demo

.dockerignore

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

.gitattributes

@@ -1,11 +1,3 @@
-llama/**/*.cpp linguist-vendored
-llama/**/*.hpp linguist-vendored
-llama/**/*.h linguist-vendored
-llama/**/*.c linguist-vendored
-llama/**/*.cu linguist-vendored
-llama/**/*.cuh linguist-vendored
-llama/**/*.m linguist-vendored
-llama/**/*.metal linguist-vendored
-
+llm/ext_server/* linguist-vendored
 * text=auto
 *.go text eol=lf

.github/workflows/release.yaml

@@ -1,9 +1,5 @@
 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:
@@ -12,7 +8,7 @@ on:
 jobs:
   # Full build of the Mac assets
   build-darwin:
-    runs-on: macos-13
+    runs-on: macos-12
     environment: release
     steps:
       - uses: actions/checkout@v4
@@ -43,8 +39,8 @@ jobs:
           APPLE_PASSWORD: ${{ secrets.APPLE_PASSWORD }}
           APPLE_TEAM_ID: ${{ vars.APPLE_TEAM_ID }}
           APPLE_ID: ${{ vars.APPLE_ID }}
-          SDKROOT: /Applications/Xcode_14.1.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX.sdk
-          DEVELOPER_DIR: /Applications/Xcode_14.1.0.app/Contents/Developer
+          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
         run: |
           ./scripts/build_darwin.sh
 
@@ -52,8 +48,8 @@ jobs:
         with:
           name: dist-darwin
           path: |
-            dist/Ollama-darwin.zip
-            dist/ollama-darwin
+            dist/*arwin*
+            !dist/*-cov
 
   # Windows builds take a long time to both install the dependencies and build, so parallelize
   # CPU generation step
@@ -64,286 +60,14 @@ 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
-      - name: Add msys paths
-        run: |
-          echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-      - name: Install msys2 tools
-        run: |
-          Start-Process "c:\msys64\usr\bin\pacman.exe" -ArgumentList @("-S", "--noconfirm", "mingw-w64-clang-x86_64-gcc-compat", "mingw-w64-clang-x86_64-clang") -NoNewWindow -Wait
-      - uses: actions/setup-go@v5
-        with:
-          go-version-file: go.mod
-          cache: true
-      - run: |
-          import-module 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
-          Enter-VsDevShell -vsinstallpath 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise' -skipautomaticlocation -DevCmdArguments '-arch=x64 -no_logo'
-          if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
-          make
-        name: make
-      - uses: actions/upload-artifact@v4
-        with:
-          name: generate-windows-cpu
-          path: |
-            build/**/*
-            dist/windows-amd64/**
-
-  # ROCm generation step
-  generate-windows-rocm:
-    environment: release
-    runs-on: windows
-    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
-      - name: Add msys paths
-        run: |
-          echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-      - name: Install msys2 tools
-        run: |
-          Start-Process "c:\msys64\usr\bin\pacman.exe" -ArgumentList @("-S", "--noconfirm", "mingw-w64-clang-x86_64-gcc-compat", "mingw-w64-clang-x86_64-clang") -NoNewWindow -Wait
-      - uses: actions/setup-go@v5
-        with:
-          go-version-file: go.mod
-          cache: true
-      # ROCM installation steps
-      - name: 'Cache ROCm installer'
-        id: cache-rocm
-        uses: actions/cache@v4
-        with:
-          path: rocm-install.exe
-          key: ${{ env.ROCM_WINDOWS_URL }}
-      - name: 'Conditionally Download ROCm'
-        if: steps.cache-rocm.outputs.cache-hit != 'true'
-        run: |
-          $ErrorActionPreference = "Stop"
-          Invoke-WebRequest -Uri "${env:ROCM_WINDOWS_URL}" -OutFile "rocm-install.exe"
-      - 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
-          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'
-          if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
-          make -C llama print-HIP_PATH print-HIP_LIB_DIR
-          make rocm
-      - uses: actions/upload-artifact@v4
-        with:
-          name: generate-windows-rocm
-          path: |
-            build/**/*
-            dist/windows-amd64/**
-
-  # CUDA generation step
-  generate-windows-cuda:
-    environment: release
-    runs-on: windows
-    strategy:
-      matrix:
-        cuda:
-          - 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
-      - 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
-      - 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
-      - uses: actions/setup-go@v5
-        with:
-          go-version-file: go.mod
-          cache: true
-      # 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: |
-          $ErrorActionPreference = "Stop"
-          Invoke-WebRequest -Uri "${{ 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" | 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 $env:GITHUB_ENV -Encoding utf8 -Append
-
-      - name: make cuda runner
-        run: |
-          import-module 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
-          Enter-VsDevShell -vsinstallpath 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise' -skipautomaticlocation -DevCmdArguments '-arch=x64 -no_logo'
-          if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
-          make cuda_v$(($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)
-  # Output of this build is aggregated into the final x86 build
-  # for a unified windows installer
-  windows-arm64:
-    runs-on: windows-arm64
-    environment: release
-    env:
-      KEY_CONTAINER: ${{ vars.KEY_CONTAINER }}
-    steps:
-      # The current Windows arm64 beta image has effectively zero dev tools installed...
-      - name: Install git and gzip
-        run: |
-          Set-ExecutionPolicy Bypass -Scope Process -Force
-          [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072
-          iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))
-          choco install -y --no-progress git gzip
-          echo "C:\Program Files\Git\cmd" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-          echo "C:\ProgramData\chocolatey\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-      # pacman is buggy on win arm64, so we avoid using it, but rely on the binary artifacts
-      # we download the sfx (7zip bundle) which isn't fully set up, but the binaries we need to build work
-      - name: Install msys2 x64
-        run: |
-          $url="https://github.com/msys2/msys2-installer/releases/download/2024-07-27/msys2-base-x86_64-20240727.sfx.exe"
-          write-host "Downloading MSYS2"
-          Invoke-WebRequest -Uri "$url" -outfile "${env:RUNNER_TEMP}\msys2.exe"
-          write-host "Installing msys2"
-          Start-Process "${env:RUNNER_TEMP}\msys2.exe" -ArgumentList @(
-              '-y', '-oC:\'
-              ) -NoNewWindow -Wait
-          echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-      # since pacman isn't reliable, we just download the tar file and extract directly
-      - name: Downloading and extracting msys2 make tar file
-        run: |
-          $url="https://mirror.msys2.org/msys/x86_64/make-4.4.1-2-x86_64.pkg.tar.zst"
-          write-host "Downloading make"
-          Invoke-WebRequest -Uri "$url" -outfile c:\msys64\make.tar.zst
-          cd c:\msys64; tar -xf make.tar.zst
-          rm c:\msys64\make.tar.zst
-      - name: Verify Make works properly
-        run: |
-          echo $env:PATH
-          make --version
-      - name: Install Visual Studio 2022
-        run: |
-          $components = @(
-            "Microsoft.VisualStudio.Component.CoreEditor",
-            "Microsoft.VisualStudio.Workload.CoreEditor",
-            "Microsoft.VisualStudio.Component.Roslyn.Compiler",
-            "Microsoft.Component.MSBuild",
-            "Microsoft.VisualStudio.Component.TextTemplating",
-            "Microsoft.VisualStudio.Component.Debugger.JustInTime",
-            "Microsoft.VisualStudio.Component.VC.CoreIde",
-            "Microsoft.VisualStudio.Component.VC.Tools.x86.x64",
-            "Microsoft.VisualStudio.Component.Windows11SDK.22621",
-            "Microsoft.VisualStudio.Component.VC.Tools.ARM64EC",
-            "Microsoft.VisualStudio.Component.VC.Tools.ARM64",
-            "Microsoft.VisualStudio.Component.VC.ATL",
-            "Microsoft.VisualStudio.Component.VC.ATL.ARM64",
-            "Microsoft.VisualStudio.Component.Graphics",
-            "Microsoft.VisualStudio.Component.VC.Redist.14.Latest",
-            "Microsoft.VisualStudio.ComponentGroup.NativeDesktop.Core",
-            "Microsoft.VisualStudio.Component.Windows11Sdk.WindowsPerformanceToolkit",
-            "Microsoft.VisualStudio.Component.CppBuildInsights",
-            "Microsoft.VisualStudio.Component.VC.DiagnosticTools",
-            "Microsoft.VisualStudio.ComponentGroup.WebToolsExtensions.CMake",
-            "Microsoft.VisualStudio.Component.VC.CMake.Project",
-            "Microsoft.VisualStudio.Component.VC.ASAN",
-            "Microsoft.VisualStudio.Component.Vcpkg",
-            "Microsoft.VisualStudio.Workload.NativeDesktop"
-          )
-          $config = @{
-                "version" = "1.0"
-                "components"  = $components
-                "extensions"  = @()
-            }
-          $configPath = "${env:RUNNER_TEMP}\vsconfig"
-          $config | ConvertTo-Json | Out-File -FilePath $configPath
-          $bootstrapperFilePath = "${env:RUNNER_TEMP}\vs_community.exe"
-          write-host "Downloading Visual Studio 2022"
-          Invoke-WebRequest -Uri "https://aka.ms/vs/17/release/vs_community.exe" -outfile $bootstrapperFilePath
-          $bootstrapperArgumentList = ('/c', $bootstrapperFilePath, '--config', $configPath, '--quiet', '--wait' )
-          write-host "Installing Visual Studio 2022"
-          $process = Start-Process -FilePath cmd.exe -ArgumentList $bootstrapperArgumentList -Wait -PassThru
-          $exitCode = $process.ExitCode
-          write-host $exitCode
-      # pacman in mingw/msys2 is ~broken on windows arm right now - hangs consistently during attempts to install
-      # so we'll use this alternative GCC binary
-      - name: Install llvm-mingw GCC
-        run: |
-          $gcc_url="https://github.com/mstorsjo/llvm-mingw/releases/download/20240619/llvm-mingw-20240619-ucrt-aarch64.zip"
-          write-host "Downloading llvm-mingw"
-          Invoke-WebRequest -Uri "${gcc_url}" -OutFile "${env:RUNNER_TEMP}\gcc.zip"
-          write-host "Unpacking llvm-mingw"
-          expand-archive -path "${env:RUNNER_TEMP}\gcc.zip" -destinationpath "c:\"
-          mv c:\llvm-mingw-* c:\llvm-mingw
-          echo "c:\llvm-mingw\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-      - name: Verify GCC
-        run: |
-          echo $env:PATH
-          gcc --version
-      - uses: actions/checkout@v4
-      - name: Set Version
-        run: |
-          $ver=${env:GITHUB_REF_NAME}.trim("v")
-          echo VERSION=$ver | Out-File -FilePath ${env:GITHUB_ENV} -Encoding utf8 -Append
       - uses: 'google-github-actions/auth@v2'
         with:
           project_id: 'ollama'
           credentials_json: '${{ secrets.GOOGLE_SIGNING_CREDENTIALS }}'
-      - run: echo "${{ vars.OLLAMA_CERT }}" | Out-File -FilePath ollama_inc.crt -Encoding utf8
+      - run: echo "${{ vars.OLLAMA_CERT }}" > ollama_inc.crt
       - name: install Windows SDK 8.1 to get signtool
         run: |
           $ErrorActionPreference = "Stop"
@@ -368,23 +92,180 @@ jobs:
       - run: go get ./...
       - run: |
           $gopath=(get-command go).source | split-path -parent
-          $gccpath=(get-command gcc).source | split-path -parent
-          import-module 'C:\Program Files\Microsoft Visual Studio\2022\Community\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
-          Enter-VsDevShell -Arch arm64 -vsinstallpath 'C:\Program Files\Microsoft Visual Studio\2022\Community' -skipautomaticlocation
-          $env:PATH="$gopath;$gccpath;$env:PATH"
-          echo $env:PATH
-          $env:ARCH="arm64"
-          .\scripts\build_windows.ps1 buildOllama buildApp gatherDependencies sign distZip
-        name: 'Windows Build'
+          & "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Launch-VsDevShell.ps1"
+          cd $env:GITHUB_WORKSPACE
+          $env:CMAKE_SYSTEM_VERSION="10.0.22621.0"
+          $env:PATH="$gopath;$env:PATH"
+          go generate -x ./...
+        name: go generate
       - uses: actions/upload-artifact@v4
         with:
-          name: windows-arm64
+          name: generate-windows-cpu
           path: |
-            dist/windows-arm64/**
-            dist/windows-arm64-app.exe
-            dist/ollama-windows-arm64.zip
+            llm/build/**/bin/*
+            llm/build/**/*.a
+            dist/windows-amd64/**
 
-  # Import the prior generation steps plus the full arm64 build, and build the final windows assets
+  # ROCm generation step
+  generate-windows-rocm:
+    environment: release
+    runs-on: windows
+    env:
+      KEY_CONTAINER: ${{ vars.KEY_CONTAINER }}
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set Version
+        shell: bash
+        run: echo "VERSION=${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"
+      - uses: actions/setup-go@v5
+        with:
+          go-version-file: go.mod
+          cache: true
+      - name: 'Install ROCm'
+        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"
+      - 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
+          & "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Launch-VsDevShell.ps1"
+          cd $env:GITHUB_WORKSPACE
+          $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)
+          go generate -x ./...
+        name: go generate
+      - name: 'gather rocm dependencies'
+        run: |
+          $HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path)
+          md "dist\deps\bin\rocblas\library"
+          cp "${HIP_PATH}\bin\hipblas.dll" "dist\deps\bin\"
+          cp "${HIP_PATH}\bin\rocblas.dll" "dist\deps\bin\"
+          cp "${HIP_PATH}\bin\rocblas\library\*" "dist\deps\bin\rocblas\library\"
+      - uses: actions/upload-artifact@v4
+        with:
+          name: generate-windows-rocm
+          path: |
+            llm/build/**/bin/*
+            dist/windows-amd64/**
+      - uses: actions/upload-artifact@v4
+        with:
+          name: windows-rocm-deps
+          path: dist/deps/*
+
+  # CUDA generation step
+  generate-windows-cuda:
+    environment: release
+    runs-on: windows
+    env:
+      KEY_CONTAINER: ${{ vars.KEY_CONTAINER }}
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set Version
+        shell: bash
+        run: echo "VERSION=${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"
+      - uses: actions/setup-go@v5
+        with:
+          go-version-file: go.mod
+          cache: true
+      - name: 'Install CUDA'
+        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"
+          $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: go generate
+        run: |
+          $gopath=(get-command go).source | split-path -parent
+          $cudabin=(get-command nvcc).source | split-path
+          & "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Launch-VsDevShell.ps1"
+          cd $env:GITHUB_WORKSPACE
+          $env:CMAKE_SYSTEM_VERSION="10.0.22621.0"
+          $env:PATH="$gopath;$cudabin;$env:PATH"
+          $env:OLLAMA_SKIP_CPU_GENERATE="1"
+          go generate -x ./...
+      - name: 'gather cuda dependencies'
+        run: |
+          $NVIDIA_DIR=(resolve-path 'C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\*\bin\')[0]
+          md "dist\deps"
+          cp "${NVIDIA_DIR}\cudart64_*.dll" "dist\deps\"
+          cp "${NVIDIA_DIR}\cublas64_*.dll" "dist\deps\"
+          cp "${NVIDIA_DIR}\cublasLt64_*.dll" "dist\deps\"
+      - uses: actions/upload-artifact@v4
+        with:
+          name: generate-windows-cuda
+          path: |
+            llm/build/**/bin/*
+            dist/windows-amd64/**
+      - uses: actions/upload-artifact@v4
+        with:
+          name: windows-cuda-deps
+          path: dist/deps/*
+
+  # Import the prior generation steps and build the final windows assets
   build-windows:
     environment: release
     runs-on: windows
@@ -392,7 +273,6 @@ jobs:
       - generate-windows-cuda
       - generate-windows-rocm
       - generate-windows-cpu
-      - windows-arm64
     env:
       KEY_CONTAINER: ${{ vars.KEY_CONTAINER }}
     steps:
@@ -424,24 +304,6 @@ jobs:
           write-host "Installing plugin"
           & "${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
-      - 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
       - uses: actions/setup-go@v5
         with:
           go-version-file: go.mod
@@ -452,24 +314,24 @@ jobs:
           name: generate-windows-cpu
       - uses: actions/download-artifact@v4
         with:
-          name: generate-windows-cuda-11.3
+          name: generate-windows-cuda
       - uses: actions/download-artifact@v4
         with:
-          name: generate-windows-cuda-12.4
+          name: windows-cuda-deps
+      - uses: actions/download-artifact@v4
+        with:
+          name: windows-rocm-deps
       - uses: actions/download-artifact@v4
         with:
           name: generate-windows-rocm
-      - uses: actions/download-artifact@v4
-        with:
-          name: windows-arm64
-          path: dist
-      - run: dir build
+      - run: dir llm/build
       - 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'
+          $gopath=(get-command go).source | split-path -parent
+          & "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Launch-VsDevShell.ps1"
+          cd $env:GITHUB_WORKSPACE
+          $env:CMAKE_SYSTEM_VERSION="10.0.22621.0"
+          $env:PATH="$gopath;$env:PATH"
           $env:OLLAMA_SKIP_GENERATE="1"
-          $env:ARCH="amd64"
-          if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
           & .\scripts\build_windows.ps1
       - uses: actions/upload-artifact@v4
         with:
@@ -483,7 +345,9 @@ jobs:
     environment: release
     runs-on: linux
     env:
-      PLATFORM: linux/amd64
+      OLLAMA_SKIP_MANIFEST_CREATE: '1'
+      BUILD_ARCH: amd64
+      PUSH: '1'
     steps:
       - uses: actions/checkout@v4
         with:
@@ -491,8 +355,15 @@ jobs:
       - name: Set Version
         shell: bash
         run: echo "VERSION=${GITHUB_REF_NAME#v}" >> $GITHUB_ENV
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ vars.DOCKER_USER }}
+          password: ${{ secrets.DOCKER_ACCESS_TOKEN }}
       - run: |
           ./scripts/build_linux.sh
+          ./scripts/build_docker.sh
+          mv dist/deps/* dist/
       - uses: actions/upload-artifact@v4
         with:
           name: dist-linux-amd64
@@ -506,7 +377,9 @@ jobs:
     environment: release
     runs-on: linux-arm64
     env:
-      PLATFORM: linux/arm64
+      OLLAMA_SKIP_MANIFEST_CREATE: '1'
+      BUILD_ARCH: arm64
+      PUSH: '1'
     steps:
       - uses: actions/checkout@v4
         with:
@@ -535,8 +408,14 @@ jobs:
           sudo usermod -aG docker $USER
           sudo apt-get install acl
           sudo setfacl --modify user:$USER:rw /var/run/docker.sock
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ vars.DOCKER_USER }}
+          password: ${{ secrets.DOCKER_ACCESS_TOKEN }}
       - run: |
           ./scripts/build_linux.sh
+          ./scripts/build_docker.sh
       - uses: actions/upload-artifact@v4
         with:
           name: dist-linux-arm64
@@ -544,178 +423,6 @@ jobs:
             dist/*linux*
             !dist/*-cov
 
-  # Container image build
-  build-container-image:
-    environment: release
-    strategy:
-      matrix:
-        runner:
-          - linux
-          - linux-arm64
-    runs-on: ${{ matrix.runner }}
-    env:
-      FINAL_IMAGE_REPO: ollama/ollama
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          submodules: recursive
-      - name: 'Install Docker'
-        if: ${{ startsWith(matrix.runner, 'linux-arm64') }}
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y ca-certificates curl
-          sudo install -m 0755 -d /etc/apt/keyrings
-          sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc
-          sudo chmod a+r /etc/apt/keyrings/docker.asc
-          echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/ubuntu \
-            $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
-            sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
-          sudo apt-get update
-          sudo apt-get install -y docker-ce docker-ce-cli containerd.io
-          sudo usermod -aG docker $USER
-          sudo apt-get install acl
-          sudo setfacl --modify user:$USER:rw /var/run/docker.sock
-      - name: Docker meta
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: ${{ env.FINAL_IMAGE_REPO }}
-          flavor: |
-            latest=false
-          tags: |
-            type=ref,enable=true,priority=600,prefix=0.0.0-pr,suffix=,event=pr
-            type=semver,pattern={{version}}
-      - name: Set Version
-        shell: bash
-        run: |
-          machine=$(uname -m)
-          case ${machine} in
-            x86_64) echo ARCH=amd64; echo PLATFORM_PAIR=linux-amd64 ;;
-            aarch64) echo ARCH=arm64; echo PLATFORM_PAIR=linux-arm64 ;;
-          esac >>$GITHUB_ENV
-          echo GOFLAGS="'-ldflags=-w -s \"-X=github.com/ollama/ollama/version.Version=${{ env.DOCKER_METADATA_OUTPUT_VERSION }}\" \"-X=github.com/ollama/ollama/server.mode=release\"'" >>$GITHUB_ENV
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ vars.DOCKER_USER }}
-          password: ${{ secrets.DOCKER_ACCESS_TOKEN }}
-      - name: Build and push by digest
-        id: build
-        uses: docker/build-push-action@v6
-        with:
-          context: "."
-          platforms: linux/${{ env.ARCH }}
-          build-args: |
-            GOFLAGS
-          outputs: type=image,name=${{ env.FINAL_IMAGE_REPO }},push-by-digest=true,name-canonical=true,push=true
-      - name: Export digest
-        run: |
-          mkdir -p /tmp/digests
-          digest="${{ steps.build.outputs.digest }}"
-          touch "/tmp/digests/${digest#sha256:}"
-      - name: Upload digest
-        uses: actions/upload-artifact@v4
-        with:
-          name: digests-${{ env.PLATFORM_PAIR }}
-          path: /tmp/digests/*
-          if-no-files-found: error
-          retention-days: 1
-  merge:
-    environment: release
-    runs-on: linux
-    needs:
-      - build-container-image
-    env:
-      FINAL_IMAGE_REPO: ollama/ollama
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          submodules: recursive
-      - name: Download digests
-        uses: actions/download-artifact@v4
-        with:
-          path: /tmp/digests
-          pattern: digests-*
-          merge-multiple: true
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-      - name: Docker meta
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: ${{ env.FINAL_IMAGE_REPO }}
-          flavor: |
-            latest=false
-          tags: |
-            type=ref,enable=true,priority=600,prefix=0.0.0-pr,suffix=,event=pr
-            type=semver,pattern={{version}}
-      - name: Set Version
-        shell: bash
-        run: |
-          machine=$(uname -m)
-          case ${machine} in
-            x86_64) echo ARCH=amd64; echo PLATFORM_PAIR=linux-amd64 ;;
-            aarch64) echo ARCH=arm64; echo PLATFORM_PAIR=linux-arm64 ;;
-          esac >>$GITHUB_ENV
-          echo GOFLAGS="'-ldflags=-w -s \"-X=github.com/ollama/ollama/version.Version=${{ env.DOCKER_METADATA_OUTPUT_VERSION }}\" \"-X=github.com/ollama/ollama/server.mode=release\"'" >>$GITHUB_ENV
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ vars.DOCKER_USER }}
-          password: ${{ secrets.DOCKER_ACCESS_TOKEN }}
-      - name: Create manifest list and push
-        working-directory: /tmp/digests
-        run: |
-          docker buildx imagetools create $(jq -cr '.tags | map("-t " + .) | join(" ")' <<< "$DOCKER_METADATA_OUTPUT_JSON") \
-            $(printf '${{ env.FINAL_IMAGE_REPO }}@sha256:%s ' *)
-      - name: Inspect image
-        run: |
-          docker buildx imagetools inspect ${{ env.FINAL_IMAGE_REPO }}:${{ steps.meta.outputs.version }}          
-  build-container-image-rocm:
-    environment: release
-    runs-on: linux
-    env:
-      FINAL_IMAGE_REPO: ollama/ollama
-      ARCH: amd64
-      PLATFORM_PAIR: linux-amd64
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          submodules: recursive
-      - name: Docker meta
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: ${{ env.FINAL_IMAGE_REPO }}
-          flavor: |
-            latest=false
-          tags: |
-            type=ref,enable=true,priority=600,prefix=0.0.0-pr,suffix=,event=pr
-            type=semver,pattern={{version}}
-      - name: Set Version
-        shell: bash
-        run: |
-          echo GOFLAGS="'-ldflags=-w -s \"-X=github.com/ollama/ollama/version.Version=${{ env.DOCKER_METADATA_OUTPUT_VERSION }}\" \"-X=github.com/ollama/ollama/server.mode=release\"'" >>$GITHUB_ENV
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ vars.DOCKER_USER }}
-          password: ${{ secrets.DOCKER_ACCESS_TOKEN }}
-      - name: Build and push by digest
-        id: build
-        uses: docker/build-push-action@v6
-        with:
-          context: "."
-          target: runtime-rocm
-          build-args: |
-            GOFLAGS
-          tags: ${{ env.FINAL_IMAGE_REPO }}:${{ env.DOCKER_METADATA_OUTPUT_VERSION}}-rocm
-          push: true
-
   # Aggregate all the assets and ship a release
   release:
     needs:
@@ -728,6 +435,8 @@ jobs:
     permissions:
       contents: write
     env:
+      OLLAMA_SKIP_IMAGE_BUILD: '1'
+      PUSH: '1'
       GH_TOKEN: ${{ github.token }}
     steps:
       - uses: actions/checkout@v4
@@ -736,6 +445,12 @@ jobs:
         run: |
           echo "VERSION=${GITHUB_REF_NAME#v}" >> $GITHUB_ENV
           echo "RELEASE_VERSION=$(echo ${GITHUB_REF_NAME} | cut -f1 -d-)" >> $GITHUB_ENV
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ vars.DOCKER_USER }}
+          password: ${{ secrets.DOCKER_ACCESS_TOKEN }}
+      - run: ./scripts/build_docker.sh
       - name: Retrieve built artifact
         uses: actions/download-artifact@v4
         with:
@@ -744,8 +459,7 @@ jobs:
           merge-multiple: true
       - run: |
           ls -lh dist/
-          (cd dist; find . -type f | xargs sha256sum > ../sha256sum.txt)
-          mv sha256sum.txt dist/
+          (cd dist; sha256sum * > sha256sum.txt)
           cat dist/sha256sum.txt
       - name: Create or update Release
         run: |

.github/workflows/test.yaml

@@ -1,11 +1,5 @@
 name: test
 
-env:
-  ROCM_WINDOWS_URL: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe
-  MSYS2_URL: https://github.com/msys2/msys2-installer/releases/download/2024-07-27/msys2-x86_64-20240727.exe
-  CUDA_12_WINDOWS_URL: https://developer.download.nvidia.com/compute/cuda/12.4.0/local_installers/cuda_12.4.0_551.61_windows.exe
-  CUDA_12_WINDOWS_VER: 12.4
-
 concurrency:
   # For PRs, later CI runs preempt previous ones. e.g. a force push on a PR
   # cancels running CI jobs and starts all new ones.
@@ -27,7 +21,9 @@ jobs:
   changes:
     runs-on: ubuntu-latest
     outputs:
-      RUNNERS: ${{ steps.changes.outputs.RUNNERS }}
+      GENERATE: ${{ steps.changes.outputs.GENERATE }}
+      GENERATE_CUDA: ${{ steps.changes.outputs.GENERATE_CUDA }}
+      GENERATE_ROCM: ${{ steps.changes.outputs.GENERATE_ROCM }}
     steps:
       - uses: actions/checkout@v4
         with:
@@ -42,167 +38,14 @@ jobs:
           }
 
           {
-            echo RUNNERS=$(changed 'llama/**')
+            echo GENERATE=$(changed 'llm/llama.cpp' 'llm/patches/**' 'llm/ext_server/**' 'llm/generate/**')
+            echo GENERATE_CUDA=$(changed 'llm/llama.cpp' 'llm/patches/**' 'llm/ext_server/**' 'llm/generate/**')
+            echo GENERATE_ROCM=$(changed 'llm/llama.cpp' 'llm/patches/**' 'llm/ext_server/**' 'llm/generate/**')
           } >>$GITHUB_OUTPUT
 
-  runners-linux-cuda:
+  generate:
     needs: [changes]
-    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
-    strategy:
-      matrix:
-        cuda-version:
-          - '11.8.0'
-    runs-on: linux
-    container: nvidia/cuda:${{ matrix.cuda-version }}-devel-ubuntu20.04
-    steps:
-      - run: |
-          apt-get update && apt-get install -y git build-essential curl
-        env:
-          DEBIAN_FRONTEND: noninteractive
-      - uses: actions/checkout@v4
-      - uses: actions/setup-go@v4
-        with:
-          go-version-file: go.mod
-          cache: true
-      - run: go get ./...
-      - run: |
-          git config --global --add safe.directory /__w/ollama/ollama
-          cores=$(grep '^core id' /proc/cpuinfo |sort -u|wc -l)
-          make -j $cores cuda_v11
-  runners-linux-rocm:
-    needs: [changes]
-    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
-    strategy:
-      matrix:
-        rocm-version:
-          - '6.1.2'
-    runs-on: linux
-    container: rocm/dev-ubuntu-20.04:${{ matrix.rocm-version }}
-    steps:
-      - run: |
-          apt-get update && apt-get install -y git build-essential curl rocm-libs
-        env:
-          DEBIAN_FRONTEND: noninteractive
-      - uses: actions/checkout@v4
-      - uses: actions/setup-go@v4
-        with:
-          go-version-file: go.mod
-          cache: true
-      - run: go get ./...
-      - run: |
-          git config --global --add safe.directory /__w/ollama/ollama
-          cores=$(grep '^core id' /proc/cpuinfo |sort -u|wc -l)
-          make -j $cores rocm
-
-  # ROCm generation step
-  runners-windows-rocm:
-    needs: [changes]
-    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
-    runs-on: windows
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-go@v5
-        with:
-          go-version-file: go.mod
-          cache: true
-      - name: Set make jobs default
-        run: |
-          echo "MAKEFLAGS=--jobs=$((Get-ComputerInfo -Property CsProcessors).CsProcessors.NumberOfCores)" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
-
-      # ROCM installation steps
-      - name: 'Cache ROCm installer'
-        id: cache-rocm
-        uses: actions/cache@v4
-        with:
-          path: rocm-install.exe
-          key: ${{ env.ROCM_WINDOWS_URL }}
-      - name: 'Conditionally Download ROCm'
-        if: steps.cache-rocm.outputs.cache-hit != 'true'
-        run: |
-          $ErrorActionPreference = "Stop"
-          Invoke-WebRequest -Uri "${env:ROCM_WINDOWS_URL}" -OutFile "rocm-install.exe"
-      - 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
-          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: |
-          echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-      - name: Install msys2 tools
-        run: |
-          Start-Process "c:\msys64\usr\bin\pacman.exe" -ArgumentList @("-S", "--noconfirm", "mingw-w64-clang-x86_64-gcc-compat", "mingw-w64-clang-x86_64-clang") -NoNewWindow -Wait
-
-      - name: make rocm runner
-        run: |
-          import-module 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
-          Enter-VsDevShell -vsinstallpath 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise' -skipautomaticlocation -DevCmdArguments '-arch=x64 -no_logo'
-          if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
-          make -C llama print-HIP_PATH print-HIP_LIB_DIR
-          make rocm
-
-  # CUDA generation step
-  runners-windows-cuda:
-    needs: [changes]
-    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
-    runs-on: windows
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-go@v5
-        with:
-          go-version-file: go.mod
-          cache: true
-      - name: Set make jobs default
-        run: |
-          echo "MAKEFLAGS=--jobs=$((Get-ComputerInfo -Property CsProcessors).CsProcessors.NumberOfCores)" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
-
-      # CUDA installation steps
-      - name: 'Cache CUDA installer'
-        id: cache-cuda
-        uses: actions/cache@v4
-        with:
-          path: cuda-install.exe
-          key: ${{ env.CUDA_12_WINDOWS_URL }}
-      - name: 'Conditionally Download CUDA'
-        if: steps.cache-cuda.outputs.cache-hit != 'true'
-        run: |
-          $ErrorActionPreference = "Stop"
-          Invoke-WebRequest -Uri "${env:CUDA_12_WINDOWS_URL}" -OutFile "cuda-install.exe"
-      - name: 'Install CUDA'
-        run: |
-          $subpackages = @("cudart", "nvcc", "cublas", "cublas_dev") | foreach-object {"${_}_${{ env.CUDA_12_WINDOWS_VER }}"}
-          Start-Process "cuda-install.exe" -ArgumentList (@("-s") + $subpackages) -NoNewWindow -Wait
-      - name: 'Verify CUDA'
-        run: |
-          & (resolve-path "c:\Program Files\NVIDIA*\CUDA\v*\bin\nvcc.exe")[0] --version
-          $cudaPath=((resolve-path "c:\Program Files\NVIDIA*\CUDA\v*\bin\nvcc.exe")[0].path | split-path | split-path)
-          $cudaVer=($cudaPath | split-path -leaf ) -replace 'v(\d+).(\d+)', '$1_$2' 
-          echo "$cudaPath\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-          echo "CUDA_PATH=$cudaPath" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
-          echo "CUDA_PATH_V${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: |
-          echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-      - name: Install msys2 tools
-        run: |
-          Start-Process "c:\msys64\usr\bin\pacman.exe" -ArgumentList @("-S", "--noconfirm", "mingw-w64-clang-x86_64-gcc-compat", "mingw-w64-clang-x86_64-clang") -NoNewWindow -Wait
-      - name: make cuda runner
-        run: |
-          import-module 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
-          Enter-VsDevShell -vsinstallpath 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise' -skipautomaticlocation -DevCmdArguments '-arch=x64 -no_logo'
-          if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
-          make cuda_v$(($env:CUDA_PATH | split-path -leaf) -replace 'v(\d+).*', '$1')
-
-  runners-cpu:
-    needs: [changes]
-    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
+    if: ${{ needs.changes.outputs.GENERATE == 'True' }}
     strategy:
       matrix:
         os: [ubuntu-latest, macos-latest, windows-2019]
@@ -215,7 +58,6 @@ jobs:
     runs-on: ${{ matrix.os }}
     env:
       GOARCH: ${{ matrix.arch }}
-      ARCH: ${{ matrix.arch }}
       CGO_ENABLED: '1'
     steps:
       - uses: actions/checkout@v4
@@ -223,31 +65,173 @@ jobs:
         with:
           go-version-file: go.mod
           cache: true
-      - name: Add msys paths
-        if: ${{ startsWith(matrix.os, 'windows-') }}
-        run: |
-          echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-      - name: Install msys2 tools
-        if: ${{ startsWith(matrix.os, 'windows-') }}
-        run: |
-          Start-Process "c:\msys64\usr\bin\pacman.exe" -ArgumentList @("-S", "--noconfirm", "mingw-w64-clang-x86_64-gcc-compat", "mingw-w64-clang-x86_64-clang") -NoNewWindow -Wait
-      - name: 'Build Windows Go Runners'
-        if: ${{ startsWith(matrix.os, 'windows-') }}
-        run: |
+      - run: go get ./...
+      - run: |
           $gopath=(get-command go).source | split-path -parent
           $gccpath=(get-command gcc).source | split-path -parent
-          import-module 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
-          Enter-VsDevShell -vsinstallpath 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise' -skipautomaticlocation -DevCmdArguments '-arch=x64 -no_logo'
+          & "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Launch-VsDevShell.ps1"
+          cd $env:GITHUB_WORKSPACE
           $env:CMAKE_SYSTEM_VERSION="10.0.22621.0"
           $env:PATH="$gopath;$gccpath;$env:PATH"
           echo $env:PATH
-          if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
-          make -j 4      
-      - name: 'Build Unix Go Runners'
+          go generate -x ./...
+        if: ${{ startsWith(matrix.os, 'windows-') }}
+        name: 'Windows Go Generate'
+      - run: go generate -x ./...
         if: ${{ ! startsWith(matrix.os, 'windows-') }}
-        run: make -j 4
+        name: 'Unix Go Generate'
       - run: go build .
+      - uses: actions/upload-artifact@v4
+        with:
+          name: ${{ matrix.os }}-${{ matrix.arch }}-libraries
+          path: |
+            llm/build/**/bin/*
+            llm/build/**/*.a
+  generate-cuda:
+    needs: [changes]
+    if: ${{ needs.changes.outputs.GENERATE_CUDA == 'True' }}
+    strategy:
+      matrix:
+        cuda-version:
+          - '11.8.0'
+    runs-on: linux
+    container: nvidia/cuda:${{ matrix.cuda-version }}-devel-ubuntu20.04
+    steps:
+      - run: |
+          apt-get update && apt-get install -y git build-essential curl
+          curl -fsSL https://github.com/Kitware/CMake/releases/download/v3.28.1/cmake-3.28.1-linux-x86_64.tar.gz \
+            | tar -zx -C /usr --strip-components 1
+        env:
+          DEBIAN_FRONTEND: noninteractive
+      - uses: actions/checkout@v4
+      - uses: actions/setup-go@v4
+        with:
+          go-version-file: go.mod
+          cache: true
+      - run: go get ./...
+      - run: |
+          git config --global --add safe.directory /__w/ollama/ollama
+          go generate -x ./...
+        env:
+          OLLAMA_SKIP_CPU_GENERATE: '1'
+      - uses: actions/upload-artifact@v4
+        with:
+          name: cuda-${{ matrix.cuda-version }}-libraries
+          path: |
+            llm/build/**/bin/*
+            dist/windows-amd64/**
+  generate-rocm:
+    needs: [changes]
+    if: ${{ needs.changes.outputs.GENERATE_ROCM == 'True' }}
+    strategy:
+      matrix:
+        rocm-version:
+          - '6.1.2'
+    runs-on: linux
+    container: rocm/dev-ubuntu-20.04:${{ matrix.rocm-version }}
+    steps:
+      - run: |
+          apt-get update && apt-get install -y git build-essential curl rocm-libs
+          curl -fsSL https://github.com/Kitware/CMake/releases/download/v3.28.1/cmake-3.28.1-linux-x86_64.tar.gz \
+            | tar -zx -C /usr --strip-components 1
+        env:
+          DEBIAN_FRONTEND: noninteractive
+      - uses: actions/checkout@v4
+      - uses: actions/setup-go@v4
+        with:
+          go-version-file: go.mod
+          cache: true
+      - run: go get ./...
+      - run: |
+          git config --global --add safe.directory /__w/ollama/ollama
+          go generate -x ./...
+        env:
+          OLLAMA_SKIP_CPU_GENERATE: '1'
+      - uses: actions/upload-artifact@v4
+        with:
+          name: rocm-${{ matrix.rocm-version }}-libraries
+          path: |
+            llm/build/**/bin/*
+            dist/windows-amd64/**
+
+  # ROCm generation step
+  generate-windows-rocm:
+    needs: [changes]
+    if: ${{ needs.changes.outputs.GENERATE_ROCM == 'True' }}
+    runs-on: windows
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-go@v5
+        with:
+          go-version-file: go.mod
+          cache: true
+      - name: 'Install ROCm'
+        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"
+      - 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
+          & "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Launch-VsDevShell.ps1"
+          cd $env:GITHUB_WORKSPACE
+          $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)
+          go generate -x ./...
+        name: go generate
+        env:
+          OLLAMA_SKIP_CPU_GENERATE: '1'
+      # TODO - do we need any artifacts?
+
+  # CUDA generation step
+  generate-windows-cuda:
+    needs: [changes]
+    if: ${{ needs.changes.outputs.GENERATE_CUDA == 'True' }}
+    runs-on: windows
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-go@v5
+        with:
+          go-version-file: go.mod
+          cache: true
+      - name: 'Install CUDA'
+        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"
+          $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: go generate
+        run: |
+          $gopath=(get-command go).source | split-path -parent
+          $cudabin=(get-command nvcc).source | split-path
+          & "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Launch-VsDevShell.ps1"
+          cd $env:GITHUB_WORKSPACE
+          $env:CMAKE_SYSTEM_VERSION="10.0.22621.0"
+          $env:PATH="$gopath;$cudabin;$env:PATH"
+          $env:OLLAMA_SKIP_CPU_GENERATE="1"
+          go generate -x ./...
+        env:
+          OLLAMA_SKIP_CPU_GENERATE: '1'
+      # TODO - do we need any artifacts?
 
   lint:
     strategy:
@@ -279,9 +263,17 @@ jobs:
             arm64) echo ARCH=arm64 ;;
           esac >>$GITHUB_ENV
         shell: bash
+      - run: |
+          mkdir -p llm/build/linux/$ARCH/stub/bin
+          touch llm/build/linux/$ARCH/stub/bin/ollama_llama_server
+        if: ${{ startsWith(matrix.os, 'ubuntu-') }}
+      - run: |
+          mkdir -p llm/build/darwin/$ARCH/stub/bin
+          touch llm/build/darwin/$ARCH/stub/bin/ollama_llama_server
+        if: ${{ startsWith(matrix.os, 'macos-') }}
       - uses: golangci/golangci-lint-action@v6
         with:
-          args: --timeout 10m0s -v
+          args: --timeout 8m0s -v
   test:
     strategy:
       matrix:
@@ -296,6 +288,9 @@ jobs:
     env:
       GOARCH: ${{ matrix.arch }}
       CGO_ENABLED: '1'
+      OLLAMA_CPU_TARGET: 'static'
+      OLLAMA_SKIP_CPU_GENERATE: '1'
+      OLLAMA_SKIP_METAL_GENERATE: '1'
     steps:
       - uses: actions/checkout@v4
         with:
@@ -306,21 +301,23 @@ jobs:
           cache: true
       - run: |
           case ${{ matrix.arch }} in
-            amd64) echo ARCH=amd64 ;;
+            amd64) echo ARCH=x86_64 ;;
             arm64) echo ARCH=arm64 ;;
           esac >>$GITHUB_ENV
         shell: bash
+      - run: |
+          mkdir -p llm/build/linux/$ARCH/stub/bin
+          touch llm/build/linux/$ARCH/stub/bin/ollama_llama_server
+        if: ${{ startsWith(matrix.os, 'ubuntu-') }}
+      - run: |
+          mkdir -p llm/build/darwin/$ARCH/stub/bin
+          touch llm/build/darwin/$ARCH/stub/bin/ollama_llama_server
+        if: ${{ startsWith(matrix.os, 'macos-') }}
+        shell: bash
+      - run: go generate ./...
       - run: go build
       - run: go test -v ./...
-
-  patches:
-    needs: [changes]
-    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/upload-artifact@v4
         with:
-          submodules: recursive
-      - name: Verify patches carry all the changes
-        run: |
-          make apply-patches sync && git diff --compact-summary --exit-code llama
+          name: ${{ matrix.os }}-binaries
+          path: ollama

.gitignore

@@ -5,14 +5,11 @@
 .swp
 dist
 ollama
+ggml-metal.metal
 .cache
 *.exe
 .idea
 test_data
 *.crt
 llm/build
-build/*/*/*
-!build/**/placeholder
-llama/build
 __debug_bin*
-llama/vendor

.gitmodules

@@ -0,0 +1,4 @@
+[submodule "llama.cpp"]
+	path = llm/llama.cpp
+	url = https://github.com/ggerganov/llama.cpp.git
+	shallow = true

.golangci.yaml

@@ -32,10 +32,6 @@ linters:
 linters-settings:
   gci:
     sections: [standard, default, localmodule]
-  staticcheck:
-    checks:
-      - all
-      - -SA1019 # omit Deprecated check
 severity:
   default-severity: error
   rules:

CONTRIBUTING.md

@@ -18,7 +18,7 @@ See the [development documentation](./docs/development.md) for instructions on h
 
 * New features: new features (e.g. API fields, environment variables) add surface area to Ollama and make it harder to maintain in the long run as they cannot be removed without potentially breaking users in the future.
 * Refactoring: large code improvements are important, but can be harder or take longer to review and merge.
-* Documentation: small updates to fill in or correct missing documentation is helpful, however large documentation additions can be hard to maintain over time.
+* Documentation: small updates to fill in or dorrect missing documentation is helpful, however large documentation additions can be hard to maintain over time.
 
