fix ref

The numa flag may be having a performance impact on multi-socket systems with GPU loads

.gitattributes

@@ -1 +1,3 @@
 llm/ext_server/* linguist-vendored
+* text=auto
+*.go text eol=lf

.github/ISSUE_TEMPLATE/10_bug_report.yml

@@ -0,0 +1,60 @@
+name: Bug report
+labels: [bug]
+description: Something isn't working right.
+body:
+  - type: textarea
+    id: description
+    attributes:
+      label: What is the issue?
+      description: What happened? What did you expect to happen?
+    validations:
+      required: true
+  - type: dropdown
+    id: os
+    attributes:
+      label: OS
+      description: Which operating system are you using?
+      multiple: true
+      options:
+        - Linux
+        - macOS
+        - Windows
+        - Docker
+        - WSL2
+    validations:
+      required: false
+  - type: dropdown
+    id: gpu
+    attributes:
+      label: GPU
+      description: Which GPU are you using?
+      multiple: true
+      options:
+        - Nvidia
+        - AMD
+        - Intel
+        - Apple
+        - Other
+    validations:
+      required: false
+  - type: dropdown
+    id: cpu
+    attributes:
+      label: CPU
+      description: Which CPU are you using?
+      multiple: true
+      options:
+        - Intel
+        - AMD
+        - Apple
+        - Other
+    validations:
+      required: false
+  - type: input
+    id: version
+    attributes:
+      label: Ollama version
+      description: What version of Ollama are you using? (`ollama --version`)
+      placeholder: e.g., 0.1.32
+    validations:
+      required: false

.github/ISSUE_TEMPLATE/10_model_request.yml

@@ -1,18 +0,0 @@
-name: Model request
-description: Request a new model for the library
-labels: [mr]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Please check if your Model request is [already available](https://ollama.com/search) or that you cannot [import it](https://github.com/ollama/ollama/blob/main/docs/import.md#import-a-model) yourself.
-        Tell us about which Model you'd like to see in the library!
-  - type: textarea
-    id: problem
-    attributes:
-      label: What model would you like?
-      description: Please provide a link to the model.
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for filing a model request!

.github/ISSUE_TEMPLATE/20_feature_request.md

@@ -0,0 +1,6 @@
+---
+name: Feature request
+about: Request a new feature
+labels: feature request
+---
+

.github/ISSUE_TEMPLATE/20_feature_request.yml

@@ -1,41 +0,0 @@
-name: Feature request
-description: Propose a new feature
-labels: [needs-triage, fr]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Please check if your feature request is [already filed](https://github.com/ollama/ollama/issues).
-        Tell us about your idea!
-  - type: textarea
-    id: problem
-    attributes:
-      label: What are you trying to do?
-      description: Tell us about the problem you're trying to solve.
-    validations:
-      required: false
-  - type: textarea
-    id: solution
-    attributes:
-      label: How should we solve this?
-      description: If you have an idea of how you'd like to see this feature work, let us know.
-    validations:
-      required: false
-  - type: textarea
-    id: alternative
-    attributes:
-      label: What is the impact of not solving this?
-      description: (How) Are you currently working around the issue?
-    validations:
-      required: false
-  - type: textarea
-    id: context
-    attributes:
-      label: Anything else?
-      description: Any additional context to share, e.g., links
-    validations:
-      required: false
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for filing a feature request!

.github/ISSUE_TEMPLATE/30_model_request.md

@@ -0,0 +1,5 @@
+---
+name: Model request
+about: Request support for a new model to be added to Ollama
+labels: model request
+---

.github/ISSUE_TEMPLATE/90_bug_report.yml

@@ -1,125 +0,0 @@
-name: Bug report
-description: File a bug report. If you need help, please join our Discord server.
-labels: [needs-triage, bug]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Please check if your bug is [already filed](https://github.com/ollama/ollama/issues) before filing a new one.
-  - type: textarea
-    id: what-happened
-    attributes:
-      label: What is the issue?
-      description: What happened? What did you expect to happen?
-    validations:
-      required: true
-  - type: textarea
-    id: what-was-expected
-    attributes:
-      label: What did you expect to see?
-      description: What did you expect to see/happen instead?
-    validations:
-      required: false
-  - type: textarea
-    id: steps
-    attributes:
-      label: Steps to reproduce
-      description: What are the steps you took that hit this issue?
-    validations:
-      required: false
-  - type: textarea
-    id: changes
-    attributes:
-      label: Are there any recent changes that introduced the issue?
-      description: If so, what are those changes?
-    validations:
-      required: false
-  - type: dropdown
-    id: os
-    attributes:
-      label: OS
-      description: What OS are you using? You may select more than one.
-      multiple: true
-      options:
-        - Linux
-        - macOS
-        - Windows
-        - Other
-    validations:
-      required: false
-  - type: dropdown
-    id: architecture
-    attributes:
-      label: Architecture
-      description: What architecture are you using? You may select more than one.
-      multiple: true
-      options:
-        - arm64
-        - amd64
-        - x86
-        - Other
-  - type: dropdown
-    id: platform
-    attributes:
-      label: Platform
-      description: What platform are you using? You may select more than one.
-      multiple: true
-      options:
-        - Docker
-        - WSL
-        - WSL2
-    validations:
-      required: false
-  - type: input
-    id: ollama-version
-    attributes:
-      label: Ollama version
-      description: What Ollama version are you using? (`ollama --version`)
-      placeholder: e.g., 1.14.4
-    validations:
-      required: false
-  - type: dropdown
-    id: gpu
-    attributes:
-      label: GPU
-      description: What GPU, if any, are you using? You may select more than one.
-      multiple: true
-      options:
-        - Nvidia
-        - AMD
-        - Intel
-        - Apple
-        - Other
-    validations:
-      required: false
-  - type: textarea
-    id: gpu-info
-    attributes:
-      label: GPU info
-      description: What GPU info do you have? (`nvidia-smi`, `rocminfo`, `system_profiler SPDisplaysDataType`, etc.)
-    validations:
-      required: false
-  - type: dropdown
-    id: cpu
-    attributes:
-      label: CPU
-      description: What CPU are you using? You may select more than one.
-      multiple: true
-      options:
-        - Intel
-        - AMD
-        - Apple
-        - Other
-    validations:
-      required: false
-  - type: textarea
-    id: other-software
-    attributes:
-      label: Other software
-      description: What other software are you using that might be related to this issue?
-    validations:
-      required: false
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for filing a bug report!

.github/workflows/release.yaml

@@ -28,6 +28,7 @@ jobs:
           security unlock-keychain -p password build.keychain
           security import certificate.p12 -k build.keychain -P $MACOS_SIGNING_KEY_PASSWORD -T /usr/bin/codesign
           security set-key-partition-list -S apple-tool:,apple:,codesign: -s -k password build.keychain
+          security set-keychain-settings -lut 3600 build.keychain
       - uses: actions/setup-go@v5
         with:
           go-version-file: go.mod
@@ -103,6 +104,7 @@ jobs:
           path: |
             llm/build/**/bin/*
             llm/build/**/*.a
+            dist/windows-amd64/**
 
   # ROCm generation step
   generate-windows-rocm:
@@ -145,7 +147,7 @@ jobs:
         run: |
           $ErrorActionPreference = "Stop"
           write-host "downloading AMD HIP Installer"
-          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-23.Q4-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
+          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
           write-host "Installing AMD HIP"
           Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
           write-host "Completed AMD HIP"
@@ -173,7 +175,9 @@ jobs:
       - uses: actions/upload-artifact@v4
         with:
           name: generate-windows-rocm
-          path: llm/build/**/bin/*
+          path: |
+            llm/build/**/bin/*
+            dist/windows-amd64/**
       - uses: actions/upload-artifact@v4
         with:
           name: windows-rocm-deps
@@ -183,6 +187,13 @@ jobs:
   generate-windows-cuda:
     environment: release
     runs-on: windows
+    strategy:
+      matrix:
+        cuda:
+          - version: "11"
+            url: 'https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda_11.3.1_465.89_win10.exe'
+          - version: "12"
+            url: 'https://developer.download.nvidia.com/compute/cuda/12.4.0/local_installers/cuda_12.4.0_551.61_windows.exe'
     env:
       KEY_CONTAINER: ${{ vars.KEY_CONTAINER }}
     steps:
@@ -216,11 +227,11 @@ jobs:
         with:
           go-version-file: go.mod
           cache: true
-      - name: 'Install CUDA'
+      - name: 'Install CUDA ${{ matrix.cuda.version }}'
         run: |
           $ErrorActionPreference = "Stop"
           write-host "downloading CUDA Installer"
-          Invoke-WebRequest -Uri "https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda_11.3.1_465.89_win10.exe" -OutFile "${env:RUNNER_TEMP}\cuda-install.exe"
+          Invoke-WebRequest -Uri "${{ matrix.cuda.url }}" -OutFile "${env:RUNNER_TEMP}\cuda-install.exe"
           write-host "Installing CUDA"
           Start-Process "${env:RUNNER_TEMP}\cuda-install.exe" -ArgumentList '-s' -NoNewWindow -Wait
           write-host "Completed CUDA"
@@ -252,13 +263,16 @@ jobs:
           cp "${NVIDIA_DIR}\cublasLt64_*.dll" "dist\deps\"
       - uses: actions/upload-artifact@v4
         with:
-          name: generate-windows-cuda
+          name: generate-windows-cuda-${{ matrix.cuda.version }}
-          path: llm/build/**/bin/*
+          path: |
+            llm/build/**/bin/*
+            dist/windows-amd64/**
       - uses: actions/upload-artifact@v4
         with:
-          name: windows-cuda-deps
+          name: windows-cuda-deps-${{ matrix.cuda.version }}
           path: dist/deps/*
 
+
   # Import the prior generation steps and build the final windows assets
   build-windows:
     environment: release
@@ -306,23 +320,24 @@ jobs:
       - uses: actions/download-artifact@v4
         with:
           name: generate-windows-cpu
-          path: llm/build
       - uses: actions/download-artifact@v4
         with:
-          name: generate-windows-cuda
+          name: generate-windows-cuda-11
-          path: llm/build
       - uses: actions/download-artifact@v4
         with:
-          name: windows-cuda-deps
+          name: generate-windows-cuda-12
-          path: dist/deps
+      - uses: actions/download-artifact@v4
+        with:
+          name: windows-cuda-deps-11
+      - uses: actions/download-artifact@v4
+        with:
+          name: windows-cuda-deps-12
       - uses: actions/download-artifact@v4
         with:
           name: windows-rocm-deps
-          path: dist/deps
       - uses: actions/download-artifact@v4
         with:
           name: generate-windows-rocm
-          path: llm/build
       - run: dir llm/build
       - run: |
           $gopath=(get-command go).source | split-path -parent
@@ -331,13 +346,13 @@ jobs:
           $env:CMAKE_SYSTEM_VERSION="10.0.22621.0"
           $env:PATH="$gopath;$env:PATH"
           $env:OLLAMA_SKIP_GENERATE="1"
-          $env:NVIDIA_DIR=$(resolve-path ".\dist\deps")
-          $env:HIP_PATH=$(resolve-path ".\dist\deps")
           & .\scripts\build_windows.ps1
       - uses: actions/upload-artifact@v4
         with:
           name: dist-windows
-          path: dist/*.exe
+          path: |
+            dist/OllamaSetup.exe
+            dist/ollama-windows-*.zip
 
   # Linux x86 assets built using the container based build
   build-linux-amd64:
@@ -362,7 +377,6 @@ jobs:
       - run: |
           ./scripts/build_linux.sh
           ./scripts/build_docker.sh
-          mv dist/deps/* dist/
       - uses: actions/upload-artifact@v4
         with:
           name: dist-linux-amd64
@@ -436,6 +450,7 @@ jobs:
     env:
       OLLAMA_SKIP_IMAGE_BUILD: '1'
       PUSH: '1'
+      GH_TOKEN: ${{ github.token }}
     steps:
       - uses: actions/checkout@v4
       - name: Set Version
@@ -457,17 +472,25 @@ jobs:
           merge-multiple: true
       - run: |
           ls -lh dist/
-          (cd dist; sha256sum * > sha256sum.txt)
+          (cd dist; find . -type f | xargs sha256sum > ../sha256sum.txt)
+          mv sha256sum.txt dist/
+          mv dist/linux-???64 .
+          mv dist/linux-amd64-rocm .
           cat dist/sha256sum.txt
-      - uses: ncipollo/release-action@v1
+      - name: Create or update Release
-        with:
+        run: |
-          name: ${{ env.RELEASE_VERSION }}
+          echo "Looking for existing release for ${{ env.RELEASE_VERSION }}"
-          allowUpdates: true
+          OLD_TAG=$(gh release ls --json name,tagName | jq -r ".[] | select(.name == \"${{ env.RELEASE_VERSION }}\") | .tagName")
-          artifacts: 'dist/*'
+          if [ -n "$OLD_TAG" ]; then
-          draft: true
+            echo "Updating release ${{ env.RELEASE_VERSION }} to point to new tag ${GITHUB_REF_NAME}"
-          prerelease: true
+            gh release edit ${OLD_TAG} --tag ${GITHUB_REF_NAME}
-          omitBodyDuringUpdate: true
+          else
-          generateReleaseNotes: true
+            echo "Creating new release ${{ env.RELEASE_VERSION }} pointing to tag ${GITHUB_REF_NAME}"
-          omitDraftDuringUpdate: true
+            gh release create ${GITHUB_REF_NAME} \
-          omitPrereleaseDuringUpdate: true
+              --title ${{ env.RELEASE_VERSION }} \
-          replacesArtifacts: true
+              --draft \
+              --generate-notes \
+              --prerelease
+          fi
+          echo "Uploading artifacts for tag ${GITHUB_REF_NAME}"
+          gh release upload ${GITHUB_REF_NAME} dist/* --clobber

.github/workflows/test.yaml

@@ -1,11 +1,20 @@
 name: test
 
+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.
+  #
+  # For non-PR pushes, concurrency.group needs to be unique for every distinct
+  # CI run we want to have happen. Use run_id, which in practice means all
+  # non-PR CI runs will be allowed to run without preempting each other.
+  group: ${{ github.workflow }}-$${{ github.pull_request.number || github.run_id }}
+  cancel-in-progress: true
+
 on:
   pull_request:
     paths:
       - '**/*'
       - '!docs/**'
-      - '!examples/**'
       - '!README.md'
 
 jobs:
@@ -22,14 +31,16 @@ jobs:
       - id: changes
         run: |
           changed() {
-            git diff-tree -r --no-commit-id --name-only ${{ github.event.pull_request.base.sha }} ${{ github.event.pull_request.head.sha }} \
+            git diff-tree -r --no-commit-id --name-only \
-              | xargs python3 -c "import sys; print(any([x.startswith('$1') for x in sys.argv[1:]]))"
+              $(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 GENERATE=$(changed llm/)
+            echo GENERATE=$(changed 'llm/llama.cpp' 'llm/patches/**' 'llm/ext_server/**' 'llm/generate/**')
-            echo GENERATE_CUDA=$(changed llm/)
+            echo GENERATE_CUDA=$(changed 'llm/llama.cpp' 'llm/patches/**' 'llm/ext_server/**' 'llm/generate/**')
-            echo GENERATE_ROCM=$(changed llm/)
+            echo GENERATE_ROCM=$(changed 'llm/llama.cpp' 'llm/patches/**' 'llm/ext_server/**' 'llm/generate/**')
           } >>$GITHUB_OUTPUT
 
   generate:
@@ -47,6 +58,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     env:
       GOARCH: ${{ matrix.arch }}
+      CGO_ENABLED: '1'
     steps:
       - uses: actions/checkout@v4
       - uses: actions/setup-go@v5
@@ -68,6 +80,7 @@ jobs:
       - run: go generate -x ./...
         if: ${{ ! startsWith(matrix.os, 'windows-') }}
         name: 'Unix Go Generate'
+      - run: go build .
       - uses: actions/upload-artifact@v4
         with:
           name: ${{ matrix.os }}-${{ matrix.arch }}-libraries
@@ -104,14 +117,16 @@ jobs:
       - uses: actions/upload-artifact@v4
         with:
           name: cuda-${{ matrix.cuda-version }}-libraries
-          path: llm/build/**/bin/*
+          path: |
+            llm/build/**/bin/*
+            dist/windows-amd64/**
   generate-rocm:
     needs: [changes]
     if: ${{ needs.changes.outputs.GENERATE_ROCM == 'True' }}
     strategy:
       matrix:
         rocm-version:
-          - '6.0.2'
+          - '6.1.2'
     runs-on: linux
     container: rocm/dev-ubuntu-20.04:${{ matrix.rocm-version }}
     steps:
@@ -135,7 +150,9 @@ jobs:
       - uses: actions/upload-artifact@v4
         with:
           name: rocm-${{ matrix.rocm-version }}-libraries
-          path: llm/build/**/bin/*
+          path: |
+            llm/build/**/bin/*
+            dist/windows-amd64/**
 
   # ROCm generation step
   generate-windows-rocm:
@@ -152,7 +169,7 @@ jobs:
         run: |
           $ErrorActionPreference = "Stop"
           write-host "downloading AMD HIP Installer"
-          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-23.Q4-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
+          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
           write-host "Installing AMD HIP"
           Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
           write-host "Completed AMD HIP"
@@ -254,14 +271,9 @@ jobs:
           mkdir -p llm/build/darwin/$ARCH/stub/bin
           touch llm/build/darwin/$ARCH/stub/bin/ollama_llama_server
         if: ${{ startsWith(matrix.os, 'macos-') }}
-      - run: |
+      - uses: golangci/golangci-lint-action@v6
-          mkdir -p llm/build/windows/$ARCH/stub/bin
-          touch llm/build/windows/$ARCH/stub/bin/ollama_llama_server
-        if: ${{ startsWith(matrix.os, 'windows-') }}
-        shell: bash
-      - uses: golangci/golangci-lint-action@v4
         with:
-          args: --timeout 8m0s
+          args: --timeout 8m0s -v
   test:
     strategy:
       matrix:
@@ -277,6 +289,8 @@ jobs:
       GOARCH: ${{ matrix.arch }}
       CGO_ENABLED: '1'
       OLLAMA_CPU_TARGET: 'static'
+      OLLAMA_SKIP_CPU_GENERATE: '1'
+      OLLAMA_SKIP_METAL_GENERATE: '1'
     steps:
       - uses: actions/checkout@v4
         with:
@@ -285,7 +299,6 @@ jobs:
         with:
           go-version-file: go.mod
           cache: true
-      - run: go get
       - run: |
           case ${{ matrix.arch }} in
             amd64) echo ARCH=x86_64 ;;
@@ -300,10 +313,6 @@ jobs:
           mkdir -p llm/build/darwin/$ARCH/stub/bin
           touch llm/build/darwin/$ARCH/stub/bin/ollama_llama_server
         if: ${{ startsWith(matrix.os, 'macos-') }}
-      - run: |
-          mkdir -p llm/build/windows/$ARCH/stub/bin
-          touch llm/build/windows/$ARCH/stub/bin/ollama_llama_server
-        if: ${{ startsWith(matrix.os, 'windows-') }}
         shell: bash
       - run: go generate ./...
       - run: go build

.gitignore

@@ -12,3 +12,4 @@ ggml-metal.metal
 test_data
 *.crt
 llm/build
+__debug_bin*

.golangci.yaml

@@ -7,11 +7,37 @@ linters:
     - bodyclose
     - containedctx
     - contextcheck
+    - errcheck
     - exportloopref
+    - gci
     - gocheckcompilerdirectives
-    # FIXME: for some reason this errors on windows
+    - gofmt
-    # - gofmt
+    - gofumpt
-    # - goimports
+    - gosimple
+    - govet
+    - ineffassign
+    - intrange
+    - makezero
     - misspell
     - nilerr
+    - nolintlint
+    - nosprintfhostport
+    - staticcheck
+    - tenv
+    - unconvert
     - unused
+    - usestdlibvars
+    - wastedassign
+    - whitespace
+linters-settings:
+  gci:
+    sections: [standard, default, localmodule]
+severity:
+  default-severity: error
+  rules:
+    - linters:
+        - gofmt
+        - goimports
+        - intrange
+        - usestdlibvars
+      severity: info

CONTRIBUTING.md

@@ -0,0 +1,37 @@
+# Contributing to Ollama
+
+Thank you for your interest in contributing to Ollama! Here are a few guidelines to help get you started.
+
+## Set up
+
+See the [development documentation](./docs/development.md) for instructions on how to build and run Ollama locally.
+
+## Pull requests
+
+### Ideal issues
+
+* [Bugs](https://github.com/ollama/ollama/issues?q=is%3Aissue+is%3Aopen+label%3Abug): issues where Ollama stops working or where it results in an unexpected error.
+* [Performance](https://github.com/ollama/ollama/issues?q=is%3Aissue+is%3Aopen+label%3Aperformance): issues to make Ollama faster at model inference, downloading or uploading.
+* [Security](https://github.com/ollama/ollama/blob/main/SECURITY.md): issues that could lead to a security vulnerability. As mentioned in [SECURITY.md](https://github.com/ollama/ollama/blob/main/SECURITY.md), please do not disclose security vulnerabilities publicly.
+
+### Issues that are harder to review
+
+* New features: new features (e.g. API fields, environment variables) add surface area to Ollama and make it harder to maintain in the long run as they cannot be removed without potentially breaking users in the future.
+* Refactoring: large code improvements are important, but can be harder or take longer to review and merge.
+* Documentation: small updates to fill in or dorrect missing documentation is helpful, however large documentation additions can be hard to maintain over time.
+
+### Issues that may not be accepted
+
+* Changes that break backwards compatibility in Ollama's API (including the OpenAI-compatible API)
+* Changes that add significant friction to the user experience
+* Changes that create a large future maintenance burden for maintainers and contributors
+
+### Best practices
+
+* Commit messages: please leave both a title and a description in your commit messages. The title should be a short summary of the changes, with a leading word that explains the section of the code being changed (e.g. `api: fix parsing of prompt field`) . In the description, leave a short 2-3 sentences that explain more about the change and its impact.
+* Tests: please add test coverage to changes where possible.
+* Minimize dependencies: avoid adding new dependencies unless absolutely necessary.
+
+## Need help?
+
+If you need help with anything, feel free to reach out to us on our [Discord server](https://discord.gg/ollama).

Dockerfile

@@ -1,8 +1,10 @@
-ARG GOLANG_VERSION=1.22.1
+ARG GOLANG_VERSION=1.22.5
 ARG CMAKE_VERSION=3.22.1
-# this CUDA_VERSION corresponds with the one specified in docs/gpu.md
+ARG CUDA_VERSION_11=11.3.1
-ARG CUDA_VERSION=11.3.1
+ARG CUDA_V11_ARCHITECTURES="50;52;53;60;61;62;70;72;75;80;86"
-ARG ROCM_VERSION=6.0.2
+ARG CUDA_VERSION_12=12.4.0
+ARG CUDA_V12_ARCHITECTURES="60;61;62;70;72;75;80;86;87;89;90;90a"
+ARG ROCM_VERSION=6.1.2
 
 # Copy the minimal context we need to run the generate scripts
 FROM scratch AS llm-code
@@ -10,7 +12,7 @@ COPY .git .git
 COPY .gitmodules .gitmodules
 COPY llm llm
 
-FROM --platform=linux/amd64 nvidia/cuda:$CUDA_VERSION-devel-centos7 AS cuda-build-amd64
+FROM --platform=linux/amd64 nvidia/cuda:$CUDA_VERSION_11-devel-centos7 AS cuda-11-build-amd64
 ARG CMAKE_VERSION
 COPY ./scripts/rh_linux_deps.sh /
 RUN CMAKE_VERSION=${CMAKE_VERSION} sh /rh_linux_deps.sh
@@ -18,9 +20,34 @@ ENV PATH /opt/rh/devtoolset-10/root/usr/bin:$PATH
 COPY --from=llm-code / /go/src/github.com/ollama/ollama/
 WORKDIR /go/src/github.com/ollama/ollama/llm/generate
 ARG CGO_CFLAGS
-RUN OLLAMA_SKIP_CPU_GENERATE=1 sh gen_linux.sh
+ARG CUDA_V11_ARCHITECTURES
+ENV GOARCH amd64 
+RUN --mount=type=cache,target=/root/.ccache \
+    OLLAMA_SKIP_STATIC_GENERATE=1 \
+    OLLAMA_SKIP_CPU_GENERATE=1 \
+    CMAKE_CUDA_ARCHITECTURES="${CUDA_V11_ARCHITECTURES}" \
+    CUDA_VARIANT="_v11" \
+    bash gen_linux.sh
 
-FROM --platform=linux/arm64 nvidia/cuda:$CUDA_VERSION-devel-rockylinux8 AS cuda-build-arm64
+FROM --platform=linux/amd64 nvidia/cuda:$CUDA_VERSION_12-devel-centos7 AS cuda-12-build-amd64
+ARG CMAKE_VERSION
+COPY ./scripts/rh_linux_deps.sh /
+RUN CMAKE_VERSION=${CMAKE_VERSION} sh /rh_linux_deps.sh
+ENV PATH /opt/rh/devtoolset-10/root/usr/bin:$PATH
+COPY --from=llm-code / /go/src/github.com/ollama/ollama/
+WORKDIR /go/src/github.com/ollama/ollama/llm/generate
+ARG CGO_CFLAGS
+ARG CUDA_V12_ARCHITECTURES
+ENV GOARCH amd64 
+RUN --mount=type=cache,target=/root/.ccache \
+    OLLAMA_SKIP_STATIC_GENERATE=1 \
+    OLLAMA_SKIP_CPU_GENERATE=1 \
+    CMAKE_CUDA_ARCHITECTURES="${CUDA_V12_ARCHITECTURES}" \
+    CUDA_VARIANT="_v12" \
+    OLLAMA_CUSTOM_CUDA_DEFS="-DGGML_CUDA_USE_GRAPHS=on" \
+    bash gen_linux.sh
+
+FROM --platform=linux/arm64 nvidia/cuda:$CUDA_VERSION_11-devel-rockylinux8 AS cuda-11-build-server-arm64
 ARG CMAKE_VERSION
 COPY ./scripts/rh_linux_deps.sh /
 RUN CMAKE_VERSION=${CMAKE_VERSION} sh /rh_linux_deps.sh
@@ -28,7 +55,32 @@ ENV PATH /opt/rh/gcc-toolset-10/root/usr/bin:$PATH
 COPY --from=llm-code / /go/src/github.com/ollama/ollama/
 WORKDIR /go/src/github.com/ollama/ollama/llm/generate
 ARG CGO_CFLAGS
-RUN OLLAMA_SKIP_CPU_GENERATE=1 sh gen_linux.sh
+ARG CUDA_V11_ARCHITECTURES
+ENV GOARCH arm64 
+RUN OLLAMA_SKIP_STATIC_GENERATE=1 \
+    OLLAMA_SKIP_CPU_GENERATE=1 \
+    CMAKE_CUDA_ARCHITECTURES="${CUDA_V11_ARCHITECTURES}" \
+    CUDA_VARIANT="_v11" \
+    bash gen_linux.sh
+
+FROM --platform=linux/arm64 nvidia/cuda:$CUDA_VERSION_12-devel-rockylinux8 AS cuda-12-build-server-arm64
+ARG CMAKE_VERSION
+COPY ./scripts/rh_linux_deps.sh /
+RUN CMAKE_VERSION=${CMAKE_VERSION} sh /rh_linux_deps.sh
+ENV PATH /opt/rh/gcc-toolset-10/root/usr/bin:$PATH
+COPY --from=llm-code / /go/src/github.com/ollama/ollama/
+WORKDIR /go/src/github.com/ollama/ollama/llm/generate
+ARG CGO_CFLAGS
+ARG CUDA_V12_ARCHITECTURES
+ENV GOARCH arm64 
+RUN --mount=type=cache,target=/root/.ccache \
+    OLLAMA_SKIP_STATIC_GENERATE=1 \
+    OLLAMA_SKIP_CPU_GENERATE=1 \
+    CMAKE_CUDA_ARCHITECTURES="${CUDA_V12_ARCHITECTURES}" \
+    CUDA_VARIANT="_v12" \
+    OLLAMA_CUSTOM_CUDA_DEFS="-DGGML_CUDA_USE_GRAPHS=on" \
+    bash gen_linux.sh
+
 
 FROM --platform=linux/amd64 rocm/dev-centos-7:${ROCM_VERSION}-complete AS rocm-build-amd64
 ARG CMAKE_VERSION
@@ -40,15 +92,11 @@ COPY --from=llm-code / /go/src/github.com/ollama/ollama/
 WORKDIR /go/src/github.com/ollama/ollama/llm/generate
 ARG CGO_CFLAGS
 ARG AMDGPU_TARGETS
-RUN OLLAMA_SKIP_CPU_GENERATE=1 sh gen_linux.sh
+ENV GOARCH amd64 
-RUN mkdir /tmp/scratch && \
+RUN --mount=type=cache,target=/root/.ccache \
-    for dep in $(cat /go/src/github.com/ollama/ollama/llm/llama.cpp/build/linux/x86_64/rocm*/lib/deps.txt) ; do \
+    OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_SKIP_CPU_GENERATE=1 bash gen_linux.sh
-        cp ${dep} /tmp/scratch/ || exit 1 ; \
+RUN mkdir -p ../../dist/linux-amd64-rocm/lib/ollama && \
-    done && \
+    (cd /opt/rocm/lib && tar cf - rocblas/library) | (cd ../../dist/linux-amd64-rocm/lib/ollama && tar xf - )
-    (cd /opt/rocm/lib && tar cf - rocblas/library) | (cd /tmp/scratch/ && tar xf - ) && \
-    mkdir -p /go/src/github.com/ollama/ollama/dist/deps/ && \
-    (cd /tmp/scratch/ && tar czvf /go/src/github.com/ollama/ollama/dist/deps/ollama-linux-amd64-rocm.tgz . )
-
 
