change name to context_length

Co-authored-by: Michael Yang <mxyng@pm.me>

.github/workflows/release.yaml

@@ -1,5 +1,9 @@
 name: release
 
+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
+
 on:
   push:
     tags:
@@ -8,7 +12,7 @@ on:
 jobs:
   # Full build of the Mac assets
   build-darwin:
-    runs-on: macos-12
+    runs-on: macos-13
     environment: release
     steps:
       - uses: actions/checkout@v4
@@ -39,8 +43,8 @@ jobs:
           APPLE_PASSWORD: ${{ secrets.APPLE_PASSWORD }}
           APPLE_TEAM_ID: ${{ vars.APPLE_TEAM_ID }}
           APPLE_ID: ${{ vars.APPLE_ID }}
-          SDKROOT: /Applications/Xcode_13.4.1.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX.sdk
-          DEVELOPER_DIR: /Applications/Xcode_13.4.1.app/Contents/Developer
+          SDKROOT: /Applications/Xcode_14.1.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX.sdk
+          DEVELOPER_DIR: /Applications/Xcode_14.1.0.app/Contents/Developer
         run: |
           ./scripts/build_darwin.sh
 
@@ -60,70 +64,33 @@ jobs:
       KEY_CONTAINER: ${{ vars.KEY_CONTAINER }}
     steps:
       - uses: actions/checkout@v4
+      - name: Set make jobs default
+        run: |
+          echo "MAKEFLAGS=--jobs=$((Get-ComputerInfo -Property CsProcessors).CsProcessors.NumberOfCores)" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
       - name: Set Version
         shell: bash
         run: echo "VERSION=${GITHUB_REF_NAME#v}" >> $GITHUB_ENV
-      - uses: 'google-github-actions/auth@v2'
-        with:
-          project_id: 'ollama'
-          credentials_json: '${{ secrets.GOOGLE_SIGNING_CREDENTIALS }}'
-      - run: echo "${{ vars.OLLAMA_CERT }}" > ollama_inc.crt
-      - name: install Windows SDK 8.1 to get signtool
+      - name: Add msys paths
         run: |
-          $ErrorActionPreference = "Stop"
-          write-host "downloading SDK"
-          Invoke-WebRequest -Uri "https://go.microsoft.com/fwlink/p/?LinkId=323507" -OutFile "${env:RUNNER_TEMP}\sdksetup.exe"
-          Start-Process "${env:RUNNER_TEMP}\sdksetup.exe" -ArgumentList @("/q") -NoNewWindow -Wait
-          write-host "Win SDK 8.1 installed"
-          gci -path 'C:\Program Files (x86)\Windows Kits\' -r -fi 'signtool.exe'
-      - name: install signing plugin
-        run: |
-          $ErrorActionPreference = "Stop"
-          write-host "downloading plugin"
-          Invoke-WebRequest -Uri "https://github.com/GoogleCloudPlatform/kms-integrations/releases/download/cng-v1.0/kmscng-1.0-windows-amd64.zip" -OutFile "${env:RUNNER_TEMP}\plugin.zip"
-          Expand-Archive -Path "${env:RUNNER_TEMP}\plugin.zip" -DestinationPath ${env:RUNNER_TEMP}\plugin\
-          write-host "Installing plugin"
-          & "${env:RUNNER_TEMP}\plugin\*\kmscng.msi" /quiet
-          write-host "plugin installed"
-      - name: Install msys2
-        run: |
-          $msys2_url="https://github.com/msys2/msys2-installer/releases/download/2024-07-27/msys2-x86_64-20240727.exe"
-          write-host "Downloading msys2"
-          Invoke-WebRequest -Uri "${msys2_url}" -OutFile "${env:RUNNER_TEMP}\msys2.exe"
-          write-host "Installing msys2"
-          Start-Process "${env:RUNNER_TEMP}\msys2.exe" -ArgumentList @("in", "--confirm-command", "--accept-messages", "--root", "C:/msys64") -NoNewWindow -Wait
           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", "make") -NoNewWindow -Wait
-          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-      - name: verify tools
-        run: |
-          get-command gcc
-          gcc --version
-          get-command make
-          make --version
+          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: go get ./...
       - run: |
-          $gopath=(get-command go).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;$env:PATH"
-          $cores = (Get-ComputerInfo -Property CsProcessors).CsProcessors.NumberOfCores
           if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
-          make -j $cores
+          make dist
         name: make
       - uses: actions/upload-artifact@v4
         with:
           name: generate-windows-cpu
           path: |
-            build/**/*
-            build/**/*.a
             dist/windows-amd64/**
 
   # ROCm generation step
@@ -134,82 +101,53 @@ jobs:
       KEY_CONTAINER: ${{ vars.KEY_CONTAINER }}
     steps:
       - uses: actions/checkout@v4
+      - name: Set make jobs default
+        run: |
+          echo "MAKEFLAGS=--jobs=$((Get-ComputerInfo -Property CsProcessors).CsProcessors.NumberOfCores)" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
       - name: Set Version
         shell: bash
         run: echo "VERSION=${GITHUB_REF_NAME#v}" >> $GITHUB_ENV
-      - uses: 'google-github-actions/auth@v2'
-        with:
-          project_id: 'ollama'
-          credentials_json: '${{ secrets.GOOGLE_SIGNING_CREDENTIALS }}'
-      - run: echo "${{ vars.OLLAMA_CERT }}" > ollama_inc.crt
-      - name: install Windows SDK 8.1 to get signtool
+      - name: Add msys paths
         run: |
-          $ErrorActionPreference = "Stop"
-          write-host "downloading SDK"
-          Invoke-WebRequest -Uri "https://go.microsoft.com/fwlink/p/?LinkId=323507" -OutFile "${env:RUNNER_TEMP}\sdksetup.exe"
-          Start-Process "${env:RUNNER_TEMP}\sdksetup.exe" -ArgumentList @("/q") -NoNewWindow -Wait
-          write-host "Win SDK 8.1 installed"
-          gci -path 'C:\Program Files (x86)\Windows Kits\' -r -fi 'signtool.exe'
-      - name: install signing plugin
-        run: |
-          $ErrorActionPreference = "Stop"
-          write-host "downloading plugin"
-          Invoke-WebRequest -Uri "https://github.com/GoogleCloudPlatform/kms-integrations/releases/download/cng-v1.0/kmscng-1.0-windows-amd64.zip" -OutFile "${env:RUNNER_TEMP}\plugin.zip"
-          Expand-Archive -Path "${env:RUNNER_TEMP}\plugin.zip" -DestinationPath ${env:RUNNER_TEMP}\plugin\
-          write-host "Installing plugin"
-          & "${env:RUNNER_TEMP}\plugin\*\kmscng.msi" /quiet
-          write-host "plugin installed"
-      - name: Install msys2
-        run: |
-          $msys2_url="https://github.com/msys2/msys2-installer/releases/download/2024-07-27/msys2-x86_64-20240727.exe"
-          write-host "Downloading msys2"
-          Invoke-WebRequest -Uri "${msys2_url}" -OutFile "${env:RUNNER_TEMP}\msys2.exe"
-          write-host "Installing msys2"
-          Start-Process "${env:RUNNER_TEMP}\msys2.exe" -ArgumentList @("in", "--confirm-command", "--accept-messages", "--root", "C:/msys64") -NoNewWindow -Wait
           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", "make") -NoNewWindow -Wait
-          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-      - name: verify tools
-        run: |
-          get-command gcc
-          gcc --version
-          get-command make
-          make --version
+          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
-      - name: 'Install ROCm'
+      # 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: |
           $ErrorActionPreference = "Stop"
-          write-host "downloading AMD HIP Installer"
-          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
-          write-host "Installing AMD HIP"
-          Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
-          write-host "Completed AMD HIP"
+          Invoke-WebRequest -Uri "${env:ROCM_WINDOWS_URL}" -OutFile "rocm-install.exe"
+      - name: 'Install ROCm'
+        run: |
+          Start-Process "rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
       - name: 'Verify ROCm'
         run: |
           & 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version
-      - run: go get ./...
-      - run: |
-          $gopath=(get-command go).source | split-path -parent
+          echo "HIP_PATH=$(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: 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'
-          $env:CMAKE_SYSTEM_VERSION="10.0.22621.0"
-          $env:PATH="$gopath;$env:PATH"
-          $env:OLLAMA_SKIP_CPU_GENERATE="1"
-          $env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path)
-          $cores = (Get-ComputerInfo -Property CsProcessors).CsProcessors.NumberOfCores
           if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
-          make -j $cores
-        name: make
+          make help-runners
+          make dist_rocm
       - uses: actions/upload-artifact@v4
         with:
           name: generate-windows-rocm
           path: |
-            build/**/*
             dist/windows-amd64/**
 
   # CUDA generation step
@@ -219,39 +157,20 @@ jobs:
     strategy:
       matrix:
         cuda:
-          - version: "11"
-            url: 'https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda_11.3.1_465.89_win10.exe'
-          - version: "12"
-            url: 'https://developer.download.nvidia.com/compute/cuda/12.4.0/local_installers/cuda_12.4.0_551.61_windows.exe'
+          - version: "11.3"
+            url: https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda_11.3.1_465.89_win10.exe
+          - version: "12.4"
+            url: https://developer.download.nvidia.com/compute/cuda/12.4.0/local_installers/cuda_12.4.0_551.61_windows.exe
     env:
       KEY_CONTAINER: ${{ vars.KEY_CONTAINER }}
     steps:
       - uses: actions/checkout@v4
+      - name: Set make jobs default
+        run: |
+          echo "MAKEFLAGS=--jobs=$((Get-ComputerInfo -Property CsProcessors).CsProcessors.NumberOfCores)" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
       - name: Set Version
         shell: bash
         run: echo "VERSION=${GITHUB_REF_NAME#v}" >> $GITHUB_ENV
-      - uses: 'google-github-actions/auth@v2'
-        with:
-          project_id: 'ollama'
-          credentials_json: '${{ secrets.GOOGLE_SIGNING_CREDENTIALS }}'
-      - run: echo "${{ vars.OLLAMA_CERT }}" > ollama_inc.crt
-      - name: install Windows SDK 8.1 to get signtool
-        run: |
-          $ErrorActionPreference = "Stop"
-          write-host "downloading SDK"
-          Invoke-WebRequest -Uri "https://go.microsoft.com/fwlink/p/?LinkId=323507" -OutFile "${env:RUNNER_TEMP}\sdksetup.exe"
-          Start-Process "${env:RUNNER_TEMP}\sdksetup.exe" -ArgumentList @("/q") -NoNewWindow -Wait
-          write-host "Win SDK 8.1 installed"
-          gci -path 'C:\Program Files (x86)\Windows Kits\' -r -fi 'signtool.exe'
-      - name: install signing plugin
-        run: |
-          $ErrorActionPreference = "Stop"
-          write-host "downloading plugin"
-          Invoke-WebRequest -Uri "https://github.com/GoogleCloudPlatform/kms-integrations/releases/download/cng-v1.0/kmscng-1.0-windows-amd64.zip" -OutFile "${env:RUNNER_TEMP}\plugin.zip"
-          Expand-Archive -Path "${env:RUNNER_TEMP}\plugin.zip" -DestinationPath ${env:RUNNER_TEMP}\plugin\
-          write-host "Installing plugin"
-          & "${env:RUNNER_TEMP}\plugin\*\kmscng.msi" /quiet
-          write-host "plugin installed"
       - name: Install msys2
         run: |
           $msys2_url="https://github.com/msys2/msys2-installer/releases/download/2024-07-27/msys2-x86_64-20240727.exe"
@@ -274,40 +193,42 @@ jobs:
         with:
           go-version-file: go.mod
           cache: true
-      - name: 'Install CUDA ${{ matrix.cuda.version }}'
+      # CUDA installation steps
+      - name: 'Cache CUDA installer'
+        id: cache-cuda
+        uses: actions/cache@v4
+        with:
+          path: cuda-install.exe
+          key: ${{ matrix.cuda.url }}
+      - name: 'Conditionally Download CUDA'
+        if: steps.cache-cuda.outputs.cache-hit != 'true'
         run: |
           $ErrorActionPreference = "Stop"
-          write-host "downloading CUDA Installer"
-          Invoke-WebRequest -Uri "${{ matrix.cuda.url }}" -OutFile "${env:RUNNER_TEMP}\cuda-install.exe"
-          write-host "Installing CUDA"
-          Start-Process "${env:RUNNER_TEMP}\cuda-install.exe" -ArgumentList '-s' -NoNewWindow -Wait
-          write-host "Completed CUDA"
+          Invoke-WebRequest -Uri "${{ matrix.cuda.url }}" -OutFile "cuda-install.exe"
+      - name: 'Install CUDA'
+        run: |
+          $subpackages = @("cudart", "nvcc", "cublas", "cublas_dev") | foreach-object {"${_}_${{ matrix.cuda.version }}"}
+          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" >> $env:GITHUB_PATH
-          echo "CUDA_PATH=$cudaPath" >> $env:GITHUB_ENV
-          echo "CUDA_PATH_V${cudaVer}=$cudaPath" >> $env:GITHUB_ENV
-          echo "CUDA_PATH_VX_Y=CUDA_PATH_V${cudaVer}" >> $env:GITHUB_ENV
-      - name: 'Verify CUDA'
-        run: nvcc -V
-      - run: go get ./...
-      - name: make
+          echo "$cudaPath\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+          echo "CUDA_PATH=$cudaPath" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+          echo "CUDA_PATH_V${cudaVer}=$cudaPath" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+          echo "CUDA_PATH_VX_Y=CUDA_PATH_V${cudaVer}" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+
+      - name: make cuda runner
         run: |
-          $gopath=(get-command go).source | split-path -parent
-          $cudabin=(get-command nvcc).source | split-path
           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;$cudabin;$env:PATH"
-          $env:OLLAMA_SKIP_CPU_GENERATE="1"
-          $cores = (Get-ComputerInfo -Property CsProcessors).CsProcessors.NumberOfCores
           if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
-          make -j $cores
+          make dist_cuda_v$(($env:CUDA_PATH | split-path -leaf) -replace 'v(\d+).*', '$1')
       - uses: actions/upload-artifact@v4
         with:
           name: generate-windows-cuda-${{ matrix.cuda.version }}
           path: |
-            build/**/*
             dist/windows-amd64/**
 
   # windows arm64 generate, go build, and zip file (no installer)
@@ -450,7 +371,7 @@ jobs:
           $env:PATH="$gopath;$gccpath;$env:PATH"
           echo $env:PATH
           $env:ARCH="arm64"
-          .\scripts\build_windows.ps1 buildOllama buildApp gatherDependencies distZip
+          .\scripts\build_windows.ps1 buildOllama buildApp gatherDependencies sign distZip
         name: 'Windows Build'
       - uses: actions/upload-artifact@v4
         with:
@@ -526,26 +447,26 @@ jobs:
       - uses: actions/download-artifact@v4
         with:
           name: generate-windows-cpu
+          path: dist/windows-amd64/
       - uses: actions/download-artifact@v4
         with:
-          name: generate-windows-cuda-11
+          name: generate-windows-cuda-11.3
+          path: dist/windows-amd64/
       - uses: actions/download-artifact@v4
         with:
-          name: generate-windows-cuda-12
+          name: generate-windows-cuda-12.4
+          path: dist/windows-amd64/
       - uses: actions/download-artifact@v4
         with:
           name: generate-windows-rocm
+          path: dist/windows-amd64/
       - uses: actions/download-artifact@v4
         with:
           name: windows-arm64
           path: dist
-      - run: dir build
       - run: |
-          $gopath=(get-command go).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;$env:PATH"
           $env:OLLAMA_SKIP_GENERATE="1"
           $env:ARCH="amd64"
           if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }

.github/workflows/test.yaml

@@ -1,5 +1,11 @@
 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.
@@ -99,49 +105,45 @@ jobs:
         with:
           go-version-file: go.mod
           cache: true
-      - name: 'Install ROCm'
+      - name: Set make jobs default
+        run: |
+          echo "MAKEFLAGS=--jobs=$((Get-ComputerInfo -Property CsProcessors).CsProcessors.NumberOfCores)" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+
+      # 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: |
           $ErrorActionPreference = "Stop"
-          write-host "downloading AMD HIP Installer"
-          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
-          write-host "Installing AMD HIP"
-          Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
-          write-host "Completed AMD HIP"
+          Invoke-WebRequest -Uri "${env:ROCM_WINDOWS_URL}" -OutFile "rocm-install.exe"
+      - name: 'Install ROCm'
+        run: |
+          Start-Process "rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
       - name: 'Verify ROCm'
         run: |
           & 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version
-      - name: Install msys2
+          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
         run: |
-          $msys2_url="https://github.com/msys2/msys2-installer/releases/download/2024-07-27/msys2-x86_64-20240727.exe"
-          write-host "Downloading msys2"
-          Invoke-WebRequest -Uri "${msys2_url}" -OutFile "${env:RUNNER_TEMP}\msys2.exe"
-          write-host "Installing msys2"
-          Start-Process "${env:RUNNER_TEMP}\msys2.exe" -ArgumentList @("in", "--confirm-command", "--accept-messages", "--root", "C:/msys64") -NoNewWindow -Wait
           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", "make") -NoNewWindow -Wait
-          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-      - name: verify tools
+          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: |
-          get-command gcc
-          gcc --version
-          get-command make
-          make --version
-      - run: go get ./...
-      - run: |
-          $gopath=(get-command go).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'
-          $env:PATH="$gopath;$env:PATH"
-          $env:OLLAMA_SKIP_CPU_GENERATE="1"
-          $env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path)
-          $cores = (Get-ComputerInfo -Property CsProcessors).CsProcessors.NumberOfCores
-          write-host $env:HIP_PATH
           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 -j $cores rocm
-        name: make
+          make rocm
 
   # CUDA generation step
   runners-windows-cuda:
@@ -154,55 +156,49 @@ jobs:
         with:
           go-version-file: go.mod
           cache: true
-      - name: 'Install CUDA'
+      - 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: |
           $ErrorActionPreference = "Stop"
-          write-host "downloading CUDA Installer"
-          Invoke-WebRequest -Uri "https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda_11.3.1_465.89_win10.exe" -OutFile "${env:RUNNER_TEMP}\cuda-install.exe"
-          write-host "Installing CUDA"
-          Start-Process "${env:RUNNER_TEMP}\cuda-install.exe" -ArgumentList '-s' -NoNewWindow -Wait
-          write-host "Completed CUDA"
+          Invoke-WebRequest -Uri "${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" >> $env:GITHUB_PATH
-          echo "CUDA_PATH=$cudaPath" >> $env:GITHUB_ENV
-          echo "CUDA_PATH_V${cudaVer}=$cudaPath" >> $env:GITHUB_ENV
-          echo "CUDA_PATH_VX_Y=CUDA_PATH_V${cudaVer}" >> $env:GITHUB_ENV
-      - name: 'Verify CUDA'
-        run: nvcc -V
-      - name: Install msys2
+          echo "$cudaPath\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+          echo "CUDA_PATH=$cudaPath" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+          echo "CUDA_PATH_V${cudaVer}=$cudaPath" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+          echo "CUDA_PATH_VX_Y=CUDA_PATH_V${cudaVer}" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+
+      - name: Add msys paths
         run: |
-          $msys2_url="https://github.com/msys2/msys2-installer/releases/download/2024-07-27/msys2-x86_64-20240727.exe"
-          write-host "Downloading msys2"
-          Invoke-WebRequest -Uri "${msys2_url}" -OutFile "${env:RUNNER_TEMP}\msys2.exe"
-          write-host "Installing msys2"
-          Start-Process "${env:RUNNER_TEMP}\msys2.exe" -ArgumentList @("in", "--confirm-command", "--accept-messages", "--root", "C:/msys64") -NoNewWindow -Wait
           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", "make") -NoNewWindow -Wait
-          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-      - name: verify tools
+          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 cuda runner
         run: |
-          get-command gcc
-          gcc --version
-          get-command make
-          make --version
-      - run: go get ./...
-      - name: make
-        run: |
-          $gopath=(get-command go).source | split-path -parent
-          $cudabin=(get-command nvcc).source | split-path
           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;$cudabin;$env:PATH"
-          $env:OLLAMA_SKIP_CPU_GENERATE="1"
-          $cores = (Get-ComputerInfo -Property CsProcessors).CsProcessors.NumberOfCores
           if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
-          make -j $cores cuda_v11
-        env:
-          OLLAMA_SKIP_CPU_GENERATE: '1'
+          make cuda_v$(($env:CUDA_PATH | split-path -leaf) -replace 'v(\d+).*', '$1')
 
   runners-cpu:
     needs: [changes]
@@ -227,28 +223,15 @@ jobs:
         with:
           go-version-file: go.mod
           cache: true
-      - run: go get ./...
-      - name: Install msys2
+      - name: Add msys paths
         if: ${{ startsWith(matrix.os, 'windows-') }}
         run: |
-          $msys2_url="https://github.com/msys2/msys2-installer/releases/download/2024-07-27/msys2-x86_64-20240727.exe"
-          write-host "Downloading msys2"
-          Invoke-WebRequest -Uri "${msys2_url}" -OutFile "${env:RUNNER_TEMP}\msys2.exe"
-          write-host "Installing msys2"
-          Start-Process "${env:RUNNER_TEMP}\msys2.exe" -ArgumentList @("in", "--confirm-command", "--accept-messages", "--root", "C:/msys64") -NoNewWindow -Wait
           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", "make") -NoNewWindow -Wait
-          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-      - name: verify tools
-        if: ${{ startsWith(matrix.os, 'windows-') }}
-        run: |
-          get-command gcc
-          gcc --version
-          get-command make
-          make --version
+          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: |
@@ -260,7 +243,7 @@ jobs:
           $env:PATH="$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      
+          make -j 4
       - name: 'Build Unix Go Runners'
         if: ${{ ! startsWith(matrix.os, 'windows-') }}
         run: make -j 4
@@ -286,6 +269,15 @@ jobs:
       - 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
@@ -298,7 +290,7 @@ jobs:
         shell: bash
       - uses: golangci/golangci-lint-action@v6
         with:
-          args: --timeout 8m0s -v
+          args: --timeout 10m0s -v
   test:
     strategy:
       matrix:
@@ -317,6 +309,15 @@ jobs:
       - 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: ${{ startsWith(matrix.os, 'windows-') }}
+        run: |
+          Start-Process "c:\msys64\usr\bin\pacman.exe" -ArgumentList @("-S", "--noconfirm", "mingw-w64-clang-x86_64-gcc-compat", "mingw-w64-clang-x86_64-clang") -NoNewWindow -Wait
       - uses: actions/setup-go@v5
         with:
           go-version-file: go.mod
@@ -327,8 +328,7 @@ jobs:
             arm64) echo ARCH=arm64 ;;
           esac >>$GITHUB_ENV
         shell: bash
-      - run: go build
-      - run: go test -v ./...
+      - run: go test ./...
 
   patches:
     needs: [changes]
@@ -340,4 +340,4 @@ jobs:
           submodules: recursive
       - name: Verify patches carry all the changes
         run: |
-          make apply-patches sync && git diff --compact-summary --exit-code llama
+          make apply-patches sync && git diff --compact-summary --exit-code llama

.gitignore

@@ -10,9 +10,6 @@ ollama
 .idea
 test_data
 *.crt
-llm/build
-build/*/*/*
-!build/**/placeholder
 llama/build
 __debug_bin*
 llama/vendor

.golangci.yaml

@@ -8,8 +8,6 @@ linters:
     - containedctx
     - contextcheck
     - errcheck
-    - exportloopref
-    - gci
     - gocheckcompilerdirectives
     - gofmt
     - gofumpt
@@ -30,8 +28,6 @@ linters:
     - wastedassign
     - whitespace
 linters-settings:
-  gci:
-    sections: [standard, default, localmodule]
   staticcheck:
     checks:
       - all

.prettierrc.json

@@ -1,10 +0,0 @@
-{
-  "trailingComma": "es5",
-  "tabWidth": 2,
-  "useTabs": false,
-  "semi": false,
-  "singleQuote": true,
-  "jsxSingleQuote": true,
-  "printWidth": 120,
-  "arrowParens": "avoid"
-}

Dockerfile

@@ -1,10 +1,9 @@
 ARG GOLANG_VERSION=1.22.8
-ARG CMAKE_VERSION=3.22.1
 ARG CUDA_VERSION_11=11.3.1
-ARG CUDA_V11_ARCHITECTURES="50;52;53;60;61;62;70;72;75;80;86"
 ARG CUDA_VERSION_12=12.4.0
-ARG CUDA_V12_ARCHITECTURES="60;61;62;70;72;75;80;86;87;89;90;90a"
 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
 #
@@ -13,24 +12,22 @@ ARG ROCM_VERSION=6.1.2
 #
 ### Then incremental builds will be much faster in this container
 #
-# make -C llama -j 10 && go build -trimpath -o dist/linux-amd64/ollama .
+# make -j 10 dist
 #
 FROM --platform=linux/amd64 rocm/dev-centos-7:${ROCM_VERSION}-complete AS unified-builder-amd64
-ARG CMAKE_VERSION
 ARG GOLANG_VERSION
 ARG CUDA_VERSION_11
 ARG CUDA_VERSION_12
 COPY ./scripts/rh_linux_deps.sh /
 ENV PATH /opt/rh/devtoolset-10/root/usr/bin:/usr/local/cuda/bin:$PATH
 ENV LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64
-ENV LIBRARY_PATH=/usr/local/cuda/lib64/stubs:/opt/amdgpu/lib64
-RUN CMAKE_VERSION=${CMAKE_VERSION} GOLANG_VERSION=${GOLANG_VERSION} sh /rh_linux_deps.sh
+RUN GOLANG_VERSION=${GOLANG_VERSION} sh /rh_linux_deps.sh
 RUN yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-rhel7.repo && \
     dnf clean all && \
     dnf install -y \
     zsh \
-    cuda-$(echo ${CUDA_VERSION_11} | cut -f1-2 -d. | sed -e "s/\./-/g") \
-    cuda-$(echo ${CUDA_VERSION_12} | cut -f1-2 -d. | sed -e "s/\./-/g")
+    cuda-toolkit-$(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
@@ -44,12 +41,11 @@ ENTRYPOINT [ "zsh" ]
 # docker run --platform linux/arm64 --rm -it -v $(pwd):/go/src/github.com/ollama/ollama/ builder-arm64
 #
 FROM --platform=linux/arm64 rockylinux:8 AS unified-builder-arm64
-ARG CMAKE_VERSION
 ARG GOLANG_VERSION
 ARG CUDA_VERSION_11
 ARG CUDA_VERSION_12
 COPY ./scripts/rh_linux_deps.sh /
-RUN CMAKE_VERSION=${CMAKE_VERSION} GOLANG_VERSION=${GOLANG_VERSION} sh /rh_linux_deps.sh
+RUN GOLANG_VERSION=${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 && \
@@ -60,86 +56,85 @@ RUN yum-config-manager --add-repo https://developer.download.nvidia.com/compute/
 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 amd64
+ENV GOARCH arm64
 ENV CGO_ENABLED 1
 WORKDIR /go/src/github.com/ollama/ollama/
 ENTRYPOINT [ "zsh" ]
 
