llm: consider null format same as empty value

2026-01-04 13:39:28 -05:00 · 2024-12-17 09:16:01 -08:00
727 changed files with 32906 additions and 30213 deletions
--- a/.gitattributes
+++ b/.gitattributes
@@ -7,14 +7,5 @@ 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
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -1,5 +1,11 @@
 name: test

+env:
+  ROCM_WINDOWS_URL: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe
+  MSYS2_URL: https://github.com/msys2/msys2-installer/releases/download/2024-07-27/msys2-x86_64-20240727.exe
+  CUDA_12_WINDOWS_URL: https://developer.download.nvidia.com/compute/cuda/12.4.0/local_installers/cuda_12.4.0_551.61_windows.exe
+  CUDA_12_WINDOWS_VER: 12.4
+
 concurrency:
  # For PRs, later CI runs preempt previous ones. e.g. a force push on a PR
  # cancels running CI jobs and starts all new ones.
@@ -21,7 +27,7 @@ jobs:
  changes:
    runs-on: ubuntu-latest
    outputs:
-      changed: ${{ steps.changes.outputs.changed }}
+      RUNNERS: ${{ steps.changes.outputs.RUNNERS }}
    steps:
      - uses: actions/checkout@v4
        with:
@@ -29,61 +35,309 @@ jobs:
      - id: changes
        run: |
          changed() {
-            local BASE=${{ 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" \
+            git diff-tree -r --no-commit-id --name-only \
+              $(git merge-base ${{ github.event.pull_request.base.sha }} ${{ github.event.pull_request.head.sha }}) \
+              ${{ github.event.pull_request.head.sha }} \
              | xargs python3 -c "import sys; from pathlib import Path; print(any(Path(x).match(glob) for x in sys.argv[1:] for glob in '$*'.split(' ')))"
          }

-          echo changed=$(changed 'llama/llama.cpp/**' 'ml/backend/ggml/ggml/**') | tee -a $GITHUB_OUTPUT
+          {
+            echo RUNNERS=$(changed 'llama/**')
+          } >>$GITHUB_OUTPUT

-  linux:
+  runners-linux-cuda:
    needs: [changes]
-    if: ${{ needs.changes.outputs.changed == 'True' }}
+    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
    strategy:
      matrix:
-        include:
-          - container: nvidia/cuda:11.8.0-devel-ubuntu22.04
-            preset: CUDA
-          - container: rocm/dev-ubuntu-22.04:6.1.2
-            preset: ROCm
-            extra-packages: rocm-libs
-    runs-on: ubuntu-latest
-    container: ${{ matrix.container }}
+        cuda-version:
+          - '11.8.0'
+    runs-on: linux
+    container: nvidia/cuda:${{ matrix.cuda-version }}-devel-ubuntu20.04
    steps:
-      - uses: actions/checkout@v4
      - run: |
-          apt-get update
-          apt-get install -y cmake pkg-config ${{ matrix.extra-packages }}
+          apt-get update && apt-get install -y git build-essential curl
        env:
          DEBIAN_FRONTEND: noninteractive
+      - uses: actions/checkout@v4
+      - uses: actions/setup-go@v4
+        with:
+          go-version-file: go.mod
+          cache: true
+      - run: go get ./...
      - run: |
-          cmake --preset ${{ matrix.preset }}
-          cmake --build --preset ${{ matrix.preset }} --parallel
-
-  test:
+          git config --global --add safe.directory /__w/ollama/ollama
+          cores=$(grep '^core id' /proc/cpuinfo |sort -u|wc -l)
+          make -j $cores cuda_v11
+  runners-linux-rocm:
+    needs: [changes]
+    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
    strategy:
      matrix:
-        os: [ubuntu-latest, macos-latest, windows-latest]
+        rocm-version:
+          - '6.1.2'
+    runs-on: linux
+    container: rocm/dev-ubuntu-20.04:${{ matrix.rocm-version }}
+    steps:
+      - run: |
+          apt-get update && apt-get install -y git build-essential curl rocm-libs
+        env:
+          DEBIAN_FRONTEND: noninteractive
+      - uses: actions/checkout@v4
+      - uses: actions/setup-go@v4
+        with:
+          go-version-file: go.mod
+          cache: true
+      - run: go get ./...
+      - run: |
+          git config --global --add safe.directory /__w/ollama/ollama
+          cores=$(grep '^core id' /proc/cpuinfo |sort -u|wc -l)
+          make -j $cores rocm
+
+  # ROCm generation step
+  runners-windows-rocm:
+    needs: [changes]
+    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
+    runs-on: windows
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-go@v5
+        with:
+          go-version-file: go.mod
+          cache: true
+      - name: Set make jobs default
+        run: |
+          echo "MAKEFLAGS=--jobs=$((Get-ComputerInfo -Property CsProcessors).CsProcessors.NumberOfCores)" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+
+      # ROCM installation steps
+      - name: 'Cache ROCm installer'
+        id: cache-rocm
+        uses: actions/cache@v4
+        with:
+          path: rocm-install.exe
+          key: ${{ env.ROCM_WINDOWS_URL }}
+      - name: 'Conditionally Download ROCm'
+        if: steps.cache-rocm.outputs.cache-hit != 'true'
+        run: |
+          $ErrorActionPreference = "Stop"
+          Invoke-WebRequest -Uri "${env:ROCM_WINDOWS_URL}" -OutFile "rocm-install.exe"
+      - name: 'Install ROCm'
+        run: |
+          Start-Process "rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
+      - name: 'Verify ROCm'
+        run: |
+          & 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version
+          echo "HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path | select -first 1)" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+
+      - name: Add msys paths
+        run: |
+          echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+      - name: Install msys2 tools
+        run: |
+          Start-Process "c:\msys64\usr\bin\pacman.exe" -ArgumentList @("-S", "--noconfirm", "mingw-w64-clang-x86_64-gcc-compat", "mingw-w64-clang-x86_64-clang") -NoNewWindow -Wait
+
+      - name: make rocm runner
+        run: |
+          import-module 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
+          Enter-VsDevShell -vsinstallpath 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise' -skipautomaticlocation -DevCmdArguments '-arch=x64 -no_logo'
+          if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
+          make -C llama print-HIP_PATH print-HIP_LIB_DIR
+          make rocm
+
+  # CUDA generation step
+  runners-windows-cuda:
+    needs: [changes]
+    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
+    runs-on: windows
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-go@v5
+        with:
+          go-version-file: go.mod
+          cache: true
+      - name: Set make jobs default
+        run: |
+          echo "MAKEFLAGS=--jobs=$((Get-ComputerInfo -Property CsProcessors).CsProcessors.NumberOfCores)" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+
+      # CUDA installation steps
+      - name: 'Cache CUDA installer'
+        id: cache-cuda
+        uses: actions/cache@v4
+        with:
+          path: cuda-install.exe
+          key: ${{ env.CUDA_12_WINDOWS_URL }}
+      - name: 'Conditionally Download CUDA'
+        if: steps.cache-cuda.outputs.cache-hit != 'true'
+        run: |
+          $ErrorActionPreference = "Stop"
+          Invoke-WebRequest -Uri "${env:CUDA_12_WINDOWS_URL}" -OutFile "cuda-install.exe"
+      - name: 'Install CUDA'
+        run: |
+          $subpackages = @("cudart", "nvcc", "cublas", "cublas_dev") | foreach-object {"${_}_${{ env.CUDA_12_WINDOWS_VER }}"}
+          Start-Process "cuda-install.exe" -ArgumentList (@("-s") + $subpackages) -NoNewWindow -Wait
+      - name: 'Verify CUDA'
+        run: |
+          & (resolve-path "c:\Program Files\NVIDIA*\CUDA\v*\bin\nvcc.exe")[0] --version
+          $cudaPath=((resolve-path "c:\Program Files\NVIDIA*\CUDA\v*\bin\nvcc.exe")[0].path | split-path | split-path)
+          $cudaVer=($cudaPath | split-path -leaf ) -replace 'v(\d+).(\d+)', '$1_$2' 
+          echo "$cudaPath\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+          echo "CUDA_PATH=$cudaPath" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+          echo "CUDA_PATH_V${cudaVer}=$cudaPath" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+          echo "CUDA_PATH_VX_Y=CUDA_PATH_V${cudaVer}" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
+
+      - name: Add msys paths
+        run: |
+          echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+      - name: Install msys2 tools
+        run: |
+          Start-Process "c:\msys64\usr\bin\pacman.exe" -ArgumentList @("-S", "--noconfirm", "mingw-w64-clang-x86_64-gcc-compat", "mingw-w64-clang-x86_64-clang") -NoNewWindow -Wait
+      - name: make cuda runner
+        run: |
+          import-module 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
+          Enter-VsDevShell -vsinstallpath 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise' -skipautomaticlocation -DevCmdArguments '-arch=x64 -no_logo'
+          if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
+          make cuda_v$(($env:CUDA_PATH | split-path -leaf) -replace 'v(\d+).*', '$1')
+
+  runners-cpu:
+    needs: [changes]
+    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest, macos-latest, windows-2019]
+        arch: [amd64, arm64]
+        exclude:
+          - os: ubuntu-latest
+            arch: arm64
+          - os: windows-2019
+            arch: arm64
    runs-on: ${{ matrix.os }}
    env:
+      GOARCH: ${{ matrix.arch }}
+      ARCH: ${{ matrix.arch }}
      CGO_ENABLED: '1'
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-go@v5
        with:
          go-version-file: go.mod
+          cache: true
+      - name: Add msys paths
+        if: ${{ startsWith(matrix.os, 'windows-') }}
+        run: |
+          echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+      - name: Install msys2 tools
+        if: ${{ startsWith(matrix.os, 'windows-') }}
+        run: |
+          Start-Process "c:\msys64\usr\bin\pacman.exe" -ArgumentList @("-S", "--noconfirm", "mingw-w64-clang-x86_64-gcc-compat", "mingw-w64-clang-x86_64-clang") -NoNewWindow -Wait
+      - name: 'Build Windows Go Runners'
+        if: ${{ startsWith(matrix.os, 'windows-') }}
+        run: |
+          $gopath=(get-command go).source | split-path -parent
+          $gccpath=(get-command gcc).source | split-path -parent
+          import-module 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
+          Enter-VsDevShell -vsinstallpath 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise' -skipautomaticlocation -DevCmdArguments '-arch=x64 -no_logo'
+          $env:CMAKE_SYSTEM_VERSION="10.0.22621.0"
+          $env:PATH="$gopath;$gccpath;$env:PATH"
+          echo $env:PATH
+          if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
+          make -j 4
+      - name: 'Build Unix Go Runners'
+        if: ${{ ! startsWith(matrix.os, 'windows-') }}
+        run: make -j 4
+      - run: go build .
+
+  lint:
+    strategy:
+      matrix:
+        os: [ubuntu-latest, macos-latest, windows-2019]
+        arch: [amd64, arm64]
+        exclude:
+          - os: ubuntu-latest
+            arch: arm64
+          - os: windows-2019
+            arch: arm64
+          - os: macos-latest
+            arch: amd64
+    runs-on: ${{ matrix.os }}
+    env:
+      GOARCH: ${{ matrix.arch }}
+      CGO_ENABLED: '1'
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          submodules: recursive
+      - name: Add msys paths
+        if: ${{ startsWith(matrix.os, 'windows-') }}
+        run: |
+          echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+      - name: Install msys2 tools
+        if: ${{ startsWith(matrix.os, 'windows-') }}
+        run: |
+          Start-Process "c:\msys64\usr\bin\pacman.exe" -ArgumentList @("-S", "--noconfirm", "mingw-w64-clang-x86_64-gcc-compat", "mingw-w64-clang-x86_64-clang") -NoNewWindow -Wait
+      - uses: actions/setup-go@v5
+        with:
+          go-version-file: go.mod
+          cache: false
+      - 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
+  test:
+    strategy:
+      matrix:
+        os: [ubuntu-latest, macos-latest, windows-2019]
+        arch: [amd64]
+        exclude:
+          - os: ubuntu-latest
+            arch: arm64
+          - os: windows-2019
+            arch: arm64
+    runs-on: ${{ matrix.os }}
+    env:
+      GOARCH: ${{ matrix.arch }}
+      CGO_ENABLED: '1'
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          submodules: recursive
+      - name: Add msys paths
+        if: ${{ startsWith(matrix.os, 'windows-') }}
+        run: |
+          echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+          echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+      - name: Install msys2 tools
+        if: ${{ startsWith(matrix.os, 'windows-') }}
+        run: |
+          Start-Process "c:\msys64\usr\bin\pacman.exe" -ArgumentList @("-S", "--noconfirm", "mingw-w64-clang-x86_64-gcc-compat", "mingw-w64-clang-x86_64-clang") -NoNewWindow -Wait
+      - uses: actions/setup-go@v5
+        with:
+          go-version-file: go.mod
+          cache: true
+      - run: |
+          case ${{ matrix.arch }} in
+            amd64) echo ARCH=amd64 ;;
+            arm64) echo ARCH=arm64 ;;
+          esac >>$GITHUB_ENV
+        shell: bash
      - run: go test ./...