 ### Issues that may not be accepted
 

Dockerfile

@@ -1,263 +1,131 @@
-ARG GOLANG_VERSION=1.22.8
+ARG GOLANG_VERSION=1.22.5
 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"
+# this CUDA_VERSION corresponds with the one specified in docs/gpu.md
+ARG CUDA_VERSION=11.3.1
 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
-#
-# docker build --platform linux/amd64 -t builder-amd64 -f Dockerfile --target unified-builder-amd64 .
-# docker run --platform linux/amd64 --rm -it -v $(pwd):/go/src/github.com/ollama/ollama/ builder-amd64
-#
-### Then incremental builds will be much faster in this container
-#
-# make -j 10 && go build -trimpath -o dist/linux-amd64/ollama .
-#
-FROM --platform=linux/amd64 rocm/dev-centos-7:${ROCM_VERSION}-complete AS unified-builder-amd64
+# Copy the minimal context we need to run the generate scripts
+FROM scratch AS llm-code
+COPY .git .git
+COPY .gitmodules .gitmodules
+COPY llm llm
+
+FROM --platform=linux/amd64 nvidia/cuda:$CUDA_VERSION-devel-centos7 AS cuda-build-amd64
 ARG CMAKE_VERSION
-ARG GOLANG_VERSION
-ARG CUDA_VERSION_11
-ARG CUDA_VERSION_12
 COPY ./scripts/rh_linux_deps.sh /
-ENV PATH /opt/rh/devtoolset-10/root/usr/bin:/usr/local/cuda/bin:$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=${GOLANG_VERSION} sh /rh_linux_deps.sh
-RUN yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-rhel7.repo && \
-    dnf clean all && \
-    dnf install -y \
-    zsh \
-    cuda-$(echo ${CUDA_VERSION_11} | cut -f1-2 -d. | sed -e "s/\./-/g") \
-    cuda-$(echo ${CUDA_VERSION_12} | cut -f1-2 -d. | sed -e "s/\./-/g")
-# TODO intel oneapi goes here...
-ENV GOARCH amd64
-ENV CGO_ENABLED 1
-WORKDIR /go/src/github.com/ollama/ollama/
-ENTRYPOINT [ "zsh" ]
+RUN CMAKE_VERSION=${CMAKE_VERSION} sh /rh_linux_deps.sh
+ENV PATH /opt/rh/devtoolset-10/root/usr/bin:$PATH
+COPY --from=llm-code / /go/src/github.com/ollama/ollama/
+WORKDIR /go/src/github.com/ollama/ollama/llm/generate
+ARG CGO_CFLAGS
+RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_SKIP_CPU_GENERATE=1 sh gen_linux.sh
 
-### To create a local image for building linux binaries on mac or linux/arm64 with efficient incremental builds
-# Note: this does not contain jetson variants
-#
-# docker build --platform linux/arm64 -t builder-arm64 -f Dockerfile --target unified-builder-arm64 .
-# docker run --platform linux/arm64 --rm -it -v $(pwd):/go/src/github.com/ollama/ollama/ builder-arm64
-#
-FROM --platform=linux/arm64 rockylinux:8 AS unified-builder-arm64
+FROM --platform=linux/arm64 nvidia/cuda:$CUDA_VERSION-devel-rockylinux8 AS cuda-build-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 yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/sbsa/cuda-rhel8.repo && \
-    dnf config-manager --set-enabled appstream && \
-    dnf clean all && \
-    dnf install -y \
-    zsh \
-    cuda-toolkit-$(echo ${CUDA_VERSION_11} | cut -f1-2 -d. | sed -e "s/\./-/g") \
-    cuda-toolkit-$(echo ${CUDA_VERSION_12} | cut -f1-2 -d. | sed -e "s/\./-/g")
-ENV PATH /opt/rh/gcc-toolset-10/root/usr/bin:$PATH:/usr/local/cuda/bin
-ENV LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64
-ENV LIBRARY_PATH=/usr/local/cuda/lib64/stubs:/opt/amdgpu/lib64
-ENV GOARCH amd64
-ENV CGO_ENABLED 1
-WORKDIR /go/src/github.com/ollama/ollama/
-ENTRYPOINT [ "zsh" ]
-
-FROM --platform=linux/amd64 unified-builder-amd64 AS runners-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
-RUN --mount=type=cache,target=/root/.ccache \
-    if grep "^flags" /proc/cpuinfo|grep avx>/dev/null; then \
-        make -j $(expr $(nproc) / 2 ) ; \
-    else \
-        make -j 5 ; \
-    fi
-
-FROM --platform=linux/arm64 unified-builder-arm64 AS runners-arm64
-COPY . .
-ARG OLLAMA_SKIP_CUDA_GENERATE
-ARG OLLAMA_SKIP_CUDA_11_GENERATE
-ARG OLLAMA_SKIP_CUDA_12_GENERATE
-ARG CUDA_V11_ARCHITECTURES
-ARG CUDA_V12_ARCHITECTURES
-ARG OLLAMA_FAST_BUILD
-RUN --mount=type=cache,target=/root/.ccache \
-    make -j 5
-
-# Jetsons need to be built in discrete stages
-FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${JETPACK_5} AS runners-jetpack5-arm64
-ARG GOLANG_VERSION
-RUN apt-get update && apt-get install -y git curl ccache && \
-    curl -s -L https://dl.google.com/go/go${GOLANG_VERSION}.linux-arm64.tar.gz | tar xz -C /usr/local && \
-    ln -s /usr/local/go/bin/go /usr/local/bin/go && \
-    ln -s /usr/local/go/bin/gofmt /usr/local/bin/gofmt && \
-    apt-get clean && rm -rf /var/lib/apt/lists/*
-WORKDIR /go/src/github.com/ollama/ollama/
-COPY . .
+RUN CMAKE_VERSION=${CMAKE_VERSION} sh /rh_linux_deps.sh
+ENV PATH /opt/rh/gcc-toolset-10/root/usr/bin:$PATH
+COPY --from=llm-code / /go/src/github.com/ollama/ollama/
+WORKDIR /go/src/github.com/ollama/ollama/llm/generate
 ARG CGO_CFLAGS
-ENV GOARCH arm64
-RUN --mount=type=cache,target=/root/.ccache \
-    make -j 5 cuda_v11 \
-        CUDA_ARCHITECTURES="72;87" \
-        GPU_RUNNER_VARIANT=_jetpack5 \
-        CGO_EXTRA_LDFLAGS_LINUX=-L/usr/local/cuda/lib64/stubs \
-        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
+RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_SKIP_CPU_GENERATE=1 sh gen_linux.sh
 
-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 . .
+FROM --platform=linux/amd64 rocm/dev-centos-7:${ROCM_VERSION}-complete AS rocm-build-amd64
+ARG CMAKE_VERSION
+COPY ./scripts/rh_linux_deps.sh /
+RUN CMAKE_VERSION=${CMAKE_VERSION} sh /rh_linux_deps.sh
+ENV PATH /opt/rh/devtoolset-10/root/usr/bin:$PATH
+ENV LIBRARY_PATH /opt/amdgpu/lib64
+COPY --from=llm-code / /go/src/github.com/ollama/ollama/
+WORKDIR /go/src/github.com/ollama/ollama/llm/generate
 ARG CGO_CFLAGS
-ENV GOARCH arm64
-RUN --mount=type=cache,target=/root/.ccache \
-    make -j 5 cuda_v12 \
-        CUDA_ARCHITECTURES="87" \
-        GPU_RUNNER_VARIANT=_jetpack6 \
-        CGO_EXTRA_LDFLAGS_LINUX=-L/usr/local/cuda/lib64/stubs \
-        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
+ARG AMDGPU_TARGETS
+RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_SKIP_CPU_GENERATE=1 sh gen_linux.sh
+RUN mkdir /tmp/scratch && \
+    for dep in $(zcat /go/src/github.com/ollama/ollama/llm/build/linux/x86_64/rocm*/bin/deps.txt.gz) ; do \
+        cp ${dep} /tmp/scratch/ || exit 1 ; \
+    done && \
+    (cd /opt/rocm/lib && tar cf - rocblas/library) | (cd /tmp/scratch/ && tar xf - ) && \
+    mkdir -p /go/src/github.com/ollama/ollama/dist/deps/ && \
+    (cd /tmp/scratch/ && tar czvf /go/src/github.com/ollama/ollama/dist/deps/ollama-linux-amd64-rocm.tgz . )
 
 
-# Intermediate stages used for ./scripts/build_linux.sh
-FROM --platform=linux/amd64 centos:7 AS builder-amd64
+FROM --platform=linux/amd64 centos:7 AS cpu-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:$PATH
-ENV CGO_ENABLED 1
-ENV GOARCH amd64
-WORKDIR /go/src/github.com/ollama/ollama
-
-FROM --platform=linux/amd64 builder-amd64 AS build-amd64
-COPY . .
-COPY --from=runners-amd64 /go/src/github.com/ollama/ollama/dist/ dist/
-COPY --from=runners-amd64 /go/src/github.com/ollama/ollama/build/ build/
-ARG GOFLAGS
+COPY --from=llm-code / /go/src/github.com/ollama/ollama/
+ARG OLLAMA_CUSTOM_CPU_DEFS
 ARG CGO_CFLAGS
-ARG OLLAMA_SKIP_ROCM_GENERATE
-RUN --mount=type=cache,target=/root/.ccache \
-    go build -trimpath -o dist/linux-amd64/bin/ollama .
-RUN cd dist/linux-$GOARCH && \
-    tar --exclude runners -cf - . | pigz --best > ../ollama-linux-$GOARCH.tgz
-RUN if [ -z ${OLLAMA_SKIP_ROCM_GENERATE} ] ; then \
-    cd dist/linux-$GOARCH-rocm && \
-    tar -cf - . | pigz --best > ../ollama-linux-$GOARCH-rocm.tgz ;\
-    fi
+WORKDIR /go/src/github.com/ollama/ollama/llm/generate
 
-FROM --platform=linux/arm64 rockylinux:8 AS builder-arm64
+FROM --platform=linux/amd64 cpu-builder-amd64 AS static-build-amd64
+RUN OLLAMA_CPU_TARGET="static" sh gen_linux.sh
+FROM --platform=linux/amd64 cpu-builder-amd64 AS cpu-build-amd64
+RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu" sh gen_linux.sh
+FROM --platform=linux/amd64 cpu-builder-amd64 AS cpu_avx-build-amd64
+RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu_avx" sh gen_linux.sh
+FROM --platform=linux/amd64 cpu-builder-amd64 AS cpu_avx2-build-amd64
+RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu_avx2" sh gen_linux.sh
+
+FROM --platform=linux/arm64 rockylinux:8 AS cpu-builder-arm64
 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/gcc-toolset-10/root/usr/bin:$PATH
+COPY --from=llm-code / /go/src/github.com/ollama/ollama/
+ARG OLLAMA_CUSTOM_CPU_DEFS
+ARG CGO_CFLAGS
+WORKDIR /go/src/github.com/ollama/ollama/llm/generate
+
+FROM --platform=linux/arm64 cpu-builder-arm64 AS static-build-arm64
+RUN OLLAMA_CPU_TARGET="static" sh gen_linux.sh
+FROM --platform=linux/arm64 cpu-builder-arm64 AS cpu-build-arm64
+RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu" sh gen_linux.sh
+
+
+# Intermediate stage used for ./scripts/build_linux.sh
+FROM --platform=linux/amd64 cpu-build-amd64 AS build-amd64
 ENV CGO_ENABLED 1
-ENV GOARCH arm64
-WORKDIR /go/src/github.com/ollama/ollama
-
-FROM --platform=linux/arm64 builder-arm64 AS build-arm64
-COPY . .
-COPY --from=runners-arm64 /go/src/github.com/ollama/ollama/dist/ dist/
-COPY --from=runners-arm64 /go/src/github.com/ollama/ollama/build/ build/
-COPY --from=runners-jetpack5-arm64 /go/src/github.com/ollama/ollama/dist/ dist/
-COPY --from=runners-jetpack5-arm64 /go/src/github.com/ollama/ollama/build/ build/
-COPY --from=runners-jetpack6-arm64 /go/src/github.com/ollama/ollama/dist/ dist/
-COPY --from=runners-jetpack6-arm64 /go/src/github.com/ollama/ollama/build/ build/
-ARG GOFLAGS
-ARG CGO_CFLAGS
-RUN --mount=type=cache,target=/root/.ccache \
-    go build -trimpath -o dist/linux-arm64/bin/ollama .
-RUN cd dist/linux-$GOARCH && \
-    tar --exclude runners -cf - . | pigz --best > ../ollama-linux-$GOARCH.tgz
-RUN cd dist/linux-$GOARCH-jetpack5 && \
-    tar --exclude runners -cf - . | pigz --best > ../ollama-linux-$GOARCH-jetpack5.tgz
-RUN cd dist/linux-$GOARCH-jetpack6 && \
-    tar --exclude runners -cf - . | pigz --best > ../ollama-linux-$GOARCH-jetpack6.tgz
-
-FROM --platform=linux/amd64 scratch AS dist-amd64
-COPY --from=build-amd64 /go/src/github.com/ollama/ollama/dist/ollama-linux-*.tgz /
-FROM --platform=linux/arm64 scratch AS dist-arm64
-COPY --from=build-arm64 /go/src/github.com/ollama/ollama/dist/ollama-linux-*.tgz /
-FROM dist-$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 . .
+COPY --from=static-build-amd64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
+COPY --from=cpu_avx-build-amd64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
+COPY --from=cpu_avx2-build-amd64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
+COPY --from=cuda-build-amd64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
+COPY --from=rocm-build-amd64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
+COPY --from=rocm-build-amd64 /go/src/github.com/ollama/ollama/dist/deps/ ./dist/deps/
 ARG GOFLAGS
 ARG CGO_CFLAGS
-RUN --mount=type=cache,target=/root/.ccache \
-    go build -trimpath -o dist/linux-amd64/bin/ollama .
+RUN go build -trimpath .
 
-FROM --platform=linux/arm64 builder-arm64 AS container-build-arm64
+# Intermediate stage used for ./scripts/build_linux.sh
+FROM --platform=linux/arm64 cpu-build-arm64 AS build-arm64
+ENV CGO_ENABLED 1
+ARG GOLANG_VERSION
 WORKDIR /go/src/github.com/ollama/ollama
 COPY . .
+COPY --from=static-build-arm64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
+COPY --from=cuda-build-arm64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
 ARG GOFLAGS
 ARG CGO_CFLAGS
-RUN --mount=type=cache,target=/root/.ccache \
-    go build -trimpath -o dist/linux-arm64/bin/ollama .
+RUN go build -trimpath .
 
-# For amd64 container images, filter out cuda/rocm to minimize size
-FROM runners-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
-RUN rm -rf \
-    ./dist/linux-amd64/lib/ollama/libggml_cuda*.so \
-    ./dist/linux-amd64/lib/ollama/libcu*.so* \
-    ./dist/linux-amd64/lib/ollama/runners/cuda*
-
-FROM --platform=linux/amd64 ubuntu:22.04 AS runtime-amd64
-RUN apt-get update && \
-    apt-get install -y ca-certificates && \
-    apt-get clean && rm -rf /var/lib/apt/lists/*
-COPY --from=container-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
-COPY --from=build-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack5/lib/ /lib/
-COPY --from=build-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack6/lib/ /lib/
-RUN apt-get update && \
-    apt-get install -y ca-certificates && \
-    apt-get clean && rm -rf /var/lib/apt/lists/*
-COPY --from=container-build-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/bin/ /bin/
-COPY --from=cpu-build-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/lib/ /lib/
-COPY --from=cuda-11-build-runner-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/lib/ /lib/
-COPY --from=cuda-12-build-runner-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/lib/ /lib/
-COPY --from=cuda-build-jetpack5-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/lib/ /lib/
-COPY --from=cuda-build-jetpack6-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/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
-# Frontload the rocm libraries which are large, and rarely change to increase chance of a common layer
-# across releases
-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 && \
-    apt-get clean && rm -rf /var/lib/apt/lists/*
-COPY --from=container-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/
+# Runtime stages
+FROM --platform=linux/amd64 ubuntu:22.04 as runtime-amd64
+RUN apt-get update && apt-get install -y ca-certificates
+COPY --from=build-amd64 /go/src/github.com/ollama/ollama/ollama /bin/ollama
+FROM --platform=linux/arm64 ubuntu:22.04 as runtime-arm64
+RUN apt-get update && apt-get install -y ca-certificates
+COPY --from=build-arm64 /go/src/github.com/ollama/ollama/ollama /bin/ollama
 
+# Radeon images are much larger so we keep it distinct from the CPU/CUDA image
+FROM --platform=linux/amd64 rocm/dev-centos-7:${ROCM_VERSION}-complete as runtime-rocm
+RUN update-pciids
+COPY --from=build-amd64 /go/src/github.com/ollama/ollama/ollama /bin/ollama
 EXPOSE 11434
 ENV OLLAMA_HOST 0.0.0.0
 

Makefile

@@ -1,4 +0,0 @@
-GOALS := $(or $(MAKECMDGOALS),all)
-.PHONY: $(GOALS)
-$(GOALS):
-	$(MAKE) -C llama $@

README.md

@@ -12,7 +12,7 @@ Get up and running with large language models.
 
 [Download](https://ollama.com/download/Ollama-darwin.zip)
 
-### Windows
+### Windows preview
 
 [Download](https://ollama.com/download/OllamaSetup.exe)
 
@@ -35,10 +35,10 @@ The official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama) `olla
 
 ## Quickstart
 
-To run and chat with [Llama 3.2](https://ollama.com/library/llama3.2):
+To run and chat with [Llama 3.1](https://ollama.com/library/llama3.1):
 
 ```
-ollama run llama3.2
+ollama run llama3.1
 ```
 
 ## Model library
@@ -47,28 +47,24 @@ 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 3.2 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          | 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 3.1          | 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`             |
 
 > [!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.
@@ -103,16 +99,16 @@ See the [guide](docs/import.md) on importing models for more information.
 
 ### Customize a prompt
 
-Models from the Ollama library can be customized with a prompt. For example, to customize the `llama3.2` model:
+Models from the Ollama library can be customized with a prompt. For example, to customize the `llama3.1` model:
 
 ```
-ollama pull llama3.2
+ollama pull llama3.1
 ```
 
 Create a `Modelfile`:
 
 ```
-FROM llama3.2
+FROM llama3.1
 
 # set the temperature to 1 [higher is more creative, lower is more coherent]
 PARAMETER temperature 1
@@ -147,7 +143,7 @@ ollama create mymodel -f ./Modelfile
 ### Pull a model
 
 ```
-ollama pull llama3.2
+ollama pull llama3.1
 ```
 
 > This command can also be used to update a local model. Only the diff will be pulled.
@@ -155,13 +151,13 @@ ollama pull llama3.2
 ### Remove a model
 
 ```
-ollama rm llama3.2
+ollama rm llama3.1
 ```
 
 ### Copy a model
 
 ```
-ollama cp llama3.2 my-model
+ollama cp llama3.1 my-model
 ```
 
 ### Multiline input
@@ -185,14 +181,14 @@ The image features a yellow smiley face, which is likely the central focus of th
 ### Pass the prompt as an argument
 
 ```
-$ ollama run llama3.2 "Summarize this file: $(cat README.md)"
+$ ollama run llama3.1 "Summarize this file: $(cat README.md)"
  Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications.
 ```
 
 ### Show model information
 
 ```
-ollama show llama3.2
+ollama show llama3.1
 ```
 
 ### List models on your computer
@@ -201,18 +197,6 @@ ollama show llama3.2
 ollama list
 ```
 
-### List which models are currently loaded
-
-```
-ollama ps
-```
-
-### Stop a model which is currently running
-
-```
-ollama stop llama3.2
-```
-
 ### Start Ollama
 
 `ollama serve` is used when you want to start ollama without running the desktop application.
@@ -232,7 +216,7 @@ Next, start the server:
 Finally, in a separate shell, run a model:
 
 ```
-./ollama run llama3.2
+./ollama run llama3.1
 ```
 