-FROM --platform=linux/amd64 unified-builder-amd64 AS runners-amd64
+FROM --platform=linux/amd64 unified-builder-amd64 AS build-amd64
 COPY . .
 ARG OLLAMA_SKIP_CUDA_GENERATE
-ARG OLLAMA_SKIP_CUDA_11_GENERATE
-ARG OLLAMA_SKIP_CUDA_12_GENERATE
 ARG OLLAMA_SKIP_ROCM_GENERATE
-ARG CUDA_V11_ARCHITECTURES
-ARG CUDA_V12_ARCHITECTURES
 ARG OLLAMA_FAST_BUILD
+ARG VERSION
+ARG CUSTOM_CPU_FLAGS
 RUN --mount=type=cache,target=/root/.ccache \
     if grep "^flags" /proc/cpuinfo|grep avx>/dev/null; then \
-        make -C llama -j $(expr $(nproc) / 2 ) ; \
+        make -j $(nproc) dist ; \
     else \
-        make -C llama -j 5 ; \
+        make -j 5 dist ; \
     fi
-
-FROM --platform=linux/arm64 unified-builder-arm64 AS runners-arm64
-COPY . .
-ARG OLLAMA_SKIP_CUDA_GENERATE
-ARG OLLAMA_SKIP_CUDA_11_GENERATE
-ARG OLLAMA_SKIP_CUDA_12_GENERATE
-ARG CUDA_V11_ARCHITECTURES
-ARG CUDA_V12_ARCHITECTURES
-ARG OLLAMA_FAST_BUILD
-RUN --mount=type=cache,target=/root/.ccache \
-    make -C llama -j 8
-
-
-# Intermediate stages used for ./scripts/build_linux.sh
-FROM --platform=linux/amd64 centos:7 AS builder-amd64
-ARG CMAKE_VERSION
-ARG GOLANG_VERSION
-COPY ./scripts/rh_linux_deps.sh /
-RUN CMAKE_VERSION=${CMAKE_VERSION} GOLANG_VERSION=${GOLANG_VERSION} sh /rh_linux_deps.sh
-ENV PATH /opt/rh/devtoolset-10/root/usr/bin:$PATH
-ENV CGO_ENABLED 1
-ENV GOARCH amd64
-WORKDIR /go/src/github.com/ollama/ollama
-
-FROM --platform=linux/amd64 builder-amd64 AS build-amd64
-COPY . .
-COPY --from=runners-amd64 /go/src/github.com/ollama/ollama/dist/ dist/
-COPY --from=runners-amd64 /go/src/github.com/ollama/ollama/build/ build/
-ARG GOFLAGS
-ARG CGO_CFLAGS
-ARG OLLAMA_SKIP_ROCM_GENERATE
-RUN --mount=type=cache,target=/root/.ccache \
-    go build -trimpath -o dist/linux-amd64/bin/ollama .
 RUN cd dist/linux-$GOARCH && \
-    tar --exclude runners -cf - . | pigz --best > ../ollama-linux-$GOARCH.tgz
+    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
 
-FROM --platform=linux/arm64 rockylinux:8 AS builder-arm64
-ARG CMAKE_VERSION
+# Jetsons need to be built in discrete stages
+FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${JETPACK_5} AS runners-jetpack5-arm64
 ARG GOLANG_VERSION
-COPY ./scripts/rh_linux_deps.sh /
-RUN CMAKE_VERSION=${CMAKE_VERSION} GOLANG_VERSION=${GOLANG_VERSION} sh /rh_linux_deps.sh
-ENV PATH /opt/rh/gcc-toolset-10/root/usr/bin:$PATH
-ENV CGO_ENABLED 1
-ENV GOARCH arm64
-WORKDIR /go/src/github.com/ollama/ollama
-
-FROM --platform=linux/arm64 builder-arm64 AS build-arm64
+RUN apt-get update && apt-get install -y git curl ccache && \
+    curl -s -L https://dl.google.com/go/go${GOLANG_VERSION}.linux-arm64.tar.gz | tar xz -C /usr/local && \
+    ln -s /usr/local/go/bin/go /usr/local/bin/go && \
+    ln -s /usr/local/go/bin/gofmt /usr/local/bin/gofmt && \
+    apt-get clean && rm -rf /var/lib/apt/lists/*
+WORKDIR /go/src/github.com/ollama/ollama/
 COPY . .
-COPY --from=runners-arm64 /go/src/github.com/ollama/ollama/dist/ dist/
-COPY --from=runners-arm64 /go/src/github.com/ollama/ollama/build/ build/
-ARG GOFLAGS
 ARG CGO_CFLAGS
+ENV GOARCH arm64
+ARG VERSION
 RUN --mount=type=cache,target=/root/.ccache \
-    go build -trimpath -o dist/linux-arm64/bin/ollama .
+    make -j 5 dist_cuda_v11 \
+        CUDA_ARCHITECTURES="72;87" \
+        GPU_RUNNER_VARIANT=_jetpack5 \
+        DIST_LIB_DIR=/go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack5/lib/ollama \
+        DIST_GPU_RUNNER_DEPS_DIR=/go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack5/lib/ollama/cuda_jetpack5
+
+FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${JETPACK_6} AS runners-jetpack6-arm64
+ARG GOLANG_VERSION
+RUN apt-get update && apt-get install -y git curl ccache && \
+    curl -s -L https://dl.google.com/go/go${GOLANG_VERSION}.linux-arm64.tar.gz | tar xz -C /usr/local && \
+    ln -s /usr/local/go/bin/go /usr/local/bin/go && \
+    ln -s /usr/local/go/bin/gofmt /usr/local/bin/gofmt && \
+    apt-get clean && rm -rf /var/lib/apt/lists/*
+WORKDIR /go/src/github.com/ollama/ollama/
+COPY . .
+ARG CGO_CFLAGS
+ENV GOARCH arm64
+ARG VERSION
+RUN --mount=type=cache,target=/root/.ccache \
+    make -j 5 dist_cuda_v12 \
+        CUDA_ARCHITECTURES="87" \
+        GPU_RUNNER_VARIANT=_jetpack6 \
+        DIST_LIB_DIR=/go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack6/lib/ollama \
+        DIST_GPU_RUNNER_DEPS_DIR=/go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack6/lib/ollama/cuda_jetpack6
+
+FROM --platform=linux/arm64 unified-builder-arm64 AS build-arm64
+COPY . .
+ARG OLLAMA_SKIP_CUDA_GENERATE
+ARG OLLAMA_FAST_BUILD
+ARG VERSION
+RUN --mount=type=cache,target=/root/.ccache \
+    make -j 5 dist
+COPY --from=runners-jetpack5-arm64 /go/src/github.com/ollama/ollama/dist/ dist/
+COPY --from=runners-jetpack6-arm64 /go/src/github.com/ollama/ollama/dist/ dist/
 RUN cd dist/linux-$GOARCH && \
-    tar --exclude runners -cf - . | pigz --best > ../ollama-linux-$GOARCH.tgz
+    tar -cf - . | pigz --best > ../ollama-linux-$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
 COPY --from=build-amd64 /go/src/github.com/ollama/ollama/dist/ollama-linux-*.tgz /
@@ -148,30 +143,13 @@ COPY --from=build-arm64 /go/src/github.com/ollama/ollama/dist/ollama-linux-*.tgz
 FROM dist-$TARGETARCH AS dist
 
 
-# Optimized container images do not cary nested payloads
-FROM --platform=linux/amd64 builder-amd64 AS container-build-amd64
-WORKDIR /go/src/github.com/ollama/ollama
-COPY . .
-ARG GOFLAGS
-ARG CGO_CFLAGS
-RUN --mount=type=cache,target=/root/.ccache \
-    go build -trimpath -o dist/linux-amd64/bin/ollama .
-
-FROM --platform=linux/arm64 builder-arm64 AS container-build-arm64
-WORKDIR /go/src/github.com/ollama/ollama
-COPY . .
-ARG GOFLAGS
-ARG CGO_CFLAGS
-RUN --mount=type=cache,target=/root/.ccache \
-    go build -trimpath -o dist/linux-arm64/bin/ollama .
-
 # For amd64 container images, filter out cuda/rocm to minimize size
-FROM runners-amd64 AS runners-cuda-amd64
+FROM build-amd64 AS runners-cuda-amd64
 RUN rm -rf \
     ./dist/linux-amd64/lib/ollama/libggml_hipblas.so \
     ./dist/linux-amd64/lib/ollama/runners/rocm*
 
-FROM runners-amd64 AS runners-rocm-amd64
+FROM build-amd64 AS runners-rocm-amd64
 RUN rm -rf \
     ./dist/linux-amd64/lib/ollama/libggml_cuda*.so \
     ./dist/linux-amd64/lib/ollama/libcu*.so* \
@@ -180,16 +158,19 @@ RUN rm -rf \
 FROM --platform=linux/amd64 ubuntu:22.04 AS runtime-amd64
 RUN apt-get update && \
     apt-get install -y ca-certificates && \
-    rm -rf /var/lib/apt/lists/*
-COPY --from=container-build-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/bin/ /bin/
+    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
 RUN apt-get update && \
     apt-get install -y ca-certificates && \
-    rm -rf /var/lib/apt/lists/*
-COPY --from=container-build-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/bin/ /bin/
-COPY --from=runners-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/lib/ /lib/
+    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/
+
 
 # ROCm libraries larger so we keep it distinct from the CPU/CUDA image
 FROM --platform=linux/amd64 ubuntu:22.04 AS runtime-rocm
@@ -198,8 +179,8 @@ FROM --platform=linux/amd64 ubuntu:22.04 AS runtime-rocm
 COPY --from=build-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64-rocm/lib/ /lib/
 RUN apt-get update && \
     apt-get install -y ca-certificates && \
-    rm -rf /var/lib/apt/lists/*
-COPY --from=container-build-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/bin/ /bin/
+    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

Makefile

@@ -1,4 +1,103 @@
-GOALS := $(or $(MAKECMDGOALS),all)
-.PHONY: $(GOALS)
-$(GOALS):
-	$(MAKE) -C llama $@
+# top level makefile for Ollama
+include make/common-defs.make
+
+
+# Determine which if any GPU runners we should build
+include make/cuda-v11-defs.make
+include make/cuda-v12-defs.make
+include make/rocm-defs.make
+
+ifeq ($(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 $(RUNNER_TARGETS)
+
+# Handy debugging for make variables
+print-%:
+	@echo '$*=$($*)'

README.md

@@ -1,11 +1,11 @@
 <div align="center">
- <img alt="ollama" height="200px" src="https://github.com/ollama/ollama/assets/3325447/0d0b44e2-8f4a-4e99-9b52-a5c1c741c8f7">
+  <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>
 
 # Ollama
 
-[![Discord](https://dcbadge.vercel.app/api/server/ollama?style=flat&compact=true)](https://discord.gg/ollama)
-
 Get up and running with large language models.
 
 ### macOS
@@ -33,6 +33,11 @@ The official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama) `olla
 - [ollama-python](https://github.com/ollama/ollama-python)
 - [ollama-js](https://github.com/ollama/ollama-js)
 
+### Community
+
+- [Discord](https://discord.gg/ollama)
+- [Reddit](https://reddit.com/r/ollama)
+
 ## Quickstart
 
 To run and chat with [Llama 3.2](https://ollama.com/library/llama3.2):
@@ -47,26 +52,28 @@ Ollama supports a list of models available on [ollama.com/library](https://ollam
 
 Here are some example models that can be downloaded:
 
-| Model              | Parameters | Size  | Download                       |
-| ------------------ | ---------- | ----- | ------------------------------ |
-| Llama 3.2          | 3B         | 2.0GB | `ollama run llama3.2`          |
-| Llama 3.2          | 1B         | 1.3GB | `ollama run llama3.2:1b`       |
-| Llama 3.1          | 8B         | 4.7GB | `ollama run llama3.1`          |
-| Llama 3.1          | 70B        | 40GB  | `ollama run llama3.1:70b`      |
-| Llama 3.1          | 405B       | 231GB | `ollama run llama3.1:405b`     |
-| Phi 3 Mini         | 3.8B       | 2.3GB | `ollama run phi3`              |
-| Phi 3 Medium       | 14B        | 7.9GB | `ollama run phi3:medium`       |
-| 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`       |
-| Starling           | 7B         | 4.1GB | `ollama run starling-lm`       |
-| 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`             |
+| Model              | Parameters | Size  | Download                         |
+| ------------------ | ---------- | ----- | -------------------------------- |
+| 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`         |
+| Llama 3.2 Vision   | 11B        | 7.9GB | `ollama run llama3.2-vision`     |
+| Llama 3.2 Vision   | 90B        | 55GB  | `ollama run llama3.2-vision:90b` |
+| 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`                |
+| 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`         |
+| Starling           | 7B         | 4.1GB | `ollama run starling-lm`         |
+| 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`               |
 
 > [!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.
@@ -95,7 +102,7 @@ Ollama supports importing GGUF models in the Modelfile:
    ollama run example
    ```
 
-### Import from PyTorch or Safetensors
+### Import from Safetensors
 
 See the [guide](docs/import.md) on importing models for more information.
 