  patches:
+    needs: [changes]
+    if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
-      - name: Verify patches apply cleanly and do not change files
+        with:
+          submodules: recursive
+      - name: Verify patches carry all the changes
        run: |
-          make -f Makefile2 clean checkout sync
-          git diff --compact-summary --exit-code
+          make apply-patches sync && git diff --compact-summary --exit-code llama
--- a/.prettierrc.json
+++ b/.prettierrc.json
@@ -0,0 +1,10 @@
+{
+  "trailingComma": "es5",
+  "tabWidth": 2,
+  "useTabs": false,
+  "semi": false,
+  "singleQuote": true,
+  "jsxSingleQuote": true,
+  "printWidth": 120,
+  "arrowParens": "avoid"
+}
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,45 +0,0 @@
-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_CPU_ALL_VARIANTS ON)
-set(GGML_CUDA_PEER_MAX_BATCH_SIZE 128)
-set(GGML_LLAMAFILE ON)
-
-set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib)
-
-include_directories(${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)
-
-check_language(CUDA)
-if(CMAKE_CUDA_COMPILER)
-    add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-cuda)
-endif()
-
-check_language(HIP)
-if(CMAKE_HIP_COMPILER)
-    add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-hip)
-endif()
--- a/CMakePresets.json
+++ b/CMakePresets.json
@@ -1,96 +0,0 @@
-{
-  "version": 3,
-  "configurePresets": [
-    {
-      "name": "Default",
-      "binaryDir": "${sourceDir}/build",
-      "cacheVariables": {
-        "CMAKE_BUILD_TYPE": "Release"
-      }
-    },
-    {
-      "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" ]
-    },
-    {
-      "name": "ROCm 6",
-      "inherits": [ "ROCm" ],
-      "cacheVariables": {
-        "CMAKE_HIP_ARCHITECTURES": "gfx900;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102"
-      }
-    }
-  ],
-  "buildPresets": [
-    {
-      "name": "Default",
-      "configurePreset": "Default"
-    },
-    {
-      "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"
-    }
-  ]
-}
--- a/1
+++ b/1
@@ -67,7 +67,6 @@ ARG OLLAMA_SKIP_CUDA_GENERATE
 ARG OLLAMA_SKIP_ROCM_GENERATE
 ARG OLLAMA_FAST_BUILD
 ARG VERSION
-ARG CUSTOM_CPU_FLAGS
 RUN --mount=type=cache,target=/root/.ccache \
    if grep "^flags" /proc/cpuinfo|grep avx>/dev/null; then \
        make -j $(nproc) dist ; \
--- a/66
+++ b/66
@@ -1,66 +0,0 @@
-ARG CUDA_11_VERSION=11.3
-ARG CUDA_12_VERSION=12.4
-ARG ROCM_VERSION=6.1.2
-ARG JETPACK_5_VERSION=r35.4.1
-ARG JETPACK_6_VERSION=r36.2.0
-ARG CMAKE_VERSION=3.31.2
-
-FROM --platform=linux/amd64 rocm/dev-centos-7:${ROCM_VERSION}-complete AS base
-ARG CMAKE_VERSION
-RUN curl -fsSL https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}-linux-x86_64.tar.gz | tar xz -C /usr --strip-components 1
-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-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-rhel7.repo
-
-# FROM --platform=linux/arm64 rockylinux:8 AS base
-# ARG CMAKE_VERSION
-# RUN curl -fsSL https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}-linux-aarch64.tar.gz | tar xz -C /usr --strip-components 1
-# RUN yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/sbsa/cuda-rhel8.repo
-
-FROM base AS amd64
-ARG CUDA_11_VERSION
-ARG CUDA_12_VERSION
-RUN yum install -y cuda-toolkit-${CUDA_11_VERSION//./-} \
-    && yum install -y cuda-toolkit-${CUDA_12_VERSION//./-}
-COPY CMakeLists.txt CMakeLists.txt
-COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
-
-FROM --platform=linux/amd64 amd64 AS cuda_11
-ENV PATH=/usr/local/cuda-${CUDA_11_VERSION}/bin:$PATH
-RUN cmake -S . -B build -DCMAKE_CUDA_ARCHITECTURES="50;52;53;60;61;62;70;72;75;80;86"
-RUN cmake --build build --target ggml-cuda -j
-
-FROM --platform=linux/amd64 amd64 AS cuda_12
-ENV PATH=/usr/local/cuda-${CUDA_12_VERSION}/bin:$PATH
-RUN cmake -S . -B build -DCMAKE_CUDA_ARCHITECTURES="60;61;62;70;72;75;80;86;87;89;90;90a"
-RUN cmake --build build --target ggml-cuda -j
-
-FROM --platform=linux/amd64 amd64 AS rocm
-RUN cmake -S . -B build -DCMAKE_HIP_ARCHITECTURES="gfx900;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102"
-RUN cmake --build build --target ggml-hip -j
-
-FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${JETPACK_5_VERSION} AS jetpack_5
-ARG CMAKE_VERSION
-RUN curl -fsSL https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}-linux-aarch64.tar.gz | tar xz -C /usr --strip-components 1
-COPY CMakeLists.txt .
-COPY ml/backend/ggml/ggml .
-RUN cmake -S . -B build \
-    -DCMAKE_CUDA_ARCHITECTURES="72;87"
-RUN cmake --build build --target ggml-cuda
-
-FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${JETPACK_6_VERSION} AS jetpack_6
-ARG CMAKE_VERSION
-RUN curl -fsSL https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}-linux-aarch64.tar.gz | tar xz -C /usr --strip-components 1
-COPY CMakeLists.txt .
-COPY ml/backend/ggml/ggml .
-RUN cmake -S . -B build \
-    -DCMAKE_CUDA_ARCHITECTURES="87"
-RUN cmake --build build --target ggml-cuda
-
-FROM --platform=linux/amd64 golang:1.23
-COPY --from=cuda_11 build/ml/backend/ggml/ggml/src/ggml-cuda/libggml-cuda.so libggml-cuda-11.so
-COPY --from=cuda_12 build/ml/backend/ggml/ggml/src/ggml-cuda/libggml-cuda.so libggml-cuda-12.so
-COPY --from=rocm build/ml/backend/ggml/ggml/src/ggml-hip/libggml-hip.so libggml-hip.so
-
-# FROM --platform=linux/arm64 golang:1.23
-# COPY --from=jetpack_5 build/ml/backend/ggml/ggml/src/ggml-cuda/libggml-cuda.so libggml-cuda-jetpack-5.so
-# COPY --from=jetpack_6 build/ml/backend/ggml/ggml/src/ggml-cuda/libggml-cuda.so libggml-cuda-jetpack-6.so
--- a/103
+++ b/103
@@ -0,0 +1,103 @@
+# top level makefile for Ollama
+include make/common-defs.make
+
+
+# Determine which if any GPU runners we should build
+include make/cuda-v11-defs.make
+include make/cuda-v12-defs.make
+include make/rocm-defs.make
+
+ifeq ($(CUSTOM_CPU_FLAGS),)
+ifeq ($(ARCH),amd64)
+	RUNNER_TARGETS=cpu
+endif
+# Without CUSTOM_CPU_FLAGS we default to build both v11 and v12 if present
+ifeq ($(OLLAMA_SKIP_CUDA_GENERATE),)
+ifneq ($(CUDA_11_COMPILER),)
+	RUNNER_TARGETS += cuda_v11
+endif
+ifneq ($(CUDA_12_COMPILER),)
+	RUNNER_TARGETS += cuda_v12
+endif
+endif
+else # CUSTOM_CPU_FLAGS is set, we'll build only the latest cuda version detected
+ifneq ($(CUDA_12_COMPILER),)
+	RUNNER_TARGETS += cuda_v12
+else ifneq ($(CUDA_11_COMPILER),)
+	RUNNER_TARGETS += cuda_v11
+endif
+endif
+
+ifeq ($(OLLAMA_SKIP_ROCM_GENERATE),)
+ifneq ($(HIP_COMPILER),)
+	RUNNER_TARGETS += rocm
+endif
+endif
+
+
+all: runners exe
+
+dist: $(addprefix dist_, $(RUNNER_TARGETS)) dist_exe
+
+dist_%:
+	@$(MAKE) --no-print-directory -f make/Makefile.$* dist
+
+runners: $(RUNNER_TARGETS)
+
+$(RUNNER_TARGETS):
+	@$(MAKE) --no-print-directory -f make/Makefile.$@
+
+exe dist_exe:
+	@$(MAKE) --no-print-directory -f make/Makefile.ollama $@
+
+help-sync apply-patches create-patches sync sync-clean:
+	@$(MAKE) --no-print-directory -f make/Makefile.sync $@
+
+test integration lint:
+	@$(MAKE) --no-print-directory -f make/Makefile.test $@
+
+clean:
+	rm -rf $(BUILD_DIR) $(DIST_LIB_DIR) $(OLLAMA_EXE) $(DIST_OLLAMA_EXE)
+	go clean -cache
+
+help:
+	@echo "The following make targets will help you build Ollama"
+	@echo ""
+	@echo "	make all   		# (default target) Build Ollama llm subprocess runners, and the primary ollama executable"
+	@echo "	make runners		# Build Ollama llm subprocess runners; after you may use 'go build .' to build the primary ollama exectuable"
+	@echo "	make <runner>		# Build specific runners. Enabled: '$(RUNNER_TARGETS)'"
+	@echo "	make dist		# Build the runners and primary ollama executable for distribution"
+	@echo "	make help-sync 		# Help information on vendor update targets"
+	@echo "	make help-runners 	# Help information on runner targets"
+	@echo ""
+	@echo "The following make targets will help you test Ollama"
+	@echo ""
+	@echo "	make test   		# Run unit tests"
+	@echo "	make integration	# Run integration tests.  You must 'make all' first"
+	@echo "	make lint   		# Run lint and style tests"
+	@echo ""
+	@echo "For more information see 'docs/development.md'"
+	@echo ""
+
+
+help-runners:
+	@echo "The following runners will be built based on discovered GPU libraries: '$(RUNNER_TARGETS)'"
+	@echo ""
+	@echo "GPU Runner CPU Flags: '$(GPU_RUNNER_CPU_FLAGS)'  (Override with CUSTOM_CPU_FLAGS)"
+	@echo ""
+	@echo "# CUDA_PATH sets the location where CUDA toolkits are present"
+	@echo "CUDA_PATH=$(CUDA_PATH)"
+	@echo "	CUDA_11_PATH=$(CUDA_11_PATH)"
+	@echo "	CUDA_11_COMPILER=$(CUDA_11_COMPILER)"
+	@echo "	CUDA_12_PATH=$(CUDA_12_PATH)"
+	@echo "	CUDA_12_COMPILER=$(CUDA_12_COMPILER)"
+	@echo ""
+	@echo "# HIP_PATH sets the location where the ROCm toolkit is present"
+	@echo "HIP_PATH=$(HIP_PATH)"
+	@echo "	HIP_COMPILER=$(HIP_COMPILER)"
+
+.PHONY: all exe dist help help-sync help-runners test integration lint runners clean $(RUNNER_TARGETS)
+
+# Handy debugging for make variables
+print-%:
+	@echo '$*=$($*)'
--- a/46
+++ b/46
@@ -1,46 +0,0 @@
-UPSTREAM=https://github.com/ggerganov/llama.cpp.git
-WORKDIR=llama/vendor
-FETCH_HEAD=46e3556e01b824e52395fb050b29804b6cff2a7c
-
-all: sync
-
-.PHONY: sync
-sync: llama/llama.cpp ml/backend/ggml/ggml
-
-.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))
--- a/README.md
+++ b/README.md
@@ -1,11 +1,11 @@
 <div align="center">
-  <a href="https://ollama.com" />
-    <img alt="ollama" height="200px" src="https://github.com/ollama/ollama/assets/3325447/0d0b44e2-8f4a-4e99-9b52-a5c1c741c8f7">
-  </a>
+ <img alt="ollama" height="200px" src="https://github.com/ollama/ollama/assets/3325447/0d0b44e2-8f4a-4e99-9b52-a5c1c741c8f7">
 </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
@@ -33,11 +33,6 @@ 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):
@@ -61,8 +56,8 @@ Here are some example models that can be downloaded:
 | Llama 3.2 Vision   | 90B        | 55GB  | `ollama run llama3.2-vision:90b` |
 | Llama 3.1          | 8B         | 4.7GB | `ollama run llama3.1`            |
 | Llama 3.1          | 405B       | 231GB | `ollama run llama3.1:405b`       |
-| Phi 4              | 14B        | 9.1GB | `ollama run phi4`                |
 | Phi 3 Mini         | 3.8B       | 2.3GB | `ollama run phi3`                |
+| Phi 3 Medium       | 14B        | 7.9GB | `ollama run phi3:medium`         |
 | Gemma 2            | 2B         | 1.6GB | `ollama run gemma2:2b`           |
 | Gemma 2            | 9B         | 5.5GB | `ollama run gemma2`              |
 | Gemma 2            | 27B        | 16GB  | `ollama run gemma2:27b`          |
@@ -102,7 +97,7 @@ Ollama supports importing GGUF models in the Modelfile:
   ollama run example
   ```

-### Import from Safetensors
+### Import from PyTorch or Safetensors

 See the [guide](docs/import.md) on importing models for more information.

@@ -137,7 +132,7 @@ ollama run mario
 Hello! It's your friend Mario.
 ```

-For more information on working with a Modelfile, see the [Modelfile](docs/modelfile.md) documentation.
+For more examples, see the [examples](examples) directory. For more information on working with a Modelfile, see the [Modelfile](docs/modelfile.md) documentation.

 ## CLI Reference

@@ -303,7 +298,6 @@ 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)
@@ -333,7 +327,6 @@ 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)
@@ -368,7 +361,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Abbey](https://github.com/US-Artificial-Intelligence/abbey) (A configurable AI interface server with notebooks, document storage, and YouTube support)
 - [Minima](https://github.com/dmayboroda/minima) (RAG with on-premises or fully local workflow)
 - [aidful-ollama-model-delete](https://github.com/AidfulAI/aidful-ollama-model-delete) (User interface for simplified model cleanup)
- [Perplexica](https://github.com/ItzCrazyKns/Perplexica) (An AI-powered search engine & an open-source alternative to Perplexity AI)

 ### Cloud

@@ -381,7 +373,6 @@ 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)
@@ -436,12 +427,10 @@ 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)
@@ -530,7 +519,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [AI Summmary Helper plugin](https://github.com/philffm/ai-summary-helper)
 - [TextCraft](https://github.com/suncloudsmoon/TextCraft) (Copilot in Word alternative using Ollama)
 - [Alfred Ollama](https://github.com/zeitlings/alfred-ollama) (Alfred Workflow)
- [TextLLaMA](https://github.com/adarshM84/TextLLaMA) A Chrome Extension that helps you write emails, correct grammar, and translate into any language

 ### Supported backends

--- a/api/examples/README.md
+++ b/api/examples/README.md
@@ -1,17 +0,0 @@
-# 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)
-
--- a/api/types.go
+++ b/api/types.go
@@ -225,6 +225,7 @@ 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"`
 }

@@ -294,21 +295,17 @@ type EmbeddingResponse struct {

 // CreateRequest is the request passed to [Client.Create].
 type CreateRequest struct {
-	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"`
+	Model     string `json:"model"`
+	Modelfile string `json:"modelfile"`
+	Stream    *bool  `json:"stream,omitempty"`
+	Quantize  string `json:"quantize,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"`
 }
@@ -605,6 +602,7 @@ func DefaultOptions() Options {
 		Mirostat:         0,
 		MirostatTau:      5.0,
 		MirostatEta:      0.1,
+		PenalizeNewline:  true,
 		Seed:             -1,

 		Runner: Runner{
--- a/cache/cache.go
+++ b/cache/cache.go
@@ -1,63 +0,0 @@
-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
-}
--- a/cmd/cmd.go
+++ b/cmd/cmd.go
@@ -1,10 +1,13 @@
 package cmd

 import (
+	"archive/zip"
 	"bufio"
+	"bytes"
 	"context"
 	"crypto/ed25519"
 	"crypto/rand"
+	"crypto/sha256"
 	"encoding/json"
 	"encoding/pem"
 	"errors"
@@ -43,11 +46,15 @@ import (
 	"github.com/ollama/ollama/version"
 )

-var errModelfileNotFound = errors.New("specified Modelfile wasn't found")
+var (
+	errModelNotFound     = errors.New("no Modelfile or safetensors files found")
+	errModelfileNotFound = errors.New("specified Modelfile wasn't found")
+)

 func getModelfileName(cmd *cobra.Command) (string, error) {
-	filename, _ := cmd.Flags().GetString("file")
+	fn, _ := cmd.Flags().GetString("file")

+	filename := fn
 	if filename == "" {
 		filename = "Modelfile"
 	}
@@ -59,7 +66,7 @@ func getModelfileName(cmd *cobra.Command) (string, error) {

 	_, err = os.Stat(absName)
 	if err != nil {
-		return filename, err
+		return fn, err
 	}

 	return absName, nil
@@ -95,52 +102,68 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}

-	status := "gathering model components"
-	spinner := progress.NewSpinner(status)
-	p.Add(status, spinner)
-
-	req, err := modelfile.CreateRequest(filepath.Dir(filename))
+	home, err := os.UserHomeDir()
 	if err != nil {
 		return err
 	}
-	spinner.Stop()

-	req.Name = args[0]
-	quantize, _ := cmd.Flags().GetString("quantize")
-	if quantize != "" {
-		req.Quantize = quantize
-	}
+	status := "transferring model data"
+	spinner := progress.NewSpinner(status)
+	p.Add(status, spinner)
+	defer p.Stop()

 	client, err := api.ClientFromEnvironment()
 	if err != nil {
 		return err
 	}

-	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
+	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:])
 			}
-			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 {
+			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 {
 				return err
 			}
-			fileMap[filepath.Base(f)] = digest
+
+			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
 		}
-		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)
@@ -160,23 +183,145 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 		return 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")
-		}
+	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 {
 		return err
 	}

 	return nil
 }

-func createBlob(cmd *cobra.Command, client *api.Client, path string, digest string, p *progress.Progress) (string, error) {
-	realPath, err := filepath.EvalSymlinks(path)
+func tempZipFiles(path string) (string, error) {
+	tempfile, err := os.CreateTemp("", "ollama-tf")
 	if err != nil {
 		return "", err
 	}
+	defer tempfile.Close()

-	bin, err := os.Open(realPath)
+	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)
 	if err != nil {
 		return "", err
 	}
@@ -189,11 +334,18 @@ func createBlob(cmd *cobra.Command, client *api.Client, path string, digest stri
 	}
 	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 := fmt.Sprintf("copying file %s 0%%", digest)
-	spinner := progress.NewSpinner(status)
-	p.Add(status, spinner)
-	defer spinner.Stop()
+	status := "transferring model data 0%"
+	spinner.SetMessage(status)

 	done := make(chan struct{})
 	defer close(done)
@@ -204,14 +356,15 @@ func createBlob(cmd *cobra.Command, client *api.Client, path string, digest stri
 		for {
 			select {
 			case <-ticker.C:
-				spinner.SetMessage(fmt.Sprintf("copying file %s %d%%", digest, int(100*pw.n.Load()/fileSize)))
+				spinner.SetMessage(fmt.Sprintf("transferring model data %d%%", int(100*pw.n.Load()/fileSize)))
 			case <-done:
-				spinner.SetMessage(fmt.Sprintf("copying file %s 100%%", digest))
+				spinner.SetMessage("transferring model data 100%")
 				return
 			}
 		}
 	}()