 FROM --platform=linux/amd64 centos:7 AS cpu-builder-amd64
 ARG CMAKE_VERSION
@@ -59,32 +107,40 @@ ENV PATH /opt/rh/devtoolset-10/root/usr/bin:$PATH
 COPY --from=llm-code / /go/src/github.com/ollama/ollama/
 ARG OLLAMA_CUSTOM_CPU_DEFS
 ARG CGO_CFLAGS
+ENV GOARCH amd64 
 WORKDIR /go/src/github.com/ollama/ollama/llm/generate
 
 FROM --platform=linux/amd64 cpu-builder-amd64 AS static-build-amd64
-RUN OLLAMA_CPU_TARGET="static" sh gen_linux.sh
+RUN --mount=type=cache,target=/root/.ccache \
+    OLLAMA_CPU_TARGET="static" bash gen_linux.sh
 FROM --platform=linux/amd64 cpu-builder-amd64 AS cpu-build-amd64
-RUN OLLAMA_CPU_TARGET="cpu" sh gen_linux.sh
+RUN --mount=type=cache,target=/root/.ccache \
+    OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu" bash gen_linux.sh
 FROM --platform=linux/amd64 cpu-builder-amd64 AS cpu_avx-build-amd64
-RUN OLLAMA_CPU_TARGET="cpu_avx" sh gen_linux.sh
+RUN --mount=type=cache,target=/root/.ccache \
+    OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu_avx" bash gen_linux.sh
 FROM --platform=linux/amd64 cpu-builder-amd64 AS cpu_avx2-build-amd64
-RUN OLLAMA_CPU_TARGET="cpu_avx2" sh gen_linux.sh
+RUN --mount=type=cache,target=/root/.ccache \
+    OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu_avx2" bash gen_linux.sh
 
-FROM --platform=linux/arm64 centos:7 AS cpu-builder-arm64
+FROM --platform=linux/arm64 rockylinux:8 AS cpu-builder-arm64
 ARG CMAKE_VERSION
 ARG GOLANG_VERSION
 COPY ./scripts/rh_linux_deps.sh /
 RUN CMAKE_VERSION=${CMAKE_VERSION} GOLANG_VERSION=${GOLANG_VERSION} sh /rh_linux_deps.sh
-ENV PATH /opt/rh/devtoolset-10/root/usr/bin:$PATH
+ENV PATH /opt/rh/gcc-toolset-10/root/usr/bin:$PATH
 COPY --from=llm-code / /go/src/github.com/ollama/ollama/
 ARG OLLAMA_CUSTOM_CPU_DEFS
 ARG CGO_CFLAGS
+ENV GOARCH arm64
 WORKDIR /go/src/github.com/ollama/ollama/llm/generate
 
 FROM --platform=linux/arm64 cpu-builder-arm64 AS static-build-arm64
-RUN OLLAMA_CPU_TARGET="static" sh gen_linux.sh
+RUN --mount=type=cache,target=/root/.ccache \
+    OLLAMA_CPU_TARGET="static" bash gen_linux.sh
 FROM --platform=linux/arm64 cpu-builder-arm64 AS cpu-build-arm64
-RUN OLLAMA_CPU_TARGET="cpu" sh gen_linux.sh
+RUN --mount=type=cache,target=/root/.ccache \
+    OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu" bash gen_linux.sh
 
 
 # Intermediate stage used for ./scripts/build_linux.sh
@@ -95,12 +151,16 @@ COPY . .
 COPY --from=static-build-amd64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
 COPY --from=cpu_avx-build-amd64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
 COPY --from=cpu_avx2-build-amd64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
-COPY --from=cuda-build-amd64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
+COPY --from=cuda-11-build-amd64 /go/src/github.com/ollama/ollama/dist/ dist/
+COPY --from=cuda-11-build-amd64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
+COPY --from=cuda-12-build-amd64 /go/src/github.com/ollama/ollama/dist/ dist/
+COPY --from=cuda-12-build-amd64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
+COPY --from=rocm-build-amd64 /go/src/github.com/ollama/ollama/dist/ dist/
 COPY --from=rocm-build-amd64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
-COPY --from=rocm-build-amd64 /go/src/github.com/ollama/ollama/dist/deps/ ./dist/deps/
 ARG GOFLAGS
 ARG CGO_CFLAGS
-RUN go build -trimpath .
+RUN --mount=type=cache,target=/root/.ccache \
+    go build -trimpath -o dist/linux-amd64/bin/ollama .
 
 # Intermediate stage used for ./scripts/build_linux.sh
 FROM --platform=linux/arm64 cpu-build-arm64 AS build-arm64
@@ -109,23 +169,36 @@ ARG GOLANG_VERSION
 WORKDIR /go/src/github.com/ollama/ollama
 COPY . .
 COPY --from=static-build-arm64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
-COPY --from=cuda-build-arm64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
+COPY --from=cuda-11-build-server-arm64 /go/src/github.com/ollama/ollama/dist/ dist/
+COPY --from=cuda-11-build-server-arm64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
+COPY --from=cuda-12-build-server-arm64 /go/src/github.com/ollama/ollama/dist/ dist/
+COPY --from=cuda-12-build-server-arm64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
 ARG GOFLAGS
 ARG CGO_CFLAGS
-RUN go build -trimpath .
+RUN --mount=type=cache,target=/root/.ccache \
+    go build -trimpath -o dist/linux-arm64/bin/ollama .
+
+# Strip out ROCm dependencies to keep the primary image lean
+FROM --platform=linux/amd64 ubuntu:22.04 as amd64-libs-without-rocm
+COPY --from=build-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/lib/ /scratch/
+RUN cd /scratch/ollama/ && rm -rf rocblas libamd* libdrm* libroc* libhip* libhsa* 
 
 # Runtime stages
 FROM --platform=linux/amd64 ubuntu:22.04 as runtime-amd64
+COPY --from=amd64-libs-without-rocm /scratch/ /lib/
 RUN apt-get update && apt-get install -y ca-certificates
-COPY --from=build-amd64 /go/src/github.com/ollama/ollama/ollama /bin/ollama
+COPY --from=build-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/bin/ /bin/
+
 FROM --platform=linux/arm64 ubuntu:22.04 as runtime-arm64
+COPY --from=build-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/lib/ /lib/
 RUN apt-get update && apt-get install -y ca-certificates
-COPY --from=build-arm64 /go/src/github.com/ollama/ollama/ollama /bin/ollama
+COPY --from=build-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/bin/ /bin/
 
 # Radeon images are much larger so we keep it distinct from the CPU/CUDA image
 FROM --platform=linux/amd64 rocm/dev-centos-7:${ROCM_VERSION}-complete as runtime-rocm
 RUN update-pciids
-COPY --from=build-amd64 /go/src/github.com/ollama/ollama/ollama /bin/ollama
+COPY --from=build-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/bin/ /bin/
+RUN ln -s /opt/rocm/lib /lib/ollama
 EXPOSE 11434
 ENV OLLAMA_HOST 0.0.0.0
 

README.md

@@ -1,12 +1,12 @@
 <div align="center">
-  <img alt="ollama" height="200px" src="https://github.com/ollama/ollama/assets/3325447/0d0b44e2-8f4a-4e99-9b52-a5c1c741c8f7">
+ <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 locally.
+Get up and running with large language models.
 
 ### macOS
 
@@ -35,10 +35,10 @@ The official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama) `olla
 
 ## Quickstart
 
-To run and chat with [Llama 2](https://ollama.com/library/llama2):
+To run and chat with [Llama 3.1](https://ollama.com/library/llama3.1):
 
 ```
-ollama run llama2
+ollama run llama3.1
 ```
 
 ## Model library
@@ -49,23 +49,25 @@ Here are some example models that can be downloaded:
 
 | Model              | Parameters | Size  | Download                       |
 | ------------------ | ---------- | ----- | ------------------------------ |
-| Llama 2            | 7B         | 3.8GB | `ollama run llama2`            |
+| Llama 3.1          | 8B         | 4.7GB | `ollama run llama3.1`          |
+| Llama 3.1          | 70B        | 40GB  | `ollama run llama3.1:70b`      |
+| Llama 3.1          | 405B       | 231GB | `ollama run llama3.1:405b`     |
+| Phi 3 Mini         | 3.8B       | 2.3GB | `ollama run phi3`              |
+| Phi 3 Medium       | 14B        | 7.9GB | `ollama run phi3:medium`       |
+| Gemma 2            | 2B         | 1.6GB | `ollama run gemma2:2b`         |
+| Gemma 2            | 9B         | 5.5GB | `ollama run gemma2`            |
+| Gemma 2            | 27B        | 16GB  | `ollama run gemma2:27b`        |
 | Mistral            | 7B         | 4.1GB | `ollama run mistral`           |
-| Dolphin Phi        | 2.7B       | 1.6GB | `ollama run dolphin-phi`       |
+| Moondream 2        | 1.4B       | 829MB | `ollama run moondream`         |
-| Phi-2              | 2.7B       | 1.7GB | `ollama run phi`               |
 | Neural Chat        | 7B         | 4.1GB | `ollama run neural-chat`       |
 | Starling           | 7B         | 4.1GB | `ollama run starling-lm`       |
 | Code Llama         | 7B         | 3.8GB | `ollama run codellama`         |
 | Llama 2 Uncensored | 7B         | 3.8GB | `ollama run llama2-uncensored` |
-| Llama 2 13B        | 13B        | 7.3GB | `ollama run llama2:13b`        |
-| Llama 2 70B        | 70B        | 39GB  | `ollama run llama2:70b`        |
-| Orca Mini          | 3B         | 1.9GB | `ollama run orca-mini`         |
-| Vicuna             | 7B         | 3.8GB | `ollama run vicuna`            |
 | LLaVA              | 7B         | 4.5GB | `ollama run llava`             |
-| Gemma              | 2B         | 1.4GB | `ollama run gemma:2b`          |
+| Solar              | 10.7B      | 6.1GB | `ollama run solar`             |
-| Gemma              | 7B         | 4.8GB | `ollama run gemma:7b`          |
 
-> Note: You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
+> [!NOTE]
+> You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
 
 ## Customize a model
 
@@ -97,16 +99,16 @@ See the [guide](docs/import.md) on importing models for more information.
 
 ### Customize a prompt
 
-Models from the Ollama library can be customized with a prompt. For example, to customize the `llama2` model:
+Models from the Ollama library can be customized with a prompt. For example, to customize the `llama3.1` model:
 
 ```
-ollama pull llama2
+ollama pull llama3.1
 ```
 
 Create a `Modelfile`:
 
 ```
-FROM llama2
+FROM llama3.1
 
 # set the temperature to 1 [higher is more creative, lower is more coherent]
 PARAMETER temperature 1
@@ -141,7 +143,7 @@ ollama create mymodel -f ./Modelfile
 ### Pull a model
 
 ```
-ollama pull llama2
+ollama pull llama3.1
 ```
 
 > This command can also be used to update a local model. Only the diff will be pulled.
@@ -149,13 +151,13 @@ ollama pull llama2
 ### Remove a model
 
 ```
-ollama rm llama2
+ollama rm llama3.1
 ```
 
 ### Copy a model
 
 ```
-ollama cp llama2 my-llama2
+ollama cp llama3.1 my-model
 ```
 
 ### Multiline input
@@ -172,17 +174,23 @@ I'm a basic program that prints the famous "Hello, world!" message to the consol
 ### Multimodal models
 
 ```
->>> What's in this image? /Users/jmorgan/Desktop/smile.png
+ollama run llava "What's in this image? /Users/jmorgan/Desktop/smile.png"
 The image features a yellow smiley face, which is likely the central focus of the picture.
 ```
 
-### Pass in prompt as arguments
+### Pass the prompt as an argument
 
 ```
-$ ollama run llama2 "Summarize this file: $(cat README.md)"
+$ ollama run llama3.1 "Summarize this file: $(cat README.md)"
  Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications.
 ```
 
+### Show model information
+
+```
+ollama show llama3.1
+```
+
 ### List models on your computer
 
 ```
@@ -195,25 +203,7 @@ ollama list
 
 ## Building
 
-Install `cmake` and `go`:
+See the [developer guide](https://github.com/ollama/ollama/blob/main/docs/development.md)
-
-```
-brew install cmake go
-```
-
-Then generate dependencies:
-
-```
-go generate ./...
-```
-
-Then build the binary:
-
-```
-go build .
-```
-
-More detailed instructions can be found in the [developer guide](https://github.com/ollama/ollama/blob/main/docs/development.md)
 
 ### Running local builds
 
@@ -226,7 +216,7 @@ Next, start the server:
 Finally, in a separate shell, run a model:
 
 ```
-./ollama run llama2
+./ollama run llama3.1
 ```
 