 ## REST API
@@ -243,7 +227,7 @@ Ollama has a REST API for running and managing models.
 
 ```
 curl http://localhost:11434/api/generate -d '{
-  "model": "llama3.2",
+  "model": "llama3.1",
   "prompt":"Why is the sky blue?"
 }'
 ```
@@ -252,7 +236,7 @@ curl http://localhost:11434/api/generate -d '{
 
 ```
 curl http://localhost:11434/api/chat -d '{
-  "model": "llama3.2",
+  "model": "llama3.1",
   "messages": [
     { "role": "user", "content": "why is the sky blue?" }
   ]
@@ -311,30 +295,13 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [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)
-- [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)
-- [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)
 - [Sidellama](https://github.com/gyopak/sidellama) (browser-based LLM client)
 - [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)
-- [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
-- [PyOllaMx](https://github.com/kspviswa/pyOllaMx) - macOS application capable of chatting with both Ollama and Apple MLX models.
-- [Claude Dev](https://github.com/saoudrizwan/claude-dev) - VSCode extension for multi-file/whole-repo coding
-- [Cherry Studio](https://github.com/kangfenmao/cherry-studio) (Desktop client with Ollama support)
-- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
-- [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)
-- [LLMChat](https://github.com/trendy-design/llmchat) (Privacy focused, 100% local, intuitive all-in-one chat interface)
-- [ARGO](https://github.com/xark-argo/argo) (Locally download and run Ollama and Huggingface models with RAG on Mac/Windows/Linux)
-- [G1](https://github.com/bklieger-groq/g1) (Prototype of using prompting strategies to improve the LLM's reasoning through o1-like reasoning chains.)
-- [Ollama App](https://github.com/JHubi1/ollama-app) (Modern and easy-to-use multi-platform client for Ollama)
-- [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)
 
 ### Terminal
 
@@ -359,12 +326,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [podman-ollama](https://github.com/ericcurtin/podman-ollama)
 - [gollama](https://github.com/sammcj/gollama)
 - [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
-- [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.
-
-### Apple Vision Pro
-- [Enchanted](https://github.com/AugustDev/enchanted)
 
 ### Database
 
@@ -374,28 +335,23 @@ See the [API documentation](./docs/api.md) for all endpoints.
 ### Package managers
 
 - [Pacman](https://archlinux.org/packages/extra/x86_64/ollama/)
-- [Gentoo](https://github.com/gentoo/guru/tree/master/app-misc/ollama)
 - [Helm Chart](https://artifacthub.io/packages/helm/ollama-helm/ollama)
 - [Guix channel](https://codeberg.org/tusharhero/ollama-guix)
-- [Nix package](https://search.nixos.org/packages?channel=24.05&show=ollama&from=0&size=50&sort=relevance&type=packages&query=ollama)
-- [Flox](https://flox.dev/blog/ollama-part-one)
 
 ### Libraries
 
-- [LangChain](https://python.langchain.com/docs/integrations/llms/ollama) and [LangChain.js](https://js.langchain.com/docs/integrations/chat/ollama/) with [example](https://js.langchain.com/docs/tutorials/local_rag/)
+- [LangChain](https://python.langchain.com/docs/integrations/llms/ollama) and [LangChain.js](https://js.langchain.com/docs/modules/model_io/models/llms/integrations/ollama) with [example](https://js.langchain.com/docs/use_cases/question_answering/local_retrieval_qa)
 - [Firebase Genkit](https://firebase.google.com/docs/genkit/plugins/ollama)
-- [crewAI](https://github.com/crewAIInc/crewAI)
 - [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)
-- [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/llm/ollama/) and [LlamaIndexTS](https://ts.llamaindex.ai/modules/llms/available_llms/ollama)
+- [LlamaIndex](https://gpt-index.readthedocs.io/en/stable/examples/llm/ollama.html)
 - [LiteLLM](https://github.com/BerriAI/litellm)
-- [OllamaFarm for Go](https://github.com/presbrey/ollamafarm)
 - [OllamaSharp for .NET](https://github.com/awaescher/OllamaSharp)
 - [Ollama for Ruby](https://github.com/gbaptista/ollama-ai)
 - [Ollama-rs for Rust](https://github.com/pepperoni21/ollama-rs)
 - [Ollama-hpp for C++](https://github.com/jmont-dev/ollama-hpp)
-- [Ollama4j for Java](https://github.com/ollama4j/ollama4j)
+- [Ollama4j for Java](https://github.com/amithkoujalgi/ollama4j)
 - [ModelFusion Typescript Library](https://modelfusion.dev/integration/model-provider/ollama)
 - [OllamaKit for Swift](https://github.com/kevinhermawan/OllamaKit)
 - [Ollama for Dart](https://github.com/breitburg/dart-ollama)
@@ -412,20 +368,11 @@ 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)
-- [Gollm](https://docs.gollm.co/examples/ollama-example)
-- [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)
-- [Ollama for Swift](https://github.com/mattt/ollama-swift)
-- [GoLamify](https://github.com/prasad89/golamify)
 
 ### Mobile
 
 - [Enchanted](https://github.com/AugustDev/enchanted)
 - [Maid](https://github.com/Mobile-Artificial-Intelligence/maid)
-- [Ollama App](https://github.com/JHubi1/ollama-app) (Modern and easy-to-use multi-platform client for Ollama)
-- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
 
 ### Extensions & Plugins
 
@@ -450,18 +397,11 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [twinny](https://github.com/rjmacarthy/twinny) (Copilot and Copilot chat alternative using Ollama)
 - [Wingman-AI](https://github.com/RussellCanfield/wingman-ai) (Copilot code and chat alternative using Ollama and Hugging Face)
 - [Page Assist](https://github.com/n4ze3m/page-assist) (Chrome Extension)
-- [Plasmoid Ollama Control](https://github.com/imoize/plasmoid-ollamacontrol) (KDE Plasma extension that allows you to quickly manage/control Ollama model)
 - [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)
 - [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)
-- [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/jk011ru/vnc-lm) (A containerized Discord bot with support for attachments and web links)
-- [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)
-- [TextCraft](https://github.com/suncloudsmoon/TextCraft) (Copilot in Word alternative using Ollama)
 
 ### Supported backends
 

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 listening. The format of this variable
+// port on which the ollama service is listenting. The format of this variable
 // is:
 //
 //	<scheme>://<host>:<port>

api/types.go

@@ -12,7 +12,7 @@ import (
 	"time"
 )
 
-// StatusError is an error with an HTTP status code and message.
+// StatusError is an error with and HTTP status code.
 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
-	// [Client.Generate]. It can be used to keep a short conversational memory.
+	// Generate call. 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.
@@ -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 enables streaming of returned responses; true by default.
+	// Stream enable streaming of returned response; true by default.
 	Stream *bool `json:"stream,omitempty"`
 
 	// Format is the format to return the response in (e.g. "json").
 	Format string `json:"format"`
 
 	// KeepAlive controls how long the model will stay loaded into memory
-	// following the request.
+	// followin the request.
 	KeepAlive *Duration `json:"keep_alive,omitempty"`
 
 	// Tools is an optional list of tools the model has access to.
@@ -203,8 +203,8 @@ type Metrics struct {
 	EvalDuration       time.Duration `json:"eval_duration,omitempty"`
 }
 
-// Options specified in [GenerateRequest].  If you add a new option here, also
-// add it to the API docs.
+// Options specified in [GenerateRequest], if you add a new option here add it
+// to the API docs also.
 type Options struct {
 	Runner
 
@@ -236,7 +236,7 @@ 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"` // Deprecated: This option is ignored
+	F16KV     bool  `json:"f16_kv,omitempty"`
 	LogitsAll bool  `json:"logits_all,omitempty"`
 	VocabOnly bool  `json:"vocab_only,omitempty"`
 	UseMMap   *bool `json:"use_mmap,omitempty"`
@@ -296,17 +296,15 @@ type EmbeddingResponse struct {
 // CreateRequest is the request passed to [Client.Create].
 type CreateRequest struct {
 	Model     string `json:"model"`
+	Path      string `json:"path"`
 	Modelfile string `json:"modelfile"`
 	Stream    *bool  `json:"stream,omitempty"`
 	Quantize  string `json:"quantize,omitempty"`
 
-	// Deprecated: set the model name with Model instead
+	// Name is deprecated, see Model
 	Name string `json:"name"`
 
-	// Deprecated: set the file content with Modelfile instead
-	Path string `json:"path"`
-
-	// Deprecated: use Quantize instead
+	// Quantization is deprecated, see Quantize
 	Quantization string `json:"quantization,omitempty"`
 }
 
@@ -314,7 +312,7 @@ type CreateRequest struct {
 type DeleteRequest struct {
 	Model string `json:"model"`
 
-	// Deprecated: set the model name with Model instead
+	// Name is deprecated, see Model
 	Name string `json:"name"`
 }
 
@@ -329,7 +327,7 @@ type ShowRequest struct {
 
 	Options map[string]interface{} `json:"options"`
 
-	// Deprecated: set the model name with Model instead
+	// Name is deprecated, see Model
 	Name string `json:"name"`
 }
 
@@ -361,7 +359,7 @@ type PullRequest struct {
 	Password string `json:"password"`
 	Stream   *bool  `json:"stream,omitempty"`
 
-	// Deprecated: set the model name with Model instead
+	// Name is deprecated, see Model
 	Name string `json:"name"`
 }
 
@@ -382,7 +380,7 @@ type PushRequest struct {
 	Password string `json:"password"`
 	Stream   *bool  `json:"stream,omitempty"`
 
-	// Deprecated: set the model name with Model instead
+	// Name is deprecated, see Model
 	Name string `json:"name"`
 }
 
@@ -613,6 +611,7 @@ 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/lifecycle/lifecycle.go

@@ -11,12 +11,10 @@ import (
 
 	"github.com/ollama/ollama/app/store"
 	"github.com/ollama/ollama/app/tray"
-	"github.com/ollama/ollama/envconfig"
 )
 
 func Run() {
 	InitLogging()
-	slog.Info("app config", "env", envconfig.Values())
 
 	ctx, cancel := context.WithCancel(context.Background())
 	var done chan int

app/lifecycle/paths.go

@@ -36,13 +36,8 @@ func init() {
 		ServerLogFile = filepath.Join(AppDataDir, "server.log")
 		UpgradeLogFile = filepath.Join(AppDataDir, "upgrade.log")
 
-		exe, err := os.Executable()
-		if err != nil {
-			slog.Warn("error discovering executable directory", "error", err)
-			AppDir = filepath.Join(localAppData, "Programs", "Ollama")
-		} else {
-			AppDir = filepath.Dir(exe)
-		}
+		// Executables are stored in APPDATA
+		AppDir = filepath.Join(localAppData, "Programs", "Ollama")
 
 		// Make sure we have PATH set correctly for any spawned children
 		paths := strings.Split(os.Getenv("PATH"), ";")
@@ -69,7 +64,7 @@ func init() {
 		}
 
 		// Make sure our logging dir exists
-		_, err = os.Stat(AppDataDir)
+		_, err := os.Stat(AppDataDir)
 		if errors.Is(err, os.ErrNotExist) {
 			if err := os.MkdirAll(AppDataDir, 0o755); err != nil {
 				slog.Error(fmt.Sprintf("create ollama dir %s: %v", AppDataDir, err))

app/lifecycle/server.go

@@ -18,17 +18,11 @@ func getCLIFullPath(command string) string {
 	var cmdPath string
 	appExe, err := os.Executable()
 	if err == nil {
-		// Check both the same location as the tray app, as well as ./bin
 		cmdPath = filepath.Join(filepath.Dir(appExe), command)
 		_, err := os.Stat(cmdPath)
 		if err == nil {
 			return cmdPath
 		}
-		cmdPath = filepath.Join(filepath.Dir(appExe), "bin", command)
-		_, err = os.Stat(cmdPath)
-		if err == nil {
-			return cmdPath
-		}
 	}
 	cmdPath, err = exec.LookPath(command)
 	if err == nil {

app/lifecycle/updater_windows.go

@@ -26,15 +26,19 @@ func DoUpgrade(cancel context.CancelFunc, done chan int) error {
 	slog.Info("starting upgrade with " + installerExe)
 	slog.Info("upgrade log file " + UpgradeLogFile)
 
-	// make the upgrade show progress, but non interactive
+	// When running in debug mode, we'll be "verbose" and let the installer pop up and prompt
 	installArgs := []string{
 		"/CLOSEAPPLICATIONS",                    // Quit the tray app if it's still running
 		"/LOG=" + filepath.Base(UpgradeLogFile), // Only relative seems reliable, so set pwd
 		"/FORCECLOSEAPPLICATIONS",               // Force close the tray app - might be needed
-		"/SP",                                   // Skip the "This will install... Do you wish to continue" prompt
-		"/NOCANCEL",                             // Disable the ability to cancel upgrade mid-flight to avoid partially installed upgrades
-		"/SILENT",
 	}
+	// make the upgrade as quiet as possible (no GUI, no prompts)
+	installArgs = append(installArgs,
+		"/SP", // Skip the "This will install... Do you wish to continue" prompt
+		"/SUPPRESSMSGBOXES",
+		"/SILENT",
+		"/VERYSILENT",
+	)
 
 	// Safeguard in case we have requests in flight that need to drain...
 	slog.Info("Waiting for server to shutdown")

app/ollama.iss

@@ -28,8 +28,8 @@ AppPublisher={#MyAppPublisher}
 AppPublisherURL={#MyAppURL}
 AppSupportURL={#MyAppURL}
 AppUpdatesURL={#MyAppURL}
-ArchitecturesAllowed=x64compatible arm64
-ArchitecturesInstallIn64BitMode=x64compatible arm64
+ArchitecturesAllowed=x64 arm64
+ArchitecturesInstallIn64BitMode=x64 arm64
 DefaultDirName={localappdata}\Programs\{#MyAppName}
 DefaultGroupName={#MyAppName}
 DisableProgramGroupPage=yes
@@ -48,13 +48,12 @@ OutputDir=..\dist\
 SetupLogging=yes
 CloseApplications=yes
 RestartApplications=no
-RestartIfNeededByRun=no
 
 ; https://jrsoftware.org/ishelp/index.php?topic=setup_wizardimagefile
 WizardSmallImageFile=.\assets\setup.bmp
 
-; Ollama requires Windows 10 22H2 or newer for proper unicode rendering
-; TODO: consider setting this to 10.0.19045
+; TODO verifty actual min windows version...
+; OG Win 10
 MinVersion=10.0.10240
 
 ; First release that supports WinRT UI Composition for win32 apps
@@ -87,21 +86,21 @@ Name: "english"; MessagesFile: "compiler:Default.isl"
 DialogFontSize=12
 
 [Files]
-#if DirExists("..\dist\windows-amd64")
-Source: "..\dist\windows-amd64-app.exe"; DestDir: "{app}"; DestName: "{#MyAppExeName}" ;Check: not IsArm64();  Flags: ignoreversion 64bit
-Source: "..\dist\windows-amd64\ollama.exe"; DestDir: "{app}"; Check: not IsArm64(); Flags: ignoreversion 64bit
-Source: "..\dist\windows-amd64\lib\ollama\*"; DestDir: "{app}\lib\ollama\"; Check: not IsArm64(); Flags: ignoreversion 64bit recursesubdirs
-#endif
-
-#if DirExists("..\dist\windows-arm64")
-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: ".\app.exe"; DestDir: "{app}"; DestName: "{#MyAppExeName}" ; Flags: ignoreversion 64bit
+Source: "..\ollama.exe"; DestDir: "{app}"; Flags: ignoreversion 64bit
+Source: "..\dist\windows-{#ARCH}\ollama_runners\*"; DestDir: "{app}\ollama_runners"; Flags: ignoreversion 64bit recursesubdirs
 Source: "..\dist\ollama_welcome.ps1"; DestDir: "{app}"; Flags: ignoreversion
 Source: ".\assets\app.ico"; DestDir: "{app}"; Flags: ignoreversion
+#if DirExists("..\dist\windows-amd64\cuda")
+  Source: "..\dist\windows-amd64\cuda\*"; DestDir: "{app}\cuda\"; Flags: ignoreversion recursesubdirs
+#endif
+#if DirExists("..\dist\windows-amd64\oneapi")
+  Source: "..\dist\windows-amd64\oneapi\*"; DestDir: "{app}\oneapi\"; Flags: ignoreversion recursesubdirs
+#endif
+#if DirExists("..\dist\windows-amd64\rocm")
+  Source: "..\dist\windows-amd64\rocm\*"; DestDir: "{app}\rocm\"; Flags: ignoreversion recursesubdirs
+#endif
+
 
 [Icons]
 Name: "{group}\{#MyAppName}"; Filename: "{app}\{#MyAppExeName}"; IconFilename: "{app}\app.ico"
@@ -109,9 +108,6 @@ Name: "{userstartup}\{#MyAppName}"; Filename: "{app}\{#MyAppExeName}"; IconFilen
 Name: "{userprograms}\{#MyAppName}"; Filename: "{app}\{#MyAppExeName}"; IconFilename: "{app}\app.ico"
 
 [Run]
-#if DirExists("..\dist\windows-arm64")
-Filename: "{tmp}\vc_redist.arm64.exe"; Parameters: "/install /passive /norestart"; Check: IsArm64() and vc_redist_needed(); StatusMsg: "Installing VC++ Redistributables..."; Flags: waituntilterminated
-#endif
 Filename: "{cmd}"; Parameters: "/C set PATH={app};%PATH% & ""{app}\{#MyAppExeName}"""; Flags: postinstall nowait runhidden
 
 [UninstallRun]
@@ -136,13 +132,13 @@ Type: filesandordirs; Name: "{%TEMP}\ollama*"
 Type: filesandordirs; Name: "{%LOCALAPPDATA}\Programs\Ollama"
 
 [Messages]
-WizardReady=Ollama
+WizardReady=Ollama Windows Preview
 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.
 
 
 ;FinishedHeadingLabel=Run your first model
-;FinishedLabel=%nRun this command in a PowerShell or cmd terminal.%n%n%n    ollama run llama3.2
+;FinishedLabel=%nRun this command in a PowerShell or cmd terminal.%n%n%n    ollama run llama3.1
 ;ClickFinish=%n
 
 [Registry]
@@ -167,39 +163,3 @@ begin
   { Pos() returns 0 if not found }
   Result := Pos(';' + ExpandConstant(Param) + ';', ';' + OrigPath + ';') = 0;
 end;
-
-{ --- VC Runtime libraries discovery code - Only install vc_redist if it isn't already installed ----- }
-const VCRTL_MIN_V1 = 14;
-const VCRTL_MIN_V2 = 40;
-const VCRTL_MIN_V3 = 33807;
-const VCRTL_MIN_V4 = 0;
-
- // check if the minimum required vc redist is installed (by looking the registry)
-function vc_redist_needed (): Boolean;
-var
-  sRegKey: string;
-  v1: Cardinal;
-  v2: Cardinal;
-  v3: Cardinal;
-  v4: Cardinal;
-begin
-  sRegKey := 'SOFTWARE\WOW6432Node\Microsoft\VisualStudio\14.0\VC\Runtimes\arm64';
-  if (RegQueryDWordValue (HKEY_LOCAL_MACHINE, sRegKey, 'Major', v1)  and
-      RegQueryDWordValue (HKEY_LOCAL_MACHINE, sRegKey, 'Minor', v2) and
-      RegQueryDWordValue (HKEY_LOCAL_MACHINE, sRegKey, 'Bld', v3) and
-      RegQueryDWordValue (HKEY_LOCAL_MACHINE, sRegKey, 'RBld', v4)) then
-  begin
-    Log ('VC Redist version: ' + IntToStr (v1) +
-        '.' + IntToStr (v2) + '.' + IntToStr (v3) +
-        '.' + IntToStr (v4));
-    { Version info was found. Return true if later or equal to our
-       minimal required version RTL_MIN_Vx }
-    Result := not (
-        (v1 > VCRTL_MIN_V1) or ((v1 = VCRTL_MIN_V1) and
-         ((v2 > VCRTL_MIN_V2) or ((v2 = VCRTL_MIN_V2) and
-          ((v3 > VCRTL_MIN_V3) or ((v3 = VCRTL_MIN_V3) and
-           (v4 >= VCRTL_MIN_V4)))))));
-  end
-  else
-    Result := TRUE;
-end;

app/ollama_welcome.ps1

@@ -4,5 +4,5 @@ write-host "Welcome to Ollama!"
 write-host ""
 write-host "Run your first model:"
 write-host ""
-write-host "`tollama run llama3.2"
+write-host "`tollama run llama3.1"
 write-host ""

app/tray/wintray/menus.go

@@ -11,13 +11,12 @@ import (
 )
 
 const (
-	_ = iota
-	updateAvailableMenuID
-	updateMenuID
-	separatorMenuID
-	diagLogsMenuID
-	diagSeparatorMenuID
-	quitMenuID
+	updateAvailableMenuID = 1
+	updateMenuID          = updateAvailableMenuID + 1
+	separatorMenuID       = updateMenuID + 1
+	diagLogsMenuID        = separatorMenuID + 1
+	diagSeparatorMenuID   = diagLogsMenuID + 1
+	quitMenuID            = diagSeparatorMenuID + 1
 )
 
 func (t *winTray) initMenus() error {

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

@@ -2,7 +2,6 @@ package cmd
 
 import (
 	"archive/zip"
-	"bufio"
 	"bytes"
 	"context"
 	"crypto/ed25519"
@@ -21,7 +20,7 @@ import (
 	"path/filepath"
 	"regexp"
 	"runtime"
-	"strconv"
+	"slices"
 	"strings"
 	"sync/atomic"
 	"syscall"
@@ -46,58 +45,28 @@ import (
 	"github.com/ollama/ollama/version"
 )
 
-var (
-	errModelNotFound     = errors.New("no Modelfile or safetensors files found")
-	errModelfileNotFound = errors.New("specified Modelfile wasn't found")
-)
-
-func getModelfileName(cmd *cobra.Command) (string, error) {
-	fn, _ := cmd.Flags().GetString("file")
-
-	filename := fn
-	if filename == "" {
-		filename = "Modelfile"
-	}
-
-	absName, err := filepath.Abs(filename)
-	if err != nil {
-		return "", err
-	}
-
-	_, err = os.Stat(absName)
-	if err != nil {
-		return fn, err
-	}
-
-	return absName, nil
-}
-
 func CreateHandler(cmd *cobra.Command, args []string) error {
+	filename, _ := cmd.Flags().GetString("file")
+	filename, err := filepath.Abs(filename)
+	if err != nil {
+		return err
+	}
+
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		return err
+	}
+
 	p := progress.NewProgress(os.Stderr)
 	defer p.Stop()
 
-	var reader io.Reader
-
-	filename, err := getModelfileName(cmd)
-	if os.IsNotExist(err) {
-		if filename == "" {
-			reader = strings.NewReader("FROM .\n")
-		} else {
-			return errModelfileNotFound
-		}
-	} else if err != nil {
+	f, err := os.Open(filename)
+	if err != nil {
 		return err
-	} else {
-		f, err := os.Open(filename)
-		if err != nil {
-			return err
-		}
-
-		reader = f
-		defer f.Close()
 	}
+	defer f.Close()
 
-	modelfile, err := parser.ParseFile(reader)
+	modelfile, err := parser.ParseFile(f)
 	if err != nil {
 		return err
 	}
@@ -112,11 +81,6 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 	p.Add(status, spinner)
 	defer p.Stop()
 
-	client, err := api.ClientFromEnvironment()
-	if err != nil {
-		return err
-	}
-
 	for i := range modelfile.Commands {
 		switch modelfile.Commands[i].Name {
 		case "model", "adapter":
@@ -240,12 +204,6 @@ func tempZipFiles(path string) (string, error) {
 		// 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
@@ -255,7 +213,7 @@ func tempZipFiles(path string) (string, error) {
 		// covers consolidated.x.pth, consolidated.pth
 		files = append(files, pt...)
 	} else {
-		return "", errModelNotFound
+		return "", errors.New("no safetensors or torch files found")
 	}
 
 	// add configuration files, json files are detected as text/plain
@@ -265,14 +223,6 @@ func tempZipFiles(path string) (string, error) {
 	}
 	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
@@ -302,11 +252,6 @@ func tempZipFiles(path string) (string, error) {
 			return "", err
 		}
 
-		zfi.Name, err = filepath.Rel(path, file)
-		if err != nil {
-			return "", err
-		}
-
 		zf, err := zipfile.CreateHeader(zfi)
 		if err != nil {
 			return "", err
@@ -380,39 +325,6 @@ func (w *progressWriter) Write(p []byte) (n int, err error) {
 	return len(p), nil
 }
 
-func loadOrUnloadModel(cmd *cobra.Command, opts *runOptions) error {
-	p := progress.NewProgress(os.Stderr)
-	defer p.StopAndClear()
-
-	spinner := progress.NewSpinner("")
-	p.Add("", spinner)
-
-	client, err := api.ClientFromEnvironment()
-	if err != nil {
-		return err
-	}
-
-	req := &api.GenerateRequest{
-		Model:     opts.Model,
-		KeepAlive: opts.KeepAlive,
-	}
-
-	return client.Generate(cmd.Context(), req, func(api.GenerateResponse) error { return nil })
-}
-
-func StopHandler(cmd *cobra.Command, args []string) error {
-	opts := &runOptions{
-		Model:     args[0],
-		KeepAlive: &api.Duration{Duration: 0},
-	}
-	if err := loadOrUnloadModel(cmd, opts); err != nil {
-		if strings.Contains(err.Error(), "not found") {
-			return fmt.Errorf("couldn't find model \"%s\" to stop", args[0])
-		}
-	}
-	return nil
-}
-
 func RunHandler(cmd *cobra.Command, args []string) error {
 	interactive := true
 
@@ -487,11 +399,11 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
-	opts.MultiModal = len(info.ProjectorInfo) != 0
+	opts.MultiModal = slices.Contains(info.Details.Families, "clip")
 	opts.ParentModel = info.Details.ParentModel
 
 	if interactive {
-		if err := loadOrUnloadModel(cmd, &opts); err != nil {
+		if err := loadModel(cmd, &opts); err != nil {
 			return err
 		}
 
@@ -647,7 +559,7 @@ func ListHandler(cmd *cobra.Command, args []string) error {
 	table.SetHeaderLine(false)
 	table.SetBorder(false)
 	table.SetNoWhiteSpace(true)
-	table.SetTablePadding("    ")
+	table.SetTablePadding("\t")
 	table.AppendBulk(data)
 	table.Render()
 
@@ -682,15 +594,7 @@ func ListRunningHandler(cmd *cobra.Command, args []string) error {
 				cpuPercent := math.Round(float64(sizeCPU) / float64(m.Size) * 100)
 				procStr = fmt.Sprintf("%d%%/%d%% CPU/GPU", int(cpuPercent), int(100-cpuPercent))
 			}
-
-			var until string
-			delta := time.Since(m.ExpiresAt)
-			if delta > 0 {
-				until = "Stopping..."
-			} else {
-				until = format.HumanTime(m.ExpiresAt, "Never")
-			}
-			data = append(data, []string{m.Name, m.Digest[:12], format.HumanBytes(m.Size), procStr, until})
+			data = append(data, []string{m.Name, m.Digest[:12], format.HumanBytes(m.Size), procStr, format.HumanTime(m.ExpiresAt, "Never")})
 		}
 	}
 
@@ -701,7 +605,7 @@ func ListRunningHandler(cmd *cobra.Command, args []string) error {
 	table.SetHeaderLine(false)
 	table.SetBorder(false)
 	table.SetNoWhiteSpace(true)
-	table.SetTablePadding("    ")
+	table.SetTablePadding("\t")
 	table.AppendBulk(data)
 	table.Render()
 
@@ -714,17 +618,6 @@ func DeleteHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
-	// Unload the model if it's running before deletion
-	opts := &runOptions{
-		Model:     args[0],
-		KeepAlive: &api.Duration{Duration: 0},
-	}
-	if err := loadOrUnloadModel(cmd, opts); err != nil {
-		if !strings.Contains(err.Error(), "not found") {
-			return fmt.Errorf("unable to stop existing running model \"%s\": %s", args[0], err)
-		}
-	}
-
 	for _, name := range args {
 		req := api.DeleteRequest{Name: name}
 		if err := client.Delete(cmd.Context(), &req); err != nil {
@@ -800,97 +693,130 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
 		case "parameters":
 			fmt.Println(resp.Parameters)
 		case "system":
-			fmt.Print(resp.System)
+			fmt.Println(resp.System)
 		case "template":
-			fmt.Print(resp.Template)
+			fmt.Println(resp.Template)
 		}
 
 		return nil
 	}
 
-	return showInfo(resp, os.Stdout)
+	showInfo(resp)
+
+	return nil
 }
 
-func showInfo(resp *api.ShowResponse, w io.Writer) error {
-	tableRender := func(header string, rows func() [][]string) {
-		fmt.Fprintln(w, " ", header)
-		table := tablewriter.NewWriter(w)
-		table.SetAlignment(tablewriter.ALIGN_LEFT)
-		table.SetBorder(false)
-		table.SetNoWhiteSpace(true)
-		table.SetTablePadding("    ")
+func showInfo(resp *api.ShowResponse) {
+	arch := resp.ModelInfo["general.architecture"].(string)
 
-		switch header {
-		case "Template", "System", "License":
-			table.SetColWidth(100)
-		}
-
-		table.AppendBulk(rows())
-		table.Render()
-		fmt.Fprintln(w)
+	modelData := [][]string{
+		{"arch", arch},
+		{"parameters", resp.Details.ParameterSize},
+		{"quantization", resp.Details.QuantizationLevel},
+		{"context length", fmt.Sprintf("%v", resp.ModelInfo[fmt.Sprintf("%s.context_length", arch)].(float64))},
+		{"embedding length", fmt.Sprintf("%v", resp.ModelInfo[fmt.Sprintf("%s.embedding_length", arch)].(float64))},
 	}
 
-	tableRender("Model", func() (rows [][]string) {
-		if resp.ModelInfo != nil {
-			arch := resp.ModelInfo["general.architecture"].(string)
-			rows = append(rows, []string{"", "architecture", arch})
-			rows = append(rows, []string{"", "parameters", format.HumanNumber(uint64(resp.ModelInfo["general.parameter_count"].(float64)))})
-			rows = append(rows, []string{"", "context length", strconv.FormatFloat(resp.ModelInfo[fmt.Sprintf("%s.context_length", arch)].(float64), 'f', -1, 64)})
-			rows = append(rows, []string{"", "embedding length", strconv.FormatFloat(resp.ModelInfo[fmt.Sprintf("%s.embedding_length", arch)].(float64), 'f', -1, 64)})
-		} else {
-			rows = append(rows, []string{"", "architecture", resp.Details.Family})
-			rows = append(rows, []string{"", "parameters", resp.Details.ParameterSize})
-		}
-		rows = append(rows, []string{"", "quantization", resp.Details.QuantizationLevel})
-		return
-	})
+	mainTableData := [][]string{
+		{"Model"},
+		{renderSubTable(modelData, false)},
+	}
 
 	if resp.ProjectorInfo != nil {
-		tableRender("Projector", func() (rows [][]string) {
-			arch := resp.ProjectorInfo["general.architecture"].(string)
-			rows = append(rows, []string{"", "architecture", arch})
-			rows = append(rows, []string{"", "parameters", format.HumanNumber(uint64(resp.ProjectorInfo["general.parameter_count"].(float64)))})
-			rows = append(rows, []string{"", "embedding length", strconv.FormatFloat(resp.ProjectorInfo[fmt.Sprintf("%s.vision.embedding_length", arch)].(float64), 'f', -1, 64)})
-			rows = append(rows, []string{"", "dimensions", strconv.FormatFloat(resp.ProjectorInfo[fmt.Sprintf("%s.vision.projection_dim", arch)].(float64), 'f', -1, 64)})
-			return
-		})
+		projectorData := [][]string{
+			{"arch", "clip"},
+			{"parameters", format.HumanNumber(uint64(resp.ProjectorInfo["general.parameter_count"].(float64)))},
+		}
+
+		if projectorType, ok := resp.ProjectorInfo["clip.projector_type"]; ok {
+			projectorData = append(projectorData, []string{"projector type", projectorType.(string)})
+		}
+
+		projectorData = append(projectorData,
+			[]string{"embedding length", fmt.Sprintf("%v", resp.ProjectorInfo["clip.vision.embedding_length"].(float64))},
+			[]string{"projection dimensionality", fmt.Sprintf("%v", resp.ProjectorInfo["clip.vision.projection_dim"].(float64))},
+		)
+
+		mainTableData = append(mainTableData,
+			[]string{"Projector"},
+			[]string{renderSubTable(projectorData, false)},
+		)
 	}
 
 	if resp.Parameters != "" {
-		tableRender("Parameters", func() (rows [][]string) {
-			scanner := bufio.NewScanner(strings.NewReader(resp.Parameters))
-			for scanner.Scan() {
-				if text := scanner.Text(); text != "" {
-					rows = append(rows, append([]string{""}, strings.Fields(text)...))
-				}
-			}
-			return
-		})
-	}
-
-	head := func(s string, n int) (rows [][]string) {
-		scanner := bufio.NewScanner(strings.NewReader(s))
-		for scanner.Scan() && (len(rows) < n || n < 0) {
-			if text := scanner.Text(); text != "" {
-				rows = append(rows, []string{"", strings.TrimSpace(text)})
-			}
-		}
-		return
+		mainTableData = append(mainTableData, []string{"Parameters"}, []string{formatParams(resp.Parameters)})
 	}
 
 	if resp.System != "" {
-		tableRender("System", func() [][]string {
-			return head(resp.System, 2)
-		})
+		mainTableData = append(mainTableData, []string{"System"}, []string{renderSubTable(twoLines(resp.System), true)})
 	}
 
 	if resp.License != "" {
-		tableRender("License", func() [][]string {
-			return head(resp.License, 2)
-		})
+		mainTableData = append(mainTableData, []string{"License"}, []string{renderSubTable(twoLines(resp.License), true)})
 	}
 
-	return nil
+	table := tablewriter.NewWriter(os.Stdout)
+	table.SetAutoWrapText(false)
+	table.SetBorder(false)
+	table.SetAlignment(tablewriter.ALIGN_LEFT)
+
+	for _, v := range mainTableData {
+		table.Append(v)
+	}
+
+	table.Render()
+}
+
+func renderSubTable(data [][]string, file bool) string {
+	var buf bytes.Buffer
+	table := tablewriter.NewWriter(&buf)
+	table.SetAutoWrapText(!file)
+	table.SetBorder(false)
+	table.SetNoWhiteSpace(true)
+	table.SetTablePadding("\t")
+	table.SetAlignment(tablewriter.ALIGN_LEFT)
+
+	for _, v := range data {
+		table.Append(v)
+	}
+
+	table.Render()
+
+	renderedTable := buf.String()
+	lines := strings.Split(renderedTable, "\n")
+	for i, line := range lines {
+		lines[i] = "\t" + line
+	}
+
+	return strings.Join(lines, "\n")
+}
+
+func twoLines(s string) [][]string {
+	lines := strings.Split(s, "\n")
+	res := [][]string{}
+
+	count := 0
+	for _, line := range lines {
+		line = strings.TrimSpace(line)
+		if line != "" {
+			count++
+			res = append(res, []string{line})
+			if count == 2 {
+				return res
+			}
+		}
+	}
+	return res
+}
+
+func formatParams(s string) string {
+	lines := strings.Split(s, "\n")
+	table := [][]string{}
+
+	for _, line := range lines {
+		table = append(table, strings.Fields(line))
+	}
+	return renderSubTable(table, false)
 }
 
 func CopyHandler(cmd *cobra.Command, args []string) error {
@@ -1318,7 +1244,7 @@ func NewCLI() *cobra.Command {
 	log.SetFlags(log.LstdFlags | log.Lshortfile)
 	cobra.EnableCommandSorting = false
 
-	if runtime.GOOS == "windows" && term.IsTerminal(int(os.Stdout.Fd())) {
+	if runtime.GOOS == "windows" {
 		console.ConsoleFromFile(os.Stdin) //nolint:errcheck
 	}
 
@@ -1350,7 +1276,7 @@ func NewCLI() *cobra.Command {
 		RunE:    CreateHandler,
 	}
 
-	createCmd.Flags().StringP("file", "f", "", "Name of the Modelfile (default \"Modelfile\"")
+	createCmd.Flags().StringP("file", "f", "Modelfile", "Name of the Modelfile")
 	createCmd.Flags().StringP("quantize", "q", "", "Quantize model to this level (e.g. q4_0)")
 
 	showCmd := &cobra.Command{
@@ -1380,15 +1306,6 @@ func NewCLI() *cobra.Command {
 	runCmd.Flags().Bool("insecure", false, "Use an insecure registry")
 	runCmd.Flags().Bool("nowordwrap", false, "Don't wrap words to the next line automatically")
 	runCmd.Flags().String("format", "", "Response format (e.g. json)")
-
-	stopCmd := &cobra.Command{
-		Use:     "stop MODEL",
-		Short:   "Stop a running model",
-		Args:    cobra.ExactArgs(1),
-		PreRunE: checkServerHeartbeat,
-		RunE:    StopHandler,
-	}
-
 	serveCmd := &cobra.Command{
 		Use:     "serve",
 		Aliases: []string{"start"},
@@ -1456,7 +1373,6 @@ func NewCLI() *cobra.Command {
 		createCmd,
 		showCmd,
 		runCmd,
-		stopCmd,
 		pullCmd,
 		pushCmd,
 		listCmd,
@@ -1483,8 +1399,6 @@ func NewCLI() *cobra.Command {
 				envVars["OLLAMA_TMPDIR"],
 				envVars["OLLAMA_FLASH_ATTENTION"],
 				envVars["OLLAMA_LLM_LIBRARY"],
-				envVars["OLLAMA_GPU_OVERHEAD"],
-				envVars["OLLAMA_LOAD_TIMEOUT"],
 			})
 		default:
 			appendEnvDocs(cmd, envs)
@@ -1496,7 +1410,6 @@ func NewCLI() *cobra.Command {
 		createCmd,
 		showCmd,
 		runCmd,
-		stopCmd,
 		pullCmd,
 		pushCmd,
 		listCmd,

cmd/cmd_test.go

@@ -1,371 +0,0 @@
-package cmd
-
-import (
-	"bytes"
-	"context"
-	"encoding/json"
-	"net/http"
-	"net/http/httptest"
-	"os"
-	"path/filepath"
-	"strings"
-	"testing"
-
-	"github.com/google/go-cmp/cmp"
-	"github.com/spf13/cobra"
-
-	"github.com/ollama/ollama/api"
-)
-
-func TestShowInfo(t *testing.T) {
-	t.Run("bare details", func(t *testing.T) {
-		var b bytes.Buffer
-		if err := showInfo(&api.ShowResponse{
-			Details: api.ModelDetails{
-				Family:            "test",
-				ParameterSize:     "7B",
-				QuantizationLevel: "FP16",
-			},
-		}, &b); err != nil {
-			t.Fatal(err)
-		}
-
-		expect := `  Model
-    architecture    test    
-    parameters      7B      
-    quantization    FP16    
-
-`
-
-		if diff := cmp.Diff(expect, b.String()); diff != "" {
-			t.Errorf("unexpected output (-want +got):\n%s", diff)
-		}
-	})
-
-	t.Run("bare model info", func(t *testing.T) {
-		var b bytes.Buffer
-		if err := showInfo(&api.ShowResponse{
-			ModelInfo: map[string]any{
-				"general.architecture":    "test",
-				"general.parameter_count": float64(7_000_000_000),
-				"test.context_length":     float64(0),
-				"test.embedding_length":   float64(0),
-			},
-			Details: api.ModelDetails{
-				Family:            "test",
-				ParameterSize:     "7B",
-				QuantizationLevel: "FP16",
-			},
-		}, &b); err != nil {
-			t.Fatal(err)
-		}
-
-		expect := `  Model
-    architecture        test    
-    parameters          7B      
-    context length      0       
-    embedding length    0       
-    quantization        FP16    
-
-`
-		if diff := cmp.Diff(expect, b.String()); diff != "" {
-			t.Errorf("unexpected output (-want +got):\n%s", diff)
-		}
-	})
-
-	t.Run("parameters", func(t *testing.T) {
-		var b bytes.Buffer
-		if err := showInfo(&api.ShowResponse{
-			Details: api.ModelDetails{
-				Family:            "test",
-				ParameterSize:     "7B",
-				QuantizationLevel: "FP16",
-			},
-			Parameters: `
-			stop never
-			stop gonna
-			stop give
-			stop you
-			stop up
-			temperature 99`,
-		}, &b); err != nil {
-			t.Fatal(err)
-		}
-
-		expect := `  Model
-    architecture    test    
-    parameters      7B      
-    quantization    FP16    
-
-  Parameters
-    stop           never    
-    stop           gonna    
-    stop           give     
-    stop           you      
-    stop           up       
-    temperature    99       
-
-`
-		if diff := cmp.Diff(expect, b.String()); diff != "" {
-			t.Errorf("unexpected output (-want +got):\n%s", diff)
-		}
-	})
-
-	t.Run("project info", func(t *testing.T) {
-		var b bytes.Buffer
-		if err := showInfo(&api.ShowResponse{
-			Details: api.ModelDetails{
-				Family:            "test",
-				ParameterSize:     "7B",
-				QuantizationLevel: "FP16",
-			},
-			ProjectorInfo: map[string]any{
-				"general.architecture":         "clip",
-				"general.parameter_count":      float64(133_700_000),
-				"clip.vision.embedding_length": float64(0),
-				"clip.vision.projection_dim":   float64(0),
-			},
-		}, &b); err != nil {
-			t.Fatal(err)
-		}
-
-		expect := `  Model
-    architecture    test    
-    parameters      7B      
-    quantization    FP16    
-
-  Projector
-    architecture        clip       
-    parameters          133.70M    
-    embedding length    0          
-    dimensions          0          
-
-`
-		if diff := cmp.Diff(expect, b.String()); diff != "" {
-			t.Errorf("unexpected output (-want +got):\n%s", diff)
-		}
-	})
-
-	t.Run("system", func(t *testing.T) {
-		var b bytes.Buffer
-		if err := showInfo(&api.ShowResponse{
-			Details: api.ModelDetails{
-				Family:            "test",
-				ParameterSize:     "7B",
-				QuantizationLevel: "FP16",
-			},
-			System: `You are a pirate!
-Ahoy, matey!
-Weigh anchor!
-			`,
-		}, &b); err != nil {
-			t.Fatal(err)
-		}
-
-		expect := `  Model
-    architecture    test    
-    parameters      7B      
-    quantization    FP16    
-
-  System
-    You are a pirate!    
-    Ahoy, matey!         
-
-`
-		if diff := cmp.Diff(expect, b.String()); diff != "" {
-			t.Errorf("unexpected output (-want +got):\n%s", diff)
-		}
-	})
-
-	t.Run("license", func(t *testing.T) {
-		var b bytes.Buffer
-		license, err := os.ReadFile(filepath.Join("..", "LICENSE"))
-		if err != nil {
-			t.Fatal(err)
-		}
-
-		if err := showInfo(&api.ShowResponse{
-			Details: api.ModelDetails{
-				Family:            "test",
-				ParameterSize:     "7B",
-				QuantizationLevel: "FP16",
-			},
-			License: string(license),
-		}, &b); err != nil {
-			t.Fatal(err)
-		}
-
-		expect := `  Model
-    architecture    test    
-    parameters      7B      
-    quantization    FP16    
-
-  License
-    MIT License             
-    Copyright (c) Ollama    
-
-`
-		if diff := cmp.Diff(expect, b.String()); diff != "" {
-			t.Errorf("unexpected output (-want +got):\n%s", diff)
-		}
-	})
-}
-
-func TestDeleteHandler(t *testing.T) {
-	stopped := false
-	mockServer := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
-		if r.URL.Path == "/api/delete" && r.Method == http.MethodDelete {
-			var req api.DeleteRequest
-			if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
-				http.Error(w, err.Error(), http.StatusBadRequest)
-				return
-			}
-			if req.Name == "test-model" {
-				w.WriteHeader(http.StatusOK)
-			} else {
-				w.WriteHeader(http.StatusNotFound)
-			}
-			return
-		}
-		if r.URL.Path == "/api/generate" && r.Method == http.MethodPost {
-			var req api.GenerateRequest
-			if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
-				http.Error(w, err.Error(), http.StatusBadRequest)
-				return
-			}
-			if req.Model == "test-model" {
-				w.WriteHeader(http.StatusOK)
-				if err := json.NewEncoder(w).Encode(api.GenerateResponse{
-					Done: true,
-				}); err != nil {
-					http.Error(w, err.Error(), http.StatusInternalServerError)
-				}
-				stopped = true
-				return
-			} else {
-				w.WriteHeader(http.StatusNotFound)
-				if err := json.NewEncoder(w).Encode(api.GenerateResponse{
-					Done: false,
-				}); err != nil {
-					http.Error(w, err.Error(), http.StatusInternalServerError)
-				}
-			}
-		}
-	}))
-
-	t.Setenv("OLLAMA_HOST", mockServer.URL)
-	t.Cleanup(mockServer.Close)
-
-	cmd := &cobra.Command{}
-	cmd.SetContext(context.TODO())
-	if err := DeleteHandler(cmd, []string{"test-model"}); err != nil {
-		t.Fatalf("DeleteHandler failed: %v", err)
-	}
-	if !stopped {
-		t.Fatal("Model was not stopped before deletion")
-	}
-
-	err := DeleteHandler(cmd, []string{"test-model-not-found"})
-	if err == nil || !strings.Contains(err.Error(), "unable to stop existing running model \"test-model-not-found\"") {
-		t.Fatalf("DeleteHandler failed: expected error about stopping non-existent model, got %v", err)
-	}
-}
-
-func TestGetModelfileName(t *testing.T) {
-	tests := []struct {
-		name          string
-		modelfileName string
-		fileExists    bool
-		expectedName  string
-		expectedErr   error
-	}{
-		{
-			name:          "no modelfile specified, no modelfile exists",
-			modelfileName: "",
-			fileExists:    false,
-			expectedName:  "",
-			expectedErr:   os.ErrNotExist,
-		},
-		{
-			name:          "no modelfile specified, modelfile exists",
-			modelfileName: "",
-			fileExists:    true,
-			expectedName:  "Modelfile",
-			expectedErr:   nil,
-		},
-		{
-			name:          "modelfile specified, no modelfile exists",
-			modelfileName: "crazyfile",
-			fileExists:    false,
-			expectedName:  "crazyfile",
-			expectedErr:   os.ErrNotExist,
-		},
-		{
-			name:          "modelfile specified, modelfile exists",
-			modelfileName: "anotherfile",
-			fileExists:    true,
-			expectedName:  "anotherfile",
-			expectedErr:   nil,
-		},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			cmd := &cobra.Command{
-				Use: "fakecmd",
-			}
-			cmd.Flags().String("file", "", "path to modelfile")
-
-			var expectedFilename string
-
-			if tt.fileExists {
-				tempDir, err := os.MkdirTemp("", "modelfiledir")
-				defer os.RemoveAll(tempDir)
-				if err != nil {
-					t.Fatalf("temp modelfile dir creation failed: %v", err)
-				}
-				var fn string
-				if tt.modelfileName != "" {
-					fn = tt.modelfileName
-				} else {
-					fn = "Modelfile"
-				}
-
-				tempFile, err := os.CreateTemp(tempDir, fn)
-				if err != nil {
-					t.Fatalf("temp modelfile creation failed: %v", err)
-				}
-
-				expectedFilename = tempFile.Name()
-				err = cmd.Flags().Set("file", expectedFilename)
-				if err != nil {
-					t.Fatalf("couldn't set file flag: %v", err)
-				}
-			} else {
-				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)
-					}
-				}
-			}
-
-			actualFilename, actualErr := getModelfileName(cmd)
-
-			if actualFilename != expectedFilename {
-				t.Errorf("expected filename: '%s' actual filename: '%s'", expectedFilename, actualFilename)
-			}
-
-			if tt.expectedErr != os.ErrNotExist {
-				if actualErr != tt.expectedErr {
-					t.Errorf("expected err: %v actual err: %v", tt.expectedErr, actualErr)
-				}
-			} else {
-				if !os.IsNotExist(actualErr) {
-					t.Errorf("expected err: %v actual err: %v", tt.expectedErr, actualErr)
-				}
-			}
-		})
-	}
-}

cmd/interactive.go

@@ -18,6 +18,7 @@ import (
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/parser"
+	"github.com/ollama/ollama/progress"
 	"github.com/ollama/ollama/readline"
 	"github.com/ollama/ollama/types/errtypes"
 )
@@ -30,6 +31,26 @@ const (
 	MultilineSystem
 )
 
+func loadModel(cmd *cobra.Command, opts *runOptions) error {
+	p := progress.NewProgress(os.Stderr)
+	defer p.StopAndClear()
+
+	spinner := progress.NewSpinner("")
+	p.Add("", spinner)
+
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		return err
+	}
+
+	chatReq := &api.ChatRequest{
+		Model:     opts.Model,
+		KeepAlive: opts.KeepAlive,
+	}
+
+	return client.Chat(cmd.Context(), chatReq, func(api.ChatResponse) error { return nil })
+}
+
 func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 	usage := func() {
 		fmt.Fprintln(os.Stderr, "Available Commands:")
@@ -196,7 +217,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 			opts.Model = args[1]
 			opts.Messages = []api.Message{}
 			fmt.Printf("Loading model '%s'\n", opts.Model)
-			if err := loadOrUnloadModel(cmd, &opts); err != nil {
+			if err := loadModel(cmd, &opts); err != nil {
 				return err
 			}
 			continue
@@ -350,7 +371,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 
 				switch args[1] {
 				case "info":
-					_ = showInfo(resp, os.Stderr)
+					showInfo(resp)
 				case "license":
 					if resp.License == "" {
 						fmt.Println("No license was specified for this model.")
@@ -442,6 +463,13 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 					return err
 				}
 
+				// clear all previous images for better responses
+				if len(images) > 0 {
+					for i := range opts.Messages {
+						opts.Messages[i].Images = nil
+					}
+				}
+
 				newMessage.Content = msg
 				newMessage.Images = images
 			}
@@ -494,22 +522,28 @@ func buildModelfile(opts runOptions) string {
 }
 
 func normalizeFilePath(fp string) string {
-	return strings.NewReplacer(
-		"\\ ", " ", // Escaped space
-		"\\(", "(", // Escaped left parenthesis
-		"\\)", ")", // Escaped right parenthesis
-		"\\[", "[", // Escaped left square bracket
-		"\\]", "]", // Escaped right square bracket
-		"\\{", "{", // Escaped left curly brace
-		"\\}", "}", // Escaped right curly brace
-		"\\$", "$", // Escaped dollar sign
-		"\\&", "&", // Escaped ampersand
-		"\\;", ";", // Escaped semicolon
-		"\\'", "'", // Escaped single quote
-		"\\\\", "\\", // Escaped backslash
-		"\\*", "*", // Escaped asterisk
-		"\\?", "?", // Escaped question mark
-	).Replace(fp)
+	// Define a map of escaped characters and their replacements
+	replacements := map[string]string{
+		"\\ ":  " ",  // Escaped space
+		"\\(":  "(",  // Escaped left parenthesis
+		"\\)":  ")",  // Escaped right parenthesis
+		"\\[":  "[",  // Escaped left square bracket
+		"\\]":  "]",  // Escaped right square bracket
