Merge branch 'main' into drifkin/array-head-count-simple

The correct constant to remove all entries to the end of the sequence
for the Ollama engine is math.MaxInt32. -1 is used by the old engine.

The impact of this is currently minimal because it would only occur
in situations that are not supported by the implemented models or
rarely used options.

.dockerignore

@@ -3,7 +3,9 @@ ollama
 app
 macapp
 dist
+build
 .env
 .cache
 test_data
-llama/build
+.git
+

.gitattributes

@@ -7,5 +7,18 @@ llama/**/*.cuh linguist-vendored
 llama/**/*.m linguist-vendored
 llama/**/*.metal linguist-vendored
 
+ml/backend/**/*.c linguist-vendored
+ml/backend/**/*.h linguist-vendored
+ml/backend/**/*.cpp linguist-vendored
+ml/backend/**/*.hpp linguist-vendored
+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

@@ -9,6 +9,14 @@ 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/test.yaml

@@ -1,11 +1,5 @@
 name: test
 
-env:
-  ROCM_WINDOWS_URL: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe
-  MSYS2_URL: https://github.com/msys2/msys2-installer/releases/download/2024-07-27/msys2-x86_64-20240727.exe
-  CUDA_12_WINDOWS_URL: https://developer.download.nvidia.com/compute/cuda/12.4.0/local_installers/cuda_12.4.0_551.61_windows.exe
-  CUDA_12_WINDOWS_VER: 12.4
-
 concurrency:
   # For PRs, later CI runs preempt previous ones. e.g. a force push on a PR
   # cancels running CI jobs and starts all new ones.
@@ -27,7 +21,7 @@ jobs:
   changes:
     runs-on: ubuntu-latest
     outputs:
-      RUNNERS: ${{ steps.changes.outputs.RUNNERS }}
+      changed: ${{ steps.changes.outputs.changed }}
     steps:
       - uses: actions/checkout@v4
         with:
@@ -35,309 +29,213 @@ jobs:
       - id: changes
         run: |
           changed() {
-            git diff-tree -r --no-commit-id --name-only \
+            local BASE=${{ github.event.pull_request.base.sha }}
-              $(git merge-base ${{ github.event.pull_request.base.sha }} ${{ github.event.pull_request.head.sha }}) \
+            local HEAD=${{ github.event.pull_request.head.sha }}
-              ${{ github.event.pull_request.head.sha }} \
+            local MERGE_BASE=$(git merge-base $BASE $HEAD)
+            git diff-tree -r --no-commit-id --name-only "$MERGE_BASE" "$HEAD" \
               | xargs python3 -c "import sys; from pathlib import Path; print(any(Path(x).match(glob) for x in sys.argv[1:] for glob in '$*'.split(' ')))"
           }
 
-          {
+          echo changed=$(changed 'llama/llama.cpp/**' 'ml/backend/ggml/ggml/**') | tee -a $GITHUB_OUTPUT
-            echo RUNNERS=$(changed 'llama/**')
-          } >>$GITHUB_OUTPUT
 
-  runners-linux-cuda:
+  linux:
     needs: [changes]
-    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
+    if: needs.changes.outputs.changed == 'True'
     strategy:
       matrix:
-        cuda-version:
+        include:
-          - '11.8.0'
+          - preset: CPU
+          - preset: CUDA
+            container: nvidia/cuda:12.8.1-devel-ubuntu22.04
+            flags: '-DCMAKE_CUDA_ARCHITECTURES=87'
+          - preset: ROCm
+            container: rocm/dev-ubuntu-22.04:6.1.2
+            extra-packages: rocm-libs
+            flags: '-DAMDGPU_TARGETS=gfx1010 -DCMAKE_PREFIX_PATH=/opt/rocm'
     runs-on: linux
-    container: nvidia/cuda:${{ matrix.cuda-version }}-devel-ubuntu20.04
+    container: ${{ matrix.container }}
     steps:
+      - uses: actions/checkout@v4
       - run: |
-          apt-get update && apt-get install -y git build-essential curl
+          [ -n "${{ matrix.container }}" ] || sudo=sudo
+          $sudo apt-get update
+          $sudo apt-get install -y cmake ccache ${{ matrix.extra-packages }}
         env:
           DEBIAN_FRONTEND: noninteractive
-      - uses: actions/checkout@v4
+      - uses: actions/cache@v4
-      - uses: actions/setup-go@v4
         with:
-          go-version-file: go.mod
+          path: /github/home/.cache/ccache
-          cache: true
+          key: ccache-${{ runner.os }}-${{ runner.arch }}-${{ matrix.preset }}
-      - run: go get ./...
       - run: |
-          git config --global --add safe.directory /__w/ollama/ollama
+          cmake --preset ${{ matrix.preset }} ${{ matrix.flags }}
-          cores=$(grep '^core id' /proc/cpuinfo |sort -u|wc -l)
+          cmake --build --preset ${{ matrix.preset }} --parallel
-          make -j $cores cuda_v11
+
-  runners-linux-rocm:
+  windows:
     needs: [changes]
-    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
+    if: needs.changes.outputs.changed == 'True'
     strategy:
       matrix:
-        rocm-version:
+        include:
-          - '6.1.2'
+          - preset: CPU
-    runs-on: linux
+          - preset: CUDA
-    container: rocm/dev-ubuntu-20.04:${{ matrix.rocm-version }}
+            install: https://developer.download.nvidia.com/compute/cuda/12.8.0/local_installers/cuda_12.8.0_571.96_windows.exe
-    steps:
+            flags: '-DCMAKE_CUDA_ARCHITECTURES=80'
-      - run: |
+          - preset: ROCm
-          apt-get update && apt-get install -y git build-essential curl rocm-libs
+            install: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q4-WinSvr2022-For-HIP.exe
-        env:
+            flags: '-DAMDGPU_TARGETS=gfx1010'
-          DEBIAN_FRONTEND: noninteractive
-      - uses: actions/checkout@v4
-      - uses: actions/setup-go@v4
-        with:
-          go-version-file: go.mod
-          cache: true
-      - run: go get ./...
-      - run: |
-          git config --global --add safe.directory /__w/ollama/ollama
-          cores=$(grep '^core id' /proc/cpuinfo |sort -u|wc -l)
-          make -j $cores rocm
-
-  # ROCm generation step
-  runners-windows-rocm:
-    needs: [changes]
-    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
     runs-on: windows
     steps:
-      - uses: actions/checkout@v4
+      - run: |
-      - uses: actions/setup-go@v5
+          choco install -y --no-progress ccache ninja
+          ccache -o cache_dir=${{ github.workspace }}\.ccache
+      - if: matrix.preset == 'CUDA' || matrix.preset == 'ROCm'
+        id: cache-install
+        uses: actions/cache/restore@v4
         with:
-          go-version-file: go.mod
+          path: |
-          cache: true
+            C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA
-      - name: Set make jobs default
+            C:\Program Files\AMD\ROCm
-        run: |
+          key: ${{ matrix.install }}
-          echo "MAKEFLAGS=--jobs=$((Get-ComputerInfo -Property CsProcessors).CsProcessors.NumberOfCores)" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+      - if: matrix.preset == 'CUDA'
-
+        name: Install CUDA ${{ matrix.cuda-version }}
-      # ROCM installation steps
-      - name: 'Cache ROCm installer'
-        id: cache-rocm
-        uses: actions/cache@v4
-        with:
-          path: rocm-install.exe
-          key: ${{ env.ROCM_WINDOWS_URL }}
-      - name: 'Conditionally Download ROCm'
-        if: steps.cache-rocm.outputs.cache-hit != 'true'
         run: |
           $ErrorActionPreference = "Stop"
-          Invoke-WebRequest -Uri "${env:ROCM_WINDOWS_URL}" -OutFile "rocm-install.exe"
+          if ("${{ steps.cache-install.outputs.cache-hit }}" -ne 'true') {
-      - name: 'Install ROCm'
+            Invoke-WebRequest -Uri "${{ matrix.install }}" -OutFile "install.exe"
-        run: |
+            Start-Process -FilePath .\install.exe -ArgumentList (@("-s", "cudart_12.8", "nvcc_12.8", "cublas_12.8", "cublas_dev_12.8")) -NoNewWindow -Wait
-          Start-Process "rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
+          }
-      - name: 'Verify ROCm'
-        run: |
-          & 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version
-          echo "HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path | select -first 1)" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
 
-      - name: Add msys paths
+          $cudaPath = (Resolve-Path "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\*").path
-        run: |
-          echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-      - name: Install msys2 tools
-        run: |
-          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
-
-      - name: make rocm runner
-        run: |
-          import-module 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
-          Enter-VsDevShell -vsinstallpath 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise' -skipautomaticlocation -DevCmdArguments '-arch=x64 -no_logo'
-          if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
-          make -C llama print-HIP_PATH print-HIP_LIB_DIR
-          make rocm
-
-  # CUDA generation step
-  runners-windows-cuda:
-    needs: [changes]
-    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
-    runs-on: windows
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-go@v5
-        with:
-          go-version-file: go.mod
-          cache: true
-      - name: Set make jobs default
-        run: |
-          echo "MAKEFLAGS=--jobs=$((Get-ComputerInfo -Property CsProcessors).CsProcessors.NumberOfCores)" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
-
-      # CUDA installation steps
-      - name: 'Cache CUDA installer'
-        id: cache-cuda
-        uses: actions/cache@v4
-        with:
-          path: cuda-install.exe
-          key: ${{ env.CUDA_12_WINDOWS_URL }}
-      - name: 'Conditionally Download CUDA'
-        if: steps.cache-cuda.outputs.cache-hit != 'true'
-        run: |
-          $ErrorActionPreference = "Stop"
-          Invoke-WebRequest -Uri "${env:CUDA_12_WINDOWS_URL}" -OutFile "cuda-install.exe"
-      - name: 'Install CUDA'
-        run: |
-          $subpackages = @("cudart", "nvcc", "cublas", "cublas_dev") | foreach-object {"${_}_${{ env.CUDA_12_WINDOWS_VER }}"}
-          Start-Process "cuda-install.exe" -ArgumentList (@("-s") + $subpackages) -NoNewWindow -Wait
-      - name: 'Verify CUDA'
-        run: |
-          & (resolve-path "c:\Program Files\NVIDIA*\CUDA\v*\bin\nvcc.exe")[0] --version
-          $cudaPath=((resolve-path "c:\Program Files\NVIDIA*\CUDA\v*\bin\nvcc.exe")[0].path | split-path | split-path)
-          $cudaVer=($cudaPath | split-path -leaf ) -replace 'v(\d+).(\d+)', '$1_$2' 
           echo "$cudaPath\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-          echo "CUDA_PATH=$cudaPath" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+      - if: matrix.preset == 'ROCm'
-          echo "CUDA_PATH_V${cudaVer}=$cudaPath" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+        name: Install ROCm ${{ matrix.rocm-version }}
-          echo "CUDA_PATH_VX_Y=CUDA_PATH_V${cudaVer}" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+        run: |
+          $ErrorActionPreference = "Stop"
+          if ("${{ steps.cache-install.outputs.cache-hit }}" -ne 'true') {
+            Invoke-WebRequest -Uri "${{ matrix.install }}" -OutFile "install.exe"
+            Start-Process -FilePath .\install.exe -ArgumentList '-install' -NoNewWindow -Wait
+          }
 
-      - name: Add msys paths
+          $hipPath = (Resolve-Path "C:\Program Files\AMD\ROCm\*").path
-        run: |
+          echo "$hipPath\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-          echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+          echo "CC=$hipPath\bin\clang.exe" | Out-File -FilePath $env:GITHUB_ENV -Append
-          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+          echo "CXX=$hipPath\bin\clang++.exe" | Out-File -FilePath $env:GITHUB_ENV -Append
-      - name: Install msys2 tools
+      - if: ${{ !cancelled() && steps.cache-install.outputs.cache-hit != 'true' }}
-        run: |
+        uses: actions/cache/save@v4
-          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
+        with:
-      - name: make cuda runner
+          path: |
-        run: |
+            C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA
-          import-module 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
+            C:\Program Files\AMD\ROCm
-          Enter-VsDevShell -vsinstallpath 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise' -skipautomaticlocation -DevCmdArguments '-arch=x64 -no_logo'
+          key: ${{ matrix.install }}
-          if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
-          make cuda_v$(($env:CUDA_PATH | split-path -leaf) -replace 'v(\d+).*', '$1')
-
-  runners-cpu:
-    needs: [changes]
-    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
-    strategy:
-      matrix:
-        os: [ubuntu-latest, macos-latest, windows-2019]
-        arch: [amd64, arm64]
-        exclude:
-          - os: ubuntu-latest
-            arch: arm64
-          - os: windows-2019
-            arch: arm64
-    runs-on: ${{ matrix.os }}
-    env:
-      GOARCH: ${{ matrix.arch }}
-      ARCH: ${{ matrix.arch }}
-      CGO_ENABLED: '1'
-    steps:
       - uses: actions/checkout@v4
-      - uses: actions/setup-go@v5
+      - uses: actions/cache@v4
         with:
-          go-version-file: go.mod
+          path: ${{ github.workspace }}\.ccache
-          cache: true
+          key: ccache-${{ runner.os }}-${{ runner.arch }}-${{ matrix.preset }}
-      - name: Add msys paths
-        if: ${{ startsWith(matrix.os, 'windows-') }}
-        run: |
-          echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-      - name: Install msys2 tools
-        if: ${{ startsWith(matrix.os, 'windows-') }}
-        run: |
-          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
-      - name: 'Build Windows Go Runners'
-        if: ${{ startsWith(matrix.os, 'windows-') }}
-        run: |
-          $gopath=(get-command go).source | split-path -parent
-          $gccpath=(get-command gcc).source | split-path -parent
-          import-module 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
-          Enter-VsDevShell -vsinstallpath 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise' -skipautomaticlocation -DevCmdArguments '-arch=x64 -no_logo'
-          $env:CMAKE_SYSTEM_VERSION="10.0.22621.0"
-          $env:PATH="$gopath;$gccpath;$env:PATH"
-          echo $env:PATH
-          if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
-          make -j 4
-      - name: 'Build Unix Go Runners'
-        if: ${{ ! startsWith(matrix.os, 'windows-') }}
-        run: make -j 4
-      - run: go build .
-
-  lint:
-    strategy:
-      matrix:
-        os: [ubuntu-latest, macos-latest, windows-2019]
-        arch: [amd64, arm64]
-        exclude:
-          - os: ubuntu-latest
-            arch: arm64
-          - os: windows-2019
-            arch: arm64
-          - os: macos-latest
-            arch: amd64
-    runs-on: ${{ matrix.os }}
-    env:
-      GOARCH: ${{ matrix.arch }}
-      CGO_ENABLED: '1'
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          submodules: recursive
-      - name: Add msys paths
-        if: ${{ startsWith(matrix.os, 'windows-') }}
-        run: |
-          echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-      - name: Install msys2 tools
-        if: ${{ startsWith(matrix.os, 'windows-') }}
-        run: |
-          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
-      - uses: actions/setup-go@v5
-        with:
-          go-version-file: go.mod
-          cache: false
       - run: |
-          case ${{ matrix.arch }} in
+          Import-Module 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
-            amd64) echo ARCH=x86_64 ;;
+          Enter-VsDevShell -VsInstallPath 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise' -SkipAutomaticLocation  -DevCmdArguments '-arch=x64 -no_logo'
-            arm64) echo ARCH=arm64 ;;
+          cmake --preset "${{ matrix.preset }}" ${{ matrix.flags }}
-          esac >>$GITHUB_ENV
+          cmake --build --parallel --preset "${{ matrix.preset }}"
-        shell: bash
+        env:
-      - uses: golangci/golangci-lint-action@v6
+          CMAKE_GENERATOR: Ninja
-        with:
-          args: --timeout 10m0s -v
-  test:
-    strategy:
-      matrix:
-        os: [ubuntu-latest, macos-latest, windows-2019]
-        arch: [amd64]
-        exclude:
-          - os: ubuntu-latest
-            arch: arm64
-          - os: windows-2019
-            arch: arm64
-    runs-on: ${{ matrix.os }}
-    env:
-      GOARCH: ${{ matrix.arch }}
-      CGO_ENABLED: '1'
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          submodules: recursive
-      - name: Add msys paths
-        if: ${{ startsWith(matrix.os, 'windows-') }}
-        run: |
-          echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-      - name: Install msys2 tools
-        if: ${{ startsWith(matrix.os, 'windows-') }}
-        run: |
-          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
-      - uses: actions/setup-go@v5
-        with:
-          go-version-file: go.mod
-          cache: true
-      - run: |
-          case ${{ matrix.arch }} in
-            amd64) echo ARCH=amd64 ;;
-            arm64) echo ARCH=arm64 ;;
-          esac >>$GITHUB_ENV
-        shell: bash
-      - run: go test ./...
 
-  patches:
+  go_mod_tidy:
-    needs: [changes]
-    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
     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:
+        os: [ubuntu-latest, macos-latest, windows-latest]
+    runs-on: ${{ matrix.os }}
+    env:
+      CGO_ENABLED: '1'
+      GOEXPERIMENT: 'synctest'
+    steps:
+      - name: checkout
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # 4.2.2
+
+      - name: cache restore
+        uses: actions/cache/restore@1bd1e32a3bdc45362d1e726936510720a7c30a57 # v4.2.0
         with:
-          submodules: recursive
+          # Note: unlike the other setups, this is only grabbing the mod download
-      - name: Verify patches carry all the changes
+          # 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: |
-          make apply-patches sync && git diff --compact-summary --exit-code llama
+          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
+
+      - 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
+    steps:
+      - uses: actions/checkout@v4
+      - name: Verify patches apply cleanly and do not change files
+        run: |
+          make -f Makefile.sync clean checkout apply-patches sync
+          git diff --compact-summary --exit-code

.gitignore

@@ -4,12 +4,13 @@
 .venv
 .swp
 dist
-ollama
+build
 .cache
 *.exe
 .idea
 test_data
 *.crt
-llama/build
 __debug_bin*
-llama/vendor
+llama/build
+llama/vendor
+/ollama

.golangci.yaml

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

CMakeLists.txt

@@ -0,0 +1,133 @@
+cmake_minimum_required(VERSION 3.21)
+
+project(Ollama C CXX)
+
+include(CheckLanguage)
+
+find_package(Threads REQUIRED)
+
+set(CMAKE_BUILD_TYPE Release)
+set(BUILD_SHARED_LIBS ON)
+
+set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_STANDARD_REQUIRED ON)
+set(CMAKE_CXX_EXTENSIONS OFF)
+
+set(GGML_BUILD ON)
+set(GGML_SHARED ON)
+set(GGML_CCACHE ON)
+set(GGML_BACKEND_DL ON)
+set(GGML_BACKEND_SHARED ON)
+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")
+    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)
+
+set(CMAKE_RUNTIME_OUTPUT_DIRECTORY         ${OLLAMA_BUILD_DIR})
+set(CMAKE_RUNTIME_OUTPUT_DIRECTORY_DEBUG   ${OLLAMA_BUILD_DIR})
+set(CMAKE_RUNTIME_OUTPUT_DIRECTORY_RELEASE ${OLLAMA_BUILD_DIR})
+set(CMAKE_LIBRARY_OUTPUT_DIRECTORY         ${OLLAMA_BUILD_DIR})
+set(CMAKE_LIBRARY_OUTPUT_DIRECTORY_DEBUG   ${OLLAMA_BUILD_DIR})
+set(CMAKE_LIBRARY_OUTPUT_DIRECTORY_RELEASE ${OLLAMA_BUILD_DIR})
+
+include_directories(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src)
+include_directories(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/include)
+include_directories(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-cpu)
+include_directories(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-cpu/amx)
+
+set(GGML_CPU ON)
+add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src)
+set_property(TARGET ggml PROPERTY EXCLUDE_FROM_ALL TRUE)
+
+get_target_property(CPU_VARIANTS ggml-cpu MANUALLY_ADDED_DEPENDENCIES)
+if(NOT CPU_VARIANTS)
+    set(CPU_VARIANTS "ggml-cpu")
+endif()
+
+install(TARGETS ggml-base ${CPU_VARIANTS}
+    RUNTIME_DEPENDENCIES
+        PRE_EXCLUDE_REGEXES ".*"
+    RUNTIME DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT CPU
+    LIBRARY DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT CPU
+    FRAMEWORK DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT CPU
+)
+
+check_language(CUDA)
+if(CMAKE_CUDA_COMPILER)
+    if(CMAKE_VERSION VERSION_GREATER_EQUAL "3.24" AND NOT CMAKE_CUDA_ARCHITECTURES)
+        set(CMAKE_CUDA_ARCHITECTURES "native")
+    endif()
+
+    find_package(CUDAToolkit)
+    add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-cuda)
+    set(OLLAMA_CUDA_INSTALL_DIR ${OLLAMA_INSTALL_DIR}/cuda_v${CUDAToolkit_VERSION_MAJOR})
+    install(TARGETS ggml-cuda
+        RUNTIME_DEPENDENCIES
+            DIRECTORIES ${CUDAToolkit_BIN_DIR} ${CUDAToolkit_LIBRARY_DIR}
+            PRE_INCLUDE_REGEXES cublas cublasLt cudart
+            PRE_EXCLUDE_REGEXES ".*"
+        RUNTIME DESTINATION ${OLLAMA_CUDA_INSTALL_DIR} COMPONENT CUDA
+        LIBRARY DESTINATION ${OLLAMA_CUDA_INSTALL_DIR} COMPONENT CUDA
+    )
+endif()
+
+set(WINDOWS_AMDGPU_TARGETS_EXCLUDE_REGEX "^gfx(906|908|90a|1200|1201):xnack[+-]$"
+    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[+-]$\"."
+)
+
+check_language(HIP)
+if(CMAKE_HIP_COMPILER)
+    set(HIP_PLATFORM "amd")
+
+    find_package(hip REQUIRED)
+    if(NOT AMDGPU_TARGETS)
+        list(FILTER AMDGPU_TARGETS INCLUDE REGEX "^gfx(900|94[012]|101[02]|1030|110[012]|120[01])$")
+    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_EXCLUDE_REGEXES ".*"
+                POST_EXCLUDE_REGEXES "system32"
+            RUNTIME DESTINATION ${OLLAMA_HIP_INSTALL_DIR} COMPONENT HIP
+            LIBRARY DESTINATION ${OLLAMA_HIP_INSTALL_DIR} COMPONENT HIP
+        )
+
+        foreach(HIP_LIB_BIN_INSTALL_DIR IN ITEMS ${HIP_BIN_INSTALL_DIR} ${HIP_LIB_INSTALL_DIR})
+            if(EXISTS ${HIP_LIB_BIN_INSTALL_DIR}/rocblas)
+                install(DIRECTORY ${HIP_LIB_BIN_INSTALL_DIR}/rocblas DESTINATION ${OLLAMA_HIP_INSTALL_DIR} COMPONENT HIP)
+                break()
+            endif()
+        endforeach()
+    endif()
+endif()