 ## REST API
@@ -237,7 +227,7 @@ Ollama has a REST API for running and managing models.
 
 ```
 curl http://localhost:11434/api/generate -d '{
-  "model": "llama2",
+  "model": "llama3.1",
   "prompt":"Why is the sky blue?"
 }'
 ```
@@ -246,7 +236,7 @@ curl http://localhost:11434/api/generate -d '{
 
 ```
 curl http://localhost:11434/api/chat -d '{
-  "model": "mistral",
+  "model": "llama3.1",
   "messages": [
     { "role": "user", "content": "why is the sky blue?" }
   ]
@@ -259,16 +249,18 @@ See the [API documentation](./docs/api.md) for all endpoints.
 
 ### Web & Desktop
 
+- [Open WebUI](https://github.com/open-webui/open-webui)
+- [Enchanted (macOS native)](https://github.com/AugustDev/enchanted)
+- [Hollama](https://github.com/fmaclen/hollama)
 - [Lollms-Webui](https://github.com/ParisNeo/lollms-webui)
 - [LibreChat](https://github.com/danny-avila/LibreChat)
 - [Bionic GPT](https://github.com/bionic-gpt/bionic-gpt)
-- [Enchanted (macOS native)](https://github.com/AugustDev/enchanted)
 - [HTML UI](https://github.com/rtcfirefly/ollama-ui)
 - [Saddle](https://github.com/jikkuatwork/saddle)
 - [Chatbot UI](https://github.com/ivanfioravanti/chatbot-ollama)
+- [Chatbot UI v2](https://github.com/mckaywrigley/chatbot-ui)
 - [Typescript UI](https://github.com/ollama-interface/Ollama-Gui?tab=readme-ov-file)
 - [Minimalistic React UI for Ollama Models](https://github.com/richawo/minimal-llm-ui)
-- [Open WebUI](https://github.com/open-webui/open-webui)
 - [Ollamac](https://github.com/kevinhermawan/Ollamac)
 - [big-AGI](https://github.com/enricoros/big-AGI/blob/main/docs/config-local-ollama.md)
 - [Cheshire Cat assistant framework](https://github.com/cheshire-cat-ai/core)
@@ -286,13 +278,30 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [OllamaGUI](https://github.com/enoch1118/ollamaGUI)
 - [OpenAOE](https://github.com/InternLM/OpenAOE)
 - [Odin Runes](https://github.com/leonid20000/OdinRunes)
-- [LLM-X: Progressive Web App](https://github.com/mrdjohnson/llm-x)
+- [LLM-X](https://github.com/mrdjohnson/llm-x) (Progressive Web App)
 - [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)
-- [ChatOllama: Open Source Chatbot based on Ollama with Knowledge Bases](https://github.com/sugarforever/chat-ollama)
+- [QA-Pilot](https://github.com/reid41/QA-Pilot) (Chat with Code Repository)
-- [CRAG Ollama Chat: Simple Web Search with Corrective RAG](https://github.com/Nagi-ovo/CRAG-Ollama-Chat)
+- [ChatOllama](https://github.com/sugarforever/chat-ollama) (Open Source Chatbot based on Ollama with Knowledge Bases)
-- [RAGFlow: Open-source Retrieval-Augmented Generation engine based on deep document understanding](https://github.com/infiniflow/ragflow)
+- [CRAG Ollama Chat](https://github.com/Nagi-ovo/CRAG-Ollama-Chat) (Simple Web Search with Corrective RAG)
+- [RAGFlow](https://github.com/infiniflow/ragflow) (Open-source Retrieval-Augmented Generation engine based on deep document understanding)
+- [StreamDeploy](https://github.com/StreamDeploy-DevRel/streamdeploy-llm-app-scaffold) (LLM Application Scaffold)
+- [chat](https://github.com/swuecho/chat) (chat web app for teams)
+- [Lobe Chat](https://github.com/lobehub/lobe-chat) with [Integrating Doc](https://lobehub.com/docs/self-hosting/examples/ollama)
+- [Ollama RAG Chatbot](https://github.com/datvodinh/rag-chatbot.git) (Local Chat with multiple PDFs using Ollama and RAG)
+- [BrainSoup](https://www.nurgo-software.com/products/brainsoup) (Flexible native client with RAG & multi-agent automation)
+- [macai](https://github.com/Renset/macai) (macOS client for Ollama, ChatGPT, and other compatible API back-ends)
+- [Olpaka](https://github.com/Otacon/olpaka) (User-friendly Flutter Web App for Ollama)
+- [OllamaSpring](https://github.com/CrazyNeil/OllamaSpring) (Ollama Client for macOS)
+- [LLocal.in](https://github.com/kartikm7/llocal) (Easy to use Electron Desktop Client for Ollama)
+- [Ollama with Google Mesop](https://github.com/rapidarchitect/ollama_mesop/) (Mesop Chat Client implementation with Ollama)
+- [Kerlig AI](https://www.kerlig.com/) (AI writing assistant for macOS)
+- [AI Studio](https://github.com/MindWorkAI/AI-Studio)
+- [Sidellama](https://github.com/gyopak/sidellama) (browser-based LLM client)
+- [LLMStack](https://github.com/trypromptly/LLMStack) (No-code multi-agent framework to build LLM agents and workflows)
+- [BoltAI for Mac](https://boltai.com) (AI Chat Client for Mac)
+- [Harbor](https://github.com/av/harbor) (Containerized LLM Toolkit with Ollama as default backend)
 
 ### Terminal
 
@@ -308,32 +317,40 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Oatmeal](https://github.com/dustinblackman/oatmeal)
 - [cmdh](https://github.com/pgibler/cmdh)
 - [ooo](https://github.com/npahlfer/ooo)
+- [shell-pilot](https://github.com/reid41/shell-pilot)
 - [tenere](https://github.com/pythops/tenere)
 - [llm-ollama](https://github.com/taketwo/llm-ollama) for [Datasette's LLM CLI](https://llm.datasette.io/en/stable/).
 - [typechat-cli](https://github.com/anaisbetts/typechat-cli)
 - [ShellOracle](https://github.com/djcopley/ShellOracle)
 - [tlm](https://github.com/yusufcanb/tlm)
+- [podman-ollama](https://github.com/ericcurtin/podman-ollama)
+- [gollama](https://github.com/sammcj/gollama)
+- [Ollama eBook Summary](https://github.com/cognitivetech/ollama-ebook-summary/)
 
 ### Database
 
-- [MindsDB](https://github.com/mindsdb/mindsdb/blob/staging/mindsdb/integrations/handlers/ollama_handler/README.md)
+- [MindsDB](https://github.com/mindsdb/mindsdb/blob/staging/mindsdb/integrations/handlers/ollama_handler/README.md) (Connects Ollama models with nearly 200 data platforms and apps)
 - [chromem-go](https://github.com/philippgille/chromem-go/blob/v0.5.0/embed_ollama.go) with [example](https://github.com/philippgille/chromem-go/tree/v0.5.0/examples/rag-wikipedia-ollama)
 
 ### Package managers
 
 - [Pacman](https://archlinux.org/packages/extra/x86_64/ollama/)
 - [Helm Chart](https://artifacthub.io/packages/helm/ollama-helm/ollama)
+- [Guix channel](https://codeberg.org/tusharhero/ollama-guix)
 
 ### Libraries
 
 - [LangChain](https://python.langchain.com/docs/integrations/llms/ollama) and [LangChain.js](https://js.langchain.com/docs/modules/model_io/models/llms/integrations/ollama) with [example](https://js.langchain.com/docs/use_cases/question_answering/local_retrieval_qa)
+- [Firebase Genkit](https://firebase.google.com/docs/genkit/plugins/ollama)
 - [LangChainGo](https://github.com/tmc/langchaingo/) with [example](https://github.com/tmc/langchaingo/tree/main/examples/ollama-completion-example)
 - [LangChain4j](https://github.com/langchain4j/langchain4j) with [example](https://github.com/langchain4j/langchain4j-examples/tree/main/ollama-examples/src/main/java)
+- [LangChainRust](https://github.com/Abraxas-365/langchain-rust) with [example](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/llm_ollama.rs)
 - [LlamaIndex](https://gpt-index.readthedocs.io/en/stable/examples/llm/ollama.html)
 - [LiteLLM](https://github.com/BerriAI/litellm)
 - [OllamaSharp for .NET](https://github.com/awaescher/OllamaSharp)
 - [Ollama for Ruby](https://github.com/gbaptista/ollama-ai)
 - [Ollama-rs for Rust](https://github.com/pepperoni21/ollama-rs)
+- [Ollama-hpp for C++](https://github.com/jmont-dev/ollama-hpp)
 - [Ollama4j for Java](https://github.com/amithkoujalgi/ollama4j)
 - [ModelFusion Typescript Library](https://modelfusion.dev/integration/model-provider/ollama)
 - [OllamaKit for Swift](https://github.com/kevinhermawan/OllamaKit)
@@ -344,9 +361,13 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Haystack](https://github.com/deepset-ai/haystack-integrations/blob/main/integrations/ollama.md)
 - [Elixir LangChain](https://github.com/brainlid/langchain)
 - [Ollama for R - rollama](https://github.com/JBGruber/rollama)
+- [Ollama for R - ollama-r](https://github.com/hauselin/ollama-r)
 - [Ollama-ex for Elixir](https://github.com/lebrunel/ollama-ex)
 - [Ollama Connector for SAP ABAP](https://github.com/b-tocs/abap_btocs_ollama)
 - [Testcontainers](https://testcontainers.com/modules/ollama/)
+- [Portkey](https://portkey.ai/docs/welcome/integration-guides/ollama)
+- [PromptingTools.jl](https://github.com/svilupp/PromptingTools.jl) with an [example](https://svilupp.github.io/PromptingTools.jl/dev/examples/working_with_ollama)
+- [LlamaScript](https://github.com/Project-Llama/llamascript)
 
 ### Mobile
 
@@ -366,14 +387,23 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Ollama Telegram Bot](https://github.com/ruecat/ollama-telegram)
 - [Hass Ollama Conversation](https://github.com/ej52/hass-ollama-conversation)
 - [Rivet plugin](https://github.com/abrenneke/rivet-plugin-ollama)
-- [Llama Coder](https://github.com/ex3ndr/llama-coder) (Copilot alternative using Ollama)
 - [Obsidian BMO Chatbot plugin](https://github.com/longy2k/obsidian-bmo-chatbot)
 - [Cliobot](https://github.com/herval/cliobot) (Telegram bot with Ollama support)
 - [Copilot for Obsidian plugin](https://github.com/logancyang/obsidian-copilot)
 - [Obsidian Local GPT plugin](https://github.com/pfrankov/obsidian-local-gpt)
 - [Open Interpreter](https://docs.openinterpreter.com/language-model-setup/local-models/ollama)
+- [Llama Coder](https://github.com/ex3ndr/llama-coder) (Copilot alternative using Ollama)
+- [Ollama Copilot](https://github.com/bernardo-bruning/ollama-copilot) (Proxy that allows you to use ollama as a copilot like Github copilot)
 - [twinny](https://github.com/rjmacarthy/twinny) (Copilot and Copilot chat alternative using Ollama)
-- [Wingman-AI](https://github.com/RussellCanfield/wingman-ai) (Copilot code and chat alternative using Ollama and HuggingFace)
+- [Wingman-AI](https://github.com/RussellCanfield/wingman-ai) (Copilot code and chat alternative using Ollama and Hugging Face)
 - [Page Assist](https://github.com/n4ze3m/page-assist) (Chrome Extension)
 - [AI Telegram Bot](https://github.com/tusharhero/aitelegrambot) (Telegram bot using Ollama in backend)
 - [AI ST Completion](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (Sublime Text 4 AI assistant plugin with Ollama support)
+- [Discord-Ollama Chat Bot](https://github.com/kevinthedang/discord-ollama) (Generalized TypeScript Discord Bot w/ Tuning Documentation)
+- [Discord AI chat/moderation bot](https://github.com/rapmd73/Companion) Chat/moderation bot written in python. Uses Ollama to create personalities.
+- [Headless Ollama](https://github.com/nischalj10/headless-ollama) (Scripts to automatically install ollama client & models on any OS for apps that depends on ollama server)
+
+### Supported backends
+
+- [llama.cpp](https://github.com/ggerganov/llama.cpp) project founded by Georgi Gerganov.
+

SECURITY.md

@@ -0,0 +1,25 @@
+# Security
+
+The Ollama maintainer team takes security seriously and will actively work to resolve security issues.
+
+## Reporting a vulnerability
+
+If you discover a security vulnerability, please do not open a public issue. Instead, please report it by emailing hello@ollama.com. We ask that you give us sufficient time to investigate and address the vulnerability before disclosing it publicly.
+
+Please include the following details in your report:
+- A description of the vulnerability
+- Steps to reproduce the issue
+- Your assessment of the potential impact
+- Any possible mitigations
+
+## Security best practices
+
+While the maintainer team does their best to secure Ollama, users are encouraged to implement their own security best practices, such as:
+
+- Regularly updating to the latest version of Ollama
+- Securing access to hosted instances of Ollama
+- Monitoring systems for unusual activity
+
+## Contact
+
+For any other questions or concerns related to security, please contact us at hello@ollama.com

api/client.go

@@ -1,3 +1,16 @@
+// Package api implements the client-side API for code wishing to interact
+// with the ollama service. The methods of the [Client] type correspond to
+// the ollama REST API as described in [the API documentation].
+// The ollama command-line client itself uses this package to interact with
+// the backend service.
+//
+// # Examples
+//
+// Several examples of using this package are available [in the GitHub
+// repository].
+//
+// [the API documentation]: https://github.com/ollama/ollama/blob/main/docs/api.md
+// [in the GitHub repository]: https://github.com/ollama/ollama/tree/main/examples
 package api
 
 import (
@@ -5,19 +18,20 @@ import (
 	"bytes"
 	"context"
 	"encoding/json"
+	"errors"
 	"fmt"
 	"io"
-	"net"
 	"net/http"
 	"net/url"
-	"os"
 	"runtime"
-	"strings"
 
+	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
 	"github.com/ollama/ollama/version"
 )
 
+// Client encapsulates client state for interacting with the ollama
+// service. Use [ClientFromEnvironment] to create new Clients.
 type Client struct {
 	base *url.URL
 	http *http.Client
@@ -39,41 +53,29 @@ func checkError(resp *http.Response, body []byte) error {
 	return apiError
 }
 
+// ClientFromEnvironment creates a new [Client] using configuration from the
+// environment variable OLLAMA_HOST, which points to the network host and
+// port on which the ollama service is listenting. The format of this variable
+// is:
+//
+//	<scheme>://<host>:<port>
+//
+// If the variable is not specified, a default ollama host and port will be
+// used.
 func ClientFromEnvironment() (*Client, error) {
-	defaultPort := "11434"
-
-	scheme, hostport, ok := strings.Cut(os.Getenv("OLLAMA_HOST"), "://")
-	switch {
-	case !ok:
-		scheme, hostport = "http", os.Getenv("OLLAMA_HOST")
-	case scheme == "http":
-		defaultPort = "80"
-	case scheme == "https":
-		defaultPort = "443"
-	}
-
-	// trim trailing slashes
-	hostport = strings.TrimRight(hostport, "/")
-
-	host, port, err := net.SplitHostPort(hostport)
-	if err != nil {
-		host, port = "127.0.0.1", defaultPort
-		if ip := net.ParseIP(strings.Trim(hostport, "[]")); ip != nil {
-			host = ip.String()
-		} else if hostport != "" {
-			host = hostport
-		}
-	}
-
 	return &Client{
-		base: &url.URL{
+		base: envconfig.Host(),
-			Scheme: scheme,
-			Host:   net.JoinHostPort(host, port),
-		},
 		http: http.DefaultClient,
 	}, nil
 }
 
+func NewClient(base *url.URL, http *http.Client) *Client {
+	return &Client{
+		base: base,
+		http: http,
+	}
+}
+
 func (c *Client) do(ctx context.Context, method, path string, reqData, respData any) error {
 	var reqBody io.Reader
 	var data []byte
@@ -171,7 +173,7 @@ func (c *Client) stream(ctx context.Context, method, path string, data any, fn f
 		}
 
 		if errorResponse.Error != "" {
-			return fmt.Errorf(errorResponse.Error)
+			return errors.New(errorResponse.Error)
 		}
 
 		if response.StatusCode >= http.StatusBadRequest {
@@ -190,8 +192,14 @@ func (c *Client) stream(ctx context.Context, method, path string, data any, fn f
 	return nil
 }
 
+// GenerateResponseFunc is a function that [Client.Generate] invokes every time
+// a response is received from the service. If this function returns an error,
+// [Client.Generate] will stop generating and return this error.
 type GenerateResponseFunc func(GenerateResponse) error
 
+// Generate generates a response for a given prompt. The req parameter should
+// be populated with prompt details. fn is called for each response (there may
+// be multiple responses, e.g. in case streaming is enabled).
 func (c *Client) Generate(ctx context.Context, req *GenerateRequest, fn GenerateResponseFunc) error {
 	return c.stream(ctx, http.MethodPost, "/api/generate", req, func(bts []byte) error {
 		var resp GenerateResponse
@@ -203,8 +211,15 @@ func (c *Client) Generate(ctx context.Context, req *GenerateRequest, fn Generate
 	})
 }
 
+// ChatResponseFunc is a function that [Client.Chat] invokes every time
+// a response is received from the service. If this function returns an error,
+// [Client.Chat] will stop generating and return this error.
 type ChatResponseFunc func(ChatResponse) error
 
+// Chat generates the next message in a chat. [ChatRequest] may contain a
+// sequence of messages which can be used to maintain chat history with a model.
+// fn is called for each response (there may be multiple responses, e.g. if case
+// streaming is enabled).
 func (c *Client) Chat(ctx context.Context, req *ChatRequest, fn ChatResponseFunc) error {
 	return c.stream(ctx, http.MethodPost, "/api/chat", req, func(bts []byte) error {
 		var resp ChatResponse
@@ -216,8 +231,14 @@ func (c *Client) Chat(ctx context.Context, req *ChatRequest, fn ChatResponseFunc
 	})
 }
 
+// PullProgressFunc is a function that [Client.Pull] invokes every time there
+// is progress with a "pull" request sent to the service. If this function
+// returns an error, [Client.Pull] will stop the process and return this error.
 type PullProgressFunc func(ProgressResponse) error
 
+// Pull downloads a model from the ollama library. fn is called each time
+// progress is made on the request and can be used to display a progress bar,
+// etc.
 func (c *Client) Pull(ctx context.Context, req *PullRequest, fn PullProgressFunc) error {
 	return c.stream(ctx, http.MethodPost, "/api/pull", req, func(bts []byte) error {
 		var resp ProgressResponse
@@ -229,8 +250,14 @@ func (c *Client) Pull(ctx context.Context, req *PullRequest, fn PullProgressFunc
 	})
 }
 
+// PushProgressFunc is a function that [Client.Push] invokes when progress is
+// made.
+// It's similar to other progress function types like [PullProgressFunc].
 type PushProgressFunc func(ProgressResponse) error
 
+// Push uploads a model to the model library; requires registering for ollama.ai
+// and adding a public key first. fn is called each time progress is made on
+// the request and can be used to display a progress bar, etc.
 func (c *Client) Push(ctx context.Context, req *PushRequest, fn PushProgressFunc) error {
 	return c.stream(ctx, http.MethodPost, "/api/push", req, func(bts []byte) error {
 		var resp ProgressResponse
@@ -242,8 +269,15 @@ func (c *Client) Push(ctx context.Context, req *PushRequest, fn PushProgressFunc
 	})
 }
 
+// CreateProgressFunc is a function that [Client.Create] invokes when progress
+// is made.
+// It's similar to other progress function types like [PullProgressFunc].
 type CreateProgressFunc func(ProgressResponse) error
 
+// Create creates a model from a [Modelfile]. fn is a progress function that
+// behaves similarly to other methods (see [Client.Pull]).
+//
+// [Modelfile]: https://github.com/ollama/ollama/blob/main/docs/modelfile.md
 func (c *Client) Create(ctx context.Context, req *CreateRequest, fn CreateProgressFunc) error {
 	return c.stream(ctx, http.MethodPost, "/api/create", req, func(bts []byte) error {
 		var resp ProgressResponse
@@ -255,6 +289,7 @@ func (c *Client) Create(ctx context.Context, req *CreateRequest, fn CreateProgre
 	})
 }
 
+// List lists models that are available locally.
 func (c *Client) List(ctx context.Context) (*ListResponse, error) {
 	var lr ListResponse
 	if err := c.do(ctx, http.MethodGet, "/api/tags", nil, &lr); err != nil {
@@ -263,6 +298,17 @@ func (c *Client) List(ctx context.Context) (*ListResponse, error) {
 	return &lr, nil
 }
 
+// ListRunning lists running models.
+func (c *Client) ListRunning(ctx context.Context) (*ProcessResponse, error) {
+	var lr ProcessResponse
+	if err := c.do(ctx, http.MethodGet, "/api/ps", nil, &lr); err != nil {
+		return nil, err
+	}
+	return &lr, nil
+}
+
+// Copy copies a model - creating a model with another name from an existing
+// model.
 func (c *Client) Copy(ctx context.Context, req *CopyRequest) error {
 	if err := c.do(ctx, http.MethodPost, "/api/copy", req, nil); err != nil {
 		return err
@@ -270,6 +316,7 @@ func (c *Client) Copy(ctx context.Context, req *CopyRequest) error {
 	return nil
 }
 
+// Delete deletes a model and its data.
 func (c *Client) Delete(ctx context.Context, req *DeleteRequest) error {
 	if err := c.do(ctx, http.MethodDelete, "/api/delete", req, nil); err != nil {
 		return err
@@ -277,6 +324,7 @@ func (c *Client) Delete(ctx context.Context, req *DeleteRequest) error {
 	return nil
 }
 
+// Show obtains model information, including details, modelfile, license etc.
 func (c *Client) Show(ctx context.Context, req *ShowRequest) (*ShowResponse, error) {
 	var resp ShowResponse
 	if err := c.do(ctx, http.MethodPost, "/api/show", req, &resp); err != nil {
@@ -285,12 +333,25 @@ func (c *Client) Show(ctx context.Context, req *ShowRequest) (*ShowResponse, err
 	return &resp, nil
 }
 
+// Heartbeat checks if the server has started and is responsive; if yes, it
+// returns nil, otherwise an error.
 func (c *Client) Heartbeat(ctx context.Context) error {
 	if err := c.do(ctx, http.MethodHead, "/", nil, nil); err != nil {
 		return err
 	}
 	return nil
 }
+
+// Embed generates embeddings from a model.
+func (c *Client) Embed(ctx context.Context, req *EmbedRequest) (*EmbedResponse, error) {
+	var resp EmbedResponse
+	if err := c.do(ctx, http.MethodPost, "/api/embed", req, &resp); err != nil {
+		return nil, err
+	}
+	return &resp, nil
+}
+
+// Embeddings generates an embedding from a model.
 func (c *Client) Embeddings(ctx context.Context, req *EmbeddingRequest) (*EmbeddingResponse, error) {
 	var resp EmbeddingResponse
 	if err := c.do(ctx, http.MethodPost, "/api/embeddings", req, &resp); err != nil {
@@ -299,10 +360,13 @@ func (c *Client) Embeddings(ctx context.Context, req *EmbeddingRequest) (*Embedd
 	return &resp, nil
 }
 
+// CreateBlob creates a blob from a file on the server. digest is the
+// expected SHA256 digest of the file, and r represents the file.
 func (c *Client) CreateBlob(ctx context.Context, digest string, r io.Reader) error {
 	return c.do(ctx, http.MethodPost, fmt.Sprintf("/api/blobs/%s", digest), r, nil)
 }
 
+// Version returns the Ollama server version as a string.
 func (c *Client) Version(ctx context.Context) (string, error) {
 	var version struct {
 		Version string `json:"version"`

api/client_test.go

@@ -1,6 +1,8 @@
 package api
 
-import "testing"
+import (
+	"testing"
+)
 
 func TestClientFromEnvironment(t *testing.T) {
 	type testCase struct {

api/types.go

@@ -3,6 +3,7 @@ package api
 import (
 	"encoding/json"
 	"fmt"
+	"log/slog"
 	"math"
 	"os"
 	"reflect"
@@ -11,6 +12,7 @@ import (
 	"time"
 )
 
+// StatusError is an error with and HTTP status code.
 type StatusError struct {
 	StatusCode   int
 	Status       string
@@ -31,43 +33,161 @@ func (e StatusError) Error() string {
 	}
 }
 
+// ImageData represents the raw binary data of an image file.
 type ImageData []byte
 
+// GenerateRequest describes a request sent by [Client.Generate]. While you
+// have to specify the Model and Prompt fields, all the other fields have
+// reasonable defaults for basic uses.
 type GenerateRequest struct {
-	Model     string      `json:"model"`
+	// Model is the model name; it should be a name familiar to Ollama from
-	Prompt    string      `json:"prompt"`
+	// the library at https://ollama.com/library
-	System    string      `json:"system"`
+	Model string `json:"model"`
-	Template  string      `json:"template"`
-	Context   []int       `json:"context,omitempty"`
-	Stream    *bool       `json:"stream,omitempty"`
-	Raw       bool        `json:"raw,omitempty"`
-	Format    string      `json:"format"`
-	KeepAlive *Duration   `json:"keep_alive,omitempty"`
-	Images    []ImageData `json:"images,omitempty"`
 
-	Options map[string]interface{} `json:"options"`
+	// Prompt is the textual prompt to send to the model.
-}
+	Prompt string `json:"prompt"`
 
-type ChatRequest struct {
+	// Suffix is the text that comes after the inserted text.
-	Model     string    `json:"model"`
+	Suffix string `json:"suffix"`
-	Messages  []Message `json:"messages"`
+
-	Stream    *bool     `json:"stream,omitempty"`
+	// System overrides the model's default system message/prompt.
-	Format    string    `json:"format"`
+	System string `json:"system"`
+
+	// Template overrides the model's default prompt template.
+	Template string `json:"template"`
+
+	// Context is the context parameter returned from a previous call to
+	// Generate call. It can be used to keep a short conversational memory.
+	Context []int `json:"context,omitempty"`
+
+	// Stream specifies whether the response is streaming; it is true by default.
+	Stream *bool `json:"stream,omitempty"`
+
+	// Raw set to true means that no formatting will be applied to the prompt.
+	Raw bool `json:"raw,omitempty"`
+
+	// Format specifies the format to return a response in.
+	Format string `json:"format"`
+
+	// KeepAlive controls how long the model will stay loaded in memory following
+	// this request.
 	KeepAlive *Duration `json:"keep_alive,omitempty"`
 
+	// Images is an optional list of base64-encoded images accompanying this
+	// request, for multimodal models.
+	Images []ImageData `json:"images,omitempty"`
+
+	// Options lists model-specific options. For example, temperature can be
+	// set through this field, if the model supports it.
 	Options map[string]interface{} `json:"options"`
 }
 
-type Message struct {
+// ChatRequest describes a request sent by [Client.Chat].
-	Role    string      `json:"role"` // one of ["system", "user", "assistant"]
+type ChatRequest struct {
-	Content string      `json:"content"`
+	// Model is the model name, as in [GenerateRequest].
-	Images  []ImageData `json:"images,omitempty"`
+	Model string `json:"model"`
+
+	// Messages is the messages of the chat - can be used to keep a chat memory.
+	Messages []Message `json:"messages"`
+
+	// Stream enable streaming of returned response; true by default.
+	Stream *bool `json:"stream,omitempty"`
+
+	// Format is the format to return the response in (e.g. "json").
+	Format string `json:"format"`
+
+	// KeepAlive controls how long the model will stay loaded into memory
+	// followin the request.
+	KeepAlive *Duration `json:"keep_alive,omitempty"`
+
+	// Tools is an optional list of tools the model has access to.
+	Tools `json:"tools,omitempty"`
+
+	// Options lists model-specific options.
+	Options map[string]interface{} `json:"options"`
 }
 
+type Tools []Tool
+
+func (t Tools) String() string {
+	bts, _ := json.Marshal(t)
+	return string(bts)
+}
+
+func (t Tool) String() string {
+	bts, _ := json.Marshal(t)
+	return string(bts)
+}
+
+// Message is a single message in a chat sequence. The message contains the
