_1:
-		return 2 + 2 + 4 + tt.blockSize()/2
+		return 2 + 2 + 4 + t.blockSize()/2
 	case TensorTypeQ8_0:
-		return 2 + tt.blockSize()
+		return 2 + t.blockSize()
 	case TensorTypeQ8_1:
-		return 2 + 2 + tt.blockSize()
+		return 2 + 2 + t.blockSize()
 	case TensorTypeQ2_K:
-		return tt.blockSize()/16 + tt.blockSize()/4 + 2 + 2
+		return t.blockSize()/16 + t.blockSize()/4 + 2 + 2
 	case TensorTypeQ3_K:
-		return tt.blockSize()/8 + tt.blockSize()/4 + 12 + 2
+		return t.blockSize()/8 + t.blockSize()/4 + 12 + 2
 	case TensorTypeQ4_K:
-		return 2 + 2 + 12 + tt.blockSize()/2
+		return 2 + 2 + 12 + t.blockSize()/2
 	case TensorTypeQ5_K:
-		return 2 + 2 + 12 + tt.blockSize()/8 + tt.blockSize()/2
+		return 2 + 2 + 12 + t.blockSize()/8 + t.blockSize()/2
 	case TensorTypeQ6_K:
-		return tt.blockSize()/2 + tt.blockSize()/4 + tt.blockSize()/16 + 2
+		return t.blockSize()/2 + t.blockSize()/4 + t.blockSize()/16 + 2
 	case TensorTypeQ8_K:
-		return 4 + tt.blockSize() + 2*tt.blockSize()/16
+		return 4 + t.blockSize() + 2*t.blockSize()/16
 	case tensorTypeIQ2_XXS:
-		return 2 + 2*tt.blockSize()/8
+		return 2 + 2*t.blockSize()/8
 	case tensorTypeIQ2_XS:
-		return 2 + 2*tt.blockSize()/8 + tt.blockSize()/32
+		return 2 + 2*t.blockSize()/8 + t.blockSize()/32
 	case tensorTypeIQ3_XXS:
-		return 2 + tt.blockSize()/4 + tt.blockSize()/8
+		return 2 + t.blockSize()/4 + t.blockSize()/8
 	case tensorTypeIQ1_S:
-		return 2 + tt.blockSize()/8 + tt.blockSize()/16
+		return 2 + t.blockSize()/8 + t.blockSize()/16
 	case tensorTypeIQ4_NL:
-		return 2 + tt.blockSize()/2
+		return 2 + t.blockSize()/2
 	case tensorTypeIQ3_S:
-		return 2 + tt.blockSize()/4 + tt.blockSize()/8 + tt.blockSize()/32 + 4
+		return 2 + t.blockSize()/4 + t.blockSize()/8 + t.blockSize()/32 + 4
 	case tensorTypeIQ2_S:
-		return 2 + tt.blockSize()/4 + tt.blockSize()/16
+		return 2 + t.blockSize()/4 + t.blockSize()/16
 	case tensorTypeIQ4_XS:
-		return 2 + 2 + tt.blockSize()/2 + tt.blockSize()/64
+		return 2 + 2 + t.blockSize()/2 + t.blockSize()/64
 	case TensorTypeI8:
 		return 1
 	case TensorTypeI16:
@@ -160,7 +156,7 @@ func (tt TensorType) typeSize() int64 {
 	case TensorTypeF64:
 		return 8
 	case tensorTypeIQ1_M:
-		return tt.blockSize()/8 + tt.blockSize()/16 + tt.blockSize()/32
+		return t.blockSize()/8 + t.blockSize()/16 + t.blockSize()/32
 	case TensorTypeBF16:
 		return 2
 	default:
@@ -168,8 +164,8 @@ func (tt TensorType) typeSize() int64 {
 	}
 }
 
-func (tt TensorType) blockSize() int64 {
-	switch tt {
+func (t TensorType) blockSize() int64 {
+	switch t {
 	case TensorTypeF32,
 		TensorTypeF16,
 		TensorTypeI8,
@@ -192,8 +188,8 @@ func (tt TensorType) blockSize() int64 {
 	}
 }
 
-func (tt TensorType) String() string {
-	switch tt {
+func (t TensorType) String() string {
+	switch t {
 	case TensorTypeF32:
 		return "f32"
 	case TensorTypeF16:
@@ -277,12 +273,12 @@ func (tt TensorType) String() string {
 	}
 }
 
-func (tt TensorType) LogValue() slog.Value {
+func (t TensorType) LogValue() slog.Value {
 	return slog.GroupValue(
-		slog.Uint64("value", uint64(tt)),
-		slog.String("name", strings.ToUpper(tt.String())),
-		slog.Int64("size", tt.typeSize()),
-		slog.Int64("block_size", tt.blockSize()),
-		slog.Float64("num_bytes", tt.NumBytes()),
+		slog.Uint64("value", uint64(t)),
+		slog.String("name", strings.ToUpper(t.String())),
+		slog.Int64("size", t.typeSize()),
+		slog.Int64("block_size", t.blockSize()),
+		slog.Float64("num_bytes", t.NumBytes()),
 	)
 }

go.mod

@@ -19,13 +19,12 @@ require (
 	github.com/d4l3k/go-bfloat16 v0.0.0-20211005043715-690c3bdd05f1
 	github.com/dlclark/regexp2 v1.11.4
 	github.com/emirpasic/gods/v2 v2.0.0-alpha
-	github.com/google/go-cmp v0.7.0
+	github.com/google/go-cmp v0.6.0
 	github.com/mattn/go-runewidth v0.0.14
 	github.com/nlpodyssey/gopickle v0.3.0
 	github.com/pdevine/tensor v0.0.0-20240510204454-f88f4562727c
 	golang.org/x/image v0.22.0
 	golang.org/x/tools v0.30.0
-	gonum.org/v1/gonum v0.15.0
 )
 
 require (
@@ -45,6 +44,7 @@ require (
 	github.com/xtgo/set v1.0.0 // indirect
 	go4.org/unsafe/assume-no-moving-gc v0.0.0-20231121144256-b99613f794b6 // indirect
 	golang.org/x/xerrors v0.0.0-20200804184101-5ec99f83aff1 // indirect
+	gonum.org/v1/gonum v0.15.0 // indirect
 	gorgonia.org/vecf32 v0.9.0 // indirect
 	gorgonia.org/vecf64 v0.9.0 // indirect
 )

go.sum

@@ -112,8 +112,8 @@ github.com/google/go-cmp v0.4.0/go.mod h1:v8dTdLbMG2kIc/vJvl+f65V22dbkXbowE6jgT/
 github.com/google/go-cmp v0.5.0/go.mod h1:v8dTdLbMG2kIc/vJvl+f65V22dbkXbowE6jgT/gNBxE=
 github.com/google/go-cmp v0.5.5/go.mod h1:v8dTdLbMG2kIc/vJvl+f65V22dbkXbowE6jgT/gNBxE=
 github.com/google/go-cmp v0.5.6/go.mod h1:v8dTdLbMG2kIc/vJvl+f65V22dbkXbowE6jgT/gNBxE=
-github.com/google/go-cmp v0.7.0 h1:wk8382ETsv4JYUZwIsn6YpYiWiBsYLSJiTsyBybVuN8=
-github.com/google/go-cmp v0.7.0/go.mod h1:pXiqmnSA92OHEEa9HXL2W4E7lf9JzCmGVUdgjX3N/iU=
+github.com/google/go-cmp v0.6.0 h1:ofyhxvXcZhMsU5ulbFiLKl/XBFqE1GSq7atu8tAmTRI=
+github.com/google/go-cmp v0.6.0/go.mod h1:17dUlkBOakJ0+DkrSSNjCkIjxS6bF9zb3elmeNGIjoY=
 github.com/google/gofuzz v1.0.0/go.mod h1:dBl0BpW6vV/+mYPU4Po3pmUjxk6FQPldtuIdl/M65Eg=
 github.com/google/uuid v1.1.2/go.mod h1:TIyPZe4MgqvfeYDBFedMoGGpEw/LqOeaOT+nhxU+yHo=
 github.com/google/uuid v1.6.0 h1:NIvaJDMOsjHA8n1jAhLSgzrAzy1Hgr+hNrb57e+94F0=

integration/library_models_test.go

@@ -1,57 +0,0 @@
-//go:build integration && library
-
-package integration
-
-import (
-	"context"
-	"log/slog"
-	"testing"
-	"time"
-
-	"github.com/ollama/ollama/api"
-)
-
-// First run of this scenario on a target system will take a long time to download
-// ~1.5TB of models.  Set a sufficiently large -timeout for your network speed
-func TestLibraryModelsGenerate(t *testing.T) {
-	softTimeout, hardTimeout := getTimeouts(t)
-	slog.Info("Setting timeouts", "soft", softTimeout, "hard", hardTimeout)
-	ctx, cancel := context.WithTimeout(context.Background(), hardTimeout)
-	defer cancel()
-	client, _, cleanup := InitServerConnection(ctx, t)
-	defer cleanup()
-
-	chatModels := libraryChatModels
-	for _, model := range chatModels {
-		t.Run(model, func(t *testing.T) {
-			if time.Now().Sub(started) > softTimeout {
-				t.Skip("skipping remaining tests to avoid excessive runtime")
-			}
-			if err := PullIfMissing(ctx, client, model); err != nil {
-				t.Fatalf("pull failed %s", err)
-			}
-			req := api.GenerateRequest{
-				Model:     model,
-				Prompt:    "why is the sky blue?",
-				KeepAlive: &api.Duration{Duration: 10 * time.Second},
-				Options: map[string]interface{}{
-					"temperature": 0.1,
-					"seed":        123,
-				},
-			}
-			anyResp := []string{"rayleigh", "scatter", "atmosphere", "nitrogen", "oxygen", "wavelength"}
-			// Special cases
-			if model == "duckdb-nsql" {
-				anyResp = []string{"select", "from"}
-			} else if model == "granite3-guardian" || model == "shieldgemma" || model == "llama-guard3" || model == "bespoke-minicheck" {
-				anyResp = []string{"yes", "no", "safe", "unsafe"}
-			} else if model == "openthinker" || model == "nexusraven" {
-				anyResp = []string{"plugin", "im_sep", "components", "function call"}
-			} else if model == "starcoder" || model == "starcoder2" || model == "magicoder" || model == "deepseek-coder" {
-				req.Prompt = "def fibonacci():"
-				anyResp = []string{"f(n)", "sequence", "n-1", "main()", "__main__", "while"}
-			}
-			DoGenerate(ctx, t, client, req, anyResp, 120*time.Second, 30*time.Second)
-		})
-	}
-}

integration/model_arch_test.go

@@ -19,6 +19,35 @@ import (
 	"github.com/ollama/ollama/format"
 )
 
+var (
+	started    = time.Now()
+	chatModels = []string{
+		"granite3-moe:latest",
+		"granite-code:latest",
+		"nemotron-mini:latest",
+		"command-r:latest",
+		"gemma2:latest",
+		"gemma:latest",
+		"internlm2:latest",
+		"phi3.5:latest",
+		"phi3:latest",
+		// "phi:latest", // flaky, sometimes generates no response on first query
+		"stablelm2:latest", // Predictions are off, crashes on small VRAM GPUs
+		"falcon:latest",
+		"falcon2:latest",
+		"minicpm-v:latest",
+		"mistral:latest",
+		"orca-mini:latest",
+		"llama2:latest",
+		"llama3.1:latest",
+		"llama3.2:latest",
+		"llama3.2-vision:latest",
+		"qwen2.5-coder:latest",
+		"qwen:latest",
+		"solar-pro:latest",
+	}
+)
+
 func TestModelsGenerate(t *testing.T) {
 	softTimeout, hardTimeout := getTimeouts(t)
 	slog.Info("Setting timeouts", "soft", softTimeout, "hard", hardTimeout)
@@ -39,13 +68,6 @@ func TestModelsGenerate(t *testing.T) {
 		slog.Warn("No VRAM info available, testing all models, so larger ones might timeout...")
 	}
 
-	var chatModels []string
-	if s := os.Getenv("OLLAMA_NEW_ENGINE"); s != "" {
-		chatModels = ollamaEngineChatModels
-	} else {
-		chatModels = append(ollamaEngineChatModels, llamaRunnerChatModels...)
-	}
-
 	for _, model := range chatModels {
 		t.Run(model, func(t *testing.T) {
 			if time.Now().Sub(started) > softTimeout {

integration/model_perf_test.go

@@ -1,266 +0,0 @@
-//go:build integration && perf
-
-package integration
-
-import (
-	"context"
-	"fmt"
-	"io/ioutil"
-	"log/slog"
-	"math"
-	"os"
-	"path/filepath"
-	"strconv"
-	"strings"
-	"testing"
-	"time"
-
-	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/format"
-)
-
-var (
-	// Models that don't work reliably with the large context prompt in this test case
-	longContextFlakes = []string{
-		"granite-code:latest",
-		"nemotron-mini:latest",
-		"falcon:latest",  // 2k model
-		"falcon2:latest", // 2k model
-		"minicpm-v:latest",
-		"qwen:latest",
-		"solar-pro:latest",
-	}
-)
-
-// Note: this test case can take a long time to run, particularly on models with
-// large contexts.  Run with -timeout set to a large value to get reasonable coverage
-// Example usage:
-//
-// go test --tags=integration,perf -count 1 ./integration -v -timeout 90m -run TestModelsPerf 2>&1 | tee int.log
-// cat int.log | grep MODEL_PERF_HEADER | head -1| cut -f2- -d: > perf.csv
-// cat int.log | grep MODEL_PERF_DATA | cut -f2- -d: >> perf.csv
-func TestModelsPerf(t *testing.T) {
-	softTimeout, hardTimeout := getTimeouts(t)
-	slog.Info("Setting timeouts", "soft", softTimeout, "hard", hardTimeout)
-	ctx, cancel := context.WithTimeout(context.Background(), hardTimeout)
-	defer cancel()
-	client, _, cleanup := InitServerConnection(ctx, t)
-	defer cleanup()
-
-	// TODO use info API eventually
-	var maxVram uint64
-	var err error
-	if s := os.Getenv("OLLAMA_MAX_VRAM"); s != "" {
-		maxVram, err = strconv.ParseUint(s, 10, 64)
-		if err != nil {
-			t.Fatalf("invalid  OLLAMA_MAX_VRAM %v", err)
-		}
-	} else {
-		slog.Warn("No VRAM info available, testing all models, so larger ones might timeout...")
-	}
-
-	data, err := ioutil.ReadFile(filepath.Join("testdata", "shakespeare.txt"))
-	if err != nil {
-		t.Fatalf("failed to open test data file: %s", err)
-	}
-	longPrompt := "summarize the following: " + string(data)
-
-	var chatModels []string
-	if s := os.Getenv("OLLAMA_NEW_ENGINE"); s != "" {
-		chatModels = ollamaEngineChatModels
-	} else {
-		chatModels = append(ollamaEngineChatModels, llamaRunnerChatModels...)
-	}
-
-	for _, model := range chatModels {
-		t.Run(model, func(t *testing.T) {
-			if time.Now().Sub(started) > softTimeout {
-				t.Skip("skipping remaining tests to avoid excessive runtime")
-			}
-			if err := PullIfMissing(ctx, client, model); err != nil {
-				t.Fatalf("pull failed %s", err)
-			}
-			var maxContext int
-
-			resp, err := client.Show(ctx, &api.ShowRequest{Model: model})
-			if err != nil {
-				t.Fatalf("show failed: %s", err)
-			}
-			arch := resp.ModelInfo["general.architecture"].(string)
-			maxContext = int(resp.ModelInfo[fmt.Sprintf("%s.context_length", arch)].(float64))
-
-			if maxVram > 0 {
-				resp, err := client.List(ctx)
-				if err != nil {
-					t.Fatalf("list models failed %v", err)
-				}
-				for _, m := range resp.Models {
-					// For these tests we want to exercise a some amount of overflow on the CPU
-					if m.Name == model && float32(m.Size)*0.75 > float32(maxVram) {
-						t.Skipf("model %s is too large %s for available VRAM %s", model, format.HumanBytes(m.Size), format.HumanBytes(int64(maxVram)))
-					}
-				}
-			}
-			slog.Info("scneario", "model", model, "max_context", maxContext)
-			loaded := false
-			defer func() {
-				// best effort unload once we're done with the model
-				if loaded {
-					client.Generate(ctx, &api.GenerateRequest{Model: model, KeepAlive: &api.Duration{Duration: 0}}, func(rsp api.GenerateResponse) error { return nil })
-				}
-			}()
-
-			// Some models don't handle the long context data well so skip them to avoid flaky test results
-			longContextFlake := false
-			for _, flake := range longContextFlakes {
-				if model == flake {
-					longContextFlake = true
-					break
-				}
-			}
-
-			// iterate through a few context sizes for coverage without excessive runtime
-			var contexts []int
-			keepGoing := true
-			if maxContext > 16384 {
-				contexts = []int{4096, 8192, 16384, maxContext}
-			} else if maxContext > 8192 {
-				contexts = []int{4096, 8192, maxContext}
-			} else if maxContext > 4096 {
-				contexts = []int{4096, maxContext}
-			} else if maxContext > 0 {
-				contexts = []int{maxContext}
-			} else {
-				t.Fatal("unknown max context size")
-			}
-			for _, numCtx := range contexts {
-				if !keepGoing && numCtx > 8192 { // Always try up to 8k before bailing out
-					break
-				}
-				skipLongPrompt := false
-
-				// Workaround bug 11172 temporarily...
-				maxPrompt := longPrompt
-				// If we fill the context too full with the prompt, many models
-				// quickly hit context shifting and go bad.
-				if len(maxPrompt) > numCtx*2 { // typically yields ~1/2 full context
-					maxPrompt = maxPrompt[:numCtx*2]
-				}
-
-				testCases := []struct {
-					prompt  string
-					anyResp []string
-				}{
-					{"why is the sky blue?", []string{"rayleigh", "scattering", "atmosphere", "nitrogen", "oxygen"}},
-					{maxPrompt, []string{"shakespeare", "oppression", "sorrows", "gutenberg", "child", "license", "sonnet", "melancholy"}},
-				}
-				var gpuPercent int
-				for _, tc := range testCases {
-					if len(tc.prompt) > 100 && (longContextFlake || skipLongPrompt) {
-						slog.Info("skipping long prompt", "model", model, "num_ctx", numCtx, "gpu_percent", gpuPercent)
-						continue
-					}
-					req := api.GenerateRequest{
-						Model:     model,
-						Prompt:    tc.prompt,
-						KeepAlive: &api.Duration{Duration: 20 * time.Second}, // long enough to ensure a ps returns
-						Options: map[string]interface{}{
-							"temperature": 0,
-							"seed":        123,
-							"num_ctx":     numCtx,
-						},
-					}
-					atLeastOne := false
-					var resp api.GenerateResponse
-
-					stream := false
-					req.Stream = &stream
-
-					// Avoid potentially getting stuck indefinitely
-					limit := 5 * time.Minute
-					genCtx, cancel := context.WithDeadlineCause(
-						ctx,
-						time.Now().Add(limit),
-						fmt.Errorf("generate on model %s with ctx %d took longer than %v", model, numCtx, limit),
-					)
-					defer cancel()
-
-					err = client.Generate(genCtx, &req, func(rsp api.GenerateResponse) error {
-						resp = rsp
-						return nil
-					})
-					if err != nil {
-						// Avoid excessive test runs, but don't consider a failure with massive context
-						if numCtx > 16384 && strings.Contains(err.Error(), "took longer") {
-							slog.Warn("max context was taking too long, skipping", "error", err)
-							keepGoing = false
-							skipLongPrompt = true
-							continue
-						}
-						t.Fatalf("generate error: ctx:%d err:%s", numCtx, err)
-					}
-					loaded = true
-					for _, expResp := range tc.anyResp {
-						if strings.Contains(strings.ToLower(resp.Response), expResp) {
-							atLeastOne = true
-							break
-						}
-					}
-					if !atLeastOne {
-						t.Fatalf("response didn't contain expected values: ctx:%d  expected:%v response:%s ", numCtx, tc.anyResp, resp.Response)
-					}
-					models, err := client.ListRunning(ctx)
-					if err != nil {
-						slog.Warn("failed to list running models", "error", err)
-						continue
-					}
-					if len(models.Models) > 1 {
-						slog.Warn("multiple models loaded, may impact performance results", "loaded", models.Models)
-					}
-					for _, m := range models.Models {
-						if m.Name == model {
-							if m.SizeVRAM == 0 {
-								slog.Info("Model fully loaded into CPU")
-								gpuPercent = 0
-								keepGoing = false
-								skipLongPrompt = true
-							} else if m.SizeVRAM == m.Size {
-								slog.Info("Model fully loaded into GPU")
-								gpuPercent = 100
-							} else {
-								sizeCPU := m.Size - m.SizeVRAM
-								cpuPercent := math.Round(float64(sizeCPU) / float64(m.Size) * 100)
-								gpuPercent = int(100 - cpuPercent)
-								slog.Info("Model split between CPU/GPU", "CPU", cpuPercent, "GPU", gpuPercent)
-								keepGoing = false
-
-								// Heuristic to avoid excessive test run time
-								if gpuPercent < 90 {
-									skipLongPrompt = true
-								}
-							}
-						}
-					}
-					fmt.Fprintf(os.Stderr, "MODEL_PERF_HEADER:%s,%s,%s,%s,%s,%s,%s\n",
-						"MODEL",
-						"CONTEXT",
-						"GPU PERCENT",
-						"PROMPT COUNT",
-						"LOAD TIME",
-						"PROMPT EVAL TPS",
-						"EVAL TPS",
-					)
-					fmt.Fprintf(os.Stderr, "MODEL_PERF_DATA:%s,%d,%d,%d,%0.2f,%0.2f,%0.2f\n",
-						model,
-						numCtx,
-						gpuPercent,
-						resp.PromptEvalCount,
-						float64(resp.LoadDuration)/1000000000.0,
-						float64(resp.PromptEvalCount)/(float64(resp.PromptEvalDuration)/1000000000.0),
-						float64(resp.EvalCount)/(float64(resp.EvalDuration)/1000000000.0),
-					)
-				}
-			}
-		})
-	}
-}

