ci: fix linux archive (#8862)

the find returns intermediate directories which pulls the parent
directories. it also omits files under lib/ollama.

switch back to globbing

.dockerignore

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

.gitattributes

@@ -7,5 +7,18 @@ llama/**/*.cuh linguist-vendored
 llama/**/*.m linguist-vendored
 llama/**/*.metal linguist-vendored
 
+ml/backend/**/*.c linguist-vendored
+ml/backend/**/*.h linguist-vendored
+ml/backend/**/*.cpp linguist-vendored
+ml/backend/**/*.hpp linguist-vendored
+ml/backend/**/*.cu linguist-vendored
+ml/backend/**/*.cuh linguist-vendored
+ml/backend/**/*.m linguist-vendored
+ml/backend/**/*.metal linguist-vendored
+ml/backend/**/CMakeLists.txt linguist-vendored
+
+llama/build-info.cpp linguist-generated
+ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-embed.s linguist-generated
+
 * text=auto
 *.go text eol=lf

.github/ISSUE_TEMPLATE/10_bug_report.yml

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

.github/workflows/test.yaml

@@ -1,11 +1,5 @@
 name: test
 
-env:
-  ROCM_WINDOWS_URL: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe
-  MSYS2_URL: https://github.com/msys2/msys2-installer/releases/download/2024-07-27/msys2-x86_64-20240727.exe
-  CUDA_12_WINDOWS_URL: https://developer.download.nvidia.com/compute/cuda/12.4.0/local_installers/cuda_12.4.0_551.61_windows.exe
-  CUDA_12_WINDOWS_VER: 12.4
-
 concurrency:
   # For PRs, later CI runs preempt previous ones. e.g. a force push on a PR
   # cancels running CI jobs and starts all new ones.
@@ -27,7 +21,7 @@ jobs:
   changes:
     runs-on: ubuntu-latest
     outputs:
-      RUNNERS: ${{ steps.changes.outputs.RUNNERS }}
+      changed: ${{ steps.changes.outputs.changed }}
     steps:
       - uses: actions/checkout@v4
         with:
@@ -35,309 +29,139 @@ jobs:
       - id: changes
         run: |
           changed() {
-            git diff-tree -r --no-commit-id --name-only \
-              $(git merge-base ${{ github.event.pull_request.base.sha }} ${{ github.event.pull_request.head.sha }}) \
-              ${{ github.event.pull_request.head.sha }} \
+            local BASE=${{ github.event.pull_request.base.sha }}
+            local HEAD=${{ github.event.pull_request.head.sha }}
+            local MERGE_BASE=$(git merge-base $BASE $HEAD)
+            git diff-tree -r --no-commit-id --name-only "$MERGE_BASE" "$HEAD" \
               | xargs python3 -c "import sys; from pathlib import Path; print(any(Path(x).match(glob) for x in sys.argv[1:] for glob in '$*'.split(' ')))"
           }
 
-          {
-            echo RUNNERS=$(changed 'llama/**')
-          } >>$GITHUB_OUTPUT
+          echo changed=$(changed 'llama/llama.cpp/**' 'ml/backend/ggml/ggml/**') | tee -a $GITHUB_OUTPUT
 
-  runners-linux-cuda:
+  linux:
     needs: [changes]
-    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
+    if: needs.changes.outputs.changed == 'True'
     strategy:
       matrix:
-        cuda-version:
-          - '11.8.0'
+        include:
+          - preset: CPU
+          - preset: CUDA
+            container: nvidia/cuda:11.8.0-devel-ubuntu22.04
+            flags: '-DCMAKE_CUDA_ARCHITECTURES=87'
+          - preset: ROCm
+            container: rocm/dev-ubuntu-22.04:6.1.2
+            extra-packages: rocm-libs
+            flags: '-DAMDGPU_TARGETS=gfx1010 -DCMAKE_PREFIX_PATH=/opt/rocm'
     runs-on: linux
-    container: nvidia/cuda:${{ matrix.cuda-version }}-devel-ubuntu20.04
+    container: ${{ matrix.container }}
     steps:
+      - uses: actions/checkout@v4
       - run: |
-          apt-get update && apt-get install -y git build-essential curl
+          [ -n "${{ matrix.container }}" ] || sudo=sudo
+          $sudo apt-get update
+          $sudo apt-get install -y cmake ccache ${{ matrix.extra-packages }}
         env:
           DEBIAN_FRONTEND: noninteractive
-      - uses: actions/checkout@v4
-      - uses: actions/setup-go@v4
+      - uses: actions/cache@v4
         with:
-          go-version-file: go.mod
-          cache: true
-      - run: go get ./...
+          path: /github/home/.cache/ccache
+          key: ccache-${{ runner.os }}-${{ runner.arch }}-${{ matrix.preset }}
       - 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:
+          cmake --preset ${{ matrix.preset }} ${{ matrix.flags }}
+          cmake --build --preset ${{ matrix.preset }} --parallel
+
+  windows:
     needs: [changes]
-    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
+    if: needs.changes.outputs.changed == 'True'
     strategy:
       matrix:
-        rocm-version:
-          - '6.1.2'
-    runs-on: linux
-    container: rocm/dev-ubuntu-20.04:${{ 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: ${{ needs.changes.outputs.RUNNERS == 'True' }}
+        include:
+          - preset: CPU
+          - preset: CUDA
+            install: https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_522.06_windows.exe
+            flags: '-DCMAKE_CUDA_ARCHITECTURES=87'
+          - preset: ROCm
+            install: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe
+            flags: '-DAMDGPU_TARGETS=gfx1010'
     runs-on: windows
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-go@v5
+      - run: |
+          choco install -y --no-progress ccache ninja
+          ccache -o cache_dir=${{ github.workspace }}\.ccache
+      - if: matrix.preset == 'CUDA' || matrix.preset == 'ROCm'
+        id: cache-install
+        uses: actions/cache/restore@v4
         with:
-          go-version-file: go.mod
-          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'
+          path: |
+            C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA
+            C:\Program Files\AMD\ROCm
+          key: ${{ matrix.install }}
+      - if: matrix.preset == 'CUDA'
+        name: Install CUDA ${{ matrix.cuda-version }}
         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 $env:GITHUB_ENV -Encoding utf8 -Append
+          if ("${{ steps.cache-install.outputs.cache-hit }}" -ne 'true') {
+            Invoke-WebRequest -Uri "${{ matrix.install }}" -OutFile "install.exe"
+            Start-Process -FilePath .\install.exe -ArgumentList (@("-s", "cudart_11.8", "nvcc_11.8", "cublas_11.8", "cublas_dev_11.8")) -NoNewWindow -Wait
+          }
 
-      - 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' 
+          $cudaPath = (Resolve-Path "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\*").path
           echo "$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
+      - if: matrix.preset == 'ROCm'
+        name: Install ROCm ${{ matrix.rocm-version }}
+        run: |
+          $ErrorActionPreference = "Stop"
+          if ("${{ steps.cache-install.outputs.cache-hit }}" -ne 'true') {
+            Invoke-WebRequest -Uri "${{ matrix.install }}" -OutFile "install.exe"
+            Start-Process -FilePath .\install.exe -ArgumentList '-install' -NoNewWindow -Wait
+          }
 
-      - name: Add msys paths
-        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' }}
-    strategy:
-      matrix:
-        os: [ubuntu-latest, macos-latest, windows-2019]
-        arch: [amd64, arm64]
-        exclude:
-          - os: ubuntu-latest
-            arch: arm64
-          - os: windows-2019
-            arch: arm64
-    runs-on: ${{ matrix.os }}
-    env:
-      GOARCH: ${{ matrix.arch }}
-      ARCH: ${{ matrix.arch }}
-      CGO_ENABLED: '1'
-    steps:
+          $hipPath = (Resolve-Path "C:\Program Files\AMD\ROCm\*").path
+          echo "$hipPath\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+          echo "CC=$hipPath\bin\clang.exe" | Out-File -FilePath $env:GITHUB_ENV -Append
+          echo "CXX=$hipPath\bin\clang++.exe" | Out-File -FilePath $env:GITHUB_ENV -Append
+      - if: ${{ !cancelled() && steps.cache-install.outputs.cache-hit != 'true' }}
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA
+            C:\Program Files\AMD\ROCm
+          key: ${{ matrix.install }}
       - uses: actions/checkout@v4
-      - uses: actions/setup-go@v5
+      - uses: actions/cache@v4
         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: |
-          $gopath=(get-command go).source | split-path -parent
-          $gccpath=(get-command gcc).source | split-path -parent
-          import-module 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
-          Enter-VsDevShell -vsinstallpath 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise' -skipautomaticlocation -DevCmdArguments '-arch=x64 -no_logo'
-          $env:CMAKE_SYSTEM_VERSION="10.0.22621.0"
-          $env:PATH="$gopath;$gccpath;$env:PATH"
-          echo $env:PATH
-          if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
-          make -j 4
-      - name: 'Build Unix Go Runners'
-        if: ${{ ! startsWith(matrix.os, 'windows-') }}
-        run: make -j 4
-      - run: go build .
-
-  lint:
-    strategy:
-      matrix:
-        os: [ubuntu-latest, macos-latest, windows-2019]
-        arch: [amd64, arm64]
-        exclude:
-          - os: ubuntu-latest
-            arch: arm64
-          - os: windows-2019
-            arch: arm64
-          - os: macos-latest
-            arch: amd64
-    runs-on: ${{ matrix.os }}
-    env:
-      GOARCH: ${{ matrix.arch }}
-      CGO_ENABLED: '1'
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          submodules: recursive
-      - name: Add msys paths
-        if: ${{ startsWith(matrix.os, 'windows-') }}
-        run: |
-          echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-      - name: Install msys2 tools
-        if: ${{ startsWith(matrix.os, 'windows-') }}
-        run: |
-          Start-Process "c:\msys64\usr\bin\pacman.exe" -ArgumentList @("-S", "--noconfirm", "mingw-w64-clang-x86_64-gcc-compat", "mingw-w64-clang-x86_64-clang") -NoNewWindow -Wait
-      - uses: actions/setup-go@v5
-        with:
-          go-version-file: go.mod
-          cache: false
+          path: ${{ github.workspace }}\.ccache
+          key: ccache-${{ runner.os }}-${{ runner.arch }}-${{ matrix.preset }}
       - run: |
-          case ${{ matrix.arch }} in
-            amd64) echo ARCH=x86_64 ;;
-            arm64) echo ARCH=arm64 ;;
-          esac >>$GITHUB_ENV
-        shell: bash
-      - uses: golangci/golangci-lint-action@v6
-        with:
-          args: --timeout 10m0s -v
+          Import-Module 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
+          Enter-VsDevShell -VsInstallPath 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise' -SkipAutomaticLocation  -DevCmdArguments '-arch=x64 -no_logo'
+          cmake --preset "${{ matrix.preset }}" ${{ matrix.flags }}
+          cmake --build --parallel --preset "${{ matrix.preset }}"
+        env:
+          CMAKE_GENERATOR: Ninja
+
   test:
     strategy:
       matrix:
-        os: [ubuntu-latest, macos-latest, windows-2019]
-        arch: [amd64]
-        exclude:
-          - os: ubuntu-latest
-            arch: arm64
-          - os: windows-2019
-            arch: arm64
+        os: [ubuntu-latest, macos-latest, windows-latest]
     runs-on: ${{ matrix.os }}
     env:
-      GOARCH: ${{ matrix.arch }}
       CGO_ENABLED: '1'
     steps:
       - uses: actions/checkout@v4
-        with:
-          submodules: recursive
-      - name: Add msys paths
-        if: ${{ startsWith(matrix.os, 'windows-') }}
-        run: |
-          echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
-      - name: Install msys2 tools
-        if: ${{ startsWith(matrix.os, 'windows-') }}
-        run: |
-          Start-Process "c:\msys64\usr\bin\pacman.exe" -ArgumentList @("-S", "--noconfirm", "mingw-w64-clang-x86_64-gcc-compat", "mingw-w64-clang-x86_64-clang") -NoNewWindow -Wait
       - uses: actions/setup-go@v5
         with:
           go-version-file: go.mod
-          cache: true
-      - run: |
-          case ${{ matrix.arch }} in
-            amd64) echo ARCH=amd64 ;;
-            arm64) echo ARCH=arm64 ;;
-          esac >>$GITHUB_ENV
-        shell: bash
+      - uses: golangci/golangci-lint-action@v6
+        with:
+          args: --timeout 10m0s -v
       - run: go test ./...
 
   patches:
-    needs: [changes]
-    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-        with:
-          submodules: recursive
-      - name: Verify patches carry all the changes
+      - name: Verify patches apply cleanly and do not change files
         run: |
-          make apply-patches sync && git diff --compact-summary --exit-code llama
+          make -f Makefile.sync clean sync
+          git diff --compact-summary --exit-code

.gitignore

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

.golangci.yaml

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

.prettierrc.json

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

CMakeLists.txt

@@ -0,0 +1,117 @@
+cmake_minimum_required(VERSION 3.21)
+
+project(Ollama C CXX)
+
+include(CheckLanguage)
+
+find_package(Threads REQUIRED)
+
+set(CMAKE_BUILD_TYPE Release)
+set(BUILD_SHARED_LIBS ON)
+
+set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_STANDARD_REQUIRED ON)
+set(CMAKE_CXX_EXTENSIONS OFF)
+
+set(GGML_BUILD ON)
+set(GGML_SHARED ON)
+set(GGML_CCACHE ON)
+set(GGML_BACKEND_DL ON)
+set(GGML_BACKEND_SHARED ON)
+set(GGML_SCHED_MAX_COPIES 4)
+
+set(GGML_LLAMAFILE ON)
+set(GGML_CUDA_PEER_MAX_BATCH_SIZE 128)
+set(GGML_CUDA_GRAPHS ON)
+
+if((NOT CMAKE_OSX_ARCHITECTURES MATCHES "arm64")
+    OR (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_SYSTEM_PROCESSOR MATCHES "arm|aarch64|ARM64|ARMv[0-9]+"))
+    set(GGML_CPU_ALL_VARIANTS ON)
+endif()
+
+if (CMAKE_OSX_ARCHITECTURES MATCHES "x86_64")
+    set(CMAKE_BUILD_RPATH "@loader_path")
+    set(CMAKE_INSTALL_RPATH "@loader_path")
+endif()
+
+set(OLLAMA_BUILD_DIR ${CMAKE_BINARY_DIR}/lib/ollama)
+set(OLLAMA_INSTALL_DIR ${CMAKE_INSTALL_PREFIX}/lib/ollama)
+
+set(CMAKE_RUNTIME_OUTPUT_DIRECTORY         ${OLLAMA_BUILD_DIR})
+set(CMAKE_RUNTIME_OUTPUT_DIRECTORY_DEBUG   ${OLLAMA_BUILD_DIR})
+set(CMAKE_RUNTIME_OUTPUT_DIRECTORY_RELEASE ${OLLAMA_BUILD_DIR})
+set(CMAKE_LIBRARY_OUTPUT_DIRECTORY         ${OLLAMA_BUILD_DIR})
+set(CMAKE_LIBRARY_OUTPUT_DIRECTORY_DEBUG   ${OLLAMA_BUILD_DIR})
+set(CMAKE_LIBRARY_OUTPUT_DIRECTORY_RELEASE ${OLLAMA_BUILD_DIR})
+
+include_directories(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src)
+include_directories(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/include)
+include_directories(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-cpu)
+include_directories(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-cpu/amx)
+
+set(GGML_CPU ON)
+add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src)
+set_property(TARGET ggml PROPERTY EXCLUDE_FROM_ALL TRUE)
+
+get_target_property(CPU_VARIANTS ggml-cpu MANUALLY_ADDED_DEPENDENCIES)
+if(NOT CPU_VARIANTS)
+    set(CPU_VARIANTS "ggml-cpu")
+endif()
+
+install(TARGETS ggml-base ${CPU_VARIANTS}
+    RUNTIME_DEPENDENCIES
+        PRE_EXCLUDE_REGEXES ".*"
+    RUNTIME DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT CPU
+    LIBRARY DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT CPU
+    FRAMEWORK DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT CPU
+)
+
+check_language(CUDA)
+if(CMAKE_CUDA_COMPILER)
+    if(CMAKE_VERSION VERSION_GREATER_EQUAL "3.24" AND NOT CMAKE_CUDA_ARCHITECTURES)
+        set(CMAKE_CUDA_ARCHITECTURES "native")
+    endif()
+
+    find_package(CUDAToolkit)
+    add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-cuda)
+    set(OLLAMA_CUDA_INSTALL_DIR ${OLLAMA_INSTALL_DIR}/cuda_v${CUDAToolkit_VERSION_MAJOR})
+    install(TARGETS ggml-cuda
+        RUNTIME_DEPENDENCIES
+            DIRECTORIES ${CUDAToolkit_BIN_DIR} ${CUDAToolkit_LIBRARY_DIR}
+            PRE_INCLUDE_REGEXES cublas cublasLt cudart
+            PRE_EXCLUDE_REGEXES ".*"
+        RUNTIME DESTINATION ${OLLAMA_CUDA_INSTALL_DIR} COMPONENT CUDA
+        LIBRARY DESTINATION ${OLLAMA_CUDA_INSTALL_DIR} COMPONENT CUDA
+    )
+endif()
+
+check_language(HIP)
+if(CMAKE_HIP_COMPILER)
+    set(HIP_PLATFORM "amd")
+
+    find_package(hip REQUIRED)
+    if(NOT AMDGPU_TARGETS)
+        list(FILTER AMDGPU_TARGETS INCLUDE REGEX "^gfx(900|94[012]|101[02]|1030|110[012])$")
+    endif()
+
+    if(AMDGPU_TARGETS)
+        add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-hip)
+        set(OLLAMA_HIP_INSTALL_DIR ${OLLAMA_INSTALL_DIR}/rocm)
+        install(TARGETS ggml-hip
+            RUNTIME_DEPENDENCIES
+                DIRECTORIES ${HIP_BIN_INSTALL_DIR} ${HIP_LIB_INSTALL_DIR}
+                PRE_INCLUDE_REGEXES amdhip64 hipblas rocblas amd_comgr hsa_runtime64 rocprofiler-register drm_amdgpu drm numa
+                PRE_EXCLUDE_REGEXES ".*"
+                POST_EXCLUDE_REGEXES "system32"
+            RUNTIME DESTINATION ${OLLAMA_HIP_INSTALL_DIR} COMPONENT HIP
+            LIBRARY DESTINATION ${OLLAMA_HIP_INSTALL_DIR} COMPONENT HIP
+        )
+
+        foreach(HIP_LIB_BIN_INSTALL_DIR IN ITEMS ${HIP_BIN_INSTALL_DIR} ${HIP_LIB_INSTALL_DIR})
+            if(EXISTS ${HIP_LIB_BIN_INSTALL_DIR}/rocblas)
+                install(DIRECTORY ${HIP_LIB_BIN_INSTALL_DIR}/rocblas DESTINATION ${OLLAMA_HIP_INSTALL_DIR} COMPONENT HIP)
+                break()
+            endif()
+        endforeach()
+    endif()
+endif()

