use range

* add build to .dockerignore

* test: only build one arch

* add build to .gitignore

* fix ccache path

* filter amdgpu targets

* only filter if autodetecting

* Don't clobber gpu list for default runner

This ensures the GPU specific environment variables are set properly

* explicitly set CXX compiler for HIP

* Update build_windows.ps1

This isn't complete, but is close.  Dependencies are missing, and it only builds the "default" preset.

* build: add ollama subdir

* add .git to .dockerignore

* docs: update development.md

* update build_darwin.sh

* remove unused scripts

* llm: add cwd and build/lib/ollama to library paths

* default DYLD_LIBRARY_PATH to LD_LIBRARY_PATH in runner on macOS

* add additional cmake output vars for msvc

* interim edits to make server detection logic work with dll directories like lib/ollama/cuda_v12

* remove unncessary filepath.Dir, cleanup

* add hardware-specific directory to path

* use absolute server path

* build: linux arm

* cmake install targets

* remove unused files

* ml: visit each library path once

* build: skip cpu variants on arm

* build: install cpu targets

* build: fix workflow

* shorter names

* fix rocblas install

* docs: clean up development.md

* consistent build dir removal in development.md

* silence -Wimplicit-function-declaration build warnings in ggml-cpu

* update readme

* update development readme

* llm: update library lookup logic now that there is one runner (#8587)

* tweak development.md

* update docs

* add windows cuda/rocm tests

---------

Co-authored-by: jmorganca <jmorganca@gmail.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>

.dockerignore

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

.gitattributes

@@ -7,5 +7,14 @@ 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
+
 * text=auto
 *.go text eol=lf

.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:
@@ -35,292 +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
-      - 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
       - uses: actions/setup-go@v5
         with:
           go-version-file: go.mod
-          cache: true
-      - run: |
-          case ${{ matrix.arch }} in
-            amd64) echo ARCH=amd64 ;;
-            arm64) echo ARCH=arm64 ;;
-          esac >>$GITHUB_ENV
-        shell: bash
-      - run: go build
-      - run: go test -v ./...
+      - 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 checkout sync
+          git diff --compact-summary --exit-code

.gitignore

@@ -4,15 +4,13 @@
 .venv
 .swp
 dist
+build
 ollama
 .cache
 *.exe
 .idea
 test_data
 *.crt
-llm/build
-build/*/*/*
-!build/**/placeholder
-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 +1,112 @@
+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()
+
+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

@@ -1,272 +1,128 @@
-ARG GOLANG_VERSION=1.22.8
-ARG CMAKE_VERSION=3.22.1
-ARG CUDA_VERSION_11=11.3.1
-ARG CUDA_V11_ARCHITECTURES="50;52;53;60;61;62;70;72;75;80;86"
-ARG CUDA_VERSION_12=12.4.0
-ARG CUDA_V12_ARCHITECTURES="60;61;62;70;72;75;80;86;87;89;90;90a"
-ARG ROCM_VERSION=6.1.2
-ARG JETPACK_6=r36.2.0
-ARG JETPACK_5=r35.4.1
+# 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 $(pwd):/go/src/github.com/ollama/ollama/ builder-amd64
-#
-### Then incremental builds will be much faster in this container
-#
-# make -j 10 && go build -trimpath -o dist/linux-amd64/ollama .
-#
-FROM --platform=linux/amd64 rocm/dev-centos-7:${ROCM_VERSION}-complete AS unified-builder-amd64
-ARG CMAKE_VERSION
-ARG GOLANG_VERSION
-ARG CUDA_VERSION_11
-ARG CUDA_VERSION_12
-COPY ./scripts/rh_linux_deps.sh /
-ENV PATH /opt/rh/devtoolset-10/root/usr/bin:/usr/local/cuda/bin:$PATH
-ENV LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64
-ENV LIBRARY_PATH=/usr/local/cuda/lib64/stubs:/opt/amdgpu/lib64
-RUN CMAKE_VERSION=${CMAKE_VERSION} GOLANG_VERSION=${GOLANG_VERSION} sh /rh_linux_deps.sh
-RUN yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-rhel7.repo && \
-    dnf clean all && \
-    dnf install -y \
-    zsh \
-    cuda-$(echo ${CUDA_VERSION_11} | cut -f1-2 -d. | sed -e "s/\./-/g") \
-    cuda-$(echo ${CUDA_VERSION_12} | cut -f1-2 -d. | sed -e "s/\./-/g")
-# TODO intel oneapi goes here...
-ENV GOARCH amd64
-ENV CGO_ENABLED 1
-WORKDIR /go/src/github.com/ollama/ollama/
-ENTRYPOINT [ "zsh" ]
+ARG FLAVOR=${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 $(pwd):/go/src/github.com/ollama/ollama/ builder-arm64
-#
-FROM --platform=linux/arm64 rockylinux:8 AS unified-builder-arm64
-ARG CMAKE_VERSION
-ARG GOLANG_VERSION
-ARG CUDA_VERSION_11
-ARG CUDA_VERSION_12
-COPY ./scripts/rh_linux_deps.sh /
-RUN CMAKE_VERSION=${CMAKE_VERSION} GOLANG_VERSION=${GOLANG_VERSION} sh /rh_linux_deps.sh
-RUN yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/sbsa/cuda-rhel8.repo && \
-    dnf config-manager --set-enabled appstream && \
-    dnf clean all && \
-    dnf install -y \
-    zsh \
-    cuda-toolkit-$(echo ${CUDA_VERSION_11} | cut -f1-2 -d. | sed -e "s/\./-/g") \
-    cuda-toolkit-$(echo ${CUDA_VERSION_12} | cut -f1-2 -d. | sed -e "s/\./-/g")
-ENV PATH /opt/rh/gcc-toolset-10/root/usr/bin:$PATH:/usr/local/cuda/bin
-ENV LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64
-ENV LIBRARY_PATH=/usr/local/cuda/lib64/stubs:/opt/amdgpu/lib64
-ENV GOARCH amd64
-ENV CGO_ENABLED 1
-WORKDIR /go/src/github.com/ollama/ollama/
-ENTRYPOINT [ "zsh" ]
+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 runners-amd64
-COPY . .
-ARG OLLAMA_SKIP_CUDA_GENERATE
-ARG OLLAMA_SKIP_CUDA_11_GENERATE
-ARG OLLAMA_SKIP_CUDA_12_GENERATE
-ARG OLLAMA_SKIP_ROCM_GENERATE
-ARG CUDA_V11_ARCHITECTURES
-ARG CUDA_V12_ARCHITECTURES
-ARG OLLAMA_FAST_BUILD
+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 ) ; \
-    else \
-        make -j 5 ; \
-    fi
+    cmake --preset 'CPU' \
+        && cmake --build --parallel --preset 'CPU' \
+        && cmake --install build --component CPU --strip --parallel 8
 
-FROM --platform=linux/arm64 unified-builder-arm64 AS runners-arm64
-COPY . .
-ARG OLLAMA_SKIP_CUDA_GENERATE
-ARG OLLAMA_SKIP_CUDA_11_GENERATE
-ARG OLLAMA_SKIP_CUDA_12_GENERATE
-ARG CUDA_V11_ARCHITECTURES
-ARG CUDA_V12_ARCHITECTURES
-ARG OLLAMA_FAST_BUILD
+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
+    cmake --preset 'CUDA 11' \
+        && cmake --build --parallel --preset 'CUDA 11' \
+        && cmake --install build --component CUDA --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-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 cuda_v11 \
-        CUDA_ARCHITECTURES="72;87" \
-        GPU_RUNNER_VARIANT=_jetpack5 \
-        CGO_EXTRA_LDFLAGS_LINUX=-L/usr/local/cuda/lib64/stubs \
-        DIST_LIB_DIR=/go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack5/lib/ollama \
-        DIST_GPU_RUNNER_DEPS_DIR=/go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack5/lib/ollama/cuda_jetpack5
+    cmake --preset 'CUDA 12' \
+        && cmake --build --parallel --preset 'CUDA 12' \
+        && 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 rocm-6
 RUN --mount=type=cache,target=/root/.ccache \
-    make -j 5 cuda_v12 \
-        CUDA_ARCHITECTURES="87" \
-        GPU_RUNNER_VARIANT=_jetpack6 \
-        CGO_EXTRA_LDFLAGS_LINUX=-L/usr/local/cuda/lib64/stubs \
-        DIST_LIB_DIR=/go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack6/lib/ollama \
-        DIST_GPU_RUNNER_DEPS_DIR=/go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack6/lib/ollama/cuda_jetpack6
+    cmake --preset 'ROCm 6' \
+        && cmake --build --parallel --preset 'ROCm 6' \
+        && cmake --install build --component HIP --strip --parallel 8
 
-
-# Intermediate stages used for ./scripts/build_linux.sh
-FROM --platform=linux/amd64 centos:7 AS builder-amd64
-ARG CMAKE_VERSION
-ARG GOLANG_VERSION
-COPY ./scripts/rh_linux_deps.sh /
-RUN CMAKE_VERSION=${CMAKE_VERSION} GOLANG_VERSION=${GOLANG_VERSION} sh /rh_linux_deps.sh
-ENV PATH /opt/rh/devtoolset-10/root/usr/bin:$PATH
-ENV CGO_ENABLED 1
-ENV GOARCH amd64
-WORKDIR /go/src/github.com/ollama/ollama
-
-FROM --platform=linux/amd64 builder-amd64 AS build-amd64
-COPY . .
-COPY --from=runners-amd64 /go/src/github.com/ollama/ollama/dist/ dist/
-COPY --from=runners-amd64 /go/src/github.com/ollama/ollama/build/ build/
-ARG GOFLAGS
-ARG CGO_CFLAGS
-ARG OLLAMA_SKIP_ROCM_GENERATE
+FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${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-$(uname -m).tar.gz | tar xz -C /usr/local --strip-components 1
+COPY CMakeLists.txt CMakePresets.json .
+COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
 RUN --mount=type=cache,target=/root/.ccache \
-    go build -trimpath -o dist/linux-amd64/bin/ollama .
-RUN cd dist/linux-$GOARCH && \
-    tar --exclude runners -cf - . | pigz --best > ../ollama-linux-$GOARCH.tgz
-RUN if [ -z ${OLLAMA_SKIP_ROCM_GENERATE} ] ; then \
-    cd dist/linux-$GOARCH-rocm && \
-    tar -cf - . | pigz --best > ../ollama-linux-$GOARCH-rocm.tgz ;\
-    fi
+    cmake --preset 'JetPack 5' \
+        && cmake --build --parallel --preset 'JetPack 5' \
+        && cmake --install build --component CUDA --strip --parallel 8
 