+// role ("system", "user", or "assistant"), the content and an optional list
+// of images.
+type Message struct {
+	Role      string      `json:"role"`
+	Content   string      `json:"content"`
+	Images    []ImageData `json:"images,omitempty"`
+	ToolCalls []ToolCall  `json:"tool_calls,omitempty"`
+}
+
+func (m *Message) UnmarshalJSON(b []byte) error {
+	type Alias Message
+	var a Alias
+	if err := json.Unmarshal(b, &a); err != nil {
+		return err
+	}
+
+	*m = Message(a)
+	m.Role = strings.ToLower(m.Role)
+	return nil
+}
+
+type ToolCall struct {
+	Function ToolCallFunction `json:"function"`
+}
+
+type ToolCallFunction struct {
+	Name      string                    `json:"name"`
+	Arguments ToolCallFunctionArguments `json:"arguments"`
+}
+
+type ToolCallFunctionArguments map[string]any
+
+func (t *ToolCallFunctionArguments) String() string {
+	bts, _ := json.Marshal(t)
+	return string(bts)
+}
+
+type Tool struct {
+	Type     string       `json:"type"`
+	Function ToolFunction `json:"function"`
+}
+
+type ToolFunction struct {
+	Name        string `json:"name"`
+	Description string `json:"description"`
+	Parameters  struct {
+		Type       string   `json:"type"`
+		Required   []string `json:"required"`
+		Properties map[string]struct {
+			Type        string   `json:"type"`
+			Description string   `json:"description"`
+			Enum        []string `json:"enum,omitempty"`
+		} `json:"properties"`
+	} `json:"parameters"`
+}
+
+func (t *ToolFunction) String() string {
+	bts, _ := json.Marshal(t)
+	return string(bts)
+}
+
+// ChatResponse is the response returned by [Client.Chat]. Its fields are
+// similar to [GenerateResponse].
 type ChatResponse struct {
-	Model     string    `json:"model"`
+	Model      string    `json:"model"`
-	CreatedAt time.Time `json:"created_at"`
+	CreatedAt  time.Time `json:"created_at"`
-	Message   Message   `json:"message"`
+	Message    Message   `json:"message"`
+	DoneReason string    `json:"done_reason,omitempty"`
 
 	Done bool `json:"done"`
 
@@ -83,7 +203,8 @@ type Metrics struct {
 	EvalDuration       time.Duration `json:"eval_duration,omitempty"`
 }
 
-// Options specified in GenerateRequest, if you add a new option here add it to the API docs also
+// Options specified in [GenerateRequest], if you add a new option here add it
+// to the API docs also.
 type Options struct {
 	Runner
 
@@ -93,6 +214,7 @@ type Options struct {
 	NumPredict       int      `json:"num_predict,omitempty"`
 	TopK             int      `json:"top_k,omitempty"`
 	TopP             float32  `json:"top_p,omitempty"`
+	MinP             float32  `json:"min_p,omitempty"`
 	TFSZ             float32  `json:"tfs_z,omitempty"`
 	TypicalP         float32  `json:"typical_p,omitempty"`
 	RepeatLastN      int      `json:"repeat_last_n,omitempty"`
@@ -109,49 +231,84 @@ type Options struct {
 
 // Runner options which must be set when the model is loaded into memory
 type Runner struct {
-	UseNUMA   bool `json:"numa,omitempty"`
+	NumCtx    int   `json:"num_ctx,omitempty"`
-	NumCtx    int  `json:"num_ctx,omitempty"`
+	NumBatch  int   `json:"num_batch,omitempty"`
-	NumBatch  int  `json:"num_batch,omitempty"`
+	NumGPU    int   `json:"num_gpu,omitempty"`
-	NumGQA    int  `json:"num_gqa,omitempty"`
+	MainGPU   int   `json:"main_gpu,omitempty"`
-	NumGPU    int  `json:"num_gpu,omitempty"`
+	LowVRAM   bool  `json:"low_vram,omitempty"`
-	MainGPU   int  `json:"main_gpu,omitempty"`
+	F16KV     bool  `json:"f16_kv,omitempty"`
-	LowVRAM   bool `json:"low_vram,omitempty"`
+	LogitsAll bool  `json:"logits_all,omitempty"`
-	F16KV     bool `json:"f16_kv,omitempty"`
+	VocabOnly bool  `json:"vocab_only,omitempty"`
-	LogitsAll bool `json:"logits_all,omitempty"`
+	UseMMap   *bool `json:"use_mmap,omitempty"`
-	VocabOnly bool `json:"vocab_only,omitempty"`
+	UseMLock  bool  `json:"use_mlock,omitempty"`
-	UseMMap   bool `json:"use_mmap,omitempty"`
+	NumThread int   `json:"num_thread,omitempty"`
-	UseMLock  bool `json:"use_mlock,omitempty"`
-	NumThread int  `json:"num_thread,omitempty"`
-
-	// Unused: RopeFrequencyBase is ignored. Instead the value in the model will be used
-	RopeFrequencyBase float32 `json:"rope_frequency_base,omitempty"`
-	// Unused: RopeFrequencyScale is ignored. Instead the value in the model will be used
-	RopeFrequencyScale float32 `json:"rope_frequency_scale,omitempty"`
 }
 
-type EmbeddingRequest struct {
+// EmbedRequest is the request passed to [Client.Embed].
-	Model     string    `json:"model"`
+type EmbedRequest struct {
-	Prompt    string    `json:"prompt"`
+	// Model is the model name.
+	Model string `json:"model"`
+
+	// Input is the input to embed.
+	Input any `json:"input"`
+
+	// KeepAlive controls how long the model will stay loaded in memory following
+	// this request.
 	KeepAlive *Duration `json:"keep_alive,omitempty"`
 
+	Truncate *bool `json:"truncate,omitempty"`
+
+	// Options lists model-specific options.
 	Options map[string]interface{} `json:"options"`
 }
 
+// EmbedResponse is the response from [Client.Embed].
+type EmbedResponse struct {
+	Model      string      `json:"model"`
+	Embeddings [][]float32 `json:"embeddings"`
+
+	TotalDuration   time.Duration `json:"total_duration,omitempty"`
+	LoadDuration    time.Duration `json:"load_duration,omitempty"`
+	PromptEvalCount int           `json:"prompt_eval_count,omitempty"`
+}
+
+// EmbeddingRequest is the request passed to [Client.Embeddings].
+type EmbeddingRequest struct {
+	// Model is the model name.
+	Model string `json:"model"`
+
+	// Prompt is the textual prompt to embed.
+	Prompt string `json:"prompt"`
+
+	// KeepAlive controls how long the model will stay loaded in memory following
+	// this request.
+	KeepAlive *Duration `json:"keep_alive,omitempty"`
+
+	// Options lists model-specific options.
+	Options map[string]interface{} `json:"options"`
+}
+
+// EmbeddingResponse is the response from [Client.Embeddings].
 type EmbeddingResponse struct {
 	Embedding []float64 `json:"embedding"`
 }
 
+// CreateRequest is the request passed to [Client.Create].
 type CreateRequest struct {
-	Model        string `json:"model"`
+	Model     string `json:"model"`
-	Path         string `json:"path"`
+	Path      string `json:"path"`
-	Modelfile    string `json:"modelfile"`
+	Modelfile string `json:"modelfile"`
-	Stream       *bool  `json:"stream,omitempty"`
+	Stream    *bool  `json:"stream,omitempty"`
-	Quantization string `json:"quantization,omitempty"`
+	Quantize  string `json:"quantize,omitempty"`
 
 	// Name is deprecated, see Model
 	Name string `json:"name"`
+
+	// Quantization is deprecated, see Quantize
+	Quantization string `json:"quantization,omitempty"`
 }
 
+// DeleteRequest is the request passed to [Client.Delete].
 type DeleteRequest struct {
 	Model string `json:"model"`
 
@@ -159,10 +316,14 @@ type DeleteRequest struct {
 	Name string `json:"name"`
 }
 
+// ShowRequest is the request passed to [Client.Show].
 type ShowRequest struct {
-	Model    string `json:"model"`
+	Model  string `json:"model"`
-	System   string `json:"system"`
+	System string `json:"system"`
+
+	// Template is deprecated
 	Template string `json:"template"`
+	Verbose  bool   `json:"verbose"`
 
 	Options map[string]interface{} `json:"options"`
 
@@ -170,21 +331,27 @@ type ShowRequest struct {
 	Name string `json:"name"`
 }
 
+// ShowResponse is the response returned from [Client.Show].
 type ShowResponse struct {
-	License    string       `json:"license,omitempty"`
+	License       string         `json:"license,omitempty"`
-	Modelfile  string       `json:"modelfile,omitempty"`
+	Modelfile     string         `json:"modelfile,omitempty"`
-	Parameters string       `json:"parameters,omitempty"`
+	Parameters    string         `json:"parameters,omitempty"`
-	Template   string       `json:"template,omitempty"`
+	Template      string         `json:"template,omitempty"`
-	System     string       `json:"system,omitempty"`
+	System        string         `json:"system,omitempty"`
-	Details    ModelDetails `json:"details,omitempty"`
+	Details       ModelDetails   `json:"details,omitempty"`
-	Messages   []Message    `json:"messages,omitempty"`
+	Messages      []Message      `json:"messages,omitempty"`
+	ModelInfo     map[string]any `json:"model_info,omitempty"`
+	ProjectorInfo map[string]any `json:"projector_info,omitempty"`
+	ModifiedAt    time.Time      `json:"modified_at,omitempty"`
 }
 
+// CopyRequest is the request passed to [Client.Copy].
 type CopyRequest struct {
 	Source      string `json:"source"`
 	Destination string `json:"destination"`
 }
 
+// PullRequest is the request passed to [Client.Pull].
 type PullRequest struct {
 	Model    string `json:"model"`
 	Insecure bool   `json:"insecure,omitempty"`
@@ -196,6 +363,8 @@ type PullRequest struct {
 	Name string `json:"name"`
 }
 
+// ProgressResponse is the response passed to progress functions like
+// [PullProgressFunc] and [PushProgressFunc].
 type ProgressResponse struct {
 	Status    string `json:"status"`
 	Digest    string `json:"digest,omitempty"`
@@ -203,6 +372,7 @@ type ProgressResponse struct {
 	Completed int64  `json:"completed,omitempty"`
 }
 
+// PushRequest is the request passed to [Client.Push].
 type PushRequest struct {
 	Model    string `json:"model"`
 	Insecure bool   `json:"insecure,omitempty"`
@@ -214,11 +384,18 @@ type PushRequest struct {
 	Name string `json:"name"`
 }
 
+// ListResponse is the response from [Client.List].
 type ListResponse struct {
-	Models []ModelResponse `json:"models"`
+	Models []ListModelResponse `json:"models"`
 }
 
-type ModelResponse struct {
+// ProcessResponse is the response from [Client.Process].
+type ProcessResponse struct {
+	Models []ProcessModelResponse `json:"models"`
+}
+
+// ListModelResponse is a single model description in [ListResponse].
+type ListModelResponse struct {
 	Name       string       `json:"name"`
 	Model      string       `json:"model"`
 	ModifiedAt time.Time    `json:"modified_at"`
@@ -227,21 +404,53 @@ type ModelResponse struct {
 	Details    ModelDetails `json:"details,omitempty"`
 }
 
+// ProcessModelResponse is a single model description in [ProcessResponse].
+type ProcessModelResponse struct {
+	Name      string       `json:"name"`
+	Model     string       `json:"model"`
+	Size      int64        `json:"size"`
+	Digest    string       `json:"digest"`
+	Details   ModelDetails `json:"details,omitempty"`
+	ExpiresAt time.Time    `json:"expires_at"`
+	SizeVRAM  int64        `json:"size_vram"`
+}
+
+type RetrieveModelResponse struct {
+	Id      string `json:"id"`
+	Object  string `json:"object"`
+	Created int64  `json:"created"`
+	OwnedBy string `json:"owned_by"`
+}
+
 type TokenResponse struct {
 	Token string `json:"token"`
 }
 
+// GenerateResponse is the response passed into [GenerateResponseFunc].
 type GenerateResponse struct {
-	Model     string    `json:"model"`
+	// Model is the model name that generated the response.
-	CreatedAt time.Time `json:"created_at"`
+	Model string `json:"model"`
-	Response  string    `json:"response"`
 
-	Done    bool  `json:"done"`
+	// CreatedAt is the timestamp of the response.
+	CreatedAt time.Time `json:"created_at"`
+
+	// Response is the textual response itself.
+	Response string `json:"response"`
+
+	// Done specifies if the response is complete.
+	Done bool `json:"done"`
+
+	// DoneReason is the reason the model stopped generating text.
+	DoneReason string `json:"done_reason,omitempty"`
+
+	// Context is an encoding of the conversation used in this response; this
+	// can be sent in the next request to keep a conversational memory.
 	Context []int `json:"context,omitempty"`
 
 	Metrics
 }
 
+// ModelDetails provides details about a model.
 type ModelDetails struct {
 	ParentModel       string   `json:"parent_model"`
 	Format            string   `json:"format"`
@@ -279,8 +488,6 @@ func (m *Metrics) Summary() {
 	}
 }
 
-var ErrInvalidOpts = fmt.Errorf("invalid options")
-
 func (opts *Options) FromMap(m map[string]interface{}) error {
 	valueOpts := reflect.ValueOf(opts).Elem() // names of the fields in the options struct
 	typeOpts := reflect.TypeOf(opts).Elem()   // types of the fields in the options struct
@@ -294,81 +501,94 @@ func (opts *Options) FromMap(m map[string]interface{}) error {
 		}
 	}
 
-	invalidOpts := []string{}
 	for key, val := range m {
-		if opt, ok := jsonOpts[key]; ok {
+		opt, ok := jsonOpts[key]
-			field := valueOpts.FieldByName(opt.Name)
+		if !ok {
-			if field.IsValid() && field.CanSet() {
+			slog.Warn("invalid option provided", "option", key)
-				if val == nil {
+			continue
-					continue
+		}
-				}
 
-				switch field.Kind() {
+		field := valueOpts.FieldByName(opt.Name)
-				case reflect.Int:
+		if field.IsValid() && field.CanSet() {
-					switch t := val.(type) {
+			if val == nil {
-					case int64:
+				continue
-						field.SetInt(t)
+			}
-					case float64:
+
-						// when JSON unmarshals numbers, it uses float64, not int
+			switch field.Kind() {
-						field.SetInt(int64(t))
+			case reflect.Int:
-					default:
+				switch t := val.(type) {
-						return fmt.Errorf("option %q must be of type integer", key)
+				case int64:
+					field.SetInt(t)
+				case float64:
+					// when JSON unmarshals numbers, it uses float64, not int
+					field.SetInt(int64(t))
+				default:
+					return fmt.Errorf("option %q must be of type integer", key)
+				}
+			case reflect.Bool:
+				val, ok := val.(bool)
+				if !ok {
+					return fmt.Errorf("option %q must be of type boolean", key)
+				}
+				field.SetBool(val)
+			case reflect.Float32:
+				// JSON unmarshals to float64
+				val, ok := val.(float64)
+				if !ok {
+					return fmt.Errorf("option %q must be of type float32", key)
+				}
+				field.SetFloat(val)
+			case reflect.String:
+				val, ok := val.(string)
+				if !ok {
+					return fmt.Errorf("option %q must be of type string", key)
+				}
+				field.SetString(val)
+			case reflect.Slice:
+				// JSON unmarshals to []interface{}, not []string
+				val, ok := val.([]interface{})
+				if !ok {
+					return fmt.Errorf("option %q must be of type array", key)
+				}
+				// convert []interface{} to []string
+				slice := make([]string, len(val))
+				for i, item := range val {
+					str, ok := item.(string)
+					if !ok {
+						return fmt.Errorf("option %q must be of an array of strings", key)
 					}
-				case reflect.Bool:
+					slice[i] = str
+				}
+				field.Set(reflect.ValueOf(slice))
+			case reflect.Pointer:
+				var b bool
+				if field.Type() == reflect.TypeOf(&b) {
 					val, ok := val.(bool)
 					if !ok {
 						return fmt.Errorf("option %q must be of type boolean", key)
 					}
-					field.SetBool(val)
+					field.Set(reflect.ValueOf(&val))
-				case reflect.Float32:
+				} else {
-					// JSON unmarshals to float64
+					return fmt.Errorf("unknown type loading config params: %v %v", field.Kind(), field.Type())
-					val, ok := val.(float64)
-					if !ok {
-						return fmt.Errorf("option %q must be of type float32", key)
-					}
-					field.SetFloat(val)
-				case reflect.String:
-					val, ok := val.(string)
-					if !ok {
-						return fmt.Errorf("option %q must be of type string", key)
-					}
-					field.SetString(val)
-				case reflect.Slice:
-					// JSON unmarshals to []interface{}, not []string
-					val, ok := val.([]interface{})
-					if !ok {
-						return fmt.Errorf("option %q must be of type array", key)
-					}
-					// convert []interface{} to []string
-					slice := make([]string, len(val))
-					for i, item := range val {
-						str, ok := item.(string)
-						if !ok {
-							return fmt.Errorf("option %q must be of an array of strings", key)
-						}
-						slice[i] = str
-					}
-					field.Set(reflect.ValueOf(slice))
-				default:
-					return fmt.Errorf("unknown type loading config params: %v", field.Kind())
 				}
+			default:
+				return fmt.Errorf("unknown type loading config params: %v", field.Kind())
 			}
-		} else {
-			invalidOpts = append(invalidOpts, key)
 		}
 	}
 
-	if len(invalidOpts) > 0 {
-		return fmt.Errorf("%w: %v", ErrInvalidOpts, strings.Join(invalidOpts, ", "))
-	}
 	return nil
 }
 
+// DefaultOptions is the default set of options for [GenerateRequest]; these
+// values are used unless the user specifies other values explicitly.
 func DefaultOptions() Options {
 	return Options{
 		// options set on request to runner
-		NumPredict:       -1,
+		NumPredict: -1,
-		NumKeep:          0,
+
+		// set a minimal num_keep to avoid issues on context shifts
+		NumKeep:          4,
 		Temperature:      0.8,
 		TopK:             40,
 		TopP:             0.9,
@@ -389,13 +609,11 @@ func DefaultOptions() Options {
 			NumCtx:    2048,
 			NumBatch:  512,
 			NumGPU:    -1, // -1 here indicates that NumGPU should be set dynamically
-			NumGQA:    1,
+			NumThread: 0,  // let the runtime decide
-			NumThread: 0, // let the runtime decide
 			LowVRAM:   false,
 			F16KV:     true,
 			UseMLock:  false,
-			UseMMap:   true,
+			UseMMap:   nil,
-			UseNUMA:   false,
 		},
 	}
 }
@@ -404,6 +622,13 @@ type Duration struct {
 	time.Duration
 }
 
+func (d Duration) MarshalJSON() ([]byte, error) {
+	if d.Duration < 0 {
+		return []byte("-1"), nil
+	}
+	return []byte("\"" + d.Duration.String() + "\""), nil
+}
+
 func (d *Duration) UnmarshalJSON(b []byte) (err error) {
 	var v any
 	if err := json.Unmarshal(b, &v); err != nil {
@@ -417,7 +642,7 @@ func (d *Duration) UnmarshalJSON(b []byte) (err error) {
 		if t < 0 {
 			d.Duration = time.Duration(math.MaxInt64)
 		} else {
-			d.Duration = time.Duration(t * float64(time.Second))
+			d.Duration = time.Duration(int(t) * int(time.Second))
 		}
 	case string:
 		d.Duration, err = time.ParseDuration(t)
@@ -427,6 +652,8 @@ func (d *Duration) UnmarshalJSON(b []byte) (err error) {
 		if d.Duration < 0 {
 			d.Duration = time.Duration(math.MaxInt64)
 		}
+	default:
+		return fmt.Errorf("Unsupported type: '%s'", reflect.TypeOf(v))
 	}
 
 	return nil
@@ -482,6 +709,17 @@ func FormatParams(params map[string][]string) (map[string]interface{}, error) {
 				case reflect.Slice:
 					// TODO: only string slices are supported right now
 					out[key] = vals
+				case reflect.Pointer:
+					var b bool
+					if field.Type() == reflect.TypeOf(&b) {
+						boolVal, err := strconv.ParseBool(vals[0])
+						if err != nil {
+							return nil, fmt.Errorf("invalid bool value %s", vals)
+						}
+						out[key] = &boolVal
+					} else {
+						return nil, fmt.Errorf("unknown type %s for %s", field.Kind(), key)
+					}
 				default:
 					return nil, fmt.Errorf("unknown type %s for %s", field.Kind(), key)
 				}

api/types_test.go

@@ -2,6 +2,7 @@ package api
 
 import (
 	"encoding/json"
+	"errors"
 	"math"
 	"testing"
 	"time"
@@ -21,6 +22,11 @@ func TestKeepAliveParsingFromJSON(t *testing.T) {
 			req:  `{ "keep_alive": 42 }`,
 			exp:  &Duration{42 * time.Second},
 		},
+		{
+			name: "Positive Float",
+			req:  `{ "keep_alive": 42.5 }`,
+			exp:  &Duration{42 * time.Second},
+		},
 		{
 			name: "Positive Integer String",
 			req:  `{ "keep_alive": "42m" }`,
@@ -31,6 +37,11 @@ func TestKeepAliveParsingFromJSON(t *testing.T) {
 			req:  `{ "keep_alive": -1 }`,
 			exp:  &Duration{math.MaxInt64},
 		},
+		{
+			name: "Negative Float",
+			req:  `{ "keep_alive": -3.14 }`,
+			exp:  &Duration{math.MaxInt64},
+		},
 		{
 			name: "Negative Integer String",
 			req:  `{ "keep_alive": "-1m" }`,
@@ -48,3 +59,175 @@ func TestKeepAliveParsingFromJSON(t *testing.T) {
 		})
 	}
 }
+
+func TestDurationMarshalUnmarshal(t *testing.T) {
+	tests := []struct {
+		name     string
+		input    time.Duration
+		expected time.Duration
+	}{
+		{
+			"negative duration",
+			time.Duration(-1),
+			time.Duration(math.MaxInt64),
+		},
+		{
+			"positive duration",
+			42 * time.Second,
+			42 * time.Second,
+		},
+		{
+			"another positive duration",
+			42 * time.Minute,
+			42 * time.Minute,
+		},
+		{
+			"zero duration",
+			time.Duration(0),
+			time.Duration(0),
+		},
+		{
+			"max duration",
+			time.Duration(math.MaxInt64),
+			time.Duration(math.MaxInt64),
+		},
+	}
+
+	for _, test := range tests {
+		t.Run(test.name, func(t *testing.T) {
+			b, err := json.Marshal(Duration{test.input})
+			require.NoError(t, err)
+
+			var d Duration
+			err = json.Unmarshal(b, &d)
+			require.NoError(t, err)
+
+			assert.Equal(t, test.expected, d.Duration, "input %v, marshalled %v, got %v", test.input, string(b), d.Duration)
+		})
+	}
+}
+
+func TestUseMmapParsingFromJSON(t *testing.T) {
+	tr := true
+	fa := false
+	tests := []struct {
+		name string
+		req  string
+		exp  *bool
+	}{
+		{
+			name: "Undefined",
+			req:  `{ }`,
+			exp:  nil,
+		},
+		{
+			name: "True",
+			req:  `{ "use_mmap": true }`,
+			exp:  &tr,
+		},
+		{
+			name: "False",
+			req:  `{ "use_mmap": false }`,
+			exp:  &fa,
+		},
+	}
+
+	for _, test := range tests {
+		t.Run(test.name, func(t *testing.T) {
+			var oMap map[string]interface{}
+			err := json.Unmarshal([]byte(test.req), &oMap)
+			require.NoError(t, err)
+			opts := DefaultOptions()
+			err = opts.FromMap(oMap)
+			require.NoError(t, err)
+			assert.Equal(t, test.exp, opts.UseMMap)
+		})
+	}
+}
+
+func TestUseMmapFormatParams(t *testing.T) {
+	tr := true
+	fa := false
+	tests := []struct {
+		name string
+		req  map[string][]string
+		exp  *bool
+		err  error
+	}{
+		{
+			name: "True",
+			req: map[string][]string{
+				"use_mmap": {"true"},
+			},
+			exp: &tr,
+			err: nil,
+		},
+		{
+			name: "False",
+			req: map[string][]string{
+				"use_mmap": {"false"},
+			},
+			exp: &fa,
+			err: nil,
+		},
+		{
+			name: "Numeric True",
+			req: map[string][]string{
+				"use_mmap": {"1"},
+			},
+			exp: &tr,
+			err: nil,
+		},
+		{
+			name: "Numeric False",
+			req: map[string][]string{
+				"use_mmap": {"0"},
+			},
+			exp: &fa,
+			err: nil,
+		},
+		{
+			name: "invalid string",
+			req: map[string][]string{
+				"use_mmap": {"foo"},
+			},
+			exp: nil,
+			err: errors.New("invalid bool value [foo]"),
+		},
+	}
+
+	for _, test := range tests {
+		t.Run(test.name, func(t *testing.T) {
+			resp, err := FormatParams(test.req)
+			require.Equal(t, test.err, err)
+			respVal, ok := resp["use_mmap"]
+			if test.exp != nil {
+				assert.True(t, ok, "resp: %v", resp)
+				assert.Equal(t, *test.exp, *respVal.(*bool))
+			}
+		})
+	}
+}
+
+func TestMessage_UnmarshalJSON(t *testing.T) {
+	tests := []struct {
+		input    string
+		expected string
+	}{
+		{`{"role": "USER", "content": "Hello!"}`, "user"},
+		{`{"role": "System", "content": "Initialization complete."}`, "system"},
+		{`{"role": "assistant", "content": "How can I help you?"}`, "assistant"},
+		{`{"role": "TOOl", "content": "Access granted."}`, "tool"},
+	}
+
+	for _, test := range tests {
+		var msg Message
+		if err := json.Unmarshal([]byte(test.input), &msg); err != nil {
+			t.Errorf("Unexpected error: %v", err)
+		}
+
+		if msg.Role != test.expected {
+			t.Errorf("role not lowercased: got %v, expected %v", msg.Role, test.expected)
+		}
+	}
+}

app/lifecycle/getstarted_nonwindows.go

@@ -2,8 +2,8 @@
 
 package lifecycle
 
-import "fmt"
+import "errors"
 
 func GetStarted() error {
-	return fmt.Errorf("GetStarted not implemented")
+	return errors.New("not implemented")
 }

app/lifecycle/getstarted_windows.go

@@ -34,7 +34,6 @@ func GetStarted() error {
 		Sys:   &syscall.SysProcAttr{CreationFlags: CREATE_NEW_CONSOLE, HideWindow: false},
 	}
 	proc, err := os.StartProcess(args[0], args, attrs)
-
 	if err != nil {
 		return fmt.Errorf("unable to start getting started shell %w", err)
 	}

app/lifecycle/logging.go

@@ -5,12 +5,16 @@ import (
 	"log/slog"
 	"os"
 	"path/filepath"
+	"strconv"
+	"strings"
+
+	"github.com/ollama/ollama/envconfig"
 )
 
 func InitLogging() {
 	level := slog.LevelInfo
 
-	if debug := os.Getenv("OLLAMA_DEBUG"); debug != "" {
+	if envconfig.Debug() {
 		level = slog.LevelDebug
 	}
 
@@ -22,7 +26,8 @@ func InitLogging() {
 		logFile = os.Stderr
 		// TODO - write one-line to the app.log file saying we're running in console mode to help avoid confusion
 	} else {
-		logFile, err = os.OpenFile(AppLogFile, os.O_APPEND|os.O_WRONLY|os.O_CREATE, 0755)
+		rotateLogs(AppLogFile)
+		logFile, err = os.OpenFile(AppLogFile, os.O_APPEND|os.O_WRONLY|os.O_CREATE, 0o755)
 		if err != nil {
 			slog.Error(fmt.Sprintf("failed to create server log %v", err))
 			return
@@ -44,3 +49,32 @@ func InitLogging() {
 
 	slog.Info("ollama app started")
 }
+
+func rotateLogs(logFile string) {
+	if _, err := os.Stat(logFile); os.IsNotExist(err) {
+		return
+	}
+	index := strings.LastIndex(logFile, ".")
+	pre := logFile[:index]
+	post := "." + logFile[index+1:]
+	for i := LogRotationCount; i > 0; i-- {
+		older := pre + "-" + strconv.Itoa(i) + post
+		newer := pre + "-" + strconv.Itoa(i-1) + post
+		if i == 1 {
+			newer = pre + post
+		}
+		if _, err := os.Stat(newer); err == nil {
+			if _, err := os.Stat(older); err == nil {
+				err := os.Remove(older)
+				if err != nil {
+					slog.Warn("Failed to remove older log", "older", older, "error", err)
+					continue
+				}
+			}
+			err := os.Rename(newer, older)
+			if err != nil {
+				slog.Warn("Failed to rotate log", "older", older, "newer", newer, "error", err)
+			}
+		}
+	}
+}

app/lifecycle/logging_nonwindows.go

@@ -5,5 +5,5 @@ package lifecycle
 import "log/slog"
 
 func ShowLogs() {
-	slog.Warn("ShowLogs not yet implemented")
+	slog.Warn("not implemented")
 }

app/lifecycle/logging_test.go

@@ -0,0 +1,44 @@
+package lifecycle
+
+import (
+	"os"
+	"path/filepath"
+	"strconv"
+	"testing"
+
+	"github.com/stretchr/testify/assert"
+	"github.com/stretchr/testify/require"
+)
+
+func TestRotateLogs(t *testing.T) {
+	logDir := t.TempDir()
+	logFile := filepath.Join(logDir, "testlog.log")
+
+	// No log exists
+	rotateLogs(logFile)
+
+	require.NoError(t, os.WriteFile(logFile, []byte("1"), 0o644))
+	assert.FileExists(t, logFile)
+	// First rotation
+	rotateLogs(logFile)
+	assert.FileExists(t, filepath.Join(logDir, "testlog-1.log"))
+	assert.NoFileExists(t, filepath.Join(logDir, "testlog-2.log"))
+	assert.NoFileExists(t, logFile)
+
+	// Should be a no-op without a new log
+	rotateLogs(logFile)
+	assert.FileExists(t, filepath.Join(logDir, "testlog-1.log"))
+	assert.NoFileExists(t, filepath.Join(logDir, "testlog-2.log"))
+	assert.NoFileExists(t, logFile)
+
+	for i := 2; i <= LogRotationCount+1; i++ {
+		require.NoError(t, os.WriteFile(logFile, []byte(strconv.Itoa(i)), 0o644))