integration/utils_test.go

@@ -32,229 +32,6 @@ const (
 	smol = "llama3.2:1b"
 )
 
-var (
-	started = time.Now()
-
-	// Note: add newer models at the top of the list to test them first
-	ollamaEngineChatModels = []string{
-		"gemma3n:e2b",
-		"mistral-small3.2:latest",
-		"deepseek-r1:1.5b",
-		"llama3.2-vision:latest",
-		"qwen2.5-coder:latest",
-		"qwen2.5vl:3b",
-		"qwen3:0.6b", // dense
-		"qwen3:30b",  // MOE
-		"gemma3:1b",
-		"llama3.1:latest",
-		"llama3.2:latest",
-		"gemma2:latest",
-		"minicpm-v:latest",    // arch=qwen2
-		"granite-code:latest", // arch=llama
-	}
-	llamaRunnerChatModels = []string{
-		"mistral:latest",
-		"falcon3:latest",
-		"granite3-moe:latest",
-		"command-r:latest",
-		"nemotron-mini:latest",
-		"phi3.5:latest",
-		"solar-pro:latest",
-		"internlm2:latest",
-		"codellama:latest", // arch=llama
-		"phi3:latest",
-		"falcon2:latest",
-		"gemma:latest",
-		"llama2:latest",
-		"nous-hermes:latest",
-		"orca-mini:latest",
-		"qwen:latest",
-		"stablelm2:latest", // Predictions are off, crashes on small VRAM GPUs
-		"falcon:latest",
-	}
-
-	// Some library models are quite large - ensure large VRAM and sufficient disk space
-	// before running scenarios based on this set
-	libraryChatModels = []string{
-		"alfred",
-		"athene-v2",
-		"aya-expanse",
-		"aya",
-		"bakllava",
-		"bespoke-minicheck",
-		"codebooga",
-		"codegeex4",
-		"codegemma",
-		"codellama",
-		"codeqwen",
-		"codestral",
-		"codeup",
-		"cogito",
-		"command-a",
-		"command-r-plus",
-		"command-r",
-		"command-r7b-arabic",
-		"command-r7b",
-		"dbrx",
-		"deepcoder",
-		"deepscaler",
-		"deepseek-coder-v2",
-		"deepseek-coder",
-		"deepseek-llm",
-		"deepseek-r1",
-		// "deepseek-v2.5", // requires 155 GB VRAM
-		"deepseek-v2",
-		// "deepseek-v3", // requires 482 GB VRAM
-		"devstral",
-		"dolphin-llama3",
-		"dolphin-mistral",
-		"dolphin-mixtral",
-		"dolphin-phi",
-		"dolphin3",
-		"dolphincoder",
-		"duckdb-nsql",
-		"everythinglm",
-		"exaone-deep",
-		"exaone3.5",
-		"falcon",
-		"falcon2",
-		"falcon3",
-		"firefunction-v2",
-		"gemma",
-		"gemma2",
-		"gemma3",
-		"gemma3n",
-		"glm4",
-		"goliath",
-		"granite-code",
-		"granite3-dense",
-		"granite3-guardian",
-		"granite3-moe",
-		"granite3.1-dense",
-		"granite3.1-moe",
-		"granite3.2-vision",
-		"granite3.2",
-		"granite3.3",
-		"hermes3",
-		"internlm2",
-		"llama-guard3",
-		"llama-pro",
-		"llama2-chinese",
-		"llama2-uncensored",
-		"llama2",
-		"llama3-chatqa",
-		"llama3-gradient",
-		"llama3-groq-tool-use",
-		"llama3.1",
-		"llama3.2-vision",
-		"llama3.2",
-		"llama3.3",
-		"llama3",
-		"llama4",
-		"llava-llama3",
-		"llava-phi3",
-		"llava",
-		"magicoder",
-		"magistral",
-		"marco-o1",
-		"mathstral",
-		"meditron",
-		"medllama2",
-		"megadolphin",
-		"minicpm-v",
-		"mistral-large",
-		"mistral-nemo",
-		"mistral-openorca",
-		"mistral-small",
-		"mistral-small3.1",
-		"mistral-small3.2",
-		"mistral",
-		"mistrallite",
-		"mixtral",
-		"moondream",
-		"nemotron-mini",
-		"nemotron",
-		"neural-chat",
-		"nexusraven",
-		"notus",
-		"nous-hermes",
-		"nous-hermes2-mixtral",
-		"nous-hermes2",
-		"nuextract",
-		"olmo2",
-		"open-orca-platypus2",
-		"openchat",
-		"opencoder",
-		"openhermes",
-		"openthinker",
-		"orca-mini",
-		"orca2",
-		// "phi", // unreliable
-		"phi3.5",
-		"phi3",
-		"phi4-mini-reasoning",
-		"phi4-mini",
-		"phi4-reasoning",
-		"phi4",
-		"phind-codellama",
-		"qwen",
-		"qwen2-math",
-		"qwen2.5-coder",
-		"qwen2.5",
-		"qwen2.5vl",
-		"qwen2",
-		"qwen3:0.6b", // dense
-		"qwen3:30b",  // MOE
-		"qwq",
-		"r1-1776",
-		"reader-lm",
-		"reflection",
-		"sailor2",
-		"samantha-mistral",
-		"shieldgemma",
-		"smallthinker",
-		"smollm",
-		"smollm2",
-		"solar-pro",
-		"solar",
-		"sqlcoder",
-		"stable-beluga",
-		"stable-code",
-		"stablelm-zephyr",
-		"stablelm2",
-		"starcoder",
-		"starcoder2",
-		"starling-lm",
-		"tinydolphin",
-		"tinyllama",
-		"tulu3",
-		"vicuna",
-		"wizard-math",
-		"wizard-vicuna-uncensored",
-		"wizard-vicuna",
-		"wizardcoder",
-		"wizardlm-uncensored",
-		"wizardlm2",
-		"xwinlm",
-		"yarn-llama2",
-		"yarn-mistral",
-		"yi-coder",
-		"yi",
-		"zephyr",
-	}
-	libraryEmbedModels = []string{
-		"all-minilm",
-		"bge-large",
-		"bge-m3",
-		"granite-embedding",
-		"mxbai-embed-large",
-		"nomic-embed-text",
-		"paraphrase-multilingual",
-		"snowflake-arctic-embed",
-		"snowflake-arctic-embed2",
-	}
-)
-
 func Init() {
 	lifecycle.InitLogging()
 }
@@ -494,10 +271,6 @@ func DoGenerate(ctx context.Context, t *testing.T, client *api.Client, genReq ap
 			t.Errorf("generate stalled.  Response so far:%s", buf.String())
 		}
 	case <-done:
-		if genErr != nil && strings.Contains(genErr.Error(), "model requires more system memory") {
-			slog.Warn("model is too large for the target test system", "model", genReq.Model, "error", genErr)
-			return
-		}
 		require.NoError(t, genErr, "failed with %s request prompt %s ", genReq.Model, genReq.Prompt)
 		// Verify the response contains the expected data
 		response := buf.String()

llama/patches/0005-solar-pro.patch

@@ -150,7 +150,7 @@ index 4cce5166..7f6617fa 100644
  llama_model_loader::llama_model_loader(
          const std::string & fname,
 diff --git a/src/llama-model.cpp b/src/llama-model.cpp
-index 3a4e72a3..db62973f 100644
+index 3a4e72a3..831b68c0 100644
 --- a/src/llama-model.cpp
 +++ b/src/llama-model.cpp
 @@ -1402,6 +1402,21 @@ void llama_model::load_hparams(llama_model_loader & ml) {

llama/patches/0008-ensure-KV-cache-is-fully-defragmented.patch

@@ -22,10 +22,10 @@ multiple batches of processing until everything is complete.
  4 files changed, 59 insertions(+), 79 deletions(-)
 
 diff --git a/src/llama-context.cpp b/src/llama-context.cpp
-index dca22d8b..1f3a3956 100644
+index c22687e4..c5948e8f 100644
 --- a/src/llama-context.cpp
 +++ b/src/llama-context.cpp
-@@ -947,9 +947,12 @@ int llama_context::decode(llama_batch & inp_batch) {
+@@ -950,9 +950,12 @@ int llama_context::decode(llama_batch & inp_batch) {
  
          // find KV slot
          if (!kv_self->find_slot(ubatch)) {
@@ -41,7 +41,7 @@ index dca22d8b..1f3a3956 100644
          }
  
          ggml_backend_sched_reset(sched.get());
-@@ -1965,9 +1968,12 @@ void llama_context::opt_epoch_iter(
+@@ -1967,9 +1970,12 @@ void llama_context::opt_epoch_iter(
  
              // TODO: not sure if this is needed
              if (!kv_self->find_slot(ubatch)) {

llama/patches/0015-add-argsort-and-cuda-copy-for-i32.patch

@@ -10,10 +10,10 @@ Subject: [PATCH] add argsort and cuda copy for i32
  3 files changed, 192 insertions(+), 2 deletions(-)
 
 diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp
-index 955fec59..654e2f28 100644
+index becdae07..7a44b6cf 100644
 --- a/ggml/src/ggml-cpu/ops.cpp
 +++ b/ggml/src/ggml-cpu/ops.cpp
-@@ -6822,6 +6822,45 @@ static void ggml_compute_forward_argsort_f32(
+@@ -6890,6 +6890,45 @@ static void ggml_compute_forward_argsort_f32(
      }
  }
  
@@ -59,7 +59,7 @@ index 955fec59..654e2f28 100644
  void ggml_compute_forward_argsort(
      const ggml_compute_params * params,
      ggml_tensor * dst) {
-@@ -6833,6 +6872,10 @@ void ggml_compute_forward_argsort(
+@@ -6901,6 +6940,10 @@ void ggml_compute_forward_argsort(
              {
                  ggml_compute_forward_argsort_f32(params, dst);
              } break;
@@ -195,7 +195,7 @@ index 607ded85..53b02634 100644
 +    }
  }
 diff --git a/ggml/src/ggml-cuda/cpy.cu b/ggml/src/ggml-cuda/cpy.cu
-index d027271f..4abd01d7 100644
+index 2d46176e..47383486 100644
 --- a/ggml/src/ggml-cuda/cpy.cu
 +++ b/ggml/src/ggml-cuda/cpy.cu
 @@ -38,6 +38,13 @@ static __device__ void cpy_1_f16_f32(const char * cxi, char * cdsti) {
@@ -257,7 +257,7 @@ index d027271f..4abd01d7 100644
  static __device__ void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) {
      const float * xi = (const float *) cxi;
      block_q8_0 * dsti = (block_q8_0 *) cdsti;
-@@ -633,6 +678,8 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
+@@ -631,6 +676,8 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
          ggml_cpy_f16_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
      } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
          ggml_cpy_f16_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream, dest_ptrs_d, graph_cpynode_index);
@@ -266,7 +266,7 @@ index d027271f..4abd01d7 100644
      } else {
          GGML_ABORT("%s: unsupported type combination (%s to %s)\n", __func__,
                  ggml_type_name(src0->type), ggml_type_name(src1->type));
-@@ -688,6 +735,8 @@ void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) {
+@@ -686,6 +733,8 @@ void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) {
          return (void*) cpy_f32_f16<cpy_1_f32_f16>;
      } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) {
          return (void*) cpy_f32_f16<cpy_1_f16_f32>;

llama/patches/0017-ggml-Export-GPU-UUIDs.patch

@@ -7,31 +7,31 @@ This enables matching up devices and information reported by the backend
 with tools (e.g. nvidia-smi) and system management libraries (e.g. nvml).
 ---
  ggml/include/ggml-backend.h      |  1 +
- ggml/src/ggml-cuda/ggml-cuda.cu  | 39 ++++++++++++++++++++++++++++++++
+ ggml/src/ggml-cuda/ggml-cuda.cu  | 33 ++++++++++++++++++++++++++++++++
  ggml/src/ggml-metal/ggml-metal.m |  1 +
- 3 files changed, 41 insertions(+)
+ 3 files changed, 35 insertions(+)
 
 diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h
-index 74e46716..48839339 100644
+index 74e46716..a880df33 100644
 --- a/ggml/include/ggml-backend.h
 +++ b/ggml/include/ggml-backend.h
 @@ -152,6 +152,7 @@ extern "C" {
      struct ggml_backend_dev_props {
          const char * name;
          const char * description;
-+        const char * id;
++        const char * uuid;
          size_t memory_free;
          size_t memory_total;
          enum ggml_backend_dev_type type;
 diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
-index cb0d8528..d6960174 100644
+index cb0d8528..4c829153 100644
 --- a/ggml/src/ggml-cuda/ggml-cuda.cu
 +++ b/ggml/src/ggml-cuda/ggml-cuda.cu
 @@ -2884,6 +2884,7 @@ struct ggml_backend_cuda_device_context {
      int device;
      std::string name;
      std::string description;
-+    std::string id;
++    std::string uuid;
  };
  
  static const char * ggml_backend_cuda_device_get_name(ggml_backend_dev_t dev) {
@@ -39,9 +39,9 @@ index cb0d8528..d6960174 100644
      return ctx->description.c_str();
  }
  
-+static const char * ggml_backend_cuda_device_get_id(ggml_backend_dev_t dev) {
++static const char * ggml_backend_cuda_device_get_uuid(ggml_backend_dev_t dev) {
 +    ggml_backend_cuda_device_context * ctx = (ggml_backend_cuda_device_context *)dev->context;
-+    return ctx->id.c_str();
++    return ctx->uuid.c_str();
 +}
 +
  static void ggml_backend_cuda_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
@@ -51,17 +51,17 @@ index cb0d8528..d6960174 100644
  static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) {
      props->name        = ggml_backend_cuda_device_get_name(dev);
      props->description = ggml_backend_cuda_device_get_description(dev);
-+    props->id          = ggml_backend_cuda_device_get_id(dev);
++    props->uuid        = ggml_backend_cuda_device_get_uuid(dev);
      props->type        = ggml_backend_cuda_device_get_type(dev);
      ggml_backend_cuda_device_get_memory(dev, &props->memory_free, &props->memory_total);
  