@@ -130,7 +137,7 @@ ollama run mario
 Hello! It's your friend Mario.
 ```
 
-For more examples, see the [examples](examples) directory. For more information on working with a Modelfile, see the [Modelfile](docs/modelfile.md) documentation.
+For more information on working with a Modelfile, see the [Modelfile](docs/modelfile.md) documentation.
 
 ## CLI Reference
 
@@ -296,7 +303,8 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [AnythingLLM (Docker + MacOs/Windows/Linux native app)](https://github.com/Mintplex-Labs/anything-llm)
 - [Ollama Basic Chat: Uses HyperDiv Reactive UI](https://github.com/rapidarchitect/ollama_basic_chat)
 - [Ollama-chats RPG](https://github.com/drazdra/ollama-chats)
-- [QA-Pilot](https://github.com/reid41/QA-Pilot) (Chat with Code Repository)
+- [IntelliBar](https://intellibar.app/) (AI-powered assistant for macOS)
+- [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)
 - [RAGFlow](https://github.com/infiniflow/ragflow) (Open-source Retrieval-Augmented Generation engine based on deep document understanding)
@@ -306,11 +314,17 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Ollama RAG Chatbot](https://github.com/datvodinh/rag-chatbot.git) (Local Chat with multiple PDFs using Ollama and RAG)
 - [BrainSoup](https://www.nurgo-software.com/products/brainsoup) (Flexible native client with RAG & multi-agent automation)
 - [macai](https://github.com/Renset/macai) (macOS client for Ollama, ChatGPT, and other compatible API back-ends)
+- [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)
 - [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 )
 - [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)
+- [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)
 - [AI Studio](https://github.com/MindWorkAI/AI-Studio)
@@ -318,6 +332,9 @@ 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)
+- [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
@@ -327,16 +344,45 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [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)
 - [LLMChat](https://github.com/trendy-design/llmchat) (Privacy focused, 100% local, intuitive all-in-one chat interface)
+- [Local Multimodal AI Chat](https://github.com/Leon-Sander/Local-Multimodal-AI-Chat) (Ollama-based LLM Chat with support for multiple features, including PDF RAG, voice chat, image-based interactions, and integration with OpenAI.)
 - [ARGO](https://github.com/xark-argo/argo) (Locally download and run Ollama and Huggingface models with RAG on Mac/Windows/Linux)
+- [OrionChat](https://github.com/EliasPereirah/OrionChat) - OrionChat is a web interface for chatting with different AI providers
 - [G1](https://github.com/bklieger-groq/g1) (Prototype of using prompting strategies to improve the LLM's reasoning through o1-like reasoning chains.)
+- [Web management](https://github.com/lemonit-eric-mao/ollama-web-management) (Web management page)
+- [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)
+- [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)
+- [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)
+- [VT](https://github.com/vinhnx/vt.ai) (A minimal multimodal AI chat app, with dynamic conversation routing. Supports local models via Ollama)
+- [Nosia](https://github.com/nosia-ai/nosia) (Easy to install and use RAG platform based on Ollama)
+- [Witsy](https://github.com/nbonamy/witsy) (An AI Desktop application available for Mac/Windows/Linux)
+- [Abbey](https://github.com/US-Artificial-Intelligence/abbey) (A configurable AI interface server with notebooks, document storage, and YouTube support)
+- [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)
+- [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.)
+
+### Cloud
+
+- [Google Cloud](https://cloud.google.com/run/docs/tutorials/gpu-gemma2-with-ollama)
+- [Fly.io](https://fly.io/docs/python/do-more/add-ollama/)
+- [Koyeb](https://www.koyeb.com/deploy/ollama)
 
 ### Terminal
 
 - [oterm](https://github.com/ggozad/oterm)
 - [Ellama Emacs client](https://github.com/s-kostyaev/ellama)
 - [Emacs client](https://github.com/zweifisch/ollama)
+- [neollama](https://github.com/paradoxical-dev/neollama) UI client for interacting with models from within Neovim
 - [gen.nvim](https://github.com/David-Kunz/gen.nvim)
 - [ollama.nvim](https://github.com/nomnivore/ollama.nvim)
 - [ollero.nvim](https://github.com/marco-souza/ollero.nvim)
@@ -346,7 +392,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Oatmeal](https://github.com/dustinblackman/oatmeal)
 - [cmdh](https://github.com/pgibler/cmdh)
 - [ooo](https://github.com/npahlfer/ooo)
-- [shell-pilot](https://github.com/reid41/shell-pilot)
+- [shell-pilot](https://github.com/reid41/shell-pilot)(Interact with models via pure shell scripts on Linux or macOS)
 - [tenere](https://github.com/pythops/tenere)
 - [llm-ollama](https://github.com/taketwo/llm-ollama) for [Datasette's LLM CLI](https://llm.datasette.io/en/stable/).
 - [typechat-cli](https://github.com/anaisbetts/typechat-cli)
@@ -354,17 +400,28 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [tlm](https://github.com/yusufcanb/tlm)
 - [podman-ollama](https://github.com/ericcurtin/podman-ollama)
 - [gollama](https://github.com/sammcj/gollama)
+- [ParLlama](https://github.com/paulrobello/parllama)
 - [Ollama eBook Summary](https://github.com/cognitivetech/ollama-ebook-summary/)
 - [Ollama Mixture of Experts (MOE) in 50 lines of code](https://github.com/rapidarchitect/ollama_moe)
 - [vim-intelligence-bridge](https://github.com/pepo-ec/vim-intelligence-bridge) Simple interaction of "Ollama" with the Vim editor
+- [x-cmd ollama](https://x-cmd.com/mod/ollama)
+- [bb7](https://github.com/drunkwcodes/bb7)
+- [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
+- [orbiton](https://github.com/xyproto/orbiton) Configuration-free text editor and IDE with support for tab completion with Ollama.
 
 ### Apple Vision Pro
+
 - [Enchanted](https://github.com/AugustDev/enchanted)
 
 ### Database
 
+- [pgai](https://github.com/timescale/pgai) - PostgreSQL as a vector database (Create and search embeddings from Ollama models using pgvector)
+   - [Get started guide](https://github.com/timescale/pgai/blob/main/docs/vectorizer-quick-start.md)
 - [MindsDB](https://github.com/mindsdb/mindsdb/blob/staging/mindsdb/integrations/handlers/ollama_handler/README.md) (Connects Ollama models with nearly 200 data platforms and apps)
 - [chromem-go](https://github.com/philippgille/chromem-go/blob/v0.5.0/embed_ollama.go) with [example](https://github.com/philippgille/chromem-go/tree/v0.5.0/examples/rag-wikipedia-ollama)
+- [Kangaroo](https://github.com/dbkangaroo/kangaroo) (AI-powered SQL client and admin tool for popular databases)
 
 ### Package managers
 
@@ -380,9 +437,13 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [LangChain](https://python.langchain.com/docs/integrations/llms/ollama) and [LangChain.js](https://js.langchain.com/docs/integrations/chat/ollama/) with [example](https://js.langchain.com/docs/tutorials/local_rag/)
 - [Firebase Genkit](https://firebase.google.com/docs/genkit/plugins/ollama)
 - [crewAI](https://github.com/crewAIInc/crewAI)
+- [Yacana](https://remembersoftwares.github.io/yacana/) (User-friendly multi-agent framework for brainstorming and executing predetermined flows with built-in tool integration)
+- [Spring AI](https://github.com/spring-projects/spring-ai) with [reference](https://docs.spring.io/spring-ai/reference/api/chat/ollama-chat.html) and [example](https://github.com/tzolov/ollama-tools)
 - [LangChainGo](https://github.com/tmc/langchaingo/) with [example](https://github.com/tmc/langchaingo/tree/main/examples/ollama-completion-example)
 - [LangChain4j](https://github.com/langchain4j/langchain4j) with [example](https://github.com/langchain4j/langchain4j-examples/tree/main/ollama-examples/src/main/java)
 - [LangChainRust](https://github.com/Abraxas-365/langchain-rust) with [example](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/llm_ollama.rs)
+- [LangChain for .NET](https://github.com/tryAGI/LangChain) with [example](https://github.com/tryAGI/LangChain/blob/main/examples/LangChain.Samples.OpenAI/Program.cs)
+- [LLPhant](https://github.com/theodo-group/LLPhant?tab=readme-ov-file#ollama)
 - [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/llm/ollama/) and [LlamaIndexTS](https://ts.llamaindex.ai/modules/llms/available_llms/ollama)
 - [LiteLLM](https://github.com/BerriAI/litellm)
 - [OllamaFarm for Go](https://github.com/presbrey/ollamafarm)
@@ -407,12 +468,21 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Portkey](https://portkey.ai/docs/welcome/integration-guides/ollama)
 - [PromptingTools.jl](https://github.com/svilupp/PromptingTools.jl) with an [example](https://svilupp.github.io/PromptingTools.jl/dev/examples/working_with_ollama)
 - [LlamaScript](https://github.com/Project-Llama/llamascript)
+- [llm-axe](https://github.com/emirsahin1/llm-axe) (Python Toolkit for Building LLM Powered Apps)
 - [Gollm](https://docs.gollm.co/examples/ollama-example)
+- [Gollama for Golang](https://github.com/jonathanhecl/gollama)
 - [Ollamaclient for Golang](https://github.com/xyproto/ollamaclient)
 - [High-level function abstraction in Go](https://gitlab.com/tozd/go/fun)
 - [Ollama PHP](https://github.com/ArdaGnsrn/ollama-php)
 - [Agents-Flex for Java](https://github.com/agents-flex/agents-flex) with [example](https://github.com/agents-flex/agents-flex/tree/main/agents-flex-llm/agents-flex-llm-ollama/src/test/java/com/agentsflex/llm/ollama)
+- [Parakeet](https://github.com/parakeet-nest/parakeet) is a GoLang library, made to simplify the development of small generative AI applications with Ollama.
+- [Haverscript](https://github.com/andygill/haverscript) with [examples](https://github.com/andygill/haverscript/tree/main/examples)
 - [Ollama for Swift](https://github.com/mattt/ollama-swift)
+- [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)
+- [LlmTornado](https://github.com/lofcz/llmtornado) (C# library providing a unified interface for major FOSS & Commercial inference APIs)
 
 ### Mobile
 
@@ -426,6 +496,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Raycast extension](https://github.com/MassimilianoPasquini97/raycast_ollama)
 - [Discollama](https://github.com/mxyng/discollama) (Discord bot inside the Ollama discord channel)
 - [Continue](https://github.com/continuedev/continue)
+- [Vibe](https://github.com/thewh1teagle/vibe) (Transcribe and analyze meetings with Ollama)
 - [Obsidian Ollama plugin](https://github.com/hinterdupfinger/obsidian-ollama)
 - [Logseq Ollama plugin](https://github.com/omagdy7/ollama-logseq)
 - [NotesOllama](https://github.com/andersrex/notesollama) (Apple Notes Ollama plugin)
@@ -448,14 +519,27 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [AI Telegram Bot](https://github.com/tusharhero/aitelegrambot) (Telegram bot using Ollama in backend)
 - [AI ST Completion](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (Sublime Text 4 AI assistant plugin with Ollama support)
 - [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)
-- [vnc-lm](https://github.com/jk011ru/vnc-lm) (A containerized Discord bot with support for attachments and web links)
+- [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.)
 - [LSP-AI](https://github.com/SilasMarvin/lsp-ai) (Open-source language server for AI-powered functionality)
 - [QodeAssist](https://github.com/Palm1r/QodeAssist) (AI-powered coding assistant plugin for Qt Creator)
 - [Obsidian Quiz Generator plugin](https://github.com/ECuiDev/obsidian-quiz-generator)
+- [AI Summmary Helper plugin](https://github.com/philffm/ai-summary-helper)
+- [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
 
 ### Supported backends
 
 - [llama.cpp](https://github.com/ggerganov/llama.cpp) project founded by Georgi Gerganov.
 
+### Observability
+
+- [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.

api/client.go

@@ -55,7 +55,7 @@ func checkError(resp *http.Response, body []byte) error {
 
 // ClientFromEnvironment creates a new [Client] using configuration from the
 // environment variable OLLAMA_HOST, which points to the network host and
-// port on which the ollama service is listenting. The format of this variable
+// port on which the ollama service is listening. The format of this variable
 // is:
 //
 //	<scheme>://<host>:<port>

api/examples/README.md

@@ -0,0 +1,17 @@
+# Ollama API Examples
+
+Run the examples in this directory with:
+
+```
+go run example_name/main.go
+```
+## Chat - Chat with a model
+- [chat/main.go](chat/main.go)
+
+## Generate - Generate text from a model
+- [generate/main.go](generate/main.go)
+- [generate-streaming/main.go](generate-streaming/main.go)
+
+## Pull - Pull a model
+- [pull-progress/main.go](pull-progress/main.go)
+

api/types.go

@@ -12,7 +12,7 @@ import (
 	"time"
 )
 
-// StatusError is an error with and HTTP status code.
+// StatusError is an error with an HTTP status code and message.
 type StatusError struct {
 	StatusCode   int
 	Status       string
@@ -57,7 +57,7 @@ type GenerateRequest struct {
 	Template string `json:"template"`
 
 	// Context is the context parameter returned from a previous call to
-	// Generate call. It can be used to keep a short conversational memory.
+	// [Client.Generate]. It can be used to keep a short conversational memory.
 	Context []int `json:"context,omitempty"`
 
 	// Stream specifies whether the response is streaming; it is true by default.
@@ -67,7 +67,7 @@ type GenerateRequest struct {
 	Raw bool `json:"raw,omitempty"`
 
 	// Format specifies the format to return a response in.
-	Format string `json:"format"`
+	Format json.RawMessage `json:"format,omitempty"`
 
 	// KeepAlive controls how long the model will stay loaded in memory following
 	// this request.
@@ -90,14 +90,14 @@ type ChatRequest struct {
 	// Messages is the messages of the chat - can be used to keep a chat memory.
 	Messages []Message `json:"messages"`
 
-	// Stream enable streaming of returned response; true by default.
+	// Stream enables streaming of returned responses; true by default.
 	Stream *bool `json:"stream,omitempty"`
 
 	// Format is the format to return the response in (e.g. "json").
-	Format string `json:"format"`
+	Format json.RawMessage `json:"format,omitempty"`
 
 	// KeepAlive controls how long the model will stay loaded into memory
-	// followin the request.
+	// following the request.
 	KeepAlive *Duration `json:"keep_alive,omitempty"`
 
 	// Tools is an optional list of tools the model has access to.
@@ -146,6 +146,7 @@ type ToolCall struct {
 }
 
 type ToolCallFunction struct {
+	Index     int                       `json:"index,omitempty"`
 	Name      string                    `json:"name"`
 	Arguments ToolCallFunctionArguments `json:"arguments"`
 }
@@ -203,8 +204,8 @@ type Metrics struct {
 	EvalDuration       time.Duration `json:"eval_duration,omitempty"`
 }
 
-// Options specified in [GenerateRequest], if you add a new option here add it
-// to the API docs also.
+// Options specified in [GenerateRequest].  If you add a new option here, also
+// add it to the API docs.
 type Options struct {
 	Runner
 
@@ -215,7 +216,6 @@ type Options struct {
 	TopK             int      `json:"top_k,omitempty"`
 	TopP             float32  `json:"top_p,omitempty"`
 	MinP             float32  `json:"min_p,omitempty"`
-	TFSZ             float32  `json:"tfs_z,omitempty"`
 	TypicalP         float32  `json:"typical_p,omitempty"`
 	RepeatLastN      int      `json:"repeat_last_n,omitempty"`
 	Temperature      float32  `json:"temperature,omitempty"`
@@ -225,7 +225,6 @@ type Options struct {
 	Mirostat         int      `json:"mirostat,omitempty"`
 	MirostatTau      float32  `json:"mirostat_tau,omitempty"`
 	MirostatEta      float32  `json:"mirostat_eta,omitempty"`
-	PenalizeNewline  bool     `json:"penalize_newline,omitempty"`
 	Stop             []string `json:"stop,omitempty"`
 }
 
@@ -236,7 +235,7 @@ type Runner struct {
 	NumGPU    int   `json:"num_gpu,omitempty"`
 	MainGPU   int   `json:"main_gpu,omitempty"`
 	LowVRAM   bool  `json:"low_vram,omitempty"`
-	F16KV     bool  `json:"f16_kv,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"`
@@ -295,17 +294,21 @@ type EmbeddingResponse struct {
 
 // CreateRequest is the request passed to [Client.Create].
 type CreateRequest struct {
-	Model     string `json:"model"`
-	Modelfile string `json:"modelfile"`
-	Stream    *bool  `json:"stream,omitempty"`
-	Quantize  string `json:"quantize,omitempty"`
+	Model    string `json:"model"`
+	Stream   *bool  `json:"stream,omitempty"`
+	Quantize string `json:"quantize,omitempty"`
+
+	From       string            `json:"from,omitempty"`
+	Files      map[string]string `json:"files,omitempty"`
+	Adapters   map[string]string `json:"adapters,omitempty"`
+	Template   string            `json:"template,omitempty"`
+	License    any               `json:"license,omitempty"`
+	System     string            `json:"system,omitempty"`
+	Parameters map[string]any    `json:"parameters,omitempty"`
+	Messages   []Message         `json:"messages,omitempty"`
 
 	// Deprecated: set the model name with Model instead
 	Name string `json:"name"`
-
-	// Deprecated: set the file content with Modelfile instead
-	Path string `json:"path"`
-
 	// Deprecated: use Quantize instead
 	Quantization string `json:"quantization,omitempty"`
 }
@@ -594,7 +597,6 @@ func DefaultOptions() Options {
 		Temperature:      0.8,
 		TopK:             40,
 		TopP:             0.9,
-		TFSZ:             1.0,
 		TypicalP:         1.0,
 		RepeatLastN:      64,
 		RepeatPenalty:    1.1,
@@ -603,7 +605,6 @@ func DefaultOptions() Options {
 		Mirostat:         0,
 		MirostatTau:      5.0,
 		MirostatEta:      0.1,
-		PenalizeNewline:  true,
 		Seed:             -1,
 
 		Runner: Runner{
@@ -613,7 +614,6 @@ func DefaultOptions() Options {
 			NumGPU:    -1, // -1 here indicates that NumGPU should be set dynamically
 			NumThread: 0,  // let the runtime decide
 			LowVRAM:   false,
-			F16KV:     true,
 			UseMLock:  false,
 			UseMMap:   nil,
 		},

app/ollama.iss

@@ -97,7 +97,6 @@ Source: "..\dist\windows-amd64\lib\ollama\*"; DestDir: "{app}\lib\ollama\"; Chec
 Source: "..\dist\windows-arm64\vc_redist.arm64.exe"; DestDir: "{tmp}"; Check: IsArm64() and vc_redist_needed(); Flags: deleteafterinstall
 Source: "..\dist\windows-arm64-app.exe"; DestDir: "{app}"; DestName: "{#MyAppExeName}" ;Check: IsArm64();  Flags: ignoreversion 64bit
 Source: "..\dist\windows-arm64\ollama.exe"; DestDir: "{app}"; Check: IsArm64(); Flags: ignoreversion 64bit
-Source: "..\dist\windows-arm64\lib\ollama\*"; DestDir: "{app}\lib\ollama\"; Check: IsArm64(); Flags: ignoreversion 64bit recursesubdirs
 #endif
 
 Source: "..\dist\ollama_welcome.ps1"; DestDir: "{app}"; Flags: ignoreversion
@@ -136,7 +135,7 @@ Type: filesandordirs; Name: "{%TEMP}\ollama*"
 Type: filesandordirs; Name: "{%LOCALAPPDATA}\Programs\Ollama"
 
 [Messages]
-WizardReady=Ollama Windows Preview
+WizardReady=Ollama
 ReadyLabel1=%nLet's get you up and running with your own large language models.
 SetupAppRunningError=Another Ollama installer is running.%n%nPlease cancel or finish the other installer, then click OK to continue with this install, or Cancel to exit.
 

app/store/store.go

@@ -64,7 +64,7 @@ func initStore() {
 		slog.Debug(fmt.Sprintf("unexpected error searching for store: %s", err))
 	}
 	slog.Debug("initializing new store")
-	store.ID = uuid.New().String()
+	store.ID = uuid.NewString()
 	writeStore(getStorePath())
 }
 

app/tray/wintray/eventloop.go

@@ -98,7 +98,7 @@ func (t *winTray) wndProc(hWnd windows.Handle, message uint32, wParam, lParam ui
 		}
 		err = t.wcex.unregister()
 		if err != nil {
-			slog.Error(fmt.Sprintf("failed to uregister windo %s", err))
+			slog.Error(fmt.Sprintf("failed to unregister window %s", err))
 		}
 	case WM_DESTROY:
 		// same as WM_ENDSESSION, but throws 0 exit code after all

app/tray/wintray/menus.go

@@ -39,7 +39,7 @@ func (t *winTray) UpdateAvailable(ver string) error {
 		if err := t.addOrUpdateMenuItem(updateAvailableMenuID, 0, updateAvailableMenuTitle, true); err != nil {
 			return fmt.Errorf("unable to create menu entries %w", err)
 		}
-		if err := t.addOrUpdateMenuItem(updateMenuID, 0, updateMenutTitle, false); err != nil {
+		if err := t.addOrUpdateMenuItem(updateMenuID, 0, updateMenuTitle, false); err != nil {
 			return fmt.Errorf("unable to create menu entries %w", err)
 		}
 		if err := t.addSeparatorMenuItem(separatorMenuID, 0); err != nil {

app/tray/wintray/messages.go

@@ -10,6 +10,6 @@ const (
 
 	quitMenuTitle            = "Quit Ollama"
 	updateAvailableMenuTitle = "An update is available"
-	updateMenutTitle         = "Restart to update"
+	updateMenuTitle          = "Restart to update"
 	diagLogsMenuTitle        = "View logs"
 )

app/tray/wintray/tray.go

@@ -361,7 +361,7 @@ func (t *winTray) showMenu() error {
 
 	boolRet, _, err = pTrackPopupMenu.Call(
 		uintptr(t.menus[0]),
-		TPM_BOTTOMALIGN|TPM_LEFTALIGN,
+		TPM_BOTTOMALIGN|TPM_LEFTALIGN|TPM_RIGHTBUTTON,
 		uintptr(p.X),
 		uintptr(p.Y),
 		0,

app/tray/wintray/w32api.go

@@ -67,6 +67,7 @@ const (
 	SW_HIDE             = 0
 	TPM_BOTTOMALIGN     = 0x0020
 	TPM_LEFTALIGN       = 0x0000
+	TPM_RIGHTBUTTON     = 0x0002
 	WM_CLOSE            = 0x0010
 	WM_USER             = 0x0400
 	WS_CAPTION          = 0x00C00000

build/darwin/amd64/placeholder

@@ -1 +0,0 @@
-This is here to make sure the build/ directory exists for the go:embed command

build/darwin/arm64/placeholder

@@ -1 +0,0 @@
-This is here to make sure the build/ directory exists for the go:embed command

build/embed_darwin_amd64.go

@@ -1,8 +0,0 @@
-package build
-
-import "embed"
-
-// Darwin payloads separated by architecture to avoid duplicate payloads when cross compiling
-
-//go:embed darwin/amd64/*
-var EmbedFS embed.FS

build/embed_darwin_arm64.go

@@ -1,8 +0,0 @@
-package build
-
-import "embed"
-
-// Darwin payloads separated by architecture to avoid duplicate payloads when cross compiling
-
-//go:embed darwin/arm64/*
-var EmbedFS embed.FS

build/embed_linux.go

@@ -1,6 +0,0 @@
-package build
-
-import "embed"
-
-//go:embed linux/*
-var EmbedFS embed.FS

build/embed_unused.go

@@ -1,8 +0,0 @@
-//go:build !linux && !darwin
-
-package build
-
-import "embed"
-
-// unused on windows
-var EmbedFS embed.FS

build/linux/amd64/placeholder

@@ -1 +0,0 @@
-This is here to make sure the build/ directory exists for the go:embed command

build/linux/arm64/placeholder

@@ -1 +0,0 @@
-This is here to make sure the build/ directory exists for the go:embed command

cmd/cmd.go

@@ -1,13 +1,11 @@
 package cmd
 
 import (
-	"archive/zip"
 	"bufio"
-	"bytes"
 	"context"
 	"crypto/ed25519"
 	"crypto/rand"
-	"crypto/sha256"
+	"encoding/json"
 	"encoding/pem"
 	"errors"
 	"fmt"
@@ -19,7 +17,6 @@ import (
 	"os"
 	"os/signal"
 	"path/filepath"
-	"regexp"
 	"runtime"
 	"strconv"
 	"strings"
@@ -35,26 +32,22 @@ import (
 	"golang.org/x/term"
 
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/auth"
 	"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/server"
-	"github.com/ollama/ollama/types/errtypes"
 	"github.com/ollama/ollama/types/model"
 	"github.com/ollama/ollama/version"
 )
 
-var (
-	errModelNotFound     = errors.New("no Modelfile or safetensors files found")
-	errModelfileNotFound = errors.New("specified Modelfile wasn't found")
-)
+var errModelfileNotFound = errors.New("specified Modelfile wasn't found")
 
 func getModelfileName(cmd *cobra.Command) (string, error) {
-	fn, _ := cmd.Flags().GetString("file")
+	filename, _ := cmd.Flags().GetString("file")
 
-	filename := fn
 	if filename == "" {
 		filename = "Modelfile"
 	}
@@ -66,7 +59,7 @@ func getModelfileName(cmd *cobra.Command) (string, error) {
 
 	_, err = os.Stat(absName)
 	if err != nil {
-		return fn, err
+		return "", err
 	}
 
 	return absName, nil
@@ -102,68 +95,52 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
-	home, err := os.UserHomeDir()
+	status := "gathering model components"
+	spinner := progress.NewSpinner(status)
+	p.Add(status, spinner)
+
+	req, err := modelfile.CreateRequest(filepath.Dir(filename))
 	if err != nil {
 		return err
 	}
+	spinner.Stop()
 
-	status := "transferring model data"
-	spinner := progress.NewSpinner(status)
-	p.Add(status, spinner)
-	defer p.Stop()
+	req.Name = args[0]
+	quantize, _ := cmd.Flags().GetString("quantize")
+	if quantize != "" {
+		req.Quantize = quantize
+	}
 
 	client, err := api.ClientFromEnvironment()
 	if err != nil {
 		return err
 	}
 
-	for i := range modelfile.Commands {
-		switch modelfile.Commands[i].Name {
-		case "model", "adapter":
-			path := modelfile.Commands[i].Args
-			if path == "~" {
-				path = home
-			} else if strings.HasPrefix(path, "~/") {
-				path = filepath.Join(home, path[2:])
-			}
-
-			if !filepath.IsAbs(path) {
-				path = filepath.Join(filepath.Dir(filename), path)
-			}
-
-			fi, err := os.Stat(path)
-			if errors.Is(err, os.ErrNotExist) && modelfile.Commands[i].Name == "model" {
-				continue
-			} else if err != nil {
+	if len(req.Files) > 0 {
+		fileMap := map[string]string{}
+		for f, digest := range req.Files {
+			if _, err := createBlob(cmd, client, f, digest, p); err != nil {
 				return err
 			}
-
-			if fi.IsDir() {
-				// this is likely a safetensors or pytorch directory
-				// TODO make this work w/ adapters
-				tempfile, err := tempZipFiles(path)
-				if err != nil {
-					return err
-				}
-				defer os.RemoveAll(tempfile)
-
-				path = tempfile
-			}
-
-			digest, err := createBlob(cmd, client, path, spinner)
-			if err != nil {
-				return err
-			}
-
-			modelfile.Commands[i].Args = "@" + digest
+			fileMap[filepath.Base(f)] = digest
 		}
+		req.Files = fileMap
+	}
+
+	if len(req.Adapters) > 0 {
+		fileMap := map[string]string{}
+		for f, digest := range req.Adapters {
+			if _, err := createBlob(cmd, client, f, digest, p); err != nil {
+				return err
+			}
+			fileMap[filepath.Base(f)] = digest
+		}
+		req.Adapters = fileMap
 	}
 
 	bars := make(map[string]*progress.Bar)
 	fn := func(resp api.ProgressResponse) error {
 		if resp.Digest != "" {
-			spinner.Stop()
-
 			bar, ok := bars[resp.Digest]
 			if !ok {
 				bar = progress.NewBar(fmt.Sprintf("pulling %s...", resp.Digest[7:19]), resp.Total, resp.Completed)
@@ -183,145 +160,23 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 		return nil
 	}
 
-	quantize, _ := cmd.Flags().GetString("quantize")
-
-	request := api.CreateRequest{Name: args[0], Modelfile: modelfile.String(), Quantize: quantize}
-	if err := client.Create(cmd.Context(), &request, fn); err != nil {
+	if err := client.Create(cmd.Context(), req, fn); err != nil {
+		if strings.Contains(err.Error(), "path or Modelfile are required") {
+			return fmt.Errorf("the ollama server must be updated to use `ollama create` with this client")
+		}
 		return err
 	}
 
 	return nil
 }
 
-func tempZipFiles(path string) (string, error) {
-	tempfile, err := os.CreateTemp("", "ollama-tf")
+func createBlob(cmd *cobra.Command, client *api.Client, path string, digest string, p *progress.Progress) (string, error) {
+	realPath, err := filepath.EvalSymlinks(path)
 	if err != nil {
 		return "", err
 	}
-	defer tempfile.Close()
 
-	detectContentType := func(path string) (string, error) {
-		f, err := os.Open(path)
-		if err != nil {
-			return "", err
-		}
-		defer f.Close()
-
-		var b bytes.Buffer
-		b.Grow(512)
-
-		if _, err := io.CopyN(&b, f, 512); err != nil && !errors.Is(err, io.EOF) {
-			return "", err
-		}
-
-		contentType, _, _ := strings.Cut(http.DetectContentType(b.Bytes()), ";")
-		return contentType, nil
-	}
-
-	glob := func(pattern, contentType string) ([]string, error) {
-		matches, err := filepath.Glob(pattern)
-		if err != nil {
-			return nil, err
-		}
-
-		for _, safetensor := range matches {
-			if ct, err := detectContentType(safetensor); err != nil {
-				return nil, err
-			} else if ct != contentType {
-				return nil, fmt.Errorf("invalid content type: expected %s for %s", ct, safetensor)
-			}
-		}
-
-		return matches, nil
-	}
-
-	var files []string
-	if st, _ := glob(filepath.Join(path, "model*.safetensors"), "application/octet-stream"); len(st) > 0 {
-		// safetensors files might be unresolved git lfs references; skip if they are
-		// covers model-x-of-y.safetensors, model.fp32-x-of-y.safetensors, model.safetensors
-		files = append(files, st...)
-	} else if st, _ := glob(filepath.Join(path, "adapters.safetensors"), "application/octet-stream"); len(st) > 0 {
-		// covers adapters.safetensors
-		files = append(files, st...)
-	} else if st, _ := glob(filepath.Join(path, "adapter_model.safetensors"), "application/octet-stream"); len(st) > 0 {
-		// covers adapter_model.safetensors
-		files = append(files, st...)
-	} else if pt, _ := glob(filepath.Join(path, "pytorch_model*.bin"), "application/zip"); len(pt) > 0 {
-		// pytorch files might also be unresolved git lfs references; skip if they are
-		// covers pytorch_model-x-of-y.bin, pytorch_model.fp32-x-of-y.bin, pytorch_model.bin
-		files = append(files, pt...)
-	} else if pt, _ := glob(filepath.Join(path, "consolidated*.pth"), "application/zip"); len(pt) > 0 {
-		// pytorch files might also be unresolved git lfs references; skip if they are
-		// covers consolidated.x.pth, consolidated.pth
-		files = append(files, pt...)
-	} else {
-		return "", errModelNotFound
-	}
-
-	// add configuration files, json files are detected as text/plain
-	js, err := glob(filepath.Join(path, "*.json"), "text/plain")
-	if err != nil {
-		return "", err
-	}
-	files = append(files, js...)
-
-	// bert models require a nested config.json
-	// TODO(mxyng): merge this with the glob above
-	js, err = glob(filepath.Join(path, "**/*.json"), "text/plain")
-	if err != nil {
-		return "", err
-	}
-	files = append(files, js...)
-
-	if tks, _ := glob(filepath.Join(path, "tokenizer.model"), "application/octet-stream"); len(tks) > 0 {
-		// add tokenizer.model if it exists, tokenizer.json is automatically picked up by the previous glob
-		// tokenizer.model might be a unresolved git lfs reference; error if it is
-		files = append(files, tks...)
-	} else if tks, _ := glob(filepath.Join(path, "**/tokenizer.model"), "text/plain"); len(tks) > 0 {
-		// some times tokenizer.model is in a subdirectory (e.g. meta-llama/Meta-Llama-3-8B)
-		files = append(files, tks...)
-	}
-
-	zipfile := zip.NewWriter(tempfile)
-	defer zipfile.Close()
-
-	for _, file := range files {
-		f, err := os.Open(file)
-		if err != nil {
-			return "", err
-		}
-		defer f.Close()
-
-		fi, err := f.Stat()
-		if err != nil {
-			return "", err
-		}
-
-		zfi, err := zip.FileInfoHeader(fi)
-		if err != nil {
-			return "", err
-		}
-
-		zfi.Name, err = filepath.Rel(path, file)
-		if err != nil {
-			return "", err
-		}
-
-		zf, err := zipfile.CreateHeader(zfi)
-		if err != nil {
-			return "", err
-		}
-
-		if _, err := io.Copy(zf, f); err != nil {
-			return "", err
-		}
-	}
-
-	return tempfile.Name(), nil
-}
-
-func createBlob(cmd *cobra.Command, client *api.Client, path string, spinner *progress.Spinner) (string, error) {
-	bin, err := os.Open(path)
+	bin, err := os.Open(realPath)
 	if err != nil {
 		return "", err
 	}
@@ -334,18 +189,11 @@ func createBlob(cmd *cobra.Command, client *api.Client, path string, spinner *pr
 	}
 	fileSize := fileInfo.Size()
 
-	hash := sha256.New()
-	if _, err := io.Copy(hash, bin); err != nil {
-		return "", err
-	}
-
-	if _, err := bin.Seek(0, io.SeekStart); err != nil {
-		return "", err
-	}
-
 	var pw progressWriter
-	status := "transferring model data 0%"
-	spinner.SetMessage(status)
+	status := fmt.Sprintf("copying file %s 0%%", digest)
+	spinner := progress.NewSpinner(status)
+	p.Add(status, spinner)
+	defer spinner.Stop()
 
 	done := make(chan struct{})
 	defer close(done)
@@ -356,15 +204,14 @@ func createBlob(cmd *cobra.Command, client *api.Client, path string, spinner *pr
 		for {
 			select {
 			case <-ticker.C:
-				spinner.SetMessage(fmt.Sprintf("transferring model data %d%%", int(100*pw.n.Load()/fileSize)))
+				spinner.SetMessage(fmt.Sprintf("copying file %s %d%%", digest, int(100*pw.n.Load()/fileSize)))
 			case <-done:
-				spinner.SetMessage("transferring model data 100%")
+				spinner.SetMessage(fmt.Sprintf("copying file %s 100%%", digest))
 				return
 			}
 		}
 	}()
 
-	digest := fmt.Sprintf("sha256:%x", hash.Sum(nil))
 	if err = client.CreateBlob(cmd.Context(), digest, io.TeeReader(bin, &pw)); err != nil {
 		return "", err
 	}
@@ -456,6 +303,10 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 	if len(prompts) > 0 {
 		interactive = false
 	}
+	// Be quiet if we're redirecting to a pipe or file
+	if !term.IsTerminal(int(os.Stdout.Fd())) {
+		interactive = false
+	}
 
 	nowrap, err := cmd.Flags().GetBool("nowordwrap")
 	if err != nil {
@@ -512,47 +363,6 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 	return generate(cmd, opts)
 }
 
-func errFromUnknownKey(unknownKeyErr error) error {
-	// find SSH public key in the error message
-	sshKeyPattern := `ssh-\w+ [^\s"]+`
-	re := regexp.MustCompile(sshKeyPattern)
-	matches := re.FindStringSubmatch(unknownKeyErr.Error())
-
-	if len(matches) > 0 {
-		serverPubKey := matches[0]
-
-		localPubKey, err := auth.GetPublicKey()
-		if err != nil {
-			return unknownKeyErr
-		}
-
-		if runtime.GOOS == "linux" && serverPubKey != localPubKey {
-			// try the ollama service public key
-			svcPubKey, err := os.ReadFile("/usr/share/ollama/.ollama/id_ed25519.pub")
-			if err != nil {
-				return unknownKeyErr
-			}
-			localPubKey = strings.TrimSpace(string(svcPubKey))
-		}
-
-		// check if the returned public key matches the local public key, this prevents adding a remote key to the user's account
-		if serverPubKey != localPubKey {
-			return unknownKeyErr
-		}
-
-		var msg strings.Builder
-		msg.WriteString(unknownKeyErr.Error())
-		msg.WriteString("\n\nYour ollama key is:\n")
-		msg.WriteString(localPubKey)
-		msg.WriteString("\nAdd your key at:\n")
-		msg.WriteString("https://ollama.com/settings/keys")
-
-		return errors.New(msg.String())
-	}
-
-	return unknownKeyErr
-}
-
 func PushHandler(cmd *cobra.Command, args []string) error {
 	client, err := api.ClientFromEnvironment()
 	if err != nil {
@@ -599,6 +409,8 @@ func PushHandler(cmd *cobra.Command, args []string) error {
 	}
 
 	request := api.PushRequest{Name: args[0], Insecure: insecure}
+
+	n := model.ParseName(args[0])
 	if err := client.Push(cmd.Context(), &request, fn); err != nil {
 		if spinner != nil {
 			spinner.Stop()
@@ -606,18 +418,19 @@ func PushHandler(cmd *cobra.Command, args []string) error {
 		if strings.Contains(err.Error(), "access denied") {
 			return errors.New("you are not authorized to push to this namespace, create the model under a namespace you own")
 		}
-		host := model.ParseName(args[0]).Host
-		isOllamaHost := strings.HasSuffix(host, ".ollama.ai") || strings.HasSuffix(host, ".ollama.com")
-		if strings.Contains(err.Error(), errtypes.UnknownOllamaKeyErrMsg) && isOllamaHost {
-			// the user has not added their ollama key to ollama.com
-			// re-throw an error with a more user-friendly message
-			return errFromUnknownKey(err)
-		}
-
 		return err
 	}
 
+	p.Stop()
 	spinner.Stop()
+
+	destination := n.String()
+	if strings.HasSuffix(n.Host, ".ollama.ai") || strings.HasSuffix(n.Host, ".ollama.com") {
+		destination = "https://ollama.com/" + strings.TrimSuffix(n.DisplayShortest(), ":latest")
+	}
+	fmt.Printf("\nYou can find your model at:\n\n")
+	fmt.Printf("\t%s\n", destination)
+
 	return nil
 }
 
@@ -635,7 +448,7 @@ func ListHandler(cmd *cobra.Command, args []string) error {
 	var data [][]string
 
 	for _, m := range models.Models {
-		if len(args) == 0 || strings.HasPrefix(m.Name, args[0]) {
+		if len(args) == 0 || strings.HasPrefix(strings.ToLower(m.Name), strings.ToLower(args[0])) {
 			data = append(data, []string{m.Name, m.Digest[:12], format.HumanBytes(m.Size), format.HumanTime(m.ModifiedAt, "Never")})
 		}
 	}
@@ -800,9 +613,9 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
 		case "parameters":
 			fmt.Println(resp.Parameters)
 		case "system":
-			fmt.Println(resp.System)
+			fmt.Print(resp.System)
 		case "template":
-			fmt.Println(resp.Template)
+			fmt.Print(resp.Template)
 		}
 
 		return nil
@@ -1072,10 +885,14 @@ func chat(cmd *cobra.Command, opts runOptions) (*api.Message, error) {
 		return nil
 	}
 
+	if opts.Format == "json" {
+		opts.Format = `"` + opts.Format + `"`
+	}
+
 	req := &api.ChatRequest{
 		Model:    opts.Model,
 		Messages: opts.Messages,
-		Format:   opts.Format,
+		Format:   json.RawMessage(opts.Format),
 		Options:  opts.Options,
 	}
 
@@ -1157,12 +974,16 @@ func generate(cmd *cobra.Command, opts runOptions) error {
 		}
 	}
 
+	if opts.Format == "json" {
+		opts.Format = `"` + opts.Format + `"`
+	}
+
 	request := api.GenerateRequest{
 		Model:     opts.Model,
 		Prompt:    opts.Prompt,
 		Context:   generateContext,
 		Images:    opts.Images,
-		Format:    opts.Format,
+		Format:    json.RawMessage(opts.Format),
 		System:    opts.System,
 		Options:   opts.Options,
 		KeepAlive: opts.KeepAlive,
@@ -1448,6 +1269,19 @@ func NewCLI() *cobra.Command {
 		RunE:    DeleteHandler,
 	}
 
+	runnerCmd := &cobra.Command{
+		Use:    "runner",
+		Short:  llama.PrintSystemInfo(),
+		Hidden: true,
+		RunE: func(cmd *cobra.Command, args []string) error {
+			return runner.Execute(os.Args[1:])
+		},
+		FParseErrWhitelist: cobra.FParseErrWhitelist{UnknownFlags: true},
+	}
+	runnerCmd.SetHelpFunc(func(cmd *cobra.Command, args []string) {
+		_ = runner.Execute(args[1:])
+	})
+
 	envVars := envconfig.AsMap()
 
 	envs := []envconfig.EnvVar{envVars["OLLAMA_HOST"]}
@@ -1482,6 +1316,7 @@ func NewCLI() *cobra.Command {
 				envVars["OLLAMA_SCHED_SPREAD"],
 				envVars["OLLAMA_TMPDIR"],
 				envVars["OLLAMA_FLASH_ATTENTION"],
+				envVars["OLLAMA_KV_CACHE_TYPE"],
 				envVars["OLLAMA_LLM_LIBRARY"],
 				envVars["OLLAMA_GPU_OVERHEAD"],
 				envVars["OLLAMA_LOAD_TIMEOUT"],
@@ -1503,6 +1338,7 @@ func NewCLI() *cobra.Command {
 		psCmd,
 		copyCmd,
 		deleteCmd,
+		runnerCmd,
 	)
 
 	return rootCmd

cmd/cmd_test.go

@@ -4,10 +4,10 @@ import (
 	"bytes"
 	"context"
 	"encoding/json"
+	"io"
 	"net/http"
 	"net/http/httptest"
 	"os"
-	"path/filepath"
 	"strings"
 	"testing"
 
@@ -179,18 +179,14 @@ Weigh anchor!
 
 	t.Run("license", func(t *testing.T) {
 		var b bytes.Buffer
-		license, err := os.ReadFile(filepath.Join("..", "LICENSE"))
-		if err != nil {
-			t.Fatal(err)
-		}
-
+		license := "MIT License\nCopyright (c) Ollama\n"
 		if err := showInfo(&api.ShowResponse{
 			Details: api.ModelDetails{
 				Family:            "test",
 				ParameterSize:     "7B",
 				QuantizationLevel: "FP16",
 			},
-			License: string(license),
+			License: license,
 		}, &b); err != nil {
 			t.Fatal(err)
 		}
@@ -297,7 +293,7 @@ func TestGetModelfileName(t *testing.T) {
 			name:          "modelfile specified, no modelfile exists",
 			modelfileName: "crazyfile",
 			fileExists:    false,
-			expectedName:  "crazyfile",
+			expectedName:  "",
 			expectedErr:   os.ErrNotExist,
 		},
 		{
@@ -342,8 +338,8 @@ func TestGetModelfileName(t *testing.T) {
 					t.Fatalf("couldn't set file flag: %v", err)
 				}
 			} else {
+				expectedFilename = tt.expectedName
 				if tt.modelfileName != "" {
-					expectedFilename = tt.modelfileName
 					err := cmd.Flags().Set("file", tt.modelfileName)
 					if err != nil {
 						t.Fatalf("couldn't set file flag: %v", err)
@@ -369,3 +365,254 @@ func TestGetModelfileName(t *testing.T) {
 		})
 	}
 }
+
+func TestPushHandler(t *testing.T) {
+	tests := []struct {
+		name           string
+		modelName      string
+		serverResponse map[string]func(w http.ResponseWriter, r *http.Request)
+		expectedError  string
+		expectedOutput string
+	}{
+		{
+			name:      "successful push",
+			modelName: "test-model",
+			serverResponse: map[string]func(w http.ResponseWriter, r *http.Request){
+				"/api/push": func(w http.ResponseWriter, r *http.Request) {
+					if r.Method != http.MethodPost {
+						t.Errorf("expected POST request, got %s", r.Method)
+					}
+
+					var req api.PushRequest
+					if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
+						http.Error(w, err.Error(), http.StatusBadRequest)
+						return
+					}
+
+					if req.Name != "test-model" {
+						t.Errorf("expected model name 'test-model', got %s", req.Name)
+					}
+
+					// Simulate progress updates
+					responses := []api.ProgressResponse{
+						{Status: "preparing manifest"},
+						{Digest: "sha256:abc123456789", Total: 100, Completed: 50},
+						{Digest: "sha256:abc123456789", Total: 100, Completed: 100},
+					}
+
+					for _, resp := range responses {
+						if err := json.NewEncoder(w).Encode(resp); err != nil {
+							http.Error(w, err.Error(), http.StatusInternalServerError)
+							return
+						}
+						w.(http.Flusher).Flush()
+					}
+				},
+			},
+			expectedOutput: "\nYou can find your model at:\n\n\thttps://ollama.com/test-model\n",
+		},
+		{
+			name:      "unauthorized push",
+			modelName: "unauthorized-model",
+			serverResponse: map[string]func(w http.ResponseWriter, r *http.Request){
+				"/api/push": func(w http.ResponseWriter, r *http.Request) {
+					w.Header().Set("Content-Type", "application/json")
+					w.WriteHeader(http.StatusUnauthorized)
+					err := json.NewEncoder(w).Encode(map[string]string{
+						"error": "access denied",
+					})
+					if err != nil {
+						t.Fatal(err)
+					}
+				},
+			},
+			expectedError: "you are not authorized to push to this namespace, create the model under a namespace you own",
+		},
+	}
+
+	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 handler, ok := tt.serverResponse[r.URL.Path]; ok {
+					handler(w, r)
+					return
+				}
+				http.Error(w, "not found", http.StatusNotFound)
+			}))
+			defer mockServer.Close()
+
+			t.Setenv("OLLAMA_HOST", mockServer.URL)
+
+			cmd := &cobra.Command{}
+			cmd.Flags().Bool("insecure", false, "")
+			cmd.SetContext(context.TODO())
+
+			// Redirect stderr to capture progress output
+			oldStderr := os.Stderr
+			r, w, _ := os.Pipe()
+			os.Stderr = w
+
+			// Capture stdout for the "Model pushed" message
+			oldStdout := os.Stdout
+			outR, outW, _ := os.Pipe()
+			os.Stdout = outW
+
+			err := PushHandler(cmd, []string{tt.modelName})
+
+			// Restore stderr
+			w.Close()
+			os.Stderr = oldStderr
+			// drain the pipe
+			if _, err := io.ReadAll(r); err != nil {
+				t.Fatal(err)
+			}
+
+			// Restore stdout and get output
+			outW.Close()
+			os.Stdout = oldStdout
+			stdout, _ := io.ReadAll(outR)
+
+			if tt.expectedError == "" {
+				if err != nil {
+					t.Errorf("expected no error, got %v", err)
+				}
+				if tt.expectedOutput != "" {
+					if got := string(stdout); got != tt.expectedOutput {
+						t.Errorf("expected output %q, got %q", 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
+		modelName      string
+		modelFile      string
+		serverResponse map[string]func(w http.ResponseWriter, r *http.Request)
+		expectedError  string
+		expectedOutput string
+	}{
+		{
+			name:      "successful create",
+			modelName: "test-model",
+			modelFile: "FROM foo",
+			serverResponse: map[string]func(w http.ResponseWriter, r *http.Request){
+				"/api/create": func(w http.ResponseWriter, r *http.Request) {
+					if r.Method != http.MethodPost {
+						t.Errorf("expected POST request, got %s", r.Method)
+					}
+
+					req := api.CreateRequest{}
+					if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
+						http.Error(w, err.Error(), http.StatusBadRequest)
+						return
+					}
+
+					if req.Name != "test-model" {
+						t.Errorf("expected model name 'test-model', got %s", req.Name)
+					}
+
+					if req.From != "foo" {
+						t.Errorf("expected from 'foo', got %s", req.From)
+					}
+
+					responses := []api.ProgressResponse{
+						{Status: "using existing layer sha256:56bb8bd477a519ffa694fc449c2413c6f0e1d3b1c88fa7e3c9d88d3ae49d4dcb"},
+						{Status: "writing manifest"},
+						{Status: "success"},
+					}
+
+					for _, resp := range responses {
+						if err := json.NewEncoder(w).Encode(resp); err != nil {
+							http.Error(w, err.Error(), http.StatusInternalServerError)
+							return
+						}
+						w.(http.Flusher).Flush()
+					}
+				},
+			},
+			expectedOutput: "",
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			mockServer := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
+				handler, ok := tt.serverResponse[r.URL.Path]
+				if !ok {
+					t.Errorf("unexpected request to %s", r.URL.Path)
+					http.Error(w, "not found", http.StatusNotFound)
+					return
+				}
+				handler(w, r)
+			}))
+			t.Setenv("OLLAMA_HOST", mockServer.URL)
+			t.Cleanup(mockServer.Close)
+			tempFile, err := os.CreateTemp("", "modelfile")
+			if err != nil {
+				t.Fatal(err)
+			}