+		assert.FileExists(t, logFile)
+		rotateLogs(logFile)
+		assert.NoFileExists(t, logFile)
+		for j := 1; j < i; j++ {
+			assert.FileExists(t, filepath.Join(logDir, "testlog-"+strconv.Itoa(j)+".log"))
+		}
+		assert.NoFileExists(t, filepath.Join(logDir, "testlog-"+strconv.Itoa(i+1)+".log"))
+	}
+}

app/lifecycle/paths.go

@@ -16,11 +16,12 @@ var (
 	AppDir     = "/opt/Ollama"
 	AppDataDir = "/opt/Ollama"
 	// TODO - should there be a distinct log dir?
-	UpdateStageDir = "/tmp"
+	UpdateStageDir   = "/tmp"
-	AppLogFile     = "/tmp/ollama_app.log"
+	AppLogFile       = "/tmp/ollama_app.log"
-	ServerLogFile  = "/tmp/ollama.log"
+	ServerLogFile    = "/tmp/ollama.log"
-	UpgradeLogFile = "/tmp/ollama_update.log"
+	UpgradeLogFile   = "/tmp/ollama_update.log"
-	Installer      = "OllamaSetup.exe"
+	Installer        = "OllamaSetup.exe"
+	LogRotationCount = 5
 )
 
 func init() {
@@ -69,7 +70,6 @@ func init() {
 				slog.Error(fmt.Sprintf("create ollama dir %s: %v", AppDataDir, err))
 			}
 		}
-
 	} else if runtime.GOOS == "darwin" {
 		// TODO
 		AppName += ".app"

app/lifecycle/server.go

@@ -9,14 +9,13 @@ import (
 	"os"
 	"os/exec"
 	"path/filepath"
-	"syscall"
 	"time"
 
 	"github.com/ollama/ollama/api"
 )
 
 func getCLIFullPath(command string) string {
-	cmdPath := ""
+	var cmdPath string
 	appExe, err := os.Executable()
 	if err == nil {
 		cmdPath = filepath.Join(filepath.Dir(appExe), command)
@@ -44,37 +43,35 @@ func getCLIFullPath(command string) string {
 	return command
 }
 
-func SpawnServer(ctx context.Context, command string) (chan int, error) {
+func start(ctx context.Context, command string) (*exec.Cmd, error) {
-	done := make(chan int)
-
-	logDir := filepath.Dir(ServerLogFile)
-	_, err := os.Stat(logDir)
-	if errors.Is(err, os.ErrNotExist) {
-		if err := os.MkdirAll(logDir, 0o755); err != nil {
-			return done, fmt.Errorf("create ollama server log dir %s: %v", logDir, err)
-		}
-	}
-
 	cmd := getCmd(ctx, getCLIFullPath(command))
-	// send stdout and stderr to a file
 	stdout, err := cmd.StdoutPipe()
 	if err != nil {
-		return done, fmt.Errorf("failed to spawn server stdout pipe %s", err)
+		return nil, fmt.Errorf("failed to spawn server stdout pipe: %w", err)
 	}
 	stderr, err := cmd.StderrPipe()
 	if err != nil {
-		return done, fmt.Errorf("failed to spawn server stderr pipe %s", err)
+		return nil, fmt.Errorf("failed to spawn server stderr pipe: %w", err)
-	}
-	stdin, err := cmd.StdinPipe()
-	if err != nil {
-		return done, fmt.Errorf("failed to spawn server stdin pipe %s", err)
 	}
 
-	// TODO - rotation
+	rotateLogs(ServerLogFile)
-	logFile, err := os.OpenFile(ServerLogFile, os.O_APPEND|os.O_WRONLY|os.O_CREATE, 0755)
+	logFile, err := os.OpenFile(ServerLogFile, os.O_APPEND|os.O_WRONLY|os.O_CREATE, 0o755)
 	if err != nil {
-		return done, fmt.Errorf("failed to create server log %w", err)
+		return nil, fmt.Errorf("failed to create server log: %w", err)
 	}
+
+	logDir := filepath.Dir(ServerLogFile)
+	_, err = os.Stat(logDir)
+	if err != nil {
+		if !errors.Is(err, os.ErrNotExist) {
+			return nil, fmt.Errorf("stat ollama server log dir %s: %v", logDir, err)
+		}
+
+		if err := os.MkdirAll(logDir, 0o755); err != nil {
+			return nil, fmt.Errorf("create ollama server log dir %s: %v", logDir, err)
+		}
+	}
+
 	go func() {
 		defer logFile.Close()
 		io.Copy(logFile, stdout) //nolint:errcheck
@@ -87,19 +84,29 @@ func SpawnServer(ctx context.Context, command string) (chan int, error) {
 	// Re-wire context done behavior to attempt a graceful shutdown of the server
 	cmd.Cancel = func() error {
 		if cmd.Process != nil {
-			cmd.Process.Signal(os.Interrupt) //nolint:errcheck
+			err := terminate(cmd)
+			if err != nil {
+				slog.Warn("error trying to gracefully terminate server", "err", err)
+				return cmd.Process.Kill()
+			}
+
 			tick := time.NewTicker(10 * time.Millisecond)
 			defer tick.Stop()
+
 			for {
 				select {
 				case <-tick.C:
-					// OS agnostic "is it still running"
+					exited, err := isProcessExited(cmd.Process.Pid)
-					if proc, err := os.FindProcess(int(cmd.Process.Pid)); err != nil || errors.Is(proc.Signal(syscall.Signal(0)), os.ErrProcessDone) {
+					if err != nil {
-						return nil //nolint:nilerr
+						return err
+					}
+
+					if exited {
+						return nil
 					}
 				case <-time.After(5 * time.Second):
 					slog.Warn("graceful server shutdown timeout, killing", "pid", cmd.Process.Pid)
-					cmd.Process.Kill() //nolint:errcheck
+					return cmd.Process.Kill()
 				}
 			}
 		}
@@ -108,19 +115,33 @@ func SpawnServer(ctx context.Context, command string) (chan int, error) {
 
 	// run the command and wait for it to finish
 	if err := cmd.Start(); err != nil {
-		return done, fmt.Errorf("failed to start server %w", err)
+		return nil, fmt.Errorf("failed to start server %w", err)
 	}
 	if cmd.Process != nil {
 		slog.Info(fmt.Sprintf("started ollama server with pid %d", cmd.Process.Pid))
 	}
 	slog.Info(fmt.Sprintf("ollama server logs %s", ServerLogFile))
 
+	return cmd, nil
+}
+
+func SpawnServer(ctx context.Context, command string) (chan int, error) {
+	done := make(chan int)
+
 	go func() {
 		// Keep the server running unless we're shuttind down the app
 		crashCount := 0
 		for {
+			slog.Info("starting server...")
+			cmd, err := start(ctx, command)
+			if err != nil {
+				crashCount++
+				slog.Error(fmt.Sprintf("failed to start server %s", err))
+				time.Sleep(500 * time.Millisecond * time.Duration(crashCount))
+				continue
+			}
+
 			cmd.Wait() //nolint:errcheck
-			stdin.Close()
 			var code int
 			if cmd.ProcessState != nil {
 				code = cmd.ProcessState.ExitCode()
@@ -134,15 +155,12 @@ func SpawnServer(ctx context.Context, command string) (chan int, error) {
 			default:
 				crashCount++
 				slog.Warn(fmt.Sprintf("server crash %d - exit code %d - respawning", crashCount, code))
-				time.Sleep(500 * time.Millisecond)
+				time.Sleep(500 * time.Millisecond * time.Duration(crashCount))
-				if err := cmd.Start(); err != nil {
+				break
-					slog.Error(fmt.Sprintf("failed to restart server %s", err))
-					// Keep trying, but back off if we keep failing
-					time.Sleep(time.Duration(crashCount) * time.Second)
-				}
 			}
 		}
 	}()
+
 	return done, nil
 }
 

app/lifecycle/server_unix.go

@@ -4,9 +4,35 @@ package lifecycle
 
 import (
 	"context"
+	"errors"
+	"fmt"
+	"os"
 	"os/exec"
+	"syscall"
 )
 
 func getCmd(ctx context.Context, cmd string) *exec.Cmd {
 	return exec.CommandContext(ctx, cmd, "serve")
 }
+
+func terminate(cmd *exec.Cmd) error {
+	return cmd.Process.Signal(os.Interrupt)
+}
+
+func isProcessExited(pid int) (bool, error) {
+	proc, err := os.FindProcess(pid)
+	if err != nil {
+		return false, fmt.Errorf("failed to find process: %v", err)
+	}
+
+	err = proc.Signal(syscall.Signal(0))
+	if err != nil {
+		if errors.Is(err, os.ErrProcessDone) || errors.Is(err, syscall.ESRCH) {
+			return true, nil
+		}
+
+		return false, fmt.Errorf("error signaling process: %v", err)
+	}
+
+	return false, nil
+}

app/lifecycle/server_windows.go

@@ -2,12 +2,90 @@ package lifecycle
 
 import (
 	"context"
+	"fmt"
 	"os/exec"
 	"syscall"
+
+	"golang.org/x/sys/windows"
 )
 
 func getCmd(ctx context.Context, exePath string) *exec.Cmd {
 	cmd := exec.CommandContext(ctx, exePath, "serve")
-	cmd.SysProcAttr = &syscall.SysProcAttr{HideWindow: true, CreationFlags: 0x08000000}
+	cmd.SysProcAttr = &syscall.SysProcAttr{
+		HideWindow:    true,
+		CreationFlags: windows.CREATE_NEW_PROCESS_GROUP,
+	}
+
 	return cmd
 }
+
+func terminate(cmd *exec.Cmd) error {
+	dll, err := windows.LoadDLL("kernel32.dll")
+	if err != nil {
+		return err
+	}
+	//nolint:errcheck
+	defer dll.Release()
+
+	pid := cmd.Process.Pid
+
+	f, err := dll.FindProc("AttachConsole")
+	if err != nil {
+		return err
+	}
+
+	r1, _, err := f.Call(uintptr(pid))
+	if r1 == 0 && err != syscall.ERROR_ACCESS_DENIED {
+		return err
+	}
+
+	f, err = dll.FindProc("SetConsoleCtrlHandler")
+	if err != nil {
+		return err
+	}
+
+	r1, _, err = f.Call(0, 1)
+	if r1 == 0 {
+		return err
+	}
+
+	f, err = dll.FindProc("GenerateConsoleCtrlEvent")
+	if err != nil {
+		return err
+	}
+
+	r1, _, err = f.Call(windows.CTRL_BREAK_EVENT, uintptr(pid))
+	if r1 == 0 {
+		return err
+	}
+
+	r1, _, err = f.Call(windows.CTRL_C_EVENT, uintptr(pid))
+	if r1 == 0 {
+		return err
+	}
+
+	return nil
+}
+
+const STILL_ACTIVE = 259
+
+func isProcessExited(pid int) (bool, error) {
+	hProcess, err := windows.OpenProcess(windows.PROCESS_QUERY_INFORMATION, false, uint32(pid))
+	if err != nil {
+		return false, fmt.Errorf("failed to open process: %v", err)
+	}
+	//nolint:errcheck
+	defer windows.CloseHandle(hProcess)
+
+	var exitCode uint32
+	err = windows.GetExitCodeProcess(hProcess, &exitCode)
+	if err != nil {
+		return false, fmt.Errorf("failed to get exit code: %v", err)
+	}
+
+	if exitCode == STILL_ACTIVE {
+		return false, nil
+	}
+
+	return true, nil
+}

app/lifecycle/updater.go

@@ -15,6 +15,7 @@ import (
 	"path"
 	"path/filepath"
 	"runtime"
+	"strconv"
 	"strings"
 	"time"
 
@@ -46,7 +47,7 @@ func IsNewReleaseAvailable(ctx context.Context) (bool, UpdateResponse) {
 	query.Add("os", runtime.GOOS)
 	query.Add("arch", runtime.GOARCH)
 	query.Add("version", version.Version)
-	query.Add("ts", fmt.Sprintf("%d", time.Now().Unix()))
+	query.Add("ts", strconv.FormatInt(time.Now().Unix(), 10))
 
 	nonce, err := auth.NewNonce(rand.Reader, 16)
 	if err != nil {
@@ -78,7 +79,7 @@ func IsNewReleaseAvailable(ctx context.Context) (bool, UpdateResponse) {
 	}
 	defer resp.Body.Close()
 
-	if resp.StatusCode == 204 {
+	if resp.StatusCode == http.StatusNoContent {
 		slog.Debug("check update response 204 (current version is up to date)")
 		return false, updateResp
 	}
@@ -87,7 +88,7 @@ func IsNewReleaseAvailable(ctx context.Context) (bool, UpdateResponse) {
 		slog.Warn(fmt.Sprintf("failed to read body response: %s", err))
 	}
 
-	if resp.StatusCode != 200 {
+	if resp.StatusCode != http.StatusOK {
 		slog.Info(fmt.Sprintf("check update error %d - %.96s", resp.StatusCode, string(body)))
 		return false, updateResp
 	}
@@ -114,7 +115,7 @@ func DownloadNewRelease(ctx context.Context, updateResp UpdateResponse) error {
 	if err != nil {
 		return fmt.Errorf("error checking update: %w", err)
 	}
-	if resp.StatusCode != 200 {
+	if resp.StatusCode != http.StatusOK {
 		return fmt.Errorf("unexpected status attempting to download update %d", resp.StatusCode)
 	}
 	resp.Body.Close()

app/lifecycle/updater_nonwindows.go

@@ -4,9 +4,9 @@ package lifecycle
 
 import (
 	"context"
-	"fmt"
+	"errors"
 )
 
 func DoUpgrade(cancel context.CancelFunc, done chan int) error {
-	return fmt.Errorf("DoUpgrade not yet implemented")
+	return errors.New("not implemented")
 }

app/lifecycle/updater_windows.go

@@ -2,6 +2,7 @@ package lifecycle
 
 import (
 	"context"
+	"errors"
 	"fmt"
 	"log/slog"
 	"os"
@@ -15,7 +16,7 @@ func DoUpgrade(cancel context.CancelFunc, done chan int) error {
 		return fmt.Errorf("failed to lookup downloads: %s", err)
 	}
 	if len(files) == 0 {
-		return fmt.Errorf("no update downloads found")
+		return errors.New("no update downloads found")
 	} else if len(files) > 1 {
 		// Shouldn't happen
 		slog.Warn(fmt.Sprintf("multiple downloads found, using first one %v", files))
@@ -31,16 +32,13 @@ func DoUpgrade(cancel context.CancelFunc, done chan int) error {
 		"/LOG=" + filepath.Base(UpgradeLogFile), // Only relative seems reliable, so set pwd
 		"/FORCECLOSEAPPLICATIONS",               // Force close the tray app - might be needed
 	}
-	// When we're not in debug mode, make the upgrade as quiet as possible (no GUI, no prompts)
+	// make the upgrade as quiet as possible (no GUI, no prompts)
-	// TODO - temporarily disable since we're pinning in debug mode for the preview
-	// if debug := os.Getenv("OLLAMA_DEBUG"); debug == "" {
 	installArgs = append(installArgs,
 		"/SP", // Skip the "This will install... Do you wish to continue" prompt
 		"/SUPPRESSMSGBOXES",
 		"/SILENT",
 		"/VERYSILENT",
 	)
-	// }
 
 	// Safeguard in case we have requests in flight that need to drain...
 	slog.Info("Waiting for server to shutdown")
@@ -67,7 +65,7 @@ func DoUpgrade(cancel context.CancelFunc, done chan int) error {
 		}
 	} else {
 		// TODO - some details about why it didn't start, or is this a pedantic error case?
-		return fmt.Errorf("installer process did not start")
+		return errors.New("installer process did not start")
 	}
 
 	// TODO should we linger for a moment and check to make sure it's actually running by checking the pid?

app/ollama.iss

@@ -87,18 +87,11 @@ DialogFontSize=12
 
 [Files]
 Source: ".\app.exe"; DestDir: "{app}"; DestName: "{#MyAppExeName}" ; Flags: ignoreversion 64bit
-Source: "..\ollama.exe"; DestDir: "{app}"; Flags: ignoreversion 64bit
+Source: "..\ollama.exe"; DestDir: "{app}\bin"; Flags: ignoreversion 64bit
-Source: "..\dist\windeps\*.dll"; DestDir: "{app}"; Flags: ignoreversion 64bit
+Source: "..\dist\windows-{#ARCH}\lib\ollama\runners\*"; DestDir: "{app}\lib\ollama\runners"; Flags: ignoreversion 64bit recursesubdirs
 Source: "..\dist\ollama_welcome.ps1"; DestDir: "{app}"; Flags: ignoreversion
 Source: ".\assets\app.ico"; DestDir: "{app}"; Flags: ignoreversion
-; Assumes v5.7, may need adjustments for v6
+Source: "..\dist\windows-amd64\lib\ollama\*"; DestDir: "{app}\lib\ollama\"; Flags: ignoreversion recursesubdirs
-#if GetEnv("HIP_PATH") != ""
-  Source: "{#GetEnv('HIP_PATH')}\bin\hipblas.dll"; DestDir: "{app}\rocm\"; Flags: ignoreversion
-  Source: "{#GetEnv('HIP_PATH')}\bin\rocblas.dll"; DestDir: "{app}\rocm\"; Flags: ignoreversion
-  ; amdhip64.dll dependency comes from the driver and must be installed already
-  Source: "{#GetEnv('HIP_PATH')}\bin\rocblas\library\*"; DestDir: "{app}\rocm\rocblas\library\"; Flags: ignoreversion
-#endif
-
 
 [Icons]
 Name: "{group}\{#MyAppName}"; Filename: "{app}\{#MyAppExeName}"; IconFilename: "{app}\app.ico"
@@ -106,7 +99,7 @@ Name: "{userstartup}\{#MyAppName}"; Filename: "{app}\{#MyAppExeName}"; IconFilen
 Name: "{userprograms}\{#MyAppName}"; Filename: "{app}\{#MyAppExeName}"; IconFilename: "{app}\app.ico"
 
 [Run]
-Filename: "{cmd}"; Parameters: "/C set PATH={app};%PATH% & ""{app}\{#MyAppExeName}"""; Flags: postinstall nowait runhidden
+Filename: "{cmd}"; Parameters: "/C set PATH={app}\bin;%PATH% & ""{app}\{#MyAppExeName}"""; Flags: postinstall nowait runhidden
 
 [UninstallRun]
 ; Filename: "{cmd}"; Parameters: "/C ""taskkill /im ''{#MyAppExeName}'' /f /t"; Flags: runhidden
@@ -125,6 +118,10 @@ Type: filesandordirs; Name: "{%USERPROFILE}\.ollama\models"
 Type: filesandordirs; Name: "{%USERPROFILE}\.ollama\history"
 ; NOTE: if the user has a custom OLLAMA_MODELS it will be preserved
 
+[InstallDelete]
+Type: filesandordirs; Name: "{%TEMP}\ollama*"
+Type: filesandordirs; Name: "{%LOCALAPPDATA}\Programs\Ollama"
+
 [Messages]
 WizardReady=Ollama Windows Preview
 ReadyLabel1=%nLet's get you up and running with your own large language models.
@@ -132,13 +129,13 @@ SetupAppRunningError=Another Ollama installer is running.%n%nPlease cancel or fi
 
 
 ;FinishedHeadingLabel=Run your first model
-;FinishedLabel=%nRun this command in a PowerShell or cmd terminal.%n%n%n    ollama run llama2
+;FinishedLabel=%nRun this command in a PowerShell or cmd terminal.%n%n%n    ollama run llama3.1
 ;ClickFinish=%n
 
 [Registry]
 Root: HKCU; Subkey: "Environment"; \
-    ValueType: expandsz; ValueName: "Path"; ValueData: "{olddata};{app}"; \
+    ValueType: expandsz; ValueName: "Path"; ValueData: "{olddata};{app}\bin"; \
-    Check: NeedsAddPath('{app}')
+    Check: NeedsAddPath('{app}\bin')
 
 [Code]
 

app/ollama_welcome.ps1

@@ -4,5 +4,5 @@ write-host "Welcome to Ollama!"
 write-host ""
 write-host "Run your first model:"
 write-host ""
-write-host "`tollama run llama2"
+write-host "`tollama run llama3.1"
 write-host ""

app/store/store.go

@@ -29,7 +29,6 @@ func GetID() string {
 		initStore()
 	}
 	return store.ID
-
 }
 
 func GetFirstTimeRun() bool {

app/tray/tray.go

@@ -24,10 +24,5 @@ func NewTray() (commontray.OllamaTray, error) {
 		return nil, fmt.Errorf("failed to load icon %s: %w", iconName, err)
 	}
 
-	tray, err := InitPlatformTray(icon, updateIcon)
+	return InitPlatformTray(icon, updateIcon)
-	if err != nil {
-		return nil, err
-	}
-
-	return tray, nil
 }

app/tray/tray_nonwindows.go

@@ -3,11 +3,11 @@
 package tray
 
 import (
-	"fmt"
+	"errors"
 
 	"github.com/ollama/ollama/app/tray/commontray"
 )
 
 func InitPlatformTray(icon, updateIcon []byte) (commontray.OllamaTray, error) {
-	return nil, fmt.Errorf("NOT IMPLEMENTED YET")
+	return nil, errors.New("not implemented")
 }

app/tray/wintray/eventloop.go

@@ -11,9 +11,7 @@ import (
 	"golang.org/x/sys/windows"
 )
 
-var (
+var quitOnce sync.Once
-	quitOnce sync.Once
-)
 
 func (t *winTray) Run() {
 	nativeLoop()
@@ -47,7 +45,6 @@ func nativeLoop() {
 		default:
 			pTranslateMessage.Call(uintptr(unsafe.Pointer(m))) //nolint:errcheck
 			pDispatchMessage.Call(uintptr(unsafe.Pointer(m)))  //nolint:errcheck
-
 		}
 	}
 }
@@ -160,8 +157,8 @@ func (t *winTray) wndProc(hWnd windows.Handle, message uint32, wParam, lParam ui
 		lResult, _, _ = pDefWindowProc.Call(
 			uintptr(hWnd),
 			uintptr(message),
-			uintptr(wParam),
+			wParam,
-			uintptr(lParam),
+			lParam,
 		)
 	}
 	return

app/tray/wintray/menus.go

@@ -11,12 +11,12 @@ import (
 )
 
 const (
-	updatAvailableMenuID = 1
+	updateAvailableMenuID = 1
-	updateMenuID         = updatAvailableMenuID + 1
+	updateMenuID          = updateAvailableMenuID + 1
-	separatorMenuID      = updateMenuID + 1
+	separatorMenuID       = updateMenuID + 1
-	diagLogsMenuID       = separatorMenuID + 1
+	diagLogsMenuID        = separatorMenuID + 1
-	diagSeparatorMenuID  = diagLogsMenuID + 1
+	diagSeparatorMenuID   = diagLogsMenuID + 1
-	quitMenuID           = diagSeparatorMenuID + 1
+	quitMenuID            = diagSeparatorMenuID + 1
 )
 
 func (t *winTray) initMenus() error {
@@ -35,7 +35,7 @@ func (t *winTray) initMenus() error {
 func (t *winTray) UpdateAvailable(ver string) error {
 	if !t.updateNotified {
 		slog.Debug("updating menu and sending notification for new update")
-		if err := t.addOrUpdateMenuItem(updatAvailableMenuID, 0, updateAvailableMenuTitle, true); err != nil {
+		if err := t.addOrUpdateMenuItem(updateAvailableMenuID, 0, updateAvailableMenuTitle, true); err != nil {
 			return fmt.Errorf("unable to create menu entries %w", err)
 		}
 		if err := t.addOrUpdateMenuItem(updateMenuID, 0, updateMenutTitle, false); err != nil {

app/tray/wintray/tray.go

@@ -11,10 +11,12 @@ import (
 	"path/filepath"
 	"sort"
 	"sync"
+	"syscall"
 	"unsafe"
 
-	"github.com/ollama/ollama/app/tray/commontray"
 	"golang.org/x/sys/windows"
+
+	"github.com/ollama/ollama/app/tray/commontray"
 )
 
 // Helpful sources: https://github.com/golang/exp/blob/master/shiny/driver/internal/win32
@@ -186,7 +188,7 @@ func (t *winTray) initInstance() error {
 	t.muNID.Lock()
 	defer t.muNID.Unlock()
 	t.nid = &notifyIconData{
-		Wnd:             windows.Handle(t.window),
+		Wnd:             t.window,
 		ID:              100,
 		Flags:           NIF_MESSAGE,
 		CallbackMessage: t.wmSystrayMessage,
@@ -197,7 +199,6 @@ func (t *winTray) initInstance() error {
 }
 
 func (t *winTray) createMenu() error {
-
 	menuHandle, _, err := pCreatePopupMenu.Call()
 	if menuHandle == 0 {
 		return err
@@ -246,7 +247,7 @@ func (t *winTray) addOrUpdateMenuItem(menuItemId uint32, parentId uint32, title
 	mi := menuItemInfo{
 		Mask:     MIIM_FTYPE | MIIM_STRING | MIIM_ID | MIIM_STATE,
 		Type:     MFT_STRING,
-		ID:       uint32(menuItemId),
+		ID:       menuItemId,
 		TypeData: titlePtr,
 		Cch:      uint32(len(title)),
 	}
@@ -302,11 +303,10 @@ func (t *winTray) addOrUpdateMenuItem(menuItemId uint32, parentId uint32, title
 }
 
 func (t *winTray) addSeparatorMenuItem(menuItemId, parentId uint32) error {
-
 	mi := menuItemInfo{
 		Mask: MIIM_FTYPE | MIIM_ID | MIIM_STATE,
 		Type: MFT_SEPARATOR,
-		ID:   uint32(menuItemId),
+		ID:   menuItemId,
 	}
 
 	mi.Size = uint32(unsafe.Sizeof(mi))
@@ -416,7 +416,7 @@ func iconBytesToFilePath(iconBytes []byte) (string, error) {
 	iconFilePath := filepath.Join(os.TempDir(), "ollama_temp_icon_"+dataHash)
 
 	if _, err := os.Stat(iconFilePath); os.IsNotExist(err) {
-		if err := os.WriteFile(iconFilePath, iconBytes, 0644); err != nil {
+		if err := os.WriteFile(iconFilePath, iconBytes, 0o644); err != nil {
 			return "", err
 		}
 	}
@@ -426,7 +426,6 @@ func iconBytesToFilePath(iconBytes []byte) (string, error) {
 // Loads an image from file and shows it in tray.
 // Shell_NotifyIcon: https://msdn.microsoft.com/en-us/library/windows/desktop/bb762159(v=vs.85).aspx
 func (t *winTray) setIcon(src string) error {
-
 	h, err := t.loadIconFrom(src)
 	if err != nil {
 		return err
@@ -435,7 +434,12 @@ func (t *winTray) setIcon(src string) error {
 	t.muNID.Lock()
 	defer t.muNID.Unlock()
 	t.nid.Icon = h
-	t.nid.Flags |= NIF_ICON
+	t.nid.Flags |= NIF_ICON | NIF_TIP
+	if toolTipUTF16, err := syscall.UTF16FromString(commontray.ToolTip); err == nil {
+		copy(t.nid.Tip[:], toolTipUTF16)
+	} else {
+		return err
+	}
 	t.nid.Size = uint32(unsafe.Sizeof(*t.nid))
 
 	return t.nid.modify()
@@ -444,7 +448,6 @@ func (t *winTray) setIcon(src string) error {
 // Loads an image from file to be shown in tray or menu item.
 // LoadImage: https://msdn.microsoft.com/en-us/library/windows/desktop/ms648045(v=vs.85).aspx
 func (t *winTray) loadIconFrom(src string) (windows.Handle, error) {
-
 	// Save and reuse handles of loaded images
 	t.muLoadedImages.RLock()
 	h, ok := t.loadedImages[src]

app/tray/wintray/w32api.go

@@ -61,6 +61,7 @@ const (
 	MIIM_SUBMENU        = 0x00000004
 	MIM_APPLYTOSUBMENUS = 0x80000000
 	NIF_ICON            = 0x00000002
+	NIF_TIP             = 0x00000004
 	NIF_INFO            = 0x00000010
 	NIF_MESSAGE         = 0x00000001
 	SW_HIDE             = 0

auth/auth.go

@@ -5,17 +5,50 @@ import (
 	"context"
 	"crypto/rand"
 	"encoding/base64"
+	"errors"
 	"fmt"
 	"io"
 	"log/slog"
 	"os"
 	"path/filepath"
+	"strings"
 
 	"golang.org/x/crypto/ssh"
 )
 
 const defaultPrivateKey = "id_ed25519"
 
+func keyPath() (string, error) {
+	home, err := os.UserHomeDir()
+	if err != nil {
+		return "", err
+	}
+
+	return filepath.Join(home, ".ollama", defaultPrivateKey), nil
+}
+
+func GetPublicKey() (string, error) {
+	keyPath, err := keyPath()
+	if err != nil {
+		return "", err
+	}
+
+	privateKeyFile, err := os.ReadFile(keyPath)
+	if err != nil {
+		slog.Info(fmt.Sprintf("Failed to load private key: %v", err))
+		return "", err
+	}
+
+	privateKey, err := ssh.ParsePrivateKey(privateKeyFile)
+	if err != nil {
+		return "", err
+	}
+
+	publicKey := ssh.MarshalAuthorizedKey(privateKey.PublicKey())
+
+	return strings.TrimSpace(string(publicKey)), nil
+}
+
 func NewNonce(r io.Reader, length int) (string, error) {
 	nonce := make([]byte, length)
 	if _, err := io.ReadFull(r, nonce); err != nil {
@@ -26,13 +59,11 @@ func NewNonce(r io.Reader, length int) (string, error) {
 }
 
 func Sign(ctx context.Context, bts []byte) (string, error) {
-	home, err := os.UserHomeDir()
+	keyPath, err := keyPath()
 	if err != nil {
 		return "", err
 	}
 
-	keyPath := filepath.Join(home, ".ollama", defaultPrivateKey)
-
 	privateKeyFile, err := os.ReadFile(keyPath)
 	if err != nil {
 		slog.Info(fmt.Sprintf("Failed to load private key: %v", err))
@@ -48,7 +79,7 @@ func Sign(ctx context.Context, bts []byte) (string, error) {
 	publicKey := ssh.MarshalAuthorizedKey(privateKey.PublicKey())
 	parts := bytes.Split(publicKey, []byte(" "))
 	if len(parts) < 2 {
-		return "", fmt.Errorf("malformed public key")
+		return "", errors.New("malformed public key")
 	}
 
 	signedData, err := privateKey.Sign(rand.Reader, bts)

cmd/interactive.go

@@ -1,6 +1,7 @@
 package cmd
 
 import (
+	"cmp"
 	"errors"
 	"fmt"
 	"io"
@@ -8,15 +9,18 @@ import (
 	"os"
 	"path/filepath"
 	"regexp"
-	"sort"
+	"slices"
 	"strings"
 
 	"github.com/spf13/cobra"
-	"golang.org/x/exp/slices"
+	"golang.org/x/exp/maps"
 
 	"github.com/ollama/ollama/api"
+	"github.com/ollama/ollama/envconfig"
+	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/progress"
 	"github.com/ollama/ollama/readline"
+	"github.com/ollama/ollama/types/errtypes"
 )
 
 type MultilineState int
@@ -25,75 +29,36 @@ const (
 	MultilineNone MultilineState = iota
 	MultilinePrompt
 	MultilineSystem
-	MultilineTemplate
 )
 
 func loadModel(cmd *cobra.Command, opts *runOptions) error {
-	client, err := api.ClientFromEnvironment()
-	if err != nil {
-		return err
-	}
-
 	p := progress.NewProgress(os.Stderr)
 	defer p.StopAndClear()
 
 	spinner := progress.NewSpinner("")
 	p.Add("", spinner)
 
-	showReq := api.ShowRequest{Name: opts.Model}
+	client, err := api.ClientFromEnvironment()
-	showResp, err := client.Show(cmd.Context(), &showReq)
 	if err != nil {
 		return err
 	}
-	opts.MultiModal = slices.Contains(showResp.Details.Families, "clip")
-	opts.ParentModel = showResp.Details.ParentModel
-
-	if len(showResp.Messages) > 0 {
-		opts.Messages = append(opts.Messages, showResp.Messages...)
-	}
 
 	chatReq := &api.ChatRequest{
-		Model:    opts.Model,
+		Model:     opts.Model,
-		Messages: []api.Message{},
+		KeepAlive: opts.KeepAlive,
-	}
-	err = client.Chat(cmd.Context(), chatReq, func(resp api.ChatResponse) error {
-		p.StopAndClear()
-		if len(opts.Messages) > 0 {
-			for _, msg := range opts.Messages {
-				switch msg.Role {
-				case "user":
-					fmt.Printf(">>> %s\n", msg.Content)
-				case "assistant":
-					state := &displayResponseState{}
-					displayResponse(msg.Content, opts.WordWrap, state)
-					fmt.Println()
-					fmt.Println()
-				}
-			}
-		}
-		return nil
-	})
-	if err != nil {
-		return err
 	}
 
-	return nil
+	return client.Chat(cmd.Context(), chatReq, func(api.ChatResponse) error { return nil })
 }
 
 func generateInteractive(cmd *cobra.Command, opts runOptions) error {
-	opts.Messages = make([]api.Message, 0)
-
-	err := loadModel(cmd, &opts)
-	if err != nil {
-		return err
-	}
-
 	usage := func() {
 		fmt.Fprintln(os.Stderr, "Available Commands:")
 		fmt.Fprintln(os.Stderr, "  /set            Set session variables")
 		fmt.Fprintln(os.Stderr, "  /show           Show model information")
 		fmt.Fprintln(os.Stderr, "  /load <model>   Load a session or model")
 		fmt.Fprintln(os.Stderr, "  /save <model>   Save your current session")