-@@ -3458,6 +3465,38 @@ ggml_backend_reg_t ggml_backend_cuda_reg() {
+@@ -3458,6 +3465,32 @@ ggml_backend_reg_t ggml_backend_cuda_reg() {
                  CUDA_CHECK(cudaGetDeviceProperties(&prop, i));
                  dev_ctx->description = prop.name;
  
 +                #if !defined(GGML_USE_HIP)
-+                char id[64];
-+                snprintf(id, sizeof(id),
++                char uuid[64];
++                snprintf(uuid, sizeof(uuid),
 +                    "GPU-%02x%02x%02x%02x-%02x%02x-%02x%02x-%02x%02x-%02x%02x%02x%02x%02x%02x",
 +                    (unsigned char)prop.uuid.bytes[0],
 +                    (unsigned char)prop.uuid.bytes[1],
@@ -80,29 +80,23 @@ index cb0d8528..d6960174 100644
 +                    (unsigned char)prop.uuid.bytes[14],
 +                    (unsigned char)prop.uuid.bytes[15]
 +                  );
-+                dev_ctx->id = id;
++                dev_ctx->uuid = uuid;
 +                #else
-+                #ifdef _WIN32
-+                char id[16];
-+                snprintf(id, sizeof(id), "%d", i);
-+                dev_ctx->id = id;
-+                #else
-+                dev_ctx->id = "GPU-" + std::string(prop.uuid.bytes, 16);
-+                #endif
++                dev_ctx->uuid = "GPU-" + std::string(prop.uuid.bytes, 16);
 +                #endif
 +
                  ggml_backend_dev_t dev = new ggml_backend_device {
                      /* .iface   = */ ggml_backend_cuda_device_interface,
                      /* .reg     = */ &reg,
 diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
-index 1b56f858..a9eeebc6 100644
+index 1b56f858..ee4f2dcb 100644
 --- a/ggml/src/ggml-metal/ggml-metal.m
 +++ b/ggml/src/ggml-metal/ggml-metal.m
 @@ -5703,6 +5703,7 @@ static enum ggml_backend_dev_type ggml_backend_metal_device_get_type(ggml_backen
  static void ggml_backend_metal_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
      props->name        = ggml_backend_metal_device_get_name(dev);
      props->description = ggml_backend_metal_device_get_description(dev);
-+    props->id          = "0";
++    props->uuid        = "0";
      props->type        = ggml_backend_metal_device_get_type(dev);
      ggml_backend_metal_device_get_memory(dev, &props->memory_free, &props->memory_total);
      props->caps = (struct ggml_backend_dev_caps) {

llama/patches/0018-temporary-prevent-rocm-cuda-mixed-loading.patch

@@ -1,32 +0,0 @@
-From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
-From: Daniel Hiltgen <daniel@ollama.com>
-Date: Sun, 22 Jun 2025 09:22:05 -0700
-Subject: [PATCH] temporary prevent rocm+cuda mixed loading
-
----
- ggml/src/ggml-backend-reg.cpp | 12 ++++++++++--
- 1 file changed, 10 insertions(+), 2 deletions(-)
-
-diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp
-index 4e67d243..8f49f084 100644
---- a/ggml/src/ggml-backend-reg.cpp
-+++ b/ggml/src/ggml-backend-reg.cpp
-@@ -573,8 +573,16 @@ void ggml_backend_load_all_from_path(const char * dir_path) {
- 
-     ggml_backend_load_best("blas", silent, dir_path);
-     ggml_backend_load_best("cann", silent, dir_path);
--    ggml_backend_load_best("cuda", silent, dir_path);
--    ggml_backend_load_best("hip", silent, dir_path);
-+
-+    // Avoid mixed hip+cuda configurations
-+    const char * hip_devices = std::getenv("HIP_VISIBLE_DEVICES");
-+    const char * rocr_devices = std::getenv("ROCR_VISIBLE_DEVICES"); 
-+    if (!hip_devices && !rocr_devices) {
-+        ggml_backend_load_best("cuda", silent, dir_path);
-+    } else {
-+        ggml_backend_load_best("hip", silent, dir_path);
-+    }
-+    
-     ggml_backend_load_best("kompute", silent, dir_path);
-     ggml_backend_load_best("metal", silent, dir_path);
-     ggml_backend_load_best("rpc", silent, dir_path);

llama/patches/0019-metal-add-mean-kernel-14267.patch

@@ -1,169 +0,0 @@
-From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
-From: Georgi Gerganov <ggerganov@gmail.com>
-Date: Thu, 19 Jun 2025 08:05:21 +0300
-Subject: [PATCH] metal : add mean kernel (#14267)
-
-* metal : add mean kernel
-
-ggml-ci
-
-* cont : dedup implementation
-
-ggml-ci
----
- ggml/src/ggml-metal/ggml-metal.m     | 33 ++++++++++++++++---
- ggml/src/ggml-metal/ggml-metal.metal | 48 ++++++++++++++++++++++------
- 2 files changed, 67 insertions(+), 14 deletions(-)
-
-diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
-index ee4f2dcb..f20f5615 100644
---- a/ggml/src/ggml-metal/ggml-metal.m
-+++ b/ggml/src/ggml-metal/ggml-metal.m
-@@ -489,6 +489,7 @@ enum ggml_metal_kernel_type {
-     GGML_METAL_KERNEL_TYPE_COS,
-     GGML_METAL_KERNEL_TYPE_NEG,
-     GGML_METAL_KERNEL_TYPE_SUM_ROWS,
-+    GGML_METAL_KERNEL_TYPE_MEAN,
-     GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32,
-     GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32,
-     GGML_METAL_KERNEL_TYPE_ARGMAX,
-@@ -1436,6 +1437,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
-         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_COS,                             cos,                             true);
-         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NEG,                             neg,                             true);
-         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS,                        sum_rows,                        true);
-+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MEAN,                            mean,                            true);
-         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGMAX,                          argmax,                          true);
-         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32,                 pool_2d_avg_f32,                 true);
-         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32,                 pool_2d_max_f32,                 true);
-@@ -1634,6 +1636,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
-         case GGML_OP_LOG:
-             return false; // TODO: implement
-         case GGML_OP_SUM_ROWS:
-+        case GGML_OP_MEAN:
-         case GGML_OP_SOFT_MAX:
-         case GGML_OP_GROUP_NORM:
-             return has_simdgroup_reduction && ggml_is_contiguous(op->src[0]);
-@@ -2362,11 +2365,30 @@ static bool ggml_metal_encode_node(
-                 [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
-             } break;
-         case GGML_OP_SUM_ROWS:
-+        case GGML_OP_MEAN:
-             {
-                 GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));
- 
--                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline;
-+                id<MTLComputePipelineState> pipeline = nil;
-+
-+                switch (dst->op) {
-+                    case GGML_OP_SUM_ROWS:
-+                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline;
-+                        break;
-+                    case GGML_OP_MEAN:
-+                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MEAN].pipeline;
-+                        break;
-+                    default:
-+                        GGML_ABORT("fatal error");
-+                }
-+
-+                int nth = 32; // SIMD width
-+
-+                while (nth < ne00 && nth < (int) pipeline.maxTotalThreadsPerThreadgroup) {
-+                    nth *= 2;
-+                }
- 
-+                nth = MIN(nth, ne00);
- 
-                 ggml_metal_kargs_sum_rows args = {
-                    /*.ne00 =*/ ne00,
-@@ -2396,11 +2418,12 @@ static bool ggml_metal_encode_node(
-                 };
- 
-                 [encoder setComputePipelineState:pipeline];
--                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
--                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
--                [encoder setBytes:&args length:sizeof(args) atIndex:2];
-+                [encoder setBytes:&args length:sizeof(args) atIndex:0];
-+                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
-+                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
-+                [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];
- 
--                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
-+                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
-             } break;
-         case GGML_OP_SOFT_MAX:
-             {
-diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
-index 9cfddf45..08e8d807 100644
---- a/ggml/src/ggml-metal/ggml-metal.metal
-+++ b/ggml/src/ggml-metal/ggml-metal.metal
-@@ -956,31 +956,61 @@ kernel void kernel_neg(
-     dst[tpig] = -src0[tpig];
- }
- 
-+template <bool norm>
- kernel void kernel_sum_rows(
-+        constant ggml_metal_kargs_sum_rows & args,
-         device const float * src0,
-         device       float * dst,
--        constant ggml_metal_kargs_sum_rows & args,
--        uint3 tpig[[thread_position_in_grid]]) {
--    int64_t i3 = tpig.z;
--    int64_t i2 = tpig.y;
--    int64_t i1 = tpig.x;
-+        threadgroup  float * shmem_f32 [[threadgroup(0)]],
-+        uint3   tgpig[[threadgroup_position_in_grid]],
-+        ushort3 tpitg[[thread_position_in_threadgroup]],
-+        ushort  sgitg[[simdgroup_index_in_threadgroup]],
-+        ushort  tiisg[[thread_index_in_simdgroup]],
-+        ushort3   ntg[[threads_per_threadgroup]]) {
-+    int64_t i3 = tgpig.z;
-+    int64_t i2 = tgpig.y;
-+    int64_t i1 = tgpig.x;
- 
-     if (i3 >= args.ne03 || i2 >= args.ne02 || i1 >= args.ne01) {
-         return;
-     }
- 
-+    if (sgitg == 0) {
-+        shmem_f32[tiisg] = 0.0f;
-+    }
-+
-     device const float * src_row = (device const float *) ((device const char *) src0 + i1*args.nb01 + i2*args.nb02 + i3*args.nb03);
-     device       float * dst_row = (device       float *) ((device       char *) dst  + i1*args.nb1  + i2*args.nb2  + i3*args.nb3);
- 
--    float row_sum = 0;
-+    float sumf = 0;
- 
--    for (int64_t i0 = 0; i0 < args.ne00; i0++) {
--        row_sum += src_row[i0];
-+    for (int64_t i0 = tpitg.x; i0 < args.ne00; i0 += ntg.x) {
-+        sumf += src_row[i0];
-     }
- 
--    dst_row[0] = row_sum;
-+    sumf = simd_sum(sumf);
-+
-+    threadgroup_barrier(mem_flags::mem_threadgroup);
-+
-+    if (tiisg == 0) {
-+        shmem_f32[sgitg] = sumf;
-+    }
-+
-+    threadgroup_barrier(mem_flags::mem_threadgroup);
-+
-+    sumf = shmem_f32[tiisg];
-+    sumf = simd_sum(sumf);
-+
-+    if (tpitg.x == 0) {
-+        dst_row[0] = norm ? sumf / args.ne00 : sumf;
-+    }
- }
- 
-+typedef decltype(kernel_sum_rows<false>) kernel_sum_rows_t;
-+
-+template [[host_name("kernel_sum_rows")]] kernel kernel_sum_rows_t kernel_sum_rows<false>;
-+template [[host_name("kernel_mean")]]     kernel kernel_sum_rows_t kernel_sum_rows<true>;
-+
- template<typename T>
- kernel void kernel_soft_max(
-         device const  char * src0,

llm/memory.go

@@ -151,12 +151,7 @@ func EstimateGPULayers(gpus []discover.GpuInfo, f *ggml.GGML, projectors []strin
 	}
 
 	if graphPartialOffload == 0 {
-		headsKV := f.KV().HeadCountKVMin()
-		if headsKV == 0 {
-			headsKV = 1
-		}
-		gqa := f.KV().HeadCountMax() / headsKV
-		graphPartialOffload = gqa * kvTotal / 6
+		graphPartialOffload = f.KV().GQA() * kvTotal / 6
 	}
 	if graphFullOffload == 0 {
 		graphFullOffload = graphPartialOffload

llm/server.go

@@ -139,13 +139,6 @@ func NewLlamaServer(gpus discover.GpuInfoList, modelPath string, f *ggml.GGML, a
 		gpus = discover.GetCPUInfo()
 	}
 
-	// Verify the requested context size is <= the model training size
-	trainCtx := f.KV().ContextLength()
-	if opts.NumCtx/numParallel > int(trainCtx) && trainCtx > 0 {
-		slog.Warn("requested context size too large for model", "num_ctx", opts.NumCtx, "num_parallel", numParallel, "n_ctx_train", trainCtx)
-		opts.NumCtx = int(trainCtx) * numParallel
-	}
-
 	estimate := EstimateGPULayers(gpus, f, projectors, opts, numParallel)
 	if len(gpus) > 1 || gpus[0].Library != "cpu" {
 		switch {
@@ -318,7 +311,7 @@ func NewLlamaServer(gpus discover.GpuInfoList, modelPath string, f *ggml.GGML, a
 		params = append(params, "--mmproj", projectors[0])
 	}
 
-	// iterate through compatible GPU libraries such as 'cuda_v12', 'rocm', etc.
+	// iterate through compatible GPU libraries such as 'cuda_v12', 'cuda_v11', 'rocm', etc.
 	// adding each library's respective path to the LD_LIBRARY_PATH, until finally running
 	// without any LD_LIBRARY_PATH flags
 	for {

ml/backend.go

@@ -124,9 +124,9 @@ type DeviceMemory struct {
 	// may not be persistent across instances of the runner.
 	Name string
 
-	// ID is an identifier for the device for matching with system
-	// management libraries.
-	ID string
+	// UUID is a unique persistent identifier for the device for matching
+	// with system management libraries
+	UUID string
 
 	// Weights is the per-layer memory needed for the model weights.
 	Weights []Memory
@@ -156,8 +156,8 @@ func (m DeviceMemory) LogValue() slog.Value {
 		attrs = append(attrs, slog.Any("Graph", m.Graph))
 	}
 
-	if len(attrs) > 0 && m.ID != "" {
-		attrs = append([]slog.Attr{slog.String("ID", m.ID)}, attrs...)
+	if len(attrs) > 0 && m.UUID != "" {
+		attrs = append([]slog.Attr{slog.String("UUID", m.UUID)}, attrs...)
 	}
 
 	return slog.GroupValue(attrs...)
@@ -253,7 +253,6 @@ type Tensor interface {
 
 	Neg(ctx Context) Tensor
 	Add(ctx Context, t2 Tensor) Tensor
-	Sub(ctx Context, t2 Tensor) Tensor
 	Mul(ctx Context, t2 Tensor) Tensor
 	Div(ctx Context, t2 Tensor) Tensor
 
@@ -277,7 +276,6 @@ type Tensor interface {
 	Tanh(ctx Context) Tensor
 	GELU(ctx Context) Tensor
 	SILU(ctx Context) Tensor
-	RELU(ctx Context) Tensor
 	Sigmoid(ctx Context) Tensor
 
 	Reshape(ctx Context, shape ...int) Tensor
@@ -299,12 +297,6 @@ type Tensor interface {
 
 	TopK(ctx Context, k int) Tensor
 	Argsort(ctx Context) Tensor
-	Mean(ctx Context) Tensor
-	Variance(ctx Context) Tensor
-	Stddev(ctx Context) Tensor
-	Sqr(ctx Context) Tensor
-	Sqrt(ctx Context) Tensor
-	Clamp(ctx Context, min, max float32) Tensor
 }
 
 // ScaledDotProductAttention implements a fused attention

ml/backend/ggml/ggml.go

@@ -138,7 +138,7 @@ func New(modelPath string, params ml.BackendParams) (ml.Backend, error) {
 	requiredMemory.CPU.Name = C.GoString(C.ggml_backend_dev_name(cpuDeviceBufferType.d))
 	var props C.struct_ggml_backend_dev_props
 	C.ggml_backend_dev_get_props(cpuDeviceBufferType.d, &props)
-	requiredMemory.CPU.ID = C.GoString(props.id)
+	requiredMemory.CPU.UUID = C.GoString(props.uuid)
 	requiredMemory.CPU.Weights = make([]ml.Memory, blocks+1)
 	requiredMemory.CPU.Cache = make([]ml.Memory, blocks+1)
 
@@ -155,7 +155,7 @@ func New(modelPath string, params ml.BackendParams) (ml.Backend, error) {
 		requiredMemory.GPUs[i].Name = C.GoString(C.ggml_backend_dev_name(d))
 		var props C.struct_ggml_backend_dev_props
 		C.ggml_backend_dev_get_props(d, &props)
-		requiredMemory.GPUs[i].ID = C.GoString(props.id)
+		requiredMemory.GPUs[i].UUID = C.GoString(props.uuid)
 		requiredMemory.GPUs[i].Weights = make([]ml.Memory, blocks+1)
 		requiredMemory.GPUs[i].Cache = make([]ml.Memory, blocks+1)
 	}
@@ -297,9 +297,7 @@ func New(modelPath string, params ml.BackendParams) (ml.Backend, error) {
 			if _, ok := meta.Tensors().GroupLayers()["output"]; !ok && t.Name == "token_embd.weight" {
 				createTensor(tensor{source: t, target: "output.weight"}, output.bts, blocks)
 			}
-		case contains(t.Name, "cls", "output", "output_norm",
-			"altup_proj", "altup_unembd_proj",
-			"per_layer_token_embd", "per_layer_model_proj", "per_layer_proj_norm"):
+		case contains(t.Name, "cls", "output", "output_norm"):
 			createTensor(tensor{source: t}, output.bts, blocks)
 		case strings.HasPrefix(t.Name, "v.") || strings.HasPrefix(t.Name, "mm."):
 			// TODO: assign vision tensors to the gpu if possible
@@ -355,26 +353,6 @@ func New(modelPath string, params ml.BackendParams) (ml.Backend, error) {
 		bbs[c] = b
 	}
 