+			defer os.Remove(tempFile.Name())
+
+			if _, err := tempFile.WriteString(tt.modelFile); err != nil {
+				t.Fatal(err)
+			}
+			if err := tempFile.Close(); err != nil {
+				t.Fatal(err)
+			}
+
+			cmd := &cobra.Command{}
+			cmd.Flags().String("file", "", "")
+			if err := cmd.Flags().Set("file", tempFile.Name()); err != nil {
+				t.Fatal(err)
+			}
+
+			cmd.Flags().Bool("insecure", false, "")
+			cmd.SetContext(context.TODO())
+
+			// Redirect stderr to capture progress output
+			oldStderr := os.Stderr
+			r, w, _ := os.Pipe()
+			os.Stderr = w
+
+			// Capture stdout for the "Model pushed" message
+			oldStdout := os.Stdout
+			outR, outW, _ := os.Pipe()
+			os.Stdout = outW
+
+			err = CreateHandler(cmd, []string{tt.modelName})
+
+			// Restore stderr
+			w.Close()
+			os.Stderr = oldStderr
+			// drain the pipe
+			if _, err := io.ReadAll(r); err != nil {
+				t.Fatal(err)
+			}
+
+			// Restore stdout and get output
+			outW.Close()
+			os.Stdout = oldStdout
+			stdout, _ := io.ReadAll(outR)
+
+			if tt.expectedError == "" {
+				if err != nil {
+					t.Errorf("expected no error, got %v", err)
+				}
+
+				if tt.expectedOutput != "" {
+					if got := string(stdout); got != tt.expectedOutput {
+						t.Errorf("expected output %q, got %q", tt.expectedOutput, got)
+					}
+				}
+			}
+		})
+	}
+}

cmd/interactive.go

@@ -13,11 +13,9 @@ import (
 	"strings"
 
 	"github.com/spf13/cobra"
-	"golang.org/x/exp/maps"
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/envconfig"
-	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/readline"
 	"github.com/ollama/ollama/types/errtypes"
 )
@@ -213,10 +211,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 				return err
 			}
 
-			req := &api.CreateRequest{
-				Name:      args[1],
-				Modelfile: buildModelfile(opts),
-			}
+			req := NewCreateRequest(args[1], opts)
 			fn := func(resp api.ProgressResponse) error { return nil }
 			err = client.Create(cmd.Context(), req, fn)
 			if err != nil {
@@ -319,8 +314,6 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 						opts.Messages = append(opts.Messages, newMessage)
 					}
 					fmt.Println("Set system message.")
-					sb.Reset()
-
 					sb.Reset()
 					continue
 				default:
@@ -461,36 +454,25 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 	}
 }
 
-func buildModelfile(opts runOptions) string {
-	var f parser.File
-	f.Commands = append(f.Commands, parser.Command{Name: "model", Args: cmp.Or(opts.ParentModel, opts.Model)})
+func NewCreateRequest(name string, opts runOptions) *api.CreateRequest {
+	req := &api.CreateRequest{
+		Name: name,
+		From: cmp.Or(opts.ParentModel, opts.Model),
+	}
 
 	if opts.System != "" {
-		f.Commands = append(f.Commands, parser.Command{Name: "system", Args: opts.System})
+		req.System = opts.System
 	}
 
-	keys := maps.Keys(opts.Options)
-	slices.Sort(keys)
-	for _, k := range keys {
-		v := opts.Options[k]
-		var cmds []parser.Command
-		switch t := v.(type) {
-		case []string:
-			for _, s := range t {
-				cmds = append(cmds, parser.Command{Name: k, Args: s})
-			}
-		default:
-			cmds = append(cmds, parser.Command{Name: k, Args: fmt.Sprintf("%v", t)})
-		}
-
-		f.Commands = append(f.Commands, cmds...)
+	if len(opts.Options) > 0 {
+		req.Parameters = opts.Options
 	}
 
-	for _, msg := range opts.Messages {
-		f.Commands = append(f.Commands, parser.Command{Name: "message", Args: fmt.Sprintf("%s: %s", msg.Role, msg.Content)})
+	if len(opts.Messages) > 0 {
+		req.Messages = opts.Messages
 	}
 
-	return f.String()
+	return req
 }
 
 func normalizeFilePath(fp string) string {
@@ -516,7 +498,7 @@ func extractFileNames(input string) []string {
 	// Regex to match file paths starting with optional drive letter, / ./ \ or .\ and include escaped or unescaped spaces (\ or %20)
 	// and followed by more characters and a file extension
 	// This will capture non filename strings, but we'll check for file existence to remove mismatches
-	regexPattern := `(?:[a-zA-Z]:)?(?:\./|/|\\)[\S\\ ]+?\.(?i:jpg|jpeg|png|svg)\b`
+	regexPattern := `(?:[a-zA-Z]:)?(?:\./|/|\\)[\S\\ ]+?\.(?i:jpg|jpeg|png)\b`
 	re := regexp.MustCompile(regexPattern)
 
 	return re.FindAllString(input, -1)

cmd/interactive_test.go

@@ -3,105 +3,50 @@ package cmd
 import (
 	"testing"
 
-	"github.com/google/go-cmp/cmp"
 	"github.com/stretchr/testify/assert"
-
-	"github.com/ollama/ollama/api"
 )
 
 func TestExtractFilenames(t *testing.T) {
 	// Unix style paths
 	input := ` some preamble 
- ./relative\ path/one.png inbetween1 ./not a valid two.jpg inbetween2
-/unescaped space /three.jpeg inbetween3 /valid\ path/dir/four.png "./quoted with spaces/five.svg`
+ ./relative\ path/one.png inbetween1 ./not a valid two.jpg inbetween2 ./1.svg
+/unescaped space /three.jpeg inbetween3 /valid\ path/dir/four.png "./quoted with spaces/five.JPG`
 	res := extractFileNames(input)
 	assert.Len(t, res, 5)
 	assert.Contains(t, res[0], "one.png")
 	assert.Contains(t, res[1], "two.jpg")
 	assert.Contains(t, res[2], "three.jpeg")
 	assert.Contains(t, res[3], "four.png")
-	assert.Contains(t, res[4], "five.svg")
+	assert.Contains(t, res[4], "five.JPG")
 	assert.NotContains(t, res[4], '"')
-	assert.NotContains(t, res, "inbtween")
+	assert.NotContains(t, res, "inbetween1")
+	assert.NotContains(t, res, "./1.svg")
 
 	// Windows style paths
 	input = ` some preamble
  c:/users/jdoe/one.png inbetween1 c:/program files/someplace/two.jpg inbetween2 
  /absolute/nospace/three.jpeg inbetween3 /absolute/with space/four.png inbetween4
-./relative\ path/five.svg inbetween5 "./relative with/spaces/six.png inbetween6
-d:\path with\spaces\seven.svg inbetween7 c:\users\jdoe\eight.png inbetween8 
- d:\program files\someplace\nine.png inbetween9 "E:\program files\someplace\ten.svg some ending
+./relative\ path/five.JPG inbetween5 "./relative with/spaces/six.png inbetween6
+d:\path with\spaces\seven.JPEG inbetween7 c:\users\jdoe\eight.png inbetween8 
+ d:\program files\someplace\nine.png inbetween9 "E:\program files\someplace\ten.PNG some ending
 `
 	res = extractFileNames(input)
 	assert.Len(t, res, 10)
-	assert.NotContains(t, res, "inbtween")
+	assert.NotContains(t, res, "inbetween2")
 	assert.Contains(t, res[0], "one.png")
 	assert.Contains(t, res[0], "c:")
 	assert.Contains(t, res[1], "two.jpg")
 	assert.Contains(t, res[1], "c:")
 	assert.Contains(t, res[2], "three.jpeg")
 	assert.Contains(t, res[3], "four.png")
-	assert.Contains(t, res[4], "five.svg")
+	assert.Contains(t, res[4], "five.JPG")
 	assert.Contains(t, res[5], "six.png")
-	assert.Contains(t, res[6], "seven.svg")
+	assert.Contains(t, res[6], "seven.JPEG")
 	assert.Contains(t, res[6], "d:")
 	assert.Contains(t, res[7], "eight.png")
 	assert.Contains(t, res[7], "c:")
 	assert.Contains(t, res[8], "nine.png")
 	assert.Contains(t, res[8], "d:")
-	assert.Contains(t, res[9], "ten.svg")
+	assert.Contains(t, res[9], "ten.PNG")
 	assert.Contains(t, res[9], "E:")
 }
-
-func TestModelfileBuilder(t *testing.T) {
-	opts := runOptions{
-		Model:  "hork",
-		System: "You are part horse and part shark, but all hork. Do horklike things",
-		Messages: []api.Message{
-			{Role: "user", Content: "Hey there hork!"},
-			{Role: "assistant", Content: "Yes it is true, I am half horse, half shark."},
-		},
-		Options: map[string]any{
-			"temperature":      0.9,
-			"seed":             42,
-			"penalize_newline": false,
-			"stop":             []string{"hi", "there"},
-		},
-	}
-
-	t.Run("model", func(t *testing.T) {
-		expect := `FROM hork
-SYSTEM You are part horse and part shark, but all hork. Do horklike things
-PARAMETER penalize_newline false
-PARAMETER seed 42
-PARAMETER stop hi
-PARAMETER stop there
-PARAMETER temperature 0.9
-MESSAGE user Hey there hork!
-MESSAGE assistant Yes it is true, I am half horse, half shark.
-`
-
-		actual := buildModelfile(opts)
-		if diff := cmp.Diff(expect, actual); diff != "" {
-			t.Errorf("mismatch (-want +got):\n%s", diff)
-		}
-	})
-
-	t.Run("parent model", func(t *testing.T) {
-		opts.ParentModel = "horseshark"
-		expect := `FROM horseshark
-SYSTEM You are part horse and part shark, but all hork. Do horklike things
-PARAMETER penalize_newline false
-PARAMETER seed 42
-PARAMETER stop hi
-PARAMETER stop there
-PARAMETER temperature 0.9
-MESSAGE user Hey there hork!
-MESSAGE assistant Yes it is true, I am half horse, half shark.
-`
-		actual := buildModelfile(opts)
-		if diff := cmp.Diff(expect, actual); diff != "" {
-			t.Errorf("mismatch (-want +got):\n%s", diff)
-		}
-	})
-}

cmd/runner/main.go

@@ -0,0 +1,15 @@
+package main
+
+import (
+	"fmt"
+	"os"
+
+	"github.com/ollama/ollama/llama/runner"
+)
+
+func main() {
+	if err := runner.Execute(os.Args[1:]); err != nil {
+		fmt.Fprintf(os.Stderr, "error: %s\n", err)
+		os.Exit(1)
+	}
+}

convert/convert.go

@@ -187,8 +187,12 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
 		conv = &gemma2Model{}
 	case "Phi3ForCausalLM":
 		conv = &phi3Model{}
+	case "Qwen2ForCausalLM":
+		conv = &qwen2Model{}
 	case "BertModel":
 		conv = &bertModel{}
+	case "CohereForCausalLM":
+		conv = &commandrModel{}
 	default:
 		return errors.New("unsupported architecture")
 	}

convert/convert_commandr.go

@@ -0,0 +1,76 @@
+package convert
+
+import (
+	"cmp"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type commandrModel struct {
+	ModelParameters
+	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
+	HiddenSize            uint32  `json:"hidden_size"`
+	HiddenLayers          uint32  `json:"num_hidden_layers"`
+	IntermediateSize      uint32  `json:"intermediate_size"`
+	NumAttentionHeads     uint32  `json:"num_attention_heads"`
+	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
+	LayerNormEPS          float32 `json:"layer_norm_eps"`
+	RopeTheta             float32 `json:"rope_theta"`
+	UseQKNorm             bool    `json:"use_qk_norm"`
+	MaxLength             uint32  `json:"model_max_length"`
+	LogitScale            float32 `json:"logit_scale"`
+	NCtx                  uint32  `json:"n_ctx"`
+}
+
+var _ ModelConverter = (*commandrModel)(nil)
+
+func (p *commandrModel) KV(t *Tokenizer) llm.KV {
+	kv := p.ModelParameters.KV(t)
+	kv["general.architecture"] = "command-r"
+	kv["general.name"] = "command-r"
+	kv["command-r.context_length"] = cmp.Or(p.MaxLength, p.MaxPositionEmbeddings, p.NCtx)
+	kv["command-r.embedding_length"] = p.HiddenSize
+	kv["command-r.block_count"] = p.HiddenLayers
+	kv["command-r.feed_forward_length"] = p.IntermediateSize
+	kv["command-r.attention.head_count"] = p.NumAttentionHeads
+	kv["command-r.attention.head_count_kv"] = p.NumKeyValueHeads
+	kv["command-r.attention.layer_norm_epsilon"] = p.LayerNormEPS
+	kv["command-r.rope.freq_base"] = p.RopeTheta
+	kv["command-r.max_position_embeddings"] = cmp.Or(p.MaxLength, p.MaxPositionEmbeddings)
+	kv["command-r.logit_scale"] = p.LogitScale
+	kv["command-r.rope.scaling.type"] = "none"
+
+	return kv
+}
+
+func (p *commandrModel) Tensors(ts []Tensor) []llm.Tensor {
+	var out []llm.Tensor
+	for _, t := range ts {
+		out = append(out, llm.Tensor{
+			Name:     t.Name(),
+			Kind:     t.Kind(),
+			Shape:    t.Shape(),
+			WriterTo: t,
+		})
+	}
+
+	return out
+}
+
+func (p *commandrModel) Replacements() []string {
+	return []string{
+		"self_attn.q_norm", "attn_q_norm",
+		"self_attn.k_norm", "attn_k_norm",
+		"model.layers", "blk",
+		"input_layernorm", "attn_norm",
+		"mlp.down_proj", "ffn_down",
+		"mlp.gate_proj", "ffn_gate",
+		"mlp.up_proj", "ffn_up",
+		"self_attn.k_proj", "attn_k",
+		"self_attn.o_proj", "attn_output",
+		"self_attn.q_proj", "attn_q",
+		"self_attn.v_proj", "attn_v",
+		"model.norm", "output_norm",
+		"model.embed_tokens", "token_embd",
+	}
+}

convert/convert_qwen2.go

@@ -0,0 +1,78 @@
+package convert
+
+import "github.com/ollama/ollama/llm"
+
+type qwen2Model struct {
+	ModelParameters
+	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
+	HiddenSize            uint32  `json:"hidden_size"`
+	HiddenLayers          uint32  `json:"num_hidden_layers"`
+	IntermediateSize      uint32  `json:"intermediate_size"`
+	NumAttentionHeads     uint32  `json:"num_attention_heads"`
+	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
+	RopeTheta             float32 `json:"rope_theta"`
+	RopeScaling           struct {
+		Type                          string     `json:"type"`
+		Factor                        ropeFactor `json:"factor"`
+		OriginalMaxPositionEmbeddings uint32     `json:"original_max_position_embeddings"`
+	} `json:"rope_scaling"`
+	RMSNormEPS float32 `json:"rms_norm_eps"`
+}
+
+var _ ModelConverter = (*qwen2Model)(nil)
+
+func (q *qwen2Model) KV(t *Tokenizer) llm.KV {
+	kv := q.ModelParameters.KV(t)
+	kv["general.architecture"] = "qwen2"
+	kv["qwen2.block_count"] = q.HiddenLayers
+	kv["qwen2.context_length"] = q.MaxPositionEmbeddings
+	kv["qwen2.embedding_length"] = q.HiddenSize
+	kv["qwen2.feed_forward_length"] = q.IntermediateSize
+	kv["qwen2.attention.head_count"] = q.NumAttentionHeads
+	kv["qwen2.attention.head_count_kv"] = q.NumKeyValueHeads
+	kv["qwen2.rope.freq_base"] = q.RopeTheta
+	kv["qwen2.attention.layer_norm_rms_epsilon"] = q.RMSNormEPS
+
+	switch q.RopeScaling.Type {
+	case "":
+		// no scaling
+	case "yarn":
+		kv["qwen2.rope.scaling.type"] = q.RopeScaling.Type
+		kv["qwen2.rope.scaling.factor"] = q.RopeScaling.Factor
+	default:
+		panic("unknown rope scaling type")
+	}
+	return kv
+}
+
+func (q *qwen2Model) Tensors(ts []Tensor) []llm.Tensor {
+	var out []llm.Tensor
+	for _, t := range ts {
+		out = append(out, llm.Tensor{
+			Name:     t.Name(),
+			Kind:     t.Kind(),
+			Shape:    t.Shape(),
+			WriterTo: t,
+		})
+	}
+
+	return out
+}
+
+func (p *qwen2Model) Replacements() []string {
+	return []string{
+		"lm_head", "output",
+		"model.embed_tokens", "token_embd",
+		"model.layers", "blk",
+		"input_layernorm", "attn_norm",
+		"self_attn.k_proj", "attn_k",
+		"self_attn.v_proj", "attn_v",
+		"self_attn.q_proj", "attn_q",
+		"self_attn.o_proj", "attn_output",
+		"mlp.down_proj", "ffn_down",
+		"mlp.gate_proj", "ffn_gate",
+		"mlp.up_proj", "ffn_up",
+		"post_attention_layernorm", "ffn_norm",
+		"model.norm", "output_norm",
+	}
+}

convert/convert_test.go

@@ -108,6 +108,8 @@ func TestConvertModel(t *testing.T) {
 		"Phi-3-mini-128k-instruct",
 		"all-MiniLM-L6-v2",
 		"gemma-2-9b-it",
+		"Qwen2.5-0.5B-Instruct",
+		"c4ai-command-r-v01",
 	}
 
 	for i := range cases {

convert/sentencepiece/sentencepiece_model.pb.go

@@ -331,7 +331,7 @@ type TrainerSpec struct {
 	// Reserved special meta tokens.
 	// * -1 is not used.
 	// * unk_id must not be -1.
-	// Id must starts with 0 and be contigous.
+	// Id must start with 0 and be contiguous.
 	UnkId    *int32  `protobuf:"varint,40,opt,name=unk_id,json=unkId,def=0" json:"unk_id,omitempty"`  // <unk>
 	BosId    *int32  `protobuf:"varint,41,opt,name=bos_id,json=bosId,def=1" json:"bos_id,omitempty"`  // <s>
 	EosId    *int32  `protobuf:"varint,42,opt,name=eos_id,json=eosId,def=2" json:"eos_id,omitempty"`  // </s>

convert/sentencepiece_model.proto

@@ -213,7 +213,7 @@ message TrainerSpec {
   // Reserved special meta tokens.
   // * -1 is not used.
   // * unk_id must not be -1.
-  // Id must starts with 0 and be contigous.
+  // Id must start with 0 and be contiguous.
   optional int32 unk_id = 40 [default = 0];   // <unk>
   optional int32 bos_id = 41 [default = 1];   // <s>
   optional int32 eos_id = 42 [default = 2];   // </s>

convert/testdata/Qwen2.5-0.5B-Instruct.json

@@ -0,0 +1,314 @@
+{
+    "general.architecture": "qwen2",
+    "general.file_type": "1",
+    "general.parameter_count": "494032768",
+    "general.quantization_version": "2",
+    "output_norm.weight": "93a01a6db3419e85320a244bbf8ae81c43033b1d10c342bea3797ff2ce348390",
+    "qwen2.attention.head_count": "14",
+    "qwen2.attention.head_count_kv": "2",
+    "qwen2.attention.layer_norm_rms_epsilon": "1e-06",
+    "qwen2.block_count": "24",
+    "qwen2.context_length": "32768",
+    "qwen2.embedding_length": "896",
+    "qwen2.feed_forward_length": "4864",
+    "qwen2.rope.freq_base": "1e+06",
+    "token_embd.weight": "d74257dc547b48be5ae7b93f1c9af072c0c42dbbb85503078e25c59cd09e68d0",
+    "tokenizer.ggml.add_eos_token": "false",
+    "tokenizer.ggml.add_padding_token": "false",
+    "tokenizer.ggml.eos_token_id": "151645",
+    "tokenizer.ggml.merges": "6b1b1c58f1223d74f9095929d3e6416cdd74784440221a5507b87b8197f2bfd2",
+    "tokenizer.ggml.model": "gpt2",
+    "tokenizer.ggml.padding_token_id": "151643",
+    "tokenizer.ggml.pre": "qwen2",
+    "tokenizer.ggml.scores": "94e247e531e8b0fa3d248f3de09c9beae0c87da8106208a8edfaac0b8ec4b53d",
+    "tokenizer.ggml.token_type": "b178dbc9d1b2e08f84d02918e00fc2de2619a250e6c188c91a6605f701860055",
+    "tokenizer.ggml.tokens": "1d93f6679b23a1152b725f7f473792d54d53c1040c5250d3e46b42f81e0a1a34",
+    "blk.0.attn_k.bias": "5ce6617845f66c34515978d23d52e729c298d8bffa28c356a0428bef17142cf1",
+    "blk.0.attn_k.weight": "a960832a9e0e83e4d95402e5d1a01cc74300fcca0c381237162126330e1a7af8",
+    "blk.0.attn_norm.weight": "32c7d51cd0958f1f1771174192db341f9770516d7595a2f0fd18a4d78bd5aba3",
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+  }

convert/testdata/c4ai-command-r-v01.json

@@ -0,0 +1,344 @@
+{
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+    "blk.35.attn_k.weight": "52513fc0cd4e997a842729af7d21dd09399bce0a339558374738be266d0fa2f0",
+    "blk.35.attn_norm.weight": "e5281fa911964263ccf1630b14762edbd41d0b9472d6ec695fc600fed4892c35",
+    "blk.35.attn_output.weight": "b391d6705d5dc6f48326b5fd16573f679edf64109d86fb729a498819676590ca",
+    "blk.35.attn_q.weight": "d16446921966db9b0e0539626ad22a2511ace780e59379d6a4162d8c5441440b",
+    "blk.35.attn_v.weight": "9d8cdf23ffdb0c5c74106843390b94b24c9f33ef0eb9998d39f78c73390101ea",
+    "blk.35.ffn_down.weight": "938eb6301f7bbf162d7dd965682a5ed11d0a4a530c6fedd7e5469ce80012fc17",
+    "blk.35.ffn_gate.weight": "5ad84f5a0c8edcfea1ecf1a3e3d21d85ceda0c4ad9e3c6ca68885eeff8ed3c2f",
+    "blk.35.ffn_up.weight": "1c4330d9dc71bf4c98812c34356c51f520f47610a534152aa6d29284b758090d",
+    "blk.36.attn_k.weight": "ef720655e5ca2465f13db2dfc4732fb4ef2c9d53acde52f514fd4f301e974081",
+    "blk.36.attn_norm.weight": "88f4b9310b3c8c2644e3029160cd35678c79dfa59280430e03f5c29a6fe84a58",
+    "blk.36.attn_output.weight": "aec6f915fffd7bb72cd783273e871b4f09605950089d45e72059d1316b6c4b01",
+    "blk.36.attn_q.weight": "72f9408a2405d42f8db6ce5fcf1d26a3660b6f225fc60e77d0277109cfcb82ed",
+    "blk.36.attn_v.weight": "0f3b3d851dc44b3893ef53f6cca5b4acc9658bacfe1cc2d13c3d704ddd409b67",
+    "blk.36.ffn_down.weight": "470aec48ce8c5129a6654d9fd26fcae72776f9fc1429a8bb05818072a876475d",
+    "blk.36.ffn_gate.weight": "7f5f296d09cf55679767b5d15de3eff489c456782119f25204be4b1647f18dcf",
+    "blk.36.ffn_up.weight": "b7ef74a1f7ffb4982711d93f1787be3a70edc3d2358d5203c41d8900508037d4",
+    "blk.37.attn_k.weight": "c4ffa5412e4ff2dcfe1aed991c1f54169fd171a4c7638e4b9f21a1ca64c5e1d6",
+    "blk.37.attn_norm.weight": "4eb6c888d841cccfacf5b963f8611120f6ff24b84af0b5714fd9ab36dcda422f",
+    "blk.37.attn_output.weight": "db2a7bbf9682f9f6eea672dae8e150738f1bf74dbc80edc7022017a3f040c8ac",
+    "blk.37.attn_q.weight": "e38c0462aff139afcbab289189823527e453abc9e541154adde5e7af88cacf0b",
+    "blk.37.attn_v.weight": "952eb2492ed452a72f96bcc12d4b2affad9dfdf46ee39ce4a5d7b57a5dc301e5",
+    "blk.37.ffn_down.weight": "25f23a8fbc44febf6dc4848fd7fe03a580e2822bd3b3b5a51f4990826bfe3e4e",
+    "blk.37.ffn_gate.weight": "707da5eb40118b035305d3262444382351f170a20a537386a70e90c5a83a7817",
+    "blk.37.ffn_up.weight": "d2d2ba5cfc4ef47338dd7384219e22bf030a5a2209e0354d88f5bbaaafd20e87",
+    "blk.38.attn_k.weight": "abc4bb189dedf7ce661e79028427623a4f91ac091c2cd60e31b58bc62b1cda71",
+    "blk.38.attn_norm.weight": "9f4803a7d03fd40fcb83d85f84eb1d5682ea4e5bb084f210c02850675d804c3d",
+    "blk.38.attn_output.weight": "77cb66007f1a41df7135d0e7f900ceb499c2f667dfc3f1a6ac01a3203bbd3ccf",
+    "blk.38.attn_q.weight": "d94a8b26cd375bf2bcaa76597e314aa8268ee50a479d00931e5e0e021feadb5d",
+    "blk.38.attn_v.weight": "660c907888bc5016dc69b7d35fe6f55c7ded697c93be0e2d332a2f17aff88758",
+    "blk.38.ffn_down.weight": "6f06173bae5b00ffaf88ef383619a8b9c6a8d0d5c6494695d17f6c1de1a68a13",
+    "blk.38.ffn_gate.weight": "89f99be149d03f116527bfcabe073c50001c874de40fb6e817f6619027f3cd05",
+    "blk.38.ffn_up.weight": "8d57557c8d5e2d2688b73f01dddf1ce8d5194990cda6358153320aea88aac7f8",
+    "blk.39.attn_k.weight": "21be09c988b46c8393e6c2ec9230f3b5136eb7607dd1953ba92d0811c2f0dd75",
+    "blk.39.attn_norm.weight": "ba7c1912dd1c4e2d16917201f62396fd0600e4a451137eaddff255548c209abd",
+    "blk.39.attn_output.weight": "acfaf4abb3fd27fd899b5563c3877f176b597d8f6cdb2f2fd3f3a0bd4da15ed6",
+    "blk.39.attn_q.weight": "e8adbc140d4c8f0db2a27ca584c5531d5b1e080555fe627e34d80d0814a92bed",
+    "blk.39.attn_v.weight": "92f96b0e1f724e73a0f90a76c145654418844c04a6d4b14c05eb5af8a62bf8dc",
+    "blk.39.ffn_down.weight": "4d9ee7c65fc16fe95d10c47b79ac6a525741947600a64b5fcea5d300a82c50de",
+    "blk.39.ffn_gate.weight": "7e18507989f39b32191133d2657c2ee3b74f42f070579204d727eb72215793d1",
+    "blk.39.ffn_up.weight": "22cda752269c9757ba918abede1df95bb0f83a5c772dea13c8deea3d5f2723d9",
+    "output_norm.weight": "2858cf0e39d32caf52b7861378ace076000241e147f10b9eb21d8a5cd149e3cb"
+}

convert/tokenizer.go

@@ -10,6 +10,7 @@ import (
 	"log/slog"
 	"os"
 	"slices"
+	"strings"
 
 	"golang.org/x/exp/maps"
 )
@@ -60,7 +61,25 @@ func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error)
 			addedTokens[t.Content] = t
 		}
 
-		t.Merges = tt.Model.Merges
+		if len(tt.Model.Merges) == 0 {
+			// noop; merges is empty
+		} else if err := json.Unmarshal(tt.Model.Merges, &t.Merges); err == nil {
+			// noop; merges is []string
+		} else if merges, err := func() ([][]string, error) {
+			var merges [][]string
+			if err := json.Unmarshal(tt.Model.Merges, &merges); err != nil {
+				return nil, err
+			}
+
+			return merges, nil
+		}(); err == nil {
+			t.Merges = make([]string, len(merges))
+			for i := range merges {
+				t.Merges[i] = strings.Join(merges[i], " ")
+			}
+		} else {
+			return nil, fmt.Errorf("could not parse tokenizer merges. expected []string or [][]string: %w", err)
+		}
 
 		sha256sum := sha256.New()
 		for _, pt := range tt.PreTokenizer.PreTokenizers {
@@ -81,6 +100,8 @@ func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error)
 			t.Pre = "deepseek-llm"
 		case "21cde974d587f0d54dc8d56b183cc1e6239600172035c68fbd6d4b9f8da0576e":
 			t.Pre = "deepseek-coder"
+		case "1ff7f41064896984db5d1bb6ff64fa4bc29007d08c1b439e505b7392777a319e":
+			t.Pre = "qwen2"
 		case "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855":
 			// noop, empty pretokenizer
 		default:
@@ -156,9 +177,9 @@ func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error)
 type tokenizer struct {
 	AddedTokens []token `json:"added_tokens"`
 	Model       struct {
-		Type   string         `json:"type"`
-		Vocab  map[string]int `json:"vocab"`
-		Merges []string       `json:"merges"`
+		Type   string          `json:"type"`
+		Vocab  map[string]int  `json:"vocab"`
+		Merges json.RawMessage `json:"merges"`
 	} `json:"model"`
 
 	PreTokenizer struct {

convert/tokenizer_test.go

@@ -191,6 +191,62 @@ func TestParseTokenizer(t *testing.T) {
 				Pre: "default",
 			},
 		},
+		{
+			name: "list string merges",
+			fsys: createTokenizerFS(t, t.TempDir(), map[string]io.Reader{
+				"tokenizer.json": strings.NewReader(`{
+					"model": {
+						"merges": [
+							"a b",
+							"c d",
+							"e f"
+						]
+					}
+				}`),
+			}),
+			want: &Tokenizer{
+				Vocabulary: &Vocabulary{
+					Model: "gpt2",
+				},
+				Merges: []string{
+					"a b",
+					"c d",
+					"e f",
+				},
+				Pre: "default",
+			},
+		},
+		{
+			name: "list list string merges",
+			fsys: createTokenizerFS(t, t.TempDir(), map[string]io.Reader{
+				"tokenizer.json": strings.NewReader(`{
+					"model": {
+						"merges": [
+							[
+								"a", "b"
+							],
+							[
+								"c", "d"
+							],
+							[
+								"e", "f"
+							]
+						]
+					}
+				}`),
+			}),
+			want: &Tokenizer{
+				Vocabulary: &Vocabulary{
+					Model: "gpt2",
+				},
+				Merges: []string{
+					"a b",
+					"c d",
+					"e f",
+				},
+				Pre: "default",
+			},
+		},
 	}
 
 	for _, tt := range cases {

discover/amd_linux.go

@@ -77,6 +77,7 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
 
 	gfxOverride := envconfig.HsaOverrideGfxVersion()
 	var supported []string
+	depPaths := LibraryDirs()
 	libDir := ""
 
 	// The amdgpu driver always exposes the host CPU(s) first, but we have to skip them and subtract
@@ -300,8 +301,11 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
 			})
 			continue
 		}
-
-		if int(major) < RocmComputeMin {
+		minVer, err := strconv.Atoi(RocmComputeMajorMin)
+		if err != nil {
+			slog.Error("invalid RocmComputeMajorMin setting", "value", RocmComputeMajorMin, "error", err)
+		}
+		if int(major) < minVer {
 			reason := fmt.Sprintf("amdgpu too old gfx%d%x%x", major, minor, patch)
 			slog.Warn(reason, "gpu", gpuID)
 			unsupportedGPUs = append(unsupportedGPUs, UnsupportedGPUInfo{
@@ -349,8 +353,9 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
 				})
 				return nil, err
 			}
+			depPaths = append(depPaths, libDir)
 		}
-		gpuInfo.DependencyPath = libDir
+		gpuInfo.DependencyPath = depPaths
 
 		if gfxOverride == "" {
 			// Only load supported list once

discover/amd_windows.go

@@ -50,12 +50,14 @@ 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()
@@ -111,7 +113,7 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
 				UnreliableFreeMemory: true,
 
 				ID:             strconv.Itoa(i), // TODO this is probably wrong if we specify visible devices
-				DependencyPath: libDir,
+				DependencyPath: depPaths,
 				MinimumMemory:  rocmMinimumMemory,
 				Name:           name,
 				Compute:        gfx,
@@ -182,7 +184,7 @@ func (gpus RocmGPUInfoList) RefreshFreeMemory() error {
 	hl, err := NewHipLib()
 	if err != nil {
 		slog.Debug(err.Error())
-		return nil
+		return err
 	}
 	defer hl.Release()
 

discover/cpu_common.go

@@ -5,21 +5,8 @@ import (
 	"path/filepath"
 	"runtime"
 	"strings"
-
-	"golang.org/x/sys/cpu"
 )
 
-func GetCPUCapability() CPUCapability {
-	if cpu.X86.HasAVX2 {
-		return CPUCapabilityAVX2
-	}
-	if cpu.X86.HasAVX {
-		return CPUCapabilityAVX
-	}
-	// else LCD
-	return CPUCapabilityNone
-}
-
 func IsNUMA() bool {
 	if runtime.GOOS != "linux" {
 		// numa support in llama.cpp is linux only

discover/gpu.go

@@ -16,12 +16,14 @@ import (
 	"os"
 	"path/filepath"
 	"runtime"
+	"strconv"
 	"strings"
 	"sync"
 	"unsafe"
 
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
+	"github.com/ollama/ollama/runners"
 )
 
 type cudaHandles struct {
@@ -45,7 +47,6 @@ const (
 var (
 	gpuMutex      sync.Mutex
 	bootstrapped  bool
-	cpuCapability CPUCapability
 	cpus          []CPUInfo
 	cudaGPUs      []CudaGPUInfo
 	nvcudaLibPath string
@@ -64,9 +65,13 @@ var (
 )
 
 // With our current CUDA compile flags, older than 5.0 will not work properly
-var CudaComputeMin = [2]C.int{5, 0}
+// (string values used to allow ldflags overrides at build time)
+var (
+	CudaComputeMajorMin = "5"
+	CudaComputeMinorMin = "0"
+)
 
-var RocmComputeMin = 9
+var RocmComputeMajorMin = "9"
 
 // TODO find a better way to detect iGPU instead of minimum memory
 const IGPUMemLimit = 1 * format.GibiByte // 512G is what they typically report, so anything less than 1G must be iGPU
@@ -101,9 +106,9 @@ func initCudaHandles() *cudaHandles {
 		localAppData := os.Getenv("LOCALAPPDATA")
 		cudartMgmtPatterns = []string{filepath.Join(localAppData, "Programs", "Ollama", CudartMgmtName)}
 	}
-	libDir := LibraryDir()
-	if libDir != "" {
-		cudartMgmtPatterns = []string{filepath.Join(libDir, CudartMgmtName)}
+	libDirs := LibraryDirs()
+	for _, d := range libDirs {
+		cudartMgmtPatterns = append(cudartMgmtPatterns, filepath.Join(d, CudartMgmtName))
 	}
 	cudartMgmtPatterns = append(cudartMgmtPatterns, CudartGlobs...)
 
@@ -219,16 +224,23 @@ func GetGPUInfo() GpuInfoList {
 
 	if !bootstrapped {
 		slog.Info("looking for compatible GPUs")
+		cudaComputeMajorMin, err := strconv.Atoi(CudaComputeMajorMin)
+		if err != nil {
+			slog.Error("invalid CudaComputeMajorMin setting", "value", CudaComputeMajorMin, "error", err)
+		}
+		cudaComputeMinorMin, err := strconv.Atoi(CudaComputeMinorMin)
+		if err != nil {
+			slog.Error("invalid CudaComputeMinorMin setting", "value", CudaComputeMinorMin, "error", err)
+		}
 		bootstrapErrors = []error{}
 		needRefresh = false
-		cpuCapability = GetCPUCapability()
 		var memInfo C.mem_info_t
 
 		mem, err := GetCPUMem()
 		if err != nil {
 			slog.Warn("error looking up system memory", "error", err)
 		}
-		depPath := LibraryDir()
+		depPaths := LibraryDirs()
 		details, err := GetCPUDetails()
 		if err != nil {
 			slog.Warn("failed to lookup CPU details", "error", err)
@@ -238,24 +250,14 @@ func GetGPUInfo() GpuInfoList {
 				GpuInfo: GpuInfo{
 					memInfo:        mem,
 					Library:        "cpu",
-					Variant:        cpuCapability.String(),
+					Variant:        runners.GetCPUCapability().String(),
 					ID:             "0",
-					DependencyPath: depPath,
+					DependencyPath: depPaths,
 				},
 				CPUs: details,
 			},
 		}
 
-		// Fallback to CPU mode if we're lacking required vector extensions on x86
-		if cpuCapability < GPURunnerCPUCapability && runtime.GOARCH == "amd64" {
-			err := fmt.Errorf("CPU does not have minimum vector extensions, GPU inference disabled.  Required:%s  Detected:%s", GPURunnerCPUCapability, cpuCapability)
-			slog.Warn(err.Error())
-			bootstrapErrors = append(bootstrapErrors, err)
-			bootstrapped = true
-			// No need to do any GPU discovery, since we can't run on them
-			return GpuInfoList{cpus[0].GpuInfo}
-		}
-
 		// Load ALL libraries
 		cHandles = initCudaHandles()
 
@@ -292,19 +294,23 @@ func GetGPUInfo() GpuInfoList {
 				gpuInfo.DriverMajor = driverMajor
 				gpuInfo.DriverMinor = driverMinor
 				variant := cudaVariant(gpuInfo)
-				if depPath != "" {
-					gpuInfo.DependencyPath = depPath
+				if depPaths != nil {
+					gpuInfo.DependencyPath = depPaths
 					// Check for variant specific directory
 					if variant != "" {
-						if _, err := os.Stat(filepath.Join(depPath, "cuda_"+variant)); err == nil {
-							gpuInfo.DependencyPath = filepath.Join(depPath, "cuda_"+variant)
+						for _, d := range depPaths {
+							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
+								gpuInfo.DependencyPath = append([]string{filepath.Join(d, "cuda_"+variant)}, gpuInfo.DependencyPath...)
+								break
+							}
 						}
 					}
 				}
 				gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
 				gpuInfo.Variant = variant
 
-				if memInfo.major < CudaComputeMin[0] || (memInfo.major == CudaComputeMin[0] && memInfo.minor < CudaComputeMin[1]) {
+				if int(memInfo.major) < cudaComputeMajorMin || (int(memInfo.major) == cudaComputeMajorMin && int(memInfo.minor) < cudaComputeMinorMin) {
 					unsupportedGPUs = append(unsupportedGPUs,
 						UnsupportedGPUInfo{
 							GpuInfo: gpuInfo.GpuInfo,
@@ -316,7 +322,9 @@ func GetGPUInfo() GpuInfoList {
 				// query the management library as well so we can record any skew between the two
 				// which represents overhead on the GPU we must set aside on subsequent updates
 				if cHandles.nvml != nil {
-					C.nvml_get_free(*cHandles.nvml, C.int(gpuInfo.index), &memInfo.free, &memInfo.total, &memInfo.used)
+					uuid := C.CString(gpuInfo.ID)
+					defer C.free(unsafe.Pointer(uuid))
+					C.nvml_get_free(*cHandles.nvml, uuid, &memInfo.free, &memInfo.total, &memInfo.used)
 					if memInfo.err != nil {
 						slog.Warn("error looking up nvidia GPU memory", "error", C.GoString(memInfo.err))
 						C.free(unsafe.Pointer(memInfo.err))
@@ -368,7 +376,7 @@ 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 = depPath
+						gpuInfo.DependencyPath = depPaths
 						oneapiGPUs = append(oneapiGPUs, gpuInfo)
 					}
 				}
@@ -383,6 +391,8 @@ func GetGPUInfo() GpuInfoList {
 		if len(cudaGPUs) == 0 && len(rocmGPUs) == 0 && len(oneapiGPUs) == 0 {
 			slog.Info("no compatible GPUs were discovered")
 		}
+
+		// TODO verify we have runners for the discovered GPUs, filter out any that aren't supported with good error messages
 	}
 
 	// For detected GPUs, load library if not loaded
@@ -417,7 +427,9 @@ func GetGPUInfo() GpuInfoList {
 		}
 		for i, gpu := range cudaGPUs {
 			if cHandles.nvml != nil {
-				C.nvml_get_free(*cHandles.nvml, C.int(gpu.index), &memInfo.free, &memInfo.total, &memInfo.used)
+				uuid := C.CString(gpu.ID)
+				defer C.free(unsafe.Pointer(uuid))
+				C.nvml_get_free(*cHandles.nvml, uuid, &memInfo.free, &memInfo.total, &memInfo.used)
 			} else if cHandles.cudart != nil {
 				C.cudart_bootstrap(*cHandles.cudart, C.int(gpu.index), &memInfo)
 			} else if cHandles.nvcuda != nil {
@@ -505,7 +517,10 @@ func FindGPULibs(baseLibName string, defaultPatterns []string) []string {
 	slog.Debug("Searching for GPU library", "name", baseLibName)
 
 	// Start with our bundled libraries
-	patterns := []string{filepath.Join(LibraryDir(), baseLibName)}
+	patterns := []string{}
+	for _, d := range LibraryDirs() {
+		patterns = append(patterns, filepath.Join(d, baseLibName))
+	}
 
 	switch runtime.GOOS {
 	case "windows":
@@ -527,7 +542,6 @@ func FindGPULibs(baseLibName string, defaultPatterns []string) []string {
 	patterns = append(patterns, defaultPatterns...)
 	slog.Debug("gpu library search", "globs", patterns)
 	for _, pattern := range patterns {
-
 		// Nvidia PhysX known to return bogus results
 		if strings.Contains(pattern, "PhysX") {
 			slog.Debug("skipping PhysX cuda library path", "path", pattern)
@@ -701,32 +715,26 @@ func (l GpuInfoList) GetVisibleDevicesEnv() (string, string) {
 	}
 }
 
-func LibraryDir() string {
-	// On Windows/linux we bundle the dependencies at the same level as the executable
+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)
+		}
 	}
-	cwd, err := os.Getwd()
+	rDir := runners.Locate()
 	if err != nil {
-		slog.Warn("failed to lookup working directory", "error", err)
+		slog.Warn("unable to locate gpu dependency libraries", "error", err)
+	} else {
+		paths = append(paths, filepath.Dir(rDir))
 	}
-	// Scan for any of our dependeices, and pick first match
-	for _, root := range []string{filepath.Dir(appExe), filepath.Join(filepath.Dir(appExe), envconfig.LibRelativeToExe()), cwd} {
-		libDep := filepath.Join("lib", "ollama")
-		if _, err := os.Stat(filepath.Join(root, libDep)); err == nil {
-			return filepath.Join(root, libDep)
-		}
-		// Developer mode, local build
-		if _, err := os.Stat(filepath.Join(root, runtime.GOOS+"-"+runtime.GOARCH, libDep)); err == nil {
-			return filepath.Join(root, runtime.GOOS+"-"+runtime.GOARCH, libDep)
-		}
-		if _, err := os.Stat(filepath.Join(root, "dist", runtime.GOOS+"-"+runtime.GOARCH, libDep)); err == nil {
-			return filepath.Join(root, "dist", runtime.GOOS+"-"+runtime.GOARCH, libDep)
-		}
-	}
-	slog.Warn("unable to locate gpu dependency libraries")
-	return ""
+	return paths
 }
 
 func GetSystemInfo() SystemInfo {

discover/gpu_darwin.go

@@ -15,6 +15,7 @@ import (
 	"syscall"
 
 	"github.com/ollama/ollama/format"
+	"github.com/ollama/ollama/runners"
 )
 
 const (
@@ -27,7 +28,7 @@ func GetGPUInfo() GpuInfoList {
 		return []GpuInfo{
 			{
 				Library: "cpu",
-				Variant: GetCPUCapability().String(),
+				Variant: runners.GetCPUCapability().String(),
 				memInfo: mem,
 			},
 		}
@@ -50,7 +51,7 @@ func GetCPUInfo() GpuInfoList {
 	return []GpuInfo{
 		{
 			Library: "cpu",
-			Variant: GetCPUCapability().String(),
+			Variant: runners.GetCPUCapability().String(),
 			memInfo: mem,
 		},
 	}

discover/gpu_info_nvcuda.c

@@ -4,6 +4,7 @@
 #include "gpu_info_nvcuda.h"
 
 void nvcuda_init(char *nvcuda_lib_path, nvcuda_init_resp_t *resp) {
+  LOG(resp->ch.verbose, "initializing %s\n", nvcuda_lib_path);
   CUresult ret;
   resp->err = NULL;
   resp->num_devices = 0;
@@ -57,8 +58,10 @@ void nvcuda_init(char *nvcuda_lib_path, nvcuda_init_resp_t *resp) {
       resp->cudaErr = -1;
       return;
     }
+    LOG(resp->ch.verbose, "dlsym: %s - %p\n", l[i].s, *l[i].p);
   }
 
+  LOG(resp->ch.verbose, "calling cuInit\n");
   ret = (*resp->ch.cuInit)(0);
   if (ret != CUDA_SUCCESS) {
     LOG(resp->ch.verbose, "cuInit err: %d\n", ret);
@@ -75,15 +78,18 @@ void nvcuda_init(char *nvcuda_lib_path, nvcuda_init_resp_t *resp) {
   resp->ch.driver_minor = 0;
 
   // Report driver version if we're in verbose mode, ignore errors
+  LOG(resp->ch.verbose, "calling cuDriverGetVersion\n");
   ret = (*resp->ch.cuDriverGetVersion)(&version);
   if (ret != CUDA_SUCCESS) {
     LOG(resp->ch.verbose, "cuDriverGetVersion failed: %d\n", ret);
   } else {
+    LOG(resp->ch.verbose, "raw version 0x%x\n", version);
     resp->ch.driver_major = version / 1000;
     resp->ch.driver_minor = (version - (resp->ch.driver_major * 1000)) / 10;
     LOG(resp->ch.verbose, "CUDA driver version: %d.%d\n", resp->ch.driver_major, resp->ch.driver_minor);
   }
 
+  LOG(resp->ch.verbose, "calling cuDeviceGetCount\n");
   ret = (*resp->ch.cuDeviceGetCount)(&resp->num_devices);
   if (ret != CUDA_SUCCESS) {
     LOG(resp->ch.verbose, "cuDeviceGetCount err: %d\n", ret);
@@ -94,6 +100,7 @@ void nvcuda_init(char *nvcuda_lib_path, nvcuda_init_resp_t *resp) {
     resp->cudaErr = ret;
     return;
   }
+  LOG(resp->ch.verbose, "device count %d\n", resp->num_devices);
 }
 
 const int buflen = 256;

discover/gpu_info_nvml.c

@@ -17,7 +17,7 @@ void nvml_init(char *nvml_lib_path, nvml_init_resp_t *resp) {
   } l[] = {
       {"nvmlInit_v2", (void *)&resp->ch.nvmlInit_v2},
       {"nvmlShutdown", (void *)&resp->ch.nvmlShutdown},
-      {"nvmlDeviceGetHandleByIndex", (void *)&resp->ch.nvmlDeviceGetHandleByIndex},
+      {"nvmlDeviceGetHandleByUUID", (void *)&resp->ch.nvmlDeviceGetHandleByUUID},
       {"nvmlDeviceGetMemoryInfo", (void *)&resp->ch.nvmlDeviceGetMemoryInfo},
       {NULL, NULL},
   };
@@ -67,20 +67,20 @@ void nvml_init(char *nvml_lib_path, nvml_init_resp_t *resp) {
 }
 
 
-void nvml_get_free(nvml_handle_t h, int device_id, uint64_t *free, uint64_t *total, uint64_t *used) {
+void nvml_get_free(nvml_handle_t h, char *uuid, uint64_t *free, uint64_t *total, uint64_t *used) {
     nvmlDevice_t device;
     nvmlMemory_t memInfo = {0};
     nvmlReturn_t ret;
-    ret = (*h.nvmlDeviceGetHandleByIndex)(device_id, &device);
+    ret = (*h.nvmlDeviceGetHandleByUUID)((const char *)(uuid), &device);
     if (ret != NVML_SUCCESS) {
-        LOG(1, "unable to get device handle %d: %d", device_id, ret);
+        LOG(1, "unable to get device handle %s: %d", uuid, ret);
         *free = 0;
         return;
     }
 
     ret = (*h.nvmlDeviceGetMemoryInfo)(device, &memInfo);
     if (ret != NVML_SUCCESS) {
-        LOG(1, "device memory info lookup failure %d: %d", device_id, ret);
+        LOG(1, "device memory info lookup failure %s: %d", uuid, ret);
         *free = 0;
         return;
     }

discover/gpu_info_nvml.h

@@ -25,7 +25,7 @@ typedef struct nvml_handle {
   uint16_t verbose;
   nvmlReturn_t (*nvmlInit_v2)(void);
   nvmlReturn_t (*nvmlShutdown)(void);
-  nvmlReturn_t (*nvmlDeviceGetHandleByIndex)(unsigned int, nvmlDevice_t *);
+  nvmlReturn_t (*nvmlDeviceGetHandleByUUID)(const char *, nvmlDevice_t *);
   nvmlReturn_t (*nvmlDeviceGetMemoryInfo)(nvmlDevice_t, nvmlMemory_t *);
 } nvml_handle_t;
 
@@ -41,7 +41,7 @@ typedef struct nvml_compute_capability {
 } nvml_compute_capability_t;
 
 void nvml_init(char *nvml_lib_path, nvml_init_resp_t *resp);
-void nvml_get_free(nvml_handle_t ch,  int device_id, uint64_t *free, uint64_t *total, uint64_t *used);
+void nvml_get_free(nvml_handle_t ch, char *uuid, uint64_t *free, uint64_t *total, uint64_t *used);
 void nvml_release(nvml_handle_t ch);
 
 #endif  // __GPU_INFO_NVML_H__

discover/gpu_windows.go

@@ -209,7 +209,7 @@ func processSystemLogicalProcessorInforationList(buf []byte) []*winPackage {
 		}
 	}
 
-	// Sumarize the results
+	// Summarize the results
 	for i, pkg := range packages {
 		slog.Info("", "package", i, "cores", pkg.coreCount, "efficiency", pkg.efficiencyCoreCount, "threads", pkg.threadCount)
 	}

discover/types.go

@@ -5,6 +5,7 @@ import (
 	"log/slog"
 
 	"github.com/ollama/ollama/format"
+	"github.com/ollama/ollama/runners"
 )
 
 type memInfo struct {
@@ -25,7 +26,7 @@ type GpuInfo struct { // TODO better name maybe "InferenceProcessor"?
 	MinimumMemory uint64 `json:"-"`
 
 	// Any extra PATH/LD_LIBRARY_PATH dependencies required for the Library to operate properly
-	DependencyPath string `json:"lib_path,omitempty"`
+	DependencyPath []string `json:"lib_path,omitempty"`
 
 	// Extra environment variables specific to the GPU as list of [key,value]
 	EnvWorkarounds [][2]string `json:"envs,omitempty"`
@@ -47,6 +48,13 @@ type GpuInfo struct { // TODO better name maybe "InferenceProcessor"?
 	// TODO other performance capability info to help in scheduling decisions
 }
 
+func (gpu GpuInfo) RunnerName() string {
+	if gpu.Variant != "" {
+		return gpu.Library + "_" + gpu.Variant
+	}
+	return gpu.Library
+}
+
 type CPUInfo struct {
 	GpuInfo
 	CPUs []CPU
@@ -99,7 +107,7 @@ func (l GpuInfoList) ByLibrary() []GpuInfoList {
 	for _, info := range l {
 		found := false
 		requested := info.Library
-		if info.Variant != CPUCapabilityNone.String() {
+		if info.Variant != runners.CPUCapabilityNone.String() {
 			requested += "_" + info.Variant
 		}
 		for i, lib := range libs {
@@ -140,29 +148,6 @@ func (a ByFreeMemory) Len() int           { return len(a) }
 func (a ByFreeMemory) Swap(i, j int)      { a[i], a[j] = a[j], a[i] }
 func (a ByFreeMemory) Less(i, j int) bool { return a[i].FreeMemory < a[j].FreeMemory }
 
-type CPUCapability uint32
-
-// Override at build time when building base GPU runners
-var GPURunnerCPUCapability = CPUCapabilityAVX
-
-const (
-	CPUCapabilityNone CPUCapability = iota
-	CPUCapabilityAVX
-	CPUCapabilityAVX2
-	// TODO AVX512
-)
-
-func (c CPUCapability) String() string {
-	switch c {
-	case CPUCapabilityAVX:
-		return "avx"
-	case CPUCapabilityAVX2:
-		return "avx2"
-	default:
-		return "no vector extensions"
-	}
-}
-
 type SystemInfo struct {
 	System          CPUInfo              `json:"system"`
 	GPUs            []GpuInfo            `json:"gpus"`
@@ -183,3 +168,17 @@ func (si SystemInfo) GetOptimalThreadCount() int {
 
 	return coreCount
 }
+
+// For each GPU, check if it does NOT support flash attention
+func (l GpuInfoList) FlashAttentionSupported() bool {
+	for _, gpu := range l {
+		supportsFA := gpu.Library == "metal" ||
+			(gpu.Library == "cuda" && gpu.DriverMajor >= 7) ||
+			gpu.Library == "rocm"
+
+		if !supportsFA {
+			return false
+		}
+	}
+	return true
+}

docs/README.md

@@ -2,7 +2,7 @@
 
 ### Getting Started
 * [Quickstart](../README.md#quickstart)
-* [Examples](../examples)
+* [Examples](./examples.md)
 * [Importing models](./import.md)
 * [Linux Documentation](./linux.md)
 * [Windows Documentation](./windows.md)

docs/api.md

@@ -13,6 +13,7 @@
 - [Push a Model](#push-a-model)
 - [Generate Embeddings](#generate-embeddings)
 - [List Running Models](#list-running-models)
+- [Version](#version)
 
 ## Conventions
 
@@ -45,14 +46,18 @@ Generate a response for a given prompt with a provided model. This is a streamin
 
 Advanced parameters (optional):
 
-- `format`: the format to return a response in. Currently the only accepted value is `json`
+- `format`: the format to return a response in. Format can be `json` or a JSON schema
 - `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.md#valid-parameters-and-values) such as `temperature`
 - `system`: system message to (overrides what is defined in the `Modelfile`)
 - `template`: the prompt template to use (overrides what is defined in the `Modelfile`)
-- `context`: the context parameter returned from a previous request to `/generate`, this can be used to keep a short conversational memory
 - `stream`: if `false` the response will be returned as a single response object, rather than a stream of objects
 - `raw`: if `true` no formatting will be applied to the prompt. You may choose to use the `raw` parameter if you are specifying a full templated prompt in your request to the API
 - `keep_alive`: controls how long the model will stay loaded into memory following the request (default: `5m`)
+- `context` (deprecated): the context parameter returned from a previous request to `/generate`, this can be used to keep a short conversational memory
+
+#### Structured outputs
+
+Structured outputs are supported by providing a JSON schema in the `format` parameter. The model will generate a response that matches the schema. See the [structured outputs](#request-structured-outputs) example below.
 
 #### JSON mode
 
@@ -185,6 +190,52 @@ curl http://localhost:11434/api/generate -d '{
 }
 ```
 