CMakePresets.json

@@ -0,0 +1,99 @@
+{
+  "version": 3,
+  "configurePresets": [
+    {
+      "name": "Default",
+      "binaryDir": "${sourceDir}/build",
+      "installDir": "${sourceDir}/dist",
+      "cacheVariables": {
+        "CMAKE_BUILD_TYPE": "Release"
+      }
+    },
+    {
+      "name": "CPU",
+      "inherits": [ "Default" ]
+    },
+    {
+      "name": "CUDA",
+      "inherits": [ "Default" ]
+    },
+    {
+      "name": "CUDA 12",
+      "inherits": [ "CUDA" ],
+      "cacheVariables": {
+        "CMAKE_CUDA_ARCHITECTURES": "50;60;61;70;75;80;86;87;89;90;90a;120",
+        "CMAKE_CUDA_FLAGS": "-Wno-deprecated-gpu-targets"
+      }
+    },
+    {
+      "name": "JetPack 5",
+      "inherits": [ "CUDA" ],
+      "cacheVariables": {
+        "CMAKE_CUDA_ARCHITECTURES": "72;87"
+      }
+    },
+    {
+      "name": "JetPack 6",
+      "inherits": [ "CUDA" ],
+      "cacheVariables": {
+        "CMAKE_CUDA_ARCHITECTURES": "87"
+      }
+    },
+    {
+      "name": "ROCm",
+      "inherits": [ "Default" ],
+      "cacheVariables": {
+        "CMAKE_HIP_PLATFORM": "amd"
+      }
+    },
+    {
+      "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-"
+      }
+    }
+  ],
+  "buildPresets": [
+    {
+      "name": "Default",
+      "configurePreset": "Default",
+      "configuration": "Release"
+    },
+    {
+      "name": "CPU",
+      "configurePreset": "Default",
+      "targets": [ "ggml-cpu" ]
+    },
+    {
+      "name": "CUDA",
+      "configurePreset": "CUDA",
+      "targets": [ "ggml-cuda" ]
+    },
+    {
+      "name": "CUDA 12",
+      "inherits": [ "CUDA" ],
+      "configurePreset": "CUDA 12"
+    },
+    {
+      "name": "JetPack 5",
+      "inherits": [ "CUDA" ],
+      "configurePreset": "JetPack 5"
+    },
+    {
+      "name": "JetPack 6",
+      "inherits": [ "CUDA" ],
+      "configurePreset": "JetPack 6"
+    },
+    {
+      "name": "ROCm",
+      "configurePreset": "ROCm",
+      "targets": [ "ggml-hip" ]
+    },
+    {
+      "name": "ROCm 6",
+      "inherits": [ "ROCm" ],
+      "configurePreset": "ROCm 6"
+    }
+  ]
+}

CONTRIBUTING.md

@@ -6,8 +6,6 @@ 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.
@@ -26,11 +24,64 @@ 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
 
-### Best practices
+## Proposing a (non-trivial) change
 
-* 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.
+> By "non-trivial", we mean a change that is not a bug fix or small
-* Tests: please add test coverage to changes where possible.
+> documentation update. If you are unsure, please ask us on our [Discord
-* Minimize dependencies: avoid adding new dependencies unless absolutely necessary.
+> server](https://discord.gg/ollama).
+
+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

@@ -1,201 +1,116 @@
-ARG GOLANG_VERSION=1.22.8
+# vim: filetype=dockerfile
-ARG CUDA_VERSION_11=11.3.1
-ARG CUDA_VERSION_12=12.4.0
-ARG ROCM_VERSION=6.1.2
-ARG JETPACK_6=r36.2.0
-ARG JETPACK_5=r35.4.1
 
-### To create a local image for building linux binaries on mac or windows with efficient incremental builds
+ARG FLAVOR=${TARGETARCH}
-#
-# docker build --platform linux/amd64 -t builder-amd64 -f Dockerfile --target unified-builder-amd64 .
-# docker run --platform linux/amd64 --rm -it -v $(pwd):/go/src/github.com/ollama/ollama/ builder-amd64
-#
-### Then incremental builds will be much faster in this container
-#
-# make -j 10 dist
-#
-FROM --platform=linux/amd64 rocm/dev-centos-7:${ROCM_VERSION}-complete AS unified-builder-amd64
-ARG GOLANG_VERSION
-ARG CUDA_VERSION_11
-ARG CUDA_VERSION_12
-COPY ./scripts/rh_linux_deps.sh /
-ENV PATH /opt/rh/devtoolset-10/root/usr/bin:/usr/local/cuda/bin:$PATH
-ENV LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64
-RUN GOLANG_VERSION=${GOLANG_VERSION} sh /rh_linux_deps.sh
-RUN yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-rhel7.repo && \
-    dnf clean all && \
-    dnf install -y \
-    zsh \
-    cuda-toolkit-$(echo ${CUDA_VERSION_11} | cut -f1-2 -d. | sed -e "s/\./-/g") \
-    cuda-toolkit-$(echo ${CUDA_VERSION_12} | cut -f1-2 -d. | sed -e "s/\./-/g")
-# TODO intel oneapi goes here...
-ENV GOARCH amd64
-ENV CGO_ENABLED 1
-WORKDIR /go/src/github.com/ollama/ollama/
-ENTRYPOINT [ "zsh" ]
 
-### To create a local image for building linux binaries on mac or linux/arm64 with efficient incremental builds
+ARG ROCMVERSION=6.3.3
-# Note: this does not contain jetson variants
+ARG JETPACK5VERSION=r35.4.1
-#
+ARG JETPACK6VERSION=r36.4.0
-# docker build --platform linux/arm64 -t builder-arm64 -f Dockerfile --target unified-builder-arm64 .
+ARG CMAKEVERSION=3.31.2
-# docker run --platform linux/arm64 --rm -it -v $(pwd):/go/src/github.com/ollama/ollama/ builder-arm64
-#
-FROM --platform=linux/arm64 rockylinux:8 AS unified-builder-arm64
-ARG GOLANG_VERSION
-ARG CUDA_VERSION_11
-ARG CUDA_VERSION_12
-COPY ./scripts/rh_linux_deps.sh /
-RUN GOLANG_VERSION=${GOLANG_VERSION} sh /rh_linux_deps.sh
-RUN yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/sbsa/cuda-rhel8.repo && \
-    dnf config-manager --set-enabled appstream && \
-    dnf clean all && \
-    dnf install -y \
-    zsh \
-    cuda-toolkit-$(echo ${CUDA_VERSION_11} | cut -f1-2 -d. | sed -e "s/\./-/g") \
-    cuda-toolkit-$(echo ${CUDA_VERSION_12} | cut -f1-2 -d. | sed -e "s/\./-/g")
-ENV PATH /opt/rh/gcc-toolset-10/root/usr/bin:$PATH:/usr/local/cuda/bin
-ENV LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64
-ENV LIBRARY_PATH=/usr/local/cuda/lib64/stubs:/opt/amdgpu/lib64
-ENV GOARCH arm64
-ENV CGO_ENABLED 1
-WORKDIR /go/src/github.com/ollama/ollama/
-ENTRYPOINT [ "zsh" ]
 
-FROM --platform=linux/amd64 unified-builder-amd64 AS build-amd64
+FROM --platform=linux/amd64 rocm/dev-almalinux-8:${ROCMVERSION}-complete AS base-amd64
-COPY . .
+RUN yum install -y yum-utils \
-ARG OLLAMA_SKIP_CUDA_GENERATE
+    && dnf install -y ccache \
-ARG OLLAMA_SKIP_ROCM_GENERATE
+    && yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo
-ARG OLLAMA_FAST_BUILD
+
-ARG VERSION
+FROM --platform=linux/arm64 almalinux:8 AS base-arm64
-ARG CUSTOM_CPU_FLAGS
+# install epel-release for ccache
+RUN yum install -y yum-utils epel-release \
+    && dnf install -y clang ccache \
+    && yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/sbsa/cuda-rhel8.repo
+ENV CC=clang CXX=clang++
+
+FROM base-${TARGETARCH} AS base
+ARG CMAKEVERSION
+RUN curl -fsSL https://github.com/Kitware/CMake/releases/download/v${CMAKEVERSION}/cmake-${CMAKEVERSION}-linux-$(uname -m).tar.gz | tar xz -C /usr/local --strip-components 1
+COPY CMakeLists.txt CMakePresets.json .
+COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
+ENV LDFLAGS=-s
+
+FROM base AS cpu
+RUN dnf install -y gcc-toolset-11-gcc gcc-toolset-11-gcc-c++
+ENV PATH=/opt/rh/gcc-toolset-11/root/usr/bin:$PATH
 RUN --mount=type=cache,target=/root/.ccache \
-    if grep "^flags" /proc/cpuinfo|grep avx>/dev/null; then \
+    cmake --preset 'CPU' \
-        make -j $(nproc) dist ; \
+        && cmake --build --parallel --preset 'CPU' \
-    else \
+        && cmake --install build --component CPU --strip --parallel 8
-        make -j 5 dist ; \
-    fi
-RUN cd dist/linux-$GOARCH && \
-    tar -cf - . | pigz --best > ../ollama-linux-$GOARCH.tgz
-RUN if [ -z ${OLLAMA_SKIP_ROCM_GENERATE} ] ; then \
-    cd dist/linux-$GOARCH-rocm && \
-    tar -cf - . | pigz --best > ../ollama-linux-$GOARCH-rocm.tgz ;\
-    fi
 
-# Jetsons need to be built in discrete stages
+FROM base AS cuda-12
-FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${JETPACK_5} AS runners-jetpack5-arm64
+ARG CUDA12VERSION=12.8
-ARG GOLANG_VERSION
+RUN dnf install -y cuda-toolkit-${CUDA12VERSION//./-}
-RUN apt-get update && apt-get install -y git curl ccache && \
+ENV PATH=/usr/local/cuda-12/bin:$PATH
-    curl -s -L https://dl.google.com/go/go${GOLANG_VERSION}.linux-arm64.tar.gz | tar xz -C /usr/local && \
-    ln -s /usr/local/go/bin/go /usr/local/bin/go && \
-    ln -s /usr/local/go/bin/gofmt /usr/local/bin/gofmt && \
-    apt-get clean && rm -rf /var/lib/apt/lists/*
-WORKDIR /go/src/github.com/ollama/ollama/
-COPY . .
-ARG CGO_CFLAGS
-ENV GOARCH arm64
-ARG VERSION
 RUN --mount=type=cache,target=/root/.ccache \
-    make -j 5 dist_cuda_v11 \
+    cmake --preset 'CUDA 12' \
-        CUDA_ARCHITECTURES="72;87" \
+        && cmake --build --parallel --preset 'CUDA 12' \
-        GPU_RUNNER_VARIANT=_jetpack5 \
+        && cmake --install build --component CUDA --strip --parallel 8
-        DIST_LIB_DIR=/go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack5/lib/ollama \
-        DIST_GPU_RUNNER_DEPS_DIR=/go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack5/lib/ollama/cuda_jetpack5
 
-FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${JETPACK_6} AS runners-jetpack6-arm64
+FROM base AS rocm-6
-ARG GOLANG_VERSION
+ENV PATH=/opt/rocm/hcc/bin:/opt/rocm/hip/bin:/opt/rocm/bin:/opt/rocm/hcc/bin:$PATH
-RUN apt-get update && apt-get install -y git curl ccache && \
-    curl -s -L https://dl.google.com/go/go${GOLANG_VERSION}.linux-arm64.tar.gz | tar xz -C /usr/local && \
-    ln -s /usr/local/go/bin/go /usr/local/bin/go && \
-    ln -s /usr/local/go/bin/gofmt /usr/local/bin/gofmt && \
-    apt-get clean && rm -rf /var/lib/apt/lists/*
-WORKDIR /go/src/github.com/ollama/ollama/
-COPY . .
-ARG CGO_CFLAGS
-ENV GOARCH arm64
-ARG VERSION
 RUN --mount=type=cache,target=/root/.ccache \
-    make -j 5 dist_cuda_v12 \
+    cmake --preset 'ROCm 6' \
-        CUDA_ARCHITECTURES="87" \
+        && cmake --build --parallel --preset 'ROCm 6' \
-        GPU_RUNNER_VARIANT=_jetpack6 \
+        && cmake --install build --component HIP --strip --parallel 8
-        DIST_LIB_DIR=/go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack6/lib/ollama \
-        DIST_GPU_RUNNER_DEPS_DIR=/go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack6/lib/ollama/cuda_jetpack6
 
-FROM --platform=linux/arm64 unified-builder-arm64 AS build-arm64
+FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${JETPACK5VERSION} AS jetpack-5
-COPY . .
+ARG CMAKEVERSION
-ARG OLLAMA_SKIP_CUDA_GENERATE
+RUN apt-get update && apt-get install -y curl ccache \
-ARG OLLAMA_FAST_BUILD
+    && curl -fsSL https://github.com/Kitware/CMake/releases/download/v${CMAKEVERSION}/cmake-${CMAKEVERSION}-linux-$(uname -m).tar.gz | tar xz -C /usr/local --strip-components 1
-ARG VERSION
+COPY CMakeLists.txt CMakePresets.json .
+COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
 RUN --mount=type=cache,target=/root/.ccache \
-    make -j 5 dist
+    cmake --preset 'JetPack 5' \
-COPY --from=runners-jetpack5-arm64 /go/src/github.com/ollama/ollama/dist/ dist/
+        && cmake --build --parallel --preset 'JetPack 5' \
-COPY --from=runners-jetpack6-arm64 /go/src/github.com/ollama/ollama/dist/ dist/
+        && cmake --install build --component CUDA --strip --parallel 8
-RUN cd dist/linux-$GOARCH && \
-    tar -cf - . | pigz --best > ../ollama-linux-$GOARCH.tgz
-RUN cd dist/linux-$GOARCH-jetpack5 && \
-    tar -cf - . | pigz --best > ../ollama-linux-$GOARCH-jetpack5.tgz
-RUN cd dist/linux-$GOARCH-jetpack6 && \
-    tar -cf - . | pigz --best > ../ollama-linux-$GOARCH-jetpack6.tgz
 
-FROM --platform=linux/amd64 scratch AS dist-amd64
+FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${JETPACK6VERSION} AS jetpack-6
-COPY --from=build-amd64 /go/src/github.com/ollama/ollama/dist/ollama-linux-*.tgz /
+ARG CMAKEVERSION
-FROM --platform=linux/arm64 scratch AS dist-arm64
+RUN apt-get update && apt-get install -y curl ccache \
-COPY --from=build-arm64 /go/src/github.com/ollama/ollama/dist/ollama-linux-*.tgz /
+    && curl -fsSL https://github.com/Kitware/CMake/releases/download/v${CMAKEVERSION}/cmake-${CMAKEVERSION}-linux-$(uname -m).tar.gz | tar xz -C /usr/local --strip-components 1
-FROM dist-$TARGETARCH AS dist
+COPY CMakeLists.txt CMakePresets.json .
+COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
+RUN --mount=type=cache,target=/root/.ccache \
+    cmake --preset 'JetPack 6' \
+        && cmake --build --parallel --preset 'JetPack 6' \
+        && cmake --install build --component CUDA --strip --parallel 8
 
+FROM base AS build
+WORKDIR /go/src/github.com/ollama/ollama
+COPY go.mod go.sum .
+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
+COPY . .
+ARG GOFLAGS="'-ldflags=-w -s'"
+ENV CGO_ENABLED=1
+RUN --mount=type=cache,target=/root/.cache/go-build \
+    go build -trimpath -buildmode=pie -o /bin/ollama .
 
-# For amd64 container images, filter out cuda/rocm to minimize size
+FROM --platform=linux/amd64 scratch AS amd64
-FROM build-amd64 AS runners-cuda-amd64
+COPY --from=cuda-12 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_v12
-RUN rm -rf \
-    ./dist/linux-amd64/lib/ollama/libggml_hipblas.so \
-    ./dist/linux-amd64/lib/ollama/runners/rocm*
 
-FROM build-amd64 AS runners-rocm-amd64
+FROM --platform=linux/arm64 scratch AS arm64
-RUN rm -rf \
+COPY --from=cuda-12 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_v12
-    ./dist/linux-amd64/lib/ollama/libggml_cuda*.so \
+COPY --from=jetpack-5 dist/lib/ollama/cuda_v11 /lib/ollama/cuda_jetpack5
-    ./dist/linux-amd64/lib/ollama/libcu*.so* \
+COPY --from=jetpack-6 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_jetpack6
-    ./dist/linux-amd64/lib/ollama/runners/cuda*
 
-FROM --platform=linux/amd64 ubuntu:22.04 AS runtime-amd64
+FROM scratch AS rocm
-RUN apt-get update && \
+COPY --from=rocm-6 dist/lib/ollama/rocm /lib/ollama/rocm
-    apt-get install -y ca-certificates && \
-    apt-get clean && rm -rf /var/lib/apt/lists/*
-COPY --from=build-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/bin/ /bin/
-COPY --from=runners-cuda-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/lib/ /lib/
 
-FROM --platform=linux/arm64 ubuntu:22.04 AS runtime-arm64
+FROM ${FLAVOR} AS archive
-RUN apt-get update && \
+COPY --from=cpu dist/lib/ollama /lib/ollama
-    apt-get install -y ca-certificates && \
+COPY --from=build /bin/ollama /bin/ollama
-    apt-get clean && rm -rf /var/lib/apt/lists/*
-COPY --from=build-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/bin/ /bin/
-COPY --from=build-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/lib/ /lib/
-COPY --from=runners-jetpack5-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack5/lib/ /lib/
-COPY --from=runners-jetpack6-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack6/lib/ /lib/
 
-
+FROM ubuntu:20.04
-# ROCm libraries larger so we keep it distinct from the CPU/CUDA image
+RUN apt-get update \
-FROM --platform=linux/amd64 ubuntu:22.04 AS runtime-rocm
+    && apt-get install -y ca-certificates \
-# Frontload the rocm libraries which are large, and rarely change to increase chance of a common layer
+    && apt-get clean \
-# across releases
+    && rm -rf /var/lib/apt/lists/*
-COPY --from=build-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64-rocm/lib/ /lib/
+COPY --from=archive /bin /usr/bin
-RUN apt-get update && \
-    apt-get install -y ca-certificates && \
-    apt-get clean && rm -rf /var/lib/apt/lists/*
-COPY --from=build-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/bin/ /bin/
-COPY --from=runners-rocm-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/lib/ /lib/
-
-EXPOSE 11434
-ENV OLLAMA_HOST 0.0.0.0
-
-ENTRYPOINT ["/bin/ollama"]
-CMD ["serve"]
-
-FROM runtime-$TARGETARCH
-EXPOSE 11434
-ENV OLLAMA_HOST 0.0.0.0
 ENV PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
+COPY --from=archive /lib/ollama /usr/lib/ollama
 ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
 ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
 ENV NVIDIA_VISIBLE_DEVICES=all
-
+ENV OLLAMA_HOST=0.0.0.0:11434
+EXPOSE 11434
 ENTRYPOINT ["/bin/ollama"]
 CMD ["serve"]

Makefile

@@ -1,103 +0,0 @@
-# top level makefile for Ollama
-include make/common-defs.make
-
-
-# Determine which if any GPU runners we should build
-include make/cuda-v11-defs.make
-include make/cuda-v12-defs.make
-include make/rocm-defs.make
-
-ifeq ($(CUSTOM_CPU_FLAGS),)
-ifeq ($(ARCH),amd64)
-	RUNNER_TARGETS=cpu
-endif
-# Without CUSTOM_CPU_FLAGS we default to build both v11 and v12 if present
-ifeq ($(OLLAMA_SKIP_CUDA_GENERATE),)
-ifneq ($(CUDA_11_COMPILER),)
-	RUNNER_TARGETS += cuda_v11
-endif
-ifneq ($(CUDA_12_COMPILER),)
-	RUNNER_TARGETS += cuda_v12
-endif
-endif
-else # CUSTOM_CPU_FLAGS is set, we'll build only the latest cuda version detected
-ifneq ($(CUDA_12_COMPILER),)
-	RUNNER_TARGETS += cuda_v12
-else ifneq ($(CUDA_11_COMPILER),)
-	RUNNER_TARGETS += cuda_v11
-endif
-endif
-
-ifeq ($(OLLAMA_SKIP_ROCM_GENERATE),)
-ifneq ($(HIP_COMPILER),)
-	RUNNER_TARGETS += rocm
-endif
-endif
-
-
-all: runners exe
-
-dist: $(addprefix dist_, $(RUNNER_TARGETS)) dist_exe
-
-dist_%:
-	@$(MAKE) --no-print-directory -f make/Makefile.$* dist
-
-runners: $(RUNNER_TARGETS)
-
-$(RUNNER_TARGETS):
-	@$(MAKE) --no-print-directory -f make/Makefile.$@
-
-exe dist_exe:
-	@$(MAKE) --no-print-directory -f make/Makefile.ollama $@
-
-help-sync apply-patches create-patches sync sync-clean:
-	@$(MAKE) --no-print-directory -f make/Makefile.sync $@
-
-test integration lint:
-	@$(MAKE) --no-print-directory -f make/Makefile.test $@
-
-clean:
-	rm -rf $(BUILD_DIR) $(DIST_LIB_DIR) $(OLLAMA_EXE) $(DIST_OLLAMA_EXE)
-	go clean -cache
-
-help:
-	@echo "The following make targets will help you build Ollama"
-	@echo ""
-	@echo "	make all   		# (default target) Build Ollama llm subprocess runners, and the primary ollama executable"
-	@echo "	make runners		# Build Ollama llm subprocess runners; after you may use 'go build .' to build the primary ollama exectuable"
-	@echo "	make <runner>		# Build specific runners. Enabled: '$(RUNNER_TARGETS)'"
-	@echo "	make dist		# Build the runners and primary ollama executable for distribution"
-	@echo "	make help-sync 		# Help information on vendor update targets"
-	@echo "	make help-runners 	# Help information on runner targets"
-	@echo ""
-	@echo "The following make targets will help you test Ollama"
-	@echo ""
-	@echo "	make test   		# Run unit tests"
-	@echo "	make integration	# Run integration tests.  You must 'make all' first"
-	@echo "	make lint   		# Run lint and style tests"
-	@echo ""
-	@echo "For more information see 'docs/development.md'"
-	@echo ""
-
-
-help-runners:
-	@echo "The following runners will be built based on discovered GPU libraries: '$(RUNNER_TARGETS)'"
-	@echo ""
-	@echo "GPU Runner CPU Flags: '$(GPU_RUNNER_CPU_FLAGS)'  (Override with CUSTOM_CPU_FLAGS)"
-	@echo ""
-	@echo "# CUDA_PATH sets the location where CUDA toolkits are present"
-	@echo "CUDA_PATH=$(CUDA_PATH)"
-	@echo "	CUDA_11_PATH=$(CUDA_11_PATH)"
-	@echo "	CUDA_11_COMPILER=$(CUDA_11_COMPILER)"
-	@echo "	CUDA_12_PATH=$(CUDA_12_PATH)"
-	@echo "	CUDA_12_COMPILER=$(CUDA_12_COMPILER)"
-	@echo ""
-	@echo "# HIP_PATH sets the location where the ROCm toolkit is present"
-	@echo "HIP_PATH=$(HIP_PATH)"
-	@echo "	HIP_COMPILER=$(HIP_COMPILER)"
-
-.PHONY: all exe dist help help-sync help-runners test integration lint runners clean $(RUNNER_TARGETS)
-
-# Handy debugging for make variables
-print-%:
-	@echo '$*=$($*)'

