model: test byte pair encoding

.gitattributes

@@ -15,10 +15,6 @@ ml/backend/**/*.cu linguist-vendored
 ml/backend/**/*.cuh linguist-vendored
 ml/backend/**/*.m linguist-vendored
 ml/backend/**/*.metal linguist-vendored
-ml/backend/**/CMakeLists.txt linguist-vendored
-
-llama/build-info.cpp linguist-generated
-ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-embed.s linguist-generated
 
 * text=auto
 *.go text eol=lf

.github/ISSUE_TEMPLATE/10_bug_report.yml

@@ -9,14 +9,6 @@ body:
       description: What happened? What did you expect to happen?
     validations:
       required: true
-  - type: textarea
-    id: logs
-    attributes:
-      label: Relevant log output
-      description: Please copy and paste any relevant log output. See [Troubleshooting Guide](https://github.com/ollama/ollama/blob/main/docs/troubleshooting.md#how-to-troubleshoot-issues) for details.
-      render: shell
-    validations:
-      required: false
   - type: dropdown
     id: os
     attributes:

.github/workflows/release.yaml

@@ -5,10 +5,6 @@ on:
     tags:
       - 'v*'
 
-env:
-  CGO_CFLAGS: '-O3'
-  CGO_CXXFLAGS: '-O3'
-
 jobs:
   setup-environment:
     runs-on: ubuntu-latest
@@ -81,7 +77,7 @@ jobs:
           path: dist/darwin-arm64
       - run: |
           export VERSION=${GITHUB_REF_NAME#v}
-          ./scripts/build_darwin.sh sign macapp
+          ./scripts/build_darwin.sh macapp sign
         env:
           APPLE_IDENTITY: ${{ secrets.APPLE_IDENTITY }}
           APPLE_PASSWORD: ${{ secrets.APPLE_PASSWORD }}
@@ -111,13 +107,13 @@ jobs:
           - 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
+            install: https://developer.download.nvidia.com/compute/cuda/12.4.0/local_installers/cuda_12.4.0_551.61_windows.exe
-            cuda-version: '12.8'
+            cuda-version: '12.4'
           - 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
+            install: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe
-            rocm-version: '6.2'
+            rocm-version: '6.1'
     runs-on: ${{ matrix.arch == 'arm64' && format('{0}-{1}', matrix.os, matrix.arch) || matrix.os }}
     environment: release
     env:
@@ -160,10 +156,6 @@ jobs:
           echo "$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
-      - if: matrix.preset == 'CPU'
-        run: |
-          echo "CC=clang.exe" | Out-File -FilePath $env:GITHUB_ENV -Append
-          echo "CXX=clang++.exe" | Out-File -FilePath $env:GITHUB_ENV -Append
       - if: ${{ !cancelled() && steps.cache-install.outputs.cache-hit != 'true' }}
         uses: actions/cache/save@v4
         with:
@@ -201,38 +193,33 @@ jobs:
     env:
       GOFLAGS: ${{ needs.setup-environment.outputs.GOFLAGS }}
     steps:
-      - name: Install AMD64 system dependencies
+      - name: Install system dependencies
-        if: matrix.arch == 'amd64'
         run: |
           $ErrorActionPreference = "Stop"
-          Start-Process "C:\msys64\usr\bin\pacman.exe" -ArgumentList @("-S", "--noconfirm", "mingw-w64-clang-x86_64-gcc-compat", "mingw-w64-clang-x86_64-clang") -NoNewWindow -Wait
+          if ("${{ matrix.arch }}" -eq 'amd64') {
-          echo "C:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+            Start-Process "C:\msys64\usr\bin\pacman.exe" -ArgumentList @("-S", "--noconfirm", "mingw-w64-clang-x86_64-gcc-compat", "mingw-w64-clang-x86_64-clang") -NoNewWindow -Wait
-          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+            echo "C:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-      - name: Install ARM64 system dependencies
+            echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-        if: matrix.arch == 'arm64'
+          } elseif ("${{ matrix.arch }}" -eq 'arm64') {
-        run: |
+            Set-ExecutionPolicy Bypass -Scope Process -Force
-          $ErrorActionPreference = "Stop"
+            [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072
-          Set-ExecutionPolicy Bypass -Scope Process -Force
+            iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))
-          [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072
+            echo "C:\ProgramData\chocolatey\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-          iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))
-          echo "C:\ProgramData\chocolatey\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
 
-          choco install -y --no-progress git gzip
+            choco install -y --no-progress git gzip
-          echo "C:\Program Files\Git\cmd" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+            echo "C:\Program Files\Git\cmd" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
 
-          Invoke-WebRequest -Uri "https://github.com/mstorsjo/llvm-mingw/releases/download/20240619/llvm-mingw-20240619-ucrt-aarch64.zip" -OutFile "${{ runner.temp }}\llvm-mingw-ucrt-aarch64.zip"
+            Invoke-WebRequest -Uri "https://github.com/mstorsjo/llvm-mingw/releases/download/20240619/llvm-mingw-20240619-ucrt-aarch64.zip" -OutFile "${{ runner.temp }}\llvm-mingw-ucrt-aarch64.zip"
-          Expand-Archive -Path ${{ runner.temp }}\llvm-mingw-ucrt-aarch64.zip -DestinationPath "C:\Program Files\"
+            Expand-Archive -Path ${{ runner.temp }}\llvm-mingw-ucrt-aarch64.zip -DestinationPath "C:\Program Files\"
-          $installPath=(Resolve-Path -Path "C:\Program Files\llvm-mingw-*-ucrt-aarch64").path
+            $installPath=(Resolve-Path -Path "C:\Program Files\llvm-mingw-*-ucrt-aarch64").path
-          echo $installPath\bin | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+            echo $installPath\bin | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+          }
       - uses: actions/checkout@v4
       - uses: actions/setup-go@v5
         with:
           go-version-file: go.mod
       - run: |
           go build -o dist/${{ 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(.*)", '$1'
           & .\scripts\build_windows.ps1 buildApp
@@ -246,7 +233,7 @@ jobs:
             dist\${{ matrix.os }}-${{ matrix.arch }}-app.exe
 
   windows-sign:
-    runs-on: windows-2022
+    runs-on: windows
     environment: release
     needs: [windows-depends, windows-build]
     steps:
@@ -267,18 +254,16 @@ jobs:
           echo "${{ vars.OLLAMA_CERT }}" >ollama_inc.crt
       - uses: actions/download-artifact@v4
         with:
-          pattern: build-windows-*
+          name: build-windows-*
           path: dist\
           merge-multiple: true
       - uses: actions/download-artifact@v4
         with:
-          pattern: depends-windows-amd64-*
+          name: 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
@@ -292,13 +277,10 @@ jobs:
         include:
           - os: linux
             arch: amd64
-            target: archive
+            targets: 'archive rocm'
-          - os: linux
-            arch: amd64
-            target: rocm
           - os: linux
             arch: arm64
-            target: archive
+            targets: archive
     runs-on: ${{ matrix.arch == 'arm64' && format('{0}-{1}', matrix.os, matrix.arch) || matrix.os }}
     environment: release
     needs: setup-environment
@@ -307,106 +289,38 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - uses: docker/setup-buildx-action@v3
-      - uses: docker/build-push-action@v6
-        with:
-          context: .
-          platforms: ${{ matrix.os }}/${{ matrix.arch }}
-          target: ${{ matrix.target }}
-          build-args: |
-            GOFLAGS=${{ env.GOFLAGS }}
-            CGO_CFLAGS=${{ env.CGO_CFLAGS }}
-            CGO_CXXFLAGS=${{ env.CGO_CXXFLAGS }}
-          outputs: type=local,dest=dist/${{ matrix.os }}-${{ matrix.arch }}
-          cache-from: type=registry,ref=ollama/ollama:latest
-          cache-to: type=inline
       - run: |
-          for COMPONENT in bin/* lib/ollama/*; do
+          apt-get update && apt-get install pigz
-            case "$COMPONENT" in
+          for TARGET in ${{ matrix.targets }}; do docker buildx build --platform $PLATFORM --target $TARGET --output type=local,dest=dist/$PLATFORM .; done
-              bin/ollama)               echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
+          tar c -C dist/$PLATFORM . | pigz -9cv >dist/ollama-${PLATFORM//\//-}.tgz
-              lib/ollama/*.so)          echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
+        env:
-              lib/ollama/cuda_v11)      echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
+          PLATFORM: ${{ matrix.os }}/${{ matrix.arch }}
-              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 }}-${{ matrix.arch }}
-      - run: |
-          for ARCHIVE in dist/${{ matrix.os }}-${{ matrix.arch }}/*.tar.in; do
-            tar c -C dist/${{ matrix.os }}-${{ matrix.arch }} -T $ARCHIVE --owner 0 --group 0 | pigz -9vc >$(basename ${ARCHIVE//.*/}.tgz);
-          done
       - uses: actions/upload-artifact@v4
         with:
-          name: dist-${{ matrix.os }}-${{ matrix.arch }}-${{ matrix.target }}
+          name: dist-${{ matrix.os }}-${{ matrix.arch }}
           path: |
-            *.tgz
+            dist/ollama-${{ matrix.os }}-${{ matrix.arch }}.tgz
 
-  # Build each Docker variant (OS, arch, and flavor) separately. Using QEMU is unreliable and slower.
+  docker-build:
-  docker-build-push:
     strategy:
       matrix:
         include:
-          - os: linux
+          - flavor: 'latest=false'
-            arch: arm64
+            platforms: linux/amd64,linux/arm64
             build-args: |
-              CGO_CFLAGS
+              GOFLAGS=${{ needs.setup-environment.outputs.GOFLAGS }}
-              CGO_CXXFLAGS
+          - flavor: 'latest=false,suffix=rocm'
-              GOFLAGS
+            platforms: linux/amd64
-          - os: linux
-            arch: amd64
             build-args: |
-              CGO_CFLAGS
+              GOFLAGS=${{ needs.setup-environment.outputs.GOFLAGS }}
-              CGO_CXXFLAGS
-              GOFLAGS
-          - os: linux
-            arch: amd64
-            suffix: '-rocm'
-            build-args: |
-              CGO_CFLAGS
-              CGO_CXXFLAGS
-              GOFLAGS
               FLAVOR=rocm
-    runs-on: ${{ matrix.arch == 'arm64' && format('{0}-{1}', matrix.os, matrix.arch) || matrix.os }}
-    environment: release
-    needs: setup-environment
-    env:
-      GOFLAGS: ${{ needs.setup-environment.outputs.GOFLAGS }}
-    steps:
-      - uses: actions/checkout@v4
-      - uses: docker/setup-buildx-action@v3
-      - uses: docker/login-action@v3
-        with:
-          username: ${{ vars.DOCKER_USER }}
-          password: ${{ secrets.DOCKER_ACCESS_TOKEN }}
-      - id: build-push
-        uses: docker/build-push-action@v6
-        with:
-          context: .
-          platforms: ${{ matrix.os }}/${{ matrix.arch }}
-          build-args: ${{ matrix.build-args }}
-          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 }}
-          echo "${{ steps.build-push.outputs.digest }}" >${{ matrix.os }}-${{ matrix.arch }}-${{ matrix.suffix }}.txt
-        working-directory: ${{ runner.temp }}
-      - uses: actions/upload-artifact@v4
-        with:
-          name: digest-${{ matrix.os }}-${{ matrix.arch }}-${{ matrix.suffix }}
-          path: |
-            ${{ runner.temp }}/${{ matrix.os }}-${{ matrix.arch }}-${{ matrix.suffix }}.txt
-
-  # Merge Docker images for the same flavor into a single multi-arch manifest
-  docker-merge-push:
-    strategy:
-      matrix:
-        suffix: ['', '-rocm']
     runs-on: linux
     environment: release
-    needs: [docker-build-push]
+    needs: setup-environment
     steps:
+      - uses: actions/checkout@v4
+      - uses: docker/setup-qemu-action@v2
+      - uses: docker/setup-buildx-action@v2
       - uses: docker/login-action@v3
         with:
           username: ${{ vars.DOCKER_USER }}
@@ -414,39 +328,22 @@ jobs:
       - id: metadata
         uses: docker/metadata-action@v4
         with:
-          flavor: |
+          flavor: ${{ matrix.flavor }}
-            latest=false
-            suffix=${{ matrix.suffix }}
           images: |
             ollama/ollama
           tags: |
-            type=ref,enable=true,priority=600,prefix=pr-,event=pr
             type=semver,pattern={{version}}
-      - uses: actions/download-artifact@v4
+      - uses: docker/build-push-action@v6
         with:
-          pattern: digest-*
+          context: .
-          path: ${{ runner.temp }}
+          push: true
-          merge-multiple: true
+          platforms: ${{ matrix.platforms }}
-      - run: |
+          build-args: ${{ matrix.build-args }}
-          docker buildx imagetools create $(echo '${{ steps.metadata.outputs.json }}' | jq -cr '.tags | map("-t", .) | join(" ")') $(cat *-${{ matrix.suffix }}.txt | xargs printf 'ollama/ollama@%s ')
+          tags: ${{ steps.metadata.outputs.tags }}
-          docker buildx imagetools inspect ollama/ollama:${{ steps.metadata.outputs.version }}
+          labels: ${{ steps.metadata.outputs.labels }}
-        working-directory: ${{ runner.temp }}
+          cache-from: type=registry,ref=ollama/ollama:latest
-
+          cache-to: type=inline
-  # Trigger downstream release process
+          provenance: false
-  trigger:
-    runs-on: ubuntu-latest
-    environment: release
-    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:
@@ -459,24 +356,33 @@ jobs:
       GH_TOKEN: ${{ github.token }}
     steps:
       - uses: actions/checkout@v4
+      - name: Set Version
+        shell: bash
+        run: |
       - uses: actions/download-artifact@v4
         with:
-          name: dist-darwin
           path: dist
+          pattern: dist-darwin
       - uses: actions/download-artifact@v4
         with:
-          name: dist-windows
           path: dist
+          pattern: dist-windows
       - uses: actions/download-artifact@v4
         with:
+          path: dist
           pattern: dist-linux-*
+      - uses: actions/download-artifact@v4
+        with:
           path: dist
-          merge-multiple: true
+          pattern: dist-windows
-      - run: find . -type f -not -name 'sha256sum.txt' | xargs sha256sum | tee sha256sum.txt
+      - run: |
-        working-directory: dist
+          ls -lh dist/
+          (cd dist; find . -type f | xargs sha256sum > ../sha256sum.txt)
+          mv sha256sum.txt dist/
+          cat dist/sha256sum.txt
       - name: Create or update Release
         run: |
-          RELEASE_VERSION="$(echo ${GITHUB_REF_NAME} | cut -f1 -d-)"
+          RELEASE_VERSION=$(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")

.github/workflows/test.yaml

@@ -78,10 +78,10 @@ jobs:
         include:
           - preset: CPU
           - preset: CUDA
-            install: https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda_11.3.1_465.89_win10.exe
+            install: https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_522.06_windows.exe
-            flags: '-DCMAKE_CUDA_ARCHITECTURES=80'
+            flags: '-DCMAKE_CUDA_ARCHITECTURES=87'
           - preset: ROCm
-            install: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q4-WinSvr2022-For-HIP.exe
+            install: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe
             flags: '-DAMDGPU_TARGETS=gfx1010'
     runs-on: windows
     steps:
@@ -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_11.3", "nvcc_11.3", "cublas_11.3", "cublas_dev_11.3")) -NoNewWindow -Wait
+            Start-Process -FilePath .\install.exe -ArgumentList (@("-s", "cudart_11.8", "nvcc_11.8", "cublas_11.8", "cublas_dev_11.8")) -NoNewWindow -Wait
           }
 
           $cudaPath = (Resolve-Path "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\*").path
@@ -140,13 +140,6 @@ jobs:
         env:
           CMAKE_GENERATOR: Ninja
 
-  go_mod_tidy:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - name: check that 'go mod tidy' is clean
-        run: go mod tidy --diff || (echo "Please run 'go mod tidy'." && exit 1)
-
   test:
     strategy:
       matrix:
@@ -154,82 +147,15 @@ jobs:
     runs-on: ${{ matrix.os }}
     env:
       CGO_ENABLED: '1'
-      GOEXPERIMENT: 'synctest'
     steps:
-      - name: checkout
+      - uses: actions/checkout@v4
-        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # 4.2.2
+      - uses: actions/setup-go@v5
-
-      - name: cache restore
-        uses: actions/cache/restore@1bd1e32a3bdc45362d1e726936510720a7c30a57 # v4.2.0
         with:
-          # Note: unlike the other setups, this is only grabbing the mod download
-          # cache, rather than the whole mod directory, as the download cache
-          # contains zips that can be unpacked in parallel faster than they can be
-          # fetched and extracted by tar
-          path: |
-            ~/.cache/go-build
-            ~/go/pkg/mod/cache
-            ~\AppData\Local\go-build
-          # NOTE: The -3- here should be incremented when the scheme of data to be
-          # cached changes (e.g. path above changes).
-          key: ${{ github.job }}-${{ runner.os }}-${{ matrix.goarch }}-${{ matrix.buildflags }}-go-3-${{ hashFiles('**/go.sum') }}-${{ github.run_id }}
-          restore-keys: |
-            ${{ github.job }}-${{ runner.os }}-${{ matrix.goarch }}-${{ matrix.buildflags }}-go-3-${{ hashFiles('**/go.sum') }}
-            ${{ github.job }}-${{ runner.os }}-${{ matrix.goarch }}-${{ matrix.buildflags }}-go-3-
-
-      - name: Setup Go
-        uses: actions/setup-go@v5
-        with:
-          # The caching strategy of setup-go is less than ideal, and wastes
-          # time by not saving artifacts due to small failures like the linter
-          # complaining, etc. This means subsequent have to rebuild their world
-          # again until all checks pass. For instance, if you mispell a word,
-          # you're punished until you fix it. This is more hostile than
-          # helpful.
-          cache: false
-
           go-version-file: go.mod
-
-      # It is tempting to run this in a platform independent way, but the past
-      # shows this codebase will see introductions of platform specific code
-      # generation, and so we need to check this per platform to ensure we
-      # don't abuse go generate on specific platforms.
-      - name: check that 'go generate' is clean
-        if: always()
-        run: |
-          go generate ./...
-          git diff --name-only --exit-code || (echo "Please run 'go generate ./...'." && exit 1)
-
-      - name: go test
-        if: always()
-        run: go test -count=1 -benchtime=1x ./...
-
-      # TODO(bmizerany): replace this heavy tool with just the
-      # tools/checks/binaries we want and then make them all run in parallel
-      # across jobs, not on a single tiny vm on Github Actions.
       - uses: golangci/golangci-lint-action@v6
         with:
           args: --timeout 10m0s -v
-
+      - run: go test ./...
-      - name: cache save
-        # Always save the cache, even if the job fails. The artifacts produced
-        # during the building of test binaries are not all for naught. They can
-        # be used to speed up subsequent runs.
-        if: always()
-
-        uses: actions/cache/save@1bd1e32a3bdc45362d1e726936510720a7c30a57 # v4.2.0
-        with:
-          # Note: unlike the other setups, this is only grabbing the mod download
-          # cache, rather than the whole mod directory, as the download cache
-          # contains zips that can be unpacked in parallel faster than they can be
-          # fetched and extracted by tar
-          path: |
-            ~/.cache/go-build
-            ~/go/pkg/mod/cache
-            ~\AppData\Local\go-build
-          # NOTE: The -3- here should be incremented when the scheme of data to be
-          # cached changes (e.g. path above changes).
-          key: ${{ github.job }}-${{ runner.os }}-${{ matrix.goarch }}-${{ matrix.buildflags }}-go-3-${{ hashFiles('**/go.sum') }}-${{ github.run_id }}
 
   patches:
     runs-on: ubuntu-latest
@@ -237,5 +163,5 @@ jobs:
       - uses: actions/checkout@v4
       - name: Verify patches apply cleanly and do not change files
         run: |
-          make -f Makefile.sync clean checkout apply-patches sync
+          make -f Makefile.sync clean checkout sync
-          git diff --compact-summary --exit-code
+          git diff --compact-summary --exit-code

.gitignore

@@ -5,6 +5,7 @@
 .swp
 dist
 build
+ollama
 .cache
 *.exe
 .idea
@@ -13,4 +14,3 @@ test_data
 __debug_bin*
 llama/build
 llama/vendor
-/ollama

.golangci.yaml

@@ -6,6 +6,8 @@ linters:
     - bidichk
     - bodyclose
     - containedctx
+    - contextcheck
+    - errcheck
     - gocheckcompilerdirectives
     - gofmt
     - gofumpt
@@ -19,13 +21,12 @@ linters:
     - nolintlint
     - nosprintfhostport
     - staticcheck
+    - tenv
     - unconvert
-    - usetesting
+    - unused
+    - usestdlibvars
     - wastedassign
     - whitespace
-  disable:
-    - usestdlibvars
-    - errcheck
 linters-settings:
   staticcheck:
     checks:
@@ -38,4 +39,5 @@ severity:
         - gofmt
         - goimports
         - intrange
+        - usestdlibvars
       severity: info

CMakeLists.txt

@@ -23,19 +23,12 @@ set(GGML_SCHED_MAX_COPIES 4)
 set(GGML_LLAMAFILE ON)
 set(GGML_CUDA_PEER_MAX_BATCH_SIZE 128)
 set(GGML_CUDA_GRAPHS ON)
-set(GGML_CUDA_FA ON)
-set(GGML_CUDA_COMPRESSION_MODE default)
 
-if((CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_OSX_ARCHITECTURES MATCHES "arm64")
+if((NOT CMAKE_OSX_ARCHITECTURES MATCHES "arm64")
     OR (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_SYSTEM_PROCESSOR MATCHES "arm|aarch64|ARM64|ARMv[0-9]+"))
     set(GGML_CPU_ALL_VARIANTS ON)
 endif()
 
-if (CMAKE_OSX_ARCHITECTURES MATCHES "x86_64")
-    set(CMAKE_BUILD_RPATH "@loader_path")
-    set(CMAKE_INSTALL_RPATH "@loader_path")
-endif()
-
 set(OLLAMA_BUILD_DIR ${CMAKE_BINARY_DIR}/lib/ollama)
 set(OLLAMA_INSTALL_DIR ${CMAKE_INSTALL_PREFIX}/lib/ollama)
 
@@ -87,36 +80,22 @@ if(CMAKE_CUDA_COMPILER)
     )
 endif()
 
-set(WINDOWS_AMDGPU_TARGETS_EXCLUDE_REGEX "^gfx(906|908|90a|1200|1201):xnack[+-]$"
-    CACHE STRING
-    "Regular expression describing AMDGPU_TARGETS not supported on Windows. Override to force building these targets. Default \"^gfx(906|908|90a|1200|1201):xnack[+-]$\"."
-)
-
 check_language(HIP)
 if(CMAKE_HIP_COMPILER)
     set(HIP_PLATFORM "amd")
 
     find_package(hip REQUIRED)
     if(NOT AMDGPU_TARGETS)
-        list(FILTER AMDGPU_TARGETS INCLUDE REGEX "^gfx(900|94[012]|101[02]|1030|110[012]|120[01])$")
+        list(FILTER AMDGPU_TARGETS INCLUDE REGEX "^gfx(900|94[012]|101[02]|1030|110[012])$")
-    elseif(WIN32 AND WINDOWS_AMDGPU_TARGETS_EXCLUDE_REGEX)
-        list(FILTER AMDGPU_TARGETS EXCLUDE REGEX ${WINDOWS_AMDGPU_TARGETS_EXCLUDE_REGEX})
     endif()
 
     if(AMDGPU_TARGETS)
         add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-hip)
-
-        if (WIN32)
-            target_compile_definitions(ggml-hip PRIVATE GGML_CUDA_NO_PEER_COPY)
-        endif()
-
-        target_compile_definitions(ggml-hip PRIVATE GGML_HIP_NO_VMM)
-
         set(OLLAMA_HIP_INSTALL_DIR ${OLLAMA_INSTALL_DIR}/rocm)
         install(TARGETS ggml-hip
             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_INCLUDE_REGEXES amdhip64 hipblas rocblas amd_comgr hsa_runtime64 rocprofiler-register drm_amdgpu drm numa
                 PRE_EXCLUDE_REGEXES ".*"
                 POST_EXCLUDE_REGEXES "system32"
             RUNTIME DESTINATION ${OLLAMA_HIP_INSTALL_DIR} COMPONENT HIP

CMakePresets.json

@@ -21,16 +21,14 @@
       "name": "CUDA 11",
       "inherits": [ "CUDA" ],
       "cacheVariables": {
-        "CMAKE_CUDA_ARCHITECTURES": "50;52;53;60;61;70;75;80;86",
+        "CMAKE_CUDA_ARCHITECTURES": "50;52;53;60;61;62;70;72;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_ARCHITECTURES": "60;61;62;70;72;75;80;86;87;89;90;90a"
-        "CMAKE_CUDA_FLAGS": "-Wno-deprecated-gpu-targets"
       }
     },
     {
@@ -58,7 +56,7 @@
       "name": "ROCm 6",
       "inherits": [ "ROCm" ],
       "cacheVariables": {
-        "AMDGPU_TARGETS": "gfx900;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102;gfx1151;gfx1200;gfx1201;gfx906:xnack-;gfx908:xnack-;gfx90a:xnack+;gfx90a:xnack-"
+        "AMDGPU_TARGETS": "gfx900;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102"
       }
     }
   ],

CONTRIBUTING.md

@@ -6,6 +6,8 @@ Thank you for your interest in contributing to Ollama! Here are a few guidelines
 
 See the [development documentation](./docs/development.md) for instructions on how to build and run Ollama locally.
 
+## Pull requests
+
 ### Ideal issues
 
 * [Bugs](https://github.com/ollama/ollama/issues?q=is%3Aissue+is%3Aopen+label%3Abug): issues where Ollama stops working or where it results in an unexpected error.
@@ -24,64 +26,11 @@ See the [development documentation](./docs/development.md) for instructions on h
 * Changes that add significant friction to the user experience
 * Changes that create a large future maintenance burden for maintainers and contributors
 
-## Proposing a (non-trivial) change
+### Best practices
 
-> By "non-trivial", we mean a change that is not a bug fix or small
+* Commit messages: please leave both a title and a description in your commit messages. The title should be a short summary of the changes, with a leading word that explains the section of the code being changed (e.g. `api: fix parsing of prompt field`) . In the description, leave a short 2-3 sentences that explain more about the change and its impact.
-> documentation update. If you are unsure, please ask us on our [Discord
+* Tests: please add test coverage to changes where possible.
-> server](https://discord.gg/ollama).
+* Minimize dependencies: avoid adding new dependencies unless absolutely necessary.
-
-Before opening a non-trivial Pull Request, please open an issue to discuss the change and
-get feedback from the maintainers. This helps us understand the context of the
-change and how it fits into Ollama's roadmap and prevents us from duplicating
-work or you from spending time on a change that we may not be able to accept.
-
-Tips for proposals:
-
-* Explain the problem you are trying to solve, not what you are trying to do.
-* Explain why the change is important.
-* Explain how the change will be used.
-* Explain how the change will be tested.
-
-Additionally, for bonus points: Provide draft documentation you would expect to
-see if the change were accepted.
-
-## Pull requests
-
-**Commit messages**
-
-The title should look like:
-
-    <package>: <short description>
-
-The package is the most affected Go package. If the change does not affect Go
-code, then use the directory name instead. Changes to a single well-known
-file in the root directory may use the file name.
-
-The short description should start with a lowercase letter and be a
-continuation of the sentence:
-
-      "This changes Ollama to..."
-
-Examples:
-
-      llm/backend/mlx: support the llama architecture
-      CONTRIBUTING: provide clairity on good commit messages, and bad
-
-Bad Examples:
-
-      feat: add more emoji
-      fix: was not using famous web framework
-      chore: generify code
-
-**Tests**
-
-Please include tests. Strive to test behavior, not implementation.
-
-**New dependencies**
-
-Dependencies should be added sparingly. If you are adding a new dependency,
-please explain why it is necessary and what other ways you attempted that
-did not work without it.
 
 ## Need help?
 

Dockerfile

@@ -2,24 +2,22 @@
 
 ARG FLAVOR=${TARGETARCH}
 
-ARG ROCMVERSION=6.3.3
+ARG ROCMVERSION=6.1.2
 ARG JETPACK5VERSION=r35.4.1
-ARG JETPACK6VERSION=r36.4.0
+ARG JETPACK6VERSION=r36.2.0
 ARG CMAKEVERSION=3.31.2
 
-# 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-centos-7:${ROCMVERSION}-complete AS base-amd64
-FROM --platform=linux/amd64 rocm/dev-almalinux-8:${ROCMVERSION}-complete AS base-amd64
+RUN sed -i -e 's/mirror.centos.org/vault.centos.org/g' -e 's/^#.*baseurl=http/baseurl=http/g' -e 's/^mirrorlist=http/#mirrorlist=http/g' /etc/yum.repos.d/*.repo \
-RUN yum install -y yum-utils \
+    && yum install -y yum-utils devtoolset-10-gcc devtoolset-10-gcc-c++ \
-    && yum-config-manager --add-repo https://dl.rockylinux.org/vault/rocky/8.5/AppStream/\$basearch/os/ \
+    && yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-rhel7.repo \
-    && rpm --import https://dl.rockylinux.org/pub/rocky/RPM-GPG-KEY-Rocky-8 \
+    && curl -s -L https://github.com/ccache/ccache/releases/download/v4.10.2/ccache-4.10.2-linux-x86_64.tar.xz | tar -Jx -C /usr/local/bin --strip-components 1
-    && 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 \
+ENV PATH=/opt/rh/devtoolset-10/root/usr/bin:/opt/rh/devtoolset-11/root/usr/bin:$PATH
-    && yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo
-ENV PATH=/opt/rh/gcc-toolset-10/root/usr/bin:$PATH
 
-FROM --platform=linux/arm64 almalinux:8 AS base-arm64
+FROM --platform=linux/arm64 rockylinux:8 AS base-arm64
 # install epel-release for ccache
 RUN yum install -y yum-utils epel-release \
-    && dnf install -y clang ccache \
+    && yum install -y clang ccache \
     && yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/sbsa/cuda-rhel8.repo
 ENV CC=clang CXX=clang++
 
@@ -31,8 +29,9 @@ COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
 ENV LDFLAGS=-s
 
 FROM base AS cpu
-RUN dnf install -y gcc-toolset-11-gcc gcc-toolset-11-gcc-c++
+# amd64 uses gcc which requires devtoolset-11 for AVX extensions while arm64 uses clang
-ENV PATH=/opt/rh/gcc-toolset-11/root/usr/bin:$PATH
+RUN if [ "$(uname -m)" = "x86_64" ]; then yum install -y devtoolset-11-gcc devtoolset-11-gcc-c++; fi
+ENV PATH=/opt/rh/devtoolset-11/root/usr/bin:$PATH
 RUN --mount=type=cache,target=/root/.ccache \
     cmake --preset 'CPU' \
         && cmake --build --parallel --preset 'CPU' \
@@ -40,7 +39,7 @@ RUN --mount=type=cache,target=/root/.ccache \
 
 FROM base AS cuda-11
 ARG CUDA11VERSION=11.3
-RUN dnf install -y cuda-toolkit-${CUDA11VERSION//./-}
+RUN yum install -y cuda-toolkit-${CUDA11VERSION//./-}
 ENV PATH=/usr/local/cuda-11/bin:$PATH
 RUN --mount=type=cache,target=/root/.ccache \
     cmake --preset 'CUDA 11' \
@@ -48,8 +47,8 @@ RUN --mount=type=cache,target=/root/.ccache \
         && cmake --install build --component CUDA --strip --parallel 8
 
 FROM base AS cuda-12
-ARG CUDA12VERSION=12.8
+ARG CUDA12VERSION=12.4
-RUN dnf install -y cuda-toolkit-${CUDA12VERSION//./-}
+RUN yum install -y cuda-toolkit-${CUDA12VERSION//./-}
 ENV PATH=/usr/local/cuda-12/bin:$PATH
 RUN --mount=type=cache,target=/root/.ccache \
     cmake --preset 'CUDA 12' \
@@ -57,7 +56,6 @@ RUN --mount=type=cache,target=/root/.ccache \
         && cmake --install build --component CUDA --strip --parallel 8
 
 FROM base AS rocm-6
-ENV PATH=/opt/rocm/hcc/bin:/opt/rocm/hip/bin:/opt/rocm/bin:/opt/rocm/hcc/bin:$PATH
 RUN --mount=type=cache,target=/root/.ccache \
     cmake --preset 'ROCm 6' \
         && cmake --build --parallel --preset 'ROCm 6' \
@@ -86,11 +84,10 @@ RUN --mount=type=cache,target=/root/.ccache \
         && cmake --install build --component CUDA --strip --parallel 8
 
 FROM base AS build
-WORKDIR /go/src/github.com/ollama/ollama
+ARG GOVERSION=1.23.4
-COPY go.mod go.sum .
+RUN curl -fsSL https://golang.org/dl/go${GOVERSION}.linux-$(case $(uname -m) in x86_64) echo amd64 ;; aarch64) echo arm64 ;; esac).tar.gz | tar xz -C /usr/local
-RUN curl -fsSL https://golang.org/dl/go$(awk '/^go/ { print $2 }' go.mod).linux-$(case $(uname -m) in x86_64) echo amd64 ;; aarch64) echo arm64 ;; esac).tar.gz | tar xz -C /usr/local
 ENV PATH=/usr/local/go/bin:$PATH
-RUN go mod download
+WORKDIR /go/src/github.com/ollama/ollama
 COPY . .
 ARG GOFLAGS="'-ldflags=-w -s'"
 ENV CGO_ENABLED=1
@@ -104,10 +101,10 @@ COPY --from=cuda-12 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_v12
 FROM --platform=linux/arm64 scratch AS arm64
 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-5 dist/lib/ollama/cuda_v11 lib/ollama/cuda_jetpack5
-COPY --from=jetpack-6 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_jetpack6
+COPY --from=jetpack-6 dist/lib/ollama/cuda_v12 lib/ollama/cuda_jetpack6
 
-FROM scratch AS rocm
+FROM --platform=linux/arm64 scratch AS rocm
 COPY --from=rocm-6 dist/lib/ollama/rocm /lib/ollama/rocm
 
 FROM ${FLAVOR} AS archive

Makefile.sync

@@ -1,6 +1,6 @@
 UPSTREAM=https://github.com/ggerganov/llama.cpp.git
 WORKDIR=llama/vendor
-FETCH_HEAD=de4c07f93783a1a96456a44dc16b9db538ee1618
+FETCH_HEAD=46e3556e01b824e52395fb050b29804b6cff2a7c
 
 .PHONY: help
 help:
@@ -15,30 +15,23 @@ help:
 	@echo "    make -f $(lastword $(MAKEFILE_LIST)) clean sync"
 
 .PHONY: sync
-sync: llama/build-info.cpp ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-embed.metal
+sync: llama/llama.cpp ml/backend/ggml/ggml apply-patches
-
-llama/build-info.cpp: llama/build-info.cpp.in llama/llama.cpp
-	sed -e 's|@FETCH_HEAD@|$(FETCH_HEAD)|' <$< >$@
-
-ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-embed.metal: ml/backend/ggml/ggml
-	go generate ./$(@D)
 
 .PHONY: llama/llama.cpp
-llama/llama.cpp: llama/vendor/
+llama/llama.cpp: llama/vendor/ apply-patches
 	rsync -arvzc -f "merge $@/.rsync-filter" $< $@
 
-.PHONY: ml/backend/ggml/ggml
+.PHONY: ml/backend/ggml/ggml apply-patches
-ml/backend/ggml/ggml: llama/vendor/ggml/
+ml/backend/ggml/ggml: llama/vendor/ggml/ apply-patches
 	rsync -arvzc -f "merge $@/.rsync-filter" $< $@
 
 PATCHES=$(wildcard llama/patches/*.patch)
-PATCHED=$(join $(dir $(PATCHES)), $(addsuffix ed, $(addprefix ., $(notdir $(PATCHES)))))
 
 .PHONY: apply-patches
 .NOTPARALLEL:
-apply-patches: $(PATCHED)
+apply-patches: $(addsuffix ed, $(PATCHES))
 
-llama/patches/.%.patched: llama/patches/%.patch
+%.patched: %.patch
 	@if git -c user.name=nobody -c 'user.email=<>' -C $(WORKDIR) am -3 $(realpath $<); then touch $@; else git -C $(WORKDIR) am --abort; exit 1; fi
 
 .PHONY: checkout
@@ -60,4 +53,4 @@ format-patches: llama/patches
 
 .PHONE: clean
 clean: checkout
-	$(RM) llama/patches/.*.patched
+	$(RM) $(addsuffix ed, $(PATCHES))

README.md

@@ -1,5 +1,5 @@
 <div align="center">
-  <a href="https://ollama.com">
+  <a href="https://ollama.com" />
     <img alt="ollama" height="200px" src="https://github.com/ollama/ollama/assets/3325447/0d0b44e2-8f4a-4e99-9b52-a5c1c741c8f7">
   </a>
 </div>
@@ -18,7 +18,7 @@ Get up and running with large language models.
 
 ### Linux
 
-```shell
+```
 curl -fsSL https://ollama.com/install.sh | sh
 ```
 
@@ -42,7 +42,7 @@ The official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama) `olla
 
 To run and chat with [Llama 3.2](https://ollama.com/library/llama3.2):
 
-```shell
+```
 ollama run llama3.2
 ```
 
@@ -54,15 +54,6 @@ Here are some example models that can be downloaded:
 
 | Model              | Parameters | Size  | Download                         |
 | ------------------ | ---------- | ----- | -------------------------------- |
-| Gemma 3            | 1B         | 815MB | `ollama run gemma3:1b`           |
-| Gemma 3            | 4B         | 3.3GB | `ollama run gemma3`              |
-| Gemma 3            | 12B        | 8.1GB | `ollama run gemma3:12b`          |
-| Gemma 3            | 27B        | 17GB  | `ollama run gemma3:27b`          |
-| QwQ                | 32B        | 20GB  | `ollama run qwq`                 |
-| DeepSeek-R1        | 7B         | 4.7GB | `ollama run deepseek-r1`         |
-| DeepSeek-R1        | 671B       | 404GB | `ollama run deepseek-r1:671b`    |
-| Llama 4            | 109B       | 67GB  | `ollama run llama4:scout`        |
-| Llama 4            | 400B       | 245GB | `ollama run llama4:maverick`     |
 | Llama 3.3          | 70B        | 43GB  | `ollama run llama3.3`            |
 | Llama 3.2          | 3B         | 2.0GB | `ollama run llama3.2`            |
 | Llama 3.2          | 1B         | 1.3GB | `ollama run llama3.2:1b`         |
@@ -71,7 +62,10 @@ Here are some example models that can be downloaded:
 | Llama 3.1          | 8B         | 4.7GB | `ollama run llama3.1`            |
 | Llama 3.1          | 405B       | 231GB | `ollama run llama3.1:405b`       |
 | Phi 4              | 14B        | 9.1GB | `ollama run phi4`                |
-| Phi 4 Mini         | 3.8B       | 2.5GB | `ollama run phi4-mini`           |
+| Phi 3 Mini         | 3.8B       | 2.3GB | `ollama run phi3`                |
+| Gemma 2            | 2B         | 1.6GB | `ollama run gemma2:2b`           |
+| Gemma 2            | 9B         | 5.5GB | `ollama run gemma2`              |
+| Gemma 2            | 27B        | 16GB  | `ollama run gemma2:27b`          |
 | Mistral            | 7B         | 4.1GB | `ollama run mistral`             |
 | Moondream 2        | 1.4B       | 829MB | `ollama run moondream`           |
 | Neural Chat        | 7B         | 4.1GB | `ollama run neural-chat`         |
@@ -79,7 +73,7 @@ Here are some example models that can be downloaded:
 | Code Llama         | 7B         | 3.8GB | `ollama run codellama`           |
 | Llama 2 Uncensored | 7B         | 3.8GB | `ollama run llama2-uncensored`   |
 | LLaVA              | 7B         | 4.5GB | `ollama run llava`               |
-| Granite-3.3         | 8B         | 4.9GB | `ollama run granite3.3`          |
+| Solar              | 10.7B      | 6.1GB | `ollama run solar`               |
 
 > [!NOTE]
 > You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
@@ -98,13 +92,13 @@ Ollama supports importing GGUF models in the Modelfile:
 
 2. Create the model in Ollama
 
-   ```shell
+   ```
    ollama create example -f Modelfile
    ```
 
 3. Run the model
 
-   ```shell
+   ```
    ollama run example
    ```
 
@@ -116,7 +110,7 @@ See the [guide](docs/import.md) on importing models for more information.
 
 Models from the Ollama library can be customized with a prompt. For example, to customize the `llama3.2` model:
 
-```shell
+```
 ollama pull llama3.2
 ```
 
@@ -151,13 +145,13 @@ For more information on working with a Modelfile, see the [Modelfile](docs/model
 
 `ollama create` is used to create a model from a Modelfile.
 
-```shell
+```
 ollama create mymodel -f ./Modelfile
 ```
 
 ### Pull a model
 
-```shell
+```
 ollama pull llama3.2
 ```
 
@@ -165,13 +159,13 @@ ollama pull llama3.2
 
 ### Remove a model
 
-```shell
+```
 ollama rm llama3.2
 ```
 
 ### Copy a model
 
-```shell
+```
 ollama cp llama3.2 my-model
 ```
 
@@ -190,39 +184,37 @@ I'm a basic program that prints the famous "Hello, world!" message to the consol
 
 ```
 ollama run llava "What's in this image? /Users/jmorgan/Desktop/smile.png"
+The image features a yellow smiley face, which is likely the central focus of the picture.
 ```
 
-> **Output**: The image features a yellow smiley face, which is likely the central focus of the picture.
-
 ### Pass the prompt as an argument
 
-```shell
-ollama run llama3.2 "Summarize this file: $(cat README.md)"
 ```
-
+$ ollama run llama3.2 "Summarize this file: $(cat README.md)"
-> **Output**: Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications.
+ Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications.
+```
 
 ### Show model information
 
-```shell
+```
 ollama show llama3.2
 ```
 
 ### List models on your computer
 
-```shell
+```
 ollama list
 ```
 
 ### List which models are currently loaded
 
-```shell
+```
 ollama ps
 ```
 
 ### Stop a model which is currently running
 
-```shell
+```
 ollama stop llama3.2
 ```
 
@@ -238,13 +230,13 @@ See the [developer guide](https://github.com/ollama/ollama/blob/main/docs/develo
 
 Next, start the server:
 
-```shell
+```
 ./ollama serve
 ```
 
 Finally, in a separate shell, run a model:
 
-```shell
+```
 ./ollama run llama3.2
 ```
 
@@ -254,7 +246,7 @@ Ollama has a REST API for running and managing models.
 
 ### Generate a response
 
-```shell
+```
 curl http://localhost:11434/api/generate -d '{
   "model": "llama3.2",
   "prompt":"Why is the sky blue?"
@@ -263,7 +255,7 @@ curl http://localhost:11434/api/generate -d '{
 
 ### Chat with a model
 
-```shell
+```
 curl http://localhost:11434/api/chat -d '{
   "model": "llama3.2",
   "messages": [
@@ -279,7 +271,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
 ### Web & Desktop
 
 - [Open WebUI](https://github.com/open-webui/open-webui)
-- [SwiftChat (macOS with ReactNative)](https://github.com/aws-samples/swift-chat)
 - [Enchanted (macOS native)](https://github.com/AugustDev/enchanted)
 - [Hollama](https://github.com/fmaclen/hollama)
 - [Lollms-Webui](https://github.com/ParisNeo/lollms-webui)
@@ -287,13 +278,12 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Bionic GPT](https://github.com/bionic-gpt/bionic-gpt)
 - [HTML UI](https://github.com/rtcfirefly/ollama-ui)
 - [Saddle](https://github.com/jikkuatwork/saddle)
-- [TagSpaces](https://www.tagspaces.org) (A platform for file-based apps, [utilizing Ollama](https://docs.tagspaces.org/ai/) for the generation of tags and descriptions)
 - [Chatbot UI](https://github.com/ivanfioravanti/chatbot-ollama)
 - [Chatbot UI v2](https://github.com/mckaywrigley/chatbot-ui)
 - [Typescript UI](https://github.com/ollama-interface/Ollama-Gui?tab=readme-ov-file)
 - [Minimalistic React UI for Ollama Models](https://github.com/richawo/minimal-llm-ui)
 - [Ollamac](https://github.com/kevinhermawan/Ollamac)
-- [big-AGI](https://github.com/enricoros/big-AGI)
+- [big-AGI](https://github.com/enricoros/big-AGI/blob/main/docs/config-local-ollama.md)
 - [Cheshire Cat assistant framework](https://github.com/cheshire-cat-ai/core)
 - [Amica](https://github.com/semperai/amica)
 - [chatd](https://github.com/BruceMacD/chatd)
@@ -314,8 +304,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Ollama Basic Chat: Uses HyperDiv Reactive UI](https://github.com/rapidarchitect/ollama_basic_chat)
 - [Ollama-chats RPG](https://github.com/drazdra/ollama-chats)
 - [IntelliBar](https://intellibar.app/) (AI-powered assistant for macOS)
-- [Jirapt](https://github.com/AliAhmedNada/jirapt) (Jira Integration to generate issues, tasks, epics)
-- [ojira](https://github.com/AliAhmedNada/ojira) (Jira chrome plugin to easily generate descriptions for tasks)
 - [QA-Pilot](https://github.com/reid41/QA-Pilot) (Interactive chat tool that can leverage Ollama models for rapid understanding and navigation of GitHub code repositories)
 - [ChatOllama](https://github.com/sugarforever/chat-ollama) (Open Source Chatbot based on Ollama with Knowledge Bases)
 - [CRAG Ollama Chat](https://github.com/Nagi-ovo/CRAG-Ollama-Chat) (Simple Web Search with Corrective RAG)
@@ -329,14 +317,13 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [RWKV-Runner](https://github.com/josStorer/RWKV-Runner) (RWKV offline LLM deployment tool, also usable as a client for ChatGPT and Ollama)
 - [Ollama Grid Search](https://github.com/dezoito/ollama-grid-search) (app to evaluate and compare models)
 - [Olpaka](https://github.com/Otacon/olpaka) (User-friendly Flutter Web App for Ollama)
-- [Casibase](https://casibase.org) (An open source AI knowledge base and dialogue system combining the latest RAG, SSO, ollama support, and multiple large language models.)
 - [OllamaSpring](https://github.com/CrazyNeil/OllamaSpring) (Ollama Client for macOS)
 - [LLocal.in](https://github.com/kartikm7/llocal) (Easy to use Electron Desktop Client for Ollama)
 - [Shinkai Desktop](https://github.com/dcSpark/shinkai-apps) (Two click install Local AI using Ollama + Files + RAG)
-- [AiLama](https://github.com/zeyoyt/ailama) (A Discord User App that allows you to interact with Ollama anywhere in Discord)
+- [AiLama](https://github.com/zeyoyt/ailama) (A Discord User App that allows you to interact with Ollama anywhere in discord )
 - [Ollama with Google Mesop](https://github.com/rapidarchitect/ollama_mesop/) (Mesop Chat Client implementation with Ollama)
 - [R2R](https://github.com/SciPhi-AI/R2R) (Open-source RAG engine)
-- [Ollama-Kis](https://github.com/elearningshow/ollama-kis) (A simple easy-to-use GUI with sample custom LLM for Drivers Education)
+- [Ollama-Kis](https://github.com/elearningshow/ollama-kis) (A simple easy to use GUI with sample custom LLM for Drivers Education)
 - [OpenGPA](https://opengpa.org) (Open-source offline-first Enterprise Agentic Application)
 - [Painting Droid](https://github.com/mateuszmigas/painting-droid) (Painting app with AI integrations)
 - [Kerlig AI](https://www.kerlig.com/) (AI writing assistant for macOS)
@@ -345,16 +332,16 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [LLMStack](https://github.com/trypromptly/LLMStack) (No-code multi-agent framework to build LLM agents and workflows)
 - [BoltAI for Mac](https://boltai.com) (AI Chat Client for Mac)
 - [Harbor](https://github.com/av/harbor) (Containerized LLM Toolkit with Ollama as default backend)
-- [PyGPT](https://github.com/szczyglis-dev/py-gpt) (AI desktop assistant for Linux, Windows, and Mac)
+- [PyGPT](https://github.com/szczyglis-dev/py-gpt) (AI desktop assistant for Linux, Windows and Mac)
-- [Alpaca](https://github.com/Jeffser/Alpaca) (An Ollama client application for Linux and macOS made with GTK4 and Adwaita)
+- [Alpaca](https://github.com/Jeffser/Alpaca) (An Ollama client application for linux and macos made with GTK4 and Adwaita)
 - [AutoGPT](https://github.com/Significant-Gravitas/AutoGPT/blob/master/docs/content/platform/ollama.md) (AutoGPT Ollama integration)
 - [Go-CREW](https://www.jonathanhecl.com/go-crew/) (Powerful Offline RAG in Golang)
 - [PartCAD](https://github.com/openvmp/partcad/) (CAD model generation with OpenSCAD and CadQuery)
-- [Ollama4j Web UI](https://github.com/ollama4j/ollama4j-web-ui) - Java-based Web UI for Ollama built with Vaadin, Spring Boot, and Ollama4j
+- [Ollama4j Web UI](https://github.com/ollama4j/ollama4j-web-ui) - Java-based Web UI for Ollama built with Vaadin, Spring Boot and Ollama4j
 - [PyOllaMx](https://github.com/kspviswa/pyOllaMx) - macOS application capable of chatting with both Ollama and Apple MLX models.
-- [Cline](https://github.com/cline/cline) - Formerly known as Claude Dev is a VSCode extension for multi-file/whole-repo coding
+- [Claude Dev](https://github.com/saoudrizwan/claude-dev) - VSCode extension for multi-file/whole-repo coding
 - [Cherry Studio](https://github.com/kangfenmao/cherry-studio) (Desktop client with Ollama support)
-- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy-focused LLM chat interface with optional encryption)
+- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
 - [Archyve](https://github.com/nickthecook/archyve) (RAG-enabling document library)
 - [crewAI with Mesop](https://github.com/rapidarchitect/ollama-crew-mesop) (Mesop Web Interface to run crewAI with Ollama)
 - [Tkinter-based client](https://github.com/chyok/ollama-gui) (Python tkinter-based Client for Ollama)
@@ -366,13 +353,12 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Web management](https://github.com/lemonit-eric-mao/ollama-web-management) (Web management page)
 - [Promptery](https://github.com/promptery/promptery) (desktop client for Ollama.)
 - [Ollama App](https://github.com/JHubi1/ollama-app) (Modern and easy-to-use multi-platform client for Ollama)
-- [chat-ollama](https://github.com/annilq/chat-ollama) (a React Native client for Ollama)
 - [SpaceLlama](https://github.com/tcsenpai/spacellama) (Firefox and Chrome extension to quickly summarize web pages with ollama in a sidebar)
 - [YouLama](https://github.com/tcsenpai/youlama) (Webapp to quickly summarize any YouTube video, supporting Invidious as well)
 - [DualMind](https://github.com/tcsenpai/dualmind) (Experimental app allowing two models to talk to each other in the terminal or in a web interface)
 - [ollamarama-matrix](https://github.com/h1ddenpr0cess20/ollamarama-matrix) (Ollama chatbot for the Matrix chat protocol)
 - [ollama-chat-app](https://github.com/anan1213095357/ollama-chat-app) (Flutter-based chat app)
-- [Perfect Memory AI](https://www.perfectmemory.ai/) (Productivity AI assists personalized by what you have seen on your screen, heard, and said in the meetings)
+- [Perfect Memory AI](https://www.perfectmemory.ai/) (Productivity AI assists personalized by what you have seen on your screen, heard and said in the meetings)
 - [Hexabot](https://github.com/hexastack/hexabot) (A conversational AI builder)
 - [Reddit Rate](https://github.com/rapidarchitect/reddit_analyzer) (Search and Rate Reddit topics with a weighted summation)
 - [OpenTalkGpt](https://github.com/adarshM84/OpenTalkGpt) (Chrome Extension to manage open-source models supported by Ollama, create custom models, and chat with models from a user-friendly UI)
@@ -383,28 +369,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Minima](https://github.com/dmayboroda/minima) (RAG with on-premises or fully local workflow)
 - [aidful-ollama-model-delete](https://github.com/AidfulAI/aidful-ollama-model-delete) (User interface for simplified model cleanup)
 - [Perplexica](https://github.com/ItzCrazyKns/Perplexica) (An AI-powered search engine & an open-source alternative to Perplexity AI)
-- [Ollama Chat WebUI for Docker ](https://github.com/oslook/ollama-webui) (Support for local docker deployment, lightweight ollama webui)
 - [AI Toolkit for Visual Studio Code](https://aka.ms/ai-tooklit/ollama-docs) (Microsoft-official VSCode extension to chat, test, evaluate models with Ollama support, and use them in your AI applications.)
-- [MinimalNextOllamaChat](https://github.com/anilkay/MinimalNextOllamaChat) (Minimal Web UI for Chat and Model Control)
-- [Chipper](https://github.com/TilmanGriesel/chipper) AI interface for tinkerers (Ollama, Haystack RAG, Python)
-- [ChibiChat](https://github.com/CosmicEventHorizon/ChibiChat) (Kotlin-based Android app to chat with Ollama and Koboldcpp API endpoints)
-- [LocalLLM](https://github.com/qusaismael/localllm) (Minimal Web-App to run ollama models on it with a GUI)
-- [Ollamazing](https://github.com/buiducnhat/ollamazing) (Web extension to run Ollama models)
-- [OpenDeepResearcher-via-searxng](https://github.com/benhaotang/OpenDeepResearcher-via-searxng) (A Deep Research equivalent endpoint with Ollama support for running locally)
-- [AntSK](https://github.com/AIDotNet/AntSK) (Out-of-the-box & Adaptable RAG Chatbot)
-- [MaxKB](https://github.com/1Panel-dev/MaxKB/) (Ready-to-use & flexible RAG Chatbot)
-- [yla](https://github.com/danielekp/yla) (Web interface to freely interact with your customized models)
-- [LangBot](https://github.com/RockChinQ/LangBot) (LLM-based instant messaging bots platform, with Agents, RAG features, supports multiple platforms)
-- [1Panel](https://github.com/1Panel-dev/1Panel/) (Web-based Linux Server Management Tool)
-- [AstrBot](https://github.com/Soulter/AstrBot/) (User-friendly LLM-based multi-platform chatbot with a WebUI, supporting RAG, LLM agents, and plugins integration)
-- [Reins](https://github.com/ibrahimcetin/reins) (Easily tweak parameters, customize system prompts per chat, and enhance your AI experiments with reasoning model support.)
-- [Flufy](https://github.com/Aharon-Bensadoun/Flufy) (A beautiful chat interface for interacting with Ollama's API. Built with React, TypeScript, and Material-UI.)
-- [Ellama](https://github.com/zeozeozeo/ellama) (Friendly native app to chat with an Ollama instance)
-- [screenpipe](https://github.com/mediar-ai/screenpipe) Build agents powered by your screen history
-- [Ollamb](https://github.com/hengkysteen/ollamb) (Simple yet rich in features, cross-platform built with Flutter and designed for Ollama. Try the [web demo](https://hengkysteen.github.io/demo/ollamb/).)
-- [Writeopia](https://github.com/Writeopia/Writeopia) (Text editor with integration with Ollama)
-- [AppFlowy](https://github.com/AppFlowy-IO/AppFlowy) (AI collaborative workspace with Ollama, cross-platform and self-hostable)
-- [Lumina](https://github.com/cushydigit/lumina.git) (A lightweight, minimal React.js frontend for interacting with Ollama servers)
 