CMakePresets.json

@@ -0,0 +1,110 @@
+{
+  "version": 3,
+  "configurePresets": [
+    {
+      "name": "Default",
+      "binaryDir": "${sourceDir}/build",
+      "installDir": "${sourceDir}/dist",
+      "cacheVariables": {
+        "CMAKE_BUILD_TYPE": "Release"
+      }
+    },
+    {
+      "name": "CPU",
+      "inherits": [ "Default" ]
+    },
+    {
+      "name": "CUDA",
+      "inherits": [ "Default" ]
+    },
+    {
+      "name": "CUDA 11",
+      "inherits": [ "CUDA" ],
+      "cacheVariables": {
+        "CMAKE_CUDA_ARCHITECTURES": "50;52;53;60;61;62;70;72;75;80;86"
+      }
+    },
+    {
+      "name": "CUDA 12",
+      "inherits": [ "CUDA" ],
+      "cacheVariables": {
+        "CMAKE_CUDA_ARCHITECTURES": "60;61;62;70;72;75;80;86;87;89;90;90a"
+      }
+    },
+    {
+      "name": "JetPack 5",
+      "inherits": [ "CUDA" ],
+      "cacheVariables": {
+        "CMAKE_CUDA_ARCHITECTURES": "72;87"
+      }
+    },
+    {
+      "name": "JetPack 6",
+      "inherits": [ "CUDA" ],
+      "cacheVariables": {
+        "CMAKE_CUDA_ARCHITECTURES": "87"
+      }
+    },
+    {
+      "name": "ROCm",
+      "inherits": [ "Default" ],
+      "cacheVariables": {
+        "CMAKE_HIP_PLATFORM": "amd"
+      }
+    },
+    {
+      "name": "ROCm 6",
+      "inherits": [ "ROCm" ],
+      "cacheVariables": {
+        "AMDGPU_TARGETS": "gfx900;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102"
+      }
+    }
+  ],
+  "buildPresets": [
+    {
+      "name": "Default",
+      "configurePreset": "Default",
+      "configuration": "Release"
+    },
+    {
+      "name": "CPU",
+      "configurePreset": "Default",
+      "targets": [ "ggml-cpu" ]
+    },
+    {
+      "name": "CUDA",
+      "configurePreset": "CUDA",
+      "targets": [ "ggml-cuda" ]
+    },
+    {
+      "name": "CUDA 11",
+      "inherits": [ "CUDA" ],
+      "configurePreset": "CUDA 11"
+    },
+    {
+      "name": "CUDA 12",
+      "inherits": [ "CUDA" ],
+      "configurePreset": "CUDA 12"
+    },
+    {
+      "name": "JetPack 5",
+      "inherits": [ "CUDA" ],
+      "configurePreset": "JetPack 5"
+    },
+    {
+      "name": "JetPack 6",
+      "inherits": [ "CUDA" ],
+      "configurePreset": "JetPack 6"
+    },
+    {
+      "name": "ROCm",
+      "configurePreset": "ROCm",
+      "targets": [ "ggml-hip" ]
+    },
+    {
+      "name": "ROCm 6",
+      "inherits": [ "ROCm" ],
+      "configurePreset": "ROCm 6"
+    }
+  ]
+}

Dockerfile

@@ -1,196 +1,128 @@
-ARG GOLANG_VERSION=1.22.8
-ARG CUDA_VERSION_11=11.3.1
-ARG CUDA_VERSION_12=12.4.0
-ARG ROCM_VERSION=6.1.2
-ARG JETPACK_6=r36.2.0
-ARG JETPACK_5=r35.4.1
+# vim: filetype=dockerfile
 
-### 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 $(pwd):/go/src/github.com/ollama/ollama/ builder-amd64
-#
-### Then incremental builds will be much faster in this container
-#
-# make -j 10 dist
-#
-FROM --platform=linux/amd64 rocm/dev-centos-7:${ROCM_VERSION}-complete AS unified-builder-amd64
-ARG GOLANG_VERSION
-ARG CUDA_VERSION_11
-ARG CUDA_VERSION_12
-COPY ./scripts/rh_linux_deps.sh /
-ENV PATH /opt/rh/devtoolset-10/root/usr/bin:/usr/local/cuda/bin:$PATH
-ENV LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64
-RUN GOLANG_VERSION=${GOLANG_VERSION} sh /rh_linux_deps.sh
-RUN yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-rhel7.repo && \
-    dnf clean all && \
-    dnf install -y \
-    zsh \
-    cuda-toolkit-$(echo ${CUDA_VERSION_11} | cut -f1-2 -d. | sed -e "s/\./-/g") \
-    cuda-toolkit-$(echo ${CUDA_VERSION_12} | cut -f1-2 -d. | sed -e "s/\./-/g")
-# TODO intel oneapi goes here...
-ENV GOARCH amd64
-ENV CGO_ENABLED 1
-WORKDIR /go/src/github.com/ollama/ollama/
-ENTRYPOINT [ "zsh" ]
+ARG FLAVOR=${TARGETARCH}
 
-### 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 $(pwd):/go/src/github.com/ollama/ollama/ builder-arm64
-#
-FROM --platform=linux/arm64 rockylinux:8 AS unified-builder-arm64
-ARG GOLANG_VERSION
-ARG CUDA_VERSION_11
-ARG CUDA_VERSION_12
-COPY ./scripts/rh_linux_deps.sh /
-RUN GOLANG_VERSION=${GOLANG_VERSION} sh /rh_linux_deps.sh
-RUN yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/sbsa/cuda-rhel8.repo && \
-    dnf config-manager --set-enabled appstream && \
-    dnf clean all && \
-    dnf install -y \
-    zsh \
-    cuda-toolkit-$(echo ${CUDA_VERSION_11} | cut -f1-2 -d. | sed -e "s/\./-/g") \
-    cuda-toolkit-$(echo ${CUDA_VERSION_12} | cut -f1-2 -d. | sed -e "s/\./-/g")
-ENV PATH /opt/rh/gcc-toolset-10/root/usr/bin:$PATH:/usr/local/cuda/bin
-ENV LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64
-ENV LIBRARY_PATH=/usr/local/cuda/lib64/stubs:/opt/amdgpu/lib64
-ENV GOARCH arm64
-ENV CGO_ENABLED 1
-WORKDIR /go/src/github.com/ollama/ollama/
-ENTRYPOINT [ "zsh" ]
+ARG ROCMVERSION=6.1.2
+ARG JETPACK5VERSION=r35.4.1
+ARG JETPACK6VERSION=r36.2.0
+ARG CMAKEVERSION=3.31.2
 
-FROM --platform=linux/amd64 unified-builder-amd64 AS build-amd64
-COPY . .
-ARG OLLAMA_SKIP_CUDA_GENERATE
-ARG OLLAMA_SKIP_ROCM_GENERATE
-ARG OLLAMA_FAST_BUILD
+FROM --platform=linux/amd64 rocm/dev-centos-7:${ROCMVERSION}-complete AS base-amd64
+RUN sed -i -e 's/mirror.centos.org/vault.centos.org/g' -e 's/^#.*baseurl=http/baseurl=http/g' -e 's/^mirrorlist=http/#mirrorlist=http/g' /etc/yum.repos.d/*.repo \
+    && yum install -y yum-utils devtoolset-10-gcc devtoolset-10-gcc-c++ \
+    && yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-rhel7.repo \
+    && curl -s -L https://github.com/ccache/ccache/releases/download/v4.10.2/ccache-4.10.2-linux-x86_64.tar.xz | tar -Jx -C /usr/local/bin --strip-components 1
+ENV PATH=/opt/rh/devtoolset-10/root/usr/bin:/opt/rh/devtoolset-11/root/usr/bin:$PATH
+
+FROM --platform=linux/arm64 rockylinux:8 AS base-arm64
+# install epel-release for ccache
+RUN yum install -y yum-utils epel-release \
+    && yum install -y clang ccache \
+    && yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/sbsa/cuda-rhel8.repo
+ENV CC=clang CXX=clang++
+
+FROM base-${TARGETARCH} AS base
+ARG CMAKEVERSION
+RUN curl -fsSL https://github.com/Kitware/CMake/releases/download/v${CMAKEVERSION}/cmake-${CMAKEVERSION}-linux-$(uname -m).tar.gz | tar xz -C /usr/local --strip-components 1
+COPY CMakeLists.txt CMakePresets.json .
+COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
+ENV LDFLAGS=-s
+
+FROM base AS cpu
+# amd64 uses gcc which requires devtoolset-11 for AVX extensions while arm64 uses clang
+RUN if [ "$(uname -m)" = "x86_64" ]; then yum install -y devtoolset-11-gcc devtoolset-11-gcc-c++; fi
+ENV PATH=/opt/rh/devtoolset-11/root/usr/bin:$PATH
 RUN --mount=type=cache,target=/root/.ccache \
-    if grep "^flags" /proc/cpuinfo|grep avx>/dev/null; then \
-        make -j $(expr $(nproc) / 2 ) dist ; \
-    else \
-        make -j 5 dist ; \
-    fi
-RUN cd dist/linux-$GOARCH && \
-    tar -cf - . | pigz --best > ../ollama-linux-$GOARCH.tgz
-RUN if [ -z ${OLLAMA_SKIP_ROCM_GENERATE} ] ; then \
-    cd dist/linux-$GOARCH-rocm && \
-    tar -cf - . | pigz --best > ../ollama-linux-$GOARCH-rocm.tgz ;\
-    fi
+    cmake --preset 'CPU' \
+        && cmake --build --parallel --preset 'CPU' \
+        && cmake --install build --component CPU --strip --parallel 8
 
-# 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 . .
-ARG CGO_CFLAGS
-ENV GOARCH arm64
+FROM base AS cuda-11
+ARG CUDA11VERSION=11.3
+RUN yum install -y cuda-toolkit-${CUDA11VERSION//./-}
+ENV PATH=/usr/local/cuda-11/bin:$PATH
 RUN --mount=type=cache,target=/root/.ccache \
-    make -j 5 dist_cuda_v11 \
-        CUDA_ARCHITECTURES="72;87" \
-        GPU_RUNNER_VARIANT=_jetpack5 \
-        DIST_LIB_DIR=/go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack5/lib/ollama \
-        DIST_GPU_RUNNER_DEPS_DIR=/go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack5/lib/ollama/cuda_jetpack5
+    cmake --preset 'CUDA 11' \
+        && cmake --build --parallel --preset 'CUDA 11' \
+        && cmake --install build --component CUDA --strip --parallel 8
 
-FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${JETPACK_6} AS runners-jetpack6-arm64
-ARG GOLANG_VERSION
-RUN apt-get update && apt-get install -y git curl ccache && \
-    curl -s -L https://dl.google.com/go/go${GOLANG_VERSION}.linux-arm64.tar.gz | tar xz -C /usr/local && \
-    ln -s /usr/local/go/bin/go /usr/local/bin/go && \
-    ln -s /usr/local/go/bin/gofmt /usr/local/bin/gofmt && \
-    apt-get clean && rm -rf /var/lib/apt/lists/*
-WORKDIR /go/src/github.com/ollama/ollama/
-COPY . .
-ARG CGO_CFLAGS
-ENV GOARCH arm64
+FROM base AS cuda-12
+ARG CUDA12VERSION=12.4
+RUN yum install -y cuda-toolkit-${CUDA12VERSION//./-}
+ENV PATH=/usr/local/cuda-12/bin:$PATH
 RUN --mount=type=cache,target=/root/.ccache \
-    make -j 5 dist_cuda_v12 \
-        CUDA_ARCHITECTURES="87" \
-        GPU_RUNNER_VARIANT=_jetpack6 \
-        DIST_LIB_DIR=/go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack6/lib/ollama \
-        DIST_GPU_RUNNER_DEPS_DIR=/go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack6/lib/ollama/cuda_jetpack6
+    cmake --preset 'CUDA 12' \
+        && cmake --build --parallel --preset 'CUDA 12' \
+        && cmake --install build --component CUDA --strip --parallel 8
 
-FROM --platform=linux/arm64 unified-builder-arm64 AS build-arm64
-COPY . .
-ARG OLLAMA_SKIP_CUDA_GENERATE
-ARG OLLAMA_FAST_BUILD
+FROM base AS rocm-6
 RUN --mount=type=cache,target=/root/.ccache \
-    make -j 5 dist
-COPY --from=runners-jetpack5-arm64 /go/src/github.com/ollama/ollama/dist/ dist/
-COPY --from=runners-jetpack6-arm64 /go/src/github.com/ollama/ollama/dist/ dist/
-RUN cd dist/linux-$GOARCH && \
-    tar -cf - . | pigz --best > ../ollama-linux-$GOARCH.tgz
-RUN cd dist/linux-$GOARCH-jetpack5 && \
-    tar -cf - . | pigz --best > ../ollama-linux-$GOARCH-jetpack5.tgz
-RUN cd dist/linux-$GOARCH-jetpack6 && \
-    tar -cf - . | pigz --best > ../ollama-linux-$GOARCH-jetpack6.tgz
+    cmake --preset 'ROCm 6' \
+        && cmake --build --parallel --preset 'ROCm 6' \
+        && cmake --install build --component HIP --strip --parallel 8
 
-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-$TARGETARCH AS dist
+FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${JETPACK5VERSION} AS jetpack-5
+ARG CMAKEVERSION
+RUN apt-get update && apt-get install -y curl ccache \
+    && curl -fsSL https://github.com/Kitware/CMake/releases/download/v${CMAKEVERSION}/cmake-${CMAKEVERSION}-linux-$(uname -m).tar.gz | tar xz -C /usr/local --strip-components 1
+COPY CMakeLists.txt CMakePresets.json .
+COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
+RUN --mount=type=cache,target=/root/.ccache \
+    cmake --preset 'JetPack 5' \
+        && cmake --build --parallel --preset 'JetPack 5' \
+        && cmake --install build --component CUDA --strip --parallel 8
 
+FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${JETPACK6VERSION} AS jetpack-6
+ARG CMAKEVERSION
+RUN apt-get update && apt-get install -y curl ccache \
+    && curl -fsSL https://github.com/Kitware/CMake/releases/download/v${CMAKEVERSION}/cmake-${CMAKEVERSION}-linux-$(uname -m).tar.gz | tar xz -C /usr/local --strip-components 1
+COPY CMakeLists.txt CMakePresets.json .
+COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
+RUN --mount=type=cache,target=/root/.ccache \
+    cmake --preset 'JetPack 6' \
+        && cmake --build --parallel --preset 'JetPack 6' \
+        && cmake --install build --component CUDA --strip --parallel 8
 
-# For amd64 container images, filter out cuda/rocm to minimize size
-FROM build-amd64 AS runners-cuda-amd64
-RUN rm -rf \
-    ./dist/linux-amd64/lib/ollama/libggml_hipblas.so \
-    ./dist/linux-amd64/lib/ollama/runners/rocm*
+FROM base AS build
+ARG GOVERSION=1.23.4
+RUN curl -fsSL https://golang.org/dl/go${GOVERSION}.linux-$(case $(uname -m) in x86_64) echo amd64 ;; aarch64) echo arm64 ;; esac).tar.gz | tar xz -C /usr/local
+ENV PATH=/usr/local/go/bin:$PATH
+WORKDIR /go/src/github.com/ollama/ollama
+COPY . .
+ARG GOFLAGS="'-ldflags=-w -s'"
+ENV CGO_ENABLED=1
+RUN --mount=type=cache,target=/root/.cache/go-build \
+    go build -trimpath -buildmode=pie -o /bin/ollama .
 