+		"\\{":  "{",  // Escaped left curly brace
+		"\\}":  "}",  // Escaped right curly brace
+		"\\$":  "$",  // Escaped dollar sign
+		"\\&":  "&",  // Escaped ampersand
+		"\\;":  ";",  // Escaped semicolon
+		"\\'":  "'",  // Escaped single quote
+		"\\\\": "\\", // Escaped backslash
+		"\\*":  "*",  // Escaped asterisk
+		"\\?":  "?",  // Escaped question mark
+	}
+
+	for escaped, actual := range replacements {
+		fp = strings.ReplaceAll(fp, escaped, actual)
+	}
+	return fp
 }
 
 func extractFileNames(input string) []string {
@@ -529,9 +563,10 @@ func extractFileData(input string) (string, []api.ImageData, error) {
 	for _, fp := range filePaths {
 		nfp := normalizeFilePath(fp)
 		data, err := getImageData(nfp)
-		if errors.Is(err, os.ErrNotExist) {
-			continue
-		} else if err != nil {
+		if err != nil {
+			if os.IsNotExist(err) {
+				continue
+			}
 			fmt.Fprintf(os.Stderr, "Couldn't process image: %q\n", err)
 			return "", imgs, err
 		}
@@ -539,7 +574,7 @@ func extractFileData(input string) (string, []api.ImageData, error) {
 		input = strings.ReplaceAll(input, fp, "")
 		imgs = append(imgs, data)
 	}
-	return strings.TrimSpace(input), imgs, nil
+	return input, imgs, nil
 }
 
 func getImageData(filePath string) ([]byte, error) {

convert/convert.go

@@ -7,27 +7,16 @@ import (
 	"io"
 	"io/fs"
 	"log/slog"
-	"strings"
 
 	"github.com/ollama/ollama/llm"
 )
 
-type ModelParameters struct {
+type Parameters struct {
 	Architectures []string `json:"architectures"`
 	VocabSize     uint32   `json:"vocab_size"`
 }
 
-type AdapterParameters struct {
-	Alpha          uint32 `json:"lora_alpha"`
-	LoraLayers     uint32 `json:"lora_layers"`
-	LoraParameters struct {
-		Rank  uint32  `json:"rank"`
-		Alpha float32 `json:"alpha"`
-		Scale float32 `json:"scale"`
-	} `json:"lora_parameters"`
-}
-
-func (ModelParameters) KV(t *Tokenizer) llm.KV {
+func (Parameters) KV(t *Tokenizer) llm.KV {
 	kv := llm.KV{
 		"general.file_type":            uint32(1),
 		"general.quantization_version": uint32(2),
@@ -54,119 +43,40 @@ func (ModelParameters) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p AdapterParameters) KV() llm.KV {
-	var alpha float32
-	if p.LoraParameters.Alpha == 0 {
-		alpha = float32(p.Alpha)
-	} else {
-		alpha = p.LoraParameters.Alpha
-	}
-
-	kv := llm.KV{
-		"adapter.lora.alpha": alpha,
-		"adapter.type":       "lora",
-		"general.file_type":  uint32(1),
-		"general.type":       "adapter",
-		"general.version":    "v0.2",
-	}
-
-	return kv
-}
-
-func (ModelParameters) specialTokenTypes() []string {
+func (Parameters) specialTokenTypes() []string {
 	return []string{
 		"bos", "eos", "unk", "sep", "pad", "cls", "mask",
 	}
 }
 
-func (ModelParameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []llm.Tensor) error {
+func (Parameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []llm.Tensor) error {
 	return llm.WriteGGUF(ws, kv, ts)
 }
 
-func (AdapterParameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []llm.Tensor) error {
-	return llm.WriteGGUF(ws, kv, ts)
-}
-
-type ModelConverter interface {
+type Converter interface {
 	// KV maps parameters to LLM key-values
 	KV(*Tokenizer) llm.KV
 	// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
 	Tensors([]Tensor) []llm.Tensor
-	// Replacements returns a list of string pairs to replace in tensor names.
-	// See [strings.Replacer](https://pkg.go.dev/strings#Replacer) for details
-	Replacements() []string
 
+	// tensorName returns the LLM tensor name for a specific input name
+	tensorName(string) string
 	// specialTokenTypes returns any special token types the model uses
 	specialTokenTypes() []string
-	// writeFile writes the model to the provided io.WriteSeeker
 	writeFile(io.WriteSeeker, llm.KV, []llm.Tensor) error
 }
 
-type moreParser interface {
-	parseMore(fs.FS) error
-}
-
-type AdapterConverter interface {
-	// KV maps parameters to LLM key-values
-	KV(llm.KV) llm.KV
-	// Tensors maps input tensors to LLM tensors. Adapter specific modifications can be done here.
-	Tensors([]Tensor) []llm.Tensor
-	// Replacements returns a list of string pairs to replace in tensor names.
-	// See [strings.Replacer](https://pkg.go.dev/strings#Replacer) for details
-	Replacements() []string
-
-	writeFile(io.WriteSeeker, llm.KV, []llm.Tensor) error
-}
-
-func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV llm.KV) error {
-	bts, err := fs.ReadFile(fsys, "adapter_config.json")
-	if err != nil {
-		return err
-	}
-
-	var p AdapterParameters
-	if err := json.Unmarshal(bts, &p); err != nil {
-		return err
-	}
-
-	arch, ok := baseKV["general.architecture"]
-	if !ok {
-		return errors.New("architecture not set for the base model")
-	}
-
-	var conv AdapterConverter
-	switch arch {
-	case "llama":
-		conv = &llamaAdapter{}
-	case "gemma2":
-		conv = &gemma2Adapter{}
-	default:
-		return errors.New("unsupported architecture")
-	}
-
-	ts, err := parseTensors(fsys, strings.NewReplacer(conv.Replacements()...))
-	if err != nil {
-		return err
-	}
-
-	if err := json.Unmarshal(bts, conv); err != nil {
-		return err
-	}
-
-	return conv.writeFile(ws, conv.KV(baseKV), conv.Tensors(ts))
-}
-
 // Convert writes an Ollama compatible model to the provided io.WriteSeeker based on configurations
 // and files it finds in the input path.
 // Supported input model formats include safetensors.
 // Supported input tokenizers files include tokenizer.json (preferred) and tokenizer.model.
-func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
+func Convert(fsys fs.FS, ws io.WriteSeeker) error {
 	bts, err := fs.ReadFile(fsys, "config.json")
 	if err != nil {
 		return err
 	}
 
-	var p ModelParameters
+	var p Parameters
 	if err := json.Unmarshal(bts, &p); err != nil {
 		return err
 	}
@@ -175,20 +85,16 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
 		return errors.New("unknown architecture")
 	}
 
-	var conv ModelConverter
+	var conv Converter
 	switch p.Architectures[0] {
 	case "LlamaForCausalLM", "MistralForCausalLM":
-		conv = &llamaModel{}
+		conv = &llama{}
 	case "MixtralForCausalLM":
-		conv = &mixtralModel{}
+		conv = &mixtral{}
 	case "GemmaForCausalLM":
-		conv = &gemmaModel{}
-	case "Gemma2ForCausalLM":
-		conv = &gemma2Model{}
+		conv = &gemma{}
 	case "Phi3ForCausalLM":
-		conv = &phi3Model{}
-	case "BertModel":
-		conv = &bertModel{}
+		conv = &phi3{}
 	default:
 		return errors.New("unsupported architecture")
 	}
@@ -197,33 +103,23 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
 		return err
 	}
 
-	if t, ok := conv.(moreParser); ok {
-		if err := t.parseMore(fsys); err != nil {
-			return err
-		}
-	}
-
 	t, err := parseTokenizer(fsys, conv.specialTokenTypes())
 	if err != nil {
 		return err
 	}
 
-	vocabSize := int(p.VocabSize)
-	switch {
-	case vocabSize > len(t.Vocabulary.Tokens):
-		slog.Warn("vocabulary is smaller than expected, padding with dummy tokens", "expect", vocabSize, "actual", len(t.Vocabulary.Tokens))
+	if vocabSize := int(p.VocabSize); vocabSize > len(t.Vocabulary.Tokens) {
+		slog.Warn("vocabulary is smaller than expected, padding with dummy tokens", "expect", p.VocabSize, "actual", len(t.Vocabulary.Tokens))
 		for i := range vocabSize - len(t.Vocabulary.Tokens) {
 			t.Vocabulary.Tokens = append(t.Vocabulary.Tokens, fmt.Sprintf("[PAD%d]", i))
 			t.Vocabulary.Scores = append(t.Vocabulary.Scores, -1)
 			t.Vocabulary.Types = append(t.Vocabulary.Types, tokenTypeUserDefined)
 		}
-	case vocabSize < len(t.Vocabulary.Tokens):
-		return fmt.Errorf("vocabulary is larger than expected '%d' instead of '%d'", len(t.Vocabulary.Tokens), vocabSize)
-	default:
+	} else {
 		slog.Debug("vocabulary", "size", len(t.Vocabulary.Tokens))
 	}
 
-	ts, err := parseTensors(fsys, strings.NewReplacer(conv.Replacements()...))
+	ts, err := parseTensors(fsys)
 	if err != nil {
 		return err
 	}

convert/convert_bert.go

@@ -1,174 +0,0 @@
-package convert
-
-import (
-	"cmp"
-	"encoding/json"
-	"io/fs"
-	"path/filepath"
-	"slices"
-	"strings"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type bertModel struct {
-	ModelParameters
-	NLayers               uint32  `json:"n_layers"`
-	NumHiddenLayers       uint32  `json:"num_hidden_layers"`
-	NLayer                uint32  `json:"n_layer"`
-	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
-	NCtx                  uint32  `json:"n_ctx"`
-	HiddenSize            uint32  `json:"hidden_size"`
-	NEmbd                 uint32  `json:"n_embd"`
-	IntermediateSize      uint32  `json:"intermediate_size"`
-	NInner                uint32  `json:"n_inner"`
-	NumAttentionHeads     uint32  `json:"num_attention_heads"`
-	NHead                 uint32  `json:"n_head"`
-	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
-	LayerNormEPS          float32 `json:"layer_norm_eps"`
-	LayerNormEpsilon      float32 `json:"layer_norm_epsilon"`
-	NormEpsilon           float32 `json:"norm_epsilon"`
-
-	PoolingType uint32
-}
-
-var (
-	_ ModelConverter = (*bertModel)(nil)
-	_ moreParser     = (*bertModel)(nil)
-)
-
-func (p *bertModel) parseMore(fsys fs.FS) error {
-	bts, err := fs.ReadFile(fsys, "modules.json")
-	if err != nil {
-		return err
-	}
-
-	var modules []struct {
-		Type string `json:"type"`
-		Path string `json:"path"`
-	}
-
-	if err := json.Unmarshal(bts, &modules); err != nil {
-		return err
-	}
-
-	var pooling string
-	for _, m := range modules {
-		if m.Type == "sentence_transformers.models.Pooling" {
-			pooling = m.Path
-			break
-		}
-	}
-
-	if pooling != "" {
-		bts, err := fs.ReadFile(fsys, filepath.Join(pooling, "config.json"))
-		if err != nil {
-			return err
-		}
-
-		var pc struct {
-			PoolingModeCLSToken   bool `json:"pooling_mode_cls_token"`
-			PoolingModeMeanTokens bool `json:"pooling_mode_mean_tokens"`
-		}
-
-		if err := json.Unmarshal(bts, &pc); err != nil {
-			return err
-		}
-
-		if pc.PoolingModeMeanTokens {
-			p.PoolingType = 1
-		} else if pc.PoolingModeCLSToken {
-			p.PoolingType = 2
-		}
-	}
-
-	return nil
-}
-
-func (p *bertModel) KV(t *Tokenizer) llm.KV {
-	kv := p.ModelParameters.KV(t)
-	kv["general.architecture"] = "bert"
-	kv["bert.attention.causal"] = false
-	kv["bert.pooling_type"] = p.PoolingType
-
-	kv["bert.block_count"] = cmp.Or(p.NLayers, p.NumHiddenLayers, p.NLayer)
-
-	if contextLength := cmp.Or(p.MaxPositionEmbeddings, p.NCtx); contextLength > 0 {
-		kv["bert.context_length"] = contextLength
-	}
-
-	if embeddingLength := cmp.Or(p.HiddenSize, p.NEmbd); embeddingLength > 0 {
-		kv["bert.embedding_length"] = cmp.Or(p.HiddenSize, p.NEmbd)
-	}
-
-	if feedForwardLength := cmp.Or(p.IntermediateSize, p.NInner); feedForwardLength > 0 {
-		kv["bert.feed_forward_length"] = cmp.Or(p.IntermediateSize, p.NInner)
-	}
-
-	if headCount := cmp.Or(p.NumAttentionHeads, p.NHead); headCount > 0 {
-		kv["bert.attention.head_count"] = cmp.Or(p.NumAttentionHeads, p.NHead)
-	}
-
-	if layerNormEpsilon := cmp.Or(p.LayerNormEPS, p.LayerNormEpsilon, p.NormEpsilon); layerNormEpsilon > 0 {
-		kv["bert.attention.layer_norm_epsilon"] = layerNormEpsilon
-	}
-
-	kv["tokenizer.ggml.model"] = "bert"
-	kv["tokenizer.ggml.token_type_count"] = uint32(2)
-
-	// convert to phantom space tokens
-	for i, e := range t.Tokens {
-		if strings.HasPrefix(e, "[") && strings.HasSuffix(e, "]") {
-			// noop
-		} else if strings.HasPrefix(e, "##") {
-			t.Tokens[i] = e[2:]
-		} else {
-			t.Tokens[i] = "\u2581" + e
-		}
-	}
-
-	kv["tokenizer.ggml.tokens"] = t.Tokens
-
-	return kv
-}
-
-func (p *bertModel) Tensors(ts []Tensor) []llm.Tensor {
-	var out []llm.Tensor
-	for _, t := range ts {
-		if slices.Contains([]string{
-			"embeddings.position_ids",
-			"pooler.dense.weight",
-			"pooler.dense.bias",
-		}, t.Name()) {
-			continue
-		}
-
-		out = append(out, llm.Tensor{
-			Name:     t.Name(),
-			Kind:     t.Kind(),
-			Shape:    t.Shape(),
-			WriterTo: t,
-		})
-	}
-
-	return out
-}
-
-func (bertModel) Replacements() []string {
-	return []string{
-		"encoder.layer", "blk",
-		"encoder.layers", "blk",
-		"embeddings.word_embeddings", "token_embd",
-		"embeddings.token_type_embeddings", "token_types",
-		"embeddings.LayerNorm", "token_embd_norm",
-		"embeddings.position_embeddings", "position_embd",
-		"attention.self.query", "attn_q",
-		"attention.self.key", "attn_k",
-		"attention.self.value", "attn_v",
-		"attention.output.dense", "attn_output",
-		"attention.output.LayerNorm", "attn_output_norm",
-		"intermediate.dense", "ffn_up",
-		"output.dense", "ffn_down",
-		"output.LayerNorm", "layer_output_norm",
-	}
-}

convert/convert_gemma.go

@@ -9,8 +9,8 @@ import (
 	"github.com/ollama/ollama/llm"
 )
 
-type gemmaModel struct {
-	ModelParameters
+type gemma struct {
+	Parameters
 	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
 	HiddenSize            uint32  `json:"hidden_size"`
 	HiddenLayers          uint32  `json:"num_hidden_layers"`
@@ -21,11 +21,12 @@ type gemmaModel struct {
 	HeadDim               uint32  `json:"head_dim"`
 }
 
-var _ ModelConverter = (*gemmaModel)(nil)
+var _ Converter = (*gemma)(nil)
 
-func (p *gemmaModel) KV(t *Tokenizer) llm.KV {
-	kv := p.ModelParameters.KV(t)
+func (p *gemma) KV(t *Tokenizer) llm.KV {
+	kv := p.Parameters.KV(t)
 	kv["general.architecture"] = "gemma"
+	kv["general.name"] = "gemma"
 	kv["gemma.context_length"] = p.MaxPositionEmbeddings
 	kv["gemma.embedding_length"] = p.HiddenSize
 	kv["gemma.block_count"] = p.HiddenLayers
@@ -42,15 +43,16 @@ func (p *gemmaModel) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p *gemmaModel) Tensors(ts []Tensor) []llm.Tensor {
+func (p *gemma) Tensors(ts []Tensor) []llm.Tensor {
 	var out []llm.Tensor
 	for _, t := range ts {
-		if strings.HasSuffix(t.Name(), "_norm.weight") {
+		name := p.tensorName(t.Name())
+		if strings.HasSuffix(name, "_norm.weight") {
 			t.SetRepacker(p.addOne)
 		}
 
 		out = append(out, llm.Tensor{
-			Name:     t.Name(),
+			Name:     name,
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),
 			WriterTo: t,
@@ -60,8 +62,8 @@ func (p *gemmaModel) Tensors(ts []Tensor) []llm.Tensor {
 	return out
 }
 
-func (p *gemmaModel) Replacements() []string {
-	return []string{
+func (p *gemma) tensorName(n string) string {
+	return strings.NewReplacer(
 		"model.embed_tokens", "token_embd",
 		"model.norm", "output_norm",
 		"model.layers", "blk",
@@ -74,10 +76,11 @@ func (p *gemmaModel) Replacements() []string {
 		"mlp.down_proj", "ffn_down",
 		"mlp.up_proj", "ffn_up",
 		"post_attention_layernorm", "ffn_norm",
-	}
+		"block_sparse_moe.gate", "ffn_inp",
+	).Replace(n)
 }
 
-func (*gemmaModel) addOne(_ string, data []float32, shape []uint64) ([]float32, error) {
+func (*gemma) addOne(_ string, data []float32, shape []uint64) ([]float32, error) {
 	n := tensor.New(tensor.WithShape(int(shape[0])), tensor.WithBacking(data))
 	ones := tensor.Ones(tensor.Float32, int(shape[0]))
 

convert/convert_gemma2.go

@@ -1,53 +0,0 @@
-package convert
-
-import (
-	"github.com/ollama/ollama/llm"
-)
-
-type gemma2Model struct {
-	gemmaModel
-	SlidingWindow         uint32  `json:"sliding_window"`
-	AttentionLogitSoftcap float32 `json:"attn_logit_softcapping"`
-	FinalLogitSoftcap     float32 `json:"final_logit_softcapping"`
-}
-
-func (p *gemma2Model) KV(t *Tokenizer) llm.KV {
-	kv := p.ModelParameters.KV(t)
-	kv["general.architecture"] = "gemma2"
-	kv["gemma2.context_length"] = p.MaxPositionEmbeddings
-	kv["gemma2.embedding_length"] = p.HiddenSize
-	kv["gemma2.block_count"] = p.HiddenLayers
-	kv["gemma2.feed_forward_length"] = p.IntermediateSize
-	kv["gemma2.attention.head_count"] = p.NumAttentionHeads
-	kv["gemma2.attention.head_count_kv"] = p.NumKeyValueHeads
-	kv["gemma2.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
-	kv["gemma2.attention.key_length"] = p.HeadDim
-	kv["gemma2.attention.value_length"] = p.HeadDim
-	kv["gemma2.attention.sliding_window"] = p.SlidingWindow
-	kv["gemma2.attn_logit_softcapping"] = p.AttentionLogitSoftcap
-	kv["gemma2.final_logit_softcapping"] = p.FinalLogitSoftcap
-	kv["tokenizer.ggml.eot_token_id"] = uint32(107)
-	kv["tokenizer.ggml.middle_token_id"] = uint32(68)
-	kv["tokenizer.ggml.prefix_token_id"] = uint32(67)
-	kv["tokenizer.ggml.suffix_token_id"] = uint32(69)
-	return kv
-}
-
-func (p *gemma2Model) Replacements() []string {
-	return []string{
-		"model.embed_tokens", "token_embd",
-		"model.norm", "output_norm",
-		"model.layers", "blk",
-		"input_layernorm", "attn_norm",
-		"self_attn.q_proj", "attn_q",
-		"self_attn.k_proj", "attn_k",
-		"self_attn.v_proj", "attn_v",
-		"self_attn.o_proj", "attn_output",
-		"mlp.gate_proj", "ffn_gate",
-		"mlp.down_proj", "ffn_down",
-		"mlp.up_proj", "ffn_up",
-		"post_attention_layernorm", "post_attention_norm",
-		"pre_feedforward_layernorm", "ffn_norm",
-		"post_feedforward_layernorm", "post_ffw_norm",
-	}
-}

convert/convert_gemma2_adapter.go

@@ -1,91 +0,0 @@
-package convert
-
-import (
-	"strings"
-
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type gemma2Adapter struct {
-	AdapterParameters
-}
-
-var _ AdapterConverter = (*gemma2Adapter)(nil)
-
-func (p *gemma2Adapter) KV(baseKV llm.KV) llm.KV {
-	kv := p.AdapterParameters.KV()
-	kv["general.architecture"] = "gemma2"
-	return kv
-}
-
-func (p *gemma2Adapter) Tensors(ts []Tensor) []llm.Tensor {
-	var out []llm.Tensor
-	for _, t := range ts {
-		shape := t.Shape()
-		if (strings.HasSuffix(t.Name(), "weight.lora_a") && shape[0] > shape[1]) ||
-			(strings.HasSuffix(t.Name(), "weight.lora_b") && shape[0] < shape[1]) {
-			shape[0], shape[1] = shape[1], shape[0]
-			t.SetRepacker(p.repack)
-		}
-
-		out = append(out, llm.Tensor{
-			Name:     t.Name(),
-			Kind:     t.Kind(),
-			Shape:    t.Shape(),
-			WriterTo: t,
-		})
-	}
-
-	return out
-}
-
-func (p *gemma2Adapter) Replacements() []string {
-	return []string{
-		"base_model.model.", "",
-		"model.layers", "blk",
-		"self_attn.q_proj", "attn_q",
-		"self_attn.k_proj", "attn_k",
-		"self_attn.v_proj", "attn_v",
-		"self_attn.o_proj", "attn_output",
-		"mlp.gate_proj", "ffn_gate",
-		"mlp.down_proj", "ffn_down",
-		"mlp.up_proj", "ffn_up",
-		"lora_A.weight", "weight.lora_a",
-		"lora_B.weight", "weight.lora_b",
-		"lora_a", "weight.lora_a",
-		"lora_b", "weight.lora_b",
-	}
-}
-
-func (p *gemma2Adapter) repack(name string, data []float32, shape []uint64) ([]float32, error) {
-	dims := []int{int(shape[1]), int(shape[0])}
-
-	n := tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
-
-	if err := n.T(1, 0); err != nil {
-		return nil, err
-	}
-
-	if err := n.Reshape(dims...); err != nil {
-		return nil, err
-	}
-
-	if err := n.Transpose(); err != nil {
-		return nil, err
-	}
-
-	ts, err := native.SelectF32(n, 1)
-	if err != nil {
-		return nil, err
-	}
-
-	var f32s []float32
-	for _, t := range ts {
-		f32s = append(f32s, t...)
-	}
-
-	return f32s, nil
-}

convert/convert_llama.go

@@ -3,7 +3,6 @@ package convert
 import (
 	"cmp"
 	"fmt"
-	"math"
 	"strings"
 
 	"github.com/pdevine/tensor"
@@ -12,8 +11,8 @@ import (
 	"github.com/ollama/ollama/llm"
 )
 
-type llamaModel struct {
-	ModelParameters
+type llama struct {
+	Parameters
 	NLayers               uint32  `json:"n_layers"`
 	NumHiddenLayers       uint32  `json:"num_hidden_layers"`
 	NLayer                uint32  `json:"n_layer"`
@@ -28,14 +27,8 @@ type llamaModel struct {
 	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
 	RopeTheta             float32 `json:"rope_theta"`
 	RopeScaling           struct {
-		Type                            string  `json:"type"`
-		RopeType                        string  `json:"rope_type"`
-		Factor                          float32 `json:"factor"`
-		LowFrequencyFactor              float32 `json:"low_freq_factor"`
-		HighFrequencyFactor             float32 `json:"high_freq_factor"`
-		OriginalMaxPositionalEmbeddings uint32  `json:"original_max_positional_embeddings"`
-
-		factors ropeFactor
+		Type   string  `json:"type"`
+		Factor float32 `json:"factor"`
 	} `json:"rope_scaling"`
 	RMSNormEPS       float32 `json:"rms_norm_eps"`
 	LayerNormEPS     float32 `json:"layer_norm_eps"`
@@ -44,11 +37,12 @@ type llamaModel struct {
 	HeadDim          uint32  `json:"head_dim"`
 }
 
-var _ ModelConverter = (*llamaModel)(nil)
+var _ Converter = (*llama)(nil)
 
-func (p *llamaModel) KV(t *Tokenizer) llm.KV {
-	kv := p.ModelParameters.KV(t)
+func (p *llama) KV(t *Tokenizer) llm.KV {
+	kv := p.Parameters.KV(t)
 	kv["general.architecture"] = "llama"
+	kv["general.name"] = "llama"
 	kv["llama.vocab_size"] = p.VocabSize
 
 	kv["llama.block_count"] = cmp.Or(p.NLayers, p.NumHiddenLayers, p.NLayer)
@@ -77,27 +71,6 @@ func (p *llamaModel) KV(t *Tokenizer) llm.KV {
 	if p.RopeScaling.Type == "linear" {
 		kv["llama.rope.scaling.type"] = p.RopeScaling.Type
 		kv["llama.rope.scaling.factor"] = p.RopeScaling.Factor
-	} else if p.RopeScaling.RopeType == "llama3" {
-		dim := p.HiddenSize / p.NumAttentionHeads
-		for i := uint32(0); i < dim; i += 2 {
-			factor := cmp.Or(p.RopeScaling.Factor, 8.0)
-			factorLow := cmp.Or(p.RopeScaling.LowFrequencyFactor, 1.0)
-			factorHigh := cmp.Or(p.RopeScaling.HighFrequencyFactor, 4.0)
-
-			original := cmp.Or(p.RopeScaling.OriginalMaxPositionalEmbeddings, 8192)
-			lambdaLow := float32(original) / factorLow
-			lambdaHigh := float32(original) / factorHigh
-
-			lambda := 2 * math.Pi * math.Pow(float64(p.RopeTheta), float64(i)/float64(dim))
-			if lambda < float64(lambdaHigh) {
-				p.RopeScaling.factors = append(p.RopeScaling.factors, 1.0)
-			} else if lambda > float64(lambdaLow) {
-				p.RopeScaling.factors = append(p.RopeScaling.factors, factor)
-			} else {
-				smooth := (float32(original)/float32(lambda) - factorLow) / (factorHigh - factorLow)
-				p.RopeScaling.factors = append(p.RopeScaling.factors, 1.0/((1-smooth)/factor+smooth))
-			}
-		}
 	}
 
 	if p.NumKeyValueHeads > 0 {
@@ -120,26 +93,17 @@ func (p *llamaModel) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p *llamaModel) Tensors(ts []Tensor) []llm.Tensor {
+func (p *llama) Tensors(ts []Tensor) []llm.Tensor {
 	var out []llm.Tensor
-
-	if p.RopeScaling.factors != nil {
-		out = append(out, llm.Tensor{
-			Name:     "rope_freqs.weight",
-			Kind:     0,
-			Shape:    []uint64{uint64(len(p.RopeScaling.factors))},
-			WriterTo: p.RopeScaling.factors,
-		})
-	}
-
 	for _, t := range ts {
-		if strings.HasSuffix(t.Name(), "attn_q.weight") ||
-			strings.HasSuffix(t.Name(), "attn_k.weight") {
+		name := p.tensorName(t.Name())
+		if strings.HasSuffix(name, "attn_q.weight") ||
+			strings.HasSuffix(name, "attn_k.weight") {
 			t.SetRepacker(p.repack)
 		}
 
 		out = append(out, llm.Tensor{
-			Name:     t.Name(),
+			Name:     name,
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),
 			WriterTo: t,
@@ -149,8 +113,8 @@ func (p *llamaModel) Tensors(ts []Tensor) []llm.Tensor {
 	return out
 }
 
-func (p *llamaModel) Replacements() []string {
-	return []string{
+func (p *llama) tensorName(n string) string {
+	return strings.NewReplacer(
 		"lm_head", "output",
 		"model.embed_tokens", "token_embd",
 		"model.norm", "output_norm",
@@ -164,19 +128,21 @@ func (p *llamaModel) Replacements() []string {
 		"mlp.down_proj", "ffn_down",
 		"mlp.up_proj", "ffn_up",
 		"post_attention_layernorm", "ffn_norm",
-	}
+		// mixtral
+		"block_sparse_moe.gate", "ffn_gate_inp",
+	).Replace(n)
 }
 
-func (p *llamaModel) repack(name string, data []float32, shape []uint64) ([]float32, error) {
+func (p *llama) repack(name string, data []float32, shape []uint64) ([]float32, error) {
 	var dims []int
 	for _, dim := range shape {
 		dims = append(dims, int(dim))
 	}
 
 	var heads uint32
-	if strings.HasSuffix(name, "attn_q.weight") {
+	if strings.HasSuffix(name, "q_proj.weight") {
 		heads = p.NumAttentionHeads
-	} else if strings.HasSuffix(name, "attn_k.weight") {
+	} else if strings.HasSuffix(name, "k_proj.weight") {
 		heads = cmp.Or(p.NumKeyValueHeads, p.NumAttentionHeads)
 	} else {
 		return nil, fmt.Errorf("unknown tensor for repack: %s", name)

convert/convert_llama_adapter.go

@@ -1,169 +0,0 @@
-package convert
-
-import (
-	"cmp"
-	"strings"
-
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type llamaAdapter struct {
-	AdapterParameters
-	NumAttentionHeads uint32 `json:"num_attention_heads"`
-	NumKeyValueHeads  uint32 `json:"num_key_value_heads"`
-}
-
-var _ AdapterConverter = (*llamaAdapter)(nil)
-
-func (p *llamaAdapter) KV(baseKV llm.KV) llm.KV {
-	kv := p.AdapterParameters.KV()
-	kv["general.architecture"] = "llama"
-	kv["llama.attention.head_count"] = baseKV["llama.attention.head_count"]
-	kv["llama.attention.head_count_kv"] = baseKV["llama.attention.head_count_kv"]
-
-	p.NumAttentionHeads = baseKV["llama.attention.head_count"].(uint32)
-
-	return kv
-}
-
-func (p *llamaAdapter) Tensors(ts []Tensor) []llm.Tensor {
-	var out []llm.Tensor
-	for _, t := range ts {
-		shape := t.Shape()
-		if (strings.HasSuffix(t.Name(), "weight.lora_a") && shape[0] > shape[1]) ||
-			(strings.HasSuffix(t.Name(), "weight.lora_b") && shape[0] < shape[1]) {
-			shape[0], shape[1] = shape[1], shape[0]
-			t.SetRepacker(p.repackAndTranspose)
-		} else {
-			t.SetRepacker(p.repack)
-		}
-
-		out = append(out, llm.Tensor{
-			Name:     t.Name(),
-			Kind:     t.Kind(),
-			Shape:    shape,
-			WriterTo: t,
-		})
-	}
-
-	return out
-}
-
-func (p *llamaAdapter) Replacements() []string {
-	return []string{
-		"base_model.model.", "",
-		"model.layers", "blk",
-		"self_attn.q_proj", "attn_q",
-		"self_attn.k_proj", "attn_k",
-		"self_attn.v_proj", "attn_v",
-		"self_attn.o_proj", "attn_output",
-		"mlp.gate_proj", "ffn_gate",
-		"mlp.down_proj", "ffn_down",
-		"mlp.up_proj", "ffn_up",
-		"lora_A.weight", "weight.lora_a",
-		"lora_B.weight", "weight.lora_b",
-		"lora_a", "weight.lora_a",
-		"lora_b", "weight.lora_b",
-	}
-}
-
-func (p *llamaAdapter) repack(name string, data []float32, shape []uint64) ([]float32, error) {
-	dims := []int{int(shape[1]), int(shape[0])}
-
-	var heads uint32
-	if strings.HasSuffix(name, "attn_q.weight.lora_a") {
-		heads = p.NumAttentionHeads
-	} else if strings.HasSuffix(name, "attn_k.weight.lora_a") {
-		heads = cmp.Or(p.NumKeyValueHeads, p.NumAttentionHeads)
-	} else {
-		return data, nil
-	}
-
-	n := tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
-
-	if err := n.Reshape(append([]int{int(heads), 2, dims[0] / int(heads) / 2}, dims[1:]...)...); err != nil {
-		return nil, err
-	}
-
-	if err := n.T(0, 2, 1, 3); err != nil {
-		return nil, err
-	}
-
-	if err := n.Reshape(dims...); err != nil {
-		return nil, err
-	}
-
-	if err := n.Transpose(); err != nil {
-		return nil, err
-	}
-
-	ts, err := native.SelectF32(n, 1)
-	if err != nil {
-		return nil, err
-	}
-
-	var f32s []float32
-	for _, t := range ts {
-		f32s = append(f32s, t...)
-	}
-
-	return f32s, nil
-}
-
-func (p *llamaAdapter) repackAndTranspose(name string, data []float32, shape []uint64) ([]float32, error) {
-	dims := []int{int(shape[1]), int(shape[0])}
-
-	n := tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
-
-	var heads uint32
-	if strings.HasSuffix(name, "attn_q.weight.lora_a") {
-		heads = p.NumAttentionHeads
-	} else if strings.HasSuffix(name, "attn_k.weight.lora_a") {
-		heads = cmp.Or(p.NumKeyValueHeads, p.NumAttentionHeads)
-	}
-
-	if heads > 0 {
-		if err := n.Reshape(append([]int{int(heads), 2, dims[0] / int(heads) / 2}, dims[1:]...)...); err != nil {
-			return nil, err
-		}
-
-		if err := n.T(0, 2, 1, 3); err != nil {
-			return nil, err
-		}
-
-		if err := n.Reshape(dims...); err != nil {
-			return nil, err
-		}
-
-		if err := n.Transpose(); err != nil {
-			return nil, err
-		}
-	}
-
-	if err := n.T(1, 0); err != nil {
-		return nil, err
-	}
-
-	if err := n.Reshape(dims...); err != nil {
-		return nil, err
-	}
-
-	if err := n.Transpose(); err != nil {
-		return nil, err
-	}
-
-	ts, err := native.SelectF32(n, 1)
-	if err != nil {
-		return nil, err
-	}
-
-	var f32s []float32
-	for _, t := range ts {
-		f32s = append(f32s, t...)
-	}
-
-	return f32s, nil
-}

convert/convert_mixtral.go

@@ -9,14 +9,16 @@ import (
 	"github.com/ollama/ollama/llm"
 )
 
-type mixtralModel struct {
-	llamaModel
+type mixtral struct {
+	llama
 	NumLocalExperts    uint32 `json:"num_local_experts"`
 	NumExpertsPerToken uint32 `json:"num_experts_per_tok"`
 }
 
-func (p *mixtralModel) KV(t *Tokenizer) llm.KV {
-	kv := p.llamaModel.KV(t)
+var _ Converter = (*mixtral)(nil)
+
+func (p *mixtral) KV(t *Tokenizer) llm.KV {
+	kv := p.llama.KV(t)
 
 	if p.NumLocalExperts > 0 {
 		kv["llama.expert_count"] = p.NumLocalExperts
@@ -29,7 +31,7 @@ func (p *mixtralModel) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p *mixtralModel) Tensors(ts []Tensor) []llm.Tensor {
+func (p *mixtral) Tensors(ts []Tensor) []llm.Tensor {
 	oldnew := []string{
 		"model.layers", "blk",
 		"w1", "ffn_gate_exps",
@@ -67,14 +69,7 @@ func (p *mixtralModel) Tensors(ts []Tensor) []llm.Tensor {
 		})
 	}
 
-	return append(out, p.llamaModel.Tensors(ts)...)
-}
-
-func (p *mixtralModel) Replacements() []string {
-	return append(
-		p.llamaModel.Replacements(),
-		"block_sparse_moe.gate", "ffn_gate_inp",
-	)
+	return append(out, p.llama.Tensors(ts)...)
 }
 
 type experts []Tensor

convert/convert_phi3.go

@@ -11,8 +11,8 @@ import (
 	"github.com/ollama/ollama/llm"
 )
 
-type phi3Model struct {
-	ModelParameters
+type phi3 struct {
+	Parameters
 	NumHiddenLayers   uint32  `json:"num_hidden_layers"`
 	NLayers           uint32  `json:"n_layers"`
 	HiddenSize        uint32  `json:"hidden_size"`
@@ -35,11 +35,12 @@ type phi3Model struct {
 	SlidingWindow                 uint32  `json:"sliding_window"`
 }
 
-var _ ModelConverter = (*phi3Model)(nil)
+var _ Converter = (*phi3)(nil)
 
-func (p *phi3Model) KV(t *Tokenizer) llm.KV {
-	kv := p.ModelParameters.KV(t)
+func (p *phi3) KV(t *Tokenizer) llm.KV {
+	kv := p.Parameters.KV(t)
 	kv["general.architecture"] = "phi3"
+	kv["general.name"] = "phi3"
 	kv["phi3.context_length"] = p.MaxPositionEmbeddings
 	kv["phi3.embedding_length"] = cmp.Or(p.HiddenSize, p.NEmbd)
 	kv["phi3.feed_forward_length"] = p.IntermediateSize
@@ -68,12 +69,13 @@ func (p *phi3Model) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p *phi3Model) Tensors(ts []Tensor) []llm.Tensor {
+func (p *phi3) Tensors(ts []Tensor) []llm.Tensor {
 	var addRopeFactors sync.Once
 
 	out := make([]llm.Tensor, 0, len(ts)+2)
 	for _, t := range ts {
-		if strings.HasPrefix(t.Name(), "blk.0.") {
+		name := p.tensorName(t.Name())
+		if strings.HasPrefix(name, "blk.0.") {
 			addRopeFactors.Do(func() {
 				out = append(out, llm.Tensor{
 					Name:     "rope_factors_long.weight",
@@ -90,7 +92,7 @@ func (p *phi3Model) Tensors(ts []Tensor) []llm.Tensor {
 		}
 
 		out = append(out, llm.Tensor{
-			Name:     t.Name(),
+			Name:     name,
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),
 			WriterTo: t,
@@ -100,8 +102,8 @@ func (p *phi3Model) Tensors(ts []Tensor) []llm.Tensor {
 	return out
 }
 
-func (p *phi3Model) Replacements() []string {
-	return []string{
+func (p *phi3) tensorName(n string) string {
+	return strings.NewReplacer(
 		"lm_head", "output",
 		"model.embed_tokens", "token_embd",
 		"model.norm", "output_norm",
@@ -112,7 +114,7 @@ func (p *phi3Model) Replacements() []string {
 		"mlp.down_proj", "ffn_down",
 		"mlp.gate_up_proj", "ffn_up",
 		"post_attention_layernorm", "ffn_norm",
-	}
+	).Replace(n)
 }
 
 type ropeFactor []float32

convert/convert_test.go

@@ -1,9 +1,7 @@
 package convert
 
 import (
-	"bytes"
 	"crypto/sha256"
-	"encoding/binary"
 	"encoding/hex"
 	"encoding/json"
 	"flag"
@@ -15,7 +13,6 @@ import (
 	"os"
 	"path/filepath"
 	"slices"
-	"strings"
 	"testing"
 
 	"golang.org/x/exp/maps"
@@ -23,13 +20,7 @@ import (
 	"github.com/ollama/ollama/llm"
 )
 
-type tensorData struct {
-	Offsets []int  `json:"data_offsets"`
-	Type    string `json:"dtype"`
-	Shape   []int  `json:"shape"`
-}
-
-func convertFull(t *testing.T, fsys fs.FS) (*os.File, llm.KV, *llm.Tensors) {
+func convertFull(t *testing.T, fsys fs.FS) (*os.File, llm.KV, llm.Tensors) {
 	t.Helper()
 
 	f, err := os.CreateTemp(t.TempDir(), "f16")
@@ -38,7 +29,7 @@ func convertFull(t *testing.T, fsys fs.FS) (*os.File, llm.KV, *llm.Tensors) {
 	}
 	defer f.Close()
 
-	if err := ConvertModel(fsys, f); err != nil {
+	if err := Convert(fsys, f); err != nil {
 		t.Fatal(err)
 	}
 
@@ -60,34 +51,6 @@ func convertFull(t *testing.T, fsys fs.FS) (*os.File, llm.KV, *llm.Tensors) {
 	return r, m.KV(), m.Tensors()
 }
 
-func generateResultsJSON(t *testing.T, f *os.File, kv llm.KV, tensors *llm.Tensors) map[string]string {
-	actual := make(map[string]string)
-	for k, v := range kv {
-		if s, ok := v.(json.Marshaler); !ok {
-			actual[k] = fmt.Sprintf("%v", v)
-		} else {
-			bts, err := json.Marshal(s)
-			if err != nil {
-				t.Fatal(err)
-			}
-
-			actual[k] = fmt.Sprintf("%x", sha256.Sum256(bts))
-		}
-	}
-
-	for _, tensor := range tensors.Items {
-		sha256sum := sha256.New()
-		sr := io.NewSectionReader(f, int64(tensors.Offset+tensor.Offset), int64(tensor.Size()))
-		if _, err := io.Copy(sha256sum, sr); err != nil {
-			t.Fatal(err)
-		}
-
-		actual[tensor.Name] = hex.EncodeToString(sha256sum.Sum(nil))
-	}
-
-	return actual
-}
-
 func TestMain(m *testing.M) {
 	var level slog.Level
 	flag.TextVar(&level, "level", slog.LevelInfo, "log level")
@@ -96,18 +59,14 @@ func TestMain(m *testing.M) {
 	os.Exit(m.Run())
 }
 
-func TestConvertModel(t *testing.T) {
+func TestConvertFull(t *testing.T) {
 	cases := []string{
 		"Meta-Llama-3-8B-Instruct",
-		"Meta-Llama-3.1-8B-Instruct",
 		"Mistral-7B-Instruct-v0.2",
 		"Mixtral-8x7B-Instruct-v0.1",
 		"gemma-2b-it",
-		"gemma-2-2b-it",
 		// microsoft/Phi-3-mini-128-instruct@d548c233192db00165d842bf8edff054bb3212f8
 		"Phi-3-mini-128k-instruct",
-		"all-MiniLM-L6-v2",
-		"gemma-2-9b-it",
 	}
 
 	for i := range cases {
@@ -123,7 +82,29 @@ func TestConvertModel(t *testing.T) {
 			}
 
 			f, kv, tensors := convertFull(t, os.DirFS(p))
-			actual := generateResultsJSON(t, f, kv, tensors)
+			actual := make(map[string]string)
+			for k, v := range kv {
+				if s, ok := v.(json.Marshaler); !ok {
+					actual[k] = fmt.Sprintf("%v", v)
+				} else {
+					bts, err := json.Marshal(s)
+					if err != nil {
+						t.Fatal(err)
+					}
+
+					actual[k] = fmt.Sprintf("%x", sha256.Sum256(bts))
+				}
+			}
+
+			for _, tensor := range tensors.Items {
+				sha256sum := sha256.New()
+				sr := io.NewSectionReader(f, int64(tensors.Offset+tensor.Offset), int64(tensor.Size()))
+				if _, err := io.Copy(sha256sum, sr); err != nil {
+					t.Fatal(err)
+				}
+
+				actual[tensor.Name] = hex.EncodeToString(sha256sum.Sum(nil))
+			}
 
 			expectFile, err := os.Open(filepath.Join("testdata", fmt.Sprintf("%s.json", tt)))
 			if err != nil {
@@ -147,330 +128,3 @@ func TestConvertModel(t *testing.T) {
 		})
 	}
 }
-
-func TestConvertInvalidTensorNames(t *testing.T) {
-	f, err := os.CreateTemp(t.TempDir(), "testmodel")
-	if err != nil {
-		t.Fatal(err)
-	}
-	defer f.Close()
-
-	tempDir := t.TempDir()
-
-	td := map[string]*tensorData{}
-	offset := 4096
-
-	td["model.layers.0.self_attn.q_proj.weight"] = &tensorData{
-		Offsets: []int{0, offset},
-		Type:    "F32",
-		Shape:   []int{4096, 4096},
-	}
-	td["blk.0.attn_q.weight"] = &tensorData{
-		Offsets: []int{offset, offset * 2},
-		Type:    "F32",
-		Shape:   []int{4096, 4096},
-	}
-	generateSafetensorTestData(t, tempDir, td)
-
-	err = ConvertModel(os.DirFS(tempDir), f)
-	if err == nil || !strings.HasPrefix(err.Error(), "duplicate tensor name") {
-		t.Errorf("expected error but didn't get one")
-	}
-}
-
-func TestConvertInvalidDatatype(t *testing.T) {
-	f, err := os.CreateTemp(t.TempDir(), "testmodel")
-	if err != nil {
-		t.Fatal(err)
-	}
-	defer f.Close()
-
-	tempDir := t.TempDir()
-
-	td := map[string]*tensorData{}
-	offset := 4096 * 14336
-
-	td["model.layers.0.mlp.down_proj.weight"] = &tensorData{
-		Offsets: []int{0, offset},
-		Type:    "I8",
-		Shape:   []int{4096, 14336},
-	}
-	td["model.layers.0.mlp.down_proj.weight_format"] = &tensorData{
-		Offsets: []int{offset, offset},
-		Type:    "U8",
-		Shape:   []int{},
-	}
-	generateSafetensorTestData(t, tempDir, td)
-
-	err = ConvertModel(os.DirFS(tempDir), f)
-	if err == nil || err.Error() != "unsupported safetensors model" {
-		t.Errorf("expected error but didn't get one")
-	}
-}
-
-func generateSafetensorTestData(t *testing.T, tempDir string, tensorData map[string]*tensorData) {
-	data, err := json.Marshal(tensorData)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	var buf bytes.Buffer
-
-	l := int64(len(data))
-	err = binary.Write(&buf, binary.LittleEndian, l)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	_, err = buf.Write(data)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	fdata, err := os.Create(filepath.Join(tempDir, "model-00001-of-00001.safetensors"))
-	if err != nil {
-		t.Fatal(err)
-	}
-	defer fdata.Close()
-
-	_, err = fdata.Write(buf.Bytes())
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	configData := `
-{
-  "architectures": [
-    "LlamaForCausalLM"
-  ]
-}
-`
-
-	f, err := os.Create(filepath.Join(tempDir, "config.json"))
-	if err != nil {
-		t.Fatal(err)
-	}
-	defer f.Close()
-
-	_, err = f.WriteString(configData)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	tokenizerData := `
-{
-}
-`
-
-	f, err = os.Create(filepath.Join(tempDir, "tokenizer.json"))
-	if err != nil {
-		t.Fatal(err)
-	}
-	defer f.Close()
-
-	_, err = f.WriteString(tokenizerData)
-	if err != nil {
-		t.Fatal(err)
-	}
-}
-
-func TestConvertAdapter(t *testing.T) {
-	type AdapterCase struct {
-		Name     string
-		BaseKV   map[string]any
-		Expected map[string]string
-	}
-
-	cases := []AdapterCase{
-		{
-			Name: "discollama",
-			BaseKV: map[string]any{
-				"general.architecture":          "llama",
-				"llama.attention.head_count":    uint32(32),
-				"llama.attention.head_count_kv": uint32(8),
-			},
-			Expected: map[string]string{
-				"general.architecture":          "llama",
-				"general.file_type":             "1",
-				"general.parameter_count":       "106496",
-				"general.type":                  "adapter",
-				"general.version":               "v0.2",
-				"adapter.lora.alpha":            "16",
-				"adapter.type":                  "lora",
-				"llama.attention.head_count":    "32",
-				"llama.attention.head_count_kv": "8",
-				"blk.31.attn_q.weight.lora_a":   "0eb3318b02cd313429bcc7621b539fdbb10240fea190c56c9e5f93fcd37a4e50",
-				"blk.31.attn_q.weight.lora_b":   "0eb3318b02cd313429bcc7621b539fdbb10240fea190c56c9e5f93fcd37a4e50",
-				"blk.31.attn_v.weight.lora_a":   "0eb3318b02cd313429bcc7621b539fdbb10240fea190c56c9e5f93fcd37a4e50",
-				"blk.31.attn_v.weight.lora_b":   "071dcafe89df065d6e1c935ecb8fdf6479b3c202eb912e7da938597673ff5857",
-			},
-		},
-	}
-
-	for _, c := range cases {
-		t.Run(c.Name, func(t *testing.T) {
-			t.Parallel()
-
-			f, err := os.CreateTemp(t.TempDir(), "f16")
-			if err != nil {
-				t.Fatal(err)
-			}
-			defer f.Close()
-
-			tempDir := t.TempDir()
-			generateLoraTestData(t, tempDir)
-
-			if err = ConvertAdapter(os.DirFS(tempDir), f, c.BaseKV); err != nil {
-				t.Fatal(err)
-			}
-
-			r, err := os.Open(f.Name())
-			if err != nil {
-				t.Fatal(err)
-			}
-			defer r.Close()
-
-			m, _, err := llm.DecodeGGML(r, math.MaxInt)
-			if err != nil {
-				t.Fatal(err)
-			}
-
-			if _, err := r.Seek(0, io.SeekStart); err != nil {
-				t.Fatal(err)
-			}
-
-			actual := generateResultsJSON(t, r, m.KV(), m.Tensors())
-
-			keys := maps.Keys(c.Expected)
-			slices.Sort(keys)
-			for _, k := range keys {
-				if v, ok := actual[k]; !ok {
-					t.Errorf("missing %s", k)
-				} else if v != c.Expected[k] {
-					t.Errorf("unexpected %s: want %s, got %s", k, c.Expected[k], v)
-				}
-			}
-		})
-	}
-}
-
-func generateLoraTestData(t *testing.T, tempDir string) {
-	offset := 4096 * 8 * 4
-
-	td := map[string]*tensorData{"__metadata__": nil}
-	td["model.layers.31.self_attn.q_proj.lora_a"] = &tensorData{
-		Offsets: []int{0, offset},
-		Type:    "F32",
-		Shape:   []int{4096, 8},
-	}
-	td["model.layers.31.self_attn.q_proj.lora_b"] = &tensorData{
-		Offsets: []int{offset, offset * 2},
-		Type:    "F32",
-		Shape:   []int{8, 4096},
-	}
-	td["model.layers.31.self_attn.v_proj.lora_a"] = &tensorData{
-		Offsets: []int{offset * 2, offset * 3},
-		Type:    "F32",
-		Shape:   []int{4096, 8},
-	}
-	td["model.layers.31.self_attn.v_proj.lora_b"] = &tensorData{
-		Offsets: []int{offset * 3, offset*3 + 8*1024*4},
-		Type:    "F32",
-		Shape:   []int{8, 1024},
-	}
-
-	data, err := json.Marshal(td)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	var buf bytes.Buffer
-
-	l := int64(len(data))
-	err = binary.Write(&buf, binary.LittleEndian, l)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	_, err = buf.Write(data)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	// write some data for the tensors
-
-	ones := make([]float32, 4096*8)
-	for i := range ones {
-		ones[i] = float32(1)
-	}
-
-	for range 3 {
-		err = binary.Write(&buf, binary.LittleEndian, ones)
-		if err != nil {
-			t.Fatal(err)
-		}
-	}
-
-	ones = make([]float32, 1024*8)
-	for i := range ones {
-		ones[i] = float32(1)
-	}
-
-	err = binary.Write(&buf, binary.LittleEndian, ones)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	fdata, err := os.Create(filepath.Join(tempDir, "adapters.safetensors"))
-	if err != nil {
-		t.Fatal(err)
-	}
-	defer fdata.Close()
-
-	_, err = fdata.Write(buf.Bytes())
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	configData := `
-{
-    "adapter_path": "adapters-test",
-    "batch_size": 8,
-    "config": "config-tiny.json",
-    "data": "../discollama-completion",
-    "grad_checkpoint": null,
-    "iters": 1000,
-    "learning_rate": 1e-05,
-    "lora_layers": 1,
-    "lora_parameters": {
-        "rank": 8,
-        "alpha": 16,
-        "dropout": 0.0,
-        "scale": 2.0
-    },
-    "lr_schedule": null,
-    "max_seq_length": 2048,
-    "model": "/Users/pdevine/git/Meta-Llama-3-8B-Instruct",
-    "resume_adapter_file": null,
-    "save_every": 100,
-    "seed": 0,
-    "steps_per_eval": 200,
-    "steps_per_report": 10,
-    "test": false,
-    "test_batches": 500,
-    "train": true,
-    "use_dora": false,
-    "val_batches": 25
-}
-`
-	f, err := os.Create(filepath.Join(tempDir, "adapter_config.json"))
-	if err != nil {
-		t.Fatal(err)
-	}
-	defer f.Close()
-
-	_, err = f.WriteString(configData)
-	if err != nil {
-		t.Fatal(err)
-	}
-}

convert/reader.go

@@ -35,9 +35,7 @@ const (
 )
 
 func (t tensorBase) Kind() uint32 {
-	if strings.HasSuffix(t.name, ".ffn_gate_inp.weight") ||
-		t.name == "token_types.weight" {
-		// these tensors are always F32
+	if strings.HasSuffix(t.name, ".block_sparse_moe.gate.weight") {
 		return 0
 	}
 
@@ -57,15 +55,13 @@ func (t *tensorBase) SetRepacker(fn repacker) {
 
 type repacker func(string, []float32, []uint64) ([]float32, error)
 
-func parseTensors(fsys fs.FS, replacer *strings.Replacer) ([]Tensor, error) {
+func parseTensors(fsys fs.FS) ([]Tensor, error) {
 	patterns := []struct {
 		Pattern string
-		Func    func(fs.FS, *strings.Replacer, ...string) ([]Tensor, error)
+		Func    func(fs.FS, ...string) ([]Tensor, error)
 	}{
 		{"model-*-of-*.safetensors", parseSafetensors},
 		{"model.safetensors", parseSafetensors},
-		{"adapters.safetensors", parseSafetensors},
-		{"adapter_model.safetensors", parseSafetensors},
 		{"pytorch_model-*-of-*.bin", parseTorch},
 		{"pytorch_model.bin", parseTorch},
 		{"consolidated.*.pth", parseTorch},
@@ -78,7 +74,7 @@ func parseTensors(fsys fs.FS, replacer *strings.Replacer) ([]Tensor, error) {
 		}
 
 		if len(matches) > 0 {
-			return pattern.Func(fsys, replacer, matches...)
+			return pattern.Func(fsys, matches...)
 		}
 	}
 

convert/reader_safetensors.go

@@ -4,12 +4,10 @@ import (
 	"bytes"
 	"encoding/binary"
 	"encoding/json"
-	"errors"
 	"fmt"
 	"io"
 	"io/fs"
 	"slices"
-	"strings"
 
 	"github.com/d4l3k/go-bfloat16"
 	"github.com/x448/float16"
@@ -22,7 +20,7 @@ type safetensorMetadata struct {
 	Offsets []int64  `json:"data_offsets"`
 }
 
-func parseSafetensors(fsys fs.FS, replacer *strings.Replacer, ps ...string) ([]Tensor, error) {
+func parseSafetensors(fsys fs.FS, ps ...string) ([]Tensor, error) {
 	var ts []Tensor
 	for _, p := range ps {
 		f, err := fsys.Open(p)
@@ -49,19 +47,8 @@ func parseSafetensors(fsys fs.FS, replacer *strings.Replacer, ps ...string) ([]T
 		keys := maps.Keys(headers)
 		slices.Sort(keys)
 
-		names := make(map[string]struct{}, len(keys))
-
 		for _, key := range keys {
 			if value := headers[key]; value.Type != "" {
-				// bitsandbytes quantized models are unsupported
-				if len(value.Shape) == 0 {
-					return nil, errors.New("unsupported safetensors model")
-				}
-				ggufName := replacer.Replace(key)
-				if _, ok := names[ggufName]; ok {
-					return nil, fmt.Errorf("duplicate tensor name '%s' was found for this model", ggufName)
-				}
-				names[ggufName] = struct{}{}
 				ts = append(ts, safetensor{
 					fs:     fsys,
 					path:   p,
@@ -69,7 +56,7 @@ func parseSafetensors(fsys fs.FS, replacer *strings.Replacer, ps ...string) ([]T
 					offset: safetensorsPad(n, value.Offsets[0]),
 					size:   safetensorsPad(n, value.Offsets[1]) - safetensorsPad(n, value.Offsets[0]),
 					tensorBase: &tensorBase{