+	digest := fmt.Sprintf("sha256:%x", hash.Sum(nil))
 	if err = client.CreateBlob(cmd.Context(), digest, io.TeeReader(bin, &pw)); err != nil {
 		return "", err
 	}
--- a/cmd/cmd_test.go
+++ b/cmd/cmd_test.go
@@ -279,7 +279,7 @@ func TestGetModelfileName(t *testing.T) {
 			name:          "no modelfile specified, no modelfile exists",
 			modelfileName: "",
 			fileExists:    false,
-			expectedName:  "Modelfile",
+			expectedName:  "",
 			expectedErr:   os.ErrNotExist,
 		},
 		{
@@ -338,8 +338,8 @@ func TestGetModelfileName(t *testing.T) {
 					t.Fatalf("couldn't set file flag: %v", err)
 				}
 			} else {
-				expectedFilename = tt.expectedName
 				if tt.modelfileName != "" {
+					expectedFilename = tt.modelfileName
 					err := cmd.Flags().Set("file", tt.modelfileName)
 					if err != nil {
 						t.Fatalf("couldn't set file flag: %v", err)
@@ -489,130 +489,3 @@ 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)
-					}
-				}
-			}
-		})
-	}
-}
--- a/cmd/interactive.go
+++ b/cmd/interactive.go
@@ -13,9 +13,11 @@ 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"
 )
@@ -211,7 +213,10 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 				return err
 			}

-			req := NewCreateRequest(args[1], opts)
+			req := &api.CreateRequest{
+				Name:      args[1],
+				Modelfile: buildModelfile(opts),
+			}
 			fn := func(resp api.ProgressResponse) error { return nil }
 			err = client.Create(cmd.Context(), req, fn)
 			if err != nil {
@@ -454,25 +459,36 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 	}
 }

-func NewCreateRequest(name string, opts runOptions) *api.CreateRequest {
-	req := &api.CreateRequest{
-		Name: name,
-		From: cmp.Or(opts.ParentModel, opts.Model),
-	}
+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)})

 	if opts.System != "" {
-		req.System = opts.System
+		f.Commands = append(f.Commands, parser.Command{Name: "system", Args: opts.System})
 	}

-	if len(opts.Options) > 0 {
-		req.Parameters = opts.Options
+	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.Messages) > 0 {
-		req.Messages = opts.Messages
+	for _, msg := range opts.Messages {
+		f.Commands = append(f.Commands, parser.Command{Name: "message", Args: fmt.Sprintf("%s: %s", msg.Role, msg.Content)})
 	}

-	return req
+	return f.String()
 }

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

-	"github.com/ollama/ollama/fs/ggml"
+	"github.com/ollama/ollama/llm"
 )

 type ModelParameters struct {
@@ -27,8 +27,8 @@ type AdapterParameters struct {
 	} `json:"lora_parameters"`
 }

-func (ModelParameters) KV(t *Tokenizer) ggml.KV {
-	kv := ggml.KV{
+func (ModelParameters) KV(t *Tokenizer) llm.KV {
+	kv := llm.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) ggml.KV {
 	return kv
 }

-func (p AdapterParameters) KV() ggml.KV {
+func (p AdapterParameters) KV() llm.KV {
 	var alpha float32
 	if p.LoraParameters.Alpha == 0 {
 		alpha = float32(p.Alpha)
@@ -62,7 +62,7 @@ func (p AdapterParameters) KV() ggml.KV {
 		alpha = p.LoraParameters.Alpha
 	}

-	kv := ggml.KV{
+	kv := llm.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 ggml.KV, ts []ggml.Tensor) error {
-	return ggml.WriteGGUF(ws, kv, ts)
+func (ModelParameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []llm.Tensor) error {
+	return llm.WriteGGUF(ws, kv, ts)
 }

-func (AdapterParameters) writeFile(ws io.WriteSeeker, kv 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)
 }

 type ModelConverter interface {
 	// KV maps parameters to LLM key-values
-	KV(*Tokenizer) ggml.KV
+	KV(*Tokenizer) llm.KV
 	// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
-	Tensors([]Tensor) []ggml.Tensor
+	Tensors([]Tensor) []llm.Tensor
 	// Replacements returns a list of string pairs to replace in tensor names.
 	// See [strings.Replacer](https://pkg.go.dev/strings#Replacer) for details
 	Replacements() []string
@@ -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, ggml.KV, []ggml.Tensor) error
+	writeFile(io.WriteSeeker, llm.KV, []llm.Tensor) error
 }

 type moreParser interface {
@@ -108,17 +108,17 @@ type moreParser interface {

 type AdapterConverter interface {
 	// KV maps parameters to LLM key-values
-	KV(ggml.KV) ggml.KV
+	KV(llm.KV) llm.KV
 	// Tensors maps input tensors to LLM tensors. Adapter specific modifications can be done here.
-	Tensors([]Tensor) []ggml.Tensor
+	Tensors([]Tensor) []llm.Tensor
 	// Replacements returns a list of string pairs to replace in tensor names.
 	// See [strings.Replacer](https://pkg.go.dev/strings#Replacer) for details
 	Replacements() []string

-	writeFile(io.WriteSeeker, ggml.KV, []ggml.Tensor) error
+	writeFile(io.WriteSeeker, llm.KV, []llm.Tensor) error
 }

-func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV ggml.KV) error {
+func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV llm.KV) error {
 	bts, err := fs.ReadFile(fsys, "adapter_config.json")
 	if err != nil {
 		return err
@@ -187,8 +187,6 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
 		conv = &gemma2Model{}
 	case "Phi3ForCausalLM":
 		conv = &phi3Model{}
-	case "Qwen2ForCausalLM":
-		conv = &qwen2Model{}
 	case "BertModel":
 		conv = &bertModel{}
 	default:
--- a/convert/convert_bert.go
+++ b/convert/convert_bert.go
@@ -8,7 +8,7 @@ import (
 	"slices"
 	"strings"

-	"github.com/ollama/ollama/fs/ggml"
+	"github.com/ollama/ollama/llm"
 )

 type bertModel struct {
@@ -85,7 +85,7 @@ func (p *bertModel) parseMore(fsys fs.FS) error {
 	return nil
 }

-func (p *bertModel) KV(t *Tokenizer) ggml.KV {
+func (p *bertModel) KV(t *Tokenizer) llm.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) ggml.KV {
 	return kv
 }

-func (p *bertModel) Tensors(ts []Tensor) []ggml.Tensor {
-	var out []ggml.Tensor
+func (p *bertModel) Tensors(ts []Tensor) []llm.Tensor {
+	var out []llm.Tensor
 	for _, t := range ts {
 		if slices.Contains([]string{
 			"embeddings.position_ids",
@@ -143,7 +143,7 @@ func (p *bertModel) Tensors(ts []Tensor) []ggml.Tensor {
 			continue
 		}

-		out = append(out, ggml.Tensor{
+		out = append(out, llm.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),
--- a/convert/convert_gemma.go
+++ b/convert/convert_gemma.go
@@ -6,7 +6,7 @@ import (
 	"github.com/pdevine/tensor"
 	"github.com/pdevine/tensor/native"

-	"github.com/ollama/ollama/fs/ggml"
+	"github.com/ollama/ollama/llm"
 )

 type gemmaModel struct {
@@ -23,7 +23,7 @@ type gemmaModel struct {

 var _ ModelConverter = (*gemmaModel)(nil)

-func (p *gemmaModel) KV(t *Tokenizer) ggml.KV {
+func (p *gemmaModel) KV(t *Tokenizer) llm.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) ggml.KV {
 	return kv
 }

-func (p *gemmaModel) Tensors(ts []Tensor) []ggml.Tensor {
-	var out []ggml.Tensor
+func (p *gemmaModel) Tensors(ts []Tensor) []llm.Tensor {
+	var out []llm.Tensor
 	for _, t := range ts {
 		if strings.HasSuffix(t.Name(), "_norm.weight") {
 			t.SetRepacker(p.addOne)
 		}

-		out = append(out, ggml.Tensor{
+		out = append(out, llm.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),
--- a/convert/convert_gemma2.go
+++ b/convert/convert_gemma2.go
@@ -1,6 +1,8 @@
 package convert

-import "github.com/ollama/ollama/fs/ggml"
+import (
+	"github.com/ollama/ollama/llm"
+)

 type gemma2Model struct {
 	gemmaModel
@@ -9,7 +11,7 @@ type gemma2Model struct {
 	FinalLogitSoftcap     float32 `json:"final_logit_softcapping"`
 }

-func (p *gemma2Model) KV(t *Tokenizer) ggml.KV {
+func (p *gemma2Model) KV(t *Tokenizer) llm.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "gemma2"
 	kv["gemma2.context_length"] = p.MaxPositionEmbeddings
--- a/convert/convert_gemma2_adapter.go
+++ b/convert/convert_gemma2_adapter.go
@@ -6,7 +6,7 @@ import (
 	"github.com/pdevine/tensor"
 	"github.com/pdevine/tensor/native"

-	"github.com/ollama/ollama/fs/ggml"
+	"github.com/ollama/ollama/llm"
 )

 type gemma2Adapter struct {
@@ -15,14 +15,14 @@ type gemma2Adapter struct {

 var _ AdapterConverter = (*gemma2Adapter)(nil)

-func (p *gemma2Adapter) KV(baseKV ggml.KV) ggml.KV {
+func (p *gemma2Adapter) KV(baseKV llm.KV) llm.KV {
 	kv := p.AdapterParameters.KV()
 	kv["general.architecture"] = "gemma2"
 	return kv
 }

-func (p *gemma2Adapter) Tensors(ts []Tensor) []ggml.Tensor {
-	var out []ggml.Tensor
+func (p *gemma2Adapter) Tensors(ts []Tensor) []llm.Tensor {
+	var out []llm.Tensor
 	for _, t := range ts {
 		shape := t.Shape()
 		if (strings.HasSuffix(t.Name(), "weight.lora_a") && shape[0] > shape[1]) ||
@@ -31,7 +31,7 @@ func (p *gemma2Adapter) Tensors(ts []Tensor) []ggml.Tensor {
 			t.SetRepacker(p.repack)
 		}

-		out = append(out, ggml.Tensor{
+		out = append(out, llm.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),
--- a/convert/convert_llama.go
+++ b/convert/convert_llama.go
@@ -9,7 +9,7 @@ import (
 	"github.com/pdevine/tensor"
 	"github.com/pdevine/tensor/native"

-	"github.com/ollama/ollama/fs/ggml"
+	"github.com/ollama/ollama/llm"
 )

 type llamaModel struct {
@@ -46,7 +46,7 @@ type llamaModel struct {

 var _ ModelConverter = (*llamaModel)(nil)

-func (p *llamaModel) KV(t *Tokenizer) ggml.KV {
+func (p *llamaModel) KV(t *Tokenizer) llm.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) ggml.KV {
 	return kv
 }

-func (p *llamaModel) Tensors(ts []Tensor) []ggml.Tensor {
-	var out []ggml.Tensor
+func (p *llamaModel) Tensors(ts []Tensor) []llm.Tensor {
+	var out []llm.Tensor

 	if p.RopeScaling.factors != nil {
-		out = append(out, ggml.Tensor{
+		out = append(out, llm.Tensor{
 			Name:     "rope_freqs.weight",
 			Kind:     0,
 			Shape:    []uint64{uint64(len(p.RopeScaling.factors))},
@@ -138,7 +138,7 @@ func (p *llamaModel) Tensors(ts []Tensor) []ggml.Tensor {
 			t.SetRepacker(p.repack)
 		}

-		out = append(out, ggml.Tensor{
+		out = append(out, llm.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),
--- a/convert/convert_llama_adapter.go
+++ b/convert/convert_llama_adapter.go
@@ -7,7 +7,7 @@ import (
 	"github.com/pdevine/tensor"
 	"github.com/pdevine/tensor/native"

-	"github.com/ollama/ollama/fs/ggml"
+	"github.com/ollama/ollama/llm"
 )

 type llamaAdapter struct {
@@ -18,7 +18,7 @@ type llamaAdapter struct {

 var _ AdapterConverter = (*llamaAdapter)(nil)

-func (p *llamaAdapter) KV(baseKV ggml.KV) ggml.KV {
+func (p *llamaAdapter) KV(baseKV llm.KV) llm.KV {
 	kv := p.AdapterParameters.KV()
 	kv["general.architecture"] = "llama"
 	kv["llama.attention.head_count"] = baseKV["llama.attention.head_count"]
@@ -29,8 +29,8 @@ func (p *llamaAdapter) KV(baseKV ggml.KV) ggml.KV {
 	return kv
 }

-func (p *llamaAdapter) Tensors(ts []Tensor) []ggml.Tensor {
-	var out []ggml.Tensor
+func (p *llamaAdapter) Tensors(ts []Tensor) []llm.Tensor {
+	var out []llm.Tensor
 	for _, t := range ts {
 		shape := t.Shape()
 		if (strings.HasSuffix(t.Name(), "weight.lora_a") && shape[0] > shape[1]) ||
@@ -41,7 +41,7 @@ func (p *llamaAdapter) Tensors(ts []Tensor) []ggml.Tensor {
 			t.SetRepacker(p.repack)
 		}

-		out = append(out, ggml.Tensor{
+		out = append(out, llm.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    shape,
--- a/convert/convert_mixtral.go
+++ b/convert/convert_mixtral.go
@@ -6,7 +6,7 @@ import (
 	"slices"
 	"strings"

-	"github.com/ollama/ollama/fs/ggml"
+	"github.com/ollama/ollama/llm"
 )

 type mixtralModel struct {
@@ -15,7 +15,7 @@ type mixtralModel struct {
 	NumExpertsPerToken uint32 `json:"num_experts_per_tok"`
 }

-func (p *mixtralModel) KV(t *Tokenizer) ggml.KV {
+func (p *mixtralModel) KV(t *Tokenizer) llm.KV {
 	kv := p.llamaModel.KV(t)

 	if p.NumLocalExperts > 0 {
@@ -29,7 +29,7 @@ func (p *mixtralModel) KV(t *Tokenizer) ggml.KV {
 	return kv
 }

-func (p *mixtralModel) Tensors(ts []Tensor) []ggml.Tensor {
+func (p *mixtralModel) Tensors(ts []Tensor) []llm.Tensor {
 	oldnew := []string{
 		"model.layers", "blk",
 		"w1", "ffn_gate_exps",
@@ -56,10 +56,10 @@ func (p *mixtralModel) Tensors(ts []Tensor) []ggml.Tensor {
 		return true
 	})

-	var out []ggml.Tensor
+	var out []llm.Tensor
 	for n, e := range experts {
 		// TODO(mxyng): sanity check experts
-		out = append(out, ggml.Tensor{
+		out = append(out, llm.Tensor{
 			Name:     n,
 			Kind:     e[0].Kind(),
 			Shape:    append([]uint64{uint64(len(e))}, e[0].Shape()...),
--- a/convert/convert_phi3.go
+++ b/convert/convert_phi3.go
@@ -8,7 +8,7 @@ import (
 	"strings"
 	"sync"

-	"github.com/ollama/ollama/fs/ggml"
+	"github.com/ollama/ollama/llm"
 )

 type phi3Model struct {
@@ -37,7 +37,7 @@ type phi3Model struct {

 var _ ModelConverter = (*phi3Model)(nil)

-func (p *phi3Model) KV(t *Tokenizer) ggml.KV {
+func (p *phi3Model) KV(t *Tokenizer) llm.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) ggml.KV {
 	return kv
 }

-func (p *phi3Model) Tensors(ts []Tensor) []ggml.Tensor {
+func (p *phi3Model) Tensors(ts []Tensor) []llm.Tensor {
 	var addRopeFactors sync.Once

-	out := make([]ggml.Tensor, 0, len(ts)+2)
+	out := make([]llm.Tensor, 0, len(ts)+2)
 	for _, t := range ts {
 		if strings.HasPrefix(t.Name(), "blk.0.") {
 			addRopeFactors.Do(func() {
-				out = append(out, ggml.Tensor{
+				out = append(out, llm.Tensor{
 					Name:     "rope_factors_long.weight",
 					Kind:     0,
 					Shape:    []uint64{uint64(len(p.RopeScaling.LongFactor))},
 					WriterTo: p.RopeScaling.LongFactor,
-				}, ggml.Tensor{
+				}, llm.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) []ggml.Tensor {
 			})
 		}