-FROM build-amd64 AS runners-rocm-amd64
-RUN rm -rf \
-    ./dist/linux-amd64/lib/ollama/libggml_cuda*.so \
-    ./dist/linux-amd64/lib/ollama/libcu*.so* \
-    ./dist/linux-amd64/lib/ollama/runners/cuda*
+FROM --platform=linux/amd64 scratch AS amd64
+COPY --from=cuda-11 dist/lib/ollama/cuda_v11 /lib/ollama/cuda_v11
+COPY --from=cuda-12 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_v12
 
-FROM --platform=linux/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=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 scratch AS arm64
+COPY --from=cuda-11 dist/lib/ollama/cuda_v11 /lib/ollama/cuda_v11
+COPY --from=cuda-12 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_v12
+COPY --from=jetpack-5 dist/lib/ollama/cuda_v11 lib/ollama/cuda_jetpack5
+COPY --from=jetpack-6 dist/lib/ollama/cuda_v12 lib/ollama/cuda_jetpack6
 
-FROM --platform=linux/arm64 ubuntu:22.04 AS runtime-arm64
-RUN apt-get update && \
-    apt-get install -y ca-certificates && \
-    apt-get clean && rm -rf /var/lib/apt/lists/*
-COPY --from=build-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/bin/ /bin/
-COPY --from=build-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/lib/ /lib/
-COPY --from=runners-jetpack5-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack5/lib/ /lib/
-COPY --from=runners-jetpack6-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack6/lib/ /lib/
+FROM --platform=linux/arm64 scratch AS rocm
+COPY --from=rocm-6 dist/lib/ollama/rocm /lib/ollama/rocm
 
+FROM ${FLAVOR} AS archive
+COPY --from=cpu dist/lib/ollama /lib/ollama
+COPY --from=build /bin/ollama /bin/ollama
 
-# 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=build-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/bin/ /bin/
-COPY --from=runners-rocm-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/lib/ /lib/
-
-EXPOSE 11434
-ENV OLLAMA_HOST 0.0.0.0
-
-ENTRYPOINT ["/bin/ollama"]
-CMD ["serve"]
-
-FROM runtime-$TARGETARCH
-EXPOSE 11434
-ENV OLLAMA_HOST 0.0.0.0
+FROM ubuntu:20.04
+RUN apt-get update \
+    && apt-get install -y ca-certificates \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+COPY --from=archive /bin /usr/bin
 ENV PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
+COPY --from=archive /lib/ollama /usr/lib/ollama
 ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
 ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
 ENV NVIDIA_VISIBLE_DEVICES=all
-
+ENV OLLAMA_HOST=0.0.0.0:11434
+EXPOSE 11434
 ENTRYPOINT ["/bin/ollama"]
 CMD ["serve"]

Makefile

@@ -1,105 +0,0 @@
-# top level makefile for Ollama
-include make/common-defs.make
-
-
-# Determine which if any GPU runners we should build
-include make/cuda-v11-defs.make
-include make/cuda-v12-defs.make
-include make/rocm-defs.make
-
-ifeq ($(CUSTOM_CPU_FLAGS),)
-ifneq ($(OS),darwin)
-ifeq ($(ARCH),amd64)
-	RUNNER_TARGETS=cpu
-endif
-endif
-# Without CUSTOM_CPU_FLAGS we default to build both v11 and v12 if present
-ifeq ($(OLLAMA_SKIP_CUDA_GENERATE),)
-ifneq ($(CUDA_11_COMPILER),)
-	RUNNER_TARGETS += cuda_v11
-endif
-ifneq ($(CUDA_12_COMPILER),)
-	RUNNER_TARGETS += cuda_v12
-endif
-endif
-else # CUSTOM_CPU_FLAGS is set, we'll build only the latest cuda version detected
-ifneq ($(CUDA_12_COMPILER),)
-	RUNNER_TARGETS += cuda_v12
-else ifneq ($(CUDA_11_COMPILER),)
-	RUNNER_TARGETS += cuda_v11
-endif
-endif
-
-ifeq ($(OLLAMA_SKIP_ROCM_GENERATE),)
-ifneq ($(HIP_COMPILER),)
-	RUNNER_TARGETS += rocm
-endif
-endif
-
-
-all: runners exe
-
-dist: $(addprefix dist_, $(RUNNER_TARGETS)) dist_exe
-
-dist_%:
-	@$(MAKE) --no-print-directory -f make/Makefile.$* dist
-
-runners: $(RUNNER_TARGETS)
-
-$(RUNNER_TARGETS):
-	@$(MAKE) --no-print-directory -f make/Makefile.$@
-
-exe dist_exe:
-	@$(MAKE) --no-print-directory -f make/Makefile.ollama $@
-
-help-sync apply-patches create-patches sync sync-clean:
-	@$(MAKE) --no-print-directory -f make/Makefile.sync $@
-
-test integration lint:
-	@$(MAKE) --no-print-directory -f make/Makefile.test $@
-
-clean:
-	rm -rf $(BUILD_DIR) $(DIST_LIB_DIR) $(OLLAMA_EXE) $(DIST_OLLAMA_EXE)
-	go clean -cache
-
-help:
-	@echo "The following make targets will help you build Ollama"
-	@echo ""
-	@echo "	make all   		# (default target) Build Ollama llm subprocess runners, and the primary ollama executable"
-	@echo "	make runners		# Build Ollama llm subprocess runners; after you may use 'go build .' to build the primary ollama exectuable"
-	@echo "	make <runner>		# Build specific runners. Enabled: '$(RUNNER_TARGETS)'"
-	@echo "	make dist		# Build the runners and primary ollama executable for distribution"
-	@echo "	make help-sync 		# Help information on vendor update targets"
-	@echo "	make help-runners 	# Help information on runner targets"
-	@echo ""
-	@echo "The following make targets will help you test Ollama"
-	@echo ""
-	@echo "	make test   		# Run unit tests"
-	@echo "	make integration	# Run integration tests.  You must 'make all' first"
-	@echo "	make lint   		# Run lint and style tests"
-	@echo ""
-	@echo "For more information see 'docs/development.md'"
-	@echo ""
-
-
-help-runners:
-	@echo "The following runners will be built based on discovered GPU libraries: '$(RUNNER_TARGETS)'"
-	@echo ""
-	@echo "GPU Runner CPU Flags: '$(GPU_RUNNER_CPU_FLAGS)'  (Override with CUSTOM_CPU_FLAGS)"
-	@echo ""
-	@echo "# CUDA_PATH sets the location where CUDA toolkits are present"
-	@echo "CUDA_PATH=$(CUDA_PATH)"
-	@echo "	CUDA_11_PATH=$(CUDA_11_PATH)"
-	@echo "	CUDA_11_COMPILER=$(CUDA_11_COMPILER)"
-	@echo "	CUDA_12_PATH=$(CUDA_12_PATH)"
-	@echo "	CUDA_12_COMPILER=$(CUDA_12_COMPILER)"
-	@echo ""
-	@echo "# HIP_PATH sets the location where the ROCm toolkit is present"
-	@echo "HIP_PATH=$(HIP_PATH)"
-	@echo "	HIP_COMPILER=$(HIP_COMPILER)"
-
-.PHONY: all exe dist help help-sync help-runners test integration lint runners clean $(RUNNER_TARGETS)
-
-# Handy debugging for make variables
-print-%:
-	@echo '$*=$($*)'

Makefile.sync

@@ -0,0 +1,60 @@
+UPSTREAM=https://github.com/ggerganov/llama.cpp.git
+WORKDIR=llama/vendor
+FETCH_HEAD=46e3556e01b824e52395fb050b29804b6cff2a7c
+
+.PHONY: help
+help:
+	@echo "Available targets:"
+	@echo "    sync                 Sync with upstream repositories"
+	@echo "    checkout             Checkout upstream repository"
+	@echo "    apply-patches        Apply patches to local repository"
+	@echo "    format-patches       Format patches from local repository"
+	@echo "    clean                Clean local repository"
+	@echo
+	@echo "Example:"
+	@echo "    make -f $(lastword $(MAKEFILE_LIST)) clean sync"
+
+.PHONY: sync
+sync: llama/build-info.cpp llama/llama.cpp ml/backend/ggml/ggml apply-patches
+
+.PHONY: llama/build-info.cpp
+llama/build-info.cpp: llama/build-info.cpp.in
+	sed -e 's|@FETCH_HEAD@|$(FETCH_HEAD)|' $< > $@
+
+.PHONY: llama/llama.cpp
+llama/llama.cpp: llama/vendor/ apply-patches
+	rsync -arvzc -f "merge $@/.rsync-filter" $< $@
+
+.PHONY: ml/backend/ggml/ggml apply-patches
+ml/backend/ggml/ggml: llama/vendor/ggml/ apply-patches
+	rsync -arvzc -f "merge $@/.rsync-filter" $< $@
+
+PATCHES=$(wildcard llama/patches/*.patch)
+
+.PHONY: apply-patches
+.NOTPARALLEL:
+apply-patches: $(addsuffix ed, $(PATCHES))
+
+%.patched: %.patch
+	@if git -c user.name=nobody -c 'user.email=<>' -C $(WORKDIR) am -3 $(realpath $<); then touch $@; else git -C $(WORKDIR) am --abort; exit 1; fi
+
+.PHONY: checkout
+checkout: $(WORKDIR)
+	git -C $(WORKDIR) fetch
+	git -C $(WORKDIR) checkout -f $(FETCH_HEAD)
+
+$(WORKDIR):
+	git clone $(UPSTREAM) $(WORKDIR)
+
+.PHONE: format-patches
+format-patches: llama/patches
+	git -C $(WORKDIR) format-patch \
+		--no-signature \
+		--no-numbered \
+		--zero-commit \
+		-o $(realpath $<) \
+		$(FETCH_HEAD)
+
+.PHONE: clean
+clean: checkout
+	$(RM) $(addsuffix ed, $(PATCHES))

README.md

@@ -1,11 +1,11 @@
 <div align="center">
- <img alt="ollama" height="200px" src="https://github.com/ollama/ollama/assets/3325447/0d0b44e2-8f4a-4e99-9b52-a5c1c741c8f7">
+  <a href="https://ollama.com" />
+    <img alt="ollama" height="200px" src="https://github.com/ollama/ollama/assets/3325447/0d0b44e2-8f4a-4e99-9b52-a5c1c741c8f7">
+  </a>
 </div>
 
 # Ollama
 
-[![Discord](https://dcbadge.vercel.app/api/server/ollama?style=flat&compact=true)](https://discord.gg/ollama)
-
 Get up and running with large language models.
 
 ### macOS
@@ -33,6 +33,11 @@ The official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama) `olla
 - [ollama-python](https://github.com/ollama/ollama-python)
 - [ollama-js](https://github.com/ollama/ollama-js)
 
+### Community
+
+- [Discord](https://discord.gg/ollama)
+- [Reddit](https://reddit.com/r/ollama)
+
 ## Quickstart
 
 To run and chat with [Llama 3.2](https://ollama.com/library/llama3.2):
@@ -56,8 +61,8 @@ Here are some example models that can be downloaded:
 | Llama 3.2 Vision   | 90B        | 55GB  | `ollama run llama3.2-vision:90b` |
 | Llama 3.1          | 8B         | 4.7GB | `ollama run llama3.1`            |
 | Llama 3.1          | 405B       | 231GB | `ollama run llama3.1:405b`       |
+| Phi 4              | 14B        | 9.1GB | `ollama run phi4`                |
 | Phi 3 Mini         | 3.8B       | 2.3GB | `ollama run phi3`                |
-| 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`          |
@@ -97,7 +102,7 @@ Ollama supports importing GGUF models in the Modelfile:
    ollama run example
    ```
 
-### Import from PyTorch or Safetensors
+### Import from Safetensors
 
 See the [guide](docs/import.md) on importing models for more information.
 