-FROM --platform=linux/arm64 rockylinux:8 AS builder-arm64
-ARG CMAKE_VERSION
-ARG GOLANG_VERSION
-COPY ./scripts/rh_linux_deps.sh /
-RUN CMAKE_VERSION=${CMAKE_VERSION} GOLANG_VERSION=${GOLANG_VERSION} sh /rh_linux_deps.sh
-ENV PATH /opt/rh/gcc-toolset-10/root/usr/bin:$PATH
-ENV CGO_ENABLED 1
-ENV GOARCH arm64
-WORKDIR /go/src/github.com/ollama/ollama
-
-FROM --platform=linux/arm64 builder-arm64 AS build-arm64
-COPY . .
-COPY --from=runners-arm64 /go/src/github.com/ollama/ollama/dist/ dist/
-COPY --from=runners-arm64 /go/src/github.com/ollama/ollama/build/ build/
-COPY --from=runners-jetpack5-arm64 /go/src/github.com/ollama/ollama/dist/ dist/
-COPY --from=runners-jetpack5-arm64 /go/src/github.com/ollama/ollama/build/ build/
-COPY --from=runners-jetpack6-arm64 /go/src/github.com/ollama/ollama/dist/ dist/
-COPY --from=runners-jetpack6-arm64 /go/src/github.com/ollama/ollama/build/ build/
-ARG GOFLAGS
-ARG CGO_CFLAGS
+FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${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-$(uname -m).tar.gz | tar xz -C /usr/local --strip-components 1
+COPY CMakeLists.txt CMakePresets.json .
+COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
 RUN --mount=type=cache,target=/root/.ccache \
-    go build -trimpath -o dist/linux-arm64/bin/ollama .
-RUN cd dist/linux-$GOARCH && \
-    tar --exclude runners -cf - . | pigz --best > ../ollama-linux-$GOARCH.tgz
-RUN cd dist/linux-$GOARCH-jetpack5 && \
-    tar --exclude runners -cf - . | pigz --best > ../ollama-linux-$GOARCH-jetpack5.tgz
-RUN cd dist/linux-$GOARCH-jetpack6 && \
-    tar --exclude runners -cf - . | pigz --best > ../ollama-linux-$GOARCH-jetpack6.tgz
+    cmake --preset 'JetPack 6' \
+        && cmake --build --parallel --preset 'JetPack 6' \
+        && cmake --install build --component CUDA --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-$TARGETARCH AS dist
-
-
-# Optimized container images do not cary nested payloads
-FROM --platform=linux/amd64 builder-amd64 AS container-build-amd64
+FROM base AS build
+ARG GOVERSION=1.23.4
+RUN curl -fsSL https://golang.org/dl/go${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
-ARG CGO_CFLAGS
-RUN --mount=type=cache,target=/root/.ccache \
-    go build -trimpath -o dist/linux-amd64/bin/ollama .
+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 --platform=linux/arm64 builder-arm64 AS container-build-arm64
-WORKDIR /go/src/github.com/ollama/ollama
-COPY . .
-ARG GOFLAGS
-ARG CGO_CFLAGS
-RUN --mount=type=cache,target=/root/.ccache \
-    go build -trimpath -o dist/linux-arm64/bin/ollama .
+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
 
-# For amd64 container images, filter out cuda/rocm to minimize size
-FROM runners-amd64 AS runners-cuda-amd64
-RUN rm -rf \
-    ./dist/linux-amd64/lib/ollama/libggml_hipblas.so \
-    ./dist/linux-amd64/lib/ollama/runners/rocm*
+FROM --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 runners-amd64 AS runners-rocm-amd64
-RUN rm -rf \
-    ./dist/linux-amd64/lib/ollama/libggml_cuda*.so \
-    ./dist/linux-amd64/lib/ollama/libcu*.so* \
-    ./dist/linux-amd64/lib/ollama/runners/cuda*
+FROM --platform=linux/arm64 scratch AS rocm
+COPY --from=rocm-6 dist/lib/ollama/rocm /lib/ollama/rocm
 
-FROM --platform=linux/amd64 ubuntu:22.04 AS runtime-amd64
-RUN apt-get update && \
-    apt-get install -y ca-certificates && \
-    apt-get clean && rm -rf /var/lib/apt/lists/*
-COPY --from=container-build-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/bin/ /bin/
-COPY --from=runners-cuda-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/lib/ /lib/
+FROM ${FLAVOR} AS archive
+COPY --from=cpu dist/lib/ollama /lib/ollama
+COPY --from=build /bin/ollama /bin/ollama
 
-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=container-build-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/bin/ /bin/
-COPY --from=runners-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/lib/ /lib/
-COPY --from=runners-jetpack5-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack5/lib/ /lib/
-COPY --from=runners-jetpack6-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack6/lib/ /lib/
-
-
-# ROCm libraries larger so we keep it distinct from the CPU/CUDA image
-FROM --platform=linux/amd64 ubuntu:22.04 AS runtime-rocm
-# Frontload the rocm libraries which are large, and rarely change to increase chance of a common layer
-# across releases
-COPY --from=build-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64-rocm/lib/ /lib/
-RUN apt-get update && \
-    apt-get install -y ca-certificates && \
-    apt-get clean && rm -rf /var/lib/apt/lists/*
-COPY --from=container-build-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/bin/ /bin/
-COPY --from=runners-rocm-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/lib/ /lib/
-
-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

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

Makefile.sync

@@ -0,0 +1,56 @@
+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/llama.cpp ml/backend/ggml/ggml apply-patches
+
+.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):
@@ -49,15 +54,15 @@ Here are some example models that can be downloaded:
 
 | Model              | Parameters | Size  | Download                         |
 | ------------------ | ---------- | ----- | -------------------------------- |
+| Llama 3.3          | 70B        | 43GB  | `ollama run llama3.3`            |
 | Llama 3.2          | 3B         | 2.0GB | `ollama run llama3.2`            |
 | Llama 3.2          | 1B         | 1.3GB | `ollama run llama3.2:1b`         |
 | Llama 3.2 Vision   | 11B        | 7.9GB | `ollama run llama3.2-vision`     |
 | Llama 3.2 Vision   | 90B        | 55GB  | `ollama run llama3.2-vision:90b` |
 | Llama 3.1          | 8B         | 4.7GB | `ollama run llama3.1`            |
-| Llama 3.1          | 70B        | 40GB  | `ollama run llama3.1:70b`        |
 | 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)
@@ -346,6 +353,9 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Web management](https://github.com/lemonit-eric-mao/ollama-web-management) (Web management page)
 - [Promptery](https://github.com/promptery/promptery) (desktop client for Ollama.)
 - [Ollama App](https://github.com/JHubi1/ollama-app) (Modern and easy-to-use multi-platform client for Ollama)
+- [SpaceLlama](https://github.com/tcsenpai/spacellama) (Firefox and Chrome extension to quickly summarize web pages with ollama in a sidebar)
+- [YouLama](https://github.com/tcsenpai/youlama) (Webapp to quickly summarize any YouTube video, supporting Invidious as well)
+- [DualMind](https://github.com/tcsenpai/dualmind) (Experimental app allowing two models to talk to each other in the terminal or in a web interface)
 - [ollamarama-matrix](https://github.com/h1ddenpr0cess20/ollamarama-matrix) (Ollama chatbot for the Matrix chat protocol)
 - [ollama-chat-app](https://github.com/anan1213095357/ollama-chat-app) (Flutter-based chat app)
 - [Perfect Memory AI](https://www.perfectmemory.ai/) (Productivity AI assists personalized by what you have seen on your screen, heard and said in the meetings)
@@ -354,8 +364,12 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [OpenTalkGpt](https://github.com/adarshM84/OpenTalkGpt) (Chrome Extension to manage open-source models supported by Ollama, create custom models, and chat with models from a user-friendly UI)
 - [VT](https://github.com/vinhnx/vt.ai) (A minimal multimodal AI chat app, with dynamic conversation routing. Supports local models via Ollama)
 - [Nosia](https://github.com/nosia-ai/nosia) (Easy to install and use RAG platform based on Ollama)
-- [Witsy](https://github.com/nbonamy/witsy) (An AI Desktop application avaiable for Mac/Windows/Linux)
+- [Witsy](https://github.com/nbonamy/witsy) (An AI Desktop application available for Mac/Windows/Linux)
 - [Abbey](https://github.com/US-Artificial-Intelligence/abbey) (A configurable AI interface server with notebooks, document storage, and YouTube support)
+- [Minima](https://github.com/dmayboroda/minima) (RAG with on-premises or fully local workflow)
+- [aidful-ollama-model-delete](https://github.com/AidfulAI/aidful-ollama-model-delete) (User interface for simplified model cleanup)
+- [Perplexica](https://github.com/ItzCrazyKns/Perplexica) (An AI-powered search engine & an open-source alternative to Perplexity AI)
+- [AI Toolkit for Visual Studio Code](https://aka.ms/ai-tooklit/ollama-docs) (Microsoft-official VSCode extension to chat, test, evaluate models with Ollama support, and use them in your AI applications.)
 
 ### Cloud
 
@@ -368,6 +382,7 @@ 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)
@@ -402,8 +417,11 @@ 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)
 
 ### Package managers
 
@@ -419,10 +437,12 @@ 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)
@@ -462,6 +482,7 @@ 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
 
@@ -504,12 +525,14 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Terraform AWS Ollama & Open WebUI](https://github.com/xuyangbocn/terraform-aws-self-host-llm) (A Terraform module to deploy on AWS a ready-to-use Ollama service, together with its front end Open WebUI service.)
 - [node-red-contrib-ollama](https://github.com/jakubburkiewicz/node-red-contrib-ollama)
 - [Local AI Helper](https://github.com/ivostoykov/localAI) (Chrome and Firefox extensions that enable interactions with the active tab and customisable API endpoints. Includes secure storage for user prompts.)
-- [vnc-lm](https://github.com/jk011ru/vnc-lm) (A containerized Discord bot with support for attachments and web links)
+- [vnc-lm](https://github.com/jake83741/vnc-lm) (Discord bot for messaging with LLMs through Ollama and LiteLLM. Seamlessly move between local and flagship models.)
 - [LSP-AI](https://github.com/SilasMarvin/lsp-ai) (Open-source language server for AI-powered functionality)
 - [QodeAssist](https://github.com/Palm1r/QodeAssist) (AI-powered coding assistant plugin for Qt Creator)
 - [Obsidian Quiz Generator plugin](https://github.com/ECuiDev/obsidian-quiz-generator)
+- [AI Summmary Helper plugin](https://github.com/philffm/ai-summary-helper)
 - [TextCraft](https://github.com/suncloudsmoon/TextCraft) (Copilot in Word alternative using Ollama)
 - [Alfred Ollama](https://github.com/zeitlings/alfred-ollama) (Alfred Workflow)
+- [TextLLaMA](https://github.com/adarshM84/TextLLaMA) A Chrome Extension that helps you write emails, correct grammar, and translate into any language
 
 ### Supported backends
 
@@ -518,4 +541,5 @@ 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.