-						name:  ggufName,
+						name:  key,
 						shape: value.Shape,
 					},
 				})

convert/reader_torch.go

@@ -3,13 +3,12 @@ package convert
 import (
 	"io"
 	"io/fs"
-	"strings"
 
 	"github.com/nlpodyssey/gopickle/pytorch"
 	"github.com/nlpodyssey/gopickle/types"
 )
 
-func parseTorch(fsys fs.FS, replacer *strings.Replacer, ps ...string) ([]Tensor, error) {
+func parseTorch(fsys fs.FS, ps ...string) ([]Tensor, error) {
 	var ts []Tensor
 	for _, p := range ps {
 		pt, err := pytorch.Load(p)
@@ -28,7 +27,7 @@ func parseTorch(fsys fs.FS, replacer *strings.Replacer, ps ...string) ([]Tensor,
 			ts = append(ts, torch{
 				storage: t.(*pytorch.Tensor).Source,
 				tensorBase: &tensorBase{
-					name:  replacer.Replace(k.(string)),
+					name:  k.(string),
 					shape: shape,
 				},
 			})

convert/testdata/Meta-Llama-3.1-8B-Instruct.json

@@ -1,3 +0,0 @@
-{
-  "rope_freqs.weight": "80fd5efb2f729381785b293a091a268cfeceb0079167f6ece9b07070e662b222"
-}

convert/testdata/all-MiniLM-L6-v2.json

@@ -1,124 +0,0 @@
-{
-  "general.architecture": "bert",
-  "general.file_type": "1",
-  "general.quantization_version": "2",
-  "bert.attention.causal": "false",
-  "bert.attention.head_count": "12",
-  "bert.attention.layer_norm_epsilon": "1e-12",
-  "bert.block_count": "6",
-  "bert.context_length": "512",
-  "bert.embedding_length": "384",
-  "bert.feed_forward_length": "1536",
-  "bert.pooling_type": "1",
-  "tokenizer.ggml.model": "bert",
-  "tokenizer.ggml.padding_token_id": "0",
-  "tokenizer.ggml.unknown_token_id": "100",
-  "tokenizer.ggml.cls_token_id": "101",
-  "tokenizer.ggml.seperator_token_id": "102",
-  "tokenizer.ggml.mask_token_id": "103",
-  "tokenizer.ggml.token_type_count": "2",
-  "tokenizer.ggml.scores": "6db964fe67338aca57790481a390121ff3dd643eebe49f7dd308029ad99abb6f",
-  "tokenizer.ggml.token_type": "98d247c5404b6b18f05f133b92dd56edf6efefefac326794b00d7b351f6c5aa1",
-  "tokenizer.ggml.tokens": "9efe405e229a45ff9916f54c475d151d2200cd2ab0006f347abfb069cf096c86",
-  "token_embd.weight": "8c1ee80a9ea4f65aa385ba30112010068af3d209bebc6e149d3d4589c2cd0a5a",
-  "position_embd.weight": "6c516f0b1c4e2388ab90394dd80ad69e4e4509b890982fc3408108ae66210eb6",
-  "token_types.weight": "f879f8e422ed211948f28b560d3c5e17aae7993f063b51196a28cf5c0fb3da21",
-  "token_embd_norm.weight": "75076e095d717aab96f8b6beeee503c27940d9a76f2b891a0e3de72f8a6043e4",
-  "token_embd_norm.bias": "298735285ffe944e1bf03e5d35c7280326b85cf121bde9874f1af5dc51ab939d",
-  "blk.0.attn_q.weight": "ab0923ce4c1549175112dcdfcc860fe30137f991e03ea6857fb5993670adaf6c",
-  "blk.0.attn_q.bias": "a3ec29551dabf976e1d34256b8ab5ab7b758f3ed9742c3cafdbd984d5441df62",
-  "blk.0.attn_k.weight": "4c1038a6d035c3e9ffed7fa672b614627814752503755fbad0cfb76a41ad71ba",
-  "blk.0.attn_k.bias": "e0363930eb588d91816aa3d230bb03b6e2551c165117b80b8d60397413819ef9",
-  "blk.0.attn_v.weight": "425e2e53e3f00ce98d29c3e6a161eb55d3e6ae0d96fdb9f6242d1c4fd6eef4b3",
-  "blk.0.attn_v.bias": "6579173a1e65ee124fbd0bd53cbdca4225515b4f2c5f18fb1bfd000f5978f9bb",
-  "blk.0.attn_output.weight": "a6d70a08cd7164de5d12af65d86d657c3db35aaecde778b2b3fda9193c4c9802",
-  "blk.0.attn_output.bias": "2b8d12c4f9a9c5bfaa29c597839568f6e0525cb41eeaf64ddeb6bd84dfeb9701",
-  "blk.0.attn_output_norm.weight": "bbe6e502a473228b525aeed26cc31b7db123ad63bdc5a6eebac6ea70b8b51d62",
-  "blk.0.attn_output_norm.bias": "36eaacaf0007c5c62daea97aab0115390c0682914f78482e37eb76885f4b7a50",
-  "blk.0.ffn_up.weight": "24654561c76ce387d125759ba843f06b904ef721fcceaeff6ccc62180a48e874",
-  "blk.0.ffn_up.bias": "fd3f0126aa1d95768fa60eb6f4ab8a2763cfcb7e5405f35b92353031d86f4d34",
-  "blk.0.ffn_down.weight": "97a829763a6a5bf3329ceb4d39c424ba4787d61653a5b0bbd1f84782e4d4e0ca",
-  "blk.0.ffn_down.bias": "7aa980c30ae8b4ee7f69df28808dbf5c431f56ccc4a80340f644a0419f16c054",
-  "blk.0.layer_output_norm.weight": "ef30dad4c2a083ae1ff5039a2a6cda60ecc89bf1e486a6f8c0d15f50589603f8",
-  "blk.0.layer_output_norm.bias": "8b1b77e67568b1bce43fc476de1b177c53ff688d66beb66995e8eb3dc290da8a",
-  "blk.1.attn_q.weight": "284331622a1f6f9b87ccee4f652bd66a394ca493c4d93be4d1844e4f6159ad10",
-  "blk.1.attn_q.bias": "e24ebd4860330e08f6bfdd077a82db0bee33f4c8846cf1db26327a34754c7069",
-  "blk.1.attn_k.weight": "729dd0d555544b5bd0f7580b3c8b384256b974605f0e7487b95f295aa032997d",
-  "blk.1.attn_k.bias": "2aa51a828a858f35473f54477583fea54ce2ccc34ea60fbd1d228fbe9bca827f",
-  "blk.1.attn_v.weight": "6be304671cc311d5ca5c103f2b51467ee800c589bc5b8101e09ff5aed1f68c21",
-  "blk.1.attn_v.bias": "43bcbab78a8819e07f723bc9e5b737b71e87a7594f15234e882b63e327a64199",
-  "blk.1.attn_output.weight": "15ec8a1a12b26c9976445308a09f748ab0e4bef0f583d13ab08c3129f8738d73",
-  "blk.1.attn_output.bias": "dac2146f4baa6ed16f6c0dc7443831fb7ec79bedcceafd80d1a4b628a1bb072d",
-  "blk.1.attn_output_norm.weight": "d2151eb33bffac536787a4c9a5d2b31c7a80b17c4611877842a3cce2cd6e98d8",
-  "blk.1.attn_output_norm.bias": "31e1b779716dafb855d2cf5631ee168a0ccf372eb9c6ea6091f66fa97a9b9d2d",
-  "blk.1.ffn_up.weight": "a57547fc3fc3b77406f5cdcb0c87af9bc184701f175c39c1f35297826fce3cc7",
-  "blk.1.ffn_up.bias": "123be6d541d086202913c75d878c54d59a749f3af7b58f7ef9eb9e7c62a24c9a",
-  "blk.1.ffn_down.weight": "cfdb79788377e5cbded8790cd41b9e66c397ecab75474071fcd7cf32d30f9613",
-  "blk.1.ffn_down.bias": "bcb58315519a573097960891c9ae41cf4c685ab78c3e0e77471471758a7eae88",
-  "blk.1.layer_output_norm.weight": "819b554271452bfb1d84c2603b90377b2e41a0ac1e3aa8b417ccf9dce63375bd",
-  "blk.1.layer_output_norm.bias": "47a3433ac27f5ce8947fb38dd491f3706df4ef6adb0ddf74612bf0f54b19e164",
-  "blk.2.attn_q.weight": "1557a9ea852b1880551f7290e00aded4f35e6c4180fdcbed1b0039bf805f639e",
-  "blk.2.attn_q.bias": "c3bfe5f3066f655fd36b055530997b59ff33ef013563aaeb3cb8ff07dabd59a9",
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-  "blk.2.attn_k.bias": "9b839bc0e79974a0b3f5d1895972bc6f5c9a1bc16052e1af786e6a530758152d",
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-  "blk.2.attn_v.bias": "e7e6f0089fded1659a867ab736c220d9653ea7da6b1b94baf5c8d30a748b63ab",
-  "blk.2.attn_output.weight": "a1db121c7d33806b349cadd050300a57db49fdc91224fd07c9ac43bf4299dc79",
-  "blk.2.attn_output.bias": "7675128b6a92555cd955c820311e91e9417d31f48848f45d047b4100c62148b3",
-  "blk.2.attn_output_norm.weight": "5b4595e0fbcba67a700c4331adf746d2fba3546364a4db5607ae241947bb1a21",
-  "blk.2.attn_output_norm.bias": "7b8e16826ea30e5a2ba0b02e0095a901775981a296e98819625320e983060d08",
-  "blk.2.ffn_up.weight": "a0d815d946ac07a65095c4ae4df77b818845e6d97795c7d82f55e689d944db59",
-  "blk.2.ffn_up.bias": "ce37c0a4174d6bf773ded7bd016ede627ad3bdb8bc99b9992a18dc8e8898f252",
-  "blk.2.ffn_down.weight": "f6231d2a25426fbd45b9f1160aa484220eb227ceef0348c4a6a6de890606e5ef",
-  "blk.2.ffn_down.bias": "429e00556e8dc63a785238b309b9d83738500c1ef6d736fe6526ad88ea496d27",
-  "blk.2.layer_output_norm.weight": "651457a573adf3f7dd9ee5dfe1c8e89389e94443993aab77ec6a0b05aa621e35",
-  "blk.2.layer_output_norm.bias": "41fbbeda7fd89b0cef5f945ae44011c316982390401d6f75ba8c6d365e185247",
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-  "blk.3.attn_k.weight": "6b892da6a17d4d3265265a15f695864a31813ee8c8e710ae9bc9e1adbc6c9a18",
-  "blk.3.attn_k.bias": "40b8067b641a56014cee42548240aa8930820958b1933004892b5f04fbaef39e",
-  "blk.3.attn_v.weight": "9fcd5922319dd2a461082a5ce040c1dfe65d87d70ca6547dd0b46eeecc3eeb2b",
-  "blk.3.attn_v.bias": "b528c56212e66931fdbe267ac327a9c2f87cd03baff3ea719e30afe681da15f1",
-  "blk.3.attn_output.weight": "e3b178c1b03981e75510e0d277af23ea59cc404b5394e61bd32291825719b502",
-  "blk.3.attn_output.bias": "712c84d39a6a5a9c06a09da8fd9939ba0d5525524a4bba61ea4de09b48f45cae",
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-  "blk.3.ffn_up.weight": "4ef4f3b9a1ea6ab2ef2eb6e8b008e06a44790d099d97482a05a51e39a29afac0",
-  "blk.3.ffn_up.bias": "06a4296dda16f452675c51f108079fe7722552d6521c737d97734943818b9a2b",
-  "blk.3.ffn_down.weight": "f114b2bebe392c7d80433bb880c6730293aa4561b0b0370dcdaf7472daebd847",
-  "blk.3.ffn_down.bias": "2c8e67831d28a3bf613fc7912ae3259b63d72abcaf4d30efd8800758400158de",
-  "blk.3.layer_output_norm.weight": "a1dfeb7b5a51dd56447312ca41e2ad2f361a3ea12ddc355127f5f4219fb0a482",
-  "blk.3.layer_output_norm.bias": "1ed630021b25c6c6fc93fd32988b9907df966d4982a93081f639aac3044618ab",
-  "blk.4.attn_q.weight": "b5fae4c1f9a5f33a2a2e816ac0c01c25f422e4efdd59ef1ed93da2610e5370fc",
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-  "blk.4.layer_output_norm.bias": "62142a81e813a9e636333b2b805d6bc3b17c5e7cd4b15adce1ada6bc9a32563c",
-  "blk.5.attn_q.weight": "afc1dff080a72c3daad01384b1448d476aaf789871017c8ff8e144788887995d",
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-  "blk.5.attn_k.weight": "59e30c1ed8acd2cbb01de5f62e7804015b9ecf98ba157d98cab016344639eda5",
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-}

convert/testdata/gemma-2-2b-it.json

@@ -1,312 +0,0 @@
-{
-  "general.architecture": "gemma2",
-  "general.file_type": "1",
-  "general.quantization_version": "2",
-  "gemma2.block_count": "26",
-  "gemma2.context_length": "8192",
-  "gemma2.embedding_length": "2304",
-  "gemma2.feed_forward_length": "9216",
-  "gemma2.attention.head_count": "8",
-  "gemma2.attention.head_count_kv": "4",
-  "gemma2.attention.key_length": "256",
-  "gemma2.attention.value_length": "256",
-  "gemma2.attention.layer_norm_rms_epsilon": "1e-06",
-  "tokenizer.ggml.model": "llama",
-  "tokenizer.ggml.add_bos_token": "true",
-  "tokenizer.ggml.add_eos_token": "false",
-  "tokenizer.ggml.bos_token_id": "2",
-  "tokenizer.ggml.eos_token_id": "1",
-  "tokenizer.ggml.padding_token_id": "0",
-  "tokenizer.ggml.unknown_token_id": "3",
-  "tokenizer.ggml.scores": "0872465d173867d755d3ee728f882b9dc2057a0bfd596fe1e3d131522f1250d8",
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-  "blk.0.post_attention_norm.weight": "32e135f1f258ffe407018899e39af1725d59d66d60022b9a21575ba160e0357a",
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-  "blk.1.attn_norm.weight": "6833104c7fbf35a7e799ae56c262b97fffa14789642aee14381b25acd21ed80a",
-  "blk.1.attn_output.weight": "14c39481369087bf292ac9a3ab2ef166f9fe376a9f90c246653213ef264febdc",
-  "blk.1.attn_q.weight": "443f64ae2229f857c69d6bebb7800b685786cb77884c3ae19d4286aeed081325",
-  "blk.1.attn_v.weight": "0df482de2038f1e4c8a7733ac0ddb69ad90759dab5968b942af0155588de4c4a",
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-  "blk.14.ffn_gate.weight": "6d0d6a74bfe9692e8f8eedff0fc34fc4fa1c8687794f35f2e2b033ab2d7510b8",
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-  "blk.14.ffn_up.weight": "7d69ea1424007ffc9c12247dd0308c616e93ac02a59ec341cfa48f92d6ce3b10",
-  "blk.14.post_attention_norm.weight": "65b9712834d9445d4236bec362f3fb795c20d60c541b3dc6dbb7914d9b493e41",
-  "blk.14.post_ffw_norm.weight": "9c6a8da2e4e437d5cfdf3b9097e9f8b64bf07946a048badec20f4d374613f38f",
-  "blk.15.attn_k.weight": "864bc618303a0e4ee67fb1d5e751de61e936cd51e96669dd86f8cd08f2305045",
-  "blk.15.attn_norm.weight": "f9f4187da6eeadc2fc5921d8fe669741697d16c13d71e4aaeb73b82f50dc577e",
-  "blk.15.attn_output.weight": "ce2419a0b097036b2a31f2f4ad731d5814bcc2ef4c511786e24471e5eefd273b",
-  "blk.15.attn_q.weight": "9539db5a970d11ebe99722d1e13fcd635e250033630811efe583d2f97778e4a9",
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-  "blk.15.ffn_gate.weight": "1b14529d57056b79037f6cb5008132e62cc35992353b38dda59572274623103b",
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-  "blk.18.ffn_up.weight": "78a1816662378ce56cc870e705174492781897b3afd2d4d97a51f10f2f2987c1",
-  "blk.18.post_attention_norm.weight": "a58dde3f12df3e94cbc27d87c8ea86f89af8a388a506446ff6758f05399b05fc",
-  "blk.18.post_ffw_norm.weight": "cebf90cc143577d483cca27b032dfd82031ee59bdf17c0e2cf60a0a3ad5bf996",
-  "blk.19.attn_k.weight": "4683375d0599ac9e2232196aae1e90af13a14cae26e865465de5c8e257bb2055",
-  "blk.19.attn_norm.weight": "f3eba936bfb1814bbcb0a1d62739eb66daac839df8c9c836fe0e94860df88525",
-  "blk.19.attn_output.weight": "51c0f01d38a9dcfe9bdbc4643576fab164c1d9e4b7168b7695c0ee55e6965667",
-  "blk.19.attn_q.weight": "28d15b69b8416f2e7ddc88fe381cb1e2ef2ad705fb1c268139ba96498cc74848",
-  "blk.19.attn_v.weight": "6860f1cd720638e63a981fa2c0b4db900129826bcb9823c9ddf9fb8b1b9f3383",
-  "blk.19.ffn_down.weight": "bc7f2d7827ee01c2dd41401c7b3b1700ad3a4ff620e8bb734f92630d342dcc7f",
-  "blk.19.ffn_gate.weight": "54d03ef69ba373fc410fbca8f1e34a565d58e4296d9a035ff7e48340b9c848e7",
-  "blk.19.ffn_norm.weight": "9178fc796a340ee6e8128ca74c0cb6203d1adbed6927af4e5ac7863da57affc7",
-  "blk.19.ffn_up.weight": "a77bd708026c6e83ad5c79c223278e74621bcf74a9641c7818d96b595daaad20",
-  "blk.19.post_attention_norm.weight": "ae94aa26f4c411bf9496a6fd4a6df64ee589ee1ae9a04b531d45acc95721e582",
-  "blk.19.post_ffw_norm.weight": "9ad210700edeef12133bdcff04bf1c7f62b49f6f4a9ba483c7cdc59857c24a5c",
-  "blk.20.attn_k.weight": "e35bce1e9f4a7a09ef34721f57ea38cfca68c272f52d923fe50af8308f66cfaa",
-  "blk.20.attn_norm.weight": "644800f6926fd34f233795c4dec1151a295d2138ca8cac33e3e48167d26f8b41",
-  "blk.20.attn_output.weight": "8d3758cd236471741e1ad66c0710cb79077dc8c7a3a292d35bc551c0c5abe627",
-  "blk.20.attn_q.weight": "c333b1f0f6f956b5d73891df10b1a0321e55fc31c40d623a24e1f52caa6a998b",
-  "blk.20.attn_v.weight": "8562b418d0c4868a050fb19fa3fcaf50a8cf1c669f537d666c80c7b3a04714e1",
-  "blk.20.ffn_down.weight": "97efb608ac44cc804198faec3ee66eafe56ced6b7ca5359700c6f1df75b7205e",
-  "blk.20.ffn_gate.weight": "5c61151d86f28415c73c73d90ec088c646cbe5c1640197caf58eb501ba7db293",
-  "blk.20.ffn_norm.weight": "24bbe0a701afd4bbeea65b3edde712b3cbb2281043bbc43dbf250582453116ed",
-  "blk.20.ffn_up.weight": "e170cf68e249566aa99eb6f6b265679bf9a5a6b76830ba24e7e130c2515910c4",
-  "blk.20.post_attention_norm.weight": "e092d751cfe20dbf2d348358f3b38397bd83e4ed94d6bbaa6bbaddcd902b2ac4",
-  "blk.20.post_ffw_norm.weight": "219a18a47dcba76e669e4322223a5a9227bd3db1de3fbd3d3cfb22e54a783c5a",
-  "blk.21.attn_k.weight": "c3a095ebddb42c63824f1c98da65263dc88e4d790a26aa1632840b44f5cc7cb1",
-  "blk.21.attn_norm.weight": "ef8bbaded5fbc45ad9cf3985ae02174524e7090fe6362811124f942ef643bec7",
-  "blk.21.attn_output.weight": "668f018aba72baac6252aa3ad58569ddd55ab751a0dd8d7bcc9fb9b6efb4bf53",
-  "blk.21.attn_q.weight": "e759c65663089f3bbbd51847934c185e680c82f1249065d5d487da638e519e6d",
-  "blk.21.attn_v.weight": "2ff57762686cf9ba1f5a6be76503454b97556ce67f4ac98254bd0562231197ba",
-  "blk.21.ffn_down.weight": "3fd106556fb721b1c28ae3f4026bc83eb1b08ed910f2ba5f466c6b5f327d91cb",
-  "blk.21.ffn_gate.weight": "338022d882f4b6619e8054a6fb909696fa3eef3013cf69b65c3cacdfc5b9e42c",
-  "blk.21.ffn_norm.weight": "1e77660c23a3f9653ee721a863d1960f773d87437cabc4dc0a6e17ee3d4e5e44",
-  "blk.21.ffn_up.weight": "7d31b20fbc2e6eba8f350f170069dc36f0cb12f68fbc4206ec5022a74085ebcb",
-  "blk.21.post_attention_norm.weight": "9638bae8d8bdcd7ed68da282979cd84a07c41ff9cabcaea94ebc846a1803db23",
-  "blk.21.post_ffw_norm.weight": "d622ef11115fe0cbe04b727d5a3b6371e7f39bf08c8d5eb9bc6da52e3f3cfb9d",
-  "blk.22.attn_k.weight": "5c321cb29deffbe57de200dd206a62005f1e80acb86c4fd2349dd44c8d3594fd",
-  "blk.22.attn_norm.weight": "198d949705d7170a331d75889d8c7500c3635254dac2cc6aa4dc35d556584536",
-  "blk.22.attn_output.weight": "19805cd5d7025b457e5d41d70db8b3fd63c2dd0e4a94d3ef1704d50ef4e749e8",
-  "blk.22.attn_q.weight": "177836cd583fc87405975ddc21ebfebdaa090a0363799664c72caa3da851ae2c",
-  "blk.22.attn_v.weight": "fea255692483e30d0108f9e4e250eb3ed7dbda8d83f499b06519b8c223ae6096",
-  "blk.22.ffn_down.weight": "00cb8939f03e5817d6d412de8cf2c923c9568d5493e382cec7faf5718fb034eb",
-  "blk.22.ffn_gate.weight": "b0591065b91281b2fbd8a9567f3568d40479f680e1f0a29e27ae213f37642489",
-  "blk.22.ffn_norm.weight": "96b5c5d0737c2ceb8fc869f54adb9e5f46e28cb7b177c40f49fa926b923c00f8",
-  "blk.22.ffn_up.weight": "81f472185b24344ab0594ea8246cc6e200e0dc1cab4943e74fbe4ca19d5a9701",
-  "blk.22.post_attention_norm.weight": "27fa9aa6260aa3071e0391e1a1d49322dcb6e8072315b8a9b7064087108dbd06",
-  "blk.22.post_ffw_norm.weight": "f37e1dcd7f643d9545675ffe9dc527a11eba86eb204989c2f44f636b266d896a",
-  "blk.23.attn_k.weight": "5d82f36658a56c3f94d0bb2d61f65509c966fa6568f81812e0d3e338b380ef8c",
-  "blk.23.attn_norm.weight": "b7983f88d9cad88bc88a528923e6da592ad20e699965b223ebc10840fe1f4fec",
-  "blk.23.attn_output.weight": "59f97f80f430d71606aab0158a195aed29ccd3405e6c0a5c41c809be8eb01898",
-  "blk.23.attn_q.weight": "53ac4789fe958919cc02ea4222bcd64c0ea1b4baa54304bff46635bdf42f7490",
-  "blk.23.attn_v.weight": "ec8abe09b9e84dbb52c7a068094657c6d3c62fe551ba8d7c3a3f23da622e9756",
-  "blk.23.ffn_down.weight": "3cf547eccb1b82aa64f208cee9682d7f558ca84e0aead7d9d3d1420d90f3d992",
-  "blk.23.ffn_gate.weight": "366aa2486d911ba81eb519119e13807deacf7e9908bc1975a2a63e00d6b10124",
-  "blk.23.ffn_norm.weight": "6d1d4a4af34bb7dc090ac87d6457d398c3e0fb68bd2e2b60b099dc318b6cfac3",
-  "blk.23.ffn_up.weight": "53f76692e253f5d2420b3f200c731b9f3b7a83e379920b4a067c729b4674aa4d",
-  "blk.23.post_attention_norm.weight": "7c952fa0efa76b3f048c8c4c9e8dcb5e3724d231327eda6423a34d3f3d3367de",
-  "blk.23.post_ffw_norm.weight": "7ab188cfe61f0a91b40309a0ab6bfa99f19d0ff2a37b6ac10e5f0c7f44eb5270",
-  "blk.24.attn_k.weight": "225798792f9bfdd10eff0505ebe61e0aad0209c17b431f6044ee7968ffe8c198",
-  "blk.24.attn_norm.weight": "635e3c1ebf5219bbebfc40ef164bc32d2b726ef595a94da64ac524ae878e2915",
-  "blk.24.attn_output.weight": "482f5bb2db8d9ed22b253d9a3296333b239efe698e5992e5d77e7e12dc2a5cf5",
-  "blk.24.attn_q.weight": "43805bbccddb65d58fffc4be9b5c374d4e1df1395ec1e1ffb4bcff03e98d5adb",
-  "blk.24.attn_v.weight": "fa741af54b4a3b1775d32f59134756090c5df2e7345a12a2d8db94fe289667a7",
-  "blk.24.ffn_down.weight": "83c6351e3162626b276f524a57836144625c2556dbe321b57cbd8fd486a68fab",
-  "blk.24.ffn_gate.weight": "fbe66be0d84d12cea5176cc7eaef64382ffc7324cd9d6266a3342dc43442f2ac",
-  "blk.24.ffn_norm.weight": "77c1445a8639ad24938bdf0280233eea2362d47391421833dfa72ec756dfc1e8",
-  "blk.24.ffn_up.weight": "78235ac729ee23c1cf1ae543751e3af32776d8808cee6e529c2a625a1f027654",
-  "blk.24.post_attention_norm.weight": "161f71b6d07628d43e4ae51a4c9088ec6ca2db123a17986a14505d83fdd04dad",
-  "blk.24.post_ffw_norm.weight": "cf1ba692aa683368b02ac413e69b2521b98c69a5274eacbb54165b53bf38a8b2",
-  "blk.25.attn_k.weight": "057a56bd8c8d2b41608d1f71faa3052902152ddf85e47669ad950c1c3e77c33f",
-  "blk.25.attn_norm.weight": "b7179fe02c334da556ddcf6c1b502245639a728c4cbba8b552d8e1df4565ee9d",
-  "blk.25.attn_output.weight": "4fed8b05b08a0ff75ffd022701bbeb52f17b23d09332a1ddcba737244bd0d3b0",
-  "blk.25.attn_q.weight": "c52e99f5d38bf7538d6106a0bbf38ac6dc6296bca9a3f849afa384ea67b4af01",
-  "blk.25.attn_v.weight": "c49c23d8e1cfa6a8eb971eb69942204890c6d7d830dc8774c84b108a80598912",
-  "blk.25.ffn_down.weight": "c08d4dc8412b19fdc870c164b83c341b236ec6fe7bb4a9bcfe0dc100faa20286",
-  "blk.25.ffn_gate.weight": "1a4cb3f36735d59181721471452807903006539e5e1b5ceb4f72d1d7ae134127",
-  "blk.25.ffn_norm.weight": "8fd6bd0dcec5198761525a36992a57c9ec5e9da60a22092839a84ae8c4e87f26",
-  "blk.25.ffn_up.weight": "3a00f39bdd5f31dc5e3b281d2002e1ac4f2475d49a0ac1d7720a25b377dcd04a",
-  "blk.25.post_attention_norm.weight": "e5f31a648612c859b6d21c9ee426e87a86cb1973dfdd86276c767371d9cef5ad",
-  "blk.25.post_ffw_norm.weight": "553c3bd774922c99c2384380a142d019881d30dbf0fe3bf9430dabfb3f6cbd33",
-  "output_norm.weight": "49445c4585ab0a8135717a0bdb1cda4a062a030177d0119561d91542aec5744b"
-}

convert/testdata/gemma-2-9b-it.json

@@ -1,6 +0,0 @@
-{
-  "general.architecture": "gemma2",
-  "gemma2.attention.sliding_window": "4096",
-  "gemma2.attn_logit_softcapping": "50",
-  "gemma2.final_logit_softcapping": "30"
-}

convert/tokenizer.go

@@ -1,6 +1,7 @@
 package convert
 
 import (
+	"cmp"
 	"crypto/sha256"
 	"encoding/hex"
 	"encoding/json"
@@ -10,8 +11,6 @@ import (
 	"log/slog"
 	"os"
 	"slices"
-
-	"golang.org/x/exp/maps"
 )
 
 const (
@@ -100,21 +99,8 @@ func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error)
 		}
 
 		if template, ok := p["chat_template"]; ok {
-			var s []struct {
-				Name     string `json:"name"`
-				Template string `json:"template"`
-			}
-			if err := json.Unmarshal(template, &t.Template); err == nil {
-				// noop
-			} else if err := json.Unmarshal(template, &s); err == nil {
-				for _, e := range s {
-					if e.Name == "default" {
-						t.Template = e.Template
-						break
-					}
-				}
-			} else {
-				return nil, fmt.Errorf("invalid chat_template: %w", err)
+			if err := json.Unmarshal(template, &t.Template); err != nil {
+				return nil, err
 			}
 		}
 
@@ -154,6 +140,7 @@ func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error)
 }
 
 type tokenizer struct {
+	Version     string  `json:"version"`
 	AddedTokens []token `json:"added_tokens"`
 	Model       struct {
 		Type   string         `json:"type"`
@@ -197,32 +184,32 @@ func parseVocabularyFromTokenizer(fsys fs.FS) (*Vocabulary, error) {
 		return nil, err
 	}
 
-	tokens := make(map[int]token, len(t.Model.Vocab))
+	var tokens []token
 	for k, v := range t.Model.Vocab {
-		tokens[v] = token{
+		tokens = append(tokens, token{
 			ID:      v,
 			Content: k,
-		}
+		})
 	}
 
-	for _, token := range t.AddedTokens {
-		token.UserDefined = true
-		tokens[token.ID] = token
+	for _, t := range t.AddedTokens {
+		t.UserDefined = true
+		tokens = append(tokens, t)
 	}
 
-	keys := maps.Keys(tokens)
-	slices.Sort(keys)
+	slices.SortFunc(tokens, func(i, j token) int {
+		return cmp.Compare(i.ID, j.ID)
+	})
 
 	v := Vocabulary{Model: "gpt2"}
-	for _, k := range keys {
-		token := tokens[k]
-		v.Tokens = append(v.Tokens, token.Content)
-		v.Scores = append(v.Scores, float32(token.ID))
+	for _, t := range tokens {
+		v.Tokens = append(v.Tokens, t.Content)
+		v.Scores = append(v.Scores, float32(t.ID))
 
 		switch {
-		case token.Special:
+		case t.Special:
 			v.Types = append(v.Types, tokenTypeControl)
-		case token.UserDefined:
+		case t.UserDefined:
 			v.Types = append(v.Types, tokenTypeUserDefined)
 		default:
 			v.Types = append(v.Types, tokenTypeNormal)
@@ -251,7 +238,7 @@ func parseVocabulary(fsys fs.FS) (*Vocabulary, error) {
 		return pattern.Func(fsys)
 	}
 
-	return nil, errors.New("unknown tokenizer format")
+	return nil, errors.New("unknown tensor format")
 }
 
 type SpecialVocabulary struct {

convert/tokenizer_spm.go

@@ -15,11 +15,6 @@ import (
 )
 
 func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
-	ast, err := parseAdditionalSpecialTokens(fsys)
-	if err != nil {
-		return nil, err
-	}
-
 	bts, err := fs.ReadFile(fsys, "tokenizer.model")
 	if err != nil {
 		return nil, err
@@ -42,12 +37,7 @@ func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
 			sentencepiece.ModelProto_SentencePiece_BYTE:
 			v.Types = append(v.Types, int32(t))
 		default:
-			tt := int32(sentencepiece.ModelProto_SentencePiece_NORMAL)
-			if slices.Contains(ast, piece.GetPiece()) {
-				tt = int32(sentencepiece.ModelProto_SentencePiece_CONTROL)
-			}
-
-			v.Types = append(v.Types, tt)
+			v.Types = append(v.Types, int32(sentencepiece.ModelProto_SentencePiece_NORMAL))
 		}
 	}
 
@@ -91,23 +81,3 @@ func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
 
 	return &v, nil
 }
-
-func parseAdditionalSpecialTokens(fsys fs.FS) ([]string, error) {
-	f, err := fsys.Open("special_tokens_map.json")
-	if errors.Is(err, os.ErrNotExist) {
-		return nil, nil
-	} else if err != nil {
-		return nil, err
-	}
-	defer f.Close()
-
-	var m struct {
-		AdditionalSpecialTokens []string `json:"additional_special_tokens"`
-	}
-
-	if err := json.NewDecoder(f).Decode(&m); err != nil {
-		return nil, err
-	}
-
-	return m.AdditionalSpecialTokens, nil
-}

convert/tokenizer_test.go

@@ -1,208 +0,0 @@
-package convert
-
-import (
-	"io"
-	"io/fs"
-	"os"
-	"path/filepath"
-	"strings"
-	"testing"
-
-	"github.com/google/go-cmp/cmp"
-)
-
-func createTokenizerFS(t *testing.T, dir string, files map[string]io.Reader) fs.FS {
-	t.Helper()
-
-	for k, v := range files {
-		if err := func() error {
-			f, err := os.Create(filepath.Join(dir, k))
-			if err != nil {
-				return err
-			}
-			defer f.Close()
-
-			if _, err := io.Copy(f, v); err != nil {
-				return err
-			}
-
-			return nil
-		}(); err != nil {
-			t.Fatalf("unexpected error: %v", err)
-		}
-	}
-
-	return os.DirFS(dir)
-}
-
-func TestParseTokenizer(t *testing.T) {
-	cases := []struct {
-		name              string
-		fsys              fs.FS
-		specialTokenTypes []string
-		want              *Tokenizer
-	}{
-		{
-			name: "string chat template",
-			fsys: createTokenizerFS(t, t.TempDir(), map[string]io.Reader{
-				"tokenizer.json": strings.NewReader(`{}`),
-				"tokenizer_config.json": strings.NewReader(`{
-					"chat_template": "<default template>"
-				}`),
-			}),
-			want: &Tokenizer{
-				Vocabulary: &Vocabulary{Model: "gpt2"},
-				Pre:        "default",
-				Template:   "<default template>",
-			},
-		},
-		{
-			name: "list chat template",
-			fsys: createTokenizerFS(t, t.TempDir(), map[string]io.Reader{
-				"tokenizer.json": strings.NewReader(`{}`),
-				"tokenizer_config.json": strings.NewReader(`{
-					"chat_template": [
-						{
-							"name": "default",
-							"template": "<default template>"
-						},
-						{
-							"name": "tools",
-							"template": "<tools template>"
-						}
-					]
-				}`),
-			}),
-			want: &Tokenizer{
-				Vocabulary: &Vocabulary{Model: "gpt2"},
-				Pre:        "default",
-				Template:   "<default template>",
-			},
-		},
-		{
-			name: "added tokens",
-			fsys: createTokenizerFS(t, t.TempDir(), map[string]io.Reader{
-				"tokenizer.json": strings.NewReader(`{
-					"added_tokens": [
-						{
-							"id": 999,
-							"content": "<unused999>",
-							"special": false
-						}
-					]
-				}`),
-			}),
-			want: &Tokenizer{
-				Vocabulary: &Vocabulary{
-					Model:  "gpt2",
-					Tokens: []string{"<unused999>"},
-					Scores: []float32{999},
-					Types:  []int32{4},
-				},
-				Pre: "default",
-			},
-		},
-		{
-			name: "added tokens overlap vocab",
-			fsys: createTokenizerFS(t, t.TempDir(), map[string]io.Reader{
-				"tokenizer.json": strings.NewReader(`{
-					"added_tokens": [
-						{
-							"id": 0,
-							"content": "<pad>",
-							"special": true
-						}
-					],
-					"model": {
-						"vocab": {
-							"<pad>": 0
-						}
-					}
-				}`),
-			}),
-			want: &Tokenizer{
-				Vocabulary: &Vocabulary{
-					Model:  "gpt2",
-					Tokens: []string{"<pad>"},
-					Scores: []float32{0},
-					Types:  []int32{3},
-				},
-				Pre: "default",
-			},
-		},
-		{
-			name: "special token types",
-			fsys: createTokenizerFS(t, t.TempDir(), map[string]io.Reader{
-				"tokenizer.json": strings.NewReader(`{
-					"added_tokens": [
-						{
-							"id": 0,
-							"content": "<pad>",
-							"special": true
-						},
-						{
-							"id": 1,
-							"content": "<eos>",
-							"special": true
-						},
-						{
-							"id": 2,
-							"content": "<bos>",
-							"special": true
-						},
-						{
-							"id": 3,
-							"content": "<unk>",
-							"special": true
-						}
-					],
-					"model": {
-						"vocab": {
-							"<pad>": 0,
-							"<eos>": 1,
-							"<bos>": 2,
-							"<unk>": 3
-						}
-					}
-				}`),
-				"tokenizer_config.json": strings.NewReader(`{
-					"add_bos_token": true,
-					"add_eos_token": false,
-					"bos_token": "<bos>",
-					"eos_token": "<eos>",
-					"pad_token": "<pad>",
-					"unk_token": "<unk>"
-				}`),
-			}),
-			specialTokenTypes: []string{"pad", "eos", "bos", "unk"},
-			want: &Tokenizer{
-				Vocabulary: &Vocabulary{
-					Model:  "gpt2",
-					Tokens: []string{"<pad>", "<eos>", "<bos>", "<unk>"},
-					Scores: []float32{0, 1, 2, 3},
-					Types:  []int32{3, 3, 3, 3},
-				},
-				SpecialVocabulary: []*SpecialVocabulary{
-					{Type: "pad", Content: "<pad>", ID: 0, AddToken: false},
-					{Type: "eos", Content: "<eos>", ID: 1, AddToken: false},
-					{Type: "bos", Content: "<bos>", ID: 2, AddToken: true},
-					{Type: "unk", Content: "<unk>", ID: 3, AddToken: false},
-				},
-				Pre: "default",
-			},
-		},
-	}
-
-	for _, tt := range cases {
-		t.Run(tt.name, func(t *testing.T) {
-			tokenizer, err := parseTokenizer(tt.fsys, tt.specialTokenTypes)
-			if err != nil {
-				t.Fatalf("unexpected error: %v", err)
-			}
-
-			if diff := cmp.Diff(tt.want, tokenizer); diff != "" {
-				t.Errorf("unexpected tokenizer (-want +got):\n%s", diff)
-			}
-		})
-	}
-}

discover/cuda_common.go

@@ -1,64 +0,0 @@
-//go:build linux || windows
-
-package discover
-
-import (
-	"log/slog"
-	"os"
-	"regexp"
-	"runtime"
-	"strconv"
-	"strings"
-)
-
-// Jetson devices have JETSON_JETPACK="x.y.z" factory set to the Jetpack version installed.
-// Included to drive logic for reducing Ollama-allocated overhead on L4T/Jetson devices.
-var CudaTegra string = os.Getenv("JETSON_JETPACK")
-
-func cudaGetVisibleDevicesEnv(gpuInfo []GpuInfo) (string, string) {
-	ids := []string{}
-	for _, info := range gpuInfo {
-		if info.Library != "cuda" {
-			// TODO shouldn't happen if things are wired correctly...
-			slog.Debug("cudaGetVisibleDevicesEnv skipping over non-cuda device", "library", info.Library)
-			continue
-		}
-		ids = append(ids, info.ID)
-	}
-	return "CUDA_VISIBLE_DEVICES", strings.Join(ids, ",")
-}
-
-func cudaVariant(gpuInfo CudaGPUInfo) string {
-	if runtime.GOARCH == "arm64" && runtime.GOOS == "linux" {
-		if CudaTegra != "" {
-			ver := strings.Split(CudaTegra, ".")
-			if len(ver) > 0 {
-				return "jetpack" + ver[0]
-			}
-		} else if data, err := os.ReadFile("/etc/nv_tegra_release"); err == nil {
-			r := regexp.MustCompile(` R(\d+) `)
-			m := r.FindSubmatch(data)
-			if len(m) != 2 {
-				slog.Info("Unexpected format for /etc/nv_tegra_release.  Set JETSON_JETPACK to select version")
-			} else {
-				if l4t, err := strconv.Atoi(string(m[1])); err == nil {
-					// Note: mapping from L4t -> JP is inconsistent (can't just subtract 30)
-					// https://developer.nvidia.com/embedded/jetpack-archive
-					switch l4t {
-					case 35:
-						return "jetpack5"
-					case 36:
-						return "jetpack6"
-					default:
-						slog.Info("unsupported L4T version", "nv_tegra_release", string(data))
-					}
-				}
-			}
-		}
-	}
-
-	if gpuInfo.computeMajor < 6 || gpuInfo.DriverMajor < 12 || (gpuInfo.DriverMajor == 12 && gpuInfo.DriverMinor == 0) {
-		return "v11"
-	}
-	return "v12"
-}

discover/gpu_linux.go

@@ -1,199 +0,0 @@
-package discover
-
-import (
-	"bufio"
-	"fmt"
-	"io"
-	"os"
-	"reflect"
-	"regexp"
-	"sort"
-	"strings"
-
-	"github.com/ollama/ollama/format"
-)
-
-var CudartGlobs = []string{
-	"/usr/local/cuda/lib64/libcudart.so*",
-	"/usr/lib/x86_64-linux-gnu/nvidia/current/libcudart.so*",
-	"/usr/lib/x86_64-linux-gnu/libcudart.so*",
-	"/usr/lib/wsl/lib/libcudart.so*",
-	"/usr/lib/wsl/drivers/*/libcudart.so*",
-	"/opt/cuda/lib64/libcudart.so*",
-	"/usr/local/cuda*/targets/aarch64-linux/lib/libcudart.so*",
-	"/usr/lib/aarch64-linux-gnu/nvidia/current/libcudart.so*",
-	"/usr/lib/aarch64-linux-gnu/libcudart.so*",
-	"/usr/local/cuda/lib*/libcudart.so*",
-	"/usr/lib*/libcudart.so*",
-	"/usr/local/lib*/libcudart.so*",
-}
-
-var NvmlGlobs = []string{}
-
-var NvcudaGlobs = []string{
-	"/usr/local/cuda*/targets/*/lib/libcuda.so*",
-	"/usr/lib/*-linux-gnu/nvidia/current/libcuda.so*",
-	"/usr/lib/*-linux-gnu/libcuda.so*",
-	"/usr/lib/wsl/lib/libcuda.so*",
-	"/usr/lib/wsl/drivers/*/libcuda.so*",
-	"/opt/cuda/lib*/libcuda.so*",
-	"/usr/local/cuda/lib*/libcuda.so*",
-	"/usr/lib*/libcuda.so*",
-	"/usr/local/lib*/libcuda.so*",
-}
-
-var OneapiGlobs = []string{
-	"/usr/lib/x86_64-linux-gnu/libze_intel_gpu.so*",
-	"/usr/lib*/libze_intel_gpu.so*",
-}
-
-var (
-	CudartMgmtName = "libcudart.so*"
-	NvcudaMgmtName = "libcuda.so*"
-	NvmlMgmtName   = "" // not currently wired on linux
-	OneapiMgmtName = "libze_intel_gpu.so*"
-)
-
-func GetCPUMem() (memInfo, error) {
-	var mem memInfo
-	var total, available, free, buffers, cached, freeSwap uint64
-	f, err := os.Open("/proc/meminfo")
-	if err != nil {
-		return mem, err
-	}
-	defer f.Close()
-	s := bufio.NewScanner(f)
-	for s.Scan() {
-		line := s.Text()
-		switch {
-		case strings.HasPrefix(line, "MemTotal:"):
-			_, err = fmt.Sscanf(line, "MemTotal:%d", &total)
-		case strings.HasPrefix(line, "MemAvailable:"):
-			_, err = fmt.Sscanf(line, "MemAvailable:%d", &available)
-		case strings.HasPrefix(line, "MemFree:"):
-			_, err = fmt.Sscanf(line, "MemFree:%d", &free)
-		case strings.HasPrefix(line, "Buffers:"):
-			_, err = fmt.Sscanf(line, "Buffers:%d", &buffers)
-		case strings.HasPrefix(line, "Cached:"):
-			_, err = fmt.Sscanf(line, "Cached:%d", &cached)
-		case strings.HasPrefix(line, "SwapFree:"):
-			_, err = fmt.Sscanf(line, "SwapFree:%d", &freeSwap)
-		default:
-			continue
-		}
-		if err != nil {
-			return mem, err
-		}
-	}
-	mem.TotalMemory = total * format.KibiByte
-	mem.FreeSwap = freeSwap * format.KibiByte
-	if available > 0 {
-		mem.FreeMemory = available * format.KibiByte
-	} else {
-		mem.FreeMemory = (free + buffers + cached) * format.KibiByte
-	}
-	return mem, nil
-}
-
-const CpuInfoFilename = "/proc/cpuinfo"
-
-type linuxCpuInfo struct {
-	ID         string `cpuinfo:"processor"`
-	VendorID   string `cpuinfo:"vendor_id"`
-	ModelName  string `cpuinfo:"model name"`
-	PhysicalID string `cpuinfo:"physical id"`
-	Siblings   string `cpuinfo:"siblings"`
-	CoreID     string `cpuinfo:"core id"`
-}
-
-func GetCPUDetails() ([]CPU, error) {
-	file, err := os.Open(CpuInfoFilename)
-	if err != nil {
-		return nil, err
-	}
-	return linuxCPUDetails(file)
-}
-
-func linuxCPUDetails(file io.Reader) ([]CPU, error) {
-	reColumns := regexp.MustCompile("\t+: ")
-	scanner := bufio.NewScanner(file)
-	cpuInfos := []linuxCpuInfo{}
-	cpu := &linuxCpuInfo{}
-	for scanner.Scan() {
-		line := scanner.Text()
-		if sl := reColumns.Split(line, 2); len(sl) > 1 {
-			t := reflect.TypeOf(cpu).Elem()
-			s := reflect.ValueOf(cpu).Elem()
-			for i := range t.NumField() {
-				field := t.Field(i)
-				tag := field.Tag.Get("cpuinfo")
-				if tag == sl[0] {
-					s.FieldByName(field.Name).SetString(sl[1])
-					break
-				}
-			}
-		} else if strings.TrimSpace(line) == "" && cpu.ID != "" {
-			cpuInfos = append(cpuInfos, *cpu)
-			cpu = &linuxCpuInfo{}
-		}
-	}
-	if cpu.ID != "" {
-		cpuInfos = append(cpuInfos, *cpu)
-	}
-
-	// Process the sockets/cores/threads
-	socketByID := map[string]*CPU{}
-	coreBySocket := map[string]map[string]struct{}{}
-	threadsByCoreBySocket := map[string]map[string]int{}
-	for _, c := range cpuInfos {
-		if _, found := socketByID[c.PhysicalID]; !found {
-			socketByID[c.PhysicalID] = &CPU{
-				ID:        c.PhysicalID,
-				VendorID:  c.VendorID,
-				ModelName: c.ModelName,
-			}
-			coreBySocket[c.PhysicalID] = map[string]struct{}{}
-			threadsByCoreBySocket[c.PhysicalID] = map[string]int{}
-		}
-		if c.CoreID != "" {
-			coreBySocket[c.PhysicalID][c.PhysicalID+":"+c.CoreID] = struct{}{}
-			threadsByCoreBySocket[c.PhysicalID][c.PhysicalID+":"+c.CoreID]++
-		} else {
-			coreBySocket[c.PhysicalID][c.PhysicalID+":"+c.ID] = struct{}{}
-			threadsByCoreBySocket[c.PhysicalID][c.PhysicalID+":"+c.ID]++
-		}
-	}
-
-	// Tally up the values from the tracking maps
-	for id, s := range socketByID {
-		s.CoreCount = len(coreBySocket[id])
-		s.ThreadCount = 0
-		for _, tc := range threadsByCoreBySocket[id] {
-			s.ThreadCount += tc
-		}
-
-		// This only works if HT is enabled, consider a more reliable model, maybe cache size comparisons?
-		efficiencyCoreCount := 0
-		for _, threads := range threadsByCoreBySocket[id] {
-			if threads == 1 {
-				efficiencyCoreCount++
-			}
-		}
-		if efficiencyCoreCount == s.CoreCount {
-			// 1:1 mapping means they're not actually efficiency cores, but regular cores
-			s.EfficiencyCoreCount = 0
-		} else {
-			s.EfficiencyCoreCount = efficiencyCoreCount
-		}
-	}
-	keys := make([]string, 0, len(socketByID))
-	result := make([]CPU, 0, len(socketByID))
-	for k := range socketByID {
-		keys = append(keys, k)
-	}
-	sort.Strings(keys)
-	for _, k := range keys {
-		result = append(result, *socketByID[k])
-	}
-	return result, nil
-}

discover/gpu_test.go

@@ -1,60 +0,0 @@
-package discover
-
-import (
-	"runtime"
-	"testing"
-
-	"github.com/stretchr/testify/assert"
-	"github.com/stretchr/testify/require"
-)
-
-func TestBasicGetGPUInfo(t *testing.T) {
-	info := GetGPUInfo()
-	assert.NotEmpty(t, len(info))
-	assert.Contains(t, "cuda rocm cpu metal", info[0].Library)
-	if info[0].Library != "cpu" {
-		assert.Greater(t, info[0].TotalMemory, uint64(0))
-		assert.Greater(t, info[0].FreeMemory, uint64(0))
-	}
-}
-
-func TestCPUMemInfo(t *testing.T) {
-	info, err := GetCPUMem()
-	require.NoError(t, err)
-	switch runtime.GOOS {
-	case "darwin":
-		t.Skip("CPU memory not populated on darwin")
-	case "linux", "windows":
-		assert.Greater(t, info.TotalMemory, uint64(0))
-		assert.Greater(t, info.FreeMemory, uint64(0))
-	default:
-		return
-	}
-}
-
-func TestByLibrary(t *testing.T) {
-	type testCase struct {
-		input  []GpuInfo
-		expect int
-	}
-
-	testCases := map[string]*testCase{
-		"empty":                    {input: []GpuInfo{}, expect: 0},
-		"cpu":                      {input: []GpuInfo{{Library: "cpu"}}, expect: 1},
-		"cpu + GPU":                {input: []GpuInfo{{Library: "cpu"}, {Library: "cuda"}}, expect: 2},
-		"cpu + 2 GPU no variant":   {input: []GpuInfo{{Library: "cpu"}, {Library: "cuda"}, {Library: "cuda"}}, expect: 2},
-		"cpu + 2 GPU same variant": {input: []GpuInfo{{Library: "cpu"}, {Library: "cuda", Variant: "v11"}, {Library: "cuda", Variant: "v11"}}, expect: 2},
-		"cpu + 2 GPU diff variant": {input: []GpuInfo{{Library: "cpu"}, {Library: "cuda", Variant: "v11"}, {Library: "cuda", Variant: "v12"}}, expect: 3},
-	}
-
-	for k, v := range testCases {
-		t.Run(k, func(t *testing.T) {
-			resp := (GpuInfoList)(v.input).ByLibrary()
-			if len(resp) != v.expect {
-				t.Fatalf("expected length %d, got %d => %+v", v.expect, len(resp), resp)
-			}
-		})
-	}
-}
-
-// TODO - add some logic to figure out card type through other means and actually verify we got back what we expected

discover/gpu_windows.go

@@ -1,234 +0,0 @@
-package discover
-
-import (
-	"fmt"
-	"log/slog"
-	"syscall"
-	"unsafe"
-)
-
-type MEMORYSTATUSEX struct {
-	length               uint32
-	MemoryLoad           uint32
-	TotalPhys            uint64
-	AvailPhys            uint64
-	TotalPageFile        uint64
-	AvailPageFile        uint64
-	TotalVirtual         uint64
-	AvailVirtual         uint64
-	AvailExtendedVirtual uint64
-}
-
-var (
-	k32                              = syscall.NewLazyDLL("kernel32.dll")
-	globalMemoryStatusExProc         = k32.NewProc("GlobalMemoryStatusEx")
-	sizeofMemoryStatusEx             = uint32(unsafe.Sizeof(MEMORYSTATUSEX{}))
-	GetLogicalProcessorInformationEx = k32.NewProc("GetLogicalProcessorInformationEx")
-)
-
-var CudartGlobs = []string{
-	"c:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v*\\bin\\cudart64_*.dll",
-}
-
-var NvmlGlobs = []string{
-	"c:\\Windows\\System32\\nvml.dll",
-}
-
-var NvcudaGlobs = []string{
-	"c:\\windows\\system*\\nvcuda.dll",
-}
-
-var OneapiGlobs = []string{
-	"c:\\Windows\\System32\\DriverStore\\FileRepository\\*\\ze_intel_gpu64.dll",
-}
-
-var (
-	CudartMgmtName = "cudart64_*.dll"
-	NvcudaMgmtName = "nvcuda.dll"
-	NvmlMgmtName   = "nvml.dll"
-	OneapiMgmtName = "ze_intel_gpu64.dll"
-)
-
-func GetCPUMem() (memInfo, error) {
-	memStatus := MEMORYSTATUSEX{length: sizeofMemoryStatusEx}
-	r1, _, err := globalMemoryStatusExProc.Call(uintptr(unsafe.Pointer(&memStatus)))
-	if r1 == 0 {
-		return memInfo{}, fmt.Errorf("GlobalMemoryStatusEx failed: %w", err)
-	}
-	return memInfo{TotalMemory: memStatus.TotalPhys, FreeMemory: memStatus.AvailPhys, FreeSwap: memStatus.AvailPageFile}, nil
-}
-
-type LOGICAL_PROCESSOR_RELATIONSHIP uint32
-
-const (
-	RelationProcessorCore LOGICAL_PROCESSOR_RELATIONSHIP = iota
-	RelationNumaNode
-	RelationCache
-	RelationProcessorPackage
-	RelationGroup
-	RelationProcessorDie
-	RelationNumaNodeEx
-	RelationProcessorModule
-)
-const RelationAll LOGICAL_PROCESSOR_RELATIONSHIP = 0xffff
-
-type GROUP_AFFINITY struct {
-	Mask     uintptr // KAFFINITY
-	Group    uint16
-	Reserved [3]uint16
-}
-
-type PROCESSOR_RELATIONSHIP struct {
-	Flags           byte
-	EfficiencyClass byte
-	Reserved        [20]byte
-	GroupCount      uint16
-	GroupMask       [1]GROUP_AFFINITY // len GroupCount
-}
-
-// Omitted unused structs: NUMA_NODE_RELATIONSHIP CACHE_RELATIONSHIP GROUP_RELATIONSHIP
-
-type SYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX struct {
-	Relationship LOGICAL_PROCESSOR_RELATIONSHIP
-	Size         uint32
-	U            [1]byte // Union len Size
-	// PROCESSOR_RELATIONSHIP
-	// NUMA_NODE_RELATIONSHIP
-	// CACHE_RELATIONSHIP
-	// GROUP_RELATIONSHIP
-}
-
-func (group *GROUP_AFFINITY) IsMember(target *GROUP_AFFINITY) bool {
-	if group == nil || target == nil {
-		return false
-	}
-	return group.Mask&target.Mask != 0
-}
-
-type winPackage struct {
-	groups              []*GROUP_AFFINITY
-	coreCount           int // performance cores = coreCount - efficiencyCoreCount
-	efficiencyCoreCount int
-	threadCount         int
-}
-
-func (pkg *winPackage) IsMember(target *GROUP_AFFINITY) bool {
-	for _, group := range pkg.groups {
-		if group.IsMember(target) {
-			return true
-		}
-	}
-	return false
-}
-
-func getLogicalProcessorInformationEx() ([]byte, error) {
-	buf := make([]byte, 1)
-	bufSize := len(buf)
-	ret, _, err := GetLogicalProcessorInformationEx.Call(
-		uintptr(RelationAll),
-		uintptr(unsafe.Pointer(&buf[0])),
-		uintptr(unsafe.Pointer(&bufSize)),
-	)
-	if ret != 0 {
-		return nil, fmt.Errorf("failed to determine size info ret:%d %w", ret, err)
-	}
-
-	buf = make([]byte, bufSize)
-	ret, _, err = GetLogicalProcessorInformationEx.Call(
-		uintptr(RelationAll),
-		uintptr(unsafe.Pointer(&buf[0])),
-		uintptr(unsafe.Pointer(&bufSize)),
-	)
-	if ret == 0 {
-		return nil, fmt.Errorf("failed to gather processor information ret:%d buflen:%d %w", ret, bufSize, err)
-	}
-	return buf, nil
-}
-
-func processSystemLogicalProcessorInforationList(buf []byte) []*winPackage {
-	var slpi *SYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX
-	// Find all the packages first
-	packages := []*winPackage{}
-	for bufOffset := 0; bufOffset < len(buf); bufOffset += int(slpi.Size) {
-		slpi = (*SYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX)(unsafe.Pointer(&buf[bufOffset]))
-		if slpi.Relationship != RelationProcessorPackage {
-			continue
-		}
-		pr := (*PROCESSOR_RELATIONSHIP)(unsafe.Pointer(&slpi.U[0]))
-		pkg := &winPackage{}
-		ga0 := unsafe.Pointer(&pr.GroupMask[0])
-		for j := range pr.GroupCount {
-			gm := (*GROUP_AFFINITY)(unsafe.Pointer(uintptr(ga0) + uintptr(j)*unsafe.Sizeof(GROUP_AFFINITY{})))
-			pkg.groups = append(pkg.groups, gm)
-		}
-		packages = append(packages, pkg)
-	}
-
-	slog.Info("packages", "count", len(packages))
-
-	// To identify efficiency cores we have to compare the relative values
-	// Larger values are "less efficient" (aka, more performant)
-	var maxEfficiencyClass byte
-	for bufOffset := 0; bufOffset < len(buf); bufOffset += int(slpi.Size) {
-		slpi = (*SYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX)(unsafe.Pointer(&buf[bufOffset]))
-		if slpi.Relationship != RelationProcessorCore {
-			continue
-		}
-		pr := (*PROCESSOR_RELATIONSHIP)(unsafe.Pointer(&slpi.U[0]))
-		if pr.EfficiencyClass > maxEfficiencyClass {
-			maxEfficiencyClass = pr.EfficiencyClass
-		}
-	}
-	if maxEfficiencyClass > 0 {
-		slog.Info("efficiency cores detected", "maxEfficiencyClass", maxEfficiencyClass)
-	}
-
-	// then match up the Cores to the Packages, count up cores, threads and efficiency cores
-	for bufOffset := 0; bufOffset < len(buf); bufOffset += int(slpi.Size) {
-		slpi = (*SYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX)(unsafe.Pointer(&buf[bufOffset]))
-		if slpi.Relationship != RelationProcessorCore {
-			continue
-		}
-		pr := (*PROCESSOR_RELATIONSHIP)(unsafe.Pointer(&slpi.U[0]))
-		ga0 := unsafe.Pointer(&pr.GroupMask[0])
-		for j := range pr.GroupCount {
-			gm := (*GROUP_AFFINITY)(unsafe.Pointer(uintptr(ga0) + uintptr(j)*unsafe.Sizeof(GROUP_AFFINITY{})))
-			for _, pkg := range packages {
-				if pkg.IsMember(gm) {
-					pkg.coreCount++
-					if pr.Flags == 0 {
-						pkg.threadCount++
-					} else {
-						pkg.threadCount += 2
-					}
-					if pr.EfficiencyClass < maxEfficiencyClass {
-						pkg.efficiencyCoreCount++
-					}
-				}
-			}
-		}
-	}
-
-	// Sumarize the results
-	for i, pkg := range packages {
-		slog.Info("", "package", i, "cores", pkg.coreCount, "efficiency", pkg.efficiencyCoreCount, "threads", pkg.threadCount)
-	}
-
-	return packages
-}
-
-func GetCPUDetails() ([]CPU, error) {
-	buf, err := getLogicalProcessorInformationEx()
-	if err != nil {
-		return nil, err
-	}
-	packages := processSystemLogicalProcessorInforationList(buf)
-	cpus := make([]CPU, len(packages))
-
-	for i, pkg := range packages {
-		cpus[i].CoreCount = pkg.coreCount
-		cpus[i].EfficiencyCoreCount = pkg.efficiencyCoreCount
-		cpus[i].ThreadCount = pkg.threadCount
-	}
-	return cpus, nil
-}

docs/api.md

@@ -69,7 +69,7 @@ Enable JSON mode by setting the `format` parameter to `json`. This will structur
 