@@ -132,7 +137,7 @@ ollama run mario
 Hello! It's your friend Mario.
 ```
 
-For more examples, see the [examples](examples) directory. For more information on working with a Modelfile, see the [Modelfile](docs/modelfile.md) documentation.
+For more information on working with a Modelfile, see the [Modelfile](docs/modelfile.md) documentation.
 
 ## CLI Reference
 
@@ -298,6 +303,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [AnythingLLM (Docker + MacOs/Windows/Linux native app)](https://github.com/Mintplex-Labs/anything-llm)
 - [Ollama Basic Chat: Uses HyperDiv Reactive UI](https://github.com/rapidarchitect/ollama_basic_chat)
 - [Ollama-chats RPG](https://github.com/drazdra/ollama-chats)
+- [IntelliBar](https://intellibar.app/) (AI-powered assistant for macOS)
 - [QA-Pilot](https://github.com/reid41/QA-Pilot) (Interactive chat tool that can leverage Ollama models for rapid understanding and navigation of GitHub code repositories)
 - [ChatOllama](https://github.com/sugarforever/chat-ollama) (Open Source Chatbot based on Ollama with Knowledge Bases)
 - [CRAG Ollama Chat](https://github.com/Nagi-ovo/CRAG-Ollama-Chat) (Simple Web Search with Corrective RAG)
@@ -327,6 +333,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [BoltAI for Mac](https://boltai.com) (AI Chat Client for Mac)
 - [Harbor](https://github.com/av/harbor) (Containerized LLM Toolkit with Ollama as default backend)
 - [PyGPT](https://github.com/szczyglis-dev/py-gpt) (AI desktop assistant for Linux, Windows and Mac)
+- [Alpaca](https://github.com/Jeffser/Alpaca) (An Ollama client application for linux and macos made with GTK4 and Adwaita)
 - [AutoGPT](https://github.com/Significant-Gravitas/AutoGPT/blob/master/docs/content/platform/ollama.md) (AutoGPT Ollama integration)
 - [Go-CREW](https://www.jonathanhecl.com/go-crew/) (Powerful Offline RAG in Golang)
 - [PartCAD](https://github.com/openvmp/partcad/) (CAD model generation with OpenSCAD and CadQuery)
@@ -361,6 +368,10 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Abbey](https://github.com/US-Artificial-Intelligence/abbey) (A configurable AI interface server with notebooks, document storage, and YouTube support)
 - [Minima](https://github.com/dmayboroda/minima) (RAG with on-premises or fully local workflow)
 - [aidful-ollama-model-delete](https://github.com/AidfulAI/aidful-ollama-model-delete) (User interface for simplified model cleanup)
+- [Perplexica](https://github.com/ItzCrazyKns/Perplexica) (An AI-powered search engine & an open-source alternative to Perplexity AI)
+- [AI Toolkit for Visual Studio Code](https://aka.ms/ai-tooklit/ollama-docs) (Microsoft-official VSCode extension to chat, test, evaluate models with Ollama support, and use them in your AI applications.)
+- [MinimalNextOllamaChat](https://github.com/anilkay/MinimalNextOllamaChat) (Minimal Web UI for Chat and Model Control)
+- [Chipper](https://github.com/TilmanGriesel/chipper) AI interface for tinkerers (Ollama, Haystack RAG, Python)
 
 ### Cloud
 
@@ -373,6 +384,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [oterm](https://github.com/ggozad/oterm)
 - [Ellama Emacs client](https://github.com/s-kostyaev/ellama)
 - [Emacs client](https://github.com/zweifisch/ollama)
+- [neollama](https://github.com/paradoxical-dev/neollama) UI client for interacting with models from within Neovim
 - [gen.nvim](https://github.com/David-Kunz/gen.nvim)
 - [ollama.nvim](https://github.com/nomnivore/ollama.nvim)
 - [ollero.nvim](https://github.com/marco-souza/ollero.nvim)
@@ -407,6 +419,8 @@ See the [API documentation](./docs/api.md) for all endpoints.
 
 ### Database
 
+- [pgai](https://github.com/timescale/pgai) - PostgreSQL as a vector database (Create and search embeddings from Ollama models using pgvector)
+   - [Get started guide](https://github.com/timescale/pgai/blob/main/docs/vectorizer-quick-start.md)
 - [MindsDB](https://github.com/mindsdb/mindsdb/blob/staging/mindsdb/integrations/handlers/ollama_handler/README.md) (Connects Ollama models with nearly 200 data platforms and apps)
 - [chromem-go](https://github.com/philippgille/chromem-go/blob/v0.5.0/embed_ollama.go) with [example](https://github.com/philippgille/chromem-go/tree/v0.5.0/examples/rag-wikipedia-ollama)
 - [Kangaroo](https://github.com/dbkangaroo/kangaroo) (AI-powered SQL client and admin tool for popular databases)
@@ -425,10 +439,12 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [LangChain](https://python.langchain.com/docs/integrations/llms/ollama) and [LangChain.js](https://js.langchain.com/docs/integrations/chat/ollama/) with [example](https://js.langchain.com/docs/tutorials/local_rag/)
 - [Firebase Genkit](https://firebase.google.com/docs/genkit/plugins/ollama)
 - [crewAI](https://github.com/crewAIInc/crewAI)
+- [Yacana](https://remembersoftwares.github.io/yacana/) (User-friendly multi-agent framework for brainstorming and executing predetermined flows with built-in tool integration)
 - [Spring AI](https://github.com/spring-projects/spring-ai) with [reference](https://docs.spring.io/spring-ai/reference/api/chat/ollama-chat.html) and [example](https://github.com/tzolov/ollama-tools)
 - [LangChainGo](https://github.com/tmc/langchaingo/) with [example](https://github.com/tmc/langchaingo/tree/main/examples/ollama-completion-example)
 - [LangChain4j](https://github.com/langchain4j/langchain4j) with [example](https://github.com/langchain4j/langchain4j-examples/tree/main/ollama-examples/src/main/java)
 - [LangChainRust](https://github.com/Abraxas-365/langchain-rust) with [example](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/llm_ollama.rs)
+- [LangChain for .NET](https://github.com/tryAGI/LangChain) with [example](https://github.com/tryAGI/LangChain/blob/main/examples/LangChain.Samples.OpenAI/Program.cs)
 - [LLPhant](https://github.com/theodo-group/LLPhant?tab=readme-ov-file#ollama)
 - [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/llm/ollama/) and [LlamaIndexTS](https://ts.llamaindex.ai/modules/llms/available_llms/ollama)
 - [LiteLLM](https://github.com/BerriAI/litellm)
@@ -468,6 +484,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [GoLamify](https://github.com/prasad89/golamify)
 - [Ollama for Haskell](https://github.com/tusharad/ollama-haskell)
 - [multi-llm-ts](https://github.com/nbonamy/multi-llm-ts) (A Typescript/JavaScript library allowing access to different LLM in unified API)
+- [LlmTornado](https://github.com/lofcz/llmtornado) (C# library providing a unified interface for major FOSS & Commercial inference APIs)
 
 ### Mobile
 
@@ -517,6 +534,8 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [AI Summmary Helper plugin](https://github.com/philffm/ai-summary-helper)
 - [TextCraft](https://github.com/suncloudsmoon/TextCraft) (Copilot in Word alternative using Ollama)
 - [Alfred Ollama](https://github.com/zeitlings/alfred-ollama) (Alfred Workflow)
+- [TextLLaMA](https://github.com/adarshM84/TextLLaMA) A Chrome Extension that helps you write emails, correct grammar, and translate into any language
+- [Simple-Discord-AI](https://github.com/zyphixor/simple-discord-ai)
 
 ### Supported backends
 
@@ -525,4 +544,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
 ### Observability
 
 - [OpenLIT](https://github.com/openlit/openlit) is an OpenTelemetry-native tool for monitoring Ollama Applications & GPUs using traces and metrics.
-- [HoneyHive](https://docs.honeyhive.ai/integrations/ollama) is an AI observability and evaluation platform for AI agents. Use HoneyHive to evaluate agent performance, interrogate failures, and monitor quality in production. 
+- [HoneyHive](https://docs.honeyhive.ai/integrations/ollama) is an AI observability and evaluation platform for AI agents. Use HoneyHive to evaluate agent performance, interrogate failures, and monitor quality in production.
+- [Langfuse](https://langfuse.com/docs/integrations/ollama) is an open source LLM observability platform that enables teams to collaboratively monitor, evaluate and debug AI applications.
+- [MLflow Tracing](https://mlflow.org/docs/latest/llms/tracing/index.html#automatic-tracing) is an open source LLM observability tool with a convenient API to log and visualize traces, making it easy to debug and evaluate GenAI applications.

api/examples/README.md

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

api/types.go

@@ -225,7 +225,6 @@ type Options struct {
 	Mirostat         int      `json:"mirostat,omitempty"`
 	MirostatTau      float32  `json:"mirostat_tau,omitempty"`
 	MirostatEta      float32  `json:"mirostat_eta,omitempty"`
-	PenalizeNewline  bool     `json:"penalize_newline,omitempty"`
 	Stop             []string `json:"stop,omitempty"`
 }
 
@@ -295,17 +294,21 @@ type EmbeddingResponse struct {
 
 // CreateRequest is the request passed to [Client.Create].
 type CreateRequest struct {
-	Model     string `json:"model"`
-	Modelfile string `json:"modelfile"`
-	Stream    *bool  `json:"stream,omitempty"`
-	Quantize  string `json:"quantize,omitempty"`
+	Model    string `json:"model"`
+	Stream   *bool  `json:"stream,omitempty"`
+	Quantize string `json:"quantize,omitempty"`
+
+	From       string            `json:"from,omitempty"`
+	Files      map[string]string `json:"files,omitempty"`
+	Adapters   map[string]string `json:"adapters,omitempty"`
+	Template   string            `json:"template,omitempty"`
+	License    any               `json:"license,omitempty"`
+	System     string            `json:"system,omitempty"`
+	Parameters map[string]any    `json:"parameters,omitempty"`
+	Messages   []Message         `json:"messages,omitempty"`
 
 	// Deprecated: set the model name with Model instead
 	Name string `json:"name"`
-
-	// Deprecated: set the file content with Modelfile instead
-	Path string `json:"path"`
-
 	// Deprecated: use Quantize instead
 	Quantization string `json:"quantization,omitempty"`
 }
@@ -602,7 +605,6 @@ func DefaultOptions() Options {
 		Mirostat:         0,
 		MirostatTau:      5.0,
 		MirostatEta:      0.1,
-		PenalizeNewline:  true,
 		Seed:             -1,
 
 		Runner: Runner{

cmd/cmd.go

@@ -1,13 +1,10 @@
 package cmd
 
 import (
-	"archive/zip"
 	"bufio"
-	"bytes"
 	"context"
 	"crypto/ed25519"
 	"crypto/rand"
-	"crypto/sha256"
 	"encoding/json"
 	"encoding/pem"
 	"errors"
@@ -46,15 +43,11 @@ import (
 	"github.com/ollama/ollama/version"
 )
 
-var (
-	errModelNotFound     = errors.New("no Modelfile or safetensors files found")
-	errModelfileNotFound = errors.New("specified Modelfile wasn't found")
-)
+var errModelfileNotFound = errors.New("specified Modelfile wasn't found")
 
 func getModelfileName(cmd *cobra.Command) (string, error) {
-	fn, _ := cmd.Flags().GetString("file")
+	filename, _ := cmd.Flags().GetString("file")
 
-	filename := fn
 	if filename == "" {
 		filename = "Modelfile"
 	}
@@ -66,7 +59,7 @@ func getModelfileName(cmd *cobra.Command) (string, error) {
 
 	_, err = os.Stat(absName)
 	if err != nil {
-		return fn, err
+		return "", err
 	}
 
 	return absName, nil
@@ -102,68 +95,52 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
-	home, err := os.UserHomeDir()
+	status := "gathering model components"
+	spinner := progress.NewSpinner(status)
+	p.Add(status, spinner)
+
+	req, err := modelfile.CreateRequest(filepath.Dir(filename))
 	if err != nil {
 		return err
 	}
+	spinner.Stop()
 
-	status := "transferring model data"
-	spinner := progress.NewSpinner(status)
-	p.Add(status, spinner)
-	defer p.Stop()
+	req.Name = args[0]
+	quantize, _ := cmd.Flags().GetString("quantize")
+	if quantize != "" {
+		req.Quantize = quantize
+	}
 
 	client, err := api.ClientFromEnvironment()
 	if err != nil {
 		return err
 	}
 
-	for i := range modelfile.Commands {
-		switch modelfile.Commands[i].Name {
-		case "model", "adapter":
-			path := modelfile.Commands[i].Args
-			if path == "~" {
-				path = home
-			} else if strings.HasPrefix(path, "~/") {
-				path = filepath.Join(home, path[2:])
-			}
-
-			if !filepath.IsAbs(path) {
-				path = filepath.Join(filepath.Dir(filename), path)
-			}
-
-			fi, err := os.Stat(path)
-			if errors.Is(err, os.ErrNotExist) && modelfile.Commands[i].Name == "model" {
-				continue
-			} else if err != nil {
+	if len(req.Files) > 0 {
+		fileMap := map[string]string{}
+		for f, digest := range req.Files {
+			if _, err := createBlob(cmd, client, f, digest, p); err != nil {
 				return err
 			}
-
-			if fi.IsDir() {
-				// this is likely a safetensors or pytorch directory
-				// TODO make this work w/ adapters
-				tempfile, err := tempZipFiles(path)
-				if err != nil {
-					return err
-				}
-				defer os.RemoveAll(tempfile)
-
-				path = tempfile
-			}
-
-			digest, err := createBlob(cmd, client, path, spinner)
-			if err != nil {
-				return err
-			}
-
-			modelfile.Commands[i].Args = "@" + digest
+			fileMap[filepath.Base(f)] = digest
 		}
+		req.Files = fileMap
+	}
+
+	if len(req.Adapters) > 0 {
+		fileMap := map[string]string{}
+		for f, digest := range req.Adapters {
+			if _, err := createBlob(cmd, client, f, digest, p); err != nil {
+				return err
+			}
+			fileMap[filepath.Base(f)] = digest
+		}
+		req.Adapters = fileMap
 	}
 
 	bars := make(map[string]*progress.Bar)
 	fn := func(resp api.ProgressResponse) error {
 		if resp.Digest != "" {
-			spinner.Stop()
-
 			bar, ok := bars[resp.Digest]
 			if !ok {
 				bar = progress.NewBar(fmt.Sprintf("pulling %s...", resp.Digest[7:19]), resp.Total, resp.Completed)
@@ -183,145 +160,23 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 		return nil
 	}
 
-	quantize, _ := cmd.Flags().GetString("quantize")
-
-	request := api.CreateRequest{Name: args[0], Modelfile: modelfile.String(), Quantize: quantize}
-	if err := client.Create(cmd.Context(), &request, fn); err != nil {
+	if err := client.Create(cmd.Context(), req, fn); err != nil {
+		if strings.Contains(err.Error(), "path or Modelfile are required") {
+			return fmt.Errorf("the ollama server must be updated to use `ollama create` with this client")
+		}
 		return err
 	}
 
 	return nil
 }
 
-func tempZipFiles(path string) (string, error) {
-	tempfile, err := os.CreateTemp("", "ollama-tf")
+func createBlob(cmd *cobra.Command, client *api.Client, path string, digest string, p *progress.Progress) (string, error) {
+	realPath, err := filepath.EvalSymlinks(path)
 	if err != nil {
 		return "", err
 	}
-	defer tempfile.Close()
 
-	detectContentType := func(path string) (string, error) {
-		f, err := os.Open(path)
-		if err != nil {
-			return "", err
-		}
-		defer f.Close()
-
-		var b bytes.Buffer
-		b.Grow(512)
-
-		if _, err := io.CopyN(&b, f, 512); err != nil && !errors.Is(err, io.EOF) {
-			return "", err
-		}
-
-		contentType, _, _ := strings.Cut(http.DetectContentType(b.Bytes()), ";")
-		return contentType, nil
-	}
-
-	glob := func(pattern, contentType string) ([]string, error) {
-		matches, err := filepath.Glob(pattern)
-		if err != nil {
-			return nil, err
-		}
-
-		for _, safetensor := range matches {
-			if ct, err := detectContentType(safetensor); err != nil {
-				return nil, err
-			} else if ct != contentType {
-				return nil, fmt.Errorf("invalid content type: expected %s for %s", ct, safetensor)
-			}
-		}
-
-		return matches, nil
-	}
-
-	var files []string
-	if st, _ := glob(filepath.Join(path, "model*.safetensors"), "application/octet-stream"); len(st) > 0 {
-		// safetensors files might be unresolved git lfs references; skip if they are
-		// covers model-x-of-y.safetensors, model.fp32-x-of-y.safetensors, model.safetensors
-		files = append(files, st...)
-	} else if st, _ := glob(filepath.Join(path, "adapters.safetensors"), "application/octet-stream"); len(st) > 0 {
-		// covers adapters.safetensors
-		files = append(files, st...)
-	} else if st, _ := glob(filepath.Join(path, "adapter_model.safetensors"), "application/octet-stream"); len(st) > 0 {
-		// covers adapter_model.safetensors
-		files = append(files, st...)
-	} else if pt, _ := glob(filepath.Join(path, "pytorch_model*.bin"), "application/zip"); len(pt) > 0 {
-		// pytorch files might also be unresolved git lfs references; skip if they are
-		// covers pytorch_model-x-of-y.bin, pytorch_model.fp32-x-of-y.bin, pytorch_model.bin
-		files = append(files, pt...)
-	} else if pt, _ := glob(filepath.Join(path, "consolidated*.pth"), "application/zip"); len(pt) > 0 {
-		// pytorch files might also be unresolved git lfs references; skip if they are
-		// covers consolidated.x.pth, consolidated.pth
-		files = append(files, pt...)
-	} else {
-		return "", errModelNotFound
-	}
-
-	// add configuration files, json files are detected as text/plain
-	js, err := glob(filepath.Join(path, "*.json"), "text/plain")
-	if err != nil {
-		return "", err
-	}
-	files = append(files, js...)
-
-	// bert models require a nested config.json
-	// TODO(mxyng): merge this with the glob above
-	js, err = glob(filepath.Join(path, "**/*.json"), "text/plain")
-	if err != nil {
-		return "", err
-	}
-	files = append(files, js...)
-
-	if tks, _ := glob(filepath.Join(path, "tokenizer.model"), "application/octet-stream"); len(tks) > 0 {
-		// add tokenizer.model if it exists, tokenizer.json is automatically picked up by the previous glob
-		// tokenizer.model might be a unresolved git lfs reference; error if it is
-		files = append(files, tks...)
-	} else if tks, _ := glob(filepath.Join(path, "**/tokenizer.model"), "text/plain"); len(tks) > 0 {
-		// some times tokenizer.model is in a subdirectory (e.g. meta-llama/Meta-Llama-3-8B)
-		files = append(files, tks...)
-	}
-
-	zipfile := zip.NewWriter(tempfile)
-	defer zipfile.Close()
-
-	for _, file := range files {
-		f, err := os.Open(file)
-		if err != nil {
-			return "", err
-		}
-		defer f.Close()
-
-		fi, err := f.Stat()
-		if err != nil {
-			return "", err
-		}
-
-		zfi, err := zip.FileInfoHeader(fi)
-		if err != nil {
-			return "", err
-		}
-
-		zfi.Name, err = filepath.Rel(path, file)
-		if err != nil {
-			return "", err
-		}
-
-		zf, err := zipfile.CreateHeader(zfi)
-		if err != nil {
-			return "", err
-		}
-
-		if _, err := io.Copy(zf, f); err != nil {
-			return "", err
-		}
-	}
-
-	return tempfile.Name(), nil
-}
-
-func createBlob(cmd *cobra.Command, client *api.Client, path string, spinner *progress.Spinner) (string, error) {
-	bin, err := os.Open(path)
+	bin, err := os.Open(realPath)
 	if err != nil {
 		return "", err
 	}
@@ -334,18 +189,11 @@ func createBlob(cmd *cobra.Command, client *api.Client, path string, spinner *pr
 	}
 	fileSize := fileInfo.Size()
 
-	hash := sha256.New()
-	if _, err := io.Copy(hash, bin); err != nil {
-		return "", err
-	}
-
-	if _, err := bin.Seek(0, io.SeekStart); err != nil {
-		return "", err
-	}
-
 	var pw progressWriter
-	status := "transferring model data 0%"
-	spinner.SetMessage(status)
+	status := fmt.Sprintf("copying file %s 0%%", digest)
+	spinner := progress.NewSpinner(status)
+	p.Add(status, spinner)
+	defer spinner.Stop()
 
 	done := make(chan struct{})
 	defer close(done)
@@ -356,15 +204,14 @@ func createBlob(cmd *cobra.Command, client *api.Client, path string, spinner *pr
 		for {
 			select {
 			case <-ticker.C:
-				spinner.SetMessage(fmt.Sprintf("transferring model data %d%%", int(100*pw.n.Load()/fileSize)))
+				spinner.SetMessage(fmt.Sprintf("copying file %s %d%%", digest, int(100*pw.n.Load()/fileSize)))
 			case <-done:
-				spinner.SetMessage("transferring model data 100%")
+				spinner.SetMessage(fmt.Sprintf("copying file %s 100%%", digest))
 				return
 			}
 		}
 	}()
 
-	digest := fmt.Sprintf("sha256:%x", hash.Sum(nil))
 	if err = client.CreateBlob(cmd.Context(), digest, io.TeeReader(bin, &pw)); err != nil {
 		return "", err
 	}
@@ -601,7 +448,7 @@ func ListHandler(cmd *cobra.Command, args []string) error {
 	var data [][]string
 
 	for _, m := range models.Models {
-		if len(args) == 0 || strings.HasPrefix(m.Name, args[0]) {
+		if len(args) == 0 || strings.HasPrefix(strings.ToLower(m.Name), strings.ToLower(args[0])) {
 			data = append(data, []string{m.Name, m.Digest[:12], format.HumanBytes(m.Size), format.HumanTime(m.ModifiedAt, "Never")})
 		}
 	}

cmd/cmd_test.go

@@ -293,7 +293,7 @@ func TestGetModelfileName(t *testing.T) {
 			name:          "modelfile specified, no modelfile exists",
 			modelfileName: "crazyfile",
 			fileExists:    false,
-			expectedName:  "crazyfile",
+			expectedName:  "",
 			expectedErr:   os.ErrNotExist,
 		},
 		{
@@ -338,8 +338,8 @@ func TestGetModelfileName(t *testing.T) {
 					t.Fatalf("couldn't set file flag: %v", err)
 				}
 			} else {
+				expectedFilename = tt.expectedName
 				if tt.modelfileName != "" {
-					expectedFilename = tt.modelfileName
 					err := cmd.Flags().Set("file", tt.modelfileName)
 					if err != nil {
 						t.Fatalf("couldn't set file flag: %v", err)
@@ -489,3 +489,130 @@ func TestPushHandler(t *testing.T) {
 		})
 	}
 }
+
+func TestCreateHandler(t *testing.T) {
+	tests := []struct {
+		name           string
+		modelName      string
+		modelFile      string
+		serverResponse map[string]func(w http.ResponseWriter, r *http.Request)
+		expectedError  string
+		expectedOutput string
+	}{
+		{
+			name:      "successful create",
+			modelName: "test-model",
+			modelFile: "FROM foo",
+			serverResponse: map[string]func(w http.ResponseWriter, r *http.Request){
+				"/api/create": func(w http.ResponseWriter, r *http.Request) {
+					if r.Method != http.MethodPost {
+						t.Errorf("expected POST request, got %s", r.Method)
+					}
+
+					req := api.CreateRequest{}
+					if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
+						http.Error(w, err.Error(), http.StatusBadRequest)
+						return
+					}
+
+					if req.Name != "test-model" {
+						t.Errorf("expected model name 'test-model', got %s", req.Name)
+					}
+
+					if req.From != "foo" {
+						t.Errorf("expected from 'foo', got %s", req.From)
+					}
+
+					responses := []api.ProgressResponse{
+						{Status: "using existing layer sha256:56bb8bd477a519ffa694fc449c2413c6f0e1d3b1c88fa7e3c9d88d3ae49d4dcb"},
+						{Status: "writing manifest"},
+						{Status: "success"},
+					}
+
+					for _, resp := range responses {
+						if err := json.NewEncoder(w).Encode(resp); err != nil {
+							http.Error(w, err.Error(), http.StatusInternalServerError)
+							return
+						}
+						w.(http.Flusher).Flush()
+					}
+				},
+			},
+			expectedOutput: "",
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			mockServer := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
+				handler, ok := tt.serverResponse[r.URL.Path]
+				if !ok {
+					t.Errorf("unexpected request to %s", r.URL.Path)
+					http.Error(w, "not found", http.StatusNotFound)
+					return
+				}
+				handler(w, r)
+			}))
+			t.Setenv("OLLAMA_HOST", mockServer.URL)
+			t.Cleanup(mockServer.Close)
+			tempFile, err := os.CreateTemp("", "modelfile")
+			if err != nil {
+				t.Fatal(err)
+			}
+			defer os.Remove(tempFile.Name())
+
+			if _, err := tempFile.WriteString(tt.modelFile); err != nil {
+				t.Fatal(err)
+			}
+			if err := tempFile.Close(); err != nil {
+				t.Fatal(err)
+			}
+
+			cmd := &cobra.Command{}
+			cmd.Flags().String("file", "", "")
+			if err := cmd.Flags().Set("file", tempFile.Name()); err != nil {
+				t.Fatal(err)
+			}
+
+			cmd.Flags().Bool("insecure", false, "")
+			cmd.SetContext(context.TODO())
+
+			// Redirect stderr to capture progress output
+			oldStderr := os.Stderr
+			r, w, _ := os.Pipe()
+			os.Stderr = w
+
+			// Capture stdout for the "Model pushed" message
+			oldStdout := os.Stdout
+			outR, outW, _ := os.Pipe()
+			os.Stdout = outW
+
+			err = CreateHandler(cmd, []string{tt.modelName})
+
+			// Restore stderr
+			w.Close()
+			os.Stderr = oldStderr
+			// drain the pipe
+			if _, err := io.ReadAll(r); err != nil {
+				t.Fatal(err)
+			}
+
+			// Restore stdout and get output
+			outW.Close()
+			os.Stdout = oldStdout
+			stdout, _ := io.ReadAll(outR)
+
+			if tt.expectedError == "" {
+				if err != nil {
+					t.Errorf("expected no error, got %v", err)
+				}
+
+				if tt.expectedOutput != "" {
+					if got := string(stdout); got != tt.expectedOutput {
+						t.Errorf("expected output %q, got %q", tt.expectedOutput, got)
+					}
+				}
+			}
+		})
+	}
+}

cmd/interactive.go

@@ -13,11 +13,9 @@ import (
 	"strings"
 
 	"github.com/spf13/cobra"
-	"golang.org/x/exp/maps"
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/envconfig"
-	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/readline"
 	"github.com/ollama/ollama/types/errtypes"
 )
@@ -213,10 +211,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 				return err
 			}
 
-			req := &api.CreateRequest{
-				Name:      args[1],
-				Modelfile: buildModelfile(opts),
-			}
+			req := NewCreateRequest(args[1], opts)
 			fn := func(resp api.ProgressResponse) error { return nil }
 			err = client.Create(cmd.Context(), req, fn)
 			if err != nil {