Makefile.sync

@@ -0,0 +1,60 @@
+UPSTREAM=https://github.com/ggerganov/llama.cpp.git
+WORKDIR=llama/vendor
+FETCH_HEAD=e1e8e0991ffd9e99a445c6812bb519d5bac9f4b5
+
+.PHONY: help
+help:
+	@echo "Available targets:"
+	@echo "    sync                 Sync with upstream repositories"
+	@echo "    checkout             Checkout upstream repository"
+	@echo "    apply-patches        Apply patches to local repository"
+	@echo "    format-patches       Format patches from local repository"
+	@echo "    clean                Clean local repository"
+	@echo
+	@echo "Example:"
+	@echo "    make -f $(lastword $(MAKEFILE_LIST)) clean sync"
+
+.PHONY: sync
+sync: llama/build-info.cpp llama/llama.cpp ml/backend/ggml/ggml
+
+.PHONY: llama/build-info.cpp
+llama/build-info.cpp: llama/build-info.cpp.in
+	sed -e 's|@FETCH_HEAD@|$(FETCH_HEAD)|' $< > $@
+
+.PHONY: llama/llama.cpp
+llama/llama.cpp: llama/vendor/
+	rsync -arvzc -f "merge $@/.rsync-filter" $< $@
+
+.PHONY: ml/backend/ggml/ggml
+ml/backend/ggml/ggml: llama/vendor/ggml/
+	rsync -arvzc -f "merge $@/.rsync-filter" $< $@
+
+PATCHES=$(wildcard llama/patches/*.patch)
+
+.PHONY: apply-patches
+.NOTPARALLEL:
+apply-patches: $(addsuffix ed, $(PATCHES))
+
+%.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
+checkout: $(WORKDIR)
+	git -C $(WORKDIR) fetch
+	git -C $(WORKDIR) checkout -f $(FETCH_HEAD)
+
+$(WORKDIR):
+	git clone $(UPSTREAM) $(WORKDIR)
+
+.PHONE: format-patches
+format-patches: llama/patches
+	git -C $(WORKDIR) format-patch \
+		--no-signature \
+		--no-numbered \
+		--zero-commit \
+		-o $(realpath $<) \
+		$(FETCH_HEAD)
+
+.PHONE: clean
+clean: checkout
+	$(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
 ```
 
@@ -54,6 +54,15 @@ 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`         |
@@ -62,10 +71,7 @@ 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 3 Mini         | 3.8B       | 2.3GB | `ollama run phi3`                |
+| Phi 4 Mini         | 3.8B       | 2.5GB | `ollama run phi4-mini`           |
-| 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`         |
@@ -73,7 +79,7 @@ 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`               |
-| Solar              | 10.7B      | 6.1GB | `ollama run solar`               |
+| Granite-3.3         | 8B         | 4.9GB | `ollama run granite3.3`          |
 
 > [!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.
@@ -92,13 +98,13 @@ 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
    ```
 
@@ -110,7 +116,7 @@ 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
 ```
 
@@ -145,13 +151,13 @@ 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
 ```
 
@@ -159,13 +165,13 @@ ollama pull llama3.2
 
 ### Remove a model
 
-```
+```shell
 ollama rm llama3.2
 ```
 
 ### Copy a model
 
-```
+```shell
 ollama cp llama3.2 my-model
 ```
 
@@ -184,37 +190,39 @@ 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)"
+
- 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.
+> **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.
-```
 
 ### 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
 ```
 
@@ -230,13 +238,13 @@ 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
 ```
 
@@ -246,7 +254,7 @@ 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?"
@@ -255,7 +263,7 @@ curl http://localhost:11434/api/generate -d '{
 
 ### Chat with a model
 
-```
+```shell
 curl http://localhost:11434/api/chat -d '{
   "model": "llama3.2",
   "messages": [
@@ -271,6 +279,7 @@ 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)
@@ -278,12 +287,13 @@ 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/blob/main/docs/config-local-ollama.md)
+- [big-AGI](https://github.com/enricoros/big-AGI)
 - [Cheshire Cat assistant framework](https://github.com/cheshire-cat-ai/core)
 - [Amica](https://github.com/semperai/amica)
 - [chatd](https://github.com/BruceMacD/chatd)
@@ -304,6 +314,7 @@ 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)
 - [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)
@@ -317,13 +328,14 @@ 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)
@@ -332,16 +344,16 @@ 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.
-- [Claude Dev](https://github.com/saoudrizwan/claude-dev) - VSCode extension for multi-file/whole-repo coding
+- [Cline](https://github.com/cline/cline) - Formerly known as Claude Dev is a 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)
@@ -353,12 +365,13 @@ 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)
@@ -369,7 +382,28 @@ 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
 
@@ -409,10 +443,14 @@ 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
@@ -427,14 +465,15 @@ 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?channel=24.05&show=ollama&from=0&size=50&sort=relevance&type=packages&query=ollama)
+- [Nix package](https://search.nixos.org/packages?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/llms/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/chat/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)
@@ -481,15 +520,22 @@ 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 unified API)
+- [multi-llm-ts](https://github.com/nbonamy/multi-llm-ts) (A Typescript/JavaScript library allowing access to different LLM in a 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)
 
 ### 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
 
@@ -511,7 +557,7 @@ 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)
@@ -521,8 +567,8 @@ 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 depends on ollama server)
+- [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)
-- [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.)
@@ -533,13 +579,18 @@ 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)
 
 ### 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/examples
+// [in the GitHub repository]: https://github.com/ollama/ollama/tree/main/api/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 *bytes.Buffer
+	var buf io.Reader
 	if data != nil {
 		bts, err := json.Marshal(data)
 		if err != nil {

api/client_test.go

@@ -1,6 +1,12 @@
 package api
 
 import (
+	"encoding/json"
+	"fmt"
+	"net/http"
+	"net/http/httptest"
+	"net/url"
+	"strings"
 	"testing"
 )
 
@@ -43,3 +49,206 @@ 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

@@ -2,9 +2,10 @@
 
 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

@@ -10,6 +10,9 @@ 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.
@@ -73,13 +76,13 @@ type GenerateRequest struct {
 	// this request.
 	KeepAlive *Duration `json:"keep_alive,omitempty"`
 
-	// Images is an optional list of base64-encoded images accompanying this
+	// Images is an optional list of raw image bytes 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]interface{} `json:"options"`
+	Options map[string]any `json:"options"`
 }
 
 // ChatRequest describes a request sent by [Client.Chat].
@@ -104,7 +107,7 @@ type ChatRequest struct {
 	Tools `json:"tools,omitempty"`
 
 	// Options lists model-specific options.
-	Options map[string]interface{} `json:"options"`
+	Options map[string]any `json:"options"`
 }
 
 type Tools []Tool
@@ -160,19 +163,65 @@ 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        string   `json:"type"`
+			Type        PropertyType `json:"type"`
-			Description string   `json:"description"`
+			Items       any          `json:"items,omitempty"`
-			Enum        []string `json:"enum,omitempty"`
+			Description string       `json:"description"`
+			Enum        []any        `json:"enum,omitempty"`
 		} `json:"properties"`
 	} `json:"parameters"`
 }
@@ -222,9 +271,6 @@ 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"`
 }
 
@@ -234,12 +280,7 @@ 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"`
 }
 
@@ -258,7 +299,7 @@ type EmbedRequest struct {
 	Truncate *bool `json:"truncate,omitempty"`
 
 	// Options lists model-specific options.
-	Options map[string]interface{} `json:"options"`
+	Options map[string]any `json:"options"`
 }
 
 // EmbedResponse is the response from [Client.Embed].
@@ -284,7 +325,7 @@ type EmbeddingRequest struct {
 	KeepAlive *Duration `json:"keep_alive,omitempty"`
 
 	// Options lists model-specific options.
-	Options map[string]interface{} `json:"options"`
+	Options map[string]any `json:"options"`
 }
 
 // EmbeddingResponse is the response from [Client.Embeddings].
@@ -330,7 +371,7 @@ type ShowRequest struct {
 	Template string `json:"template"`
 	Verbose  bool   `json:"verbose"`
 
-	Options map[string]interface{} `json:"options"`
+	Options map[string]any `json:"options"`
 
 	// Deprecated: set the model name with Model instead
 	Name string `json:"name"`
@@ -338,16 +379,18 @@ 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"`
-	ModifiedAt    time.Time      `json:"modified_at,omitempty"`
+	Tensors       []Tensor           `json:"tensors,omitempty"`
+	Capabilities  []model.Capability `json:"capabilities,omitempty"`
+	ModifiedAt    time.Time          `json:"modified_at,omitempty"`
 }
 
 // CopyRequest is the request passed to [Client.Copy].
@@ -359,9 +402,9 @@ type CopyRequest struct {
 // PullRequest is the request passed to [Client.Pull].
 type PullRequest struct {
 	Model    string `json:"model"`
-	Insecure bool   `json:"insecure,omitempty"`
+	Insecure bool   `json:"insecure,omitempty"` // Deprecated: ignored
-	Username string `json:"username"`
+	Username string `json:"username"`           // Deprecated: ignored
-	Password string `json:"password"`
+	Password string `json:"password"`           // Deprecated: ignored
 	Stream   *bool  `json:"stream,omitempty"`
 
 	// Deprecated: set the model name with Model instead
@@ -420,13 +463,6 @@ 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"`
 }
@@ -465,6 +501,13 @@ 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)
@@ -493,7 +536,7 @@ func (m *Metrics) Summary() {
 	}
 }
 
-func (opts *Options) FromMap(m map[string]interface{}) error {
+func (opts *Options) FromMap(m map[string]any) 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
 
@@ -550,12 +593,12 @@ func (opts *Options) FromMap(m map[string]interface{}) error {
 				}
 				field.SetString(val)
 			case reflect.Slice:
-				// JSON unmarshals to []interface{}, not []string
+				// JSON unmarshals to []any, not []string
-				val, ok := val.([]interface{})
+				val, ok := val.([]any)
 				if !ok {
 					return fmt.Errorf("option %q must be of type array", key)
 				}
-				// convert []interface{} to []string
+				// convert []any to []string
 				slice := make([]string, len(val))
 				for i, item := range val {
 					str, ok := item.(string)
@@ -602,19 +645,14 @@ 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:    2048,
+			NumCtx:    int(envconfig.ContextLength()),
 			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,
 		},
 	}
@@ -662,7 +700,7 @@ 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]interface{}, error) {
+func FormatParams(params map[string][]string) (map[string]any, 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
@@ -676,7 +714,7 @@ func FormatParams(params map[string][]string) (map[string]interface{}, error) {
 		}
 	}
 
-	out := make(map[string]interface{})
+	out := make(map[string]any)
 	// 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]interface{}
+			var oMap map[string]any
 			err := json.Unmarshal([]byte(test.req), &oMap)
 			require.NoError(t, err)
 			opts := DefaultOptions()
@@ -231,3 +231,144 @@ 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
 ```

benchmark/server_benchmark_test.go

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

cmd/cmd.go

@@ -18,6 +18,8 @@ import (
 	"os/signal"
 	"path/filepath"
 	"runtime"
+	"slices"
+	"sort"
 	"strconv"
 	"strings"
 	"sync/atomic"
@@ -29,17 +31,18 @@ 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"
 )
 
@@ -105,7 +108,7 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 	}
 	spinner.Stop()
 
-	req.Name = args[0]
+	req.Model = args[0]
 	quantize, _ := cmd.Flags().GetString("quantize")
 	if quantize != "" {
 		req.Quantize = quantize
@@ -116,34 +119,54 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
-	if len(req.Files) > 0 {
+	var g errgroup.Group
-		fileMap := map[string]string{}
+	g.SetLimit(max(runtime.GOMAXPROCS(0)-1, 1))
-		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
-		req.Files = fileMap
+			//       instead this should take the path relative to the model directory
+			//       but the current implementation does not allow this
+			files.Store(filepath.Base(f), digest)
+			return nil
+		})
 	}
 
-	if len(req.Adapters) > 0 {
+	adapters := syncmap.NewSyncMap[string, string]()
-		fileMap := map[string]string{}
+	for f, digest := range req.Adapters {
-		for f, digest := range req.Adapters {
+		g.Go(func() error {
 			if _, err := createBlob(cmd, client, f, digest, p); err != nil {
 				return err
 			}
-			fileMap[filepath.Base(f)] = digest
+
-		}
+			// TODO: same here
-		req.Adapters = fileMap
+			adapters.Store(filepath.Base(f), digest)
+			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 {
-				bar = progress.NewBar(fmt.Sprintf("pulling %s...", resp.Digest[7:19]), resp.Total, resp.Completed)
+				msg := resp.Status
+				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)
 			}
@@ -212,7 +235,7 @@ 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
@@ -256,6 +279,7 @@ 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
 }
@@ -266,7 +290,7 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 	opts := runOptions{
 		Model:    args[0],
 		WordWrap: os.Getenv("TERM") == "xterm-256color",
-		Options:  map[string]interface{}{},
+		Options:  map[string]any{},
 	}
 
 	format, err := cmd.Flags().GetString("format")
@@ -338,7 +362,21 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
-	opts.MultiModal = len(info.ProjectorInfo) != 0
+	opts.MultiModal = slices.Contains(info.Capabilities, model.CapabilityVision)
+
+	// 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 {
@@ -559,8 +597,9 @@ 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} {
+	for _, boolErr := range []error{errLicense, errModelfile, errParams, errSystem, errTemplate, errVerbose} {
 		if boolErr != nil {
 			return errors.New("error retrieving flags")
 		}
@@ -598,7 +637,7 @@ 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]}
+	req := api.ShowRequest{Name: args[0], Verbose: verbose}
 	resp, err := client.Show(cmd.Context(), &req)
 	if err != nil {
 		return err
@@ -621,10 +660,10 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
 		return nil
 	}
 
-	return showInfo(resp, os.Stdout)
+	return showInfo(resp, verbose, os.Stdout)
 }
 
-func showInfo(resp *api.ShowResponse, w io.Writer) error {
+func showInfo(resp *api.ShowResponse, verbose bool, w io.Writer) error {
 	tableRender := func(header string, rows func() [][]string) {
 		fmt.Fprintln(w, " ", header)
 		table := tablewriter.NewWriter(w)
@@ -658,6 +697,15 @@ func showInfo(resp *api.ShowResponse, 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)
@@ -681,6 +729,47 @@ func showInfo(resp *api.ShowResponse, 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) {
@@ -741,13 +830,38 @@ 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 {
-				bar = progress.NewBar(fmt.Sprintf("pulling %s...", resp.Digest[7:19]), resp.Total, resp.Completed)
+				name, isDigest := strings.CutPrefix(resp.Digest, "sha256:")
+				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)
 			}
@@ -767,11 +881,7 @@ func PullHandler(cmd *cobra.Command, args []string) error {
 	}
 
 	request := api.PullRequest{Name: args[0], Insecure: insecure}
-	if err := client.Pull(cmd.Context(), &request, fn); err != nil {
+	return client.Pull(cmd.Context(), &request, fn)
-		return err
-	}
-
-	return nil
 }
 
 type generateContextKey string
@@ -785,7 +895,7 @@ type runOptions struct {
 	Format      string
 	System      string
 	Images      []api.ImageData
-	Options     map[string]interface{}
+	Options     map[string]any
 	MultiModal  bool
 	KeepAlive   *api.Duration
 }
@@ -1187,6 +1297,7 @@ 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]",
@@ -1271,7 +1382,6 @@ 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:])
@@ -1314,7 +1424,6 @@ 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

@@ -2,7 +2,6 @@ package cmd
 
 import (
 	"bytes"
-	"context"
 	"encoding/json"
 	"io"
 	"net/http"
@@ -10,11 +9,13 @@ 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) {
@@ -26,7 +27,7 @@ func TestShowInfo(t *testing.T) {
 				ParameterSize:     "7B",
 				QuantizationLevel: "FP16",
 			},
-		}, &b); err != nil {
+		}, false, &b); err != nil {
 			t.Fatal(err)
 		}
 
@@ -56,7 +57,7 @@ func TestShowInfo(t *testing.T) {
 				ParameterSize:     "7B",
 				QuantizationLevel: "FP16",
 			},
-		}, &b); err != nil {
+		}, false, &b); err != nil {
 			t.Fatal(err)
 		}
 
@@ -67,6 +68,60 @@ 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)
@@ -88,7 +143,7 @@ func TestShowInfo(t *testing.T) {
 			stop you
 			stop up
 			temperature 99`,
-		}, &b); err != nil {
+		}, false, &b); err != nil {
 			t.Fatal(err)
 		}
 
@@ -125,7 +180,7 @@ func TestShowInfo(t *testing.T) {
 				"clip.vision.embedding_length": float64(0),
 				"clip.vision.projection_dim":   float64(0),
 			},
-		}, &b); err != nil {
+		}, false, &b); err != nil {
 			t.Fatal(err)
 		}
 
@@ -158,7 +213,7 @@ func TestShowInfo(t *testing.T) {
 Ahoy, matey!
 Weigh anchor!
 			`,
-		}, &b); err != nil {
+		}, false, &b); err != nil {
 			t.Fatal(err)
 		}
 
@@ -187,7 +242,7 @@ Weigh anchor!
 				QuantizationLevel: "FP16",
 			},
 			License: license,
-		}, &b); err != nil {
+		}, false, &b); err != nil {
 			t.Fatal(err)
 		}
 
@@ -205,6 +260,34 @@ 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) {
@@ -253,7 +336,7 @@ func TestDeleteHandler(t *testing.T) {
 	t.Cleanup(mockServer.Close)
 
 	cmd := &cobra.Command{}
-	cmd.SetContext(context.TODO())
+	cmd.SetContext(t.Context())
 	if err := DeleteHandler(cmd, []string{"test-model"}); err != nil {
 		t.Fatalf("DeleteHandler failed: %v", err)
 	}
@@ -315,11 +398,6 @@ 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
@@ -327,10 +405,11 @@ func TestGetModelfileName(t *testing.T) {
 					fn = "Modelfile"
 				}
 
-				tempFile, err := os.CreateTemp(tempDir, fn)
+				tempFile, err := os.CreateTemp(t.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)
@@ -445,7 +524,7 @@ func TestPushHandler(t *testing.T) {
 
 			cmd := &cobra.Command{}
 			cmd.Flags().Bool("insecure", false, "")
-			cmd.SetContext(context.TODO())
+			cmd.SetContext(t.Context())
 
 			// Redirect stderr to capture progress output
 			oldStderr := os.Stderr
@@ -490,6 +569,96 @@ 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
@@ -515,7 +684,7 @@ func TestCreateHandler(t *testing.T) {
 						return
 					}
 
-					if req.Name != "test-model" {
+					if req.Model != "test-model" {
 						t.Errorf("expected model name 'test-model', got %s", req.Name)
 					}
 
@@ -555,7 +724,7 @@ func TestCreateHandler(t *testing.T) {
 			}))
 			t.Setenv("OLLAMA_HOST", mockServer.URL)
 			t.Cleanup(mockServer.Close)
-			tempFile, err := os.CreateTemp("", "modelfile")
+			tempFile, err := os.CreateTemp(t.TempDir(), "modelfile")
 			if err != nil {
 				t.Fatal(err)
 			}
@@ -575,7 +744,7 @@ func TestCreateHandler(t *testing.T) {
 			}
 
 			cmd.Flags().Bool("insecure", false, "")
-			cmd.SetContext(context.TODO())
+			cmd.SetContext(t.Context())
 
 			// Redirect stderr to capture progress output
 			oldStderr := os.Stderr
@@ -616,3 +785,132 @@ 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

@@ -18,6 +18,7 @@ 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
@@ -195,6 +196,10 @@ 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
@@ -343,7 +348,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 
 				switch args[1] {
 				case "info":
-					_ = showInfo(resp, os.Stderr)
+					_ = showInfo(resp, false, os.Stderr)
 				case "license":
 					if resp.License == "" {
 						fmt.Println("No license was specified for this model.")
@@ -455,9 +460,16 @@ 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{
-		Name: name,
+		Model: name,
-		From: cmp.Or(opts.ParentModel, opts.Model),
+		From:  cmp.Or(parentModel, opts.Model),
 	}
 
 	if opts.System != "" {
@@ -491,6 +503,7 @@ func normalizeFilePath(fp string) string {
 		"\\\\", "\\", // Escaped backslash
 		"\\*", "*", // Escaped asterisk
 		"\\?", "?", // Escaped question mark
+		"\\~", "~", // Escaped tilde
 	).Replace(fp)
 }
 

cmd/runner/main.go

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

convert/convert.go

@@ -4,17 +4,23 @@ import (
 	"encoding/json"
 	"errors"
 	"fmt"
-	"io"
 	"io/fs"
 	"log/slog"
+	"os"
+	"slices"
 	"strings"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type ModelParameters struct {
-	Architectures []string `json:"architectures"`
+	Architectures []string       `json:"architectures"`
-	VocabSize     uint32   `json:"vocab_size"`
+	VocabSize     uint32         `json:"vocab_size"`
+	TextModel     TextParameters `json:"text_config"`
+}
+
+type TextParameters struct {
+	VocabSize uint32 `json:"vocab_size"`
 }
 
 type AdapterParameters struct {
@@ -27,8 +33,8 @@ type AdapterParameters struct {
 	} `json:"lora_parameters"`
 }
 
-func (ModelParameters) KV(t *Tokenizer) llm.KV {
+func (ModelParameters) KV(t *Tokenizer) ggml.KV {
-	kv := llm.KV{
+	kv := ggml.KV{
 		"general.file_type":            uint32(1),
 		"general.quantization_version": uint32(2),
 		"tokenizer.ggml.pre":           t.Pre,
@@ -54,7 +60,7 @@ func (ModelParameters) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p AdapterParameters) KV() llm.KV {
+func (p AdapterParameters) KV() ggml.KV {
 	var alpha float32
 	if p.LoraParameters.Alpha == 0 {
 		alpha = float32(p.Alpha)
@@ -62,7 +68,7 @@ func (p AdapterParameters) KV() llm.KV {
 		alpha = p.LoraParameters.Alpha
 	}
 
-	kv := llm.KV{
+	kv := ggml.KV{
 		"adapter.lora.alpha": alpha,
 		"adapter.type":       "lora",
 		"general.file_type":  uint32(1),
@@ -79,27 +85,17 @@ func (ModelParameters) specialTokenTypes() []string {
 	}
 }
 
-func (ModelParameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []llm.Tensor) error {
-	return llm.WriteGGUF(ws, kv, ts)
-}
-
-func (AdapterParameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []llm.Tensor) error {
-	return llm.WriteGGUF(ws, kv, ts)
-}
-
 type ModelConverter interface {
 	// KV maps parameters to LLM key-values
-	KV(*Tokenizer) llm.KV
+	KV(*Tokenizer) ggml.KV
 	// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
-	Tensors([]Tensor) []llm.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, llm.KV, []llm.Tensor) error
 }
 
 type moreParser interface {
@@ -108,17 +104,15 @@ type moreParser interface {
 
 type AdapterConverter interface {
 	// KV maps parameters to LLM key-values
-	KV(llm.KV) llm.KV
+	KV(ggml.KV) ggml.KV
 	// Tensors maps input tensors to LLM tensors. Adapter specific modifications can be done here.
-	Tensors([]Tensor) []llm.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, llm.KV, []llm.Tensor) error
 }
 