+#### Request (Structured outputs)
+
+##### Request
+
+```shell
+curl -X POST http://localhost:11434/api/generate -H "Content-Type: application/json" -d '{
+  "model": "llama3.1:8b",
+  "prompt": "Ollama is 22 years old and is busy saving the world. Respond using JSON",
+  "stream": false,
+  "format": {
+    "type": "object",
+    "properties": {
+      "age": {
+        "type": "integer"
+      },
+      "available": {
+        "type": "boolean"
+      }
+    },
+    "required": [
+      "age",
+      "available"
+    ]
+  }
+}'
+```
+
+##### Response
+
+```json
+{
+  "model": "llama3.1:8b",
+  "created_at": "2024-12-06T00:48:09.983619Z",
+  "response": "{\n  \"age\": 22,\n  \"available\": true\n}",
+  "done": true,
+  "done_reason": "stop",
+  "context": [1, 2, 3],
+  "total_duration": 1075509083,
+  "load_duration": 567678166,
+  "prompt_eval_count": 28,
+  "prompt_eval_duration": 236000000,
+  "eval_count": 16,
+  "eval_duration": 269000000
+}
+```
+
 #### Request (JSON mode)
 
 > [!IMPORTANT]
@@ -337,7 +388,6 @@ curl http://localhost:11434/api/generate -d '{
     "top_k": 20,
     "top_p": 0.9,
     "min_p": 0.0,
-    "tfs_z": 0.5,
     "typical_p": 0.7,
     "repeat_last_n": 33,
     "temperature": 0.8,
@@ -355,7 +405,6 @@ curl http://localhost:11434/api/generate -d '{
     "num_gpu": 1,
     "main_gpu": 0,
     "low_vram": false,
-    "f16_kv": true,
     "vocab_only": false,
     "use_mmap": true,
     "use_mlock": false,
@@ -457,11 +506,15 @@ The `message` object has the following fields:
 