 ### Cloud
 
@@ -444,14 +409,10 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [SwollamaCLI](https://github.com/marcusziade/Swollama) bundled with the Swollama Swift package. [Demo](https://github.com/marcusziade/Swollama?tab=readme-ov-file#cli-usage)
 - [aichat](https://github.com/sigoden/aichat) All-in-one LLM CLI tool featuring Shell Assistant, Chat-REPL, RAG, AI tools & agents, with access to OpenAI, Claude, Gemini, Ollama, Groq, and more.
 - [PowershAI](https://github.com/rrg92/powershai) PowerShell module that brings AI to terminal on Windows, including support for Ollama
-- [DeepShell](https://github.com/Abyss-c0re/deepshell) Your self-hosted AI assistant. Interactive Shell, Files and Folders analysis.
 - [orbiton](https://github.com/xyproto/orbiton) Configuration-free text editor and IDE with support for tab completion with Ollama.
-- [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)
 
 ### Apple Vision Pro
 
-- [SwiftChat](https://github.com/aws-samples/swift-chat) (Cross-platform AI chat app supporting Apple Vision Pro via "Designed for iPad")
 - [Enchanted](https://github.com/AugustDev/enchanted)
 
 ### Database
@@ -466,15 +427,14 @@ See the [API documentation](./docs/api.md) for all endpoints.
 
 - [Pacman](https://archlinux.org/packages/extra/x86_64/ollama/)
 - [Gentoo](https://github.com/gentoo/guru/tree/master/app-misc/ollama)
-- [Homebrew](https://formulae.brew.sh/formula/ollama)
 - [Helm Chart](https://artifacthub.io/packages/helm/ollama-helm/ollama)
 - [Guix channel](https://codeberg.org/tusharhero/ollama-guix)
-- [Nix package](https://search.nixos.org/packages?show=ollama&from=0&size=50&sort=relevance&type=packages&query=ollama)
+- [Nix package](https://search.nixos.org/packages?channel=24.05&show=ollama&from=0&size=50&sort=relevance&type=packages&query=ollama)
 - [Flox](https://flox.dev/blog/ollama-part-one)
 
 ### Libraries
 
-- [LangChain](https://python.langchain.com/docs/integrations/chat/ollama/) and [LangChain.js](https://js.langchain.com/docs/integrations/chat/ollama/) with [example](https://js.langchain.com/docs/tutorials/local_rag/)
+- [LangChain](https://python.langchain.com/docs/integrations/llms/ollama) and [LangChain.js](https://js.langchain.com/docs/integrations/chat/ollama/) with [example](https://js.langchain.com/docs/tutorials/local_rag/)
 - [Firebase Genkit](https://firebase.google.com/docs/genkit/plugins/ollama)
 - [crewAI](https://github.com/crewAIInc/crewAI)
 - [Yacana](https://remembersoftwares.github.io/yacana/) (User-friendly multi-agent framework for brainstorming and executing predetermined flows with built-in tool integration)
@@ -521,23 +481,15 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Swollama for Swift](https://github.com/marcusziade/Swollama) with [DocC](https://marcusziade.github.io/Swollama/documentation/swollama/)
 - [GoLamify](https://github.com/prasad89/golamify)
 - [Ollama for Haskell](https://github.com/tusharad/ollama-haskell)
-- [multi-llm-ts](https://github.com/nbonamy/multi-llm-ts) (A Typescript/JavaScript library allowing access to different LLM in a unified API)
+- [multi-llm-ts](https://github.com/nbonamy/multi-llm-ts) (A Typescript/JavaScript library allowing access to different LLM in unified API)
 - [LlmTornado](https://github.com/lofcz/llmtornado) (C# library providing a unified interface for major FOSS & Commercial inference APIs)
-- [Ollama for Zig](https://github.com/dravenk/ollama-zig)
-- [Abso](https://github.com/lunary-ai/abso) (OpenAI-compatible TypeScript SDK for any LLM provider)
-- [Nichey](https://github.com/goodreasonai/nichey) is a Python package for generating custom wikis for your research topic
-- [Ollama for D](https://github.com/kassane/ollama-d)
-- [OllamaPlusPlus](https://github.com/HardCodeDev777/OllamaPlusPlus) (Very simple C++ library for Ollama)
 
 ### Mobile
 
-- [SwiftChat](https://github.com/aws-samples/swift-chat) (Lightning-fast Cross-platform AI chat app with native UI for Android, iOS, and iPad)
 - [Enchanted](https://github.com/AugustDev/enchanted)
 - [Maid](https://github.com/Mobile-Artificial-Intelligence/maid)
 - [Ollama App](https://github.com/JHubi1/ollama-app) (Modern and easy-to-use multi-platform client for Ollama)
-- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy-focused LLM chat interface with optional encryption)
+- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
-- [Ollama Android Chat](https://github.com/sunshine0523/OllamaServer) (No need for Termux, start the Ollama service with one click on an Android device)
-- [Reins](https://github.com/ibrahimcetin/reins) (Easily tweak parameters, customize system prompts per chat, and enhance your AI experiments with reasoning model support.)
 
 ### Extensions & Plugins
 
@@ -559,7 +511,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Obsidian Local GPT plugin](https://github.com/pfrankov/obsidian-local-gpt)
 - [Open Interpreter](https://docs.openinterpreter.com/language-model-setup/local-models/ollama)
 - [Llama Coder](https://github.com/ex3ndr/llama-coder) (Copilot alternative using Ollama)
-- [Ollama Copilot](https://github.com/bernardo-bruning/ollama-copilot) (Proxy that allows you to use Ollama as a copilot like GitHub Copilot)
+- [Ollama Copilot](https://github.com/bernardo-bruning/ollama-copilot) (Proxy that allows you to use ollama as a copilot like Github copilot)
 - [twinny](https://github.com/rjmacarthy/twinny) (Copilot and Copilot chat alternative using Ollama)
 - [Wingman-AI](https://github.com/RussellCanfield/wingman-ai) (Copilot code and chat alternative using Ollama and Hugging Face)
 - [Page Assist](https://github.com/n4ze3m/page-assist) (Chrome Extension)
@@ -569,8 +521,8 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Discord-Ollama Chat Bot](https://github.com/kevinthedang/discord-ollama) (Generalized TypeScript Discord Bot w/ Tuning Documentation)
 - [ChatGPTBox: All in one browser extension](https://github.com/josStorer/chatGPTBox) with [Integrating Tutorial](https://github.com/josStorer/chatGPTBox/issues/616#issuecomment-1975186467)
 - [Discord AI chat/moderation bot](https://github.com/rapmd73/Companion) Chat/moderation bot written in python. Uses Ollama to create personalities.
-- [Headless Ollama](https://github.com/nischalj10/headless-ollama) (Scripts to automatically install ollama client & models on any OS for apps that depend on ollama server)
+- [Headless Ollama](https://github.com/nischalj10/headless-ollama) (Scripts to automatically install ollama client & models on any OS for apps that depends on ollama server)
-- [Terraform AWS Ollama & Open WebUI](https://github.com/xuyangbocn/terraform-aws-self-host-llm) (A Terraform module to deploy on AWS a ready-to-use Ollama service, together with its front-end Open WebUI service.)
+- [Terraform AWS Ollama & Open WebUI](https://github.com/xuyangbocn/terraform-aws-self-host-llm) (A Terraform module to deploy on AWS a ready-to-use Ollama service, together with its front end Open WebUI service.)
 - [node-red-contrib-ollama](https://github.com/jakubburkiewicz/node-red-contrib-ollama)
 - [Local AI Helper](https://github.com/ivostoykov/localAI) (Chrome and Firefox extensions that enable interactions with the active tab and customisable API endpoints. Includes secure storage for user prompts.)
 - [vnc-lm](https://github.com/jake83741/vnc-lm) (Discord bot for messaging with LLMs through Ollama and LiteLLM. Seamlessly move between local and flagship models.)
@@ -581,19 +533,13 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [TextCraft](https://github.com/suncloudsmoon/TextCraft) (Copilot in Word alternative using Ollama)
 - [Alfred Ollama](https://github.com/zeitlings/alfred-ollama) (Alfred Workflow)
 - [TextLLaMA](https://github.com/adarshM84/TextLLaMA) A Chrome Extension that helps you write emails, correct grammar, and translate into any language
-- [Simple-Discord-AI](https://github.com/zyphixor/simple-discord-ai)
-- [LLM Telegram Bot](https://github.com/innightwolfsleep/llm_telegram_bot) (telegram bot, primary for RP. Oobabooga-like buttons, [A1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) API integration e.t.c)
-- [mcp-llm](https://github.com/sammcj/mcp-llm) (MCP Server to allow LLMs to call other LLMs)
-- [UnityCodeLama](https://github.com/HardCodeDev777/UnityCodeLama) (Unity Edtior tool to analyze scripts via Ollama)
 
 ### Supported backends
 
 - [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.
+
-- [Lunary](https://lunary.ai/docs/integrations/ollama) is the leading open-source LLM observability platform. It provides a variety of enterprise-grade features such as real-time analytics, prompt templates management, PII masking, and comprehensive agent tracing.
 - [OpenLIT](https://github.com/openlit/openlit) is an OpenTelemetry-native tool for monitoring Ollama Applications & GPUs using traces and metrics.
 - [HoneyHive](https://docs.honeyhive.ai/integrations/ollama) is an AI observability and evaluation platform for AI agents. Use HoneyHive to evaluate agent performance, interrogate failures, and monitor quality in production.
 - [Langfuse](https://langfuse.com/docs/integrations/ollama) is an open source LLM observability platform that enables teams to collaboratively monitor, evaluate and debug AI applications.
-- [MLflow Tracing](https://mlflow.org/docs/latest/llms/tracing/index.html#automatic-tracing) is an open source LLM observability tool with a convenient API to log and visualize traces, making it easy to debug and evaluate GenAI applications.

api/client.go

@@ -10,7 +10,7 @@
 // repository].
 //
 // [the API documentation]: https://github.com/ollama/ollama/blob/main/docs/api.md
-// [in the GitHub repository]: https://github.com/ollama/ollama/tree/main/api/examples
+// [in the GitHub repository]: https://github.com/ollama/ollama/tree/main/examples
 package api
 
 import (
@@ -132,7 +132,7 @@ func (c *Client) do(ctx context.Context, method, path string, reqData, respData
 const maxBufferSize = 512 * format.KiloByte
 
 func (c *Client) stream(ctx context.Context, method, path string, data any, fn func([]byte) error) error {
-	var buf io.Reader
+	var buf *bytes.Buffer
 	if data != nil {
 		bts, err := json.Marshal(data)
 		if err != nil {

api/client_test.go

@@ -1,12 +1,6 @@
 package api
 
 import (
-	"encoding/json"
-	"fmt"
-	"net/http"
-	"net/http/httptest"
-	"net/url"
-	"strings"
 	"testing"
 )
 
@@ -49,206 +43,3 @@ func TestClientFromEnvironment(t *testing.T) {
 		})
 	}
 }
-
-// testError represents an internal error type with status code and message
-// this is used since the error response from the server is not a standard error struct
-type testError struct {
-	message    string
-	statusCode int
-}
-
-func (e testError) Error() string {
-	return e.message
-}
-
-func TestClientStream(t *testing.T) {
-	testCases := []struct {
-		name      string
-		responses []any
-		wantErr   string
-	}{
-		{
-			name: "immediate error response",
-			responses: []any{
-				testError{
-					message:    "test error message",
-					statusCode: http.StatusBadRequest,
-				},
-			},
-			wantErr: "test error message",
-		},
-		{
-			name: "error after successful chunks, ok response",
-			responses: []any{
-				ChatResponse{Message: Message{Content: "partial response 1"}},
-				ChatResponse{Message: Message{Content: "partial response 2"}},
-				testError{
-					message:    "mid-stream error",
-					statusCode: http.StatusOK,
-				},
-			},
-			wantErr: "mid-stream error",
-		},
-		{
-			name: "successful stream completion",
-			responses: []any{
-				ChatResponse{Message: Message{Content: "chunk 1"}},
-				ChatResponse{Message: Message{Content: "chunk 2"}},
-				ChatResponse{
-					Message:    Message{Content: "final chunk"},
-					Done:       true,
-					DoneReason: "stop",
-				},
-			},
-		},
-	}
-
-	for _, tc := range testCases {
-		t.Run(tc.name, func(t *testing.T) {
-			ts := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
-				flusher, ok := w.(http.Flusher)
-				if !ok {
-					t.Fatal("expected http.Flusher")
-				}
-
-				w.Header().Set("Content-Type", "application/x-ndjson")
-
-				for _, resp := range tc.responses {
-					if errResp, ok := resp.(testError); ok {
-						w.WriteHeader(errResp.statusCode)
-						err := json.NewEncoder(w).Encode(map[string]string{
-							"error": errResp.message,
-						})
-						if err != nil {
-							t.Fatal("failed to encode error response:", err)
-						}
-						return
-					}
-
-					if err := json.NewEncoder(w).Encode(resp); err != nil {
-						t.Fatalf("failed to encode response: %v", err)
-					}
-					flusher.Flush()
-				}
-			}))
-			defer ts.Close()
-
-			client := NewClient(&url.URL{Scheme: "http", Host: ts.Listener.Addr().String()}, http.DefaultClient)
-
-			var receivedChunks []ChatResponse
-			err := client.stream(t.Context(), http.MethodPost, "/v1/chat", nil, func(chunk []byte) error {
-				var resp ChatResponse
-				if err := json.Unmarshal(chunk, &resp); err != nil {
-					return fmt.Errorf("failed to unmarshal chunk: %w", err)
-				}
-				receivedChunks = append(receivedChunks, resp)
-				return nil
-			})
-
-			if tc.wantErr != "" {
-				if err == nil {
-					t.Fatal("expected error but got nil")
-				}
-				if !strings.Contains(err.Error(), tc.wantErr) {
-					t.Errorf("expected error containing %q, got %v", tc.wantErr, err)
-				}
-				return
-			}
-			if err != nil {
-				t.Errorf("unexpected error: %v", err)
-			}
-		})
-	}
-}
-
-func TestClientDo(t *testing.T) {
-	testCases := []struct {
-		name     string
-		response any
-		wantErr  string
-	}{
-		{
-			name: "immediate error response",
-			response: testError{
-				message:    "test error message",
-				statusCode: http.StatusBadRequest,
-			},
-			wantErr: "test error message",
-		},
-		{
-			name: "server error response",
-			response: testError{
-				message:    "internal error",
-				statusCode: http.StatusInternalServerError,
-			},
-			wantErr: "internal error",
-		},
-		{
-			name: "successful response",
-			response: struct {
-				ID      string `json:"id"`
-				Success bool   `json:"success"`
-			}{
-				ID:      "msg_123",
-				Success: true,
-			},
-		},
-	}
-
-	for _, tc := range testCases {
-		t.Run(tc.name, func(t *testing.T) {
-			ts := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
-				if errResp, ok := tc.response.(testError); ok {
-					w.WriteHeader(errResp.statusCode)
-					err := json.NewEncoder(w).Encode(map[string]string{
-						"error": errResp.message,
-					})
-					if err != nil {
-						t.Fatal("failed to encode error response:", err)
-					}
-					return
-				}
-
-				w.Header().Set("Content-Type", "application/json")
-				if err := json.NewEncoder(w).Encode(tc.response); err != nil {
-					t.Fatalf("failed to encode response: %v", err)
-				}
-			}))
-			defer ts.Close()
-
-			client := NewClient(&url.URL{Scheme: "http", Host: ts.Listener.Addr().String()}, http.DefaultClient)
-
-			var resp struct {
-				ID      string `json:"id"`
-				Success bool   `json:"success"`
-			}
-			err := client.do(t.Context(), http.MethodPost, "/v1/messages", nil, &resp)
-
-			if tc.wantErr != "" {
-				if err == nil {
-					t.Fatalf("got nil, want error %q", tc.wantErr)
-				}
-				if err.Error() != tc.wantErr {
-					t.Errorf("error message mismatch: got %q, want %q", err.Error(), tc.wantErr)
-				}
-				return
-			}
-
-			if err != nil {
-				t.Fatalf("got error %q, want nil", err)
-			}
-
-			if expectedResp, ok := tc.response.(struct {
-				ID      string `json:"id"`
-				Success bool   `json:"success"`
-			}); ok {
-				if resp.ID != expectedResp.ID {
-					t.Errorf("response ID mismatch: got %q, want %q", resp.ID, expectedResp.ID)
-				}
-				if resp.Success != expectedResp.Success {
-					t.Errorf("response Success mismatch: got %v, want %v", resp.Success, expectedResp.Success)
-				}
-			}
-		})
-	}
-}

api/examples/README.md

@@ -2,10 +2,9 @@
 
 Run the examples in this directory with:
 
-```shell
+```
 go run example_name/main.go
 ```
-
 ## Chat - Chat with a model
 - [chat/main.go](chat/main.go)
 

api/types.go

@@ -10,9 +10,6 @@ import (
 	"strconv"
 	"strings"
 	"time"
-
-	"github.com/ollama/ollama/envconfig"
-	"github.com/ollama/ollama/types/model"
 )
 
 // StatusError is an error with an HTTP status code and message.
@@ -76,13 +73,13 @@ type GenerateRequest struct {
 	// this request.
 	KeepAlive *Duration `json:"keep_alive,omitempty"`
 
-	// Images is an optional list of raw image bytes accompanying this
+	// Images is an optional list of base64-encoded images accompanying this
 	// request, for multimodal models.
 	Images []ImageData `json:"images,omitempty"`
 
 	// Options lists model-specific options. For example, temperature can be
 	// set through this field, if the model supports it.
-	Options map[string]any `json:"options"`
+	Options map[string]interface{} `json:"options"`
 }
 
 // ChatRequest describes a request sent by [Client.Chat].
@@ -107,7 +104,7 @@ type ChatRequest struct {
 	Tools `json:"tools,omitempty"`
 
 	// Options lists model-specific options.
-	Options map[string]any `json:"options"`
+	Options map[string]interface{} `json:"options"`
 }
 
 type Tools []Tool
@@ -163,65 +160,19 @@ func (t *ToolCallFunctionArguments) String() string {
 
 type Tool struct {
 	Type     string       `json:"type"`
-	Items    any          `json:"items,omitempty"`
 	Function ToolFunction `json:"function"`
 }
 
-// PropertyType can be either a string or an array of strings
-type PropertyType []string
-
-// UnmarshalJSON implements the json.Unmarshaler interface
-func (pt *PropertyType) UnmarshalJSON(data []byte) error {
-	// Try to unmarshal as a string first
-	var s string
-	if err := json.Unmarshal(data, &s); err == nil {
-		*pt = []string{s}
-		return nil
-	}
-
-	// If that fails, try to unmarshal as an array of strings
-	var a []string
-	if err := json.Unmarshal(data, &a); err != nil {
-		return err
-	}
-	*pt = a
-	return nil
-}
-
-// MarshalJSON implements the json.Marshaler interface
-func (pt PropertyType) MarshalJSON() ([]byte, error) {
-	if len(pt) == 1 {
-		// If there's only one type, marshal as a string
-		return json.Marshal(pt[0])
-	}
-	// Otherwise marshal as an array
-	return json.Marshal([]string(pt))
-}
-
-// String returns a string representation of the PropertyType
-func (pt PropertyType) String() string {
-	if len(pt) == 0 {
-		return ""
-	}
-	if len(pt) == 1 {
-		return pt[0]
-	}
-	return fmt.Sprintf("%v", []string(pt))
-}
-
 type ToolFunction struct {
 	Name        string `json:"name"`
 	Description string `json:"description"`
 	Parameters  struct {
 		Type       string   `json:"type"`
-		Defs       any      `json:"$defs,omitempty"`
-		Items      any      `json:"items,omitempty"`
 		Required   []string `json:"required"`
 		Properties map[string]struct {
-			Type        PropertyType `json:"type"`
+			Type        string   `json:"type"`
-			Items       any          `json:"items,omitempty"`
+			Description string   `json:"description"`
-			Description string       `json:"description"`
+			Enum        []string `json:"enum,omitempty"`
-			Enum        []any        `json:"enum,omitempty"`
 		} `json:"properties"`
 	} `json:"parameters"`
 }
@@ -271,6 +222,9 @@ type Options struct {
 	RepeatPenalty    float32  `json:"repeat_penalty,omitempty"`
 	PresencePenalty  float32  `json:"presence_penalty,omitempty"`
 	FrequencyPenalty float32  `json:"frequency_penalty,omitempty"`
+	Mirostat         int      `json:"mirostat,omitempty"`
+	MirostatTau      float32  `json:"mirostat_tau,omitempty"`
+	MirostatEta      float32  `json:"mirostat_eta,omitempty"`
 	Stop             []string `json:"stop,omitempty"`
 }
 
@@ -280,7 +234,12 @@ type Runner struct {
 	NumBatch  int   `json:"num_batch,omitempty"`
 	NumGPU    int   `json:"num_gpu,omitempty"`
 	MainGPU   int   `json:"main_gpu,omitempty"`
+	LowVRAM   bool  `json:"low_vram,omitempty"`
+	F16KV     bool  `json:"f16_kv,omitempty"` // Deprecated: This option is ignored
+	LogitsAll bool  `json:"logits_all,omitempty"`
+	VocabOnly bool  `json:"vocab_only,omitempty"`
 	UseMMap   *bool `json:"use_mmap,omitempty"`
+	UseMLock  bool  `json:"use_mlock,omitempty"`
 	NumThread int   `json:"num_thread,omitempty"`
 }
 
@@ -299,7 +258,7 @@ type EmbedRequest struct {
 	Truncate *bool `json:"truncate,omitempty"`
 
 	// Options lists model-specific options.
-	Options map[string]any `json:"options"`
+	Options map[string]interface{} `json:"options"`
 }
 
 // EmbedResponse is the response from [Client.Embed].
@@ -325,7 +284,7 @@ type EmbeddingRequest struct {
 	KeepAlive *Duration `json:"keep_alive,omitempty"`
 
 	// Options lists model-specific options.
-	Options map[string]any `json:"options"`
+	Options map[string]interface{} `json:"options"`
 }
 
 // EmbeddingResponse is the response from [Client.Embeddings].
@@ -371,7 +330,7 @@ type ShowRequest struct {
 	Template string `json:"template"`
 	Verbose  bool   `json:"verbose"`
 
-	Options map[string]any `json:"options"`
+	Options map[string]interface{} `json:"options"`
 
 	// Deprecated: set the model name with Model instead
 	Name string `json:"name"`
@@ -379,18 +338,16 @@ type ShowRequest struct {
 
 // ShowResponse is the response returned from [Client.Show].
 type ShowResponse struct {
-	License       string             `json:"license,omitempty"`
+	License       string         `json:"license,omitempty"`
-	Modelfile     string             `json:"modelfile,omitempty"`
+	Modelfile     string         `json:"modelfile,omitempty"`
-	Parameters    string             `json:"parameters,omitempty"`
+	Parameters    string         `json:"parameters,omitempty"`
-	Template      string             `json:"template,omitempty"`
+	Template      string         `json:"template,omitempty"`
-	System        string             `json:"system,omitempty"`
+	System        string         `json:"system,omitempty"`
-	Details       ModelDetails       `json:"details,omitempty"`
+	Details       ModelDetails   `json:"details,omitempty"`
-	Messages      []Message          `json:"messages,omitempty"`
+	Messages      []Message      `json:"messages,omitempty"`
-	ModelInfo     map[string]any     `json:"model_info,omitempty"`
+	ModelInfo     map[string]any `json:"model_info,omitempty"`
-	ProjectorInfo map[string]any     `json:"projector_info,omitempty"`
+	ProjectorInfo map[string]any `json:"projector_info,omitempty"`
-	Tensors       []Tensor           `json:"tensors,omitempty"`
+	ModifiedAt    time.Time      `json:"modified_at,omitempty"`
-	Capabilities  []model.Capability `json:"capabilities,omitempty"`
-	ModifiedAt    time.Time          `json:"modified_at,omitempty"`
 }
 
 // CopyRequest is the request passed to [Client.Copy].
@@ -402,9 +359,9 @@ type CopyRequest struct {
 // PullRequest is the request passed to [Client.Pull].
 type PullRequest struct {
 	Model    string `json:"model"`
-	Insecure bool   `json:"insecure,omitempty"` // Deprecated: ignored
+	Insecure bool   `json:"insecure,omitempty"`
-	Username string `json:"username"`           // Deprecated: ignored
+	Username string `json:"username"`
-	Password string `json:"password"`           // Deprecated: ignored
+	Password string `json:"password"`
 	Stream   *bool  `json:"stream,omitempty"`
 
 	// Deprecated: set the model name with Model instead
@@ -463,6 +420,13 @@ type ProcessModelResponse struct {
 	SizeVRAM  int64        `json:"size_vram"`
 }
 
+type RetrieveModelResponse struct {
+	Id      string `json:"id"`
+	Object  string `json:"object"`
+	Created int64  `json:"created"`
+	OwnedBy string `json:"owned_by"`
+}
+
 type TokenResponse struct {
 	Token string `json:"token"`
 }
@@ -501,13 +465,6 @@ type ModelDetails struct {
 	QuantizationLevel string   `json:"quantization_level"`
 }
 
-// Tensor describes the metadata for a given tensor.
-type Tensor struct {
-	Name  string   `json:"name"`
-	Type  string   `json:"type"`
-	Shape []uint64 `json:"shape"`
-}
-
 func (m *Metrics) Summary() {
 	if m.TotalDuration > 0 {
 		fmt.Fprintf(os.Stderr, "total duration:       %v\n", m.TotalDuration)
@@ -536,7 +493,7 @@ func (m *Metrics) Summary() {
 	}
 }
 
-func (opts *Options) FromMap(m map[string]any) error {
+func (opts *Options) FromMap(m map[string]interface{}) error {
 	valueOpts := reflect.ValueOf(opts).Elem() // names of the fields in the options struct
 	typeOpts := reflect.TypeOf(opts).Elem()   // types of the fields in the options struct
 
@@ -593,12 +550,12 @@ func (opts *Options) FromMap(m map[string]any) error {
 				}
 				field.SetString(val)
 			case reflect.Slice:
-				// JSON unmarshals to []any, not []string
+				// JSON unmarshals to []interface{}, not []string
-				val, ok := val.([]any)
+				val, ok := val.([]interface{})
 				if !ok {
 					return fmt.Errorf("option %q must be of type array", key)
 				}
-				// convert []any to []string
+				// convert []interface{} to []string
 				slice := make([]string, len(val))
 				for i, item := range val {
 					str, ok := item.(string)
@@ -645,14 +602,19 @@ func DefaultOptions() Options {
 		RepeatPenalty:    1.1,
 		PresencePenalty:  0.0,
 		FrequencyPenalty: 0.0,
+		Mirostat:         0,
+		MirostatTau:      5.0,
+		MirostatEta:      0.1,
 		Seed:             -1,
 
 		Runner: Runner{
 			// options set when the model is loaded
-			NumCtx:    int(envconfig.ContextLength()),
+			NumCtx:    2048,
 			NumBatch:  512,
 			NumGPU:    -1, // -1 here indicates that NumGPU should be set dynamically
 			NumThread: 0,  // let the runtime decide
+			LowVRAM:   false,
+			UseMLock:  false,
 			UseMMap:   nil,
 		},
 	}
@@ -700,7 +662,7 @@ func (d *Duration) UnmarshalJSON(b []byte) (err error) {
 }
 
 // FormatParams converts specified parameter options to their correct types
-func FormatParams(params map[string][]string) (map[string]any, error) {
+func FormatParams(params map[string][]string) (map[string]interface{}, error) {
 	opts := Options{}
 	valueOpts := reflect.ValueOf(&opts).Elem() // names of the fields in the options struct
 	typeOpts := reflect.TypeOf(opts)           // types of the fields in the options struct
@@ -714,7 +676,7 @@ func FormatParams(params map[string][]string) (map[string]any, error) {
 		}
 	}
 
-	out := make(map[string]any)
+	out := make(map[string]interface{})
 	// iterate params and set values based on json struct tags
 	for key, vals := range params {
 		if opt, ok := jsonOpts[key]; !ok {

api/types_test.go

@@ -134,7 +134,7 @@ func TestUseMmapParsingFromJSON(t *testing.T) {
 
 	for _, test := range tests {
 		t.Run(test.name, func(t *testing.T) {
-			var oMap map[string]any
+			var oMap map[string]interface{}
 			err := json.Unmarshal([]byte(test.req), &oMap)
 			require.NoError(t, err)
 			opts := DefaultOptions()
@@ -231,144 +231,3 @@ func TestMessage_UnmarshalJSON(t *testing.T) {
 		}
 	}
 }
-
-func TestToolFunction_UnmarshalJSON(t *testing.T) {
-	tests := []struct {
-		name    string
-		input   string
-		wantErr string
-	}{
-		{
-			name: "valid enum with same types",
-			input: `{
-				"name": "test",
-				"description": "test function",
-				"parameters": {
-					"type": "object",
-					"required": ["test"],
-					"properties": {
-						"test": {
-							"type": "string",
-							"description": "test prop",
-							"enum": ["a", "b", "c"]
-						}
-					}
-				}
-			}`,
-			wantErr: "",
-		},
-		{
-			name: "empty enum array",
-			input: `{
-				"name": "test",
-				"description": "test function",
-				"parameters": {
-					"type": "object",
-					"required": ["test"],
-					"properties": {
-						"test": {
-							"type": "string",
-							"description": "test prop",
-							"enum": []
-						}
-					}
-				}
-			}`,
-			wantErr: "",
-		},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			var tf ToolFunction
-			err := json.Unmarshal([]byte(tt.input), &tf)
-
-			if tt.wantErr != "" {
-				require.Error(t, err)
-				assert.Contains(t, err.Error(), tt.wantErr)
-			} else {
-				require.NoError(t, err)
-			}
-		})
-	}
-}
-
-func TestPropertyType_UnmarshalJSON(t *testing.T) {
-	tests := []struct {
-		name     string
-		input    string
-		expected PropertyType
-	}{
-		{
-			name:     "string type",
-			input:    `"string"`,
-			expected: PropertyType{"string"},
-		},
-		{
-			name:     "array of types",
-			input:    `["string", "number"]`,
-			expected: PropertyType{"string", "number"},
-		},
-		{
-			name:     "array with single type",
-			input:    `["string"]`,
-			expected: PropertyType{"string"},
-		},
-	}
-
-	for _, test := range tests {
-		t.Run(test.name, func(t *testing.T) {
-			var pt PropertyType
-			if err := json.Unmarshal([]byte(test.input), &pt); err != nil {
-				t.Errorf("Unexpected error: %v", err)
-			}
-
-			if len(pt) != len(test.expected) {
-				t.Errorf("Length mismatch: got %v, expected %v", len(pt), len(test.expected))
-			}
-
-			for i, v := range pt {
-				if v != test.expected[i] {
-					t.Errorf("Value mismatch at index %d: got %v, expected %v", i, v, test.expected[i])
-				}
-			}
-		})
-	}
-}
-
-func TestPropertyType_MarshalJSON(t *testing.T) {
-	tests := []struct {
-		name     string
-		input    PropertyType
-		expected string
-	}{
-		{
-			name:     "single type",
-			input:    PropertyType{"string"},
-			expected: `"string"`,
-		},
-		{
-			name:     "multiple types",
-			input:    PropertyType{"string", "number"},
-			expected: `["string","number"]`,
-		},
-		{
-			name:     "empty type",
-			input:    PropertyType{},
-			expected: `[]`,
-		},
-	}
-
-	for _, test := range tests {
-		t.Run(test.name, func(t *testing.T) {
-			data, err := json.Marshal(test.input)
-			if err != nil {
-				t.Errorf("Unexpected error: %v", err)
-			}
-
-			if string(data) != test.expected {
-				t.Errorf("Marshaled data mismatch: got %v, expected %v", string(data), test.expected)
-			}
-		})
-	}
-}

app/README.md

@@ -17,6 +17,6 @@ If you want to build the installer, youll need to install
 In the top directory of this repo, run the following powershell script
 to build the ollama CLI, ollama app, and ollama installer.
 
-```powershell
+```
 powershell -ExecutionPolicy Bypass -File .\scripts\build_windows.ps1
 ```

app/lifecycle/logging.go

@@ -4,14 +4,20 @@ import (
 	"fmt"
 	"log/slog"
 	"os"
+	"path/filepath"
 	"strconv"
 	"strings"
 
 	"github.com/ollama/ollama/envconfig"
-	"github.com/ollama/ollama/logutil"
 )
 
 func InitLogging() {
+	level := slog.LevelInfo
+
+	if envconfig.Debug() {
+		level = slog.LevelDebug
+	}
+
 	var logFile *os.File
 	var err error
 	// Detect if we're a GUI app on windows, and if not, send logs to console
@@ -27,8 +33,20 @@ func InitLogging() {
 			return
 		}
 	}
+	handler := slog.NewTextHandler(logFile, &slog.HandlerOptions{
+		Level:     level,
+		AddSource: true,
+		ReplaceAttr: func(_ []string, attr slog.Attr) slog.Attr {
+			if attr.Key == slog.SourceKey {
+				source := attr.Value.Any().(*slog.Source)
+				source.File = filepath.Base(source.File)
+			}
+			return attr
+		},
+	})
+
+	slog.SetDefault(slog.New(handler))
 
-	slog.SetDefault(logutil.NewLogger(logFile, envconfig.LogLevel()))
 	slog.Info("ollama app started")
 }
 

benchmark/server_benchmark_test.go

@@ -1,178 +0,0 @@
-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)
-}

cache/cache.go

@@ -0,0 +1,63 @@
+package cache
+
+import (
+	"github.com/ollama/ollama/ml"
+)
+
+type Options struct {
+	Position int
+}
+
+type Cache interface {
+	Sub(i int) Cache
+	Put(ctx ml.Context, key, value ml.Tensor, opts Options) (ml.Tensor, ml.Tensor)
+}
+
+type Simple struct {
+	DType    ml.DType
+	Capacity int
+
+	keys, values []ml.Tensor
+}
+
+func (c *Simple) Sub(i int) Cache {
+	if i >= len(c.keys) {
+		c.keys = append(c.keys, make([]ml.Tensor, i-len(c.keys)+1)...)
+		c.values = append(c.values, make([]ml.Tensor, i-len(c.values)+1)...)
+	}
+
+	return &Simple{
+		keys:     c.keys[i : i+1],
+		values:   c.values[i : i+1],
+		Capacity: c.Capacity,
+		DType:    c.DType,
+	}
+}
+
+func (c *Simple) Put(ctx ml.Context, key, value ml.Tensor, opts Options) (ml.Tensor, ml.Tensor) {
+	if c.keys[0] == nil || c.values[0] == nil {
+		c.keys[0] = ctx.Zeros(c.DType, int(key.Dim(0)*key.Dim(1))*c.Capacity)
+		c.values[0] = ctx.Zeros(c.DType, int(value.Dim(0)*value.Dim(1))*c.Capacity)
+	}
+
+	ctx.Forward(key.Copy(ctx, c.keys[0].View(ctx, int(key.Stride(2))*opts.Position, int(key.Dim(0)*key.Dim(1)*key.Dim(2)))))
+	ctx.Forward(value.Copy(ctx, c.values[0].View(ctx, int(value.Stride(2))*opts.Position, int(value.Dim(0)*value.Dim(1)*value.Dim(2)))))
+
+	n := min(c.Capacity, int(key.Dim(2))+opts.Position)
+
+	key = c.keys[0].View(ctx, 0,
+		int(key.Dim(0)), int(key.Stride(1)),
+		int(key.Dim(1)), int(key.Stride(2)),
+		n,
+	)
+
+	value = c.values[0].View(ctx, 0,
+		int(value.Dim(0)), int(value.Stride(1)),
+		int(value.Dim(1)), int(value.Stride(2)),
+		n,
+	)
+
+	// TODO shift context if necessary
+
+	return key, value
+}

cmd/cmd.go

@@ -18,8 +18,6 @@ import (
 	"os/signal"
 	"path/filepath"
 	"runtime"
-	"slices"
-	"sort"
 	"strconv"
 	"strings"
 	"sync/atomic"
@@ -31,18 +29,17 @@ import (
 	"github.com/olekukonko/tablewriter"
 	"github.com/spf13/cobra"
 	"golang.org/x/crypto/ssh"
-	"golang.org/x/sync/errgroup"
 	"golang.org/x/term"
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
+	"github.com/ollama/ollama/llama"
+	"github.com/ollama/ollama/llama/runner"
 	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/progress"
-	"github.com/ollama/ollama/runner"
 	"github.com/ollama/ollama/server"
 	"github.com/ollama/ollama/types/model"
-	"github.com/ollama/ollama/types/syncmap"
 	"github.com/ollama/ollama/version"
 )
 
@@ -108,7 +105,7 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 	}
 	spinner.Stop()
 
-	req.Model = args[0]
+	req.Name = args[0]
 	quantize, _ := cmd.Flags().GetString("quantize")
 	if quantize != "" {
 		req.Quantize = quantize
@@ -119,54 +116,34 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
-	var g errgroup.Group
+	if len(req.Files) > 0 {
-	g.SetLimit(max(runtime.GOMAXPROCS(0)-1, 1))
+		fileMap := map[string]string{}
-
+		for f, digest := range req.Files {
-	files := syncmap.NewSyncMap[string, string]()
-	for f, digest := range req.Files {
-		g.Go(func() error {
 			if _, err := createBlob(cmd, client, f, digest, p); err != nil {
 				return err
 			}
-
+			fileMap[filepath.Base(f)] = digest
-			// TODO: this is incorrect since the file might be in a subdirectory
+		}
-			//       instead this should take the path relative to the model directory
+		req.Files = fileMap
-			//       but the current implementation does not allow this
-			files.Store(filepath.Base(f), digest)
-			return nil
-		})
 	}
 
-	adapters := syncmap.NewSyncMap[string, string]()
+	if len(req.Adapters) > 0 {
-	for f, digest := range req.Adapters {
+		fileMap := map[string]string{}
-		g.Go(func() error {
+		for f, digest := range req.Adapters {
 			if _, err := createBlob(cmd, client, f, digest, p); err != nil {
 				return err
 			}
-
+			fileMap[filepath.Base(f)] = digest
-			// TODO: same here
+		}
-			adapters.Store(filepath.Base(f), digest)
+		req.Adapters = fileMap
-			return nil
-		})
 	}
 
-	if err := g.Wait(); err != nil {
-		return err
-	}
-
-	req.Files = files.Items()
-	req.Adapters = adapters.Items()
-
 	bars := make(map[string]*progress.Bar)
 	fn := func(resp api.ProgressResponse) error {
 		if resp.Digest != "" {
 			bar, ok := bars[resp.Digest]
 			if !ok {
-				msg := resp.Status
+				bar = progress.NewBar(fmt.Sprintf("pulling %s...", resp.Digest[7:19]), resp.Total, resp.Completed)
-				if msg == "" {
-					msg = fmt.Sprintf("pulling %s...", resp.Digest[7:19])
-				}
-				bar = progress.NewBar(msg, resp.Total, resp.Completed)
 				bars[resp.Digest] = bar
 				p.Add(resp.Digest, bar)
 			}
@@ -235,7 +212,7 @@ func createBlob(cmd *cobra.Command, client *api.Client, path string, digest stri
 		}
 	}()
 
-	if err := client.CreateBlob(cmd.Context(), digest, io.TeeReader(bin, &pw)); err != nil {
+	if err = client.CreateBlob(cmd.Context(), digest, io.TeeReader(bin, &pw)); err != nil {
 		return "", err
 	}
 	return digest, nil
@@ -279,7 +256,6 @@ func StopHandler(cmd *cobra.Command, args []string) error {
 		if strings.Contains(err.Error(), "not found") {
 			return fmt.Errorf("couldn't find model \"%s\" to stop", args[0])
 		}
-		return err
 	}
 	return nil
 }
@@ -290,7 +266,7 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 	opts := runOptions{
 		Model:    args[0],
 		WordWrap: os.Getenv("TERM") == "xterm-256color",
-		Options:  map[string]any{},
+		Options:  map[string]interface{}{},
 	}
 
 	format, err := cmd.Flags().GetString("format")
@@ -362,21 +338,7 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
-	opts.MultiModal = slices.Contains(info.Capabilities, model.CapabilityVision)
+	opts.MultiModal = len(info.ProjectorInfo) != 0
-
-	// TODO: remove the projector info and vision info checks below,
-	// these are left in for backwards compatibility with older servers
-	// that don't have the capabilities field in the model info
-	if len(info.ProjectorInfo) != 0 {
-		opts.MultiModal = true
-	}
-	for k := range info.ModelInfo {
-		if strings.Contains(k, ".vision.") {
-			opts.MultiModal = true
-			break
-		}
-	}
-
 	opts.ParentModel = info.Details.ParentModel
 
 	if interactive {
@@ -597,9 +559,8 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
 	parameters, errParams := cmd.Flags().GetBool("parameters")
 	system, errSystem := cmd.Flags().GetBool("system")
 	template, errTemplate := cmd.Flags().GetBool("template")
-	verbose, errVerbose := cmd.Flags().GetBool("verbose")
 
-	for _, boolErr := range []error{errLicense, errModelfile, errParams, errSystem, errTemplate, errVerbose} {
+	for _, boolErr := range []error{errLicense, errModelfile, errParams, errSystem, errTemplate} {
 		if boolErr != nil {
 			return errors.New("error retrieving flags")
 		}
@@ -637,7 +598,7 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
 		return errors.New("only one of '--license', '--modelfile', '--parameters', '--system', or '--template' can be specified")
 	}
 
-	req := api.ShowRequest{Name: args[0], Verbose: verbose}
+	req := api.ShowRequest{Name: args[0]}
 	resp, err := client.Show(cmd.Context(), &req)
 	if err != nil {
 		return err
@@ -660,10 +621,10 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
 		return nil
 	}
 
-	return showInfo(resp, verbose, os.Stdout)
+	return showInfo(resp, os.Stdout)
 }
 
-func showInfo(resp *api.ShowResponse, verbose bool, w io.Writer) error {
+func showInfo(resp *api.ShowResponse, w io.Writer) error {
 	tableRender := func(header string, rows func() [][]string) {
 		fmt.Fprintln(w, " ", header)
 		table := tablewriter.NewWriter(w)
@@ -697,15 +658,6 @@ func showInfo(resp *api.ShowResponse, verbose bool, w io.Writer) error {
 		return
 	})
 
-	if len(resp.Capabilities) > 0 {
-		tableRender("Capabilities", func() (rows [][]string) {
-			for _, capability := range resp.Capabilities {
-				rows = append(rows, []string{"", capability.String()})
-			}
-			return
-		})
-	}
-
 	if resp.ProjectorInfo != nil {
 		tableRender("Projector", func() (rows [][]string) {
 			arch := resp.ProjectorInfo["general.architecture"].(string)
@@ -729,47 +681,6 @@ func showInfo(resp *api.ShowResponse, verbose bool, w io.Writer) error {
 		})
 	}
 
-	if resp.ModelInfo != nil && verbose {
-		tableRender("Metadata", func() (rows [][]string) {
-			keys := make([]string, 0, len(resp.ModelInfo))
-			for k := range resp.ModelInfo {
-				keys = append(keys, k)
-			}
-			sort.Strings(keys)
-
-			for _, k := range keys {
-				var v string
-				switch vData := resp.ModelInfo[k].(type) {
-				case bool:
-					v = fmt.Sprintf("%t", vData)
-				case string:
-					v = vData
-				case float64:
-					v = fmt.Sprintf("%g", vData)
-				case []any:
-					n := 3
-					if len(vData) < n {
-						n = len(vData)
-					}
-					v = fmt.Sprintf("%v", vData[:n])
-				default:
-					v = fmt.Sprintf("%T", vData)
-				}
-				rows = append(rows, []string{"", k, v})
-			}
-			return
-		})
-	}
-
-	if len(resp.Tensors) > 0 && verbose {
-		tableRender("Tensors", func() (rows [][]string) {
-			for _, t := range resp.Tensors {
-				rows = append(rows, []string{"", t.Name, t.Type, fmt.Sprint(t.Shape)})
-			}
-			return
-		})
-	}
-
 	head := func(s string, n int) (rows [][]string) {
 		scanner := bufio.NewScanner(strings.NewReader(s))
 		for scanner.Scan() && (len(rows) < n || n < 0) {
@@ -830,38 +741,13 @@ func PullHandler(cmd *cobra.Command, args []string) error {
 
 	fn := func(resp api.ProgressResponse) error {
 		if resp.Digest != "" {
-			if resp.Completed == 0 {
-				// This is the initial status update for the
-				// layer, which the server sends before
-				// beginning the download, for clients to
-				// compute total size and prepare for
-				// downloads, if needed.
-				//
-				// Skipping this here to avoid showing a 0%
-				// progress bar, which *should* clue the user
-				// into the fact that many things are being
-				// downloaded and that the current active
-				// download is not that last. However, in rare
-				// cases it seems to be triggering to some, and
-				// it isn't worth explaining, so just ignore
-				// and regress to the old UI that keeps giving
-				// you the "But wait, there is more!" after
-				// each "100% done" bar, which is "better."
-				return nil
-			}
-
 			if spinner != nil {
 				spinner.Stop()
 			}
 
 			bar, ok := bars[resp.Digest]
 			if !ok {
-				name, isDigest := strings.CutPrefix(resp.Digest, "sha256:")
+				bar = progress.NewBar(fmt.Sprintf("pulling %s...", resp.Digest[7:19]), resp.Total, resp.Completed)
-				name = strings.TrimSpace(name)
-				if isDigest {
-					name = name[:min(12, len(name))]
-				}
-				bar = progress.NewBar(fmt.Sprintf("pulling %s:", name), resp.Total, resp.Completed)
 				bars[resp.Digest] = bar
 				p.Add(resp.Digest, bar)
 			}
@@ -881,7 +767,11 @@ func PullHandler(cmd *cobra.Command, args []string) error {
 	}
 
 	request := api.PullRequest{Name: args[0], Insecure: insecure}
-	return client.Pull(cmd.Context(), &request, fn)
+	if err := client.Pull(cmd.Context(), &request, fn); err != nil {
+		return err
+	}
+
+	return nil
 }
 
 type generateContextKey string
@@ -895,7 +785,7 @@ type runOptions struct {
 	Format      string
 	System      string
 	Images      []api.ImageData
-	Options     map[string]any
+	Options     map[string]interface{}
 	MultiModal  bool
 	KeepAlive   *api.Duration
 }
@@ -1297,7 +1187,6 @@ func NewCLI() *cobra.Command {
 	showCmd.Flags().Bool("parameters", false, "Show parameters of a model")
 	showCmd.Flags().Bool("template", false, "Show template of a model")
 	showCmd.Flags().Bool("system", false, "Show system message of a model")
-	showCmd.Flags().BoolP("verbose", "v", false, "Show detailed model information")
 
 	runCmd := &cobra.Command{
 		Use:     "run MODEL [PROMPT]",
@@ -1382,6 +1271,7 @@ func NewCLI() *cobra.Command {
 
 	runnerCmd := &cobra.Command{
 		Use:    "runner",
+		Short:  llama.PrintSystemInfo(),
 		Hidden: true,
 		RunE: func(cmd *cobra.Command, args []string) error {
 			return runner.Execute(os.Args[1:])
@@ -1424,6 +1314,7 @@ func NewCLI() *cobra.Command {
 				envVars["OLLAMA_NOPRUNE"],
 				envVars["OLLAMA_ORIGINS"],
 				envVars["OLLAMA_SCHED_SPREAD"],
+				envVars["OLLAMA_TMPDIR"],
 				envVars["OLLAMA_FLASH_ATTENTION"],
 				envVars["OLLAMA_KV_CACHE_TYPE"],
 				envVars["OLLAMA_LLM_LIBRARY"],

cmd/cmd_test.go

@@ -2,6 +2,7 @@ package cmd
 
 import (
 	"bytes"
+	"context"
 	"encoding/json"
 	"io"
 	"net/http"
@@ -9,13 +10,11 @@ import (
 	"os"
 	"strings"
 	"testing"
-	"time"
 
 	"github.com/google/go-cmp/cmp"
 	"github.com/spf13/cobra"
 
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/types/model"
 )
 
 func TestShowInfo(t *testing.T) {
@@ -27,7 +26,7 @@ func TestShowInfo(t *testing.T) {
 				ParameterSize:     "7B",
 				QuantizationLevel: "FP16",
 			},
-		}, false, &b); err != nil {
+		}, &b); err != nil {
 			t.Fatal(err)
 		}
 
@@ -57,7 +56,7 @@ func TestShowInfo(t *testing.T) {
 				ParameterSize:     "7B",
 				QuantizationLevel: "FP16",
 			},
-		}, false, &b); err != nil {
+		}, &b); err != nil {
 			t.Fatal(err)
 		}
 