-func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV llm.KV) error {
+func ConvertAdapter(fsys fs.FS, f *os.File, baseKV ggml.KV) error {
 	bts, err := fs.ReadFile(fsys, "adapter_config.json")
 	if err != nil {
 		return err
@@ -153,14 +147,14 @@ func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV llm.KV) error {
 		return err
 	}
 
-	return conv.writeFile(ws, conv.KV(baseKV), conv.Tensors(ts))
+	return writeFile(f, 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, ws io.WriteSeeker) error {
+func ConvertModel(fsys fs.FS, f *os.File) error {
 	bts, err := fs.ReadFile(fsys, "config.json")
 	if err != nil {
 		return err
@@ -177,14 +171,20 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
 
 	var conv ModelConverter
 	switch p.Architectures[0] {
-	case "LlamaForCausalLM", "MistralForCausalLM":
+	case "LlamaForCausalLM":
 		conv = &llamaModel{}
+	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":
@@ -194,7 +194,7 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
 	case "CohereForCausalLM":
 		conv = &commandrModel{}
 	default:
-		return errors.New("unsupported architecture")
+		return fmt.Errorf("unsupported architecture %q", p.Architectures[0])
 	}
 
 	if err := json.Unmarshal(bts, conv); err != nil {
@@ -213,7 +213,14 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
 	}
 
 	vocabSize := int(p.VocabSize)
+	if vocabSize == 0 {
+		tVocabSize := int(p.TextModel.VocabSize)
+		vocabSize = tVocabSize
+	}
+
 	switch {
+	case vocabSize == 0:
+		slog.Warn("vocabulary size was not explicitly set by the model", "default size", len(t.Vocabulary.Tokens))
 	case vocabSize > 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) {
@@ -232,5 +239,13 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
 		return err
 	}
 
-	return conv.writeFile(ws, conv.KV(t), conv.Tensors(ts))
+	return writeFile(f, 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

@@ -8,7 +8,7 @@ import (
 	"slices"
 	"strings"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type bertModel struct {
@@ -85,7 +85,7 @@ func (p *bertModel) parseMore(fsys fs.FS) error {
 	return nil
 }
 
-func (p *bertModel) KV(t *Tokenizer) llm.KV {
+func (p *bertModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "bert"
 	kv["bert.attention.causal"] = false
@@ -132,8 +132,8 @@ func (p *bertModel) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p *bertModel) Tensors(ts []Tensor) []llm.Tensor {
+func (p *bertModel) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []llm.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) []llm.Tensor {
 			continue
 		}
 
-		out = append(out, llm.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_commandr.go

@@ -3,7 +3,7 @@ package convert
 import (
 	"cmp"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type commandrModel struct {
@@ -24,7 +24,7 @@ type commandrModel struct {
 
 var _ ModelConverter = (*commandrModel)(nil)
 
-func (p *commandrModel) KV(t *Tokenizer) llm.KV {
+func (p *commandrModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "command-r"
 	kv["general.name"] = "command-r"
@@ -43,10 +43,10 @@ func (p *commandrModel) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p *commandrModel) Tensors(ts []Tensor) []llm.Tensor {
+func (p *commandrModel) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []llm.Tensor
+	var out []*ggml.Tensor
 	for _, t := range ts {
-		out = append(out, llm.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_gemma.go

@@ -6,7 +6,7 @@ import (
 	"github.com/pdevine/tensor"
 	"github.com/pdevine/tensor/native"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type gemmaModel struct {
@@ -23,7 +23,7 @@ type gemmaModel struct {
 
 var _ ModelConverter = (*gemmaModel)(nil)
 
-func (p *gemmaModel) KV(t *Tokenizer) llm.KV {
+func (p *gemmaModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "gemma"
 	kv["gemma.context_length"] = p.MaxPositionEmbeddings
@@ -42,14 +42,14 @@ func (p *gemmaModel) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p *gemmaModel) Tensors(ts []Tensor) []llm.Tensor {
+func (p *gemmaModel) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []llm.Tensor
+	var out []*ggml.Tensor
 	for _, t := range ts {
-		if strings.HasSuffix(t.Name(), "_norm.weight") {
+		if !strings.HasPrefix(t.Name(), "v.") && strings.HasSuffix(t.Name(), "_norm.weight") {
 			t.SetRepacker(p.addOne)
 		}
 
-		out = append(out, llm.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_gemma2.go

@@ -1,8 +1,6 @@
 package convert
 
-import (
+import "github.com/ollama/ollama/fs/ggml"
-	"github.com/ollama/ollama/llm"
-)
 
 type gemma2Model struct {
 	gemmaModel
@@ -11,7 +9,7 @@ type gemma2Model struct {
 	FinalLogitSoftcap     float32 `json:"final_logit_softcapping"`
 }
 
-func (p *gemma2Model) KV(t *Tokenizer) llm.KV {
+func (p *gemma2Model) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "gemma2"
 	kv["gemma2.context_length"] = p.MaxPositionEmbeddings

convert/convert_gemma2_adapter.go

@@ -6,7 +6,7 @@ import (
 	"github.com/pdevine/tensor"
 	"github.com/pdevine/tensor/native"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type gemma2Adapter struct {
@@ -15,14 +15,14 @@ type gemma2Adapter struct {
 
 var _ AdapterConverter = (*gemma2Adapter)(nil)
 
-func (p *gemma2Adapter) KV(baseKV llm.KV) llm.KV {
+func (p *gemma2Adapter) KV(baseKV ggml.KV) ggml.KV {
 	kv := p.AdapterParameters.KV()
 	kv["general.architecture"] = "gemma2"
 	return kv
 }
 
-func (p *gemma2Adapter) Tensors(ts []Tensor) []llm.Tensor {
+func (p *gemma2Adapter) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []llm.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) []llm.Tensor {
 			t.SetRepacker(p.repack)
 		}
 
-		out = append(out, llm.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_gemma3.go

@@ -0,0 +1,142 @@
+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

@@ -9,7 +9,7 @@ import (
 	"github.com/pdevine/tensor"
 	"github.com/pdevine/tensor/native"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type llamaModel struct {
@@ -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"`
-		OriginalMaxPositionalEmbeddings uint32  `json:"original_max_positional_embeddings"`
+		OriginalMaxPositionEmbeddings uint32  `json:"original_max_position_embeddings"`
 
 		factors ropeFactor
 	} `json:"rope_scaling"`
@@ -42,11 +42,13 @@ 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)
 
-func (p *llamaModel) KV(t *Tokenizer) llm.KV {
+func (p *llamaModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "llama"
 	kv["llama.vocab_size"] = p.VocabSize
@@ -70,6 +72,10 @@ func (p *llamaModel) KV(t *Tokenizer) llm.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
 	}
@@ -84,7 +90,7 @@ func (p *llamaModel) KV(t *Tokenizer) llm.KV {
 			factorLow := cmp.Or(p.RopeScaling.LowFrequencyFactor, 1.0)
 			factorHigh := cmp.Or(p.RopeScaling.HighFrequencyFactor, 4.0)
 
-			original := cmp.Or(p.RopeScaling.OriginalMaxPositionalEmbeddings, 8192)
+			original := cmp.Or(p.RopeScaling.OriginalMaxPositionEmbeddings, 8192)
 			lambdaLow := float32(original) / factorLow
 			lambdaHigh := float32(original) / factorHigh
 
@@ -120,11 +126,11 @@ func (p *llamaModel) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p *llamaModel) Tensors(ts []Tensor) []llm.Tensor {
+func (p *llamaModel) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []llm.Tensor
+	var out []*ggml.Tensor
 
 	if p.RopeScaling.factors != nil {
-		out = append(out, llm.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     "rope_freqs.weight",
 			Kind:     0,
 			Shape:    []uint64{uint64(len(p.RopeScaling.factors))},
@@ -133,12 +139,13 @@ func (p *llamaModel) Tensors(ts []Tensor) []llm.Tensor {
 	}
 
 	for _, t := range ts {
-		if strings.HasSuffix(t.Name(), "attn_q.weight") ||
+		if strings.HasSuffix(t.Name(), "attn_q.weight") || strings.HasSuffix(t.Name(), "attn_k.weight") {
-			strings.HasSuffix(t.Name(), "attn_k.weight") {
+			if !p.skipRepack {
-			t.SetRepacker(p.repack)
+				t.SetRepacker(p.repack)
+			}
 		}
 
-		out = append(out, llm.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_llama4.go

@@ -0,0 +1,169 @@
+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

@@ -7,7 +7,7 @@ import (
 	"github.com/pdevine/tensor"
 	"github.com/pdevine/tensor/native"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type llamaAdapter struct {
@@ -18,7 +18,7 @@ type llamaAdapter struct {
 
 var _ AdapterConverter = (*llamaAdapter)(nil)
 
-func (p *llamaAdapter) KV(baseKV llm.KV) llm.KV {
+func (p *llamaAdapter) KV(baseKV ggml.KV) ggml.KV {
 	kv := p.AdapterParameters.KV()
 	kv["general.architecture"] = "llama"
 	kv["llama.attention.head_count"] = baseKV["llama.attention.head_count"]
@@ -29,8 +29,8 @@ func (p *llamaAdapter) KV(baseKV llm.KV) llm.KV {
 	return kv
 }
 
-func (p *llamaAdapter) Tensors(ts []Tensor) []llm.Tensor {
+func (p *llamaAdapter) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []llm.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) []llm.Tensor {
 			t.SetRepacker(p.repack)
 		}
 
-		out = append(out, llm.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    shape,

convert/convert_mistral.go

@@ -0,0 +1,190 @@
+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

@@ -6,7 +6,7 @@ import (
 	"slices"
 	"strings"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type mixtralModel struct {
@@ -15,7 +15,7 @@ type mixtralModel struct {
 	NumExpertsPerToken uint32 `json:"num_experts_per_tok"`
 }
 
-func (p *mixtralModel) KV(t *Tokenizer) llm.KV {
+func (p *mixtralModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.llamaModel.KV(t)
 
 	if p.NumLocalExperts > 0 {
@@ -29,7 +29,7 @@ func (p *mixtralModel) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p *mixtralModel) Tensors(ts []Tensor) []llm.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) []llm.Tensor {
 		return true
 	})
 
-	var out []llm.Tensor
+	var out []*ggml.Tensor
 	for n, e := range experts {
 		// TODO(mxyng): sanity check experts
-		out = append(out, llm.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     n,
 			Kind:     e[0].Kind(),
 			Shape:    append([]uint64{uint64(len(e))}, e[0].Shape()...),

convert/convert_phi3.go

@@ -8,7 +8,7 @@ import (
 	"strings"
 	"sync"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type phi3Model struct {
@@ -37,7 +37,7 @@ type phi3Model struct {
 
 var _ ModelConverter = (*phi3Model)(nil)
 
-func (p *phi3Model) KV(t *Tokenizer) llm.KV {
+func (p *phi3Model) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "phi3"
 	kv["phi3.context_length"] = p.MaxPositionEmbeddings
@@ -68,19 +68,19 @@ func (p *phi3Model) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p *phi3Model) Tensors(ts []Tensor) []llm.Tensor {
+func (p *phi3Model) Tensors(ts []Tensor) []*ggml.Tensor {
 	var addRopeFactors sync.Once
 
-	out := make([]llm.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, llm.Tensor{
+				out = append(out, &ggml.Tensor{
 					Name:     "rope_factors_long.weight",
 					Kind:     0,
 					Shape:    []uint64{uint64(len(p.RopeScaling.LongFactor))},
 					WriterTo: p.RopeScaling.LongFactor,
-				}, llm.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) []llm.Tensor {
 			})
 		}
 
-		out = append(out, llm.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),
@@ -118,6 +118,5 @@ func (p *phi3Model) Replacements() []string {
 type ropeFactor []float32
 
 func (r ropeFactor) WriteTo(w io.Writer) (int64, error) {
-	err := binary.Write(w, binary.LittleEndian, r)
+	return 0, binary.Write(w, binary.LittleEndian, r)
-	return 0, err
 }

convert/convert_qwen2.go

@@ -1,6 +1,6 @@
 package convert
 
-import "github.com/ollama/ollama/llm"
+import "github.com/ollama/ollama/fs/ggml"
 
 type qwen2Model struct {
 	ModelParameters
@@ -21,7 +21,7 @@ type qwen2Model struct {
 
 var _ ModelConverter = (*qwen2Model)(nil)
 
-func (q *qwen2Model) KV(t *Tokenizer) llm.KV {
+func (q *qwen2Model) KV(t *Tokenizer) ggml.KV {
 	kv := q.ModelParameters.KV(t)
 	kv["general.architecture"] = "qwen2"
 	kv["qwen2.block_count"] = q.HiddenLayers
@@ -45,10 +45,10 @@ func (q *qwen2Model) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (q *qwen2Model) Tensors(ts []Tensor) []llm.Tensor {
+func (q *qwen2Model) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []llm.Tensor
+	var out []*ggml.Tensor
 	for _, t := range ts {
-		out = append(out, llm.Tensor{
+		out = append(out, &ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_test.go

@@ -11,7 +11,6 @@ import (
 	"io"
 	"io/fs"
 	"log/slog"
-	"math"
 	"os"
 	"path/filepath"
 	"slices"
@@ -20,7 +19,7 @@ import (
 
 	"golang.org/x/exp/maps"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type tensorData struct {
@@ -29,7 +28,7 @@ type tensorData struct {
 	Shape   []int  `json:"shape"`
 }
 
-func convertFull(t *testing.T, fsys fs.FS) (*os.File, llm.KV, *llm.Tensors) {
+func convertFull(t *testing.T, fsys fs.FS) (*os.File, ggml.KV, ggml.Tensors) {
 	t.Helper()
 
 	f, err := os.CreateTemp(t.TempDir(), "f16")
@@ -48,7 +47,7 @@ func convertFull(t *testing.T, fsys fs.FS) (*os.File, llm.KV, *llm.Tensors) {
 	}
 	t.Cleanup(func() { r.Close() })
 
-	m, _, err := llm.DecodeGGML(r, math.MaxInt)
+	m, _, err := ggml.Decode(r, -1)
 	if err != nil {
 		t.Fatal(err)
 	}
@@ -60,7 +59,7 @@ func convertFull(t *testing.T, fsys fs.FS) (*os.File, llm.KV, *llm.Tensors) {
 	return r, m.KV(), m.Tensors()
 }
 
-func generateResultsJSON(t *testing.T, f *os.File, kv llm.KV, tensors *llm.Tensors) map[string]string {
+func generateResultsJSON(t *testing.T, f *os.File, kv ggml.KV, tensors ggml.Tensors) map[string]string {
 	actual := make(map[string]string)
 	for k, v := range kv {
 		if s, ok := v.(json.Marshaler); !ok {
@@ -75,7 +74,7 @@ func generateResultsJSON(t *testing.T, f *os.File, kv llm.KV, tensors *llm.Tenso
 		}
 	}
 
-	for _, tensor := range tensors.Items {
+	for _, tensor := range tensors.Items() {
 		sha256sum := sha256.New()
 		sr := io.NewSectionReader(f, int64(tensors.Offset+tensor.Offset), int64(tensor.Size()))
 		if _, err := io.Copy(sha256sum, sr); err != nil {
@@ -131,6 +130,7 @@ 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 := llm.DecodeGGML(r, math.MaxInt)
+			m, _, err := ggml.Decode(r, -1)
 			if err != nil {
 				t.Fatal(err)
 			}

convert/fs.go

@@ -1,58 +0,0 @@
-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

@@ -11,14 +11,15 @@ 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 {
@@ -36,7 +37,8 @@ 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" {
 		// these tensors are always F32
 		return 0
 	}
@@ -51,21 +53,18 @@ 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)
 	}{
-		{"model-*-of-*.safetensors", parseSafetensors},
+		{"*.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

@@ -94,6 +94,21 @@ 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

@@ -43,6 +43,17 @@ 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 = []interface{}{
+var file_sentencepiece_model_proto_goTypes = []any{
 	(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 interface{}, i int) interface{} {
+		file_sentencepiece_model_proto_msgTypes[0].Exporter = func(v any, i int) any {
 			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 interface{}, i int) interface{} {
+		file_sentencepiece_model_proto_msgTypes[1].Exporter = func(v any, i int) any {
 			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 interface{}, i int) interface{} {
+		file_sentencepiece_model_proto_msgTypes[2].Exporter = func(v any, i int) any {
 			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 interface{}, i int) interface{} {
+		file_sentencepiece_model_proto_msgTypes[3].Exporter = func(v any, i int) any {
 			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 interface{}, i int) interface{} {
+		file_sentencepiece_model_proto_msgTypes[4].Exporter = func(v any, i int) any {
 			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 interface{}, i int) interface{} {
+		file_sentencepiece_model_proto_msgTypes[5].Exporter = func(v any, i int) any {
 			switch v := v.(*ModelProto_SentencePiece); i {
 			case 0:
 				return &v.state

convert/tokenizer_spm.go

@@ -6,7 +6,9 @@ import (
 	"errors"
 	"fmt"
 	"io/fs"
+	"log/slog"
 	"os"
+	"reflect"
 	"slices"
 
 	"google.golang.org/protobuf/proto"
@@ -15,6 +17,8 @@ import (
 )
 
 func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
+	slog.Debug("using spm vocabulary")
+
 	ast, err := parseAdditionalSpecialTokens(fsys)
 	if err != nil {
 		return nil, err
@@ -43,10 +47,19 @@ 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)
 		}
 	}
@@ -78,10 +91,16 @@ func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
 		return cmp.Compare(i.id, j.id)
 	})
 
-	n := len(v.Tokens)
+	for _, t := range ts {
-	for i, t := range ts {
+		if t.id < len(v.Tokens) {
-		if t.id != i+n {
+			if v.Tokens[t.id] == t.content {
-			return nil, fmt.Errorf("invalid token id: %d", t.id)
+				slog.Warn("tokenizer", "duplicate token", t.content, "id", 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)
@@ -92,7 +111,15 @@ func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
 	return &v, nil
 }
 
-func parseAdditionalSpecialTokens(fsys fs.FS) ([]string, error) {
+type specialToken struct {
+	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
@@ -102,12 +129,43 @@ func parseAdditionalSpecialTokens(fsys fs.FS) ([]string, error) {
 	defer f.Close()
 
 	var m struct {
-		AdditionalSpecialTokens []string `json:"additional_special_tokens"`
+		AdditionalSpecialTokens any `json:"additional_special_tokens"`
 	}
 
 	if err := json.NewDecoder(f).Decode(&m); err != nil {
 		return nil, err
 	}
 
-	return m.AdditionalSpecialTokens, nil
+	var ast []specialToken
+
+	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/amd_common.go

@@ -9,8 +9,6 @@ import (
 	"path/filepath"
 	"runtime"
 	"strings"
-
-	"github.com/ollama/ollama/envconfig"
 )
 
 // Determine if the given ROCm lib directory is usable by checking for existence of some glob patterns
@@ -41,13 +39,10 @@ func commonAMDValidateLibDir() (string, error) {
 	// Favor our bundled version
 
 	// Installer payload location if we're running the installed binary
-	exe, err := os.Executable()
+	rocmTargetDir := filepath.Join(LibOllamaPath, "rocm")
-	if err == nil {
+	if rocmLibUsable(rocmTargetDir) {
-		rocmTargetDir := filepath.Join(filepath.Dir(exe), envconfig.LibRelativeToExe(), "lib", "ollama")
+		slog.Debug("detected ROCM next to ollama executable " + rocmTargetDir)
-		if rocmLibUsable(rocmTargetDir) {
+		return rocmTargetDir, nil
-			slog.Debug("detected ROCM next to ollama executable " + rocmTargetDir)
-			return rocmTargetDir, nil
-		}
 	}
 
 	// Prefer explicit HIP env var

discover/amd_linux.go

@@ -77,8 +77,7 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
 
 	gfxOverride := envconfig.HsaOverrideGfxVersion()
 	var supported []string
-	depPaths := LibraryDirs()
+	var libDir string
-	libDir := ""
 
 	// The amdgpu driver always exposes the host CPU(s) first, but we have to skip them and subtract
 	// from the other IDs to get alignment with the HIP libraries expectations (zero is the first GPU, not the CPU)
@@ -353,9 +352,8 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
 				})
 				return nil, err
 			}
-			depPaths = append(depPaths, libDir)
 		}
-		gpuInfo.DependencyPath = depPaths
+		gpuInfo.DependencyPath = []string{libDir}
 
 		if gfxOverride == "" {
 			// Only load supported list once

discover/amd_windows.go

@@ -5,7 +5,6 @@ import (
 	"errors"
 	"fmt"
 	"log/slog"
-	"os"
 	"path/filepath"
 	"slices"
 	"strconv"
@@ -50,14 +49,13 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
 		slog.Info(err.Error())
 		return nil, err
 	}
-	depPaths := LibraryDirs()
+
 	libDir, err := AMDValidateLibDir()
 	if err != nil {
 		err = fmt.Errorf("unable to verify rocm library: %w", err)
 		slog.Warn(err.Error())
 		return nil, err
 	}
-	depPaths = append(depPaths, libDir)
 
 	var supported []string
 	gfxOverride := envconfig.HsaOverrideGfxVersion()
@@ -113,7 +111,7 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
 				UnreliableFreeMemory: true,
 
 				ID:             strconv.Itoa(i), // TODO this is probably wrong if we specify visible devices
-				DependencyPath: depPaths,
+				DependencyPath: []string{libDir},
 				MinimumMemory:  rocmMinimumMemory,
 				Name:           name,
 				Compute:        gfx,
@@ -164,9 +162,7 @@ func AMDValidateLibDir() (string, error) {
 	}
 
 	// Installer payload (if we're running from some other location)
-	localAppData := os.Getenv("LOCALAPPDATA")
+	rocmTargetDir := filepath.Join(LibOllamaPath, "rocm")
-	appDir := filepath.Join(localAppData, "Programs", "Ollama")
-	rocmTargetDir := filepath.Join(appDir, envconfig.LibRelativeToExe(), "lib", "ollama")
 	if rocmLibUsable(rocmTargetDir) {
 		slog.Debug("detected ollama installed ROCm at " + rocmTargetDir)
 		return rocmTargetDir, 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]interface{}{}
+	ids := map[string]any{}
 	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

@@ -3,6 +3,7 @@
 package discover
 
 import (
+	"fmt"
 	"log/slog"
 	"os"
 	"regexp"
@@ -57,7 +58,10 @@ func cudaVariant(gpuInfo CudaGPUInfo) string {
 		}
 	}
 
-	if gpuInfo.computeMajor < 6 || gpuInfo.DriverMajor < 12 || (gpuInfo.DriverMajor == 12 && gpuInfo.DriverMinor == 0) {
+	// driver 12.0 has problems with the cuda v12 library, so run v11 on those older drivers
+	if gpuInfo.DriverMajor < 12 || (gpuInfo.DriverMajor == 12 && gpuInfo.DriverMinor == 0) {
+		// The detected driver is older than Feb 2023
+		slog.Warn("old CUDA driver detected - please upgrade to a newer driver", "version", fmt.Sprintf("%d.%d", gpuInfo.DriverMajor, gpuInfo.DriverMinor))
 		return "v11"
 	}
 	return "v12"

discover/gpu.go

@@ -23,7 +23,6 @@ import (
 
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
-	"github.com/ollama/ollama/runners"
 )
 
 type cudaHandles struct {
@@ -101,15 +100,7 @@ func initCudaHandles() *cudaHandles {
 