-		out = append(out, ggml.Tensor{
+		out = append(out, llm.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),
--- a/convert/convert_qwen2.go
+++ b/convert/convert_qwen2.go
@@ -1,79 +0,0 @@
-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",
-	}
-}
--- a/convert/convert_test.go
+++ b/convert/convert_test.go
@@ -20,7 +20,7 @@ import (

 	"golang.org/x/exp/maps"

-	"github.com/ollama/ollama/fs/ggml"
+	"github.com/ollama/ollama/llm"
 )

 type tensorData struct {
@@ -29,7 +29,7 @@ type tensorData struct {
 	Shape   []int  `json:"shape"`
 }

-func convertFull(t *testing.T, fsys fs.FS) (*os.File, ggml.KV, ggml.Tensors) {
+func convertFull(t *testing.T, fsys fs.FS) (*os.File, llm.KV, *llm.Tensors) {
 	t.Helper()

 	f, err := os.CreateTemp(t.TempDir(), "f16")
@@ -48,7 +48,7 @@ func convertFull(t *testing.T, fsys fs.FS) (*os.File, ggml.KV, ggml.Tensors) {
 	}
 	t.Cleanup(func() { r.Close() })

-	m, _, err := ggml.Decode(r, math.MaxInt)
+	m, _, err := llm.DecodeGGML(r, math.MaxInt)
 	if err != nil {
 		t.Fatal(err)
 	}
@@ -60,7 +60,7 @@ func convertFull(t *testing.T, fsys fs.FS) (*os.File, ggml.KV, ggml.Tensors) {
 	return r, m.KV(), m.Tensors()
 }

-func generateResultsJSON(t *testing.T, f *os.File, kv ggml.KV, tensors ggml.Tensors) map[string]string {
+func generateResultsJSON(t *testing.T, f *os.File, kv llm.KV, tensors *llm.Tensors) map[string]string {
 	actual := make(map[string]string)
 	for k, v := range kv {
 		if s, ok := v.(json.Marshaler); !ok {
@@ -75,7 +75,7 @@ func generateResultsJSON(t *testing.T, f *os.File, kv ggml.KV, tensors ggml.Tens
 		}
 	}

-	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 {
@@ -108,7 +108,6 @@ func TestConvertModel(t *testing.T) {
 		"Phi-3-mini-128k-instruct",
 		"all-MiniLM-L6-v2",
 		"gemma-2-9b-it",
-		"Qwen2.5-0.5B-Instruct",
 	}

 	for i := range cases {
@@ -331,7 +330,7 @@ func TestConvertAdapter(t *testing.T) {
 			}
 			defer r.Close()

-			m, _, err := ggml.Decode(r, math.MaxInt)
+			m, _, err := llm.DecodeGGML(r, math.MaxInt)
 			if err != nil {
 				t.Fatal(err)
 			}
--- a/convert/testdata/Qwen2.5-0.5B-Instruct.json
+++ b/convert/testdata/Qwen2.5-0.5B-Instruct.json
@@ -1,314 +0,0 @@
-{
-    "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",
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-    "blk.3.ffn_norm.weight": "7389fc240a282949580ea2f5b0d7973ac79f32f76dc0155b537bb6b751f8e27a",
-    "blk.3.ffn_up.weight": "2a945f47090df9cb16f92f1f06c520f156f8e232182eaaed09f257b8947a2a62",
-    "blk.4.attn_k.bias": "62533c31f0de498187593f238c6597503fef2a92e920cd540a96bc5311b3b2a0",
-    "blk.4.attn_k.weight": "93e829868bffd980a8e589b9c4566cd81e6ce4296a5f357a2ae93febe1284156",
-    "blk.4.attn_norm.weight": "9e0aaa4bbdd1389890f8abec20533f3ab16d61b872b1a8dbd623023921c660a9",
-    "blk.4.attn_output.weight": "74467d6f44357d67f452ac49da861468b38e98057017bd38bc9a449f9d3538e6",
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-    "blk.4.attn_q.weight": "e5bfbafd94a4d530f3769f5edbba8cc08d9b5bee8f66ebf4cb54e69bc0b7f63b",
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-    "blk.4.ffn_down.weight": "9d91b06e89d52f4365dece7eaeec50f81e52cb2407b333248a81e6e2f84c05b8",
-    "blk.4.ffn_gate.weight": "bf6350a79c6a6ee9146edfd788b88d4a4c2b54db1aa0adcc1464dbba8a84b646",
-    "blk.4.ffn_norm.weight": "11a70a6b9f7ce336292f4e3a2c6c92d366d4ee4306ad4fdb1870fde107e9cc31",
-    "blk.4.ffn_up.weight": "64f23f493d02b147a72a59605e6b7dd1c4c74f6813a38a2a60818bd66f697347",
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-    "blk.5.attn_k.weight": "0e682f75870abf9efaca10dac5f04c580f42820ecf4e234d43af967019acb86f",
-    "blk.5.attn_norm.weight": "01efae7653705e741932fcd79dff3be643d7e97f4b5719b887835dffe44b3a82",
-    "blk.5.attn_output.weight": "69e841d00d196acc489cd70bc5ffbbb63530ac5fabb169d40c4fb3a32ebb8ed8",
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-    "blk.5.attn_v.bias": "39940a9b353ceed3edfd4a39b985c9520490aa1b9f11749c94fdf6d879d1a259",
-    "blk.5.attn_v.weight": "839f84b828cf83aecf479a0dc7bc86cce05145ef77dcf29916dc3e0680f5b665",
-    "blk.5.ffn_down.weight": "1f48cbb0960f15e06ab8a3754ade792995a655856389ddbca629c07e89d1b114",
-    "blk.5.ffn_gate.weight": "33d8219fce3189e1aab376039896eebd4ad36ebd26a8278cd19b26e4357e4f81",
-    "blk.5.ffn_norm.weight": "0f4a0f83d37127fa4483f2905cb4f38ef6ddc71584b6cb05632c62a9af313dda",
-    "blk.5.ffn_up.weight": "22a64a11e5f0a1ff45ca327bf9e1efa258f085ff6a96edc398b7474f725b4514",
-    "blk.6.attn_k.bias": "baa91df99d4df2d25e8d590bca4e334b97f2d9aa3df8e748fedc8a6188499111",
-    "blk.6.attn_k.weight": "121f3b9f4b9491996499392e2688a929cafe102a67920b4cb2a039349c43d8eb",
-    "blk.6.attn_norm.weight": "b4cf987e923d71f2f84c58d20ea8af7576b225bf61952145b489fdd395e3d411",
-    "blk.6.attn_output.weight": "a112642150a138d54b2a4038042fd33619035a35694771e966f3575856c635d6",
-    "blk.6.attn_q.bias": "a97ea10469cdfa3fdddf8bad6de683ef99f6170eb8d29d15dcf6bf4bce37c5a3",
-    "blk.6.attn_q.weight": "d80c787019317a87361de6bbc7df6701357216bdd9b404522cede34a719a5500",
-    "blk.6.attn_v.bias": "d846269db9cd77ae28da26ba0914cace1b6754bd5301af9c44607085dfcbd2d7",
-    "blk.6.attn_v.weight": "06567c433e8a391647633291b50828a076ad7c2436106bb9278c60a3f8fccb3b",
-    "blk.6.ffn_down.weight": "f15f66f56b3c474eac8c6315c5fff07c3e29c6e483d7efd4d303c7f43814be91",
-    "blk.6.ffn_gate.weight": "47768f89c6da8eefb29adb766ff4eb38c9dfd79320bbc1386248319fcbcf567f",
-    "blk.6.ffn_norm.weight": "7f8195e6b148212967145fc9d86ce36b699cff0de026042245c2d344f1ef8510",
-    "blk.6.ffn_up.weight": "53d7707ae4347aadb445289f9f87a008b72df5cb855b00080a605442fdd8edf3",
-    "blk.7.attn_k.bias": "63e274df3217dde25b8369a383e480fe4f6b403a74385f15ac0b5db71dce2744",
-    "blk.7.attn_k.weight": "f6fce88602f5945eee09767acbcad387d132614e6da39ae359f2bbf380d94b1f",
-    "blk.7.attn_norm.weight": "bbf5dc7336c0f9a511afef6bf5efeffd78f1b83940850c3eb7eb20c621b75656",
-    "blk.7.attn_output.weight": "d9fb907a138396a859cecbfcb377927308dc93c24c7fb52dba5eb59265feadec",
-    "blk.7.attn_q.bias": "f02ba1318346af77e309f40aee716e2de7ee8cab67e67b17636db9bf40894fb0",
-    "blk.7.attn_q.weight": "54a691e824be287a61c35c172edc01922ed792d2addeee029afc17ba6c7e11b9",
-    "blk.7.attn_v.bias": "3a4f182f51e84ce862d558fb2751b91802b65d74596bb14d624808513a8a83ec",
-    "blk.7.attn_v.weight": "a142fe6e106d3ab484e2dc6f9c72b8fc0a385279dde08deb1ad1fd05ac25deb1",
-    "blk.7.ffn_down.weight": "8daf7e8c430d183a4d6ab3eb575fafa4b5e31689f68b290c8b370411ad9d0f12",
-    "blk.7.ffn_gate.weight": "a2a786b45eb660994254b48e2aaf22f3e9821cfb383dee0ba04cc4350a2f8e72",
-    "blk.7.ffn_norm.weight": "73828bbc8c9610cc139fcf03e96272648cdc291263251fe3a67367408deb69e1",
-    "blk.7.ffn_up.weight": "e85dd0f63fed449ce16893c5795ea6a050a2d7a66d9534410a227e22c905dafa",
-    "blk.8.attn_k.bias": "91a752a6e2c364e5ee6a015770fe289aece4911ae6c6bbfe74ac52f465465f93",
-    "blk.8.attn_k.weight": "99c069e92c43a2efb74e23188256b3cabbbe06399878e681ce203a05d5da378a",
-    "blk.8.attn_norm.weight": "c76d36d3cc06aa2a9edb1abf9f602bb7ed61ac9d61f8ef7ed736a1e619abe717",
-    "blk.8.attn_output.weight": "ee5ff156a2625e1f203f65e69b514f9df04bd9a5e82b28e3876e16cf1c6f65c5",
-    "blk.8.attn_q.bias": "8fbd868a93b330c8b0418b488c5301f42a7eb0c58445a4e515d56777f1d96ed5",
-    "blk.8.attn_q.weight": "9f20ef86e80098ba52a3a31ebcc315bea3a614dac9cba7ac1db02f156db9b577",
-    "blk.8.attn_v.bias": "c4813571d5d618742183a7890c0b89cd7f18e210c758f63aad564659bc38a26d",
-    "blk.8.attn_v.weight": "ea88e1a4cf8bd56e9a88ada427d2b0cd352234827640757ee2a9ed594fb67a53",
-    "blk.8.ffn_down.weight": "b0d1a7495811580b189aaa3e20ea871d6d01ed7b6c23e59825078ef786944ff2",
-    "blk.8.ffn_gate.weight": "0a17c0caa0b06721c49b59b2a63a5dcbf744dd1cffa55962b404ba910c658a62",
-    "blk.8.ffn_norm.weight": "f15f109d4a8e9d1ff7c71fa5bc6373df7ee80c5f7d1de3fa0d4849d747e36bcb",
-    "blk.8.ffn_up.weight": "bbf4c5c4c5c8a0f9ae8b88e3cc8b86f81b98148722d5a350995af176c0b774f2",
-    "blk.9.attn_k.bias": "a7f60d962686b8ca60f69643e0e0fa8614688be738fb0b1c6bd54de35c2beb5e",
-    "blk.9.attn_k.weight": "dd80ce4adb00e338fc04b307e4c18a27071f4ba4397184a24d765e6e4a268ef4",
-    "blk.9.attn_norm.weight": "721e6487547e2b3986ab4b4e2500ceade59d908bccf4436e1e8031f246deb2bd",
-    "blk.9.attn_output.weight": "5a800af39107b363861e5f5173483cdcd644d8ac3b0c8a443b9c759d71285db8",
-    "blk.9.attn_q.bias": "0a19b4925ea8ca8067acc909b058adc327de3874cfc94cc9eb4a106d3f370123",
-    "blk.9.attn_q.weight": "93e84906684c0c7ede79967236d9fc8344da84a9f1daa04e8295c2c9b6b26a24",
-    "blk.9.attn_v.bias": "615421f812f821e230ecde4e6da35d868823248355ce7e4e51e2d650ead565f9",
-    "blk.9.attn_v.weight": "7f4913e289aefd9ceecbdaf9767b1e95303f5d59dd67ecb2cc15768477f4d08e",
-    "blk.9.ffn_down.weight": "95d1b3933221e87dc4af70dd566daec9498bf358070b8d26f1fc70766a84a152",
-    "blk.9.ffn_gate.weight": "530f2d04f6a1fbffaaa5f2fbc3a328ebed7b330e3af14b4fc7d8a51b13ad8d42",
-    "blk.9.ffn_norm.weight": "28077de416217ea1df94b96017bef4cc562ab62e51b1a03a671c70abc29ce52a",
-    "blk.9.ffn_up.weight": "b87b6190778aaee4695938e24ac6c90dbbee6dce7c5c2ab5bc26ba4564581822"
-  }
--- a/convert/tokenizer.go
+++ b/convert/tokenizer.go
@@ -100,8 +100,6 @@ 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:
--- a/discover/gpu.go
+++ b/discover/gpu.go
@@ -542,6 +542,7 @@ func FindGPULibs(baseLibName string, defaultPatterns []string) []string {
 	patterns = append(patterns, defaultPatterns...)
 	slog.Debug("gpu library search", "globs", patterns)
 	for _, pattern := range patterns {
+
 		// Nvidia PhysX known to return bogus results
 		if strings.Contains(pattern, "PhysX") {
 			slog.Debug("skipping PhysX cuda library path", "path", pattern)
@@ -718,18 +719,23 @@ func (l GpuInfoList) GetVisibleDevicesEnv() (string, string) {
 func LibraryDirs() []string {
 	// dependencies can exist wherever we found the runners (e.g. build tree for developers) and relative to the executable
 	// This can be simplified once we no longer carry runners as payloads
-	exe, err := os.Executable()
+	paths := []string{}
+	appExe, err := os.Executable()
 	if err != nil {
 		slog.Warn("failed to lookup executable path", "error", err)
-		return nil
+	} else {
+		appRelative := filepath.Join(filepath.Dir(appExe), envconfig.LibRelativeToExe(), "lib", "ollama")
+		if _, err := os.Stat(appRelative); err == nil {
+			paths = append(paths, appRelative)
+		}
 	}
-
-	lib := filepath.Join(filepath.Dir(exe), envconfig.LibRelativeToExe(), "lib", "ollama")
-	if _, err := os.Stat(lib); err != nil {
-		return nil
+	rDir := runners.Locate()
+	if err != nil {
+		slog.Warn("unable to locate gpu dependency libraries", "error", err)
+	} else {
+		paths = append(paths, filepath.Dir(rDir))
 	}
-
-	return []string{lib}
+	return paths
 }

 func GetSystemInfo() SystemInfo {
--- a/docs/api.md
+++ b/docs/api.md
@@ -13,7 +13,6 @@
 - [Push a Model](#push-a-model)
 - [Generate Embeddings](#generate-embeddings)
 - [List Running Models](#list-running-models)
- [Version](#version)

 ## Conventions

@@ -928,25 +927,14 @@ A single JSON object is returned:
 POST /api/create
 ```

-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.
+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.

 ### Parameters

 - `model`: name of the model to create
- `from`: (optional) name of an existing model to create the new model from
- `files`: (optional) a dictionary of file names to SHA256 digests of blobs to create the model from
- `adapters`: (optional) a dictionary of file names to SHA256 digests of blobs for LORA adapters
- `template`: (optional) the prompt template for the model
- `license`: (optional) a string or list of strings containing the license or licenses for the model
- `system`: (optional) a string containing the system prompt for the model
- `parameters`: (optional) a dictionary of parameters for the model (see [Modelfile](./modelfile.md#valid-parameters-and-values) for a list of parameters)
- `messages`: (optional) a list of message objects used to create a conversation
+- `modelfile` (optional): contents of the Modelfile
 - `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
@@ -972,15 +960,14 @@ If you are creating a model from a safetensors directory or from a GGUF file, yo

 #### Create a new model

-Create a new model from an existing model.
+Create a new model from a `Modelfile`.

 ##### Request