@@ -459,36 +454,25 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 	}
 }
 
-func buildModelfile(opts runOptions) string {
-	var f parser.File
-	f.Commands = append(f.Commands, parser.Command{Name: "model", Args: cmp.Or(opts.ParentModel, opts.Model)})
+func NewCreateRequest(name string, opts runOptions) *api.CreateRequest {
+	req := &api.CreateRequest{
+		Name: name,
+		From: cmp.Or(opts.ParentModel, opts.Model),
+	}
 
 	if opts.System != "" {
-		f.Commands = append(f.Commands, parser.Command{Name: "system", Args: opts.System})
+		req.System = opts.System
 	}
 
-	keys := maps.Keys(opts.Options)
-	slices.Sort(keys)
-	for _, k := range keys {
-		v := opts.Options[k]
-		var cmds []parser.Command
-		switch t := v.(type) {
-		case []string:
-			for _, s := range t {
-				cmds = append(cmds, parser.Command{Name: k, Args: s})
-			}
-		default:
-			cmds = append(cmds, parser.Command{Name: k, Args: fmt.Sprintf("%v", t)})
-		}
-
-		f.Commands = append(f.Commands, cmds...)
+	if len(opts.Options) > 0 {
+		req.Parameters = opts.Options
 	}
 
-	for _, msg := range opts.Messages {
-		f.Commands = append(f.Commands, parser.Command{Name: "message", Args: fmt.Sprintf("%s: %s", msg.Role, msg.Content)})
+	if len(opts.Messages) > 0 {
+		req.Messages = opts.Messages
 	}
 
-	return f.String()
+	return req
 }
 
 func normalizeFilePath(fp string) string {

cmd/interactive_test.go

@@ -3,10 +3,7 @@ package cmd
 import (
 	"testing"
 
-	"github.com/google/go-cmp/cmp"
 	"github.com/stretchr/testify/assert"
-
-	"github.com/ollama/ollama/api"
 )
 
 func TestExtractFilenames(t *testing.T) {
@@ -53,56 +50,3 @@ d:\path with\spaces\seven.JPEG inbetween7 c:\users\jdoe\eight.png inbetween8
 	assert.Contains(t, res[9], "ten.PNG")
 	assert.Contains(t, res[9], "E:")
 }
-
-func TestModelfileBuilder(t *testing.T) {
-	opts := runOptions{
-		Model:  "hork",
-		System: "You are part horse and part shark, but all hork. Do horklike things",
-		Messages: []api.Message{
-			{Role: "user", Content: "Hey there hork!"},
-			{Role: "assistant", Content: "Yes it is true, I am half horse, half shark."},
-		},
-		Options: map[string]any{
-			"temperature":      0.9,
-			"seed":             42,
-			"penalize_newline": false,
-			"stop":             []string{"hi", "there"},
-		},
-	}
-
-	t.Run("model", func(t *testing.T) {
-		expect := `FROM hork
-SYSTEM You are part horse and part shark, but all hork. Do horklike things
-PARAMETER penalize_newline false
-PARAMETER seed 42
-PARAMETER stop hi
-PARAMETER stop there
-PARAMETER temperature 0.9
-MESSAGE user Hey there hork!
-MESSAGE assistant Yes it is true, I am half horse, half shark.
-`
-
-		actual := buildModelfile(opts)
-		if diff := cmp.Diff(expect, actual); diff != "" {
-			t.Errorf("mismatch (-want +got):\n%s", diff)
-		}
-	})
-
-	t.Run("parent model", func(t *testing.T) {
-		opts.ParentModel = "horseshark"
-		expect := `FROM horseshark
-SYSTEM You are part horse and part shark, but all hork. Do horklike things
-PARAMETER penalize_newline false
-PARAMETER seed 42
-PARAMETER stop hi
-PARAMETER stop there
-PARAMETER temperature 0.9
-MESSAGE user Hey there hork!
-MESSAGE assistant Yes it is true, I am half horse, half shark.
-`
-		actual := buildModelfile(opts)
-		if diff := cmp.Diff(expect, actual); diff != "" {
-			t.Errorf("mismatch (-want +got):\n%s", diff)
-		}
-	})
-}

convert/convert.go

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

convert/convert_commandr.go

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

convert/convert_qwen2.go

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

convert/convert_test.go

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

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

@@ -0,0 +1,314 @@
+{
+    "general.architecture": "qwen2",
+    "general.file_type": "1",
+    "general.parameter_count": "494032768",
+    "general.quantization_version": "2",
+    "output_norm.weight": "93a01a6db3419e85320a244bbf8ae81c43033b1d10c342bea3797ff2ce348390",
+    "qwen2.attention.head_count": "14",
+    "qwen2.attention.head_count_kv": "2",
+    "qwen2.attention.layer_norm_rms_epsilon": "1e-06",
+    "qwen2.block_count": "24",
+    "qwen2.context_length": "32768",
+    "qwen2.embedding_length": "896",
+    "qwen2.feed_forward_length": "4864",
+    "qwen2.rope.freq_base": "1e+06",
+    "token_embd.weight": "d74257dc547b48be5ae7b93f1c9af072c0c42dbbb85503078e25c59cd09e68d0",
+    "tokenizer.ggml.add_eos_token": "false",
+    "tokenizer.ggml.add_padding_token": "false",
+    "tokenizer.ggml.eos_token_id": "151645",
+    "tokenizer.ggml.merges": "6b1b1c58f1223d74f9095929d3e6416cdd74784440221a5507b87b8197f2bfd2",
+    "tokenizer.ggml.model": "gpt2",
+    "tokenizer.ggml.padding_token_id": "151643",
+    "tokenizer.ggml.pre": "qwen2",
+    "tokenizer.ggml.scores": "94e247e531e8b0fa3d248f3de09c9beae0c87da8106208a8edfaac0b8ec4b53d",
+    "tokenizer.ggml.token_type": "b178dbc9d1b2e08f84d02918e00fc2de2619a250e6c188c91a6605f701860055",
+    "tokenizer.ggml.tokens": "1d93f6679b23a1152b725f7f473792d54d53c1040c5250d3e46b42f81e0a1a34",
+    "blk.0.attn_k.bias": "5ce6617845f66c34515978d23d52e729c298d8bffa28c356a0428bef17142cf1",
+    "blk.0.attn_k.weight": "a960832a9e0e83e4d95402e5d1a01cc74300fcca0c381237162126330e1a7af8",
+    "blk.0.attn_norm.weight": "32c7d51cd0958f1f1771174192db341f9770516d7595a2f0fd18a4d78bd5aba3",
+    "blk.0.attn_output.weight": "c67e6e7e868354a11bf9121c70ee56c140b20eec611a8955e7dfe54a21d40a98",
+    "blk.0.attn_q.bias": "3e9e994eb1f03bccfc82f8bb3c324c920d42d547e07de5be83be12c428645063",
+    "blk.0.attn_q.weight": "dc12132f789b97cfa1e3f5775ceb835247fa67aa47400fd09c8f9f3769208583",
+    "blk.0.attn_v.bias": "a3fd0757b31fdc78af5ec320332d239c1a79d34e8804df06c5454e86955e8cc9",
+    "blk.0.attn_v.weight": "f43094a2134c7ee2dcc52aac3c8b7d9d64fb0295a8adb94cabfd49213f017b84",
+    "blk.0.ffn_down.weight": "18c2aec92db14f21976838a8c35d5575f80d0e4b1e05ccc0d8388d5877e80147",
+    "blk.0.ffn_gate.weight": "a3a1c4ef38f8f750eabadfe3d83bbb0f77941eec1cc1a388e51852e99c8691f6",
+    "blk.0.ffn_norm.weight": "b59b779c42d44b5c4cec41e39b4eb61e0491a07c1b3e946ccb5b8d5c657eda3f",
+    "blk.0.ffn_up.weight": "db64f09987ea59449e90abae5a2ffcc20efd9203f0eebec77a6aacb5809d6cff",
+    "blk.1.attn_k.bias": "a5c8c5671703ec0aa0143ff70a20ffdd67b5d5790ca1dfa5bba4e87e4071ed9f",
+    "blk.1.attn_k.weight": "835c7c7cc95b3cb2e55bd9cac585aa0760a033896621d3e06421f3378c540f7d",
+    "blk.1.attn_norm.weight": "f4c36fb6c14fce721fab0de78cc118d6f66e3a3d3ea0017bb14aade24c3c5434",
+    "blk.1.attn_output.weight": "cc1e80310c97cef068e48e40b7096f32fa2138519d6209c6a1a9994985999016",
+    "blk.1.attn_q.bias": "bc332780e66b0aac80ec5e63ac32344919a840db2fcc8f87bcef16a43a54138e",
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+    "blk.1.ffn_norm.weight": "4ac774ee8afaee119610c46aa1ff89fc6c9084a29d226075bc4aa4d2f15f746c",
+    "blk.1.ffn_up.weight": "042d81ab5f1983d85c81213232f3bfc05a9302d9dfaa98d931ebba326b6058b8",
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convert/testdata/c4ai-command-r-v01.json

@@ -0,0 +1,344 @@
+{
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+    "blk.30.attn_output.weight": "4cf0ccca04b792573b756655a24fc89cfb1f272da8305633f0bc66ef14990b93",
+    "blk.30.attn_q.weight": "21e07d6cba6c50d65350289258209717174a13c42be57e8141d69712cbaf32c1",
+    "blk.30.attn_v.weight": "65a8ca29c7237b3182ccf03e2fc94e84f9a53d0e160fb679ab401c853170dd9c",
+    "blk.30.ffn_down.weight": "8b00500a6d00d84058f6658ee1d6f06fb4fcae2f90d4341792259362923b3c13",
+    "blk.30.ffn_gate.weight": "5bc0e19ab7a31b50ac2118ad1b36e31055271a322cd8ff661d47c3ac0210703c",
+    "blk.30.ffn_up.weight": "f37a0561955725bd59ee2d064fa9f4e00a12a1b620b624db3bc3add5330bc321",
+    "blk.31.attn_k.weight": "9a5663edda227f5d87533897146764f8e8a7481b9e71fae197c39204f8463221",
+    "blk.31.attn_norm.weight": "060a4f438a1ee5e220b5b5278ad2f5c085a428bf38c515766781815597c87529",
+    "blk.31.attn_output.weight": "6ada5d3cad9dea4780ffbb43302bb6ccc2f24eddd0fc4f5f84c9ce0fc0c6e5dd",
+    "blk.31.attn_q.weight": "bb5d08c08603907981ad388d5d8b70fcc9b98034ba264b8474c8890cc0297af0",
+    "blk.31.attn_v.weight": "e01b4252ea9c6a889c32b21144b441a347464d04536ef4f6572425be55759796",
+    "blk.31.ffn_down.weight": "8ba4d679c36e93ba65ba03180385ef35ea86b3b7cdf2fded9df59369f1c09630",
+    "blk.31.ffn_gate.weight": "e5b41dc93645f8b5e8eebae3ada3ea43a18f97ce2654228655170b07b463ccb0",
+    "blk.31.ffn_up.weight": "25b88cdddc8b547af294ed107d3d1312e90b983cae87936fa6062ecd8ea02539",
+    "blk.32.attn_k.weight": "4bcf86dc0858c8ca2fbdf6aa76674d43eb698f78979fdc1a38f556a7af1facc4",
+    "blk.32.attn_norm.weight": "cdcc12f3b8b9773c6722736bfb748a2729230b21478cbcc4104859d3148df815",
+    "blk.32.attn_output.weight": "d43f1196822995ed89a9365c97054753a8b30ce20b6e273c8edcc42673a1e141",
+    "blk.32.attn_q.weight": "ebf2972bb3865cbc5be4840113a322089752038344beab2a0122c7cb4fb399b6",
+    "blk.32.attn_v.weight": "714db81704ff34fa137512903c1013acee7877467473e46600728b9240582eb7",
+    "blk.32.ffn_down.weight": "2cde3da1258bb170a79d5d3cdfe10c86a71eb34b77da46b74c5ed71e7f4fe274",
+    "blk.32.ffn_gate.weight": "c7e1ed792532613ff9d4e5834b6536e2e0f47df2303bc0fdaa90aac0c1f4e8db",
+    "blk.32.ffn_up.weight": "d8d6f13fe66a716e28f79101a29817f0c0d6f99969a6f017d51bafd1a16c600c",
+    "blk.33.attn_k.weight": "a0a28f6cbca88da00cab2ca37094d9b0503bf9defdae77b91895b911c408cbb6",
+    "blk.33.attn_norm.weight": "0251200c24cc8445607ace6dc8c5aa0566567997262b7cca53a11ac23cc564b2",
+    "blk.33.attn_output.weight": "b2423205bdf6a1096d43c44d8d12f1a84fcd4e1bb70fcf6dc8542b8b8a71a13c",
+    "blk.33.attn_q.weight": "00b425c3ef71065ce5e0234e702bf38143b4952da78a85f52ab2c2e3073d97ab",
+    "blk.33.attn_v.weight": "035edd2335df816c42c765a5e66b9d9b9e15a822a8dc1863508145499c942c14",
+    "blk.33.ffn_down.weight": "4894a923a3db75bae4496ba3ce5f28796ad31fe33996a066271fb8654964310e",
+    "blk.33.ffn_gate.weight": "8f6c819b8bbfbe3357fae89e1ac5a3d58be85b3b04be3bacf7b62775869046ff",
+    "blk.33.ffn_up.weight": "257c3544b5b544fd5d839665bf5caf107a329b59dbc3751efcaa24ae63c56179",
+    "blk.34.attn_k.weight": "b6cd8bba892e38dac4a2ebc3ba1bce49e71b967fc436fde30c6d76f54a18935f",
+    "blk.34.attn_norm.weight": "2b3c8e60a064cba9955752bbbbdd92c71ba5c2f1bd721097bdbe88b5abc68787",
+    "blk.34.attn_output.weight": "8cc272551c9aaca9db5a660c6927bab94a0243d74a30b2bc165f06bd577714ea",
+    "blk.34.attn_q.weight": "74b561eb4792484e6a94b58fe2583848c3ae28ff2f1bf3d02939a0cfdfa49990",
+    "blk.34.attn_v.weight": "dba19e24ff05154dc5a1f55c023729303a583d13d68732ce22ea74d4410dc8f0",
+    "blk.34.ffn_down.weight": "76eca5dfeb274c35774e0bf9f22ee420ed9085c8e99aa2cd5a236e4918b44c61",
+    "blk.34.ffn_gate.weight": "9af0862d5fcbc24732846488e653db8242a467765c0cdbc00332b3a40256b4a6",
+    "blk.34.ffn_up.weight": "2a03126bf73587eaba99ece2066103d12e47bcd4ce30ff6c17b2f383b81d40df",
+    "blk.35.attn_k.weight": "52513fc0cd4e997a842729af7d21dd09399bce0a339558374738be266d0fa2f0",
+    "blk.35.attn_norm.weight": "e5281fa911964263ccf1630b14762edbd41d0b9472d6ec695fc600fed4892c35",
+    "blk.35.attn_output.weight": "b391d6705d5dc6f48326b5fd16573f679edf64109d86fb729a498819676590ca",
+    "blk.35.attn_q.weight": "d16446921966db9b0e0539626ad22a2511ace780e59379d6a4162d8c5441440b",
+    "blk.35.attn_v.weight": "9d8cdf23ffdb0c5c74106843390b94b24c9f33ef0eb9998d39f78c73390101ea",
+    "blk.35.ffn_down.weight": "938eb6301f7bbf162d7dd965682a5ed11d0a4a530c6fedd7e5469ce80012fc17",
+    "blk.35.ffn_gate.weight": "5ad84f5a0c8edcfea1ecf1a3e3d21d85ceda0c4ad9e3c6ca68885eeff8ed3c2f",
+    "blk.35.ffn_up.weight": "1c4330d9dc71bf4c98812c34356c51f520f47610a534152aa6d29284b758090d",
+    "blk.36.attn_k.weight": "ef720655e5ca2465f13db2dfc4732fb4ef2c9d53acde52f514fd4f301e974081",
+    "blk.36.attn_norm.weight": "88f4b9310b3c8c2644e3029160cd35678c79dfa59280430e03f5c29a6fe84a58",
+    "blk.36.attn_output.weight": "aec6f915fffd7bb72cd783273e871b4f09605950089d45e72059d1316b6c4b01",
+    "blk.36.attn_q.weight": "72f9408a2405d42f8db6ce5fcf1d26a3660b6f225fc60e77d0277109cfcb82ed",
+    "blk.36.attn_v.weight": "0f3b3d851dc44b3893ef53f6cca5b4acc9658bacfe1cc2d13c3d704ddd409b67",
+    "blk.36.ffn_down.weight": "470aec48ce8c5129a6654d9fd26fcae72776f9fc1429a8bb05818072a876475d",
+    "blk.36.ffn_gate.weight": "7f5f296d09cf55679767b5d15de3eff489c456782119f25204be4b1647f18dcf",
+    "blk.36.ffn_up.weight": "b7ef74a1f7ffb4982711d93f1787be3a70edc3d2358d5203c41d8900508037d4",
+    "blk.37.attn_k.weight": "c4ffa5412e4ff2dcfe1aed991c1f54169fd171a4c7638e4b9f21a1ca64c5e1d6",
+    "blk.37.attn_norm.weight": "4eb6c888d841cccfacf5b963f8611120f6ff24b84af0b5714fd9ab36dcda422f",
+    "blk.37.attn_output.weight": "db2a7bbf9682f9f6eea672dae8e150738f1bf74dbc80edc7022017a3f040c8ac",
+    "blk.37.attn_q.weight": "e38c0462aff139afcbab289189823527e453abc9e541154adde5e7af88cacf0b",
+    "blk.37.attn_v.weight": "952eb2492ed452a72f96bcc12d4b2affad9dfdf46ee39ce4a5d7b57a5dc301e5",
+    "blk.37.ffn_down.weight": "25f23a8fbc44febf6dc4848fd7fe03a580e2822bd3b3b5a51f4990826bfe3e4e",
+    "blk.37.ffn_gate.weight": "707da5eb40118b035305d3262444382351f170a20a537386a70e90c5a83a7817",
+    "blk.37.ffn_up.weight": "d2d2ba5cfc4ef47338dd7384219e22bf030a5a2209e0354d88f5bbaaafd20e87",
+    "blk.38.attn_k.weight": "abc4bb189dedf7ce661e79028427623a4f91ac091c2cd60e31b58bc62b1cda71",
+    "blk.38.attn_norm.weight": "9f4803a7d03fd40fcb83d85f84eb1d5682ea4e5bb084f210c02850675d804c3d",
+    "blk.38.attn_output.weight": "77cb66007f1a41df7135d0e7f900ceb499c2f667dfc3f1a6ac01a3203bbd3ccf",
+    "blk.38.attn_q.weight": "d94a8b26cd375bf2bcaa76597e314aa8268ee50a479d00931e5e0e021feadb5d",
+    "blk.38.attn_v.weight": "660c907888bc5016dc69b7d35fe6f55c7ded697c93be0e2d332a2f17aff88758",
+    "blk.38.ffn_down.weight": "6f06173bae5b00ffaf88ef383619a8b9c6a8d0d5c6494695d17f6c1de1a68a13",
+    "blk.38.ffn_gate.weight": "89f99be149d03f116527bfcabe073c50001c874de40fb6e817f6619027f3cd05",
+    "blk.38.ffn_up.weight": "8d57557c8d5e2d2688b73f01dddf1ce8d5194990cda6358153320aea88aac7f8",
+    "blk.39.attn_k.weight": "21be09c988b46c8393e6c2ec9230f3b5136eb7607dd1953ba92d0811c2f0dd75",
+    "blk.39.attn_norm.weight": "ba7c1912dd1c4e2d16917201f62396fd0600e4a451137eaddff255548c209abd",
+    "blk.39.attn_output.weight": "acfaf4abb3fd27fd899b5563c3877f176b597d8f6cdb2f2fd3f3a0bd4da15ed6",
+    "blk.39.attn_q.weight": "e8adbc140d4c8f0db2a27ca584c5531d5b1e080555fe627e34d80d0814a92bed",
+    "blk.39.attn_v.weight": "92f96b0e1f724e73a0f90a76c145654418844c04a6d4b14c05eb5af8a62bf8dc",
+    "blk.39.ffn_down.weight": "4d9ee7c65fc16fe95d10c47b79ac6a525741947600a64b5fcea5d300a82c50de",
+    "blk.39.ffn_gate.weight": "7e18507989f39b32191133d2657c2ee3b74f42f070579204d727eb72215793d1",
+    "blk.39.ffn_up.weight": "22cda752269c9757ba918abede1df95bb0f83a5c772dea13c8deea3d5f2723d9",
+    "output_norm.weight": "2858cf0e39d32caf52b7861378ace076000241e147f10b9eb21d8a5cd149e3cb"
+}

convert/tokenizer.go

@@ -100,6 +100,8 @@ func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error)
 			t.Pre = "deepseek-llm"
 		case "21cde974d587f0d54dc8d56b183cc1e6239600172035c68fbd6d4b9f8da0576e":
 			t.Pre = "deepseek-coder"
+		case "1ff7f41064896984db5d1bb6ff64fa4bc29007d08c1b439e505b7392777a319e":
+			t.Pre = "qwen2"
 		case "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855":
 			// noop, empty pretokenizer
 		default:

discover/amd_common.go

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

discover/amd_linux.go

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

discover/amd_windows.go

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

discover/gpu.go

@@ -23,7 +23,6 @@ import (
 
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
-	"github.com/ollama/ollama/runners"
 )
 
 type cudaHandles struct {
@@ -101,15 +100,7 @@ func initCudaHandles() *cudaHandles {
 
 	// Aligned with driver, we can't carry as payloads
 	nvcudaMgmtPatterns := NvcudaGlobs
-
-	if runtime.GOOS == "windows" {
-		localAppData := os.Getenv("LOCALAPPDATA")
-		cudartMgmtPatterns = []string{filepath.Join(localAppData, "Programs", "Ollama", CudartMgmtName)}
-	}
-	libDirs := LibraryDirs()
-	for _, d := range libDirs {
-		cudartMgmtPatterns = append(cudartMgmtPatterns, filepath.Join(d, CudartMgmtName))
-	}
+	cudartMgmtPatterns = append(cudartMgmtPatterns, filepath.Join(LibOllamaPath, "cuda_v*", CudartMgmtName))
 	cudartMgmtPatterns = append(cudartMgmtPatterns, CudartGlobs...)
 
 	if len(NvmlGlobs) > 0 {
@@ -240,7 +231,7 @@ func GetGPUInfo() GpuInfoList {
 		if err != nil {
 			slog.Warn("error looking up system memory", "error", err)
 		}
-		depPaths := LibraryDirs()
+
 		details, err := GetCPUDetails()
 		if err != nil {
 			slog.Warn("failed to lookup CPU details", "error", err)
@@ -248,11 +239,9 @@ func GetGPUInfo() GpuInfoList {
 		cpus = []CPUInfo{
 			{
 				GpuInfo: GpuInfo{
-					memInfo:        mem,
-					Library:        "cpu",
-					Variant:        runners.GetCPUCapability().String(),
-					ID:             "0",
-					DependencyPath: depPaths,
+					memInfo: mem,
+					Library: "cpu",
+					ID:      "0",
 				},
 				CPUs: details,
 			},
@@ -294,17 +283,13 @@ func GetGPUInfo() GpuInfoList {
 				gpuInfo.DriverMajor = driverMajor
 				gpuInfo.DriverMinor = driverMinor
 				variant := cudaVariant(gpuInfo)
-				if depPaths != nil {
-					gpuInfo.DependencyPath = depPaths
-					// Check for variant specific directory
-					if variant != "" {
-						for _, d := range depPaths {
-							if _, err := os.Stat(filepath.Join(d, "cuda_"+variant)); err == nil {
-								// Put the variant directory first in the search path to avoid runtime linking to the wrong library
-								gpuInfo.DependencyPath = append([]string{filepath.Join(d, "cuda_"+variant)}, gpuInfo.DependencyPath...)
-								break
-							}
-						}
+
+				// Start with our bundled libraries
+				if variant != "" {
+					variantPath := filepath.Join(LibOllamaPath, "cuda_"+variant)
+					if _, err := os.Stat(variantPath); err == nil {
+						// Put the variant directory first in the search path to avoid runtime linking to the wrong library
+						gpuInfo.DependencyPath = append([]string{variantPath}, gpuInfo.DependencyPath...)
 					}
 				}
 				gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
@@ -376,7 +361,7 @@ func GetGPUInfo() GpuInfoList {
 						gpuInfo.FreeMemory = uint64(memInfo.free)
 						gpuInfo.ID = C.GoString(&memInfo.gpu_id[0])
 						gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
-						gpuInfo.DependencyPath = depPaths
+						gpuInfo.DependencyPath = []string{LibOllamaPath}
 						oneapiGPUs = append(oneapiGPUs, gpuInfo)
 					}
 				}
@@ -512,37 +497,33 @@ func GetGPUInfo() GpuInfoList {
 
 func FindGPULibs(baseLibName string, defaultPatterns []string) []string {
 	// Multiple GPU libraries may exist, and some may not work, so keep trying until we exhaust them
-	var ldPaths []string
 	gpuLibPaths := []string{}
 	slog.Debug("Searching for GPU library", "name", baseLibName)
 
-	// Start with our bundled libraries
-	patterns := []string{}
-	for _, d := range LibraryDirs() {
-		patterns = append(patterns, filepath.Join(d, baseLibName))
-	}
+	// search our bundled libraries first
+	patterns := []string{filepath.Join(LibOllamaPath, baseLibName)}
 
+	var ldPaths []string
 	switch runtime.GOOS {
 	case "windows":
-		ldPaths = strings.Split(os.Getenv("PATH"), ";")
+		ldPaths = strings.Split(os.Getenv("PATH"), string(os.PathListSeparator))
 	case "linux":
-		ldPaths = strings.Split(os.Getenv("LD_LIBRARY_PATH"), ":")
-	default:
-		return gpuLibPaths
+		ldPaths = strings.Split(os.Getenv("LD_LIBRARY_PATH"), string(os.PathListSeparator))
 	}
 
-	// Then with whatever we find in the PATH/LD_LIBRARY_PATH
-	for _, ldPath := range ldPaths {
-		d, err := filepath.Abs(ldPath)
+	// then search the system's LD_LIBRARY_PATH
+	for _, p := range ldPaths {
+		p, err := filepath.Abs(p)
 		if err != nil {
 			continue
 		}
-		patterns = append(patterns, filepath.Join(d, baseLibName))
+		patterns = append(patterns, filepath.Join(p, baseLibName))
 	}
+
+	// finally, search the default patterns provided by the caller
 	patterns = append(patterns, defaultPatterns...)
 	slog.Debug("gpu library search", "globs", patterns)
 	for _, pattern := range patterns {
-
 		// Nvidia PhysX known to return bogus results
 		if strings.Contains(pattern, "PhysX") {
 			slog.Debug("skipping PhysX cuda library path", "path", pattern)
@@ -716,28 +697,6 @@ func (l GpuInfoList) GetVisibleDevicesEnv() (string, string) {
 	}
 }
 
-func LibraryDirs() []string {
-	// dependencies can exist wherever we found the runners (e.g. build tree for developers) and relative to the executable
-	// This can be simplified once we no longer carry runners as payloads
-	paths := []string{}
-	appExe, err := os.Executable()
-	if err != nil {
-		slog.Warn("failed to lookup executable path", "error", err)
-	} else {
-		appRelative := filepath.Join(filepath.Dir(appExe), envconfig.LibRelativeToExe(), "lib", "ollama")
-		if _, err := os.Stat(appRelative); err == nil {
-			paths = append(paths, appRelative)
-		}
-	}
-	rDir := runners.Locate()
-	if err != nil {
-		slog.Warn("unable to locate gpu dependency libraries", "error", err)
-	} else {
-		paths = append(paths, filepath.Dir(rDir))
-	}
-	return paths
-}
-
 func GetSystemInfo() SystemInfo {
 	gpus := GetGPUInfo()
 	gpuMutex.Lock()

discover/gpu_darwin.go

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

discover/path.go

@@ -0,0 +1,57 @@
+package discover
+
+import (
+	"os"
+	"path/filepath"
+	"runtime"
+)
+
+// LibPath is a path to lookup dynamic libraries
+// in development it's usually 'build/lib/ollama'
+// in distribution builds it's 'lib/ollama' on Windows
+// '../lib/ollama' on Linux and the executable's directory on macOS
+// note: distribution builds, additional GPU-specific libraries are
+// found in subdirectories of the returned path, such as
+// 'cuda_v11', 'cuda_v12', 'rocm', etc.
+var LibOllamaPath string = func() string {
+	exe, err := os.Executable()
+	if err != nil {
+		return ""
+	}
+
+	exe, err = filepath.EvalSymlinks(exe)
+	if err != nil {
+		return ""
+	}
+
+	var libPath string
+	switch runtime.GOOS {
+	case "windows":
+		libPath = filepath.Join(filepath.Dir(exe), "lib", "ollama")
+	case "linux":
+		libPath = filepath.Join(filepath.Dir(exe), "..", "lib", "ollama")
+	case "darwin":
+		libPath = filepath.Dir(exe)
+	}
+
+	cwd, err := os.Getwd()
+	if err != nil {
+		return ""
+	}
+
+	paths := []string{
+		libPath,
+
+		// build paths for development
+		filepath.Join(filepath.Dir(exe), "build", "lib", "ollama"),
+		filepath.Join(cwd, "build", "lib", "ollama"),
+	}
+
+	for _, p := range paths {
+		if _, err := os.Stat(p); err == nil {
+			return p
+		}
+	}
+
+	return filepath.Dir(exe)
+}()

discover/types.go

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

docs/README.md

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

docs/api.md

@@ -13,6 +13,7 @@
 - [Push a Model](#push-a-model)
 - [Generate Embeddings](#generate-embeddings)
 - [List Running Models](#list-running-models)
+- [Version](#version)
 
 ## Conventions
 
@@ -305,7 +306,7 @@ curl http://localhost:11434/api/generate -d '{
 
 #### Response
 
-```
+```json
 {
   "model": "llava",
   "created_at": "2023-11-03T15:36:02.583064Z",
@@ -494,14 +495,14 @@ Generate the next message in a chat with a provided model. This is a streaming e
 
 - `model`: (required) the [model name](#model-names)
 - `messages`: the messages of the chat, this can be used to keep a chat memory
-- `tools`: tools for the model to use if supported. Requires `stream` to be set to `false`
+- `tools`: list of tools in JSON for the model to use if supported
 
 The `message` object has the following fields:
 
 - `role`: the role of the message, either `system`, `user`, `assistant`, or `tool`
 - `content`: the content of the message
 - `images` (optional): a list of images to include in the message (for multimodal models such as `llava`)
-- `tool_calls` (optional): a list of tools the model wants to use
+- `tool_calls` (optional): a list of tools in JSON that the model wants to use
 
 Advanced parameters (optional):
 
@@ -794,7 +795,7 @@ curl http://localhost:11434/api/chat -d '{
 
 ##### Request
 
-```
+```shell
 curl http://localhost:11434/api/chat -d '{
   "model": "llama3.2",
   "messages": [
@@ -869,7 +870,7 @@ If the messages array is empty, the model will be loaded into memory.
 
 ##### Request
 
-```
+```shell
 curl http://localhost:11434/api/chat -d '{
   "model": "llama3.2",
   "messages": []
@@ -896,7 +897,7 @@ If the messages array is empty and the `keep_alive` parameter is set to `0`, a m
 
 ##### Request
 
-```
+```shell
 curl http://localhost:11434/api/chat -d '{
   "model": "llama3.2",
   "messages": [],
@@ -927,14 +928,25 @@ A single JSON object is returned:
 POST /api/create
 ```
 