 	// Aligned with driver, we can't carry as payloads
 	nvcudaMgmtPatterns := NvcudaGlobs
-
+	cudartMgmtPatterns = append(cudartMgmtPatterns, filepath.Join(LibOllamaPath, "cuda_v*", CudartMgmtName))
-	if runtime.GOOS == "windows" {
-		localAppData := os.Getenv("LOCALAPPDATA")
-		cudartMgmtPatterns = []string{filepath.Join(localAppData, "Programs", "Ollama", CudartMgmtName)}
-	}
-	libDirs := LibraryDirs()
-	for _, d := range libDirs {
-		cudartMgmtPatterns = append(cudartMgmtPatterns, filepath.Join(d, CudartMgmtName))
-	}
 	cudartMgmtPatterns = append(cudartMgmtPatterns, CudartGlobs...)
 
 	if len(NvmlGlobs) > 0 {
@@ -240,7 +231,7 @@ func GetGPUInfo() GpuInfoList {
 		if err != nil {
 			slog.Warn("error looking up system memory", "error", err)
 		}
-		depPaths := LibraryDirs()
+
 		details, err := GetCPUDetails()
 		if err != nil {
 			slog.Warn("failed to lookup CPU details", "error", err)
@@ -248,11 +239,9 @@ func GetGPUInfo() GpuInfoList {
 		cpus = []CPUInfo{
 			{
 				GpuInfo: GpuInfo{
-					memInfo:        mem,
+					memInfo: mem,
-					Library:        "cpu",
+					Library: "cpu",
-					Variant:        runners.GetCPUCapability().String(),
+					ID:      "0",
-					ID:             "0",
-					DependencyPath: depPaths,
 				},
 				CPUs: details,
 			},
@@ -294,17 +283,13 @@ func GetGPUInfo() GpuInfoList {
 				gpuInfo.DriverMajor = driverMajor
 				gpuInfo.DriverMinor = driverMinor
 				variant := cudaVariant(gpuInfo)
-				if depPaths != nil {
+
-					gpuInfo.DependencyPath = depPaths
+				// Start with our bundled libraries
-					// Check for variant specific directory
+				if variant != "" {
-					if variant != "" {
+					variantPath := filepath.Join(LibOllamaPath, "cuda_"+variant)
-						for _, d := range depPaths {
+					if _, err := os.Stat(variantPath); err == nil {
-							if _, err := os.Stat(filepath.Join(d, "cuda_"+variant)); err == nil {
+						// Put the variant directory first in the search path to avoid runtime linking to the wrong library
-								// Put the variant directory first in the search path to avoid runtime linking to the wrong library
+						gpuInfo.DependencyPath = append([]string{variantPath}, gpuInfo.DependencyPath...)
-								gpuInfo.DependencyPath = append([]string{filepath.Join(d, "cuda_"+variant)}, gpuInfo.DependencyPath...)
-								break
-							}
-						}
 					}
 				}
 				gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
@@ -376,7 +361,7 @@ func GetGPUInfo() GpuInfoList {
 						gpuInfo.FreeMemory = uint64(memInfo.free)
 						gpuInfo.ID = C.GoString(&memInfo.gpu_id[0])
 						gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
-						gpuInfo.DependencyPath = depPaths
+						gpuInfo.DependencyPath = []string{LibOllamaPath}
 						oneapiGPUs = append(oneapiGPUs, gpuInfo)
 					}
 				}
@@ -512,33 +497,30 @@ func GetGPUInfo() GpuInfoList {
 
 func FindGPULibs(baseLibName string, defaultPatterns []string) []string {
 	// Multiple GPU libraries may exist, and some may not work, so keep trying until we exhaust them
-	var ldPaths []string
 	gpuLibPaths := []string{}
 	slog.Debug("Searching for GPU library", "name", baseLibName)
 
-	// Start with our bundled libraries
+	// search our bundled libraries first
-	patterns := []string{}
+	patterns := []string{filepath.Join(LibOllamaPath, baseLibName)}
-	for _, d := range LibraryDirs() {
-		patterns = append(patterns, filepath.Join(d, baseLibName))
-	}
 
+	var ldPaths []string
 	switch runtime.GOOS {
 	case "windows":
-		ldPaths = strings.Split(os.Getenv("PATH"), ";")
+		ldPaths = strings.Split(os.Getenv("PATH"), string(os.PathListSeparator))
 	case "linux":
-		ldPaths = strings.Split(os.Getenv("LD_LIBRARY_PATH"), ":")
+		ldPaths = strings.Split(os.Getenv("LD_LIBRARY_PATH"), string(os.PathListSeparator))
-	default:
-		return gpuLibPaths
 	}
 
-	// Then with whatever we find in the PATH/LD_LIBRARY_PATH
+	// then search the system's LD_LIBRARY_PATH
-	for _, ldPath := range ldPaths {
+	for _, p := range ldPaths {
-		d, err := filepath.Abs(ldPath)
+		p, err := filepath.Abs(p)
 		if err != nil {
 			continue
 		}
-		patterns = append(patterns, filepath.Join(d, baseLibName))
+		patterns = append(patterns, filepath.Join(p, baseLibName))
 	}
+
+	// finally, search the default patterns provided by the caller
 	patterns = append(patterns, defaultPatterns...)
 	slog.Debug("gpu library search", "globs", patterns)
 	for _, pattern := range patterns {
@@ -715,28 +697,6 @@ func (l GpuInfoList) GetVisibleDevicesEnv() (string, string) {
 	}
 }
 
-func LibraryDirs() []string {
-	// dependencies can exist wherever we found the runners (e.g. build tree for developers) and relative to the executable
-	// This can be simplified once we no longer carry runners as payloads
-	paths := []string{}
-	appExe, err := os.Executable()
-	if err != nil {
-		slog.Warn("failed to lookup executable path", "error", err)
-	} else {
-		appRelative := filepath.Join(filepath.Dir(appExe), envconfig.LibRelativeToExe(), "lib", "ollama")
-		if _, err := os.Stat(appRelative); err == nil {
-			paths = append(paths, appRelative)
-		}
-	}
-	rDir := runners.Locate()
-	if err != nil {
-		slog.Warn("unable to locate gpu dependency libraries", "error", err)
-	} else {
-		paths = append(paths, filepath.Dir(rDir))
-	}
-	return paths
-}
-
 func GetSystemInfo() SystemInfo {
 	gpus := GetGPUInfo()
 	gpuMutex.Lock()

discover/gpu_darwin.go

@@ -15,7 +15,6 @@ import (
 	"syscall"
 
 	"github.com/ollama/ollama/format"
-	"github.com/ollama/ollama/runners"
 )
 
 const (
@@ -28,7 +27,6 @@ func GetGPUInfo() GpuInfoList {
 		return []GpuInfo{
 			{
 				Library: "cpu",
-				Variant: runners.GetCPUCapability().String(),
 				memInfo: mem,
 			},
 		}
@@ -51,7 +49,6 @@ func GetCPUInfo() GpuInfoList {
 	return []GpuInfo{
 		{
 			Library: "cpu",
-			Variant: runners.GetCPUCapability().String(),
 			memInfo: mem,
 		},
 	}

discover/gpu_info.h

@@ -27,12 +27,14 @@
 
 #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

@@ -1,6 +1,7 @@
 #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) {
@@ -58,7 +59,7 @@ 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 == CUDA_ERROR_INSUFFICIENT_DRIVER) {
+    if (ret == CUDART_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;
     }
@@ -168,9 +169,9 @@ 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 %lu\n", resp->gpu_id, resp->total);
+  LOG(h.verbose, "[%s] CUDA totalMem %" PRId64 "\n", resp->gpu_id, resp->total);
-  LOG(h.verbose, "[%s] CUDA freeMem %lu\n", resp->gpu_id, resp->free);
+  LOG(h.verbose, "[%s] CUDA freeMem %" PRId64 "\n", resp->gpu_id, resp->free);
-  LOG(h.verbose, "[%s] CUDA usedMem %lu\n", resp->gpu_id, resp->used);
+  LOG(h.verbose, "[%s] CUDA usedMem %" PRId64 "\n", resp->gpu_id, resp->used);
   LOG(h.verbose, "[%s] Compute Capability %d.%d\n", resp->gpu_id, resp->major, resp->minor);
 }
 
@@ -180,4 +181,4 @@ void cudart_release(cudart_handle_t h) {
   h.handle = NULL;
 }
 
-#endif  // __APPLE__
+#endif  // __APPLE__

discover/gpu_info_nvcuda.c

@@ -1,6 +1,7 @@
 #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) {
@@ -193,8 +194,8 @@ 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 %lu mb\n", resp->gpu_id, resp->total / 1024 / 1024);
+  LOG(h.verbose, "[%s] CUDA totalMem %" PRId64 "mb\n", resp->gpu_id, resp->total / 1024 / 1024);
-  LOG(h.verbose, "[%s] CUDA freeMem %lu mb\n", resp->gpu_id, resp->free / 1024 / 1024);
+  LOG(h.verbose, "[%s] CUDA freeMem %" PRId64 "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);
 
   
@@ -247,4 +248,4 @@ void nvcuda_release(nvcuda_handle_t h) {
   h.handle = NULL;
 }
 
-#endif  // __APPLE__
+#endif  // __APPLE__

discover/gpu_linux.go

@@ -111,6 +111,7 @@ func GetCPUDetails() ([]CPU, error) {
 	if err != nil {
 		return nil, err
 	}
+	defer file.Close()
 	return linuxCPUDetails(file)
 }
 
@@ -168,13 +169,11 @@ 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

@@ -0,0 +1,56 @@
+package discover
+
+import (
+	"os"
+	"path/filepath"
+	"runtime"
+)
+
+// LibPath is a path to lookup dynamic libraries
+// in development it's usually 'build/lib/ollama'
+// in distribution builds it's 'lib/ollama' on Windows
+// '../lib/ollama' on Linux and the executable's directory on macOS
+// note: distribution builds, additional GPU-specific libraries are
+// found in subdirectories of the returned path, such as
+// 'cuda_v12', 'rocm', etc.
+var LibOllamaPath string = func() string {
+	exe, err := os.Executable()
+	if err != nil {
+		return ""
+	}
+
+	if eval, err := filepath.EvalSymlinks(exe); err == nil {
+		exe = eval
+	}
+
+	var libPath string
+	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()
+	if err != nil {
+		return ""
+	}
+
+	paths := []string{
+		libPath,
+
+		// build paths for development
+		filepath.Join(filepath.Dir(exe), "build", "lib", "ollama"),
+		filepath.Join(cwd, "build", "lib", "ollama"),
+	}
+
+	for _, p := range paths {
+		if _, err := os.Stat(p); err == nil {
+			return p
+		}
+	}
+
+	return filepath.Dir(exe)
+}()

discover/types.go

@@ -5,7 +5,6 @@ import (
 	"log/slog"
 
 	"github.com/ollama/ollama/format"
-	"github.com/ollama/ollama/runners"
 )
 
 type memInfo struct {
@@ -107,7 +106,7 @@ func (l GpuInfoList) ByLibrary() []GpuInfoList {
 	for _, info := range l {
 		found := false
 		requested := info.Library
-		if info.Variant != runners.CPUCapabilityNone.String() {
+		if info.Variant != "" {
 			requested += "_" + info.Variant
 		}
 		for i, lib := range libs {

docs/api.md

@@ -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
 
-```json
+```json5
 {
   "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",
@@ -394,9 +394,6 @@ 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,
@@ -404,10 +401,7 @@ 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
   }
 }'
@@ -485,7 +479,7 @@ A single JSON object is returned:
 
 ## Generate a chat completion
 
-```shell
+```
 POST /api/chat
 ```
 
@@ -495,14 +489,14 @@ 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`: tools for the model to use if supported. Requires `stream` to be set to `false`
+- `tools`: list of tools in JSON for the model to use if supported
 
 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 the model wants to use
+- `tool_calls` (optional): a list of tools in JSON that the model wants to use
 
 Advanced parameters (optional):
 
@@ -558,6 +552,10 @@ 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,
@@ -795,7 +793,7 @@ curl http://localhost:11434/api/chat -d '{
 
 ##### Request
 
-```
+```shell
 curl http://localhost:11434/api/chat -d '{
   "model": "llama3.2",
   "messages": [
@@ -870,7 +868,7 @@ 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": []
@@ -878,6 +876,7 @@ curl http://localhost:11434/api/chat -d '{
 ```
 
 ##### Response
+
 ```json
 {
   "model": "llama3.2",
@@ -897,7 +896,7 @@ 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": [],
@@ -924,7 +923,7 @@ A single JSON object is returned:
 
 ## Create a Model
 
-```shell
+```
 POST /api/create
 ```
 
@@ -1020,7 +1019,7 @@ curl http://localhost:11434/api/create -d '{
 
 A stream of JSON objects is returned:
 
-```
+```json
 {"status":"quantizing F16 model to Q4_K_M"}
 {"status":"creating new layer sha256:667b0c1932bc6ffc593ed1d03f895bf2dc8dc6df21db3042284a6f4416b06a29"}
 {"status":"using existing layer sha256:11ce4ee3e170f6adebac9a991c22e22ab3f8530e154ee669954c4bc73061c258"}
@@ -1051,7 +1050,7 @@ 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"}
@@ -1118,7 +1117,7 @@ Return 200 OK if the blob exists, 404 Not Found if it does not.
 
 ## Push a Blob
 
-```shell
+```
 POST /api/blobs/:digest
 ```
 
@@ -1142,7 +1141,7 @@ Return 201 Created if the blob was successfully created, 400 Bad Request if the
 
 ## List Local Models
 
-```shell
+```
 GET /api/tags
 ```
 
@@ -1195,7 +1194,7 @@ A single JSON object will be returned.
 
 ## Show Model Information
 
-```shell
+```
 POST /api/show
 ```
 
@@ -1212,13 +1211,13 @@ Show information about a model including details, modelfile, template, parameter
 
 ```shell
 curl http://localhost:11434/api/show -d '{
-  "model": "llama3.2"
+  "model": "llava"
 }'
 ```
 
 #### Response
 
-```json
+```json5
 {
   "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|>\"",
@@ -1255,13 +1254,17 @@ 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
 ```
 
@@ -1284,7 +1287,7 @@ 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
 ```
 
@@ -1310,7 +1313,7 @@ Returns a 200 OK if successful, 404 Not Found if the model to be deleted doesn't
 
 ## Pull a Model
 
-```shell
+```
 POST /api/pull
 ```
 
@@ -1382,7 +1385,7 @@ if `stream` is set to false, then the response is a single JSON object:
 
 ## Push a Model
 
-```shell
+```
 POST /api/push
 ```
 
@@ -1447,7 +1450,7 @@ If `stream` is set to `false`, then the response is a single JSON object:
 
 ## Generate Embeddings
 
-```shell
+```
 POST /api/embed
 ```
 
@@ -1515,7 +1518,7 @@ curl http://localhost:11434/api/embed -d '{
 ```
 
 ## List Running Models
-```shell
+```
 GET /api/ps
 ```
 
@@ -1562,7 +1565,7 @@ A single JSON object will be returned.
 
 > Note: this endpoint has been superseded by `/api/embed`
 
-```shell
+```
 POST /api/embeddings
 ```
 
@@ -1602,7 +1605,7 @@ curl http://localhost:11434/api/embeddings -d '{
 
 ## Version
 
-```shell
+```
 GET /api/version
 ```
 

docs/benchmark.md

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

docs/development.md

@@ -1,165 +1,159 @@
 # Development
 
-Install required tools:
+Install prerequisites:
 
-- go version 1.22 or higher
+- [Go](https://go.dev/doc/install)
-- OS specific C/C++ compiler (see below)
+- 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.
-- GNU Make
 
+Then build and run Ollama from the root directory of the repository:
 
-## Overview
+```shell
-
+go run . serve
-Ollama uses a mix of Go and C/C++ code to interface with GPUs.  The C/C++ code is compiled with both CGO and GPU library specific compilers.  A set of GNU Makefiles are used to compile the project.  GPU Libraries are auto-detected based on the typical environment variables used by the respective libraries, but can be overridden if necessary.  The default make target will build the runners and primary Go Ollama application that will run within the repo directory.  Throughout the examples below `-j 5` is suggested for 5 parallel jobs to speed up the build.  You can adjust the job count based on your CPU Core count to reduce build times.  If you want to relocate the built binaries, use the `dist` target and recursively copy the files in `./dist/$OS-$ARCH/` to your desired location. To learn more about the other make targets use `make help`
-
-Once you have built the GPU/CPU runners, you can compile the main application with `go build .` 
-
-### MacOS
-
-[Download Go](https://go.dev/dl/)
-
-```bash
-make -j 5
 ```
 
-Now you can run `ollama`:
+## macOS (Apple Silicon)
 
-```bash
+macOS Apple Silicon supports Metal which is built-in to the Ollama binary. No additional steps are required.
-./ollama
+
+## macOS (Intel)
+
+Install prerequisites:
+
+- [CMake](https://cmake.org/download/) or `brew install cmake`
+
+Then, configure and build the project:
+
+```shell
+cmake -B build
+cmake --build build
 ```
 
-#### Xcode 15 warnings
+Lastly, run Ollama:
 
-If you are using Xcode newer than version 14, you may see a warning during `go build` about `ld: warning: ignoring duplicate libraries: '-lobjc'` due to Golang issue https://github.com/golang/go/issues/67799 which can be safely ignored.  You can suppress the warning with `export CGO_LDFLAGS="-Wl,-no_warn_duplicate_libraries"`
+```shell
-
+go run . serve
-### Linux
-
-#### Linux CUDA (NVIDIA)
-
-_Your operating system distribution may already have packages for NVIDIA CUDA. Distro packages are often preferable, but instructions are distro-specific. Please consult distro-specific docs for dependencies if available!_
-
-Install `make`, `gcc` and `golang` as well as [NVIDIA CUDA](https://developer.nvidia.com/cuda-downloads)
-development and runtime packages.
-
-Typically the makefile will auto-detect CUDA, however, if your Linux distro
-or installation approach uses alternative paths, you can specify the location by
-overriding `CUDA_PATH` to the location of the CUDA toolkit. You can customize
-a set of target CUDA architectures by setting `CUDA_ARCHITECTURES` (e.g. `CUDA_ARCHITECTURES=50;60;70`)
-
-```
-make -j 5
 ```
 
-If both v11 and v12 tookkits are detected, runners for both major versions will be built by default.  You can build just v12 with `make cuda_v12`
+## Windows
 
-#### Older Linux CUDA (NVIDIA)
+Install prerequisites:
 
-To support older GPUs with Compute Capability 3.5 or 3.7, you will need to use an older version of the Driver from [Unix Driver Archive](https://www.nvidia.com/en-us/drivers/unix/) (tested with 470) and [CUDA Toolkit Archive](https://developer.nvidia.com/cuda-toolkit-archive) (tested with cuda V11).  When you build Ollama, you will need to set two make variable to adjust the minimum compute capability Ollama supports via `make -j 5 CUDA_ARCHITECTURES="35;37;50;52" EXTRA_GOLDFLAGS="\"-X=github.com/ollama/ollama/discover.CudaComputeMajorMin=3\" \"-X=github.com/ollama/ollama/discover.CudaComputeMinorMin=5\""`.  To find the Compute Capability of your older GPU, refer to [GPU Compute Capability](https://developer.nvidia.com/cuda-gpus).
+- [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/)
+    - [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)
 
-#### Linux ROCm (AMD)
+Then, configure and build the project:
 
-_Your operating system distribution may already have packages for AMD ROCm. Distro packages are often preferable, but instructions are distro-specific. Please consult distro-specific docs for dependencies if available!_
+```shell
-
+cmake -B build
-Install [ROCm](https://rocm.docs.amd.com/en/latest/) development packages first, as well as `make`, `gcc`, and `golang`.
+cmake --build build --config Release
-
-Typically the build scripts will auto-detect ROCm, however, if your Linux distro
-or installation approach uses unusual paths, you can specify the location by
-specifying an environment variable `HIP_PATH` to the location of the ROCm
-install (typically `/opt/rocm`). You can also customize
-the AMD GPU targets by setting HIP_ARCHS (e.g. `HIP_ARCHS=gfx1101;gfx1102`)
-
-```
-make -j 5
 ```
 
-ROCm requires elevated privileges to access the GPU at runtime. On most distros you can add your user account to the `render` group, or run as root.
+> [!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
+> ```
 
-#### Containerized Linux Build
 
-If you have Docker and buildx available, you can build linux binaries with `./scripts/build_linux.sh` which has the CUDA and ROCm dependencies included. The resulting artifacts are placed in `./dist`  and by default the script builds both arm64 and amd64 binaries.  If you want to build only amd64, you can build with `PLATFORM=linux/amd64 ./scripts/build_linux.sh`
+Lastly, run Ollama:
 
-### Windows
+```shell
-
+go run . serve
-The following tools are required as a minimal development environment to build CPU inference support.
-
-- Go version 1.22 or higher
-  - https://go.dev/dl/
-- Git
-  - https://git-scm.com/download/win
-- clang with gcc compat and Make.  There are multiple options on how to go about installing these tools on Windows.  We have verified the following, but others may work as well:  
-  - [MSYS2](https://www.msys2.org/)
-    - After installing, from an MSYS2 terminal, run `pacman -S mingw-w64-clang-x86_64-gcc-compat mingw-w64-clang-x86_64-clang make` to install the required tools
-  - Assuming you used the default install prefix for msys2 above, add `C:\msys64\clang64\bin` and `c:\msys64\usr\bin` to your environment variable `PATH` where you will perform the build steps below (e.g. system-wide, account-level, powershell, cmd, etc.)
-
-> [!NOTE]  
-> Due to bugs in the GCC C++ library for unicode support, Ollama should be built with clang on windows.
-
-```
-make -j 5
 ```
 
-#### GPU Support
+## Windows (ARM)
 
-The GPU tools require the Microsoft native build tools.  To build either CUDA or ROCm, you must first install MSVC via Visual Studio:
+Windows ARM does not support additional acceleration libraries at this time.  Do not use cmake, simply `go run` or `go build`.
 
-- Make sure to select `Desktop development with C++` as a Workload during the Visual Studio install
+## Linux
-- You must complete the Visual Studio install and run it once **BEFORE** installing CUDA or ROCm for the tools to properly register
-- Add the location of the **64 bit (x64)** compiler (`cl.exe`) to your `PATH`
-- Note: the default Developer Shell may configure the 32 bit (x86) compiler which will lead to build failures.  Ollama requires a 64 bit toolchain.
 
-#### Windows CUDA (NVIDIA)
+Install prerequisites:
 
-In addition to the common Windows development tools and MSVC described above:
+- [CMake](https://cmake.org/download/) or `sudo apt install cmake` or `sudo dnf install cmake`
+- (Optional) AMD GPU support
+    - [ROCm](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/quick-start.html)
+- (Optional) NVIDIA GPU support
+    - [CUDA SDK](https://developer.nvidia.com/cuda-downloads)
 
-- [NVIDIA CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html)
+> [!IMPORTANT]
+> Ensure prerequisites are in `PATH` before running CMake.
 
-#### Windows ROCm (AMD Radeon)
 
-In addition to the common Windows development tools and MSVC described above:
+Then, configure and build the project:
 
-- [AMD HIP](https://www.amd.com/en/developer/resources/rocm-hub/hip-sdk.html)
+```shell
-
+cmake -B build
-#### Windows arm64
+cmake --build build
-
-The default `Developer PowerShell for VS 2022` may default to x86 which is not what you want.  To ensure you get an arm64 development environment, start a plain PowerShell terminal and run:
-
-```powershell
-import-module 'C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\Tools\\Microsoft.VisualStudio.DevShell.dll'
-Enter-VsDevShell -Arch arm64 -vsinstallpath 'C:\\Program Files\\Microsoft Visual Studio\\2022\\Community' -skipautomaticlocation
 ```
 