@@ -68,60 +67,6 @@ func TestShowInfo(t *testing.T) {
     embedding length    0       
     quantization        FP16    
 
-`
-		if diff := cmp.Diff(expect, b.String()); diff != "" {
-			t.Errorf("unexpected output (-want +got):\n%s", diff)
-		}
-	})
-
-	t.Run("verbose model", func(t *testing.T) {
-		var b bytes.Buffer
-		if err := showInfo(&api.ShowResponse{
-			Details: api.ModelDetails{
-				Family:            "test",
-				ParameterSize:     "8B",
-				QuantizationLevel: "FP16",
-			},
-			Parameters: `
-			stop up`,
-			ModelInfo: map[string]any{
-				"general.architecture":    "test",
-				"general.parameter_count": float64(8_000_000_000),
-				"some.true_bool":          true,
-				"some.false_bool":         false,
-				"test.context_length":     float64(1000),
-				"test.embedding_length":   float64(11434),
-			},
-			Tensors: []api.Tensor{
-				{Name: "blk.0.attn_k.weight", Type: "BF16", Shape: []uint64{42, 3117}},
-				{Name: "blk.0.attn_q.weight", Type: "FP16", Shape: []uint64{3117, 42}},
-			},
-		}, true, &b); err != nil {
-			t.Fatal(err)
-		}
-
-		expect := `  Model
-    architecture        test     
-    parameters          8B       
-    context length      1000     
-    embedding length    11434    
-    quantization        FP16     
-
-  Parameters
-    stop    up    
-
-  Metadata
-    general.architecture       test     
-    general.parameter_count    8e+09    
-    some.false_bool            false    
-    some.true_bool             true     
-    test.context_length        1000     
-    test.embedding_length      11434    
-
-  Tensors
-    blk.0.attn_k.weight    BF16    [42 3117]    
-    blk.0.attn_q.weight    FP16    [3117 42]    
-
 `
 		if diff := cmp.Diff(expect, b.String()); diff != "" {
 			t.Errorf("unexpected output (-want +got):\n%s", diff)
@@ -143,7 +88,7 @@ func TestShowInfo(t *testing.T) {
 			stop you
 			stop up
 			temperature 99`,
-		}, false, &b); err != nil {
+		}, &b); err != nil {
 			t.Fatal(err)
 		}
 
@@ -180,7 +125,7 @@ func TestShowInfo(t *testing.T) {
 				"clip.vision.embedding_length": float64(0),
 				"clip.vision.projection_dim":   float64(0),
 			},
-		}, false, &b); err != nil {
+		}, &b); err != nil {
 			t.Fatal(err)
 		}
 
@@ -213,7 +158,7 @@ func TestShowInfo(t *testing.T) {
 Ahoy, matey!
 Weigh anchor!
 			`,
-		}, false, &b); err != nil {
+		}, &b); err != nil {
 			t.Fatal(err)
 		}
 
@@ -242,7 +187,7 @@ Weigh anchor!
 				QuantizationLevel: "FP16",
 			},
 			License: license,
-		}, false, &b); err != nil {
+		}, &b); err != nil {
 			t.Fatal(err)
 		}
 
@@ -260,34 +205,6 @@ Weigh anchor!
 			t.Errorf("unexpected output (-want +got):\n%s", diff)
 		}
 	})
-
-	t.Run("capabilities", func(t *testing.T) {
-		var b bytes.Buffer
-		if err := showInfo(&api.ShowResponse{
-			Details: api.ModelDetails{
-				Family:            "test",
-				ParameterSize:     "7B",
-				QuantizationLevel: "FP16",
-			},
-			Capabilities: []model.Capability{model.CapabilityVision, model.CapabilityTools},
-		}, false, &b); err != nil {
-			t.Fatal(err)
-		}
-
-		expect := "  Model\n" +
-			"    architecture    test    \n" +
-			"    parameters      7B      \n" +
-			"    quantization    FP16    \n" +
-			"\n" +
-			"  Capabilities\n" +
-			"    vision    \n" +
-			"    tools     \n" +
-			"\n"
-
-		if diff := cmp.Diff(expect, b.String()); diff != "" {
-			t.Errorf("unexpected output (-want +got):\n%s", diff)
-		}
-	})
 }
 
 func TestDeleteHandler(t *testing.T) {
@@ -336,7 +253,7 @@ func TestDeleteHandler(t *testing.T) {
 	t.Cleanup(mockServer.Close)
 
 	cmd := &cobra.Command{}
-	cmd.SetContext(t.Context())
+	cmd.SetContext(context.TODO())
 	if err := DeleteHandler(cmd, []string{"test-model"}); err != nil {
 		t.Fatalf("DeleteHandler failed: %v", err)
 	}
@@ -398,6 +315,11 @@ func TestGetModelfileName(t *testing.T) {
 			var expectedFilename string
 
 			if tt.fileExists {
+				tempDir, err := os.MkdirTemp("", "modelfiledir")
+				defer os.RemoveAll(tempDir)
+				if err != nil {
+					t.Fatalf("temp modelfile dir creation failed: %v", err)
+				}
 				var fn string
 				if tt.modelfileName != "" {
 					fn = tt.modelfileName
@@ -405,11 +327,10 @@ func TestGetModelfileName(t *testing.T) {
 					fn = "Modelfile"
 				}
 
-				tempFile, err := os.CreateTemp(t.TempDir(), fn)
+				tempFile, err := os.CreateTemp(tempDir, fn)
 				if err != nil {
 					t.Fatalf("temp modelfile creation failed: %v", err)
 				}
-				defer tempFile.Close()
 
 				expectedFilename = tempFile.Name()
 				err = cmd.Flags().Set("file", expectedFilename)
@@ -524,7 +445,7 @@ func TestPushHandler(t *testing.T) {
 
 			cmd := &cobra.Command{}
 			cmd.Flags().Bool("insecure", false, "")
-			cmd.SetContext(t.Context())
+			cmd.SetContext(context.TODO())
 
 			// Redirect stderr to capture progress output
 			oldStderr := os.Stderr
@@ -569,96 +490,6 @@ func TestPushHandler(t *testing.T) {
 	}
 }
 
-func TestListHandler(t *testing.T) {
-	tests := []struct {
-		name           string
-		args           []string
-		serverResponse []api.ListModelResponse
-		expectedError  string
-		expectedOutput string
-	}{
-		{
-			name: "list all models",
-			args: []string{},
-			serverResponse: []api.ListModelResponse{
-				{Name: "model1", Digest: "sha256:abc123", Size: 1024, ModifiedAt: time.Now().Add(-24 * time.Hour)},
-				{Name: "model2", Digest: "sha256:def456", Size: 2048, ModifiedAt: time.Now().Add(-48 * time.Hour)},
-			},
-			expectedOutput: "NAME      ID              SIZE      MODIFIED     \n" +
-				"model1    sha256:abc12    1.0 KB    24 hours ago    \n" +
-				"model2    sha256:def45    2.0 KB    2 days ago      \n",
-		},
-		{
-			name: "filter models by prefix",
-			args: []string{"model1"},
-			serverResponse: []api.ListModelResponse{
-				{Name: "model1", Digest: "sha256:abc123", Size: 1024, ModifiedAt: time.Now().Add(-24 * time.Hour)},
-				{Name: "model2", Digest: "sha256:def456", Size: 2048, ModifiedAt: time.Now().Add(-24 * time.Hour)},
-			},
-			expectedOutput: "NAME      ID              SIZE      MODIFIED     \n" +
-				"model1    sha256:abc12    1.0 KB    24 hours ago    \n",
-		},
-		{
-			name:          "server error",
-			args:          []string{},
-			expectedError: "server error",
-		},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			mockServer := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
-				if r.URL.Path != "/api/tags" || r.Method != http.MethodGet {
-					t.Errorf("unexpected request to %s %s", r.Method, r.URL.Path)
-					http.Error(w, "not found", http.StatusNotFound)
-					return
-				}
-
-				if tt.expectedError != "" {
-					http.Error(w, tt.expectedError, http.StatusInternalServerError)
-					return
-				}
-
-				response := api.ListResponse{Models: tt.serverResponse}
-				if err := json.NewEncoder(w).Encode(response); err != nil {
-					t.Fatal(err)
-				}
-			}))
-			defer mockServer.Close()
-
-			t.Setenv("OLLAMA_HOST", mockServer.URL)
-
-			cmd := &cobra.Command{}
-			cmd.SetContext(t.Context())
-
-			// Capture stdout
-			oldStdout := os.Stdout
-			r, w, _ := os.Pipe()
-			os.Stdout = w
-
-			err := ListHandler(cmd, tt.args)
-
-			// Restore stdout and get output
-			w.Close()
-			os.Stdout = oldStdout
-			output, _ := io.ReadAll(r)
-
-			if tt.expectedError == "" {
-				if err != nil {
-					t.Errorf("expected no error, got %v", err)
-				}
-				if got := string(output); got != tt.expectedOutput {
-					t.Errorf("expected output:\n%s\ngot:\n%s", tt.expectedOutput, got)
-				}
-			} else {
-				if err == nil || !strings.Contains(err.Error(), tt.expectedError) {
-					t.Errorf("expected error containing %q, got %v", tt.expectedError, err)
-				}
-			}
-		})
-	}
-}
-
 func TestCreateHandler(t *testing.T) {
 	tests := []struct {
 		name           string
@@ -684,7 +515,7 @@ func TestCreateHandler(t *testing.T) {
 						return
 					}
 
-					if req.Model != "test-model" {
+					if req.Name != "test-model" {
 						t.Errorf("expected model name 'test-model', got %s", req.Name)
 					}
 
@@ -724,7 +555,7 @@ func TestCreateHandler(t *testing.T) {
 			}))
 			t.Setenv("OLLAMA_HOST", mockServer.URL)
 			t.Cleanup(mockServer.Close)
-			tempFile, err := os.CreateTemp(t.TempDir(), "modelfile")
+			tempFile, err := os.CreateTemp("", "modelfile")
 			if err != nil {
 				t.Fatal(err)
 			}
@@ -744,7 +575,7 @@ func TestCreateHandler(t *testing.T) {
 			}
 
 			cmd.Flags().Bool("insecure", false, "")
-			cmd.SetContext(t.Context())
+			cmd.SetContext(context.TODO())
 
 			// Redirect stderr to capture progress output
 			oldStderr := os.Stderr
@@ -785,132 +616,3 @@ func TestCreateHandler(t *testing.T) {
 		})
 	}
 }
-
-func TestNewCreateRequest(t *testing.T) {
-	tests := []struct {
-		name     string
-		from     string
-		opts     runOptions
-		expected *api.CreateRequest
-	}{
-		{
-			"basic test",
-			"newmodel",
-			runOptions{
-				Model:       "mymodel",
-				ParentModel: "",
-				Prompt:      "You are a fun AI agent",
-				Messages:    []api.Message{},
-				WordWrap:    true,
-			},
-			&api.CreateRequest{
-				From:  "mymodel",
-				Model: "newmodel",
-			},
-		},
-		{
-			"parent model test",
-			"newmodel",
-			runOptions{
-				Model:       "mymodel",
-				ParentModel: "parentmodel",
-				Messages:    []api.Message{},
-				WordWrap:    true,
-			},
-			&api.CreateRequest{
-				From:  "parentmodel",
-				Model: "newmodel",
-			},
-		},
-		{
-			"parent model as filepath test",
-			"newmodel",
-			runOptions{
-				Model:       "mymodel",
-				ParentModel: "/some/file/like/etc/passwd",
-				Messages:    []api.Message{},
-				WordWrap:    true,
-			},
-			&api.CreateRequest{
-				From:  "mymodel",
-				Model: "newmodel",
-			},
-		},
-		{
-			"parent model as windows filepath test",
-			"newmodel",
-			runOptions{
-				Model:       "mymodel",
-				ParentModel: "D:\\some\\file\\like\\etc\\passwd",
-				Messages:    []api.Message{},
-				WordWrap:    true,
-			},
-			&api.CreateRequest{
-				From:  "mymodel",
-				Model: "newmodel",
-			},
-		},
-		{
-			"options test",
-			"newmodel",
-			runOptions{
-				Model:       "mymodel",
-				ParentModel: "parentmodel",
-				Options: map[string]any{
-					"temperature": 1.0,
-				},
-			},
-			&api.CreateRequest{
-				From:  "parentmodel",
-				Model: "newmodel",
-				Parameters: map[string]any{
-					"temperature": 1.0,
-				},
-			},
-		},
-		{
-			"messages test",
-			"newmodel",
-			runOptions{
-				Model:       "mymodel",
-				ParentModel: "parentmodel",
-				System:      "You are a fun AI agent",
-				Messages: []api.Message{
-					{
-						Role:    "user",
-						Content: "hello there!",
-					},
-					{
-						Role:    "assistant",
-						Content: "hello to you!",
-					},
-				},
-				WordWrap: true,
-			},
-			&api.CreateRequest{
-				From:   "parentmodel",
-				Model:  "newmodel",
-				System: "You are a fun AI agent",
-				Messages: []api.Message{
-					{
-						Role:    "user",
-						Content: "hello there!",
-					},
-					{
-						Role:    "assistant",
-						Content: "hello to you!",
-					},
-				},
-			},
-		},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			actual := NewCreateRequest(tt.from, tt.opts)
-			if !cmp.Equal(actual, tt.expected) {
-				t.Errorf("expected output %#v, got %#v", tt.expected, actual)
-			}
-		})
-	}
-}

cmd/interactive.go

@@ -18,7 +18,6 @@ import (
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/readline"
 	"github.com/ollama/ollama/types/errtypes"
-	"github.com/ollama/ollama/types/model"
 )
 
 type MultilineState int
@@ -44,7 +43,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 		fmt.Fprintln(os.Stderr, "Use \"\"\" to begin a multi-line message.")
 
 		if opts.MultiModal {
-			fmt.Fprintf(os.Stderr, "Use %s to include .jpg, .png, or .webp images.\n", filepath.FromSlash("/path/to/file"))
+			fmt.Fprintf(os.Stderr, "Use %s to include .jpg or .png images.\n", filepath.FromSlash("/path/to/file"))
 		}
 
 		fmt.Fprintln(os.Stderr, "")
@@ -196,10 +195,6 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 			opts.Messages = []api.Message{}
 			fmt.Printf("Loading model '%s'\n", opts.Model)
 			if err := loadOrUnloadModel(cmd, &opts); err != nil {
-				if strings.Contains(err.Error(), "not found") {
-					fmt.Printf("error: %v\n", err)
-					continue
-				}
 				return err
 			}
 			continue
@@ -348,7 +343,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 
 				switch args[1] {
 				case "info":
-					_ = showInfo(resp, false, os.Stderr)
+					_ = showInfo(resp, os.Stderr)
 				case "license":
 					if resp.License == "" {
 						fmt.Println("No license was specified for this model.")
@@ -460,16 +455,9 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 }
 
 func NewCreateRequest(name string, opts runOptions) *api.CreateRequest {
-	parentModel := opts.ParentModel
-
-	modelName := model.ParseName(parentModel)
-	if !modelName.IsValid() {
-		parentModel = ""
-	}
-
 	req := &api.CreateRequest{
-		Model: name,
+		Name: name,
-		From:  cmp.Or(parentModel, opts.Model),
+		From: cmp.Or(opts.ParentModel, opts.Model),
 	}
 
 	if opts.System != "" {
@@ -503,7 +491,6 @@ func normalizeFilePath(fp string) string {
 		"\\\\", "\\", // Escaped backslash
 		"\\*", "*", // Escaped asterisk
 		"\\?", "?", // Escaped question mark
-		"\\~", "~", // Escaped tilde
 	).Replace(fp)
 }
 
@@ -511,7 +498,7 @@ func extractFileNames(input string) []string {
 	// Regex to match file paths starting with optional drive letter, / ./ \ or .\ and include escaped or unescaped spaces (\ or %20)
 	// and followed by more characters and a file extension
 	// This will capture non filename strings, but we'll check for file existence to remove mismatches
-	regexPattern := `(?:[a-zA-Z]:)?(?:\./|/|\\)[\S\\ ]+?\.(?i:jpg|jpeg|png|webp)\b`
+	regexPattern := `(?:[a-zA-Z]:)?(?:\./|/|\\)[\S\\ ]+?\.(?i:jpg|jpeg|png)\b`
 	re := regexp.MustCompile(regexPattern)
 
 	return re.FindAllString(input, -1)
@@ -531,8 +518,6 @@ func extractFileData(input string) (string, []api.ImageData, error) {
 			return "", imgs, err
 		}
 		fmt.Fprintf(os.Stderr, "Added image '%s'\n", nfp)
-		input = strings.ReplaceAll(input, "'"+nfp+"'", "")
-		input = strings.ReplaceAll(input, "'"+fp+"'", "")
 		input = strings.ReplaceAll(input, fp, "")
 		imgs = append(imgs, data)
 	}
@@ -553,7 +538,7 @@ func getImageData(filePath string) ([]byte, error) {
 	}
 
 	contentType := http.DetectContentType(buf)
-	allowedTypes := []string{"image/jpeg", "image/jpg", "image/png", "image/webp"}
+	allowedTypes := []string{"image/jpeg", "image/jpg", "image/png"}
 	if !slices.Contains(allowedTypes, contentType) {
 		return nil, fmt.Errorf("invalid image type: %s", contentType)
 	}

cmd/interactive_test.go

@@ -1,8 +1,6 @@
 package cmd
 
 import (
-	"os"
-	"path/filepath"
 	"testing"
 
 	"github.com/stretchr/testify/assert"
@@ -12,17 +10,14 @@ func TestExtractFilenames(t *testing.T) {
 	// Unix style paths
 	input := ` some preamble 
  ./relative\ path/one.png inbetween1 ./not a valid two.jpg inbetween2 ./1.svg
-/unescaped space /three.jpeg inbetween3 /valid\ path/dir/four.png "./quoted with spaces/five.JPG
+/unescaped space /three.jpeg inbetween3 /valid\ path/dir/four.png "./quoted with spaces/five.JPG`
-/unescaped space /six.webp inbetween6 /valid\ path/dir/seven.WEBP`
 	res := extractFileNames(input)
-	assert.Len(t, res, 7)
+	assert.Len(t, res, 5)
 	assert.Contains(t, res[0], "one.png")
 	assert.Contains(t, res[1], "two.jpg")
 	assert.Contains(t, res[2], "three.jpeg")
 	assert.Contains(t, res[3], "four.png")
 	assert.Contains(t, res[4], "five.JPG")
-	assert.Contains(t, res[5], "six.webp")
-	assert.Contains(t, res[6], "seven.WEBP")
 	assert.NotContains(t, res[4], '"')
 	assert.NotContains(t, res, "inbetween1")
 	assert.NotContains(t, res, "./1.svg")
@@ -33,12 +28,10 @@ func TestExtractFilenames(t *testing.T) {
  /absolute/nospace/three.jpeg inbetween3 /absolute/with space/four.png inbetween4
 ./relative\ path/five.JPG inbetween5 "./relative with/spaces/six.png inbetween6
 d:\path with\spaces\seven.JPEG inbetween7 c:\users\jdoe\eight.png inbetween8 
- d:\program files\someplace\nine.png inbetween9 "E:\program files\someplace\ten.PNG
+ d:\program files\someplace\nine.png inbetween9 "E:\program files\someplace\ten.PNG some ending
-c:/users/jdoe/eleven.webp inbetween11 c:/program files/someplace/twelve.WebP inbetween12
-d:\path with\spaces\thirteen.WEBP some ending
 `
 	res = extractFileNames(input)
-	assert.Len(t, res, 13)
+	assert.Len(t, res, 10)
 	assert.NotContains(t, res, "inbetween2")
 	assert.Contains(t, res[0], "one.png")
 	assert.Contains(t, res[0], "c:")
@@ -56,31 +49,4 @@ d:\path with\spaces\thirteen.WEBP some ending
 	assert.Contains(t, res[8], "d:")
 	assert.Contains(t, res[9], "ten.PNG")
 	assert.Contains(t, res[9], "E:")
-	assert.Contains(t, res[10], "eleven.webp")
-	assert.Contains(t, res[10], "c:")
-	assert.Contains(t, res[11], "twelve.WebP")
-	assert.Contains(t, res[11], "c:")
-	assert.Contains(t, res[12], "thirteen.WEBP")
-	assert.Contains(t, res[12], "d:")
-}
-
-// Ensure that file paths wrapped in single quotes are removed with the quotes.
-func TestExtractFileDataRemovesQuotedFilepath(t *testing.T) {
-	dir := t.TempDir()
-	fp := filepath.Join(dir, "img.jpg")
-	data := make([]byte, 600)
-	copy(data, []byte{
-		0xff, 0xd8, 0xff, 0xe0, 0x00, 0x10, 'J', 'F', 'I', 'F',
-		0x00, 0x01, 0x01, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-		0xff, 0xd9,
-	})
-	if err := os.WriteFile(fp, data, 0o600); err != nil {
-		t.Fatalf("failed to write test image: %v", err)
-	}
-
-	input := "before '" + fp + "' after"
-	cleaned, imgs, err := extractFileData(input)
-	assert.NoError(t, err)
-	assert.Len(t, imgs, 1)
-	assert.Equal(t, cleaned, "before  after")
 }

cmd/runner/main.go

@@ -4,7 +4,7 @@ import (
 	"fmt"
 	"os"
 
-	"github.com/ollama/ollama/runner"
+	"github.com/ollama/ollama/llama/runner"
 )
 
 func main() {

convert/convert.go

@@ -1,14 +1,12 @@
 package convert
 
 import (
-	"cmp"
 	"encoding/json"
 	"errors"
 	"fmt"
+	"io"
 	"io/fs"
 	"log/slog"
-	"os"
-	"slices"
 	"strings"
 
 	"github.com/ollama/ollama/fs/ggml"
@@ -17,10 +15,6 @@ import (
 type ModelParameters struct {
 	Architectures []string `json:"architectures"`
 	VocabSize     uint32   `json:"vocab_size"`
-
-	TextModel struct {
-		VocabSize uint32 `json:"vocab_size"`
-	} `json:"text_config"`
 }
 
 type AdapterParameters struct {
@@ -85,17 +79,27 @@ func (ModelParameters) specialTokenTypes() []string {
 	}
 }
 
+func (ModelParameters) writeFile(ws io.WriteSeeker, kv ggml.KV, ts []ggml.Tensor) error {
+	return ggml.WriteGGUF(ws, kv, ts)
+}
+
+func (AdapterParameters) writeFile(ws io.WriteSeeker, kv ggml.KV, ts []ggml.Tensor) error {
+	return ggml.WriteGGUF(ws, kv, ts)
+}
+
 type ModelConverter interface {
 	// KV maps parameters to LLM key-values
 	KV(*Tokenizer) ggml.KV
 	// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
-	Tensors([]Tensor) []*ggml.Tensor
+	Tensors([]Tensor) []ggml.Tensor
 	// Replacements returns a list of string pairs to replace in tensor names.
 	// See [strings.Replacer](https://pkg.go.dev/strings#Replacer) for details
 	Replacements() []string
 
 	// specialTokenTypes returns any special token types the model uses
 	specialTokenTypes() []string
+	// writeFile writes the model to the provided io.WriteSeeker
+	writeFile(io.WriteSeeker, ggml.KV, []ggml.Tensor) error
 }
 
 type moreParser interface {
@@ -106,13 +110,15 @@ type AdapterConverter interface {
 	// KV maps parameters to LLM key-values
 	KV(ggml.KV) ggml.KV
 	// Tensors maps input tensors to LLM tensors. Adapter specific modifications can be done here.
-	Tensors([]Tensor) []*ggml.Tensor
+	Tensors([]Tensor) []ggml.Tensor
 	// Replacements returns a list of string pairs to replace in tensor names.
 	// See [strings.Replacer](https://pkg.go.dev/strings#Replacer) for details
 	Replacements() []string
+
+	writeFile(io.WriteSeeker, ggml.KV, []ggml.Tensor) error
 }
 
-func ConvertAdapter(fsys fs.FS, f *os.File, baseKV ggml.KV) error {
+func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV ggml.KV) error {
 	bts, err := fs.ReadFile(fsys, "adapter_config.json")
 	if err != nil {
 		return err
@@ -147,14 +153,14 @@ func ConvertAdapter(fsys fs.FS, f *os.File, baseKV ggml.KV) error {
 		return err
 	}
 
-	return writeFile(f, conv.KV(baseKV), conv.Tensors(ts))
+	return conv.writeFile(ws, conv.KV(baseKV), conv.Tensors(ts))
 }
 
 // Convert writes an Ollama compatible model to the provided io.WriteSeeker based on configurations
 // and files it finds in the input path.
 // Supported input model formats include safetensors.
 // Supported input tokenizers files include tokenizer.json (preferred) and tokenizer.model.
-func ConvertModel(fsys fs.FS, f *os.File) error {
+func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
 	bts, err := fs.ReadFile(fsys, "config.json")
 	if err != nil {
 		return err
@@ -171,34 +177,24 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
 
 	var conv ModelConverter
 	switch p.Architectures[0] {
-	case "LlamaForCausalLM":
+	case "LlamaForCausalLM", "MistralForCausalLM":
 		conv = &llamaModel{}
-	case "MllamaForConditionalGeneration":
-		conv = &mllamaModel{}
-	case "Llama4ForConditionalGeneration":
-		conv = &llama4Model{}
-	case "Mistral3ForConditionalGeneration":
-		conv = &mistral3Model{}
 	case "MixtralForCausalLM":
 		conv = &mixtralModel{}
 	case "GemmaForCausalLM":
 		conv = &gemmaModel{}
 	case "Gemma2ForCausalLM":
 		conv = &gemma2Model{}
-	case "Gemma3ForCausalLM", "Gemma3ForConditionalGeneration":
-		conv = &gemma3Model{Architecture: p.Architectures[0]}
 	case "Phi3ForCausalLM":
 		conv = &phi3Model{}
 	case "Qwen2ForCausalLM":
 		conv = &qwen2Model{}
-	case "Qwen2_5_VLForConditionalGeneration":
-		conv = &qwen25VLModel{}
 	case "BertModel":
 		conv = &bertModel{}
 	case "CohereForCausalLM":
 		conv = &commandrModel{}
 	default:
-		return fmt.Errorf("unsupported architecture %q", p.Architectures[0])
+		return errors.New("unsupported architecture")
 	}
 
 	if err := json.Unmarshal(bts, conv); err != nil {
@@ -216,22 +212,17 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
 		return err
 	}
 
-	vocabSize := int(cmp.Or(p.VocabSize, p.TextModel.VocabSize))
+	vocabSize := int(p.VocabSize)
-
 	switch {
-	case vocabSize == 0:
-		slog.Debug("vocabulary size was not explicitly set by the model", "default size", len(t.Vocabulary.Tokens))
 	case vocabSize > len(t.Vocabulary.Tokens):
-		slog.Debug("vocabulary is smaller than expected, padding with dummy tokens", "expect", vocabSize, "actual", len(t.Vocabulary.Tokens))
+		slog.Warn("vocabulary is smaller than expected, padding with dummy tokens", "expect", vocabSize, "actual", len(t.Vocabulary.Tokens))
 		for i := range vocabSize - len(t.Vocabulary.Tokens) {
 			t.Vocabulary.Tokens = append(t.Vocabulary.Tokens, fmt.Sprintf("[PAD%d]", i))
 			t.Vocabulary.Scores = append(t.Vocabulary.Scores, -1)
 			t.Vocabulary.Types = append(t.Vocabulary.Types, tokenTypeUserDefined)
 		}
 	case vocabSize < len(t.Vocabulary.Tokens):
-		slog.Debug("vocabulary is larger than expected", "want", vocabSize, "got", len(t.Vocabulary.Tokens))
+		return fmt.Errorf("vocabulary is larger than expected '%d' instead of '%d'", len(t.Vocabulary.Tokens), vocabSize)
-		p.VocabSize = uint32(len(t.Vocabulary.Tokens))
-		p.TextModel.VocabSize = uint32(len(t.Vocabulary.Tokens))
 	default:
 		slog.Debug("vocabulary", "size", len(t.Vocabulary.Tokens))
 	}
@@ -241,13 +232,5 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
 		return err
 	}
 
-	return writeFile(f, conv.KV(t), conv.Tensors(ts))
+	return conv.writeFile(ws, conv.KV(t), conv.Tensors(ts))
-}
-
-func writeFile(f *os.File, kv ggml.KV, ts []*ggml.Tensor) error {
-	for i := range ts {
-		ts[i].Shape = slices.Clone(ts[i].Shape)
-		slices.Reverse(ts[i].Shape)
-	}
-	return ggml.WriteGGUF(f, kv, ts)
 }

convert/convert_bert.go

@@ -132,8 +132,8 @@ func (p *bertModel) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p *bertModel) Tensors(ts []Tensor) []*ggml.Tensor {
+func (p *bertModel) Tensors(ts []Tensor) []ggml.Tensor {
-	var out []*ggml.Tensor
+	var out []ggml.Tensor
 	for _, t := range ts {
 		if slices.Contains([]string{
 			"embeddings.position_ids",
@@ -143,7 +143,7 @@ func (p *bertModel) Tensors(ts []Tensor) []*ggml.Tensor {
 			continue
 		}
 
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_commandr.go

@@ -43,10 +43,10 @@ func (p *commandrModel) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p *commandrModel) Tensors(ts []Tensor) []*ggml.Tensor {
+func (p *commandrModel) Tensors(ts []Tensor) []ggml.Tensor {
-	var out []*ggml.Tensor
+	var out []ggml.Tensor
 	for _, t := range ts {
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_gemma.go

@@ -42,14 +42,14 @@ func (p *gemmaModel) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p *gemmaModel) Tensors(ts []Tensor) []*ggml.Tensor {
+func (p *gemmaModel) Tensors(ts []Tensor) []ggml.Tensor {
-	var out []*ggml.Tensor
+	var out []ggml.Tensor
 	for _, t := range ts {
-		if !strings.HasPrefix(t.Name(), "v.") && strings.HasSuffix(t.Name(), "_norm.weight") {
+		if strings.HasSuffix(t.Name(), "_norm.weight") {
 			t.SetRepacker(p.addOne)
 		}
 
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_gemma2_adapter.go

@@ -21,8 +21,8 @@ func (p *gemma2Adapter) KV(baseKV ggml.KV) ggml.KV {
 	return kv
 }
 
-func (p *gemma2Adapter) Tensors(ts []Tensor) []*ggml.Tensor {
+func (p *gemma2Adapter) Tensors(ts []Tensor) []ggml.Tensor {
-	var out []*ggml.Tensor
+	var out []ggml.Tensor
 	for _, t := range ts {
 		shape := t.Shape()
 		if (strings.HasSuffix(t.Name(), "weight.lora_a") && shape[0] > shape[1]) ||
@@ -31,7 +31,7 @@ func (p *gemma2Adapter) Tensors(ts []Tensor) []*ggml.Tensor {
 			t.SetRepacker(p.repack)
 		}
 
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_gemma3.go

@@ -1,142 +0,0 @@
-package convert
-
-import (
-	"cmp"
-
-	"github.com/ollama/ollama/fs/ggml"
-)
-
-type gemma3Model struct {
-	gemmaModel
-	Architecture string
-	TextModel    struct {
-		HeadDim          uint32 `json:"head_dim"`
-		HiddenSize       uint32 `json:"hidden_size"`
-		HiddenLayers     uint32 `json:"num_hidden_layers"`
-		IntermediateSize uint32 `json:"intermediate_size"`
-		SlidingWindow    uint32 `json:"sliding_window"`
-	} `json:"text_config"`
-	VisionModel struct {
-		NumAttentionHeads uint32  `json:"num_attention_heads"` // attention.head_count 16
-		LayerNormEpsilon  float32 `json:"layer_norm_eps"`      // attention.layer_norm_epsilon 1e-05
-		NumHiddenLayers   uint32  `json:"num_hidden_layers"`   // block_count 32
-		HiddenSize        uint32  `json:"hidden_size"`         // embedding_length 1280
-		IntermediateSize  uint32  `json:"intermediate_size"`   // feed_forward_length 5120
-		ImageSize         uint32  `json:"image_size"`          // image_size 560
-		NumChannels       uint32  `json:"num_channels"`        // num_channels 3
-		PatchSize         uint32  `json:"patch_size"`          // patch_size 14
-	} `json:"vision_config"`
-	MaxPositionEmbeddings    uint32  `json:"max_position_embeddings"`
-	NumAttentionHeads        uint32  `json:"num_attention_heads"`
-	NumKeyValueHeads         uint32  `json:"num_key_value_heads"`
-	RMSNormEPS               float32 `json:"rms_norm_eps"`
-	HeadDim                  uint32  `json:"head_dim"`
-	FinalLogitSoftcap        float32 `json:"final_logit_softcapping"`
-	RopeLocalTheta           float32 `json:"rope_local_base_freq"`
-	RopeGlobalTheta          float32 `json:"rope_global_base_freq"`
-	SlidingWindow            uint32  `json:"sliding_window"`
-	MultiModalTokensPerImage uint32  `json:"mm_tokens_per_image"`
-}
-
-const (
-	gemma4BLayerCount  = 34
-	gemma12BLayerCount = 48
-	gemma27BLayerCount = 62
-)
-
-func (p *gemma3Model) KV(t *Tokenizer) ggml.KV {
-	kv := p.ModelParameters.KV(t)
-	kv["general.architecture"] = "gemma3"
-
-	numBlocks := cmp.Or(p.HiddenLayers, p.TextModel.HiddenLayers)
-	kv["gemma3.block_count"] = numBlocks
-
-	var (
-		numHeads   uint32
-		numKVHeads uint32
-	)
-
-	switch numBlocks {
-	case gemma4BLayerCount:
-		numHeads = 8
-		numKVHeads = 4
-	case gemma12BLayerCount:
-		numHeads = 16
-		numKVHeads = 8
-	case gemma27BLayerCount:
-		numHeads = 32
-		numKVHeads = 16
-	default:
-		numHeads = p.NumAttentionHeads
-		numKVHeads = p.NumKeyValueHeads
-	}
-
-	kv["gemma3.attention.head_count"] = numHeads
-	kv["gemma3.attention.head_count_kv"] = numKVHeads
-
-	switch p.Architecture {
-	case "Gemma3ForCausalLM":
-		kv["gemma3.context_length"] = p.MaxPositionEmbeddings
-		kv["gemma3.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
-		kv["gemma3.attention.key_length"] = p.HeadDim
-		kv["gemma3.attention.value_length"] = p.HeadDim
-		kv["gemma3.attention.sliding_window"] = p.SlidingWindow
-		kv["gemma3.final_logit_softcapping"] = cmp.Or(p.FinalLogitSoftcap, 30)
-		kv["gemma3.rope.local.freq_base"] = cmp.Or(p.RopeLocalTheta, 10000.0)
-		kv["gemma3.rope.global.freq_base"] = cmp.Or(p.RopeGlobalTheta, 1000000.0)
-		kv["gemma3.embedding_length"] = p.HiddenSize
-		kv["gemma3.feed_forward_length"] = p.IntermediateSize
-	default:
-		kv["gemma3.context_length"] = cmp.Or(p.MaxPositionEmbeddings, 131072)
-		kv["gemma3.embedding_length"] = p.TextModel.HiddenSize
-		kv["gemma3.feed_forward_length"] = p.TextModel.IntermediateSize
-		kv["gemma3.attention.sliding_window"] = p.TextModel.SlidingWindow
-		kv["gemma3.vision.block_count"] = p.VisionModel.NumHiddenLayers
-		kv["gemma3.vision.embedding_length"] = p.VisionModel.HiddenSize
-		kv["gemma3.vision.feed_forward_length"] = p.VisionModel.IntermediateSize
-		kv["gemma3.vision.image_size"] = p.VisionModel.ImageSize
-		kv["gemma3.vision.patch_size"] = p.VisionModel.PatchSize
-		kv["gemma3.vision.num_channels"] = cmp.Or(p.VisionModel.NumChannels, 3)
-		kv["gemma3.vision.attention.head_count"] = p.VisionModel.NumAttentionHeads
-		kv["gemma3.vision.attention.layer_norm_epsilon"] = cmp.Or(p.VisionModel.LayerNormEpsilon, 1e-6)
-		kv["gemma3.attention.key_length"] = cmp.Or(p.TextModel.HeadDim, 256)
-		kv["gemma3.attention.value_length"] = cmp.Or(p.TextModel.HeadDim, 256)
-	}
-
-	if p.MultiModalTokensPerImage > 0 {
-		kv["gemma3.mm.tokens_per_image"] = p.MultiModalTokensPerImage
-	}
-
-	return kv
-}
-
-func (p *gemma3Model) Replacements() []string {
-	return []string{
-		"lm_head", "output",
-		"model.embed_tokens", "token_embd",
-		"model.norm", "output_norm",
-		"vision_tower.vision_model.embeddings", "v",
-		"vision_tower.vision_model", "v",
-		"vision_model.vision_model.embeddings", "v",
-		"vision_model.vision_model", "v",
-		"language_model.", "",
-		"model.layers", "blk",
-		"encoder.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",
-		"self_attn.out_proj", "attn_output",
-		"mlp.gate_proj", "ffn_gate",
-		"mlp.down_proj", "ffn_down",
-		"mlp.up_proj", "ffn_up",
-		"post_attention_layernorm", "post_attention_norm",
-		"pre_feedforward_layernorm", "ffn_norm",
-		"post_feedforward_layernorm", "post_ffw_norm",
-		"input_projection_weight", "input_projection.weight",
-		"multi_modal_projector", "mm",
-	}
-}

convert/convert_llama.go

@@ -28,12 +28,12 @@ type llamaModel struct {
 	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
 	RopeTheta             float32 `json:"rope_theta"`
 	RopeScaling           struct {
-		Type                          string  `json:"type"`
+		Type                            string  `json:"type"`
-		RopeType                      string  `json:"rope_type"`
+		RopeType                        string  `json:"rope_type"`
-		Factor                        float32 `json:"factor"`
+		Factor                          float32 `json:"factor"`
-		LowFrequencyFactor            float32 `json:"low_freq_factor"`
+		LowFrequencyFactor              float32 `json:"low_freq_factor"`
-		HighFrequencyFactor           float32 `json:"high_freq_factor"`
+		HighFrequencyFactor             float32 `json:"high_freq_factor"`
-		OriginalMaxPositionEmbeddings uint32  `json:"original_max_position_embeddings"`
+		OriginalMaxPositionalEmbeddings uint32  `json:"original_max_positional_embeddings"`
 
 		factors ropeFactor
 	} `json:"rope_scaling"`
@@ -42,8 +42,6 @@ type llamaModel struct {
 	LayerNormEpsilon float32 `json:"layer_norm_epsilon"`
 	NormEpsilon      float32 `json:"norm_epsilon"`
 	HeadDim          uint32  `json:"head_dim"`
-
-	skipRepack bool
 }
 
 var _ ModelConverter = (*llamaModel)(nil)
@@ -72,10 +70,6 @@ func (p *llamaModel) KV(t *Tokenizer) ggml.KV {
 		kv["llama.rope.dimension_count"] = p.HiddenSize / headCount
 	}
 
-	if p.HeadDim > 0 {
-		kv["llama.attention.head_dim"] = p.HeadDim
-	}
-
 	if p.RopeTheta > 0 {
 		kv["llama.rope.freq_base"] = p.RopeTheta
 	}
@@ -90,7 +84,7 @@ func (p *llamaModel) KV(t *Tokenizer) ggml.KV {
 			factorLow := cmp.Or(p.RopeScaling.LowFrequencyFactor, 1.0)
 			factorHigh := cmp.Or(p.RopeScaling.HighFrequencyFactor, 4.0)
 
-			original := cmp.Or(p.RopeScaling.OriginalMaxPositionEmbeddings, 8192)
+			original := cmp.Or(p.RopeScaling.OriginalMaxPositionalEmbeddings, 8192)
 			lambdaLow := float32(original) / factorLow
 			lambdaHigh := float32(original) / factorHigh
 
@@ -126,11 +120,11 @@ func (p *llamaModel) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p *llamaModel) Tensors(ts []Tensor) []*ggml.Tensor {
+func (p *llamaModel) Tensors(ts []Tensor) []ggml.Tensor {
-	var out []*ggml.Tensor
+	var out []ggml.Tensor
 
 	if p.RopeScaling.factors != nil {
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     "rope_freqs.weight",
 			Kind:     0,
 			Shape:    []uint64{uint64(len(p.RopeScaling.factors))},
@@ -139,13 +133,12 @@ func (p *llamaModel) Tensors(ts []Tensor) []*ggml.Tensor {
 	}
 
 	for _, t := range ts {
-		if strings.HasSuffix(t.Name(), "attn_q.weight") || strings.HasSuffix(t.Name(), "attn_k.weight") {
+		if strings.HasSuffix(t.Name(), "attn_q.weight") ||
-			if !p.skipRepack {
+			strings.HasSuffix(t.Name(), "attn_k.weight") {
-				t.SetRepacker(p.repack)
+			t.SetRepacker(p.repack)
-			}
 		}
 
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_llama4.go

@@ -1,169 +0,0 @@
-package convert
-
-import (
-	"slices"
-	"strings"
-
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-
-	"github.com/ollama/ollama/fs/ggml"
-)
-
-type llama4Model struct {
-	ModelParameters
-	TextModel struct {
-		llamaModel
-		NumExpertsPerToken     uint32 `json:"num_experts_per_tok"`
-		NumLocalExperts        uint32 `json:"num_local_experts"`
-		InterleaveMOELayerStep uint32 `json:"interleave_moe_layer_step"`
-		UseQKNorm              bool   `json:"use_qk_norm"`
-		IntermediateSizeMLP    uint32 `json:"intermediate_size_mlp"`
-		AttentionChunkSize     uint32 `json:"attention_chunk_size"`
-	} `json:"text_config"`
-	VisionModel struct {
-		NumHiddenLayers   uint32  `json:"num_hidden_layers"`
-		HiddenSize        uint32  `json:"hidden_size"`
-		IntermediateSize  uint32  `json:"intermediate_size"`
-		NumAttentionHeads uint32  `json:"num_attention_heads"`
-		ImageSize         uint32  `json:"image_size"`
-		PatchSize         uint32  `json:"patch_size"`
-		RopeTheta         float32 `json:"rope_theta"`
-		NormEpsilon       float32 `json:"norm_eps"`
-		PixelShuffleRatio float32 `json:"pixel_shuffle_ratio"`
-	} `json:"vision_config"`
-}
-
-// KV implements ModelConverter.
-func (p *llama4Model) KV(t *Tokenizer) ggml.KV {
-	kv := p.ModelParameters.KV(t)
-	kv["general.architecture"] = "llama4"
-
-	for k, v := range p.TextModel.KV(t) {
-		if strings.HasPrefix(k, "llama.") {
-			kv[strings.ReplaceAll(k, "llama.", "llama4.")] = v
-		}
-	}
-
-	kv["llama4.feed_forward_length"] = p.TextModel.IntermediateSizeMLP
-	kv["llama4.expert_feed_forward_length"] = p.TextModel.IntermediateSize
-
-	kv["llama4.expert_count"] = p.TextModel.NumLocalExperts
-	kv["llama4.expert_used_count"] = p.TextModel.NumExpertsPerToken
-	kv["llama4.interleave_moe_layer_step"] = p.TextModel.InterleaveMOELayerStep
-	kv["llama4.use_qk_norm"] = p.TextModel.UseQKNorm
-	kv["llama4.attention.chunk_size"] = p.TextModel.AttentionChunkSize
-
-	kv["llama4.vision.block_count"] = p.VisionModel.NumHiddenLayers
-	kv["llama4.vision.embedding_length"] = p.VisionModel.HiddenSize
-	kv["llama4.vision.feed_forward_length"] = p.VisionModel.IntermediateSize
-	kv["llama4.vision.attention.head_count"] = p.VisionModel.NumAttentionHeads
-	kv["llama4.vision.image_size"] = p.VisionModel.ImageSize
-	kv["llama4.vision.patch_size"] = p.VisionModel.PatchSize
-	kv["llama4.vision.rope.freq_base"] = p.VisionModel.RopeTheta
-	kv["llama4.vision.layer_norm_epsilon"] = p.VisionModel.NormEpsilon
-	kv["llama4.vision.pixel_shuffle_ratio"] = p.VisionModel.PixelShuffleRatio
-	return kv
-}
-
-// Replacements implements ModelConverter.
-func (p *llama4Model) Replacements() []string {
-	return append(
-		p.TextModel.Replacements(),
-		"language_model.", "",
-		"vision_model", "v",
-		"multi_modal_projector", "mm",
-		"feed_forward.down_proj", "ffn_down",
-		"feed_forward.up_proj", "ffn_up",
-		"feed_forward.gate_proj", "ffn_gate",
-		"feed_forward.", "ffn_",
-		"shared_expert.down_proj", "down_shexp",
-		"shared_expert.gate_proj", "gate_shexp",
-		"shared_expert.up_proj", "up_shexp",
-		"experts.down_proj", "down_exps.weight",
-		"experts.gate_up_proj", "gate_up_exps.weight",
-		"router", "gate_inp",
-		"patch_embedding.linear", "patch_embedding",
-	)
-}
-
-// Tensors implements ModelConverter.
-func (p *llama4Model) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []*ggml.Tensor
-
-	var textTensors []Tensor
-	for _, t := range ts {
-		if strings.HasPrefix(t.Name(), "v.") || strings.HasPrefix(t.Name(), "mm.") {
-			out = append(out, &ggml.Tensor{
-				Name:     t.Name(),
-				Kind:     t.Kind(),
-				Shape:    t.Shape(),
-				WriterTo: t,
-			})
-		} else if strings.Contains(t.Name(), "ffn_gate_up_exps") {
-			// gate and up projectors are fused
-			// dims[1], dims[2] must be swapped
-			// [experts, hidden_size, intermediate_size * 2] --> [experts, intermediate_size, hidden_size]
-			halfDim := int(t.Shape()[2]) / 2
-
-			newShape := slices.Clone(t.Shape())
-			newShape[1], newShape[2] = newShape[2]/2, newShape[1]
-			for i, name := range []string{"ffn_gate_exps", "ffn_up_exps"} {
-				// clone tensor since we need separate repackers
-				tt := t.Clone()
-				tt.SetRepacker(p.repack(nil, nil, tensor.S(i*halfDim, (i+1)*halfDim)))
-				out = append(out, &ggml.Tensor{
-					Name:     strings.ReplaceAll(tt.Name(), "ffn_gate_up_exps", name),
-					Kind:     tt.Kind(),
-					Shape:    newShape,
-					WriterTo: tt,
-				})
-			}
-		} else if strings.Contains(t.Name(), "ffn_down_exps") {
-			// dims[1], dims[2] must be swapped
-			// [experts, intermediate_size, hidden_size] --> [experts, hidden_size, intermediate_size]
-			t.SetRepacker(p.repack())
-			newShape := slices.Clone(t.Shape())
-			newShape[1], newShape[2] = newShape[2], newShape[1]
-			out = append(out, &ggml.Tensor{
-				Name:     t.Name(),
-				Kind:     t.Kind(),
-				Shape:    newShape,
-				WriterTo: t,
-			})
-		} else {
-			textTensors = append(textTensors, t)
-		}
-	}
-
-	p.TextModel.skipRepack = true
-	out = append(out, p.TextModel.Tensors(textTensors)...)
-	return out
-}
-
-func (p *llama4Model) repack(slice ...tensor.Slice) Repacker {
-	return func(name string, data []float32, shape []uint64) ([]float32, error) {
-		dims := make([]int, len(shape))
-		for i, dim := range shape {
-			dims[i] = int(dim)
-		}
-
-		var t tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
-		t, err := t.Slice(slice...)
-		if err != nil {
-			return nil, err
-		}
-
-		if err := t.T(0, 2, 1); err != nil {
-			return nil, err
-		}
-
-		t = tensor.Materialize(t)
-		// flatten tensor so it can be return as a vector
-		if err := t.Reshape(t.Shape().TotalSize()); err != nil {
-			return nil, err
-		}
-
-		return native.VectorF32(t.(*tensor.Dense))
-	}
-}

convert/convert_llama_adapter.go

@@ -29,8 +29,8 @@ func (p *llamaAdapter) KV(baseKV ggml.KV) ggml.KV {
 	return kv
 }
 
-func (p *llamaAdapter) Tensors(ts []Tensor) []*ggml.Tensor {
+func (p *llamaAdapter) Tensors(ts []Tensor) []ggml.Tensor {
-	var out []*ggml.Tensor
+	var out []ggml.Tensor
 	for _, t := range ts {
 		shape := t.Shape()
 		if (strings.HasSuffix(t.Name(), "weight.lora_a") && shape[0] > shape[1]) ||
@@ -41,7 +41,7 @@ func (p *llamaAdapter) Tensors(ts []Tensor) []*ggml.Tensor {
 			t.SetRepacker(p.repack)
 		}
 