+		fmt.Fprintln(os.Stderr, "  /clear          Clear session context")
 		fmt.Fprintln(os.Stderr, "  /bye            Exit")
 		fmt.Fprintln(os.Stderr, "  /?, /help       Help for a command")
 		fmt.Fprintln(os.Stderr, "  /? shortcuts    Help for keyboard shortcuts")
@@ -111,7 +76,6 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 		fmt.Fprintln(os.Stderr, "Available Commands:")
 		fmt.Fprintln(os.Stderr, "  /set parameter ...     Set a parameter")
 		fmt.Fprintln(os.Stderr, "  /set system <string>   Set system message")
-		fmt.Fprintln(os.Stderr, "  /set template <string> Set prompt template")
 		fmt.Fprintln(os.Stderr, "  /set history           Enable history")
 		fmt.Fprintln(os.Stderr, "  /set nohistory         Disable history")
 		fmt.Fprintln(os.Stderr, "  /set wordwrap          Enable wordwrap")
@@ -131,6 +95,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 		fmt.Fprintln(os.Stderr, "   Alt + f            Move forward (right) one word")
 		fmt.Fprintln(os.Stderr, "  Ctrl + k            Delete the sentence after the cursor")
 		fmt.Fprintln(os.Stderr, "  Ctrl + u            Delete the sentence before the cursor")
+		fmt.Fprintln(os.Stderr, "  Ctrl + w            Delete the word before the cursor")
 		fmt.Fprintln(os.Stderr, "")
 		fmt.Fprintln(os.Stderr, "  Ctrl + l            Clear the screen")
 		fmt.Fprintln(os.Stderr, "  Ctrl + c            Stop the model from responding")
@@ -156,12 +121,13 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 		fmt.Fprintln(os.Stderr, "  /set parameter num_predict <int>      Max number of tokens to predict")
 		fmt.Fprintln(os.Stderr, "  /set parameter top_k <int>            Pick from top k num of tokens")
 		fmt.Fprintln(os.Stderr, "  /set parameter top_p <float>          Pick token based on sum of probabilities")
+		fmt.Fprintln(os.Stderr, "  /set parameter min_p <float>          Pick token based on top token probability * min_p")
 		fmt.Fprintln(os.Stderr, "  /set parameter num_ctx <int>          Set the context size")
 		fmt.Fprintln(os.Stderr, "  /set parameter temperature <float>    Set creativity level")
 		fmt.Fprintln(os.Stderr, "  /set parameter repeat_penalty <float> How strongly to penalize repetitions")
 		fmt.Fprintln(os.Stderr, "  /set parameter repeat_last_n <int>    Set how far back to look for repetitions")
 		fmt.Fprintln(os.Stderr, "  /set parameter num_gpu <int>          The number of layers to send to the GPU")
-		fmt.Fprintln(os.Stderr, "  /set parameter stop \"<string>\", ...   Set the stop parameters")
+		fmt.Fprintln(os.Stderr, "  /set parameter stop <string> <string> ...   Set the stop parameters")
 		fmt.Fprintln(os.Stderr, "")
 	}
 
@@ -175,6 +141,10 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 		return err
 	}
 
+	if envconfig.NoHistory() {
+		scanner.HistoryDisable()
+	}
+
 	fmt.Print(readline.StartBracketedPaste)
 	defer fmt.Printf(readline.EndBracketedPaste)
 
@@ -216,10 +186,6 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 				opts.Messages = append(opts.Messages, api.Message{Role: "system", Content: opts.System})
 				fmt.Println("Set system message.")
 				sb.Reset()
-			case MultilineTemplate:
-				opts.Template = sb.String()
-				fmt.Println("Set prompt template.")
-				sb.Reset()
 			}
 
 			multiline = MultilineNone
@@ -275,11 +241,22 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 			fn := func(resp api.ProgressResponse) error { return nil }
 			err = client.Create(cmd.Context(), req, fn)
 			if err != nil {
-				fmt.Println("error: couldn't save model")
+				if strings.Contains(err.Error(), errtypes.InvalidModelNameErrMsg) {
+					fmt.Printf("error: The model name '%s' is invalid\n", args[1])
+					continue
+				}
 				return err
 			}
 			fmt.Printf("Created new model '%s'\n", args[1])
 			continue
+		case strings.HasPrefix(line, "/clear"):
+			opts.Messages = []api.Message{}
+			if opts.System != "" {
+				newMessage := api.Message{Role: "system", Content: opts.System}
+				opts.Messages = append(opts.Messages, newMessage)
+			}
+			fmt.Println("Cleared session context")
+			continue
 		case strings.HasPrefix(line, "/set"):
 			args := strings.Fields(line)
 			if len(args) > 1 {
@@ -327,17 +304,13 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 					}
 					fmt.Printf("Set parameter '%s' to '%s'\n", args[2], strings.Join(params, ", "))
 					opts.Options[args[2]] = fp[args[2]]
-				case "system", "template":
+				case "system":
 					if len(args) < 3 {
 						usageSet()
 						continue
 					}
 
-					if args[1] == "system" {
+					multiline = MultilineSystem
-						multiline = MultilineSystem
-					} else if args[1] == "template" {
-						multiline = MultilineTemplate
-					}
 
 					line := strings.Join(args[2:], " ")
 					line, ok := strings.CutPrefix(line, `"""`)
@@ -357,23 +330,17 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 						continue
 					}
 
-					if args[1] == "system" {
+					opts.System = sb.String() // for display in modelfile
-						opts.System = sb.String() // for display in modelfile
+					newMessage := api.Message{Role: "system", Content: sb.String()}
-						newMessage := api.Message{Role: "system", Content: sb.String()}
+					// Check if the slice is not empty and the last message is from 'system'
-						// Check if the slice is not empty and the last message is from 'system'
+					if len(opts.Messages) > 0 && opts.Messages[len(opts.Messages)-1].Role == "system" {
-						if len(opts.Messages) > 0 && opts.Messages[len(opts.Messages)-1].Role == "system" {
+						// Replace the last message
-							// Replace the last message
+						opts.Messages[len(opts.Messages)-1] = newMessage
-							opts.Messages[len(opts.Messages)-1] = newMessage
+					} else {
-						} else {
+						opts.Messages = append(opts.Messages, newMessage)
-							opts.Messages = append(opts.Messages, newMessage)
-						}
-						fmt.Println("Set system message.")
-						sb.Reset()
-					} else if args[1] == "template" {
-						opts.Template = sb.String()
-						fmt.Println("Set prompt template.")
-						sb.Reset()
 					}
+					fmt.Println("Set system message.")
+					sb.Reset()
 
 					sb.Reset()
 					continue
@@ -392,10 +359,9 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 					return err
 				}
 				req := &api.ShowRequest{
-					Name:     opts.Model,
+					Name:    opts.Model,
-					System:   opts.System,
+					System:  opts.System,
-					Template: opts.Template,
+					Options: opts.Options,
-					Options:  opts.Options,
 				}
 				resp, err := client.Show(cmd.Context(), req)
 				if err != nil {
@@ -405,15 +371,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 
 				switch args[1] {
 				case "info":
-					fmt.Println("Model details:")
+					showInfo(resp)
-					if len(resp.Details.Families) > 0 {
-						fmt.Printf("Family              %s\n", strings.Join(resp.Details.Families, ", "))
-					} else if resp.Details.Family != "" {
-						fmt.Printf("Family              %s\n", resp.Details.Family)
-					}
-					fmt.Printf("Parameter Size      %s\n", resp.Details.ParameterSize)
-					fmt.Printf("Quantization Level  %s\n", resp.Details.QuantizationLevel)
-					fmt.Println("")
 				case "license":
 					if resp.License == "" {
 						fmt.Println("No license was specified for this model.")
@@ -446,12 +404,9 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 						fmt.Println("No system message was specified for this model.")
 					}
 				case "template":
-					switch {
+					if resp.Template != "" {
-					case opts.Template != "":
-						fmt.Println(opts.Template + "\n")
-					case resp.Template != "":
 						fmt.Println(resp.Template)
-					default:
+					} else {
 						fmt.Println("No prompt template was specified for this model.")
 					}
 				default:
@@ -535,35 +490,35 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 }
 
 func buildModelfile(opts runOptions) string {
-	var mf strings.Builder
+	var f parser.File
-	model := opts.ParentModel
+	f.Commands = append(f.Commands, parser.Command{Name: "model", Args: cmp.Or(opts.ParentModel, opts.Model)})
-	if model == "" {
+
-		model = opts.Model
-	}
-	fmt.Fprintf(&mf, "FROM %s\n", model)
 	if opts.System != "" {
-		fmt.Fprintf(&mf, "SYSTEM \"\"\"%s\"\"\"\n", opts.System)
+		f.Commands = append(f.Commands, parser.Command{Name: "system", Args: opts.System})
 	}
 
-	if opts.Template != "" {
+	keys := maps.Keys(opts.Options)
-		fmt.Fprintf(&mf, "TEMPLATE \"\"\"%s\"\"\"\n", opts.Template)
+	slices.Sort(keys)
-	}
-
-	keys := make([]string, 0)
-	for k := range opts.Options {
-		keys = append(keys, k)
-	}
-	sort.Strings(keys)
 	for _, k := range keys {
-		fmt.Fprintf(&mf, "PARAMETER %s %v\n", k, opts.Options[k])
+		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...)
 	}
-	fmt.Fprintln(&mf)
 
 	for _, msg := range opts.Messages {
-		fmt.Fprintf(&mf, "MESSAGE %s \"\"\"%s\"\"\"\n", msg.Role, msg.Content)
+		f.Commands = append(f.Commands, parser.Command{Name: "message", Args: fmt.Sprintf("%s: %s", msg.Role, msg.Content)})
 	}
 
-	return mf.String()
+	return f.String()
 }
 
 func normalizeFilePath(fp string) string {
@@ -649,7 +604,7 @@ func getImageData(filePath string) ([]byte, error) {
 	// Check if the file size exceeds 100MB
 	var maxSize int64 = 100 * 1024 * 1024 // 100MB in bytes
 	if info.Size() > maxSize {
-		return nil, fmt.Errorf("file size exceeds maximum limit (100MB)")
+		return nil, errors.New("file size exceeds maximum limit (100MB)")
 	}
 
 	buf = make([]byte, info.Size())

cmd/interactive_test.go

@@ -1,10 +1,9 @@
 package cmd
 
 import (
-	"bytes"
 	"testing"
-	"text/template"
 
+	"github.com/google/go-cmp/cmp"
 	"github.com/stretchr/testify/assert"
 
 	"github.com/ollama/ollama/api"
@@ -56,61 +55,53 @@ d:\path with\spaces\seven.svg inbetween7 c:\users\jdoe\eight.png inbetween8
 
 func TestModelfileBuilder(t *testing.T) {
 	opts := runOptions{
-		Model:    "hork",
+		Model:  "hork",
-		System:   "You are part horse and part shark, but all hork. Do horklike things",
+		System: "You are part horse and part shark, but all hork. Do horklike things",
-		Template: "This is a template.",
 		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]interface{}{},
+		Options: map[string]any{
+			"temperature":      0.9,
+			"seed":             42,
+			"penalize_newline": false,
+			"stop":             []string{"hi", "there"},
+		},
 	}
 
-	opts.Options["temperature"] = 0.9
+	t.Run("model", func(t *testing.T) {
-	opts.Options["seed"] = 42
+		expect := `FROM hork
-	opts.Options["penalize_newline"] = false
+SYSTEM You are part horse and part shark, but all hork. Do horklike things
-	opts.Options["stop"] = []string{"hi", "there"}
-
-	mf := buildModelfile(opts)
-	expectedModelfile := `FROM {{.Model}}
-SYSTEM """{{.System}}"""
-TEMPLATE """{{.Template}}"""
 PARAMETER penalize_newline false
 PARAMETER seed 42
-PARAMETER stop [hi there]
+PARAMETER stop hi
+PARAMETER stop there
 PARAMETER temperature 0.9
-
+MESSAGE user Hey there hork!
-MESSAGE user """Hey there hork!"""
+MESSAGE assistant Yes it is true, I am half horse, half shark.
-MESSAGE assistant """Yes it is true, I am half horse, half shark."""
 `
 
-	tmpl, err := template.New("").Parse(expectedModelfile)
+		actual := buildModelfile(opts)
-	assert.Nil(t, err)
+		if diff := cmp.Diff(expect, actual); diff != "" {
+			t.Errorf("mismatch (-want +got):\n%s", diff)
+		}
+	})
 
-	var buf bytes.Buffer
+	t.Run("parent model", func(t *testing.T) {
-	err = tmpl.Execute(&buf, opts)
+		opts.ParentModel = "horseshark"
-	assert.Nil(t, err)
+		expect := `FROM horseshark
-	assert.Equal(t, buf.String(), mf)
+SYSTEM You are part horse and part shark, but all hork. Do horklike things
-
-	opts.ParentModel = "horseshark"
-	mf = buildModelfile(opts)
-	expectedModelfile = `FROM {{.ParentModel}}
-SYSTEM """{{.System}}"""
-TEMPLATE """{{.Template}}"""
 PARAMETER penalize_newline false
 PARAMETER seed 42
-PARAMETER stop [hi there]
+PARAMETER stop hi
+PARAMETER stop there
 PARAMETER temperature 0.9
-
+MESSAGE user Hey there hork!
-MESSAGE user """Hey there hork!"""
+MESSAGE assistant Yes it is true, I am half horse, half shark.
-MESSAGE assistant """Yes it is true, I am half horse, half shark."""
 `
-
+		actual := buildModelfile(opts)
-	tmpl, err = template.New("").Parse(expectedModelfile)
+		if diff := cmp.Diff(expect, actual); diff != "" {
-	assert.Nil(t, err)
+			t.Errorf("mismatch (-want +got):\n%s", diff)
-
+		}
-	var parentBuf bytes.Buffer
+	})
-	err = tmpl.Execute(&parentBuf, opts)
-	assert.Nil(t, err)
-	assert.Equal(t, parentBuf.String(), mf)
 }

cmd/start.go

@@ -0,0 +1,27 @@
+//go:build darwin || windows
+
+package cmd
+
+import (
+	"context"
+	"errors"
+	"time"
+
+	"github.com/ollama/ollama/api"
+)
+
+func waitForServer(ctx context.Context, client *api.Client) error {
+	// wait for the server to start
+	timeout := time.After(5 * time.Second)
+	tick := time.Tick(500 * time.Millisecond)
+	for {
+		select {
+		case <-timeout:
+			return errors.New("timed out waiting for server to start")
+		case <-tick:
+			if err := client.Heartbeat(ctx); err == nil {
+				return nil // server has started
+			}
+		}
+	}
+}

cmd/start_darwin.go

@@ -2,7 +2,7 @@ package cmd
 
 import (
 	"context"
-	"fmt"
+	"errors"
 	"os"
 	"os/exec"
 	"strings"
@@ -20,7 +20,7 @@ func startApp(ctx context.Context, client *api.Client) error {
 		return err
 	}
 	if !strings.Contains(link, "Ollama.app") {
-		return fmt.Errorf("could not find ollama app")
+		return errors.New("could not find ollama app")
 	}
 	path := strings.Split(link, "Ollama.app")
 	if err := exec.Command("/usr/bin/open", "-a", path[0]+"Ollama.app").Run(); err != nil {

cmd/start_default.go

@@ -4,11 +4,11 @@ package cmd
 
 import (
 	"context"
-	"fmt"
+	"errors"
 
 	"github.com/ollama/ollama/api"
 )
 
 func startApp(ctx context.Context, client *api.Client) error {
-	return fmt.Errorf("could not connect to ollama server, run 'ollama serve' to start it")
+	return errors.New("could not connect to ollama server, run 'ollama serve' to start it")
 }

cmd/start_windows.go

@@ -31,7 +31,7 @@ func startApp(ctx context.Context, client *api.Client) error {
 			// Finally look in the path
 			appExe, err = exec.LookPath(AppName)
 			if err != nil {
-				return fmt.Errorf("could not locate ollama app")
+				return errors.New("could not locate ollama app")
 			}
 		}
 	}

convert/convert.go

@@ -1,433 +1,228 @@
 package convert
 
 import (
-	"bytes"
-	"cmp"
-	"encoding/binary"
 	"encoding/json"
+	"errors"
 	"fmt"
 	"io"
+	"io/fs"
 	"log/slog"
-	"os"
+	"strings"
-	"path/filepath"
-	"regexp"
-	"slices"
 
-	"github.com/d4l3k/go-bfloat16"
-	"github.com/mitchellh/mapstructure"
-	"github.com/x448/float16"
-	"google.golang.org/protobuf/proto"
-
-	"github.com/ollama/ollama/convert/sentencepiece"
 	"github.com/ollama/ollama/llm"
 )
 
-type Params struct {
+type ModelParameters struct {
-	Architectures    []string `json:"architectures"`
+	Architectures []string `json:"architectures"`
-	VocabSize        int      `json:"vocab_size"`
+	VocabSize     uint32   `json:"vocab_size"`
-	HiddenSize       int      `json:"hidden_size"`       // n_embd
-	HiddenLayers     int      `json:"num_hidden_layers"` // n_layer
-	ContextSize      int      `json:"max_position_embeddings"`
-	IntermediateSize int      `json:"intermediate_size"`
-	AttentionHeads   int      `json:"num_attention_heads"` // n_head
-	KeyValHeads      int      `json:"num_key_value_heads"`
-	NormEPS          float64  `json:"rms_norm_eps"`
-	BoSTokenID       int      `json:"bos_token_id"`
-	EoSTokenID       int      `json:"eos_token_id"`
-	HeadDimension    int      `json:"head_dim"`
-	PaddingTokenID   int      `json:"pad_token_id"`
-
-	ByteOrder
 }
 
-type ByteOrder interface {
+type AdapterParameters struct {
-	binary.ByteOrder
+	Alpha          uint32 `json:"lora_alpha"`
-	binary.AppendByteOrder
+	LoraLayers     uint32 `json:"lora_layers"`
+	LoraParameters struct {
+		Rank  uint32  `json:"rank"`
+		Alpha float32 `json:"alpha"`
+		Scale float32 `json:"scale"`
+	} `json:"lora_parameters"`
 }
 
-type MetaData struct {
+func (ModelParameters) KV(t *Tokenizer) llm.KV {
-	Type    string `mapstructure:"dtype"`
+	kv := llm.KV{
-	Shape   []int  `mapstructure:"shape"`
+		"general.file_type":            uint32(1),
-	Offsets []int  `mapstructure:"data_offsets"`
+		"general.quantization_version": uint32(2),
+		"tokenizer.ggml.pre":           t.Pre,
+		"tokenizer.ggml.model":         t.Vocabulary.Model,
+		"tokenizer.ggml.tokens":        t.Vocabulary.Tokens,
+		"tokenizer.ggml.scores":        t.Vocabulary.Scores,
+		"tokenizer.ggml.token_type":    t.Vocabulary.Types,
+	}
+
+	if len(t.Merges) > 0 {
+		kv["tokenizer.ggml.merges"] = t.Merges
+	}
+
+	if t.Template != "" {
+		kv["tokenizer.chat_template"] = t.Template
+	}
+
+	for _, sv := range t.SpecialVocabulary {
+		kv[fmt.Sprintf("tokenizer.ggml.%s_token_id", sv.Key())] = uint32(sv.ID)
+		kv[fmt.Sprintf("tokenizer.ggml.add_%s_token", sv.Key())] = sv.AddToken
+	}
+
+	return kv
 }
 
-type ModelArch interface {
+func (p AdapterParameters) KV() llm.KV {
-	GetTensors() error
+	var alpha float32
-	LoadVocab() error
+	if p.LoraParameters.Alpha == 0 {
-	WriteGGUF() (string, error)
+		alpha = float32(p.Alpha)
+	} else {
+		alpha = p.LoraParameters.Alpha
+	}
+
+	kv := llm.KV{
+		"adapter.lora.alpha": alpha,
+		"adapter.type":       "lora",
+		"general.file_type":  uint32(1),
+		"general.type":       "adapter",
+		"general.version":    "v0.2",
+	}
+
+	return kv
 }
 
-type ModelData struct {
+func (ModelParameters) specialTokenTypes() []string {
-	Path    string
+	return []string{
-	Name    string
+		"bos", "eos", "unk", "sep", "pad", "cls", "mask",
-	Params  *Params
+	}
-	Vocab   *Vocab
-	Tensors []llm.Tensor
 }
 
-func ReadSafeTensors(fn string, offset uint64, params *Params) ([]llm.Tensor, uint64, error) {
+func (ModelParameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []llm.Tensor) error {
-	f, err := os.Open(fn)
+	return llm.WriteGGUF(ws, kv, ts)
+}
+
+func (AdapterParameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []llm.Tensor) error {
+	return llm.WriteGGUF(ws, kv, ts)
+}
+
+type ModelConverter interface {
+	// KV maps parameters to LLM key-values
+	KV(*Tokenizer) llm.KV
+	// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
+	Tensors([]Tensor) []llm.Tensor
+	// Replacements returns a list of string pairs to replace in tensor names.
+	// See [strings.Replacer](https://pkg.go.dev/strings#Replacer) for details
+	Replacements() []string
+
+	// specialTokenTypes returns any special token types the model uses
+	specialTokenTypes() []string
+	// writeFile writes the model to the provided io.WriteSeeker
+	writeFile(io.WriteSeeker, llm.KV, []llm.Tensor) error
+}
+
+type moreParser interface {
+	parseMore(fs.FS) error
+}
+
+type AdapterConverter interface {
+	// KV maps parameters to LLM key-values
+	KV(llm.KV) llm.KV
+	// Tensors maps input tensors to LLM tensors. Adapter specific modifications can be done here.
+	Tensors([]Tensor) []llm.Tensor
+	// Replacements returns a list of string pairs to replace in tensor names.
+	// See [strings.Replacer](https://pkg.go.dev/strings#Replacer) for details
+	Replacements() []string
+
+	writeFile(io.WriteSeeker, llm.KV, []llm.Tensor) error
+}
+
+func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV llm.KV) error {
+	bts, err := fs.ReadFile(fsys, "adapter_config.json")
 	if err != nil {
-		return nil, 0, err
+		return err
-	}
-	defer f.Close()
-
-	var jsonSize uint64
-	if err := binary.Read(f, binary.LittleEndian, &jsonSize); err != nil {
-		return nil, 0, err
 	}
 
-	buf := make([]byte, jsonSize)
+	var p AdapterParameters
-	_, err = io.ReadFull(f, buf)
+	if err := json.Unmarshal(bts, &p); err != nil {
+		return err
+	}
+
+	arch, ok := baseKV["general.architecture"]
+	if !ok {
+		return errors.New("architecture not set for the base model")
+	}
+
+	var conv AdapterConverter
+	switch arch {
+	case "llama":
+		conv = &llamaAdapter{}
+	case "gemma2":
+		conv = &gemma2Adapter{}
+	default:
+		return errors.New("unsupported architecture")
+	}
+
+	ts, err := parseTensors(fsys, strings.NewReplacer(conv.Replacements()...))
 	if err != nil {
-		return nil, 0, err
+		return err
 	}
 
-	d := json.NewDecoder(bytes.NewBuffer(buf))
+	if err := json.Unmarshal(bts, conv); err != nil {
-	d.UseNumber()
+		return err
-	var parsed map[string]interface{}
-	if err = d.Decode(&parsed); err != nil {
-		return nil, 0, err
 	}
 
-	var keys []string
+	return conv.writeFile(ws, conv.KV(baseKV), conv.Tensors(ts))
-	for k := range parsed {
-		keys = append(keys, k)
-	}
-
-	slices.Sort(keys)
-
-	slog.Info("converting layers")
-
-	var tensors []llm.Tensor
-	for _, k := range keys {
-		vals := parsed[k].(map[string]interface{})
-		var data MetaData
-		if err = mapstructure.Decode(vals, &data); err != nil {
-			return nil, 0, err
-		}
-
-		var size uint64
-		var kind uint32
-		switch len(data.Shape) {
-		case 0:
-			// metadata
-			continue
-		case 1:
-			// convert to float32
-			kind = 0
-			size = uint64(data.Shape[0] * 4)
-		case 2:
-			// convert to float16
-			kind = 1
-			size = uint64(data.Shape[0] * data.Shape[1] * 2)
-		}
-
-		ggufName, err := GetTensorName(k)
-		if err != nil {
-			slog.Error("%v", err)
-			return nil, 0, err
-		}
-
-		shape := []uint64{0, 0, 0, 0}
-		for i := range data.Shape {
-			shape[i] = uint64(data.Shape[i])
-		}
-
-		t := llm.Tensor{
-			Name:   ggufName,
-			Kind:   kind,
-			Offset: offset,
-			Shape:  shape[:],
-		}
-
-		t.WriterTo = safetensorWriterTo{
-			t:        &t,
-			params:   params,
-			bo:       params.ByteOrder,
-			filename: fn,
-			start:    uint64(data.Offsets[0]),
-			end:      uint64(data.Offsets[1]),
-			padding:  8 + jsonSize,
-		}
-
-		slog.Debug(fmt.Sprintf("%v", t))
-		tensors = append(tensors, t)
-		offset += size
-	}
-	return tensors, offset, nil
 }
 
-func GetSafeTensors(dirpath string, params *Params) ([]llm.Tensor, error) {
+// Convert writes an Ollama compatible model to the provided io.WriteSeeker based on configurations
-	var tensors []llm.Tensor
+// and files it finds in the input path.
-	files, err := filepath.Glob(filepath.Join(dirpath, "/model-*.safetensors"))
+// Supported input model formats include safetensors.
+// Supported input tokenizers files include tokenizer.json (preferred) and tokenizer.model.
+func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
+	bts, err := fs.ReadFile(fsys, "config.json")
 	if err != nil {
-		return nil, err
+		return err
 	}
 
-	var offset uint64
+	var p ModelParameters
-	for _, f := range files {
+	if err := json.Unmarshal(bts, &p); err != nil {
-		var t []llm.Tensor
+		return err
-		var err error
+	}
-		t, offset, err = ReadSafeTensors(f, offset, params)
+
-		if err != nil {
+	if len(p.Architectures) < 1 {
-			slog.Error("%v", err)
+		return errors.New("unknown architecture")
-			return nil, err
+	}
+
+	var conv ModelConverter
+	switch p.Architectures[0] {
+	case "LlamaForCausalLM", "MistralForCausalLM":
+		conv = &llamaModel{}
+	case "MixtralForCausalLM":
+		conv = &mixtralModel{}
+	case "GemmaForCausalLM":
+		conv = &gemmaModel{}
+	case "Gemma2ForCausalLM":
+		conv = &gemma2Model{}
+	case "Phi3ForCausalLM":
+		conv = &phi3Model{}
+	case "BertModel":
+		conv = &bertModel{}
+	default:
+		return errors.New("unsupported architecture")
+	}
+
+	if err := json.Unmarshal(bts, conv); err != nil {
+		return err
+	}
+
+	if t, ok := conv.(moreParser); ok {
+		if err := t.parseMore(fsys); err != nil {
+			return err
 		}
-		tensors = append(tensors, t...)
 	}
-	return tensors, nil
-}
 
-func GetParams(dirpath string) (*Params, error) {
+	t, err := parseTokenizer(fsys, conv.specialTokenTypes())
-	f, err := os.Open(filepath.Join(dirpath, "config.json"))
 	if err != nil {
-		return nil, err
+		return err
 	}
-	defer f.Close()
 
-	var params Params
+	if vocabSize := int(p.VocabSize); vocabSize > len(t.Vocabulary.Tokens) {
+		slog.Warn("vocabulary is smaller than expected, padding with dummy tokens", "expect", p.VocabSize, "actual", len(t.Vocabulary.Tokens))
+		for i := range vocabSize - len(t.Vocabulary.Tokens) {
+			t.Vocabulary.Tokens = append(t.Vocabulary.Tokens, fmt.Sprintf("[PAD%d]", i))
+			t.Vocabulary.Scores = append(t.Vocabulary.Scores, -1)
+			t.Vocabulary.Types = append(t.Vocabulary.Types, tokenTypeUserDefined)
+		}
+	} else {
+		slog.Debug("vocabulary", "size", len(t.Vocabulary.Tokens))
+	}
 
-	d := json.NewDecoder(f)
+	ts, err := parseTensors(fsys, strings.NewReplacer(conv.Replacements()...))
-	err = d.Decode(&params)
 	if err != nil {
-		return nil, err
+		return err
 	}
 
-	params.ByteOrder = binary.LittleEndian
+	return conv.writeFile(ws, conv.KV(t), conv.Tensors(ts))
-	return &params, nil
-}
-
-// Details on gguf's tokenizer can be found at:
-// https://github.com/ggerganov/ggml/blob/master/docs/gguf.md#tokenizer
-type Vocab struct {
-	Tokens []string
-	Scores []float32
-	Types  []int32
-}
-
-func LoadSentencePieceTokens(dirpath string, vocabSize int) (*Vocab, error) {
-	slog.Info(fmt.Sprintf("reading vocab from %s", filepath.Join(dirpath, "tokenizer.model")))
-	in, err := os.ReadFile(filepath.Join(dirpath, "tokenizer.model"))
-	if err != nil {
-		return nil, err
-	}
-
-	// To regenerate sentencepiece from the protobufs use:
-	// protoc -I=./ --go_out=./ sentencepiece_model.proto
-	modelProto := &sentencepiece.ModelProto{}
-	if err := proto.Unmarshal(in, modelProto); err != nil {
-		return nil, err
-	}
-
-	v := &Vocab{
-		Tokens: make([]string, 0),
-		Scores: make([]float32, 0),
-		Types:  make([]int32, 0),
-	}
-
-	pieces := modelProto.GetPieces()
-	for _, p := range pieces {
-		v.Tokens = append(v.Tokens, p.GetPiece())
-		v.Scores = append(v.Scores, p.GetScore())
-		t := p.GetType()
-		switch t {
-		case sentencepiece.ModelProto_SentencePiece_UNKNOWN:
-		case sentencepiece.ModelProto_SentencePiece_CONTROL:
-		case sentencepiece.ModelProto_SentencePiece_UNUSED:
-		case sentencepiece.ModelProto_SentencePiece_BYTE:
-		default:
-			t = sentencepiece.ModelProto_SentencePiece_NORMAL
-		}
-		v.Types = append(v.Types, int32(t))
-	}
-
-	slog.Info(fmt.Sprintf("vocab size: %d", len(v.Tokens)))
-
-	// add any additional tokens
-	addIn, err := os.ReadFile(filepath.Join(dirpath, "added_tokens.json"))
-	if os.IsNotExist(err) {
-		return v, nil
-	} else if err != nil {
-		return nil, err
-	}
-
-	slog.Info("reading user defined tokens")
-
-	var extraTokenData map[string]int