-Create a model from a [`Modelfile`](./modelfile.md). It is recommended to set `modelfile` to the content of the Modelfile rather than just set `path`. This is a requirement for remote create. Remote model creation must also create any file blobs, fields such as `FROM` and `ADAPTER`, explicitly with the server using [Create a Blob](#create-a-blob) and the value to the path indicated in the response.
+Create a model from:
+ * another model;
+ * a safetensors directory; or
+ * a GGUF file.
+
+If you are creating a model from a safetensors directory or from a GGUF file, you must [create a blob](#create-a-blob) for each of the files and then use the file name and SHA256 digest associated with each blob in the `files` field.
 
 ### Parameters
 
 - `model`: name of the model to create
-- `modelfile` (optional): contents of the Modelfile
+- `from`: (optional) name of an existing model to create the new model from
+- `files`: (optional) a dictionary of file names to SHA256 digests of blobs to create the model from
+- `adapters`: (optional) a dictionary of file names to SHA256 digests of blobs for LORA adapters
+- `template`: (optional) the prompt template for the model
+- `license`: (optional) a string or list of strings containing the license or licenses for the model
+- `system`: (optional) a string containing the system prompt for the model
+- `parameters`: (optional) a dictionary of parameters for the model (see [Modelfile](./modelfile.md#valid-parameters-and-values) for a list of parameters)
+- `messages`: (optional) a list of message objects used to create a conversation
 - `stream`: (optional) if `false` the response will be returned as a single response object, rather than a stream of objects
-- `path` (optional): path to the Modelfile
 - `quantize` (optional): quantize a non-quantized (e.g. float16) model
 
 #### Quantization types
@@ -960,14 +972,15 @@ Create a model from a [`Modelfile`](./modelfile.md). It is recommended to set `m
 
 #### Create a new model
 
-Create a new model from a `Modelfile`.
+Create a new model from an existing model.
 