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    shape,

convert/convert_mistral.go

@@ -1,190 +0,0 @@
-package convert
-
-import (
-	"cmp"
-	"fmt"
-	"strings"
-
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-
-	"github.com/ollama/ollama/fs/ggml"
-)
-
-type mistral3Model struct {
-	ModelParameters
-	ImageTokenIndex    uint32 `json:"image_token_index"`
-	SpatialMergeSize   uint32 `json:"spatial_merge_size"`
-	VisionFeatureLayer int32  `json:"vision_feature_layer"`
-	TextModel          struct {
-		NumHiddenLayers       uint32  `json:"num_hidden_layers"`
-		MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
-		HiddenSize            uint32  `json:"hidden_size"`
-		IntermediateSize      uint32  `json:"intermediate_size"`
-		NumAttentionHeads     uint32  `json:"num_attention_heads"`
-		NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
-		RopeTheta             float32 `json:"rope_theta"`
-		RMSNormEPS            float32 `json:"rms_norm_eps"`
-		HeadDim               uint32  `json:"head_dim"`
-		SlidingWindow         *uint32 `json:"sliding_window"`
-		HiddenAct             string  `json:"hidden_act"`
-		VocabSize             uint32  `json:"vocab_size"`
-	} `json:"text_config"`
-	VisionModel struct {
-		NumAttentionHeads uint32  `json:"num_attention_heads"`
-		NumHiddenLayers   uint32  `json:"num_hidden_layers"`
-		HiddenSize        uint32  `json:"hidden_size"`
-		IntermediateSize  uint32  `json:"intermediate_size"`
-		ImageSize         uint32  `json:"image_size"`
-		NumChannels       uint32  `json:"num_channels"`
-		PatchSize         uint32  `json:"patch_size"`
-		HeadDim           uint32  `json:"head_dim"`
-		HiddenAct         string  `json:"hidden_act"`
-		RopeTheta         float32 `json:"rope_theta"`
-	} `json:"vision_config"`
-	MultiModalProjectorBias bool   `json:"multimodal_projector_bias"`
-	ProjectorHiddenAct      string `json:"projector_hidden_act"`
-}
-
-func (p *mistral3Model) KV(t *Tokenizer) ggml.KV {
-	kv := p.ModelParameters.KV(t)
-	kv["general.architecture"] = "mistral3"
-	kv["mistral3.vocab_size"] = p.TextModel.VocabSize
-
-	// Text configuration
-	kv["mistral3.block_count"] = p.TextModel.NumHiddenLayers
-	kv["mistral3.context_length"] = p.TextModel.MaxPositionEmbeddings
-	kv["mistral3.embedding_length"] = p.TextModel.HiddenSize
-	kv["mistral3.feed_forward_length"] = p.TextModel.IntermediateSize
-	kv["mistral3.attention.head_count"] = p.TextModel.NumAttentionHeads
-	kv["mistral3.attention.head_count_kv"] = p.TextModel.NumKeyValueHeads
-	kv["mistral3.attention.layer_norm_rms_epsilon"] = p.TextModel.RMSNormEPS
-	kv["mistral3.attention.key_length"] = p.TextModel.HeadDim
-	kv["mistral3.attention.value_length"] = p.TextModel.HeadDim
-	kv["mistral3.rope.dimension_count"] = p.TextModel.HiddenSize / p.TextModel.NumHiddenLayers
-	kv["mistral3.rope.freq_base"] = p.TextModel.RopeTheta
-
-	// Vision configuration
-	kv["mistral3.vision.block_count"] = p.VisionModel.NumHiddenLayers
-	kv["mistral3.vision.embedding_length"] = p.VisionModel.HiddenSize
-	kv["mistral3.vision.feed_forward_length"] = p.VisionModel.IntermediateSize
-	kv["mistral3.vision.attention.head_count"] = p.VisionModel.NumAttentionHeads
-	kv["mistral3.vision.attention.key_length"] = p.VisionModel.HeadDim
-	kv["mistral3.vision.image_size"] = p.VisionModel.ImageSize
-	kv["mistral3.vision.patch_size"] = p.VisionModel.PatchSize
-	kv["mistral3.vision.num_channels"] = p.VisionModel.NumChannels
-	// kv["mistral3.vision.attention.layer_norm_epsilon"] = 1e-05 // Default value
-	kv["mistral3.vision.rope.freq_base"] = p.VisionModel.RopeTheta
-
-	// Multimodal configuration
-	kv["mistral3.image_token_index"] = p.ImageTokenIndex
-	kv["mistral3.spatial_merge_size"] = p.SpatialMergeSize
-
-	kv["mistral3.mm.projector_bias"] = p.MultiModalProjectorBias
-
-	if p.ProjectorHiddenAct != "" {
-		kv["mistral3.mm.projector_hidden_act"] = p.ProjectorHiddenAct
-	}
-
-	return kv
-}
-
-func (p *mistral3Model) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []*ggml.Tensor
-
-	for _, t := range ts {
-		if !strings.HasPrefix(t.Name(), "v.") {
-			if strings.HasSuffix(t.Name(), ".attn_q.weight") ||
-				strings.HasSuffix(t.Name(), ".attn_k.weight") {
-				t.SetRepacker(p.repack)
-			}
-		}
-
-		out = append(out, &ggml.Tensor{
-			Name:     t.Name(),
-			Kind:     t.Kind(),
-			Shape:    t.Shape(),
-			WriterTo: t,
-		})
-	}
-
-	return out
-}
-
-func (p *mistral3Model) Replacements() []string {
-	return []string{
-		"language_model.model.norm", "output_norm",
-		"language_model.model.", "",
-		"language_model.", "",
-		"layers", "blk",
-		"transformer.layers", "blk",
-		"vision_tower", "v",
-		"ln_pre", "encoder_norm",
-		"input_layernorm", "attn_norm",
-		"post_attention_layernorm", "ffn_norm",
-		"embed_tokens", "token_embd",
-		"self_attn.q_proj", "attn_q",
-		"self_attn.k_proj", "attn_k",
-		"self_attn.v_proj", "attn_v",
-		"self_attn.o_proj", "attn_output",
-		"mlp.down_proj", "ffn_down",
-		"mlp.gate_proj", "ffn_gate",
-		"mlp.up_proj", "ffn_up",
-		"attention.q_proj", "attn_q",
-		"attention.k_proj", "attn_k",
-		"attention.v_proj", "attn_v",
-		"attention.o_proj", "attn_output",
-		"attention_norm", "attn_norm",
-		"feed_forward.gate_proj", "ffn_gate",
-		"feed_forward.down_proj", "ffn_down",
-		"feed_forward.up_proj", "ffn_up",
-		"multi_modal_projector", "mm",
-		"ffn_norm", "ffn_norm",
-		"lm_head", "output",
-	}
-}
-
-func (p *mistral3Model) repack(name string, data []float32, shape []uint64) ([]float32, error) {
-	var dims []int
-	for _, dim := range shape {
-		dims = append(dims, int(dim))
-	}
-
-	var heads uint32
-	if strings.HasSuffix(name, ".attn_q.weight") {
-		heads = p.TextModel.NumAttentionHeads
-	} else if strings.HasSuffix(name, ".attn_k.weight") {
-		heads = cmp.Or(p.TextModel.NumKeyValueHeads, p.TextModel.NumAttentionHeads)
-	} else {
-		return nil, fmt.Errorf("unknown tensor for repack: %s", name)
-	}
-
-	n := tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
-	if err := n.Reshape(append([]int{int(heads), 2, dims[0] / int(heads) / 2}, dims[1:]...)...); err != nil {
-		return nil, err
-	}
-
-	if err := n.T(0, 2, 1, 3); err != nil {
-		return nil, err
-	}
-
-	if err := n.Reshape(dims...); err != nil {
-		return nil, err
-	}
-
-	if err := n.Transpose(); err != nil {
-		return nil, err
-	}
-
-	ts, err := native.SelectF32(n, 1)
-	if err != nil {
-		return nil, err
-	}
-
-	var f32s []float32
-	for _, t := range ts {
-		f32s = append(f32s, t...)
-	}
-
-	return f32s, nil
-}

convert/convert_mixtral.go

@@ -29,7 +29,7 @@ func (p *mixtralModel) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p *mixtralModel) Tensors(ts []Tensor) []*ggml.Tensor {
+func (p *mixtralModel) Tensors(ts []Tensor) []ggml.Tensor {
 	oldnew := []string{
 		"model.layers", "blk",
 		"w1", "ffn_gate_exps",
@@ -56,10 +56,10 @@ func (p *mixtralModel) Tensors(ts []Tensor) []*ggml.Tensor {
 		return true
 	})
 
-	var out []*ggml.Tensor
+	var out []ggml.Tensor
 	for n, e := range experts {
 		// TODO(mxyng): sanity check experts
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     n,
 			Kind:     e[0].Kind(),
 			Shape:    append([]uint64{uint64(len(e))}, e[0].Shape()...),

convert/convert_mllama.go

@@ -1,160 +0,0 @@
-package convert
-
-import (
-	"strings"
-
-	"github.com/ollama/ollama/fs/ggml"
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-)
-
-type mllamaModel struct {
-	ModelParameters
-	TextModel struct {
-		llamaModel
-
-		CrossAttentionLayers []int32 `json:"cross_attention_layers"`
-	} `json:"text_config"`
-	VisionModel struct {
-		NumHiddenLayers           uint32  `json:"num_hidden_layers"`
-		NumGlobalLayers           uint32  `json:"num_global_layers"`
-		IntermediateLayersIndices []int32 `json:"intermediate_layers_indices"`
-
-		HiddenSize       uint32 `json:"hidden_size"`
-		IntermediateSize uint32 `json:"intermediate_size"`
-
-		AttentionHeads uint32 `json:"attention_heads"`
-
-		ImageSize   uint32  `json:"image_size"`
-		PatchSize   uint32  `json:"patch_size"`
-		NumChannels uint32  `json:"num_channels"`
-		MaxNumTiles uint32  `json:"max_num_tiles"`
-		NormEpsilon float32 `json:"norm_eps"`
-		RopeTheta   float32 `json:"rope.freq_base"`
-	} `json:"vision_config"`
-}
-
-func (m *mllamaModel) KV(t *Tokenizer) ggml.KV {
-	kv := m.ModelParameters.KV(t)
-	kv["general.architecture"] = "mllama"
-
-	for k, v := range m.TextModel.KV(t) {
-		if strings.HasPrefix(k, "llama.") {
-			kv[strings.ReplaceAll(k, "llama.", "mllama.")] = v
-		}
-	}
-
-	kv["mllama.attention.cross_attention_layers"] = m.TextModel.CrossAttentionLayers
-
-	kv["mllama.vision.block_count"] = m.VisionModel.NumHiddenLayers
-	kv["mllama.vision.global.block_count"] = m.VisionModel.NumGlobalLayers
-	kv["mllama.vision.intermediate_layers_indices"] = m.VisionModel.IntermediateLayersIndices
-
-	kv["mllama.vision.embedding_length"] = m.VisionModel.HiddenSize
-	kv["mllama.vision.feed_forward_length"] = m.VisionModel.IntermediateSize
-
-	kv["mllama.vision.attention.head_count"] = m.VisionModel.AttentionHeads
-	kv["mllama.vision.attention.layer_norm_epsilon"] = m.VisionModel.NormEpsilon
-
-	kv["mllama.vision.image_size"] = m.VisionModel.ImageSize
-	kv["mllama.vision.patch_size"] = m.VisionModel.PatchSize
-	kv["mllama.vision.max_num_tiles"] = m.VisionModel.MaxNumTiles
-	kv["mllama.vision.num_channels"] = m.VisionModel.NumChannels
-
-	return kv
-}
-
-func (m *mllamaModel) Replacements() []string {
-	return append(
-		m.TextModel.Replacements(),
-		"language_model.", "",
-		"gate_attn", "attn_gate",
-		"gate_ffn", "ffn_gate",
-		"cross_attn.", "cross_attn_",
-		"vision_model", "v",
-		"class_embedding", "class_embd",
-		"patch_embedding", "patch_embd",
-		"gated_positional_embedding.tile_embedding", "tile_position_embd",
-		"gated_positional_embedding.embedding", "position_embd.weight",
-		"gated_positional_embedding", "position_embd",
-		"embedding.weight", "weight",
-		"pre_tile_positional_embedding", "pre_tile_position_embd",
-		"post_tile_positional_embedding", "post_tile_position_embd",
-		"layernorm_pre", "pre_ln",
-		"layernorm_post", "post_ln",
-		"global_transformer.layers", "global.blk",
-		"transformer.layers", "blk",
-		"mlp.fc1", "ffn_up",
-		"mlp.fc2", "ffn_down",
-		"multi_modal_projector", "mm.0",
-	)
-}
-
-func (m *mllamaModel) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []*ggml.Tensor
-	var text []Tensor
-	for _, t := range ts {
-		if t.Name() == "v.position_embd.gate" {
-			for _, name := range []string{"v.position_embd.gate", "v.tile_position_embd.gate"} {
-				tt := t.Clone()
-				tt.SetRepacker(m.repack(name))
-				out = append(out, &ggml.Tensor{
-					Name:     name,
-					Kind:     t.Kind(),
-					Shape:    t.Shape(),
-					WriterTo: tt,
-				})
-			}
-		} else if t.Name() == "v.pre_tile_position_embd.gate" || t.Name() == "v.post_tile_position_embd.gate" {
-			t.SetRepacker(m.repack(t.Name()))
-			out = append(out, &ggml.Tensor{
-				Name:     t.Name(),
-				Kind:     t.Kind(),
-				Shape:    t.Shape(),
-				WriterTo: t,
-			})
-		} else if strings.HasPrefix(t.Name(), "v.") || strings.HasPrefix(t.Name(), "mm.") {
-			out = append(out, &ggml.Tensor{
-				Name:     t.Name(),
-				Kind:     t.Kind(),
-				Shape:    t.Shape(),
-				WriterTo: t,
-			})
-		} else {
-			text = append(text, t)
-		}
-	}
-
-	return append(out, m.TextModel.Tensors(text)...)
-}
-
-func (m *mllamaModel) repack(name string) Repacker {
-	return func(_ string, data []float32, shape []uint64) (_ []float32, err error) {
-		dims := make([]int, len(shape))
-		for i, dim := range shape {
-			dims[i] = int(dim)
-		}
-
-		var t tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
-
-		t, err = tensor.Tanh(t)
-		if err != nil {
-			return nil, err
-		}
-
-		if name == "v.position_embd.gate" {
-			t, err = tensor.Sub(float32(1), t)
-			if err != nil {
-				return nil, err
-			}
-		}
-
-		t = tensor.Materialize(t)
-		// flatten tensor so it can be return as a vector
-		if err := t.Reshape(t.Shape().TotalSize()); err != nil {
-			return nil, err
-		}
-
-		return native.VectorF32(t.(*tensor.Dense))
-	}
-}

convert/convert_phi3.go

@@ -68,19 +68,19 @@ func (p *phi3Model) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p *phi3Model) Tensors(ts []Tensor) []*ggml.Tensor {
+func (p *phi3Model) Tensors(ts []Tensor) []ggml.Tensor {
 	var addRopeFactors sync.Once
 
-	out := make([]*ggml.Tensor, 0, len(ts)+2)
+	out := make([]ggml.Tensor, 0, len(ts)+2)
 	for _, t := range ts {
 		if strings.HasPrefix(t.Name(), "blk.0.") {
 			addRopeFactors.Do(func() {
-				out = append(out, &ggml.Tensor{
+				out = append(out, ggml.Tensor{
 					Name:     "rope_factors_long.weight",
 					Kind:     0,
 					Shape:    []uint64{uint64(len(p.RopeScaling.LongFactor))},
 					WriterTo: p.RopeScaling.LongFactor,
-				}, &ggml.Tensor{
+				}, ggml.Tensor{
 					Name:     "rope_factors_short.weight",
 					Kind:     0,
 					Shape:    []uint64{uint64(len(p.RopeScaling.ShortFactor))},
@@ -89,7 +89,7 @@ func (p *phi3Model) Tensors(ts []Tensor) []*ggml.Tensor {
 			})
 		}
 
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),
@@ -118,5 +118,6 @@ func (p *phi3Model) Replacements() []string {
 type ropeFactor []float32
 
 func (r ropeFactor) WriteTo(w io.Writer) (int64, error) {
-	return 0, binary.Write(w, binary.LittleEndian, r)
+	err := binary.Write(w, binary.LittleEndian, r)
+	return 0, err
 }

convert/convert_qwen2.go

@@ -15,7 +15,6 @@ type qwen2Model struct {
 		Type                          string     `json:"type"`
 		Factor                        ropeFactor `json:"factor"`
 		OriginalMaxPositionEmbeddings uint32     `json:"original_max_position_embeddings"`
-		MropeSection                  []int32    `json:"mrope_section"`
 	} `json:"rope_scaling"`
 	RMSNormEPS float32 `json:"rms_norm_eps"`
 }
@@ -40,18 +39,16 @@ func (q *qwen2Model) KV(t *Tokenizer) ggml.KV {
 	case "yarn":
 		kv["qwen2.rope.scaling.type"] = q.RopeScaling.Type
 		kv["qwen2.rope.scaling.factor"] = q.RopeScaling.Factor
-	case "mrope", "default":
-		kv["qwen2.rope.mrope_section"] = q.RopeScaling.MropeSection
 	default:
 		panic("unknown rope scaling type")
 	}
 	return kv
 }
 
-func (q *qwen2Model) Tensors(ts []Tensor) []*ggml.Tensor {
+func (q *qwen2Model) Tensors(ts []Tensor) []ggml.Tensor {
-	var out []*ggml.Tensor
+	var out []ggml.Tensor
 	for _, t := range ts {
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_qwen25vl.go

@@ -1,102 +0,0 @@
-package convert
-
-import (
-	"cmp"
-	"slices"
-	"strings"
-
-	"github.com/ollama/ollama/fs/ggml"
-)
-
-type qwen25VLModel struct {
-	qwen2Model
-
-	VisionModel struct {
-		Depth               uint32  `json:"depth"`
-		HiddenSize          uint32  `json:"hidden_size"`
-		NumHeads            uint32  `json:"num_heads"`
-		InChannels          uint32  `json:"in_chans"`
-		PatchSize           uint32  `json:"patch_size"`
-		SpatialMergeSize    uint32  `json:"spatial_merge_size"`
-		SpatialPatchSize    uint32  `json:"spatial_patch_size"`
-		WindowSize          uint32  `json:"window_size"`
-		RMSNormEps          float32 `json:"layer_norm_epsilon"`
-		RopeTheta           float32 `json:"rope_theta"`
-		FullAttentionBlocks []int32 `json:"fullatt_block_indexes"`
-		TemporalPatchSize   uint32  `json:"temporal_patch_size"`
-	} `json:"vision_config"`
-}
-
-var _ ModelConverter = (*qwen25VLModel)(nil)
-
-func (q *qwen25VLModel) KV(t *Tokenizer) ggml.KV {
-	kv := q.ModelParameters.KV(t)
-	kv["general.architecture"] = "qwen25vl"
-
-	for k, v := range q.qwen2Model.KV(t) {
-		if strings.HasPrefix(k, "qwen2.") {
-			kv[strings.Replace(k, "qwen2.", "qwen25vl.", 1)] = v
-		}
-	}
-
-	if q.VisionModel.FullAttentionBlocks == nil {
-		kv["qwen25vl.vision.fullatt_block_indexes"] = []int32{7, 15, 23, 31}
-	}
-
-	kv["qwen25vl.vision.block_count"] = cmp.Or(q.VisionModel.Depth, 32)
-	kv["qwen25vl.vision.embedding_length"] = q.VisionModel.HiddenSize
-	kv["qwen25vl.vision.attention.head_count"] = cmp.Or(q.VisionModel.NumHeads, 16)
-	kv["qwen25vl.vision.num_channels"] = q.VisionModel.InChannels
-	kv["qwen25vl.vision.patch_size"] = cmp.Or(q.VisionModel.PatchSize, 14)
-	kv["qwen25vl.vision.spatial_merge_size"] = cmp.Or(q.VisionModel.SpatialMergeSize, 2)
-	kv["qwen25vl.vision.spatial_patch_size"] = q.VisionModel.SpatialPatchSize
-	kv["qwen25vl.vision.window_size"] = cmp.Or(q.VisionModel.WindowSize, 112)
-	kv["qwen25vl.vision.attention.layer_norm_epsilon"] = cmp.Or(q.VisionModel.RMSNormEps, 1e-6)
-	kv["qwen25vl.vision.rope.freq_base"] = cmp.Or(q.VisionModel.RopeTheta, 1e4)
-	kv["qwen25vl.vision.fullatt_block_indexes"] = q.VisionModel.FullAttentionBlocks
-	kv["qwen25vl.vision.temporal_patch_size"] = cmp.Or(q.VisionModel.TemporalPatchSize, 2)
-
-	return kv
-}
-
-func (q *qwen25VLModel) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []*ggml.Tensor
-
-	for _, t := range ts {
-		if strings.Contains(t.Name(), "patch_embed.proj") {
-			for t := range splitDim(t, 2,
-				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,
-				strings.NewReplacer("attn.qkv", "attn_q"),
-				strings.NewReplacer("attn.qkv", "attn_k"),
-				strings.NewReplacer("attn.qkv", "attn_v"),
-			))...)
-		} else {
-			out = append(out, &ggml.Tensor{
-				Name:     t.Name(),
-				Kind:     t.Kind(),
-				Shape:    t.Shape(),
-				WriterTo: t,
-			})
-		}
-	}
-
-	return out
-}
-
-func (p *qwen25VLModel) Replacements() []string {
-	return append(
-		p.qwen2Model.Replacements(),
-		"visual", "v",
-		"blocks", "blk",
-		"attn.proj", "attn_out",
-		"norm1", "ln1",
-		"norm2", "ln2",
-	)
-}

convert/convert_test.go

@@ -11,6 +11,7 @@ import (
 	"io"
 	"io/fs"
 	"log/slog"
+	"math"
 	"os"
 	"path/filepath"
 	"slices"
@@ -47,7 +48,7 @@ func convertFull(t *testing.T, fsys fs.FS) (*os.File, ggml.KV, ggml.Tensors) {
 	}
 	t.Cleanup(func() { r.Close() })
 
-	m, _, err := ggml.Decode(r, -1)
+	m, _, err := ggml.Decode(r, math.MaxInt)
 	if err != nil {
 		t.Fatal(err)
 	}
@@ -130,7 +131,6 @@ func TestConvertModel(t *testing.T) {
 			if err != nil {
 				t.Fatal(err)
 			}
-			defer expectFile.Close()
 
 			var expect map[string]string
 			if err := json.NewDecoder(expectFile).Decode(&expect); err != nil {
@@ -332,7 +332,7 @@ func TestConvertAdapter(t *testing.T) {
 			}
 			defer r.Close()
 
-			m, _, err := ggml.Decode(r, -1)
+			m, _, err := ggml.Decode(r, math.MaxInt)
 			if err != nil {
 				t.Fatal(err)
 			}

convert/fs.go

@@ -0,0 +1,58 @@
+package convert
+
+import (
+	"archive/zip"
+	"errors"
+	"io"
+	"io/fs"
+	"os"
+	"path/filepath"
+)
+
+type ZipReader struct {
+	r *zip.Reader
+	p string
+
+	// limit is the maximum size of a file that can be read directly
+	// from the zip archive. Files larger than this size will be extracted
+	limit int64
+}
+
+func NewZipReader(r *zip.Reader, p string, limit int64) fs.FS {
+	return &ZipReader{r, p, limit}
+}
+
+func (z *ZipReader) Open(name string) (fs.File, error) {
+	r, err := z.r.Open(name)
+	if err != nil {
+		return nil, err
+	}
+	defer r.Close()
+
+	if fi, err := r.Stat(); err != nil {
+		return nil, err
+	} else if fi.Size() < z.limit {
+		return r, nil
+	}
+
+	if !filepath.IsLocal(name) {
+		return nil, zip.ErrInsecurePath
+	}
+
+	n := filepath.Join(z.p, name)
+	if _, err := os.Stat(n); errors.Is(err, os.ErrNotExist) {
+		w, err := os.Create(n)
+		if err != nil {
+			return nil, err
+		}
+		defer w.Close()
+
+		if _, err := io.Copy(w, r); err != nil {
+			return nil, err
+		}
+	} else if err != nil {
+		return nil, err
+	}
+
+	return os.Open(n)
+}

convert/reader.go

@@ -11,15 +11,14 @@ type Tensor interface {
 	Name() string
 	Shape() []uint64
 	Kind() uint32
-	SetRepacker(Repacker)
+	SetRepacker(repacker)
 	WriteTo(io.Writer) (int64, error)
-	Clone() Tensor
 }
 
 type tensorBase struct {
-	name     string
+	name  string
-	shape    []uint64
+	shape []uint64
-	repacker Repacker
+	repacker
 }
 
 func (t tensorBase) Name() string {
@@ -37,11 +36,7 @@ const (
 
 func (t tensorBase) Kind() uint32 {
 	if strings.HasSuffix(t.name, ".ffn_gate_inp.weight") ||
-		t.name == "token_types.weight" ||
+		t.name == "token_types.weight" {
-		t.name == "v.positional_embedding_vlm" ||
-		t.name == "v.tile_position_embd.weight" ||
-		t.name == "v.pre_tile_position_embd.weight" ||
-		t.name == "v.post_tile_position_embd.weight" {
 		// these tensors are always F32
 		return 0
 	}
@@ -56,18 +51,21 @@ func (t tensorBase) Kind() uint32 {
 	}
 }
 
-func (t *tensorBase) SetRepacker(fn Repacker) {
+func (t *tensorBase) SetRepacker(fn repacker) {
 	t.repacker = fn
 }
 
-type Repacker func(string, []float32, []uint64) ([]float32, error)
+type repacker func(string, []float32, []uint64) ([]float32, error)
 
 func parseTensors(fsys fs.FS, replacer *strings.Replacer) ([]Tensor, error) {
 	patterns := []struct {
 		Pattern string
 		Func    func(fs.FS, *strings.Replacer, ...string) ([]Tensor, error)
 	}{
-		{"*.safetensors", parseSafetensors},
+		{"model-*-of-*.safetensors", parseSafetensors},
+		{"model.safetensors", parseSafetensors},
+		{"adapters.safetensors", parseSafetensors},
+		{"adapter_model.safetensors", parseSafetensors},
 		{"pytorch_model-*-of-*.bin", parseTorch},
 		{"pytorch_model.bin", parseTorch},
 		{"consolidated.*.pth", parseTorch},

convert/reader_safetensors.go

@@ -94,21 +94,6 @@ type safetensor struct {
 	*tensorBase
 }
 
-func (st safetensor) Clone() Tensor {
-	return &safetensor{
-		fs:     st.fs,
-		path:   st.path,
-		dtype:  st.dtype,
-		offset: st.offset,
-		size:   st.size,
-		tensorBase: &tensorBase{
-			name:     st.name,
-			repacker: st.repacker,
-			shape:    slices.Clone(st.shape),
-		},
-	}
-}
-
 func (st safetensor) WriteTo(w io.Writer) (int64, error) {
 	f, err := st.fs.Open(st.path)
 	if err != nil {

convert/reader_torch.go

@@ -43,17 +43,6 @@ type torch struct {
 	*tensorBase
 }
 
-func (t torch) Clone() Tensor {
-	return torch{
-		storage: t.storage,
-		tensorBase: &tensorBase{
-			name:     t.name,
-			shape:    t.shape,
-			repacker: t.repacker,
-		},
-	}
-}
-
 func (pt torch) WriteTo(w io.Writer) (int64, error) {
 	return 0, nil
 }

convert/sentencepiece/sentencepiece_model.pb.go

@@ -1360,7 +1360,7 @@ func file_sentencepiece_model_proto_rawDescGZIP() []byte {
 
 var file_sentencepiece_model_proto_enumTypes = make([]protoimpl.EnumInfo, 2)
 var file_sentencepiece_model_proto_msgTypes = make([]protoimpl.MessageInfo, 6)
-var file_sentencepiece_model_proto_goTypes = []any{
+var file_sentencepiece_model_proto_goTypes = []interface{}{
 	(TrainerSpec_ModelType)(0),         // 0: sentencepiece.TrainerSpec.ModelType
 	(ModelProto_SentencePiece_Type)(0), // 1: sentencepiece.ModelProto.SentencePiece.Type
 	(*TrainerSpec)(nil),                // 2: sentencepiece.TrainerSpec
@@ -1392,7 +1392,7 @@ func file_sentencepiece_model_proto_init() {
 		return
 	}
 	if !protoimpl.UnsafeEnabled {
-		file_sentencepiece_model_proto_msgTypes[0].Exporter = func(v any, i int) any {
+		file_sentencepiece_model_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} {
 			switch v := v.(*TrainerSpec); i {
 			case 0:
 				return &v.state
@@ -1406,7 +1406,7 @@ func file_sentencepiece_model_proto_init() {
 				return nil
 			}
 		}
-		file_sentencepiece_model_proto_msgTypes[1].Exporter = func(v any, i int) any {
+		file_sentencepiece_model_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} {
 			switch v := v.(*NormalizerSpec); i {
 			case 0:
 				return &v.state
@@ -1420,7 +1420,7 @@ func file_sentencepiece_model_proto_init() {
 				return nil
 			}
 		}
-		file_sentencepiece_model_proto_msgTypes[2].Exporter = func(v any, i int) any {
+		file_sentencepiece_model_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} {
 			switch v := v.(*SelfTestData); i {
 			case 0:
 				return &v.state
@@ -1434,7 +1434,7 @@ func file_sentencepiece_model_proto_init() {
 				return nil
 			}
 		}
-		file_sentencepiece_model_proto_msgTypes[3].Exporter = func(v any, i int) any {
+		file_sentencepiece_model_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} {
 			switch v := v.(*ModelProto); i {
 			case 0:
 				return &v.state
@@ -1448,7 +1448,7 @@ func file_sentencepiece_model_proto_init() {
 				return nil
 			}
 		}
-		file_sentencepiece_model_proto_msgTypes[4].Exporter = func(v any, i int) any {
+		file_sentencepiece_model_proto_msgTypes[4].Exporter = func(v interface{}, i int) interface{} {
 			switch v := v.(*SelfTestData_Sample); i {
 			case 0:
 				return &v.state
@@ -1460,7 +1460,7 @@ func file_sentencepiece_model_proto_init() {
 				return nil
 			}
 		}
-		file_sentencepiece_model_proto_msgTypes[5].Exporter = func(v any, i int) any {
+		file_sentencepiece_model_proto_msgTypes[5].Exporter = func(v interface{}, i int) interface{} {
 			switch v := v.(*ModelProto_SentencePiece); i {
 			case 0:
 				return &v.state

convert/tensor.go

@@ -1,56 +0,0 @@
-package convert
-
-import (
-	"iter"
-	"slices"
-	"strings"
-
-	"github.com/ollama/ollama/fs/ggml"
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-)
-
-// splitDim splits a tensor along a specified dimension into multiple tensors. The dimension
-// 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) {
-		for i, replacer := range replacers {
-			shape := slices.Clone(t.Shape())
-			shape[dim] = shape[dim] / uint64(len(replacers))
-
-			slice := slices.Repeat([]tensor.Slice{nil}, len(shape))
-			slice[dim] = tensor.S(i*int(shape[dim]), (i+1)*int(shape[dim]))
-
-			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 t tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
-				t, err := t.Slice(slice...)
-				if err != nil {
-					return nil, err
-				}
-
-				t = tensor.Materialize(t)
-				// flatten tensor so it can be written as a vector
-				if err := t.Reshape(t.Shape().TotalSize()); err != nil {
-					return nil, err
-				}
-
-				return native.VectorF32(t.(*tensor.Dense))
-			})
-
-			if !yield(&ggml.Tensor{
-				Name:     replacer.Replace(t.Name()),
-				Kind:     t.Kind(),
-				Shape:    shape,
-				WriterTo: tt,
-			}) {
-				break
-			}
-		}
-	}
-}

convert/tokenizer_spm.go

@@ -6,9 +6,7 @@ import (
 	"errors"
 	"fmt"
 	"io/fs"
-	"log/slog"
 	"os"
-	"reflect"
 	"slices"
 
 	"google.golang.org/protobuf/proto"
@@ -17,8 +15,6 @@ import (
 )
 
 func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
-	slog.Debug("using spm vocabulary")
-
 	ast, err := parseAdditionalSpecialTokens(fsys)
 	if err != nil {
 		return nil, err
@@ -47,19 +43,10 @@ func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
 			v.Types = append(v.Types, int32(t))
 		default:
 			tt := int32(sentencepiece.ModelProto_SentencePiece_NORMAL)
-
+			if slices.Contains(ast, piece.GetPiece()) {
-			// temporary fix to handle gemma3 broken configs
-			if slices.Contains([]string{"<end_of_turn>", "<start_of_turn>"}, piece.GetPiece()) {
 				tt = int32(sentencepiece.ModelProto_SentencePiece_CONTROL)
 			}
 
-			for _, t := range ast {
-				if t.Content == piece.GetPiece() {
-					tt = int32(sentencepiece.ModelProto_SentencePiece_CONTROL)
-					break
-				}
-			}
-
 			v.Types = append(v.Types, tt)
 		}
 	}
@@ -91,16 +78,10 @@ func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
 		return cmp.Compare(i.id, j.id)
 	})
 
-	for _, t := range ts {
+	n := len(v.Tokens)
-		if t.id < len(v.Tokens) {
+	for i, t := range ts {
-			if v.Tokens[t.id] == t.content {
+		if t.id != i+n {
-				slog.Warn("tokenizer", "duplicate token", t.content, "id", t.id)
+			return nil, fmt.Errorf("invalid token id: %d", t.id)
-				continue
-			}
-			return nil, fmt.Errorf("token mismatch: %s != %s at pos [%d]", t.content, v.Tokens[t.id], t.id)
-		}
-		if t.id != len(v.Tokens) {
-			return nil, fmt.Errorf("invalid token id: [%d] as pos [%d]", t.id, len(v.Tokens))
 		}
 
 		v.Tokens = append(v.Tokens, t.content)
@@ -111,15 +92,7 @@ func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
 	return &v, nil
 }
 
-type specialToken struct {
+func parseAdditionalSpecialTokens(fsys fs.FS) ([]string, error) {
-	Content    string `json:"content"`
-	Lstrip     bool   `json:"lstrip"`
-	Normalized bool   `json:"normalized"`
-	Rstrip     bool   `json:"rstrip"`
-	SingleWord bool   `json:"single_word"`
-}
-
-func parseAdditionalSpecialTokens(fsys fs.FS) ([]specialToken, error) {
 	f, err := fsys.Open("special_tokens_map.json")
 	if errors.Is(err, os.ErrNotExist) {
 		return nil, nil
@@ -129,43 +102,12 @@ func parseAdditionalSpecialTokens(fsys fs.FS) ([]specialToken, error) {
 	defer f.Close()
 
 	var m struct {
-		AdditionalSpecialTokens any `json:"additional_special_tokens"`
+		AdditionalSpecialTokens []string `json:"additional_special_tokens"`
 	}
 
 	if err := json.NewDecoder(f).Decode(&m); err != nil {
 		return nil, err
 	}
 
-	var ast []specialToken
+	return m.AdditionalSpecialTokens, nil
-
-	switch st := m.AdditionalSpecialTokens.(type) {
-	case []string:
-		for _, s := range st {
-			ast = append(ast, specialToken{Content: s})
-		}
-	case []any:
-		for _, s := range st {
-			// marshal and unmarshal the object to get the special token
-			tMap := s.(map[string]any)
-			data, err := json.Marshal(tMap)
-			if err != nil {
-				return nil, err
-			}
-
-			var token specialToken
-			err = json.Unmarshal(data, &token)
-			if err != nil {
-				return nil, err
-			}
-
-			ast = append(ast, token)
-		}
-
-	default:
-		slog.Warn("special token", "unknown token", reflect.TypeOf(st))
-	}
-
-	slog.Debug("spm tokenizer", "additional tokens", ast)
-
-	return ast, nil
 }

discover/cpu_common.go

@@ -12,7 +12,7 @@ func IsNUMA() bool {
 		// numa support in llama.cpp is linux only
 		return false
 	}
-	ids := map[string]any{}
+	ids := map[string]interface{}{}
 	packageIds, _ := filepath.Glob("/sys/devices/system/cpu/cpu*/topology/physical_package_id")
 	for _, packageId := range packageIds {
 		id, err := os.ReadFile(packageId)

discover/cuda_common.go

@@ -57,8 +57,7 @@ func cudaVariant(gpuInfo CudaGPUInfo) string {
 		}
 	}
 
-	// driver 12.0 has problems with the cuda v12 library, so run v11 on those older drivers
+	if gpuInfo.computeMajor < 6 || gpuInfo.DriverMajor < 12 || (gpuInfo.DriverMajor == 12 && gpuInfo.DriverMinor == 0) {
-	if gpuInfo.DriverMajor < 12 || (gpuInfo.DriverMajor == 12 && gpuInfo.DriverMinor == 0) {
 		return "v11"
 	}
 	return "v12"

discover/gpu.go

@@ -670,7 +670,7 @@ func loadOneapiMgmt(oneapiLibPaths []string) (int, *C.oneapi_handle_t, string, e
 }
 
 func getVerboseState() C.uint16_t {
-	if envconfig.LogLevel() < slog.LevelInfo {
+	if envconfig.Debug() {
 		return C.uint16_t(1)
 	}
 	return C.uint16_t(0)

discover/gpu_info.h

@@ -27,14 +27,12 @@
 
 #endif
 
-#ifndef LOG
 #define LOG(verbose, ...) \
   do { \
     if (verbose) { \
       fprintf(stderr, __VA_ARGS__); \
     } \
   } while (0)
-#endif
 
 #ifdef __cplusplus
 extern "C" {

discover/gpu_info_cudart.c

@@ -1,7 +1,6 @@
 #ifndef __APPLE__  // TODO - maybe consider nvidia support on intel macs?
 
 #include <string.h>
-#include <inttypes.h>
 #include "gpu_info_cudart.h"
 
 void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp) {
@@ -59,7 +58,7 @@ void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp) {
     LOG(resp->ch.verbose, "cudaSetDevice err: %d\n", ret);
     UNLOAD_LIBRARY(resp->ch.handle);
     resp->ch.handle = NULL;
-    if (ret == CUDART_ERROR_INSUFFICIENT_DRIVER) {
+    if (ret == CUDA_ERROR_INSUFFICIENT_DRIVER) {
       resp->err = strdup("your nvidia driver is too old or missing.  If you have a CUDA GPU please upgrade to run ollama");
       return;
     }
@@ -169,9 +168,9 @@ void cudart_bootstrap(cudart_handle_t h, int i, mem_info_t *resp) {
   resp->free = memInfo.free;
   resp->used = memInfo.used;
 
-  LOG(h.verbose, "[%s] CUDA totalMem %" PRId64 "\n", resp->gpu_id, resp->total);
+  LOG(h.verbose, "[%s] CUDA totalMem %lu\n", resp->gpu_id, resp->total);
-  LOG(h.verbose, "[%s] CUDA freeMem %" PRId64 "\n", resp->gpu_id, resp->free);
+  LOG(h.verbose, "[%s] CUDA freeMem %lu\n", resp->gpu_id, resp->free);
-  LOG(h.verbose, "[%s] CUDA usedMem %" PRId64 "\n", resp->gpu_id, resp->used);
+  LOG(h.verbose, "[%s] CUDA usedMem %lu\n", resp->gpu_id, resp->used);
   LOG(h.verbose, "[%s] Compute Capability %d.%d\n", resp->gpu_id, resp->major, resp->minor);
 }
 
@@ -181,4 +180,4 @@ void cudart_release(cudart_handle_t h) {
   h.handle = NULL;
 }
 
-#endif  // __APPLE__
+#endif  // __APPLE__

discover/gpu_info_nvcuda.c

@@ -1,7 +1,6 @@
 #ifndef __APPLE__  // TODO - maybe consider nvidia support on intel macs?
 
 #include <string.h>
-#include <inttypes.h>
 #include "gpu_info_nvcuda.h"
 
 void nvcuda_init(char *nvcuda_lib_path, nvcuda_init_resp_t *resp) {
@@ -194,8 +193,8 @@ void nvcuda_bootstrap(nvcuda_handle_t h, int i, mem_info_t *resp) {
   resp->total = memInfo.total;
   resp->free = memInfo.free;
 
-  LOG(h.verbose, "[%s] CUDA totalMem %" PRId64 "mb\n", resp->gpu_id, resp->total / 1024 / 1024);
+  LOG(h.verbose, "[%s] CUDA totalMem %lu mb\n", resp->gpu_id, resp->total / 1024 / 1024);
-  LOG(h.verbose, "[%s] CUDA freeMem %" PRId64 "mb\n", resp->gpu_id, resp->free / 1024 / 1024);
+  LOG(h.verbose, "[%s] CUDA freeMem %lu mb\n", resp->gpu_id, resp->free / 1024 / 1024);
   LOG(h.verbose, "[%s] Compute Capability %d.%d\n", resp->gpu_id, resp->major, resp->minor);
 
   
@@ -248,4 +247,4 @@ void nvcuda_release(nvcuda_handle_t h) {
   h.handle = NULL;
 }
 
-#endif  // __APPLE__
+#endif  // __APPLE__

discover/gpu_linux.go

@@ -111,7 +111,6 @@ func GetCPUDetails() ([]CPU, error) {
 	if err != nil {
 		return nil, err
 	}
-	defer file.Close()
 	return linuxCPUDetails(file)
 }
 
@@ -169,11 +168,13 @@ func linuxCPUDetails(file io.Reader) ([]CPU, error) {
 	for id, s := range socketByID {
 		s.CoreCount = len(coreBySocket[id])
 		s.ThreadCount = 0
+		for _, tc := range threadsByCoreBySocket[id] {
+			s.ThreadCount += tc
+		}
 
 		// This only works if HT is enabled, consider a more reliable model, maybe cache size comparisons?
 		efficiencyCoreCount := 0
 		for _, threads := range threadsByCoreBySocket[id] {
-			s.ThreadCount += threads
 			if threads == 1 {
 				efficiencyCoreCount++
 			}

discover/path.go

@@ -19,18 +19,17 @@ var LibOllamaPath string = func() string {
 		return ""
 	}
 
-	if eval, err := filepath.EvalSymlinks(exe); err == nil {
+	exe, err = filepath.EvalSymlinks(exe)
-		exe = eval
+	if err != nil {
+		return ""
 	}
 
-	var libPath string
+	libPath := filepath.Dir(exe)
 	switch runtime.GOOS {
 	case "windows":
 		libPath = filepath.Join(filepath.Dir(exe), "lib", "ollama")
 	case "linux":
 		libPath = filepath.Join(filepath.Dir(exe), "..", "lib", "ollama")
-	case "darwin":
-		libPath = filepath.Dir(exe)
 	}
 
 	cwd, err := os.Getwd()
@@ -38,19 +37,17 @@ var LibOllamaPath string = func() string {
 		return ""
 	}
 
-	paths := []string{
+	// build paths for development
-		libPath,
+	buildPaths := []string{
-
-		// build paths for development
 		filepath.Join(filepath.Dir(exe), "build", "lib", "ollama"),
 		filepath.Join(cwd, "build", "lib", "ollama"),
 	}
 
-	for _, p := range paths {
+	for _, p := range buildPaths {
 		if _, err := os.Stat(p); err == nil {
 			return p
 		}
 	}
 
-	return filepath.Dir(exe)
+	return libPath
 }()

docs/api.md

@@ -19,7 +19,7 @@
 
 ### Model names
 
-Model names follow a `model:tag` format, where `model` can have an optional namespace such as `example/model`. Some examples are `orca-mini:3b-q8_0` and `llama3:70b`. The tag is optional and, if not provided, will default to `latest`. The tag is used to identify a specific version.
+Model names follow a `model:tag` format, where `model` can have an optional namespace such as `example/model`. Some examples are `orca-mini:3b-q4_1` and `llama3:70b`. The tag is optional and, if not provided, will default to `latest`. The tag is used to identify a specific version.
 
 ### Durations
 
@@ -31,7 +31,7 @@ Certain endpoints stream responses as JSON objects. Streaming can be disabled by
 
 ## Generate a completion
 
-```
+```shell
 POST /api/generate
 ```
 
@@ -173,7 +173,7 @@ curl http://localhost:11434/api/generate -d '{
 
 ##### Response
 
-```json5
+```json
 {
   "model": "codellama:code",
   "created_at": "2024-07-22T20:47:51.147561Z",
@@ -306,7 +306,7 @@ curl http://localhost:11434/api/generate -d '{
 
 #### Response
 
-```json
+```
 {
   "model": "llava",
   "created_at": "2023-11-03T15:36:02.583064Z",
@@ -394,6 +394,9 @@ curl http://localhost:11434/api/generate -d '{
     "repeat_penalty": 1.2,
     "presence_penalty": 1.5,
     "frequency_penalty": 1.0,
+    "mirostat": 1,
+    "mirostat_tau": 0.8,
+    "mirostat_eta": 0.6,
     "penalize_newline": true,
     "stop": ["\n", "user:"],
     "numa": false,
@@ -401,7 +404,10 @@ curl http://localhost:11434/api/generate -d '{
     "num_batch": 2,
     "num_gpu": 1,
     "main_gpu": 0,
+    "low_vram": false,
+    "vocab_only": false,
     "use_mmap": true,
+    "use_mlock": false,
     "num_thread": 8
   }
 }'
@@ -479,7 +485,7 @@ A single JSON object is returned:
 
 ## Generate a chat completion
 
-```
+```shell
 POST /api/chat
 ```
 
@@ -489,14 +495,14 @@ Generate the next message in a chat with a provided model. This is a streaming e
 
 - `model`: (required) the [model name](#model-names)
 - `messages`: the messages of the chat, this can be used to keep a chat memory
-- `tools`: list of tools in JSON for the model to use if supported
+- `tools`: tools for the model to use if supported. Requires `stream` to be set to `false`
 
 The `message` object has the following fields:
 
 - `role`: the role of the message, either `system`, `user`, `assistant`, or `tool`
 - `content`: the content of the message
 - `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_calls` (optional): a list of tools the model wants to use
 
 Advanced parameters (optional):
 
@@ -552,10 +558,6 @@ Final response:
 {
   "model": "llama3.2",
   "created_at": "2023-08-04T19:22:45.499127Z",
-  "message": {
-    "role": "assistant",
-    "content": ""
-  },
   "done": true,
   "total_duration": 4883583458,
   "load_duration": 1334875,
@@ -793,7 +795,7 @@ curl http://localhost:11434/api/chat -d '{
 
 ##### Request
 
-```shell
+```
 curl http://localhost:11434/api/chat -d '{
   "model": "llama3.2",
   "messages": [
@@ -868,7 +870,7 @@ If the messages array is empty, the model will be loaded into memory.
 
 ##### Request
 
-```shell
+```
 curl http://localhost:11434/api/chat -d '{
   "model": "llama3.2",
   "messages": []
@@ -876,7 +878,6 @@ curl http://localhost:11434/api/chat -d '{
 ```
 
 ##### Response
-
 ```json
 {
   "model": "llama3.2",
@@ -896,7 +897,7 @@ If the messages array is empty and the `keep_alive` parameter is set to `0`, a m
 
 ##### Request
 
-```shell
+```
 curl http://localhost:11434/api/chat -d '{
   "model": "llama3.2",
   "messages": [],
@@ -923,7 +924,7 @@ A single JSON object is returned:
 
 ## Create a Model
 
-```
+```shell
 POST /api/create
 ```
 
@@ -952,8 +953,19 @@ If you are creating a model from a safetensors directory or from a GGUF file, yo
 
 | Type | Recommended |
 | --- | :-: |
+| q2_K | |
+| q3_K_L | |
+| q3_K_M | |
+| q3_K_S | |
+| q4_0 | |
+| q4_1 | |
 | q4_K_M | * |
 | q4_K_S | |
+| q5_0 | |
+| q5_1 | |
+| q5_K_M | |
+| q5_K_S | |
+| q6_K | |
 | q8_0 | * |
 
 ### Examples
@@ -998,8 +1010,8 @@ Quantize a non-quantized model.
 
 ```shell
 curl http://localhost:11434/api/create -d '{
-  "model": "llama3.2:quantized",
+  "model": "llama3.1:quantized",
-  "from": "llama3.2:3b-instruct-fp16",
+  "from": "llama3.1:8b-instruct-fp16",
   "quantize": "q4_K_M"
 }'
 ```
@@ -1008,15 +1020,13 @@ curl http://localhost:11434/api/create -d '{
 
 A stream of JSON objects is returned:
 
-```json
+```
-{"status":"quantizing F16 model to Q4_K_M","digest":"0","total":6433687776,"completed":12302}
+{"status":"quantizing F16 model to Q4_K_M"}
-{"status":"quantizing F16 model to Q4_K_M","digest":"0","total":6433687776,"completed":6433687552}
+{"status":"creating new layer sha256:667b0c1932bc6ffc593ed1d03f895bf2dc8dc6df21db3042284a6f4416b06a29"}
-{"status":"verifying conversion"}
+{"status":"using existing layer sha256:11ce4ee3e170f6adebac9a991c22e22ab3f8530e154ee669954c4bc73061c258"}
-{"status":"creating new layer sha256:fb7f4f211b89c6c4928ff4ddb73db9f9c0cfca3e000c3e40d6cf27ddc6ca72eb"}
+{"status":"using existing layer sha256:0ba8f0e314b4264dfd19df045cde9d4c394a52474bf92ed6a3de22a4ca31a177"}
-{"status":"using existing layer sha256:966de95ca8a62200913e3f8bfbf84c8494536f1b94b49166851e76644e966396"}
-{"status":"using existing layer sha256:fcc5a6bec9daf9b561a68827b67ab6088e1dba9d1fa2a50d7bbcc8384e0a265d"}
-{"status":"using existing layer sha256:a70ff7e570d97baaf4e62ac6e6ad9975e04caa6d900d3742d37698494479e0cd"}
 {"status":"using existing layer sha256:56bb8bd477a519ffa694fc449c2413c6f0e1d3b1c88fa7e3c9d88d3ae49d4dcb"}
+{"status":"creating new layer sha256:455f34728c9b5dd3376378bfb809ee166c145b0b4c1f1a6feca069055066ef9a"}
 {"status":"writing manifest"}
 {"status":"success"}
 ```
@@ -1041,7 +1051,7 @@ curl http://localhost:11434/api/create -d '{
 
 A stream of JSON objects is returned:
 
-```json
+```
 {"status":"parsing GGUF"}
 {"status":"using existing layer sha256:432f310a77f4650a88d0fd59ecdd7cebed8d684bafea53cbff0473542964f0c3"}
 {"status":"writing manifest"}
@@ -1108,7 +1118,7 @@ Return 200 OK if the blob exists, 404 Not Found if it does not.
 
 ## Push a Blob
 
-```
+```shell
 POST /api/blobs/:digest
 ```
 
@@ -1132,7 +1142,7 @@ Return 201 Created if the blob was successfully created, 400 Bad Request if the
 
 ## List Local Models
 
-```
+```shell
 GET /api/tags
 ```
 
@@ -1154,37 +1164,29 @@ A single JSON object will be returned.
 {
   "models": [
     {
-      "name": "deepseek-r1:latest",
+      "name": "codellama:13b",
-      "model": "deepseek-r1:latest",
+      "modified_at": "2023-11-04T14:56:49.277302595-07:00",
-      "modified_at": "2025-05-10T08:06:48.639712648-07:00",
+      "size": 7365960935,
-      "size": 4683075271,
+      "digest": "9f438cb9cd581fc025612d27f7c1a6669ff83a8bb0ed86c94fcf4c5440555697",
-      "digest": "0a8c266910232fd3291e71e5ba1e058cc5af9d411192cf88b6d30e92b6e73163",
       "details": {
-        "parent_model": "",
         "format": "gguf",
-        "family": "qwen2",
+        "family": "llama",
-        "families": [
+        "families": null,
-          "qwen2"
+        "parameter_size": "13B",
-        ],
+        "quantization_level": "Q4_0"
-        "parameter_size": "7.6B",
-        "quantization_level": "Q4_K_M"
       }
     },
     {
-      "name": "llama3.2:latest",
+      "name": "llama3:latest",
-      "model": "llama3.2:latest",
+      "modified_at": "2023-12-07T09:32:18.757212583-08:00",
-      "modified_at": "2025-05-04T17:37:44.706015396-07:00",
+      "size": 3825819519,
-      "size": 2019393189,
+      "digest": "fe938a131f40e6f6d40083c9f0f430a515233eb2edaa6d72eb85c50d64f2300e",
-      "digest": "a80c4f17acd55265feec403c7aef86be0c25983ab279d83f3bcd3abbcb5b8b72",
       "details": {
-        "parent_model": "",
         "format": "gguf",
         "family": "llama",
-        "families": [
+        "families": null,
-          "llama"
+        "parameter_size": "7B",
-        ],
+        "quantization_level": "Q4_0"
-        "parameter_size": "3.2B",
-        "quantization_level": "Q4_K_M"
       }
     }
   ]
@@ -1193,7 +1195,7 @@ A single JSON object will be returned.
 