 ```shell
 curl http://localhost:11434/api/create -d '{
  "model": "mario",
-  "from": "llama3.2",
-  "system": "You are Mario from Super Mario Bros."
+  "modelfile": "FROM llama3\nSYSTEM You are mario from Super Mario Bros."
 }'
 ```

@@ -1011,7 +998,7 @@ Quantize a non-quantized model.
 ```shell
 curl http://localhost:11434/api/create -d '{
  "model": "llama3.1:quantized",
-  "from": "llama3.1:8b-instruct-fp16",
+  "modelfile": "FROM llama3.1:8b-instruct-fp16",
  "quantize": "q4_K_M"
 }'
 ```
@@ -1031,112 +1018,52 @@ A stream of JSON objects is returned:
 {"status":"success"}
 ```

-#### Create a model from GGUF

-Create a model from a GGUF file. The `files` parameter should be filled out with the file name and SHA256 digest of the GGUF file you wish to use. Use [/api/blobs/:digest](#push-a-blob) to push the GGUF file to the server before calling this API.
-
-
-##### Request
-
-```shell
-curl http://localhost:11434/api/create -d '{
-  "model": "my-gguf-model",
-  "files": {
-    "test.gguf": "sha256:432f310a77f4650a88d0fd59ecdd7cebed8d684bafea53cbff0473542964f0c3"
-  }
-}'
-```
-
-##### Response
-
-A stream of JSON objects is returned:
-
-```
-{"status":"parsing GGUF"}
-{"status":"using existing layer sha256:432f310a77f4650a88d0fd59ecdd7cebed8d684bafea53cbff0473542964f0c3"}
-{"status":"writing manifest"}
-{"status":"success"}
-```
-
-
-#### Create a model from a Safetensors directory
-
-The `files` parameter should include a dictionary of files for the safetensors model which includes the file names and SHA256 digest of each file. Use [/api/blobs/:digest](#push-a-blob) to first push each of the files to the server before calling this API. Files will remain in the cache until the Ollama server is restarted.
-
-##### Request
-
-```shell
-curl http://localhost:11434/api/create -d '{
-  "model": "fred",
-  "files": {
-    "config.json": "sha256:dd3443e529fb2290423a0c65c2d633e67b419d273f170259e27297219828e389",
-    "generation_config.json": "sha256:88effbb63300dbbc7390143fbbdd9d9fa50587b37e8bfd16c8c90d4970a74a36",
-    "special_tokens_map.json": "sha256:b7455f0e8f00539108837bfa586c4fbf424e31f8717819a6798be74bef813d05",
-    "tokenizer.json": "sha256:bbc1904d35169c542dffbe1f7589a5994ec7426d9e5b609d07bab876f32e97ab",
-    "tokenizer_config.json": "sha256:24e8a6dc2547164b7002e3125f10b415105644fcf02bf9ad8b674c87b1eaaed6",
-    "model.safetensors": "sha256:1ff795ff6a07e6a68085d206fb84417da2f083f68391c2843cd2b8ac6df8538f"
-  }
-}'
-```
-
-##### Response
-
-A stream of JSON objects is returned:
-
-```shell
-{"status":"converting model"}
-{"status":"creating new layer sha256:05ca5b813af4a53d2c2922933936e398958855c44ee534858fcfd830940618b6"}
-{"status":"using autodetected template llama3-instruct"}
-{"status":"using existing layer sha256:56bb8bd477a519ffa694fc449c2413c6f0e1d3b1c88fa7e3c9d88d3ae49d4dcb"}
-{"status":"writing manifest"}
-{"status":"success"}
-```
-
-## Check if a Blob Exists
+### Check if a Blob Exists

 ```shell
 HEAD /api/blobs/:digest
 ```

-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.
+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.

-### 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.

-## Push a Blob
+### Create a Blob

 ```shell
 POST /api/blobs/:digest
 ```

-Push a file to the Ollama server to create a "blob" (Binary Large Object).
+Create a blob from a file on the server. Returns the server file path.

-### Query Parameters
+#### Query Parameters

 - `digest`: the expected SHA256 digest of the file

-### Examples
+#### Examples

-#### Request
+##### Request

 ```shell
-curl -T model.gguf -X POST http://localhost:11434/api/blobs/sha256:29fdb92e57cf0827ded04ae6461b5931d01fa595843f55d36f5b275a52087dd2
+curl -T model.bin -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.

@@ -1599,29 +1526,3 @@ 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"
-}
-```
-
-
--- a/docs/template.md
+++ b/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.

-```go
+```gotmpl
 {{- 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.

-```go
+```gotmpl
 {{- 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.

-```go
+```gotmpl
 <PRE> {{ .Prompt }} <SUF>{{ .Suffix }} <MID>
 ```

--- a/docs/tutorials.md
+++ b/docs/tutorials.md
@@ -0,0 +1,9 @@
+# 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.
--- a/examples/.gitignore
+++ b/examples/.gitignore
@@ -0,0 +1,174 @@
+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/
--- a/examples/README.md
+++ b/examples/README.md
@@ -12,9 +12,3 @@ Ollama JavaScript examples at [ollama-js/examples](https://github.com/ollama/oll