-	// Mimic llama runner logs summarizing layers and memory
-	gpuLayers := 0
-	for _, layer := range layers {
-		if C.ggml_backend_dev_type(layer.d) == C.GGML_BACKEND_DEVICE_TYPE_GPU {
-			gpuLayers++
-		}
-	}
-	slog.Info(fmt.Sprintf("offloading %d repeating layers to GPU", gpuLayers))
-
-	switch C.ggml_backend_dev_type(output.d) {
-	case C.GGML_BACKEND_DEVICE_TYPE_CPU:
-		slog.Info("offloading output layer to CPU")
-	case C.GGML_BACKEND_DEVICE_TYPE_GPU:
-		slog.Info("offloading output layer to GPU")
-		gpuLayers++
-	case C.GGML_BACKEND_DEVICE_TYPE_ACCEL:
-		slog.Info("offloading output layer to ACCEL")
-	}
-	slog.Info(fmt.Sprintf("offloaded %d/%d layers to GPU", gpuLayers, len(layers)+1))
-
 	for bs := range maps.Values(bbs) {
 		slog.Info("model weights", "buffer", C.GoString(C.ggml_backend_buffer_name(bs)), "size", format.HumanBytes2(uint64(C.ggml_backend_buffer_get_size(bs))))
 	}
@@ -420,7 +398,7 @@ func New(modelPath string, params ml.BackendParams) (ml.Backend, error) {
 			(*C.ggml_backend_buffer_type_t)(unsafe.Pointer(&schedBufts[0])),
 			C.int(len(schedBackends)),
 			C.size_t(maxGraphNodes),
-			C._Bool(false),
+			C._Bool(len(gpus) > 1 && slices.Contains(gpus, output.d)),
 			C._Bool(false),
 		),
 		schedBackends: schedBackends,
@@ -624,9 +602,7 @@ func (c *Context) Forward(tensors ...ml.Tensor) ml.Context {
 }
 
 func (c *Context) Compute(tensors ...ml.Tensor) {
-	if status := C.ggml_backend_sched_graph_compute_async(c.b.sched, c.graph); status != C.GGML_STATUS_SUCCESS {
-		panic(fmt.Errorf("error computing ggml graph: %v", status))
-	}
+	C.ggml_backend_sched_graph_compute_async(c.b.sched, c.graph)
 	C.ggml_backend_sched_reset(c.b.sched)
 
 	needSync := true
@@ -915,13 +891,6 @@ func (t *Tensor) Add(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
 	}
 }
 
-func (t *Tensor) Sub(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_sub(ctx.(*Context).ctx, t.t, t2.(*Tensor).t),
-	}
-}
-
 func (t *Tensor) Repeat(ctx ml.Context, dim, n int) ml.Tensor {
 	if dim < 0 || dim >= C.GGML_MAX_DIMS {
 		panic("invalid dimension")
@@ -1229,13 +1198,6 @@ func (t *Tensor) SILU(ctx ml.Context) ml.Tensor {
 	}
 }
 
-func (t *Tensor) RELU(ctx ml.Context) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_relu_inplace(ctx.(*Context).ctx, t.t),
-	}
-}
-
 func (t *Tensor) Conv2D(ctx ml.Context, t2 ml.Tensor, s0, s1, p0, p1, d0, d1 int) ml.Tensor {
 	return &Tensor{
 		b: t.b,
@@ -1311,42 +1273,3 @@ func (t *Tensor) Argsort(ctx ml.Context) ml.Tensor {
 		t: C.ggml_argsort(ctx.(*Context).ctx, t.t, C.GGML_SORT_ORDER_ASC),
 	}
 }
-
-func (t *Tensor) Mean(ctx ml.Context) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_mean(ctx.(*Context).ctx, t.t),
-	}
-}
-
-func (t *Tensor) Variance(ctx ml.Context) ml.Tensor {
-	return t.Add(ctx, t.Mean(ctx).Scale(ctx, -1)).
-		Sqr(ctx).
-		SumRows(ctx).
-		Scale(ctx, 1/float64(t.Dim(0)))
-}
-
-func (t *Tensor) Stddev(ctx ml.Context) ml.Tensor {
-	return t.Variance(ctx).Sqrt(ctx)
-}
-
-func (t *Tensor) Sqr(ctx ml.Context) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_sqr(ctx.(*Context).ctx, t.t),
-	}
-}
-
-func (t *Tensor) Sqrt(ctx ml.Context) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_sqrt(ctx.(*Context).ctx, t.t),
-	}
-}
-
-func (t *Tensor) Clamp(ctx ml.Context, min, max float32) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_clamp(ctx.(*Context).ctx, t.t, C.float(min), C.float(max)),
-	}
-}

ml/backend/ggml/ggml/include/ggml-backend.h

@@ -152,7 +152,7 @@ extern "C" {
     struct ggml_backend_dev_props {
         const char * name;
         const char * description;
-        const char * id;
+        const char * uuid;
         size_t memory_free;
         size_t memory_total;
         enum ggml_backend_dev_type type;

ml/backend/ggml/ggml/src/ggml-backend-reg.cpp

@@ -573,16 +573,8 @@ void ggml_backend_load_all_from_path(const char * dir_path) {
 
     ggml_backend_load_best("blas", silent, dir_path);
     ggml_backend_load_best("cann", silent, dir_path);
-
-    // Avoid mixed hip+cuda configurations
-    const char * hip_devices = std::getenv("HIP_VISIBLE_DEVICES");
-    const char * rocr_devices = std::getenv("ROCR_VISIBLE_DEVICES"); 
-    if (!hip_devices && !rocr_devices) {
-        ggml_backend_load_best("cuda", silent, dir_path);
-    } else {
-        ggml_backend_load_best("hip", silent, dir_path);
-    }
-    
+    ggml_backend_load_best("cuda", silent, dir_path);
+    ggml_backend_load_best("hip", silent, dir_path);
     ggml_backend_load_best("kompute", silent, dir_path);
     ggml_backend_load_best("metal", silent, dir_path);
     ggml_backend_load_best("rpc", silent, dir_path);

ml/backend/ggml/ggml/src/ggml-cuda/common.cuh

@@ -362,26 +362,6 @@ static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
 #endif // FP16_AVAILABLE
 }
 
-// Row reduction kernel template - compute sum (norm=false) or mean (norm=true)
-template<bool norm>
-static __global__ void reduce_rows_f32(const float * x, float * dst, const int ncols) {
-    const int row = blockIdx.x;
-    const int col = threadIdx.x;
-
-    float sum = 0.0f;
-    for (int i = col; i < ncols; i += blockDim.x) {
-        sum += x[row * ncols + i];
-    }
-
-    sum = warp_reduce_sum(sum);
-
-    if (col != 0) {
-        return;
-    }
-
-    dst[row] = norm ? sum / ncols : sum;
-}
-
 template<int width = WARP_SIZE>
 static __device__ __forceinline__ float warp_reduce_max(float x) {
 #pragma unroll

ml/backend/ggml/ggml/src/ggml-cuda/ggml-cuda.cu

@@ -35,7 +35,6 @@
 #include "ggml-cuda/ssm-scan.cuh"
 #include "ggml-cuda/sum.cuh"
 #include "ggml-cuda/sumrows.cuh"
-#include "ggml-cuda/mean.cuh"
 #include "ggml-cuda/tsembd.cuh"
 #include "ggml-cuda/unary.cuh"
 #include "ggml-cuda/upscale.cuh"
@@ -2323,9 +2322,6 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_SUM_ROWS:
             ggml_cuda_op_sum_rows(ctx, dst);
             break;
-        case GGML_OP_MEAN:
-            ggml_cuda_op_mean(ctx, dst);
-            break;
         case GGML_OP_SSM_CONV:
             ggml_cuda_op_ssm_conv(ctx, dst);
             break;
@@ -2888,7 +2884,7 @@ struct ggml_backend_cuda_device_context {
     int device;
     std::string name;
     std::string description;
-    std::string id;
+    std::string uuid;
 };
 
 static const char * ggml_backend_cuda_device_get_name(ggml_backend_dev_t dev) {
@@ -2901,9 +2897,9 @@ static const char * ggml_backend_cuda_device_get_description(ggml_backend_dev_t
     return ctx->description.c_str();
 }
 
-static const char * ggml_backend_cuda_device_get_id(ggml_backend_dev_t dev) {
+static const char * ggml_backend_cuda_device_get_uuid(ggml_backend_dev_t dev) {
     ggml_backend_cuda_device_context * ctx = (ggml_backend_cuda_device_context *)dev->context;
-    return ctx->id.c_str();
+    return ctx->uuid.c_str();
 }
 
 static void ggml_backend_cuda_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
@@ -2920,7 +2916,7 @@ static enum ggml_backend_dev_type ggml_backend_cuda_device_get_type(ggml_backend
 static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) {
     props->name        = ggml_backend_cuda_device_get_name(dev);
     props->description = ggml_backend_cuda_device_get_description(dev);
-    props->id          = ggml_backend_cuda_device_get_id(dev);
+    props->uuid        = ggml_backend_cuda_device_get_uuid(dev);
     props->type        = ggml_backend_cuda_device_get_type(dev);
     ggml_backend_cuda_device_get_memory(dev, &props->memory_free, &props->memory_total);
 
@@ -3215,7 +3211,6 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_POOL_2D:
         case GGML_OP_SUM:
         case GGML_OP_SUM_ROWS:
-        case GGML_OP_MEAN:
         case GGML_OP_ARGSORT:
         case GGML_OP_ACC:
             return true;
@@ -3471,8 +3466,8 @@ ggml_backend_reg_t ggml_backend_cuda_reg() {
                 dev_ctx->description = prop.name;
 
                 #if !defined(GGML_USE_HIP)
-                char id[64];
-                snprintf(id, sizeof(id),
+                char uuid[64];
+                snprintf(uuid, sizeof(uuid),
                     "GPU-%02x%02x%02x%02x-%02x%02x-%02x%02x-%02x%02x-%02x%02x%02x%02x%02x%02x",
                     (unsigned char)prop.uuid.bytes[0],
                     (unsigned char)prop.uuid.bytes[1],
@@ -3491,15 +3486,9 @@ ggml_backend_reg_t ggml_backend_cuda_reg() {
                     (unsigned char)prop.uuid.bytes[14],
                     (unsigned char)prop.uuid.bytes[15]
                   );
-                dev_ctx->id = id;
+                dev_ctx->uuid = uuid;
                 #else
-                #ifdef _WIN32
-                char id[16];
-                snprintf(id, sizeof(id), "%d", i);
-                dev_ctx->id = id;
-                #else
-                dev_ctx->id = "GPU-" + std::string(prop.uuid.bytes, 16);
-                #endif
+                dev_ctx->uuid = "GPU-" + std::string(prop.uuid.bytes, 16);
                 #endif
 
                 ggml_backend_dev_t dev = new ggml_backend_device {

ml/backend/ggml/ggml/src/ggml-cuda/mean.cu

@@ -1,19 +0,0 @@
-#include "mean.cuh"
-
-void ggml_cuda_op_mean(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * src0   = dst->src[0];
-    const float *       src0_d = (const float *) src0->data;
-    float *             dst_d  = (float *) dst->data;
-    cudaStream_t        stream = ctx.stream();
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
-    GGML_ASSERT(ggml_is_contiguous(src0));
-
-    const int64_t ncols = src0->ne[0];
-    const int64_t nrows = ggml_nrows(src0);
-
-    const dim3 block_dims(WARP_SIZE, 1, 1);
-    const dim3 block_nums(nrows, 1, 1);
-    reduce_rows_f32</*norm*/ true><<<block_nums, block_dims, 0, stream>>>(src0_d, dst_d, ncols);
-}

ml/backend/ggml/ggml/src/ggml-cuda/mean.cuh

@@ -1,3 +0,0 @@
-#include "common.cuh"
-
-void ggml_cuda_op_mean(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

ml/backend/ggml/ggml/src/ggml-cuda/sumrows.cu

@@ -1,9 +1,25 @@
 #include "sumrows.cuh"
 
+static __global__ void k_sum_rows_f32(const float * x, float * dst, const int ncols) {
+    const int row = blockIdx.x;
+    const int col = threadIdx.x;
+
+    float sum = 0.0f;
+    for (int i = col; i < ncols; i += blockDim.x) {
+        sum += x[row * ncols + i];
+    }
+
+    sum = warp_reduce_sum(sum);
+
+    if (col == 0) {
+        dst[row] = sum;
+    }
+}
+
 void sum_rows_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const dim3 block_dims(WARP_SIZE, 1, 1);
     const dim3 block_nums(nrows, 1, 1);
-    reduce_rows_f32</*norm*/false><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
+    k_sum_rows_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
 }
 
 void ggml_cuda_op_sum_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
@@ -19,8 +35,5 @@ void ggml_cuda_op_sum_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const int64_t ncols = src0->ne[0];
     const int64_t nrows = ggml_nrows(src0);
 
-    const dim3 block_dims(WARP_SIZE, 1, 1);
-    const dim3 block_nums(nrows, 1, 1);
-
-    reduce_rows_f32</*norm=*/false><<<block_nums, block_dims, 0, stream>>>(src0_d, dst_d, ncols);
+    sum_rows_f32_cuda(src0_d, dst_d, ncols, nrows, stream);
 }

ml/backend/ggml/ggml/src/ggml-cuda/sumrows.cuh

@@ -1,4 +1,5 @@
 #include "common.cuh"
 
 void sum_rows_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream);
+
 void ggml_cuda_op_sum_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-embed.metal

@@ -3434,61 +3434,31 @@ kernel void kernel_neg(
     dst[tpig] = -src0[tpig];
 }
 
-template <bool norm>
 kernel void kernel_sum_rows(
-        constant ggml_metal_kargs_sum_rows & args,
         device const float * src0,
         device       float * dst,
-        threadgroup  float * shmem_f32 [[threadgroup(0)]],
-        uint3   tgpig[[threadgroup_position_in_grid]],
-        ushort3 tpitg[[thread_position_in_threadgroup]],
-        ushort  sgitg[[simdgroup_index_in_threadgroup]],
-        ushort  tiisg[[thread_index_in_simdgroup]],
-        ushort3   ntg[[threads_per_threadgroup]]) {
-    int64_t i3 = tgpig.z;
-    int64_t i2 = tgpig.y;
-    int64_t i1 = tgpig.x;
+        constant ggml_metal_kargs_sum_rows & args,
+        uint3 tpig[[thread_position_in_grid]]) {
+    int64_t i3 = tpig.z;
+    int64_t i2 = tpig.y;
+    int64_t i1 = tpig.x;
 
     if (i3 >= args.ne03 || i2 >= args.ne02 || i1 >= args.ne01) {
         return;
     }
 
-    if (sgitg == 0) {
-        shmem_f32[tiisg] = 0.0f;
-    }
-
     device const float * src_row = (device const float *) ((device const char *) src0 + i1*args.nb01 + i2*args.nb02 + i3*args.nb03);
     device       float * dst_row = (device       float *) ((device       char *) dst  + i1*args.nb1  + i2*args.nb2  + i3*args.nb3);
 
-    float sumf = 0;
+    float row_sum = 0;
 
-    for (int64_t i0 = tpitg.x; i0 < args.ne00; i0 += ntg.x) {
-        sumf += src_row[i0];
+    for (int64_t i0 = 0; i0 < args.ne00; i0++) {
+        row_sum += src_row[i0];
     }
 
-    sumf = simd_sum(sumf);
-
-    threadgroup_barrier(mem_flags::mem_threadgroup);
-
-    if (tiisg == 0) {
-        shmem_f32[sgitg] = sumf;
-    }
-
-    threadgroup_barrier(mem_flags::mem_threadgroup);
-
-    sumf = shmem_f32[tiisg];
-    sumf = simd_sum(sumf);
-
-    if (tpitg.x == 0) {
-        dst_row[0] = norm ? sumf / args.ne00 : sumf;
-    }
+    dst_row[0] = row_sum;
 }
 
-typedef decltype(kernel_sum_rows<false>) kernel_sum_rows_t;
-
-template [[host_name("kernel_sum_rows")]] kernel kernel_sum_rows_t kernel_sum_rows<false>;
-template [[host_name("kernel_mean")]]     kernel kernel_sum_rows_t kernel_sum_rows<true>;
-
 template<typename T>
 kernel void kernel_soft_max(
         device const  char * src0,

ml/backend/ggml/ggml/src/ggml-metal/ggml-metal.m

@@ -489,7 +489,6 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_COS,
     GGML_METAL_KERNEL_TYPE_NEG,
     GGML_METAL_KERNEL_TYPE_SUM_ROWS,
-    GGML_METAL_KERNEL_TYPE_MEAN,
     GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32,
     GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32,
     GGML_METAL_KERNEL_TYPE_ARGMAX,
@@ -1437,7 +1436,6 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_COS,                             cos,                             true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NEG,                             neg,                             true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS,                        sum_rows,                        true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MEAN,                            mean,                            true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGMAX,                          argmax,                          true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32,                 pool_2d_avg_f32,                 true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32,                 pool_2d_max_f32,                 true);
@@ -1636,7 +1634,6 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
         case GGML_OP_LOG:
             return false; // TODO: implement
         case GGML_OP_SUM_ROWS:
-        case GGML_OP_MEAN:
         case GGML_OP_SOFT_MAX:
         case GGML_OP_GROUP_NORM:
             return has_simdgroup_reduction && ggml_is_contiguous(op->src[0]);
@@ -2365,30 +2362,11 @@ static bool ggml_metal_encode_node(
                 [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
             } break;
         case GGML_OP_SUM_ROWS:
-        case GGML_OP_MEAN:
             {
                 GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));
 
-                id<MTLComputePipelineState> pipeline = nil;
+                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline;
 
-                switch (dst->op) {
-                    case GGML_OP_SUM_ROWS:
-                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline;
-                        break;
-                    case GGML_OP_MEAN:
-                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MEAN].pipeline;
-                        break;
-                    default:
-                        GGML_ABORT("fatal error");
-                }
-
-                int nth = 32; // SIMD width
-
-                while (nth < ne00 && nth < (int) pipeline.maxTotalThreadsPerThreadgroup) {
-                    nth *= 2;
-                }
-
-                nth = MIN(nth, ne00);
 
                 ggml_metal_kargs_sum_rows args = {
                    /*.ne00 =*/ ne00,
@@ -2418,12 +2396,11 @@ static bool ggml_metal_encode_node(
                 };
 
                 [encoder setComputePipelineState:pipeline];
-                [encoder setBytes:&args length:sizeof(args) atIndex:0];
-                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
-                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
-                [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];
+                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+                [encoder setBytes:&args length:sizeof(args) atIndex:2];
 