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

@@ -67,7 +67,7 @@ type GenerateRequest struct {
 	Raw bool `json:"raw,omitempty"`
 
 	// Format specifies the format to return a response in.
-	Format string `json:"format"`
+	Format json.RawMessage `json:"format,omitempty"`
 
 	// KeepAlive controls how long the model will stay loaded in memory following
 	// this request.
@@ -94,7 +94,7 @@ type ChatRequest struct {
 	Stream *bool `json:"stream,omitempty"`
 
 	// Format is the format to return the response in (e.g. "json").
-	Format string `json:"format"`
+	Format json.RawMessage `json:"format,omitempty"`
 
 	// KeepAlive controls how long the model will stay loaded into memory
 	// following the request.
@@ -146,6 +146,7 @@ type ToolCall struct {
 }
 
 type ToolCallFunction struct {
+	Index     int                       `json:"index,omitempty"`
 	Name      string                    `json:"name"`
 	Arguments ToolCallFunctionArguments `json:"arguments"`
 }
@@ -215,7 +216,6 @@ type Options struct {
 	TopK             int      `json:"top_k,omitempty"`
 	TopP             float32  `json:"top_p,omitempty"`
 	MinP             float32  `json:"min_p,omitempty"`
-	TFSZ             float32  `json:"tfs_z,omitempty"`
 	TypicalP         float32  `json:"typical_p,omitempty"`
 	RepeatLastN      int      `json:"repeat_last_n,omitempty"`
 	Temperature      float32  `json:"temperature,omitempty"`
@@ -225,7 +225,6 @@ type Options struct {
 	Mirostat         int      `json:"mirostat,omitempty"`
 	MirostatTau      float32  `json:"mirostat_tau,omitempty"`
 	MirostatEta      float32  `json:"mirostat_eta,omitempty"`
-	PenalizeNewline  bool     `json:"penalize_newline,omitempty"`
 	Stop             []string `json:"stop,omitempty"`
 }
 
@@ -295,17 +294,21 @@ type EmbeddingResponse struct {
 
 // CreateRequest is the request passed to [Client.Create].
 type CreateRequest struct {
-	Model     string `json:"model"`
-	Modelfile string `json:"modelfile"`
-	Stream    *bool  `json:"stream,omitempty"`
-	Quantize  string `json:"quantize,omitempty"`
+	Model    string `json:"model"`
+	Stream   *bool  `json:"stream,omitempty"`
+	Quantize string `json:"quantize,omitempty"`
+
+	From       string            `json:"from,omitempty"`
+	Files      map[string]string `json:"files,omitempty"`
+	Adapters   map[string]string `json:"adapters,omitempty"`
+	Template   string            `json:"template,omitempty"`
+	License    any               `json:"license,omitempty"`
+	System     string            `json:"system,omitempty"`
+	Parameters map[string]any    `json:"parameters,omitempty"`
+	Messages   []Message         `json:"messages,omitempty"`
 
 	// Deprecated: set the model name with Model instead
 	Name string `json:"name"`
-
-	// Deprecated: set the file content with Modelfile instead
-	Path string `json:"path"`
-
 	// Deprecated: use Quantize instead
 	Quantization string `json:"quantization,omitempty"`
 }
@@ -594,7 +597,6 @@ func DefaultOptions() Options {
 		Temperature:      0.8,
 		TopK:             40,
 		TopP:             0.9,
-		TFSZ:             1.0,
 		TypicalP:         1.0,
 		RepeatLastN:      64,
 		RepeatPenalty:    1.1,
@@ -603,7 +605,6 @@ func DefaultOptions() Options {
 		Mirostat:         0,
 		MirostatTau:      5.0,
 		MirostatEta:      0.1,
-		PenalizeNewline:  true,
 		Seed:             -1,
 
 		Runner: Runner{

app/ollama.iss

@@ -97,7 +97,6 @@ Source: "..\dist\windows-amd64\lib\ollama\*"; DestDir: "{app}\lib\ollama\"; Chec
 Source: "..\dist\windows-arm64\vc_redist.arm64.exe"; DestDir: "{tmp}"; Check: IsArm64() and vc_redist_needed(); Flags: deleteafterinstall
 Source: "..\dist\windows-arm64-app.exe"; DestDir: "{app}"; DestName: "{#MyAppExeName}" ;Check: IsArm64();  Flags: ignoreversion 64bit
 Source: "..\dist\windows-arm64\ollama.exe"; DestDir: "{app}"; Check: IsArm64(); Flags: ignoreversion 64bit
-Source: "..\dist\windows-arm64\lib\ollama\*"; DestDir: "{app}\lib\ollama\"; Check: IsArm64(); Flags: ignoreversion 64bit recursesubdirs
 #endif
 
 Source: "..\dist\ollama_welcome.ps1"; DestDir: "{app}"; Flags: ignoreversion

app/tray/wintray/eventloop.go

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

build/darwin/amd64/placeholder

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

build/darwin/arm64/placeholder

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

build/embed_darwin_amd64.go

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

build/embed_darwin_arm64.go

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

build/embed_linux.go

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

build/embed_unused.go

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

build/linux/amd64/placeholder

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

build/linux/arm64/placeholder

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

cache/cache.go

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

cmd/cmd.go

@@ -1,13 +1,11 @@
 package cmd
 
 import (
-	"archive/zip"
 	"bufio"
-	"bytes"
 	"context"
 	"crypto/ed25519"
 	"crypto/rand"
-	"crypto/sha256"
+	"encoding/json"
 	"encoding/pem"
 	"errors"
 	"fmt"
@@ -36,6 +34,8 @@ import (
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
+	"github.com/ollama/ollama/llama"
+	"github.com/ollama/ollama/llama/runner"
 	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/progress"
 	"github.com/ollama/ollama/server"
@@ -43,15 +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"
 	}
@@ -63,7 +59,7 @@ func getModelfileName(cmd *cobra.Command) (string, error) {
 
 	_, err = os.Stat(absName)
 	if err != nil {
-		return fn, err
+		return "", err
 	}
 
 	return absName, nil
@@ -99,68 +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)
@@ -180,145 +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
 	}
@@ -331,18 +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)
@@ -353,15 +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
 	}
@@ -598,7 +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")})
 		}
 	}
@@ -1035,10 +885,14 @@ func chat(cmd *cobra.Command, opts runOptions) (*api.Message, error) {
 		return nil
 	}
 
+	if opts.Format == "json" {
+		opts.Format = `"` + opts.Format + `"`
+	}
+
 	req := &api.ChatRequest{
 		Model:    opts.Model,
 		Messages: opts.Messages,
-		Format:   opts.Format,
+		Format:   json.RawMessage(opts.Format),
 		Options:  opts.Options,
 	}
 
@@ -1120,12 +974,16 @@ func generate(cmd *cobra.Command, opts runOptions) error {
 		}
 	}
 
+	if opts.Format == "json" {
+		opts.Format = `"` + opts.Format + `"`
+	}
+
 	request := api.GenerateRequest{
 		Model:     opts.Model,
 		Prompt:    opts.Prompt,
 		Context:   generateContext,
 		Images:    opts.Images,
-		Format:    opts.Format,
+		Format:    json.RawMessage(opts.Format),
 		System:    opts.System,
 		Options:   opts.Options,
 		KeepAlive: opts.KeepAlive,
@@ -1411,6 +1269,19 @@ func NewCLI() *cobra.Command {
 		RunE:    DeleteHandler,
 	}
 
+	runnerCmd := &cobra.Command{
+		Use:    "runner",
+		Short:  llama.PrintSystemInfo(),
+		Hidden: true,
+		RunE: func(cmd *cobra.Command, args []string) error {
+			return runner.Execute(os.Args[1:])
+		},
+		FParseErrWhitelist: cobra.FParseErrWhitelist{UnknownFlags: true},
+	}
+	runnerCmd.SetHelpFunc(func(cmd *cobra.Command, args []string) {
+		_ = runner.Execute(args[1:])
+	})
+
 	envVars := envconfig.AsMap()
 
 	envs := []envconfig.EnvVar{envVars["OLLAMA_HOST"]}
@@ -1445,6 +1316,7 @@ func NewCLI() *cobra.Command {
 				envVars["OLLAMA_SCHED_SPREAD"],
 				envVars["OLLAMA_TMPDIR"],
 				envVars["OLLAMA_FLASH_ATTENTION"],
+				envVars["OLLAMA_KV_CACHE_TYPE"],
 				envVars["OLLAMA_LLM_LIBRARY"],
 				envVars["OLLAMA_GPU_OVERHEAD"],
 				envVars["OLLAMA_LOAD_TIMEOUT"],
@@ -1466,6 +1338,7 @@ func NewCLI() *cobra.Command {
 		psCmd,
 		copyCmd,
 		deleteCmd,
+		runnerCmd,
 	)
 
 	return rootCmd

cmd/cmd_test.go

@@ -8,7 +8,6 @@ import (
 	"net/http"
 	"net/http/httptest"
 	"os"
-	"path/filepath"
 	"strings"
 	"testing"
 
@@ -180,18 +179,14 @@ Weigh anchor!
 
 	t.Run("license", func(t *testing.T) {
 		var b bytes.Buffer
-		license, err := os.ReadFile(filepath.Join("..", "LICENSE"))
-		if err != nil {
-			t.Fatal(err)
-		}
-
+		license := "MIT License\nCopyright (c) Ollama\n"
 		if err := showInfo(&api.ShowResponse{
 			Details: api.ModelDetails{
 				Family:            "test",
 				ParameterSize:     "7B",
 				QuantizationLevel: "FP16",
 			},
-			License: string(license),
+			License: license,
 		}, &b); err != nil {
 			t.Fatal(err)
 		}
@@ -298,7 +293,7 @@ func TestGetModelfileName(t *testing.T) {
 			name:          "modelfile specified, no modelfile exists",
 			modelfileName: "crazyfile",
 			fileExists:    false,
-			expectedName:  "crazyfile",
+			expectedName:  "",
 			expectedErr:   os.ErrNotExist,
 		},
 		{
@@ -343,8 +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)
@@ -494,3 +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 {
@@ -514,7 +498,7 @@ func extractFileNames(input string) []string {
 	// Regex to match file paths starting with optional drive letter, / ./ \ or .\ and include escaped or unescaped spaces (\ or %20)
 	// and followed by more characters and a file extension
 	// This will capture non filename strings, but we'll check for file existence to remove mismatches
-	regexPattern := `(?:[a-zA-Z]:)?(?:\./|/|\\)[\S\\ ]+?\.(?i:jpg|jpeg|png|svg)\b`
+	regexPattern := `(?:[a-zA-Z]:)?(?:\./|/|\\)[\S\\ ]+?\.(?i:jpg|jpeg|png)\b`
 	re := regexp.MustCompile(regexPattern)
 
 	return re.FindAllString(input, -1)

cmd/interactive_test.go

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

cmd/runner/main.go

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

convert/convert.go

@@ -9,7 +9,7 @@ import (
 	"log/slog"
 	"strings"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type ModelParameters struct {
@@ -27,8 +27,8 @@ type AdapterParameters struct {
 	} `json:"lora_parameters"`
 }
 