-You can confirm with `write-host $env:VSCMD_ARG_TGT_ARCH`
+Lastly, run Ollama:
 
-Follow the instructions at https://www.msys2.org/wiki/arm64/ to set up an arm64 msys2 environment.  Ollama requires gcc and mingw32-make to compile, which is not currently available on Windows arm64, but a gcc compatibility adapter is available via `mingw-w64-clang-aarch64-gcc-compat`. At a minimum you will need to install the following:
+```shell
-
+go run . serve
-```
-pacman -S mingw-w64-clang-aarch64-clang mingw-w64-clang-aarch64-gcc-compat mingw-w64-clang-aarch64-make make
 ```
 
-You will need to ensure your PATH includes go, cmake, gcc and clang mingw32-make to build ollama from source. (typically `C:\msys64\clangarm64\bin\`)
+## Docker
 
-
+```shell
-## Advanced CPU Vector Settings
+docker build .
-
-On x86, running `make` will compile several CPU runners which can run on different CPU families. At runtime, Ollama will auto-detect the best variation to load.  If GPU libraries are present at build time, Ollama also compiles GPU runners with the `AVX` CPU vector feature enabled.  This provides a good performance balance when loading large models that split across GPU and CPU with broad compatibility.  Some users may prefer no vector extensions (e.g. older Xeon/Celeron processors, or hypervisors that mask the vector features) while other users may prefer turning on many more vector extensions to further improve performance for split model loads.
-
-To customize the set of CPU vector features enabled for a CPU runner and all GPU runners, use CUSTOM_CPU_FLAGS during the build.
-
-To build without any vector flags:
-
-```
-make CUSTOM_CPU_FLAGS=""
 ```
 
-To build with both AVX and AVX2:
+### ROCm
-```
+
-make CUSTOM_CPU_FLAGS=avx,avx2
+```shell
+docker build --build-arg FLAVOR=rocm .
 ```
 
-To build with AVX512 features turned on:
+## Running tests
 
-```
+To run tests, use `go test`:
-make CUSTOM_CPU_FLAGS=avx,avx2,avx512,avx512vbmi,avx512vnni,avx512bf16
+
+```shell
+go test ./...
 ```
 
-> [!NOTE]  
+> NOTE: In rare cirumstances, you may nedd to change a package using the new
-> If you are experimenting with different flags, make sure to do a `make clean` between each change to ensure everything is rebuilt with the new compiler flags
+> "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
 
-```bash
+```shell
 docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
 ```
 
@@ -11,42 +11,46 @@ Install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-
 
 #### Install with Apt
 1.  Configure the repository
-```bash
+
-curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey \
+    ```shell
-    | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
+    curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey \
-curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list \
+        | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
-    | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' \
+    curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list \
-    | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
+        | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' \
-sudo apt-get update
+        | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
-```
+    sudo apt-get update
+    ```
+
 2.  Install the NVIDIA Container Toolkit packages
-```bash
+
-sudo apt-get install -y nvidia-container-toolkit
+    ```shell
-```
+    sudo apt-get install -y nvidia-container-toolkit
+    ```
 
 #### Install with Yum or Dnf
 1.  Configure the repository
 
-```bash
+    ```shell
-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
 
-```bash
+    ```shell
-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
 
-```bash
+```shell
 docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
 ```
 
@@ -57,7 +61,7 @@ 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
 ```
 
@@ -65,7 +69,7 @@ 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

@@ -20,11 +20,17 @@ 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 2048 tokens.
+By default, Ollama uses a context window size of 4096 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
 ```
 
@@ -46,10 +52,15 @@ 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
@@ -66,7 +77,7 @@ 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"
+    launchctl setenv OLLAMA_HOST "0.0.0.0:11434"
     ```
 
 2. Restart Ollama application.
@@ -81,14 +92,14 @@ If Ollama is run as a systemd service, environment variables should be set using
 
     ```ini
     [Service]
-    Environment="OLLAMA_HOST=0.0.0.0"
+    Environment="OLLAMA_HOST=0.0.0.0:11434"
     ```
 
 3. Save and exit.
 
 4. Reload `systemd` and restart Ollama:
 
-   ```bash
+   ```shell
    systemctl daemon-reload
    systemctl restart ollama
    ```
@@ -182,6 +193,13 @@ 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?
@@ -221,16 +239,19 @@ 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 ""
 ```
@@ -250,11 +271,13 @@ 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

@@ -1,13 +1,13 @@
 # GPU
 ## Nvidia
-Ollama supports Nvidia GPUs with compute capability 5.0+.
+Ollama supports Nvidia GPUs with compute capability 5.0+ and driver version 531 and newer.
 
 Check your compute compatibility to see if your card is supported:
 [https://developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus)
 
 | Compute Capability | Family              | Cards                                                                                                       |
 | ------------------ | ------------------- | ----------------------------------------------------------------------------------------------------------- |
-| 9.0                | NVIDIA              | `H100`                                                                                                      |
+| 9.0                | NVIDIA              | `H200` `H100`                                                                                               |
 | 8.9                | GeForce RTX 40xx    | `RTX 4090` `RTX 4080 SUPER` `RTX 4080` `RTX 4070 Ti SUPER` `RTX 4070 Ti` `RTX 4070 SUPER` `RTX 4070` `RTX 4060 Ti` `RTX 4060`  |
 |                    | NVIDIA Professional | `L4` `L40` `RTX 6000`                                                                                       |
 | 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:
 
-```bash
+```shell
 ollama create my-model
 ```
 
 Lastly, test the model:
 
-```bash
+```shell
 ollama run my-model
 ```
 

docs/linux.md

@@ -75,7 +75,7 @@ RestartSec=3
 Environment="PATH=$PATH"
 
 [Install]
-WantedBy=default.target
+WantedBy=multi-user.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=0.3.9 sh
+curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.5.7 sh
 ```
 
 ## Viewing logs
@@ -186,3 +186,9 @@ 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
@@ -69,24 +69,30 @@ To use this:
 
 To view the Modelfile of a given model, use the `ollama show --modelfile` command.
 
-  ```bash
+```shell
-  > 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|>
 
-  {{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
+> **Output**:
+>
+> ```
+> # 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
 
@@ -94,13 +100,13 @@ To view the Modelfile of a given model, use the `ollama show --modelfile` comman
 
 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
 ```
 
@@ -111,7 +117,7 @@ Additional models can be found at:
 
 #### Build from a Safetensors model
 
-```modelfile
+```
 FROM <model directory>
 ```
 
@@ -125,7 +131,7 @@ Currently supported model architectures:
 
 #### Build from a GGUF file
 
-```modelfile
+```
 FROM ./ollama-model.gguf
 ```
 
@@ -136,7 +142,7 @@ 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>
 ```
 
@@ -144,9 +150,6 @@ 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   |
@@ -183,7 +186,7 @@ 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>"""
 ```
 
@@ -193,7 +196,7 @@ The `ADAPTER` instruction specifies a fine tuned LoRA adapter that should apply
 
 #### Safetensor adapter
 
-```modelfile
+```
 ADAPTER <path to safetensor adapter>
 ```
 
@@ -204,7 +207,7 @@ Currently supported Safetensor adapters:
 
 #### GGUF adapter
 
-```modelfile
+```
 ADAPTER ./ollama-lora.gguf
 ```
 
@@ -212,7 +215,7 @@ 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>
 """
@@ -222,7 +225,7 @@ 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>
 ```
 
@@ -237,7 +240,7 @@ MESSAGE <role> <message>
 
 #### Example conversation
 
-```modelfile
+```
 MESSAGE user Is Toronto in Canada?
 MESSAGE assistant yes
 MESSAGE user Is Sacramento in Canada?

docs/openai.md

@@ -1,6 +1,7 @@
 # OpenAI compatibility
 
-> **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).
+> [!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).
 
 Ollama provides experimental compatibility with parts of the [OpenAI API](https://platform.openai.com/docs/api-reference) to help connect existing applications to Ollama.
 
@@ -59,8 +60,10 @@ 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
 
@@ -144,7 +147,7 @@ const embedding = await openai.embeddings.create({
 
 ### `curl`
 
-``` shell
+```shell
 curl http://localhost:11434/v1/chat/completions \
     -H "Content-Type: application/json" \
     -d '{
@@ -204,45 +207,6 @@ curl http://localhost:11434/v1/embeddings \
     }'
 ```
 
-## Extra arguments
-
-### Setting context length
-- `context_length` parameter can be used to set the context length for the model
-
-#### OpenAI python library 
-- OpenAI python library does not support setting context length, however this can be set for Ollama through the `extra_body` parameter
-
-```py
-completion = client.chat.completions.create(
-    model="llama3.1:8b",
-    messages=[{"role": "user", "content": "Say this is a test"}],
-    extra_body={"context_length": 4096},
-)
-```
-
-#### OpenAI JavaScript library
-- OpenAI JavaScript library does not support setting context length, however this can be set for Ollama by passing `context_length` directly with a `@ts-expect-error` as an undocumented parameter in the OpenAI JavaScript library. [See documentation here](https://github.com/openai/openai-node?tab=readme-ov-file#making-customundocumented-requests)
-
-```ts
-const chatCompletion = await openai.chat.completions.create({
-    messages: [{ role: 'user', content: 'Say this is a test' }],
-    model: 'llama3.2',
-    // @ts-expect-error context_length is an additional parameter 
-    context_length: 4096,
-})
-```
-
-#### `curl`
-```shell
-curl http://localhost:11434/v1/chat/completions \
-    -H "Content-Type: application/json" \
-    -d '{
-        "model": "llama3.2",
-        "messages": [{"role": "user", "content": "Say this is a test"}],
-        "context_length": 4096
-    }'
-```
-
 ## Endpoints
 
 ### `/v1/chat/completions`
@@ -252,7 +216,6 @@ curl http://localhost:11434/v1/chat/completions \
 - [x] Chat completions
 - [x] Streaming
 - [x] JSON mode
-- [x] Structured outputs
 - [x] Reproducible outputs
 - [x] Vision
 - [x] Tools
@@ -359,7 +322,7 @@ 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
 ```
 
@@ -379,3 +342,27 @@ curl http://localhost:11434/v1/chat/completions \
     }'
 ```
 
+### Setting the context size
+
+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:
+
+```
+FROM <some model>
+PARAMETER num_ctx <context size>
+```
+
+Use the `ollama create mymodel` command to create a new model with the updated context size. Call the API with the updated model name:
+
+```shell
+curl http://localhost:11434/v1/chat/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "mymodel",
+        "messages": [
+            {
+                "role": "user",
+                "content": "Hello!"
+            }
+        ]
+    }'
+```

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:
 
-```gotmpl
+```go
 {{- 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.
 
-```gotmpl
+```go
 [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
+journalctl -u ollama --no-pager --follow --pager-end 
 ```
 
 When you run Ollama in a **container**, the logs go to stdout/stderr in the container:
@@ -17,6 +17,7 @@ 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.
@@ -25,9 +26,9 @@ 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"
@@ -42,19 +43,20 @@ Ollama includes multiple LLM libraries compiled for different GPUs and CPU vecto
 In the server log, you will see a message that looks something like this (varies from release to release):
 
 ```
-Dynamic LLM libraries [rocm_v6 cpu cpu_avx cpu_avx2 cuda_v11 rocm_v5]
+Dynamic LLM libraries [rocm_v6 cpu cpu_avx cpu_avx2 cuda_v12 rocm_v5]
 ```
 
 **Experimental LLM Library Override**
 
 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
 ```
 
@@ -62,13 +64,13 @@ 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.
 
-```sh
+```shell
-curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION="0.1.29" sh
+curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.5.7 sh
 ```
 
-## Linux tmp noexec 
+## Linux docker
 
-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/
+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.
 
 ## NVIDIA GPU Discovery
 
@@ -97,8 +99,6 @@ 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

@@ -47,6 +47,7 @@ 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
 ```
@@ -54,14 +55,13 @@ 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 `<cmd>+R` and type in:
+the explorer window by hitting `<Ctrl>+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
 
@@ -80,9 +80,11 @@ 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 and AMD. This allows for embedding
+and GPU library dependencies for Nvidia.  If you have an AMD GPU, also download
-Ollama in existing applications, or running it as a system service via `ollama
+and extract the additional ROCm package `ollama-windows-amd64-rocm.zip` into the
-serve` with tools such as [NSSM](https://nssm.cc/).
+same directory.  This allows for embedding Ollama in existing applications, or
+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 {
 	}
 }
 
-// Origins returns a list of allowed origins. Origins can be configured via the OLLAMA_ORIGINS environment variable.
+// AllowedOrigins returns a list of allowed origins. AllowedOrigins can be configured via the OLLAMA_ORIGINS environment variable.
-func Origins() (origins []string) {
+func AllowedOrigins() (origins []string) {
 	if s := Var("OLLAMA_ORIGINS"); s != "" {
 		origins = strings.Split(s, ",")
 	}
@@ -73,6 +73,7 @@ func Origins() (origins []string) {
 		"file://*",
 		"tauri://*",
 		"vscode-webview://*",
+		"vscode-file://*",
 	)
 
 	return origins
@@ -165,6 +166,10 @@ 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 {
@@ -247,9 +252,11 @@ 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", Origins(), "A comma separated list of allowed origins"},
+		"OLLAMA_ORIGINS":           {"OLLAMA_ORIGINS", AllowedOrigins(), "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"},
@@ -288,12 +295,3 @@ func Values() map[string]string {
 func Var(key string) string {
 	return strings.Trim(strings.TrimSpace(os.Getenv(key)), "\"'")
 }
-
-// On windows, we keep the binary at the top directory, but
-// other platforms use a "bin" directory, so this returns ".."
-func LibRelativeToExe() string {
-	if runtime.GOOS == "windows" {
-		return "."
-	}
-	return ".."
-}

envconfig/config_test.go

@@ -69,6 +69,7 @@ func TestOrigins(t *testing.T) {
 			"file://*",
 			"tauri://*",
 			"vscode-webview://*",
+			"vscode-file://*",
 		}},
 		{"http://10.0.0.1", []string{
 			"http://10.0.0.1",
@@ -88,6 +89,7 @@ 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",
@@ -108,6 +110,7 @@ func TestOrigins(t *testing.T) {
 			"file://*",
 			"tauri://*",
 			"vscode-webview://*",
+			"vscode-file://*",
 		}},
 		{"http://totally.safe,http://definitely.legit", []string{
 			"http://totally.safe",
@@ -128,13 +131,14 @@ 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(Origins(), tt.expect); diff != "" {
+			if diff := cmp.Diff(AllowedOrigins(), tt.expect); diff != "" {
 				t.Errorf("%s: mismatch (-want +got):\n%s", tt.value, diff)
 			}
 		})
@@ -272,3 +276,19 @@ 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)
+			}
+		})
+	}
+}

format/bytes.go

@@ -40,8 +40,6 @@ 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

@@ -0,0 +1,91 @@
+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

@@ -12,6 +12,9 @@ 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 interface{}, b interface{}) {
+func assertEqual(t *testing.T, a any, b any) {
 	if a != b {
 		t.Errorf("Assert failed, expected %v, got %v", b, a)
 	}