 ## Show Model Information
 
-```
+```shell
 POST /api/show
 ```
 
@@ -1210,13 +1212,13 @@ Show information about a model including details, modelfile, template, parameter
 
 ```shell
 curl http://localhost:11434/api/show -d '{
-  "model": "llava"
+  "model": "llama3.2"
 }'
 ```
 
 #### Response
 
-```json5
+```json
 {
   "modelfile": "# Modelfile generated by \"ollama show\"\n# To build a new Modelfile based on this one, replace the FROM line with:\n# FROM llava:latest\n\nFROM /Users/matt/.ollama/models/blobs/sha256:200765e1283640ffbd013184bf496e261032fa75b99498a9613be4e94d63ad52\nTEMPLATE \"\"\"{{ .System }}\nUSER: {{ .Prompt }}\nASSISTANT: \"\"\"\nPARAMETER num_ctx 4096\nPARAMETER stop \"\u003c/s\u003e\"\nPARAMETER stop \"USER:\"\nPARAMETER stop \"ASSISTANT:\"",
   "parameters": "num_keep                       24\nstop                           \"<|start_header_id|>\"\nstop                           \"<|end_header_id|>\"\nstop                           \"<|eot_id|>\"",
@@ -1253,17 +1255,13 @@ curl http://localhost:11434/api/show -d '{
     "tokenizer.ggml.pre": "llama-bpe",
     "tokenizer.ggml.token_type": [],        // populates if `verbose=true`
     "tokenizer.ggml.tokens": []             // populates if `verbose=true`
-  },
+  }
-  "capabilities": [
-    "completion",
-    "vision"
-  ],
 }
 ```
 
 ## Copy a Model
 
-```
+```shell
 POST /api/copy
 ```
 
@@ -1286,7 +1284,7 @@ Returns a 200 OK if successful, or a 404 Not Found if the source model doesn't e
 
 ## Delete a Model
 
-```
+```shell
 DELETE /api/delete
 ```
 
@@ -1312,7 +1310,7 @@ Returns a 200 OK if successful, 404 Not Found if the model to be deleted doesn't
 
 ## Pull a Model
 
-```
+```shell
 POST /api/pull
 ```
 
@@ -1384,7 +1382,7 @@ if `stream` is set to false, then the response is a single JSON object:
 
 ## Push a Model
 
-```
+```shell
 POST /api/push
 ```
 
@@ -1449,7 +1447,7 @@ If `stream` is set to `false`, then the response is a single JSON object:
 
 ## Generate Embeddings
 
-```
+```shell
 POST /api/embed
 ```
 
@@ -1517,7 +1515,7 @@ curl http://localhost:11434/api/embed -d '{
 ```
 
 ## List Running Models
-```
+```shell
 GET /api/ps
 ```
 
@@ -1564,7 +1562,7 @@ A single JSON object will be returned.
 
 > Note: this endpoint has been superseded by `/api/embed`
 
-```
+```shell
 POST /api/embeddings
 ```
 
@@ -1604,7 +1602,7 @@ curl http://localhost:11434/api/embeddings -d '{
 
 ## Version
 
-```
+```shell
 GET /api/version
 ```
 

docs/benchmark.md

@@ -1,59 +0,0 @@
-# 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/development.md

@@ -3,11 +3,11 @@
 Install prerequisites:
 
 - [Go](https://go.dev/doc/install)
-- C/C++ Compiler e.g. Clang on macOS, [TDM-GCC](https://github.com/jmeubank/tdm-gcc/releases/latest) (Windows amd64) or [llvm-mingw](https://github.com/mstorsjo/llvm-mingw) (Windows arm64), GCC/Clang on Linux.
+- C/C++ Compiler e.g. Clang on macOS, [TDM-GCC](https://jmeubank.github.io/tdm-gcc/download/) (Windows amd64) or [llvm-mingw](https://github.com/mstorsjo/llvm-mingw) (Windows arm64), GCC/Clang on Linux.
 
 Then build and run Ollama from the root directory of the repository:
 
-```shell
+```
 go run . serve
 ```
 
@@ -23,14 +23,14 @@ Install prerequisites:
 
 Then, configure and build the project:
 
-```shell
+```
 cmake -B build
 cmake --build build
 ```
 
 Lastly, run Ollama:
 
-```shell
+```
 go run . serve
 ```
 
@@ -41,35 +41,36 @@ Install prerequisites:
 - [CMake](https://cmake.org/download/)
 - [Visual Studio 2022](https://visualstudio.microsoft.com/downloads/) including the Native Desktop Workload
 - (Optional) AMD GPU support
-    - [ROCm](https://rocm.docs.amd.com/en/latest/)
+    - [ROCm](https://rocm.github.io/install.html)
     - [Ninja](https://github.com/ninja-build/ninja/releases)
 - (Optional) NVIDIA GPU support
     - [CUDA SDK](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=11&target_type=exe_network)
 
+> [!IMPORTANT]
+> Ensure prerequisites are in `PATH` before running CMake.
+
+> [!IMPORTANT]
+> ROCm is not compatible with Visual Studio CMake generators. Use `-GNinja` when configuring the project.
+
+> [!IMPORTANT]
+> CUDA is only compatible with Visual Studio CMake generators.
+
 Then, configure and build the project:
 
-```shell
+```
 cmake -B build
 cmake --build build --config Release
 ```
 
-> [!IMPORTANT]
-> Building for ROCm requires additional flags:
-> ```
-> cmake -B build -G Ninja -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++
-> cmake --build build --config Release
-> ```
-
-
 Lastly, run Ollama:
 
-```shell
+```
 go run . serve
 ```
 
 ## Windows (ARM)
 
-Windows ARM does not support additional acceleration libraries at this time.  Do not use cmake, simply `go run` or `go build`.
+Windows ARM does not support additional acceleration libraries at this time.
 
 ## Linux
 
@@ -87,26 +88,26 @@ Install prerequisites:
 
 Then, configure and build the project:
 
-```shell
+```
 cmake -B build
 cmake --build build
 ```
 
 Lastly, run Ollama:
 
-```shell
+```
 go run . serve
 ```
 
 ## Docker
 
-```shell
+```
 docker build .
 ```
 
 ### ROCm
 
-```shell
+```
 docker build --build-arg FLAVOR=rocm .
 ```
 
@@ -114,46 +115,6 @@ docker build --build-arg FLAVOR=rocm .
 
 To run tests, use `go test`:
 
-```shell
+```
 go test ./...
 ```
-
-> NOTE: In rare cirumstances, you may nedd to change a package using the new
-> "synctest" package in go1.24.
->
-> If you do not have the "synctest" package enabled, you will not see build or
-> test failures resulting from your change(s), if any, locally, but CI will
-> break.
->
-> If you see failures in CI, you can either keep pushing changes to see if the
-> CI build passes, or you can enable the "synctest" package locally to see the
-> failures before pushing.
->
-> To enable the "synctest" package for testing, run the following command:
->
-> ```shell
-> GOEXPERIMENT=synctest go test ./...
-> ```
->
-> If you wish to enable synctest for all go commands, you can set the
-> `GOEXPERIMENT` environment variable in your shell profile or by using:
->
-> ```shell
-> go env -w GOEXPERIMENT=synctest
-> ```
->
-> Which will enable the "synctest" package for all go commands without needing
-> to set it for all shell sessions.
->
-> The synctest package is not required for production builds.
-
-## Library detection
-
-Ollama looks for acceleration libraries in the following paths relative to the `ollama` executable:
-
-* `./lib/ollama` (Windows)
-* `../lib/ollama` (Linux)
-* `.` (macOS)
-* `build/lib/ollama` (for development)
-
-If the libraries are not found, Ollama will not run with any acceleration libraries.

docs/docker.md

@@ -2,7 +2,7 @@
 
 ### CPU only
 
-```shell
+```bash
 docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
 ```
 
@@ -11,46 +11,42 @@ Install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-
 
 #### Install with Apt
 1.  Configure the repository
-
+```bash
-    ```shell
+curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey \
-    curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey \
+    | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
-        | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
+curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list \
-    curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list \
+    | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' \
-        | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' \
+    | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
-        | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
+sudo apt-get update
-    sudo apt-get update
+```
-    ```
-
 2.  Install the NVIDIA Container Toolkit packages
-
+```bash
-    ```shell
+sudo apt-get install -y nvidia-container-toolkit
-    sudo apt-get install -y nvidia-container-toolkit
+```
-    ```
 
 #### Install with Yum or Dnf
 1.  Configure the repository
 
-    ```shell
+```bash
-    curl -s -L https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo \
+curl -s -L https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo \
-        | sudo tee /etc/yum.repos.d/nvidia-container-toolkit.repo
+    | sudo tee /etc/yum.repos.d/nvidia-container-toolkit.repo
-    ```
+```
 
 2. Install the NVIDIA Container Toolkit packages
 
-    ```shell
+```bash
-    sudo yum install -y nvidia-container-toolkit
+sudo yum install -y nvidia-container-toolkit
-    ```
+```
 
 #### Configure Docker to use Nvidia driver
-
+```
-```shell
 sudo nvidia-ctk runtime configure --runtime=docker
 sudo systemctl restart docker
 ```
 
 #### Start the container
 
-```shell
+```bash
 docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
 ```
 
@@ -61,7 +57,7 @@ docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ol
 
 To run Ollama using Docker with AMD GPUs, use the `rocm` tag and the following command:
 
-```shell
+```
 docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama:rocm
 ```
 
@@ -69,7 +65,7 @@ docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 114
 
 Now you can run a model:
 
-```shell
+```
 docker exec -it ollama ollama run llama3.2
 ```
 

docs/faq.md

@@ -20,17 +20,11 @@ Please refer to the [GPU docs](./gpu.md).
 
 ## How can I specify the context window size?
 
-By default, Ollama uses a context window size of 4096 tokens. 
+By default, Ollama uses a context window size of 2048 tokens.
-
-This can be overridden with the `OLLAMA_CONTEXT_LENGTH` environment variable. For example, to set the default context window to 8K, use: 
-
-```shell
-OLLAMA_CONTEXT_LENGTH=8192 ollama serve
-```
 
 To change this when using `ollama run`, use `/set parameter`:
 
-```shell
+```
 /set parameter num_ctx 4096
 ```
 
@@ -52,15 +46,10 @@ Use the `ollama ps` command to see what models are currently loaded into memory.
 
 ```shell
 ollama ps
+NAME      	ID          	SIZE 	PROCESSOR	UNTIL
+llama3:70b	bcfb190ca3a7	42 GB	100% GPU 	4 minutes from now
 ```
 
-> **Output**:
->
-> ```
-> NAME      	ID          	SIZE 	PROCESSOR	UNTIL
-> llama3:70b	bcfb190ca3a7	42 GB	100% GPU 	4 minutes from now
-> ```
-
 The `Processor` column will show which memory the model was loaded in to:
 * `100% GPU` means the model was loaded entirely into the GPU
 * `100% CPU` means the model was loaded entirely in system memory
@@ -77,7 +66,7 @@ If Ollama is run as a macOS application, environment variables should be set usi
 1. For each environment variable, call `launchctl setenv`.
 
     ```bash
-    launchctl setenv OLLAMA_HOST "0.0.0.0:11434"
+    launchctl setenv OLLAMA_HOST "0.0.0.0"
     ```
 
 2. Restart Ollama application.
@@ -92,14 +81,14 @@ If Ollama is run as a systemd service, environment variables should be set using
 
     ```ini
     [Service]
-    Environment="OLLAMA_HOST=0.0.0.0:11434"
+    Environment="OLLAMA_HOST=0.0.0.0"
     ```
 
 3. Save and exit.
 
 4. Reload `systemd` and restart Ollama:
 
-   ```shell
+   ```bash
    systemctl daemon-reload
    systemctl restart ollama
    ```
@@ -193,13 +182,6 @@ cloudflared tunnel --url http://localhost:11434 --http-host-header="localhost:11
 
 Ollama allows cross-origin requests from `127.0.0.1` and `0.0.0.0` by default. Additional origins can be configured with `OLLAMA_ORIGINS`.
 
-For browser extensions, you'll need to explicitly allow the extension's origin pattern. Set `OLLAMA_ORIGINS` to include `chrome-extension://*`, `moz-extension://*`, and `safari-web-extension://*` if you wish to allow all browser extensions access, or specific extensions as needed:
-
-```
-# Allow all Chrome, Firefox, and Safari extensions
-OLLAMA_ORIGINS=chrome-extension://*,moz-extension://*,safari-web-extension://* ollama serve
-```
-
 Refer to the section [above](#how-do-i-configure-ollama-server) for how to set environment variables on your platform.
 
 ## Where are models stored?
@@ -239,19 +221,16 @@ properties.
 If you are using the API you can preload a model by sending the Ollama server an empty request. This works with both the `/api/generate` and `/api/chat` API endpoints.
 
 To preload the mistral model using the generate endpoint, use:
-
 ```shell
 curl http://localhost:11434/api/generate -d '{"model": "mistral"}'
 ```
 
 To use the chat completions endpoint, use:
-
 ```shell
 curl http://localhost:11434/api/chat -d '{"model": "mistral"}'
 ```
 
 To preload a model using the CLI, use the command:
-
 ```shell
 ollama run llama3.2 ""
 ```
@@ -271,13 +250,11 @@ If you're using the API, use the `keep_alive` parameter with the `/api/generate`
 * '0' which will unload the model immediately after generating a response
 
 For example, to preload a model and leave it in memory use:
-
 ```shell
 curl http://localhost:11434/api/generate -d '{"model": "llama3.2", "keep_alive": -1}'
 ```
 
 To unload the model and free up memory use:
-
 ```shell
 curl http://localhost:11434/api/generate -d '{"model": "llama3.2", "keep_alive": 0}'
 ```

docs/gpu.md

@@ -7,7 +7,7 @@ Check your compute compatibility to see if your card is supported:
 
 | Compute Capability | Family              | Cards                                                                                                       |
 | ------------------ | ------------------- | ----------------------------------------------------------------------------------------------------------- |
-| 9.0                | NVIDIA              | `H200` `H100`                                                                                               |
+| 9.0                | NVIDIA              | `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`                                                                                       |
 | 8.6                | GeForce RTX 30xx    | `RTX 3090 Ti` `RTX 3090` `RTX 3080 Ti` `RTX 3080` `RTX 3070 Ti` `RTX 3070` `RTX 3060 Ti` `RTX 3060` `RTX 3050 Ti` `RTX 3050`   |

docs/import.md

@@ -20,13 +20,13 @@ Make sure that you use the same base model in the `FROM` command as you used to
 
 Now run `ollama create` from the directory where the `Modelfile` was created:
 
-```shell
+```bash
 ollama create my-model
 ```
 
 Lastly, test the model:
 
-```shell
+```bash
 ollama run my-model
 ```
 

docs/linux.md

@@ -75,7 +75,7 @@ RestartSec=3
 Environment="PATH=$PATH"
 
 [Install]
-WantedBy=multi-user.target
+WantedBy=default.target
 ```
 
 Then start the service:
@@ -119,7 +119,7 @@ sudo systemctl status ollama
 
 To customize the installation of Ollama, you can edit the systemd service file or the environment variables by running:
 
-```shell
+```
 sudo systemctl edit ollama
 ```
 
@@ -152,7 +152,7 @@ Use `OLLAMA_VERSION` environment variable with the install script to install a s
 For example:
 
 ```shell
-curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.5.7 sh
+curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.3.9 sh
 ```
 
 ## Viewing logs
@@ -186,9 +186,3 @@ sudo rm -r /usr/share/ollama
 sudo userdel ollama
 sudo groupdel ollama
 ```
-
-Remove installed libraries:
-
-```shell
-sudo rm -rf /usr/local/lib/ollama
-```

docs/modelfile.md

@@ -28,7 +28,7 @@ A model file is the blueprint to create and share models with Ollama.
 
 The format of the `Modelfile`:
 
-```
+```modelfile
 # comment
 INSTRUCTION arguments
 ```
@@ -49,7 +49,7 @@ INSTRUCTION arguments
 
 An example of a `Modelfile` creating a mario blueprint:
 
-```
+```modelfile
 FROM llama3.2
 # sets the temperature to 1 [higher is more creative, lower is more coherent]
 PARAMETER temperature 1
@@ -69,30 +69,24 @@ To use this:
 
 To view the Modelfile of a given model, use the `ollama show --modelfile` command.
 
-```shell
+  ```bash
-ollama show --modelfile llama3.2
+  > ollama show --modelfile llama3.2
-```
+  # Modelfile generated by "ollama show"
+  # To build a new Modelfile based on this one, replace the FROM line with:
+  # FROM llama3.2:latest
+  FROM /Users/pdevine/.ollama/models/blobs/sha256-00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29
+  TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>
 
-> **Output**:
+  {{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
->
-> ```
-> # Modelfile generated by "ollama show"
-> # To build a new Modelfile based on this one, replace the FROM line with:
-> # FROM llama3.2:latest
-> FROM /Users/pdevine/.ollama/models/blobs/sha256-00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29
-> TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>
->
-> {{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
->
-> {{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
->
-> {{ .Response }}<|eot_id|>"""
-> PARAMETER stop "<|start_header_id|>"
-> PARAMETER stop "<|end_header_id|>"
-> PARAMETER stop "<|eot_id|>"
-> PARAMETER stop "<|reserved_special_token"
-> ```
 
+  {{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
+
+  {{ .Response }}<|eot_id|>"""
+  PARAMETER stop "<|start_header_id|>"
+  PARAMETER stop "<|end_header_id|>"
+  PARAMETER stop "<|eot_id|>"
+  PARAMETER stop "<|reserved_special_token"
+  ```
 
 ## Instructions
 
@@ -100,13 +94,13 @@ ollama show --modelfile llama3.2
 
 The `FROM` instruction defines the base model to use when creating a model.
 
-```
+```modelfile
 FROM <model name>:<tag>
 ```
 
 #### Build from existing model
 
-```
+```modelfile
 FROM llama3.2
 ```
 
@@ -117,7 +111,7 @@ Additional models can be found at:
 
 #### Build from a Safetensors model
 
-```
+```modelfile
 FROM <model directory>
 ```
 
@@ -131,7 +125,7 @@ Currently supported model architectures:
 
 #### Build from a GGUF file
 
-```
+```modelfile
 FROM ./ollama-model.gguf
 ```
 
@@ -142,7 +136,7 @@ The GGUF file location should be specified as an absolute path or relative to th
 
 The `PARAMETER` instruction defines a parameter that can be set when the model is run.
 
-```
+```modelfile
 PARAMETER <parameter> <parametervalue>
 ```
 
@@ -150,6 +144,9 @@ PARAMETER <parameter> <parametervalue>
 
 | Parameter      | Description                                                                                                                                                                                                                                             | Value Type | Example Usage        |
 | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | -------------------- |
+| mirostat       | Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)                                                                                                                                         | int        | mirostat 0           |
+| mirostat_eta   | Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1)                        | float      | mirostat_eta 0.1     |
+| mirostat_tau   | Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0)                                                                                                         | float      | mirostat_tau 5.0     |
 | 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   |
@@ -186,7 +183,7 @@ TEMPLATE """{{ if .System }}<|im_start|>system
 
 The `SYSTEM` instruction specifies the system message to be used in the template, if applicable.
 
-```
+```modelfile
 SYSTEM """<system message>"""
 ```
 
@@ -196,7 +193,7 @@ The `ADAPTER` instruction specifies a fine tuned LoRA adapter that should apply
 
 #### Safetensor adapter
 
-```
+```modelfile
 ADAPTER <path to safetensor adapter>
 ```
 
@@ -207,7 +204,7 @@ Currently supported Safetensor adapters:
 
 #### GGUF adapter
 
-```
+```modelfile
 ADAPTER ./ollama-lora.gguf
 ```
 
@@ -215,7 +212,7 @@ ADAPTER ./ollama-lora.gguf
 
 The `LICENSE` instruction allows you to specify the legal license under which the model used with this Modelfile is shared or distributed.
 
-```
+```modelfile
 LICENSE """
 <license text>
 """
@@ -225,7 +222,7 @@ LICENSE """
 
 The `MESSAGE` instruction allows you to specify a message history for the model to use when responding. Use multiple iterations of the MESSAGE command to build up a conversation which will guide the model to answer in a similar way.
 
-```
+```modelfile
 MESSAGE <role> <message>
 ```
 
@@ -240,7 +237,7 @@ MESSAGE <role> <message>
 
 #### Example conversation
 
-```
+```modelfile
 MESSAGE user Is Toronto in Canada?
 MESSAGE assistant yes
 MESSAGE user Is Sacramento in Canada?

docs/openai.md

@@ -1,7 +1,6 @@
 # OpenAI compatibility
 
-> [!NOTE]
+> **Note:** OpenAI compatibility is experimental and is subject to major adjustments including breaking changes. For fully-featured access to the Ollama API, see the Ollama [Python library](https://github.com/ollama/ollama-python), [JavaScript library](https://github.com/ollama/ollama-js) and [REST API](https://github.com/ollama/ollama/blob/main/docs/api.md).
-> OpenAI compatibility is experimental and is subject to major adjustments including breaking changes. For fully-featured access to the Ollama API, see the Ollama [Python library](https://github.com/ollama/ollama-python), [JavaScript library](https://github.com/ollama/ollama-js) and [REST API](https://github.com/ollama/ollama/blob/main/docs/api.md).
 
 Ollama provides experimental compatibility with parts of the [OpenAI API](https://platform.openai.com/docs/api-reference) to help connect existing applications to Ollama.
 
@@ -60,10 +59,8 @@ embeddings = client.embeddings.create(
     input=["why is the sky blue?", "why is the grass green?"],
 )
 ```
-
 #### Structured outputs
-
+```py
-```python
 from pydantic import BaseModel
 from openai import OpenAI
 
@@ -147,7 +144,7 @@ const embedding = await openai.embeddings.create({
 
 ### `curl`
 
-```shell
+``` shell
 curl http://localhost:11434/v1/chat/completions \
     -H "Content-Type: application/json" \
     -d '{
@@ -322,7 +319,7 @@ ollama pull llama3.2
 
 For tooling that relies on default OpenAI model names such as `gpt-3.5-turbo`, use `ollama cp` to copy an existing model name to a temporary name:
 
-```shell
+```
 ollama cp llama3.2 gpt-3.5-turbo
 ```
 
@@ -346,7 +343,7 @@ curl http://localhost:11434/v1/chat/completions \
 
 The OpenAI API does not have a way of setting the context size for a model. If you need to change the context size, create a `Modelfile` which looks like:
 
-```
+```modelfile
 FROM <some model>
 PARAMETER num_ctx <context size>
 ```

docs/template.md

@@ -12,7 +12,7 @@ A basic Go template consists of three main parts:
 
 Here's an example of a simple chat template:
 
-```go
+```gotmpl
 {{- range .Messages }}
 {{ .Role }}: {{ .Content }}
 {{- end }}
@@ -162,6 +162,6 @@ CodeLlama [7B](https://ollama.com/library/codellama:7b-code) and [13B](https://o
 
 Codestral [22B](https://ollama.com/library/codestral:22b) supports fill-in-middle.
 
-```go
+```gotmpl
 [SUFFIX]{{ .Suffix }}[PREFIX] {{ .Prompt }}
 ```

docs/troubleshooting.md

@@ -9,7 +9,7 @@ cat ~/.ollama/logs/server.log
 On **Linux** systems with systemd, the logs can be found with this command:
 
 ```shell
-journalctl -u ollama --no-pager --follow --pager-end 
+journalctl -u ollama --no-pager
 ```
 
 When you run Ollama in a **container**, the logs go to stdout/stderr in the container:
@@ -17,7 +17,6 @@ When you run Ollama in a **container**, the logs go to stdout/stderr in the cont
 ```shell
 docker logs <container-name>
 ```
-
 (Use `docker ps` to find the container name)
 
 If manually running `ollama serve` in a terminal, the logs will be on that terminal.
@@ -26,9 +25,9 @@ When you run Ollama on **Windows**, there are a few different locations. You can
 - `explorer %LOCALAPPDATA%\Ollama` to view logs.  The most recent server logs will be in `server.log` and older logs will be in `server-#.log` 
 - `explorer %LOCALAPPDATA%\Programs\Ollama` to browse the binaries (The installer adds this to your user PATH)
 - `explorer %HOMEPATH%\.ollama` to browse where models and configuration is stored
+- `explorer %TEMP%` where temporary executable files are stored in one or more `ollama*` directories
 
 To enable additional debug logging to help troubleshoot problems, first **Quit the running app from the tray menu** then in a powershell terminal
-
 ```powershell
 $env:OLLAMA_DEBUG="1"
 & "ollama app.exe"
@@ -50,13 +49,12 @@ Dynamic LLM libraries [rocm_v6 cpu cpu_avx cpu_avx2 cuda_v11 rocm_v5]
 
 You can set OLLAMA_LLM_LIBRARY to any of the available LLM libraries to bypass autodetection, so for example, if you have a CUDA card, but want to force the CPU LLM library with AVX2 vector support, use:
 
-```shell
+```
 OLLAMA_LLM_LIBRARY="cpu_avx2" ollama serve
 ```
 
 You can see what features your CPU has with the following.
-
+```
-```shell
 cat /proc/cpuinfo| grep flags | head -1
 ```
 
@@ -64,13 +62,13 @@ cat /proc/cpuinfo| grep flags | head -1
 
 If you run into problems on Linux and want to install an older version, or you'd like to try out a pre-release before it's officially released, you can tell the install script which version to install.
 
-```shell
+```sh
-curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.5.7 sh
+curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION="0.1.29" sh
 ```
 
-## Linux docker
+## Linux tmp noexec 
 
-If Ollama initially works on the GPU in a docker container, but then switches to running on CPU after some period of time with errors in the server log reporting GPU discovery failures, this can be resolved by disabling systemd cgroup management in Docker.  Edit `/etc/docker/daemon.json` on the host and add `"exec-opts": ["native.cgroupdriver=cgroupfs"]` to the docker configuration.
+If your system is configured with the "noexec" flag where Ollama stores its temporary executable files, you can specify an alternate location by setting OLLAMA_TMPDIR to a location writable by the user ollama runs as. For example OLLAMA_TMPDIR=/usr/share/ollama/
 
 ## NVIDIA GPU Discovery
 
@@ -99,6 +97,8 @@ On linux, AMD GPU access typically requires `video` and/or `render` group member
 
 When running in a container, in some Linux distributions and container runtimes, the ollama process may be unable to access the GPU.  Use `ls -lnd /dev/kfd /dev/dri /dev/dri/*` on the host system to determine the **numeric** group IDs on your system, and pass additional `--group-add ...` arguments to the container so it can access the required devices.   For example, in the following output `crw-rw---- 1 0  44 226,   0 Sep 16 16:55 /dev/dri/card0` the group ID column is `44` 
 
+If Ollama initially works on the GPU in a docker container, but then switches to running on CPU after some period of time with errors in the server log reporting GPU discovery failures, this can be resolved by disabling systemd cgroup management in Docker.  Edit `/etc/docker/daemon.json` on the host and add `"exec-opts": ["native.cgroupdriver=cgroupfs"]` to the docker configuration.
+
 If you are experiencing problems getting Ollama to correctly discover or use your GPU for inference, the following may help isolate the failure.
 - `AMD_LOG_LEVEL=3` Enable info log levels in the AMD HIP/ROCm libraries.  This can help show more detailed error codes that can help troubleshoot problems
 - `OLLAMA_DEBUG=1` During GPU discovery additional information will be reported

docs/windows.md

@@ -47,7 +47,6 @@ If Ollama is already running, Quit the tray application and relaunch it from the
 ## API Access
 
 Here's a quick example showing API access from `powershell`
-
 ```powershell
 (Invoke-WebRequest -method POST -Body '{"model":"llama3.2", "prompt":"Why is the sky blue?", "stream": false}' -uri http://localhost:11434/api/generate ).Content | ConvertFrom-json
 ```
@@ -55,13 +54,14 @@ Here's a quick example showing API access from `powershell`
 ## Troubleshooting
 
 Ollama on Windows stores files in a few different locations.  You can view them in
-the explorer window by hitting `<Ctrl>+R` and type in:
+the explorer window by hitting `<cmd>+R` and type in:
 - `explorer %LOCALAPPDATA%\Ollama` contains logs, and downloaded updates
     - *app.log* contains most resent logs from the GUI application
     - *server.log* contains the most recent server logs
     - *upgrade.log* contains log output for upgrades
 - `explorer %LOCALAPPDATA%\Programs\Ollama` contains the binaries (The installer adds this to your user PATH)
 - `explorer %HOMEPATH%\.ollama` contains models and configuration
+- `explorer %TEMP%` contains temporary executable files in one or more `ollama*` directories
 
 ## Uninstall
 
@@ -80,11 +80,9 @@ help you keep up to date.
 
 If you'd like to install or integrate Ollama as a service, a standalone
 `ollama-windows-amd64.zip` zip file is available containing only the Ollama CLI
-and GPU library dependencies for Nvidia.  If you have an AMD GPU, also download
+and GPU library dependencies for Nvidia and AMD. This allows for embedding
-and extract the additional ROCm package `ollama-windows-amd64-rocm.zip` into the
+Ollama in existing applications, or running it as a system service via `ollama
-same directory.  This allows for embedding Ollama in existing applications, or
+serve` with tools such as [NSSM](https://nssm.cc/).
-running it as a system service via `ollama serve` with tools such as
-[NSSM](https://nssm.cc/). 
 
 > [!NOTE]  
 > If you are upgrading from a prior version, you should remove the old directories first.

envconfig/config.go

@@ -53,8 +53,8 @@ func Host() *url.URL {
 	}
 }
 
-// AllowedOrigins returns a list of allowed origins. AllowedOrigins can be configured via the OLLAMA_ORIGINS environment variable.
+// Origins returns a list of allowed origins. Origins can be configured via the OLLAMA_ORIGINS environment variable.
-func AllowedOrigins() (origins []string) {
+func Origins() (origins []string) {
 	if s := Var("OLLAMA_ORIGINS"); s != "" {
 		origins = strings.Split(s, ",")
 	}
@@ -73,7 +73,6 @@ func AllowedOrigins() (origins []string) {
 		"file://*",
 		"tauri://*",
 		"vscode-webview://*",
-		"vscode-file://*",
 	)
 
 	return origins
@@ -149,22 +148,9 @@ func Bool(k string) func() bool {
 	}
 }
 
-// LogLevel returns the log level for the application.
-// Values are 0 or false INFO (Default), 1 or true DEBUG, 2 TRACE
-func LogLevel() slog.Level {
-	level := slog.LevelInfo
-	if s := Var("OLLAMA_DEBUG"); s != "" {
-		if b, _ := strconv.ParseBool(s); b {
-			level = slog.LevelDebug
-		} else if i, _ := strconv.ParseInt(s, 10, 64); i != 0 {
-			level = slog.Level(i * -4)
-		}
-	}
-
-	return level
-}
-
 var (
+	// Debug enabled additional debug information.
+	Debug = Bool("OLLAMA_DEBUG")
 	// FlashAttention enables the experimental flash attention feature.
 	FlashAttention = Bool("OLLAMA_FLASH_ATTENTION")
 	// KvCacheType is the quantization type for the K/V cache.
@@ -179,10 +165,6 @@ var (
 	IntelGPU = Bool("OLLAMA_INTEL_GPU")
 	// MultiUserCache optimizes prompt caching for multi-user scenarios
 	MultiUserCache = Bool("OLLAMA_MULTIUSER_CACHE")
-	// Enable the new Ollama engine
-	NewEngine = Bool("OLLAMA_NEW_ENGINE")
-	// ContextLength sets the default context length
-	ContextLength = Uint("OLLAMA_CONTEXT_LENGTH", 4096)
 )
 
 func String(s string) func() string {
@@ -222,6 +204,8 @@ var (
 	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.
 	MaxQueue = Uint("OLLAMA_MAX_QUEUE", 512)
+	// MaxVRAM sets a maximum VRAM override in bytes. MaxVRAM can be configured via the OLLAMA_MAX_VRAM environment variable.
+	MaxVRAM = Uint("OLLAMA_MAX_VRAM", 0)
 )
 
 func Uint64(key string, defaultValue uint64) func() uint64 {
@@ -249,7 +233,7 @@ type EnvVar struct {
 
 func AsMap() map[string]EnvVar {
 	ret := map[string]EnvVar{
-		"OLLAMA_DEBUG":             {"OLLAMA_DEBUG", LogLevel(), "Show additional debug information (e.g. OLLAMA_DEBUG=1)"},
+		"OLLAMA_DEBUG":             {"OLLAMA_DEBUG", Debug(), "Show additional debug information (e.g. OLLAMA_DEBUG=1)"},
 		"OLLAMA_FLASH_ATTENTION":   {"OLLAMA_FLASH_ATTENTION", FlashAttention(), "Enabled flash attention"},
 		"OLLAMA_KV_CACHE_TYPE":     {"OLLAMA_KV_CACHE_TYPE", KvCacheType(), "Quantization type for the K/V cache (default: f16)"},
 		"OLLAMA_GPU_OVERHEAD":      {"OLLAMA_GPU_OVERHEAD", GpuOverhead(), "Reserve a portion of VRAM per GPU (bytes)"},
@@ -263,11 +247,9 @@ func AsMap() map[string]EnvVar {
 		"OLLAMA_NOHISTORY":         {"OLLAMA_NOHISTORY", NoHistory(), "Do not preserve readline history"},
 		"OLLAMA_NOPRUNE":           {"OLLAMA_NOPRUNE", NoPrune(), "Do not prune model blobs on startup"},
 		"OLLAMA_NUM_PARALLEL":      {"OLLAMA_NUM_PARALLEL", NumParallel(), "Maximum number of parallel requests"},
-		"OLLAMA_ORIGINS":           {"OLLAMA_ORIGINS", AllowedOrigins(), "A comma separated list of allowed origins"},
+		"OLLAMA_ORIGINS":           {"OLLAMA_ORIGINS", Origins(), "A comma separated list of allowed origins"},
 		"OLLAMA_SCHED_SPREAD":      {"OLLAMA_SCHED_SPREAD", SchedSpread(), "Always schedule model across all GPUs"},
 		"OLLAMA_MULTIUSER_CACHE":   {"OLLAMA_MULTIUSER_CACHE", MultiUserCache(), "Optimize prompt caching for multi-user scenarios"},
-		"OLLAMA_CONTEXT_LENGTH":    {"OLLAMA_CONTEXT_LENGTH", ContextLength(), "Context length to use unless otherwise specified (default: 4096)"},
-		"OLLAMA_NEW_ENGINE":        {"OLLAMA_NEW_ENGINE", NewEngine(), "Enable the new Ollama engine"},
 
 		// Informational
 		"HTTP_PROXY":  {"HTTP_PROXY", String("HTTP_PROXY")(), "HTTP proxy"},

envconfig/config_test.go

@@ -1,13 +1,11 @@
 package envconfig
 
 import (
-	"log/slog"
 	"math"
 	"testing"
 	"time"
 
 	"github.com/google/go-cmp/cmp"
-	"github.com/ollama/ollama/logutil"
 )
 
 func TestHost(t *testing.T) {
@@ -71,7 +69,6 @@ func TestOrigins(t *testing.T) {
 			"file://*",
 			"tauri://*",
 			"vscode-webview://*",
-			"vscode-file://*",
 		}},
 		{"http://10.0.0.1", []string{
 			"http://10.0.0.1",
@@ -91,7 +88,6 @@ func TestOrigins(t *testing.T) {
 			"file://*",
 			"tauri://*",
 			"vscode-webview://*",
-			"vscode-file://*",
 		}},
 		{"http://172.16.0.1,https://192.168.0.1", []string{
 			"http://172.16.0.1",
@@ -112,7 +108,6 @@ func TestOrigins(t *testing.T) {
 			"file://*",
 			"tauri://*",
 			"vscode-webview://*",
-			"vscode-file://*",
 		}},
 		{"http://totally.safe,http://definitely.legit", []string{
 			"http://totally.safe",
@@ -133,14 +128,13 @@ func TestOrigins(t *testing.T) {
 			"file://*",
 			"tauri://*",
 			"vscode-webview://*",
-			"vscode-file://*",
 		}},
 	}
 	for _, tt := range cases {
 		t.Run(tt.value, func(t *testing.T) {
 			t.Setenv("OLLAMA_ORIGINS", tt.value)
 
-			if diff := cmp.Diff(AllowedOrigins(), tt.expect); diff != "" {
+			if diff := cmp.Diff(Origins(), tt.expect); diff != "" {
 				t.Errorf("%s: mismatch (-want +got):\n%s", tt.value, diff)
 			}
 		})
@@ -278,50 +272,3 @@ func TestVar(t *testing.T) {
 		})
 	}
 }
-
-func TestContextLength(t *testing.T) {
-	cases := map[string]uint{
-		"":     4096,
-		"2048": 2048,
-	}
-
-	for k, v := range cases {
-		t.Run(k, func(t *testing.T) {
-			t.Setenv("OLLAMA_CONTEXT_LENGTH", k)
-			if i := ContextLength(); i != v {
-				t.Errorf("%s: expected %d, got %d", k, v, i)
-			}
-		})
-	}
-}
-
-func TestLogLevel(t *testing.T) {
-	cases := map[string]slog.Level{
-		// Default to INFO
-		"":      slog.LevelInfo,
-		"false": slog.LevelInfo,
-		"f":     slog.LevelInfo,
-		"0":     slog.LevelInfo,
-
-		// True values enable Debug
-		"true": slog.LevelDebug,
-		"t":    slog.LevelDebug,
-
-		// Positive values increase verbosity
-		"1": slog.LevelDebug,
-		"2": logutil.LevelTrace,
-
-		// Negative values decrease verbosity
-		"-1": slog.LevelWarn,
-		"-2": slog.LevelError,
-	}
-
-	for k, v := range cases {
-		t.Run(k, func(t *testing.T) {
-			t.Setenv("OLLAMA_DEBUG", k)
-			if i := LogLevel(); i != v {
-				t.Errorf("%s: expected %d, got %d", k, v, i)
-			}
-		})
-	}
-}

format/bytes.go

@@ -40,6 +40,8 @@ func HumanBytes(b int64) string {
 	}
 
 	switch {
+	case value >= 100:
+		return fmt.Sprintf("%d %s", int(value), unit)
 	case value >= 10:
 		return fmt.Sprintf("%d %s", int(value), unit)
 	case value != math.Trunc(value):

format/bytes_test.go

@@ -1,91 +0,0 @@
-package format
-
-import (
-	"testing"
-)
-
-func TestHumanBytes(t *testing.T) {
-	type testCase struct {
-		input    int64
-		expected string
-	}
-
-	tests := []testCase{
-		// Test bytes (B)
-		{0, "0 B"},
-		{1, "1 B"},
-		{999, "999 B"},
-
-		// Test kilobytes (KB)
-		{1000, "1 KB"},
-		{1500, "1.5 KB"},
-		{999999, "999 KB"},
-
-		// Test megabytes (MB)
-		{1000000, "1 MB"},
-		{1500000, "1.5 MB"},
-		{999999999, "999 MB"},
-
-		// Test gigabytes (GB)
-		{1000000000, "1 GB"},
-		{1500000000, "1.5 GB"},
-		{999999999999, "999 GB"},
-
-		// Test terabytes (TB)
-		{1000000000000, "1 TB"},
-		{1500000000000, "1.5 TB"},
-		{1999999999999, "2.0 TB"},
-
-		// Test fractional values
-		{1234, "1.2 KB"},
-		{1234567, "1.2 MB"},
-		{1234567890, "1.2 GB"},
-	}
-
-	for _, tc := range tests {
-		t.Run(tc.expected, func(t *testing.T) {
-			result := HumanBytes(tc.input)
-			if result != tc.expected {
-				t.Errorf("Expected %s, got %s", tc.expected, result)
-			}
-		})
-	}
-}
-
-func TestHumanBytes2(t *testing.T) {
-	type testCase struct {
-		input    uint64
-		expected string
-	}
-
-	tests := []testCase{
-		// Test bytes (B)
-		{0, "0 B"},
-		{1, "1 B"},
-		{1023, "1023 B"},
-
-		// Test kibibytes (KiB)
-		{1024, "1.0 KiB"},
-		{1536, "1.5 KiB"},
-		{1048575, "1024.0 KiB"},
-
-		// Test mebibytes (MiB)
-		{1048576, "1.0 MiB"},
-		{1572864, "1.5 MiB"},
-		{1073741823, "1024.0 MiB"},
-
-		// Test gibibytes (GiB)
-		{1073741824, "1.0 GiB"},
-		{1610612736, "1.5 GiB"},
-		{2147483648, "2.0 GiB"},
-	}
-
-	for _, tc := range tests {
-		t.Run(tc.expected, func(t *testing.T) {
-			result := HumanBytes2(tc.input)
-			if result != tc.expected {
-				t.Errorf("Expected %s, got %s", tc.expected, result)
-			}
-		})
-	}
-}

format/format_test.go

@@ -12,9 +12,6 @@ func TestHumanNumber(t *testing.T) {
 
 	testCases := []testCase{
 		{0, "0"},
-		{999, "999"},
-		{1000, "1K"},
-		{1001, "1K"},
 		{1000000, "1M"},
 		{125000000, "125M"},
 		{500500000, "500.50M"},

format/time_test.go

@@ -5,7 +5,7 @@ import (
 	"time"
 )
 
-func assertEqual(t *testing.T, a any, b any) {
+func assertEqual(t *testing.T, a interface{}, b interface{}) {
 	if a != b {
 		t.Errorf("Assert failed, expected %v, got %v", b, a)
 	}

fs/config.go

@@ -1,13 +0,0 @@
-package fs
-
-type Config interface {
-	Architecture() string
-	String(string, ...string) string
-	Uint(string, ...uint32) uint32
-	Float(string, ...float32) float32
-	Bool(string, ...bool) bool
-
-	Strings(string, ...[]string) []string
-	Ints(string, ...[]int32) []int32
-	Floats(string, ...[]float32) []float32
-}

fs/ggml/ggml.go

@@ -6,7 +6,6 @@ import (
 	"fmt"
 	"io"
 	"log/slog"
-	"math"
 	"slices"
 	"strings"
 
@@ -34,15 +33,15 @@ func (kv KV) Kind() string {
 }
 
 func (kv KV) ParameterCount() uint64 {
-	return keyValue(kv, "general.parameter_count", uint64(0))
+	return keyValue[uint64](kv, "general.parameter_count")
 }
 
-func (kv KV) FileType() FileType {
+func (kv KV) FileType() fileType {
 	if t := kv.Uint("general.file_type"); t > 0 {
-		return FileType(t)
+		return fileType(t)
 	}
 
-	return FileTypeUnknown
+	return fileTypeUnknown
 }
 
 func (kv KV) BlockCount() uint64 {
@@ -101,49 +100,27 @@ func (kv KV) Float(key string, defaultValue ...float32) float32 {
 	return keyValue(kv, key, append(defaultValue, 0)...)
 }
 
-func (kv KV) Bool(key string, defaultValue ...bool) bool {
-	return keyValue(kv, key, append(defaultValue, false)...)
-}
-
 func (kv KV) Strings(key string, defaultValue ...[]string) []string {
-	return keyValue(kv, key, &array[string]{values: append(defaultValue, []string(nil))[0]}).values
+	r := keyValue(kv, key, &array{})
-}
+	s := make([]string, r.size)
+	for i := range r.size {
+		s[i] = r.values[i].(string)
+	}
 
-func (kv KV) Ints(key string, defaultValue ...[]int32) []int32 {
+	return s
-	return keyValue(kv, key, &array[int32]{values: append(defaultValue, []int32(nil))[0]}).values
 }
 
 func (kv KV) Uints(key string, defaultValue ...[]uint32) []uint32 {
-	return keyValue(kv, key, &array[uint32]{values: append(defaultValue, []uint32(nil))[0]}).values
+	r := keyValue(kv, key, &array{})
+	s := make([]uint32, r.size)
+	for i := range r.size {
+		s[i] = uint32(r.values[i].(int32))
+	}
+
+	return s
 }
 
-func (kv KV) Floats(key string, defaultValue ...[]float32) []float32 {
+func keyValue[T string | uint32 | uint64 | float32 | *array](kv KV, key string, defaultValue ...T) T {
-	return keyValue(kv, key, &array[float32]{values: append(defaultValue, []float32(nil))[0]}).values
-}
-
-func (kv KV) OllamaEngineRequired() bool {
-	return slices.Contains([]string{
-		"gemma3",
-		"mistral3",
-		"llama4",
-		"mllama",
-		"qwen25vl",
-	}, kv.Architecture())
-}
-
-type valueTypes interface {
-	uint8 | int8 | uint16 | int16 |
-		uint32 | int32 | uint64 | int64 |
-		string | float32 | float64 | bool
-}
-
-type arrayValueTypes interface {
-	*array[uint8] | *array[int8] | *array[uint16] | *array[int16] |
-		*array[uint32] | *array[int32] | *array[uint64] | *array[int64] |
-		*array[string] | *array[float32] | *array[float64] | *array[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
 	}
@@ -152,7 +129,7 @@ func keyValue[T valueTypes | arrayValueTypes](kv KV, key string, defaultValue ..
 		return val.(T)
 	}
 
-	slog.Debug("key not found", "key", key, "default", defaultValue[0])
+	slog.Warn("key not found", "key", key, "default", defaultValue[0])
 	return defaultValue[0]
 }
 
@@ -176,17 +153,19 @@ func (s Tensors) Items(prefix ...string) []*Tensor {
 	return items
 }
 
-func (ts Tensors) GroupLayers() map[string]Layer {
+func (ts Tensors) Layers() map[string]Layer {
 	layers := make(map[string]Layer)
 	for _, t := range ts.items {
 		parts := strings.Split(t.Name, ".")
-		if index := slices.IndexFunc(parts, func(s string) bool { return s == "blk" || s == "mm" }); index != -1 {
+		if i := slices.Index(parts, "blk"); i > 0 {
-			if len(parts) > index+2 {
+			parts = append([]string{
-				// blk and mm should have a number after them, join it
+				strings.Join(parts[:i], "."),
-				parts = append(
+				strings.Join(parts[i:i+2], "."),
-					[]string{strings.Join(parts[:index+2], ".")},
+			}, parts[i+2:]...)
-					parts[index+2:]...)
+		} else if i == 0 {
-			}
+			parts = append([]string{
+				strings.Join(parts[i:i+2], "."),
+			}, parts[i+2:]...)
 		}
 
 		if _, ok := layers[parts[0]]; !ok {
@@ -229,107 +208,82 @@ func (t Tensor) block() (n int) {
 }
 
 func (t Tensor) blockSize() uint64 {
-	return (TensorType)(t.Kind).BlockSize()
+	switch t.Kind {
-}
+	case 0, 1, 24, 25, 26, 27, 28, 30: // F32, F16, I8, I16, I32, I64, F64, BF16
-
-func (t TensorType) BlockSize() uint64 {
-	switch t {
-	case
-		0,  // F32
-		1,  // F16
-		24, // I8
-		25, // I16
-		26, // I32
-		27, // I64
-		28, // F64
-		30: // BF16
 		return 1
-	case
+	case 2, 3, 4, 5, 6, 7, 8, 9, 20: // Q4_0, Q4_1, Q5_0, Q5_1, Q8_0, Q8_1, IQ4_NL
-		2,  // Q4_0
-		3,  // Q4_1
-		6,  // Q5_0
-		7,  // Q5_1
-		8,  // Q8_0
-		9,  // Q8_1
-		20: // IQ4_NL
 		return 32
-	default:
+	default: // All others
 		return 256
 	}
 }
 
 func (t Tensor) typeSize() uint64 {
-	return TensorType(t.Kind).TypeSize()
+	blockSize := t.blockSize()
-}
 
-func (t TensorType) TypeSize() uint64 {
+	switch t.Kind {
-	blockSize := t.BlockSize()
+	case 0: // FP32
-
-	switch t {
-	case TensorTypeF32:
 		return 4
-	case TensorTypeF16:
+	case 1: // FP16
 		return 2
-	case TensorTypeQ4_0:
+	case 2: // Q4_0
 		return 2 + blockSize/2
-	case TensorTypeQ4_1:
+	case 3: // Q4_1
 		return 2 + 2 + blockSize/2
-	case TensorTypeQ5_0:
+	case 6: // Q5_0
 		return 2 + 4 + blockSize/2
-	case TensorTypeQ5_1:
+	case 7: // Q5_1
 		return 2 + 2 + 4 + blockSize/2
-	case TensorTypeQ8_0:
+	case 8: // Q8_0
 		return 2 + blockSize
-	case TensorTypeQ8_1:
+	case 9: // Q8_1
-		return 2 + 2 + blockSize
+		return 4 + 4 + blockSize
-	case TensorTypeQ2_K:
+	case 10: // Q2_K
 		return blockSize/16 + blockSize/4 + 2 + 2
-	case TensorTypeQ3_K:
+	case 11: // Q3_K
 		return blockSize/8 + blockSize/4 + 12 + 2
-	case TensorTypeQ4_K:
+	case 12: // Q4_K
 		return 2 + 2 + 12 + blockSize/2
-	case TensorTypeQ5_K:
+	case 13: // Q5_K
 		return 2 + 2 + 12 + blockSize/8 + blockSize/2
-	case TensorTypeQ6_K:
+	case 14: // Q6_K
 		return blockSize/2 + blockSize/4 + blockSize/16 + 2
-	case TensorTypeQ8_K:
+	case 15: // Q8_K
-		return 4 + blockSize + 2*blockSize/16
+		return 2 + blockSize + 2*blockSize/16
-	case tensorTypeIQ2_XXS:
+	case 16: // IQ2_XXS
 		return 2 + 2*blockSize/8
-	case tensorTypeIQ2_XS:
+	case 17: // IQ2_XS
 		return 2 + 2*blockSize/8 + blockSize/32
-	case tensorTypeIQ3_XXS:
+	case 18: // IQ3_XXS
 		return 2 + blockSize/4 + blockSize/8
-	case tensorTypeIQ1_S:
+	case 19: // IQ1_S
 		return 2 + blockSize/8 + blockSize/16
-	case tensorTypeIQ4_NL:
+	case 20: // IQ4_NL
 		return 2 + blockSize/2
-	case tensorTypeIQ3_S:
+	case 21: // IQ3_S
 		return 2 + blockSize/4 + blockSize/8 + blockSize/32 + 4
-	case tensorTypeIQ2_S:
+	case 22: // IQ2_S
 		return 2 + blockSize/4 + blockSize/16
-	case tensorTypeIQ4_XS:
+	case 23: // IQ4_XS
 		return 2 + 2 + blockSize/2 + blockSize/64
-	case TensorTypeI8:
+	case 24: // I8
 		return 1
-	case TensorTypeI16:
+	case 25: // I16
 		return 2
-	case TensorTypeI32:
+	case 26: // I32
 		return 4
-	case TensorTypeI64:
+	case 27: // I64
 		return 8
-	case TensorTypeF64:
+	case 28: // F64
 		return 8
-	case tensorTypeIQ1_M:
+	case 29: // IQ1_M
 		return blockSize/8 + blockSize/16 + blockSize/32
-	case TensorTypeBF16:
-		return 2
 	default:
 		return 0
 	}
 }
 
-func (t Tensor) Elements() uint64 {
+func (t Tensor) parameters() uint64 {
 	var count uint64 = 1
 	for _, n := range t.Shape {
 		count *= n
@@ -338,11 +292,7 @@ func (t Tensor) Elements() uint64 {
 }
 
 func (t Tensor) Size() uint64 {
-	return t.Elements() * t.typeSize() / t.blockSize()
+	return t.parameters() * t.typeSize() / t.blockSize()
-}
-
-func (t Tensor) Type() string {
-	return TensorType(t.Kind).String()
 }
 
 type container interface {
@@ -386,8 +336,13 @@ func DetectContentType(b []byte) string {
 // Decode decodes a GGML model from the given reader.
 //
 // It collects array values for arrays with a size less than or equal to
-// maxArraySize. If the maxArraySize is negative, all arrays are collected.
+// maxArraySize. If maxArraySize is 0, the default value of 1024 is used. If
+// the maxArraySize is negative, all arrays are collected.
 func Decode(rs io.ReadSeeker, maxArraySize int) (*GGML, int64, error) {
+	if maxArraySize == 0 {
+		maxArraySize = 1024
+	}
+
 	rs = bufioutil.NewBufferedSeeker(rs, 32<<10)
 
 	var magic uint32
@@ -422,26 +377,23 @@ func Decode(rs io.ReadSeeker, maxArraySize int) (*GGML, int64, error) {
 	}, offset, nil
 }
 
-func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType string) (kv []uint64, partialOffload, fullOffload uint64) {
+func (llm GGML) GraphSize(context, batch uint64, kvCacheType string) (kv, partialOffload, fullOffload uint64) {
-	embedding := f.KV().EmbeddingLength()
+	embedding := llm.KV().EmbeddingLength()
-	heads := f.KV().HeadCount()
+	heads := llm.KV().HeadCount()
-	headsKV := f.KV().HeadCountKV()
+	headsKV := llm.KV().HeadCountKV()
-	vocab := uint64(f.KV()["tokenizer.ggml.tokens"].(*array[string]).size)
+	vocab := uint64(llm.KV()["tokenizer.ggml.tokens"].(*array).size)
 
-	embeddingHeads := f.KV().EmbeddingHeadCount()
+	embeddingHeads := llm.KV().EmbeddingHeadCount()
-	embeddingHeadsK := f.KV().EmbeddingHeadCountK()
+	embeddingHeadsK := llm.KV().EmbeddingHeadCountK()
-	embeddingHeadsV := f.KV().EmbeddingHeadCountV()
+	embeddingHeadsV := llm.KV().EmbeddingHeadCountV()
 
-	layers := f.Tensors().GroupLayers()
+	layers := llm.Tensors().Layers()
 
 	bytesPerElement := kvCacheBytesPerElement(kvCacheType)
-	kv = make([]uint64, f.KV().BlockCount())
+	kv = uint64(float64(context*llm.KV().BlockCount()*(embeddingHeadsK+embeddingHeadsV)*headsKV) * bytesPerElement)
-	for i := range kv {
-		kv[i] = uint64(float64(context*(embeddingHeadsK+embeddingHeadsV)*headsKV) * bytesPerElement)
-	}
 