-func (ModelParameters) KV(t *Tokenizer) llm.KV {
-	kv := llm.KV{
+func (ModelParameters) KV(t *Tokenizer) ggml.KV {
+	kv := ggml.KV{
 		"general.file_type":            uint32(1),
 		"general.quantization_version": uint32(2),
 		"tokenizer.ggml.pre":           t.Pre,
@@ -54,7 +54,7 @@ func (ModelParameters) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p AdapterParameters) KV() llm.KV {
+func (p AdapterParameters) KV() ggml.KV {
 	var alpha float32
 	if p.LoraParameters.Alpha == 0 {
 		alpha = float32(p.Alpha)
@@ -62,7 +62,7 @@ func (p AdapterParameters) KV() llm.KV {
 		alpha = p.LoraParameters.Alpha
 	}
 
-	kv := llm.KV{
+	kv := ggml.KV{
 		"adapter.lora.alpha": alpha,
 		"adapter.type":       "lora",
 		"general.file_type":  uint32(1),
@@ -79,19 +79,19 @@ func (ModelParameters) specialTokenTypes() []string {
 	}
 }
 
-func (ModelParameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []llm.Tensor) error {
-	return llm.WriteGGUF(ws, kv, ts)
+func (ModelParameters) writeFile(ws io.WriteSeeker, kv ggml.KV, ts []ggml.Tensor) error {
+	return ggml.WriteGGUF(ws, kv, ts)
 }
 
-func (AdapterParameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []llm.Tensor) error {
-	return llm.WriteGGUF(ws, kv, ts)
+func (AdapterParameters) writeFile(ws io.WriteSeeker, kv ggml.KV, ts []ggml.Tensor) error {
+	return ggml.WriteGGUF(ws, kv, ts)
 }
 
 type ModelConverter interface {
 	// KV maps parameters to LLM key-values
-	KV(*Tokenizer) llm.KV
+	KV(*Tokenizer) ggml.KV
 	// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
-	Tensors([]Tensor) []llm.Tensor
+	Tensors([]Tensor) []ggml.Tensor
 	// Replacements returns a list of string pairs to replace in tensor names.
 	// See [strings.Replacer](https://pkg.go.dev/strings#Replacer) for details
 	Replacements() []string
@@ -99,7 +99,7 @@ type ModelConverter interface {
 	// specialTokenTypes returns any special token types the model uses
 	specialTokenTypes() []string
 	// writeFile writes the model to the provided io.WriteSeeker
-	writeFile(io.WriteSeeker, llm.KV, []llm.Tensor) error
+	writeFile(io.WriteSeeker, ggml.KV, []ggml.Tensor) error
 }
 
 type moreParser interface {
@@ -108,17 +108,17 @@ type moreParser interface {
 
 type AdapterConverter interface {
 	// KV maps parameters to LLM key-values
-	KV(llm.KV) llm.KV
+	KV(ggml.KV) ggml.KV
 	// Tensors maps input tensors to LLM tensors. Adapter specific modifications can be done here.
-	Tensors([]Tensor) []llm.Tensor
+	Tensors([]Tensor) []ggml.Tensor
 	// Replacements returns a list of string pairs to replace in tensor names.
 	// See [strings.Replacer](https://pkg.go.dev/strings#Replacer) for details
 	Replacements() []string
 
-	writeFile(io.WriteSeeker, llm.KV, []llm.Tensor) error
+	writeFile(io.WriteSeeker, ggml.KV, []ggml.Tensor) error
 }
 
-func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV llm.KV) error {
+func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV ggml.KV) error {
 	bts, err := fs.ReadFile(fsys, "adapter_config.json")
 	if err != nil {
 		return err
@@ -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_bert.go

@@ -8,7 +8,7 @@ import (
 	"slices"
 	"strings"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type bertModel struct {
@@ -85,7 +85,7 @@ func (p *bertModel) parseMore(fsys fs.FS) error {
 	return nil
 }
 
-func (p *bertModel) KV(t *Tokenizer) llm.KV {
+func (p *bertModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "bert"
 	kv["bert.attention.causal"] = false
@@ -132,8 +132,8 @@ func (p *bertModel) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p *bertModel) Tensors(ts []Tensor) []llm.Tensor {
-	var out []llm.Tensor
+func (p *bertModel) Tensors(ts []Tensor) []ggml.Tensor {
+	var out []ggml.Tensor
 	for _, t := range ts {
 		if slices.Contains([]string{
 			"embeddings.position_ids",
@@ -143,7 +143,7 @@ func (p *bertModel) Tensors(ts []Tensor) []llm.Tensor {
 			continue
 		}
 
-		out = append(out, llm.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_commandr.go

@@ -0,0 +1,76 @@
+package convert
+
+import (
+	"cmp"
+
+	"github.com/ollama/ollama/fs/ggml"
+)
+
+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) ggml.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) []ggml.Tensor {
+	var out []ggml.Tensor
+	for _, t := range ts {
+		out = append(out, ggml.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_gemma.go

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

convert/convert_gemma2.go

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

convert/convert_gemma2_adapter.go

@@ -6,7 +6,7 @@ import (
 	"github.com/pdevine/tensor"
 	"github.com/pdevine/tensor/native"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type gemma2Adapter struct {
@@ -15,14 +15,14 @@ type gemma2Adapter struct {
 
 var _ AdapterConverter = (*gemma2Adapter)(nil)
 
-func (p *gemma2Adapter) KV(baseKV llm.KV) llm.KV {
+func (p *gemma2Adapter) KV(baseKV ggml.KV) ggml.KV {
 	kv := p.AdapterParameters.KV()
 	kv["general.architecture"] = "gemma2"
 	return kv
 }
 
-func (p *gemma2Adapter) Tensors(ts []Tensor) []llm.Tensor {
-	var out []llm.Tensor
+func (p *gemma2Adapter) Tensors(ts []Tensor) []ggml.Tensor {
+	var out []ggml.Tensor
 	for _, t := range ts {
 		shape := t.Shape()
 		if (strings.HasSuffix(t.Name(), "weight.lora_a") && shape[0] > shape[1]) ||
@@ -31,7 +31,7 @@ func (p *gemma2Adapter) Tensors(ts []Tensor) []llm.Tensor {
 			t.SetRepacker(p.repack)
 		}
 
-		out = append(out, llm.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_llama.go

@@ -9,7 +9,7 @@ import (
 	"github.com/pdevine/tensor"
 	"github.com/pdevine/tensor/native"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type llamaModel struct {
@@ -46,7 +46,7 @@ type llamaModel struct {
 
 var _ ModelConverter = (*llamaModel)(nil)
 
-func (p *llamaModel) KV(t *Tokenizer) llm.KV {
+func (p *llamaModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "llama"
 	kv["llama.vocab_size"] = p.VocabSize
@@ -120,11 +120,11 @@ func (p *llamaModel) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p *llamaModel) Tensors(ts []Tensor) []llm.Tensor {
-	var out []llm.Tensor
+func (p *llamaModel) Tensors(ts []Tensor) []ggml.Tensor {
+	var out []ggml.Tensor
 
 	if p.RopeScaling.factors != nil {
-		out = append(out, llm.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     "rope_freqs.weight",
 			Kind:     0,
 			Shape:    []uint64{uint64(len(p.RopeScaling.factors))},
@@ -138,7 +138,7 @@ func (p *llamaModel) Tensors(ts []Tensor) []llm.Tensor {
 			t.SetRepacker(p.repack)
 		}
 
-		out = append(out, llm.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_llama_adapter.go

@@ -7,7 +7,7 @@ import (
 	"github.com/pdevine/tensor"
 	"github.com/pdevine/tensor/native"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type llamaAdapter struct {
@@ -18,7 +18,7 @@ type llamaAdapter struct {
 
 var _ AdapterConverter = (*llamaAdapter)(nil)
 
-func (p *llamaAdapter) KV(baseKV llm.KV) llm.KV {
+func (p *llamaAdapter) KV(baseKV ggml.KV) ggml.KV {
 	kv := p.AdapterParameters.KV()
 	kv["general.architecture"] = "llama"
 	kv["llama.attention.head_count"] = baseKV["llama.attention.head_count"]
@@ -29,8 +29,8 @@ func (p *llamaAdapter) KV(baseKV llm.KV) llm.KV {
 	return kv
 }
 
-func (p *llamaAdapter) Tensors(ts []Tensor) []llm.Tensor {
-	var out []llm.Tensor
+func (p *llamaAdapter) Tensors(ts []Tensor) []ggml.Tensor {
+	var out []ggml.Tensor
 	for _, t := range ts {
 		shape := t.Shape()
 		if (strings.HasSuffix(t.Name(), "weight.lora_a") && shape[0] > shape[1]) ||
@@ -41,7 +41,7 @@ func (p *llamaAdapter) Tensors(ts []Tensor) []llm.Tensor {
 			t.SetRepacker(p.repack)
 		}
 
-		out = append(out, llm.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    shape,

convert/convert_mixtral.go

@@ -6,7 +6,7 @@ import (
 	"slices"
 	"strings"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type mixtralModel struct {
@@ -15,7 +15,7 @@ type mixtralModel struct {
 	NumExpertsPerToken uint32 `json:"num_experts_per_tok"`
 }
 
-func (p *mixtralModel) KV(t *Tokenizer) llm.KV {
+func (p *mixtralModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.llamaModel.KV(t)
 
 	if p.NumLocalExperts > 0 {
@@ -29,7 +29,7 @@ func (p *mixtralModel) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p *mixtralModel) Tensors(ts []Tensor) []llm.Tensor {
+func (p *mixtralModel) Tensors(ts []Tensor) []ggml.Tensor {
 	oldnew := []string{
 		"model.layers", "blk",
 		"w1", "ffn_gate_exps",
@@ -56,10 +56,10 @@ func (p *mixtralModel) Tensors(ts []Tensor) []llm.Tensor {
 		return true
 	})
 
-	var out []llm.Tensor
+	var out []ggml.Tensor
 	for n, e := range experts {
 		// TODO(mxyng): sanity check experts
-		out = append(out, llm.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     n,
 			Kind:     e[0].Kind(),
 			Shape:    append([]uint64{uint64(len(e))}, e[0].Shape()...),

convert/convert_phi3.go

@@ -8,7 +8,7 @@ import (
 	"strings"
 	"sync"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type phi3Model struct {
@@ -37,7 +37,7 @@ type phi3Model struct {
 
 var _ ModelConverter = (*phi3Model)(nil)
 