 Advanced parameters (optional):
 
-- `format`: the format to return a response in. Currently the only accepted value is `json`
+- `format`: the format to return a response in. Format can be `json` or a JSON schema. 
 - `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.md#valid-parameters-and-values) such as `temperature`
 - `stream`: if `false` the response will be returned as a single response object, rather than a stream of objects
 - `keep_alive`: controls how long the model will stay loaded into memory following the request (default: `5m`)
 
+### Structured outputs
+
+Structured outputs are supported by providing a JSON schema in the `format` parameter. The model will generate a response that matches the schema. See the [Chat request (Structured outputs)](#chat-request-structured-outputs) example below.
+
 ### Examples
 
 #### Chat Request (Streaming)
@@ -552,6 +605,54 @@ curl http://localhost:11434/api/chat -d '{
 }
 ```
 
+#### Chat request (Structured outputs)
+
+##### Request
+
+```shell
+curl -X POST http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{
+  "model": "llama3.1",
+  "messages": [{"role": "user", "content": "Ollama is 22 years old and busy saving the world. Return a JSON object with the age and availability."}],
+  "stream": false,
+  "format": {
+    "type": "object",
+    "properties": {
+      "age": {
+        "type": "integer"
+      },
+      "available": {
+        "type": "boolean"
+      }
+    },
+    "required": [
+      "age",
+      "available"
+    ]
+  },
+  "options": {
+    "temperature": 0
+  }
+}'
+```
+
+##### Response
+
+```json
+{
+  "model": "llama3.1",
+  "created_at": "2024-12-06T00:46:58.265747Z",
+  "message": { "role": "assistant", "content": "{\"age\": 22, \"available\": false}" },
+  "done_reason": "stop",
+  "done": true,
+  "total_duration": 2254970291,
+  "load_duration": 574751416,
+  "prompt_eval_count": 34,
+  "prompt_eval_duration": 1502000000,
+  "eval_count": 12,
+  "eval_duration": 175000000
+}
+```
+
 #### Chat request (With History)
 
 Send a chat message with a conversation history. You can use this same approach to start the conversation using multi-shot or chain-of-thought prompting.
@@ -827,33 +928,65 @@ A single JSON object is returned:
 POST /api/create
 ```
 
-Create a model from a [`Modelfile`](./modelfile.md). It is recommended to set `modelfile` to the content of the Modelfile rather than just set `path`. This is a requirement for remote create. Remote model creation must also create any file blobs, fields such as `FROM` and `ADAPTER`, explicitly with the server using [Create a Blob](#create-a-blob) and the value to the path indicated in the response.
+Create a model from:
+ * another model;
+ * a safetensors directory; or
+ * a GGUF file.
+
+If you are creating a model from a safetensors directory or from a GGUF file, you must [create a blob](#create-a-blob) for each of the files and then use the file name and SHA256 digest associated with each blob in the `files` field.
 
 ### Parameters
 
-- `name`: name of the model to create
-- `modelfile` (optional): contents of the Modelfile
+- `model`: name of the model to create
+- `from`: (optional) name of an existing model to create the new model from
+- `files`: (optional) a dictionary of file names to SHA256 digests of blobs to create the model from
+- `adapters`: (optional) a dictionary of file names to SHA256 digests of blobs for LORA adapters
+- `template`: (optional) the prompt template for the model
+- `license`: (optional) a string or list of strings containing the license or licenses for the model
+- `system`: (optional) a string containing the system prompt for the model
+- `parameters`: (optional) a dictionary of parameters for the model (see [Modelfile](./modelfile.md#valid-parameters-and-values) for a list of parameters)
+- `messages`: (optional) a list of message objects used to create a conversation
 - `stream`: (optional) if `false` the response will be returned as a single response object, rather than a stream of objects
-- `path` (optional): path to the Modelfile
+- `quantize` (optional): quantize a non-quantized (e.g. float16) model
+
+#### Quantization types
+
+| Type | Recommended |
+| --- | :-: |
+| q2_K | |
+| q3_K_L | |
+| q3_K_M | |
+| q3_K_S | |
+| q4_0 | |
+| q4_1 | |
+| q4_K_M | * |
+| q4_K_S | |
+| q5_0 | |
+| q5_1 | |
+| q5_K_M | |
+| q5_K_S | |
+| q6_K | |
+| q8_0 | * |
 
 ### Examples
 
 #### Create a new model
 
-Create a new model from a `Modelfile`.
+Create a new model from an existing model.
 
 ##### Request
 
 ```shell
 curl http://localhost:11434/api/create -d '{
-  "name": "mario",
-  "modelfile": "FROM llama3\nSYSTEM You are mario from Super Mario Bros."
+  "model": "mario",
+  "from": "llama3.2",
+  "system": "You are Mario from Super Mario Bros."
 }'
 ```
 
 ##### Response
 
-A stream of JSON objects. Notice that the final JSON object shows a `"status": "success"`.
+A stream of JSON objects is returned:
 
 ```json
 {"status":"reading model metadata"}
@@ -869,51 +1002,141 @@ A stream of JSON objects. Notice that the final JSON object shows a `"status": "
 {"status":"success"}
 ```
 
-### Check if a Blob Exists
+#### Quantize a model
+
+Quantize a non-quantized model.
+
+##### Request
+
+```shell
+curl http://localhost:11434/api/create -d '{
+  "model": "llama3.1:quantized",
+  "from": "llama3.1:8b-instruct-fp16",
+  "quantize": "q4_K_M"
+}'
+```
+
+##### Response
+
+A stream of JSON objects is returned:
+
+```
+{"status":"quantizing F16 model to Q4_K_M"}
+{"status":"creating new layer sha256:667b0c1932bc6ffc593ed1d03f895bf2dc8dc6df21db3042284a6f4416b06a29"}
+{"status":"using existing layer sha256:11ce4ee3e170f6adebac9a991c22e22ab3f8530e154ee669954c4bc73061c258"}
+{"status":"using existing layer sha256:0ba8f0e314b4264dfd19df045cde9d4c394a52474bf92ed6a3de22a4ca31a177"}
+{"status":"using existing layer sha256:56bb8bd477a519ffa694fc449c2413c6f0e1d3b1c88fa7e3c9d88d3ae49d4dcb"}
+{"status":"creating new layer sha256:455f34728c9b5dd3376378bfb809ee166c145b0b4c1f1a6feca069055066ef9a"}
+{"status":"writing manifest"}
+{"status":"success"}
+```
+
+#### Create a model from GGUF
+
+Create a model from a GGUF file. The `files` parameter should be filled out with the file name and SHA256 digest of the GGUF file you wish to use. Use [/api/blobs/:digest](#push-a-blob) to push the GGUF file to the server before calling this API.
+
+
+##### Request
+
+```shell
+curl http://localhost:11434/api/create -d '{
+  "model": "my-gguf-model",
+  "files": {
+    "test.gguf": "sha256:432f310a77f4650a88d0fd59ecdd7cebed8d684bafea53cbff0473542964f0c3"
+  }
+}'
+```
+
+##### Response
+
+A stream of JSON objects is returned:
+
+```
+{"status":"parsing GGUF"}
+{"status":"using existing layer sha256:432f310a77f4650a88d0fd59ecdd7cebed8d684bafea53cbff0473542964f0c3"}
+{"status":"writing manifest"}
+{"status":"success"}
+```
+
+
+#### Create a model from a Safetensors directory
+
+The `files` parameter should include a dictionary of files for the safetensors model which includes the file names and SHA256 digest of each file. Use [/api/blobs/:digest](#push-a-blob) to first push each of the files to the server before calling this API. Files will remain in the cache until the Ollama server is restarted.
+
+##### Request
+
+```shell
+curl http://localhost:11434/api/create -d '{
+  "model": "fred",
+  "files": {
+    "config.json": "sha256:dd3443e529fb2290423a0c65c2d633e67b419d273f170259e27297219828e389",
+    "generation_config.json": "sha256:88effbb63300dbbc7390143fbbdd9d9fa50587b37e8bfd16c8c90d4970a74a36",
+    "special_tokens_map.json": "sha256:b7455f0e8f00539108837bfa586c4fbf424e31f8717819a6798be74bef813d05",
+    "tokenizer.json": "sha256:bbc1904d35169c542dffbe1f7589a5994ec7426d9e5b609d07bab876f32e97ab",
+    "tokenizer_config.json": "sha256:24e8a6dc2547164b7002e3125f10b415105644fcf02bf9ad8b674c87b1eaaed6",
+    "model.safetensors": "sha256:1ff795ff6a07e6a68085d206fb84417da2f083f68391c2843cd2b8ac6df8538f"
+  }
+}'
+```
+
+##### Response
+
+A stream of JSON objects is returned:
+
+```shell
+{"status":"converting model"}
+{"status":"creating new layer sha256:05ca5b813af4a53d2c2922933936e398958855c44ee534858fcfd830940618b6"}
+{"status":"using autodetected template llama3-instruct"}
+{"status":"using existing layer sha256:56bb8bd477a519ffa694fc449c2413c6f0e1d3b1c88fa7e3c9d88d3ae49d4dcb"}
+{"status":"writing manifest"}
+{"status":"success"}
+```
+
+## Check if a Blob Exists
 
 ```shell
 HEAD /api/blobs/:digest
 ```
 
-Ensures that the file blob used for a FROM or ADAPTER field exists on the server. This is checking your Ollama server and not Ollama.ai.
+Ensures that the file blob (Binary Large Object) used with create a model exists on the server. This checks your Ollama server and not ollama.com.
 
-#### Query Parameters
+### Query Parameters
 
 - `digest`: the SHA256 digest of the blob
 
-#### Examples
+### Examples
 
-##### Request
+#### Request
 
 ```shell
 curl -I http://localhost:11434/api/blobs/sha256:29fdb92e57cf0827ded04ae6461b5931d01fa595843f55d36f5b275a52087dd2
 ```
 
-##### Response
+#### Response
 
 Return 200 OK if the blob exists, 404 Not Found if it does not.
 
-### Create a Blob
+## Push a Blob
 
 ```shell
 POST /api/blobs/:digest
 ```
 
-Create a blob from a file on the server. Returns the server file path.
+Push a file to the Ollama server to create a "blob" (Binary Large Object).
 
-#### Query Parameters
+### Query Parameters
 
 - `digest`: the expected SHA256 digest of the file
 
-#### Examples
+### Examples
 
-##### Request
+#### Request
 
 ```shell
-curl -T model.bin -X POST http://localhost:11434/api/blobs/sha256:29fdb92e57cf0827ded04ae6461b5931d01fa595843f55d36f5b275a52087dd2
+curl -T model.gguf -X POST http://localhost:11434/api/blobs/sha256:29fdb92e57cf0827ded04ae6461b5931d01fa595843f55d36f5b275a52087dd2
 ```
 
-##### Response
+#### Response
 
 Return 201 Created if the blob was successfully created, 400 Bad Request if the digest used is not expected.
 
@@ -980,7 +1203,7 @@ Show information about a model including details, modelfile, template, parameter
 
 ### Parameters
 
-- `name`: name of the model to show
+- `model`: name of the model to show
 - `verbose`: (optional) if set to `true`, returns full data for verbose response fields
 
 ### Examples
@@ -989,7 +1212,7 @@ Show information about a model including details, modelfile, template, parameter
 
 ```shell
 curl http://localhost:11434/api/show -d '{
-  "name": "llama3.2"
+  "model": "llama3.2"
 }'
 ```
 
@@ -1069,7 +1292,7 @@ Delete a model and its data.
 
 ### Parameters
 
-- `name`: model name to delete
+- `model`: model name to delete
 
 ### Examples
 
@@ -1077,7 +1300,7 @@ Delete a model and its data.
 
 ```shell
 curl -X DELETE http://localhost:11434/api/delete -d '{
-  "name": "llama3:13b"
+  "model": "llama3:13b"
 }'
 ```
 
@@ -1095,7 +1318,7 @@ Download a model from the ollama library. Cancelled pulls are resumed from where
 
 ### Parameters
 
-- `name`: name of the model to pull
+- `model`: name of the model to pull
 - `insecure`: (optional) allow insecure connections to the library. Only use this if you are pulling from your own library during development.
 - `stream`: (optional) if `false` the response will be returned as a single response object, rather than a stream of objects
 
@@ -1105,7 +1328,7 @@ Download a model from the ollama library. Cancelled pulls are resumed from where
 
 ```shell
 curl http://localhost:11434/api/pull -d '{
-  "name": "llama3.2"
+  "model": "llama3.2"
 }'
 ```
 
@@ -1167,7 +1390,7 @@ Upload a model to a model library. Requires registering for ollama.ai and adding
 
 ### Parameters
 
-- `name`: name of the model to push in the form of `<namespace>/<model>:<tag>`
+- `model`: name of the model to push in the form of `<namespace>/<model>:<tag>`
 - `insecure`: (optional) allow insecure connections to the library. Only use this if you are pushing to your library during development.
 - `stream`: (optional) if `false` the response will be returned as a single response object, rather than a stream of objects
 
@@ -1177,7 +1400,7 @@ Upload a model to a model library. Requires registering for ollama.ai and adding
 
 ```shell
 curl http://localhost:11434/api/push -d '{
-  "name": "mattw/pygmalion:latest"
+  "model": "mattw/pygmalion:latest"
 }'
 ```
 
@@ -1376,3 +1599,29 @@ curl http://localhost:11434/api/embeddings -d '{
   ]
 }
 ```
+
+## Version
+
+```shell
+GET /api/version
+```
+
+Retrieve the Ollama version
+
+### Examples
+
+#### Request
+
+```shell
+curl http://localhost:11434/api/version
+```
+
+#### Response
+
+```json
+{
+  "version": "0.5.1"
+}
+```
+
+

docs/development.md

@@ -3,35 +3,24 @@
 Install required tools:
 
 - go version 1.22 or higher
-- gcc version 11.4.0 or higher
+- OS specific C/C++ compiler (see below)
+- GNU Make
 
 
+## Overview
+
+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/)
 
-Optionally enable debugging and more verbose logging:
-
-```bash
-# At build time
-export CGO_CFLAGS="-g"
-
-# At runtime
-export OLLAMA_DEBUG=1
-```
-
-Get the required libraries and build the native LLM code:  (Adjust the job count based on your number of processors for a faster build)
-
 ```bash
 make -j 5
 ```
 
-Then build ollama:
-
-```bash
-go build .
-```
-
 Now you can run `ollama`:
 
 ```bash
@@ -51,64 +40,42 @@ _Your operating system distribution may already have packages for NVIDIA CUDA. D
 Install `make`, `gcc` and `golang` as well as [NVIDIA CUDA](https://developer.nvidia.com/cuda-downloads)
 development and runtime packages.
 
-Typically the build scripts will auto-detect CUDA, however, if your Linux distro
-or installation approach uses unusual paths, you can specify the location by
-specifying an environment variable `CUDA_LIB_DIR` to the location of the shared
-libraries, and `CUDACXX` to the location of the nvcc compiler. You can customize
-a set of target CUDA architectures by setting `CMAKE_CUDA_ARCHITECTURES` (e.g. "50;60;70")
-
-Then generate dependencies:  (Adjust the job count based on your number of processors for a faster build)
+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
 ```
 
-Then build the binary:
+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`
 
-```
-go build .
-```
+#### Older Linux CUDA (NVIDIA)
+
+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).
 
 #### Linux ROCm (AMD)
 
-_Your operating system distribution may already have packages for AMD ROCm and CLBlast. Distro packages are often preferable, but instructions are distro-specific. Please consult distro-specific docs for dependencies if available!_
+_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!_
 
-Install [CLBlast](https://github.com/CNugteren/CLBlast/blob/master/doc/installation.md) and [ROCm](https://rocm.docs.amd.com/en/latest/) development packages first, as well as `make`, `gcc`, and `golang`.
+Install [ROCm](https://rocm.docs.amd.com/en/latest/) development packages first, as well as `make`, `gcc`, and `golang`.
 
 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 `ROCM_PATH` to the location of the ROCm
-install (typically `/opt/rocm`), and `CLBlast_DIR` to the location of the
-CLBlast install (typically `/usr/lib/cmake/CLBlast`). You can also customize
-the AMD GPU targets by setting AMDGPU_TARGETS (e.g. `AMDGPU_TARGETS="gfx1101;gfx1102"`)
-
-Then generate dependencies:  (Adjust the job count based on your number of processors for a faster build)
+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
 ```
 
-Then build the binary:
-
-```
-go build .
-```
-
 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.
 
-#### Advanced CPU Settings
-
-By default, running `make` will compile a few different variations
-of the LLM library based on common CPU families and vector math capabilities,
-including a lowest-common-denominator which should run on almost any 64 bit CPU
-somewhat slowly. At runtime, Ollama will auto-detect the optimal variation to
-load. 
-
-Custom CPU settings are not currently supported in the new Go server build but will be added back after we complete the transition.
-
 #### Containerized Linux Build
 
-If you have Docker available, you can build linux binaries with `OLLAMA_NEW_RUNNERS=1 ./scripts/build_linux.sh` which has the CUDA and ROCm dependencies included. The resulting binary is placed in `./dist`
+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`
 
 ### Windows
 
@@ -126,12 +93,8 @@ The following tools are required as a minimal development environment to build C
 > [!NOTE]  
 > Due to bugs in the GCC C++ library for unicode support, Ollama should be built with clang on windows.
 
-Then, build the `ollama` binary:
-
-```powershell
-$env:CGO_ENABLED="1"
-make -j 8
-go build .
+```
+make -j 5
 ```
 
 #### GPU Support
@@ -173,3 +136,30 @@ pacman -S mingw-w64-clang-aarch64-clang mingw-w64-clang-aarch64-gcc-compat mingw
 ```
 
 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\`)
+
+
+## Advanced CPU Vector Settings
+
+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:
+```
+make CUSTOM_CPU_FLAGS=avx,avx2
+```
+
+To build with AVX512 features turned on:
+
+```
+make CUSTOM_CPU_FLAGS=avx,avx2,avx512,avx512vbmi,avx512vnni,avx512bf16
+```
+
+> [!NOTE]  
+> 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

docs/docker.md

@@ -50,6 +50,9 @@ sudo systemctl restart docker
 docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
 ```
 
+> [!NOTE]  
+> If you're running on an NVIDIA JetPack system, Ollama can't automatically discover the correct JetPack version. Pass the environment variable JETSON_JETPACK=5 or JETSON_JETPACK=6 to the container to select version 5 or 6.
+
 ### AMD GPU
 
 To run Ollama using Docker with AMD GPUs, use the `rocm` tag and the following command:

docs/examples.md

@@ -0,0 +1,20 @@
+# Examples
+
+This directory contains different examples of using Ollama.
+
+## Python examples
+Ollama Python examples at [ollama-python/examples](https://github.com/ollama/ollama-python/tree/main/examples)
+
+
+## JavaScript examples
+Ollama JavaScript examples at [ollama-js/examples](https://github.com/ollama/ollama-js/tree/main/examples)
+
+
+## OpenAI compatibility examples
+Ollama OpenAI compatibility examples at [ollama/examples/openai](../docs/openai.md)
+
+
+## Community examples
+
+- [LangChain Ollama Python](https://python.langchain.com/docs/integrations/chat/ollama/)
+- [LangChain Ollama JS](https://js.langchain.com/docs/integrations/chat/ollama/)

docs/faq.md

@@ -151,7 +151,7 @@ Refer to the section [above](#how-do-i-configure-ollama-server) for how to set e
 
 Ollama runs an HTTP server and can be exposed using a proxy server such as Nginx. To do so, configure the proxy to forward requests and optionally set required headers (if not exposing Ollama on the network). For example, with Nginx:
 
-```
+```nginx
 server {
     listen 80;
     server_name example.com;  # Replace with your domain or IP
@@ -285,4 +285,28 @@ Note: Windows with Radeon GPUs currently default to 1 model maximum due to limit
 
 ## How does Ollama load models on multiple GPUs?
 
-Installing multiple GPUs of the same brand can be a great way to increase your available VRAM to load larger models.  When you load a new model, Ollama evaluates the required VRAM for the model against what is currently available.  If the model will entirely fit on any single GPU, Ollama will load the model on that GPU.  This typically provides the best performance as it reduces the amount of data transfering across the PCI bus during inference.  If the model does not fit entirely on one GPU, then it will be spread across all the available GPUs.
+When loading a new model, Ollama evaluates the required VRAM for the model against what is currently available.  If the model will entirely fit on any single GPU, Ollama will load the model on that GPU.  This typically provides the best performance as it reduces the amount of data transferring across the PCI bus during inference.  If the model does not fit entirely on one GPU, then it will be spread across all the available GPUs.
+
+## How can I enable Flash Attention?
+
+Flash Attention is a feature of most modern models that can significantly reduce memory usage as the context size grows.  To enable Flash Attention, set the `OLLAMA_FLASH_ATTENTION` environment variable to `1` when starting the Ollama server.
+
+## How can I set the quantization type for the K/V cache?
+
+The K/V context cache can be quantized to significantly reduce memory usage when Flash Attention is enabled.
+
+To use quantized K/V cache with Ollama you can set the following environment variable:
+
+- `OLLAMA_KV_CACHE_TYPE` - The quantization type for the K/V cache.  Default is `f16`.
+
+> Note: Currently this is a global option - meaning all models will run with the specified quantization type.
+
+The currently available K/V cache quantization types are:
+
+- `f16` - high precision and memory usage (default).
+- `q8_0` - 8-bit quantization, uses approximately 1/2 the memory of `f16` with a very small loss in precision, this usually has no noticeable impact on the model's quality (recommended if not using f16).
+- `q4_0` - 4-bit quantization, uses approximately 1/4 the memory of `f16` with a small-medium loss in precision that may be more noticeable at higher context sizes.
+
+How much the cache quantization impacts the model's response quality will depend on the model and the task.  Models that have a high GQA count (e.g. Qwen2) may see a larger impact on precision from quantization than models with a low GQA count.
+
+You may need to experiment with different quantization types to find the best balance between memory usage and quality.

docs/gpu.md

@@ -28,6 +28,7 @@ Check your compute compatibility to see if your card is supported:
 | 5.0                | GeForce GTX         | `GTX 750 Ti` `GTX 750` `NVS 810`                                                                            |
 |                    | Quadro              | `K2200` `K1200` `K620` `M1200` `M520` `M5000M` `M4000M` `M3000M` `M2000M` `M1000M` `K620M` `M600M` `M500M`  |
 
+For building locally to support older GPUs, see [developer.md](./development.md#linux-cuda-nvidia)
 
 ### GPU Selection
 
@@ -37,7 +38,7 @@ Numeric IDs may be used, however ordering may vary, so UUIDs are more reliable.
 You can discover the UUID of your GPUs by running `nvidia-smi -L` If you want to
 ignore the GPUs and force CPU usage, use an invalid GPU ID (e.g., "-1")
 
-### Laptop Suspend Resume
+### Linux Suspend Resume
 
 On linux, after a suspend/resume cycle, sometimes Ollama will fail to discover
 your NVIDIA GPU, and fallback to running on the CPU.  You can workaround this

docs/import.md

@@ -32,7 +32,7 @@ ollama run my-model
 
 Ollama supports importing adapters based on several different model architectures including:
 
-  * Llama (including Llama 2, Llama 3, and Llama 3.1);
+  * Llama (including Llama 2, Llama 3, Llama 3.1, and Llama 3.2);
   * Mistral (including Mistral 1, Mistral 2, and Mixtral); and
   * Gemma (including Gemma 1 and Gemma 2)
 
@@ -67,14 +67,12 @@ ollama run my-model
 
 Ollama supports importing models for several different architectures including:
 
-  * Llama (including Llama 2, Llama 3, and Llama 3.1);
+  * Llama (including Llama 2, Llama 3, Llama 3.1, and Llama 3.2);
   * Mistral (including Mistral 1, Mistral 2, and Mixtral);
   * Gemma (including Gemma 1 and Gemma 2); and
   * Phi3
 
-This includes importing foundation models as well as any fine tuned models which which have been _fused_ with a foundation model.
-
-
+This includes importing foundation models as well as any fine tuned models which have been _fused_ with a foundation model.
 ## Importing a GGUF based model or adapter
 
 If you have a GGUF based model or adapter it is possible to import it into Ollama. You can obtain a GGUF model or adapter by:
@@ -83,7 +81,7 @@ If you have a GGUF based model or adapter it is possible to import it into Ollam
   * converting a Safetensors adapter with the `convert_lora_to_gguf.py` from Llama.cpp; or
   * downloading a model or adapter from a place such as HuggingFace
 
-To import a GGUF model, create a `Modelfile` containg:
+To import a GGUF model, create a `Modelfile` containing:
 
 ```dockerfile
 FROM /path/to/file.gguf

docs/linux.md

@@ -10,6 +10,9 @@ curl -fsSL https://ollama.com/install.sh | sh
 
 ## Manual install
 
+> [!NOTE]
+> If you are upgrading from a prior version, you should remove the old libraries with `sudo rm -rf /usr/lib/ollama` first.
+
 Download and extract the package:
 
 ```shell
@@ -112,6 +115,21 @@ sudo systemctl status ollama
 > https://www.amd.com/en/support/linux-drivers for best support of your Radeon
 > GPU.
 
+## Customizing
+
+To customize the installation of Ollama, you can edit the systemd service file or the environment variables by running:
+
+```
+sudo systemctl edit ollama
+```
+
+Alternatively, create an override file manually in `/etc/systemd/system/ollama.service.d/override.conf`:
+
+```ini
+[Service]
+Environment="OLLAMA_DEBUG=1"
+```
+
 ## Updating
 
 Update Ollama by running the install script again:
@@ -129,7 +147,7 @@ sudo tar -C /usr -xzf ollama-linux-amd64.tgz
 
 ## Installing specific versions
 
-Use `OLLAMA_VERSION` environment variable with the install script to install a specific version of Ollama, including pre-releases. You can find the version numbers in the [releases page](https://github.com/ollama/ollama/releases). 
+Use `OLLAMA_VERSION` environment variable with the install script to install a specific version of Ollama, including pre-releases. You can find the version numbers in the [releases page](https://github.com/ollama/ollama/releases).
 
 For example:
 

docs/modelfile.md

@@ -63,12 +63,10 @@ SYSTEM You are Mario from super mario bros, acting as an assistant.
 To use this:
 
 1. Save it as a file (e.g. `Modelfile`)
-2. `ollama create choose-a-model-name -f <location of the file e.g. ./Modelfile>'`
+2. `ollama create choose-a-model-name -f <location of the file e.g. ./Modelfile>`
 3. `ollama run choose-a-model-name`
 4. Start using the model!
 
-More examples are available in the [examples directory](../examples).
-
 To view the Modelfile of a given model, use the `ollama show --modelfile` command.
 
   ```bash
@@ -120,7 +118,7 @@ FROM <model directory>
 The model directory should contain the Safetensors weights for a supported architecture.
 