 ```shell
 curl http://localhost:11434/api/generate -d '{
-  "model": "llama3.2",
+  "model": "llama3",
   "prompt": "Why is the sky blue?"
 }'
 ```
@@ -80,7 +80,7 @@ A stream of JSON objects is returned:
 
 ```json
 {
-  "model": "llama3.2",
+  "model": "llama3",
   "created_at": "2023-08-04T08:52:19.385406455-07:00",
   "response": "The",
   "done": false
@@ -102,7 +102,7 @@ To calculate how fast the response is generated in tokens per second (token/s),
 
 ```json
 {
-  "model": "llama3.2",
+  "model": "llama3",
   "created_at": "2023-08-04T19:22:45.499127Z",
   "response": "",
   "done": true,
@@ -124,7 +124,7 @@ A response can be received in one reply when streaming is off.
 
 ```shell
 curl http://localhost:11434/api/generate -d '{
-  "model": "llama3.2",
+  "model": "llama3",
   "prompt": "Why is the sky blue?",
   "stream": false
 }'
@@ -136,7 +136,7 @@ If `stream` is set to `false`, the response will be a single JSON object:
 
 ```json
 {
-  "model": "llama3.2",
+  "model": "llama3",
   "created_at": "2023-08-04T19:22:45.499127Z",
   "response": "The sky is blue because it is the color of the sky.",
   "done": true,
@@ -194,7 +194,7 @@ curl http://localhost:11434/api/generate -d '{
 
 ```shell
 curl http://localhost:11434/api/generate -d '{
-  "model": "llama3.2",
+  "model": "llama3",
   "prompt": "What color is the sky at different times of the day? Respond using JSON",
   "format": "json",
   "stream": false
@@ -205,7 +205,7 @@ curl http://localhost:11434/api/generate -d '{
 
 ```json
 {
-  "model": "llama3.2",
+  "model": "llama3",
   "created_at": "2023-11-09T21:07:55.186497Z",
   "response": "{\n\"morning\": {\n\"color\": \"blue\"\n},\n\"noon\": {\n\"color\": \"blue-gray\"\n},\n\"afternoon\": {\n\"color\": \"warm gray\"\n},\n\"evening\": {\n\"color\": \"orange\"\n}\n}\n",
   "done": true,
@@ -327,7 +327,7 @@ If you want to set custom options for the model at runtime rather than in the Mo
 
 ```shell
 curl http://localhost:11434/api/generate -d '{
-  "model": "llama3.2",
+  "model": "llama3",
   "prompt": "Why is the sky blue?",
   "stream": false,
   "options": {
@@ -355,6 +355,7 @@ 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,
@@ -367,7 +368,7 @@ curl http://localhost:11434/api/generate -d '{
 
 ```json
 {
-  "model": "llama3.2",
+  "model": "llama3",
   "created_at": "2023-08-04T19:22:45.499127Z",
   "response": "The sky is blue because it is the color of the sky.",
   "done": true,
@@ -389,7 +390,7 @@ If an empty prompt is provided, the model will be loaded into memory.
 
 ```shell
 curl http://localhost:11434/api/generate -d '{
-  "model": "llama3.2"
+  "model": "llama3"
 }'
 ```
 
@@ -399,40 +400,13 @@ A single JSON object is returned:
 
 ```json
 {
-  "model": "llama3.2",
+  "model": "llama3",
   "created_at": "2023-12-18T19:52:07.071755Z",
   "response": "",
   "done": true
 }
 ```
 
-#### Unload a model
-
-If an empty prompt is provided and the `keep_alive` parameter is set to `0`, a model will be unloaded from memory.
-
-##### Request
-
-```shell
-curl http://localhost:11434/api/generate -d '{
-  "model": "llama3.2",
-  "keep_alive": 0
-}'
-```
-
-##### Response
-
-A single JSON object is returned:
-
-```json
-{
-  "model": "llama3.2",
-  "created_at": "2024-09-12T03:54:03.516566Z",
-  "response": "",
-  "done": true,
-  "done_reason": "unload"
-}
-```
-
 ## Generate a chat completion
 
 ```shell
@@ -471,7 +445,7 @@ Send a chat message with a streaming response.
 
 ```shell
 curl http://localhost:11434/api/chat -d '{
-  "model": "llama3.2",
+  "model": "llama3",
   "messages": [
     {
       "role": "user",
@@ -487,7 +461,7 @@ A stream of JSON objects is returned:
 
 ```json
 {
-  "model": "llama3.2",
+  "model": "llama3",
   "created_at": "2023-08-04T08:52:19.385406455-07:00",
   "message": {
     "role": "assistant",
@@ -502,7 +476,7 @@ Final response:
 
 ```json
 {
-  "model": "llama3.2",
+  "model": "llama3",
   "created_at": "2023-08-04T19:22:45.499127Z",
   "done": true,
   "total_duration": 4883583458,
@@ -520,7 +494,7 @@ Final response:
 
 ```shell
 curl http://localhost:11434/api/chat -d '{
-  "model": "llama3.2",
+  "model": "llama3",
   "messages": [
     {
       "role": "user",
@@ -535,7 +509,7 @@ curl http://localhost:11434/api/chat -d '{
 
 ```json
 {
-  "model": "llama3.2",
+  "model": "registry.ollama.ai/library/llama3:latest",
   "created_at": "2023-12-12T14:13:43.416799Z",
   "message": {
     "role": "assistant",
@@ -559,7 +533,7 @@ Send a chat message with a conversation history. You can use this same approach
 
 ```shell
 curl http://localhost:11434/api/chat -d '{
-  "model": "llama3.2",
+  "model": "llama3",
   "messages": [
     {
       "role": "user",
@@ -583,7 +557,7 @@ A stream of JSON objects is returned:
 
 ```json
 {
-  "model": "llama3.2",
+  "model": "llama3",
   "created_at": "2023-08-04T08:52:19.385406455-07:00",
   "message": {
     "role": "assistant",
@@ -597,7 +571,7 @@ Final response:
 
 ```json
 {
-  "model": "llama3.2",
+  "model": "llama3",
   "created_at": "2023-08-04T19:22:45.499127Z",
   "done": true,
   "total_duration": 8113331500,
@@ -655,7 +629,7 @@ curl http://localhost:11434/api/chat -d '{
 
 ```shell
 curl http://localhost:11434/api/chat -d '{
-  "model": "llama3.2",
+  "model": "llama3",
   "messages": [
     {
       "role": "user",
@@ -673,7 +647,7 @@ curl http://localhost:11434/api/chat -d '{
 
 ```json
 {
-  "model": "llama3.2",
+  "model": "registry.ollama.ai/library/llama3:latest",
   "created_at": "2023-12-12T14:13:43.416799Z",
   "message": {
     "role": "assistant",
@@ -695,7 +669,7 @@ curl http://localhost:11434/api/chat -d '{
 
 ```
 curl http://localhost:11434/api/chat -d '{
-  "model": "llama3.2",
+  "model": "llama3.1",
   "messages": [
     {
       "role": "user",
@@ -734,7 +708,7 @@ curl http://localhost:11434/api/chat -d '{
 
 ```json
 {
-  "model": "llama3.2",
+  "model": "llama3.1",
   "created_at": "2024-07-22T20:33:28.123648Z",
   "message": {
     "role": "assistant",
@@ -762,64 +736,6 @@ curl http://localhost:11434/api/chat -d '{
 }
 ```
 
-#### Load a model
-
-If the messages array is empty, the model will be loaded into memory.
-
-##### Request
-
-```
-curl http://localhost:11434/api/chat -d '{
-  "model": "llama3.2",
-  "messages": []
-}'
-```
-
-##### Response
-```json
-{
-  "model": "llama3.2",
-  "created_at":"2024-09-12T21:17:29.110811Z",
-  "message": {
-    "role": "assistant",
-    "content": ""
-  },
-  "done_reason": "load",
-  "done": true
-}
-```
-
-#### Unload a model
-
-If the messages array is empty and the `keep_alive` parameter is set to `0`, a model will be unloaded from memory.
-
-##### Request
-
-```
-curl http://localhost:11434/api/chat -d '{
-  "model": "llama3.2",
-  "messages": [],
-  "keep_alive": 0
-}'
-```
-
-##### Response
-
-A single JSON object is returned:
-
-```json
-{
-  "model": "llama3.2",
-  "created_at":"2024-09-12T21:33:17.547535Z",
-  "message": {
-    "role": "assistant",
-    "content": ""
-  },
-  "done_reason": "unload",
-  "done": true
-}
-```
-
 ## Create a Model
 
 ```shell
@@ -988,7 +904,7 @@ Show information about a model including details, modelfile, template, parameter
 
 ```shell
 curl http://localhost:11434/api/show -d '{
-  "name": "llama3.2"
+  "name": "llama3"
 }'
 ```
 
@@ -1049,7 +965,7 @@ Copy a model. Creates a model with another name from an existing model.
 
 ```shell
 curl http://localhost:11434/api/copy -d '{
-  "source": "llama3.2",
+  "source": "llama3",
   "destination": "llama3-backup"
 }'
 ```
@@ -1104,7 +1020,7 @@ Download a model from the ollama library. Cancelled pulls are resumed from where
 
 ```shell
 curl http://localhost:11434/api/pull -d '{
-  "name": "llama3.2"
+  "name": "llama3"
 }'
 ```
 

docs/development.md

@@ -2,13 +2,15 @@
 
 Install required tools:
 