-func (p *phi3Model) KV(t *Tokenizer) llm.KV {
+func (p *phi3Model) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "phi3"
 	kv["phi3.context_length"] = p.MaxPositionEmbeddings
@@ -68,19 +68,19 @@ func (p *phi3Model) KV(t *Tokenizer) llm.KV {
 	return kv
 }
 
-func (p *phi3Model) Tensors(ts []Tensor) []llm.Tensor {
+func (p *phi3Model) Tensors(ts []Tensor) []ggml.Tensor {
 	var addRopeFactors sync.Once
 
-	out := make([]llm.Tensor, 0, len(ts)+2)
+	out := make([]ggml.Tensor, 0, len(ts)+2)
 	for _, t := range ts {
 		if strings.HasPrefix(t.Name(), "blk.0.") {
 			addRopeFactors.Do(func() {
-				out = append(out, llm.Tensor{
+				out = append(out, ggml.Tensor{
 					Name:     "rope_factors_long.weight",
 					Kind:     0,
 					Shape:    []uint64{uint64(len(p.RopeScaling.LongFactor))},
 					WriterTo: p.RopeScaling.LongFactor,
-				}, llm.Tensor{
+				}, ggml.Tensor{
 					Name:     "rope_factors_short.weight",
 					Kind:     0,
 					Shape:    []uint64{uint64(len(p.RopeScaling.ShortFactor))},
@@ -89,7 +89,7 @@ func (p *phi3Model) Tensors(ts []Tensor) []llm.Tensor {
 			})
 		}
 
-		out = append(out, llm.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_qwen2.go

@@ -0,0 +1,78 @@
+package convert
+
+import "github.com/ollama/ollama/fs/ggml"
+
+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) ggml.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) []ggml.Tensor {
+	var out []ggml.Tensor
+	for _, t := range ts {
+		out = append(out, ggml.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

@@ -20,7 +20,7 @@ import (
 
 	"golang.org/x/exp/maps"
 
-	"github.com/ollama/ollama/llm"
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type tensorData struct {
@@ -29,7 +29,7 @@ type tensorData struct {
 	Shape   []int  `json:"shape"`
 }
 
-func convertFull(t *testing.T, fsys fs.FS) (*os.File, llm.KV, *llm.Tensors) {
+func convertFull(t *testing.T, fsys fs.FS) (*os.File, ggml.KV, ggml.Tensors) {
 	t.Helper()
 
 	f, err := os.CreateTemp(t.TempDir(), "f16")
@@ -48,7 +48,7 @@ func convertFull(t *testing.T, fsys fs.FS) (*os.File, llm.KV, *llm.Tensors) {
 	}
 	t.Cleanup(func() { r.Close() })
 
-	m, _, err := llm.DecodeGGML(r, math.MaxInt)
+	m, _, err := ggml.Decode(r, math.MaxInt)
 	if err != nil {
 		t.Fatal(err)
 	}
@@ -60,7 +60,7 @@ func convertFull(t *testing.T, fsys fs.FS) (*os.File, llm.KV, *llm.Tensors) {
 	return r, m.KV(), m.Tensors()
 }
 
-func generateResultsJSON(t *testing.T, f *os.File, kv llm.KV, tensors *llm.Tensors) map[string]string {
+func generateResultsJSON(t *testing.T, f *os.File, kv ggml.KV, tensors ggml.Tensors) map[string]string {
 	actual := make(map[string]string)
 	for k, v := range kv {
 		if s, ok := v.(json.Marshaler); !ok {
@@ -75,7 +75,7 @@ func generateResultsJSON(t *testing.T, f *os.File, kv llm.KV, tensors *llm.Tenso
 		}
 	}
 
-	for _, tensor := range tensors.Items {
+	for _, tensor := range tensors.Items() {
 		sha256sum := sha256.New()
 		sr := io.NewSectionReader(f, int64(tensors.Offset+tensor.Offset), int64(tensor.Size()))
 		if _, err := io.Copy(sha256sum, sr); err != nil {
@@ -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 {
@@ -330,7 +332,7 @@ func TestConvertAdapter(t *testing.T) {
 			}
 			defer r.Close()
 
-			m, _, err := llm.DecodeGGML(r, math.MaxInt)
+			m, _, err := ggml.Decode(r, math.MaxInt)
 			if err != nil {
 				t.Fatal(err)
 			}

convert/sentencepiece/sentencepiece_model.pb.go

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

convert/sentencepiece_model.proto

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

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

@@ -0,0 +1,314 @@
+{
+    "general.architecture": "qwen2",
+    "general.file_type": "1",
+    "general.parameter_count": "494032768",
+    "general.quantization_version": "2",
+    "output_norm.weight": "93a01a6db3419e85320a244bbf8ae81c43033b1d10c342bea3797ff2ce348390",
+    "qwen2.attention.head_count": "14",
+    "qwen2.attention.head_count_kv": "2",
+    "qwen2.attention.layer_norm_rms_epsilon": "1e-06",
+    "qwen2.block_count": "24",
+    "qwen2.context_length": "32768",
+    "qwen2.embedding_length": "896",
+    "qwen2.feed_forward_length": "4864",
+    "qwen2.rope.freq_base": "1e+06",
+    "token_embd.weight": "d74257dc547b48be5ae7b93f1c9af072c0c42dbbb85503078e25c59cd09e68d0",
+    "tokenizer.ggml.add_eos_token": "false",
+    "tokenizer.ggml.add_padding_token": "false",
+    "tokenizer.ggml.eos_token_id": "151645",
+    "tokenizer.ggml.merges": "6b1b1c58f1223d74f9095929d3e6416cdd74784440221a5507b87b8197f2bfd2",
+    "tokenizer.ggml.model": "gpt2",
+    "tokenizer.ggml.padding_token_id": "151643",
+    "tokenizer.ggml.pre": "qwen2",
+    "tokenizer.ggml.scores": "94e247e531e8b0fa3d248f3de09c9beae0c87da8106208a8edfaac0b8ec4b53d",
+    "tokenizer.ggml.token_type": "b178dbc9d1b2e08f84d02918e00fc2de2619a250e6c188c91a6605f701860055",
+    "tokenizer.ggml.tokens": "1d93f6679b23a1152b725f7f473792d54d53c1040c5250d3e46b42f81e0a1a34",
+    "blk.0.attn_k.bias": "5ce6617845f66c34515978d23d52e729c298d8bffa28c356a0428bef17142cf1",
+    "blk.0.attn_k.weight": "a960832a9e0e83e4d95402e5d1a01cc74300fcca0c381237162126330e1a7af8",
+    "blk.0.attn_norm.weight": "32c7d51cd0958f1f1771174192db341f9770516d7595a2f0fd18a4d78bd5aba3",
+    "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",
+    "blk.1.attn_q.weight": "d766f06c925cce38d4b31b2165b3448e1fb49a7d561985f95d9cd2fcba52367a",
+    "blk.1.attn_v.bias": "9f486626fb6ed9ac84970a71e9b9818dd2758501fd3f61bb1c08540dcc7a8631",
+    "blk.1.attn_v.weight": "e873d1e5bd4f4d6abfd47c0f55119c2c111105838753ee273a03c5ccea25ce5c",
+    "blk.1.ffn_down.weight": "b3ce82b093f187344de04284b1783a452de1b72640914609b8f830dc81580521",
+    "blk.1.ffn_gate.weight": "5cd44ad237edaca525a28a3ac13975d1b565f576d6a8003237a341ae0d156f2e",
+    "blk.1.ffn_norm.weight": "4ac774ee8afaee119610c46aa1ff89fc6c9084a29d226075bc4aa4d2f15f746c",
+    "blk.1.ffn_up.weight": "042d81ab5f1983d85c81213232f3bfc05a9302d9dfaa98d931ebba326b6058b8",
+    "blk.10.attn_k.bias": "767ecfeacd60a2c2221ac4d76c357190849dd9cdf64ced418d9d0c7949101401",
+    "blk.10.attn_k.weight": "a9f3df343227537636be8202303453086375091944e498bad11e0b91e45e8c71",
+    "blk.10.attn_norm.weight": "01acd0e7b3e363f873dbfde6f0995ffcce83f5aaa10ff91c31dbf775035f6d5a",
+    "blk.10.attn_output.weight": "a531fe660769604ab869f01b203eb115e025cad4c0baeacdd1bcca99cf6d0264",
+    "blk.10.attn_q.bias": "356a02c9163dd660c1340fbe1e049b335ac6178891e00996131bba9ab4cb3e59",
+    "blk.10.attn_q.weight": "81be0cfb227339d83f954cd8dcf35828441211c6e1d184060e3eb76085041e2f",
+    "blk.10.attn_v.bias": "ed0450653284b62f8bf2c2db19c0ff7a6cf3cda1324d0a044c5e3db7bb692bd3",
+    "blk.10.attn_v.weight": "c1247ff7092babd2ed979883095b9aa022b2996cab1c77fb9e6176ddc1498d16",
+    "blk.10.ffn_down.weight": "fda7544965dc9af874f1062c22151c6cefc8ba08cbe15dc67aa89979e77b2de4",
+    "blk.10.ffn_gate.weight": "9f2632b1dee7304d10c70bd38d85bb1f148a628a8468f894f57975b8a2f1d945",
+    "blk.10.ffn_norm.weight": "94f8cbd6b17a4d5aabd93fa32930a687db3b11f086142f1cd71c535c11adcad4",
+    "blk.10.ffn_up.weight": "8dc2f8db0474939a277a3d89db34c3bcc3381cfea57bd05a8426a164634d9112",
+    "blk.11.attn_k.bias": "3b8e5a662b19411e3f6530714b766aad2ee41eebc8161bec9db0bc82d383a6e0",
+    "blk.11.attn_k.weight": "2c29f1ed1ce53ce9604e9ea3663c2c373157e909a0d6064a8920005f6d15dad9",
+    "blk.11.attn_norm.weight": "48f68a99c3da4ab4c9e492677b606d1b8e0e3de1fdbf6a977523f97b8c21ec31",
+    "blk.11.attn_output.weight": "5859f3838a94898b020c23040941ed88f4fcb132db400d0849f30a01f62c0f1c",
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convert/testdata/c4ai-command-r-v01.json