-	switch f.KV().Architecture() {
+	switch llm.KV().Architecture() {
-	case "llama", "llama4":
+	case "llama":
 		fullOffload = max(
 			4*batch*(1+4*embedding+context*(1+heads)),
 			4*batch*(embedding+vocab),
@@ -455,7 +407,7 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 
 		if ffnGateExpsWeight, ok := layers["blk.0"]["ffn_gate_exps.weight"]; ok {
 			// mixtral 8x22b
-			ff := uint64(f.KV().Uint("feed_forward_length"))
+			ff := uint64(llm.KV()["llama.feed_forward_length"].(uint32))
 			partialOffload = max(
 				3*ffnGateExpsWeight.Size()+4*batch*(2*ff+headsKV+embedding+context+embeddingHeads*headsKV),
 				4*(context*batch*heads+context*embeddingHeads*headsKV+batch*1024+embeddingHeads*headsKV*batch),
@@ -472,14 +424,16 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 	case "mllama":
 		var visionTokens, tiles uint64 = 1601, 4
 
-		crossAttentionLayers := f.KV().Ints("attention.cross_attention_layers")
+		if crossAttentionLayers, ok := llm.KV()["mllama.attention.cross_attention_layers"].(*array); ok {
-		for i := range kv {
+			kv = headsKV *
-			if slices.Contains(crossAttentionLayers, int32(i)) {
+				(embeddingHeadsK + embeddingHeadsV) * // one for K, one for V
-				kv[i] = headsKV * (embeddingHeadsK + embeddingHeadsV) *
+				(2* // sizeof(float16)
-					4 * // sizeof(float32)
+					(llm.KV().BlockCount()-uint64(crossAttentionLayers.size))* // num non-cross attention layers
-					visionTokens *
+					context +
-					tiles
+					4* // sizeof(float32)
-			}
+						uint64(crossAttentionLayers.size)* // num cross attention layers
+						visionTokens*
+						tiles)
 		}
 
 		fullOffload = max(
@@ -489,9 +443,9 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 		)
 
 		var ropeFreqsCount uint64
-		if ropeFreqs, ok := f.Tensors().GroupLayers()["rope_freqs"]; ok {
+		if ropeFreqs, ok := llm.Tensors().Layers()["rope_freqs"]; ok {
 			if ropeFreqsWeights, ok := ropeFreqs["weights"]; ok {
-				ropeFreqsCount = ropeFreqsWeights.Elements()
+				ropeFreqsCount = ropeFreqsWeights.parameters()
 			}
 		}
 
@@ -503,7 +457,7 @@ 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":
+	case "gemma", "gemma2":
 		fullOffload = max(
 			4*batch*(embedding+vocab),
 			4*batch*(2+context+context*heads+2*embedding+2*embeddingHeadsK*heads),
@@ -515,20 +469,6 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 				4*embeddingHeadsK*context*8+
 				embedding*embeddingHeadsK*heads*9/16,
 		)
-
-		// 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" {
-			const gemma3GlobalCacheCount = 6
-			slidingWindow := (uint64(numParallel) * uint64(f.KV().Uint("attention.sliding_window"))) + batch
-			for i := range kv {
-				// Every 6th layer is a global layer, which is the full context size that has already been set. The other
-				// layers are the smaller local (sliding) layers.
-				if (i+1)%gemma3GlobalCacheCount != 0 {
-					kv[i] = uint64(float64(slidingWindow*(embeddingHeadsK+embeddingHeadsV)*headsKV) * bytesPerElement)
-				}
-			}
-		}
 	case "command-r":
 		fullOffload = max(
 			4*batch*(embedding+vocab),
@@ -606,97 +546,21 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 	return
 }
 
-func (llm GGML) VisionGraphSize() (weights, graphSize uint64) {
-	if llm.KV().Uint("vision.block_count") == 0 {
-		return
-	}
-
-	for name, layer := range llm.Tensors().GroupLayers() {
-		if name == "v" || strings.HasPrefix(name, "v.") {
-			for _, tensor := range layer {
-				weights += tensor.Size()
-			}
-		}
-	}
-
-	imageSize := uint64(llm.KV().Uint("vision.image_size"))
-	patchSize := uint64(llm.KV().Uint("vision.patch_size"))
-	if patchSize == 0 {
-		slog.Warn("unknown patch size for vision model")
-		return
-	}
-
-	numChannels := uint64(llm.KV().Uint("vision.num_channels"))
-
-	numPatches := (imageSize / patchSize) * (imageSize / patchSize)
-	if _, ok := llm.Tensors().GroupLayers()["v"]["class_embd"]; ok {
-		numPatches++
-	}
-
-	headCount := uint64(llm.KV().Uint("vision.attention.head_count"))
-	embeddingLength := uint64(llm.KV().Uint("vision.embedding_length"))
-
-	switch llm.KV().Architecture() {
-	case "mllama":
-		numPaddedPatches := numPatches + 8 - (numPatches%8)%8
-
-		maxNumTiles := uint64(llm.KV().Uint("vision.max_num_tiles"))
-
-		graphSize = 4 * (8 +
-			imageSize*imageSize*numChannels*maxNumTiles +
-			embeddingLength*numPatches*maxNumTiles +
-			9*embeddingLength*numPaddedPatches*maxNumTiles +
-			numPaddedPatches*maxNumTiles*numPaddedPatches*maxNumTiles*headCount)
-	case "gemma3", "mistral3":
-		graphSize = 4 * (imageSize*imageSize*numChannels +
-			embeddingLength*patchSize +
-			numPatches*numPatches*headCount)
-	case "qwen25vl":
-		maxPixels := uint64(llm.KV().Uint("vision.max_pixels", 28*28*1280))
-		mergeSize := uint64(llm.KV().Uint("vision.spatial_merge_size", 2))
-		temporalPatchSize := uint64(2)
-
-		// Calculate max possible patches based on max_pixels
-		maxHeight := uint64(math.Sqrt(float64(maxPixels)))
-		maxWidth := maxPixels / maxHeight
-		maxGridHeight := maxHeight / patchSize
-		maxGridWidth := maxWidth / patchSize
-		// Account for merged patches (2x2 grid)
-		numPatches := (maxGridHeight * maxGridWidth) / (mergeSize * mergeSize)
-
-		// Calculate graph size based on typical operations in ProcessImage and createPatches
-		graphSize = 4 * (maxPixels*numChannels + // Original image storage
-			// Normalized pixels
-			maxPixels*numChannels +
-			// Patches storage (numPatches * channels * temporalPatchSize * patchSize^2)
-			numPatches*numChannels*temporalPatchSize*patchSize*patchSize +
-			// Self-attention calculations (similar to other architectures)
-			numPatches*numPatches*headCount +
-			// Additional buffer for processing
-			embeddingLength*numPatches)
-	case "llama4":
-		// vision graph is computed independently in the same schedule
-		// and is negligible compared to the worst case text graph
-	}
-
-	return weights, graphSize
-}
-
 // SupportsKVCacheType checks if the requested cache type is supported
-func (f GGML) SupportsKVCacheType(cacheType string) bool {
+func (llm GGML) SupportsKVCacheType(cacheType string) bool {
 	return slices.Contains([]string{"f16", "q8_0", "q4_0"}, cacheType)
 }
 
 // SupportsFlashAttention checks if the model supports flash attention
-func (f GGML) SupportsFlashAttention() bool {
+func (llm GGML) SupportsFlashAttention() bool {
-	_, isEmbedding := f.KV()[fmt.Sprintf("%s.pooling_type", f.KV().Architecture())]
+	_, isEmbedding := llm.KV()[fmt.Sprintf("%s.pooling_type", llm.KV().Architecture())]
 	if isEmbedding {
 		return false
 	}
 
 	// Check head counts match and are non-zero
-	headCountK := f.KV().EmbeddingHeadCountK()
+	headCountK := llm.KV().EmbeddingHeadCountK()
-	headCountV := f.KV().EmbeddingHeadCountV()
+	headCountV := llm.KV().EmbeddingHeadCountV()
 	return headCountK != 0 && headCountV != 0 && headCountK == headCountV
 }
 

fs/ggml/ggml_test.go

@@ -1,271 +0,0 @@
-package ggml
-
-import (
-	"maps"
-	"math"
-	"slices"
-	"strconv"
-	"strings"
-	"testing"
-
-	"github.com/google/go-cmp/cmp"
-)
-
-func TestTensorLayers(t *testing.T) {
-	tensors := make(map[string]*Tensor)
-	for _, name := range []string{
-		"token_embd.weight",
-		"blk.0.attn_k.weight",
-		"blk.0.attn_output.weight",
-		"blk.0.attn_q.weight",
-		"blk.0.attn_v.weight",
-		"blk.0.attn_norm.weight",
-		"blk.0.ffn_down.weight",
-		"blk.0.ffn_gate.weight",
-		"blk.0.ffn_up.weight",
-		"blk.0.ffn_norm.weight",
-		"output_norm.weight",
-		"mm.0.bias",
-		"mm.0.weight",
-		"v.blk.0.attn_k.weight",
-		"v.blk.0.attn_output.weight",
-		"v.blk.0.attn_q.weight",
-		"v.blk.0.attn_v.weight",
-		"v.blk.0.attn_norm.weight",
-		"v.blk.0.ffn_down.weight",
-		"v.blk.0.ffn_gate.weight",
-		"v.blk.0.ffn_up.weight",
-		"v.blk.0.ffn_norm.weight",
-		"v.patch_embd.weight",
-		"v.position_embd.gate",
-		"v.position_embd.weight",
-	} {
-		tensors[name] = &Tensor{Name: name}
-	}
-
-	cases := []struct {
-		name  string
-		items []*Tensor
-		want  map[string]Layer
-	}{
-		{
-			name: "text",
-			items: slices.Collect(func(yield func(*Tensor) bool) {
-				for k, v := range tensors {
-					if !strings.HasPrefix(k, "mm.") && !strings.HasPrefix(k, "v.") {
-						if !yield(v) {
-							return
-						}
-					}
-				}
-			}),
-			want: map[string]Layer{
-				"blk.0": {
-					"attn_k.weight":      tensors["blk.0.attn_k.weight"],
-					"attn_q.weight":      tensors["blk.0.attn_q.weight"],
-					"attn_v.weight":      tensors["blk.0.attn_v.weight"],
-					"attn_output.weight": tensors["blk.0.attn_output.weight"],
-					"attn_norm.weight":   tensors["blk.0.attn_norm.weight"],
-					"ffn_down.weight":    tensors["blk.0.ffn_down.weight"],
-					"ffn_gate.weight":    tensors["blk.0.ffn_gate.weight"],
-					"ffn_up.weight":      tensors["blk.0.ffn_up.weight"],
-					"ffn_norm.weight":    tensors["blk.0.ffn_norm.weight"],
-				},
-				"token_embd":  {"weight": tensors["token_embd.weight"]},
-				"output_norm": {"weight": tensors["output_norm.weight"]},
-			},
-		},
-		{
-			name: "vision",
-			items: slices.Collect(func(yield func(*Tensor) bool) {
-				for k, v := range tensors {
-					if strings.HasPrefix(k, "mm.") || strings.HasPrefix(k, "v.") {
-						if !yield(v) {
-							return
-						}
-					}
-				}
-			}),
-			want: map[string]Layer{
-				"mm.0": {
-					"bias":   tensors["mm.0.bias"],
-					"weight": tensors["mm.0.weight"],
-				},
-				"v.blk.0": {
-					"attn_k.weight":      tensors["v.blk.0.attn_k.weight"],
-					"attn_q.weight":      tensors["v.blk.0.attn_q.weight"],
-					"attn_v.weight":      tensors["v.blk.0.attn_v.weight"],
-					"attn_output.weight": tensors["v.blk.0.attn_output.weight"],
-					"attn_norm.weight":   tensors["v.blk.0.attn_norm.weight"],
-					"ffn_down.weight":    tensors["v.blk.0.ffn_down.weight"],
-					"ffn_gate.weight":    tensors["v.blk.0.ffn_gate.weight"],
-					"ffn_up.weight":      tensors["v.blk.0.ffn_up.weight"],
-					"ffn_norm.weight":    tensors["v.blk.0.ffn_norm.weight"],
-				},
-				"v": {
-					"patch_embd.weight":    tensors["v.patch_embd.weight"],
-					"position_embd.gate":   tensors["v.position_embd.gate"],
-					"position_embd.weight": tensors["v.position_embd.weight"],
-				},
-			},
-		},
-		{
-			name:  "vision and text",
-			items: slices.Collect(maps.Values(tensors)),
-			want: map[string]Layer{
-				"blk.0": {
-					"attn_k.weight":      tensors["blk.0.attn_k.weight"],
-					"attn_q.weight":      tensors["blk.0.attn_q.weight"],
-					"attn_v.weight":      tensors["blk.0.attn_v.weight"],
-					"attn_output.weight": tensors["blk.0.attn_output.weight"],
-					"attn_norm.weight":   tensors["blk.0.attn_norm.weight"],
-					"ffn_down.weight":    tensors["blk.0.ffn_down.weight"],
-					"ffn_gate.weight":    tensors["blk.0.ffn_gate.weight"],
-					"ffn_up.weight":      tensors["blk.0.ffn_up.weight"],
-					"ffn_norm.weight":    tensors["blk.0.ffn_norm.weight"],
-				},
-				"token_embd":  {"weight": tensors["token_embd.weight"]},
-				"output_norm": {"weight": tensors["output_norm.weight"]},
-				"mm.0": {
-					"bias":   tensors["mm.0.bias"],
-					"weight": tensors["mm.0.weight"],
-				},
-				"v.blk.0": {
-					"attn_k.weight":      tensors["v.blk.0.attn_k.weight"],
-					"attn_q.weight":      tensors["v.blk.0.attn_q.weight"],
-					"attn_v.weight":      tensors["v.blk.0.attn_v.weight"],
-					"attn_output.weight": tensors["v.blk.0.attn_output.weight"],
-					"attn_norm.weight":   tensors["v.blk.0.attn_norm.weight"],
-					"ffn_down.weight":    tensors["v.blk.0.ffn_down.weight"],
-					"ffn_gate.weight":    tensors["v.blk.0.ffn_gate.weight"],
-					"ffn_up.weight":      tensors["v.blk.0.ffn_up.weight"],
-					"ffn_norm.weight":    tensors["v.blk.0.ffn_norm.weight"],
-				},
-				"v": {
-					"patch_embd.weight":    tensors["v.patch_embd.weight"],
-					"position_embd.gate":   tensors["v.position_embd.gate"],
-					"position_embd.weight": tensors["v.position_embd.weight"],
-				},
-			},
-		},
-	}
-
-	for _, tt := range cases {
-		t.Run(tt.name, func(t *testing.T) {
-			got := Tensors{items: tt.items}.GroupLayers()
-			if diff := cmp.Diff(got, tt.want); diff != "" {
-				t.Errorf("unexpected layers (-got +want):\n%s", diff)
-			}
-		})
-	}
-}
-
-// ref: https://github.com/ggml-org/llama.cpp/blob/a82c9e7c23ef6db48cebfa194dc9cebbc4ac3552/ggml/src/ggml.c#L572
-func TestTensorTypes(t *testing.T) {
-	cases := []struct {
-		kind      uint32
-		blockSize uint64
-		typeSize  uint64
-	}{
-		{0, 1, 4},
-		{1, 1, 2},
-		{2, 32, 18},
-		{3, 32, 20},
-		{6, 32, 22},
-		{7, 32, 24},
-		{8, 32, 34},
-		{9, 32, 36},
-		{10, 256, 84},
-		{11, 256, 110},
-		{12, 256, 144},
-		{13, 256, 176},
-		{14, 256, 210},
-		{15, 256, 292},
-		{16, 256, 66},
-		{17, 256, 74},
-		{18, 256, 98},
-		{19, 256, 50},
-		{20, 32, 18},
-		{21, 256, 110},
-		{22, 256, 82},
-		{23, 256, 136},
-		{24, 1, 1},
-		{25, 1, 2},
-		{26, 1, 4},
-		{27, 1, 8},
-		{28, 1, 8},
-		{29, 256, 56},
-		{30, 1, 2},
-	}
-
-	for _, tt := range cases {
-		t.Run(strconv.Itoa(int(tt.kind)), func(t *testing.T) {
-			tensor := Tensor{Kind: tt.kind}
-			if tensor.blockSize() != tt.blockSize {
-				t.Errorf("unexpected block size: got=%d want=%d", tensor.blockSize(), tt.blockSize)
-			}
-
-			if tensor.typeSize() != tt.typeSize {
-				t.Errorf("unexpected type size: got=%d want=%d", tensor.typeSize(), tt.typeSize)
-			}
-		})
-	}
-}
-
-func TestKeyValue(t *testing.T) {
-	kv := KV{
-		"general.architecture": "test",
-		"test.strings":         &array[string]{size: 3, values: []string{"a", "b", "c"}},
-		"test.float32s":        &array[float32]{size: 3, values: []float32{1.0, 2.0, 3.0}},
-		"test.int32s":          &array[int32]{size: 3, values: []int32{1, 2, 3}},
-		"test.uint32s":         &array[uint32]{size: 3, values: []uint32{1, 2, 3}},
-	}
-
-	if diff := cmp.Diff(kv.Strings("strings"), []string{"a", "b", "c"}); diff != "" {
-		t.Errorf("unexpected strings (-got +want):\n%s", diff)
-	}
-
-	if diff := cmp.Diff(kv.Strings("nonexistent.strings"), []string(nil)); diff != "" {
-		t.Errorf("unexpected strings (-got +want):\n%s", diff)
-	}
-
-	if diff := cmp.Diff(kv.Strings("default.strings", []string{"ollama"}), []string{"ollama"}); diff != "" {
-		t.Errorf("unexpected strings (-got +want):\n%s", diff)
-	}
-
-	if diff := cmp.Diff(kv.Floats("float32s"), []float32{1.0, 2.0, 3.0}); diff != "" {
-		t.Errorf("unexpected float32s (-got +want):\n%s", diff)
-	}
-
-	if diff := cmp.Diff(kv.Floats("nonexistent.float32s"), []float32(nil)); diff != "" {
-		t.Errorf("unexpected float32s (-got +want):\n%s", diff)
-	}
-
-	if diff := cmp.Diff(kv.Floats("default.float32s", []float32{math.MaxFloat32}), []float32{math.MaxFloat32}); diff != "" {
-		t.Errorf("unexpected float32s (-got +want):\n%s", diff)
-	}
-
-	if diff := cmp.Diff(kv.Ints("int32s"), []int32{1, 2, 3}); diff != "" {
-		t.Errorf("unexpected int8s (-got +want):\n%s", diff)
-	}
-
-	if diff := cmp.Diff(kv.Ints("nonexistent.int32s"), []int32(nil)); diff != "" {
-		t.Errorf("unexpected int8s (-got +want):\n%s", diff)
-	}
-
-	if diff := cmp.Diff(kv.Ints("default.int32s", []int32{math.MaxInt32}), []int32{math.MaxInt32}); diff != "" {
-		t.Errorf("unexpected int8s (-got +want):\n%s", diff)
-	}
-
-	if diff := cmp.Diff(kv.Uints("uint32s"), []uint32{1, 2, 3}); diff != "" {
-		t.Errorf("unexpected uint8s (-got +want):\n%s", diff)
-	}
-
-	if diff := cmp.Diff(kv.Uints("nonexistent.uint32s"), []uint32(nil)); diff != "" {
-		t.Errorf("unexpected uint8s (-got +want):\n%s", diff)
-	}
-
-	if diff := cmp.Diff(kv.Uints("default.uint32s", []uint32{math.MaxUint32}), []uint32{math.MaxUint32}); diff != "" {
-		t.Errorf("unexpected uint8s (-got +want):\n%s", diff)
-	}
-}

fs/ggml/gguf.go

@@ -9,12 +9,8 @@ import (
 	"io"
 	"log/slog"
 	"maps"
-	"os"
-	"runtime"
 	"slices"
 	"strings"
-
-	"golang.org/x/sync/errgroup"
 )
 
 type containerGGUF struct {
@@ -40,6 +36,10 @@ type containerGGUF struct {
 	maxArraySize int
 }
 
+func (c *containerGGUF) canCollectArray(size int) bool {
+	return c.maxArraySize < 0 || size <= c.maxArraySize
+}
+
 func (c *containerGGUF) Name() string {
 	return "gguf"
 }
@@ -229,13 +229,16 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
 		}
 
 		llm.tensors = append(llm.tensors, &tensor)
-		llm.parameters += tensor.Elements()
+		llm.parameters += tensor.parameters()
 	}
 
 	// patch KV with parameter count
 	llm.kv["general.parameter_count"] = llm.parameters
 
-	alignment := llm.kv.Uint("general.alignment", 32)
+	alignment, ok := llm.kv["general.alignment"].(uint32)
+	if !ok {
+		alignment = 32
+	}
 
 	offset, err := rs.Seek(0, io.SeekCurrent)
 	if err != nil {
@@ -295,23 +298,6 @@ func readGGUFV1String(llm *gguf, r io.Reader) (string, error) {
 	return b.String(), nil
 }
 
-func readGGUFV1StringsData(llm *gguf, r io.Reader, a *array[string]) (any, error) {
-	for i := range a.size {
-		if a.values != nil {
-			e, err := readGGUFV1String(llm, r)
-			if err != nil {
-				return nil, err
-			}
-
-			a.values[i] = e
-		} else {
-			discardGGUFString(llm, r)
-		}
-	}
-
-	return a, nil
-}
-
 func discardGGUFString(llm *gguf, r io.Reader) error {
 	buf := llm.scratch[:8]
 	_, err := io.ReadFull(r, buf)
@@ -369,44 +355,78 @@ func writeGGUFString(w io.Writer, s string) error {
 	return err
 }
 
-func readGGUFStringsData(llm *gguf, r io.Reader, a *array[string]) (any, error) {
+type array struct {
-	for i := range a.size {
+	size   int
-		if a.values != nil {
+	values []any
-			e, err := readGGUFString(llm, r)
+}
-			if err != nil {
-				return nil, err
-			}
 
+func (a *array) MarshalJSON() ([]byte, error) {
+	return json.Marshal(a.values)
+}
+
+func readGGUFV1Array(llm *gguf, r io.Reader) (*array, error) {
+	t, err := readGGUF[uint32](llm, r)
+	if err != nil {
+		return nil, err
+	}
+
+	n, err := readGGUF[uint32](llm, r)
+	if err != nil {
+		return nil, err
+	}
+
+	a := &array{size: int(n)}
+	if llm.canCollectArray(int(n)) {
+		a.values = make([]any, 0, int(n))
+	}
+
+	for i := range n {
+		var e any
+		switch t {
+		case ggufTypeUint8:
+			e, err = readGGUF[uint8](llm, r)
+		case ggufTypeInt8:
+			e, err = readGGUF[int8](llm, r)
+		case ggufTypeUint16:
+			e, err = readGGUF[uint16](llm, r)
+		case ggufTypeInt16:
+			e, err = readGGUF[int16](llm, r)
+		case ggufTypeUint32:
+			e, err = readGGUF[uint32](llm, r)
+		case ggufTypeInt32:
+			e, err = readGGUF[int32](llm, r)
+		case ggufTypeUint64:
+			e, err = readGGUF[uint64](llm, r)
+		case ggufTypeInt64:
+			e, err = readGGUF[int64](llm, r)
+		case ggufTypeFloat32:
+			e, err = readGGUF[float32](llm, r)
+		case ggufTypeFloat64:
+			e, err = readGGUF[float64](llm, r)
+		case ggufTypeBool:
+			e, err = readGGUF[bool](llm, r)
+		case ggufTypeString:
+			e, err = readGGUFV1String(llm, r)
+		default:
+			return nil, fmt.Errorf("invalid array type: %d", t)
+		}
+		if err != nil {
+			return nil, err
+		}
+
+		if a.values != nil {
 			a.values[i] = e
-		} else {
-			discardGGUFString(llm, r)
 		}
 	}
 
 	return a, nil
 }
 
-type array[T any] struct {
+func readGGUFArray(llm *gguf, r io.Reader) (*array, error) {
-	// size is the actual size of the array
+	if llm.Version == 1 {
-	size int
+		return readGGUFV1Array(llm, r)
-
-	// values is the array of values. this is nil if the array is larger than configured maxSize
-	values []T
-}
-
-func (a *array[T]) MarshalJSON() ([]byte, error) {
-	return json.Marshal(a.values)
-}
-
-func newArray[T any](size, maxSize int) *array[T] {
-	a := array[T]{size: size}
-	if maxSize < 0 || size <= maxSize {
-		a.values = make([]T, size)
 	}
-	return &a
-}
 
-func readGGUFArray(llm *gguf, r io.Reader) (any, error) {
 	t, err := readGGUF[uint32](llm, r)
 	if err != nil {
 		return nil, err
@@ -417,55 +437,45 @@ func readGGUFArray(llm *gguf, r io.Reader) (any, error) {
 		return nil, err
 	}
 
-	switch t {
+	a := &array{size: int(n)}
-	case ggufTypeUint8:
+	if llm.canCollectArray(int(n)) {
-		a := newArray[uint8](int(n), llm.maxArraySize)
+		a.values = make([]any, int(n))
-		return readGGUFArrayData(llm, r, a)
-	case ggufTypeInt8:
-		a := newArray[int8](int(n), llm.maxArraySize)
-		return readGGUFArrayData(llm, r, a)
-	case ggufTypeUint16:
-		a := newArray[uint16](int(n), llm.maxArraySize)
-		return readGGUFArrayData(llm, r, a)
-	case ggufTypeInt16:
-		a := newArray[int16](int(n), llm.maxArraySize)
-		return readGGUFArrayData(llm, r, a)
-	case ggufTypeUint32:
-		a := newArray[uint32](int(n), llm.maxArraySize)
-		return readGGUFArrayData(llm, r, a)
-	case ggufTypeInt32:
-		a := newArray[int32](int(n), llm.maxArraySize)
-		return readGGUFArrayData(llm, r, a)
-	case ggufTypeUint64:
-		a := newArray[uint64](int(n), llm.maxArraySize)
-		return readGGUFArrayData(llm, r, a)
-	case ggufTypeInt64:
-		a := newArray[int64](int(n), llm.maxArraySize)
-		return readGGUFArrayData(llm, r, a)
-	case ggufTypeFloat32:
-		a := newArray[float32](int(n), llm.maxArraySize)
-		return readGGUFArrayData(llm, r, a)
-	case ggufTypeFloat64:
-		a := newArray[float64](int(n), llm.maxArraySize)
-		return readGGUFArrayData(llm, r, a)
-	case ggufTypeBool:
-		a := newArray[bool](int(n), llm.maxArraySize)
-		return readGGUFArrayData(llm, r, a)
-	case ggufTypeString:
-		a := newArray[string](int(n), llm.maxArraySize)
-		if llm.Version == 1 {
-			return readGGUFV1StringsData(llm, r, a)
-		}
-
-		return readGGUFStringsData(llm, r, a)
-	default:
-		return nil, fmt.Errorf("invalid array type: %d", t)
 	}
-}
 
-func readGGUFArrayData[T any](llm *gguf, r io.Reader, a *array[T]) (any, error) {
+	for i := range n {
-	for i := range a.size {
+		var e any
-		e, err := readGGUF[T](llm, r)
+		switch t {
+		case ggufTypeUint8:
+			e, err = readGGUF[uint8](llm, r)
+		case ggufTypeInt8:
+			e, err = readGGUF[int8](llm, r)
+		case ggufTypeUint16:
+			e, err = readGGUF[uint16](llm, r)
+		case ggufTypeInt16:
+			e, err = readGGUF[int16](llm, r)
+		case ggufTypeUint32:
+			e, err = readGGUF[uint32](llm, r)
+		case ggufTypeInt32:
+			e, err = readGGUF[int32](llm, r)
+		case ggufTypeUint64:
+			e, err = readGGUF[uint64](llm, r)
+		case ggufTypeInt64:
+			e, err = readGGUF[int64](llm, r)
+		case ggufTypeFloat32:
+			e, err = readGGUF[float32](llm, r)
+		case ggufTypeFloat64:
+			e, err = readGGUF[float64](llm, r)
+		case ggufTypeBool:
+			e, err = readGGUF[bool](llm, r)
+		case ggufTypeString:
+			if a.values != nil {
+				e, err = readGGUFString(llm, r)
+			} else {
+				err = discardGGUFString(llm, r)
+			}
+		default:
+			return nil, fmt.Errorf("invalid array type: %d", t)
+		}
 		if err != nil {
 			return nil, err
 		}
@@ -492,38 +502,23 @@ func writeGGUFArray[S ~[]E, E any](w io.Writer, t uint32, s S) error {
 		return err
 	}
 
-	if t == ggufTypeString {
-		for _, e := range any(s).([]string) {
-			if err := binary.Write(w, binary.LittleEndian, uint64(len(e))); err != nil {
-				return err
-			}
-
-			if err := binary.Write(w, binary.LittleEndian, []byte(e)); err != nil {
-				return err
-			}
-		}
-		return nil
-	}
-
 	return binary.Write(w, binary.LittleEndian, s)
 }
 
-func WriteGGUF(f *os.File, kv KV, ts []*Tensor) error {
+func WriteGGUF(ws io.WriteSeeker, kv KV, ts []Tensor) error {
-	alignment := kv.Uint("general.alignment", 32)
+	if err := binary.Write(ws, binary.LittleEndian, []byte("GGUF")); err != nil {
-
-	if err := binary.Write(f, binary.LittleEndian, []byte("GGUF")); err != nil {
 		return err
 	}
 
-	if err := binary.Write(f, binary.LittleEndian, uint32(3)); err != nil {
+	if err := binary.Write(ws, binary.LittleEndian, uint32(3)); err != nil {
 		return err
 	}
 
-	if err := binary.Write(f, binary.LittleEndian, uint64(len(ts))); err != nil {
+	if err := binary.Write(ws, binary.LittleEndian, uint64(len(ts))); err != nil {
 		return err
 	}
 
-	if err := binary.Write(f, binary.LittleEndian, uint64(len(kv))); err != nil {
+	if err := binary.Write(ws, binary.LittleEndian, uint64(len(kv))); err != nil {
 		return err
 	}
 
@@ -531,12 +526,12 @@ func WriteGGUF(f *os.File, kv KV, ts []*Tensor) error {
 	slices.Sort(keys)
 
 	for _, key := range keys {
-		if err := ggufWriteKV(f, key, kv[key]); err != nil {
+		if err := ggufWriteKV(ws, key, kv[key]); err != nil {
 			return err
 		}
 	}
 
-	slices.SortStableFunc(ts, func(a, b *Tensor) int {
+	slices.SortStableFunc(ts, func(a, b Tensor) int {
 		if i, j := a.block(), b.block(); i < 0 && j > 0 {
 			return 1
 		} else if i > 0 && j < 0 {
@@ -547,34 +542,22 @@ func WriteGGUF(f *os.File, kv KV, ts []*Tensor) error {
 	})
 
 	var s uint64
-	for i := range ts {
+	for _, t := range ts {
-		ts[i].Offset = s
+		t.Offset = s
-		if err := ggufWriteTensorInfo(f, ts[i]); err != nil {
+		if err := ggufWriteTensorInfo(ws, t); err != nil {
 			return err
 		}
-		s += ts[i].Size()
+		s += t.Size()
-		s += uint64(ggufPadding(int64(s), int64(alignment)))
 	}
 
-	offset, err := f.Seek(0, io.SeekCurrent)
+	var alignment int64 = 32
-	if err != nil {
-		return err
-	}
-	offset += ggufPadding(offset, int64(alignment))
-
-	var g errgroup.Group
-	g.SetLimit(runtime.GOMAXPROCS(0))
-	// TODO consider reducing if tensors size * gomaxprocs is larger than free memory
 	for _, t := range ts {
-		t := t
+		if err := ggufWriteTensor(ws, t, alignment); err != nil {
-		w := io.NewOffsetWriter(f, offset+int64(t.Offset))
-		g.Go(func() error {
-			_, err := t.WriteTo(w)
 			return err
-		})
+		}
 	}
 
-	return g.Wait()
+	return nil
 }
 
 func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
@@ -589,10 +572,8 @@ func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
 
 	var err error
 	switch v := v.(type) {
-	case uint32, FileType:
+	case uint32:
 		err = writeGGUF(ws, ggufTypeUint32, v)
-	case uint64:
-		err = writeGGUF(ws, ggufTypeUint64, v)
 	case float32:
 		err = writeGGUF(ws, ggufTypeFloat32, v)
 	case bool:
@@ -601,20 +582,32 @@ func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
 		err = writeGGUFString(ws, v)
 	case []int32:
 		err = writeGGUFArray(ws, ggufTypeInt32, v)
-	case *array[int32]:
-		err = writeGGUFArray(ws, ggufTypeInt32, v.values)
 	case []uint32:
 		err = writeGGUFArray(ws, ggufTypeUint32, v)
-	case *array[uint32]:
-		err = writeGGUFArray(ws, ggufTypeUint32, v.values)
 	case []float32:
 		err = writeGGUFArray(ws, ggufTypeFloat32, v)
-	case *array[float32]:
-		err = writeGGUFArray(ws, ggufTypeFloat32, v.values)
 	case []string:
-		err = writeGGUFArray(ws, ggufTypeString, v)
+		if err := binary.Write(ws, binary.LittleEndian, ggufTypeArray); err != nil {
-	case *array[string]:
+			return err
-		err = writeGGUFArray(ws, ggufTypeString, v.values)
+		}
+
+		if err := binary.Write(ws, binary.LittleEndian, ggufTypeString); err != nil {
+			return err
+		}
+
+		if err := binary.Write(ws, binary.LittleEndian, uint64(len(v))); err != nil {
+			return err
+		}
+
+		for _, e := range v {
+			if err := binary.Write(ws, binary.LittleEndian, uint64(len(e))); err != nil {
+				return err
+			}
+
+			if err := binary.Write(ws, binary.LittleEndian, []byte(e)); err != nil {
+				return err
+			}
+		}
 	default:
 		return fmt.Errorf("improper type for '%s'", k)
 	}
@@ -622,7 +615,7 @@ func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
 	return err
 }
 
-func ggufWriteTensorInfo(ws io.WriteSeeker, t *Tensor) error {
+func ggufWriteTensorInfo(ws io.WriteSeeker, t Tensor) error {
 	slog.Debug(t.Name, "kind", t.Kind, "shape", t.Shape, "offset", t.Offset)
 	if err := binary.Write(ws, binary.LittleEndian, uint64(len(t.Name))); err != nil {
 		return err
@@ -636,8 +629,8 @@ func ggufWriteTensorInfo(ws io.WriteSeeker, t *Tensor) error {
 		return err
 	}
 
-	for _, n := range t.Shape {
+	for i := range len(t.Shape) {
-		if err := binary.Write(ws, binary.LittleEndian, n); err != nil {
+		if err := binary.Write(ws, binary.LittleEndian, t.Shape[len(t.Shape)-i-1]); err != nil {
 			return err
 		}
 	}
@@ -649,6 +642,20 @@ func ggufWriteTensorInfo(ws io.WriteSeeker, t *Tensor) error {
 	return binary.Write(ws, binary.LittleEndian, t.Offset)
 }
 
+func ggufWriteTensor(ws io.WriteSeeker, t Tensor, alignment int64) error {
+	offset, err := ws.Seek(0, io.SeekCurrent)
+	if err != nil {
+		return err
+	}
+
+	if err := binary.Write(ws, binary.LittleEndian, bytes.Repeat([]byte{0}, int(ggufPadding(offset, alignment)))); err != nil {
+		return err
+	}
+
+	_, err = t.WriteTo(ws)
+	return err
+}
+
 func ggufPadding(offset, align int64) int64 {
 	return (align - offset%align) % align
 }

fs/ggml/gguf_test.go

@@ -1,63 +0,0 @@
-package ggml
-
-import (
-	"bytes"
-	"os"
-	"slices"
-	"testing"
-
-	"github.com/google/go-cmp/cmp"
-)
-
-func TestWriteGGUF(t *testing.T) {
-	w, err := os.CreateTemp(t.TempDir(), "*.bin")
-	if err != nil {
-		t.Fatal(err)
-	}
-	defer w.Close()
-
-	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, 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(ff.KV(), KV{
-		"general.alignment":       uint32(16),
-		"general.parameter_count": uint64(36),
-	}); 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/ggml/type.go

@@ -1,31 +1,26 @@
 package ggml
 
-import (
+import "fmt"
-	"fmt"
-	"log/slog"
-	"strings"
-)
 
-// FileType is the Go equivalent to llama_ftype used for gguf file typing
+type fileType uint32
-type FileType uint32
 
 const (
-	FileTypeF32 FileType = iota
+	fileTypeF32 fileType = iota
-	FileTypeF16
+	fileTypeF16
 	fileTypeQ4_0
 	fileTypeQ4_1
-	fileTypeQ4_1_F16 // unused by GGML
+	fileTypeQ4_1_F16
-	fileTypeQ4_2     // unused by GGML
+	fileTypeQ4_2 // unused
-	fileTypeQ4_3     // unused by GGML
+	fileTypeQ4_3 // unused
-	FileTypeQ8_0
+	fileTypeQ8_0
 	fileTypeQ5_0
 	fileTypeQ5_1
 	fileTypeQ2_K
 	fileTypeQ3_K_S
 	fileTypeQ3_K_M
 	fileTypeQ3_K_L
-	FileTypeQ4_K_S
+	fileTypeQ4_K_S
-	FileTypeQ4_K_M
+	fileTypeQ4_K_M
 	fileTypeQ5_K_S
 	fileTypeQ5_K_M
 	fileTypeQ6_K
@@ -37,67 +32,95 @@ const (
 	fileTypeIQ1_S
 	fileTypeIQ4_NL
 	fileTypeIQ3_S
-	fileTypeIQ3_M
 	fileTypeIQ2_S
-	fileTypeIQ2_M
 	fileTypeIQ4_XS
+	fileTypeIQ2_M
 	fileTypeIQ1_M
-	FileTypeBF16
+	fileTypeBF16
-	fileTypeQ4_0_4_4 // unused by GGML
-	fileTypeQ4_0_4_8 // unused by GGML
-	fileTypeQ4_0_8_8 // unused by GGML
-	fileTypeTQ1_0
-	fileTypeTQ2_0
 
-	FileTypeUnknown = 1024
+	fileTypeUnknown
 )
 
-// ParseFileType parses the provided GGUF file type
+func ParseFileType(s string) (fileType, error) {
-// Only Ollama supported types are considered valid
-func ParseFileType(s string) (FileType, error) {
 	switch s {
 	case "F32":
-		return FileTypeF32, nil
+		return fileTypeF32, nil
 	case "F16":
-		return FileTypeF16, nil
+		return fileTypeF16, nil
+	case "Q4_0":
+		return fileTypeQ4_0, nil
+	case "Q4_1":
+		return fileTypeQ4_1, nil
+	case "Q4_1_F16":
+		return fileTypeQ4_1_F16, nil
 	case "Q8_0":
-		return FileTypeQ8_0, nil
+		return fileTypeQ8_0, nil
+	case "Q5_0":
+		return fileTypeQ5_0, nil
+	case "Q5_1":
+		return fileTypeQ5_1, nil
+	case "Q2_K":
+		return fileTypeQ2_K, nil
+	case "Q3_K_S":
+		return fileTypeQ3_K_S, nil
+	case "Q3_K_M":
+		return fileTypeQ3_K_M, nil
+	case "Q3_K_L":
+		return fileTypeQ3_K_L, nil
 	case "Q4_K_S":
-		return FileTypeQ4_K_S, nil
+		return fileTypeQ4_K_S, nil
-	case "Q4_K_M", "Q4_K":
+	case "Q4_K_M":
-		return FileTypeQ4_K_M, nil
+		return fileTypeQ4_K_M, nil
+	case "Q5_K_S":
+		return fileTypeQ5_K_S, nil
+	case "Q5_K_M":
+		return fileTypeQ5_K_M, nil
+	case "Q6_K":
+		return fileTypeQ6_K, nil
+	case "IQ2_XXS":
+		return fileTypeIQ2_XXS, nil
+	case "IQ2_XS":
+		return fileTypeIQ2_XS, nil
+	case "Q2_K_S":
+		return fileTypeQ2_K_S, nil
+	case "IQ3_XS":
+		return fileTypeIQ3_XS, nil
+	case "IQ3_XXS":
+		return fileTypeIQ3_XXS, nil
+	case "IQ1_S":
+		return fileTypeIQ1_S, nil
+	case "IQ4_NL":
+		return fileTypeIQ4_NL, nil
+	case "IQ3_S":
+		return fileTypeIQ3_S, nil
+	case "IQ2_S":
+		return fileTypeIQ2_S, nil
+	case "IQ4_XS":
+		return fileTypeIQ4_XS, nil
+	case "IQ2_M":
+		return fileTypeIQ2_M, nil
+	case "IQ1_M":
+		return fileTypeIQ1_M, nil
 	case "BF16":
-		return FileTypeBF16, nil
+		return fileTypeBF16, nil
 	default:
-		supportedFileTypes := []FileType{
+		return fileTypeUnknown, fmt.Errorf("unknown fileType: %s", s)
-			FileTypeF32,
-			FileTypeF16,
-			FileTypeQ4_K_S,
-			FileTypeQ4_K_M,
-			FileTypeQ8_0,
-			// fsggml.FileTypeBF16, // TODO
-		}
-		strs := make([]string, len(supportedFileTypes))
-		for i := range supportedFileTypes {
-			strs[i] = supportedFileTypes[i].String()
-		}
-
-		return FileTypeUnknown, fmt.Errorf("unsupported quantization type %s - supported types are %s", s, strings.Join(strs, ", "))
 	}
 }
 
-func (t FileType) String() string {
+func (t fileType) String() string {
-	// Note: this routine will return a broader set of file types for existing models
 	switch t {
-	case FileTypeF32:
+	case fileTypeF32:
 		return "F32"
-	case FileTypeF16:
+	case fileTypeF16:
 		return "F16"
 	case fileTypeQ4_0:
 		return "Q4_0"
 	case fileTypeQ4_1:
 		return "Q4_1"
-	case FileTypeQ8_0:
+	case fileTypeQ4_1_F16:
+		return "Q4_1_F16"
+	case fileTypeQ8_0:
 		return "Q8_0"
 	case fileTypeQ5_0:
 		return "Q5_0"
@@ -111,9 +134,9 @@ func (t FileType) String() string {
 		return "Q3_K_M"
 	case fileTypeQ3_K_L:
 		return "Q3_K_L"
-	case FileTypeQ4_K_S:
+	case fileTypeQ4_K_S:
 		return "Q4_K_S"
-	case FileTypeQ4_K_M:
+	case fileTypeQ4_K_M:
 		return "Q4_K_M"
 	case fileTypeQ5_K_S:
 		return "Q5_K_S"
@@ -121,198 +144,37 @@ func (t FileType) String() string {
 		return "Q5_K_M"
 	case fileTypeQ6_K:
 		return "Q6_K"
+	case fileTypeIQ2_XXS:
+		return "IQ2_XXS"
+	case fileTypeIQ2_XS:
+		return "IQ2_XS"
 	case fileTypeQ2_K_S:
 		return "Q2_K_S"
-	case FileTypeBF16:
+	case fileTypeIQ3_XS:
+		return "IQ3_XS"
+	case fileTypeIQ3_XXS:
+		return "IQ3_XXS"
+	case fileTypeIQ1_S:
+		return "IQ1_S"
+	case fileTypeIQ4_NL:
+		return "IQ4_NL"
+	case fileTypeIQ3_S:
+		return "IQ3_S"
+	case fileTypeIQ2_S:
+		return "IQ2_S"
+	case fileTypeIQ4_XS:
+		return "IQ4_XS"
+	case fileTypeIQ2_M:
+		return "IQ2_M"
+	case fileTypeIQ1_M:
+		return "IQ1_M"
+	case fileTypeBF16:
 		return "BF16"
 	default:
 		return "unknown"
 	}
 }
 
-func (t FileType) Value() uint32 {
+func (t fileType) Value() uint32 {
 	return uint32(t)
 }
-
-func (ftype FileType) ToTensorType() TensorType {
-	switch ftype {
-	case FileTypeF32:
-		return TensorTypeF32
-	case FileTypeF16:
-		return TensorTypeF16
-	case fileTypeQ4_0:
-		return TensorTypeQ4_0
-	case fileTypeQ4_1:
-		return TensorTypeQ4_1
-	case FileTypeQ8_0:
-		return TensorTypeQ8_0
-	case fileTypeQ5_0:
-		return TensorTypeQ5_0
-	case fileTypeQ5_1:
-		return TensorTypeQ5_1
-	case fileTypeQ2_K:
-		return TensorTypeQ2_K
-	case fileTypeQ3_K_S:
-		return TensorTypeQ3_K
-	case fileTypeQ3_K_M:
-		return TensorTypeQ3_K
-	case fileTypeQ3_K_L:
-		return TensorTypeQ3_K
-	case FileTypeQ4_K_S:
-		return TensorTypeQ4_K
-	case FileTypeQ4_K_M:
-		return TensorTypeQ4_K
-	case fileTypeQ5_K_S:
-		return TensorTypeQ5_K
-	case fileTypeQ5_K_M:
-		return TensorTypeQ5_K
-	case fileTypeQ6_K:
-		return TensorTypeQ6_K
-	case fileTypeQ2_K_S:
-		return TensorTypeQ2_K
-	case FileTypeBF16:
-		return TensorTypeBF16
-	default:
-		slog.Warn("unsupported file type", "type", ftype)
-		return 0 // F32
-	}
-}
-
-// TensorType is equivalent to ggml_type for individual tensor types
-// Note: these are not the same as FileType
-type TensorType uint32
-
-const (
-	TensorTypeF32 TensorType = iota
-	TensorTypeF16
-	TensorTypeQ4_0
-	TensorTypeQ4_1
-	tensorTypeQ4_2 // unused by GGML
-	tensorTypeQ4_3 // unused by GGML
-	TensorTypeQ5_0
-	TensorTypeQ5_1
-	TensorTypeQ8_0
-	TensorTypeQ8_1
-	TensorTypeQ2_K
-	TensorTypeQ3_K
-	TensorTypeQ4_K
-	TensorTypeQ5_K
-	TensorTypeQ6_K
-	TensorTypeQ8_K
-	tensorTypeIQ2_XXS // not supported by ollama
-	tensorTypeIQ2_XS  // not supported by ollama
-	tensorTypeIQ3_XXS // not supported by ollama
-	tensorTypeIQ1_S   // not supported by ollama
-	tensorTypeIQ4_NL  // not supported by ollama
-	tensorTypeIQ3_S   // not supported by ollama
-	tensorTypeIQ2_S   // not supported by ollama
-	tensorTypeIQ4_XS  // not supported by ollama
-	TensorTypeI8
-	TensorTypeI16
-	TensorTypeI32
-	TensorTypeI64
-	TensorTypeF64
-	tensorTypeIQ1_M // not supported by ollama
-	TensorTypeBF16
-	tensorTypeQ4_0_4_4   // unused by GGML
-	tensorTypeQ4_0_4_8   // unused by GGML
-	tensorTypeQ4_0_8_8   // unused by GGML
-	tensorTypeTQ1_0      // not supported by ollama
-	tensorTypeTQ2_0      // not supported by ollama
-	tensorTypeIQ4_NL_4_4 // unused by GGML
-	tensorTypeIQ4_NL_4_8 // unused by GGML
-	tensorTypeIQ4_NL_8_8 // unused by GGML
-)
-
-// ParseFileType parses the provided GGUF file type
-// Only Ollama supported types are considered valid
-func ParseTensorType(s string) (TensorType, error) {
-	switch s {
-	case "F32":
-		return TensorTypeF32, nil
-	case "F16":
-		return TensorTypeF16, nil
-	case "Q4_0":
-		return TensorTypeQ4_0, nil
-	case "Q4_1":
-		return TensorTypeQ4_1, nil
-	case "Q5_0":
-		return TensorTypeQ5_0, nil
-	case "Q5_1":
-		return TensorTypeQ5_1, nil
-	case "Q8_0":
-		return TensorTypeQ8_0, nil
-	case "Q8_1":
-		return TensorTypeQ8_1, nil
-	case "Q2_K":
-		return TensorTypeQ2_K, nil
-	case "Q3_K":
-		return TensorTypeQ3_K, nil
-	case "Q4_K":
-		return TensorTypeQ4_K, nil
-	case "Q5_K":
-		return TensorTypeQ5_K, nil
-	case "Q6_K":
-		return TensorTypeQ6_K, nil
-	case "Q8_K":
-		return TensorTypeQ8_K, nil
-	case "F64":
-		return TensorTypeF64, nil
-	case "BF16":
-		return TensorTypeBF16, nil
-	default:
-		return 0, fmt.Errorf("unsupported quantization type %s", s)
-	}
-}
-
-func (t TensorType) IsQuantized() bool {
-	switch t {
-	case TensorTypeF32, TensorTypeF16, TensorTypeBF16:
-		return false
-	default:
-		return true
-	}
-}
-
-func (t TensorType) RowSize(ne uint64) uint64 {
-	return t.TypeSize() * ne / t.BlockSize()
-}
-
-func (t TensorType) String() string {
-	switch t {
-	case TensorTypeF32:
-		return "F32"
-	case TensorTypeF16:
-		return "F16"
-	case TensorTypeQ4_0:
-		return "Q4_0"
-	case TensorTypeQ4_1:
-		return "Q4_1"
-	case TensorTypeQ5_0:
-		return "Q5_0"
-	case TensorTypeQ5_1:
-		return "Q5_1"
-	case TensorTypeQ8_0:
-		return "Q8_0"
-	case TensorTypeQ8_1:
-		return "Q8_1"
-	case TensorTypeQ2_K:
-		return "Q2_K"
-	case TensorTypeQ3_K:
-		return "Q3_K"
-	case TensorTypeQ4_K:
-		return "Q4_K"
-	case TensorTypeQ5_K:
-		return "Q5_K"
-	case TensorTypeQ6_K:
-		return "Q6_K"
-	case TensorTypeQ8_K:
-		return "Q8_K"
-	case TensorTypeF64:
-		return "F64"
-	case TensorTypeBF16:
-		return "BF16"
-	default:
-		return "unknown"
-	}
-}

go.mod

@@ -1,6 +1,6 @@
 module github.com/ollama/ollama
 
-go 1.24.0
+go 1.23.4
 
 require (
 	github.com/containerd/console v1.0.3
@@ -11,7 +11,7 @@ require (
 	github.com/spf13/cobra v1.7.0
 	github.com/stretchr/testify v1.9.0
 	github.com/x448/float16 v0.8.4
-	golang.org/x/sync v0.12.0
+	golang.org/x/sync v0.10.0
 )
 
 require (
@@ -24,7 +24,7 @@ require (
 	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 (
@@ -44,7 +44,6 @@ 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
 )
@@ -70,12 +69,12 @@ require (
 	github.com/twitchyliquid64/golang-asm v0.15.1 // indirect
 	github.com/ugorji/go/codec v1.2.12 // indirect
 	golang.org/x/arch v0.8.0 // indirect
-	golang.org/x/crypto v0.36.0
+	golang.org/x/crypto v0.31.0
-	golang.org/x/exp v0.0.0-20250218142911-aa4b98e5adaa
+	golang.org/x/exp v0.0.0-20231110203233-9a3e6036ecaa
-	golang.org/x/net v0.38.0 // indirect
+	golang.org/x/net v0.25.0 // indirect
-	golang.org/x/sys v0.31.0
+	golang.org/x/sys v0.28.0
-	golang.org/x/term v0.30.0
+	golang.org/x/term v0.27.0
-	golang.org/x/text v0.23.0
+	golang.org/x/text v0.21.0
 	google.golang.org/protobuf v1.34.1
 	gopkg.in/yaml.v3 v3.0.1 // indirect
 )