 ## OpenAI compatibility examples
 Ollama OpenAI compatibility examples at [ollama/examples/openai](../docs/openai.md)
-
-
-## Community examples
-
- [LangChain Ollama Python](https://python.langchain.com/docs/integrations/chat/ollama/)
- [LangChain Ollama JS](https://js.langchain.com/docs/integrations/chat/ollama/)
--- a/examples/flyio/.gitignore
+++ b/examples/flyio/.gitignore
@@ -0,0 +1 @@
+fly.toml
--- a/examples/flyio/README.md
+++ b/examples/flyio/README.md
@@ -0,0 +1,67 @@
+# 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`.
--- a/api/examples/chat/main.go
+++ b/api/examples/chat/main.go
--- a/api/examples/generate-streaming/main.go
+++ b/api/examples/generate-streaming/main.go
--- a/api/examples/generate/main.go
+++ b/api/examples/generate/main.go
--- a/examples/go-http-generate/main.go
+++ b/examples/go-http-generate/main.go
@@ -0,0 +1,29 @@
+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))
+
+}
--- a/api/examples/multimodal/main.go
+++ b/api/examples/multimodal/main.go
--- a/api/examples/pull-progress/main.go
+++ b/api/examples/pull-progress/main.go
--- a/examples/jupyter-notebook/README.md
+++ b/examples/jupyter-notebook/README.md
@@ -0,0 +1,5 @@
+# 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.
--- a/examples/jupyter-notebook/ollama.ipynb
+++ b/examples/jupyter-notebook/ollama.ipynb
@@ -0,0 +1,102 @@
+{
+ "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
+}
--- a/examples/kubernetes/README.md
+++ b/examples/kubernetes/README.md
@@ -0,0 +1,38 @@
+# 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
+   ```
--- a/examples/kubernetes/cpu.yaml
+++ b/examples/kubernetes/cpu.yaml
@@ -0,0 +1,42 @@
+---
+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
--- a/examples/kubernetes/gpu.yaml
+++ b/examples/kubernetes/gpu.yaml
@@ -0,0 +1,58 @@
+---
+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
--- a/examples/langchain-python-rag-document/README.md
+++ b/examples/langchain-python-rag-document/README.md
@@ -0,0 +1,29 @@
+# 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?
+```
--- a/examples/langchain-python-rag-document/main.py
+++ b/examples/langchain-python-rag-document/main.py
@@ -0,0 +1,61 @@
+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})
--- a/examples/langchain-python-rag-document/requirements.txt
+++ b/examples/langchain-python-rag-document/requirements.txt
@@ -0,0 +1,109 @@
+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
--- a/examples/langchain-python-rag-privategpt/.gitignore
+++ b/examples/langchain-python-rag-privategpt/.gitignore
@@ -0,0 +1,170 @@
+# 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/
--- a/examples/langchain-python-rag-privategpt/LICENSE
+++ b/examples/langchain-python-rag-privategpt/LICENSE
@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
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--- a/examples/langchain-python-rag-privategpt/README.md
+++ b/examples/langchain-python-rag-privategpt/README.md
@@ -0,0 +1,91 @@
+# 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),
--- a/examples/langchain-python-rag-privategpt/constants.py
+++ b/examples/langchain-python-rag-privategpt/constants.py
@@ -0,0 +1,11 @@
+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
+)
--- a/examples/langchain-python-rag-privategpt/ingest.py
+++ b/examples/langchain-python-rag-privategpt/ingest.py
@@ -0,0 +1,170 @@
+#!/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()
--- a/examples/langchain-python-rag-privategpt/poetry.lock
+++ b/examples/langchain-python-rag-privategpt/poetry.lock
--- a/examples/langchain-python-rag-privategpt/privateGPT.py
+++ b/examples/langchain-python-rag-privategpt/privateGPT.py
@@ -0,0 +1,74 @@
+#!/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()
--- a/examples/langchain-python-rag-privategpt/pyproject.toml
+++ b/examples/langchain-python-rag-privategpt/pyproject.toml
@@ -0,0 +1,26 @@
+[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"
--- a/examples/langchain-python-rag-privategpt/requirements.txt
+++ b/examples/langchain-python-rag-privategpt/requirements.txt
@@ -0,0 +1,15 @@
+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
--- a/examples/langchain-python-rag-websummary/README.md
+++ b/examples/langchain-python-rag-websummary/README.md
@@ -0,0 +1,23 @@
+# 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
+   ```
--- a/examples/langchain-python-rag-websummary/main.py
+++ b/examples/langchain-python-rag-websummary/main.py
@@ -0,0 +1,12 @@
+from langchain_community.llms import Ollama
+from langchain_community.document_loaders import WebBaseLoader
+from langchain.chains.summarize import load_summarize_chain
+
+loader = WebBaseLoader("https://ollama.com/blog/run-llama2-uncensored-locally")
+docs = loader.load()
+
+llm = Ollama(model="llama3.2")
+chain = load_summarize_chain(llm, chain_type="stuff")
+
+result = chain.invoke(docs)
+print(result)
--- a/examples/langchain-python-rag-websummary/requirements.txt
+++ b/examples/langchain-python-rag-websummary/requirements.txt
@@ -0,0 +1 @@
+langchain==0.0.259
--- a/examples/langchain-python-simple/README.md
+++ b/examples/langchain-python-simple/README.md
@@ -0,0 +1,23 @@
+# LangChain
+
+This example is a basic "hello world" of using LangChain with Ollama.
+
+## Running the Example
+
+1. Ensure you have the `llama3.2` model installed:
+
+   ```bash
+   ollama pull llama3.2
+   ```
+
+2. Install the Python Requirements.
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Run the example:
+
+   ```bash
+   python main.py
+   ```
--- a/examples/langchain-python-simple/main.py
+++ b/examples/langchain-python-simple/main.py
@@ -0,0 +1,6 @@
+from langchain.llms import Ollama
+
+input = input("What is your question?\n> ")
+llm = Ollama(model="llama3.2")
+res = llm.invoke(input)
+print (res)
--- a/examples/langchain-python-simple/requirements.txt
+++ b/examples/langchain-python-simple/requirements.txt
@@ -0,0 +1 @@
+langchain==0.0.259
--- a/examples/langchain-typescript-simple/README.md
+++ b/examples/langchain-typescript-simple/README.md
@@ -0,0 +1,23 @@
+# LangChain
+
+This example is a basic "hello world" of using LangChain with Ollama using Node.js and Typescript.
+
+## Running the Example
+
+1. Install the prerequisites:
+
+   ```bash
+   npm install
+   ```
+
+2. Ensure the `mistral` model is available:
+
+   ```bash
+   ollama pull mistral
+   ```
+
+3. Run the example:
+
+   ```bash
+   npm start
+   ```
--- a/examples/langchain-typescript-simple/main.ts
+++ b/examples/langchain-typescript-simple/main.ts
@@ -0,0 +1,25 @@
+import { Ollama } from 'langchain/llms/ollama';
+import * as readline from "readline";
+
+async function main() {
+  const ollama = new Ollama({
+    model: 'mistral'    
+    // other parameters can be found at https://js.langchain.com/docs/api/llms_ollama/classes/Ollama
+  });
+
+  const rl = readline.createInterface({
+    input: process.stdin,
+    output: process.stdout,
+  });
+
+  rl.question("What is your question: \n", async (user_input) => {
+    const stream = await ollama.stream(user_input);
+  
+    for await (const chunk of stream) {
+      process.stdout.write(chunk);
+    }
+    rl.close();
+  })
+}
+
+main();
--- a/examples/langchain-typescript-simple/package-lock.json
+++ b/examples/langchain-typescript-simple/package-lock.json
@@ -0,0 +1,997 @@
+{
+  "name": "langchain-typescript-simple",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "dependencies": {
+        "langchain": "^0.0.165"
+      },
+      "devDependencies": {
+        "typescript": "^5.2.2"
+      }
+    },
+    "node_modules/@anthropic-ai/sdk": {
+      "version": "0.6.2",
+      "resolved": "https://registry.npmjs.org/@anthropic-ai/sdk/-/sdk-0.6.2.tgz",
+      "integrity": "sha512-fB9PUj9RFT+XjkL+E9Ol864ZIJi+1P8WnbHspN3N3/GK2uSzjd0cbVIKTGgf4v3N8MwaQu+UWnU7C4BG/fap/g==",
+      "dependencies": {
+        "@types/node": "^18.11.18",
+        "@types/node-fetch": "^2.6.4",
+        "abort-controller": "^3.0.0",
+        "agentkeepalive": "^4.2.1",
+        "digest-fetch": "^1.3.0",
+        "form-data-encoder": "1.7.2",
+        "formdata-node": "^4.3.2",
+        "node-fetch": "^2.6.7"
+      }
+    },
+    "node_modules/@types/node": {
+      "version": "18.18.4",
+      "resolved": "https://registry.npmjs.org/@types/node/-/node-18.18.4.tgz",
+      "integrity": "sha512-t3rNFBgJRugIhackit2mVcLfF6IRc0JE4oeizPQL8Zrm8n2WY/0wOdpOPhdtG0V9Q2TlW/axbF1MJ6z+Yj/kKQ=="
+    },
+    "node_modules/@types/node-fetch": {
+      "version": "2.6.6",
+      "resolved": "https://registry.npmjs.org/@types/node-fetch/-/node-fetch-2.6.6.tgz",
+      "integrity": "sha512-95X8guJYhfqiuVVhRFxVQcf4hW/2bCuoPwDasMf/531STFoNoWTT7YDnWdXHEZKqAGUigmpG31r2FE70LwnzJw==",
+      "dependencies": {
+        "@types/node": "*",
+        "form-data": "^4.0.0"
+      }
+    },
+    "node_modules/@types/retry": {
+      "version": "0.12.0",
+      "resolved": "https://registry.npmjs.org/@types/retry/-/retry-0.12.0.tgz",
+      "integrity": "sha512-wWKOClTTiizcZhXnPY4wikVAwmdYHp8q6DmC+EJUzAMsycb7HB32Kh9RN4+0gExjmPmZSAQjgURXIGATPegAvA=="
+    },
+    "node_modules/@types/uuid": {
+      "version": "9.0.5",
+      "resolved": "https://registry.npmjs.org/@types/uuid/-/uuid-9.0.5.tgz",
+      "integrity": "sha512-xfHdwa1FMJ082prjSJpoEI57GZITiQz10r3vEJCHa2khEFQjKy91aWKz6+zybzssCvXUwE1LQWgWVwZ4nYUvHQ=="
+    },
+    "node_modules/abort-controller": {
+      "version": "3.0.0",
+      "resolved": "https://registry.npmjs.org/abort-controller/-/abort-controller-3.0.0.tgz",
+      "integrity": "sha512-h8lQ8tacZYnR3vNQTgibj+tODHI5/+l06Au2Pcriv/Gmet0eaj4TwWH41sO9wnHDiQsEj19q0drzdWdeAHtweg==",
+      "dependencies": {
+        "event-target-shim": "^5.0.0"
+      },
+      "engines": {
+        "node": ">=6.5"
+      }
+    },
+    "node_modules/agentkeepalive": {
+      "version": "4.5.0",
+      "resolved": "https://registry.npmjs.org/agentkeepalive/-/agentkeepalive-4.5.0.tgz",
+      "integrity": "sha512-5GG/5IbQQpC9FpkRGsSvZI5QYeSCzlJHdpBQntCsuTOxhKD8lqKhrleg2Yi7yvMIf82Ycmmqln9U8V9qwEiJew==",
+      "dependencies": {
+        "humanize-ms": "^1.2.1"
+      },
+      "engines": {
+        "node": ">= 8.0.0"
+      }
+    },
+    "node_modules/ansi-styles": {
+      "version": "5.2.0",
+      "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-5.2.0.tgz",
+      "integrity": "sha512-Cxwpt2SfTzTtXcfOlzGEee8O+c+MmUgGrNiBcXnuWxuFJHe6a5Hz7qwhwe5OgaSYI0IJvkLqWX1ASG+cJOkEiA==",
+      "engines": {
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+      "peerDependencies": {
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+}
--- a/examples/langchain-typescript-simple/package.json
+++ b/examples/langchain-typescript-simple/package.json
@@ -0,0 +1,13 @@
+{
+  "scripts": {
+    "start": "tsx main.ts"
+  },
+  "devDependencies": {
+    "tsx": "^4.6.2",
+    "typescript": "^5.3.3"
+  },
+  "dependencies": {
+    "langchain": "^0.0.165",
+    "readline": "^1.3.0"
+  }
+}
--- a/examples/modelfile-mario/Modelfile
+++ b/examples/modelfile-mario/Modelfile
@@ -0,0 +1,5 @@
+FROM llama3.2
+PARAMETER temperature 1
+SYSTEM """
+You are Mario from super mario bros, acting as an assistant.
+"""
--- a/examples/modelfile-mario/logo.png
+++ b/examples/modelfile-mario/logo.png
--- a/examples/modelfile-mario/readme.md
+++ b/examples/modelfile-mario/readme.md
@@ -0,0 +1,43 @@
+<img src="logo.png" alt="image of Italian plumber" height="200"/>
+
+# Example character: Mario
+
+This example shows how to create a basic character using Llama 3.2 as the base model.
+
+To run this example:
+
+1. Download the Modelfile
+2. `ollama pull llama3.2` to get the base model used in the model file.
+3. `ollama create NAME -f ./Modelfile`
+4. `ollama run NAME`
+
+Ask it some questions like "Who are you?" or "Is Peach in trouble again?"
+
+## Editing this file
+
+What the model file looks like:
+
+```
+FROM llama3.2
+PARAMETER temperature 1
+SYSTEM """
+You are Mario from Super Mario Bros, acting as an assistant.
+"""
+```
+
+What if you want to change its behaviour?
+
+- Try changing the prompt
+- Try changing the parameters [Docs](https://github.com/ollama/ollama/blob/main/docs/modelfile.md)
+- Try changing the model (e.g. An uncensored model by `FROM wizard-vicuna` this is the wizard-vicuna uncensored model )
+
+Once the changes are made,
+
+1. `ollama create NAME -f ./Modelfile`
+2. `ollama run NAME`
+3. Iterate until you are happy with the results.
+
+Notes:
+
+- This example is for research purposes only. There is no affiliation with any entity.
+- When using an uncensored model, please be aware that it may generate offensive content.
--- a/examples/python-dockerit/Modelfile
+++ b/examples/python-dockerit/Modelfile
@@ -0,0 +1,20 @@
+FROM mistral
+SYSTEM """
+You are an experienced Devops engineer focused on docker. When given specifications for a particular need or application you know the best way to host that within a docker container. For instance if someone tells you they want an nginx server to host files located at /web you will answer as follows
+
+---start
+FROM nginx:alpine
+COPY /myweb /usr/share/nginx/html
+EXPOSE 80
+---end
+
+Notice that the answer you should give is just the contents of the dockerfile with no explanation and there are three dashes and the word start at the beginning and 3 dashes and the word end. The full output can be piped into a file and run as is. Here is another example. The user will ask to launch a Postgres server with a password of abc123. And the response should be
+
+---start
+FROM postgres:latest
+ENV POSTGRES_PASSWORD=abc123
+EXPOSE 5432
+---end
+
+Again it's just the contents of the dockerfile and nothing else.
+"""
--- a/examples/python-dockerit/README.md
+++ b/examples/python-dockerit/README.md
@@ -0,0 +1,31 @@
+# DockerIt
+
+DockerIt is a tool to help you build and run your application in a Docker container. It consists of a model that defines the system prompt and model weights to use, along with a python script to then build the container and run the image automatically.
+
+## Running the Example
+
+1. Ensure you have the `mattw/dockerit` model installed:
+
+   ```bash
+   ollama pull mattw/dockerit
+   ```
+
+2. Make sure Docker is running on your machine.
+
+3. Install the Python Requirements.
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+4. Run the example:
+
+   ```bash
+   python dockerit.py "simple postgres server with admin password set to 123"
+   ```
+
+5. Enter the name you would like to use for your container image.
+
+## Caveats
+
+This is a simple example. It's assuming the Dockerfile content generated is going to work. In many cases, even with simple web servers, it fails when trying to copy files that don't exist. It's simply an example of what you could possibly do.
--- a/examples/python-dockerit/dockerit.py
+++ b/examples/python-dockerit/dockerit.py
@@ -0,0 +1,17 @@
+import requests, json, docker, io, sys
+inputDescription = " ".join(sys.argv[1:])
+imageName = input("Enter the name of the image: ")
+client = docker.from_env()
+s = requests.Session()
+output=""
+with s.post('http://localhost:11434/api/generate', json={'model': 'mattw/dockerit', 'prompt': inputDescription}, stream=True) as r:
+  for line in r.iter_lines():
+    if line:
+      j = json.loads(line)
+      if "response" in j:
+        output = output +j["response"]
+output = output[output.find("---start")+9:output.find("---end")-1]
+f = io.BytesIO(bytes(output, 'utf-8'))
+client.images.build(fileobj=f, tag=imageName)
+container = client.containers.run(imageName, detach=True)
+print("Container named", container.name, " started with id: ",container.id)
--- a/examples/python-dockerit/requirements.txt
+++ b/examples/python-dockerit/requirements.txt
@@ -0,0 +1 @@
+docker
--- a/examples/python-grounded-factuality-rag-check/README.md
+++ b/examples/python-grounded-factuality-rag-check/README.md
@@ -0,0 +1,93 @@
+# RAG Hallucination Checker using Bespoke-Minicheck
+
+This example allows the user to ask questions related to a document, which can be specified via an article url. Relevant chunks are retrieved from the document and given to `llama3.2` as context to answer the question. Then each sentence in the answer is checked against the retrieved chunks using `bespoke-minicheck` to ensure that the answer does not contain hallucinations.
+
+## Running the Example
+
+1. Ensure `all-minilm` (embedding) `llama3.2` (chat) and `bespoke-minicheck` (check) models installed:
+
+   ```bash
+   ollama pull all-minilm
+   ollama pull llama3.2
+   ollama pull bespoke-minicheck
+   ```
+
+2. Install the dependencies.
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Run the example:
+
+   ```bash
+   python main.py
+   ```
+
+## Expected Output
+
+```text
+Enter the URL of an article you want to chat with, or press Enter for default example:
+
+Loaded, chunked, and embedded text from https://www.theverge.com/2024/9/12/24242439/openai-o1-model-reasoning-strawberry-chatgpt.
+
+Enter your question or type quit: Who is the CEO of openai?
+
+Retrieved chunks:
+OpenAI is releasing a new model called o1 , the first in a planned series of “ reasoning ” models that have been trained to answer more complex questions , faster than a human can . It ’ s being released alongside o1-mini , a smaller , cheaper version . And yes , if you ’ re steeped in AI rumors : this is , in fact , the extremely hyped Strawberry model . For OpenAI , o1 represents a step toward its broader goal of human-like artificial intelligence .
+
+OpenAI is releasing a new model called o1 , the first in a planned series of “ reasoning ” models that have been trained to answer more complex questions , faster than a human can . It ’ s being released alongside o1-mini , a smaller , cheaper version . And yes , if you ’ re steeped in AI rumors : this is , in fact , the extremely hyped Strawberry model . For OpenAI , o1 represents a step toward its broader goal of human-like artificial intelligence . More practically , it does a better job at writing code and solving multistep problems than previous models . But it ’ s also more expensive and slower to use than GPT-4o . OpenAI is calling this release of o1 a “ preview ” to emphasize how nascent it is . ChatGPT Plus and Team users get access to both o1-preview and o1-mini starting today , while Enterprise and Edu users will get access early next week .
+
+More practically , it does a better job at writing code and solving multistep problems than previous models . But it ’ s also more expensive and slower to use than GPT-4o . OpenAI is calling this release of o1 a “ preview ” to emphasize how nascent it is . ChatGPT Plus and Team users get access to both o1-preview and o1-mini starting today , while Enterprise and Edu users will get access early next week . OpenAI says it plans to bring o1-mini access to all the free users of ChatGPT but hasn ’ t set a release date yet . Developer access to o1 is really expensive : In the API , o1-preview is $ 15 per 1 million input tokens , or chunks of text parsed by the model , and $ 60 per 1 million output tokens . For comparison , GPT-4o costs $ 5 per 1 million input tokens and $ 15 per 1 million output tokens .
+
+OpenAI says it plans to bring o1-mini access to all the free users of ChatGPT but hasn ’ t set a release date yet . Developer access to o1 is really expensive : In the API , o1-preview is $ 15 per 1 million input tokens , or chunks of text parsed by the model , and $ 60 per 1 million output tokens . For comparison , GPT-4o costs $ 5 per 1 million input tokens and $ 15 per 1 million output tokens . The training behind o1 is fundamentally different from its predecessors , OpenAI ’ s research lead , Jerry Tworek , tells me , though the company is being vague about the exact details . He says o1 “ has been trained using a completely new optimization algorithm and a new training dataset specifically tailored for it. ” Image : OpenAI OpenAI taught previous GPT models to mimic patterns from its training data .
+
+LLM Answer:
+The text does not mention the CEO of OpenAI. It only discusses the release of a new model called o1 and some details about it, but does not provide information on the company's leadership.