+- cmake version 3.24 or higher
 - go version 1.22 or higher
 - gcc version 11.4.0 or higher
 
-
 ### MacOS
 
-[Download Go](https://go.dev/dl/)
+```bash
+brew install go cmake gcc
+```
 
 Optionally enable debugging and more verbose logging:
 
@@ -20,10 +22,10 @@ export CGO_CFLAGS="-g"
 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)
+Get the required libraries and build the native LLM code:
 
 ```bash
-make -j 5
+go generate ./...
 ```
 
 Then build ollama:
@@ -38,17 +40,13 @@ Now you can run `ollama`:
 ./ollama
 ```
 
-#### Xcode 15 warnings
-
-If you are using Xcode newer than version 14, you may see a warning during `go build` about `ld: warning: ignoring duplicate libraries: '-lobjc'` due to Golang issue https://github.com/golang/go/issues/67799 which can be safely ignored.  You can suppress the warning with `export CGO_LDFLAGS="-Wl,-no_warn_duplicate_libraries"`
-
 ### Linux
 
 #### Linux CUDA (NVIDIA)
 
 _Your operating system distribution may already have packages for NVIDIA CUDA. Distro packages are often preferable, but instructions are distro-specific. Please consult distro-specific docs for dependencies if available!_
 
-Install `make`, `gcc` and `golang` as well as [NVIDIA CUDA](https://developer.nvidia.com/cuda-downloads)
+Install `cmake` 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
@@ -57,10 +55,10 @@ 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)
+Then generate dependencies:
 
 ```
-make -j 5
+go generate ./...
 ```
 
 Then build the binary:
@@ -73,7 +71,7 @@ go build .
 
 _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!_
 
-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 [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 `cmake` 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
@@ -82,10 +80,8 @@ 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)
-
 ```
-make -j 5
+go generate ./...
 ```
 
 Then build the binary:
@@ -98,13 +94,19 @@ ROCm requires elevated privileges to access the GPU at runtime. On most distros
 
 #### Advanced CPU Settings
 
-By default, running `make` will compile a few different variations
+By default, running `go generate ./...` 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. 
+load. If you would like to build a CPU-based build customized for your
+processor, you can set `OLLAMA_CUSTOM_CPU_DEFS` to the llama.cpp flags you would
+like to use. For example, to compile an optimized binary for an Intel i9-9880H,
+you might use:
 
-Custom CPU settings are not currently supported in the new Go server build but will be added back after we complete the transition.
+```
+OLLAMA_CUSTOM_CPU_DEFS="-DGGML_AVX=on -DGGML_AVX2=on -DGGML_F16C=on -DGGML_FMA=on" go generate ./...
+go build .
+```
 
 #### Containerized Linux Build
 
@@ -112,64 +114,37 @@ If you have Docker available, you can build linux binaries with `./scripts/build
 
 ### Windows
 
-The following tools are required as a minimal development environment to build CPU inference support.
+Note: The Windows build for Ollama is still under development.
 
+First, install required tools:
+
+- MSVC toolchain - C/C++ and cmake as minimal requirements
 - Go version 1.22 or higher
-  - https://go.dev/dl/
-- Git
-  - https://git-scm.com/download/win
-- clang with gcc compat and Make.  There are multiple options on how to go about installing these tools on Windows.  We have verified the following, but others may work as well:  
+- MinGW (pick one variant) with GCC.
+  - [MinGW-w64](https://www.mingw-w64.org/)
   - [MSYS2](https://www.msys2.org/)
-    - After installing, from an MSYS2 terminal, run `pacman -S mingw-w64-clang-x86_64-gcc-compat mingw-w64-clang-x86_64-clang make` to install the required tools
-  - Assuming you used the default install prefix for msys2 above, add `C:\msys64\clang64\bin` and `c:\msys64\usr\bin` to your environment variable `PATH` where you will perform the build steps below (e.g. system-wide, account-level, powershell, cmd, etc.)
-
-> [!NOTE]  
-> Due to bugs in the GCC C++ library for unicode support, Ollama should be built with clang on windows.
+- The `ThreadJob` Powershell module: `Install-Module -Name ThreadJob -Scope CurrentUser`
 
 Then, build the `ollama` binary:
 
 ```powershell
 $env:CGO_ENABLED="1"
-make -j 8
+go generate ./...
 go build .
 ```
 
-#### GPU Support
-
-The GPU tools require the Microsoft native build tools.  To build either CUDA or ROCm, you must first install MSVC via Visual Studio:
-
-- Make sure to select `Desktop development with C++` as a Workload during the Visual Studio install
-- You must complete the Visual Studio install and run it once **BEFORE** installing CUDA or ROCm for the tools to properly register
-- Add the location of the **64 bit (x64)** compiler (`cl.exe`) to your `PATH`
-- Note: the default Developer Shell may configure the 32 bit (x86) compiler which will lead to build failures.  Ollama requires a 64 bit toolchain.
-
 #### Windows CUDA (NVIDIA)
 
-In addition to the common Windows development tools and MSVC described above:
+In addition to the common Windows development tools described above, install CUDA after installing MSVC.
 
 - [NVIDIA CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html)
 
+
 #### Windows ROCm (AMD Radeon)
 
-In addition to the common Windows development tools and MSVC described above:
+In addition to the common Windows development tools described above, install AMDs HIP package after installing MSVC.
 
 - [AMD HIP](https://www.amd.com/en/developer/resources/rocm-hub/hip-sdk.html)
+- [Strawberry Perl](https://strawberryperl.com/)
 
-#### Windows arm64
-
-The default `Developer PowerShell for VS 2022` may default to x86 which is not what you want.  To ensure you get an arm64 development environment, start a plain PowerShell terminal and run:
-
-```powershell
-import-module 'C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\Tools\\Microsoft.VisualStudio.DevShell.dll'
-Enter-VsDevShell -Arch arm64 -vsinstallpath 'C:\\Program Files\\Microsoft Visual Studio\\2022\\Community' -skipautomaticlocation
-```
-
-You can confirm with `write-host $env:VSCMD_ARG_TGT_ARCH`
-
-Follow the instructions at https://www.msys2.org/wiki/arm64/ to set up an arm64 msys2 environment.  Ollama requires gcc and mingw32-make to compile, which is not currently available on Windows arm64, but a gcc compatibility adapter is available via `mingw-w64-clang-aarch64-gcc-compat`. At a minimum you will need to install the following:
-
-```
-pacman -S mingw-w64-clang-aarch64-clang mingw-w64-clang-aarch64-gcc-compat mingw-w64-clang-aarch64-make make
-```
-
-You will need to ensure your PATH includes go, cmake, gcc and clang mingw32-make to build ollama from source. (typically `C:\msys64\clangarm64\bin\`)
+Lastly, add `ninja.exe` included with MSVC to the system path (e.g. `C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\CommonExtensions\Microsoft\CMake\Ninja`).

docs/docker.md

@@ -63,7 +63,7 @@ docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 114
 Now you can run a model:
 
 ```
-docker exec -it ollama ollama run llama3.2
+docker exec -it ollama ollama run llama3.1
 ```
 
 ### Try different models

docs/faq.md

@@ -32,7 +32,7 @@ When using the API, specify the `num_ctx` parameter:
 
 ```shell
 curl http://localhost:11434/api/generate -d '{
-  "model": "llama3.2",
+  "model": "llama3",
   "prompt": "Why is the sky blue?",
   "options": {
     "num_ctx": 4096
@@ -111,10 +111,7 @@ On Windows, Ollama inherits your user and system environment variables.
 
 ## How do I use Ollama behind a proxy?
 
-Ollama pulls models from the Internet and may require a proxy server to access the models. Use `HTTPS_PROXY` to redirect outbound requests through the proxy. Ensure the proxy certificate is installed as a system certificate. Refer to the section above for how to use environment variables on your platform.
-
-> [!NOTE]
-> Avoid setting `HTTP_PROXY`. Ollama does not use HTTP for model pulls, only HTTPS. Setting `HTTP_PROXY` may interrupt client connections to the server.
+Ollama is compatible with proxy servers if `HTTP_PROXY` or `HTTPS_PROXY` are configured. When using either variables, ensure it is set where `ollama serve` can access the values. When using `HTTPS_PROXY`, ensure the proxy certificate is installed as a system certificate. Refer to the section above for how to use environment variables on your platform.
 
 ### How do I use Ollama behind a proxy in Docker?
 
@@ -194,8 +191,6 @@ Refer to the section [above](#how-do-i-configure-ollama-server) for how to set e
 
 If a different directory needs to be used, set the environment variable `OLLAMA_MODELS` to the chosen directory.
 
-> Note: on Linux using the standard installer, the `ollama` user needs read and write access to the specified directory. To assign the directory to the `ollama` user run `sudo chown -R ollama:ollama <directory>`.
-
 Refer to the section [above](#how-do-i-configure-ollama-server) for how to set environment variables on your platform.
 
 ## How can I use Ollama in Visual Studio Code?
@@ -232,18 +227,14 @@ curl http://localhost:11434/api/chat -d '{"model": "mistral"}'
 
 To preload a model using the CLI, use the command:
 ```shell
-ollama run llama3.2 ""
+ollama run llama3.1 ""
 ```
 
 ## How do I keep a model loaded in memory or make it unload immediately?
 
-By default models are kept in memory for 5 minutes before being unloaded. This allows for quicker response times if you're making numerous requests to the LLM. If you want to immediately unload a model from memory, use the `ollama stop` command:
+By default models are kept in memory for 5 minutes before being unloaded. This allows for quicker response times if you are making numerous requests to the LLM. You may, however, want to free up the memory before the 5 minutes have elapsed or keep the model loaded indefinitely. Use the `keep_alive` parameter with either the `/api/generate` and `/api/chat` API endpoints to control how long the model is left in memory.
 
-```shell
-ollama stop llama3.2
-```
-
-If you're using the API, use the `keep_alive` parameter with the `/api/generate` and `/api/chat` endpoints to set the amount of time that a model stays in memory. The `keep_alive` parameter can be set to:
+The `keep_alive` parameter can be set to:
 * a duration string (such as "10m" or "24h")
 * a number in seconds (such as 3600)
 * any negative number which will keep the model loaded in memory (e.g. -1 or "-1m")
@@ -251,17 +242,17 @@ If you're using the API, use the `keep_alive` parameter with the `/api/generate`
 
 For example, to preload a model and leave it in memory use:
 ```shell
-curl http://localhost:11434/api/generate -d '{"model": "llama3.2", "keep_alive": -1}'
+curl http://localhost:11434/api/generate -d '{"model": "llama3", "keep_alive": -1}'
 ```
 
 To unload the model and free up memory use:
 ```shell
-curl http://localhost:11434/api/generate -d '{"model": "llama3.2", "keep_alive": 0}'
+curl http://localhost:11434/api/generate -d '{"model": "llama3", "keep_alive": 0}'
 ```
 
-Alternatively, you can change the amount of time all models are loaded into memory by setting the `OLLAMA_KEEP_ALIVE` environment variable when starting the Ollama server. The `OLLAMA_KEEP_ALIVE` variable uses the same parameter types as the `keep_alive` parameter types mentioned above. Refer to the section explaining [how to configure the Ollama server](#how-do-i-configure-ollama-server) to correctly set the environment variable.
+Alternatively, you can change the amount of time all models are loaded into memory by setting the `OLLAMA_KEEP_ALIVE` environment variable when starting the Ollama server. The `OLLAMA_KEEP_ALIVE` variable uses the same parameter types as the `keep_alive` parameter types mentioned above. Refer to section explaining [how to configure the Ollama server](#how-do-i-configure-ollama-server) to correctly set the environment variable.
 
-The `keep_alive` API parameter with the `/api/generate` and `/api/chat` API endpoints will override the `OLLAMA_KEEP_ALIVE` setting.
+If you wish to override the `OLLAMA_KEEP_ALIVE` setting, use the `keep_alive` API parameter with the `/api/generate` or `/api/chat` API endpoints.
 
 ## How do I manage the maximum number of requests the Ollama server can queue?
 

docs/gpu.md

@@ -10,7 +10,7 @@ Check your compute compatibility to see if your card is supported:
 | 9.0                | NVIDIA              | `H100`                                                                                                      |
 | 8.9                | GeForce RTX 40xx    | `RTX 4090` `RTX 4080 SUPER` `RTX 4080` `RTX 4070 Ti SUPER` `RTX 4070 Ti` `RTX 4070 SUPER` `RTX 4070` `RTX 4060 Ti` `RTX 4060`  |
 |                    | NVIDIA Professional | `L4` `L40` `RTX 6000`                                                                                       |
-| 8.6                | GeForce RTX 30xx    | `RTX 3090 Ti` `RTX 3090` `RTX 3080 Ti` `RTX 3080` `RTX 3070 Ti` `RTX 3070` `RTX 3060 Ti` `RTX 3060` `RTX 3050 Ti` `RTX 3050`   |
+| 8.6                | GeForce RTX 30xx    | `RTX 3090 Ti` `RTX 3090` `RTX 3080 Ti` `RTX 3080` `RTX 3070 Ti` `RTX 3070` `RTX 3060 Ti` `RTX 3060`         |
 |                    | NVIDIA Professional | `A40` `RTX A6000` `RTX A5000` `RTX A4000` `RTX A3000` `RTX A2000` `A10` `A16` `A2`                          |
 | 8.0                | NVIDIA              | `A100` `A30`                                                                                                |
 | 7.5                | GeForce GTX/RTX     | `GTX 1650 Ti` `TITAN RTX` `RTX 2080 Ti` `RTX 2080` `RTX 2070` `RTX 2060`                                    |
@@ -74,10 +74,6 @@ would set `HSA_OVERRIDE_GFX_VERSION="10.3.0"` as an environment variable for the
 server.  If you have an unsupported AMD GPU you can experiment using the list of
 supported types below.
 
-If you have multiple GPUs with different GFX versions, append the numeric device
-number to the environment variable to set them individually.  For example,
-`HSA_OVERRIDE_GFX_VERSION_0=10.3.0` and  `HSA_OVERRIDE_GFX_VERSION_1=11.0.0`
-
 At this time, the known supported GPU types on linux are the following LLVM Targets.
 This table shows some example GPUs that map to these LLVM targets:
 | **LLVM Target** | **An Example GPU** |
@@ -103,10 +99,9 @@ Reach out on [Discord](https://discord.gg/ollama) or file an
 ### GPU Selection
 
 If you have multiple AMD GPUs in your system and want to limit Ollama to use a
-subset, you can set `ROCR_VISIBLE_DEVICES` to a comma separated list of GPUs.
+subset, you can set `HIP_VISIBLE_DEVICES` to a comma separated list of GPUs.
 You can see the list of devices with `rocminfo`.  If you want to ignore the GPUs
-and force CPU usage, use an invalid GPU ID (e.g., "-1").  When available, use the
-`Uuid` to uniquely identify the device instead of numeric value.
+and force CPU usage, use an invalid GPU ID (e.g., "-1")
 
 ### Container Permission
 

docs/import.md

@@ -1,127 +1,44 @@
-# Importing a model
+# Import
 
-## Table of Contents
+GGUF models and select Safetensors models can be imported directly into Ollama.
 
-  * [Importing a Safetensors adapter](#Importing-a-fine-tuned-adapter-from-Safetensors-weights)
-  * [Importing a Safetensors model](#Importing-a-model-from-Safetensors-weights)
-  * [Importing a GGUF file](#Importing-a-GGUF-based-model-or-adapter)
-  * [Sharing models on ollama.com](#Sharing-your-model-on-ollamacom)
+## Import GGUF
 
-## Importing a fine tuned adapter from Safetensors weights
-
-First, create a `Modelfile` with a `FROM` command pointing at the base model you used for fine tuning, and an `ADAPTER` command which points to the directory with your Safetensors adapter:
-
-```dockerfile
-FROM <base model name>
-ADAPTER /path/to/safetensors/adapter/directory
-```
-
-Make sure that you use the same base model in the `FROM` command as you used to create the adapter otherwise you will get erratic results. Most frameworks use different quantization methods, so it's best to use non-quantized (i.e. non-QLoRA) adapters. If your adapter is in the same directory as your `Modelfile`, use `ADAPTER .` to specify the adapter path.
-
-Now run `ollama create` from the directory where the `Modelfile` was created:
-
-```bash
-ollama create my-model
-```
-
-Lastly, test the model:
-
-```bash
-ollama run my-model
-```
-
-Ollama supports importing adapters based on several different model architectures including:
-
-  * 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)
-
-You can create the adapter using a fine tuning framework or tool which can output adapters in the Safetensors format, such as:
-
-  * Hugging Face [fine tuning framework](https://huggingface.co/docs/transformers/en/training)
-  * [Unsloth](https://github.com/unslothai/unsloth)
-  * [MLX](https://github.com/ml-explore/mlx)
-
-
-## Importing a model from Safetensors weights
-
-First, create a `Modelfile` with a `FROM` command which points to the directory containing your Safetensors weights:
-
-```dockerfile
-FROM /path/to/safetensors/directory
-```
-
-If you create the Modelfile in the same directory as the weights, you can use the command `FROM .`.
-
-Now run the `ollama create` command from the directory where you created the `Modelfile`:
-
-```shell
-ollama create my-model
-```
-
-Lastly, test the model:
-
-```shell
-ollama run my-model
-```
-
-Ollama supports importing models for several different architectures including:
-
-  * 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 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:
-
-  * converting a Safetensors model with the `convert_hf_to_gguf.py` from Llama.cpp; 
-  * 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:
+A binary GGUF file can be imported directly into Ollama through a Modelfile.
 
 ```dockerfile
 FROM /path/to/file.gguf
 ```
 
-For a GGUF adapter, create the `Modelfile` with:
+## Import Safetensors
+
+If the model being imported is one of these architectures, it can be imported directly into Ollama through a Modelfile:
+
+ - LlamaForCausalLM
+ - MistralForCausalLM
+ - MixtralForCausalLM
+ - GemmaForCausalLM
+ - Phi3ForCausalLM
 
 ```dockerfile
-FROM <model name>
-ADAPTER /path/to/file.gguf
+FROM /path/to/safetensors/directory
 ```
 
-When importing a GGUF adapter, it's important to use the same base model as the base model that the adapter was created with. You can use:
+For architectures not directly convertable by Ollama, see llama.cpp's [guide](https://github.com/ggerganov/llama.cpp/blob/master/README.md#prepare-and-quantize) on conversion. After conversion, see [Import GGUF](#import-gguf).
 
- * a model from Ollama
- * a GGUF file
- * a Safetensors based model 
+## Automatic Quantization
 
-Once you have created your `Modelfile`, use the `ollama create` command to build the model.
+> [!NOTE]
+> Automatic quantization requires v0.1.35 or higher.
 
-```shell
-ollama create my-model
-```
-
-## Quantizing a Model
-
-Quantizing a model allows you to run models faster and with less memory consumption but at reduced accuracy. This allows you to run a model on more modest hardware.
-
-Ollama can quantize FP16 and FP32 based models into different quantization levels using the `-q/--quantize` flag with the `ollama create` command.
-
-First, create a Modelfile with the FP16 or FP32 based model you wish to quantize.
+Ollama is capable of quantizing FP16 or FP32 models to any of the supported quantizations with the `-q/--quantize` flag in `ollama create`.
 
 ```dockerfile
 FROM /path/to/my/gemma/f16/model
 ```
 
-Use `ollama create` to then create the quantized model.
-
 ```shell
-$ ollama create --quantize q4_K_M mymodel
+$ ollama create -q Q4_K_M mymodel
 transferring model data
 quantizing F16 model to Q4_K_M
 creating new layer sha256:735e246cc1abfd06e9cdcf95504d6789a6cd1ad7577108a70d9902fef503c1bd
@@ -132,53 +49,42 @@ success
 
 ### Supported Quantizations
 
-- `q4_0`
-- `q4_1`
-- `q5_0`
-- `q5_1`
-- `q8_0`
+- `Q4_0`
+- `Q4_1`
+- `Q5_0`
+- `Q5_1`
+- `Q8_0`
 
 #### K-means Quantizations
 
-- `q3_K_S`
-- `q3_K_M`
-- `q3_K_L`
-- `q4_K_S`
-- `q4_K_M`
-- `q5_K_S`
-- `q5_K_M`
-- `q6_K`
+- `Q3_K_S`
+- `Q3_K_M`
+- `Q3_K_L`
+- `Q4_K_S`
+- `Q4_K_M`
+- `Q5_K_S`
+- `Q5_K_M`
+- `Q6_K`
 
+## Template Detection
 
-## Sharing your model on ollama.com
+> [!NOTE]
+> Template detection requires v0.1.42 or higher.
 
-You can share any model you have created by pushing it to [ollama.com](https://ollama.com) so that other users can try it out.
+Ollama uses model metadata, specifically `tokenizer.chat_template`, to automatically create a template appropriate for the model you're importing.
 
-First, use your browser to go to the [Ollama Sign-Up](https://ollama.com/signup) page. If you already have an account, you can skip this step.
-
-<img src="images/signup.png" alt="Sign-Up" width="40%">
-
-The `Username` field will be used as part of your model's name (e.g. `jmorganca/mymodel`), so make sure you are comfortable with the username that you have selected.
-
-Now that you have created an account and are signed-in, go to the [Ollama Keys Settings](https://ollama.com/settings/keys) page.
-
-Follow the directions on the page to determine where your Ollama Public Key is located.
-
-<img src="images/ollama-keys.png" alt="Ollama Keys" width="80%">
-
-Click on the `Add Ollama Public Key` button, and copy and paste the contents of your Ollama Public Key into the text field.
-
-To push a model to [ollama.com](https://ollama.com), first make sure that it is named correctly with your username. You may have to use the `ollama cp` command to copy
-your model to give it the correct name. Once you're happy with your model's name, use the `ollama push` command to push it to [ollama.com](https://ollama.com).
-
-```shell
-ollama cp mymodel myuser/mymodel
-ollama push myuser/mymodel
+```dockerfile
+FROM /path/to/my/gemma/model
 ```
 
-Once your model has been pushed, other users can pull and run it by using the command:
-
 ```shell
-ollama run myuser/mymodel
+$ ollama create mymodel
+transferring model data
+using autodetected template gemma-instruct
+creating new layer sha256:baa2a0edc27d19cc6b7537578a9a7ba1a4e3214dc185ed5ae43692b319af7b84
+creating new layer sha256:ba66c3309914dbef07e5149a648fd1877f030d337a4f240d444ea335008943cb
+writing manifest
+success
 ```
 
+Defining a template in the Modelfile will disable this feature which may be useful if you want to use a different template than the autodetected one.

docs/linux.md

@@ -1,59 +1,40 @@
-# Linux
+# Ollama on Linux
 
 ## Install
 
-To install Ollama, run the following command:
+Install Ollama running this one-liner:
 
-```shell
+>
+
+```bash
 curl -fsSL https://ollama.com/install.sh | sh
 ```
 
+## AMD Radeon GPU support
+
+While AMD has contributed the `amdgpu` driver upstream to the official linux
+kernel source, the version is older and may not support all ROCm features. We
+recommend you install the latest driver from
+https://www.amd.com/en/support/linux-drivers for best support of your Radeon
+GPU.
+
 ## Manual install
 
-Download and extract the package:
+### Download the `ollama` binary
 
-```shell
-curl -L https://ollama.com/download/ollama-linux-amd64.tgz -o ollama-linux-amd64.tgz
-sudo tar -C /usr -xzf ollama-linux-amd64.tgz
-```
+Ollama is distributed as a self-contained binary. Download it to a directory in your PATH:
 
-Start Ollama:
-
-```shell
-ollama serve
-```
-
-In another terminal, verify that Ollama is running:
-
-```shell
-ollama -v
-```
-
-### AMD GPU install
-
-If you have an AMD GPU, also download and extract the additional ROCm package:
-
-```shell
-curl -L https://ollama.com/download/ollama-linux-amd64-rocm.tgz -o ollama-linux-amd64-rocm.tgz
-sudo tar -C /usr -xzf ollama-linux-amd64-rocm.tgz
-```
-
-### ARM64 install
-
-Download and extract the ARM64-specific package:
-
-```shell
-curl -L https://ollama.com/download/ollama-linux-arm64.tgz -o ollama-linux-arm64.tgz
-sudo tar -C /usr -xzf ollama-linux-arm64.tgz
+```bash
+sudo curl -L https://ollama.com/download/ollama-linux-amd64 -o /usr/bin/ollama
+sudo chmod +x /usr/bin/ollama
 ```
 
 ### Adding Ollama as a startup service (recommended)
 
-Create a user and group for Ollama:
+Create a user for Ollama:
 
-```shell
-sudo useradd -r -s /bin/false -U -m -d /usr/share/ollama ollama
-sudo usermod -a -G ollama $(whoami)
+```bash
+sudo useradd -r -s /bin/false -m -d /usr/share/ollama ollama
 ```
 
 Create a service file in `/etc/systemd/system/ollama.service`:
@@ -69,7 +50,6 @@ User=ollama
 Group=ollama
 Restart=always
 RestartSec=3
-Environment="PATH=$PATH"
 
 [Install]
 WantedBy=default.target
@@ -77,54 +57,47 @@ WantedBy=default.target
 
 Then start the service:
 
-```shell
+```bash
 sudo systemctl daemon-reload
 sudo systemctl enable ollama
 ```
 
-### Install CUDA drivers (optional)
+### Install CUDA drivers (optional – for Nvidia GPUs)
 
 [Download and install](https://developer.nvidia.com/cuda-downloads) CUDA.
 
 Verify that the drivers are installed by running the following command, which should print details about your GPU:
 
-```shell
+```bash
 nvidia-smi
 ```
 
-### Install AMD ROCm drivers (optional)
+### Install ROCm (optional - for Radeon GPUs)
+[Download and Install](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html)
 
-[Download and Install](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html) ROCm v6.
+Make sure to install ROCm v6
 
 ### Start Ollama
 
-Start Ollama and verify it is running:
+Start Ollama using `systemd`:
 
-```shell
+```bash
 sudo systemctl start ollama
-sudo systemctl status ollama
 ```
 
-> [!NOTE]
-> While AMD has contributed the `amdgpu` driver upstream to the official linux
-> kernel source, the version is older and may not support all ROCm features. We
-> recommend you install the latest driver from
-> https://www.amd.com/en/support/linux-drivers for best support of your Radeon
-> GPU.
+## Update
 
-## Updating
+Update ollama by running the install script again:
 
-Update Ollama by running the install script again:
-
-```shell
+```bash
 curl -fsSL https://ollama.com/install.sh | sh
 ```
 
-Or by re-downloading Ollama:
+Or by downloading the ollama binary:
 
-```shell
-curl -L https://ollama.com/download/ollama-linux-amd64.tgz -o ollama-linux-amd64.tgz
-sudo tar -C /usr -xzf ollama-linux-amd64.tgz
+```bash
+sudo curl -L https://ollama.com/download/ollama-linux-amd64 -o /usr/bin/ollama
+sudo chmod +x /usr/bin/ollama
 ```
 
 ## Installing specific versions
@@ -133,15 +106,15 @@ Use `OLLAMA_VERSION` environment variable with the install script to install a s
 
 For example:
 
-```shell
-curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.3.9 sh
+```
+curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.1.32 sh
 ```
 
 ## Viewing logs
 
 To view logs of Ollama running as a startup service, run:
 
-```shell
+```bash
 journalctl -e -u ollama
 ```
 
@@ -149,7 +122,7 @@ journalctl -e -u ollama
 
 Remove the ollama service:
 
-```shell
+```bash
 sudo systemctl stop ollama
 sudo systemctl disable ollama
 sudo rm /etc/systemd/system/ollama.service
@@ -157,13 +130,13 @@ sudo rm /etc/systemd/system/ollama.service
 
 Remove the ollama binary from your bin directory (either `/usr/local/bin`, `/usr/bin`, or `/bin`):
 
-```shell
+```bash
 sudo rm $(which ollama)
 ```
 
 Remove the downloaded models and Ollama service user and group:
 
-```shell
+```bash
 sudo rm -r /usr/share/ollama
 sudo userdel ollama
 sudo groupdel ollama

docs/modelfile.md

@@ -11,9 +11,8 @@ A model file is the blueprint to create and share models with Ollama.
 - [Examples](#examples)
 - [Instructions](#instructions)
   - [FROM (Required)](#from-required)
-    - [Build from existing model](#build-from-existing-model)
-    - [Build from a Safetensors model](#build-from-a-safetensors-model)
-    - [Build from a GGUF file](#build-from-a-gguf-file)
+    - [Build from llama3](#build-from-llama3)
+    - [Build from a bin file](#build-from-a-bin-file)
   - [PARAMETER](#parameter)
     - [Valid Parameters and Values](#valid-parameters-and-values)
   - [TEMPLATE](#template)
@@ -50,7 +49,7 @@ INSTRUCTION arguments
 An example of a `Modelfile` creating a mario blueprint:
 
 ```modelfile
-FROM llama3.2
+FROM llama3
 # sets the temperature to 1 [higher is more creative, lower is more coherent]
 PARAMETER temperature 1
 # sets the context window size to 4096, this controls how many tokens the LLM can use as context to generate the next token
@@ -72,10 +71,10 @@ More examples are available in the [examples directory](../examples).
 To view the Modelfile of a given model, use the `ollama show --modelfile` command.
 
   ```bash
-  > ollama show --modelfile llama3.2
+  > ollama show --modelfile llama3
   # Modelfile generated by "ollama show"
   # To build a new Modelfile based on this one, replace the FROM line with:
-  # FROM llama3.2:latest
+  # FROM llama3:latest
   FROM /Users/pdevine/.ollama/models/blobs/sha256-00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29
   TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>
 
@@ -100,39 +99,22 @@ The `FROM` instruction defines the base model to use when creating a model.
 FROM <model name>:<tag>
 ```
 
-#### Build from existing model
+#### Build from llama3
 
 ```modelfile
-FROM llama3.2
+FROM llama3
 ```
 
 A list of available base models:
 <https://github.com/ollama/ollama#model-library>
-Additional models can be found at:
-<https://ollama.com/library>
 
-#### Build from a Safetensors model
+#### Build from a `bin` file
 
 ```modelfile
-FROM <model directory>
+FROM ./ollama-model.bin
 ```
 
-The model directory should contain the Safetensors weights for a supported architecture.
-
-Currently supported model architectures:
-  * 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
-
-#### Build from a GGUF file
-
-```modelfile
-FROM ./ollama-model.gguf
-```
-
-The GGUF file location should be specified as an absolute path or relative to the `Modelfile` location.
-
+This bin file location should be specified as an absolute path or relative to the `Modelfile` location.
 
 ### PARAMETER
 
@@ -192,23 +174,10 @@ SYSTEM """<system message>"""
 
 ### ADAPTER
 
-The `ADAPTER` instruction specifies a fine tuned LoRA adapter that should apply to the base model. The value of the adapter should be an absolute path or a path relative to the Modelfile. The base model should be specified with a `FROM` instruction. If the base model is not the same as the base model that the adapter was tuned from the behaviour will be erratic.
-
-#### Safetensor adapter
+The `ADAPTER` instruction is an optional instruction that specifies any LoRA adapter that should apply to the base model. The value of this instruction should be an absolute path or a path relative to the Modelfile and the file must be in a GGML file format. The adapter should be tuned from the base model otherwise the behaviour is undefined.
 
 ```modelfile
-ADAPTER <path to safetensor adapter>
-```
-
-Currently supported Safetensor adapters:
-  * Llama (including Llama 2, Llama 3, and Llama 3.1)
-  * Mistral (including Mistral 1, Mistral 2, and Mixtral)
-  * Gemma (including Gemma 1 and Gemma 2)
-
-#### GGUF adapter
-
-```modelfile
-ADAPTER ./ollama-lora.gguf
+ADAPTER ./ollama-lora.bin
 ```
 
 ### LICENSE

docs/openai.md

@@ -25,7 +25,7 @@ chat_completion = client.chat.completions.create(
             'content': 'Say this is a test',
         }
     ],
-    model='llama3.2',
+    model='llama3',
 )
 
 response = client.chat.completions.create(
@@ -37,7 +37,7 @@ response = client.chat.completions.create(
                 {"type": "text", "text": "What's in this image?"},
                 {
                     "type": "image_url",
-                    "image_url": "data:image/png;base64,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",
+                    "image_url": "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",
                 },
             ],
         }
@@ -46,13 +46,13 @@ response = client.chat.completions.create(
 )
 
 completion = client.completions.create(
-    model="llama3.2",
+    model="llama3",
     prompt="Say this is a test",
 )
 
 list_completion = client.models.list()
 
-model = client.models.retrieve("llama3.2")
+model = client.models.retrieve("llama3")
 
 embeddings = client.embeddings.create(
     model="all-minilm",
@@ -74,7 +74,7 @@ const openai = new OpenAI({
 
 const chatCompletion = await openai.chat.completions.create({
     messages: [{ role: 'user', content: 'Say this is a test' }],
-    model: 'llama3.2',
+    model: 'llama3',
 })
 
 const response = await openai.chat.completions.create({
@@ -86,7 +86,7 @@ const response = await openai.chat.completions.create({
             { type: "text", text: "What's in this image?" },
             {
             type: "image_url",
-            image_url: "data:image/png;base64,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",
+            image_url: "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",
             },
         ],
         },
@@ -94,13 +94,13 @@ const response = await openai.chat.completions.create({
 })
 
 const completion = await openai.completions.create({
-    model: "llama3.2",
+    model: "llama3",
     prompt: "Say this is a test.",
 })
 
 const listCompletion = await openai.models.list()
 
-const model = await openai.models.retrieve("llama3.2")
+const model = await openai.models.retrieve("llama3")
 
 const embedding = await openai.embeddings.create({
   model: "all-minilm",
@@ -114,7 +114,7 @@ const embedding = await openai.embeddings.create({
 curl http://localhost:11434/v1/chat/completions \
     -H "Content-Type: application/json" \
     -d '{
-        "model": "llama3.2",
+        "model": "llama3",
         "messages": [
             {
                 "role": "system",
@@ -142,7 +142,7 @@ curl http://localhost:11434/v1/chat/completions \
           {
             "type": "image_url",
             "image_url": {
-               "url": "data:image/png;base64,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"
+               "url": "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"
             }
           }
         ]
@@ -154,13 +154,13 @@ curl http://localhost:11434/v1/chat/completions \
 curl http://localhost:11434/v1/completions \
     -H "Content-Type: application/json" \
     -d '{
-        "model": "llama3.2",
+        "model": "llama3",
         "prompt": "Say this is a test"
     }'
 
 curl http://localhost:11434/v1/models
 
-curl http://localhost:11434/v1/models/llama3.2
+curl http://localhost:11434/v1/models/llama3
 
 curl http://localhost:11434/v1/embeddings \
     -H "Content-Type: application/json" \
@@ -274,7 +274,7 @@ curl http://localhost:11434/v1/embeddings \
 Before using a model, pull it locally `ollama pull`:
 
 ```shell
-ollama pull llama3.2
+ollama pull llama3
 ```
 
 ### Default model names
@@ -282,7 +282,7 @@ ollama pull llama3.2
 For tooling that relies on default OpenAI model names such as `gpt-3.5-turbo`, use `ollama cp` to copy an existing model name to a temporary name:
 
 ```
-ollama cp llama3.2 gpt-3.5-turbo
+ollama cp llama3 gpt-3.5-turbo
 ```
 
 Afterwards, this new model name can be specified the `model` field:
@@ -300,28 +300,3 @@ 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