go.sum

@@ -214,16 +214,16 @@ golang.org/x/crypto v0.0.0-20190308221718-c2843e01d9a2/go.mod h1:djNgcEr1/C05ACk
 golang.org/x/crypto v0.0.0-20190510104115-cbcb75029529/go.mod h1:yigFU9vqHzYiE8UmvKecakEJjdnWj3jj499lnFckfCI=
 golang.org/x/crypto v0.0.0-20191011191535-87dc89f01550/go.mod h1:yigFU9vqHzYiE8UmvKecakEJjdnWj3jj499lnFckfCI=
 golang.org/x/crypto v0.0.0-20200622213623-75b288015ac9/go.mod h1:LzIPMQfyMNhhGPhUkYOs5KpL4U8rLKemX1yGLhDgUto=
-golang.org/x/crypto v0.36.0 h1:AnAEvhDddvBdpY+uR+MyHmuZzzNqXSe/GvuDeob5L34=
+golang.org/x/crypto v0.31.0 h1:ihbySMvVjLAeSH1IbfcRTkD/iNscyz8rGzjF/E5hV6U=
-golang.org/x/crypto v0.36.0/go.mod h1:Y4J0ReaxCR1IMaabaSMugxJES1EpwhBHhv2bDHklZvc=
+golang.org/x/crypto v0.31.0/go.mod h1:kDsLvtWBEx7MV9tJOj9bnXsPbxwJQ6csT/x4KIN4Ssk=
 golang.org/x/exp v0.0.0-20180321215751-8460e604b9de/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
 golang.org/x/exp v0.0.0-20180807140117-3d87b88a115f/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
 golang.org/x/exp v0.0.0-20190121172915-509febef88a4/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
 golang.org/x/exp v0.0.0-20190125153040-c74c464bbbf2/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
 golang.org/x/exp v0.0.0-20190306152737-a1d7652674e8/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
 golang.org/x/exp v0.0.0-20191002040644-a1355ae1e2c3/go.mod h1:NOZ3BPKG0ec/BKJQgnvsSFpcKLM5xXVWnvZS97DWHgE=
-golang.org/x/exp v0.0.0-20250218142911-aa4b98e5adaa h1:t2QcU6V556bFjYgu4L6C+6VrCPyJZ+eyRsABUPs1mz4=
+golang.org/x/exp v0.0.0-20231110203233-9a3e6036ecaa h1:FRnLl4eNAQl8hwxVVC17teOw8kdjVDVAiFMtgUdTSRQ=
-golang.org/x/exp v0.0.0-20250218142911-aa4b98e5adaa/go.mod h1:BHOTPb3L19zxehTsLoJXVaTktb06DFgmdW6Wb9s8jqk=
+golang.org/x/exp v0.0.0-20231110203233-9a3e6036ecaa/go.mod h1:zk2irFbV9DP96SEBUUAy67IdHUaZuSnrz1n472HUCLE=
 golang.org/x/image v0.0.0-20180708004352-c73c2afc3b81/go.mod h1:ux5Hcp/YLpHSI86hEcLt0YII63i6oz57MZXIpbrjZUs=
 golang.org/x/image v0.0.0-20190227222117-0694c2d4d067/go.mod h1:kZ7UVZpmo3dzQBMxlp+ypCbDeSB+sBbTgSJuh5dn5js=
 golang.org/x/image v0.0.0-20190802002840-cff245a6509b/go.mod h1:FeLwcggjj3mMvU+oOTbSwawSJRM1uh48EjtB4UJZlP0=
@@ -257,8 +257,8 @@ golang.org/x/net v0.0.0-20200822124328-c89045814202/go.mod h1:/O7V0waA8r7cgGh81R
 golang.org/x/net v0.0.0-20201021035429-f5854403a974/go.mod h1:sp8m0HH+o8qH0wwXwYZr8TS3Oi6o0r6Gce1SSxlDquU=
 golang.org/x/net v0.0.0-20210405180319-a5a99cb37ef4/go.mod h1:p54w0d4576C0XHj96bSt6lcn1PtDYWL6XObtHCRCNQM=
 golang.org/x/net v0.0.0-20210614182718-04defd469f4e/go.mod h1:9nx3DQGgdP8bBQD5qxJ1jj9UTztislL4KSBs9R2vV5Y=
-golang.org/x/net v0.38.0 h1:vRMAPTMaeGqVhG5QyLJHqNDwecKTomGeqbnfZyKlBI8=
+golang.org/x/net v0.25.0 h1:d/OCCoBEUq33pjydKrGQhw7IlUPI2Oylr+8qLx49kac=
-golang.org/x/net v0.38.0/go.mod h1:ivrbrMbzFq5J41QOQh0siUuly180yBYtLp+CKbEaFx8=
+golang.org/x/net v0.25.0/go.mod h1:JkAGAh7GEvH74S6FOH42FLoXpXbE/aqXSrIQjXgsiwM=
 golang.org/x/oauth2 v0.0.0-20180821212333-d2e6202438be/go.mod h1:N/0e6XlmueqKjAGxoOufVs8QHGRruUQn6yWY3a++T0U=
 golang.org/x/oauth2 v0.0.0-20200107190931-bf48bf16ab8d/go.mod h1:gOpvHmFTYa4IltrdGE7lF6nIHvwfUNPOp7c8zoXwtLw=
 golang.org/x/sync v0.0.0-20180314180146-1d60e4601c6f/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
@@ -268,8 +268,8 @@ golang.org/x/sync v0.0.0-20190423024810-112230192c58/go.mod h1:RxMgew5VJxzue5/jJ
 golang.org/x/sync v0.0.0-20190911185100-cd5d95a43a6e/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
 golang.org/x/sync v0.0.0-20201020160332-67f06af15bc9/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
 golang.org/x/sync v0.0.0-20210220032951-036812b2e83c/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
-golang.org/x/sync v0.12.0 h1:MHc5BpPuC30uJk597Ri8TV3CNZcTLu6B6z4lJy+g6Jw=
+golang.org/x/sync v0.10.0 h1:3NQrjDixjgGwUOCaF8w2+VYHv0Ve/vGYSbdkTa98gmQ=
-golang.org/x/sync v0.12.0/go.mod h1:1dzgHSNfp02xaA81J2MS99Qcpr2w7fw1gpm99rleRqA=
+golang.org/x/sync v0.10.0/go.mod h1:Czt+wKu1gCyEFDUtn0jG5QVvpJ6rzVqr5aXyt9drQfk=
 golang.org/x/sys v0.0.0-20180830151530-49385e6e1522/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
 golang.org/x/sys v0.0.0-20190215142949-d0b11bdaac8a/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
 golang.org/x/sys v0.0.0-20190312061237-fead79001313/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
@@ -285,17 +285,17 @@ golang.org/x/sys v0.0.0-20210510120138-977fb7262007/go.mod h1:oPkhp1MJrh7nUepCBc
 golang.org/x/sys v0.0.0-20210630005230-0f9fa26af87c/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.5.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.6.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
-golang.org/x/sys v0.31.0 h1:ioabZlmFYtWhL+TRYpcnNlLwhyxaM9kWTDEmfnprqik=
+golang.org/x/sys v0.28.0 h1:Fksou7UEQUWlKvIdsqzJmUmCX3cZuD2+P3XyyzwMhlA=
-golang.org/x/sys v0.31.0/go.mod h1:BJP2sWEmIv4KK5OTEluFJCKSidICx8ciO85XgH3Ak8k=
+golang.org/x/sys v0.28.0/go.mod h1:/VUhepiaJMQUp4+oa/7Zr1D23ma6VTLIYjOOTFZPUcA=
 golang.org/x/term v0.0.0-20201126162022-7de9c90e9dd1/go.mod h1:bj7SfCRtBDWHUb9snDiAeCFNEtKQo2Wmx5Cou7ajbmo=
-golang.org/x/term v0.30.0 h1:PQ39fJZ+mfadBm0y5WlL4vlM7Sx1Hgf13sMIY2+QS9Y=
+golang.org/x/term v0.27.0 h1:WP60Sv1nlK1T6SupCHbXzSaN0b9wUmsPoRS9b61A23Q=
-golang.org/x/term v0.30.0/go.mod h1:NYYFdzHoI5wRh/h5tDMdMqCqPJZEuNqVR5xJLd/n67g=
+golang.org/x/term v0.27.0/go.mod h1:iMsnZpn0cago0GOrHO2+Y7u7JPn5AylBrcoWkElMTSM=
 golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
 golang.org/x/text v0.3.3/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
 golang.org/x/text v0.3.5/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
 golang.org/x/text v0.3.6/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
-golang.org/x/text v0.23.0 h1:D71I7dUrlY+VX0gQShAThNGHFxZ13dGLBHQLVl1mJlY=
+golang.org/x/text v0.21.0 h1:zyQAAkrwaneQ066sspRyJaG9VNi/YJ1NfzcGB3hZ/qo=
-golang.org/x/text v0.23.0/go.mod h1:/BLNzu4aZCJ1+kcD0DNRotWKage4q2rGVAg4o22unh4=
+golang.org/x/text v0.21.0/go.mod h1:4IBbMaMmOPCJ8SecivzSH54+73PCFmPWxNTLm+vZkEQ=
 golang.org/x/tools v0.0.0-20180525024113-a5b4c53f6e8b/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
 golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
 golang.org/x/tools v0.0.0-20190114222345-bf090417da8b/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
@@ -309,8 +309,6 @@ golang.org/x/tools v0.0.0-20200130002326-2f3ba24bd6e7/go.mod h1:TB2adYChydJhpapK
 golang.org/x/tools v0.0.0-20200619180055-7c47624df98f/go.mod h1:EkVYQZoAsY45+roYkvgYkIh4xh/qjgUK9TdY2XT94GE=
 golang.org/x/tools v0.0.0-20210106214847-113979e3529a/go.mod h1:emZCQorbCU4vsT4fOWvOPXz4eW1wZW4PmDk9uLelYpA=
 golang.org/x/tools v0.1.4/go.mod h1:o0xws9oXOQQZyjljx8fwUC0k7L1pTE6eaCbjGeHmOkk=
-golang.org/x/tools v0.30.0 h1:BgcpHewrV5AUp2G9MebG4XPFI1E2W41zU1SaqVA9vJY=
-golang.org/x/tools v0.30.0/go.mod h1:c347cR/OJfw5TI+GfX7RUPNMdDRRbjvYTS0jPyvsVtY=
 golang.org/x/xerrors v0.0.0-20190717185122-a985d3407aa7/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
 golang.org/x/xerrors v0.0.0-20191011141410-1b5146add898/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
 golang.org/x/xerrors v0.0.0-20191204190536-9bdfabe68543/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=

integration/api_test.go

@@ -1,412 +0,0 @@
-//go:build integration
-
-package integration
-
-import (
-	"bytes"
-	"context"
-	"fmt"
-	"math/rand"
-	"strings"
-	"testing"
-	"time"
-
-	"github.com/ollama/ollama/api"
-)
-
-func TestAPIGenerate(t *testing.T) {
-	initialTimeout := 60 * time.Second
-	streamTimeout := 30 * time.Second
-	ctx, cancel := context.WithTimeout(context.Background(), 1*time.Minute)
-	defer cancel()
-	// Set up the test data
-	req := api.GenerateRequest{
-		Model:  smol,
-		Prompt: "why is the sky blue? be brief",
-		Options: map[string]interface{}{
-			"temperature": 0,
-			"seed":        123,
-		},
-	}
-	anyResp := []string{"rayleigh", "scattering"}
-
-	client, _, cleanup := InitServerConnection(ctx, t)
-	defer cleanup()
-	if err := PullIfMissing(ctx, client, req.Model); err != nil {
-		t.Fatalf("pull failed %s", err)
-	}
-
-	tests := []struct {
-		name   string
-		stream bool
-	}{
-		{
-			name:   "stream",
-			stream: true,
-		},
-		{
-			name:   "no_stream",
-			stream: false,
-		},
-	}
-
-	for _, test := range tests {
-		t.Run(test.name, func(t *testing.T) {
-			stallTimer := time.NewTimer(initialTimeout)
-			var buf bytes.Buffer
-			fn := func(response api.GenerateResponse) error {
-				// Fields that must always be present
-				if response.Model == "" {
-					t.Errorf("response missing model: %#v", response)
-				}
-				if response.Done {
-					// Required fields for final updates:
-					if response.DoneReason == "" && *req.Stream {
-						// TODO - is the lack of done reason on non-stream a bug?
-						t.Errorf("final response missing done_reason: %#v", response)
-					}
-					if response.Metrics.TotalDuration == 0 {
-						t.Errorf("final response missing total_duration: %#v", response)
-					}
-					if response.Metrics.LoadDuration == 0 {
-						t.Errorf("final response missing load_duration: %#v", response)
-					}
-					if response.Metrics.PromptEvalDuration == 0 {
-						t.Errorf("final response missing prompt_eval_duration: %#v", response)
-					}
-					if response.Metrics.EvalCount == 0 {
-						t.Errorf("final response missing eval_count: %#v", response)
-					}
-					if response.Metrics.EvalDuration == 0 {
-						t.Errorf("final response missing eval_duration: %#v", response)
-					}
-					if len(response.Context) == 0 {
-						t.Errorf("final response missing context: %#v", response)
-					}
-
-					// Note: caching can result in no prompt eval count, so this can't be verified reliably
-					// if response.Metrics.PromptEvalCount == 0 {
-					// 	t.Errorf("final response missing prompt_eval_count: %#v", response)
-					// }
-
-				} // else incremental response, nothing to check right now...
-				buf.Write([]byte(response.Response))
-				if !stallTimer.Reset(streamTimeout) {
-					return fmt.Errorf("stall was detected while streaming response, aborting")
-				}
-				return nil
-			}
-
-			done := make(chan int)
-			var genErr error
-			go func() {
-				req.Stream = &test.stream
-				req.Options["seed"] = rand.Int() // bust cache for prompt eval results
-				genErr = client.Generate(ctx, &req, fn)
-				done <- 0
-			}()
-
-			select {
-			case <-stallTimer.C:
-				if buf.Len() == 0 {
-					t.Errorf("generate never started.  Timed out after :%s", initialTimeout.String())
-				} else {
-					t.Errorf("generate stalled.  Response so far:%s", buf.String())
-				}
-			case <-done:
-				if genErr != nil {
-					t.Fatalf("failed with %s request prompt %s ", req.Model, req.Prompt)
-				}
-				// Verify the response contains the expected data
-				response := buf.String()
-				atLeastOne := false
-				for _, resp := range anyResp {
-					if strings.Contains(strings.ToLower(response), resp) {
-						atLeastOne = true
-						break
-					}
-				}
-				if !atLeastOne {
-					t.Errorf("none of %v found in %s", anyResp, response)
-				}
-			case <-ctx.Done():
-				t.Error("outer test context done while waiting for generate")
-			}
-		})
-	}
-
-	// Validate PS while we're at it...
-	resp, err := client.ListRunning(ctx)
-	if err != nil {
-		t.Fatalf("list models API error: %s", err)
-	}
-	if resp == nil || len(resp.Models) == 0 {
-		t.Fatalf("list models API returned empty list while model should still be loaded")
-	}
-	// Find the model we just loaded and verify some attributes
-	found := false
-	for _, model := range resp.Models {
-		if strings.Contains(model.Name, req.Model) {
-			found = true
-			if model.Model == "" {
-				t.Errorf("model field omitted: %#v", model)
-			}
-			if model.Size == 0 {
-				t.Errorf("size omitted: %#v", model)
-			}
-			if model.Digest == "" {
-				t.Errorf("digest omitted: %#v", model)
-			}
-			verifyModelDetails(t, model.Details)
-			var nilTime time.Time
-			if model.ExpiresAt == nilTime {
-				t.Errorf("expires_at omitted: %#v", model)
-			}
-			// SizeVRAM could be zero.
-		}
-	}
-	if !found {
-		t.Errorf("unable to locate running model: %#v", resp)
-	}
-}
-
-func TestAPIChat(t *testing.T) {
-	initialTimeout := 60 * time.Second
-	streamTimeout := 30 * time.Second
-	ctx, cancel := context.WithTimeout(context.Background(), 1*time.Minute)
-	defer cancel()
-	// Set up the test data
-	req := api.ChatRequest{
-		Model: smol,
-		Messages: []api.Message{
-			{
-				Role:    "user",
-				Content: "why is the sky blue?  be brief",
-			},
-		},
-		Options: map[string]interface{}{
-			"temperature": 0,
-			"seed":        123,
-		},
-	}
-	anyResp := []string{"rayleigh", "scattering"}
-
-	client, _, cleanup := InitServerConnection(ctx, t)
-	defer cleanup()
-	if err := PullIfMissing(ctx, client, req.Model); err != nil {
-		t.Fatalf("pull failed %s", err)
-	}
-
-	tests := []struct {
-		name   string
-		stream bool
-	}{
-		{
-			name:   "stream",
-			stream: true,
-		},
-		{
-			name:   "no_stream",
-			stream: false,
-		},
-	}
-
-	for _, test := range tests {
-		t.Run(test.name, func(t *testing.T) {
-			stallTimer := time.NewTimer(initialTimeout)
-			var buf bytes.Buffer
-			fn := func(response api.ChatResponse) error {
-				// Fields that must always be present
-				if response.Model == "" {
-					t.Errorf("response missing model: %#v", response)
-				}
-				if response.Done {
-					// Required fields for final updates:
-					var nilTime time.Time
-					if response.CreatedAt == nilTime {
-						t.Errorf("final response missing total_duration: %#v", response)
-					}
-					if response.DoneReason == "" {
-						t.Errorf("final response missing done_reason: %#v", response)
-					}
-					if response.Metrics.TotalDuration == 0 {
-						t.Errorf("final response missing total_duration: %#v", response)
-					}
-					if response.Metrics.LoadDuration == 0 {
-						t.Errorf("final response missing load_duration: %#v", response)
-					}
-					if response.Metrics.PromptEvalDuration == 0 {
-						t.Errorf("final response missing prompt_eval_duration: %#v", response)
-					}
-					if response.Metrics.EvalCount == 0 {
-						t.Errorf("final response missing eval_count: %#v", response)
-					}
-					if response.Metrics.EvalDuration == 0 {
-						t.Errorf("final response missing eval_duration: %#v", response)
-					}
-
-					if response.Metrics.PromptEvalCount == 0 {
-						t.Errorf("final response missing prompt_eval_count: %#v", response)
-					}
-				} // else incremental response, nothing to check right now...
-				buf.Write([]byte(response.Message.Content))
-				if !stallTimer.Reset(streamTimeout) {
-					return fmt.Errorf("stall was detected while streaming response, aborting")
-				}
-				return nil
-			}
-
-			done := make(chan int)
-			var genErr error
-			go func() {
-				req.Stream = &test.stream
-				req.Options["seed"] = rand.Int() // bust cache for prompt eval results
-				genErr = client.Chat(ctx, &req, fn)
-				done <- 0
-			}()
-
-			select {
-			case <-stallTimer.C:
-				if buf.Len() == 0 {
-					t.Errorf("chat never started.  Timed out after :%s", initialTimeout.String())
-				} else {
-					t.Errorf("chat stalled.  Response so far:%s", buf.String())
-				}
-			case <-done:
-				if genErr != nil {
-					t.Fatalf("failed with %s request prompt %v", req.Model, req.Messages)
-				}
-				// Verify the response contains the expected data
-				response := buf.String()
-				atLeastOne := false
-				for _, resp := range anyResp {
-					if strings.Contains(strings.ToLower(response), resp) {
-						atLeastOne = true
-						break
-					}
-				}
-				if !atLeastOne {
-					t.Errorf("none of %v found in %s", anyResp, response)
-				}
-			case <-ctx.Done():
-				t.Error("outer test context done while waiting for chat")
-			}
-		})
-	}
-}
-
-func TestAPIListModels(t *testing.T) {
-	ctx, cancel := context.WithTimeout(context.Background(), 10*time.Second)
-	defer cancel()
-	client, _, cleanup := InitServerConnection(ctx, t)
-	defer cleanup()
-
-	// Make sure we have at least one model so an empty list can be considered a failure
-	if err := PullIfMissing(ctx, client, smol); err != nil {
-		t.Fatalf("pull failed %s", err)
-	}
-
-	resp, err := client.List(ctx)
-	if err != nil {
-		t.Fatalf("unable to list models: %s", err)
-	}
-	if len(resp.Models) == 0 {
-		t.Fatalf("list should not be empty")
-	}
-	model := resp.Models[0]
-	if model.Name == "" {
-		t.Errorf("first model name empty: %#v", model)
-	}
-	var nilTime time.Time
-	if model.ModifiedAt == nilTime {
-		t.Errorf("first model modified_at empty: %#v", model)
-	}
-	if model.Size == 0 {
-		t.Errorf("first model size empty: %#v", model)
-	}
-	if model.Digest == "" {
-		t.Errorf("first model digest empty: %#v", model)
-	}
-	verifyModelDetails(t, model.Details)
-}
-
-func verifyModelDetails(t *testing.T, details api.ModelDetails) {
-	if details.Format == "" {
-		t.Errorf("first model details.format empty: %#v", details)
-	}
-	if details.Family == "" {
-		t.Errorf("first model details.family empty: %#v", details)
-	}
-	if details.ParameterSize == "" {
-		t.Errorf("first model details.parameter_size empty: %#v", details)
-	}
-	if details.QuantizationLevel == "" {
-		t.Errorf("first model details.quantization_level empty: %#v", details)
-	}
-}
-
-func TestAPIShowModel(t *testing.T) {
-	modelName := "llama3.2"
-	ctx, cancel := context.WithTimeout(context.Background(), 1*time.Minute)
-	defer cancel()
-	client, _, cleanup := InitServerConnection(ctx, t)
-	defer cleanup()
-
-	if err := PullIfMissing(ctx, client, modelName); err != nil {
-		t.Fatalf("pull failed %s", err)
-	}
-	resp, err := client.Show(ctx, &api.ShowRequest{Name: modelName})
-	if err != nil {
-		t.Fatalf("unable to show model: %s", err)
-	}
-	if resp.License == "" {
-		t.Errorf("%s missing license: %#v", modelName, resp)
-	}
-	if resp.Modelfile == "" {
-		t.Errorf("%s missing modelfile: %#v", modelName, resp)
-	}
-	if resp.Parameters == "" {
-		t.Errorf("%s missing parameters: %#v", modelName, resp)
-	}
-	if resp.Template == "" {
-		t.Errorf("%s missing template: %#v", modelName, resp)
-	}
-	// llama3 omits system
-	verifyModelDetails(t, resp.Details)
-	// llama3 ommits messages
-	if len(resp.ModelInfo) == 0 {
-		t.Errorf("%s missing model_info: %#v", modelName, resp)
-	}
-	// llama3 omits projectors
-	var nilTime time.Time
-	if resp.ModifiedAt == nilTime {
-		t.Errorf("%s missing modified_at: %#v", modelName, resp)
-	}
-}
-
-func TestAPIEmbeddings(t *testing.T) {
-	ctx, cancel := context.WithTimeout(context.Background(), 1*time.Minute)
-	defer cancel()
-	client, _, cleanup := InitServerConnection(ctx, t)
-	defer cleanup()
-	req := api.EmbeddingRequest{
-		Model:  "orca-mini",
-		Prompt: "why is the sky blue?",
-		Options: map[string]interface{}{
-			"temperature": 0,
-			"seed":        123,
-		},
-	}
-
-	if err := PullIfMissing(ctx, client, req.Model); err != nil {
-		t.Fatalf("pull failed %s", err)
-	}
-
-	resp, err := client.Embeddings(ctx, &req)
-	if err != nil {
-		t.Fatalf("embeddings call failed %s", err)
-	}
-	if len(resp.Embedding) == 0 {
-		t.Errorf("zero length embedding response")
-	}
-}

integration/basic_test.go

@@ -14,15 +14,15 @@ import (
 	"github.com/stretchr/testify/require"
 )
 
-func TestBlueSky(t *testing.T) {
+func TestOrcaMiniBlueSky(t *testing.T) {
 	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
 	defer cancel()
 	// Set up the test data
 	req := api.GenerateRequest{
-		Model:  smol,
+		Model:  "orca-mini",
 		Prompt: "why is the sky blue?",
 		Stream: &stream,
-		Options: map[string]any{
+		Options: map[string]interface{}{
 			"temperature": 0,
 			"seed":        123,
 		},
@@ -31,7 +31,6 @@ func TestBlueSky(t *testing.T) {
 }
 
 func TestUnicode(t *testing.T) {
-	skipUnderMinVRAM(t, 6)
 	ctx, cancel := context.WithTimeout(context.Background(), 3*time.Minute)
 	defer cancel()
 	// Set up the test data
@@ -40,7 +39,7 @@ func TestUnicode(t *testing.T) {
 		Model:  "deepseek-coder-v2:16b-lite-instruct-q2_K",
 		Prompt: "天空为什么是蓝色的?",
 		Stream: &stream,
-		Options: map[string]any{
+		Options: map[string]interface{}{
 			"temperature": 0,
 			"seed":        123,
 			// Workaround deepseek context shifting bug
@@ -62,7 +61,7 @@ func TestExtendedUnicodeOutput(t *testing.T) {
 		Model:  "gemma2:2b",
 		Prompt: "Output some smily face emoji",
 		Stream: &stream,
-		Options: map[string]any{
+		Options: map[string]interface{}{
 			"temperature": 0,
 			"seed":        123,
 		},
@@ -94,10 +93,10 @@ func TestUnicodeModelDir(t *testing.T) {
 	defer cancel()
 
 	req := api.GenerateRequest{
-		Model:  smol,
+		Model:  "orca-mini",
 		Prompt: "why is the sky blue?",
 		Stream: &stream,
-		Options: map[string]any{
+		Options: map[string]interface{}{
 			"temperature": 0,
 			"seed":        123,
 		},

integration/concurrency_test.go

@@ -21,11 +21,11 @@ func TestMultiModelConcurrency(t *testing.T) {
 	var (
 		req = [2]api.GenerateRequest{
 			{
-				Model:     "llama3.2:1b",
+				Model:     "orca-mini",
 				Prompt:    "why is the ocean blue?",
 				Stream:    &stream,
 				KeepAlive: &api.Duration{Duration: 10 * time.Second},
-				Options: map[string]any{
+				Options: map[string]interface{}{
 					"seed":        42,
 					"temperature": 0.0,
 				},
@@ -34,7 +34,7 @@ func TestMultiModelConcurrency(t *testing.T) {
 				Prompt:    "what is the origin of the us thanksgiving holiday?",
 				Stream:    &stream,
 				KeepAlive: &api.Duration{Duration: 10 * time.Second},
-				Options: map[string]any{
+				Options: map[string]interface{}{
 					"seed":        42,
 					"temperature": 0.0,
 				},
@@ -67,7 +67,7 @@ func TestMultiModelConcurrency(t *testing.T) {
 	wg.Wait()
 }
 
-func TestIntegrationConcurrentPredict(t *testing.T) {
+func TestIntegrationConcurrentPredictOrcaMini(t *testing.T) {
 	req, resp := GenerateRequests()
 	reqLimit := len(req)
 	iterLimit := 5
@@ -117,9 +117,6 @@ func TestMultiModelStress(t *testing.T) {
 	if err != nil {
 		t.Fatal(err)
 	}
-	if maxVram < 2*format.GibiByte {
-		t.Skip("VRAM less than 2G, skipping model stress tests")
-	}
 
 	type model struct {
 		name string
@@ -128,8 +125,8 @@ func TestMultiModelStress(t *testing.T) {
 
 	smallModels := []model{
 		{
-			name: "llama3.2:1b",
+			name: "orca-mini",
-			size: 2876 * format.MebiByte,
+			size: 2992 * format.MebiByte,
 		},
 		{
 			name: "phi",

integration/context_test.go

@@ -23,7 +23,7 @@ func TestLongInputContext(t *testing.T) {
 		Model:  "llama2",
 		Prompt: "Oh, don’t speak to me of Austria. Perhaps I don’t understand things, but Austria never has wished, and does not wish, for war. She is betraying us! Russia alone must save Europe. Our gracious sovereign recognizes his high vocation and will be true to it. That is the one thing I have faith in! Our good and wonderful sovereign has to perform the noblest role on earth, and he is so virtuous and noble that God will not forsake him. He will fulfill his vocation and crush the hydra of revolution, which has become more terrible than ever in the person of this murderer and villain! We alone must avenge the blood of the just one.... Whom, I ask you, can we rely on?... England with her commercial spirit will not and cannot understand the Emperor Alexander’s loftiness of soul. She has refused to evacuate Malta. She wanted to find, and still seeks, some secret motive in our actions. What answer did Novosíltsev get? None. The English have not understood and cannot understand the self-abnegation of our Emperor who wants nothing for himself, but only desires the good of mankind. And what have they promised? Nothing! And what little they have promised they will not perform! Prussia has always declared that Buonaparte is invincible, and that all Europe is powerless before him.... And I don’t believe a word that Hardenburg says, or Haugwitz either. This famous Prussian neutrality is just a trap. I have faith only in God and the lofty destiny of our adored monarch. He will save Europe! What country is this referring to?",
 		Stream: &stream,
-		Options: map[string]any{
+		Options: map[string]interface{}{
 			"temperature": 0,
 			"seed":        123,
 			"num_ctx":     128,
@@ -50,7 +50,7 @@ func TestContextExhaustion(t *testing.T) {
 		Model:  "llama2",
 		Prompt: "Write me a story with a ton of emojis?",
 		Stream: &stream,
-		Options: map[string]any{
+		Options: map[string]interface{}{
 			"temperature": 0,
 			"seed":        123,
 			"num_ctx":     128,

integration/embed_test.go

@@ -34,15 +34,13 @@ func cosineSimilarity[V float32 | float64](v1, v2 []V) V {
 func TestAllMiniLMEmbeddings(t *testing.T) {
 	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
 	defer cancel()
-	client, _, cleanup := InitServerConnection(ctx, t)
-	defer cleanup()
 
 	req := api.EmbeddingRequest{
 		Model:  "all-minilm",
 		Prompt: "why is the sky blue?",
 	}
 
-	res, err := embeddingTestHelper(ctx, client, t, req)
+	res, err := embeddingTestHelper(ctx, t, req)
 
 	if err != nil {
 		t.Fatalf("error: %v", err)
@@ -64,15 +62,13 @@ func TestAllMiniLMEmbeddings(t *testing.T) {
 func TestAllMiniLMEmbed(t *testing.T) {
 	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
 	defer cancel()
-	client, _, cleanup := InitServerConnection(ctx, t)
-	defer cleanup()
 
 	req := api.EmbedRequest{
 		Model: "all-minilm",
 		Input: "why is the sky blue?",
 	}
 
-	res, err := embedTestHelper(ctx, client, t, req)
+	res, err := embedTestHelper(ctx, t, req)
 
 	if err != nil {
 		t.Fatalf("error: %v", err)
@@ -102,15 +98,13 @@ func TestAllMiniLMEmbed(t *testing.T) {
 func TestAllMiniLMBatchEmbed(t *testing.T) {
 	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
 	defer cancel()
-	client, _, cleanup := InitServerConnection(ctx, t)
-	defer cleanup()
 
 	req := api.EmbedRequest{
 		Model: "all-minilm",
 		Input: []string{"why is the sky blue?", "why is the grass green?"},
 	}
 
-	res, err := embedTestHelper(ctx, client, t, req)
+	res, err := embedTestHelper(ctx, t, req)
 
 	if err != nil {
 		t.Fatalf("error: %v", err)
@@ -150,8 +144,6 @@ func TestAllMiniLMBatchEmbed(t *testing.T) {
 func TestAllMiniLMEmbedTruncate(t *testing.T) {
 	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
 	defer cancel()
-	client, _, cleanup := InitServerConnection(ctx, t)
-	defer cleanup()
 
 	truncTrue, truncFalse := true, false
 
@@ -190,7 +182,7 @@ func TestAllMiniLMEmbedTruncate(t *testing.T) {
 	res := make(map[string]*api.EmbedResponse)
 
 	for _, req := range reqs {
-		response, err := embedTestHelper(ctx, client, t, req.Request)
+		response, err := embedTestHelper(ctx, t, req.Request)
 		if err != nil {
 			t.Fatalf("error: %v", err)
 		}
@@ -206,7 +198,7 @@ func TestAllMiniLMEmbedTruncate(t *testing.T) {
 	}
 
 	// check that truncate set to false returns an error if context length is exceeded
-	_, err := embedTestHelper(ctx, client, t, api.EmbedRequest{
+	_, err := embedTestHelper(ctx, t, api.EmbedRequest{
 		Model:    "all-minilm",
 		Input:    "why is the sky blue?",
 		Truncate: &truncFalse,
@@ -218,7 +210,9 @@ func TestAllMiniLMEmbedTruncate(t *testing.T) {
 	}
 }
 
-func embeddingTestHelper(ctx context.Context, client *api.Client, t *testing.T, req api.EmbeddingRequest) (*api.EmbeddingResponse, error) {
+func embeddingTestHelper(ctx context.Context, t *testing.T, req api.EmbeddingRequest) (*api.EmbeddingResponse, error) {
+	client, _, cleanup := InitServerConnection(ctx, t)
+	defer cleanup()
 	if err := PullIfMissing(ctx, client, req.Model); err != nil {
 		t.Fatalf("failed to pull model %s: %v", req.Model, err)
 	}
@@ -232,7 +226,9 @@ func embeddingTestHelper(ctx context.Context, client *api.Client, t *testing.T,
 	return response, nil
 }
 
-func embedTestHelper(ctx context.Context, client *api.Client, t *testing.T, req api.EmbedRequest) (*api.EmbedResponse, error) {
+func embedTestHelper(ctx context.Context, t *testing.T, req api.EmbedRequest) (*api.EmbedResponse, error) {
+	client, _, cleanup := InitServerConnection(ctx, t)
+	defer cleanup()
 	if err := PullIfMissing(ctx, client, req.Model); err != nil {
 		t.Fatalf("failed to pull model %s: %v", req.Model, err)
 	}

integration/llm_image_test.go

@@ -12,63 +12,14 @@ import (
 	"github.com/stretchr/testify/require"
 )
 
-func TestVisionModels(t *testing.T) {
+func TestIntegrationLlava(t *testing.T) {
-	skipUnderMinVRAM(t, 6)
-	type testCase struct {
-		model string
-	}
-	testCases := []testCase{
-		{
-			model: "llava:7b",
-		},
-		{
-			model: "llama3.2-vision",
-		},
-		{
-			model: "gemma3",
-		},
-	}
-
-	for _, v := range testCases {
-		t.Run(v.model, func(t *testing.T) {
-			image, err := base64.StdEncoding.DecodeString(imageEncoding)
-			require.NoError(t, err)
-			req := api.GenerateRequest{
-				Model:  v.model,
-				Prompt: "what does the text in this image say?",
-				Stream: &stream,
-				Options: map[string]any{
-					"seed":        42,
-					"temperature": 0.0,
-				},
-				Images: []api.ImageData{
-					image,
-				},
-			}
-			ctx, cancel := context.WithTimeout(context.Background(), 5*time.Minute)
-			defer cancel()
-			client, _, cleanup := InitServerConnection(ctx, t)
-
-			// Note: sometimes it returns "the ollamas" sometimes "the ollams"
-			resp := "the ollam"
-			defer cleanup()
-			require.NoError(t, PullIfMissing(ctx, client, req.Model))
-			// llava models on CPU can be quite slow to start
-			DoGenerate(ctx, t, client, req, []string{resp}, 240*time.Second, 30*time.Second)
-		})
-	}
-}
-
-func TestIntegrationSplitBatch(t *testing.T) {
 	image, err := base64.StdEncoding.DecodeString(imageEncoding)
 	require.NoError(t, err)
 	req := api.GenerateRequest{
-		Model: "gemma3:4b",
+		Model:  "llava:7b",
-		// Fill up a chunk of the batch so the image will partially spill over into the next one
-		System: "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed aliquet, justo in malesuada lobortis, odio ligula volutpat quam, quis faucibus ipsum magna quis sapien. Aliquam in venenatis diam, eu viverra magna. Phasellus imperdiet hendrerit volutpat. Vivamus sem ex, facilisis placerat felis non, dictum elementum est. Phasellus aliquam imperdiet lacus, eget placerat ligula sodales vel. Pellentesque nec auctor mi. Curabitur arcu nisi, faucibus eget nunc id, viverra interdum mi. Curabitur ornare ipsum ex, ac euismod ex aliquam in. Vestibulum id magna at purus accumsan fermentum. Proin scelerisque posuere nunc quis interdum. Maecenas sed mollis nisl. Etiam vitae ipsum interdum, placerat est quis, tincidunt velit. Nullam tempor nibh non lorem volutpat efficitur. Cras laoreet diam imperdiet ipsum auctor bibendum. Suspendisse ultrices urna sed metus sagittis suscipit. Quisque ullamcorper aliquam nibh ut mollis. Aenean dapibus mauris pharetra, venenatis elit ac, hendrerit odio. Cras vestibulum erat tempor, lobortis justo eu, lobortis ipsum. Nam laoreet dapibus sem. Proin vel diam ultrices, elementum ante et, ornare lectus. Proin eu accumsan nisl. Praesent ac ex vitae ipsum vulputate tristique facilisis sit amet lacus. Nullam faucibus magna a pellentesque pretium. Nunc lacinia ullamcorper sollicitudin. Donec vitae accumsan turpis, sed porttitor est. Donec porttitor mi vitae augue faucibus, vel mollis diam tincidunt.",
 		Prompt: "what does the text in this image say?",
 		Stream: &stream,
-		Options: map[string]any{
+		Options: map[string]interface{}{
 			"seed":        42,
 			"temperature": 0.0,
 		},
@@ -88,6 +39,33 @@ func TestIntegrationSplitBatch(t *testing.T) {
 	DoGenerate(ctx, t, client, req, []string{resp}, 120*time.Second, 30*time.Second)
 }
 
+func TestIntegrationMllama(t *testing.T) {
+	image, err := base64.StdEncoding.DecodeString(imageEncoding)
+	require.NoError(t, err)
+	req := api.GenerateRequest{
+		// TODO fix up once we publish the final image
+		Model:  "x/llama3.2-vision",
+		Prompt: "what does the text in this image say?",
+		Stream: &stream,
+		Options: map[string]interface{}{
+			"seed":        42,
+			"temperature": 0.0,
+		},
+		Images: []api.ImageData{
+			image,
+		},
+	}
+
+	resp := "the ollamas"
+	ctx, cancel := context.WithTimeout(context.Background(), 5*time.Minute)
+	defer cancel()
+	client, _, cleanup := InitServerConnection(ctx, t)
+	defer cleanup()
+	require.NoError(t, PullIfMissing(ctx, client, req.Model))
+	// mllama models on CPU can be quite slow to start,
+	DoGenerate(ctx, t, client, req, []string{resp}, 240*time.Second, 30*time.Second)
+}
+
 const imageEncoding = `iVBORw0KGgoAAAANSUhEUgAAANIAAAB4CAYAAACHHqzKAAAAAXNSR0IArs4c6QAAAIRlWElmTU0AKgAAAAgABQESAAMAAAABAAEAAAEaAAUAAAABAAAASgEb
 AAUAAAABAAAAUgEoAAMAAAABAAIAAIdpAAQAAAABAAAAWgAAAAAAAABIAAAAAQAAAEgAAAABAAOgAQADAAAAAQABAACgAgAEAAAAAQAAANKgAwAEAAAAAQAA
 AHgAAAAAXdsepgAAAAlwSFlzAAALEwAACxMBAJqcGAAAAVlpVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6

integration/llm_test.go

@@ -17,30 +17,30 @@ var (
 	stream = false
 	req    = [2]api.GenerateRequest{
 		{
-			Model:  smol,
+			Model:  "orca-mini",
 			Prompt: "why is the ocean blue?",
 			Stream: &stream,
-			Options: map[string]any{
+			Options: map[string]interface{}{
 				"seed":        42,
 				"temperature": 0.0,
 			},
 		}, {
-			Model:  smol,
+			Model:  "orca-mini",
 			Prompt: "what is the origin of the us thanksgiving holiday?",
 			Stream: &stream,
-			Options: map[string]any{
+			Options: map[string]interface{}{
 				"seed":        42,
 				"temperature": 0.0,
 			},
 		},
 	}
 	resp = [2][]string{
-		{"sunlight", "scattering", "interact"},
+		{"sunlight"},
 		{"england", "english", "massachusetts", "pilgrims"},
 	}
 )
 
-func TestIntegrationSimple(t *testing.T) {
+func TestIntegrationSimpleOrcaMini(t *testing.T) {
 	ctx, cancel := context.WithTimeout(context.Background(), time.Second*120)
 	defer cancel()
 	GenerateTestHelper(ctx, t, req[0], resp[0])

integration/max_queue_test.go

@@ -30,9 +30,9 @@ func TestMaxQueue(t *testing.T) {
 	t.Setenv("OLLAMA_MAX_QUEUE", strconv.Itoa(threadCount))
 
 	req := api.GenerateRequest{
-		Model:  smol,
+		Model:  "orca-mini",
 		Prompt: "write a long historical fiction story about christopher columbus.  use at least 10 facts from his actual journey",
-		Options: map[string]any{
+		Options: map[string]interface{}{
 			"seed":        42,
 			"temperature": 0.0,
 		},
@@ -52,8 +52,8 @@ func TestMaxQueue(t *testing.T) {
 	embedCtx := ctx
 
 	var genwg sync.WaitGroup
-	genwg.Add(1)
 	go func() {
+		genwg.Add(1)
 		defer genwg.Done()
 		slog.Info("Starting generate request")
 		DoGenerate(ctx, t, client, req, resp, 45*time.Second, 5*time.Second)
@@ -61,7 +61,7 @@ func TestMaxQueue(t *testing.T) {
 	}()
 
 	// Give the generate a chance to get started before we start hammering on embed requests
-	time.Sleep(10 * time.Millisecond)
+	time.Sleep(5 * time.Millisecond)
 
 	threadCount += 10 // Add a few extra to ensure we push the queue past its limit
 	busyCount := 0
@@ -71,8 +71,8 @@ func TestMaxQueue(t *testing.T) {
 	counterMu := sync.Mutex{}
 	var embedwg sync.WaitGroup
 	for i := 0; i < threadCount; i++ {
-		embedwg.Add(1)
 		go func(i int) {
+			embedwg.Add(1)
 			defer embedwg.Done()
 			slog.Info("embed started", "id", i)
 			embedReq := api.EmbeddingRequest{

integration/model_arch_test.go

@@ -1,184 +0,0 @@
-//go:build integration && models
-
-package integration
-
-import (
-	"context"
-	"encoding/json"
-	"fmt"
-	"io/ioutil"
-	"log/slog"
-	"os"
-	"path/filepath"
-	"strconv"
-	"strings"
-	"testing"
-	"time"
-
-	"github.com/ollama/ollama/api"
-	"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)
-	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...")
-	}
-
-	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)
-			}
-			if maxVram > 0 {
-				resp, err := client.List(ctx)
-				if err != nil {
-					t.Fatalf("list models failed %v", err)
-				}
-				for _, m := range resp.Models {
-					if m.Name == model && float32(m.Size)*1.2 > float32(maxVram) {
-						t.Skipf("model %s is too large for available VRAM: %s > %s", model, format.HumanBytes(m.Size), format.HumanBytes(int64(maxVram)))
-					}
-				}
-			}
-			// TODO - fiddle with context size
-			req := api.GenerateRequest{
-				Model:  model,
-				Prompt: "why is the sky blue?",
-				Options: map[string]interface{}{
-					"temperature": 0,
-					"seed":        123,
-				},
-			}
-			anyResp := []string{"rayleigh", "scattering", "atmosphere", "nitrogen", "oxygen"}
-			DoGenerate(ctx, t, client, req, anyResp, 120*time.Second, 30*time.Second)
-		})
-	}
-}
-
-func TestModelsEmbed(t *testing.T) {
-	softTimeout, hardTimeout := getTimeouts(t)
-	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", "embed.json"))
-	if err != nil {
-		t.Fatalf("failed to open test data file: %s", err)
-	}
-	testCase := map[string][]float64{}
-	err = json.Unmarshal(data, &testCase)
-	if err != nil {
-		t.Fatalf("failed to load test data: %s", err)
-	}
-	for model, expected := range testCase {
-
-		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)
-			}
-			if maxVram > 0 {
-				resp, err := client.List(ctx)
-				if err != nil {
-					t.Fatalf("list models failed %v", err)
-				}
-				for _, m := range resp.Models {
-					if m.Name == model && float32(m.Size)*1.2 > float32(maxVram) {
-						t.Skipf("model %s is too large for available VRAM: %s > %s", model, format.HumanBytes(m.Size), format.HumanBytes(int64(maxVram)))
-					}
-				}
-			}
-			req := api.EmbeddingRequest{
-				Model:  model,
-				Prompt: "why is the sky blue?",
-				Options: map[string]interface{}{
-					"temperature": 0,
-					"seed":        123,
-				},
-			}
-			resp, err := client.Embeddings(ctx, &req)
-			if err != nil {
-				t.Fatalf("embeddings call failed %s", err)
-			}
-			if len(resp.Embedding) == 0 {
-				t.Errorf("zero length embedding response")
-			}
-			if len(expected) != len(resp.Embedding) {
-				expStr := make([]string, len(resp.Embedding))
-				for i, v := range resp.Embedding {
-					expStr[i] = fmt.Sprintf("%0.6f", v)
-				}
-				// When adding new models, use this output to populate the testdata/embed.json
-				fmt.Printf("expected\n%s\n", strings.Join(expStr, ", "))
-				t.Fatalf("expected %d, got %d", len(expected), len(resp.Embedding))
-			}
-			sim := cosineSimilarity(resp.Embedding, expected)
-			if sim < 0.99 {
-				t.Fatalf("expected %v, got %v (similarity: %f)", expected[0:5], resp.Embedding[0:5], sim)
-			}
-		})
-	}
-
-}

integration/quantization_test.go

@@ -1,130 +0,0 @@
-//go:build integration && models
-
-package integration
-
-import (
-	"bytes"
-	"context"
-	"fmt"
-	"log/slog"
-	"strings"
-	"testing"
-	"time"
-
-	"github.com/ollama/ollama/api"
-)
-
-func TestQuantization(t *testing.T) {
-	sourceModels := []string{
-		"qwen2.5:0.5b-instruct-fp16",
-	}
-	quantizations := []string{
-		"Q8_0",
-		"Q4_K_S",
-		"Q4_K_M",
-		"Q4_K",
-	}
-	softTimeout, hardTimeout := getTimeouts(t)
-	started := time.Now()
-	slog.Info("Setting timeouts", "soft", softTimeout, "hard", hardTimeout)
-	ctx, cancel := context.WithTimeout(context.Background(), hardTimeout)
-	defer cancel()
-	client, _, cleanup := InitServerConnection(ctx, t)
-	defer cleanup()
-
-	for _, base := range sourceModels {
-		if err := PullIfMissing(ctx, client, base); err != nil {
-			t.Fatalf("pull failed %s", err)
-		}
-		for _, quant := range quantizations {
-			newName := fmt.Sprintf("%s__%s", base, quant)
-			t.Run(newName, func(t *testing.T) {
-				if time.Now().Sub(started) > softTimeout {
-					t.Skip("skipping remaining tests to avoid excessive runtime")
-				}
-				req := &api.CreateRequest{
-					Model:        newName,
-					Quantization: quant,
-					From:         base,
-				}
-				fn := func(resp api.ProgressResponse) error {
-					// fmt.Print(".")
-					return nil
-				}
-				t.Logf("quantizing: %s -> %s", base, quant)
-				if err := client.Create(ctx, req, fn); err != nil {
-					t.Fatalf("create failed %s", err)
-				}
-				defer func() {
-					req := &api.DeleteRequest{
-						Model: newName,
-					}
-					t.Logf("deleting: %s -> %s", base, quant)
-					if err := client.Delete(ctx, req); err != nil {
-						t.Logf("failed to clean up %s: %s", req.Model, err)
-					}
-				}()
-				// Check metadata on the model
-				resp, err := client.Show(ctx, &api.ShowRequest{Name: newName})
-				if err != nil {
-					t.Fatalf("unable to show model: %s", err)
-				}
-				if !strings.Contains(resp.Details.QuantizationLevel, quant) {
-					t.Fatalf("unexpected quantization for %s:\ngot: %s", newName, resp.Details.QuantizationLevel)
-				}
-
-				stream := true
-				genReq := api.GenerateRequest{
-					Model:     newName,
-					Prompt:    "why is the sky blue?",
-					KeepAlive: &api.Duration{Duration: 3 * time.Second},
-					Options: map[string]any{
-						"seed":        42,
-						"temperature": 0.0,
-					},
-					Stream: &stream,
-				}
-				t.Logf("verifying: %s -> %s", base, quant)
-
-				// Some smaller quantizations can cause models to have poor quality
-				// or get stuck in repetition loops, so we stop as soon as we have any matches
-				anyResp := []string{"rayleigh", "scattering", "day", "sun", "moon", "color", "nitrogen", "oxygen"}
-				reqCtx, reqCancel := context.WithCancel(ctx)
-				atLeastOne := false
-				var buf bytes.Buffer
-				genfn := func(response api.GenerateResponse) error {
-					buf.Write([]byte(response.Response))
-					fullResp := strings.ToLower(buf.String())
-					for _, resp := range anyResp {
-						if strings.Contains(fullResp, resp) {
-							atLeastOne = true
-							t.Log(fullResp)
-							reqCancel()
-							break
-						}
-					}
-					return nil
-				}
-
-				done := make(chan int)
-				var genErr error
-				go func() {
-					genErr = client.Generate(reqCtx, &genReq, genfn)
-					done <- 0
-				}()
-
-				select {
-				case <-done:
-					if genErr != nil && !atLeastOne {
-						t.Fatalf("failed with %s request prompt %s ", genReq.Model, genReq.Prompt)
-					}
-				case <-ctx.Done():
-					t.Error("outer test context done while waiting for generate")
-				}
-
-				t.Logf("passed")
-
-			})
-		}
-	}
-}

integration/utils_test.go

@@ -24,14 +24,9 @@ import (
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/app/lifecycle"
-	"github.com/ollama/ollama/format"
 	"github.com/stretchr/testify/require"
 )
 
-const (
-	smol = "llama3.2:1b"
-)
-
 func Init() {
 	lifecycle.InitLogging()
 }
@@ -145,7 +140,7 @@ func PullIfMissing(ctx context.Context, client *api.Client, modelName string) er
 
 	showCtx, cancel := context.WithDeadlineCause(
 		ctx,
-		time.Now().Add(20*time.Second),
+		time.Now().Add(10*time.Second),
 		fmt.Errorf("show for existing model %s took too long", modelName),
 	)
 	defer cancel()
@@ -162,7 +157,7 @@ func PullIfMissing(ctx context.Context, client *api.Client, modelName string) er
 	}
 	slog.Info("model missing", "model", modelName)
 
-	stallDuration := 60 * time.Second // This includes checksum verification, which can take a while on larger models, and slower systems
+	stallDuration := 30 * time.Second // This includes checksum verification, which can take a while on larger models
 	stallTimer := time.NewTimer(stallDuration)
 	fn := func(resp api.ProgressResponse) error {
 		// fmt.Print(".")
@@ -217,7 +212,6 @@ func InitServerConnection(ctx context.Context, t *testing.T) (*api.Client, strin
 					slog.Error("failed to open server log", "logfile", lifecycle.ServerLogFile, "error", err)
 					return
 				}
-				defer fp.Close()
 				data, err := io.ReadAll(fp)
 				if err != nil {
 					slog.Error("failed to read server log", "logfile", lifecycle.ServerLogFile, "error", err)
@@ -289,51 +283,51 @@ func DoGenerate(ctx context.Context, t *testing.T, client *api.Client, genReq ap
 }
 