@@ -0,0 +1,344 @@
+{
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+    "blk.39.attn_norm.weight": "ba7c1912dd1c4e2d16917201f62396fd0600e4a451137eaddff255548c209abd",
+    "blk.39.attn_output.weight": "acfaf4abb3fd27fd899b5563c3877f176b597d8f6cdb2f2fd3f3a0bd4da15ed6",
+    "blk.39.attn_q.weight": "e8adbc140d4c8f0db2a27ca584c5531d5b1e080555fe627e34d80d0814a92bed",
+    "blk.39.attn_v.weight": "92f96b0e1f724e73a0f90a76c145654418844c04a6d4b14c05eb5af8a62bf8dc",
+    "blk.39.ffn_down.weight": "4d9ee7c65fc16fe95d10c47b79ac6a525741947600a64b5fcea5d300a82c50de",
+    "blk.39.ffn_gate.weight": "7e18507989f39b32191133d2657c2ee3b74f42f070579204d727eb72215793d1",
+    "blk.39.ffn_up.weight": "22cda752269c9757ba918abede1df95bb0f83a5c772dea13c8deea3d5f2723d9",
+    "output_norm.weight": "2858cf0e39d32caf52b7861378ace076000241e147f10b9eb21d8a5cd149e3cb"
+}

convert/tokenizer.go

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

convert/tokenizer_test.go

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

discover/amd_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

@@ -77,7 +77,7 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
 
 	gfxOverride := envconfig.HsaOverrideGfxVersion()
 	var supported []string
-	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)
@@ -300,8 +300,11 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
 			})
 			continue
 		}
-
-		if int(major) < RocmComputeMin {
+		minVer, err := strconv.Atoi(RocmComputeMajorMin)
+		if err != nil {
+			slog.Error("invalid RocmComputeMajorMin setting", "value", RocmComputeMajorMin, "error", err)
+		}
+		if int(major) < minVer {
 			reason := fmt.Sprintf("amdgpu too old gfx%d%x%x", major, minor, patch)
 			slog.Warn(reason, "gpu", gpuID)
 			unsupportedGPUs = append(unsupportedGPUs, UnsupportedGPUInfo{

discover/amd_windows.go

@@ -5,7 +5,6 @@ import (
 	"errors"
 	"fmt"
 	"log/slog"
-	"os"
 	"path/filepath"
 	"slices"
 	"strconv"
@@ -50,6 +49,7 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
 		slog.Info(err.Error())
 		return nil, err
 	}
+
 	libDir, err := AMDValidateLibDir()
 	if err != nil {
 		err = fmt.Errorf("unable to verify rocm library: %w", err)
@@ -162,9 +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
@@ -182,7 +180,7 @@ func (gpus RocmGPUInfoList) RefreshFreeMemory() error {
 	hl, err := NewHipLib()
 	if err != nil {
 		slog.Debug(err.Error())
-		return nil
+		return err
 	}
 	defer hl.Release()
 

discover/cpu_common.go

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

discover/gpu.go

@@ -16,6 +16,7 @@ import (
 	"os"
 	"path/filepath"
 	"runtime"
+	"strconv"
 	"strings"
 	"sync"
 	"unsafe"
@@ -45,7 +46,6 @@ const (
 var (
 	gpuMutex      sync.Mutex
 	bootstrapped  bool
-	cpuCapability CPUCapability
 	cpus          []CPUInfo
 	cudaGPUs      []CudaGPUInfo
 	nvcudaLibPath string
@@ -64,9 +64,13 @@ var (
 )
 
 // With our current CUDA compile flags, older than 5.0 will not work properly
-var CudaComputeMin = [2]C.int{5, 0}
+// (string values used to allow ldflags overrides at build time)
+var (
+	CudaComputeMajorMin = "5"
+	CudaComputeMinorMin = "0"
+)
 
-var RocmComputeMin = 9
+var RocmComputeMajorMin = "9"
 
 // TODO find a better way to detect iGPU instead of minimum memory
 const IGPUMemLimit = 1 * format.GibiByte // 512G is what they typically report, so anything less than 1G must be iGPU
@@ -96,15 +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)}
-	}
-	libDir := LibraryDir()
-	if libDir != "" {
-		cudartMgmtPatterns = []string{filepath.Join(libDir, CudartMgmtName)}
-	}
+	cudartMgmtPatterns = append(cudartMgmtPatterns, filepath.Join(LibOllamaPath, "cuda_v*", CudartMgmtName))
 	cudartMgmtPatterns = append(cudartMgmtPatterns, CudartGlobs...)
 
 	if len(NvmlGlobs) > 0 {
@@ -219,16 +215,23 @@ func GetGPUInfo() GpuInfoList {
 
 	if !bootstrapped {
 		slog.Info("looking for compatible GPUs")
+		cudaComputeMajorMin, err := strconv.Atoi(CudaComputeMajorMin)
+		if err != nil {
+			slog.Error("invalid CudaComputeMajorMin setting", "value", CudaComputeMajorMin, "error", err)
+		}
+		cudaComputeMinorMin, err := strconv.Atoi(CudaComputeMinorMin)
+		if err != nil {
+			slog.Error("invalid CudaComputeMinorMin setting", "value", CudaComputeMinorMin, "error", err)
+		}
 		bootstrapErrors = []error{}
 		needRefresh = false
-		cpuCapability = GetCPUCapability()
 		var memInfo C.mem_info_t
 
 		mem, err := GetCPUMem()
 		if err != nil {
 			slog.Warn("error looking up system memory", "error", err)
 		}
-		depPath := LibraryDir()
+
 		details, err := GetCPUDetails()
 		if err != nil {
 			slog.Warn("failed to lookup CPU details", "error", err)
@@ -236,26 +239,14 @@ func GetGPUInfo() GpuInfoList {
 		cpus = []CPUInfo{
 			{
 				GpuInfo: GpuInfo{
-					memInfo:        mem,
-					Library:        "cpu",
-					Variant:        cpuCapability.String(),
-					ID:             "0",
-					DependencyPath: []string{depPath},
+					memInfo: mem,
+					Library: "cpu",
+					ID:      "0",
 				},
 				CPUs: details,
 			},
 		}
 
-		// Fallback to CPU mode if we're lacking required vector extensions on x86
-		if cpuCapability < GPURunnerCPUCapability && runtime.GOARCH == "amd64" {
-			err := fmt.Errorf("CPU does not have minimum vector extensions, GPU inference disabled.  Required:%s  Detected:%s", GPURunnerCPUCapability, cpuCapability)
-			slog.Warn(err.Error())
-			bootstrapErrors = append(bootstrapErrors, err)
-			bootstrapped = true
-			// No need to do any GPU discovery, since we can't run on them
-			return GpuInfoList{cpus[0].GpuInfo}
-		}
-
 		// Load ALL libraries
 		cHandles = initCudaHandles()
 
@@ -292,19 +283,19 @@ func GetGPUInfo() GpuInfoList {
 				gpuInfo.DriverMajor = driverMajor
 				gpuInfo.DriverMinor = driverMinor
 				variant := cudaVariant(gpuInfo)
-				if depPath != "" {
-					gpuInfo.DependencyPath = []string{depPath}
-					// Check for variant specific directory
-					if variant != "" {
-						if _, err := os.Stat(filepath.Join(depPath, "cuda_"+variant)); err == nil {
-							gpuInfo.DependencyPath = []string{filepath.Join(depPath, "cuda_"+variant), depPath}
-						}
+
+				// 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])
 				gpuInfo.Variant = variant
 
-				if memInfo.major < CudaComputeMin[0] || (memInfo.major == CudaComputeMin[0] && memInfo.minor < CudaComputeMin[1]) {
+				if int(memInfo.major) < cudaComputeMajorMin || (int(memInfo.major) == cudaComputeMajorMin && int(memInfo.minor) < cudaComputeMinorMin) {
 					unsupportedGPUs = append(unsupportedGPUs,
 						UnsupportedGPUInfo{
 							GpuInfo: gpuInfo.GpuInfo,
@@ -370,7 +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 = []string{depPath}
+						gpuInfo.DependencyPath = []string{LibOllamaPath}
 						oneapiGPUs = append(oneapiGPUs, gpuInfo)
 					}
 				}
@@ -385,6 +376,8 @@ func GetGPUInfo() GpuInfoList {
 		if len(cudaGPUs) == 0 && len(rocmGPUs) == 0 && len(oneapiGPUs) == 0 {
 			slog.Info("no compatible GPUs were discovered")
 		}
+
+		// TODO verify we have runners for the discovered GPUs, filter out any that aren't supported with good error messages
 	}
 
 	// For detected GPUs, load library if not loaded
@@ -504,34 +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{filepath.Join(LibraryDir(), 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)
@@ -705,34 +697,6 @@ func (l GpuInfoList) GetVisibleDevicesEnv() (string, string) {
 	}
 }
 
-func LibraryDir() string {
-	// On Windows/linux we bundle the dependencies at the same level as the executable
-	appExe, err := os.Executable()
-	if err != nil {
-		slog.Warn("failed to lookup executable path", "error", err)
-	}
-	cwd, err := os.Getwd()
-	if err != nil {
-		slog.Warn("failed to lookup working directory", "error", err)
-	}
-	// Scan for any of our dependeices, and pick first match
-	for _, root := range []string{filepath.Dir(appExe), filepath.Join(filepath.Dir(appExe), envconfig.LibRelativeToExe()), cwd} {
-		libDep := filepath.Join("lib", "ollama")
-		if _, err := os.Stat(filepath.Join(root, libDep)); err == nil {
-			return filepath.Join(root, libDep)
-		}
-		// Developer mode, local build
-		if _, err := os.Stat(filepath.Join(root, runtime.GOOS+"-"+runtime.GOARCH, libDep)); err == nil {
-			return filepath.Join(root, runtime.GOOS+"-"+runtime.GOARCH, libDep)
-		}
-		if _, err := os.Stat(filepath.Join(root, "dist", runtime.GOOS+"-"+runtime.GOARCH, libDep)); err == nil {
-			return filepath.Join(root, "dist", runtime.GOOS+"-"+runtime.GOARCH, libDep)
-		}
-	}
-	slog.Warn("unable to locate gpu dependency libraries")
-	return ""
-}
-
 func GetSystemInfo() SystemInfo {
 	gpus := GetGPUInfo()
 	gpuMutex.Lock()

discover/gpu_darwin.go

@@ -27,7 +27,6 @@ func GetGPUInfo() GpuInfoList {
 		return []GpuInfo{
 			{
 				Library: "cpu",
-				Variant: GetCPUCapability().String(),
 				memInfo: mem,
 			},
 		}
@@ -50,7 +49,6 @@ func GetCPUInfo() GpuInfoList {
 	return []GpuInfo{
 		{
 			Library: "cpu",
-			Variant: GetCPUCapability().String(),
 			memInfo: mem,
 		},
 	}

discover/gpu_windows.go

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

discover/path.go

@@ -0,0 +1,53 @@
+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 ""
+	}
+
+	libPath := filepath.Dir(exe)
+	switch runtime.GOOS {
+	case "windows":
+		libPath = filepath.Join(filepath.Dir(exe), "lib", "ollama")
+	case "linux":
+		libPath = filepath.Join(filepath.Dir(exe), "..", "lib", "ollama")
+	}
+
+	cwd, err := os.Getwd()
+	if err != nil {
+		return ""
+	}
+
+	// build paths for development
+	buildPaths := []string{
+		filepath.Join(filepath.Dir(exe), "build", "lib", "ollama"),
+		filepath.Join(cwd, "build", "lib", "ollama"),
+	}
+
+	for _, p := range buildPaths {
+		if _, err := os.Stat(p); err == nil {
+			return p
+		}
+	}
+
+	return libPath
+}()

discover/types.go

@@ -47,6 +47,13 @@ type GpuInfo struct { // TODO better name maybe "InferenceProcessor"?
 	// TODO other performance capability info to help in scheduling decisions
 }
 