 Currently supported model architectures:
-  * Llama (including Llama 2, Llama 3, and Llama 3.1)
+  * Llama (including Llama 2, Llama 3, Llama 3.1, and Llama 3.2)
   * Mistral (including Mistral 1, Mistral 2, and Mixtral)
   * Gemma (including Gemma 1 and Gemma 2)
   * Phi3
@@ -155,8 +153,7 @@ PARAMETER <parameter> <parametervalue>
 | temperature    | The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)                                                                                                                                     | float      | temperature 0.7      |
 | seed           | Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. (Default: 0)                                                                                       | int        | seed 42              |
 | stop           | Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return. Multiple stop patterns may be set by specifying multiple separate `stop` parameters in a modelfile.                                      | string     | stop "AI assistant:" |
-| tfs_z          | Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (default: 1)                                               | float      | tfs_z 1              |
-| num_predict    | Maximum number of tokens to predict when generating text. (Default: 128, -1 = infinite generation, -2 = fill context)                                                                                                                                   | int        | num_predict 42       |
+| num_predict    | Maximum number of tokens to predict when generating text. (Default: -1, infinite generation)                                                                                                                                   | int        | num_predict 42       |
 | top_k          | Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)                                                                        | int        | top_k 40             |
 | top_p          | Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)                                                                 | float      | top_p 0.9            |
 | min_p          | Alternative to the top_p, and aims to ensure a balance of quality and variety. The parameter *p* represents the minimum probability for a token to be considered, relative to the probability of the most likely token. For example, with *p*=0.05 and the most likely token having a probability of 0.9, logits with a value less than 0.045 are filtered out. (Default: 0.0) | float      | min_p 0.05            |

docs/openai.md

@@ -59,6 +59,40 @@ embeddings = client.embeddings.create(
     input=["why is the sky blue?", "why is the grass green?"],
 )
 ```
+#### Structured outputs
+```py
+from pydantic import BaseModel
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")
+
+# Define the schema for the response
+class FriendInfo(BaseModel):
+    name: str
+    age: int 
+    is_available: bool
+
+class FriendList(BaseModel):
+    friends: list[FriendInfo]
+
+try:
+    completion = client.beta.chat.completions.parse(
+        temperature=0,
+        model="llama3.1:8b",
+        messages=[
+            {"role": "user", "content": "I have two friends. The first is Ollama 22 years old busy saving the world, and the second is Alonso 23 years old and wants to hang out. Return a list of friends in JSON format"}
+        ],
+        response_format=FriendList,
+    )
+
+    friends_response = completion.choices[0].message
+    if friends_response.parsed:
+        print(friends_response.parsed)
+    elif friends_response.refusal:
+        print(friends_response.refusal)
+except Exception as e:
+    print(f"Error: {e}")
+```
 
 ### OpenAI JavaScript library
 
@@ -170,6 +204,45 @@ 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`
@@ -179,9 +252,10 @@ curl http://localhost:11434/v1/embeddings \
 - [x] Chat completions
 - [x] Streaming
 - [x] JSON mode
+- [x] Structured outputs
 - [x] Reproducible outputs
 - [x] Vision
-- [x] Tools (streaming support coming soon)
+- [x] Tools
 - [ ] Logprobs
 
 #### Supported request fields
@@ -199,6 +273,8 @@ curl http://localhost:11434/v1/embeddings \
 - [x] `seed`
 - [x] `stop`
 - [x] `stream`
+- [x] `stream_options`
+  - [x] `include_usage`
 - [x] `temperature`
 - [x] `top_p`
 - [x] `max_tokens`
@@ -227,6 +303,8 @@ curl http://localhost:11434/v1/embeddings \
 - [x] `seed`
 - [x] `stop`
 - [x] `stream`
+- [x] `stream_options`
+  - [x] `include_usage`
 - [x] `temperature`
 - [x] `top_p`
 - [x] `max_tokens`
@@ -301,27 +379,3 @@ 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:
-
-```modelfile
-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

@@ -111,7 +111,7 @@ Keep the following tips and best practices in mind when working with Go template
 
 ChatML is a popular template format. It can be used for models such as Databrick's DBRX, Intel's Neural Chat, and Microsoft's Orca 2.
 
-```gotmpl
+```go
 {{- range .Messages }}<|im_start|>{{ .Role }}
 {{ .Content }}<|im_end|>
 {{ end }}<|im_start|>assistant
@@ -125,7 +125,7 @@ Tools support can be added to a model by adding a `{{ .Tools }}` node to the tem
 
 Mistral v0.3 and Mixtral 8x22B supports tool calling.
 
-```gotmpl
+```go
 {{- range $index, $_ := .Messages }}
 {{- if eq .Role "user" }}
 {{- if and (le (len (slice $.Messages $index)) 2) $.Tools }}[AVAILABLE_TOOLS] {{ json $.Tools }}[/AVAILABLE_TOOLS]
@@ -151,7 +151,7 @@ Fill-in-middle support can be added to a model by adding a `{{ .Suffix }}` node
 
 CodeLlama [7B](https://ollama.com/library/codellama:7b-code) and [13B](https://ollama.com/library/codellama:13b-code) code completion models support fill-in-middle.
 
-```gotmpl
+```go
 <PRE> {{ .Prompt }} <SUF>{{ .Suffix }} <MID>
 ```
 

docs/troubleshooting.md

@@ -80,7 +80,7 @@ If you are using a container to run Ollama, make sure you've set up the containe
 
 Sometimes the Ollama can have difficulties initializing the GPU. When you check the server logs, this can show up as various error codes, such as "3" (not initialized), "46" (device unavailable), "100" (no device), "999" (unknown), or others. The following troubleshooting techniques may help resolve the problem
 
-- If you are using a container, is the container runtime working?  Try `docker run --gpus all ubuntu nvidia-smi` - if this doesn't work, Ollama wont be able to see your NVIDIA GPU.
+- If you are using a container, is the container runtime working?  Try `docker run --gpus all ubuntu nvidia-smi` - if this doesn't work, Ollama won't be able to see your NVIDIA GPU.
 - Is the uvm driver loaded? `sudo nvidia-modprobe -u`
 - Try reloading the nvidia_uvm driver - `sudo rmmod nvidia_uvm` then `sudo modprobe nvidia_uvm`
 - Try rebooting
@@ -95,13 +95,21 @@ If none of those resolve the problem, gather additional information and file an
 
 On linux, AMD GPU access typically requires `video` and/or `render` group membership to access the `/dev/kfd` device.  If permissions are not set up correctly, Ollama will detect this and report an error in the server log.
 
-When running in a container, in some Linux distributions and container runtimes, the ollama process may be unable to access the GPU.  Use `ls -ld /dev/kfd /dev/dri /dev/dri/*` on the host system to determine the group assignments on your system, and pass additional `--group-add ...` arguments to the container so it can access the required devices.
+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
 - Check dmesg for any errors from amdgpu or kfd drivers `sudo dmesg | grep -i amdgpu` and `sudo dmesg | grep -i kfd`
 
+## Multiple AMD GPUs
+
+If you experience gibberish responses when models load across multiple AMD GPUs on Linux, see the following guide.
+
+- https://rocm.docs.amd.com/projects/radeon/en/latest/docs/install/native_linux/mgpu.html#mgpu-known-issues-and-limitations
+
 ## Windows Terminal Errors
 
 Older versions of Windows 10 (e.g., 21H1) are known to have a bug where the standard terminal program does not display control characters correctly.  This can result in a long string of strings like `←[?25h←[?25l` being displayed, sometimes erroring with `The parameter is incorrect`  To resolve this problem, please update to Win 10 22H1 or newer.

docs/tutorials.md

@@ -1,9 +0,0 @@
-# Tutorials
-
-Here is a list of ways you can use Ollama with other tools to build interesting applications.
-
-- [Using LangChain with Ollama in JavaScript](./tutorials/langchainjs.md)
-- [Using LangChain with Ollama in Python](./tutorials/langchainpy.md)
-- [Running Ollama on NVIDIA Jetson Devices](./tutorials/nvidia-jetson.md)
-
-Also be sure to check out the [examples](../examples) directory for more ways to use Ollama.

docs/tutorials/fly-gpu.md

@@ -1,83 +0,0 @@
-# Running Ollama on Fly.io GPU Instances
-
-Ollama runs with little to no configuration on [Fly.io GPU instances](https://fly.io/docs/gpus/gpu-quickstart/). If you don't have access to GPUs yet, you'll need to [apply for access](https://fly.io/gpu/) on the waitlist. Once you're accepted, you'll get an email with instructions on how to get started.
-
-Create a new app with `fly apps create`:
-
-```bash
-fly apps create
-```
-
-Then create a `fly.toml` file in a new folder that looks like this:
-
-```toml
-app = "sparkling-violet-709"
-primary_region = "ord"
-vm.size = "a100-40gb" # see https://fly.io/docs/gpus/gpu-quickstart/ for more info
-
-[build]
-  image = "ollama/ollama"
-
-[http_service]
-  internal_port = 11434
-  force_https = false
-  auto_stop_machines = true
-  auto_start_machines = true
-  min_machines_running = 0
-  processes = ["app"]
-
-[mounts]
-  source = "models"
-  destination = "/root/.ollama"
-  initial_size = "100gb"
-```
-
-Then create a [new private IPv6 address](https://fly.io/docs/reference/private-networking/#flycast-private-load-balancing) for your app:
-
-```bash
-fly ips allocate-v6 --private
-```
-
-Then deploy your app:
-
-```bash
-fly deploy
-```
-
-And finally you can access it interactively with a new Fly.io Machine:
-
-```
-fly machine run -e OLLAMA_HOST=http://your-app-name.flycast --shell ollama/ollama
-```
-
-```bash
-$ ollama run openchat:7b-v3.5-fp16
->>> How do I bake chocolate chip cookies?
- To bake chocolate chip cookies, follow these steps:
-
-1. Preheat the oven to 375°F (190°C) and line a baking sheet with parchment paper or silicone baking mat.
-
-2. In a large bowl, mix together 1 cup of unsalted butter (softened), 3/4 cup granulated sugar, and 3/4
-cup packed brown sugar until light and fluffy.
-
-3. Add 2 large eggs, one at a time, to the butter mixture, beating well after each addition. Stir in 1
-teaspoon of pure vanilla extract.
-
-4. In a separate bowl, whisk together 2 cups all-purpose flour, 1/2 teaspoon baking soda, and 1/2 teaspoon
-salt. Gradually add the dry ingredients to the wet ingredients, stirring until just combined.
-
-5. Fold in 2 cups of chocolate chips (or chunks) into the dough.
-
-6. Drop rounded tablespoons of dough onto the prepared baking sheet, spacing them about 2 inches apart.
-
-7. Bake for 10-12 minutes, or until the edges are golden brown. The centers should still be slightly soft.
-
-8. Allow the cookies to cool on the baking sheet for a few minutes before transferring them to a wire rack
-to cool completely.
-
-Enjoy your homemade chocolate chip cookies!
-```
-
-When you set it up like this, it will automatically turn off when you're done using it. Then when you access it again, it will automatically turn back on. This is a great way to save money on GPU instances when you're not using them. If you want a persistent wake-on-use connection to your Ollama instance, you can set up a [connection to your Fly network using WireGuard](https://fly.io/docs/reference/private-networking/#discovering-apps-through-dns-on-a-wireguard-connection). Then you can access your Ollama instance at `http://your-app-name.flycast`.
-
-And that's it!

docs/tutorials/langchainjs.md

@@ -1,77 +0,0 @@
-# Using LangChain with Ollama using JavaScript
-
-In this tutorial, we are going to use JavaScript with LangChain and Ollama to learn about something just a touch more recent. In August 2023, there was a series of wildfires on Maui. There is no way an LLM trained before that time can know about this, since their training data would not include anything as recent as that. So we can find the [Wikipedia article about the fires](https://en.wikipedia.org/wiki/2023_Hawaii_wildfires) and ask questions about the contents.
-
-To get started, let's just use **LangChain** to ask a simple question to a model. To do this with JavaScript, we need to install **LangChain**:
-
-```bash
-npm install @langchain/community
-```
-
-Now we can start building out our JavaScript:
-
-```javascript
-import { Ollama } from "@langchain/community/llms/ollama";
-
-const ollama = new Ollama({
-  baseUrl: "http://localhost:11434",
-  model: "llama3.2",
-});
-
-const answer = await ollama.invoke(`why is the sky blue?`);
-
-console.log(answer);
-```
-
-That will get us the same thing as if we ran `ollama run llama3.2 "why is the sky blue"` in the terminal. But we want to load a document from the web to ask a question against. **Cheerio** is a great library for ingesting a webpage, and **LangChain** uses it in their **CheerioWebBaseLoader**. So let's install **Cheerio** and build that part of the app.
-
-```bash
-npm install cheerio
-```
-
-```javascript
-import { CheerioWebBaseLoader } from "langchain/document_loaders/web/cheerio";
-
-const loader = new CheerioWebBaseLoader("https://en.wikipedia.org/wiki/2023_Hawaii_wildfires");
-const data = await loader.load();
-```
-
-That will load the document. Although this page is smaller than the Odyssey, it is certainly bigger than the context size for most LLMs. So we are going to need to split into smaller pieces, and then select just the pieces relevant to our question. This is a great use for a vector datastore. In this example, we will use the **MemoryVectorStore** that is part of **LangChain**. But there is one more thing we need to get the content into the datastore. We have to run an embeddings process that converts the tokens in the text into a series of vectors. And for that, we are going to use **Tensorflow**. There is a lot of stuff going on in this one. First, install the **Tensorflow** components that we need.
-
-```javascript
-npm install @tensorflow/tfjs-core@3.6.0 @tensorflow/tfjs-converter@3.6.0 @tensorflow-models/universal-sentence-encoder@1.3.3 @tensorflow/tfjs-node@4.10.0
-```
-
-If you just install those components without the version numbers, it will install the latest versions, but there are conflicts within **Tensorflow**, so you need to install the compatible versions.
-
-```javascript
-import { RecursiveCharacterTextSplitter } from "langchain/text_splitter"
-import { MemoryVectorStore } from "langchain/vectorstores/memory";
-import "@tensorflow/tfjs-node";
-import { TensorFlowEmbeddings } from "langchain/embeddings/tensorflow";
-
-// Split the text into 500 character chunks. And overlap each chunk by 20 characters
-const textSplitter = new RecursiveCharacterTextSplitter({
- chunkSize: 500,
- chunkOverlap: 20
-});
-const splitDocs = await textSplitter.splitDocuments(data);
-
-// Then use the TensorFlow Embedding to store these chunks in the datastore
-const vectorStore = await MemoryVectorStore.fromDocuments(splitDocs, new TensorFlowEmbeddings());
-```
-
-To connect the datastore to a question asked to a LLM, we need to use the concept at the heart of **LangChain**: the chain. Chains are a way to connect a number of activities together to accomplish a particular tasks. There are a number of chain types available, but for this tutorial we are using the **RetrievalQAChain**.
-
-```javascript
-import { RetrievalQAChain } from "langchain/chains";
-
-const retriever = vectorStore.asRetriever();
-const chain = RetrievalQAChain.fromLLM(ollama, retriever);
-const result = await chain.call({query: "When was Hawaii's request for a major disaster declaration approved?"});
-console.log(result.text)
-```
-
-So we created a retriever, which is a way to return the chunks that match a query from a datastore. And then connect the retriever and the model via a chain. Finally, we send a query to the chain, which results in an answer using our document as a source. The answer it returned was correct, August 10, 2023.
-
-And that is a simple introduction to what you can do with **LangChain** and **Ollama.**

docs/tutorials/langchainpy.md

@@ -1,85 +0,0 @@
-# Using LangChain with Ollama in Python
-
-Let's imagine we are studying the classics, such as **the Odyssey** by **Homer**. We might have a question about Neleus and his family. If you ask llama2 for that info, you may get something like:
-
-> I apologize, but I'm a large language model, I cannot provide information on individuals or families that do not exist in reality. Neleus is not a real person or character, and therefore does not have a family or any other personal details. My apologies for any confusion. Is there anything else I can help you with?
-
-This sounds like a typical censored response, but even llama2-uncensored gives a mediocre answer:
-
-> Neleus was a legendary king of Pylos and the father of Nestor, one of the Argonauts. His mother was Clymene, a sea nymph, while his father was Neptune, the god of the sea.
-
-So let's figure out how we can use **LangChain** with Ollama to ask our question to the actual document, the Odyssey by Homer, using Python.
-
-Let's start by asking a simple question that we can get an answer to from the **Llama2** model using **Ollama**. First, we need to install the **LangChain** package:
-
-`pip install langchain_community`
-
-Then we can create a model and ask the question:
-
-```python
-from langchain_community.llms import Ollama
-ollama = Ollama(
-    base_url='http://localhost:11434',
-    model="llama3"
-)
-print(ollama.invoke("why is the sky blue"))
-```
-
-Notice that we are defining the model and the base URL for Ollama.
-
-Now let's load a document to ask questions against. I'll load up the Odyssey by Homer, which you can find at Project Gutenberg. We will need **WebBaseLoader** which is part of **LangChain** and loads text from any webpage. On my machine, I also needed to install **bs4** to get that to work, so run `pip install bs4`.
-
-```python
-from langchain.document_loaders import WebBaseLoader
-loader = WebBaseLoader("https://www.gutenberg.org/files/1727/1727-h/1727-h.htm")
-data = loader.load()
-```
-
-This file is pretty big. Just the preface is 3000 tokens. Which means the full document won't fit into the context for the model. So we need to split it up into smaller pieces.
-
-```python
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-
-text_splitter=RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
-all_splits = text_splitter.split_documents(data)
-```
-
-It's split up, but we have to find the relevant splits and then submit those to the model. We can do this by creating embeddings and storing them in a vector database. We can use Ollama directly to instantiate an embedding model. We will use ChromaDB in this example for a vector database. `pip install chromadb`
-We also need to pull embedding model: `ollama pull nomic-embed-text`
-```python
-from langchain.embeddings import OllamaEmbeddings
-from langchain.vectorstores import Chroma
-oembed = OllamaEmbeddings(base_url="http://localhost:11434", model="nomic-embed-text")
-vectorstore = Chroma.from_documents(documents=all_splits, embedding=oembed)
-```
-
-Now let's ask a question from the document. **Who was Neleus, and who is in his family?** Neleus is a character in the Odyssey, and the answer can be found in our text.
-
-```python
-question="Who is Neleus and who is in Neleus' family?"
-docs = vectorstore.similarity_search(question)
-len(docs)
-```
-
-This will output the number of matches for chunks of data similar to the search.
-
-The next thing is to send the question and the relevant parts of the docs to the model to see if we can get a good answer. But we are stitching two parts of the process together, and that is called a chain. This means we need to define a chain:
-
-```python
-from langchain.chains import RetrievalQA
-qachain=RetrievalQA.from_chain_type(ollama, retriever=vectorstore.as_retriever())
-res = qachain.invoke({"query": question})
-print(res['result'])
-```
-
-The answer received from this chain was:
-
-> Neleus is a character in Homer's "Odyssey" and is mentioned in the context of Penelope's suitors. Neleus is the father of Chloris, who is married to Neleus and bears him several children, including Nestor, Chromius, Periclymenus, and Pero. Amphinomus, the son of Nisus, is also mentioned as a suitor of Penelope and is known for his good natural disposition and agreeable conversation.
-
-It's not a perfect answer, as it implies Neleus married his daughter when actually Chloris "was the youngest daughter to Amphion son of Iasus and king of Minyan Orchomenus, and was Queen in Pylos".
-
-I updated the chunk_overlap for the text splitter to 20 and tried again and got a much better answer:
-
-> Neleus is a character in Homer's epic poem "The Odyssey." He is the husband of Chloris, who is the youngest daughter of Amphion son of Iasus and king of Minyan Orchomenus. Neleus has several children with Chloris, including Nestor, Chromius, Periclymenus, and Pero.
-
-And that is a much better answer.

docs/tutorials/nvidia-jetson.md

@@ -1,15 +0,0 @@
-# Running Ollama on NVIDIA Jetson Devices
-
-Ollama runs well on [NVIDIA Jetson Devices](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/) and should run out of the box with the standard installation instructions. 
-
-The following has been tested on [JetPack 5.1.2](https://developer.nvidia.com/embedded/jetpack), but should also work on JetPack 6.0.
-
-- Install Ollama via standard Linux command (ignore the 404 error): `curl https://ollama.com/install.sh | sh`
-- Pull the model you want to use (e.g. mistral): `ollama pull mistral`
-- Start an interactive session: `ollama run mistral`
-
-And that's it!
-
-# Running Ollama in Docker
-
-When running GPU accelerated applications in Docker, it is highly recommended to use [dusty-nv jetson-containers repo](https://github.com/dusty-nv/jetson-containers).

docs/windows.md

@@ -83,3 +83,6 @@ If you'd like to install or integrate Ollama as a service, a standalone
 and GPU library dependencies for Nvidia and AMD. 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

@@ -153,6 +153,8 @@ var (
 	Debug = Bool("OLLAMA_DEBUG")
 	// FlashAttention enables the experimental flash attention feature.
 	FlashAttention = Bool("OLLAMA_FLASH_ATTENTION")
+	// KvCacheType is the quantization type for the K/V cache.
+	KvCacheType = String("OLLAMA_KV_CACHE_TYPE")
 	// NoHistory disables readline history.
 	NoHistory = Bool("OLLAMA_NOHISTORY")
 	// NoPrune disables pruning of model blobs on startup.
@@ -173,7 +175,6 @@ func String(s string) func() string {
 
 var (
 	LLMLibrary = String("OLLAMA_LLM_LIBRARY")
-	TmpDir     = String("OLLAMA_TMPDIR")
 
 	CudaVisibleDevices    = String("CUDA_VISIBLE_DEVICES")
 	HipVisibleDevices     = String("HIP_VISIBLE_DEVICES")
@@ -234,6 +235,7 @@ func AsMap() map[string]EnvVar {
 	ret := map[string]EnvVar{
 		"OLLAMA_DEBUG":             {"OLLAMA_DEBUG", Debug(), "Show additional debug information (e.g. OLLAMA_DEBUG=1)"},
 		"OLLAMA_FLASH_ATTENTION":   {"OLLAMA_FLASH_ATTENTION", FlashAttention(), "Enabled flash attention"},
+		"OLLAMA_KV_CACHE_TYPE":     {"OLLAMA_KV_CACHE_TYPE", KvCacheType(), "Quantization type for the K/V cache (default: f16)"},
 		"OLLAMA_GPU_OVERHEAD":      {"OLLAMA_GPU_OVERHEAD", GpuOverhead(), "Reserve a portion of VRAM per GPU (bytes)"},
 		"OLLAMA_HOST":              {"OLLAMA_HOST", Host(), "IP Address for the ollama server (default 127.0.0.1:11434)"},
 		"OLLAMA_KEEP_ALIVE":        {"OLLAMA_KEEP_ALIVE", KeepAlive(), "The duration that models stay loaded in memory (default \"5m\")"},
@@ -247,7 +249,6 @@ func AsMap() map[string]EnvVar {
 		"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_SCHED_SPREAD":      {"OLLAMA_SCHED_SPREAD", SchedSpread(), "Always schedule model across all GPUs"},
-		"OLLAMA_TMPDIR":            {"OLLAMA_TMPDIR", TmpDir(), "Location for temporary files"},
 		"OLLAMA_MULTIUSER_CACHE":   {"OLLAMA_MULTIUSER_CACHE", MultiUserCache(), "Optimize prompt caching for multi-user scenarios"},
 
 		// Informational

examples/.gitignore

@@ -1,174 +0,0 @@
-node_modules
-bun.lockb
-.vscode
-# OSX
-.DS_STORE
-
-
-# Models
-models/
-
-# Local Chroma db
-.chroma/
-db/
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
-venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# pytype static type analyzer
-.pytype/
-
-# Cython debug symbols
-cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/

examples/README.md

@@ -1,3 +0,0 @@
-# Examples
-
-This directory contains different examples of using Ollama.

examples/flyio/.gitignore

@@ -1 +0,0 @@
-fly.toml

examples/flyio/README.md

@@ -1,67 +0,0 @@
-# Deploy Ollama to Fly.io
-
-> Note: this example exposes a public endpoint and does not configure authentication. Use with care.
-
-## Prerequisites
-
-- Ollama: https://ollama.com/download
-- Fly.io account. Sign up for a free account: https://fly.io/app/sign-up
-
-## Steps
-
-1. Login to Fly.io
-
-    ```bash
-    fly auth login
-    ```
-
-1. Create a new Fly app
-
-    ```bash
-    fly launch --name <name> --image ollama/ollama --internal-port 11434 --vm-size shared-cpu-8x --now
-    ```
-
-1. Pull and run `orca-mini:3b`
-
-    ```bash
-    OLLAMA_HOST=https://<name>.fly.dev ollama run orca-mini:3b
-    ```
-
-`shared-cpu-8x` is a free-tier eligible machine type. For better performance, switch to a `performance` or `dedicated` machine type or attach a GPU for hardware acceleration (see below).
-
-## (Optional) Persistent Volume
-
-By default Fly Machines use ephemeral storage which is problematic if you want to use the same model across restarts without pulling it again. Create and attach a persistent volume to store the downloaded models:
-
-1. Create the Fly Volume
-
-    ```bash
-    fly volume create ollama
-    ```
-
-1. Update `fly.toml` and add `[mounts]`
-
-    ```toml
-    [mounts]
-      source = "ollama"
-      destination = "/mnt/ollama/models"
-    ```
-
-1. Update `fly.toml` and add `[env]`
-
-    ```toml
-    [env]
-      OLLAMA_MODELS = "/mnt/ollama/models"
-    ```
-
-1. Deploy your app
-
-    ```bash
-    fly deploy
-    ```
-
-## (Optional) Hardware Acceleration
-
-Fly.io GPU is currently in waitlist. Sign up for the waitlist: https://fly.io/gpu
-
-Once you've been accepted, create the app with the additional flags `--vm-gpu-kind a100-pcie-40gb` or `--vm-gpu-kind a100-pcie-80gb`.