+
+LLM Claim: The text does not mention the CEO of OpenAI.
+Is this claim supported by the context according to bespoke-minicheck? Yes
+
+LLM Claim: It only discusses the release of a new model called o1 and some details about it, but does not provide information on the company's leadership.
+Is this claim supported by the context according to bespoke-minicheck? No
+```
+
+The second claim is unsupported since the text mentions the research lead. 
+
+Another tricky example:
+
+```text
+
+Enter your question or type quit: what sets o1 apart from gpt-4o?
+
+Retrieved chunks: 
+OpenAI says it plans to bring o1-mini access to all the free users of ChatGPT but hasn ’ t set a release date yet . Developer access to o1 is really expensive : In the API , o1-preview is $ 15 per 1 million input tokens , or chunks of text parsed by the model , and $ 60 per 1 million output tokens . For comparison , GPT-4o costs $ 5 per 1 million input tokens and $ 15 per 1 million output tokens . The training behind o1 is fundamentally different from its predecessors , OpenAI ’ s research lead , Jerry Tworek , tells me , though the company is being vague about the exact details . He says o1 “ has been trained using a completely new optimization algorithm and a new training dataset specifically tailored for it. ” Image : OpenAI OpenAI taught previous GPT models to mimic patterns from its training data .
+
+He says OpenAI also tested o1 against a qualifying exam for the International Mathematics Olympiad , and while GPT-4o only correctly solved only 13 percent of problems , o1 scored 83 percent . “ We can ’ t say we solved hallucinations ” In online programming contests known as Codeforces competitions , this new model reached the 89th percentile of participants , and OpenAI claims the next update of this model will perform “ similarly to PhD students on challenging benchmark tasks in physics , chemistry and biology. ” At the same time , o1 is not as capable as GPT-4o in a lot of areas . It doesn ’ t do as well on factual knowledge about the world .
+
+More practically , it does a better job at writing code and solving multistep problems than previous models . But it ’ s also more expensive and slower to use than GPT-4o . OpenAI is calling this release of o1 a “ preview ” to emphasize how nascent it is . ChatGPT Plus and Team users get access to both o1-preview and o1-mini starting today , while Enterprise and Edu users will get access early next week . OpenAI says it plans to bring o1-mini access to all the free users of ChatGPT but hasn ’ t set a release date yet . Developer access to o1 is really expensive : In the API , o1-preview is $ 15 per 1 million input tokens , or chunks of text parsed by the model , and $ 60 per 1 million output tokens . For comparison , GPT-4o costs $ 5 per 1 million input tokens and $ 15 per 1 million output tokens .
+
+“ We can ’ t say we solved hallucinations ” In online programming contests known as Codeforces competitions , this new model reached the 89th percentile of participants , and OpenAI claims the next update of this model will perform “ similarly to PhD students on challenging benchmark tasks in physics , chemistry and biology. ” At the same time , o1 is not as capable as GPT-4o in a lot of areas . It doesn ’ t do as well on factual knowledge about the world . It also doesn ’ t have the ability to browse the web or process files and images . Still , the company believes it represents a brand-new class of capabilities . It was named o1 to indicate “ resetting the counter back to 1. ” “ I ’ m gon na be honest : I think we ’ re terrible at naming , traditionally , ” McGrew says .
+LLM Answer: According to the text, several things set o1 apart from GPT-4o:
+
+* In online programming contests (Codeforces competitions), o1 scored 83% correct solutions compared to GPT-4o's 13%.
+* The training behind o1 is "fundamentally different" from its predecessors, including a completely new optimization algorithm and a new training dataset specifically tailored for it.
+* o1 has been shown to perform similarly to PhD students on challenging benchmark tasks in physics, chemistry, and biology, while GPT-4o does not have this level of performance.
+* However, o1 is also less capable than GPT-4o in some areas, particularly with regard to factual knowledge about the world.
+
+LLM Claim: According to the text, several things set o1 apart from GPT-4o:
+
+* In online programming contests (Codeforces competitions), o1 scored 83% correct solutions compared to GPT-4o's 13%.
+Is this claim supported by the context according to bespoke-minicheck? Yes
+
+LLM Claim: * The training behind o1 is "fundamentally different" from its predecessors, including a completely new optimization algorithm and a new training dataset specifically tailored for it.
+Is this claim supported by the context according to bespoke-minicheck? Yes
+
+LLM Claim: * o1 has been shown to perform similarly to PhD students on challenging benchmark tasks in physics, chemistry, and biology, while GPT-4o does not have this level of performance.
+Is this claim supported by the context according to bespoke-minicheck? No
+
+LLM Claim: * However, o1 is also less capable than GPT-4o in some areas, particularly with regard to factual knowledge about the world.
+Is this claim supported by the context according to bespoke-minicheck? Yes
+```
+
+We see that the third claim "* o1 has been shown to perform similarly to PhD students on challenging benchmark tasks in physics, chemistry, and biology, while GPT-4o does not have this level of performance." is not supported by the context. This is because the context only mentions that o1 "is claimed to perform" which is different from "has been shown to perform".
--- a/examples/python-grounded-factuality-rag-check/main.py
+++ b/examples/python-grounded-factuality-rag-check/main.py
@@ -0,0 +1,137 @@
+import ollama
+import warnings
+from mattsollamatools import chunker
+from newspaper import Article
+import numpy as np
+from sklearn.neighbors import NearestNeighbors
+import nltk
+
+warnings.filterwarnings(
+    "ignore", category=FutureWarning, module="transformers.tokenization_utils_base"
+)
+nltk.download("punkt_tab", quiet=True)
+
+
+def getArticleText(url):
+    """Gets the text of an article from a URL.
+
+    Often there are a bunch of ads and menus on pages for a news article.
+    This uses newspaper3k to get just the text of just the article.
+    """
+    article = Article(url)
+    article.download()
+    article.parse()
+    return article.text
+
+
+def knn_search(question_embedding, embeddings, k=5):
+    """Performs K-nearest neighbors (KNN) search"""
+    X = np.array(
+        [item["embedding"] for article in embeddings for item in article["embeddings"]]
+    )
+    source_texts = [
+        item["source"] for article in embeddings for item in article["embeddings"]
+    ]
+
+    # Fit a KNN model on the embeddings
+    knn = NearestNeighbors(n_neighbors=k, metric="cosine")
+    knn.fit(X)
+
+    # Find the indices and distances of the k-nearest neighbors.
+    _, indices = knn.kneighbors(question_embedding, n_neighbors=k)
+
+    # Get the indices and source texts of the best matches
+    best_matches = [(indices[0][i], source_texts[indices[0][i]]) for i in range(k)]
+
+    return best_matches
+
+
+def check(document, claim):
+    """Checks if the claim is supported by the document by calling bespoke-minicheck.
+
+    Returns Yes/yes if the claim is supported by the document, No/no otherwise.
+    Support for logits will be added in the future.
+
+    bespoke-minicheck's system prompt is defined as:
+      'Determine whether the provided claim is consistent with the corresponding
+      document. Consistency in this context implies that all information presented in the claim
+      is substantiated by the document. If not, it should be considered inconsistent. Please
+      assess the claim's consistency with the document by responding with either "Yes" or "No".'
+
+    bespoke-minicheck's user prompt is defined as:
+      "Document: {document}\nClaim: {claim}"
+    """
+    prompt = f"Document: {document}\nClaim: {claim}"
+    response = ollama.generate(
+        model="bespoke-minicheck", prompt=prompt, options={"num_predict": 2, "temperature": 0.0}
+    )
+    return response["response"].strip()
+
+
+if __name__ == "__main__":
+    allEmbeddings = []
+    default_url = "https://www.theverge.com/2024/9/12/24242439/openai-o1-model-reasoning-strawberry-chatgpt"
+    user_input = input(
+        "Enter the URL of an article you want to chat with, or press Enter for default example: "
+    )
+    article_url = user_input.strip() if user_input.strip() else default_url
+    article = {}
+    article["embeddings"] = []
+    article["url"] = article_url
+    text = getArticleText(article_url)
+    chunks = chunker(text)
+
+    # Embed (batch) chunks using ollama
+    embeddings = ollama.embed(model="all-minilm", input=chunks)["embeddings"]
+
+    for chunk, embedding in zip(chunks, embeddings):
+        item = {}
+        item["source"] = chunk
+        item["embedding"] = embedding
+        item["sourcelength"] = len(chunk)
+        article["embeddings"].append(item)
+
+    allEmbeddings.append(article)
+
+    print(f"\nLoaded, chunked, and embedded text from {article_url}.\n")
+
+    while True:
+        # Input a question from the user
+        # For example, "Who is the chief research officer?"
+        question = input("Enter your question or type quit: ")
+
+        if question.lower() == "quit":
+            break
+
+        # Embed the user's question using ollama.embed
+        question_embedding = ollama.embed(model="all-minilm", input=question)[
+            "embeddings"
+        ]
+
+        # Perform KNN search to find the best matches (indices and source text)
+        best_matches = knn_search(question_embedding, allEmbeddings, k=4)
+
+        sourcetext = "\n\n".join([source_text for (_, source_text) in best_matches])
+
+        print(f"\nRetrieved chunks: \n{sourcetext}\n")
+
+        # Give the retrieved chunks and question to the chat model
+        system_prompt = f"Only use the following information to answer the question. Do not use anything else: {sourcetext}"
+
+        ollama_response = ollama.generate(
+            model="llama3.2",
+            prompt=question,
+            system=system_prompt,
+            options={"stream": False},
+        )
+
+        answer = ollama_response["response"]
+        print(f"LLM Answer:\n{answer}\n")
+
+        # Check each sentence in the response for grounded factuality
+        if answer:
+            for claim in nltk.sent_tokenize(answer):
+                print(f"LLM Claim: {claim}")
+                print(
+                    f"Is this claim supported by the context according to bespoke-minicheck? {check(sourcetext, claim)}\n"
+                )
--- a/examples/python-grounded-factuality-rag-check/requirements.txt
+++ b/examples/python-grounded-factuality-rag-check/requirements.txt
@@ -0,0 +1,8 @@
+ollama
+lxml==5.3.0
+lxml_html_clean==0.2.2
+mattsollamatools==0.0.25
+newspaper3k==0.2.8
+nltk==3.9.1
+numpy==1.26.4
+scikit-learn==1.5.2
--- a/examples/python-grounded-factuality-simple-check/main.py
+++ b/examples/python-grounded-factuality-simple-check/main.py
@@ -0,0 +1,53 @@
+"""Simple example to demonstrate how to use the bespoke-minicheck model."""
+
+import ollama
+
+# NOTE: ollama must be running for this to work, start the ollama app or run `ollama serve`
+
+
+def check(document, claim):
+    """Checks if the claim is supported by the document by calling bespoke-minicheck.
+
+    Returns Yes/yes if the claim is supported by the document, No/no otherwise.
+    Support for logits will be added in the future.
+
+    bespoke-minicheck's system prompt is defined as:
+      'Determine whether the provided claim is consistent with the corresponding
+      document. Consistency in this context implies that all information presented in the claim
+      is substantiated by the document. If not, it should be considered inconsistent. Please
+      assess the claim's consistency with the document by responding with either "Yes" or "No".'
+
+    bespoke-minicheck's user prompt is defined as:
+      "Document: {document}\nClaim: {claim}"
+    """
+    prompt = f"Document: {document}\nClaim: {claim}"
+    response = ollama.generate(
+        model="bespoke-minicheck", prompt=prompt, options={"num_predict": 2, "temperature": 0.0}
+    )
+    return response["response"].strip()
+
+
+def get_user_input(prompt):
+    user_input = input(prompt)
+    if not user_input:
+        exit()
+    print()
+    return user_input
+
+
+def main():
+    while True:
+        # Get a document from the user (e.g. "Ryan likes running and biking.")
+        document = get_user_input("Enter a document: ")
+        # Get a claim from the user (e.g. "Ryan likes to run.")
+        claim = get_user_input("Enter a claim: ")
+        # Check if the claim is supported by the document
+        grounded_factuality_check = check(document, claim)
+        print(
+            f"Is the claim supported by the document according to bespoke-minicheck? {grounded_factuality_check}"
+        )
+        print("\n\n")
+
+
+if __name__ == "__main__":
+    main()
--- a/examples/python-grounded-factuality-simple-check/readme.md
+++ b/examples/python-grounded-factuality-simple-check/readme.md
@@ -0,0 +1,54 @@
+# Simple Bespoke-Minicheck Example
+
+`bespoke-minicheck` is a model for checking if a claim is supported by a document. It is used through the **generate** endpoint, which is called in this example with a `prompt` that includes the expected formatting of the user input. 
+
+## Running the Example
+
+1. Ensure you have the `bespoke-minicheck` model installed:
+
+   ```bash
+   ollama pull bespoke-minicheck
+   ```
+
+2. Install the dependencies:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Run the program:
+
+   ```bash
+   python main.py
+   ```
+
+4. Enter a document and a claim when prompted:
+
+   ```bash
+   Enter a document: Roses are red.
+
+   Enter a claim: Roses are blue. 
+   ```
+
+   The claim and document are then given to the `bespoke-minicheck` as inputs, which then generates a response (Yes or No) on whether the claim is supported by the document.
+
+   ```bash
+   Is the claim supported by the document according to bespoke-minicheck? No
+   ```
+
+## More Examples
+
+Document ([source](https://en.wikipedia.org/wiki/Apple_I)): 
+> The Apple Computer 1 (Apple-1[a]), later known predominantly as the Apple I(written with a Roman numeral),[b] is an 8-bit motherboard-only personal computer designed by Steve Wozniak[5][6] and released by the Apple Computer Company (now Apple Inc.) in 1976. The company was initially formed to sell the Apple I – its first product – and would later become the world's largest technology company.[7] The idea of starting a company and selling the computer came from Wozniak's friend and Apple co-founder Steve Jobs.[8][9] One of the main innovations of the Apple I was that it included video display terminal circuitry on its circuit board, allowing it to connect to a low-cost composite video monitor or television, instead of an expensive computer terminal, compared to most existing computers at the time.
+
+Claim: 
+>The Apple I is a 16-bit computer.
+
+Expected output:
+>Is the claim supported by the document according to bespoke-minicheck? **No**
+
+Claim: 
+>Apple was originally called the Apple Computer Company.
+
+Expected output:
+>Is the claim supported by the document according to bespoke-minicheck? **Yes**
--- a/examples/python-grounded-factuality-simple-check/requirements.txt
+++ b/examples/python-grounded-factuality-simple-check/requirements.txt
@@ -0,0 +1 @@
+ollama
--- a/examples/python-json-datagenerator/predefinedschema.py
+++ b/examples/python-json-datagenerator/predefinedschema.py
@@ -0,0 +1,31 @@
+import requests
+import json
+import random
+
+model = "llama3.2"
+template = {
+  "firstName": "",
+  "lastName": "",
+  "address": {
+    "street": "",
+    "city": "",
+    "state": "",
+    "zipCode": ""
+  },
+  "phoneNumber": ""
+}
+
+prompt = f"generate one realistically believable sample data set of a persons first name, last name, address in the US, and  phone number. \nUse the following template: {json.dumps(template)}."
+
+data = {
+    "prompt": prompt,
+    "model": model,
+    "format": "json",
+    "stream": False,
+    "options": {"temperature": 2.5, "top_p": 0.99, "top_k": 100},
+}
+
+print(f"Generating a sample user")
+response = requests.post("http://localhost:11434/api/generate", json=data, stream=False)
+json_data = json.loads(response.text)
+print(json.dumps(json.loads(json_data["response"]), indent=2))
--- a/examples/python-json-datagenerator/randomaddresses.py
+++ b/examples/python-json-datagenerator/randomaddresses.py
@@ -0,0 +1,31 @@
+import requests
+import json
+import random
+
+countries = [
+    "United States",
+    "United Kingdom",
+    "the Netherlands",
+    "Germany",
+    "Mexico",
+    "Canada",
+    "France",
+]
+country = random.choice(countries)
+model = "llama3.2"
+
+prompt = f"generate one realistically believable sample data set of a persons first name, last name, address in {country}, and phone number. Do not use common names. Respond using JSON. Key names should have no backslashes, values should use plain ascii with no special characters."
+
+data = {
+    "prompt": prompt,
+    "model": model,
+    "format": "json",
+    "stream": False,
+    "options": {"temperature": 2.5, "top_p": 0.99, "top_k": 100},
+}
+
+print(f"Generating a sample user in {country}")
+response = requests.post("http://localhost:11434/api/generate", json=data, stream=False)
+json_data = json.loads(response.text)
+
+print(json.dumps(json.loads(json_data["response"]), indent=2))
--- a/examples/python-json-datagenerator/readme.md
+++ b/examples/python-json-datagenerator/readme.md
@@ -0,0 +1,60 @@
+# JSON Output Example
+
+![llmjson 2023-11-10 15_31_31](https://github.com/ollama/ollama/assets/633681/e599d986-9b4a-4118-81a4-4cfe7e22da25)
+
+There are two python scripts in this example. `randomaddresses.py` generates random addresses from different countries. `predefinedschema.py` sets a template for the model to fill in.
+
+## Running the Example
+
+1. Ensure you have the `llama3.2` model installed:
+
+   ```bash
+   ollama pull llama3.2
+   ```
+
+2. Install the Python Requirements.
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Run the Random Addresses example:
+
+   ```bash
+   python randomaddresses.py
+   ```
+
+4. Run the Predefined Schema example:
+
+   ```bash
+   python predefinedschema.py
+   ```
+
+## Review the Code
+
+Both programs are basically the same, with a different prompt for each, demonstrating two different ideas. The key part of getting JSON out of a model is to state in the prompt or system prompt that it should respond using JSON, and specifying the `format` as `json` in the data body.
+
+```python
+prompt = f"generate one realistically believable sample data set of a persons first name, last name, address in {country}, and  phone number. Do not use common names. Respond using JSON. Key names should with no backslashes, values should use plain ascii with no special characters."
+
+data = {
+    "prompt": prompt,
+    "model": model,
+    "format": "json",
+    "stream": False,
+    "options": {"temperature": 2.5, "top_p": 0.99, "top_k": 100},
+}
+```
+
+When running `randomaddresses.py` you will see that the schema changes and adapts to the chosen country.
+
+In `predefinedschema.py`, a template has been specified in the prompt as well. It's been defined as JSON and then dumped into the prompt string to make it easier to work with.
+
+Both examples turn streaming off so that we end up with the completed JSON all at once. We need to convert the `response.text` to JSON so that when we output it as a string we can set the indent spacing to make the output easy to read.
+
+```python