-                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
             } break;
         case GGML_OP_SOFT_MAX:
             {
@@ -5726,7 +5703,7 @@ static enum ggml_backend_dev_type ggml_backend_metal_device_get_type(ggml_backen
 static void ggml_backend_metal_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
     props->name        = ggml_backend_metal_device_get_name(dev);
     props->description = ggml_backend_metal_device_get_description(dev);
-    props->id          = "0";
+    props->uuid        = "0";
     props->type        = ggml_backend_metal_device_get_type(dev);
     ggml_backend_metal_device_get_memory(dev, &props->memory_free, &props->memory_total);
     props->caps = (struct ggml_backend_dev_caps) {

ml/backend/ggml/ggml/src/ggml-metal/ggml-metal.metal

@@ -956,61 +956,31 @@ kernel void kernel_neg(
     dst[tpig] = -src0[tpig];
 }
 
-template <bool norm>
 kernel void kernel_sum_rows(
-        constant ggml_metal_kargs_sum_rows & args,
         device const float * src0,
         device       float * dst,
-        threadgroup  float * shmem_f32 [[threadgroup(0)]],
-        uint3   tgpig[[threadgroup_position_in_grid]],
-        ushort3 tpitg[[thread_position_in_threadgroup]],
-        ushort  sgitg[[simdgroup_index_in_threadgroup]],
-        ushort  tiisg[[thread_index_in_simdgroup]],
-        ushort3   ntg[[threads_per_threadgroup]]) {
-    int64_t i3 = tgpig.z;
-    int64_t i2 = tgpig.y;
-    int64_t i1 = tgpig.x;
+        constant ggml_metal_kargs_sum_rows & args,
+        uint3 tpig[[thread_position_in_grid]]) {
+    int64_t i3 = tpig.z;
+    int64_t i2 = tpig.y;
+    int64_t i1 = tpig.x;
 
     if (i3 >= args.ne03 || i2 >= args.ne02 || i1 >= args.ne01) {
         return;
     }
 
-    if (sgitg == 0) {
-        shmem_f32[tiisg] = 0.0f;
-    }
-
     device const float * src_row = (device const float *) ((device const char *) src0 + i1*args.nb01 + i2*args.nb02 + i3*args.nb03);
     device       float * dst_row = (device       float *) ((device       char *) dst  + i1*args.nb1  + i2*args.nb2  + i3*args.nb3);
 
-    float sumf = 0;
+    float row_sum = 0;
 
-    for (int64_t i0 = tpitg.x; i0 < args.ne00; i0 += ntg.x) {
-        sumf += src_row[i0];
+    for (int64_t i0 = 0; i0 < args.ne00; i0++) {
+        row_sum += src_row[i0];
     }
 
-    sumf = simd_sum(sumf);
-
-    threadgroup_barrier(mem_flags::mem_threadgroup);
-
-    if (tiisg == 0) {
-        shmem_f32[sgitg] = sumf;
-    }
-
-    threadgroup_barrier(mem_flags::mem_threadgroup);
-
-    sumf = shmem_f32[tiisg];
-    sumf = simd_sum(sumf);
-
-    if (tpitg.x == 0) {
-        dst_row[0] = norm ? sumf / args.ne00 : sumf;
-    }
+    dst_row[0] = row_sum;
 }
 
-typedef decltype(kernel_sum_rows<false>) kernel_sum_rows_t;
-
-template [[host_name("kernel_sum_rows")]] kernel kernel_sum_rows_t kernel_sum_rows<false>;
-template [[host_name("kernel_mean")]]     kernel kernel_sum_rows_t kernel_sum_rows<true>;
-
 template<typename T>
 kernel void kernel_soft_max(
         device const  char * src0,

ml/backend/ggml/ggml/src/ggml-metal/metal.go

@@ -4,6 +4,6 @@ package metal
 
 //go:generate sh -c "{ echo // Code generated by 'go generate'. DO NOT EDIT.; sed -e '/__embed_ggml-common.h__/r ../ggml-common.h' -e '/__embed_ggml-common.h__/d' -e '/#include \"ggml-metal-impl.h\"/r ggml-metal-impl.h' -e '/#include \"ggml-metal-impl.h\"/d' ggml-metal.metal; } >ggml-metal-embed.metal"
 
-// #cgo CPPFLAGS: -DGGML_METAL_NDEBUG -DGGML_METAL_EMBED_LIBRARY -DGGML_METAL_USE_BF16 -I.. -I../../include
+// #cgo CPPFLAGS: -DGGML_METAL_NDEBUG -DGGML_METAL_EMBED_LIBRARY -I.. -I../../include
 // #cgo LDFLAGS: -framework Metal -framework MetalKit
 import "C"

ml/backend/ggml/ggml/src/ggml.go

@@ -5,7 +5,6 @@ package ggml
 // #cgo CPPFLAGS: -I${SRCDIR}/../include -I${SRCDIR}/ggml-cpu
 // #cgo windows CFLAGS: -Wno-dll-attribute-on-redeclaration
 // #cgo windows LDFLAGS: -lmsvcrt -static -static-libgcc -static-libstdc++
-// #cgo windows linux CPPFLAGS: -DGGML_FP16_TO_FP32=ggml_compute_fp16_to_fp32
 // #include <stdlib.h>
 // #include "ggml-backend.h"
 // extern void sink(int level, char *text, void *user_data);

ml/backend/ggml/quantization.go

@@ -10,8 +10,6 @@ package ggml
 import "C"
 
 import (
-	"iter"
-	"slices"
 	"unsafe"
 
 	fsggml "github.com/ollama/ollama/fs/ggml"
@@ -52,30 +50,34 @@ func ConvertToF32(data []byte, dtype uint32, nelements uint64) []float32 {
 	return f32s
 }
 
-func Quantize(newType fsggml.TensorType, f32s []float32, shape []uint64) iter.Seq[[]byte] {
-	return func(yield func([]byte) bool) {
-		C.ggml_quantize_init(uint32(newType))
-		defer C.ggml_quantize_free()
-
-		dims := slices.Repeat([]C.int64_t{1}, 4)
-		for i, s := range shape {
-			dims[i] = C.int64_t(s)
-		}
-
-		bts := make([]byte, C.ggml_row_size(uint32(newType), dims[0])*C.size_t(dims[1]))
-		for chunk := range dims[2] {
-			offset := chunk * dims[0] * dims[1]
-
-			n := C.ggml_quantize_chunk(
-				uint32(newType),
-				(*C.float)(&f32s[0]),
-				unsafe.Pointer(&bts[0]),
-				offset, dims[1], dims[0], nil,
-			)
-
-			if !yield(bts[:n]) {
-				return
-			}
-		}
+func Quantize(newType fsggml.TensorType, f32s []float32, shape []uint64) []byte {
+	buf := make([]byte, len(f32s)*4) // upper bound on size
+	nPerRow := C.int64_t(shape[0])
+	nrows := C.int64_t(1)
+	if len(shape) > 1 {
+		nrows = C.int64_t(shape[1])
 	}
+	shape2 := C.int64_t(1)
+	if len(shape) > 2 {
+		shape2 = C.int64_t(shape[2])
+	}
+	nelements_matrix := nPerRow * nrows
+	newSize := C.size_t(0)
+	for i03 := C.int64_t(0); i03 < shape2; i03++ {
+		f32s_03 := i03 * nelements_matrix
+		buf_03 := C.int64_t(C.ggml_row_size(uint32(newType), nPerRow)) * i03 * nrows
+		newSize += C.ggml_quantize_chunk(
+			uint32(newType),
+			(*C.float)(&f32s[f32s_03]),
+			unsafe.Pointer((uintptr)(unsafe.Pointer(&buf[0]))+uintptr(buf_03)),
+			0,
+			nrows,
+			nPerRow,
+			nil)
+	}
+	return buf[:newSize]
+}
+
+func QuantizationVersion() uint32 {
+	return uint32(C.GGML_QNT_VERSION)
 }

model/models/gemma3n/model.go

@@ -1,51 +0,0 @@
-package gemma3n
-
-import (
-	"github.com/ollama/ollama/fs"
-	"github.com/ollama/ollama/kvcache"
-	"github.com/ollama/ollama/ml"
-	"github.com/ollama/ollama/model"
-	"github.com/ollama/ollama/model/input"
-)
-
-type Model struct {
-	model.Base
-	model.SentencePieceModel
-
-	*TextModel
-}
-
-// Forward implements model.Model.
-func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
-	return m.TextModel.Forward(ctx, batch, m.Cache)
-}
-
-func New(c fs.Config) (model.Model, error) {
-	m := Model{
-		TextModel: newTextModel(c),
-		SentencePieceModel: model.NewSentencePieceModel(
-			&model.Vocabulary{
-				Values: c.Strings("tokenizer.ggml.tokens"),
-				Scores: c.Floats("tokenizer.ggml.scores"),
-				Types:  c.Ints("tokenizer.ggml.token_type"),
-				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
-				BOS:    []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
-				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
-				EOS: append(
-					[]int32{int32(c.Uint("tokenizer.ggml.eos_token_id"))},
-					c.Ints("tokenizer.ggml.eos_token_ids")...,
-				),
-			},
-		),
-	}
-
-	m.Cache = kvcache.NewWrapperCache(
-		kvcache.NewCausalCache(m.Shift),
-		kvcache.NewSWACache(int32(c.Uint("attention.sliding_window")), m.Shift),
-	)
-	return &m, nil
-}
-
-func init() {
-	model.Register("gemma3n", New)
-}