examples/go-http-generate/main.go

@@ -1,29 +0,0 @@
-package main
-
-import (
-	"bytes"
-	"fmt"
-	"io"
-	"log"
-	"net/http"
-	"os"
-)
-
-func main() {
-	body := []byte(`{"model":"mistral"}`)
-	resp, err := http.Post("http://localhost:11434/api/generate", "application/json", bytes.NewBuffer(body))
-
-	if err != nil {
-		fmt.Print(err.Error())
-		os.Exit(1)
-	}
-
-	defer resp.Body.Close()
-
-	responseData, err := io.ReadAll(resp.Body)
-	if err != nil {
-		log.Fatal(err)
-	}
-	fmt.Println(string(responseData))
-
-}

examples/jupyter-notebook/README.md

@@ -1,5 +0,0 @@
-# Ollama Jupyter Notebook
-
-This example downloads and installs Ollama in a Jupyter instance such as Google Colab. It will start the Ollama service and expose an endpoint using `ngrok` which can be used to communicate with the Ollama instance remotely.
-
-For best results, use an instance with GPU accelerator.

examples/jupyter-notebook/ollama.ipynb

@@ -1,102 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "93f59dcb-c588-41b8-a792-55d88ade739c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Download and run the Ollama Linux install script\n",
-    "!curl -fsSL https://ollama.com/install.sh | sh\n",
-    "!command -v systemctl >/dev/null && sudo systemctl stop ollama"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "658c147e-c7f8-490e-910e-62b80f577dda",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install aiohttp pyngrok\n",
-    "\n",
-    "import os\n",
-    "import asyncio\n",
-    "from aiohttp import ClientSession\n",
-    "\n",
-    "# Set LD_LIBRARY_PATH so the system NVIDIA library becomes preferred\n",
-    "# over the built-in library. This is particularly important for \n",
-    "# Google Colab which installs older drivers\n",
-    "os.environ.update({'LD_LIBRARY_PATH': '/usr/lib64-nvidia'})\n",
-    "\n",
-    "async def run(cmd):\n",
-    "  '''\n",
-    "  run is a helper function to run subcommands asynchronously.\n",
-    "  '''\n",
-    "  print('>>> starting', *cmd)\n",
-    "  p = await asyncio.subprocess.create_subprocess_exec(\n",
-    "      *cmd,\n",
-    "      stdout=asyncio.subprocess.PIPE,\n",
-    "      stderr=asyncio.subprocess.PIPE,\n",
-    "  )\n",
-    "\n",
-    "  async def pipe(lines):\n",
-    "    async for line in lines:\n",
-    "      print(line.strip().decode('utf-8'))\n",
-    "\n",
-    "  await asyncio.gather(\n",
-    "      pipe(p.stdout),\n",
-    "      pipe(p.stderr),\n",
-    "  )\n",
-    "\n",
-    "\n",
-    "await asyncio.gather(\n",
-    "    run(['ollama', 'serve']),\n",
-    "    run(['ngrok', 'http', '--log', 'stderr', '11434']),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e7735a55-9aad-4caf-8683-52e2163ba53b",
-   "metadata": {},
-   "source": [
-    "The previous cell starts two processes, `ollama` and `ngrok`. The log output will show a line like the following which describes the external address.\n",
-    "\n",
-    "```\n",
-    "t=2023-11-12T22:55:56+0000 lvl=info msg=\"started tunnel\" obj=tunnels name=command_line addr=http://localhost:11434 url=https://8249-34-125-179-11.ngrok.io\n",
-    "```\n",
-    "\n",
-    "The external address in this case is `https://8249-34-125-179-11.ngrok.io` which can be passed into `OLLAMA_HOST` to access this instance.\n",
-    "\n",
-    "```bash\n",
-    "export OLLAMA_HOST=https://8249-34-125-179-11.ngrok.io\n",
-    "ollama list\n",
-    "ollama run mistral\n",
-    "```"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

examples/kubernetes/README.md

@@ -1,38 +0,0 @@
-# Deploy Ollama to Kubernetes
-
-## Prerequisites
-
-- Ollama: https://ollama.com/download
-- Kubernetes cluster. This example will use Google Kubernetes Engine.
-
-## Steps
-
-1. Create the Ollama namespace, deployment, and service
-
-   ```bash
-   kubectl apply -f cpu.yaml
-   ```
-
-## (Optional) Hardware Acceleration
-
-Hardware acceleration in Kubernetes requires NVIDIA's [`k8s-device-plugin`](https://github.com/NVIDIA/k8s-device-plugin) which is deployed in Kubernetes in form of daemonset. Follow the link for more details.
-
-Once configured, create a GPU enabled Ollama deployment.
-
-```bash
-kubectl apply -f gpu.yaml
-```
-
-## Test
-
-1. Port forward the Ollama service to connect and use it locally
-
-   ```bash
-   kubectl -n ollama port-forward service/ollama 11434:80
-   ```
-
-1. Pull and run a model, for example `orca-mini:3b`
-
-   ```bash
-   ollama run orca-mini:3b
-   ```

examples/kubernetes/cpu.yaml

@@ -1,42 +0,0 @@
----
-apiVersion: v1
-kind: Namespace
-metadata:
-  name: ollama
----
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: ollama
-  namespace: ollama
-spec:
-  selector:
-    matchLabels:
-      name: ollama
-  template:
-    metadata:
-      labels:
-        name: ollama
-    spec:
-      containers:
-      - name: ollama
-        image: ollama/ollama:latest
-        ports:
-        - name: http
-          containerPort: 11434
-          protocol: TCP
----
-apiVersion: v1
-kind: Service
-metadata:
-  name: ollama
-  namespace: ollama
-spec:
-  type: ClusterIP
-  selector:
-    name: ollama
-  ports:
-  - port: 80
-    name: http
-    targetPort: http
-    protocol: TCP

examples/kubernetes/gpu.yaml

@@ -1,58 +0,0 @@
----
-apiVersion: v1
-kind: Namespace
-metadata:
-  name: ollama
----
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: ollama
-  namespace: ollama
-spec:
-  strategy:
-    type: Recreate
-  selector:
-    matchLabels:
-      name: ollama
-  template:
-    metadata:
-      labels:
-        name: ollama
-    spec:
-      containers:
-      - name: ollama
-        image: ollama/ollama:latest
-        env:
-        - name: PATH
-          value: /usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
-        - name: LD_LIBRARY_PATH
-          value: /usr/local/nvidia/lib:/usr/local/nvidia/lib64
-        - name: NVIDIA_DRIVER_CAPABILITIES
-          value: compute,utility
-        ports:
-        - name: http
-          containerPort: 11434
-          protocol: TCP
-        resources:
-          limits:
-            nvidia.com/gpu: 1
-      tolerations:
-      - key: nvidia.com/gpu
-        operator: Exists
-        effect: NoSchedule
----
-apiVersion: v1
-kind: Service
-metadata:
-  name: ollama
-  namespace: ollama
-spec:
-  type: ClusterIP
-  selector:
-    name: ollama
-  ports:
-  - port: 80
-    name: http
-    targetPort: http
-    protocol: TCP

examples/langchain-python-rag-document/README.md

@@ -1,29 +0,0 @@
-# LangChain Document QA
-
-This example provides an interface for asking questions to a PDF document.
-
-## Setup
-
-1. Ensure you have the `llama3.2` model installed:
-
-```
-ollama pull llama3.2
-```
-
-2. Install the Python Requirements.
-
-```
-pip install -r requirements.txt
-```
-
-## Run
-
-```
-python main.py
-```
-
-A prompt will appear, where questions may be asked:
-
-```
-Query: How many locations does WeWork have?
-```

examples/langchain-python-rag-document/main.py

@@ -1,61 +0,0 @@
-from langchain.document_loaders import OnlinePDFLoader
-from langchain.vectorstores import Chroma
-from langchain.embeddings import GPT4AllEmbeddings
-from langchain import PromptTemplate
-from langchain.llms import Ollama
-from langchain.callbacks.manager import CallbackManager
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-from langchain.chains import RetrievalQA
-import sys
-import os
-
-class SuppressStdout:
-    def __enter__(self):
-        self._original_stdout = sys.stdout
-        self._original_stderr = sys.stderr
-        sys.stdout = open(os.devnull, 'w')
-        sys.stderr = open(os.devnull, 'w')
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        sys.stdout.close()
-        sys.stdout = self._original_stdout
-        sys.stderr = self._original_stderr
-
-# load the pdf and split it into chunks
-loader = OnlinePDFLoader("https://d18rn0p25nwr6d.cloudfront.net/CIK-0001813756/975b3e9b-268e-4798-a9e4-2a9a7c92dc10.pdf")
-data = loader.load()
-
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
-all_splits = text_splitter.split_documents(data)
-
-with SuppressStdout():
-    vectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings())
-
-while True:
-    query = input("\nQuery: ")
-    if query == "exit":
-        break
-    if query.strip() == "":
-        continue
-
-    # Prompt
-    template = """Use the following pieces of context to answer the question at the end.
-    If you don't know the answer, just say that you don't know, don't try to make up an answer.
-    Use three sentences maximum and keep the answer as concise as possible.
-    {context}
-    Question: {question}
-    Helpful Answer:"""
-    QA_CHAIN_PROMPT = PromptTemplate(
-        input_variables=["context", "question"],
-        template=template,
-    )
-
-    llm = Ollama(model="llama3.2", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))
-    qa_chain = RetrievalQA.from_chain_type(
-        llm,
-        retriever=vectorstore.as_retriever(),
-        chain_type_kwargs={"prompt": QA_CHAIN_PROMPT},
-    )
-
-    result = qa_chain({"query": query})

examples/langchain-python-rag-document/requirements.txt

@@ -1,109 +0,0 @@
-absl-py==1.4.0
-aiohttp==3.8.5
-aiosignal==1.3.1
-anyio==3.7.1
-astunparse==1.6.3
-async-timeout==4.0.3
-attrs==23.1.0
-backoff==2.2.1
-beautifulsoup4==4.12.2
-bs4==0.0.1
-cachetools==5.3.1
-certifi==2023.7.22
-cffi==1.15.1
-chardet==5.2.0
-charset-normalizer==3.2.0
-Chroma==0.2.0
-chroma-hnswlib==0.7.2
-chromadb==0.4.5
-click==8.1.6
-coloredlogs==15.0.1
-cryptography==41.0.3
-dataclasses-json==0.5.14
-fastapi==0.99.1
-filetype==1.2.0
-flatbuffers==23.5.26
-frozenlist==1.4.0
-gast==0.4.0
-google-auth==2.22.0
-google-auth-oauthlib==1.0.0
-google-pasta==0.2.0
-gpt4all==1.0.8
-grpcio==1.57.0
-h11==0.14.0
-h5py==3.9.0
-httptools==0.6.0
-humanfriendly==10.0
-idna==3.4
-importlib-resources==6.0.1
-joblib==1.3.2
-keras==2.13.1
-langchain==0.0.261
-langsmith==0.0.21
-libclang==16.0.6
-lxml==4.9.3
-Markdown==3.4.4
-MarkupSafe==2.1.3
-marshmallow==3.20.1
-monotonic==1.6
-mpmath==1.3.0
-multidict==6.0.4
-mypy-extensions==1.0.0
-nltk==3.8.1
-numexpr==2.8.5
-numpy==1.24.3
-oauthlib==3.2.2
-onnxruntime==1.15.1
-openapi-schema-pydantic==1.2.4
-opt-einsum==3.3.0
-overrides==7.4.0
-packaging==23.1
-pdf2image==1.16.3
-pdfminer==20191125
-pdfminer.six==20221105
-Pillow==10.0.0
-posthog==3.0.1
-protobuf==4.24.0
-pulsar-client==3.2.0
-pyasn1==0.5.0
-pyasn1-modules==0.3.0
-pycparser==2.21
-pycryptodome==3.18.0
-pydantic==1.10.12
-PyPika==0.48.9
-python-dateutil==2.8.2
-python-dotenv==1.0.0
-python-magic==0.4.27
-PyYAML==6.0.1
-regex==2023.8.8
-requests==2.31.0
-requests-oauthlib==1.3.1
-rsa==4.9
-six==1.16.0
-sniffio==1.3.0
-soupsieve==2.4.1
-SQLAlchemy==2.0.19
-starlette==0.27.0
-sympy==1.12
-tabulate==0.9.0
-tenacity==8.2.2
-tensorboard==2.13.0
-tensorboard-data-server==0.7.1
-tensorflow==2.13.0
-tensorflow-estimator==2.13.0
-tensorflow-hub==0.14.0
-tensorflow-macos==2.13.0
-termcolor==2.3.0
-tokenizers==0.13.3
-tqdm==4.66.1
-typing-inspect==0.9.0
-typing_extensions==4.5.0
-unstructured==0.9.2
-urllib3==1.26.16
-uvicorn==0.23.2
-uvloop==0.17.0
-watchfiles==0.19.0
-websockets==11.0.3
-Werkzeug==2.3.6
-wrapt==1.15.0
-yarl==1.9.2

examples/langchain-python-rag-privategpt/.gitignore

@@ -1,170 +0,0 @@
-# OSX
-.DS_STORE
-
-# Models
-models/
-
-# Local Chroma db
-.chroma/
-db/
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
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-
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-
-# mypy
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-
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-
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-cython_debug/
-
-# PyCharm
-#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
-#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
-#  and can be added to the global gitignore or merged into this file.  For a more nuclear
-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/

examples/langchain-python-rag-privategpt/LICENSE

@@ -1,201 +0,0 @@
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examples/langchain-python-rag-privategpt/README.md

@@ -1,91 +0,0 @@
-# PrivateGPT with Llama 2 uncensored
-
-https://github.com/ollama/ollama/assets/3325447/20cf8ec6-ff25-42c6-bdd8-9be594e3ce1b
-
-> Note: this example is a slightly modified version of PrivateGPT using models such as Llama 2 Uncensored. All credit for PrivateGPT goes to Iván Martínez who is the creator of it, and you can find his GitHub repo [here](https://github.com/imartinez/privateGPT).
-
-### Setup
-
-Set up a virtual environment (optional):
-
-```
-python3 -m venv .venv
-source .venv/bin/activate
-```
-
-Install the Python dependencies:
-
-```shell
-pip install -r requirements.txt
-```
-
-Pull the model you'd like to use:
-
-```
-ollama pull llama2-uncensored
-```
-
-### Getting WeWork's latest quarterly earnings report (10-Q)
-
-```
-mkdir source_documents
-curl https://d18rn0p25nwr6d.cloudfront.net/CIK-0001813756/975b3e9b-268e-4798-a9e4-2a9a7c92dc10.pdf -o source_documents/wework.pdf
-```
-
-### Ingesting files
-
-```shell
-python ingest.py
-```
-
-Output should look like this:
-
-```shell
-Creating new vectorstore
-Loading documents from source_documents
-Loading new documents: 100%|██████████████████████| 1/1 [00:01<00:00,  1.73s/it]
-Loaded 1 new documents from source_documents
-Split into 90 chunks of text (max. 500 tokens each)
-Creating embeddings. May take some minutes...
-Using embedded DuckDB with persistence: data will be stored in: db
-Ingestion complete! You can now run privateGPT.py to query your documents
-```
-
-### Ask questions
-
-```shell
-python privateGPT.py
-
-Enter a query: How many locations does WeWork have?
-
-> Answer (took 17.7 s.):
-As of June 2023, WeWork has 777 locations worldwide, including 610 Consolidated Locations (as defined in the section entitled Key Performance Indicators).
-```
-
-### Try a different model:
-
-```
-ollama pull llama2:13b
-MODEL=llama2:13b python privateGPT.py
-```
-
-## Adding more files
-
-Put any and all your files into the `source_documents` directory
-
-The supported extensions are:
-
-- `.csv`: CSV,
-- `.docx`: Word Document,
-- `.doc`: Word Document,
-- `.enex`: EverNote,
-- `.eml`: Email,
-- `.epub`: EPub,
-- `.html`: HTML File,
-- `.md`: Markdown,
-- `.msg`: Outlook Message,
-- `.odt`: Open Document Text,
-- `.pdf`: Portable Document Format (PDF),
-- `.pptx` : PowerPoint Document,
-- `.ppt` : PowerPoint Document,
-- `.txt`: Text file (UTF-8),

examples/langchain-python-rag-privategpt/constants.py

@@ -1,11 +0,0 @@
-import os
-from chromadb.config import Settings
-
-# Define the folder for storing database
-PERSIST_DIRECTORY = os.environ.get('PERSIST_DIRECTORY', 'db')
-
-# Define the Chroma settings
-CHROMA_SETTINGS = Settings(
-        persist_directory=PERSIST_DIRECTORY,
-        anonymized_telemetry=False
-)

examples/langchain-python-rag-privategpt/ingest.py

@@ -1,170 +0,0 @@
-#!/usr/bin/env python3
-import os
-import glob
-from typing import List
-from multiprocessing import Pool
-from tqdm import tqdm
-
-from langchain.document_loaders import (
-    CSVLoader,
-    EverNoteLoader,
-    PyMuPDFLoader,
-    TextLoader,
-    UnstructuredEmailLoader,
-    UnstructuredEPubLoader,
-    UnstructuredHTMLLoader,
-    UnstructuredMarkdownLoader,
-    UnstructuredODTLoader,
-    UnstructuredPowerPointLoader,
-    UnstructuredWordDocumentLoader,
-)
-
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores import Chroma
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.docstore.document import Document
-from constants import CHROMA_SETTINGS
-
-
-# Load environment variables
-persist_directory = os.environ.get('PERSIST_DIRECTORY', 'db')
-source_directory = os.environ.get('SOURCE_DIRECTORY', 'source_documents')
-embeddings_model_name = os.environ.get('EMBEDDINGS_MODEL_NAME', 'all-MiniLM-L6-v2')
-chunk_size = 500
-chunk_overlap = 50
-
-# Custom document loaders
-class MyElmLoader(UnstructuredEmailLoader):
-    """Wrapper to fallback to text/plain when default does not work"""
-
-    def load(self) -> List[Document]:
-        """Wrapper adding fallback for elm without html"""
-        try:
-            try:
-                doc = UnstructuredEmailLoader.load(self)
-            except ValueError as e:
-                if 'text/html content not found in email' in str(e):
-                    # Try plain text
-                    self.unstructured_kwargs["content_source"]="text/plain"
-                    doc = UnstructuredEmailLoader.load(self)
-                else:
-                    raise
-        except Exception as e:
-            # Add file_path to exception message
-            raise type(e)(f"{self.file_path}: {e}") from e
-
-        return doc
-
-
-# Map file extensions to document loaders and their arguments
-LOADER_MAPPING = {
-    ".csv": (CSVLoader, {}),
-    # ".docx": (Docx2txtLoader, {}),
-    ".doc": (UnstructuredWordDocumentLoader, {}),
-    ".docx": (UnstructuredWordDocumentLoader, {}),
-    ".enex": (EverNoteLoader, {}),
-    ".eml": (MyElmLoader, {}),
-    ".epub": (UnstructuredEPubLoader, {}),
-    ".html": (UnstructuredHTMLLoader, {}),
-    ".md": (UnstructuredMarkdownLoader, {}),
-    ".odt": (UnstructuredODTLoader, {}),
-    ".pdf": (PyMuPDFLoader, {}),
-    ".ppt": (UnstructuredPowerPointLoader, {}),
-    ".pptx": (UnstructuredPowerPointLoader, {}),
-    ".txt": (TextLoader, {"encoding": "utf8"}),
-    # Add more mappings for other file extensions and loaders as needed
-}
-
-
-def load_single_document(file_path: str) -> List[Document]:
-    if os.path.getsize(file_path) != 0:
-        filename, ext = os.path.splitext(file_path)
-        if ext in LOADER_MAPPING:
-            loader_class, loader_args = LOADER_MAPPING[ext]
-            try:
-                loader = loader_class(file_path, **loader_args)
-                if loader:
-                    return loader.load()
-            except:
-                print(f"Corrupted file {file_path}. Ignoring it.")
-        else:
-            print(f"Unsupported file {file_path}. Ignoring it.")
-    else:
-        print(f"Empty file {file_path}. Ignoring it.")
-
-
-def load_documents(source_dir: str, ignored_files: List[str] = []) -> List[Document]:
-    """
-    Loads all documents from the source documents directory, ignoring specified files
-    """
-    all_files = []
-    for ext in LOADER_MAPPING:
-        all_files.extend(
-            glob.glob(os.path.join(source_dir, f"**/*{ext}"), recursive=True)
-        )
-    filtered_files = [file_path for file_path in all_files if file_path not in ignored_files]
-
-    with Pool(processes=os.cpu_count()) as pool:
-        results = []
-        with tqdm(total=len(filtered_files), desc='Loading new documents', ncols=80) as pbar:
-            for i, docs in enumerate(pool.imap_unordered(load_single_document, filtered_files)):
-                if docs:
-                    results.extend(docs)
-                pbar.update()
-
-    return results
-
-def process_documents(ignored_files: List[str] = []) -> List[Document]:
-    """
-    Load documents and split in chunks
-    """
-    print(f"Loading documents from {source_directory}")
-    documents = load_documents(source_directory, ignored_files)
-    if not documents:
-        print("No new documents to load")
-        exit(0)
-    print(f"Loaded {len(documents)} new documents from {source_directory}")
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
-    texts = text_splitter.split_documents(documents)
-    print(f"Split into {len(texts)} chunks of text (max. {chunk_size} tokens each)")
-    return texts
-
-def does_vectorstore_exist(persist_directory: str) -> bool:
-    """
-    Checks if vectorstore exists
-    """
-    if os.path.exists(os.path.join(persist_directory, 'index')):
-        if os.path.exists(os.path.join(persist_directory, 'chroma-collections.parquet')) and os.path.exists(os.path.join(persist_directory, 'chroma-embeddings.parquet')):
-            list_index_files = glob.glob(os.path.join(persist_directory, 'index/*.bin'))
-            list_index_files += glob.glob(os.path.join(persist_directory, 'index/*.pkl'))
-            # At least 3 documents are needed in a working vectorstore
-            if len(list_index_files) > 3:
-                return True
-    return False
-
-def main():
-    # Create embeddings
-    embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
-
-    if does_vectorstore_exist(persist_directory):
-        # Update and store locally vectorstore
-        print(f"Appending to existing vectorstore at {persist_directory}")
-        db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
-        collection = db.get()
-        texts = process_documents([metadata['source'] for metadata in collection['metadatas']])
-        print(f"Creating embeddings. May take some minutes...")
-        db.add_documents(texts)
-    else:
-        # Create and store locally vectorstore
-        print("Creating new vectorstore")
-        texts = process_documents()
-        print(f"Creating embeddings. May take some minutes...")
-        db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory)
-    db.persist()
-    db = None
-
-    print(f"Ingestion complete! You can now run privateGPT.py to query your documents")
-
-
-if __name__ == "__main__":
-    main()

examples/langchain-python-rag-privategpt/privateGPT.py

@@ -1,74 +0,0 @@
-#!/usr/bin/env python3
-from langchain.chains import RetrievalQA
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-from langchain.vectorstores import Chroma
-from langchain.llms import Ollama
-import chromadb
-import os
-import argparse
-import time
-
-model = os.environ.get("MODEL", "llama2-uncensored")
-# For embeddings model, the example uses a sentence-transformers model
-# https://www.sbert.net/docs/pretrained_models.html 
-# "The all-mpnet-base-v2 model provides the best quality, while all-MiniLM-L6-v2 is 5 times faster and still offers good quality."
-embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME", "all-MiniLM-L6-v2")
-persist_directory = os.environ.get("PERSIST_DIRECTORY", "db")
-target_source_chunks = int(os.environ.get('TARGET_SOURCE_CHUNKS',4))
-
-from constants import CHROMA_SETTINGS
-
-def main():
-    # Parse the command line arguments
-    args = parse_arguments()
-    embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
-
-    db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
-
-    retriever = db.as_retriever(search_kwargs={"k": target_source_chunks})
-    # activate/deactivate the streaming StdOut callback for LLMs
-    callbacks = [] if args.mute_stream else [StreamingStdOutCallbackHandler()]
-
-    llm = Ollama(model=model, callbacks=callbacks)
-
-    qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents= not args.hide_source)
-    # Interactive questions and answers
-    while True:
-        query = input("\nEnter a query: ")
-        if query == "exit":
-            break
-        if query.strip() == "":
-            continue
-
-        # Get the answer from the chain
-        start = time.time()
-        res = qa(query)
-        answer, docs = res['result'], [] if args.hide_source else res['source_documents']
-        end = time.time()
-
-        # Print the result
-        print("\n\n> Question:")
-        print(query)
-        print(answer)
-
-        # Print the relevant sources used for the answer
-        for document in docs:
-            print("\n> " + document.metadata["source"] + ":")
-            print(document.page_content)
-
-def parse_arguments():
-    parser = argparse.ArgumentParser(description='privateGPT: Ask questions to your documents without an internet connection, '
-                                                 'using the power of LLMs.')
-    parser.add_argument("--hide-source", "-S", action='store_true',
-                        help='Use this flag to disable printing of source documents used for answers.')
-
-    parser.add_argument("--mute-stream", "-M",
-                        action='store_true',
-                        help='Use this flag to disable the streaming StdOut callback for LLMs.')
-
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    main()

examples/langchain-python-rag-privategpt/pyproject.toml

@@ -1,26 +0,0 @@
-[tool.poetry]
-name = "privategpt"
-version = "0.1.0"
-description = ""
-authors = ["Ivan Martinez <ivanmartit@gmail.com>"]
-license = "Apache Version 2.0"
-readme = "README.md"
-
-[tool.poetry.dependencies]
-python = "^3.10"
-langchain = "0.0.261"
-gpt4all = "^1.0.3"
-chromadb = "^0.3.26"
-PyMuPDF = "^1.22.5"
-python-dotenv = "^1.0.0"
-unstructured = "^0.8.0"
-extract-msg = "^0.41.5"
-tabulate = "^0.9.0"
-pandoc = "^2.3"
-pypandoc = "^1.11"
-tqdm = "^4.65.0"
-sentence-transformers = "^2.2.2"
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"

examples/langchain-python-rag-privategpt/requirements.txt

@@ -1,15 +0,0 @@
-langchain==0.0.274
-gpt4all==1.0.8
-chromadb==0.5.0
-llama-cpp-python==0.1.81
-urllib3==2.0.4
-PyMuPDF==1.23.5
-python-dotenv==1.0.0
-unstructured==0.10.8
-extract-msg==0.45.0
-tabulate==0.9.0
-pandoc==2.3
-pypandoc==1.11
-tqdm==4.66.1
-sentence_transformers==2.2.2
-numpy>=1.22.2 # not directly required, pinned by Snyk to avoid a vulnerability

examples/langchain-python-rag-websummary/README.md

@@ -1,23 +0,0 @@
-# LangChain Web Summarization
-
-This example summarizes the website, [https://ollama.com/blog/run-llama2-uncensored-locally](https://ollama.com/blog/run-llama2-uncensored-locally)
-
-## Running the Example
-
-1. Ensure you have the `llama3.2` model installed:
-
-   ```bash
-   ollama pull llama3.2
-   ```
-
-2. Install the Python Requirements.
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. Run the example:
-
-   ```bash
-   python main.py
-   ```

examples/langchain-python-rag-websummary/main.py

@@ -1,12 +0,0 @@
-from langchain_community.llms import Ollama
-from langchain_community.document_loaders import WebBaseLoader
-from langchain.chains.summarize import load_summarize_chain
-
-loader = WebBaseLoader("https://ollama.com/blog/run-llama2-uncensored-locally")
-docs = loader.load()
-
-llm = Ollama(model="llama3.2")
-chain = load_summarize_chain(llm, chain_type="stuff")
-
-result = chain.invoke(docs)
-print(result)