 ##### Request
 
 ```shell
 curl http://localhost:11434/api/create -d '{
   "model": "mario",
-  "modelfile": "FROM llama3\nSYSTEM You are mario from Super Mario Bros."
+  "from": "llama3.2",
+  "system": "You are Mario from Super Mario Bros."
 }'
 ```
 
@@ -998,7 +1011,7 @@ Quantize a non-quantized model.
 ```shell
 curl http://localhost:11434/api/create -d '{
   "model": "llama3.1:quantized",
-  "modelfile": "FROM llama3.1:8b-instruct-fp16",
+  "from": "llama3.1:8b-instruct-fp16",
   "quantize": "q4_K_M"
 }'
 ```
@@ -1018,52 +1031,112 @@ A stream of JSON objects is returned:
 {"status":"success"}
 ```
 
+#### Create a model from GGUF
 
-### Check if a Blob Exists
+Create a model from a GGUF file. The `files` parameter should be filled out with the file name and SHA256 digest of the GGUF file you wish to use. Use [/api/blobs/:digest](#push-a-blob) to push the GGUF file to the server before calling this API.
+
+
+##### Request
+
+```shell
+curl http://localhost:11434/api/create -d '{
+  "model": "my-gguf-model",
+  "files": {
+    "test.gguf": "sha256:432f310a77f4650a88d0fd59ecdd7cebed8d684bafea53cbff0473542964f0c3"
+  }
+}'
+```
+
+##### Response
+
+A stream of JSON objects is returned:
+
+```
+{"status":"parsing GGUF"}
+{"status":"using existing layer sha256:432f310a77f4650a88d0fd59ecdd7cebed8d684bafea53cbff0473542964f0c3"}
+{"status":"writing manifest"}
+{"status":"success"}
+```
+
+
+#### Create a model from a Safetensors directory
+
+The `files` parameter should include a dictionary of files for the safetensors model which includes the file names and SHA256 digest of each file. Use [/api/blobs/:digest](#push-a-blob) to first push each of the files to the server before calling this API. Files will remain in the cache until the Ollama server is restarted.
+
+##### Request
+
+```shell
+curl http://localhost:11434/api/create -d '{
+  "model": "fred",
+  "files": {
+    "config.json": "sha256:dd3443e529fb2290423a0c65c2d633e67b419d273f170259e27297219828e389",
+    "generation_config.json": "sha256:88effbb63300dbbc7390143fbbdd9d9fa50587b37e8bfd16c8c90d4970a74a36",
+    "special_tokens_map.json": "sha256:b7455f0e8f00539108837bfa586c4fbf424e31f8717819a6798be74bef813d05",
+    "tokenizer.json": "sha256:bbc1904d35169c542dffbe1f7589a5994ec7426d9e5b609d07bab876f32e97ab",
+    "tokenizer_config.json": "sha256:24e8a6dc2547164b7002e3125f10b415105644fcf02bf9ad8b674c87b1eaaed6",
+    "model.safetensors": "sha256:1ff795ff6a07e6a68085d206fb84417da2f083f68391c2843cd2b8ac6df8538f"
+  }
+}'
+```
+
+##### Response
+
+A stream of JSON objects is returned:
+
+```shell
+{"status":"converting model"}
+{"status":"creating new layer sha256:05ca5b813af4a53d2c2922933936e398958855c44ee534858fcfd830940618b6"}
+{"status":"using autodetected template llama3-instruct"}
+{"status":"using existing layer sha256:56bb8bd477a519ffa694fc449c2413c6f0e1d3b1c88fa7e3c9d88d3ae49d4dcb"}
+{"status":"writing manifest"}
+{"status":"success"}
+```
+
+## Check if a Blob Exists
 
 ```shell
 HEAD /api/blobs/:digest
 ```
 
-Ensures that the file blob used for a FROM or ADAPTER field exists on the server. This is checking your Ollama server and not ollama.com.
+Ensures that the file blob (Binary Large Object) used with create a model exists on the server. This checks your Ollama server and not ollama.com.
 
-#### Query Parameters
+### Query Parameters
 
 - `digest`: the SHA256 digest of the blob
 
-#### Examples
+### Examples
 
-##### Request
+#### Request
 
 ```shell
 curl -I http://localhost:11434/api/blobs/sha256:29fdb92e57cf0827ded04ae6461b5931d01fa595843f55d36f5b275a52087dd2
 ```
 
-##### Response
+#### Response
 
 Return 200 OK if the blob exists, 404 Not Found if it does not.
 
-### Create a Blob
+## Push a Blob
 
 ```shell
 POST /api/blobs/:digest
 ```
 
-Create a blob from a file on the server. Returns the server file path.
+Push a file to the Ollama server to create a "blob" (Binary Large Object).
 
-#### Query Parameters
+### Query Parameters
 
 - `digest`: the expected SHA256 digest of the file
 
-#### Examples
+### Examples
 
-##### Request
+#### Request
 
 ```shell
-curl -T model.bin -X POST http://localhost:11434/api/blobs/sha256:29fdb92e57cf0827ded04ae6461b5931d01fa595843f55d36f5b275a52087dd2
+curl -T model.gguf -X POST http://localhost:11434/api/blobs/sha256:29fdb92e57cf0827ded04ae6461b5931d01fa595843f55d36f5b275a52087dd2
 ```
 
-##### Response
+#### Response
 
 Return 201 Created if the blob was successfully created, 400 Bad Request if the digest used is not expected.
 
@@ -1526,3 +1599,29 @@ curl http://localhost:11434/api/embeddings -d '{
   ]
 }
 ```
+
+## Version
+
+```shell
+GET /api/version
+```
+
+Retrieve the Ollama version
+
+### Examples
+
+#### Request
+
+```shell
+curl http://localhost:11434/api/version
+```
+
+#### Response
+
+```json
+{
+  "version": "0.5.1"
+}
+```
+
+

docs/development.md

@@ -1,165 +1,131 @@
 # Development
 
-Install required tools:
+Install prerequisites:
 
-- go version 1.22 or higher
-- OS specific C/C++ compiler (see below)
-- GNU Make
+- [Go](https://go.dev/doc/install)
+- C/C++ Compiler e.g. Clang on macOS, [TDM-GCC](https://jmeubank.github.io/tdm-gcc/download/) (Windows amd64) or [llvm-mingw](https://github.com/mstorsjo/llvm-mingw) (Windows arm64), GCC/Clang on Linux.
 
-
-## Overview
-
-Ollama uses a mix of Go and C/C++ code to interface with GPUs.  The C/C++ code is compiled with both CGO and GPU library specific compilers.  A set of GNU Makefiles are used to compile the project.  GPU Libraries are auto-detected based on the typical environment variables used by the respective libraries, but can be overridden if necessary.  The default make target will build the runners and primary Go Ollama application that will run within the repo directory.  Throughout the examples below `-j 5` is suggested for 5 parallel jobs to speed up the build.  You can adjust the job count based on your CPU Core count to reduce build times.  If you want to relocate the built binaries, use the `dist` target and recursively copy the files in `./dist/$OS-$ARCH/` to your desired location. To learn more about the other make targets use `make help`
-
-Once you have built the GPU/CPU runners, you can compile the main application with `go build .` 
-
-### MacOS
-
-[Download Go](https://go.dev/dl/)
-
-```bash
-make -j 5
-```
-
-Now you can run `ollama`:
-
-```bash
-./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)
-development and runtime packages.
-
-Typically the makefile will auto-detect CUDA, however, if your Linux distro
-or installation approach uses alternative paths, you can specify the location by
-overriding `CUDA_PATH` to the location of the CUDA toolkit. You can customize
-a set of target CUDA architectures by setting `CUDA_ARCHITECTURES` (e.g. `CUDA_ARCHITECTURES=50;60;70`)
+Then build and run Ollama from the root directory of the repository:
 
 ```
-make -j 5
+go run . serve
 ```
 
-If both v11 and v12 tookkits are detected, runners for both major versions will be built by default.  You can build just v12 with `make cuda_v12`
+## macOS (Apple Silicon)
 
-#### Older Linux CUDA (NVIDIA)
+macOS Apple Silicon supports Metal which is built-in to the Ollama binary. No additional steps are required.
 
-To support older GPUs with Compute Capability 3.5 or 3.7, you will need to use an older version of the Driver from [Unix Driver Archive](https://www.nvidia.com/en-us/drivers/unix/) (tested with 470) and [CUDA Toolkit Archive](https://developer.nvidia.com/cuda-toolkit-archive) (tested with cuda V11).  When you build Ollama, you will need to set two make variable to adjust the minimum compute capability Ollama supports via `make -j 5 CUDA_ARCHITECTURES="35;37;50;52" EXTRA_GOLDFLAGS="\"-X=github.com/ollama/ollama/discover.CudaComputeMajorMin=3\" \"-X=github.com/ollama/ollama/discover.CudaComputeMinorMin=5\""`.  To find the Compute Capability of your older GPU, refer to [GPU Compute Capability](https://developer.nvidia.com/cuda-gpus).
+## macOS (Intel)
 
-#### Linux ROCm (AMD)
+Install prerequisites:
 
-_Your operating system distribution may already have packages for AMD ROCm. Distro packages are often preferable, but instructions are distro-specific. Please consult distro-specific docs for dependencies if available!_
+- [CMake](https://cmake.org/download/) or `brew install cmake`
 
-Install [ROCm](https://rocm.docs.amd.com/en/latest/) development packages first, as well as `make`, `gcc`, and `golang`.
-
-Typically the build scripts will auto-detect ROCm, however, if your Linux distro
-or installation approach uses unusual paths, you can specify the location by
-specifying an environment variable `HIP_PATH` to the location of the ROCm
-install (typically `/opt/rocm`). You can also customize
-the AMD GPU targets by setting HIP_ARCHS (e.g. `HIP_ARCHS=gfx1101;gfx1102`)
+Then, configure and build the project:
 
 ```
-make -j 5
+cmake -B build
+cmake --build build
 ```
 
-ROCm requires elevated privileges to access the GPU at runtime. On most distros you can add your user account to the `render` group, or run as root.
-
-#### Containerized Linux Build
-
-If you have Docker and buildx available, you can build linux binaries with `./scripts/build_linux.sh` which has the CUDA and ROCm dependencies included. The resulting artifacts are placed in `./dist`  and by default the script builds both arm64 and amd64 binaries.  If you want to build only amd64, you can build with `PLATFORM=linux/amd64 ./scripts/build_linux.sh`
-
-### Windows
-
-The following tools are required as a minimal development environment to build CPU inference support.
-
-- Go version 1.22 or higher
-  - https://go.dev/dl/
-- Git
-  - https://git-scm.com/download/win
-- clang with gcc compat and Make.  There are multiple options on how to go about installing these tools on Windows.  We have verified the following, but others may work as well:  
-  - [MSYS2](https://www.msys2.org/)
-    - After installing, from an MSYS2 terminal, run `pacman -S mingw-w64-clang-x86_64-gcc-compat mingw-w64-clang-x86_64-clang make` to install the required tools
-  - Assuming you used the default install prefix for msys2 above, add `C:\msys64\clang64\bin` and `c:\msys64\usr\bin` to your environment variable `PATH` where you will perform the build steps below (e.g. system-wide, account-level, powershell, cmd, etc.)
-
-> [!NOTE]  
-> Due to bugs in the GCC C++ library for unicode support, Ollama should be built with clang on windows.
+Lastly, run Ollama:
 
 ```
-make -j 5
+go run . serve
 ```
 
-#### GPU Support
+## Windows
 
-The GPU tools require the Microsoft native build tools.  To build either CUDA or ROCm, you must first install MSVC via Visual Studio:
+Install prerequisites:
 
-- 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.
+- [CMake](https://cmake.org/download/)
+- [Visual Studio 2022](https://visualstudio.microsoft.com/downloads/) including the Native Desktop Workload
+- (Optional) AMD GPU support
+    - [ROCm](https://rocm.github.io/install.html)
+    - [Ninja](https://github.com/ninja-build/ninja/releases)
+- (Optional) NVIDIA GPU support
+    - [CUDA SDK](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=11&target_type=exe_network)
 
-#### Windows CUDA (NVIDIA)
+> [!IMPORTANT]
+> Ensure prerequisites are in `PATH` before running CMake.
 
-In addition to the common Windows development tools and MSVC described above:
+> [!IMPORTANT]
+> ROCm is not compatible with Visual Studio CMake generators. Use `-GNinja` when configuring the project.
 
-- [NVIDIA CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html)
+> [!IMPORTANT]
+> CUDA is only compatible with Visual Studio CMake generators.
 
-#### Windows ROCm (AMD Radeon)
-
-In addition to the common Windows development tools and MSVC described above:
-
-- [AMD HIP](https://www.amd.com/en/developer/resources/rocm-hub/hip-sdk.html)
-
-#### 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:
+Then, configure and build the project:
 
 ```
-pacman -S mingw-w64-clang-aarch64-clang mingw-w64-clang-aarch64-gcc-compat mingw-w64-clang-aarch64-make make
+cmake -B build
+cmake --build build --config Release
 ```
 
-You will need to ensure your PATH includes go, cmake, gcc and clang mingw32-make to build ollama from source. (typically `C:\msys64\clangarm64\bin\`)
-
-
-## Advanced CPU Vector Settings
-
-On x86, running `make` will compile several CPU runners which can run on different CPU families. At runtime, Ollama will auto-detect the best variation to load.  If GPU libraries are present at build time, Ollama also compiles GPU runners with the `AVX` CPU vector feature enabled.  This provides a good performance balance when loading large models that split across GPU and CPU with broad compatibility.  Some users may prefer no vector extensions (e.g. older Xeon/Celeron processors, or hypervisors that mask the vector features) while other users may prefer turning on many more vector extensions to further improve performance for split model loads.
-
-To customize the set of CPU vector features enabled for a CPU runner and all GPU runners, use CUSTOM_CPU_FLAGS during the build.
-
-To build without any vector flags:
+Lastly, run Ollama:
 
 ```
-make CUSTOM_CPU_FLAGS=""
+go run . serve
 ```
 
-To build with both AVX and AVX2:
-```
-make CUSTOM_CPU_FLAGS=avx,avx2
-```
+## Windows (ARM)
 
-To build with AVX512 features turned on:
+Windows ARM does not support additional acceleration libraries at this time.
+
+## Linux
+
+Install prerequisites:
+
+- [CMake](https://cmake.org/download/) or `sudo apt install cmake` or `sudo dnf install cmake`
+- (Optional) AMD GPU support
+    - [ROCm](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/quick-start.html)
+- (Optional) NVIDIA GPU support
+    - [CUDA SDK](https://developer.nvidia.com/cuda-downloads)
+
+> [!IMPORTANT]
+> Ensure prerequisites are in `PATH` before running CMake.
+
+
+Then, configure and build the project:
 
 ```
-make CUSTOM_CPU_FLAGS=avx,avx2,avx512,avx512vbmi,avx512vnni,avx512bf16
+cmake -B build
+cmake --build build
 ```
 
-> [!NOTE]  
-> If you are experimenting with different flags, make sure to do a `make clean` between each change to ensure everything is rebuilt with the new compiler flags
+Lastly, run Ollama:
+
+```
+go run . serve
+```
+
+## Docker
+
+```
+docker build .
+```
+
+### ROCm
+
+```
+docker build --build-arg FLAVOR=rocm .
+```
+
+## Running tests
+
+To run tests, use `go test`:
+
+```
+go test ./...
+```
+
+## Library detection
+
+Ollama looks for acceleration libraries in the following paths relative to the `ollama` executable:
+
+* `./lib/ollama` (Windows)
+* `../lib/ollama` (Linux)
+* `.` (macOS)
+* `build/lib/ollama` (for development)
+
+If the libraries are not found, Ollama will not run with any acceleration libraries.

docs/examples.md

@@ -12,3 +12,9 @@ Ollama JavaScript examples at [ollama-js/examples](https://github.com/ollama/oll
 
 ## OpenAI compatibility examples
 Ollama OpenAI compatibility examples at [ollama/examples/openai](../docs/openai.md)
+
+
+## Community examples
+
+- [LangChain Ollama Python](https://python.langchain.com/docs/integrations/chat/ollama/)
+- [LangChain Ollama JS](https://js.langchain.com/docs/integrations/chat/ollama/)

docs/gpu.md

@@ -38,7 +38,7 @@ Numeric IDs may be used, however ordering may vary, so UUIDs are more reliable.
 You can discover the UUID of your GPUs by running `nvidia-smi -L` If you want to
 ignore the GPUs and force CPU usage, use an invalid GPU ID (e.g., "-1")
 
-### Laptop Suspend Resume
+### Linux Suspend Resume
 
 On linux, after a suspend/resume cycle, sometimes Ollama will fail to discover
 your NVIDIA GPU, and fallback to running on the CPU.  You can workaround this

docs/linux.md

@@ -152,7 +152,7 @@ Use `OLLAMA_VERSION` environment variable with the install script to install a s
 For example:
 
 ```shell
-curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.3.9 sh
+curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.5.7 sh
 ```
 
 ## Viewing logs

docs/modelfile.md

@@ -67,8 +67,6 @@ To use this:
 3. `ollama run choose-a-model-name`
 4. Start using the model!
 