@@ -33,7 +33,7 @@ Omitting a template in these models puts the responsibility of correctly templat
 To add templates in your model, you'll need to add a `TEMPLATE` command to the Modelfile. Here's an example using Meta's Llama 3.
 
 ```dockerfile
-FROM llama3.2
+FROM llama3
 
 TEMPLATE """{{- if .System }}<|start_header_id|>system<|end_header_id|>
 

docs/troubleshooting.md

@@ -91,19 +91,6 @@ If none of those resolve the problem, gather additional information and file an
 - Check dmesg for any errors `sudo dmesg | grep -i nvrm` and `sudo dmesg | grep -i nvidia`
 
 
-## AMD GPU Discovery
-
-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 -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`
-
 ## 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/langchainjs.md

@@ -15,7 +15,7 @@ import { Ollama } from "@langchain/community/llms/ollama";
 
 const ollama = new Ollama({
   baseUrl: "http://localhost:11434",
-  model: "llama3.2",
+  model: "llama3.1",
 });
 
 const answer = await ollama.invoke(`why is the sky blue?`);
@@ -23,7 +23,7 @@ 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.
+That will get us the same thing as if we ran `ollama run llama3.1 "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

docs/tutorials/langchainpy.md

@@ -10,7 +10,7 @@ This sounds like a typical censored response, but even llama2-uncensored gives a
 
 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 **Llama3** model using **Ollama**. First, we need to install the **LangChain** package:
+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`
 

docs/windows.md

@@ -1,15 +1,22 @@
-# Ollama Windows
+# Ollama Windows Preview
 
-Welcome to Ollama for Windows.
+Welcome to the Ollama Windows preview.
 
 No more WSL required!
 
 Ollama now runs as a native Windows application, including NVIDIA and AMD Radeon GPU support.
-After installing Ollama for Windows, Ollama will run in the background and
+After installing Ollama Windows Preview, Ollama will run in the background and
 the `ollama` command line is available in `cmd`, `powershell` or your favorite
 terminal application. As usual the Ollama [api](./api.md) will be served on
 `http://localhost:11434`.
 
+As this is a preview release, you should expect a few bugs here and there.  If
+you run into a problem you can reach out on
+[Discord](https://discord.gg/ollama), or file an
+[issue](https://github.com/ollama/ollama/issues).
+Logs will often be helpful in diagnosing the problem (see
+[Troubleshooting](#troubleshooting) below)
+
 ## System Requirements
 
 * Windows 10 22H2 or newer, Home or Pro
@@ -18,41 +25,19 @@ terminal application. As usual the Ollama [api](./api.md) will be served on
 
 Ollama uses unicode characters for progress indication, which may render as unknown squares in some older terminal fonts in Windows 10. If you see this, try changing your terminal font settings.
 
-## Filesystem Requirements
-
-The Ollama install does not require Administrator, and installs in your home directory by default.  You'll need at least 4GB of space for the binary install.  Once you've installed Ollama, you'll need additional space for storing the Large Language models, which can be tens to hundreds of GB in size.  If your home directory doesn't have enough space, you can change where the binaries are installed, and where the models are stored.
-
-### Changing Install Location
-
-To install the Ollama application in a location different than your home directory, start the installer with the following flag
-
-```powershell
-OllamaSetup.exe /DIR="d:\some\location"
-```
-
-### Changing Model Location
-
-To change where Ollama stores the downloaded models instead of using your home directory, set the environment variable `OLLAMA_MODELS` in your user account.
-
-1. Start the Settings (Windows 11) or Control Panel (Windows 10) application and search for _environment variables_.
-
-2. Click on _Edit environment variables for your account_.
-
-3. Edit or create a new variable for your user account for `OLLAMA_MODELS` where you want the models stored
-
-4. Click OK/Apply to save.
-
-If Ollama is already running, Quit the tray application and relaunch it from the Start menu, or a new terminal started after you saved the environment variables.
-
 ## API Access
 
 Here's a quick example showing API access from `powershell`
 ```powershell
-(Invoke-WebRequest -method POST -Body '{"model":"llama3.2", "prompt":"Why is the sky blue?", "stream": false}' -uri http://localhost:11434/api/generate ).Content | ConvertFrom-json
+(Invoke-WebRequest -method POST -Body '{"model":"llama3", "prompt":"Why is the sky blue?", "stream": false}' -uri http://localhost:11434/api/generate ).Content | ConvertFrom-json
 ```
 
 ## Troubleshooting
 
+While we're in preview, `OLLAMA_DEBUG` is always enabled, which adds
+a "view logs" menu item to the app, and increases logging for the GUI app and
+server.
+
 Ollama on Windows stores files in a few different locations.  You can view them in
 the explorer window by hitting `<cmd>+R` and type in:
 - `explorer %LOCALAPPDATA%\Ollama` contains logs, and downloaded updates
@@ -63,13 +48,6 @@ the explorer window by hitting `<cmd>+R` and type in:
 - `explorer %HOMEPATH%\.ollama` contains models and configuration
 - `explorer %TEMP%` contains temporary executable files in one or more `ollama*` directories
 
-## Uninstall
-
-The Ollama Windows installer registers an Uninstaller application.  Under `Add or remove programs` in Windows Settings, you can uninstall Ollama.
-
-> [!NOTE]
-> If you have [changed the OLLAMA_MODELS location](#changing-model-location), the installer will not remove your downloaded models
-
 
 ## Standalone CLI
 

envconfig/config.go

@@ -30,7 +30,9 @@ func Host() *url.URL {
 		defaultPort = "443"
 	}
 
-	hostport, path, _ := strings.Cut(hostport, "/")
+	// trim trailing slashes
+	hostport = strings.TrimRight(hostport, "/")
+
 	host, port, err := net.SplitHostPort(hostport)
 	if err != nil {
 		host, port = "127.0.0.1", defaultPort
@@ -43,13 +45,15 @@ func Host() *url.URL {
 
 	if n, err := strconv.ParseInt(port, 10, 32); err != nil || n > 65535 || n < 0 {
 		slog.Warn("invalid port, using default", "port", port, "default", defaultPort)
-		port = defaultPort
+		return &url.URL{
+			Scheme: scheme,
+			Host:   net.JoinHostPort(host, defaultPort),
+		}
 	}
 
 	return &url.URL{
 		Scheme: scheme,
 		Host:   net.JoinHostPort(host, port),
-		Path:   path,
 	}
 }
 
@@ -72,7 +76,6 @@ func Origins() (origins []string) {
 		"app://*",
 		"file://*",
 		"tauri://*",
-		"vscode-webview://*",
 	)
 
 	return origins
@@ -113,26 +116,6 @@ func KeepAlive() (keepAlive time.Duration) {
 	return keepAlive
 }
 
-// LoadTimeout returns the duration for stall detection during model loads. LoadTimeout can be configured via the OLLAMA_LOAD_TIMEOUT environment variable.
-// Zero or Negative values are treated as infinite.
-// Default is 5 minutes.
-func LoadTimeout() (loadTimeout time.Duration) {
-	loadTimeout = 5 * time.Minute
-	if s := Var("OLLAMA_LOAD_TIMEOUT"); s != "" {
-		if d, err := time.ParseDuration(s); err == nil {
-			loadTimeout = d
-		} else if n, err := strconv.ParseInt(s, 10, 64); err == nil {
-			loadTimeout = time.Duration(n) * time.Second
-		}
-	}
-
-	if loadTimeout <= 0 {
-		return time.Duration(math.MaxInt64)
-	}
-
-	return loadTimeout
-}
-
 func Bool(k string) func() bool {
 	return func() bool {
 		if s := Var(k); s != "" {
@@ -161,8 +144,6 @@ var (
 	SchedSpread = Bool("OLLAMA_SCHED_SPREAD")
 	// IntelGPU enables experimental Intel GPU detection.
 	IntelGPU = Bool("OLLAMA_INTEL_GPU")
-	// MultiUserCache optimizes prompt caching for multi-user scenarios
-	MultiUserCache = Bool("OLLAMA_MULTIUSER_CACHE")
 )
 
 func String(s string) func() string {
@@ -182,6 +163,53 @@ var (
 	HsaOverrideGfxVersion = String("HSA_OVERRIDE_GFX_VERSION")
 )
 
+func RunnersDir() (p string) {
+	if p := Var("OLLAMA_RUNNERS_DIR"); p != "" {
+		return p
+	}
+
+	if runtime.GOOS != "windows" {
+		return
+	}
+
+	defer func() {
+		if p == "" {
+			slog.Error("unable to locate llm runner directory. Set OLLAMA_RUNNERS_DIR to the location of 'ollama_runners'")
+		}
+	}()
+
+	// On Windows we do not carry the payloads inside the main executable
+	exe, err := os.Executable()
+	if err != nil {
+		return
+	}
+
+	cwd, err := os.Getwd()
+	if err != nil {
+		return
+	}
+
+	var paths []string
+	for _, root := range []string{filepath.Dir(exe), cwd} {
+		paths = append(paths,
+			root,
+			filepath.Join(root, "windows-"+runtime.GOARCH),
+			filepath.Join(root, "dist", "windows-"+runtime.GOARCH),
+		)
+	}
+
+	// Try a few variations to improve developer experience when building from source in the local tree
+	for _, path := range paths {
+		candidate := filepath.Join(path, "ollama_runners")
+		if _, err := os.Stat(candidate); err == nil {
+			p = candidate
+			break
+		}
+	}
+
+	return p
+}
+
 func Uint(key string, defaultValue uint) func() uint {
 	return func() uint {
 		if s := Var(key); s != "" {
@@ -207,23 +235,6 @@ var (
 	MaxVRAM = Uint("OLLAMA_MAX_VRAM", 0)
 )
 
-func Uint64(key string, defaultValue uint64) func() uint64 {
-	return func() uint64 {
-		if s := Var(key); s != "" {
-			if n, err := strconv.ParseUint(s, 10, 64); err != nil {
-				slog.Warn("invalid environment variable, using default", "key", key, "value", s, "default", defaultValue)
-			} else {
-				return n
-			}
-		}
-
-		return defaultValue
-	}
-}
-
-// Set aside VRAM per GPU
-var GpuOverhead = Uint64("OLLAMA_GPU_OVERHEAD", 0)
-
 type EnvVar struct {
 	Name        string
 	Value       any
@@ -234,11 +245,9 @@ 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_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\")"},
 		"OLLAMA_LLM_LIBRARY":       {"OLLAMA_LLM_LIBRARY", LLMLibrary(), "Set LLM library to bypass autodetection"},
-		"OLLAMA_LOAD_TIMEOUT":      {"OLLAMA_LOAD_TIMEOUT", LoadTimeout(), "How long to allow model loads to stall before giving up (default \"5m\")"},
 		"OLLAMA_MAX_LOADED_MODELS": {"OLLAMA_MAX_LOADED_MODELS", MaxRunners(), "Maximum number of loaded models per GPU"},
 		"OLLAMA_MAX_QUEUE":         {"OLLAMA_MAX_QUEUE", MaxQueue(), "Maximum number of queued requests"},
 		"OLLAMA_MODELS":            {"OLLAMA_MODELS", Models(), "The path to the models directory"},
@@ -246,32 +255,18 @@ func AsMap() map[string]EnvVar {
 		"OLLAMA_NOPRUNE":           {"OLLAMA_NOPRUNE", NoPrune(), "Do not prune model blobs on startup"},
 		"OLLAMA_NUM_PARALLEL":      {"OLLAMA_NUM_PARALLEL", NumParallel(), "Maximum number of parallel requests"},
 		"OLLAMA_ORIGINS":           {"OLLAMA_ORIGINS", Origins(), "A comma separated list of allowed origins"},
+		"OLLAMA_RUNNERS_DIR":       {"OLLAMA_RUNNERS_DIR", RunnersDir(), "Location for runners"},
 		"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
-		"HTTP_PROXY":  {"HTTP_PROXY", String("HTTP_PROXY")(), "HTTP proxy"},
-		"HTTPS_PROXY": {"HTTPS_PROXY", String("HTTPS_PROXY")(), "HTTPS proxy"},
-		"NO_PROXY":    {"NO_PROXY", String("NO_PROXY")(), "No proxy"},
 	}
-
-	if runtime.GOOS != "windows" {
-		// Windows environment variables are case-insensitive so there's no need to duplicate them
-		ret["http_proxy"] = EnvVar{"http_proxy", String("http_proxy")(), "HTTP proxy"}
-		ret["https_proxy"] = EnvVar{"https_proxy", String("https_proxy")(), "HTTPS proxy"}
-		ret["no_proxy"] = EnvVar{"no_proxy", String("no_proxy")(), "No proxy"}
-	}
-
 	if runtime.GOOS != "darwin" {
 		ret["CUDA_VISIBLE_DEVICES"] = EnvVar{"CUDA_VISIBLE_DEVICES", CudaVisibleDevices(), "Set which NVIDIA devices are visible"}
-		ret["HIP_VISIBLE_DEVICES"] = EnvVar{"HIP_VISIBLE_DEVICES", HipVisibleDevices(), "Set which AMD devices are visible by numeric ID"}
-		ret["ROCR_VISIBLE_DEVICES"] = EnvVar{"ROCR_VISIBLE_DEVICES", RocrVisibleDevices(), "Set which AMD devices are visible by UUID or numeric ID"}
-		ret["GPU_DEVICE_ORDINAL"] = EnvVar{"GPU_DEVICE_ORDINAL", GpuDeviceOrdinal(), "Set which AMD devices are visible by numeric ID"}
+		ret["HIP_VISIBLE_DEVICES"] = EnvVar{"HIP_VISIBLE_DEVICES", HipVisibleDevices(), "Set which AMD devices are visible"}
+		ret["ROCR_VISIBLE_DEVICES"] = EnvVar{"ROCR_VISIBLE_DEVICES", RocrVisibleDevices(), "Set which AMD devices are visible"}
+		ret["GPU_DEVICE_ORDINAL"] = EnvVar{"GPU_DEVICE_ORDINAL", GpuDeviceOrdinal(), "Set which AMD devices are visible"}
 		ret["HSA_OVERRIDE_GFX_VERSION"] = EnvVar{"HSA_OVERRIDE_GFX_VERSION", HsaOverrideGfxVersion(), "Override the gfx used for all detected AMD GPUs"}
 		ret["OLLAMA_INTEL_GPU"] = EnvVar{"OLLAMA_INTEL_GPU", IntelGPU(), "Enable experimental Intel GPU detection"}
 	}
-
 	return ret
 }
 
@@ -287,12 +282,3 @@ func Values() map[string]string {
 func Var(key string) string {
 	return strings.Trim(strings.TrimSpace(os.Getenv(key)), "\"'")
 }
-
-// On windows, we keep the binary at the top directory, but
-// other platforms use a "bin" directory, so this returns ".."
-func LibRelativeToExe() string {
-	if runtime.GOOS == "windows" {
-		return "."
-	}
-	return ".."
-}

envconfig/config_test.go

@@ -13,35 +13,34 @@ func TestHost(t *testing.T) {
 		value  string
 		expect string
 	}{
-		"empty":               {"", "http://127.0.0.1:11434"},
-		"only address":        {"1.2.3.4", "http://1.2.3.4:11434"},
-		"only port":           {":1234", "http://:1234"},
-		"address and port":    {"1.2.3.4:1234", "http://1.2.3.4:1234"},
-		"hostname":            {"example.com", "http://example.com:11434"},
-		"hostname and port":   {"example.com:1234", "http://example.com:1234"},
-		"zero port":           {":0", "http://:0"},
-		"too large port":      {":66000", "http://:11434"},
-		"too small port":      {":-1", "http://:11434"},
-		"ipv6 localhost":      {"[::1]", "http://[::1]:11434"},
-		"ipv6 world open":     {"[::]", "http://[::]:11434"},
-		"ipv6 no brackets":    {"::1", "http://[::1]:11434"},
-		"ipv6 + port":         {"[::1]:1337", "http://[::1]:1337"},
-		"extra space":         {" 1.2.3.4 ", "http://1.2.3.4:11434"},
-		"extra quotes":        {"\"1.2.3.4\"", "http://1.2.3.4:11434"},
-		"extra space+quotes":  {" \" 1.2.3.4 \" ", "http://1.2.3.4:11434"},
-		"extra single quotes": {"'1.2.3.4'", "http://1.2.3.4:11434"},
-		"http":                {"http://1.2.3.4", "http://1.2.3.4:80"},
-		"http port":           {"http://1.2.3.4:4321", "http://1.2.3.4:4321"},
-		"https":               {"https://1.2.3.4", "https://1.2.3.4:443"},
-		"https port":          {"https://1.2.3.4:4321", "https://1.2.3.4:4321"},
-		"proxy path":          {"https://example.com/ollama", "https://example.com:443/ollama"},
+		"empty":               {"", "127.0.0.1:11434"},
+		"only address":        {"1.2.3.4", "1.2.3.4:11434"},
+		"only port":           {":1234", ":1234"},
+		"address and port":    {"1.2.3.4:1234", "1.2.3.4:1234"},
+		"hostname":            {"example.com", "example.com:11434"},
+		"hostname and port":   {"example.com:1234", "example.com:1234"},
+		"zero port":           {":0", ":0"},
+		"too large port":      {":66000", ":11434"},
+		"too small port":      {":-1", ":11434"},
+		"ipv6 localhost":      {"[::1]", "[::1]:11434"},
+		"ipv6 world open":     {"[::]", "[::]:11434"},
+		"ipv6 no brackets":    {"::1", "[::1]:11434"},
+		"ipv6 + port":         {"[::1]:1337", "[::1]:1337"},
+		"extra space":         {" 1.2.3.4 ", "1.2.3.4:11434"},
+		"extra quotes":        {"\"1.2.3.4\"", "1.2.3.4:11434"},
+		"extra space+quotes":  {" \" 1.2.3.4 \" ", "1.2.3.4:11434"},
+		"extra single quotes": {"'1.2.3.4'", "1.2.3.4:11434"},
+		"http":                {"http://1.2.3.4", "1.2.3.4:80"},
+		"http port":           {"http://1.2.3.4:4321", "1.2.3.4:4321"},
+		"https":               {"https://1.2.3.4", "1.2.3.4:443"},
+		"https port":          {"https://1.2.3.4:4321", "1.2.3.4:4321"},
 	}
 
 	for name, tt := range cases {
 		t.Run(name, func(t *testing.T) {
 			t.Setenv("OLLAMA_HOST", tt.value)
-			if host := Host(); host.String() != tt.expect {
-				t.Errorf("%s: expected %s, got %s", name, tt.expect, host.String())
+			if host := Host(); host.Host != tt.expect {
+				t.Errorf("%s: expected %s, got %s", name, tt.expect, host.Host)
 			}
 		})
 	}
@@ -68,7 +67,6 @@ func TestOrigins(t *testing.T) {
 			"app://*",
 			"file://*",
 			"tauri://*",
-			"vscode-webview://*",
 		}},
 		{"http://10.0.0.1", []string{
 			"http://10.0.0.1",
@@ -87,7 +85,6 @@ func TestOrigins(t *testing.T) {
 			"app://*",
 			"file://*",
 			"tauri://*",
-			"vscode-webview://*",
 		}},
 		{"http://172.16.0.1,https://192.168.0.1", []string{
 			"http://172.16.0.1",
@@ -107,7 +104,6 @@ func TestOrigins(t *testing.T) {
 			"app://*",
 			"file://*",
 			"tauri://*",
-			"vscode-webview://*",
 		}},
 		{"http://totally.safe,http://definitely.legit", []string{
 			"http://totally.safe",
@@ -127,7 +123,6 @@ func TestOrigins(t *testing.T) {
 			"app://*",
 			"file://*",
 			"tauri://*",
-			"vscode-webview://*",
 		}},
 	}
 	for _, tt := range cases {
@@ -219,40 +214,6 @@ func TestKeepAlive(t *testing.T) {
 	}
 }
 
-func TestLoadTimeout(t *testing.T) {
-	defaultTimeout := 5 * time.Minute
-	cases := map[string]time.Duration{
-		"":       defaultTimeout,
-		"1s":     time.Second,
-		"1m":     time.Minute,
-		"1h":     time.Hour,
-		"5m0s":   defaultTimeout,
-		"1h2m3s": 1*time.Hour + 2*time.Minute + 3*time.Second,
-		"0":      time.Duration(math.MaxInt64),
-		"60":     60 * time.Second,
-		"120":    2 * time.Minute,
-		"3600":   time.Hour,
-		"-0":     time.Duration(math.MaxInt64),
-		"-1":     time.Duration(math.MaxInt64),
-		"-1m":    time.Duration(math.MaxInt64),
-		// invalid values
-		" ":   defaultTimeout,
-		"???": defaultTimeout,
-		"1d":  defaultTimeout,
-		"1y":  defaultTimeout,
-		"1w":  defaultTimeout,
-	}
-
-	for tt, expect := range cases {
-		t.Run(tt, func(t *testing.T) {
-			t.Setenv("OLLAMA_LOAD_TIMEOUT", tt)
-			if actual := LoadTimeout(); actual != expect {
-				t.Errorf("%s: expected %s, got %s", tt, expect, actual)
-			}
-		})
-	}
-}
-
 func TestVar(t *testing.T) {
 	cases := map[string]string{
 		"value":       "value",

examples/go-chat/main.go

@@ -35,7 +35,7 @@ func main() {
 
 	ctx := context.Background()
 	req := &api.ChatRequest{
-		Model:    "llama3.2",
+		Model:    "llama3.1",
 		Messages: messages,
 	}
 

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

@@ -4,10 +4,10 @@ This example provides an interface for asking questions to a PDF document.
 
 ## Setup
 
-1. Ensure you have the `llama3.2` model installed:
+1. Ensure you have the `llama3.1` model installed:
 
 ```
-ollama pull llama3.2
+ollama pull llama3.1
 ```
 
 2. Install the Python Requirements.

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

@@ -51,7 +51,7 @@ while True:
         template=template,
     )
 
-    llm = Ollama(model="llama3.2", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))
+    llm = Ollama(model="llama3.1", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))
     qa_chain = RetrievalQA.from_chain_type(
         llm,
         retriever=vectorstore.as_retriever(),

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

@@ -1,6 +1,6 @@
 langchain==0.0.274
 gpt4all==1.0.8
-chromadb==0.5.0
+chromadb==0.4.7
 llama-cpp-python==0.1.81
 urllib3==2.0.4
 PyMuPDF==1.23.5

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

@@ -4,10 +4,10 @@ This example summarizes the website, [https://ollama.com/blog/run-llama2-uncenso
 
 ## Running the Example
 
-1. Ensure you have the `llama3.2` model installed:
+1. Ensure you have the `llama3.1` model installed:
 
    ```bash
-   ollama pull llama3.2
+   ollama pull llama3.1
    ```
 
 2. Install the Python Requirements.

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

@@ -5,7 +5,7 @@ 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")
+llm = Ollama(model="llama3.1")
 chain = load_summarize_chain(llm, chain_type="stuff")
 
 result = chain.invoke(docs)

examples/langchain-python-simple/README.md

@@ -4,10 +4,10 @@ This example is a basic "hello world" of using LangChain with Ollama.
 
 ## Running the Example
 
-1. Ensure you have the `llama3.2` model installed:
+1. Ensure you have the `llama3.1` model installed:
 
    ```bash
-   ollama pull llama3.2
+   ollama pull llama3.1
    ```
 
 2. Install the Python Requirements.

examples/langchain-python-simple/main.py

@@ -1,6 +1,6 @@
 from langchain.llms import Ollama
 
 input = input("What is your question?")
-llm = Ollama(model="llama3.2")
+llm = Ollama(model="llama3.1")
 res = llm.predict(input)
 print (res)

examples/modelfile-mario/Modelfile

@@ -1,4 +1,4 @@
-FROM llama3.2
+FROM llama3.1
 PARAMETER temperature 1
 SYSTEM """
 You are Mario from super mario bros, acting as an assistant.

examples/modelfile-mario/readme.md

@@ -2,12 +2,12 @@
 
 # Example character: Mario
 
-This example shows how to create a basic character using Llama 3.2 as the base model.
+This example shows how to create a basic character using Llama3.1 as the base model.
 
 To run this example:
 
 1. Download the Modelfile
-2. `ollama pull llama3.2` to get the base model used in the model file.
+2. `ollama pull llama3.1` to get the base model used in the model file.
 3. `ollama create NAME -f ./Modelfile`
 4. `ollama run NAME`
 
@@ -18,7 +18,7 @@ Ask it some questions like "Who are you?" or "Is Peach in trouble again?"
 What the model file looks like:
 
 ```
-FROM llama3.2
+FROM llama3.1
 PARAMETER temperature 1
 SYSTEM """
 You are Mario from Super Mario Bros, acting as an assistant.

examples/python-grounded-factuality-rag-check/README.md

@@ -1,93 +0,0 @@
-# RAG Hallucination Checker using Bespoke-Minicheck
-
-This example allows the user to ask questions related to a document, which can be specified via an article url. Relevant chunks are retreived from the document and given to `llama3.2` as context to answer the question. Then each sentence in the answer is checked against the retrieved chunks using `bespoke-minicheck` to ensure that the answer does not contain hallucinations. 
-
-## Running the Example
-
-1. Ensure `all-minilm` (embedding) `llama3.2` (chat) and `bespoke-minicheck` (check) models installed:
-
-   ```bash
-   ollama pull all-minilm
-   ollama pull llama3.2
-   ollama pull bespoke-minicheck
-   ```
-
-2. Install the dependencies.
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. Run the example:
-
-   ```bash
-   python main.py
-   ```
-
-## Expected Output
-
-```text
-Enter the URL of an article you want to chat with, or press Enter for default example:
-
-Loaded, chunked, and embedded text from https://www.theverge.com/2024/9/12/24242439/openai-o1-model-reasoning-strawberry-chatgpt.
-
-Enter your question or type quit: Who is the CEO of openai?
-
-Retrieved chunks:
-OpenAI is releasing a new model called o1 , the first in a planned series of “ reasoning ” models that have been trained to answer more complex questions , faster than a human can . It ’ s being released alongside o1-mini , a smaller , cheaper version . And yes , if you ’ re steeped in AI rumors : this is , in fact , the extremely hyped Strawberry model . For OpenAI , o1 represents a step toward its broader goal of human-like artificial intelligence .
-
-OpenAI is releasing a new model called o1 , the first in a planned series of “ reasoning ” models that have been trained to answer more complex questions , faster than a human can . It ’ s being released alongside o1-mini , a smaller , cheaper version . And yes , if you ’ re steeped in AI rumors : this is , in fact , the extremely hyped Strawberry model . For OpenAI , o1 represents a step toward its broader goal of human-like artificial intelligence . More practically , it does a better job at writing code and solving multistep problems than previous models . But it ’ s also more expensive and slower to use than GPT-4o . OpenAI is calling this release of o1 a “ preview ” to emphasize how nascent it is . ChatGPT Plus and Team users get access to both o1-preview and o1-mini starting today , while Enterprise and Edu users will get access early next week .
-
-More practically , it does a better job at writing code and solving multistep problems than previous models . But it ’ s also more expensive and slower to use than GPT-4o . OpenAI is calling this release of o1 a “ preview ” to emphasize how nascent it is . ChatGPT Plus and Team users get access to both o1-preview and o1-mini starting today , while Enterprise and Edu users will get access early next week . OpenAI says it plans to bring o1-mini access to all the free users of ChatGPT but hasn ’ t set a release date yet . Developer access to o1 is really expensive : In the API , o1-preview is $ 15 per 1 million input tokens , or chunks of text parsed by the model , and $ 60 per 1 million output tokens . For comparison , GPT-4o costs $ 5 per 1 million input tokens and $ 15 per 1 million output tokens .
-
-OpenAI says it plans to bring o1-mini access to all the free users of ChatGPT but hasn ’ t set a release date yet . Developer access to o1 is really expensive : In the API , o1-preview is $ 15 per 1 million input tokens , or chunks of text parsed by the model , and $ 60 per 1 million output tokens . For comparison , GPT-4o costs $ 5 per 1 million input tokens and $ 15 per 1 million output tokens . The training behind o1 is fundamentally different from its predecessors , OpenAI ’ s research lead , Jerry Tworek , tells me , though the company is being vague about the exact details . He says o1 “ has been trained using a completely new optimization algorithm and a new training dataset specifically tailored for it. ” Image : OpenAI OpenAI taught previous GPT models to mimic patterns from its training data .
-
-LLM Answer:
-The text does not mention the CEO of OpenAI. It only discusses the release of a new model called o1 and some details about it, but does not provide information on the company's leadership.
-
-LLM Claim: The text does not mention the CEO of OpenAI.
-Is this claim supported by the context according to bespoke-minicheck? Yes
-
-LLM Claim: It only discusses the release of a new model called o1 and some details about it, but does not provide information on the company's leadership.
-Is this claim supported by the context according to bespoke-minicheck? No
-```
-
-The second claim is unsupported since the text mentions the research lead. 
-
-Another tricky example:
-
-```text
-
-Enter your question or type quit: what sets o1 apart from gpt-4o?
-
-Retrieved chunks: 
-OpenAI says it plans to bring o1-mini access to all the free users of ChatGPT but hasn ’ t set a release date yet . Developer access to o1 is really expensive : In the API , o1-preview is $ 15 per 1 million input tokens , or chunks of text parsed by the model , and $ 60 per 1 million output tokens . For comparison , GPT-4o costs $ 5 per 1 million input tokens and $ 15 per 1 million output tokens . The training behind o1 is fundamentally different from its predecessors , OpenAI ’ s research lead , Jerry Tworek , tells me , though the company is being vague about the exact details . He says o1 “ has been trained using a completely new optimization algorithm and a new training dataset specifically tailored for it. ” Image : OpenAI OpenAI taught previous GPT models to mimic patterns from its training data .
-
-He says OpenAI also tested o1 against a qualifying exam for the International Mathematics Olympiad , and while GPT-4o only correctly solved only 13 percent of problems , o1 scored 83 percent . “ We can ’ t say we solved hallucinations ” In online programming contests known as Codeforces competitions , this new model reached the 89th percentile of participants , and OpenAI claims the next update of this model will perform “ similarly to PhD students on challenging benchmark tasks in physics , chemistry and biology. ” At the same time , o1 is not as capable as GPT-4o in a lot of areas . It doesn ’ t do as well on factual knowledge about the world .
-
-More practically , it does a better job at writing code and solving multistep problems than previous models . But it ’ s also more expensive and slower to use than GPT-4o . OpenAI is calling this release of o1 a “ preview ” to emphasize how nascent it is . ChatGPT Plus and Team users get access to both o1-preview and o1-mini starting today , while Enterprise and Edu users will get access early next week . OpenAI says it plans to bring o1-mini access to all the free users of ChatGPT but hasn ’ t set a release date yet . Developer access to o1 is really expensive : In the API , o1-preview is $ 15 per 1 million input tokens , or chunks of text parsed by the model , and $ 60 per 1 million output tokens . For comparison , GPT-4o costs $ 5 per 1 million input tokens and $ 15 per 1 million output tokens .
-
-“ We can ’ t say we solved hallucinations ” In online programming contests known as Codeforces competitions , this new model reached the 89th percentile of participants , and OpenAI claims the next update of this model will perform “ similarly to PhD students on challenging benchmark tasks in physics , chemistry and biology. ” At the same time , o1 is not as capable as GPT-4o in a lot of areas . It doesn ’ t do as well on factual knowledge about the world . It also doesn ’ t have the ability to browse the web or process files and images . Still , the company believes it represents a brand-new class of capabilities . It was named o1 to indicate “ resetting the counter back to 1. ” “ I ’ m gon na be honest : I think we ’ re terrible at naming , traditionally , ” McGrew says .
-LLM Answer: According to the text, several things set o1 apart from GPT-4o:
-
-* In online programming contests (Codeforces competitions), o1 scored 83% correct solutions compared to GPT-4o's 13%.
-* The training behind o1 is "fundamentally different" from its predecessors, including a completely new optimization algorithm and a new training dataset specifically tailored for it.
-* o1 has been shown to perform similarly to PhD students on challenging benchmark tasks in physics, chemistry, and biology, while GPT-4o does not have this level of performance.
-* However, o1 is also less capable than GPT-4o in some areas, particularly with regard to factual knowledge about the world.
-
-LLM Claim: According to the text, several things set o1 apart from GPT-4o:
-
-* In online programming contests (Codeforces competitions), o1 scored 83% correct solutions compared to GPT-4o's 13%.
-Is this claim supported by the context according to bespoke-minicheck? Yes
-
-LLM Claim: * The training behind o1 is "fundamentally different" from its predecessors, including a completely new optimization algorithm and a new training dataset specifically tailored for it.
-Is this claim supported by the context according to bespoke-minicheck? Yes
-
-LLM Claim: * o1 has been shown to perform similarly to PhD students on challenging benchmark tasks in physics, chemistry, and biology, while GPT-4o does not have this level of performance.
-Is this claim supported by the context according to bespoke-minicheck? No
-
-LLM Claim: * However, o1 is also less capable than GPT-4o in some areas, particularly with regard to factual knowledge about the world.
-Is this claim supported by the context according to bespoke-minicheck? Yes
-```
-
-We see that the third claim "* o1 has been shown to perform similarly to PhD students on challenging benchmark tasks in physics, chemistry, and biology, while GPT-4o does not have this level of performance." is not supported by the context. This is because the context only mentions that o1 "is claimed to perform" which is different from "has been shown to perform".

examples/python-grounded-factuality-rag-check/main.py

@@ -1,137 +0,0 @@
-import ollama
-import warnings
-from mattsollamatools import chunker
-from newspaper import Article
-import numpy as np
-from sklearn.neighbors import NearestNeighbors
-import nltk
-
-warnings.filterwarnings(
-    "ignore", category=FutureWarning, module="transformers.tokenization_utils_base"
-)
-nltk.download("punkt_tab", quiet=True)
-
-
-def getArticleText(url):
-    """Gets the text of an article from a URL.
-
-    Often there are a bunch of ads and menus on pages for a news article.
-    This uses newspaper3k to get just the text of just the article.
-    """
-    article = Article(url)
-    article.download()
-    article.parse()
-    return article.text
-
-
-def knn_search(question_embedding, embeddings, k=5):
-    """Performs K-nearest neighbors (KNN) search"""
-    X = np.array(
-        [item["embedding"] for article in embeddings for item in article["embeddings"]]
-    )
-    source_texts = [
-        item["source"] for article in embeddings for item in article["embeddings"]
-    ]
-
-    # Fit a KNN model on the embeddings
-    knn = NearestNeighbors(n_neighbors=k, metric="cosine")
-    knn.fit(X)
-
-    # Find the indices and distances of the k-nearest neighbors.
-    _, indices = knn.kneighbors(question_embedding, n_neighbors=k)
-
-    # Get the indices and source texts of the best matches
-    best_matches = [(indices[0][i], source_texts[indices[0][i]]) for i in range(k)]
-
-    return best_matches
-
-
-def check(document, claim):
-    """Checks if the claim is supported by the document by calling bespoke-minicheck.
-
-    Returns Yes/yes if the claim is supported by the document, No/no otherwise.
-    Support for logits will be added in the future.
-
-    bespoke-minicheck's system prompt is defined as:
-      'Determine whether the provided claim is consistent with the corresponding
-      document. Consistency in this context implies that all information presented in the claim
-      is substantiated by the document. If not, it should be considered inconsistent. Please
-      assess the claim's consistency with the document by responding with either "Yes" or "No".'
-
-    bespoke-minicheck's user prompt is defined as:
-      "Document: {document}\nClaim: {claim}"
-    """
-    prompt = f"Document: {document}\nClaim: {claim}"
-    response = ollama.generate(
-        model="bespoke-minicheck", prompt=prompt, options={"num_predict": 2, "temperature": 0.0}
-    )
-    return response["response"].strip()
-
-
-if __name__ == "__main__":
-    allEmbeddings = []
-    default_url = "https://www.theverge.com/2024/9/12/24242439/openai-o1-model-reasoning-strawberry-chatgpt"
-    user_input = input(
-        "Enter the URL of an article you want to chat with, or press Enter for default example: "
-    )
-    article_url = user_input.strip() if user_input.strip() else default_url
-    article = {}
-    article["embeddings"] = []
-    article["url"] = article_url
-    text = getArticleText(article_url)
-    chunks = chunker(text)
-
-    # Embed (batch) chunks using ollama
-    embeddings = ollama.embed(model="all-minilm", input=chunks)["embeddings"]
-
-    for chunk, embedding in zip(chunks, embeddings):
-        item = {}
-        item["source"] = chunk
-        item["embedding"] = embedding
-        item["sourcelength"] = len(chunk)
-        article["embeddings"].append(item)
-
-    allEmbeddings.append(article)
-
-    print(f"\nLoaded, chunked, and embedded text from {article_url}.\n")
-
-    while True:
-        # Input a question from the user
-        # For example, "Who is the chief research officer?"
-        question = input("Enter your question or type quit: ")
-
-        if question.lower() == "quit":
-            break
-
-        # Embed the user's question using ollama.embed
-        question_embedding = ollama.embed(model="all-minilm", input=question)[
-            "embeddings"
-        ]
-
-        # Perform KNN search to find the best matches (indices and source text)
-        best_matches = knn_search(question_embedding, allEmbeddings, k=4)
-
-        sourcetext = "\n\n".join([source_text for (_, source_text) in best_matches])
-
-        print(f"\nRetrieved chunks: \n{sourcetext}\n")
-
-        # Give the retreived chunks and question to the chat model
-        system_prompt = f"Only use the following information to answer the question. Do not use anything else: {sourcetext}"
-
-        ollama_response = ollama.generate(
-            model="llama3.2",
-            prompt=question,
-            system=system_prompt,
-            options={"stream": False},
-        )
-
-        answer = ollama_response["response"]
-        print(f"LLM Answer:\n{answer}\n")
-
-        # Check each sentence in the response for grounded factuality
-        if answer:
-            for claim in nltk.sent_tokenize(answer):
-                print(f"LLM Claim: {claim}")
-                print(
-                    f"Is this claim supported by the context according to bespoke-minicheck? {check(sourcetext, claim)}\n"
-                )

examples/python-grounded-factuality-rag-check/requirements.txt

@@ -1,8 +0,0 @@
-ollama
-lxml==5.3.0
-lxml_html_clean==0.2.2
-mattsollamatools==0.0.25
-newspaper3k==0.2.8
-nltk==3.9.1
-numpy==1.26.4
-scikit-learn==1.5.2

examples/python-grounded-factuality-simple-check/main.py

@@ -1,53 +0,0 @@
-"""Simple example to demonstrate how to use the bespoke-minicheck model."""
-
-import ollama
-
-# NOTE: ollama must be running for this to work, start the ollama app or run `ollama serve`
-
-
-def check(document, claim):
-    """Checks if the claim is supported by the document by calling bespoke-minicheck.
-
-    Returns Yes/yes if the claim is supported by the document, No/no otherwise.
-    Support for logits will be added in the future.
-
-    bespoke-minicheck's system prompt is defined as:
-      'Determine whether the provided claim is consistent with the corresponding
-      document. Consistency in this context implies that all information presented in the claim
-      is substantiated by the document. If not, it should be considered inconsistent. Please
-      assess the claim's consistency with the document by responding with either "Yes" or "No".'
-
-    bespoke-minicheck's user prompt is defined as:
-      "Document: {document}\nClaim: {claim}"
-    """
-    prompt = f"Document: {document}\nClaim: {claim}"
-    response = ollama.generate(
-        model="bespoke-minicheck", prompt=prompt, options={"num_predict": 2, "temperature": 0.0}
-    )
-    return response["response"].strip()
-
-
-def get_user_input(prompt):
-    user_input = input(prompt)
-    if not user_input:
-        exit()
-    print()
-    return user_input
-
-
-def main():
-    while True:
-        # Get a document from the user (e.g. "Ryan likes running and biking.")
-        document = get_user_input("Enter a document: ")
-        # Get a claim from the user (e.g. "Ryan likes to run.")
-        claim = get_user_input("Enter a claim: ")
-        # Check if the claim is supported by the document
-        grounded_factuality_check = check(document, claim)
-        print(
-            f"Is the claim supported by the document according to bespoke-minicheck? {grounded_factuality_check}"
-        )
-        print("\n\n")
-
-
-if __name__ == "__main__":
-    main()

examples/python-grounded-factuality-simple-check/readme.md

@@ -1,54 +0,0 @@
-# Simple Bespoke-Minicheck Example
-
-`bespoke-minicheck` is a model for checking if a claim is supported by a document. It is used through the **generate** endpoint, which is called in this example with a `prompt` that includes the expected formatting of the user input. 
-
-## Running the Example
-
-1. Ensure you have the `bespoke-minicheck` model installed:
-
-   ```bash
-   ollama pull bespoke-minicheck
-   ```
-
-2. Install the dependencies:
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. Run the program:
-
-   ```bash
-   python main.py
-   ```
-
-4. Enter a document and a claim when prompted:
-
-   ```bash
-   Enter a document: Roses are red.
-
-   Enter a claim: Roses are blue. 
-   ```
-
-   The claim and document are then given to the `bespoke-minicheck` as inputs, which then generates a response (Yes or No) on whether the claim is supported by the document.
-
-   ```bash
-   Is the claim supported by the document according to bespoke-minicheck? No
-   ```
-
-## More Examples
-
-Document ([source](https://en.wikipedia.org/wiki/Apple_I)): 
-> The Apple Computer 1 (Apple-1[a]), later known predominantly as the Apple I(written with a Roman numeral),[b] is an 8-bit motherboard-only personal computer designed by Steve Wozniak[5][6] and released by the Apple Computer Company (now Apple Inc.) in 1976. The company was initially formed to sell the Apple I – its first product – and would later become the world's largest technology company.[7] The idea of starting a company and selling the computer came from Wozniak's friend and Apple co-founder Steve Jobs.[8][9] One of the main innovations of the Apple I was that it included video display terminal circuitry on its circuit board, allowing it to connect to a low-cost composite video monitor or television, instead of an expensive computer terminal, compared to most existing computers at the time.
-
-Claim: 
->The Apple I is a 16-bit computer.
-
-Expected output:
->Is the claim supported by the document according to bespoke-minicheck? **No**
-
-Claim: 
->Apple was originally called the Apple Computer Company.
-
-Expected output:
->Is the claim supported by the document according to bespoke-minicheck? **Yes**

examples/python-grounded-factuality-simple-check/requirements.txt

@@ -1 +0,0 @@
-ollama

examples/python-json-datagenerator/predefinedschema.py

@@ -2,7 +2,7 @@ import requests
 import json
 import random
 
-model = "llama3.2"
+model = "llama3.1"
 template = {
   "firstName": "",
   "lastName": "",

examples/python-json-datagenerator/randomaddresses.py

@@ -12,7 +12,7 @@ countries = [
     "France",
 ]
 country = random.choice(countries)
-model = "llama3.2"
+model = "llama3.1"
 
 prompt = f"generate one realistically believable sample data set of a persons first name, last name, address in {country}, and phone number. Do not use common names. Respond using JSON. Key names should have no backslashes, values should use plain ascii with no special characters."
 

examples/python-json-datagenerator/readme.md

@@ -6,10 +6,10 @@ There are two python scripts in this example. `randomaddresses.py` generates ran
 
 ## Running the Example
 
-1. Ensure you have the `llama3.2` model installed:
+1. Ensure you have the `llama3.1` model installed:
 
    ```bash
-   ollama pull llama3.2
+   ollama pull llama3.1
    ```
 
 2. Install the Python Requirements.

examples/python-loganalysis/Modelfile

@@ -4,5 +4,5 @@ SYSTEM """
 You are a log file analyzer. You will receive a set of lines from a log file for some software application, find the errors and other interesting aspects of the logs, and explain them so a new user can understand what they mean. If there are any steps they can do to resolve them, list the steps in your answer.
 """
 
-PARAMETER temperature 0.3
+PARAMETER TEMPERATURE 0.3
 

examples/python-loganalysis/readme.md

@@ -21,8 +21,6 @@ You can try this with the `logtest.logfile` file included in this directory.
 2. Install the Python Requirements.
 
    ```bash
-   python3 -m venv .venv
-   source .venv/bin/activate
    pip install -r requirements.txt
    ```
 

examples/python-loganalysis/requirements.txt

@@ -1 +1 @@
-Requests>=2.32.3
+Requests==2.31.0

examples/python-simplechat/client.py

@@ -2,7 +2,7 @@ import json
 import requests
 
 # NOTE: ollama must be running for this to work, start the ollama app or run `ollama serve`
-model = "llama3.2"  # TODO: update this for whatever model you wish to use
+model = "llama3.1"  # TODO: update this for whatever model you wish to use
 
 
 def chat(messages):

examples/python-simplechat/readme.md

@@ -4,10 +4,10 @@ The **chat** endpoint is one of two ways to generate text from an LLM with Ollam
 
 ## Running the Example
 
-1. Ensure you have the `llama3.2` model installed:
+1. Ensure you have the `llama3.1` model installed:
 
    ```bash
-   ollama pull llama3.2
+   ollama pull llama3.1
    ```
 
 2. Install the Python Requirements.

examples/typescript-simplechat/client.ts

@@ -1,6 +1,6 @@
 import * as readline from "readline";
 
-const model = "llama3.2";
+const model = "llama3.1";
 type Message = {
   role: "assistant" | "user" | "system";
   content: string;