-	if err := json.Unmarshal(addIn, &extraTokenData); err != nil {
-		return nil, err
-	}
-
-	type token struct {
-		key string
-		pos int
-	}
-
-	extraTokens := make([]token, 0)
-	for k, id := range extraTokenData {
-		extraTokens = append(extraTokens, token{k, id})
-	}
-
-	slices.SortFunc(extraTokens, func(a, b token) int {
-		return cmp.Compare(a.pos, b.pos)
-	})
-
-	numToks := len(v.Tokens)
-
-	for cnt, t := range extraTokens {
-		// the token id should match the specific index for the total number of tokens
-		if t.pos != cnt+numToks {
-			return nil, fmt.Errorf("token ID '%d' for '%s' doesn't match total token size", t.pos, t.key)
-		}
-		v.Tokens = append(v.Tokens, t.key)
-		v.Scores = append(v.Scores, -1000.0)
-		v.Types = append(v.Types, int32(llm.GGUFTokenUserDefined))
-	}
-	slog.Info(fmt.Sprintf("vocab size w/ extra tokens: %d", len(v.Tokens)))
-
-	if vocabSize > len(v.Tokens) {
-		missingTokens := vocabSize - len(v.Tokens)
-		slog.Warn(fmt.Sprintf("vocab is missing %d tokens", missingTokens))
-		for cnt := 0; cnt < missingTokens; cnt++ {
-			v.Tokens = append(v.Tokens, fmt.Sprintf("<dummy%05d>", cnt+1))
-			v.Scores = append(v.Scores, -1)
-			v.Types = append(v.Types, int32(llm.GGUFTokenUserDefined))
-		}
-	}
-
-	return v, nil
-}
-
-func GetTensorName(n string) (string, error) {
-	tMap := map[string]string{
-		"model.embed_tokens.weight":                           "token_embd.weight",
-		"model.layers.(\\d+).input_layernorm.weight":          "blk.$1.attn_norm.weight",
-		"model.layers.(\\d+).mlp.down_proj.weight":            "blk.$1.ffn_down.weight",
-		"model.layers.(\\d+).mlp.gate_proj.weight":            "blk.$1.ffn_gate.weight",
-		"model.layers.(\\d+).mlp.up_proj.weight":              "blk.$1.ffn_up.weight",
-		"model.layers.(\\d+).post_attention_layernorm.weight": "blk.$1.ffn_norm.weight",
-		"model.layers.(\\d+).self_attn.k_proj.weight":         "blk.$1.attn_k.weight",
-		"model.layers.(\\d+).self_attn.o_proj.weight":         "blk.$1.attn_output.weight",
-		"model.layers.(\\d+).self_attn.q_proj.weight":         "blk.$1.attn_q.weight",
-		"model.layers.(\\d+).self_attn.v_proj.weight":         "blk.$1.attn_v.weight",
-		"lm_head.weight":    "output.weight",
-		"model.norm.weight": "output_norm.weight",
-	}
-
-	v, ok := tMap[n]
-	if ok {
-		return v, nil
-	}
-
-	// quick hack to rename the layers to gguf format
-	for k, v := range tMap {
-		re := regexp.MustCompile(k)
-		newName := re.ReplaceAllString(n, v)
-		if newName != n {
-			return newName, nil
-		}
-	}
-
-	return "", fmt.Errorf("couldn't find a layer name for '%s'", n)
-}
-
-type safetensorWriterTo struct {
-	t *llm.Tensor
-
-	params *Params
-	bo     ByteOrder
-
-	filename string
-
-	start, end, padding uint64
-	handler             func(w io.Writer, r safetensorWriterTo, f *os.File) error
-}
-
-func (r safetensorWriterTo) WriteTo(w io.Writer) (n int64, err error) {
-	f, err := os.Open(r.filename)
-	if err != nil {
-		return 0, err
-	}
-	defer f.Close()
-
-	if _, err = f.Seek(int64(r.padding+r.start), 0); err != nil {
-		return 0, err
-	}
-
-	// use the handler if one is present
-	if r.handler != nil {
-		return 0, r.handler(w, r, f)
-	}
-
-	remaining := r.end - r.start
-
-	bufSize := uint64(10240)
-	var finished bool
-	for {
-		data := make([]byte, min(bufSize, remaining))
-
-		b, err := io.ReadFull(f, data)
-		remaining -= uint64(b)
-
-		if err == io.EOF || remaining <= 0 {
-			finished = true
-		} else if err != nil {
-			return 0, err
-		}
-
-		// convert bfloat16 -> ieee float32
-		tDataF32 := bfloat16.DecodeFloat32(data)
-
-		switch r.t.Kind {
-		case 0:
-			if err := binary.Write(w, r.bo, tDataF32); err != nil {
-				return 0, err
-			}
-		case 1:
-			// convert float32 -> float16
-			tempBuf := make([]uint16, len(data)/2)
-			for cnt, v := range tDataF32 {
-				tDataF16 := float16.Fromfloat32(v)
-				tempBuf[cnt] = uint16(tDataF16)
-			}
-			if err := binary.Write(w, binary.LittleEndian, tempBuf); err != nil {
-				return 0, err
-			}
-		}
-		if finished {
-			break
-		}
-	}
-	return 0, nil
-}
-
-func GetModelArchFromParams(name, dirPath string, params *Params) (ModelArch, error) {
-	switch len(params.Architectures) {
-	case 0:
-		return nil, fmt.Errorf("No architecture specified to convert")
-	case 1:
-		switch params.Architectures[0] {
-		case "MistralForCausalLM":
-			return &MistralModel{
-				ModelData{
-					Name:   name,
-					Path:   dirPath,
-					Params: params,
-				},
-			}, nil
-		case "GemmaForCausalLM":
-			return &GemmaModel{
-				ModelData{
-					Name:   name,
-					Path:   dirPath,
-					Params: params,
-				},
-			}, nil
-		default:
-			return nil, fmt.Errorf("Models based on '%s' are not yet supported", params.Architectures[0])
-		}
-	}
-
-	return nil, fmt.Errorf("Unknown error")
 }

convert/convert_bert.go

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

convert/convert_gemma.go

@@ -0,0 +1,100 @@
+package convert
+
+import (
+	"strings"
+
+	"github.com/pdevine/tensor"
+	"github.com/pdevine/tensor/native"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type gemmaModel 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"`
+	RMSNormEPS            float32 `json:"rms_norm_eps"`
+	HeadDim               uint32  `json:"head_dim"`
+}
+
+var _ ModelConverter = (*gemmaModel)(nil)
+
+func (p *gemmaModel) KV(t *Tokenizer) llm.KV {
+	kv := p.ModelParameters.KV(t)
+	kv["general.architecture"] = "gemma"
+	kv["gemma.context_length"] = p.MaxPositionEmbeddings
+	kv["gemma.embedding_length"] = p.HiddenSize
+	kv["gemma.block_count"] = p.HiddenLayers
+	kv["gemma.feed_forward_length"] = p.IntermediateSize
+	kv["gemma.attention.head_count"] = p.NumAttentionHeads
+	kv["gemma.attention.head_count_kv"] = p.NumKeyValueHeads
+	kv["gemma.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
+	kv["gemma.attention.key_length"] = p.HeadDim
+	kv["gemma.attention.value_length"] = p.HeadDim
+	kv["tokenizer.ggml.eot_token_id"] = uint32(107)
+	kv["tokenizer.ggml.middle_token_id"] = uint32(68)
+	kv["tokenizer.ggml.prefix_token_id"] = uint32(67)
+	kv["tokenizer.ggml.suffix_token_id"] = uint32(69)
+	return kv
+}
+
+func (p *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, llm.Tensor{
+			Name:     t.Name(),
+			Kind:     t.Kind(),
+			Shape:    t.Shape(),
+			WriterTo: t,
+		})
+	}
+
+	return out
+}
+
+func (p *gemmaModel) Replacements() []string {
+	return []string{
+		"model.embed_tokens", "token_embd",
+		"model.norm", "output_norm",
+		"model.layers", "blk",
+		"input_layernorm", "attn_norm",
+		"self_attn.q_proj", "attn_q",
+		"self_attn.k_proj", "attn_k",
+		"self_attn.v_proj", "attn_v",
+		"self_attn.o_proj", "attn_output",
+		"mlp.gate_proj", "ffn_gate",
+		"mlp.down_proj", "ffn_down",
+		"mlp.up_proj", "ffn_up",
+		"post_attention_layernorm", "ffn_norm",
+	}
+}
+
+func (*gemmaModel) addOne(_ string, data []float32, shape []uint64) ([]float32, error) {
+	n := tensor.New(tensor.WithShape(int(shape[0])), tensor.WithBacking(data))
+	ones := tensor.Ones(tensor.Float32, int(shape[0]))
+
+	n, err := n.Add(ones)
+	if err != nil {
+		return nil, err
+	}
+
+	ts, err := native.SelectF32(n, 0)
+	if err != nil {
+		return nil, err
+	}
+
+	var f32s []float32
+	for _, t := range ts {
+		f32s = append(f32s, t...)
+	}
+
+	return f32s, nil
+}

convert/convert_gemma2.go

@@ -0,0 +1,43 @@
+package convert
+
+import (
+	"github.com/ollama/ollama/llm"
+)
+
+type gemma2Model struct {
+	gemmaModel
+	SlidingWindow         uint32  `json:"sliding_window"`
+	AttentionLogitSoftcap float32 `json:"attn_logit_softcapping"`
+	FinalLogitSoftcap     float32 `json:"final_logit_softcapping"`
+}
+
+func (p *gemma2Model) KV(t *Tokenizer) llm.KV {
+	kv := p.ModelParameters.KV(t)
+	kv["general.architecture"] = "gemma2"
+	kv["gemma2.context_length"] = p.MaxPositionEmbeddings
+	kv["gemma2.embedding_length"] = p.HiddenSize
+	kv["gemma2.block_count"] = p.HiddenLayers
+	kv["gemma2.feed_forward_length"] = p.IntermediateSize
+	kv["gemma2.attention.head_count"] = p.NumAttentionHeads
+	kv["gemma2.attention.head_count_kv"] = p.NumKeyValueHeads
+	kv["gemma2.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
+	kv["gemma2.attention.key_length"] = p.HeadDim
+	kv["gemma2.attention.value_length"] = p.HeadDim
+	kv["gemma2.attention.sliding_window"] = p.SlidingWindow
+	kv["gemma2.attn_logit_softcapping"] = p.AttentionLogitSoftcap
+	kv["gemma2.final_logit_softcapping"] = p.FinalLogitSoftcap
+	kv["tokenizer.ggml.eot_token_id"] = uint32(107)
+	kv["tokenizer.ggml.middle_token_id"] = uint32(68)
+	kv["tokenizer.ggml.prefix_token_id"] = uint32(67)
+	kv["tokenizer.ggml.suffix_token_id"] = uint32(69)
+	return kv
+}
+
+func (p *gemma2Model) Replacements() []string {
+	return append(
+		p.gemmaModel.Replacements(),
+		"post_attention_layernorm", "post_attention_norm",
+		"pre_feedforward_layernorm", "ffn_norm",
+		"post_feedforward_layernorm", "post_ffw_norm",
+	)
+}

convert/convert_gemma2_adapter.go

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

convert/convert_llama.go

@@ -0,0 +1,213 @@
+package convert
+
+import (
+	"cmp"
+	"fmt"
+	"math"
+	"strings"
+
+	"github.com/pdevine/tensor"
+	"github.com/pdevine/tensor/native"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type llamaModel struct {
+	ModelParameters
+	NLayers               uint32  `json:"n_layers"`
+	NumHiddenLayers       uint32  `json:"num_hidden_layers"`
+	NLayer                uint32  `json:"n_layer"`
+	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
+	NCtx                  uint32  `json:"n_ctx"`
+	HiddenSize            uint32  `json:"hidden_size"`
+	NEmbd                 uint32  `json:"n_embd"`
+	IntermediateSize      uint32  `json:"intermediate_size"`
+	NInner                uint32  `json:"n_inner"`
+	NumAttentionHeads     uint32  `json:"num_attention_heads"`
+	NHead                 uint32  `json:"n_head"`
+	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
+	RopeTheta             float32 `json:"rope_theta"`
+	RopeScaling           struct {
+		Type                            string  `json:"type"`
+		RopeType                        string  `json:"rope_type"`
+		Factor                          float32 `json:"factor"`
+		LowFrequencyFactor              float32 `json:"low_freq_factor"`
+		HighFrequencyFactor             float32 `json:"high_freq_factor"`
+		OriginalMaxPositionalEmbeddings uint32  `json:"original_max_positional_embeddings"`
+
+		factors ropeFactor
+	} `json:"rope_scaling"`
+	RMSNormEPS       float32 `json:"rms_norm_eps"`
+	LayerNormEPS     float32 `json:"layer_norm_eps"`
+	LayerNormEpsilon float32 `json:"layer_norm_epsilon"`
+	NormEpsilon      float32 `json:"norm_epsilon"`
+	HeadDim          uint32  `json:"head_dim"`
+}
+
+var _ ModelConverter = (*llamaModel)(nil)
+
+func (p *llamaModel) KV(t *Tokenizer) llm.KV {
+	kv := p.ModelParameters.KV(t)
+	kv["general.architecture"] = "llama"
+	kv["llama.vocab_size"] = p.VocabSize
+
+	kv["llama.block_count"] = cmp.Or(p.NLayers, p.NumHiddenLayers, p.NLayer)
+
+	if contextLength := cmp.Or(p.MaxPositionEmbeddings, p.NCtx); contextLength > 0 {
+		kv["llama.context_length"] = contextLength
+	}
+
+	if embeddingLength := cmp.Or(p.HiddenSize, p.NEmbd); embeddingLength > 0 {
+		kv["llama.embedding_length"] = cmp.Or(p.HiddenSize, p.NEmbd)
+	}
+
+	if feedForwardLength := cmp.Or(p.IntermediateSize, p.NInner); feedForwardLength > 0 {
+		kv["llama.feed_forward_length"] = cmp.Or(p.IntermediateSize, p.NInner)
+	}
+
+	if headCount := cmp.Or(p.NumAttentionHeads, p.NHead); headCount > 0 {
+		kv["llama.attention.head_count"] = cmp.Or(p.NumAttentionHeads, p.NHead)
+		kv["llama.rope.dimension_count"] = p.HiddenSize / headCount
+	}
+
+	if p.RopeTheta > 0 {
+		kv["llama.rope.freq_base"] = p.RopeTheta
+	}
+
+	if p.RopeScaling.Type == "linear" {
+		kv["llama.rope.scaling.type"] = p.RopeScaling.Type
+		kv["llama.rope.scaling.factor"] = p.RopeScaling.Factor
+	} else if p.RopeScaling.RopeType == "llama3" {
+		dim := p.HiddenSize / p.NumAttentionHeads
+		for i := uint32(0); i < dim; i += 2 {
+			factor := cmp.Or(p.RopeScaling.Factor, 8.0)
+			factorLow := cmp.Or(p.RopeScaling.LowFrequencyFactor, 1.0)
+			factorHigh := cmp.Or(p.RopeScaling.HighFrequencyFactor, 4.0)
+
+			original := cmp.Or(p.RopeScaling.OriginalMaxPositionalEmbeddings, 8192)
+			lambdaLow := float32(original) / factorLow
+			lambdaHigh := float32(original) / factorHigh
+
+			lambda := 2 * math.Pi * math.Pow(float64(p.RopeTheta), float64(i)/float64(dim))
+			if lambda < float64(lambdaHigh) {
+				p.RopeScaling.factors = append(p.RopeScaling.factors, 1.0)
+			} else if lambda > float64(lambdaLow) {
+				p.RopeScaling.factors = append(p.RopeScaling.factors, factor)
+			} else {
+				smooth := (float32(original)/float32(lambda) - factorLow) / (factorHigh - factorLow)
+				p.RopeScaling.factors = append(p.RopeScaling.factors, 1.0/((1-smooth)/factor+smooth))
+			}
+		}
+	}
+
+	if p.NumKeyValueHeads > 0 {
+		kv["llama.attention.head_count_kv"] = p.NumKeyValueHeads
+	}
+
+	if p.RMSNormEPS > 0 {
+		kv["llama.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
+	}
+
+	if layerNormEpsilon := cmp.Or(p.LayerNormEPS, p.LayerNormEpsilon, p.NormEpsilon); layerNormEpsilon > 0 {
+		kv["llama.attention.layer_norm_epsilon"] = layerNormEpsilon
+	}
+
+	if p.HeadDim > 0 {
+		kv["llama.attention.key_length"] = p.HeadDim
+		kv["llama.attention.value_length"] = p.HeadDim
+	}
+
+	return kv
+}
+
+func (p *llamaModel) Tensors(ts []Tensor) []llm.Tensor {
+	var out []llm.Tensor
+
+	if p.RopeScaling.factors != nil {
+		out = append(out, llm.Tensor{
+			Name:     "rope_freqs.weight",
+			Kind:     0,
+			Shape:    []uint64{uint64(len(p.RopeScaling.factors))},
+			WriterTo: p.RopeScaling.factors,
+		})
+	}
+
+	for _, t := range ts {
+		if strings.HasSuffix(t.Name(), "attn_q.weight") ||
+			strings.HasSuffix(t.Name(), "attn_k.weight") {
+			t.SetRepacker(p.repack)
+		}
+
+		out = append(out, llm.Tensor{
+			Name:     t.Name(),
+			Kind:     t.Kind(),
+			Shape:    t.Shape(),
+			WriterTo: t,
+		})
+	}
+
+	return out
+}
+
+func (p *llamaModel) Replacements() []string {
+	return []string{
+		"lm_head", "output",
+		"model.embed_tokens", "token_embd",
+		"model.norm", "output_norm",
+		"model.layers", "blk",
+		"input_layernorm", "attn_norm",
+		"self_attn.q_proj", "attn_q",
+		"self_attn.k_proj", "attn_k",
+		"self_attn.v_proj", "attn_v",
+		"self_attn.o_proj", "attn_output",
+		"mlp.gate_proj", "ffn_gate",
+		"mlp.down_proj", "ffn_down",
+		"mlp.up_proj", "ffn_up",
+		"post_attention_layernorm", "ffn_norm",
+	}
+}
+
+func (p *llamaModel) repack(name string, data []float32, shape []uint64) ([]float32, error) {
+	var dims []int
+	for _, dim := range shape {
+		dims = append(dims, int(dim))
+	}
+
+	var heads uint32
+	if strings.HasSuffix(name, "attn_q.weight") {
+		heads = p.NumAttentionHeads
+	} else if strings.HasSuffix(name, "attn_k.weight") {
+		heads = cmp.Or(p.NumKeyValueHeads, p.NumAttentionHeads)
+	} else {
+		return nil, fmt.Errorf("unknown tensor for repack: %s", name)
+	}
+
+	n := tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
+	if err := n.Reshape(append([]int{int(heads), 2, dims[0] / int(heads) / 2}, dims[1:]...)...); err != nil {
+		return nil, err
+	}
+
+	if err := n.T(0, 2, 1, 3); err != nil {
+		return nil, err
+	}
+
+	if err := n.Reshape(dims...); err != nil {
+		return nil, err
+	}
+
+	if err := n.Transpose(); err != nil {
+		return nil, err
+	}
+
+	ts, err := native.SelectF32(n, 1)
+	if err != nil {
+		return nil, err
+	}
+
+	var f32s []float32
+	for _, t := range ts {
+		f32s = append(f32s, t...)
+	}
+
+	return f32s, nil
+}

convert/convert_llama_adapter.go

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

convert/convert_mixtral.go

@@ -0,0 +1,94 @@
+package convert
+
+import (
+	"fmt"
+	"io"
+	"slices"
+	"strings"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type mixtralModel struct {
+	llamaModel
+	NumLocalExperts    uint32 `json:"num_local_experts"`
+	NumExpertsPerToken uint32 `json:"num_experts_per_tok"`
+}
+
+func (p *mixtralModel) KV(t *Tokenizer) llm.KV {
+	kv := p.llamaModel.KV(t)
+
+	if p.NumLocalExperts > 0 {
+		kv["llama.expert_count"] = p.NumLocalExperts
+	}
+
+	if p.NumExpertsPerToken > 0 {
+		kv["llama.expert_used_count"] = p.NumExpertsPerToken
+	}
+
+	return kv
+}
+
+func (p *mixtralModel) Tensors(ts []Tensor) []llm.Tensor {
+	oldnew := []string{
+		"model.layers", "blk",
+		"w1", "ffn_gate_exps",
+		"w2", "ffn_down_exps",
+		"w3", "ffn_up_exps",
+	}
+
+	for i := range p.NumLocalExperts {
+		oldnew = append(oldnew, fmt.Sprintf(".block_sparse_moe.experts.%d.", i), ".")
+	}
+
+	// group experts of the same layer (model.layers.%d) and type (w[123]) into a single tensor
+	namer := strings.NewReplacer(oldnew...)
+	experts := make(map[string]experts)
+
+	// merge experts into a single tensor while removing them from ts
+	ts = slices.DeleteFunc(ts, func(t Tensor) bool {
+		if !strings.Contains(t.Name(), ".block_sparse_moe.experts.") {
+			return false
+		}
+
+		name := namer.Replace(t.Name())
+		experts[name] = append(experts[name], t)
+		return true
+	})
+
+	var out []llm.Tensor
+	for n, e := range experts {
+		// TODO(mxyng): sanity check experts
+		out = append(out, llm.Tensor{
+			Name:     n,
+			Kind:     e[0].Kind(),
+			Shape:    append([]uint64{uint64(len(e))}, e[0].Shape()...),
+			WriterTo: e,
+		})
+	}
+
+	return append(out, p.llamaModel.Tensors(ts)...)
+}
+
+func (p *mixtralModel) Replacements() []string {
+	return append(
+		p.llamaModel.Replacements(),
+		"block_sparse_moe.gate", "ffn_gate_inp",
+	)
+}
+
+type experts []Tensor
+
+func (e experts) WriteTo(w io.Writer) (int64, error) {
+	// TODO(mxyng): experts _should_ be numerically sorted by expert but this should check
+	for _, t := range e {
+		// the canonical merged experts tensor stacks all experts along a new, 0 axis,
+		// e.g. `tensor.Stack(0, e[0], e[1:]...)`, which requires allocating temporary buffers
+		// this accomplishes the same thing by writing each expert tensor in sequence
+		if _, err := t.WriteTo(w); err != nil {
+			return 0, err
+		}
+	}
+
+	return 0, nil
+}

convert/convert_phi3.go

@@ -0,0 +1,123 @@
+package convert
+
+import (
+	"cmp"
+	"encoding/binary"
+	"io"
+	"math"
+	"strings"
+	"sync"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type phi3Model struct {
+	ModelParameters
+	NumHiddenLayers   uint32  `json:"num_hidden_layers"`
+	NLayers           uint32  `json:"n_layers"`
+	HiddenSize        uint32  `json:"hidden_size"`
+	NEmbd             uint32  `json:"n_embd"`
+	IntermediateSize  uint32  `json:"intermediate_size"`
+	NumAttentionHeads uint32  `json:"num_attention_heads"`
+	NHead             uint32  `json:"n_head"`
+	NumKeyValueHeads  uint32  `json:"num_key_value_heads"`
+	NHeadKV           uint32  `json:"n_head_kv"`
+	RopeTheta         float32 `json:"rope_theta"`
+	RopeScaling       struct {
+		Type        string     `json:"type"`
+		LongFactor  ropeFactor `json:"long_factor"`
+		ShortFactor ropeFactor `json:"short_factor"`
+	} `json:"rope_scaling"`
+	RMSNormEPS                    float32 `json:"rms_norm_eps"`
+	NPositions                    uint32  `json:"n_positions"`
+	MaxPositionEmbeddings         uint32  `json:"max_position_embeddings"`
+	OriginalMaxPositionEmbeddings uint32  `json:"original_max_position_embeddings"`
+	SlidingWindow                 uint32  `json:"sliding_window"`
+}
+
+var _ ModelConverter = (*phi3Model)(nil)
+
+func (p *phi3Model) KV(t *Tokenizer) llm.KV {
+	kv := p.ModelParameters.KV(t)
+	kv["general.architecture"] = "phi3"
+	kv["phi3.context_length"] = p.MaxPositionEmbeddings
+	kv["phi3.embedding_length"] = cmp.Or(p.HiddenSize, p.NEmbd)
+	kv["phi3.feed_forward_length"] = p.IntermediateSize
+	kv["phi3.block_count"] = cmp.Or(p.NumHiddenLayers, p.NLayers)
+	kv["phi3.attention.head_count"] = cmp.Or(p.NumAttentionHeads, p.NHead)
+	kv["phi3.attention.head_count_kv"] = cmp.Or(p.NumKeyValueHeads, p.NHeadKV)
+	kv["phi3.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
+	kv["phi3.rope.dimension_count"] = p.HiddenSize / cmp.Or(p.NumAttentionHeads, p.NHead)
+	kv["phi3.rope.freq_base"] = p.RopeTheta
+	kv["phi3.rope.scaling.original_context_length"] = p.OriginalMaxPositionEmbeddings
+	kv["phi3.attention.sliding_window"] = p.SlidingWindow
+
+	scale := float64(p.MaxPositionEmbeddings) / float64(p.OriginalMaxPositionEmbeddings)
+
+	switch p.RopeScaling.Type {
+	case "":
+		// no scaling
+	case "su", "longrope":
+		kv["phi3.rope.scaling.attn_factor"] = float32(max(math.Sqrt(1+math.Log(scale)/math.Log(float64(p.OriginalMaxPositionEmbeddings))), 1.0))
+	case "yarn":
+		kv["phi3.rope.scaling.attn_factor"] = float32(max(0.1*math.Log(scale)+1.0, 1.0))
+	default:
+		panic("unknown rope scaling type")
+	}
+
+	return kv
+}
+
+func (p *phi3Model) Tensors(ts []Tensor) []llm.Tensor {
+	var addRopeFactors sync.Once
+
+	out := make([]llm.Tensor, 0, len(ts)+2)
+	for _, t := range ts {
+		if strings.HasPrefix(t.Name(), "blk.0.") {
+			addRopeFactors.Do(func() {
+				out = append(out, llm.Tensor{
+					Name:     "rope_factors_long.weight",
+					Kind:     0,
+					Shape:    []uint64{uint64(len(p.RopeScaling.LongFactor))},
+					WriterTo: p.RopeScaling.LongFactor,
+				}, llm.Tensor{
+					Name:     "rope_factors_short.weight",
+					Kind:     0,
+					Shape:    []uint64{uint64(len(p.RopeScaling.ShortFactor))},
+					WriterTo: p.RopeScaling.ShortFactor,
+				})
+			})
+		}
+
+		out = append(out, llm.Tensor{
+			Name:     t.Name(),
+			Kind:     t.Kind(),
+			Shape:    t.Shape(),
+			WriterTo: t,
+		})
+	}
+
+	return out
+}
+
+func (p *phi3Model) Replacements() []string {
+	return []string{
+		"lm_head", "output",
+		"model.embed_tokens", "token_embd",
+		"model.norm", "output_norm",
+		"model.layers", "blk",
+		"input_layernorm", "attn_norm",
+		"self_attn.qkv_proj", "attn_qkv",
+		"self_attn.o_proj", "attn_output",
+		"mlp.down_proj", "ffn_down",
+		"mlp.gate_up_proj", "ffn_up",
+		"post_attention_layernorm", "ffn_norm",
+	}
+}
+
+type ropeFactor []float32
+
+func (r ropeFactor) WriteTo(w io.Writer) (int64, error) {
+	err := binary.Write(w, binary.LittleEndian, r)
+	return 0, err
+}

convert/convert_test.go

@@ -0,0 +1,347 @@
+package convert
+
+import (
+	"bytes"
+	"crypto/sha256"
+	"encoding/binary"
+	"encoding/hex"
+	"encoding/json"
+	"flag"
+	"fmt"
+	"io"
+	"io/fs"
+	"log/slog"
+	"math"
+	"os"
+	"path/filepath"
+	"slices"
+	"testing"
+
+	"golang.org/x/exp/maps"
+
+	"github.com/ollama/ollama/llm"
+)
+
+func convertFull(t *testing.T, fsys fs.FS) (*os.File, llm.KV, llm.Tensors) {
+	t.Helper()
+
+	f, err := os.CreateTemp(t.TempDir(), "f16")
+	if err != nil {
+		t.Fatal(err)
+	}
+	defer f.Close()
+
+	if err := ConvertModel(fsys, f); err != nil {
+		t.Fatal(err)
+	}
+
+	r, err := os.Open(f.Name())
+	if err != nil {
+		t.Fatal(err)
+	}
+	t.Cleanup(func() { r.Close() })
+
+	m, _, err := llm.DecodeGGML(r, math.MaxInt)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	if _, err := r.Seek(0, io.SeekStart); err != nil {
+		t.Fatal(err)
+	}
+
+	return r, m.KV(), m.Tensors()
+}
+
+func generateResultsJSON(t *testing.T, f *os.File, kv llm.KV, tensors llm.Tensors) map[string]string {
+	actual := make(map[string]string)
+	for k, v := range kv {
+		if s, ok := v.(json.Marshaler); !ok {
+			actual[k] = fmt.Sprintf("%v", v)
+		} else {
+			bts, err := json.Marshal(s)
+			if err != nil {
+				t.Fatal(err)
+			}
+
+			actual[k] = fmt.Sprintf("%x", sha256.Sum256(bts))
+		}
+	}
+
+	for _, tensor := range tensors.Items {
+		sha256sum := sha256.New()
+		sr := io.NewSectionReader(f, int64(tensors.Offset+tensor.Offset), int64(tensor.Size()))
+		if _, err := io.Copy(sha256sum, sr); err != nil {
+			t.Fatal(err)
+		}
+
+		actual[tensor.Name] = hex.EncodeToString(sha256sum.Sum(nil))
+	}
+
+	return actual
+}
+
+func TestMain(m *testing.M) {
+	var level slog.Level
+	flag.TextVar(&level, "level", slog.LevelInfo, "log level")
+	flag.Parse()
+	slog.SetLogLoggerLevel(level)
+	os.Exit(m.Run())
+}
+
+func TestConvertFull(t *testing.T) {
+	cases := []string{
+		"Meta-Llama-3-8B-Instruct",
+		"Meta-Llama-3.1-8B-Instruct",
+		"Mistral-7B-Instruct-v0.2",
+		"Mixtral-8x7B-Instruct-v0.1",
+		"gemma-2b-it",
+		// microsoft/Phi-3-mini-128-instruct@d548c233192db00165d842bf8edff054bb3212f8
+		"Phi-3-mini-128k-instruct",
+		"all-MiniLM-L6-v2",
+		"gemma-2-9b-it",
+	}
+
+	for i := range cases {
+		tt := cases[i]
+		t.Run(tt, func(t *testing.T) {
+			t.Parallel()
+
+			p := filepath.Join("testdata", tt)
+			if testing.Short() {
+				t.Skip("skipping in short mode")
+			} else if _, err := os.Stat(p); err != nil {
+				t.Skipf("%s not found", p)
+			}
+
+			f, kv, tensors := convertFull(t, os.DirFS(p))
+			actual := generateResultsJSON(t, f, kv, tensors)
+
+			expectFile, err := os.Open(filepath.Join("testdata", fmt.Sprintf("%s.json", tt)))
+			if err != nil {
+				t.Fatal(err)
+			}
+
+			var expect map[string]string
+			if err := json.NewDecoder(expectFile).Decode(&expect); err != nil {
+				t.Fatal(err)
+			}
+
+			keys := maps.Keys(expect)
+			slices.Sort(keys)
+			for _, k := range keys {
+				if v, ok := actual[k]; !ok {
+					t.Errorf("missing %s", k)
+				} else if v != expect[k] {
+					t.Errorf("unexpected %s: want %s, got %s", k, expect[k], v)
+				}
+			}
+		})
+	}
+}
+
+func TestConvertAdapter(t *testing.T) {
+	type AdapterCase struct {
+		Name     string
+		BaseKV   map[string]any
+		Expected map[string]string
+	}
+
+	cases := []AdapterCase{
+		{
+			Name: "discollama",
+			BaseKV: map[string]any{
+				"general.architecture":          "llama",
+				"llama.attention.head_count":    uint32(32),
+				"llama.attention.head_count_kv": uint32(8),
+			},
+			Expected: map[string]string{
+				"general.architecture":          "llama",
+				"general.file_type":             "1",
+				"general.parameter_count":       "106496",
+				"general.type":                  "adapter",
+				"general.version":               "v0.2",
+				"adapter.lora.alpha":            "16",
+				"adapter.type":                  "lora",
+				"llama.attention.head_count":    "32",
+				"llama.attention.head_count_kv": "8",
+				"blk.31.attn_q.weight.lora_a":   "0eb3318b02cd313429bcc7621b539fdbb10240fea190c56c9e5f93fcd37a4e50",
+				"blk.31.attn_q.weight.lora_b":   "0eb3318b02cd313429bcc7621b539fdbb10240fea190c56c9e5f93fcd37a4e50",
+				"blk.31.attn_v.weight.lora_a":   "0eb3318b02cd313429bcc7621b539fdbb10240fea190c56c9e5f93fcd37a4e50",