fs/config.go

@@ -0,0 +1,13 @@
+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

@@ -0,0 +1,732 @@
+package ggml
+
+import (
+	"encoding/binary"
+	"errors"
+	"fmt"
+	"io"
+	"log/slog"
+	"slices"
+	"strings"
+
+	"github.com/ollama/ollama/fs/util/bufioutil"
+)
+
+type GGML struct {
+	container
+	model
+}
+
+type model interface {
+	KV() KV
+	Tensors() Tensors
+}
+
+type KV map[string]any
+
+func (kv KV) Architecture() string {
+	return kv.String("general.architecture", "unknown")
+}
+
+func (kv KV) Kind() string {
+	return kv.String("general.type", "unknown")
+}
+
+func (kv KV) ParameterCount() uint64 {
+	val, _ := keyValue(kv, "general.parameter_count", uint64(0))
+	return val
+}
+
+func (kv KV) FileType() FileType {
+	if t := kv.Uint("general.file_type"); t > 0 {
+		return FileType(t)
+	}
+
+	return FileTypeUnknown
+}
+
+func (kv KV) BlockCount() uint64 {
+	return uint64(kv.Uint("block_count"))
+}
+
+func (kv KV) EmbeddingLength() uint64 {
+	return uint64(kv.Uint("embedding_length"))
+}
+
+func (kv KV) HeadCountMax() uint64 {
+	// TODO(drifkin): using the max value can cause an overestimation. In the
+	// future if array values become more popular, we can adapt the more invasive
+	// <https://github.com/ollama/ollama/pull/10225>
+	return uint64(kv.UintOrMaxArrayValue("attention.head_count", 1))
+}
+
+func (kv KV) HeadCountMin() uint64 {
+	return uint64(kv.UintOrMinArrayValue("attention.head_count", 1))
+}
+
+func (kv KV) HeadCountKVMax() uint64 {
+	return uint64(kv.UintOrMaxArrayValue("attention.head_count_kv", 1))
+}
+
+func (kv KV) HeadCountKVMin() uint64 {
+	return uint64(kv.UintOrMinArrayValue("attention.head_count_kv", 1))
+}
+
+func (kv KV) EmbeddingHeadCountMax() uint64 {
+	if heads := kv.HeadCountMin(); heads > 0 {
+		return kv.EmbeddingLength() / heads
+	}
+
+	return 0
+}
+
+func (kv KV) EmbeddingHeadCountK() uint64 {
+	return uint64(kv.Uint("attention.key_length", uint32(kv.EmbeddingHeadCountMax())))
+}
+
+func (kv KV) EmbeddingHeadCountV() uint64 {
+	return uint64(kv.Uint("attention.value_length", uint32(kv.EmbeddingHeadCountMax())))
+}
+
+func (kv KV) ContextLength() uint64 {
+	return uint64(kv.Uint("context_length"))
+}
+
+func (kv KV) ChatTemplate() string {
+	return kv.String("tokenizer.chat_template")
+}
+
+func (kv KV) String(key string, defaultValue ...string) string {
+	val, _ := keyValue(kv, key, append(defaultValue, "")...)
+	return val
+}
+
+func (kv KV) Uint(key string, defaultValue ...uint32) uint32 {
+	val, _ := keyValue(kv, key, append(defaultValue, 0)...)
+	return val
+}
+
+func (kv KV) Float(key string, defaultValue ...float32) float32 {
+	val, _ := keyValue(kv, key, append(defaultValue, 0)...)
+	return val
+}
+
+func (kv KV) Bool(key string, defaultValue ...bool) bool {
+	val, _ := keyValue(kv, key, append(defaultValue, false)...)
+	return val
+}
+
+func (kv KV) UintOrMaxArrayValue(key string, defaultValue uint32) uint32 {
+	_, max := kv.UintOrArrayValue(key, defaultValue)
+	return max
+}
+
+func (kv KV) UintOrMinArrayValue(key string, defaultValue uint32) uint32 {
+	min, _ := kv.UintOrArrayValue(key, defaultValue)
+	return min
+}
+
+func (kv KV) UintOrArrayValue(key string, defaultValue uint32) (uint32, uint32) {
+	if u32, ok := keyValue(kv, key, uint32(0)); ok {
+		return u32, u32
+	} else if u32s, ok := keyValue(kv, key, &array[uint32]{}); ok {
+		min := slices.Min(u32s.values)
+		max := slices.Max(u32s.values)
+		return min, max
+	} else if i32s, ok := keyValue(kv, key, &array[int32]{}); ok {
+		min := slices.Min(i32s.values)
+		max := slices.Max(i32s.values)
+		if min < 0 || max < 0 {
+			slog.Warn("array values are unexpectedly negative", "key", key, "min", min, "max", max)
+		}
+		return uint32(min), uint32(max)
+	}
+
+	return defaultValue, defaultValue
+}
+
+func (kv KV) Strings(key string, defaultValue ...[]string) []string {
+	val, _ := keyValue(kv, key, &array[string]{values: append(defaultValue, []string(nil))[0]})
+	return val.values
+}
+
+func (kv KV) Ints(key string, defaultValue ...[]int32) []int32 {
+	val, _ := keyValue(kv, key, &array[int32]{values: append(defaultValue, []int32(nil))[0]})
+	return val.values
+}
+
+func (kv KV) Uints(key string, defaultValue ...[]uint32) []uint32 {
+	val, _ := keyValue(kv, key, &array[uint32]{values: append(defaultValue, []uint32(nil))[0]})
+	return val.values
+}
+
+func (kv KV) Floats(key string, defaultValue ...[]float32) []float32 {
+	val, _ := keyValue(kv, key, &array[float32]{values: append(defaultValue, []float32(nil))[0]})
+	return val.values
+}
+
+func (kv KV) OllamaEngineRequired() bool {
+	return slices.Contains([]string{
+		"gemma3",
+		"mistral3",
+		"llama4",
+	}, 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, bool) {
+	if !strings.HasPrefix(key, "tokenizer.") && !strings.HasPrefix(key, "general.") {
+		key = kv.Architecture() + "." + key
+	}
+
+	if val, ok := kv[key].(T); ok {
+		return val, true
+	}
+
+	slog.Debug("key with type not found", "key", key, "default", defaultValue[0])
+	return defaultValue[0], false
+}
+
+type Tensors struct {
+	items  []*Tensor
+	Offset uint64
+}
+
+func (s Tensors) Items(prefix ...string) []*Tensor {
+	if len(prefix) == 0 {
+		return s.items
+	}
+
+	var items []*Tensor
+	for _, t := range s.items {
+		if strings.HasPrefix(t.Name, prefix[0]) {
+			items = append(items, t)
+		}
+	}
+
+	return items
+}
+
+func (ts Tensors) GroupLayers() 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 len(parts) > index+2 {
+				// blk and mm should have a number after them, join it
+				parts = append(
+					[]string{strings.Join(parts[:index+2], ".")},
+					parts[index+2:]...)
+			}
+		}
+
+		if _, ok := layers[parts[0]]; !ok {
+			layers[parts[0]] = make(Layer)
+		}
+
+		layers[parts[0]][strings.Join(parts[1:], ".")] = t
+	}
+
+	return layers
+}
+
+type Layer map[string]*Tensor
+
+func (l Layer) Size() (size uint64) {
+	for _, t := range l {
+		size += t.Size()
+	}
+
+	return size
+}
+
+type Tensor struct {
+	Name   string `json:"name"`
+	Kind   uint32 `json:"kind"`
+	Offset uint64 `json:"-"`
+
+	// Shape is the number of elements in each dimension
+	Shape []uint64 `json:"shape"`
+
+	io.WriterTo `json:"-"`
+}
+
+func (t Tensor) block() (n int) {
+	if _, err := fmt.Sscanf(t.Name, "blk.%d.", &n); err != nil {
+		return -1
+	}
+
+	return
+}
+
+func (t Tensor) blockSize() uint64 {
+	return (TensorType)(t.Kind).BlockSize()
+}
+
+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
+		2,  // Q4_0
+		3,  // Q4_1
+		6,  // Q5_0
+		7,  // Q5_1
+		8,  // Q8_0
+		9,  // Q8_1
+		20: // IQ4_NL
+		return 32
+	default:
+		return 256
+	}
+}
+
+func (t Tensor) typeSize() uint64 {
+	return TensorType(t.Kind).TypeSize()
+}
+
+func (t TensorType) TypeSize() uint64 {
+	blockSize := t.BlockSize()
+
+	switch t {
+	case TensorTypeF32:
+		return 4
+	case TensorTypeF16:
+		return 2
+	case TensorTypeQ4_0:
+		return 2 + blockSize/2
+	case TensorTypeQ4_1:
+		return 2 + 2 + blockSize/2
+	case TensorTypeQ5_0:
+		return 2 + 4 + blockSize/2
+	case TensorTypeQ5_1:
+		return 2 + 2 + 4 + blockSize/2
+	case TensorTypeQ8_0:
+		return 2 + blockSize
+	case TensorTypeQ8_1:
+		return 2 + 2 + blockSize
+	case TensorTypeQ2_K:
+		return blockSize/16 + blockSize/4 + 2 + 2
+	case TensorTypeQ3_K:
+		return blockSize/8 + blockSize/4 + 12 + 2
+	case TensorTypeQ4_K:
+		return 2 + 2 + 12 + blockSize/2
+	case TensorTypeQ5_K:
+		return 2 + 2 + 12 + blockSize/8 + blockSize/2
+	case TensorTypeQ6_K:
+		return blockSize/2 + blockSize/4 + blockSize/16 + 2
+	case TensorTypeQ8_K:
+		return 4 + blockSize + 2*blockSize/16
+	case tensorTypeIQ2_XXS:
+		return 2 + 2*blockSize/8
+	case tensorTypeIQ2_XS:
+		return 2 + 2*blockSize/8 + blockSize/32
+	case tensorTypeIQ3_XXS:
+		return 2 + blockSize/4 + blockSize/8
+	case tensorTypeIQ1_S:
+		return 2 + blockSize/8 + blockSize/16
+	case tensorTypeIQ4_NL:
+		return 2 + blockSize/2
+	case tensorTypeIQ3_S:
+		return 2 + blockSize/4 + blockSize/8 + blockSize/32 + 4
+	case tensorTypeIQ2_S:
+		return 2 + blockSize/4 + blockSize/16
+	case tensorTypeIQ4_XS:
+		return 2 + 2 + blockSize/2 + blockSize/64
+	case TensorTypeI8:
+		return 1
+	case TensorTypeI16:
+		return 2
+	case TensorTypeI32:
+		return 4
+	case TensorTypeI64:
+		return 8
+	case TensorTypeF64:
+		return 8
+	case tensorTypeIQ1_M:
+		return blockSize/8 + blockSize/16 + blockSize/32
+	case TensorTypeBF16:
+		return 2
+	default:
+		return 0
+	}
+}
+
+func (t Tensor) Elements() uint64 {
+	var count uint64 = 1
+	for _, n := range t.Shape {
+		count *= n
+	}
+	return count
+}
+
+func (t Tensor) Size() uint64 {
+	return t.Elements() * t.typeSize() / t.blockSize()
+}
+
+func (t Tensor) Type() string {
+	return TensorType(t.Kind).String()
+}
+
+type container interface {
+	Name() string
+	Decode(io.ReadSeeker) (model, error)
+}
+
+const (
+	// Magic constant for `ggml` files (unversioned).
+	FILE_MAGIC_GGML = 0x67676d6c
+	// Magic constant for `ggml` files (versioned, ggmf).
+	FILE_MAGIC_GGMF = 0x67676d66
+	// Magic constant for `ggml` files (versioned, ggjt).
+	FILE_MAGIC_GGJT = 0x67676a74
+	// Magic constant for `ggla` files (LoRA adapter).
+	FILE_MAGIC_GGLA = 0x67676C61
+	// Magic constant for `gguf` files (versioned, gguf)
+	FILE_MAGIC_GGUF_LE = 0x46554747
+	FILE_MAGIC_GGUF_BE = 0x47475546
+)
+
+var ErrUnsupportedFormat = errors.New("unsupported model format")
+
+func DetectContentType(b []byte) string {
+	switch binary.LittleEndian.Uint32(b[:4]) {
+	case FILE_MAGIC_GGML:
+		return "ggml"
+	case FILE_MAGIC_GGMF:
+		return "ggmf"
+	case FILE_MAGIC_GGJT:
+		return "ggjt"
+	case FILE_MAGIC_GGLA:
+		return "ggla"
+	case FILE_MAGIC_GGUF_LE, FILE_MAGIC_GGUF_BE:
+		return "gguf"
+	default:
+		return ""
+	}
+}
+
+// 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.
+func Decode(rs io.ReadSeeker, maxArraySize int) (*GGML, int64, error) {
+	rs = bufioutil.NewBufferedSeeker(rs, 32<<10)
+
+	var magic uint32
+	if err := binary.Read(rs, binary.LittleEndian, &magic); err != nil {
+		return nil, 0, err
+	}
+
+	var c container
+	switch magic {
+	case FILE_MAGIC_GGUF_LE:
+		c = &containerGGUF{ByteOrder: binary.LittleEndian, maxArraySize: maxArraySize}
+	case FILE_MAGIC_GGUF_BE:
+		c = &containerGGUF{ByteOrder: binary.BigEndian, maxArraySize: maxArraySize}
+	default:
+		return nil, 0, errors.New("invalid file magic")
+	}
+
+	model, err := c.Decode(rs)
+	if err != nil {
+		return nil, 0, err
+	}
+
+	offset, err := rs.Seek(0, io.SeekCurrent)
+	if err != nil {
+		return nil, 0, err
+	}
+
+	// final model type
+	return &GGML{
+		container: c,
+		model:     model,
+	}, offset, nil
+}
+
+func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType string) (kv []uint64, partialOffload, fullOffload uint64) {
+	embedding := f.KV().EmbeddingLength()
+	heads := f.KV().HeadCountMax()
+	headsKV := f.KV().HeadCountKVMax()
+	vocab := uint64(f.KV()["tokenizer.ggml.tokens"].(*array[string]).size)
+
+	embeddingHeads := f.KV().EmbeddingHeadCountMax()
+	embeddingHeadsK := f.KV().EmbeddingHeadCountK()
+	embeddingHeadsV := f.KV().EmbeddingHeadCountV()
+
+	layers := f.Tensors().GroupLayers()
+
+	bytesPerElement := kvCacheBytesPerElement(kvCacheType)
+	kv = make([]uint64, f.KV().BlockCount())
+	for i := range kv {
+		kv[i] = uint64(float64(context*(embeddingHeadsK+embeddingHeadsV)*headsKV) * bytesPerElement)
+	}
+
+	switch f.KV().Architecture() {
+	case "llama", "llama4":
+		fullOffload = max(
+			4*batch*(1+4*embedding+context*(1+heads)),
+			4*batch*(embedding+vocab),
+		)
+
+		partialOffload = 4 * batch * embedding
+		partialOffload += max(
+			4*batch*(1+embedding+max(context, embedding))+embedding*embedding*9/16+4*context*(batch*heads+embeddingHeads*headsKV),
+			4*batch*(embedding+vocab)+embedding*vocab*105/128,
+		)
+
+		if ffnGateExpsWeight, ok := layers["blk.0"]["ffn_gate_exps.weight"]; ok {
+			// mixtral 8x22b
+			ff := uint64(f.KV().Uint("feed_forward_length"))
+			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),
+			)
+		} else if ffnGateWeight, ok := layers["blk.0"]["ffn_gate.0.weight"]; ok {
+			// mixtral 8x7b
+			ffnGateWeight1 := ffnGateWeight.Shape[1]
+			fullOffload = 4 * batch * (2 + 3*embedding + context*(1+heads) + 2*headsKV + ffnGateWeight1)
+			partialOffload = max(
+				4*batch*(3+embeddingHeads*headsKV+embedding+context*(1+heads)+ffnGateWeight1)+(embedding*embedding+3*embedding*headsKV*ffnGateWeight1)*9/16,
+				4*batch*(1+2*embedding+context*(1+heads))+embedding*(6*context*headsKV/heads+embedding*9/16),
+			)
+		}
+	case "mllama":
+		var visionTokens, tiles uint64 = 1601, 4
+
+		crossAttentionLayers := f.KV().Ints("attention.cross_attention_layers")
+		for i := range kv {
+			if slices.Contains(crossAttentionLayers, int32(i)) {
+				kv[i] = headsKV * (embeddingHeadsK + embeddingHeadsV) *
+					4 * // sizeof(float32)
+					visionTokens *
+					tiles
+			}
+		}
+
+		fullOffload = max(
+			4*batch*(2+3*embedding+embeddingHeadsK*heads+context*(1+heads)),
+			// vocab graph
+			4*batch*(embedding+vocab),
+		)
+
+		var ropeFreqsCount uint64
+		if ropeFreqs, ok := f.Tensors().GroupLayers()["rope_freqs"]; ok {
+			if ropeFreqsWeights, ok := ropeFreqs["weights"]; ok {
+				ropeFreqsCount = ropeFreqsWeights.Elements()
+			}
+		}
+
+		partialOffload = max(
+			4*(batch*
+				(2*embedding+1+context*(1+heads)+embeddingHeadsK*heads)+
+				ropeFreqsCount+
+				embeddingHeadsK*context*headsKV),
+			// vocab graph
+			4*batch*(embedding+vocab)+embedding*vocab*105/128,
+		)
+	case "gemma", "gemma2", "gemma3":
+		fullOffload = max(
+			4*batch*(embedding+vocab),
+			4*batch*(2+context+context*heads+2*embedding+2*embeddingHeadsK*heads),
+		)
+
+		partialOffload = max(
+			4*embedding*batch+embedding*vocab*105/128+4*vocab*batch,
+			4*batch*(2*embedding+1+2*embeddingHeadsK*heads+context+context*heads)+
+				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),
+			4*batch*(2+4*embedding+context*(1+heads)),
+		)
+
+		partialOffload = max(
+			4*batch*(embedding+vocab)+embedding*vocab*105/128,
+			4*batch*(1+2*embedding+context*(1+heads))+4*embedding*context+embedding*embedding*9/16,
+		)
+	case "qwen2":
+		fullOffload = max(
+			4*batch*(embedding+vocab),
+			4*batch*(1+2*embedding+context+context*heads),
+		)
+
+		partialOffload = max(
+			4*batch*(embedding+vocab)+embedding*vocab*105/128,
+			4*(batch*(1+2*embedding+context*(1+heads))+embedding*(1+context)),
+		)
+	case "phi2":
+		fullOffload = max(
+			4*batch*(embedding+vocab),
+			4*batch*(1+4*embedding+context+context*heads),
+		)
+
+		partialOffload = max(
+			4*batch*(2*embedding+vocab)+embedding*vocab*105/128,
+			4*batch*(2+3*embedding+context+context*heads),
+		)
+	case "stablelm":
+		fullOffload = 4 * batch * (context*(1+heads) + 3*embedding + 2)
+		partialOffload = max(
+			4*batch*(vocab+2*embedding),
+			fullOffload,
+		)
+	case "deepseek2":
+		fullOffload = max(
+			4*batch*(3*embedding+vocab),
+			4*batch*(3*embedding+2+context*(1+headsKV)+2*embeddingHeadsK*headsKV),
+		)
+
+		partialOffload = max(
+			4*batch*(3*embedding+vocab)+embedding*vocab*105/128,
+			4*batch*(2*embedding+1+2*embeddingHeadsK*headsKV+context+context*headsKV)+4*embeddingHeadsK*context*headsKV+embedding*embeddingHeadsK*headsKV*9/16,
+		)
+	case "chatglm":
+		fullOffload = 4 * batch * (embedding + vocab)
+		partialOffload = 4*batch*(embedding+vocab) + embedding*vocab*105/128
+		if qkvBias, ok := layers["blk.0"]["attn_qkv.bias"]; ok {
+			fullOffload = max(
+				fullOffload,
+				4*batch*(2+
+					2*embedding+
+					context+
+					context*heads+
+					embeddingHeadsK*heads+
+					qkvBias.Shape[0]),
+			)
+
+			partialOffload = max(
+				partialOffload,
+				4*batch*(1+
+					2*embedding+
+					embeddingHeadsK*heads+
+					context+
+					context*heads)+
+					4*embeddingHeadsK*context+
+					4*context*embeddingHeadsK+
+					4*qkvBias.Shape[0],
+			)
+		}
+	}
+
+	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 "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 {
+	return slices.Contains([]string{"f16", "q8_0", "q4_0"}, cacheType)
+}
+
+// SupportsFlashAttention checks if the model supports flash attention
+func (f GGML) SupportsFlashAttention() bool {
+	_, isEmbedding := f.KV()[fmt.Sprintf("%s.pooling_type", f.KV().Architecture())]
+	if isEmbedding {
+		return false
+	}
+
+	// Check head counts match and are non-zero
+	headCountK := f.KV().EmbeddingHeadCountK()
+	headCountV := f.KV().EmbeddingHeadCountV()
+	return headCountK != 0 && headCountV != 0 && headCountK == headCountV
+}
+
+// kvCacheBytesPerElement returns the number of bytes per element for a given KV cache type
+func kvCacheBytesPerElement(cacheType string) float64 {
+	switch cacheType {
+	case "q8_0":
+		return 1 // 1/2 of fp16
+	case "q4_0":
+		return 0.5 // 1/4 of fp16
+	default:
+		return 2 // f16 (default)
+	}
+}

fs/ggml/ggml_test.go

@@ -0,0 +1,301 @@
+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)
+	}
+}
+
+func TestHeadCount(t *testing.T) {
+	valuesArray := []int32{1, 5, 3, 4}
+	cases := []struct {
+		kv   KV
+		want uint64
+	}{
+		{
+			kv: KV{
+				"general.architecture":     "abc",
+				"abc.attention.head_count": &array[int32]{values: valuesArray, size: len(valuesArray)},
+			},
+			want: uint64(5),
+		},
+		{
+			kv: KV{
+				"general.architecture":     "abc",
+				"abc.attention.head_count": uint32(3),
+			},
+			want: uint64(3),
+		},
+	}
+
+	for _, tt := range cases {
+		got := tt.kv.HeadCountMax()
+		if got != tt.want {
+			t.Errorf("unexpected max value: got=%d want=%d", got, tt.want)
+		}
+	}
+}

fs/ggml/gguf.go

@@ -1,4 +1,4 @@
-package llm
+package ggml
 
 import (
 	"bytes"
@@ -8,10 +8,13 @@ import (
 	"fmt"
 	"io"
 	"log/slog"
+	"maps"
+	"os"
+	"runtime"
 	"slices"
 	"strings"
 
-	"golang.org/x/exp/maps"
+	"golang.org/x/sync/errgroup"
 )
 
 type containerGGUF struct {
@@ -37,10 +40,6 @@ 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"
 }
@@ -110,9 +109,9 @@ func (llm *gguf) KV() KV {
 	return llm.kv
 }
 
-func (llm *gguf) Tensors() *Tensors {
+func (llm *gguf) Tensors() Tensors {
-	return &Tensors{
+	return Tensors{
-		Items:  llm.tensors,
+		items:  llm.tensors,
 		Offset: llm.tensorOffset,
 	}
 }
@@ -230,16 +229,13 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
 		}
 
 		llm.tensors = append(llm.tensors, &tensor)
-		llm.parameters += tensor.parameters()
+		llm.parameters += tensor.Elements()
 	}
 
 	// patch KV with parameter count
 	llm.kv["general.parameter_count"] = llm.parameters
 
-	alignment, ok := llm.kv["general.alignment"].(uint32)
+	alignment := llm.kv.Uint("general.alignment", 32)
-	if !ok {
-		alignment = 32
-	}
 
 	offset, err := rs.Seek(0, io.SeekCurrent)
 	if err != nil {
@@ -299,6 +295,23 @@ 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)
@@ -356,78 +369,44 @@ func writeGGUFString(w io.Writer, s string) error {
 	return err
 }
 
-type array struct {
+func readGGUFStringsData(llm *gguf, r io.Reader, a *array[string]) (any, error) {
-	size   int
+	for i := range a.size {
-	values []any
-}
-
-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 {
+			e, err := readGGUFString(llm, r)
+			if err != nil {
+				return nil, err
+			}
+
 			a.values[i] = e
+		} else {
+			discardGGUFString(llm, r)
 		}
 	}
 
 	return a, nil
 }
 
-func readGGUFArray(llm *gguf, r io.Reader) (*array, error) {
+type array[T any] struct {
-	if llm.Version == 1 {
+	// size is the actual size of the array
-		return readGGUFV1Array(llm, r)
+	size int
-	}
 
+	// 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
@@ -438,45 +417,55 @@ func readGGUFArray(llm *gguf, r io.Reader) (*array, error) {
 		return nil, err
 	}
 
-	a := &array{size: int(n)}
+	switch t {
-	if llm.canCollectArray(int(n)) {
+	case ggufTypeUint8:
-		a.values = make([]any, int(n))
+		a := newArray[uint8](int(n), llm.maxArraySize)
-	}
+		return readGGUFArrayData(llm, r, a)
-
+	case ggufTypeInt8:
-	for i := range n {
+		a := newArray[int8](int(n), llm.maxArraySize)
-		var e any
+		return readGGUFArrayData(llm, r, a)
-		switch t {
+	case ggufTypeUint16:
-		case ggufTypeUint8:
+		a := newArray[uint16](int(n), llm.maxArraySize)
-			e, err = readGGUF[uint8](llm, r)
+		return readGGUFArrayData(llm, r, a)
-		case ggufTypeInt8:
+	case ggufTypeInt16:
-			e, err = readGGUF[int8](llm, r)
+		a := newArray[int16](int(n), llm.maxArraySize)
-		case ggufTypeUint16:
+		return readGGUFArrayData(llm, r, a)
-			e, err = readGGUF[uint16](llm, r)
+	case ggufTypeUint32:
-		case ggufTypeInt16:
+		a := newArray[uint32](int(n), llm.maxArraySize)
-			e, err = readGGUF[int16](llm, r)
+		return readGGUFArrayData(llm, r, a)
-		case ggufTypeUint32:
+	case ggufTypeInt32:
-			e, err = readGGUF[uint32](llm, r)
+		a := newArray[int32](int(n), llm.maxArraySize)
-		case ggufTypeInt32:
+		return readGGUFArrayData(llm, r, a)
-			e, err = readGGUF[int32](llm, r)
+	case ggufTypeUint64:
-		case ggufTypeUint64:
+		a := newArray[uint64](int(n), llm.maxArraySize)
-			e, err = readGGUF[uint64](llm, r)
+		return readGGUFArrayData(llm, r, a)
-		case ggufTypeInt64:
+	case ggufTypeInt64:
-			e, err = readGGUF[int64](llm, r)
+		a := newArray[int64](int(n), llm.maxArraySize)
-		case ggufTypeFloat32:
+		return readGGUFArrayData(llm, r, a)
-			e, err = readGGUF[float32](llm, r)
+	case ggufTypeFloat32:
-		case ggufTypeFloat64:
+		a := newArray[float32](int(n), llm.maxArraySize)
-			e, err = readGGUF[float64](llm, r)
+		return readGGUFArrayData(llm, r, a)
-		case ggufTypeBool:
+	case ggufTypeFloat64:
-			e, err = readGGUF[bool](llm, r)
+		a := newArray[float64](int(n), llm.maxArraySize)
-		case ggufTypeString:
+		return readGGUFArrayData(llm, r, a)
-			if a.values != nil {
+	case ggufTypeBool:
-				e, err = readGGUFString(llm, r)
+		a := newArray[bool](int(n), llm.maxArraySize)
-			} else {
+		return readGGUFArrayData(llm, r, a)
-				err = discardGGUFString(llm, r)
+	case ggufTypeString:
-			}
+		a := newArray[string](int(n), llm.maxArraySize)
-		default:
+		if llm.Version == 1 {
-			return nil, fmt.Errorf("invalid array type: %d", t)
+			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 a.size {
+		e, err := readGGUF[T](llm, r)
 		if err != nil {
 			return nil, err
 		}
@@ -503,36 +492,51 @@ 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(ws io.WriteSeeker, kv KV, ts []Tensor) error {
+func WriteGGUF(f *os.File, kv KV, ts []*Tensor) error {
-	if err := binary.Write(ws, binary.LittleEndian, []byte("GGUF")); err != nil {
+	alignment := kv.Uint("general.alignment", 32)
+
+	if err := binary.Write(f, binary.LittleEndian, []byte("GGUF")); err != nil {
 		return err
 	}
 
-	if err := binary.Write(ws, binary.LittleEndian, uint32(3)); err != nil {
+	if err := binary.Write(f, binary.LittleEndian, uint32(3)); err != nil {
 		return err
 	}
 
-	if err := binary.Write(ws, binary.LittleEndian, uint64(len(ts))); err != nil {
+	if err := binary.Write(f, binary.LittleEndian, uint64(len(ts))); err != nil {
 		return err
 	}
 
-	if err := binary.Write(ws, binary.LittleEndian, uint64(len(kv))); err != nil {
+	if err := binary.Write(f, binary.LittleEndian, uint64(len(kv))); err != nil {
 		return err
 	}
 
-	keys := maps.Keys(kv)
+	keys := slices.Collect(maps.Keys(kv))
 	slices.Sort(keys)
 
 	for _, key := range keys {
-		if err := ggufWriteKV(ws, key, kv[key]); err != nil {
+		if err := ggufWriteKV(f, 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 {
@@ -543,22 +547,34 @@ func WriteGGUF(ws io.WriteSeeker, kv KV, ts []Tensor) error {
 	})
 
 	var s uint64
-	for _, t := range ts {
+	for i := range ts {
-		t.Offset = s
+		ts[i].Offset = s
-		if err := ggufWriteTensorInfo(ws, t); err != nil {
+		if err := ggufWriteTensorInfo(f, ts[i]); err != nil {
 			return err
 		}
-		s += t.Size()
+		s += ts[i].Size()
+		s += uint64(ggufPadding(int64(s), int64(alignment)))
 	}
 
-	var alignment int64 = 32
+	offset, err := f.Seek(0, io.SeekCurrent)
+	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 {
-		if err := ggufWriteTensor(ws, t, alignment); err != nil {
+		t := t
+		w := io.NewOffsetWriter(f, offset+int64(t.Offset))
+		g.Go(func() error {
+			_, err := t.WriteTo(w)
 			return err
-		}
+		})
 	}
 