model/models/gemma3n/model_text.go

@@ -1,360 +0,0 @@
-package gemma3n
-
-import (
-	"cmp"
-	"math"
-
-	"github.com/ollama/ollama/fs"
-	"github.com/ollama/ollama/kvcache"
-	"github.com/ollama/ollama/ml"
-	"github.com/ollama/ollama/ml/nn"
-	"github.com/ollama/ollama/ml/nn/fast"
-	"github.com/ollama/ollama/ml/nn/rope"
-	"github.com/ollama/ollama/model/input"
-)
-
-type TextModel struct {
-	TokenEmbedding *TextScaledWordEmbedding `gguf:"token_embd"`
-
-	*PerLayerProjector
-
-	AltupEmbd   *nn.Linear `gguf:"altup_proj"`
-	AltupUnembd *nn.Linear `gguf:"altup_unembd_proj"`
-
-	TextLayers []TextLayer `gguf:"blk"`
-	OutputNorm *nn.RMSNorm `gguf:"output_norm"`
-	Output     *nn.Linear  `gguf:"output,alt:token_embd"`
-
-	TextOptions
-}
-
-func (m *TextModel) Forward(ctx ml.Context, batch input.Batch, cache kvcache.Cache) (ml.Tensor, error) {
-	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
-	// Create a tensor of a single float32 value of 1.0 to use for altup correction
-	one := ctx.Input().FromFloatSlice([]float32{1.0}, 1)
-
-	inputs := m.TokenEmbedding.Forward(ctx, batch.Inputs, math.Sqrt(float64(m.hiddenSize)))
-	inputsPerLayer := m.PerLayerProjector.Forward(ctx, batch, inputs, &m.TextOptions)
-
-	targetMagnitude := inputs.Sqr(ctx).Mean(ctx).Sqrt(ctx)
-	targetMagnitude = targetMagnitude.Repeat(ctx, 2, m.altupInputs-1)
-
-	hiddenState := inputs.Repeat(ctx, 2, m.altupInputs-1)
-	altupProj := m.AltupEmbd.Forward(ctx, hiddenState)
-	altupProj = altupProj.Mul(ctx, targetMagnitude.Div(ctx, altupProj.Sqr(ctx).Mean(ctx).Sqrt(ctx)))
-
-	hiddenStates := inputs.Concat(ctx, altupProj, 2)
-
-	firstSharedKeyValue := m.hiddenLayers - m.sharedKeyValueLayers
-	for i, layer := range m.TextLayers {
-		if i < firstSharedKeyValue {
-			cache.SetLayer(i)
-		} else if m.isLocal(i) {
-			cache.SetLayer(firstSharedKeyValue - 2)
-		} else {
-			cache.SetLayer(firstSharedKeyValue - 1)
-		}
-
-		var layerType int
-		ropeBase := m.ropeBase
-		if m.isLocal(i) {
-			layerType = 1
-			ropeBase = m.ropeBaseLocal
-		}
-
-		cache.(*kvcache.WrapperCache).SetLayerType(layerType)
-
-		// inputPerLayer = inputsPerLayer[:, i, :]
-		inputPerLayer := inputsPerLayer.View(ctx, i*inputsPerLayer.Stride(1), inputsPerLayer.Dim(0), inputsPerLayer.Stride(2), inputsPerLayer.Dim(2))
-		hiddenStates = layer.Forward(ctx, hiddenStates, inputPerLayer, positions, one, cache, i >= firstSharedKeyValue, ropeBase, float64(m.activationSparsityScale[i]), &m.TextOptions)
-	}
-
-	// hiddenStates = hiddenStates[:, :, 0]
-	hiddenStates0 := hiddenStates.View(ctx, 0, hiddenStates.Dim(0), hiddenStates.Stride(1), hiddenStates.Dim(1))
-	targetMagnitude = hiddenStates0.Sqr(ctx).Mean(ctx).Sqrt(ctx)
-	targetMagnitude = targetMagnitude.Repeat(ctx, 2, m.altupInputs-1)
-
-	// hiddenState = hiddenStates[:, :, 1:]
-	hiddenState = hiddenStates.View(ctx, hiddenStates.Stride(2), hiddenStates.Dim(0), hiddenStates.Stride(1), hiddenStates.Dim(1), hiddenStates.Stride(2), m.altupInputs-1)
-	altupUnembdProj := m.AltupUnembd.Forward(ctx, hiddenState)
-	altupUnembdProj = altupUnembdProj.Mul(ctx, targetMagnitude.Div(ctx, altupUnembdProj.Sqr(ctx).Mean(ctx).Sqrt(ctx)))
-
-	hiddenStates = hiddenStates0.Concat(ctx, altupUnembdProj, 2)
-
-	hiddenStates = hiddenStates.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx).Mean(ctx)
-	hiddenStates = hiddenStates.Permute(ctx, 2, 0, 1, 3).Contiguous(ctx)
-	hiddenStates = hiddenStates.Rows(ctx, ctx.Input().FromIntSlice(batch.Outputs, len(batch.Outputs)))
-
-	hiddenStates = m.OutputNorm.Forward(ctx, hiddenStates, m.eps)
-	return m.Output.Forward(ctx, hiddenStates), nil
-}
-
-func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
-	ropeBase := m.ropeBase
-	if m.isLocal(layer) {
-		ropeBase = m.ropeBaseLocal
-	}
-
-	return fast.RoPE(ctx, key, shift, m.headDim(), ropeBase, m.ropeScale, rope.WithTypeNeoX()), nil
-}
-
-type TextScaledWordEmbedding struct {
-	*nn.Embedding
-}
-
-func (e TextScaledWordEmbedding) Forward(ctx ml.Context, inputIDs ml.Tensor, scale float64) ml.Tensor {
-	return e.Embedding.Forward(ctx, inputIDs).Scale(ctx, scale)
-}
-
-type PerLayerProjector struct {
-	TokenEmbedding *TextScaledWordEmbedding `gguf:"per_layer_token_embd"`
-	Projector      *nn.Linear               `gguf:"per_layer_model_proj"`
-	Norm           *nn.RMSNorm              `gguf:"per_layer_proj_norm"`
-}
-
-func (p PerLayerProjector) Forward(ctx ml.Context, batch input.Batch, inputs ml.Tensor, opts *TextOptions) ml.Tensor {
-	inputsPerLayer := p.TokenEmbedding.Forward(ctx, batch.Inputs, math.Sqrt(float64(opts.hiddenSizePerLayerInput)))
-	inputsPerLayer = inputsPerLayer.Reshape(ctx, opts.hiddenSizePerLayerInput, opts.hiddenLayers, batch.Inputs.Dim(0), batch.Inputs.Dim(1))
-
-	perLayerProjection := p.Projector.Forward(ctx, inputs)
-	perLayerProjection = perLayerProjection.Scale(ctx, math.Sqrt(float64(opts.hiddenSize)))
-	perLayerProjection = perLayerProjection.Reshape(ctx, opts.hiddenSizePerLayerInput, opts.hiddenLayers, inputs.Dim(1))
-	perLayerProjection = p.Norm.Forward(ctx, perLayerProjection, opts.eps)
-
-	if inputsPerLayer != nil {
-		perLayerProjection = perLayerProjection.Add(ctx, inputsPerLayer)
-		perLayerProjection = perLayerProjection.Scale(ctx, 1/math.Sqrt(2))
-	}
-
-	return perLayerProjection
-}
-
-type TextLayer struct {
-	*AltUp
-	*Laurel
-
-	AttentionNorm     *nn.RMSNorm `gguf:"attn_norm"`
-	Attention         *TextAttention
-	PostAttentionNorm *nn.RMSNorm `gguf:"post_attention_norm"`
-
-	MLPNorm     *nn.RMSNorm `gguf:"ffn_norm"`
-	MLP         *TextMLP
-	PostMLPNorm *nn.RMSNorm `gguf:"post_ffw_norm"`
-
-	PerLayerInputGate  *nn.Linear  `gguf:"inp_gate"`
-	PerLayerProjection *nn.Linear  `gguf:"proj"`
-	PostPerLayerNorm   *nn.RMSNorm `gguf:"post_norm"`
-}
-
-func (d TextLayer) Forward(ctx ml.Context, hiddenStates, perLayerInput, positions, one ml.Tensor, cache kvcache.Cache, sharedKV bool, ropeBase float32, activationSparsityScale float64, opts *TextOptions) ml.Tensor {
-	predictions := d.Predict(ctx, hiddenStates, opts)
-	active := opts.altupActive(ctx, predictions)
-
-	attn := d.AttentionNorm.Forward(ctx, active, opts.eps)
-	laurel := d.Laurel.Forward(ctx, attn, opts)
-
-	attn = d.Attention.Forward(ctx, attn, positions, cache, sharedKV, ropeBase, opts)
-	attn = d.PostAttentionNorm.Forward(ctx, attn, opts.eps)
-	attn = active.Add(ctx, attn)
-	attn = attn.Add(ctx, laurel).Scale(ctx, 1/math.Sqrt(2))
-
-	mlp := d.MLPNorm.Forward(ctx, attn, opts.eps)
-	mlp = d.MLP.Forward(ctx, mlp, activationSparsityScale)
-	mlp = d.PostMLPNorm.Forward(ctx, mlp, opts.eps)
-	mlp = attn.Add(ctx, mlp)
-
-	predictions = d.Correct(ctx, predictions, mlp, one, opts)
-	active = opts.altupActive(ctx, predictions)
-	if opts.altupCorrectScale {
-		active = d.ScaleCorrectedOutput(ctx, active)
-	}
-
-	active = d.PerLayerInputGate.Forward(ctx, active)
-	active = active.GELU(ctx)
-	active = active.Mul(ctx, perLayerInput)
-
-	active = d.PerLayerProjection.Forward(ctx, active)
-	active = d.PostPerLayerNorm.Forward(ctx, active, opts.eps)
-
-	// inactive := predictions[:, :, 1:]
-	inactive := predictions.View(ctx, predictions.Stride(2), predictions.Dim(0), predictions.Stride(1), predictions.Dim(1), predictions.Stride(2), predictions.Dim(2)-1)
-	active = inactive.Add(ctx, active)
-
-	predictions0 := predictions.View(ctx, 0, predictions.Dim(0), predictions.Stride(1), predictions.Dim(1))
-	return predictions0.Concat(ctx, active, 2)
-}
-
-type AltUp struct {
-	CorrectionScale       ml.Tensor   `gguf:"altup_correct_scale.weight"`
-	PredictionCoefficient *nn.Linear  `gguf:"altup_predict_coef"`
-	CorrectionCoefficient *nn.Linear  `gguf:"altup_correct_coef"`
-	Router                *nn.Linear  `gguf:"altup_router"`
-	RouterNorm            *nn.RMSNorm `gguf:"altup_router_norm"`
-}
-
-func (a AltUp) computeRouterModalities(ctx ml.Context, hiddenStates ml.Tensor, opts *TextOptions) ml.Tensor {
-	routerInputs := a.RouterNorm.Forward(ctx, hiddenStates, opts.eps).Scale(ctx, 1.0/float64(opts.hiddenSize))
-	return a.Router.Forward(ctx, routerInputs).Tanh(ctx)
-}
-
-func (a AltUp) Predict(ctx ml.Context, hiddenStates ml.Tensor, opts *TextOptions) ml.Tensor {
-	modalities := a.computeRouterModalities(ctx, opts.altupActive(ctx, hiddenStates), opts)
-
-	coefficients := a.PredictionCoefficient.Forward(ctx, modalities)
-	coefficients = coefficients.Reshape(ctx, opts.altupInputs, opts.altupInputs, coefficients.Dim(1), coefficients.Dim(2))
-
-	hiddenStates = hiddenStates.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
-	predictions := coefficients.Mulmat(ctx, hiddenStates)
-	predictions = predictions.Add(ctx, hiddenStates)
-	return predictions.Permute(ctx, 2, 0, 1, 3).Contiguous(ctx)
-}
-
-func (a AltUp) Correct(ctx ml.Context, predictions, activated, one ml.Tensor, opts *TextOptions) ml.Tensor {
-	innovation := activated.Sub(ctx, opts.altupActive(ctx, predictions))
-	innovation = innovation.Repeat(ctx, 2, opts.altupInputs)
-
-	modalities := a.computeRouterModalities(ctx, activated, opts)
-	coefficients := a.CorrectionCoefficient.Forward(ctx, modalities)
-	coefficients = coefficients.Add(ctx, one)
-
-	coefficients = coefficients.Reshape(ctx, 1, coefficients.Dim(0), coefficients.Dim(1))
-	coefficients = coefficients.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
-
-	corrected := innovation.Mul(ctx, coefficients)
-	corrected = corrected.Add(ctx, predictions)
-	return corrected
-}
-
-func (a AltUp) ScaleCorrectedOutput(ctx ml.Context, predictions ml.Tensor) ml.Tensor {
-	return predictions.Mul(ctx, a.CorrectionScale)
-}
-
-type Laurel struct {
-	LinearLeft     *nn.Linear  `gguf:"laurel_l"`
-	LinearRight    *nn.Linear  `gguf:"laurel_r"`
-	PostLaurelNorm *nn.RMSNorm `gguf:"laurel_post_norm"`
-}
-
-func (l Laurel) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *TextOptions) ml.Tensor {
-	residual := hiddenStates
-	hiddenStates = l.LinearLeft.Forward(ctx, hiddenStates)
-	hiddenStates = l.LinearRight.Forward(ctx, hiddenStates)
-	hiddenStates = l.PostLaurelNorm.Forward(ctx, hiddenStates, opts.eps)
-	return hiddenStates.Add(ctx, residual)
-}
-
-type TextAttention struct {
-	Query     *nn.Linear  `gguf:"attn_q"`
-	QueryNorm *nn.RMSNorm `gguf:"attn_q_norm"`
-	Key       *nn.Linear  `gguf:"attn_k"`
-	KeyNorm   *nn.RMSNorm `gguf:"attn_k_norm"`
-	Value     *nn.Linear  `gguf:"attn_v"`
-	Output    *nn.Linear  `gguf:"attn_output"`
-}
-
-func (attn TextAttention) Forward(ctx ml.Context, hiddenStates, positions ml.Tensor, cache kvcache.Cache, sharedKV bool, ropeBase float32, opts *TextOptions) ml.Tensor {
-	batchSize := hiddenStates.Dim(1)
-
-	query := attn.Query.Forward(ctx, hiddenStates)
-	query = query.Reshape(ctx, opts.headDim(), opts.numHeads, batchSize)
-	query = attn.QueryNorm.Forward(ctx, query, opts.eps)
-	query = fast.RoPE(ctx, query, positions, opts.headDim(), ropeBase, opts.ropeScale, rope.WithTypeNeoX())
-
-	var key, value ml.Tensor
-	if !sharedKV {
-		key = attn.Key.Forward(ctx, hiddenStates)
-		key = key.Reshape(ctx, opts.headDim(), opts.numKVHeads, batchSize)
-		key = attn.KeyNorm.Forward(ctx, key, opts.eps)
-		key = fast.RoPE(ctx, key, positions, opts.headDim(), ropeBase, opts.ropeScale, rope.WithTypeNeoX())
-
-		value = attn.Value.Forward(ctx, hiddenStates)
-		value = value.Reshape(ctx, opts.headDim(), opts.numKVHeads, batchSize)
-		value = value.RMSNorm(ctx, nil, opts.eps)
-	}
-
-	attention := nn.Attention(ctx, query, key, value, 1., cache)
-	attention = attention.Reshape(ctx, attention.Dim(0)*attention.Dim(1), batchSize)
-	return attn.Output.Forward(ctx, attention)
-}
-
-type TextMLP struct {
-	Gate *nn.Linear `gguf:"ffn_gate"`
-	Up   *nn.Linear `gguf:"ffn_up"`
-	Down *nn.Linear `gguf:"ffn_down"`
-}
-
-func (mlp TextMLP) Forward(ctx ml.Context, hiddenStates ml.Tensor, activationSparsityScale float64) ml.Tensor {
-	upStates := mlp.Up.Forward(ctx, hiddenStates)
-	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates)
-	if activationSparsityScale > 0 {
-		mean := hiddenStates.Mean(ctx)
-		std := hiddenStates.Stddev(ctx).Scale(ctx, activationSparsityScale)
-		cutoff := mean.Add(ctx, std)
-		hiddenStates = hiddenStates.Sub(ctx, cutoff).RELU(ctx)
-	}
-
-	hiddenStates = hiddenStates.GELU(ctx).Mul(ctx, upStates)
-	hiddenStates = mlp.Down.Forward(ctx, hiddenStates)
-	return hiddenStates
-}
-
-type TextOptions struct {
-	hiddenLayers            int
-	hiddenSize              int
-	hiddenSizePerLayerInput int
-	numHeads, numKVHeads    int
-	keyLength, valueLength  int
-	sharedKeyValueLayers    int
-
-	altupActiveIndex  int
-	altupInputs       int
-	altupCorrectScale bool
-
-	eps           float32
-	ropeBase      float32
-	ropeBaseLocal float32
-	ropeScale     float32
-
-	slidingWindowPattern    []bool
-	activationSparsityScale []float32
-}
-
-func (o *TextOptions) altupActive(ctx ml.Context, t ml.Tensor) ml.Tensor {
-	// t[:, :, o.altupActiveIndex]
-	return t.View(ctx, o.altupActiveIndex*t.Stride(2), t.Dim(0), t.Stride(1), t.Dim(1))
-}
-
-func (o *TextOptions) headDim() int {
-	return cmp.Or(o.keyLength, o.valueLength, o.hiddenSize/o.numHeads)
-}
-
-func (o *TextOptions) isLocal(i int) bool {
-	return o.slidingWindowPattern[i]
-}
-
-func newTextModel(c fs.Config) *TextModel {
-	return &TextModel{
-		TextLayers: make([]TextLayer, c.Uint("block_count")),
-		TextOptions: TextOptions{
-			hiddenLayers:            int(c.Uint("block_count")),
-			hiddenSize:              int(c.Uint("embedding_length")),
-			hiddenSizePerLayerInput: int(c.Uint("embedding_length_per_layer_input")),
-			numHeads:                int(c.Uint("attention.head_count")),
-			numKVHeads:              int(c.Uint("attention.head_count_kv")),
-			keyLength:               int(c.Uint("attention.key_length")),
-			valueLength:             int(c.Uint("attention.value_length")),
-			sharedKeyValueLayers:    int(c.Uint("attention.shared_kv_layers")),
-
-			altupActiveIndex: int(c.Uint("altup.active_idx")),
-			altupInputs:      int(c.Uint("altup.num_inputs")),
-
-			eps:           c.Float("attention.layer_norm_rms_epsilon", 1e-06),
-			ropeBase:      c.Float("rope.freq_base", 1_000_000),
-			ropeBaseLocal: c.Float("rope.freq_base_local", 10_000),
-			ropeScale:     c.Float("rope.freq_scale", 1.0),
-
-			slidingWindowPattern:    c.Bools("attention.sliding_window_pattern"),
-			activationSparsityScale: c.Floats("activation_sparsity_scale"),
-		},
-	}
-}

model/models/llama/model.go

@@ -2,7 +2,6 @@ package llama
 
 import (
 	"cmp"
-	"fmt"
 	"math"
 
 	"github.com/ollama/ollama/fs"
@@ -34,14 +33,6 @@ type Model struct {
 }
 
 func New(c fs.Config) (model.Model, error) {
-	// This model currently only supports the gpt2 tokenizer
-	if c.String("tokenizer.ggml.model") == "llama" {
-		return nil, fmt.Errorf("unsupported tokenizer: llama")
-	}
-	// Best effort detection of library/deepseek-coder model(s) which are incompatible
-	if c.String("general.name") == "deepseek-ai" {
-		return nil, fmt.Errorf("unsupported model: %s", c.String("general.name"))
-	}
 	m := Model{
 		BytePairEncoding: model.NewBytePairEncoding(
 			c.String("tokenizer.ggml.pretokenizer", `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`),

model/models/llama4/model_text.go

@@ -63,9 +63,9 @@ func (mlp *TextMLP) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *TextOp
 }
 
 type TextExperts struct {
-	Gate *nn.Linear `gguf:"ffn_gate_exps"`
-	Up   *nn.Linear `gguf:"ffn_up_exps"`
-	Down *nn.Linear `gguf:"ffn_down_exps"`
+	Gate ml.Tensor `gguf:"ffn_gate_exps.weight"`
+	Up   ml.Tensor `gguf:"ffn_up_exps.weight"`
+	Down ml.Tensor `gguf:"ffn_down_exps.weight"`
 }
 
 func (e *TextExperts) Forward(ctx ml.Context, hiddenStates, routerLogits ml.Tensor, opts *TextOptions) ml.Tensor {
@@ -76,9 +76,9 @@ func (e *TextExperts) Forward(ctx ml.Context, hiddenStates, routerLogits ml.Tens
 	hiddenStates = hiddenStates.Repeat(ctx, 1, opts.numExpertsUsed)
 	hiddenStates = hiddenStates.Mul(ctx, scores)
 
-	upStates := e.Up.Weight.MulmatID(ctx, hiddenStates, experts)
-	gateStates := e.Gate.Weight.MulmatID(ctx, hiddenStates, experts)
-	downStates := e.Down.Weight.MulmatID(ctx, upStates.Mul(ctx, gateStates.SILU(ctx)), experts)
+	upStates := e.Up.MulmatID(ctx, hiddenStates, experts)
+	gateStates := e.Gate.MulmatID(ctx, hiddenStates, experts)
+	downStates := e.Down.MulmatID(ctx, upStates.Mul(ctx, gateStates.SILU(ctx)), experts)
 
 	nextStates := downStates.View(ctx, 0, hiddenStates.Dim(0), downStates.Stride(2), hiddenStates.Dim(2))
 	for i := 1; i < opts.numExpertsUsed; i++ {

model/models/models.go

@@ -3,7 +3,6 @@ package models
 import (
 	_ "github.com/ollama/ollama/model/models/gemma2"
 	_ "github.com/ollama/ollama/model/models/gemma3"
-	_ "github.com/ollama/ollama/model/models/gemma3n"
 	_ "github.com/ollama/ollama/model/models/llama"
 	_ "github.com/ollama/ollama/model/models/llama4"
 	_ "github.com/ollama/ollama/model/models/mistral3"

model/models/qwen2/model.go

@@ -2,9 +2,7 @@ package qwen2
 
 import (
 	"cmp"
-	"fmt"
 	"math"
-	"strings"
 
 	"github.com/ollama/ollama/fs"
 	"github.com/ollama/ollama/kvcache"
@@ -128,14 +126,6 @@ func (m Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor
 }
 
 func New(c fs.Config) (model.Model, error) {
-	// This model currently only supports the gpt2 tokenizer
-	if c.String("tokenizer.ggml.model") == "llama" {
-		return nil, fmt.Errorf("unsupported tokenizer: llama")
-	}
-	// detect library/qwen model(s) which are incompatible
-	if strings.HasPrefix(c.String("general.name"), "Qwen2-beta") {
-		return nil, fmt.Errorf("unsupported model: %s", c.String("general.name"))
-	}
 	m := Model{
 		Layers: make([]DecoderLayer, c.Uint("block_count")),
 		BytePairEncoding: model.NewBytePairEncoding(

model/models/qwen3/model.go

@@ -66,9 +66,9 @@ type MLP interface {
 
 type sparse struct {
 	Router *nn.Linear `gguf:"ffn_gate_inp"`
-	Gate   *nn.Linear `gguf:"ffn_gate_exps"`
-	Up     *nn.Linear `gguf:"ffn_up_exps"`
-	Down   *nn.Linear `gguf:"ffn_down_exps"`
+	Gate   ml.Tensor  `gguf:"ffn_gate_exps.weight"`
+	Up     ml.Tensor  `gguf:"ffn_up_exps.weight"`
+	Down   ml.Tensor  `gguf:"ffn_down_exps.weight"`
 }
 
 func (mlp *sparse) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Options) ml.Tensor {
@@ -87,13 +87,13 @@ func (mlp *sparse) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Options
 
 	hiddenStates = hiddenStates.Reshape(ctx, hiddenStates.Dim(0), 1, hiddenStates.Dim(1))
 
-	upStates := mlp.Up.Weight.MulmatID(ctx, hiddenStates, selectedExperts)
+	upStates := mlp.Up.MulmatID(ctx, hiddenStates, selectedExperts)
 
-	hiddenStates = mlp.Gate.Weight.MulmatID(ctx, hiddenStates, selectedExperts)
+	hiddenStates = mlp.Gate.MulmatID(ctx, hiddenStates, selectedExperts)
 	hiddenStates = hiddenStates.SILU(ctx)
 	hiddenStates = hiddenStates.Mul(ctx, upStates)
 
-	experts := mlp.Down.Weight.MulmatID(ctx, hiddenStates, selectedExperts)
+	experts := mlp.Down.MulmatID(ctx, hiddenStates, selectedExperts)
 	experts = experts.Mul(ctx, routingWeights)
 
 	nextStates := experts.View(ctx, 0, experts.Dim(0), experts.Stride(2), experts.Dim(2))

model/vocabulary.go

@@ -87,7 +87,7 @@ func (v *Vocabulary) Decode(id int32) string {
 func (v *Vocabulary) SpecialVocabulary() []string {
 	v.specialOnce.Do(func() {
 		for i := range v.Values {
-			if v.Types[i] == TOKEN_TYPE_CONTROL || v.Types[i] == TOKEN_TYPE_USER_DEFINED {
+			if v.Types[i] == TOKEN_TYPE_CONTROL {
 				v.special = append(v.special, v.Values[i])
 			}
 		}

model/vocabulary_test.go

@@ -1,16 +0,0 @@
-package model
-
-import "testing"
-
-func TestVocabulary_SpecialVocabulary(t *testing.T) {
-	vocab := &Vocabulary{
-		Values: []string{"<|startoftext|>", "<|endoftext|>", "<|tool_call_start|>", "<|tool_call_end|>", "hi"},
-		Types:  []int32{TOKEN_TYPE_CONTROL, TOKEN_TYPE_CONTROL, TOKEN_TYPE_USER_DEFINED, TOKEN_TYPE_USER_DEFINED, TOKEN_TYPE_NORMAL},
-	}
-
-	specialVocab := vocab.SpecialVocabulary()
-
-	if len(specialVocab) != 4 {
-		t.Errorf("expected 4 special tokens, got %d", len(specialVocab))
-	}
-}

openai/openai.go

@@ -423,7 +423,7 @@ func fromChatRequest(r ChatCompletionRequest) (*api.ChatRequest, error) {
 						}
 					}
 
-					types := []string{"jpeg", "jpg", "png", "webp"}
+					types := []string{"jpeg", "jpg", "png"}
 					valid := false
 					for _, t := range types {
 						prefix := "data:image/" + t + ";base64,"

parser/parser.go

@@ -292,18 +292,13 @@ func filesForModel(path string) ([]string, error) {
 	}
 	files = append(files, js...)
 