+response = requests.post("http://localhost:11434/api/generate", json=data, stream=False)
+json_data = json.loads(response.text)
+
+print(json.dumps(json.loads(json_data["response"]), indent=2))
+```
--- a/examples/python-json-datagenerator/requirements.txt
+++ b/examples/python-json-datagenerator/requirements.txt
@@ -0,0 +1 @@
+Requests==2.31.0
--- a/examples/python-loganalysis/Modelfile
+++ b/examples/python-loganalysis/Modelfile
@@ -0,0 +1,8 @@
+FROM codebooga:latest
+
+SYSTEM """
+You are a log file analyzer. You will receive a set of lines from a log file for some software application, find the errors and other interesting aspects of the logs, and explain them so a new user can understand what they mean. If there are any steps they can do to resolve them, list the steps in your answer.
+"""
+
+PARAMETER temperature 0.3
+
--- a/examples/python-loganalysis/loganalysis.py
+++ b/examples/python-loganalysis/loganalysis.py
@@ -0,0 +1,41 @@
+import sys
+import re
+import requests
+import json
+
+# prelines and postlines represent the number of lines of context to include in the output around the error
+prelines = 10
+postlines = 10
+
+def find_errors_in_log_file():
+  if len(sys.argv) < 2:
+    print("Usage: python loganalysis.py <filename>")
+    return
+
+  log_file_path = sys.argv[1]
+  with open(log_file_path, 'r') as log_file:
+    log_lines = log_file.readlines()
+
+  error_logs = []
+  for i, line in enumerate(log_lines):
+      if "error" in line.lower():
+          start_index = max(0, i - prelines)
+          end_index = min(len(log_lines), i + postlines + 1)
+          error_logs.extend(log_lines[start_index:end_index])
+
+  return error_logs
+
+error_logs = find_errors_in_log_file()
+
+data = {
+  "prompt": "\n".join(error_logs), 
+  "model": "mattw/loganalyzer"
+}
+
+response = requests.post("http://localhost:11434/api/generate", json=data, stream=True)
+for line in response.iter_lines():
+  if line:
+    json_data = json.loads(line)
+    if json_data['done'] == False:
+      print(json_data['response'], end='', flush=True)
+
--- a/examples/python-loganalysis/logtest.logfile
+++ b/examples/python-loganalysis/logtest.logfile
@@ -0,0 +1,32 @@
+2023-11-10 07:17:40 /docker-entrypoint.sh: /docker-entrypoint.d/ is not empty, will attempt to perform configuration
+2023-11-10 07:17:40 /docker-entrypoint.sh: Looking for shell scripts in /docker-entrypoint.d/
+2023-11-10 07:17:40 /docker-entrypoint.sh: Launching /docker-entrypoint.d/10-listen-on-ipv6-by-default.sh
+2023-11-10 07:17:40 10-listen-on-ipv6-by-default.sh: info: Getting the checksum of /etc/nginx/conf.d/default.conf
+2023-11-10 07:17:40 10-listen-on-ipv6-by-default.sh: info: Enabled listen on IPv6 in /etc/nginx/conf.d/default.conf
+2023-11-10 07:17:40 /docker-entrypoint.sh: Sourcing /docker-entrypoint.d/15-local-resolvers.envsh
+2023-11-10 07:17:40 /docker-entrypoint.sh: Launching /docker-entrypoint.d/20-envsubst-on-templates.sh
+2023-11-10 07:17:40 /docker-entrypoint.sh: Launching /docker-entrypoint.d/30-tune-worker-processes.sh
+2023-11-10 07:17:40 /docker-entrypoint.sh: Configuration complete; ready for start up
+2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: using the "epoll" event method
+2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: nginx/1.25.3
+2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: built by gcc 12.2.0 (Debian 12.2.0-14) 
+2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: OS: Linux 6.4.16-linuxkit
+2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: getrlimit(RLIMIT_NOFILE): 1048576:1048576
+2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker processes
+2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 29
+2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 30
+2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 31
+2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 32
+2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 33
+2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 34
+2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 35
+2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 36
+2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 37
+2023-11-10 07:17:40 2023/11/10 13:17:40 [notice] 1#1: start worker process 38
+2023-11-10 07:17:44 192.168.65.1 - - [10/Nov/2023:13:17:43 +0000] "GET / HTTP/1.1" 200 615 "-" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" "-"
+2023-11-10 07:17:44 2023/11/10 13:17:44 [error] 29#29: *1 open() "/usr/share/nginx/html/favicon.ico" failed (2: No such file or directory), client: 192.168.65.1, server: localhost, request: "GET /favicon.ico HTTP/1.1", host: "localhost:8080", referrer: "http://localhost:8080/"
+2023-11-10 07:17:44 192.168.65.1 - - [10/Nov/2023:13:17:44 +0000] "GET /favicon.ico HTTP/1.1" 404 555 "http://localhost:8080/" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" "-"
+2023-11-10 07:17:50 2023/11/10 13:17:50 [error] 29#29: *1 open() "/usr/share/nginx/html/ahstat" failed (2: No such file or directory), client: 192.168.65.1, server: localhost, request: "GET /ahstat HTTP/1.1", host: "localhost:8080"
+2023-11-10 07:17:50 192.168.65.1 - - [10/Nov/2023:13:17:50 +0000] "GET /ahstat HTTP/1.1" 404 555 "-" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" "-"
+2023-11-10 07:18:53 2023/11/10 13:18:53 [error] 29#29: *1 open() "/usr/share/nginx/html/ahstat" failed (2: No such file or directory), client: 192.168.65.1, server: localhost, request: "GET /ahstat HTTP/1.1", host: "localhost:8080"
+2023-11-10 07:18:53 192.168.65.1 - - [10/Nov/2023:13:18:53 +0000] "GET /ahstat HTTP/1.1" 404 555 "-" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36" "-"
--- a/examples/python-loganalysis/readme.md
+++ b/examples/python-loganalysis/readme.md
@@ -0,0 +1,72 @@
+# Log Analysis example
+
+![loganalyzer 2023-11-10 08_53_29](https://github.com/ollama/ollama/assets/633681/ad30f1fc-321f-4953-8914-e30e24db9921)
+
+This example shows one possible way to create a log file analyzer. It uses the model **mattw/loganalyzer** which is based on **codebooga**, a 34b parameter model.
+
+To use it, run:
+
+`python loganalysis.py <logfile>`
+
+You can try this with the `logtest.logfile` file included in this directory.
+
+## Running the Example
+
+1. Ensure you have the `mattw/loganalyzer` model installed:
+
+   ```bash
+   ollama pull mattw/loganalyzer
+   ```
+
+2. Install the Python Requirements.
+
+   ```bash
+   python3 -m venv .venv
+   source .venv/bin/activate
+   pip install -r requirements.txt
+   ```
+
+3. Run the example:
+
+   ```bash
+   python loganalysis.py logtest.logfile
+   ```
+
+## Review the code
+
+The first part of this example is a Modelfile that takes `codebooga` and applies a new System Prompt:
+
+```plaintext
+SYSTEM """
+You are a log file analyzer. You will receive a set of lines from a log file for some software application, find the errors and other interesting aspects of the logs, and explain them so a new user can understand what they mean. If there are any steps they can do to resolve them, list the steps in your answer.
+"""
+```
+
+This model is available at https://ollama.com/mattw/loganalyzer. You can customize it and add to your own namespace using the command `ollama create <namespace/modelname> -f <path-to-modelfile>` then `ollama push <namespace/modelname>`.
+
+Then loganalysis.py scans all the lines in the given log file and searches for the word 'error'. When the word is found, the 10 lines before and after are set as the prompt for a call to the Generate API.
+
+```python
+data = {
+  "prompt": "\n".join(error_logs),
+  "model": "mattw/loganalyzer"
+}
+```
+
+Finally, the streamed output is parsed and the response field in the output is printed to the line.
+
+```python
+response = requests.post("http://localhost:11434/api/generate", json=data, stream=True)
+for line in response.iter_lines():
+  if line:
+    json_data = json.loads(line)
+    if json_data['done'] == False:
+      print(json_data['response'], end='')
+
+```
+
+## Next Steps
+
+There is a lot more that can be done here. This is a simple way to detect errors, looking for the word error. Perhaps it would be interesting to find anomalous activity in the logs. It could be interesting to create embeddings for each line and compare them, looking for similar lines. Or look into applying Levenshtein Distance algorithms to find similar lines to help identify the anomalous lines.
+
+Try different models and different prompts to analyze the data. You could consider adding retrieval augmented generation (RAG) to this to help understand newer log formats.
--- a/examples/python-loganalysis/requirements.txt
+++ b/examples/python-loganalysis/requirements.txt
@@ -0,0 +1 @@
+Requests>=2.32.3
--- a/examples/python-rag-newssummary/README.md
+++ b/examples/python-rag-newssummary/README.md
@@ -0,0 +1,35 @@
+# News Summarizer
+
+This example goes through a series of steps:
+
+  1. You choose a topic area (e.g., "news", "NVidia", "music", etc.).
+  2. Gets the most recent articles on that topic from various sources.
+  3. Uses Ollama to summarize each article.
+  4. Creates chunks of sentences from each article.
+  5. Uses Sentence Transformers to generate embeddings for each of those chunks.
+  6. You enter a question regarding the summaries shown.
+  7. Uses Sentence Transformers to generate an embedding for that question.
+  8. Uses the embedded question to find the most similar chunks.
+  9. Feeds all that to Ollama to generate a good answer to your question based on these news articles.
+
+This example lets you pick from a few different topic areas, then summarize the most recent x articles for that topic. It then creates chunks of sentences from each article and then generates embeddings for each of those chunks.
+
+## Running the Example
+
+1. Ensure you have the `mistral-openorca` model installed:
+
+   ```bash
+   ollama pull mistral-openorca
+   ```
+
+2. Install the Python Requirements.
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Run the example:
+
+   ```bash
+   python summ.py
+   ```
--- a/examples/python-rag-newssummary/requirements.txt
+++ b/examples/python-rag-newssummary/requirements.txt
@@ -0,0 +1,9 @@
+beautifulsoup4==4.12.2
+feedparser==6.0.10
+mattsollamatools==0.0.8
+newspaper3k==0.2.8
+nltk==3.8.1
+numpy==1.24.3
+Requests==2.31.0
+scikit_learn==1.3.0
+sentence_transformers==2.2.2
--- a/examples/python-rag-newssummary/summ.py
+++ b/examples/python-rag-newssummary/summ.py
@@ -0,0 +1,86 @@
+import curses
+import json
+from utils import get_url_for_topic, topic_urls, menu, getUrls, get_summary, getArticleText, knn_search
+import requests
+from sentence_transformers import SentenceTransformer
+from mattsollamatools import chunker
+
+if __name__ == "__main__":
+    chosen_topic = curses.wrapper(menu)
+    print("Here is your news summary:\n")
+    urls = getUrls(chosen_topic, n=5)
+    model = SentenceTransformer('all-MiniLM-L6-v2')
+    allEmbeddings = []
+
+    for url in urls:
+      article={}
+      article['embeddings'] = []
+      article['url'] = url
+      text = getArticleText(url)
+      summary = get_summary(text)
+      chunks = chunker(text)  # Use the chunk_text function from web_utils
+      embeddings = model.encode(chunks)
+      for (chunk, embedding) in zip(chunks, embeddings):
+        item = {}
+        item['source'] = chunk
+        item['embedding'] = embedding.tolist()  # Convert NumPy array to list
+        item['sourcelength'] = len(chunk)
+        article['embeddings'].append(item)
+    
+      allEmbeddings.append(article)
+
+      print(f"{summary}\n")
+
+    
+    while True:
+      context = []
+      # Input a question from the user
+      question = input("Enter your question about the news, or type quit: ")
+
+      if question.lower() == 'quit':
+        break
+
+      # Embed the user's question
+      question_embedding = model.encode([question])
+
+      # Perform KNN search to find the best matches (indices and source text)
+      best_matches = knn_search(question_embedding, allEmbeddings, k=10)
+
+
+      sourcetext=""
+      for i, (index, source_text) in enumerate(best_matches, start=1):
+          sourcetext += f"{i}. Index: {index}, Source Text: {source_text}"
+
+      systemPrompt = f"Only use the following information to answer the question. Do not use anything else: {sourcetext}"
+
+      url = "http://localhost:11434/api/generate"
+
+      payload = {
+      "model": "mistral-openorca",
+      "prompt": question, 
+      "system": systemPrompt,
+      "stream": False, 
+      "context": context
+      }
+
+      # Convert the payload to a JSON string
+      payload_json = json.dumps(payload)
+
+      # Set the headers to specify JSON content
+      headers = {
+          "Content-Type": "application/json"
+      }
+
+      # Send the POST request
+      response = requests.post(url, data=payload_json, headers=headers)
+
+      # Check the response
+      if response.status_code == 200:
+          output = json.loads(response.text)
+          context = output['context']
+          print(output['response']+ "\n")
+          
+
+      else:
+          print(f"Request failed with status code {response.status_code}")
+
--- a/examples/python-rag-newssummary/utils.py
+++ b/examples/python-rag-newssummary/utils.py
@@ -0,0 +1,108 @@
+import curses
+import feedparser
+import requests
+import unicodedata
+import json
+from newspaper import Article
+from bs4 import BeautifulSoup
+from nltk.tokenize import sent_tokenize, word_tokenize
+import numpy as np
+from sklearn.neighbors import NearestNeighbors
+from mattsollamatools import chunker
+
+# Create a dictionary to store topics and their URLs
+topic_urls = {
+    "Mac": "https://9to5mac.com/guides/mac/feed",
+    "News": "http://www.npr.org/rss/rss.php?id=1001",
+    "Nvidia": "https://nvidianews.nvidia.com/releases.xml",
+    "Raspberry Pi": "https://www.raspberrypi.com/news/feed/", 
+    "Music": "https://www.billboard.com/c/music/music-news/feed/"
+}
+
+# Use curses to create a menu of topics
+def menu(stdscr):
+    chosen_topic = get_url_for_topic(stdscr)  
+    url = topic_urls[chosen_topic] if chosen_topic in topic_urls else "Topic not found"
+    
+    stdscr.addstr(len(topic_urls) + 3, 0, f"Selected URL for {chosen_topic}: {url}")
+    stdscr.refresh()
+    
+    return chosen_topic
+
+# You have chosen a topic. Now return the url for that topic
+def get_url_for_topic(stdscr):
+    curses.curs_set(0)  # Hide the cursor
+    stdscr.clear()
+
+    stdscr.addstr(0, 0, "Choose a topic using the arrow keys (Press Enter to select):")
+
+    # Create a list of topics
+    topics = list(topic_urls.keys())
+    current_topic = 0
+
+    while True:
+        for i, topic in enumerate(topics):
+            if i == current_topic:
+                stdscr.addstr(i + 2, 2, f"> {topic}")
+            else:
+                stdscr.addstr(i + 2, 2, f"  {topic}")
+
+        stdscr.refresh()
+
+        key = stdscr.getch()
+
+        if key == curses.KEY_DOWN and current_topic < len(topics) - 1:
+            current_topic += 1
+        elif key == curses.KEY_UP and current_topic > 0:
+            current_topic -= 1
+        elif key == 10:  # Enter key
+            return topic_urls[topics[current_topic]]
+
+# Get the last N URLs from an RSS feed
+def getUrls(feed_url, n=20):
+    feed = feedparser.parse(feed_url)
+    entries = feed.entries[-n:]
+    urls = [entry.link for entry in entries]
+    return urls
+
+# Often there are a bunch of ads and menus on pages for a news article. This uses newspaper3k to get just the text of just the article.
+def getArticleText(url):
+  article = Article(url)
+  article.download()
+  article.parse()
+  return article.text
+
+def get_summary(text):
+  systemPrompt = "Write a concise summary of the text, return your responses with 5 lines that cover the key points of the text given."
+  prompt = text
+  
+  url = "http://localhost:11434/api/generate"
+
+  payload = {
+    "model": "mistral-openorca",
+    "prompt": prompt, 
+    "system": systemPrompt,
+    "stream": False
+  }
+  payload_json = json.dumps(payload)
+  headers = {"Content-Type": "application/json"}
+  response = requests.post(url, data=payload_json, headers=headers)
+
+  return json.loads(response.text)["response"]
+
+# Perform K-nearest neighbors (KNN) search
+def knn_search(question_embedding, embeddings, k=5):
+    X = np.array([item['embedding'] for article in embeddings for item in article['embeddings']])
+    source_texts = [item['source'] for article in embeddings for item in article['embeddings']]
+    
+    # Fit a KNN model on the embeddings
+    knn = NearestNeighbors(n_neighbors=k, metric='cosine')
+    knn.fit(X)
+    
+    # Find the indices and distances of the k-nearest neighbors
+    distances, indices = knn.kneighbors(question_embedding, n_neighbors=k)
+    
+    # Get the indices and source texts of the best matches
+    best_matches = [(indices[0][i], source_texts[indices[0][i]]) for i in range(k)]
+    
+    return best_matches
--- a/examples/python-simplechat/client.py
+++ b/examples/python-simplechat/client.py
@@ -0,0 +1,48 @@
+import json
+import requests
+
+# NOTE: ollama must be running for this to work, start the ollama app or run `ollama serve`
+model = "llama3.2"  # TODO: update this for whatever model you wish to use
+
+
+def chat(messages):
+    r = requests.post(
+        "http://0.0.0.0:11434/api/chat",
+        json={"model": model, "messages": messages, "stream": True},
+	stream=True
+    )
+    r.raise_for_status()
+    output = ""
+
+    for line in r.iter_lines():
+        body = json.loads(line)
+        if "error" in body:
+            raise Exception(body["error"])
+        if body.get("done") is False:
+            message = body.get("message", "")
+            content = message.get("content", "")
+            output += content
+            # the response streams one token at a time, print that as we receive it
+            print(content, end="", flush=True)
+
+        if body.get("done", False):
+            message["content"] = output
+            return message
+
+
+def main():
+    messages = []
+
+    while True:
+        user_input = input("Enter a prompt: ")
+        if not user_input:
+            exit()
+        print()
+        messages.append({"role": "user", "content": user_input})
+        message = chat(messages)
+        messages.append(message)
+        print("\n\n")
+
+
+if __name__ == "__main__":
+    main()
--- a/examples/python-simplechat/readme.md
+++ b/examples/python-simplechat/readme.md
@@ -0,0 +1,44 @@
+# Simple Chat Example
+
+The **chat** endpoint is one of two ways to generate text from an LLM with Ollama, and is introduced in version 0.1.14. At a high level, you provide the endpoint an array of objects with a role and content specified. Then with each output and prompt, you add more of those role/content objects, which builds up the history.
+
+## 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 client.py
+   ```
+
+## Review the Code
+
+You can see in the **chat** function that actually calling the endpoint is done simply with:
+
+```python
+r = requests.post(
+  "http://0.0.0.0:11434/api/chat",
+  json={"model": model, "messages": messages, "stream": True},
+)
+```
+
+With the **generate** endpoint, you need to provide a `prompt`. But with **chat**, you provide `messages`. And the resulting stream of responses includes a `message` object with a `content` field.
+
+The final JSON object doesn't provide the full content, so you will need to build the content yourself.
+
+In the **main** function, we collect `user_input` and add it as a message to our messages and that is passed to the chat function. When the LLM is done responding the output is added as another message.
+
+## Next Steps
+
+In this example, all generations are kept. You might want to experiment with summarizing everything older than 10 conversations to enable longer history with less context being used.
--- a/examples/python-simplechat/requirements.txt
+++ b/examples/python-simplechat/requirements.txt
@@ -0,0 +1 @@
+Requests==2.31.0
--- a/Show More
+++ b/Show More