-	return nil
+	return g.Wait()
 }
 
 func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
@@ -573,8 +589,10 @@ func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
 
 	var err error
 	switch v := v.(type) {
-	case uint32:
+	case uint32, FileType:
 		err = writeGGUF(ws, ggufTypeUint32, v)
+	case uint64:
+		err = writeGGUF(ws, ggufTypeUint64, v)
 	case float32:
 		err = writeGGUF(ws, ggufTypeFloat32, v)
 	case bool:
@@ -583,32 +601,20 @@ 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:
-		if err := binary.Write(ws, binary.LittleEndian, ggufTypeArray); err != nil {
+		err = writeGGUFArray(ws, ggufTypeString, v)
-			return err
+	case *array[string]:
-		}
+		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)
 	}
@@ -616,7 +622,7 @@ 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
@@ -630,8 +636,8 @@ func ggufWriteTensorInfo(ws io.WriteSeeker, t Tensor) error {
 		return err
 	}
 
-	for i := range len(t.Shape) {
+	for _, n := range t.Shape {
-		if err := binary.Write(ws, binary.LittleEndian, t.Shape[len(t.Shape)-i-1]); err != nil {
+		if err := binary.Write(ws, binary.LittleEndian, n); err != nil {
 			return err
 		}
 	}
@@ -643,20 +649,6 @@ 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

@@ -0,0 +1,63 @@
+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

@@ -0,0 +1,341 @@
+package ggml
+
+import (
+	"fmt"
+	"log/slog"
+	"strings"
+)
+
+// FileType is the Go equivalent to llama_ftype used for gguf file typing
+type FileType uint32
+
+const (
+	FileTypeF32 FileType = iota
+	FileTypeF16
+	FileTypeQ4_0
+	FileTypeQ4_1
+	fileTypeQ4_1_F16 // unused by GGML
+	fileTypeQ4_2     // unused by GGML
+	fileTypeQ4_3     // unused by GGML
+	FileTypeQ8_0
+	FileTypeQ5_0
+	FileTypeQ5_1
+	FileTypeQ2_K
+	FileTypeQ3_K_S
+	FileTypeQ3_K_M
+	FileTypeQ3_K_L
+	FileTypeQ4_K_S
+	FileTypeQ4_K_M
+	FileTypeQ5_K_S
+	FileTypeQ5_K_M
+	FileTypeQ6_K
+	fileTypeIQ2_XXS // not supported by ollama
+	fileTypeIQ2_XS  // not supported by ollama
+	FileTypeQ2_K_S
+	fileTypeIQ3_XS  // not supported by ollama
+	fileTypeIQ3_XXS // not supported by ollama
+	fileTypeIQ1_S   // not supported by ollama
+	fileTypeIQ4_NL  // not supported by ollama
+	fileTypeIQ3_S   // not supported by ollama
+	fileTypeIQ3_M   // not supported by ollama
+	fileTypeIQ2_S   // not supported by ollama
+	fileTypeIQ2_M   // not supported by ollama
+	fileTypeIQ4_XS  // not supported by ollama
+	fileTypeIQ1_M   // not supported by ollama
+	FileTypeBF16
+	fileTypeQ4_0_4_4 // unused by GGML
+	fileTypeQ4_0_4_8 // unused by GGML
+	fileTypeQ4_0_8_8 // unused by GGML
+	fileTypeTQ1_0    // not supported by ollama
+	fileTypeTQ2_0    // not supported by ollama
+
+	FileTypeUnknown = 1024
+)
+
+// ParseFileType parses the provided GGUF file type
+// Only Ollama supported types are considered valid
+func ParseFileType(s string) (FileType, error) {
+	switch s {
+	case "F32":
+		return FileTypeF32, nil
+	case "F16":
+		return FileTypeF16, nil
+	case "Q4_0":
+		return FileTypeQ4_0, nil
+	case "Q4_1":
+		return FileTypeQ4_1, nil
+	case "Q8_0":
+		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
+	case "Q4_K_M", "Q4_K":
+		return FileTypeQ4_K_M, nil
+	case "Q5_K_S":
+		return FileTypeQ5_K_S, nil
+	case "Q5_K_M", "Q5_K":
+		return FileTypeQ5_K_M, nil
+	case "Q6_K":
+		return FileTypeQ6_K, nil
+	case "Q2_K_S":
+		return FileTypeQ2_K_S, nil
+	case "BF16":
+		return FileTypeBF16, nil
+	default:
+		supportedFileTypes := []FileType{
+			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 {
+	switch t {
+	case FileTypeF32:
+		return "F32"
+	case FileTypeF16:
+		return "F16"
+	case FileTypeQ4_0:
+		return "Q4_0"
+	case FileTypeQ4_1:
+		return "Q4_1"
+	case FileTypeQ8_0:
+		return "Q8_0"
+	case FileTypeQ5_0:
+		return "Q5_0"
+	case FileTypeQ5_1:
+		return "Q5_1"
+	case FileTypeQ2_K:
+		return "Q2_K"
+	case FileTypeQ3_K_S:
+		return "Q3_K_S"
+	case FileTypeQ3_K_M:
+		return "Q3_K_M"
+	case FileTypeQ3_K_L:
+		return "Q3_K_L"
+	case FileTypeQ4_K_S:
+		return "Q4_K_S"
+	case FileTypeQ4_K_M:
+		return "Q4_K_M"
+	case FileTypeQ5_K_S:
+		return "Q5_K_S"
+	case FileTypeQ5_K_M:
+		return "Q5_K_M"
+	case FileTypeQ6_K:
+		return "Q6_K"
+	case FileTypeQ2_K_S:
+		return "Q2_K_S"
+	case FileTypeBF16:
+		return "BF16"
+	default:
+		return "unknown"
+	}
+}
+
+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 1.23.4
+go 1.24.0
 
 require (
 	github.com/containerd/console v1.0.3
@@ -11,18 +11,20 @@ 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 v0.10.0
+	golang.org/x/sync v0.12.0
 )
 
 require (
 	github.com/agnivade/levenshtein v1.1.1
 	github.com/d4l3k/go-bfloat16 v0.0.0-20211005043715-690c3bdd05f1
+	github.com/dlclark/regexp2 v1.11.4
 	github.com/emirpasic/gods/v2 v2.0.0-alpha
 	github.com/google/go-cmp v0.6.0
 	github.com/mattn/go-runewidth v0.0.14
 	github.com/nlpodyssey/gopickle v0.3.0
 	github.com/pdevine/tensor v0.0.0-20240510204454-f88f4562727c
 	golang.org/x/image v0.22.0
+	golang.org/x/tools v0.30.0
 )
 
 require (
@@ -68,12 +70,12 @@ 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 v0.31.0
+	golang.org/x/crypto v0.36.0
-	golang.org/x/exp v0.0.0-20231110203233-9a3e6036ecaa
+	golang.org/x/exp v0.0.0-20250218142911-aa4b98e5adaa
-	golang.org/x/net v0.25.0 // indirect
+	golang.org/x/net v0.38.0 // indirect
-	golang.org/x/sys v0.28.0
+	golang.org/x/sys v0.31.0
-	golang.org/x/term v0.27.0
+	golang.org/x/term v0.30.0
-	golang.org/x/text v0.21.0
+	golang.org/x/text v0.23.0
 	google.golang.org/protobuf v1.34.1
 	gopkg.in/yaml.v3 v3.0.1 // indirect
 )

go.sum

@@ -42,6 +42,8 @@ github.com/davecgh/go-spew v1.1.1 h1:vj9j/u1bqnvCEfJOwUhtlOARqs3+rkHYY13jYWTU97c
 github.com/davecgh/go-spew v1.1.1/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
 github.com/dgryski/trifles v0.0.0-20200323201526-dd97f9abfb48 h1:fRzb/w+pyskVMQ+UbP35JkH8yB7MYb4q/qhBarqZE6g=
 github.com/dgryski/trifles v0.0.0-20200323201526-dd97f9abfb48/go.mod h1:if7Fbed8SFyPtHLHbg49SI7NAdJiC5WIA09pe59rfAA=
+github.com/dlclark/regexp2 v1.11.4 h1:rPYF9/LECdNymJufQKmri9gV604RvvABwgOA8un7yAo=
+github.com/dlclark/regexp2 v1.11.4/go.mod h1:DHkYz0B9wPfa6wondMfaivmHpzrQ3v9q8cnmRbL6yW8=
 github.com/emirpasic/gods/v2 v2.0.0-alpha h1:dwFlh8pBg1VMOXWGipNMRt8v96dKAIvBehtCt6OtunU=
 github.com/emirpasic/gods/v2 v2.0.0-alpha/go.mod h1:W0y4M2dtBB9U5z3YlghmpuUhiaZT2h6yoeE+C1sCp6A=
 github.com/envoyproxy/go-control-plane v0.9.0/go.mod h1:YTl/9mNaCwkRvm6d1a2C3ymFceY/DCBVvsKhRF0iEA4=
@@ -212,16 +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 v0.31.0 h1:ihbySMvVjLAeSH1IbfcRTkD/iNscyz8rGzjF/E5hV6U=
+golang.org/x/crypto v0.36.0 h1:AnAEvhDddvBdpY+uR+MyHmuZzzNqXSe/GvuDeob5L34=
-golang.org/x/crypto v0.31.0/go.mod h1:kDsLvtWBEx7MV9tJOj9bnXsPbxwJQ6csT/x4KIN4Ssk=
+golang.org/x/crypto v0.36.0/go.mod h1:Y4J0ReaxCR1IMaabaSMugxJES1EpwhBHhv2bDHklZvc=
 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 v0.0.0-20231110203233-9a3e6036ecaa h1:FRnLl4eNAQl8hwxVVC17teOw8kdjVDVAiFMtgUdTSRQ=
+golang.org/x/exp v0.0.0-20250218142911-aa4b98e5adaa h1:t2QcU6V556bFjYgu4L6C+6VrCPyJZ+eyRsABUPs1mz4=
-golang.org/x/exp v0.0.0-20231110203233-9a3e6036ecaa/go.mod h1:zk2irFbV9DP96SEBUUAy67IdHUaZuSnrz1n472HUCLE=
+golang.org/x/exp v0.0.0-20250218142911-aa4b98e5adaa/go.mod h1:BHOTPb3L19zxehTsLoJXVaTktb06DFgmdW6Wb9s8jqk=
 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=
@@ -255,8 +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 v0.25.0 h1:d/OCCoBEUq33pjydKrGQhw7IlUPI2Oylr+8qLx49kac=
+golang.org/x/net v0.38.0 h1:vRMAPTMaeGqVhG5QyLJHqNDwecKTomGeqbnfZyKlBI8=
-golang.org/x/net v0.25.0/go.mod h1:JkAGAh7GEvH74S6FOH42FLoXpXbE/aqXSrIQjXgsiwM=
+golang.org/x/net v0.38.0/go.mod h1:ivrbrMbzFq5J41QOQh0siUuly180yBYtLp+CKbEaFx8=
 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=
@@ -266,8 +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 v0.10.0 h1:3NQrjDixjgGwUOCaF8w2+VYHv0Ve/vGYSbdkTa98gmQ=
+golang.org/x/sync v0.12.0 h1:MHc5BpPuC30uJk597Ri8TV3CNZcTLu6B6z4lJy+g6Jw=
-golang.org/x/sync v0.10.0/go.mod h1:Czt+wKu1gCyEFDUtn0jG5QVvpJ6rzVqr5aXyt9drQfk=
+golang.org/x/sync v0.12.0/go.mod h1:1dzgHSNfp02xaA81J2MS99Qcpr2w7fw1gpm99rleRqA=
 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=
@@ -283,17 +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 v0.28.0 h1:Fksou7UEQUWlKvIdsqzJmUmCX3cZuD2+P3XyyzwMhlA=
+golang.org/x/sys v0.31.0 h1:ioabZlmFYtWhL+TRYpcnNlLwhyxaM9kWTDEmfnprqik=
-golang.org/x/sys v0.28.0/go.mod h1:/VUhepiaJMQUp4+oa/7Zr1D23ma6VTLIYjOOTFZPUcA=
+golang.org/x/sys v0.31.0/go.mod h1:BJP2sWEmIv4KK5OTEluFJCKSidICx8ciO85XgH3Ak8k=
 golang.org/x/term v0.0.0-20201126162022-7de9c90e9dd1/go.mod h1:bj7SfCRtBDWHUb9snDiAeCFNEtKQo2Wmx5Cou7ajbmo=
-golang.org/x/term v0.27.0 h1:WP60Sv1nlK1T6SupCHbXzSaN0b9wUmsPoRS9b61A23Q=
+golang.org/x/term v0.30.0 h1:PQ39fJZ+mfadBm0y5WlL4vlM7Sx1Hgf13sMIY2+QS9Y=
-golang.org/x/term v0.27.0/go.mod h1:iMsnZpn0cago0GOrHO2+Y7u7JPn5AylBrcoWkElMTSM=
+golang.org/x/term v0.30.0/go.mod h1:NYYFdzHoI5wRh/h5tDMdMqCqPJZEuNqVR5xJLd/n67g=
 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 v0.21.0 h1:zyQAAkrwaneQ066sspRyJaG9VNi/YJ1NfzcGB3hZ/qo=
+golang.org/x/text v0.23.0 h1:D71I7dUrlY+VX0gQShAThNGHFxZ13dGLBHQLVl1mJlY=
-golang.org/x/text v0.21.0/go.mod h1:4IBbMaMmOPCJ8SecivzSH54+73PCFmPWxNTLm+vZkEQ=
+golang.org/x/text v0.23.0/go.mod h1:/BLNzu4aZCJ1+kcD0DNRotWKage4q2rGVAg4o22unh4=
 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=
@@ -307,6 +309,8 @@ 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

@@ -0,0 +1,412 @@
+//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 TestOrcaMiniBlueSky(t *testing.T) {
+func TestBlueSky(t *testing.T) {
 	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
 	defer cancel()
 	// Set up the test data
 	req := api.GenerateRequest{
-		Model:  "orca-mini",
+		Model:  smol,
 		Prompt: "why is the sky blue?",
 		Stream: &stream,
-		Options: map[string]interface{}{
+		Options: map[string]any{
 			"temperature": 0,
 			"seed":        123,
 		},
@@ -31,6 +31,7 @@ func TestOrcaMiniBlueSky(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
@@ -39,7 +40,7 @@ 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]interface{}{
+		Options: map[string]any{
 			"temperature": 0,
 			"seed":        123,
 			// Workaround deepseek context shifting bug
@@ -61,7 +62,7 @@ func TestExtendedUnicodeOutput(t *testing.T) {
 		Model:  "gemma2:2b",
 		Prompt: "Output some smily face emoji",
 		Stream: &stream,
-		Options: map[string]interface{}{
+		Options: map[string]any{
 			"temperature": 0,
 			"seed":        123,
 		},
@@ -93,10 +94,10 @@ func TestUnicodeModelDir(t *testing.T) {
 	defer cancel()
 
 	req := api.GenerateRequest{
-		Model:  "orca-mini",
+		Model:  smol,
 		Prompt: "why is the sky blue?",
 		Stream: &stream,
-		Options: map[string]interface{}{
+		Options: map[string]any{
 			"temperature": 0,
 			"seed":        123,
 		},

integration/concurrency_test.go

@@ -21,11 +21,11 @@ func TestMultiModelConcurrency(t *testing.T) {
 	var (
 		req = [2]api.GenerateRequest{
 			{
-				Model:     "orca-mini",
+				Model:     "llama3.2:1b",
 				Prompt:    "why is the ocean blue?",
 				Stream:    &stream,
 				KeepAlive: &api.Duration{Duration: 10 * time.Second},
-				Options: map[string]interface{}{
+				Options: map[string]any{
 					"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]interface{}{
+				Options: map[string]any{
 					"seed":        42,
 					"temperature": 0.0,
 				},
@@ -67,7 +67,7 @@ func TestMultiModelConcurrency(t *testing.T) {
 	wg.Wait()
 }
 
-func TestIntegrationConcurrentPredictOrcaMini(t *testing.T) {
+func TestIntegrationConcurrentPredict(t *testing.T) {
 	req, resp := GenerateRequests()
 	reqLimit := len(req)
 	iterLimit := 5
@@ -117,6 +117,9 @@ 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
@@ -125,8 +128,8 @@ func TestMultiModelStress(t *testing.T) {
 
 	smallModels := []model{
 		{
-			name: "orca-mini",
+			name: "llama3.2:1b",
-			size: 2992 * format.MebiByte,
+			size: 2876 * 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]interface{}{
+		Options: map[string]any{
 			"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]interface{}{
+		Options: map[string]any{
 			"temperature": 0,
 			"seed":        123,
 			"num_ctx":     128,

integration/embed_test.go

@@ -34,13 +34,15 @@ 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, t, req)
+	res, err := embeddingTestHelper(ctx, client, t, req)
 
 	if err != nil {
 		t.Fatalf("error: %v", err)
@@ -62,13 +64,15 @@ 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, t, req)
+	res, err := embedTestHelper(ctx, client, t, req)
 
 	if err != nil {
 		t.Fatalf("error: %v", err)
@@ -98,13 +102,15 @@ 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, t, req)
+	res, err := embedTestHelper(ctx, client, t, req)
 
 	if err != nil {
 		t.Fatalf("error: %v", err)
@@ -144,6 +150,8 @@ 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
 
@@ -182,7 +190,7 @@ func TestAllMiniLMEmbedTruncate(t *testing.T) {
 	res := make(map[string]*api.EmbedResponse)
 
 	for _, req := range reqs {
-		response, err := embedTestHelper(ctx, t, req.Request)
+		response, err := embedTestHelper(ctx, client, t, req.Request)
 		if err != nil {
 			t.Fatalf("error: %v", err)
 		}
@@ -198,7 +206,7 @@ func TestAllMiniLMEmbedTruncate(t *testing.T) {
 	}
 
 	// check that truncate set to false returns an error if context length is exceeded
-	_, err := embedTestHelper(ctx, t, api.EmbedRequest{
+	_, err := embedTestHelper(ctx, client, t, api.EmbedRequest{
 		Model:    "all-minilm",
 		Input:    "why is the sky blue?",
 		Truncate: &truncFalse,
@@ -210,9 +218,7 @@ func TestAllMiniLMEmbedTruncate(t *testing.T) {
 	}
 }
 
-func embeddingTestHelper(ctx context.Context, t *testing.T, req api.EmbeddingRequest) (*api.EmbeddingResponse, error) {
+func embeddingTestHelper(ctx context.Context, client *api.Client, 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)
 	}
@@ -226,9 +232,7 @@ func embeddingTestHelper(ctx context.Context, t *testing.T, req api.EmbeddingReq
 	return response, nil
 }
 
-func embedTestHelper(ctx context.Context, t *testing.T, req api.EmbedRequest) (*api.EmbedResponse, error) {
+func embedTestHelper(ctx context.Context, client *api.Client, 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

@@ -12,14 +12,63 @@ import (
 	"github.com/stretchr/testify/require"
 )
 
-func TestIntegrationLlava(t *testing.T) {
+func TestVisionModels(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:  "llava:7b",
+		Model: "gemma3:4b",
+		// 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]interface{}{
+		Options: map[string]any{
 			"seed":        42,
 			"temperature": 0.0,
 		},
@@ -39,33 +88,6 @@ func TestIntegrationLlava(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:  "orca-mini",
+			Model:  smol,
 			Prompt: "why is the ocean blue?",
 			Stream: &stream,
-			Options: map[string]interface{}{
+			Options: map[string]any{
 				"seed":        42,
 				"temperature": 0.0,
 			},
 		}, {
-			Model:  "orca-mini",
+			Model:  smol,
 			Prompt: "what is the origin of the us thanksgiving holiday?",
 			Stream: &stream,
-			Options: map[string]interface{}{
+			Options: map[string]any{
 				"seed":        42,
 				"temperature": 0.0,
 			},
 		},
 	}
 	resp = [2][]string{
-		{"sunlight"},
+		{"sunlight", "scattering", "interact"},
 		{"england", "english", "massachusetts", "pilgrims"},
 	}
 )
 
-func TestIntegrationSimpleOrcaMini(t *testing.T) {
+func TestIntegrationSimple(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:  "orca-mini",
+		Model:  smol,
 		Prompt: "write a long historical fiction story about christopher columbus.  use at least 10 facts from his actual journey",
-		Options: map[string]interface{}{
+		Options: map[string]any{
 			"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(5 * time.Millisecond)
+	time.Sleep(10 * 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

@@ -0,0 +1,184 @@
+//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

@@ -0,0 +1,130 @@
+//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

@@ -24,9 +24,14 @@ 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()
 }
@@ -140,7 +145,7 @@ func PullIfMissing(ctx context.Context, client *api.Client, modelName string) er
 
 	showCtx, cancel := context.WithDeadlineCause(
 		ctx,
-		time.Now().Add(10*time.Second),
+		time.Now().Add(20*time.Second),
 		fmt.Errorf("show for existing model %s took too long", modelName),
 	)
 	defer cancel()
@@ -157,7 +162,7 @@ func PullIfMissing(ctx context.Context, client *api.Client, modelName string) er
 	}
 	slog.Info("model missing", "model", modelName)
 
-	stallDuration := 30 * time.Second // This includes checksum verification, which can take a while on larger models
+	stallDuration := 60 * time.Second // This includes checksum verification, which can take a while on larger models, and slower systems
 	stallTimer := time.NewTimer(stallDuration)
 	fn := func(resp api.ProgressResponse) error {
 		// fmt.Print(".")
@@ -212,6 +217,7 @@ 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)
@@ -283,51 +289,51 @@ func DoGenerate(ctx context.Context, t *testing.T, client *api.Client, genReq ap
 }
 
 // Generate a set of requests
-// By default each request uses orca-mini as the model
+// By default each request uses llama3.2 as the model
 func GenerateRequests() ([]api.GenerateRequest, [][]string) {
 	return []api.GenerateRequest{
 			{
-				Model:     "orca-mini",
+				Model:     smol,
 				Prompt:    "why is the ocean blue?",
 				Stream:    &stream,
 				KeepAlive: &api.Duration{Duration: 10 * time.Second},
-				Options: map[string]interface{}{
+				Options: map[string]any{
 					"seed":        42,
 					"temperature": 0.0,
 				},
 			}, {
-				Model:     "orca-mini",
+				Model:     smol,
 				Prompt:    "why is the color of dirt brown?",
 				Stream:    &stream,
 				KeepAlive: &api.Duration{Duration: 10 * time.Second},
-				Options: map[string]interface{}{
+				Options: map[string]any{
 					"seed":        42,
 					"temperature": 0.0,
 				},
 			}, {
-				Model:     "orca-mini",
+				Model:     smol,
 				Prompt:    "what is the origin of the us thanksgiving holiday?",
 				Stream:    &stream,
 				KeepAlive: &api.Duration{Duration: 10 * time.Second},
-				Options: map[string]interface{}{
+				Options: map[string]any{
 					"seed":        42,
 					"temperature": 0.0,
 				},
 			}, {
-				Model:     "orca-mini",
+				Model:     smol,
 				Prompt:    "what is the origin of independence day?",
 				Stream:    &stream,
 				KeepAlive: &api.Duration{Duration: 10 * time.Second},
-				Options: map[string]interface{}{
+				Options: map[string]any{
 					"seed":        42,
 					"temperature": 0.0,
 				},
 			}, {
-				Model:     "orca-mini",
+				Model:     smol,
 				Prompt:    "what is the composition of air?",
 				Stream:    &stream,
 				KeepAlive: &api.Duration{Duration: 10 * time.Second},
-				Options: map[string]interface{}{
+				Options: map[string]any{
 					"seed":        42,
 					"temperature": 0.0,
 				},
@@ -341,3 +347,26 @@ 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))
+}