-	// only include tokenizer.model is tokenizer.json is not present
-	if !slices.ContainsFunc(files, func(s string) bool {
-		return slices.Contains(strings.Split(s, string(os.PathSeparator)), "tokenizer.json")
-	}) {
-		if tks, _ := glob(filepath.Join(path, "tokenizer.model"), "application/octet-stream"); len(tks) > 0 {
-			// add tokenizer.model if it exists, tokenizer.json is automatically picked up by the previous glob
-			// tokenizer.model might be a unresolved git lfs reference; error if it is
-			files = append(files, tks...)
-		} else if tks, _ := glob(filepath.Join(path, "**/tokenizer.model"), "text/plain"); len(tks) > 0 {
-			// some times tokenizer.model is in a subdirectory (e.g. meta-llama/Meta-Llama-3-8B)
-			files = append(files, tks...)
-		}
+	if tks, _ := glob(filepath.Join(path, "tokenizer.model"), "application/octet-stream"); len(tks) > 0 {
+		// add tokenizer.model if it exists, tokenizer.json is automatically picked up by the previous glob
+		// tokenizer.model might be a unresolved git lfs reference; error if it is
+		files = append(files, tks...)
+	} else if tks, _ := glob(filepath.Join(path, "**/tokenizer.model"), "text/plain"); len(tks) > 0 {
+		// some times tokenizer.model is in a subdirectory (e.g. meta-llama/Meta-Llama-3-8B)
+		files = append(files, tks...)
 	}
 
 	return files, nil

scripts/build_windows.ps1

@@ -27,6 +27,7 @@ function checkEnv() {
         $env:VCToolsRedistDir=(get-item "${MSVC_INSTALL}\VC\Redist\MSVC\*")[0]
     }
     # Locate CUDA versions
+    # Note: this assumes every version found will be built
     $cudaList=(get-item "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v*\bin\" -ea 'silentlycontinue')
     if ($cudaList.length -eq 0) {
         $d=(get-command -ea 'silentlycontinue' nvcc).path
@@ -93,6 +94,19 @@ function buildOllama() {
 
         $hashEnv = @{}
         Get-ChildItem env: | foreach { $hashEnv[$_.Name] = $_.Value }
+        if ("$script:CUDA_DIRS".Contains("v11")) {
+            $hashEnv.Keys | foreach { if ($_.Contains("CUDA_PATH_V11")) { $v11="$_" }}
+            $env:CUDAToolkit_ROOT=$hashEnv[$v11]
+            write-host "Building CUDA v11 backend libraries"
+            # Note: cuda v11 requires msvc 2019 so force the older generator
+            # to avoid 2022 (or newer) from being used as the default
+            & cmake --fresh --preset "CUDA 11" -G "Visual Studio 16 2019" --install-prefix $script:DIST_DIR
+            if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
+            & cmake --build --preset "CUDA 11"  --config Release --parallel $script:JOBS
+            if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
+            & cmake --install build --component "CUDA" --strip
+            if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
+        }
         if ("$script:CUDA_DIRS".Contains("v12")) {
             $hashEnv.Keys | foreach { if ($_.Contains("CUDA_PATH_V12")) { $v12="$_" }}
             $env:CUDAToolkit_ROOT=$hashEnv[$v12]
@@ -113,17 +127,12 @@ function buildOllama() {
             $env:HIPCXX="${env:HIP_PATH}\bin\clang++.exe"
             $env:HIP_PLATFORM="amd"
             $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}"
-            & cmake --fresh --preset "ROCm 6" -G Ninja `
-                -DCMAKE_C_COMPILER=clang `
-                -DCMAKE_CXX_COMPILER=clang++ `
-                -DCMAKE_C_FLAGS="-parallel-jobs=4 -Wno-ignored-attributes -Wno-deprecated-pragma" `
-                -DCMAKE_CXX_FLAGS="-parallel-jobs=4 -Wno-ignored-attributes -Wno-deprecated-pragma" `
-                --install-prefix $script:DIST_DIR
+            & cmake --fresh --preset "ROCm 6" -G Ninja -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ --install-prefix $script:DIST_DIR
             if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
             $env:HIPCXX=""
             $env:HIP_PLATFORM=""
             $env:CMAKE_PREFIX_PATH=""
-            & cmake --build --preset "ROCm 6" --config Release --parallel $script:JOBS
+            & cmake --build --preset "ROCm"  --config Release --parallel $script:JOBS
             if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
             & cmake --install build --component "HIP" --strip
             if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}

scripts/env.sh

@@ -10,7 +10,9 @@ OLLAMA_COMMON_BUILD_ARGS="--build-arg=VERSION \
     --build-arg=GOFLAGS \
     --build-arg=OLLAMA_CUSTOM_CPU_DEFS \
     --build-arg=OLLAMA_SKIP_CUDA_GENERATE \
+    --build-arg=OLLAMA_SKIP_CUDA_11_GENERATE \
     --build-arg=OLLAMA_SKIP_CUDA_12_GENERATE \
+    --build-arg=CUDA_V11_ARCHITECTURES \
     --build-arg=CUDA_V12_ARCHITECTURES \
     --build-arg=OLLAMA_SKIP_ROCM_GENERATE \
     --build-arg=OLLAMA_FAST_BUILD \

server/images.go

@@ -77,13 +77,15 @@ func (m *Model) Capabilities() []model.Capability {
 	if err == nil {
 		defer f.Close()
 
-		if f.KeyValue("pooling_type").Valid() {
+		embedding := f.KeyValue("pooling_type")
+		if !embedding.Value.IsNil() {
 			capabilities = append(capabilities, model.CapabilityEmbedding)
 		} else {
 			// If no embedding is specified, we assume the model supports completion
 			capabilities = append(capabilities, model.CapabilityCompletion)
 		}
-		if f.KeyValue("vision.block_count").Valid() {
+		vision := f.KeyValue("vision.block_count")
+		if !vision.Value.IsNil() {
 			capabilities = append(capabilities, model.CapabilityVision)
 		}
 	} else {

server/images_test.go

@@ -1,42 +1,266 @@
 package server
 
 import (
+	"encoding/binary"
+	"fmt"
+	"os"
+	"path/filepath"
 	"strings"
 	"testing"
 
-	"github.com/ollama/ollama/fs/ggml"
 	"github.com/ollama/ollama/template"
 	"github.com/ollama/ollama/types/model"
 )
 
+// GGUF type constants (matching gguf package)
+const (
+	typeUint8   = uint32(0)
+	typeInt8    = uint32(1)
+	typeUint16  = uint32(2)
+	typeInt16   = uint32(3)
+	typeUint32  = uint32(4)
+	typeInt32   = uint32(5)
+	typeFloat32 = uint32(6)
+	typeBool    = uint32(7)
+	typeString  = uint32(8)
+	typeArray   = uint32(9)
+	typeUint64  = uint32(10)
+	typeInt64   = uint32(11)
+	typeFloat64 = uint32(12)
+)
+
+type testTensorInfo struct {
+	Name  string
+	Shape []uint64
+	Type  uint32
+}
+
+// Helper function to create test GGUF files (matching gguf package approach)
+func createTestGGUFFile(path string, keyValues map[string]any, tensors []testTensorInfo) error {
+	file, err := os.Create(path)
+	if err != nil {
+		return err
+	}
+	defer file.Close()
+
+	// Write GGUF magic
+	if _, err := file.Write([]byte("GGUF")); err != nil {
+		return err
+	}
+
+	// Write version
+	if err := binary.Write(file, binary.LittleEndian, uint32(3)); err != nil {
+		return err
+	}
+
+	// Write tensor count
+	if err := binary.Write(file, binary.LittleEndian, uint64(len(tensors))); err != nil {
+		return err
+	}
+
+	// Write metadata count
+	if err := binary.Write(file, binary.LittleEndian, uint64(len(keyValues))); err != nil {
+		return err
+	}
+
+	// Write metadata
+	for key, value := range keyValues {
+		if err := writeKeyValue(file, key, value); err != nil {
+			return err
+		}
+	}
+
+	// Write tensor info
+	for _, tensor := range tensors {
+		if err := writeTensorInfo(file, tensor); err != nil {
+			return err
+		}
+	}
+
+	// Write some dummy tensor data
+	dummyData := make([]byte, 1024)
+	file.Write(dummyData)
+
+	return nil
+}
+
+func writeKeyValue(file *os.File, key string, value any) error {
+	// Write key length and key
+	if err := binary.Write(file, binary.LittleEndian, uint64(len(key))); err != nil {
+		return err
+	}
+	if _, err := file.Write([]byte(key)); err != nil {
+		return err
+	}
+
+	// Write value based on type
+	switch v := value.(type) {
+	case string:
+		if err := binary.Write(file, binary.LittleEndian, uint32(typeString)); err != nil {
+			return err
+		}
+		if err := binary.Write(file, binary.LittleEndian, uint64(len(v))); err != nil {
+			return err
+		}
+		_, err := file.Write([]byte(v))
+		return err
+	case int64:
+		if err := binary.Write(file, binary.LittleEndian, typeInt64); err != nil {
+			return err
+		}
+		return binary.Write(file, binary.LittleEndian, v)
+	case uint32:
+		if err := binary.Write(file, binary.LittleEndian, typeUint32); err != nil {
+			return err
+		}
+		return binary.Write(file, binary.LittleEndian, v)
+	case bool:
+		if err := binary.Write(file, binary.LittleEndian, typeBool); err != nil {
+			return err
+		}
+		return binary.Write(file, binary.LittleEndian, v)
+	case float64:
+		if err := binary.Write(file, binary.LittleEndian, uint32(typeFloat64)); err != nil {
+			return err
+		}
+		return binary.Write(file, binary.LittleEndian, v)
+	case []string:
+		if err := binary.Write(file, binary.LittleEndian, uint32(typeArray)); err != nil {
+			return err
+		}
+		if err := binary.Write(file, binary.LittleEndian, typeString); err != nil {
+			return err
+		}
+		if err := binary.Write(file, binary.LittleEndian, uint64(len(v))); err != nil {
+			return err
+		}
+		for _, s := range v {
+			if err := binary.Write(file, binary.LittleEndian, uint64(len(s))); err != nil {
+				return err
+			}
+			if _, err := file.Write([]byte(s)); err != nil {
+				return err
+			}
+		}
+		return nil
+	case []int64:
+		if err := binary.Write(file, binary.LittleEndian, uint32(typeArray)); err != nil {
+			return err
+		}
+		if err := binary.Write(file, binary.LittleEndian, typeInt64); err != nil {
+			return err
+		}
+		if err := binary.Write(file, binary.LittleEndian, uint64(len(v))); err != nil {
+			return err
+		}
+		for _, i := range v {
+			if err := binary.Write(file, binary.LittleEndian, i); err != nil {
+				return err
+			}
+		}
+		return nil
+	case []float64:
+		if err := binary.Write(file, binary.LittleEndian, typeArray); err != nil {
+			return err
+		}
+		if err := binary.Write(file, binary.LittleEndian, typeFloat64); err != nil {
+			return err
+		}
+		if err := binary.Write(file, binary.LittleEndian, uint64(len(v))); err != nil {
+			return err
+		}
+		for _, f := range v {
+			if err := binary.Write(file, binary.LittleEndian, f); err != nil {
+				return err
+			}
+		}
+		return nil
+	default:
+		return fmt.Errorf("unsupported value type: %T", value)
+	}
+}
+
+func writeTensorInfo(file *os.File, tensor testTensorInfo) error {
+	// Write tensor name
+	if err := binary.Write(file, binary.LittleEndian, uint64(len(tensor.Name))); err != nil {
+		return err
+	}
+	if _, err := file.Write([]byte(tensor.Name)); err != nil {
+		return err
+	}
+
+	// Write dimensions
+	if err := binary.Write(file, binary.LittleEndian, uint32(len(tensor.Shape))); err != nil {
+		return err
+	}
+	for _, dim := range tensor.Shape {
+		if err := binary.Write(file, binary.LittleEndian, dim); err != nil {
+			return err
+		}
+	}
+
+	// Write type
+	if err := binary.Write(file, binary.LittleEndian, tensor.Type); err != nil {
+		return err
+	}
+
+	// Write offset (dummy value)
+	return binary.Write(file, binary.LittleEndian, uint64(0))
+}
+
 func TestModelCapabilities(t *testing.T) {
+	// Create a temporary directory for test files
+	tempDir := t.TempDir()
+
+	// Create different types of mock model files
+	completionModelPath := filepath.Join(tempDir, "model.bin")
+	visionModelPath := filepath.Join(tempDir, "vision_model.bin")
+	embeddingModelPath := filepath.Join(tempDir, "embedding_model.bin")
+	// Create a simple model file for tests that don't depend on GGUF content
+	simpleModelPath := filepath.Join(tempDir, "simple_model.bin")
+
 	// Create completion model (llama architecture without vision)
-	completionModelPath, _ := createBinFile(t, ggml.KV{
+	if err := createTestGGUFFile(completionModelPath, map[string]any{
 		"general.architecture": "llama",
-	}, []*ggml.Tensor{})
+	}, []testTensorInfo{
+		{Name: "token_embd.weight", Shape: []uint64{1000, 512}, Type: 1}, // F16
+	}); err != nil {
+		t.Fatalf("Failed to create completion model file: %v", err)
+	}
 
 	// Create vision model (llama architecture with vision block count)
-	visionModelPath, _ := createBinFile(t, ggml.KV{
+	if err := createTestGGUFFile(visionModelPath, map[string]any{
 		"general.architecture":     "llama",
 		"llama.vision.block_count": uint32(1),
-	}, []*ggml.Tensor{})
+	}, []testTensorInfo{
+		{Name: "token_embd.weight", Shape: []uint64{1000, 512}, Type: 1}, // F16
+	}); err != nil {
+		t.Fatalf("Failed to create vision model file: %v", err)
+	}
 
 	// Create embedding model (bert architecture with pooling type)
-	embeddingModelPath, _ := createBinFile(t, ggml.KV{
+	if err := createTestGGUFFile(embeddingModelPath, map[string]any{
 		"general.architecture": "bert",
 		"bert.pooling_type":    uint32(1),
-	}, []*ggml.Tensor{})
+	}, []testTensorInfo{
+		{Name: "token_embd.weight", Shape: []uint64{1000, 512}, Type: 1}, // F16
+	}); err != nil {
+		t.Fatalf("Failed to create embedding model file: %v", err)
+	}
+
+	// Create simple model file for tests that don't depend on GGUF content
+	if err := os.WriteFile(simpleModelPath, []byte("dummy model data"), 0o644); err != nil {
+		t.Fatalf("Failed to create simple model file: %v", err)
+	}
 
 	toolsInsertTemplate, err := template.Parse("{{ .prompt }}{{ if .tools }}{{ .tools }}{{ end }}{{ if .suffix }}{{ .suffix }}{{ end }}")
 	if err != nil {
 		t.Fatalf("Failed to parse template: %v", err)
 	}
-
 	chatTemplate, err := template.Parse("{{ .prompt }}")
 	if err != nil {
 		t.Fatalf("Failed to parse template: %v", err)
 	}
-
 	toolsTemplate, err := template.Parse("{{ .prompt }}{{ if .tools }}{{ .tools }}{{ end }}")
 	if err != nil {
 		t.Fatalf("Failed to parse template: %v", err)
@@ -64,13 +288,21 @@ func TestModelCapabilities(t *testing.T) {
 			},
 			expectedCaps: []model.Capability{model.CapabilityCompletion, model.CapabilityTools, model.CapabilityInsert},
 		},
+		{
+			name: "model with tools and insert capability",
+			model: Model{
+				ModelPath: simpleModelPath,
+				Template:  toolsInsertTemplate,
+			},
+			expectedCaps: []model.Capability{model.CapabilityTools, model.CapabilityInsert},
+		},
 		{
 			name: "model with tools capability",
 			model: Model{
-				ModelPath: completionModelPath,
+				ModelPath: simpleModelPath,
 				Template:  toolsTemplate,
 			},
-			expectedCaps: []model.Capability{model.CapabilityCompletion, model.CapabilityTools},
+			expectedCaps: []model.Capability{model.CapabilityTools},
 		},
 		{
 			name: "model with vision capability",
@@ -135,33 +367,46 @@ func TestModelCapabilities(t *testing.T) {
 }
 
 func TestModelCheckCapabilities(t *testing.T) {
-	// Create simple model file for tests that don't depend on GGUF content
-	completionModelPath, _ := createBinFile(t, ggml.KV{
-		"general.architecture": "llama",
-	}, []*ggml.Tensor{})
+	// Create a temporary directory for test files
+	tempDir := t.TempDir()
+
+	visionModelPath := filepath.Join(tempDir, "vision_model.bin")
+	simpleModelPath := filepath.Join(tempDir, "model.bin")
+	embeddingModelPath := filepath.Join(tempDir, "embedding_model.bin")
 
 	// Create vision model (llama architecture with vision block count)
-	visionModelPath, _ := createBinFile(t, ggml.KV{
+	if err := createTestGGUFFile(visionModelPath, map[string]any{
 		"general.architecture":     "llama",
 		"llama.vision.block_count": uint32(1),
-	}, []*ggml.Tensor{})
+	}, []testTensorInfo{
+		{Name: "token_embd.weight", Shape: []uint64{1000, 512}, Type: 1}, // F16
+	}); err != nil {
+		t.Fatalf("Failed to create vision model file: %v", err)
+	}
 