+func (gpu GpuInfo) RunnerName() string {
+	if gpu.Variant != "" {
+		return gpu.Library + "_" + gpu.Variant
+	}
+	return gpu.Library
+}
+
 type CPUInfo struct {
 	GpuInfo
 	CPUs []CPU
@@ -99,7 +106,7 @@ func (l GpuInfoList) ByLibrary() []GpuInfoList {
 	for _, info := range l {
 		found := false
 		requested := info.Library
-		if info.Variant != CPUCapabilityNone.String() {
+		if info.Variant != "" {
 			requested += "_" + info.Variant
 		}
 		for i, lib := range libs {
@@ -140,29 +147,6 @@ func (a ByFreeMemory) Len() int           { return len(a) }
 func (a ByFreeMemory) Swap(i, j int)      { a[i], a[j] = a[j], a[i] }
 func (a ByFreeMemory) Less(i, j int) bool { return a[i].FreeMemory < a[j].FreeMemory }
 
-type CPUCapability uint32
-
-// Override at build time when building base GPU runners
-var GPURunnerCPUCapability = CPUCapabilityAVX
-
-const (
-	CPUCapabilityNone CPUCapability = iota
-	CPUCapabilityAVX
-	CPUCapabilityAVX2
-	// TODO AVX512
-)
-
-func (c CPUCapability) String() string {
-	switch c {
-	case CPUCapabilityAVX:
-		return "avx"
-	case CPUCapabilityAVX2:
-		return "avx2"
-	default:
-		return "no vector extensions"
-	}
-}
-
 type SystemInfo struct {
 	System          CPUInfo              `json:"system"`
 	GPUs            []GpuInfo            `json:"gpus"`
@@ -183,3 +167,17 @@ func (si SystemInfo) GetOptimalThreadCount() int {
 
 	return coreCount
 }
+
+// For each GPU, check if it does NOT support flash attention
+func (l GpuInfoList) FlashAttentionSupported() bool {
+	for _, gpu := range l {
+		supportsFA := gpu.Library == "metal" ||
+			(gpu.Library == "cuda" && gpu.DriverMajor >= 7) ||
+			gpu.Library == "rocm"
+
+		if !supportsFA {
+			return false
+		}
+	}
+	return true
+}

docs/README.md

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

docs/api.md

@@ -13,6 +13,7 @@
 - [Push a Model](#push-a-model)
 - [Generate Embeddings](#generate-embeddings)
 - [List Running Models](#list-running-models)
+- [Version](#version)
 
 ## Conventions
 
@@ -45,14 +46,18 @@ Generate a response for a given prompt with a provided model. This is a streamin
 
 Advanced parameters (optional):
 
-- `format`: the format to return a response in. Currently the only accepted value is `json`
+- `format`: the format to return a response in. Format can be `json` or a JSON schema
 - `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.md#valid-parameters-and-values) such as `temperature`
 - `system`: system message to (overrides what is defined in the `Modelfile`)
 - `template`: the prompt template to use (overrides what is defined in the `Modelfile`)
-- `context`: the context parameter returned from a previous request to `/generate`, this can be used to keep a short conversational memory
 - `stream`: if `false` the response will be returned as a single response object, rather than a stream of objects
 - `raw`: if `true` no formatting will be applied to the prompt. You may choose to use the `raw` parameter if you are specifying a full templated prompt in your request to the API
 - `keep_alive`: controls how long the model will stay loaded into memory following the request (default: `5m`)
+- `context` (deprecated): the context parameter returned from a previous request to `/generate`, this can be used to keep a short conversational memory
+
+#### Structured outputs
+
+Structured outputs are supported by providing a JSON schema in the `format` parameter. The model will generate a response that matches the schema. See the [structured outputs](#request-structured-outputs) example below.
 
 #### JSON mode
 
@@ -185,6 +190,52 @@ curl http://localhost:11434/api/generate -d '{
 }
 ```
 
+#### Request (Structured outputs)
+
+##### Request
+
+```shell
+curl -X POST http://localhost:11434/api/generate -H "Content-Type: application/json" -d '{
+  "model": "llama3.1:8b",
+  "prompt": "Ollama is 22 years old and is busy saving the world. Respond using JSON",
+  "stream": false,
+  "format": {
+    "type": "object",
+    "properties": {
+      "age": {
+        "type": "integer"
+      },
+      "available": {
+        "type": "boolean"
+      }
+    },
+    "required": [
+      "age",
+      "available"
+    ]
+  }
+}'
+```
+
+##### Response
+
+```json
+{
+  "model": "llama3.1:8b",
+  "created_at": "2024-12-06T00:48:09.983619Z",
+  "response": "{\n  \"age\": 22,\n  \"available\": true\n}",
+  "done": true,
+  "done_reason": "stop",
+  "context": [1, 2, 3],
+  "total_duration": 1075509083,
+  "load_duration": 567678166,
+  "prompt_eval_count": 28,
+  "prompt_eval_duration": 236000000,
+  "eval_count": 16,
+  "eval_duration": 269000000
+}
+```
+
 #### Request (JSON mode)
 
 > [!IMPORTANT]
@@ -337,7 +388,6 @@ curl http://localhost:11434/api/generate -d '{
     "top_k": 20,
     "top_p": 0.9,
     "min_p": 0.0,
-    "tfs_z": 0.5,
     "typical_p": 0.7,
     "repeat_last_n": 33,
     "temperature": 0.8,
@@ -456,11 +506,15 @@ The `message` object has the following fields:
 
 Advanced parameters (optional):
 
-- `format`: the format to return a response in. Currently the only accepted value is `json`
+- `format`: the format to return a response in. Format can be `json` or a JSON schema. 
 - `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.md#valid-parameters-and-values) such as `temperature`
 - `stream`: if `false` the response will be returned as a single response object, rather than a stream of objects
 - `keep_alive`: controls how long the model will stay loaded into memory following the request (default: `5m`)
 
+### Structured outputs
+
+Structured outputs are supported by providing a JSON schema in the `format` parameter. The model will generate a response that matches the schema. See the [Chat request (Structured outputs)](#chat-request-structured-outputs) example below.
+
 ### Examples
 
 #### Chat Request (Streaming)
@@ -551,6 +605,54 @@ curl http://localhost:11434/api/chat -d '{
 }
 ```
 
+#### Chat request (Structured outputs)
+
+##### Request
+
+```shell
+curl -X POST http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{
+  "model": "llama3.1",
+  "messages": [{"role": "user", "content": "Ollama is 22 years old and busy saving the world. Return a JSON object with the age and availability."}],
+  "stream": false,
+  "format": {
+    "type": "object",
+    "properties": {
+      "age": {
+        "type": "integer"
+      },
+      "available": {
+        "type": "boolean"
+      }
+    },
+    "required": [
+      "age",
+      "available"
+    ]
+  },
+  "options": {
+    "temperature": 0
+  }
+}'
+```
+
+##### Response
+
+```json
+{
+  "model": "llama3.1",
+  "created_at": "2024-12-06T00:46:58.265747Z",
+  "message": { "role": "assistant", "content": "{\"age\": 22, \"available\": false}" },
+  "done_reason": "stop",
+  "done": true,
+  "total_duration": 2254970291,
+  "load_duration": 574751416,
+  "prompt_eval_count": 34,
+  "prompt_eval_duration": 1502000000,
+  "eval_count": 12,
+  "eval_duration": 175000000
+}
+```
+
 #### Chat request (With History)
 
 Send a chat message with a conversation history. You can use this same approach to start the conversation using multi-shot or chain-of-thought prompting.
@@ -826,14 +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
@@ -859,14 +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."
 }'
 ```
 
@@ -897,7 +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"
 }'
 ```
@@ -917,52 +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.
 
@@ -1425,3 +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

@@ -1,175 +1,120 @@
 # Development
 
-Install required tools:
+Install prerequisites:
 
-- go version 1.22 or higher
-- gcc version 11.4.0 or higher
+- [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.
 
-
-### MacOS
-
-[Download Go](https://go.dev/dl/)
-
-Optionally enable debugging and more verbose logging:
-
-```bash
-# At build time
-export CGO_CFLAGS="-g"
-
-# At runtime
-export OLLAMA_DEBUG=1
-```
-
-Get the required libraries and build the native LLM code:  (Adjust the job count based on your number of processors for a faster build)
-
-```bash
-make -j 5
-```
-
-Then build ollama:
-
-```bash
-go build .
-```
-
-Now you can run `ollama`:
-
-```bash
-./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 build scripts will auto-detect CUDA, however, if your Linux distro
-or installation approach uses unusual paths, you can specify the location by
-specifying an environment variable `CUDA_LIB_DIR` to the location of the shared
-libraries, and `CUDACXX` to the location of the nvcc compiler. You can customize
-a set of target CUDA architectures by setting `CMAKE_CUDA_ARCHITECTURES` (e.g. "50;60;70")
-
-Then generate dependencies:  (Adjust the job count based on your number of processors for a faster build)
+Then build and run Ollama from the root directory of the repository:
 
 ```
-make -j 5
+go run . serve
 ```
 
-Then build the binary:
+## macOS (Apple Silicon)
+
+macOS Apple Silicon supports Metal which is built-in to the Ollama binary. No additional steps are required.
+
+## macOS (Intel)
+
+Install prerequisites:
+
+- [CMake](https://cmake.org/download/) or `brew install cmake`
+
+Then, configure and build the project:
 
 ```
-go build .
+cmake -B build
+cmake --build build
 ```
 
-#### Linux ROCm (AMD)
-
-_Your operating system distribution may already have packages for AMD ROCm and CLBlast. Distro packages are often preferable, but instructions are distro-specific. Please consult distro-specific docs for dependencies if available!_
-
-Install [CLBlast](https://github.com/CNugteren/CLBlast/blob/master/doc/installation.md) and [ROCm](https://rocm.docs.amd.com/en/latest/) development packages first, as well as `make`, `gcc`, and `golang`.
-
-Typically the build scripts will auto-detect ROCm, however, if your Linux distro
-or installation approach uses unusual paths, you can specify the location by
-specifying an environment variable `ROCM_PATH` to the location of the ROCm
-install (typically `/opt/rocm`), and `CLBlast_DIR` to the location of the
-CLBlast install (typically `/usr/lib/cmake/CLBlast`). You can also customize
-the AMD GPU targets by setting AMDGPU_TARGETS (e.g. `AMDGPU_TARGETS="gfx1101;gfx1102"`)
-
-Then generate dependencies:  (Adjust the job count based on your number of processors for a faster build)
+Lastly, run Ollama:
 
 ```
-make -j 5
+go run . serve
 ```
 