-More examples are available in the [examples directory](../examples).
-
 To view the Modelfile of a given model, use the `ollama show --modelfile` command.
 
   ```bash
@@ -155,7 +153,6 @@ PARAMETER <parameter> <parametervalue>
 | temperature    | The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)                                                                                                                                     | float      | temperature 0.7      |
 | seed           | Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. (Default: 0)                                                                                       | int        | seed 42              |
 | stop           | Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return. Multiple stop patterns may be set by specifying multiple separate `stop` parameters in a modelfile.                                      | string     | stop "AI assistant:" |
-| tfs_z          | Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (default: 1)                                               | float      | tfs_z 1              |
 | num_predict    | Maximum number of tokens to predict when generating text. (Default: -1, infinite generation)                                                                                                                                   | int        | num_predict 42       |
 | top_k          | Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)                                                                        | int        | top_k 40             |
 | top_p          | Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)                                                                 | float      | top_p 0.9            |

docs/openai.md

@@ -233,6 +233,8 @@ curl http://localhost:11434/v1/embeddings \
 - [x] `seed`
 - [x] `stop`
 - [x] `stream`
+- [x] `stream_options`
+  - [x] `include_usage`
 - [x] `temperature`
 - [x] `top_p`
 - [x] `max_tokens`
@@ -261,6 +263,8 @@ curl http://localhost:11434/v1/embeddings \
 - [x] `seed`
 - [x] `stop`
 - [x] `stream`
+- [x] `stream_options`
+  - [x] `include_usage`
 - [x] `temperature`
 - [x] `top_p`
 - [x] `max_tokens`

docs/template.md

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

docs/tutorials.md

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

envconfig/config.go

@@ -288,12 +288,3 @@ 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 ".."
-}

examples/.gitignore

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

examples/flyio/.gitignore

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

examples/flyio/README.md

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

examples/go-http-generate/main.go

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

examples/jupyter-notebook/README.md

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

examples/jupyter-notebook/ollama.ipynb

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

examples/kubernetes/README.md

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

examples/kubernetes/cpu.yaml

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

examples/kubernetes/gpu.yaml

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

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

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

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

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

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

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

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

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

examples/langchain-python-rag-privategpt/LICENSE

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -1 +0,0 @@
-langchain==0.0.259

examples/langchain-python-simple/README.md

@@ -1,23 +0,0 @@
-# LangChain
-
-This example is a basic "hello world" of using LangChain with Ollama.
-
-## Running the Example
-
-1. Ensure you have the `llama3.2` model installed:
-
-   ```bash
-   ollama pull llama3.2
-   ```
-
-2. Install the Python Requirements.
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. Run the example:
-
-   ```bash
-   python main.py
-   ```

examples/langchain-python-simple/main.py

@@ -1,6 +0,0 @@
-from langchain.llms import Ollama
-
-input = input("What is your question?\n> ")
-llm = Ollama(model="llama3.2")
-res = llm.invoke(input)
-print (res)

examples/langchain-python-simple/requirements.txt

@@ -1 +0,0 @@
-langchain==0.0.259

examples/langchain-typescript-simple/README.md

@@ -1,23 +0,0 @@
-# LangChain
-
-This example is a basic "hello world" of using LangChain with Ollama using Node.js and Typescript.
-
-## Running the Example
-
-1. Install the prerequisites:
-
-   ```bash
-   npm install
-   ```
-
-2. Ensure the `mistral` model is available:
-
-   ```bash
-   ollama pull mistral
-   ```
-
-3. Run the example:
-
-   ```bash
-   npm start
-   ```

examples/langchain-typescript-simple/main.ts

@@ -1,25 +0,0 @@
-import { Ollama } from 'langchain/llms/ollama';
-import * as readline from "readline";
-
-async function main() {
-  const ollama = new Ollama({
-    model: 'mistral'    
-    // other parameters can be found at https://js.langchain.com/docs/api/llms_ollama/classes/Ollama
-  });
-
-  const rl = readline.createInterface({
-    input: process.stdin,
-    output: process.stdout,
-  });
-
-  rl.question("What is your question: \n", async (user_input) => {
-    const stream = await ollama.stream(user_input);
-  
-    for await (const chunk of stream) {
-      process.stdout.write(chunk);
-    }
-    rl.close();
-  })
-}
-
-main();

examples/langchain-typescript-simple/package-lock.json

@@ -1,997 +0,0 @@
-{
-  "name": "langchain-typescript-simple",
-  "lockfileVersion": 3,
-  "requires": true,
-  "packages": {
-    "": {
-      "dependencies": {
-        "langchain": "^0.0.165"
-      },
-      "devDependencies": {
-        "typescript": "^5.2.2"
-      }
-    },
-    "node_modules/@anthropic-ai/sdk": {
-      "version": "0.6.2",
-      "resolved": "https://registry.npmjs.org/@anthropic-ai/sdk/-/sdk-0.6.2.tgz",
-      "integrity": "sha512-fB9PUj9RFT+XjkL+E9Ol864ZIJi+1P8WnbHspN3N3/GK2uSzjd0cbVIKTGgf4v3N8MwaQu+UWnU7C4BG/fap/g==",
-      "dependencies": {
-        "@types/node": "^18.11.18",
-        "@types/node-fetch": "^2.6.4",
-        "abort-controller": "^3.0.0",
-        "agentkeepalive": "^4.2.1",
-        "digest-fetch": "^1.3.0",
-        "form-data-encoder": "1.7.2",
-        "formdata-node": "^4.3.2",
-        "node-fetch": "^2.6.7"
-      }
-    },
-    "node_modules/@types/node": {
-      "version": "18.18.4",
-      "resolved": "https://registry.npmjs.org/@types/node/-/node-18.18.4.tgz",
-      "integrity": "sha512-t3rNFBgJRugIhackit2mVcLfF6IRc0JE4oeizPQL8Zrm8n2WY/0wOdpOPhdtG0V9Q2TlW/axbF1MJ6z+Yj/kKQ=="
-    },
-    "node_modules/@types/node-fetch": {
-      "version": "2.6.6",
-      "resolved": "https://registry.npmjs.org/@types/node-fetch/-/node-fetch-2.6.6.tgz",
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-      }
-    },
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examples/langchain-typescript-simple/package.json

@@ -1,13 +0,0 @@
-{
-  "scripts": {
-    "start": "tsx main.ts"
-  },
-  "devDependencies": {
-    "tsx": "^4.6.2",
-    "typescript": "^5.3.3"
-  },
-  "dependencies": {
-    "langchain": "^0.0.165",
-    "readline": "^1.3.0"
-  }
-}

examples/modelfile-mario/Modelfile

@@ -1,5 +0,0 @@
-FROM llama3.2
-PARAMETER temperature 1
-SYSTEM """
-You are Mario from super mario bros, acting as an assistant.
-"""

examples/modelfile-mario/readme.md

@@ -1,43 +0,0 @@
-<img src="logo.png" alt="image of Italian plumber" height="200"/>
-
-# Example character: Mario
-
-This example shows how to create a basic character using Llama 3.2 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.
-3. `ollama create NAME -f ./Modelfile`
-4. `ollama run NAME`
-
-Ask it some questions like "Who are you?" or "Is Peach in trouble again?"
-
-## Editing this file
-
-What the model file looks like:
-
-```
-FROM llama3.2
-PARAMETER temperature 1
-SYSTEM """
-You are Mario from Super Mario Bros, acting as an assistant.
-"""
-```
-
-What if you want to change its behaviour?
-
-- Try changing the prompt
-- Try changing the parameters [Docs](https://github.com/ollama/ollama/blob/main/docs/modelfile.md)
-- Try changing the model (e.g. An uncensored model by `FROM wizard-vicuna` this is the wizard-vicuna uncensored model )
-
-Once the changes are made,
-
-1. `ollama create NAME -f ./Modelfile`
-2. `ollama run NAME`
-3. Iterate until you are happy with the results.
-
-Notes:
-
-- This example is for research purposes only. There is no affiliation with any entity.
-- When using an uncensored model, please be aware that it may generate offensive content.

examples/python-dockerit/Modelfile

@@ -1,20 +0,0 @@
-FROM mistral
-SYSTEM """
-You are an experienced Devops engineer focused on docker. When given specifications for a particular need or application you know the best way to host that within a docker container. For instance if someone tells you they want an nginx server to host files located at /web you will answer as follows
-
----start
-FROM nginx:alpine
-COPY /myweb /usr/share/nginx/html
-EXPOSE 80
----end
-
-Notice that the answer you should give is just the contents of the dockerfile with no explanation and there are three dashes and the word start at the beginning and 3 dashes and the word end. The full output can be piped into a file and run as is. Here is another example. The user will ask to launch a Postgres server with a password of abc123. And the response should be
-
----start
-FROM postgres:latest
-ENV POSTGRES_PASSWORD=abc123
-EXPOSE 5432
----end
-
-Again it's just the contents of the dockerfile and nothing else.
-"""

examples/python-dockerit/README.md

@@ -1,31 +0,0 @@
-# DockerIt
-
-DockerIt is a tool to help you build and run your application in a Docker container. It consists of a model that defines the system prompt and model weights to use, along with a python script to then build the container and run the image automatically.
-
-## Running the Example
-
-1. Ensure you have the `mattw/dockerit` model installed:
-
-   ```bash
-   ollama pull mattw/dockerit
-   ```
-
-2. Make sure Docker is running on your machine.
-
-3. Install the Python Requirements.
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-4. Run the example:
-
-   ```bash
-   python dockerit.py "simple postgres server with admin password set to 123"
-   ```
-
-5. Enter the name you would like to use for your container image.
-
-## Caveats
-
-This is a simple example. It's assuming the Dockerfile content generated is going to work. In many cases, even with simple web servers, it fails when trying to copy files that don't exist. It's simply an example of what you could possibly do.

examples/python-dockerit/dockerit.py

@@ -1,17 +0,0 @@
-import requests, json, docker, io, sys
-inputDescription = " ".join(sys.argv[1:])
-imageName = input("Enter the name of the image: ")
-client = docker.from_env()
-s = requests.Session()
-output=""
-with s.post('http://localhost:11434/api/generate', json={'model': 'mattw/dockerit', 'prompt': inputDescription}, stream=True) as r:
-  for line in r.iter_lines():
-    if line:
-      j = json.loads(line)
-      if "response" in j:
-        output = output +j["response"]
-output = output[output.find("---start")+9:output.find("---end")-1]
-f = io.BytesIO(bytes(output, 'utf-8'))
-client.images.build(fileobj=f, tag=imageName)
-container = client.containers.run(imageName, detach=True)
-print("Container named", container.name, " started with id: ",container.id)

examples/python-dockerit/requirements.txt

@@ -1 +0,0 @@
-docker

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 retrieved 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 retrieved 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

@@ -1,31 +0,0 @@
-import requests
-import json
-import random
-
-model = "llama3.2"
-template = {
-  "firstName": "",
-  "lastName": "",
-  "address": {
-    "street": "",
-    "city": "",
-    "state": "",
-    "zipCode": ""
-  },
-  "phoneNumber": ""
-}
-
-prompt = f"generate one realistically believable sample data set of a persons first name, last name, address in the US, and  phone number. \nUse the following template: {json.dumps(template)}."
-
-data = {
-    "prompt": prompt,
-    "model": model,
-    "format": "json",
-    "stream": False,
-    "options": {"temperature": 2.5, "top_p": 0.99, "top_k": 100},
-}
-
-print(f"Generating a sample user")
-response = requests.post("http://localhost:11434/api/generate", json=data, stream=False)
-json_data = json.loads(response.text)
-print(json.dumps(json.loads(json_data["response"]), indent=2))

examples/python-json-datagenerator/randomaddresses.py

@@ -1,31 +0,0 @@
-import requests
-import json
-import random
-
-countries = [
-    "United States",
-    "United Kingdom",
-    "the Netherlands",
-    "Germany",
-    "Mexico",
-    "Canada",
-    "France",
-]
-country = random.choice(countries)
-model = "llama3.2"
-
-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."
-
-data = {
-    "prompt": prompt,
-    "model": model,
-    "format": "json",
-    "stream": False,
-    "options": {"temperature": 2.5, "top_p": 0.99, "top_k": 100},
-}
-
-print(f"Generating a sample user in {country}")
-response = requests.post("http://localhost:11434/api/generate", json=data, stream=False)
-json_data = json.loads(response.text)
-
-print(json.dumps(json.loads(json_data["response"]), indent=2))

examples/python-json-datagenerator/readme.md

@@ -1,60 +0,0 @@
-# JSON Output Example
-
-![llmjson 2023-11-10 15_31_31](https://github.com/ollama/ollama/assets/633681/e599d986-9b4a-4118-81a4-4cfe7e22da25)
-
-There are two python scripts in this example. `randomaddresses.py` generates random addresses from different countries. `predefinedschema.py` sets a template for the model to fill in.
-
-## Running the Example
-
-1. Ensure you have the `llama3.2` model installed:
-
-   ```bash
-   ollama pull llama3.2
-   ```
-
-2. Install the Python Requirements.
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. Run the Random Addresses example:
-
-   ```bash
-   python randomaddresses.py
-   ```
-
-4. Run the Predefined Schema example:
-
-   ```bash
-   python predefinedschema.py
-   ```
-
-## Review the Code
-
-Both programs are basically the same, with a different prompt for each, demonstrating two different ideas. The key part of getting JSON out of a model is to state in the prompt or system prompt that it should respond using JSON, and specifying the `format` as `json` in the data body.
-
-```python
-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 with no backslashes, values should use plain ascii with no special characters."
-
-data = {
-    "prompt": prompt,
-    "model": model,
-    "format": "json",
-    "stream": False,
-    "options": {"temperature": 2.5, "top_p": 0.99, "top_k": 100},
-}
-```
-
-When running `randomaddresses.py` you will see that the schema changes and adapts to the chosen country.
-
-In `predefinedschema.py`, a template has been specified in the prompt as well. It's been defined as JSON and then dumped into the prompt string to make it easier to work with.
-
-Both examples turn streaming off so that we end up with the completed JSON all at once. We need to convert the `response.text` to JSON so that when we output it as a string we can set the indent spacing to make the output easy to read.
-
-```python
-response = requests.post("http://localhost:11434/api/generate", json=data, stream=False)
-json_data = json.loads(response.text)
-
-print(json.dumps(json.loads(json_data["response"]), indent=2))
-```

examples/python-json-datagenerator/requirements.txt

@@ -1 +0,0 @@
-Requests==2.31.0

examples/python-loganalysis/Modelfile

@@ -1,8 +0,0 @@
-FROM codebooga:latest
-
-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
-

examples/python-loganalysis/loganalysis.py

@@ -1,41 +0,0 @@
-import sys
-import re
-import requests
-import json
-
-# prelines and postlines represent the number of lines of context to include in the output around the error
-prelines = 10
-postlines = 10
-
-def find_errors_in_log_file():
-  if len(sys.argv) < 2:
-    print("Usage: python loganalysis.py <filename>")
-    return
-
-  log_file_path = sys.argv[1]
-  with open(log_file_path, 'r') as log_file:
-    log_lines = log_file.readlines()
-
-  error_logs = []
-  for i, line in enumerate(log_lines):
-      if "error" in line.lower():
-          start_index = max(0, i - prelines)
-          end_index = min(len(log_lines), i + postlines + 1)
-          error_logs.extend(log_lines[start_index:end_index])
-
-  return error_logs
-
-error_logs = find_errors_in_log_file()
-
-data = {
-  "prompt": "\n".join(error_logs), 
-  "model": "mattw/loganalyzer"
-}
-
-response = requests.post("http://localhost:11434/api/generate", json=data, stream=True)
-for line in response.iter_lines():
-  if line:
-    json_data = json.loads(line)
-    if json_data['done'] == False:
-      print(json_data['response'], end='', flush=True)
-