 	// Create embedding model (bert architecture with pooling type)
-	embeddingModelPath, _ := createBinFile(t, ggml.KV{
+	if err := createTestGGUFFile(embeddingModelPath, map[string]any{
 		"general.architecture": "bert",
 		"bert.pooling_type":    uint32(1),
-	}, []*ggml.Tensor{})
+	}, []testTensorInfo{
+		{Name: "token_embd.weight", Shape: []uint64{1000, 512}, Type: 1}, // F16
+	}); err != nil {
+		t.Fatalf("Failed to create embedding model file: %v", err)
+	}
+
+	// Create simple model file for tests that don't depend on GGUF content
+	if err := os.WriteFile(simpleModelPath, []byte("dummy model data"), 0o644); err != nil {
+		t.Fatalf("Failed to create simple model file: %v", err)
+	}
 
 	toolsInsertTemplate, err := template.Parse("{{ .prompt }}{{ if .tools }}{{ .tools }}{{ end }}{{ if .suffix }}{{ .suffix }}{{ end }}")
 	if err != nil {
 		t.Fatalf("Failed to parse template: %v", err)
 	}
-
 	chatTemplate, err := template.Parse("{{ .prompt }}")
 	if err != nil {
 		t.Fatalf("Failed to parse template: %v", err)
 	}
-
 	toolsTemplate, err := template.Parse("{{ .prompt }}{{ if .tools }}{{ .tools }}{{ end }}")
 	if err != nil {
 		t.Fatalf("Failed to parse template: %v", err)
@@ -176,7 +421,7 @@ func TestModelCheckCapabilities(t *testing.T) {
 		{
 			name: "completion model without tools capability",
 			model: Model{
-				ModelPath: completionModelPath,
+				ModelPath: simpleModelPath,
 				Template:  chatTemplate,
 			},
 			checkCaps:      []model.Capability{model.CapabilityTools},
@@ -185,7 +430,7 @@ func TestModelCheckCapabilities(t *testing.T) {
 		{
 			name: "model with all needed capabilities",
 			model: Model{
-				ModelPath: completionModelPath,
+				ModelPath: simpleModelPath,
 				Template:  toolsInsertTemplate,
 			},
 			checkCaps: []model.Capability{model.CapabilityTools, model.CapabilityInsert},
@@ -193,7 +438,7 @@ func TestModelCheckCapabilities(t *testing.T) {
 		{
 			name: "model missing insert capability",
 			model: Model{
-				ModelPath: completionModelPath,
+				ModelPath: simpleModelPath,
 				Template:  toolsTemplate,
 			},
 			checkCaps:      []model.Capability{model.CapabilityInsert},
@@ -202,7 +447,7 @@ func TestModelCheckCapabilities(t *testing.T) {
 		{
 			name: "model missing vision capability",
 			model: Model{
-				ModelPath: completionModelPath,
+				ModelPath: simpleModelPath,
 				Template:  toolsTemplate,
 			},
 			checkCaps:      []model.Capability{model.CapabilityVision},
@@ -227,7 +472,7 @@ func TestModelCheckCapabilities(t *testing.T) {
 		{
 			name: "unknown capability",
 			model: Model{
-				ModelPath: completionModelPath,
+				ModelPath: simpleModelPath,
 				Template:  chatTemplate,
 			},
 			checkCaps:      []model.Capability{"unknown"},

server/internal/cache/blob/cache.go

@@ -59,7 +59,7 @@ type DiskCache struct {
 	testHookBeforeFinalWrite func(f *os.File)
 }
 
-// PutBytes is a convenience function for c.Put(d, strings.NewReader(s), int64(len(s))).
+// PutString is a convenience function for c.Put(d, strings.NewReader(s), int64(len(s))).
 func PutBytes[S string | []byte](c *DiskCache, d Digest, data S) error {
 	return c.Put(d, bytes.NewReader([]byte(data)), int64(len(data)))
 }

server/quantization.go

@@ -40,19 +40,10 @@ func (q quantizer) WriteTo(w io.Writer) (int64, error) {
 	} else {
 		f32s = ggml.ConvertToF32(data, q.from.Kind, q.from.Elements())
 	}
-
-	var n int64
-	for bts := range ggml.Quantize(newType, f32s, q.from.Shape) {
-		nn, err := w.Write(bts)
-		if err != nil {
-			return 0, err
-		}
-
-		q.progressFn(uint64(nn))
-		n += int64(nn)
-	}
-
-	return n, err
+	data = ggml.Quantize(newType, f32s, q.from.Shape)
+	n, err := w.Write(data)
+	q.progressFn(q.from.Size())
+	return int64(n), err
 }
 
 type quantizeState struct {
@@ -240,8 +231,6 @@ func newType(t *fsggml.Tensor, kv fsggml.KV, qs *quantizeState, ftype fsggml.Fil
 	// do not quantize relative position bias (T5)
 	quantize = quantize && !strings.Contains(name, "attn_rel_b.weight")
 
-	quantize = quantize && !strings.Contains(name, "per_layer_token_embd.weight")
-
 	newType := fsggml.TensorType(t.Kind)
 	if quantize {
 		// get more optimal quantization type based on the tensor shape, layer, etc.

server/quantization_test.go

@@ -2,14 +2,12 @@ package server
 
 import (
 	"bytes"
-	"encoding/binary"
 	"fmt"
 	"math"
 	"os"
 	"strings"
 	"testing"
 
-	"github.com/google/go-cmp/cmp"
 	fsggml "github.com/ollama/ollama/fs/ggml"
 	"github.com/ollama/ollama/ml/backend/ggml"
 )
@@ -259,8 +257,16 @@ func TestQuantizeModel(t *testing.T) {
 
 	for _, tt := range cases {
 		t.Run(tt.name, func(t *testing.T) {
-			p, _ := createBinFile(t, tt.kv, tt.tensors)
-			fp, err := os.Open(p)
+			f, err := os.CreateTemp(t.TempDir(), tt.name)
+			if err != nil {
+				t.Fatal(err.Error())
+			}
+			defer f.Close()
+			err = fsggml.WriteGGUF(f, tt.kv, tt.tensors)
+			if err != nil {
+				t.Fatalf("failed to create initial model: %s", err)
+			}
+			fp, err := os.Open(f.Name())
 			if err != nil {
 				t.Fatal(err.Error())
 			}
@@ -651,55 +657,3 @@ var (
 		},
 	}
 )
-
-func TestQuantizer(t *testing.T) {
-	from := fsggml.Tensor{
-		Name:  "fp32",
-		Shape: []uint64{256},
-		Kind:  uint32(fsggml.TensorTypeF32),
-	}
-
-	temp, err := os.CreateTemp(t.TempDir(), "*.bin")
-	if err != nil {
-		t.Fatalf("failed to create temp file: %v", err)
-	}
-
-	f32s := make([]float32, 256)
-	for i := range f32s {
-		f32s[i] = float32(i)
-	}
-
-	if err := binary.Write(temp, binary.LittleEndian, f32s); err != nil {
-		t.Fatalf("failed to write to temp file: %v", err)
-	}
-
-	for type_, want := range quantBytes {
-		t.Run(type_.String(), func(t *testing.T) {
-			f, err := os.Open(temp.Name())
-			if err != nil {
-				t.Fatalf("failed to open temp file: %v", err)
-			}
-			defer f.Close()
-
-			q := quantizer{
-				File: f,
-				from: &from,
-				to: &fsggml.Tensor{
-					Name:  type_.String(),
-					Shape: from.Shape,
-					Kind:  uint32(type_),
-				},
-				progressFn: func(uint64) {},
-			}
-
-			var b bytes.Buffer
-			if _, err := q.WriteTo(&b); err != nil {
-				t.Fatalf("WriteTo failed: %v", err)
-			}
-
-			if diff := cmp.Diff(b.Bytes(), want); diff != "" {
-				t.Errorf("quantized data mismatch for %s (-got +want):\n%s", type_, diff)
-			}
-		})
-	}
-}

server/routes.go

@@ -842,11 +842,8 @@ func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
 	}
 	resp.Parameters = strings.Join(params, "\n")
 
-	if len(req.Options) > 0 {
-		if m.Options == nil {
-			m.Options = make(map[string]any)
-		}
-		for k, v := range req.Options {
+	for k, v := range req.Options {
+		if _, ok := req.Options[k]; ok {
 			m.Options[k] = v
 		}
 	}
@@ -932,8 +929,7 @@ func (s *Server) ListHandler(c *gin.Context) {
 			}
 		}
 
-		// tag should never be masked
-		models = append(models, api.ListModelResponse{
+		r := api.ListModelResponse{
 			Model:      n.DisplayShortest(),
 			Name:       n.DisplayShortest(),
 			Size:       m.Size(),
@@ -946,7 +942,16 @@ func (s *Server) ListHandler(c *gin.Context) {
 				ParameterSize:     cf.ModelType,
 				QuantizationLevel: cf.FileType,
 			},
-		})
+		}
+
+		model, err := GetModel(n.String())
+		if err != nil {
+			slog.Warn("bad model details", "name", n, "error", err)
+		} else {
+			r.Capabilities = model.Capabilities()
+		}
+
+		models = append(models, r)
 	}
 
 	slices.SortStableFunc(models, func(i, j api.ListModelResponse) int {
@@ -1407,9 +1412,6 @@ func (s *Server) PsHandler(c *gin.Context) {
 			Details:   modelDetails,
 			ExpiresAt: v.expiresAt,
 		}
-		if v.Options != nil {
-			mr.ContextLength = v.Options.NumCtx / v.numParallel
-		}
 		// The scheduler waits to set expiresAt, so if a model is loading it's
 		// possible that it will be set to the unix epoch. For those cases, just
 		// calculate the time w/ the sessionDuration instead.
@@ -1532,7 +1534,12 @@ func (s *Server) ChatHandler(c *gin.Context) {
 
 	var toolParser *tools.Parser
 	if len(req.Tools) > 0 {
-		toolParser = tools.NewParser(m.Template.Template, req.Tools)
+		toolParser, err = tools.NewParser(m.Template.Template)
+		if err != nil {
+			slog.Error("failed to create tool parser", "error", err)
+			c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
+			return
+		}
 	}
 
 	ch := make(chan any)
@@ -1585,7 +1592,6 @@ func (s *Server) ChatHandler(c *gin.Context) {
 					// don't return
 				} else {
 					if r.Done {
-						res.Message.Content = toolParser.Content()
 						ch <- res
 					}
 					return

server/sched.go

@@ -57,7 +57,9 @@ type Scheduler struct {
 var defaultModelsPerGPU = 3
 
 // Default automatic value for parallel setting
-var defaultParallel = 1
+// Model will still need to fit in VRAM.  If this setting won't fit
+// we'll back off down to 1 to try to get it to fit
+var defaultParallel = 2
 
 var ErrMaxQueue = errors.New("server busy, please try again.  maximum pending requests exceeded")
 
@@ -189,7 +191,7 @@ func (s *Scheduler) processPending(ctx context.Context) {
 					}
 
 					// Load model for fitting
-					ggml, err := llm.LoadModel(pending.model.ModelPath, 1024)
+					ggml, err := llm.LoadModel(pending.model.ModelPath, 0)
 					if err != nil {
 						pending.errCh <- err
 						break

server/sched_test.go

@@ -112,7 +112,11 @@ func newScenarioRequest(t *testing.T, ctx context.Context, modelName string, est
 	b.ctx, b.ctxDone = context.WithCancel(ctx)
 	t.Helper()
 
-	p, _ := createBinFile(t, ggml.KV{
+	f, err := os.CreateTemp(t.TempDir(), modelName)
+	require.NoError(t, err)
+	defer f.Close()
+
+	require.NoError(t, ggml.WriteGGUF(f, ggml.KV{
 		"general.architecture":          "llama",
 		"llama.context_length":          uint32(32),
 		"llama.embedding_length":        uint32(4096),
@@ -125,14 +129,14 @@ func newScenarioRequest(t *testing.T, ctx context.Context, modelName string, est
 	}, []*ggml.Tensor{
 		{Name: "blk.0.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
 		{Name: "output.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
-	})
+	}))
+	require.NoError(t, err)
+
+	fname := f.Name()
+	model := &Model{Name: modelName, ModelPath: fname}
+	b.f, err = llm.LoadModel(model.ModelPath, 0)
+	require.NoError(t, err)
 
-	model := &Model{Name: modelName, ModelPath: p}
-	f, err := llm.LoadModel(model.ModelPath, 0)
-	if err != nil {
-		t.Fatal(err)
-	}
-	b.f = f
 	if duration == nil {
 		duration = &api.Duration{Duration: 5 * time.Millisecond}
 	}

template/template.go

@@ -310,23 +310,21 @@ func (t *Template) Execute(w io.Writer, v Values) error {
 }
 
 // collate messages based on role. consecutive messages of the same role are merged
-// into a single message (except for tool messages which preserve individual metadata).
-// collate also collects and returns all system messages.
+// into a single message. collate also collects and returns all system messages.
 // collate mutates message content adding image tags ([img-%d]) as needed
-// todo(parthsareen): revisit for contextual image support
 func collate(msgs []api.Message) (string, []*api.Message) {
 	var system []string
 	var collated []*api.Message
 	for i := range msgs {
-		if msgs[i].Role == "system" {
-			system = append(system, msgs[i].Content)
+		msg := msgs[i]
+		if msg.Role == "system" {
+			system = append(system, msg.Content)
 		}
 
-		// merges consecutive messages of the same role into a single message (except for tool messages)
-		if len(collated) > 0 && collated[len(collated)-1].Role == msgs[i].Role && msgs[i].Role != "tool" {
-			collated[len(collated)-1].Content += "\n\n" + msgs[i].Content
+		if len(collated) > 0 && collated[len(collated)-1].Role == msg.Role {
+			collated[len(collated)-1].Content += "\n\n" + msg.Content
 		} else {
-			collated = append(collated, &msgs[i])
+			collated = append(collated, &msg)
 		}
 	}
 

template/template_test.go

@@ -163,12 +163,10 @@ func TestParse(t *testing.T) {
 		{"{{ .System }} {{ .Prompt }} {{ .Response }}", []string{"prompt", "response", "system"}},
 		{"{{ with .Tools }}{{ . }}{{ end }} {{ .System }} {{ .Prompt }}", []string{"prompt", "response", "system", "tools"}},
 		{"{{ range .Messages }}{{ .Role }} {{ .Content }}{{ end }}", []string{"content", "messages", "role"}},
-		{"{{ range .Messages }}{{ if eq .Role \"tool\" }}Tool Result: {{ .ToolName }} {{ .Content }}{{ end }}{{ end }}", []string{"content", "messages", "role", "toolname"}},
 		{`{{- range .Messages }}
 {{- if eq .Role "system" }}SYSTEM:
 {{- else if eq .Role "user" }}USER:
 {{- else if eq .Role "assistant" }}ASSISTANT:
-{{- else if eq .Role "tool" }}TOOL: 
 {{- end }} {{ .Content }}
 {{- end }}`, []string{"content", "messages", "role"}},
 		{`{{- if .Messages }}
@@ -378,99 +376,3 @@ func TestExecuteWithSuffix(t *testing.T) {
 		})
 	}
 }
-
-func TestCollate(t *testing.T) {
-	cases := []struct {
-		name     string
-		msgs     []api.Message
-		expected []*api.Message
-		system   string
-	}{
-		{
-			name: "consecutive user messages are merged",
-			msgs: []api.Message{
-				{Role: "user", Content: "Hello"},
-				{Role: "user", Content: "How are you?"},
-			},
-			expected: []*api.Message{
-				{Role: "user", Content: "Hello\n\nHow are you?"},
-			},
-			system: "",
-		},
-		{
-			name: "consecutive tool messages are NOT merged",
-			msgs: []api.Message{
-				{Role: "tool", Content: "sunny", ToolName: "get_weather"},
-				{Role: "tool", Content: "72F", ToolName: "get_temperature"},
-			},
-			expected: []*api.Message{
-				{Role: "tool", Content: "sunny", ToolName: "get_weather"},
-				{Role: "tool", Content: "72F", ToolName: "get_temperature"},
-			},
-			system: "",
-		},
-		{
-			name: "tool messages preserve all fields",
-			msgs: []api.Message{
-				{Role: "user", Content: "What's the weather?"},
-				{Role: "tool", Content: "sunny", ToolName: "get_conditions"},
-				{Role: "tool", Content: "72F", ToolName: "get_temperature"},
-			},
-			expected: []*api.Message{
-				{Role: "user", Content: "What's the weather?"},
-				{Role: "tool", Content: "sunny", ToolName: "get_conditions"},
-				{Role: "tool", Content: "72F", ToolName: "get_temperature"},
-			},
-			system: "",
-		},
-		{
-			name: "mixed messages with system",
-			msgs: []api.Message{
-				{Role: "system", Content: "You are helpful"},
-				{Role: "user", Content: "Hello"},
-				{Role: "assistant", Content: "Hi there!"},
-				{Role: "user", Content: "What's the weather?"},
-				{Role: "tool", Content: "sunny", ToolName: "get_weather"},
-				{Role: "tool", Content: "72F", ToolName: "get_temperature"},
-				{Role: "user", Content: "Thanks"},
-			},
-			expected: []*api.Message{
-				{Role: "system", Content: "You are helpful"},
-				{Role: "user", Content: "Hello"},
-				{Role: "assistant", Content: "Hi there!"},
-				{Role: "user", Content: "What's the weather?"},
-				{Role: "tool", Content: "sunny", ToolName: "get_weather"},
-				{Role: "tool", Content: "72F", ToolName: "get_temperature"},
-				{Role: "user", Content: "Thanks"},
-			},
-			system: "You are helpful",
-		},
-	}
-
-	for _, tt := range cases {
-		t.Run(tt.name, func(t *testing.T) {
-			system, collated := collate(tt.msgs)
-			if diff := cmp.Diff(system, tt.system); diff != "" {
-				t.Errorf("system mismatch (-got +want):\n%s", diff)
-			}
-
-			// Compare the messages
-			if len(collated) != len(tt.expected) {
-				t.Errorf("expected %d messages, got %d", len(tt.expected), len(collated))
-				return
-			}
-
-			for i := range collated {
-				if collated[i].Role != tt.expected[i].Role {
-					t.Errorf("message %d role mismatch: got %q, want %q", i, collated[i].Role, tt.expected[i].Role)
-				}
-				if collated[i].Content != tt.expected[i].Content {
-					t.Errorf("message %d content mismatch: got %q, want %q", i, collated[i].Content, tt.expected[i].Content)
-				}
-				if collated[i].ToolName != tt.expected[i].ToolName {
-					t.Errorf("message %d tool name mismatch: got %q, want %q", i, collated[i].ToolName, tt.expected[i].ToolName)
-				}
-			}
-		})
-	}
-}