-Then build the binary:
+## Windows
+
+Install prerequisites:
+
+- [CMake](https://cmake.org/download/)
+- [Visual Studio 2022](https://visualstudio.microsoft.com/downloads/) including the Native Desktop Workload
+- (Optional) AMD GPU support
+    - [ROCm](https://rocm.github.io/install.html)
+    - [Ninja](https://github.com/ninja-build/ninja/releases)
+- (Optional) NVIDIA GPU support
+    - [CUDA SDK](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=11&target_type=exe_network)
+
+> [!IMPORTANT]
+> Ensure prerequisites are in `PATH` before running CMake.
+
+> [!IMPORTANT]
+> ROCm is not compatible with Visual Studio CMake generators. Use `-GNinja` when configuring the project.
+
+> [!IMPORTANT]
+> CUDA is only compatible with Visual Studio CMake generators.
+
+Then, configure and build the project:
 
 ```
-go build .
+cmake -B build
+cmake --build build --config Release
 ```
 
-ROCm requires elevated privileges to access the GPU at runtime. On most distros you can add your user account to the `render` group, or run as root.
-
-#### Advanced CPU Settings
-
-By default, running `make` will compile a few different variations
-of the LLM library based on common CPU families and vector math capabilities,
-including a lowest-common-denominator which should run on almost any 64 bit CPU
-somewhat slowly. At runtime, Ollama will auto-detect the optimal variation to
-load. 
-
-Custom CPU settings are not currently supported in the new Go server build but will be added back after we complete the transition.
-
-#### Containerized Linux Build
-
-If you have Docker available, you can build linux binaries with `./scripts/build_linux.sh` which has the CUDA and ROCm dependencies included. The resulting binary is placed in `./dist`
-
-### 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.
-
-Then, build the `ollama` binary:
-
-```powershell
-$env:CGO_ENABLED="1"
-make -j 8
-go build .
-```
-
-#### GPU Support
-
-The GPU tools require the Microsoft native build tools.  To build either CUDA or ROCm, you must first install MSVC via Visual Studio:
-
-- Make sure to select `Desktop development with C++` as a Workload during the Visual Studio install
-- You must complete the Visual Studio install and run it once **BEFORE** installing CUDA or ROCm for the tools to properly register
-- Add the location of the **64 bit (x64)** compiler (`cl.exe`) to your `PATH`
-- Note: the default Developer Shell may configure the 32 bit (x86) compiler which will lead to build failures.  Ollama requires a 64 bit toolchain.
-
-#### Windows CUDA (NVIDIA)
-
-In addition to the common Windows development tools and MSVC described above:
-
-- [NVIDIA CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html)
-
-#### Windows ROCm (AMD Radeon)
-
-In addition to the common Windows development tools and MSVC described above:
-
-- [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:
+Lastly, run Ollama:
 
 ```
-pacman -S mingw-w64-clang-aarch64-clang mingw-w64-clang-aarch64-gcc-compat mingw-w64-clang-aarch64-make make
+go run . serve
 ```
 
-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\`)
+## Windows (ARM)
+
+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:
+
+```
+cmake -B build
+cmake --build build
+```
+
+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 ./...
+```

docs/examples.md

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

docs/faq.md

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

docs/gpu.md

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

docs/linux.md

@@ -10,6 +10,9 @@ curl -fsSL https://ollama.com/install.sh | sh
 
 ## Manual install
 
+> [!NOTE]
+> If you are upgrading from a prior version, you should remove the old libraries with `sudo rm -rf /usr/lib/ollama` first.
+
 Download and extract the package:
 
 ```shell

docs/modelfile.md

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

docs/openai.md

@@ -59,6 +59,40 @@ embeddings = client.embeddings.create(
     input=["why is the sky blue?", "why is the grass green?"],
 )
 ```
+#### Structured outputs
+```py
+from pydantic import BaseModel
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")
+
+# Define the schema for the response
+class FriendInfo(BaseModel):
+    name: str
+    age: int 
+    is_available: bool
+
+class FriendList(BaseModel):
+    friends: list[FriendInfo]
+
+try:
+    completion = client.beta.chat.completions.parse(
+        temperature=0,
+        model="llama3.1:8b",
+        messages=[
+            {"role": "user", "content": "I have two friends. The first is Ollama 22 years old busy saving the world, and the second is Alonso 23 years old and wants to hang out. Return a list of friends in JSON format"}
+        ],
+        response_format=FriendList,
+    )
+
+    friends_response = completion.choices[0].message
+    if friends_response.parsed:
+        print(friends_response.parsed)
+    elif friends_response.refusal:
+        print(friends_response.refusal)
+except Exception as e:
+    print(f"Error: {e}")
+```
 
 ### OpenAI JavaScript library
 
@@ -181,7 +215,7 @@ curl http://localhost:11434/v1/embeddings \
 - [x] JSON mode
 - [x] Reproducible outputs
 - [x] Vision
-- [x] Tools (streaming support coming soon)
+- [x] Tools
 - [ ] Logprobs
 
 #### Supported request fields
@@ -199,6 +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`
@@ -227,6 +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/troubleshooting.md

@@ -80,7 +80,7 @@ If you are using a container to run Ollama, make sure you've set up the containe
 
 Sometimes the Ollama can have difficulties initializing the GPU. When you check the server logs, this can show up as various error codes, such as "3" (not initialized), "46" (device unavailable), "100" (no device), "999" (unknown), or others. The following troubleshooting techniques may help resolve the problem
 
-- If you are using a container, is the container runtime working?  Try `docker run --gpus all ubuntu nvidia-smi` - if this doesn't work, Ollama wont be able to see your NVIDIA GPU.
+- If you are using a container, is the container runtime working?  Try `docker run --gpus all ubuntu nvidia-smi` - if this doesn't work, Ollama won't be able to see your NVIDIA GPU.
 - Is the uvm driver loaded? `sudo nvidia-modprobe -u`
 - Try reloading the nvidia_uvm driver - `sudo rmmod nvidia_uvm` then `sudo modprobe nvidia_uvm`
 - Try rebooting

docs/tutorials.md

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

docs/windows.md

@@ -83,3 +83,6 @@ If you'd like to install or integrate Ollama as a service, a standalone
 and GPU library dependencies for Nvidia and AMD. This allows for embedding
 Ollama in existing applications, or running it as a system service via `ollama
 serve` with tools such as [NSSM](https://nssm.cc/).
+
+> [!NOTE]  
+> If you are upgrading from a prior version, you should remove the old directories first.

envconfig/config.go

@@ -153,6 +153,8 @@ var (
 	Debug = Bool("OLLAMA_DEBUG")
 	// FlashAttention enables the experimental flash attention feature.
 	FlashAttention = Bool("OLLAMA_FLASH_ATTENTION")
+	// KvCacheType is the quantization type for the K/V cache.
+	KvCacheType = String("OLLAMA_KV_CACHE_TYPE")
 	// NoHistory disables readline history.
 	NoHistory = Bool("OLLAMA_NOHISTORY")
 	// NoPrune disables pruning of model blobs on startup.
@@ -173,7 +175,6 @@ func String(s string) func() string {
 
 var (
 	LLMLibrary = String("OLLAMA_LLM_LIBRARY")
-	TmpDir     = String("OLLAMA_TMPDIR")
 
 	CudaVisibleDevices    = String("CUDA_VISIBLE_DEVICES")
 	HipVisibleDevices     = String("HIP_VISIBLE_DEVICES")
@@ -234,6 +235,7 @@ func AsMap() map[string]EnvVar {
 	ret := map[string]EnvVar{
 		"OLLAMA_DEBUG":             {"OLLAMA_DEBUG", Debug(), "Show additional debug information (e.g. OLLAMA_DEBUG=1)"},
 		"OLLAMA_FLASH_ATTENTION":   {"OLLAMA_FLASH_ATTENTION", FlashAttention(), "Enabled flash attention"},
+		"OLLAMA_KV_CACHE_TYPE":     {"OLLAMA_KV_CACHE_TYPE", KvCacheType(), "Quantization type for the K/V cache (default: f16)"},
 		"OLLAMA_GPU_OVERHEAD":      {"OLLAMA_GPU_OVERHEAD", GpuOverhead(), "Reserve a portion of VRAM per GPU (bytes)"},
 		"OLLAMA_HOST":              {"OLLAMA_HOST", Host(), "IP Address for the ollama server (default 127.0.0.1:11434)"},
 		"OLLAMA_KEEP_ALIVE":        {"OLLAMA_KEEP_ALIVE", KeepAlive(), "The duration that models stay loaded in memory (default \"5m\")"},
@@ -247,7 +249,6 @@ func AsMap() map[string]EnvVar {
 		"OLLAMA_NUM_PARALLEL":      {"OLLAMA_NUM_PARALLEL", NumParallel(), "Maximum number of parallel requests"},
 		"OLLAMA_ORIGINS":           {"OLLAMA_ORIGINS", Origins(), "A comma separated list of allowed origins"},
 		"OLLAMA_SCHED_SPREAD":      {"OLLAMA_SCHED_SPREAD", SchedSpread(), "Always schedule model across all GPUs"},
-		"OLLAMA_TMPDIR":            {"OLLAMA_TMPDIR", TmpDir(), "Location for temporary files"},
 		"OLLAMA_MULTIUSER_CACHE":   {"OLLAMA_MULTIUSER_CACHE", MultiUserCache(), "Optimize prompt caching for multi-user scenarios"},
 
 		// Informational
@@ -287,12 +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/README.md

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

examples/flyio/.gitignore

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

examples/flyio/README.md

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

examples/go-http-generate/main.go

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

examples/jupyter-notebook/README.md

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

examples/jupyter-notebook/ollama.ipynb

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

examples/kubernetes/README.md

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

examples/kubernetes/cpu.yaml

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

examples/kubernetes/gpu.yaml

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

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

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

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

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

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

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

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

@@ -1,170 +0,0 @@
-# OSX
-.DS_STORE
-
-# Models
-models/
-
-# Local Chroma db
-.chroma/
-db/
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
-.pytest_cache/
-cover/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-db.sqlite3-journal
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-.pybuilder/
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-#   For a library or package, you might want to ignore these files since the code is
-#   intended to run in multiple environments; otherwise, check them in:
-# .python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don't work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# poetry
-#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
-#   This is especially recommended for binary packages to ensure reproducibility, and is more
-#   commonly ignored for libraries.
-#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
-
-# pdm
-#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
-#pdm.lock
-#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
-#   in version control.
-#   https://pdm.fming.dev/#use-with-ide
-.pdm.toml
-
-# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
-__pypackages__/
-
-# Celery stuff
-celerybeat-schedule
-celerybeat.pid
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-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 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
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-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright [yyyy] [name of copyright owner]
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
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-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.

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
-   ```