 // Generate a set of requests
-// By default each request uses llama3.2 as the model
+// By default each request uses orca-mini as the model
 func GenerateRequests() ([]api.GenerateRequest, [][]string) {
 	return []api.GenerateRequest{
 			{
-				Model:     smol,
+				Model:     "orca-mini",
 				Prompt:    "why is the ocean blue?",
 				Stream:    &stream,
 				KeepAlive: &api.Duration{Duration: 10 * time.Second},
-				Options: map[string]any{
+				Options: map[string]interface{}{
 					"seed":        42,
 					"temperature": 0.0,
 				},
 			}, {
-				Model:     smol,
+				Model:     "orca-mini",
 				Prompt:    "why is the color of dirt brown?",
 				Stream:    &stream,
 				KeepAlive: &api.Duration{Duration: 10 * time.Second},
-				Options: map[string]any{
+				Options: map[string]interface{}{
 					"seed":        42,
 					"temperature": 0.0,
 				},
 			}, {
-				Model:     smol,
+				Model:     "orca-mini",
 				Prompt:    "what is the origin of the us thanksgiving holiday?",
 				Stream:    &stream,
 				KeepAlive: &api.Duration{Duration: 10 * time.Second},
-				Options: map[string]any{
+				Options: map[string]interface{}{
 					"seed":        42,
 					"temperature": 0.0,
 				},
 			}, {
-				Model:     smol,
+				Model:     "orca-mini",
 				Prompt:    "what is the origin of independence day?",
 				Stream:    &stream,
 				KeepAlive: &api.Duration{Duration: 10 * time.Second},
-				Options: map[string]any{
+				Options: map[string]interface{}{
 					"seed":        42,
 					"temperature": 0.0,
 				},
 			}, {
-				Model:     smol,
+				Model:     "orca-mini",
 				Prompt:    "what is the composition of air?",
 				Stream:    &stream,
 				KeepAlive: &api.Duration{Duration: 10 * time.Second},
-				Options: map[string]any{
+				Options: map[string]interface{}{
 					"seed":        42,
 					"temperature": 0.0,
 				},
@@ -347,26 +341,3 @@ func GenerateRequests() ([]api.GenerateRequest, [][]string) {
 			{"nitrogen", "oxygen", "carbon", "dioxide"},
 		}
 }
-
-func skipUnderMinVRAM(t *testing.T, gb uint64) {
-	// TODO use info API in the future
-	if s := os.Getenv("OLLAMA_MAX_VRAM"); s != "" {
-		maxVram, err := strconv.ParseUint(s, 10, 64)
-		require.NoError(t, err)
-		// Don't hammer on small VRAM cards...
-		if maxVram < gb*format.GibiByte {
-			t.Skip("skipping with small VRAM to avoid timeouts")
-		}
-	}
-}
-
-func getTimeouts(t *testing.T) (soft time.Duration, hard time.Duration) {
-	deadline, hasDeadline := t.Deadline()
-	if !hasDeadline {
-		return 8 * time.Minute, 10 * time.Minute
-	} else if deadline.Compare(time.Now().Add(2*time.Minute)) <= 0 {
-		t.Skip("too little time")
-		return time.Duration(0), time.Duration(0)
-	}
-	return -time.Since(deadline.Add(-2 * time.Minute)), -time.Since(deadline.Add(-20 * time.Second))
-}

kvcache/cache.go

@@ -1,77 +0,0 @@
-package kvcache
-
-import (
-	"errors"
-
-	"github.com/ollama/ollama/ml"
-	"github.com/ollama/ollama/model/input"
-)
-
-var (
-	ErrKvCacheFull  = errors.New("could not find a kv cache slot")
-	ErrNotSupported = errors.New("model does not support operation")
-)
-
-type Cache interface {
-	// ** used by model implementations **
-
-	// SetLayer sets the active layer of the cache
-	SetLayer(layer int)
-
-	// Get returns the history of key and value tensors plus a mask
-	//
-	// The shape of the tensors is documented in the specific
-	// cache implementation used.
-	Get(ctx ml.Context) (ml.Tensor, ml.Tensor, ml.Tensor)
-
-	// Put stores a batch of key and value in the cache
-	//
-	// The shape of the tensors is documented in the specific
-	// cache implementation used.
-	Put(ctx ml.Context, key, value ml.Tensor)
-
-	// SetConfig controls optimizations (mostly backend-specific) that may transform
-	// the output of the cache to work better with specific kernels. If not called,
-	// the backend settings will be used. This works well when calling Attention.
-	//
-	// The config can be overridden by models, especially if they require vanilla
-	// output when implementing their own version of attention. To do this, pass
-	// an empty ml.CacheConfig.
-	//
-	// Most models will not need to use this.
-	SetConfig(ml.CacheConfig)
-
-	// ** cache management **
-
-	// Init sets up runtime parameters.
-	// backend: Used to allocate cache data storage and execute management operations (such as defrag)
-	// dtype: The data type for storing cache entries
-	// maxSequences: The maximum number of sequences stored in the cache - across all batches
-	// capacity: The number of cache entries to store, per sequence
-	// maxBatch: The maximum number of tokens that can occur in a single batch
-	Init(backend ml.Backend, dtype ml.DType, maxSequences, capacity, maxBatch int)
-
-	// Close closes the cache and frees resources associated with it
-	Close()
-
-	// StartForward is called before the start of the model's forward pass.
-	// For each token in the coming batch, there must be a corresponding
-	// entry in positions and seqs. reserve is to preallocate memory
-	// without actually storing data in the cache.
-	StartForward(ctx ml.Context, batch input.Batch, reserve bool) error
-
-	// CopyPrefix copies tokens in the range [0, len) from srcSeq to dstSeq
-	CopyPrefix(srcSeq, dstSeq int, len int32)
-
-	// CanResume returns true if the cache can continue with the next token at
-	// the given position and sequence. Assumes that the caller has already
-	// verified the contents of the cache.
-	CanResume(seq int, pos int32) bool
-
-	// Remove deletes tokens in the range [beginIndex, endIndex) from seq. Set
-	// endIndex to math.MaxInt32 to remove everything starting at beginIndex.
-	//
-	// If an error occurs, the entire context for the sequence should be
-	// removed by calling Remove(seq, 0, math.MaxInt32)
-	Remove(seq int, beginIndex, endIndex int32) error
-}

kvcache/causal.go

@@ -1,739 +0,0 @@
-package kvcache
-
-import (
-	"errors"
-	"fmt"
-	"log/slog"
-	"math"
-	"slices"
-
-	"github.com/ollama/ollama/ml"
-	"github.com/ollama/ollama/model/input"
-)
-
-type shiftFn func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error)
-
-// Causal cache stores K and V tensors according to their position in the
-// sequence. Returns the history and a mask for attending to past tokens
-//
-// The tensors are of shape embed dim, kv heads, batch size
-// The mask is of shape history size, batch size
-type Causal struct {
-	DType      ml.DType
-	windowSize int32
-	chunkSize  int32
-
-	opts CausalOptions
-
-	// config controls mostly backend-specific optimizations
-	config *ml.CacheConfig
-
-	// ** current forward pass **
-
-	// the active layer for Get and Put
-	curLayer int
-
-	// starting location for data storage for this batch
-	curLoc int
-
-	// size of the current batch
-	curBatchSize int
-
-	// mask of the cache as used by this batch
-	curMask ml.Tensor
-
-	// locations in the cache that are needed for this batch
-	curCellRange cellRange
-
-	// curSequences is the sequences corresponding to this pass's entries in the cache
-	curSequences []int
-
-	// curPositions is the positions corresponding to this pass's entries in the cache
-	curPositions []int32
-
-	// ** cache metadata **
-
-	// for each possible location in the cache, stores the position and set of sequences
-	// that reference the data there
-	cells []cacheCell
-
-	// maps from sequence to the range of locations where it is stored in the cache
-	cellRanges map[int]cellRange
-
-	// ** cache data storage **
-
-	shiftFn      shiftFn
-	backend      ml.Backend
-	ctxs         map[int]ml.Context
-	keys, values map[int]ml.Tensor
-}
-
-type cacheCell struct {
-	pos       int32
-	sequences []int
-}
-
-type cellRange struct {
-	min int
-	max int
-}
-
-func NewCausalCache(shift shiftFn) *Causal {
-	return &Causal{
-		windowSize: math.MaxInt32,
-		shiftFn:    shift,
-		ctxs:       make(map[int]ml.Context),
-		keys:       make(map[int]ml.Tensor),
-		values:     make(map[int]ml.Tensor),
-	}
-}
-
-func NewSWACache(windowSize int32, shift shiftFn) *Causal {
-	return &Causal{
-		windowSize: windowSize,
-		shiftFn:    shift,
-		ctxs:       make(map[int]ml.Context),
-		keys:       make(map[int]ml.Tensor),
-		values:     make(map[int]ml.Tensor),
-	}
-}
-
-func NewChunkedAttentionCache(chunkSize int32, shift shiftFn) *Causal {
-	return &Causal{
-		windowSize: math.MaxInt32,
-		chunkSize:  chunkSize,
-		shiftFn:    shift,
-		ctxs:       make(map[int]ml.Context),
-		keys:       make(map[int]ml.Tensor),
-		values:     make(map[int]ml.Tensor),
-	}
-}
-
-func (c *Causal) Init(backend ml.Backend, dtype ml.DType, maxSequences, capacity, maxBatch int) {
-	if c.config == nil {
-		var config ml.CacheConfig
-		if cc, ok := backend.(ml.BackendCacheConfig); ok {
-			config = cc.CacheConfig()
-		}
-		c.config = &config
-	}
-
-	if c.config.CachePadding == 0 {
-		c.config.CachePadding = 1
-	}
-
-	if c.config.MaskBatchPadding == 0 {
-		c.config.MaskBatchPadding = 1
-	}
-
-	if c.config.MaskDType == ml.DTypeOther {
-		c.config.MaskDType = ml.DTypeF32
-	}
-
-	var cacheSize int
-	if c.windowSize == math.MaxInt32 || capacity < int(c.windowSize) {
-		cacheSize = maxSequences * capacity
-	} else {
-		cacheSize = (maxSequences * int(c.windowSize)) + maxBatch
-	}
-	cacheSize = roundUp(cacheSize, c.config.CachePadding)
-	c.cells = make([]cacheCell, cacheSize)
-
-	c.DType = dtype
-	c.cellRanges = make(map[int]cellRange)
-	c.backend = backend
-}
-
-func (c *Causal) SetConfig(config ml.CacheConfig) {
-	if c.config != nil {
-		panic("config cannot be changed after being previously set, either by the model or backend")
-	}
-
-	c.config = &config
-}
-
-func (c *Causal) Close() {
-	for _, ctx := range c.ctxs {
-		ctx.Close()
-	}
-}
-
-func (c *Causal) StartForward(ctx ml.Context, batch input.Batch, reserve bool) error {
-	c.curBatchSize = len(batch.Positions)
-	c.curSequences = batch.Sequences
-	c.curPositions = batch.Positions
-	c.opts.Except = nil
-
-	if !reserve {
-		c.updateSlidingWindow()
-
-		var err error
-		c.curLoc, err = c.findStartLoc()
-		if errors.Is(err, ErrKvCacheFull) {
-			c.defrag()
-			c.curLoc, err = c.findStartLoc()
-		}
-		if err != nil {
-			return err
-		}
-
-		c.curCellRange = newRange()
-		for i, pos := range batch.Positions {
-			seq := batch.Sequences[i]
-
-			c.cells[c.curLoc+i] = cacheCell{pos: pos, sequences: []int{seq}}
-
-			seqRange, ok := c.cellRanges[seq]
-			if !ok {
-				seqRange = newRange()
-			}
-
-			if c.curLoc+i > seqRange.max {
-				seqRange.max = c.curLoc + i
-			}
-			if seqRange.max > c.curCellRange.max {
-				c.curCellRange.max = seqRange.max
-			}
-
-			if c.curLoc+i < seqRange.min {
-				seqRange.min = c.curLoc + i
-			}
-			if seqRange.min < c.curCellRange.min {
-				c.curCellRange.min = seqRange.min
-			}
-			c.cellRanges[seq] = seqRange
-		}
-	} else {
-		// If we are reserving memory, don't update any of the cache metadata but set the size
-		// to the worst case.
-		c.curLoc = 0
-		c.curCellRange.min = 0
-		c.curCellRange.max = len(c.cells) - 1
-	}
-
-	var err error
-	c.curMask, err = c.buildMask(ctx)
-
-	return err
-}
-
-func newRange() cellRange {
-	return cellRange{
-		min: math.MaxInt,
-		max: 0,
-	}
-}
-
-// Find the first contiguous block of at least curBatchSize
-func (c *Causal) findStartLoc() (int, error) {
-	var start, count int
-	for i := range c.cells {
-		if len(c.cells[i].sequences) == 0 {
-			count++
-			if count >= c.curBatchSize {
-				return start, nil
-			}
-		} else {
-			start = i + 1
-			count = 0
-		}
-	}
-
-	return 0, fmt.Errorf("%w (cache: %v batch: %v)", ErrKvCacheFull, len(c.cells), c.curBatchSize)
-}
-
-func (c *Causal) updateSlidingWindow() {
-	if c.windowSize == math.MaxInt32 {
-		return
-	}
-
-	// create a map of unique sequences to the lowest position in that sequence
-	lowestPos := make(map[int]int32)
-	for i := range c.curPositions {
-		seq := c.curSequences[i]
-
-		pos, ok := lowestPos[seq]
-		if !ok {
-			pos = c.curPositions[i]
-		} else if c.curPositions[i] < pos {
-			pos = c.curPositions[i]
-		}
-
-		lowestPos[seq] = pos
-	}
-
-	// delete any entries that are beyond the window of the oldest position in the sequence
-	for seq, pos := range lowestPos {
-		oldRange, ok := c.cellRanges[seq]
-		if !ok {
-			continue
-		}
-
-		newRange := newRange()
-
-		for i := oldRange.min; i <= oldRange.max; i++ {
-			if slices.Contains(c.cells[i].sequences, seq) {
-				if c.cells[i].pos < pos-c.windowSize {
-					c.cells[i].sequences = slices.DeleteFunc(c.cells[i].sequences, func(s int) bool { return s == seq })
-				} else {
-					newRange.min = min(newRange.min, i)
-					newRange.max = max(newRange.max, i)
-				}
-			}
-		}
-
-		c.cellRanges[seq] = newRange
-	}
-}
-
-func roundDown(length, pad int) int {
-	return (length / pad) * pad
-}
-
-func roundUp(length, pad int) int {
-	return ((length + pad - 1) / pad) * pad
-}
-
-// Builds a mask of history x batch indicating whether for each token in the batch the
-// token in the history should apply. This is based on both the sequence and causality (the
-// position of the history is not ahead of the token in the batch).
-func (c *Causal) buildMask(ctx ml.Context) (ml.Tensor, error) {
-	// Align and pad the two dimensions as required by the backend
-	batchSize := roundUp(c.curBatchSize, c.config.MaskBatchPadding)
-
-	c.curCellRange.min = roundDown(c.curCellRange.min, c.config.CachePadding)
-	c.curCellRange.max = roundUp(c.curCellRange.max+1, c.config.CachePadding) - 1
-
-	length := c.curCellRange.max - c.curCellRange.min + 1
-	mask := make([]float32, batchSize*length)
-
-	for i := range c.curBatchSize {
-		enabled := !slices.Contains(c.opts.Except, i)
-		for j := c.curCellRange.min; j <= c.curCellRange.max; j++ {
-			if !slices.Contains(c.cells[j].sequences, c.curSequences[i]) ||
-				(enabled && c.cells[j].pos > c.curPositions[i]) ||
-				c.chunkSize > 0 && c.cells[j].pos < c.curPositions[i]-c.curPositions[i]%c.chunkSize ||
-				c.cells[j].pos < c.curPositions[i]-c.windowSize {
-				mask[i*length+(j-c.curCellRange.min)] = float32(math.Inf(-1))
-			}
-		}
-	}
-
-	// Mask out any padding tokens we added. For padding that we added to the cache history, this
-	// has already been masked out because the sequence doesn't match.
-	for i := c.curBatchSize * length; i < len(mask); i++ {
-		mask[i] = float32(math.Inf(-1))
-	}
-
-	maskTensor, err := ctx.Input().FromFloatSlice(mask, length, batchSize)
-	if err != nil {
-		return nil, err
-	}
-
-	if c.config.MaskDType != ml.DTypeF32 {
-		out := ctx.Input().Empty(c.config.MaskDType, maskTensor.Shape()...)
-		ctx.Forward(maskTensor.Copy(ctx, out))
-		maskTensor = out
-	}
-
-	return maskTensor, nil
-}
-
-func (c *Causal) moveCells(ctx ml.Context, src, dst, length int) {
-	for i, key := range c.keys {
-		if key == nil {
-			continue
-		}
-
-		kHeadDim := key.Dim(0)
-		numKVHeads := key.Dim(1)
-		rowSize := key.Stride(2)
-
-		kSrcView := key.View(ctx, rowSize*src, kHeadDim*numKVHeads*length)
-		kDstView := key.View(ctx, rowSize*dst, kHeadDim*numKVHeads*length)
-
-		value := c.values[i]
-		var vSrcView, vDstView ml.Tensor
-		if c.config.PermutedV {
-			vHeadDim := value.Dim(1)
-			elemSize := value.Stride(0)
-
-			vSrcView = value.View(ctx, elemSize*src, length, len(c.cells)*elemSize, vHeadDim*numKVHeads)
-			vDstView = value.View(ctx, elemSize*dst, length, len(c.cells)*elemSize, vHeadDim*numKVHeads)
-		} else {
-			vHeadDim := value.Dim(0)
-			rowSize := value.Stride(2)
-
-			vSrcView = value.View(ctx, rowSize*src, vHeadDim*numKVHeads*length)
-			vDstView = value.View(ctx, rowSize*dst, vHeadDim*numKVHeads*length)
-		}
-
-		ctx.Forward(
-			kSrcView.Copy(ctx, kDstView),
-			vSrcView.Copy(ctx, vDstView),
-		)
-	}
-}
-
-func (c *Causal) defrag() {
-	slog.Debug("defragmenting kv cache")
-
-	// Defrag strategy:
-	// - Search for empty holes at the beginning of the cache,
-	//   filling them with active data starting at the end
-	// - If there are contiguous elements that need to be moved,
-	//   combine them into a single operation by holding new moves
-	//   until we see that the next one is non-contiguous
-	// - Fill up the context with the maximum number of operations it
-	//   can hold then compute that and continue with a new context
-	//
-	// We could try to optimize placement by grouping blocks from
-	// the same sequences together but most likely the next forward
-	// pass will disrupt this anyways, so the real world benefit
-	// seems limited as this time.
-
-	ctx := c.backend.NewContext()
-
-	// For every move, 6 tensors are required per layer (2 views and a
-	// copy for each of k and v). We also need to refer to the original
-	// k and v cache tensors - once per layer, not per move.
-	layers := 0
-	for _, key := range c.keys {
-		if key == nil {
-			continue
-		}
-		layers++
-	}
-
-	maxMoves := (ctx.MaxGraphNodes() - 2*layers) / (6 * layers)
-	moves := 0
-
-	var pendingSrc, pendingDst, pendingLen int
-	src := len(c.cells) - 1
-
-	for dst := 0; dst < src; dst++ {
-		if len(c.cells[dst].sequences) == 0 {
-			for ; src > dst; src-- {
-				if len(c.cells[src].sequences) != 0 {
-					c.cells[dst] = c.cells[src]
-					c.cells[src] = cacheCell{}
-
-					if pendingLen > 0 {
-						if src == pendingSrc-pendingLen && dst == pendingDst+pendingLen {
-							pendingSrc = src
-							pendingLen++
-							break
-						} else {
-							c.moveCells(ctx, pendingSrc, pendingDst, pendingLen)
-							moves++
-						}
-					}
-
-					pendingSrc = src
-					pendingDst = dst
-					pendingLen = 1
-
-					break
-				}
-			}
-		}
-
-		if moves >= maxMoves {
-			ctx.Compute()
-			ctx.Close()
-			ctx = c.backend.NewContext()
-
-			moves = 0
-		}
-	}
-
-	if pendingLen > 0 {
-		c.moveCells(ctx, pendingSrc, pendingDst, pendingLen)
-		moves++
-	}
-
-	if moves > 0 {
-		ctx.Compute()
-	}
-	ctx.Close()
-
-	// Reset range metadata
-	for seq := range c.cellRanges {
-		seqRange := newRange()
-
-		for i, cell := range c.cells {
-			if slices.Contains(cell.sequences, seq) {
-				if i < seqRange.min {
-					seqRange.min = i
-				}
-				if i > seqRange.max {
-					seqRange.max = i
-				}
-			}
-		}
-
-		c.cellRanges[seq] = seqRange
-	}
-}
-
-func (c *Causal) SetLayer(layer int) {
-	c.curLayer = layer
-}
-
-type CausalOptions struct {
-	// Enabled controls whether the causal mask is generated for a particular index in a batch
-	Except []int
-}
-
-// SetCausal disables causal mask generation for a particular range of indicies in
-// the current batch for subsequent calls to Get. The state resets for the next forward pass.
-func (c *Causal) SetCausal(ctx ml.Context, opts CausalOptions) {
-	if !slices.Equal(c.opts.Except, opts.Except) {
-		c.opts = opts
-		if ctx != nil {
-			var err error
-			c.curMask, err = c.buildMask(ctx)
-			if err != nil {
-				// This error should never occur because we have previously built a mask with the same shape
-				panic(fmt.Errorf("SetCausal: %w", err))
-			}
-		}
-	}
-}
-
-func (c *Causal) Get(ctx ml.Context) (ml.Tensor, ml.Tensor, ml.Tensor) {
-	key := c.keys[c.curLayer]
-	value := c.values[c.curLayer]
-
-	kHeadDim := key.Dim(0)
-	numKVHeads := key.Dim(1)
-	rowSize := key.Stride(2)
-	cachedSize := c.curMask.Dim(0)
-
-	key = key.View(ctx, rowSize*c.curCellRange.min,
-		kHeadDim, key.Stride(1),
-		numKVHeads, key.Stride(2),
-		cachedSize,
-	)
-
-	if c.config.PermutedV {
-		vHeadDim := value.Dim(1)
-		elemSize := value.Stride(0)
-
-		value = value.View(ctx, elemSize*c.curCellRange.min,
-			cachedSize, value.Stride(1),
-			vHeadDim, value.Stride(2),
-			numKVHeads,
-		)
-	} else {
-		vHeadDim := value.Dim(0)
-		rowSize := value.Stride(2)
-
-		value = value.View(ctx, rowSize*c.curCellRange.min,
-			vHeadDim, value.Stride(1),
-			numKVHeads, value.Stride(2),
-			cachedSize,
-		)
-	}
-
-	return key, value, c.curMask
-}
-
-func (c *Causal) Put(ctx ml.Context, key, value ml.Tensor) {
-	kHeadDim := key.Dim(0)
-	vHeadDim := value.Dim(0)
-	numKVHeads := key.Dim(1)
-	batchSize := key.Dim(2)
-
-	if c.curBatchSize != batchSize {
-		panic(fmt.Errorf("inconsistent batch sizes (layer: %v, batch size: %v layer batch size: %v)", c.curLayer, c.curBatchSize, batchSize))
-	}
-
-	if _, ok := c.ctxs[c.curLayer]; !ok {
-		c.ctxs[c.curLayer] = c.backend.NewContextSize(2).Layer(c.curLayer)
-	}
-
-	if _, ok := c.keys[c.curLayer]; !ok {
-		c.keys[c.curLayer] = c.ctxs[c.curLayer].Zeros(c.DType, kHeadDim, numKVHeads, len(c.cells))
-	}
-
-	if _, ok := c.values[c.curLayer]; !ok {
-		if c.config.PermutedV {
-			c.values[c.curLayer] = c.ctxs[c.curLayer].Zeros(c.DType, len(c.cells), vHeadDim, numKVHeads)
-		} else {
-			c.values[c.curLayer] = c.ctxs[c.curLayer].Zeros(c.DType, vHeadDim, numKVHeads, len(c.cells))
-		}
-	}
-
-	rowSize := c.keys[c.curLayer].Stride(2)
-	ctx.Forward(key.Copy(ctx, c.keys[c.curLayer].View(ctx, rowSize*c.curLoc, kHeadDim*numKVHeads*batchSize)))
-
-	if c.config.PermutedV {
-		elemSize := c.values[c.curLayer].Stride(0)
-
-		value = value.Permute(ctx, 1, 2, 0, 3)
-		ctx.Forward(value.Copy(ctx, c.values[c.curLayer].View(ctx, elemSize*c.curLoc, batchSize, len(c.cells)*elemSize, vHeadDim*numKVHeads)))
-	} else {
-		rowSize := c.values[c.curLayer].Stride(2)
-
-		ctx.Forward(value.Copy(ctx, c.values[c.curLayer].View(ctx, rowSize*c.curLoc, vHeadDim*numKVHeads*batchSize)))
-	}
-}
-
-func (c *Causal) CopyPrefix(srcSeq, dstSeq int, len int32) {
-	seqRange := newRange()
-
-	for i := range c.cells {
-		// Remove the contents of dstSeq so that we only have the copied prefix, metadata will be reset at the end
-		if slices.Contains(c.cells[i].sequences, dstSeq) {
-			c.cells[i].sequences = slices.DeleteFunc(c.cells[i].sequences, func(s int) bool { return s == dstSeq })
-		}
-
-		if slices.Contains(c.cells[i].sequences, srcSeq) && c.cells[i].pos < len {
-			c.cells[i].sequences = append(c.cells[i].sequences, dstSeq)
-			if i < seqRange.min {
-				seqRange.min = i
-			}
-			if i > seqRange.max {
-				seqRange.max = i
-			}
-		}
-	}
-
-	c.cellRanges[dstSeq] = seqRange
-}
-
-func (c *Causal) CanResume(seq int, pos int32) bool {
-	if c.windowSize == math.MaxInt32 {
-		return true
-	}
-
-	seqRange, ok := c.cellRanges[seq]
-	if !ok {
-		return false
-	}
-
-	// for sliding window, check that the window of the new sequence is contained in
-	// the window of what we are storing
-	var last int32 = -1
-	for i := seqRange.min; i <= seqRange.max; i++ {
-		if slices.Contains(c.cells[i].sequences, seq) {
-			last = max(last, c.cells[i].pos)
-		}
-	}
-
-	if last == -1 {
-		return false
-	}
-
-	lastWindowStart := max(0, last-c.windowSize)
-	posWindowStart := max(0, pos-c.windowSize)
-
-	return posWindowStart >= lastWindowStart
-}
-
-func (c *Causal) shift(seq int, beginIndex, offset int32) error {
-	if c.shiftFn == nil {
-		return ErrNotSupported
-	}
-
-	ctx := c.backend.NewContext()
-	defer ctx.Close()
-
-	seqRange := c.cellRanges[seq]
-	size := seqRange.max - seqRange.min + 1
-
-	offsets := make([]int32, size)
-	for i := range offsets {
-		cell := c.cells[seqRange.min+i]
-
-		if slices.Contains(cell.sequences, seq) && cell.pos >= beginIndex {
-			offsets[i] = offset
-		}
-	}
-
-	kShift, err := ctx.Input().FromIntSlice(offsets, len(offsets))
-	if err != nil {
-		return err
-	}
-
-	for i, key := range c.keys {
-		if key == nil {
-			continue
-		}
-
-		kHeadDim := key.Dim(0)
-		numKVHeads := key.Dim(1)
-		rowSize := key.Stride(2)
-
-		key = key.View(ctx, rowSize*seqRange.min,
-			kHeadDim, key.Stride(1),
-			numKVHeads, key.Stride(2),
-			size,
-		)
-
-		roped, err := c.shiftFn(ctx, i, key, kShift)
-		if err != nil {
-			return err
-		}
-
-		ctx.Forward(roped.Copy(ctx, key))
-	}
-
-	ctx.Compute()
-
-	return nil
-}
-
-func (c *Causal) Remove(seq int, beginIndex, endIndex int32) error {
-	// TODO(jessegross): We should check to see if removing the middle of the sequence will
-	// cause the sliding window to encompass tokens that we no longer have. If so, then we
-	// should return an error, which will trigger the runner to evaluate the full history and
-	// rebuild the window. However, if we have multimodal inputs in our history, this reuse
-	// results in use after free, so we don't do it for now.
-
-	var offset int32
-	if endIndex != math.MaxInt32 {
-		offset = beginIndex - endIndex
-	}
-
-	seqRange := newRange()
-
-	for i := range c.cells {
-		if slices.Contains(c.cells[i].sequences, seq) {
-			if c.cells[i].pos >= beginIndex && c.cells[i].pos < endIndex {
-				c.cells[i].sequences = slices.DeleteFunc(c.cells[i].sequences, func(s int) bool { return s == seq })
-			} else {
-				if c.cells[i].pos >= endIndex {
-					if slices.ContainsFunc(c.cells[i].sequences, func(s int) bool { return s != seq }) {
-						return errors.New("shifting cells shared by multiple sequences not supported")
-					}
-
-					c.cells[i].pos += offset
-				}
-				if i < seqRange.min {
-					seqRange.min = i
-				}
-				if i > seqRange.max {
-					seqRange.max = i
-				}
-			}
-		}
-	}
-
-	if seqRange == newRange() {
-		delete(c.cellRanges, seq)
-		return nil
-	}
-
-	c.cellRanges[seq] = seqRange
-
-	if endIndex != math.MaxInt32 {
-		err := c.shift(seq, endIndex+offset, offset)
-		if err != nil {
-			return err
-		}
-	}
-
-	return nil
-}

kvcache/causal_test.go

@@ -1,598 +0,0 @@
-package kvcache
-
-import (
-	"math"
-	"slices"
-	"testing"
-
-	"github.com/ollama/ollama/ml"
-	"github.com/ollama/ollama/model/input"
-)
-
-type testCase struct {
-	name          string
-	in            []float32
-	inShape       []int
-	seqs          []int
-	pos           []int32
-	expected      []float32
-	expectedShape []int
-	expectedMask  []float32
-}
-
-func TestStore(t *testing.T) {
-	backend := &testBackend{}
-	cache := NewCausalCache(nil)
-	defer cache.Close()
-
-	cache.Init(backend, ml.DTypeF16, 1, 16, 16)
-
-	tests := []testCase{
-		{
-			name:          "FirstBatch",
-			in:            []float32{111, 211, 121, 221, 131, 231, 112, 212, 122, 222, 132, 232, 113, 213, 123, 223, 133, 233, 114, 214, 124, 224, 134, 234},
-			inShape:       []int{2, 3, 4},
-			seqs:          []int{0, 0, 0, 0},
-			pos:           []int32{0, 1, 2, 3},
-			expected:      []float32{111, 211, 121, 221, 131, 231, 112, 212, 122, 222, 132, 232, 113, 213, 123, 223, 133, 233, 114, 214, 124, 224, 134, 234},
-			expectedShape: []int{2, 3, 4},
-			expectedMask:  []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, float32(math.Inf(-1)), 0, 0, 0, 0},
-		},
-		{
-			name:          "SecondBatch",
-			in:            []float32{115, 215, 125, 225, 135, 235},
-			inShape:       []int{2, 3, 1},
-			seqs:          []int{0},
-			pos:           []int32{4},
-			expected:      []float32{111, 211, 121, 221, 131, 231, 112, 212, 122, 222, 132, 232, 113, 213, 123, 223, 133, 233, 114, 214, 124, 224, 134, 234, 115, 215, 125, 225, 135, 235},
-			expectedShape: []int{2, 3, 5},
-			expectedMask:  []float32{0, 0, 0, 0, 0},
-		},
-	}
-
-	testCache(t, backend, cache, tests)
-}
-
-func TestSWA(t *testing.T) {
-	backend := &testBackend{}
-	cache := NewSWACache(1, nil)
-	defer cache.Close()
-
-	cache.Init(backend, ml.DTypeF16, 1, 16, 16)
-
-	tests := []testCase{
-		{
-			name:          "FirstBatch",
-			in:            []float32{1, 2, 3, 4},
-			inShape:       []int{1, 1, 4},
-			seqs:          []int{0, 0, 0, 0},
-			pos:           []int32{0, 1, 2, 3},
-			expected:      []float32{1, 2, 3, 4},
-			expectedShape: []int{1, 1, 4},
-			expectedMask:  []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0},
-		},
-		{
-			name:          "SecondBatch",
-			in:            []float32{5, 6},
-			inShape:       []int{1, 1, 2},
-			seqs:          []int{0, 0},
-			pos:           []int32{4, 5},
-			expected:      []float32{5, 6, 3, 4},
-			expectedShape: []int{1, 1, 4},
-			expectedMask:  []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1))},
-		},
-	}
-
-	testCache(t, backend, cache, tests)
-}
-
-func TestChunkedAttention(t *testing.T) {
-	cache := NewChunkedAttentionCache(2, nil)
-	defer cache.Close()
-
-	var b testBackend
-	cache.Init(&b, ml.DTypeF16, 1, 16, 16)
-
-	x := float32(math.Inf(-1))
-
-	testCache(
-		t, &b, cache,
-		[]testCase{
-			{
-				name:          "FirstBatch",
-				in:            []float32{1, 2, 3, 4},
-				inShape:       []int{1, 1, 4},
-				seqs:          []int{0, 0, 0, 0},
-				pos:           []int32{0, 1, 2, 3},
-				expected:      []float32{1, 2, 3, 4},
-				expectedShape: []int{1, 1, 4},
-				expectedMask: []float32{
-					0, x, x, x,
-					0, 0, x, x,
-					x, x, 0, x,
-					x, x, 0, 0,
-				},
-			},
-			{
-				name:          "SecondBatch",
-				in:            []float32{5, 6, 7},
-				inShape:       []int{1, 1, 3},
-				seqs:          []int{0, 0, 0},
-				pos:           []int32{4, 5, 6},
-				expected:      []float32{1, 2, 3, 4, 5, 6, 7},
-				expectedShape: []int{1, 1, 7},
-				expectedMask: []float32{
-					x, x, x, x, 0, x, x,
-					x, x, x, x, 0, 0, x,
-					x, x, x, x, x, x, 0,
-				},
-			},
-			{
-				name:          "ThirdBatch",
-				in:            []float32{8, 9},
-				inShape:       []int{1, 1, 2},
-				seqs:          []int{0, 0},
-				pos:           []int32{7, 8},
-				expected:      []float32{1, 2, 3, 4, 5, 6, 7, 8, 9},
-				expectedShape: []int{1, 1, 9},
-				expectedMask: []float32{
-					x, x, x, x, x, x, 0, 0, x,
-					x, x, x, x, x, x, x, x, 0,
-				},
-			},
-		},
-	)
-}
-
-func TestSequences(t *testing.T) {
-	backend := &testBackend{}
-	cache := NewCausalCache(nil)
-	defer cache.Close()
-
-	cache.Init(backend, ml.DTypeF16, 1, 16, 16)
-
-	tests := []testCase{
-		{
-			name:          "FirstBatch",
-			in:            []float32{1, 2, 3, 4},
-			inShape:       []int{1, 1, 4},
-			seqs:          []int{0, 0, 1, 1},
-			pos:           []int32{0, 1, 0, 1},
-			expected:      []float32{1, 2, 3, 4},
-			expectedShape: []int{1, 1, 4},
-			expectedMask:  []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0},
-		},
-		{
-			name:          "SecondBatch",
-			in:            []float32{5, 6},
-			inShape:       []int{1, 1, 2},
-			seqs:          []int{0, 1},
-			pos:           []int32{2, 2},
-			expected:      []float32{1, 2, 3, 4, 5, 6},
-			expectedShape: []int{1, 1, 6},
-			expectedMask:  []float32{0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), 0},
-		},
-	}
-
-	testCache(t, backend, cache, tests)
-}
-
-func TestRemove(t *testing.T) {
-	backend := &testBackend{}
-	cache := NewCausalCache(func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
-		return key.Add(ctx, shift), nil
-	})
-	defer cache.Close()
-
-	cache.Init(backend, ml.DTypeF16, 1, 16, 16)
-
-	tests := []testCase{
-		{
-			name:          "FirstBatch",
-			in:            []float32{1, 2, 3, 4},
-			inShape:       []int{1, 1, 4},
-			seqs:          []int{0, 0, 1, 1},
-			pos:           []int32{0, 1, 0, 1},
-			expected:      []float32{1, 2, 3, 4},
-			expectedShape: []int{1, 1, 4},
-			expectedMask:  []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0},
-		},
-	}
-
-	testCache(t, backend, cache, tests)
-
-	err := cache.Remove(0, 1, math.MaxInt32)
-	if err != nil {
-		panic(err)
-	}
-
-	tests = []testCase{
-		{
-			name:          "RemoveEnd",
-			in:            []float32{5, 6},
-			inShape:       []int{1, 1, 2},
-			seqs:          []int{0, 1},
-			pos:           []int32{1, 2},
-			expected:      []float32{1, 2, 3, 4, 5, 6},
-			expectedShape: []int{1, 1, 6},
-			expectedMask:  []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), 0},
-		},
-	}
-
-	testCache(t, backend, cache, tests)
-
-	err = cache.Remove(0, 0, 1)
-	if err != nil {
-		panic(err)
-	}
-
-	tests = []testCase{
-		{
-			name:          "RemoveMiddle",
-			in:            []float32{7, 8},
-			inShape:       []int{1, 1, 2},
-			seqs:          []int{0, 0},
-			pos:           []int32{1, 2},
-			expected:      []float32{7, 8, 3, 4, 4},
-			expectedShape: []int{1, 1, 5},
-			expectedMask:  []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0},
-		},
-	}
-
-	testCache(t, backend, cache, tests)
-}
-
-func TestDefrag(t *testing.T) {
-	backend := &testBackend{}
-	cache := NewCausalCache(func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
-		return key.Add(ctx, shift), nil
-	})
-	defer cache.Close()
-
-	cache.Init(backend, ml.DTypeF16, 1, 16, 16)
-
-	tests := []testCase{
-		{
-			name:          "FirstBatch",
-			in:            []float32{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16},
-			inShape:       []int{1, 1, 16},
-			seqs:          []int{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
-			pos:           []int32{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15},
-			expected:      []float32{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16},
-			expectedShape: []int{1, 1, 16},
-			expectedMask:  []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
-		},
-	}
-
-	testCache(t, backend, cache, tests)
-
-	err := cache.Remove(0, 2, 4)
-	if err != nil {
-		panic(err)
-	}
-
-	err = cache.Remove(0, 13, math.MaxInt32)
-	if err != nil {
-		panic(err)
-	}
-
-	tests = []testCase{
-		{
-			name:          "Defrag",
-			in:            []float32{17, 18, 19},
-			inShape:       []int{1, 1, 3},
-			seqs:          []int{0, 0, 0},
-			pos:           []int32{16, 17, 18},
-			expected:      []float32{1, 2, 12, 13, 3, 4, 5, 6, 7, 8, 9, 10, 11, 17, 18, 19},
-			expectedShape: []int{1, 1, 16},
-			expectedMask:  []float32{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, float32(math.Inf(-1)), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
-		},
-	}
-
-	testCache(t, backend, cache, tests)
-}
-
-func TestCopy(t *testing.T) {
-	backend := &testBackend{}
-	cache := NewCausalCache(func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) { return key, nil })
-	defer cache.Close()
-
-	cache.Init(backend, ml.DTypeF16, 1, 16, 16)
-
-	tests := []testCase{
-		{
-			name:          "FirstBatch",
-			in:            []float32{1, 2, 3, 4},
-			inShape:       []int{1, 1, 4},
-			seqs:          []int{0, 0, 0, 0},
-			pos:           []int32{0, 1, 2, 3},
-			expected:      []float32{1, 2, 3, 4},
-			expectedShape: []int{1, 1, 4},
-			expectedMask:  []float32{0, float32(math.Inf(-1)), float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0, 0, float32(math.Inf(-1)), 0, 0, 0, 0},
-		},
-	}
-
-	testCache(t, backend, cache, tests)
-
-	cache.CopyPrefix(0, 1, 2)
-
-	tests = []testCase{
-		{
-			name:          "Copy",
-			in:            []float32{5, 6},
-			inShape:       []int{1, 1, 2},
-			seqs:          []int{1, 1},
-			pos:           []int32{3, 4},
-			expected:      []float32{1, 2, 3, 4, 5, 6},
-			expectedShape: []int{1, 1, 6},
-			expectedMask:  []float32{0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, float32(math.Inf(-1)), 0, 0, float32(math.Inf(-1)), float32(math.Inf(-1)), 0, 0},
-		},
-	}
-
-	testCache(t, backend, cache, tests)
-}
-
-func testCache(t *testing.T, backend ml.Backend, cache Cache, tests []testCase) {
-	for _, test := range tests {
-		t.Run(test.name, func(t *testing.T) {
-			context := backend.NewContext()
-			defer context.Close()
-
-			err := cache.StartForward(context, input.Batch{Positions: test.pos, Sequences: test.seqs}, false)
-			if err != nil {
-				panic(err)
-			}
-
-			cache.SetLayer(0)
-			tensor, _ := context.FromFloatSlice(test.in, test.inShape...)
-			cache.Put(context, tensor, tensor)
-
-			out, _, mask := cache.Get(context)
-
-			context.Forward(out, mask).Compute(out, mask)
-
-			if !slices.Equal(out.Floats(), test.expected) {
-				t.Errorf("TestCache: have %v; want %v", out.Floats(), test.expected)
-			}
-
-			if !slices.Equal(out.Shape(), test.expectedShape) {
-				t.Errorf("TestCache: has shape %v; want %v", out.Shape(), test.expectedShape)
-			}
-
-			if !slices.Equal(mask.Floats(), test.expectedMask) {
-				t.Errorf("TestCache: have mask: have %v want %v", mask.Floats(), test.expectedMask)
-			}
-		})
-	}
-}
-
-func TestCanResume(t *testing.T) {
-	backend := &testBackend{}
-	windowSize := int32(4)
-	cache := NewSWACache(windowSize, nil)
-	defer cache.Close()
-
-	cache.Init(backend, ml.DTypeF16, 1, 16, 16)
-
-	context := backend.NewContext()
-	defer context.Close()
-
-	err := cache.StartForward(context, input.Batch{
-		Positions: []int32{0, 1, 2, 3},
-		Sequences: []int{0, 0, 0, 0},
-	}, false)
-	if err != nil {
-		t.Fatalf("StartForward failed: %v", err)
-	}
-
-	cache.SetLayer(0)
-	tensor, _ := context.FromFloatSlice([]float32{1, 2, 3, 4}, 1, 1, 4)
-	cache.Put(context, tensor, tensor)
-
-	// with window size 4, nothing has slid out of the window yet
-	if !cache.CanResume(0, 0) {
-		t.Errorf("CanResume(0, 0) = false, want true (within window)")
-	}
-	if !cache.CanResume(0, 1) {
-		t.Errorf("CanResume(0, 1) = false, want true (within window)")
-	}
-	if !cache.CanResume(0, 2) {
-		t.Errorf("CanResume(0, 2) = false, want true (within window)")
-	}
-	if !cache.CanResume(0, 3) {
-		t.Errorf("CanResume(0, 3) = false, want true (latest position)")
-	}
-
-	// shift window by adding position 4
-	err = cache.StartForward(context, input.Batch{
-		Positions: []int32{4, 5},
-		Sequences: []int{0, 0},
-	}, false)
-	if err != nil {
-		t.Fatalf("StartForward failed: %v", err)
-	}
-
-	cache.SetLayer(0)
-	tensor, _ = context.FromFloatSlice([]float32{5, 6}, 1, 1, 2)
-	cache.Put(context, tensor, tensor)
-
-	// only the latest position has overlapping windows
-	if cache.CanResume(0, 0) {
-		t.Errorf("after shift: CanResume(0, 0) = true, want false (outside window)")
-	}
-	if cache.CanResume(0, 1) {
-		t.Errorf("after shift: CanResume(0, 1) = true, want false (outside window)")
-	}
-	if cache.CanResume(0, 2) {
-		t.Errorf("after shift: CanResume(0, 2) = true, want false (outside window)")
-	}
-	if cache.CanResume(0, 3) {
-		t.Errorf("after shift: CanResume(0, 3) = true, want false (outside window)")
-	}
-	if cache.CanResume(0, 4) {
-		t.Errorf("after shift: CanResume(0, 4) = true, want false (outside window)")
-	}
-	if !cache.CanResume(0, 5) {
-		t.Errorf("after shift: CanResume(0, 5) = false, want true (latest position)")
-	}
-}
-
-type testBackend struct {
-	ml.Backend
-}
-
-func (b *testBackend) NewContext() ml.Context {
-	return &testContext{}
-}
-
-func (b *testBackend) NewContextSize(int) ml.Context {
-	return &testContext{}
-}
-
-type testContext struct {
-	ml.Context
-}
-
-func (c *testContext) Empty(dtype ml.DType, shape ...int) ml.Tensor {
-	total := 0
-
-	if len(shape) > 0 {
-		total = 1
-		for _, s := range shape {
-			total *= s
-		}
-	}
-
-	return &testTensor{dtype: dtype, elementSize: 4, data: make([]float32, total), shape: shape}
-}
-
-func (c *testContext) Zeros(dtype ml.DType, shape ...int) ml.Tensor {
-	return c.Empty(dtype, shape...)
-}
-
-func (c *testContext) FromFloatSlice(s []float32, shape ...int) (ml.Tensor, error) {
-	t := c.Empty(ml.DTypeF32, shape...).(*testTensor)
-
-	copy(t.data, s)
-
-	return t, nil
-}
-
-func (c *testContext) FromIntSlice(s []int32, shape ...int) (ml.Tensor, error) {
-	f := make([]float32, len(s))
-	for i := range f {
-		f[i] = float32(s[i])
-	}
-
-	out, _ := c.FromFloatSlice(f, shape...)
-	out.(*testTensor).dtype = ml.DTypeI32
-
-	return out, nil
-}
-
-func (c *testContext) Arange(start, stop, step float32, dtype ml.DType) ml.Tensor {
-	s := make([]float32, 0, int((stop-start)/step))
-	for i := start; i < stop; i += step {
-		s = append(s, i)
-	}
-
-	out, _ := c.FromFloatSlice(s, len(s))
-	out.(*testTensor).dtype = dtype
-	return out
-}
-
-func (c *testContext) Input() ml.Context    { return c }
-func (c *testContext) Layer(int) ml.Context { return c }
-
-func (c *testContext) Forward(...ml.Tensor) ml.Context { return c }
-
-func (c *testContext) Compute(...ml.Tensor) {}
-
-func (c *testContext) Reserve() error { return nil }
-
-func (c *testContext) MaxGraphNodes() int {
-	return 10
-}
-
-func (c *testContext) Close() {}
-
-type testTensor struct {
-	ml.Tensor
-
-	dtype       ml.DType
-	elementSize int
-	data        []float32
-	shape       []int
-}
-
-func (t *testTensor) Dim(n int) int {
-	return t.shape[n]
-}
-
-func (t *testTensor) Stride(n int) int {
-	stride := t.elementSize
-	for i := range n {
-		stride *= t.shape[i]
-	}
-
-	return stride
-}
-
-func (t *testTensor) Shape() []int {
-	return t.shape
-}
-
-func (t *testTensor) DType() ml.DType {
-	return t.dtype
-}
-
-func (t *testTensor) Floats() []float32 {
-	out := make([]float32, len(t.data))
-	copy(out, t.data)
-	return out
-}
-
-func (t *testTensor) Neg(ctx ml.Context) ml.Tensor {
-	out := ctx.Empty(t.DType(), t.Shape()...).(*testTensor)
-	for i := range out.data {
-		out.data[i] = -t.data[i]
-	}
-	return out
-}
-
-func (t *testTensor) Add(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
-	out := ctx.Empty(t.DType(), t.Shape()...).(*testTensor)
-
-	for i := range out.data {
-		out.data[i] = t.data[i] + t2.(*testTensor).data[i]
-	}
-
-	return out
-}
-
-func (t *testTensor) View(ctx ml.Context, offset int, shape ...int) ml.Tensor {
-	offset /= t.elementSize
-
-	var s []int
-
-	switch len(shape) {
-	case 1:
-		s = []int{shape[0]}
-	case 5:
-		s = []int{shape[0], shape[2], shape[4]}
-	default:
-		panic("unsupported number of dimensions")
-	}
-
-	context := &testContext{}
-
-	view := context.Empty(t.dtype, s...).(*testTensor)
-	view.data = t.data[offset : offset+len(view.data)]
-
-	return view
-}
-
-func (t *testTensor) Copy(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
-	copy(t2.(*testTensor).data, t.data)
-	return nil
-}

kvcache/encoder.go

@@ -1,156 +0,0 @@
-package kvcache
-
-import (
-	"fmt"
-
-	"github.com/ollama/ollama/ml"
-	"github.com/ollama/ollama/model/input"
-)
-
-// Encoder cache stores K and V tensors that are position independent
-//
-// The tensors can be of any shape and will be returned as they were stored
-// The mask is currently always nil
-//
-// Not currently safe for multiple sequences
-type EncoderCache struct {
-	// config controls mostly backend-specific optimizations
-	config *ml.CacheConfig
-
-	// ** current forward pass **
-
-	// the active layer for Get and Put
-	curLayer int
-
-	// if something is stored during this pass, this
-	// will be the position (but there is no guarantee
-	// anything will be stored)
-	curPos int32
-
-	// curReserve indicates that this forward pass is only for
-	// memory reservation and we should not update our metadata
-	// based on it.
-	curReserve bool
-
-	// ** cache metadata **
-
-	// was something stored in the cache?
-	encoderCached bool
-
-	// position of the cached data
-	encoderPos int32
-
-	// ** cache data storage **
-	backend      ml.Backend
-	ctxs         map[int]ml.Context
-	keys, values map[int]ml.Tensor
-}
-
-func NewEncoderCache() *EncoderCache {
-	return &EncoderCache{
-		ctxs:   make(map[int]ml.Context),
-		keys:   make(map[int]ml.Tensor),
-		values: make(map[int]ml.Tensor),
-	}
-}
-
-func (c *EncoderCache) Init(backend ml.Backend, dtype ml.DType, maxSequences, capacity, maxBatch int) {
-	if c.config == nil {
-		var config ml.CacheConfig
-		if cc, ok := backend.(ml.BackendCacheConfig); ok {
-			config = cc.CacheConfig()
-		}
-		c.config = &config
-	}
-
-	if maxSequences > 1 {
-		panic(fmt.Errorf("encoder cache does not support multiple sequences; requested: %v", maxSequences))
-	}
-
-	if c.config.CachePadding != 0 && c.config.CachePadding != 1 {
-		panic(fmt.Errorf("encoder cache is unable to enforce requested CachePadding (%v)", c.config.CachePadding))
-	}
-
-	c.backend = backend
-}
-
-func (c *EncoderCache) SetConfig(config ml.CacheConfig) {
-	if c.config != nil {
-		panic("config cannot be changed after being previously set, either by the model or backend")
-	}
-
-	c.config = &config
-}
-
-func (c *EncoderCache) Close() {
-	for _, ctx := range c.ctxs {
-		ctx.Close()
-	}
-}
-
-func (c *EncoderCache) StartForward(ctx ml.Context, batch input.Batch, reserve bool) error {
-	// We work with the most recent image
-	if len(batch.Multimodal) > 0 {
-		c.curPos = batch.Positions[batch.Multimodal[len(batch.Multimodal)-1].Index]
-	}
-
-	c.curReserve = reserve
-
-	return nil
-}
-
-func (c *EncoderCache) SetLayer(layer int) {
-	c.curLayer = layer
-}
-
-func (c *EncoderCache) EncoderCached() bool {
-	return c.encoderCached
-}
-
-func (c *EncoderCache) Get(ctx ml.Context) (ml.Tensor, ml.Tensor, ml.Tensor) {
-	return c.keys[c.curLayer], c.values[c.curLayer], nil
-}
-
-func (c *EncoderCache) Put(ctx ml.Context, key, value ml.Tensor) {
-	if !c.curReserve {
-		c.encoderPos = c.curPos
-		c.encoderCached = true
-	}
-
-	if c.config.PermutedV {
-		value = value.Permute(ctx, 1, 2, 0, 3)
-	}
-
-	if _, ok := c.ctxs[c.curLayer]; !ok {
-		c.ctxs[c.curLayer] = c.backend.NewContextSize(2).Layer(c.curLayer)
-	}
-
-	if _, ok := c.keys[c.curLayer]; !ok {
-		c.keys[c.curLayer] = c.ctxs[c.curLayer].Empty(key.DType(), key.Shape()...)
-	}
-
-	if _, ok := c.values[c.curLayer]; !ok {
-		c.values[c.curLayer] = c.ctxs[c.curLayer].Empty(value.DType(), value.Shape()...)
-	}
-
-	ctx.Forward(
-		key.Copy(ctx, c.keys[c.curLayer]),
-		value.Copy(ctx, c.values[c.curLayer]),
-	)
-}
-
-func (c *EncoderCache) CopyPrefix(srcSeq, dstSeq int, len int32) {
-	panic("encoder cache does not support multiple sequences")
-}
-
-func (c *EncoderCache) CanResume(seq int, pos int32) bool {
-	return true
-}
-
-func (c *EncoderCache) Remove(seq int, beginIndex, endIndex int32) error {
-	if c.encoderPos >= beginIndex && c.encoderPos < endIndex {
-		c.encoderCached = false
-	}
-
-	return nil
-}