+				"blk.31.attn_v.weight.lora_b":   "071dcafe89df065d6e1c935ecb8fdf6479b3c202eb912e7da938597673ff5857",
+			},
+		},
+	}
+
+	for _, c := range cases {
+		t.Run(c.Name, func(t *testing.T) {
+			t.Parallel()
+
+			f, err := os.CreateTemp(t.TempDir(), "f16")
+			if err != nil {
+				t.Fatal(err)
+			}
+			defer f.Close()
+
+			tempDir := t.TempDir()
+			generateLoraTestData(t, tempDir)
+
+			if err = ConvertAdapter(os.DirFS(tempDir), f, c.BaseKV); err != nil {
+				t.Fatal(err)
+			}
+
+			r, err := os.Open(f.Name())
+			if err != nil {
+				t.Fatal(err)
+			}
+			defer r.Close()
+
+			m, _, err := llm.DecodeGGML(r, math.MaxInt)
+			if err != nil {
+				t.Fatal(err)
+			}
+
+			if _, err := r.Seek(0, io.SeekStart); err != nil {
+				t.Fatal(err)
+			}
+
+			actual := generateResultsJSON(t, r, m.KV(), m.Tensors())
+
+			keys := maps.Keys(c.Expected)
+			slices.Sort(keys)
+			for _, k := range keys {
+				if v, ok := actual[k]; !ok {
+					t.Errorf("missing %s", k)
+				} else if v != c.Expected[k] {
+					t.Errorf("unexpected %s: want %s, got %s", k, c.Expected[k], v)
+				}
+			}
+		})
+	}
+}
+
+func generateLoraTestData(t *testing.T, tempDir string) {
+	type tensorData struct {
+		Offsets []int  `json:"data_offsets"`
+		Type    string `json:"dtype"`
+		Shape   []int  `json:"shape"`
+	}
+	offset := 4096 * 8 * 4
+
+	td := map[string]*tensorData{"__metadata__": nil}
+	td["model.layers.31.self_attn.q_proj.lora_a"] = &tensorData{
+		Offsets: []int{0, offset},
+		Type:    "F32",
+		Shape:   []int{4096, 8},
+	}
+	td["model.layers.31.self_attn.q_proj.lora_b"] = &tensorData{
+		Offsets: []int{offset, offset * 2},
+		Type:    "F32",
+		Shape:   []int{8, 4096},
+	}
+	td["model.layers.31.self_attn.v_proj.lora_a"] = &tensorData{
+		Offsets: []int{offset * 2, offset * 3},
+		Type:    "F32",
+		Shape:   []int{4096, 8},
+	}
+	td["model.layers.31.self_attn.v_proj.lora_b"] = &tensorData{
+		Offsets: []int{offset * 3, offset*3 + 8*1024*4},
+		Type:    "F32",
+		Shape:   []int{8, 1024},
+	}
+
+	data, err := json.Marshal(td)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	var buf bytes.Buffer
+
+	l := int64(len(data))
+	err = binary.Write(&buf, binary.LittleEndian, l)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	_, err = buf.Write(data)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	// write some data for the tensors
+
+	ones := make([]float32, 4096*8)
+	for i := range ones {
+		ones[i] = float32(1)
+	}
+
+	for range 3 {
+		err = binary.Write(&buf, binary.LittleEndian, ones)
+		if err != nil {
+			t.Fatal(err)
+		}
+	}
+
+	ones = make([]float32, 1024*8)
+	for i := range ones {
+		ones[i] = float32(1)
+	}
+
+	err = binary.Write(&buf, binary.LittleEndian, ones)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	fdata, err := os.Create(filepath.Join(tempDir, "adapters.safetensors"))
+	if err != nil {
+		t.Fatal(err)
+	}
+	defer fdata.Close()
+
+	_, err = fdata.Write(buf.Bytes())
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	configData := `
+{
+    "adapter_path": "adapters-test",
+    "batch_size": 8,
+    "config": "config-tiny.json",
+    "data": "../discollama-completion",
+    "grad_checkpoint": null,
+    "iters": 1000,
+    "learning_rate": 1e-05,
+    "lora_layers": 1,
+    "lora_parameters": {
+        "rank": 8,
+        "alpha": 16,
+        "dropout": 0.0,
+        "scale": 2.0
+    },
+    "lr_schedule": null,
+    "max_seq_length": 2048,
+    "model": "/Users/pdevine/git/Meta-Llama-3-8B-Instruct",
+    "resume_adapter_file": null,
+    "save_every": 100,
+    "seed": 0,
+    "steps_per_eval": 200,
+    "steps_per_report": 10,
+    "test": false,
+    "test_batches": 500,
+    "train": true,
+    "use_dora": false,
+    "val_batches": 25
+}
+`
+	f, err := os.Create(filepath.Join(tempDir, "adapter_config.json"))
+	if err != nil {
+		t.Fatal(err)
+	}
+	defer f.Close()
+
+	_, err = f.WriteString(configData)
+	if err != nil {
+		t.Fatal(err)
+	}
+}

convert/fs.go

@@ -0,0 +1,58 @@
+package convert
+
+import (
+	"archive/zip"
+	"errors"
+	"io"
+	"io/fs"
+	"os"
+	"path/filepath"
+)
+
+type ZipReader struct {
+	r *zip.Reader
+	p string
+
+	// limit is the maximum size of a file that can be read directly
+	// from the zip archive. Files larger than this size will be extracted
+	limit int64
+}
+
+func NewZipReader(r *zip.Reader, p string, limit int64) fs.FS {
+	return &ZipReader{r, p, limit}
+}
+
+func (z *ZipReader) Open(name string) (fs.File, error) {
+	r, err := z.r.Open(name)
+	if err != nil {
+		return nil, err
+	}
+	defer r.Close()
+
+	if fi, err := r.Stat(); err != nil {
+		return nil, err
+	} else if fi.Size() < z.limit {
+		return r, nil
+	}
+
+	if !filepath.IsLocal(name) {
+		return nil, zip.ErrInsecurePath
+	}
+
+	n := filepath.Join(z.p, name)
+	if _, err := os.Stat(n); errors.Is(err, os.ErrNotExist) {
+		w, err := os.Create(n)
+		if err != nil {
+			return nil, err
+		}
+		defer w.Close()
+
+		if _, err := io.Copy(w, r); err != nil {
+			return nil, err
+		}
+	} else if err != nil {
+		return nil, err
+	}
+
+	return os.Open(n)
+}

convert/gemma.go

@@ -1,136 +0,0 @@
-package convert
-
-import (
-	"encoding/binary"
-	"fmt"
-	"io"
-	"log/slog"
-	"os"
-	"strings"
-
-	"github.com/d4l3k/go-bfloat16"
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type GemmaModel struct {
-	ModelData
-}
-
-func gemmaLayerHandler(w io.Writer, r safetensorWriterTo, f *os.File) error {
-	slog.Debug(fmt.Sprintf("converting '%s'", r.t.Name))
-
-	data := make([]byte, r.end-r.start)
-	if err := binary.Read(f, r.bo, data); err != nil {
-		return err
-	}
-
-	tDataF32 := bfloat16.DecodeFloat32(data)
-
-	var err error
-	tDataF32, err = addOnes(tDataF32, int(r.t.Shape[0]))
-	if err != nil {
-		return err
-	}
-
-	if err := binary.Write(w, r.bo, tDataF32); err != nil {
-		return err
-	}
-	return nil
-}
-
-func addOnes(data []float32, vectorSize int) ([]float32, error) {
-	n := tensor.New(tensor.WithShape(vectorSize), tensor.WithBacking(data))
-	ones := tensor.Ones(tensor.Float32, vectorSize)
-
-	var err error
-	n, err = n.Add(ones)
-	if err != nil {
-		return []float32{}, err
-	}
-
-	newN, err := native.SelectF32(n, 0)
-	if err != nil {
-		return []float32{}, err
-	}
-
-	var fullTensor []float32
-	for _, v := range newN {
-		fullTensor = append(fullTensor, v...)
-	}
-
-	return fullTensor, nil
-}
-
-func (m *GemmaModel) GetTensors() error {
-	t, err := GetSafeTensors(m.Path, m.Params)
-	if err != nil {
-		return err
-	}
-
-	m.Tensors = []llm.Tensor{}
-
-	for _, l := range t {
-		if strings.HasSuffix(l.Name, "norm.weight") {
-			wt := l.WriterTo.(safetensorWriterTo)
-			wt.handler = gemmaLayerHandler
-			l.WriterTo = wt
-		}
-		m.Tensors = append(m.Tensors, l)
-	}
-
-	return nil
-}
-
-func (m *GemmaModel) LoadVocab() error {
-	v, err := LoadSentencePieceTokens(m.Path, m.Params.VocabSize)
-	if err != nil {
-		return err
-	}
-	m.Vocab = v
-	return nil
-}
-
-func (m *GemmaModel) WriteGGUF() (string, error) {
-	kv := llm.KV{
-		"general.architecture":                   "gemma",
-		"general.name":                           m.Name,
-		"gemma.context_length":                   uint32(m.Params.ContextSize),
-		"gemma.embedding_length":                 uint32(m.Params.HiddenSize),
-		"gemma.block_count":                      uint32(m.Params.HiddenLayers),
-		"gemma.feed_forward_length":              uint32(m.Params.IntermediateSize),
-		"gemma.attention.head_count":             uint32(m.Params.AttentionHeads),
-		"gemma.attention.head_count_kv":          uint32(m.Params.KeyValHeads),
-		"gemma.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
-		"gemma.attention.key_length":             uint32(m.Params.HeadDimension),
-		"gemma.attention.value_length":           uint32(m.Params.HeadDimension),
-		"general.file_type":                      uint32(1),
-		"tokenizer.ggml.model":                   "llama",
-
-		"tokenizer.ggml.tokens":     m.Vocab.Tokens,
-		"tokenizer.ggml.scores":     m.Vocab.Scores,
-		"tokenizer.ggml.token_type": m.Vocab.Types,
-
-		"tokenizer.ggml.bos_token_id":     uint32(m.Params.BoSTokenID),
-		"tokenizer.ggml.eos_token_id":     uint32(m.Params.EoSTokenID),
-		"tokenizer.ggml.padding_token_id": uint32(m.Params.PaddingTokenID),
-		"tokenizer.ggml.unknown_token_id": uint32(3),
-		"tokenizer.ggml.add_bos_token":    true,
-		"tokenizer.ggml.add_eos_token":    false,
-	}
-
-	f, err := os.CreateTemp("", "ollama-gguf")
-	if err != nil {
-		return "", err
-	}
-	defer f.Close()
-
-	mod := llm.NewGGUFV3(m.Params.ByteOrder)
-	if err := mod.Encode(f, kv, m.Tensors); err != nil {
-		return "", err
-	}
-
-	return f.Name(), nil
-}

convert/mistral.go

@@ -1,173 +0,0 @@
-package convert
-
-import (
-	"encoding/binary"
-	"fmt"
-	"io"
-	"os"
-	"regexp"
-	"strings"
-
-	"github.com/d4l3k/go-bfloat16"
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-	"github.com/x448/float16"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type MistralModel struct {
-	ModelData
-}
-
-func mistralLayerHandler(w io.Writer, r safetensorWriterTo, f *os.File) error {
-	layerSize := r.end - r.start
-
-	var err error
-	tData := make([]uint16, layerSize/2)
-	if err = binary.Read(f, r.bo, tData); err != nil {
-		return err
-	}
-
-	var heads uint32
-	if strings.Contains(r.t.Name, "attn_q") {
-		heads = uint32(r.params.AttentionHeads)
-	} else if strings.Contains(r.t.Name, "attn_k") {
-		heads = uint32(r.params.KeyValHeads)
-		if heads == 0 {
-			heads = uint32(r.params.AttentionHeads)
-		}
-	} else {
-		return fmt.Errorf("unknown layer type")
-	}
-
-	tData, err = repack(tData, int(heads), r.t.Shape)
-	if err != nil {
-		return err
-	}
-
-	var buf []byte
-	for _, n := range tData {
-		buf = r.bo.AppendUint16(buf, n)
-	}
-
-	tempBuf := make([]uint16, len(tData))
-	tDataF32 := bfloat16.DecodeFloat32(buf)
-	for cnt, v := range tDataF32 {
-		tDataF16 := float16.Fromfloat32(v)
-		tempBuf[cnt] = uint16(tDataF16)
-	}
-
-	if err = binary.Write(w, r.bo, tempBuf); err != nil {
-		return err
-	}
-	return nil
-}
-
-func repack(data []uint16, heads int, shape []uint64) ([]uint16, error) {
-	n := tensor.New(tensor.WithShape(int(shape[0]), int(shape[1])), tensor.WithBacking(data))
-	origShape := n.Shape().Clone()
-
-	// reshape the tensor and swap axes 1 and 2 to unpack the layer for gguf
-	if err := n.Reshape(heads, 2, origShape[0]/heads/2, origShape[1]); err != nil {
-		return nil, err
-	}
-
-	if err := n.T(0, 2, 1, 3); err != nil {
-		return nil, err
-	}
-
-	if err := n.Reshape(origShape...); err != nil {
-		return nil, err
-	}
-
-	if err := n.Transpose(); err != nil {
-		return nil, err
-	}
-	newN, err := native.SelectU16(n, 1)
-	if err != nil {
-		return nil, err
-	}
-
-	var fullTensor []uint16
-	for _, v := range newN {
-		fullTensor = append(fullTensor, v...)
-	}
-	return fullTensor, nil
-}
-
-func (m *MistralModel) GetTensors() error {
-	t, err := GetSafeTensors(m.Path, m.Params)
-	if err != nil {
-		return err
-	}
-
-	m.Tensors = []llm.Tensor{}
-
-	pattern := `^blk\.[0-9]+\.attn_(?P<layer>q|k)\.weight$`
-	re, err := regexp.Compile(pattern)
-	if err != nil {
-		return err
-	}
-
-	for _, l := range t {
-		matches := re.FindAllStringSubmatch(l.Name, -1)
-		if len(matches) > 0 {
-			wt := l.WriterTo.(safetensorWriterTo)
-			wt.handler = mistralLayerHandler
-			l.WriterTo = wt
-		}
-		m.Tensors = append(m.Tensors, l)
-	}
-
-	return nil
-}
-
-func (m *MistralModel) LoadVocab() error {
-	v, err := LoadSentencePieceTokens(m.Path, m.Params.VocabSize)
-	if err != nil {
-		return err
-	}
-	m.Vocab = v
-	return nil
-}
-
-func (m *MistralModel) WriteGGUF() (string, error) {
-	kv := llm.KV{
-		"general.architecture":                   "llama",
-		"general.name":                           m.Name,
-		"llama.context_length":                   uint32(m.Params.ContextSize),
-		"llama.embedding_length":                 uint32(m.Params.HiddenSize),
-		"llama.block_count":                      uint32(m.Params.HiddenLayers),
-		"llama.feed_forward_length":              uint32(m.Params.IntermediateSize),
-		"llama.rope.dimension_count":             uint32(m.Params.HiddenSize / m.Params.AttentionHeads),
-		"llama.attention.head_count":             uint32(m.Params.AttentionHeads),
-		"llama.attention.head_count_kv":          uint32(m.Params.KeyValHeads),
-		"llama.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
-		"general.file_type":                      uint32(1),
-		"tokenizer.ggml.model":                   "llama",
-
-		"tokenizer.ggml.tokens":     m.Vocab.Tokens,
-		"tokenizer.ggml.scores":     m.Vocab.Scores,
-		"tokenizer.ggml.token_type": m.Vocab.Types,
-
-		"tokenizer.ggml.bos_token_id":     uint32(m.Params.BoSTokenID),
-		"tokenizer.ggml.eos_token_id":     uint32(m.Params.EoSTokenID),
-		"tokenizer.ggml.add_bos_token":    true,
-		"tokenizer.ggml.add_eos_token":    false,
-		"tokenizer.ggml.unknown_token_id": uint32(0),
-	}
-
-	f, err := os.CreateTemp("", "ollama-gguf")
-	if err != nil {
-		return "", err
-	}
-	defer f.Close()
-
-	mod := llm.NewGGUFV3(m.Params.ByteOrder)
-	if err := mod.Encode(f, kv, m.Tensors); err != nil {
-		return "", err
-	}
-
-	return f.Name(), nil
-}

convert/reader.go

@@ -0,0 +1,86 @@
+package convert
+
+import (
+	"errors"
+	"io"
+	"io/fs"
+	"strings"
+)
+
+type Tensor interface {
+	Name() string
+	Shape() []uint64
+	Kind() uint32
+	SetRepacker(repacker)
+	WriteTo(io.Writer) (int64, error)
+}
+
+type tensorBase struct {
+	name  string
+	shape []uint64
+	repacker
+}
+
+func (t tensorBase) Name() string {
+	return t.name
+}
+
+func (t tensorBase) Shape() []uint64 {
+	return t.shape
+}
+
+const (
+	tensorKindF32 uint32 = iota
+	tensorKindF16
+)
+
+func (t tensorBase) Kind() uint32 {
+	if strings.HasSuffix(t.name, ".ffn_gate_inp.weight") ||
+		t.name == "token_types.weight" {
+		// these tensors are always F32
+		return 0
+	}
+
+	switch len(t.shape) {
+	case 0:
+		panic("invalid tensor shape")
+	case 1:
+		return tensorKindF32
+	default:
+		return tensorKindF16
+	}
+}
+
+func (t *tensorBase) SetRepacker(fn repacker) {
+	t.repacker = fn
+}
+
+type repacker func(string, []float32, []uint64) ([]float32, error)
+
+func parseTensors(fsys fs.FS, replacer *strings.Replacer) ([]Tensor, error) {
+	patterns := []struct {
+		Pattern string
+		Func    func(fs.FS, *strings.Replacer, ...string) ([]Tensor, error)
+	}{
+		{"model-*-of-*.safetensors", parseSafetensors},
+		{"model.safetensors", parseSafetensors},
+		{"adapters.safetensors", parseSafetensors},
+		{"adapter_model.safetensors", parseSafetensors},
+		{"pytorch_model-*-of-*.bin", parseTorch},
+		{"pytorch_model.bin", parseTorch},
+		{"consolidated.*.pth", parseTorch},
+	}
+
+	for _, pattern := range patterns {
+		matches, err := fs.Glob(fsys, pattern.Pattern)
+		if err != nil {
+			return nil, err
+		}
+
+		if len(matches) > 0 {
+			return pattern.Func(fsys, replacer, matches...)
+		}
+	}
+
+	return nil, errors.New("unknown tensor format")
+}

convert/reader_safetensors.go

@@ -0,0 +1,151 @@
+package convert
+
+import (
+	"bytes"
+	"encoding/binary"
+	"encoding/json"
+	"fmt"
+	"io"
+	"io/fs"
+	"slices"
+	"strings"
+
+	"github.com/d4l3k/go-bfloat16"
+	"github.com/x448/float16"
+	"golang.org/x/exp/maps"
+)
+
+type safetensorMetadata struct {
+	Type    string   `json:"dtype"`
+	Shape   []uint64 `json:"shape"`
+	Offsets []int64  `json:"data_offsets"`
+}
+
+func parseSafetensors(fsys fs.FS, replacer *strings.Replacer, ps ...string) ([]Tensor, error) {
+	var ts []Tensor
+	for _, p := range ps {
+		f, err := fsys.Open(p)
+		if err != nil {
+			return nil, err
+		}
+		defer f.Close()
+
+		var n int64
+		if err := binary.Read(f, binary.LittleEndian, &n); err != nil {
+			return nil, err
+		}
+
+		b := bytes.NewBuffer(make([]byte, 0, n))
+		if _, err = io.CopyN(b, f, n); err != nil {
+			return nil, err
+		}
+
+		var headers map[string]safetensorMetadata
+		if err := json.NewDecoder(b).Decode(&headers); err != nil {
+			return nil, err
+		}
+
+		keys := maps.Keys(headers)
+		slices.Sort(keys)
+
+		for _, key := range keys {
+			if value := headers[key]; value.Type != "" {
+				ts = append(ts, safetensor{
+					fs:     fsys,
+					path:   p,
+					dtype:  value.Type,
+					offset: safetensorsPad(n, value.Offsets[0]),
+					size:   safetensorsPad(n, value.Offsets[1]) - safetensorsPad(n, value.Offsets[0]),
+					tensorBase: &tensorBase{
+						name:  replacer.Replace(key),
+						shape: value.Shape,
+					},
+				})
+			}
+		}
+	}
+
+	return ts, nil
+}
+
+// safetensorsPad returns the padded size of the safetensors file given a length n and offset s
+func safetensorsPad(n, offset int64) int64 {
+	return 8 + n + offset
+}
+
+type safetensor struct {
+	fs     fs.FS
+	path   string
+	dtype  string
+	offset int64
+	size   int64
+	*tensorBase
+}
+
+func (st safetensor) WriteTo(w io.Writer) (int64, error) {
+	f, err := st.fs.Open(st.path)
+	if err != nil {
+		return 0, err
+	}
+	defer f.Close()
+
+	if seeker, ok := f.(io.Seeker); ok {
+		if _, err := seeker.Seek(st.offset, io.SeekStart); err != nil {
+			return 0, err
+		}
+	} else {
+		if _, err := io.CopyN(io.Discard, f, st.offset); err != nil {
+			return 0, err
+		}
+	}
+
+	var f32s []float32
+	switch st.dtype {
+	case "F32":
+		f32s = make([]float32, st.size/4)
+		if err = binary.Read(f, binary.LittleEndian, f32s); err != nil {
+			return 0, err
+		}
+	case "F16":
+		u16s := make([]uint16, st.size/2)
+		if err = binary.Read(f, binary.LittleEndian, u16s); err != nil {
+			return 0, err
+		}
+
+		f32s = make([]float32, len(u16s))
+		for i := range u16s {
+			f32s[i] = float16.Frombits(u16s[i]).Float32()
+		}
+
+	case "BF16":
+		u8s := make([]uint8, st.size)
+		if err = binary.Read(f, binary.LittleEndian, u8s); err != nil {
+			return 0, err
+		}
+
+		f32s = bfloat16.DecodeFloat32(u8s)
+	default:
+		return 0, fmt.Errorf("unknown data type: %s", st.dtype)
+	}
+
+	if st.repacker != nil {
+		f32s, err = st.repacker(st.Name(), f32s, st.Shape())
+		if err != nil {
+			return 0, err
+		}
+	}
+
+	switch st.Kind() {
+	case tensorKindF32:
+		return 0, binary.Write(w, binary.LittleEndian, f32s)
+	case tensorKindF16:
+		f16s := make([]uint16, len(f32s))
+		for i := range f32s {
+			f16s[i] = float16.Fromfloat32(f32s[i]).Bits()
+		}
+
+		return 0, binary.Write(w, binary.LittleEndian, f16s)
+	default:
+		return 0, fmt.Errorf("unknown storage type: %d", st.Kind())
+	}
+}

convert/reader_torch.go

@@ -0,0 +1,48 @@
+package convert
+
+import (
+	"io"
+	"io/fs"
+	"strings"
+
+	"github.com/nlpodyssey/gopickle/pytorch"
+	"github.com/nlpodyssey/gopickle/types"
+)
+
+func parseTorch(fsys fs.FS, replacer *strings.Replacer, ps ...string) ([]Tensor, error) {
+	var ts []Tensor
+	for _, p := range ps {
+		pt, err := pytorch.Load(p)
+		if err != nil {
+			return nil, err
+		}
+
+		for _, k := range pt.(*types.Dict).Keys() {
+			t := pt.(*types.Dict).MustGet(k)
+
+			var shape []uint64
+			for dim := range t.(*pytorch.Tensor).Size {
+				shape = append(shape, uint64(dim))
+			}
+
+			ts = append(ts, torch{
+				storage: t.(*pytorch.Tensor).Source,
+				tensorBase: &tensorBase{
+					name:  replacer.Replace(k.(string)),
+					shape: shape,
+				},
+			})
+		}
+	}
+
+	return ts, nil
+}
+
+type torch struct {
+	storage pytorch.StorageInterface
+	*tensorBase
+}
+
+func (pt torch) WriteTo(w io.Writer) (int64, error) {
+	return 0, nil
+}

convert/testdata/Meta-Llama-3-8B-Instruct.json

@@ -0,0 +1,313 @@
+{
+  "general.architecture": "llama",
+  "general.file_type": "1",
+  "general.quantization_version": "2",
+  "llama.block_count": "32",
+  "llama.context_length": "8192",
+  "llama.embedding_length": "4096",
+  "llama.feed_forward_length": "14336",
+  "llama.rope.dimension_count": "128",
+  "llama.rope.freq_base": "500000",
+  "llama.vocab_size": "128256",
+  "llama.attention.head_count": "32",
+  "llama.attention.head_count_kv": "8",
+  "llama.attention.layer_norm_rms_epsilon": "1e-05",
+  "tokenizer.ggml.model": "gpt2",
+  "tokenizer.ggml.pre": "llama-bpe",
+  "tokenizer.ggml.bos_token_id": "128000",
+  "tokenizer.ggml.eos_token_id": "128009",
+  "tokenizer.ggml.merges": "d0cbac1fcc9dcf03724b8db5c9bfb593ae1cf68fb9bc72eb1d15274dcbbf618b",
+  "tokenizer.ggml.token_type": "d70a88809fd7da6f1f028622685cd64268a7a922c5d343c96f25b66327358978",
+  "tokenizer.ggml.tokens": "765b529dbcbc42dd202ce657341c63807b51f3b07e09898f6aa6196326865d5a",
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+}

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

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

convert/testdata/Mistral-7B-Instruct-v0.2.json

@@ -0,0 +1,313 @@
+{
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+}

convert/testdata/Mixtral-8x7B-Instruct-v0.1.json

@@ -0,0 +1,348 @@
+{
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+  "general.file_type": "1",
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+}

convert/testdata/Phi-3-mini-128k-instruct.json

@@ -0,0 +1,225 @@
+{
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+  "general.file_type": "1",
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convert/testdata/all-MiniLM-L6-v2.json

@@ -0,0 +1,124 @@
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convert/testdata/gemma-2-9b-it.json

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

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

@@ -0,0 +1,188 @@
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+  "blk.13.attn_norm.weight": "87e053535144723db4145aa5402acc54331b7696752d852bb9fc542ff33f0fb5",
+  "blk.13.attn_output.weight": "078145f5ad83f8b14f97a869346f7fd1583b24d1e3edadaa95d3da4242973f8f",
+  "blk.13.attn_q.weight": "3b8caf35504cbc4d1a7dd6e011a95760703b7f71e2218b030b1254f811362dd7",
+  "blk.13.attn_v.weight": "4fdf8365a603e043e5b40c4a21c84ac167f9be62794178f9d8a608dfe5653bf9",
+  "blk.13.ffn_down.weight": "a07d3abbfcacf48ba028df2cab895be32cc15022d23389a745286e79c1b1d1fd",
+  "blk.13.ffn_gate.weight": "1d2ab39666aa2909acc96787432a3ed13b19d25170f74665fadff9b17bbaffb1",
+  "blk.13.ffn_norm.weight": "4f2e809fda5f3eadf52578ee50e0ba36e53be91e55dce418c12dfe595f5f18e7",
+  "blk.13.ffn_up.weight": "8783d2720c2c37ca176a5801e0b3ef1f9cc9cf3ef1cd37af423aaf6b2a27e2bd",
+  "blk.14.attn_k.weight": "ce9428e2b55d43ae0c6690dbd56182f99adc427694ba8236b405cc8ea5035e86",
+  "blk.14.attn_norm.weight": "6abb35f9db8251d6ae954bda147c6ada2371b0574d11702e828f3c6ac99b7cc0",
+  "blk.14.attn_output.weight": "fe3880916d0ceb5bff672c88bbefb7060a545be609bf049beb2024b38221836d",
+  "blk.14.attn_q.weight": "7c8ad81be6f4a350931fd108b5f7c9e366e8c26ef62d1d85ffef5dca8fd893f8",
+  "blk.14.attn_v.weight": "e4bdedffacbebe38567a0734dfd67db90e911d9a9669fcde9a7c4ad8a0066c52",
+  "blk.14.ffn_down.weight": "ef6694dff1e05820aac0cd2b22f39ac7788b4967afc9250775575554c66aab2c",
+  "blk.14.ffn_gate.weight": "db63c4179e2db704bc505e2b4696e055b593e295a1b7c4c586fc793bdd5aab19",
+  "blk.14.ffn_norm.weight": "2796a62d832a9710148f95d533320492a33e712b2e5218659c548705bd11684d",
+  "blk.14.ffn_up.weight": "3f78c78d8c2d54df45f799d4ff902316628af296834afe4ceed63d4a324ff03e",
+  "blk.15.attn_k.weight": "6e810ee3859e07695645ee0c9a5efc7962668984a5f0a9325f47e462743b447c",
+  "blk.15.attn_norm.weight": "0956b576ae96db0b28cb09f761f801cfd9281432284664f0fe181c8d9c55d1ec",
+  "blk.15.attn_output.weight": "03a17f7e94208177aace5cc41b7f54670ba57873b7274ff6e23caf58cce110ca",
+  "blk.15.attn_q.weight": "b8edafe7d2216a6f8b4ae4905a906475490e6ea418f6e1d3cec563dbdc6fab91",
+  "blk.15.attn_v.weight": "f8ae8cae0f4cfa34a459824eba57350c3c248104ba5607e7d9dc7d7c39aaf4a6",
+  "blk.15.ffn_down.weight": "8d02eb439da852246d2ca67e9b7b6de0b090b80744355e64728a23e41926505b",
+  "blk.15.ffn_gate.weight": "ed5bf361c67db8731f186b775826f21c33bdb521111fd2d922539719a770239f",
+  "blk.15.ffn_norm.weight": "5942ca3c73209ac9a0c8bfd9b4aab7f7be7aee9aa12d9c35833493b44af76767",
+  "blk.15.ffn_up.weight": "f4bebf4ad99ec5f911327dec347be6c595814885309c7bc5647ce28c7f4d1cf5",
+  "blk.16.attn_k.weight": "756a534c19364448e0958b8948fe33891c6ccda0fbb4dfa2024e1f532a87804b",
+  "blk.16.attn_norm.weight": "386b7b9e4e6509f6af9c022d942b6c6c6cc136aeed8751ecb037c74d7c4bfb93",
+  "blk.16.attn_output.weight": "3ba1a766a25830b84d7c22178203635f9c5624caad290bc5e5d73da5d5e7a2ec",
+  "blk.16.attn_q.weight": "d39b0c91e1fda7685d50a0f7cc8d18c44b5bdc90a142c7fda0bc329cca1afa74",
+  "blk.16.attn_v.weight": "98b33fcb0ee3483cff1b06ecb44d7b7ffb4d34c268248e4d73dfdf82b2065b2f",
+  "blk.16.ffn_down.weight": "14006f5e4acb2f9416271ae562e299359cd2585739c7fc77ccbca54495563948",
+  "blk.16.ffn_gate.weight": "12f8abae2d301d8f88bedb6af98b1daecc7b0b8d05148594f931f30958d77aca",
+  "blk.16.ffn_norm.weight": "129a15a046ee96d06de288bd43c80f77a6b0fb3a159c7367154c6e4aaf362672",
+  "blk.16.ffn_up.weight": "b4a5911a45f3871ef1d4efb7dc7108645a564b70f818eccf45beebef2e844ee9",
+  "blk.17.attn_k.weight": "5e1bfcff0146ebdde3817b656952892eb671e14e75afc92fa53f84f8eecbec4c",
+  "blk.17.attn_norm.weight": "60bc988fab7c4b29ee9de599df41a8de00caa94fcd74677da011fac82f60f465",
+  "blk.17.attn_output.weight": "ba49b40d6a0b5685f749c24b0edbed3adc44dbe13b5d5e5fa1e56169fc746555",
+  "blk.17.attn_q.weight": "82bb415d24efcd14d03ace03f907bb70db6a204c76a0bdd1892e0fba165db87d",
+  "blk.17.attn_v.weight": "73dbe54beb91a899884e275ea81ffc5187a20cb7d5b68d5c299b783096999d94",
+  "blk.17.ffn_down.weight": "7c086166241e0664f8963fd1ca4ed74c737abfb2525ec20f8435821ff50158f3",
+  "blk.17.ffn_gate.weight": "51a32f78244d42a539f619c5ce661db9e6cf41636280a826d439b5444edcd28c",
+  "blk.17.ffn_norm.weight": "c4bb247fccd1ecc84875028af63dd20aaf5cbd17eb94a9bc36679c09285dccab",
+  "blk.17.ffn_up.weight": "b5886182790bc6fbadd63de9bc4ffee416f3b69a66280d197ab8c18edf769abf",
+  "output_norm.weight": "481f3097d0a20412e35b3a739b1b958487bcd41ff67744baa3c9acbddd2ee4d4"
+}

convert/tokenizer.go

@@ -0,0 +1,266 @@
+package convert
+
+import (
+	"crypto/sha256"
+	"encoding/hex"
+	"encoding/json"
+	"errors"
+	"fmt"
+	"io/fs"
+	"log/slog"
+	"os"
+	"slices"
+
+	"golang.org/x/exp/maps"
+)
+
+const (
+	_ int32 = iota
+	tokenTypeNormal
+	tokenTypeUnknown
+	tokenTypeControl
+	tokenTypeUserDefined
+	tokenTypeUnused
+	tokenTypeByte
+)
+
+type Tokenizer struct {
+	*Vocabulary
+	SpecialVocabulary []*SpecialVocabulary
+	Merges            []string
+
+	Pre      string
+	Template string
+}
+
+func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error) {
+	v, err := parseVocabulary(fsys)
+	if err != nil {
+		return nil, err
+	}
+
+	t := &Tokenizer{
+		Vocabulary: v,
+		Pre:        "default",
+	}
+
+	addedTokens := make(map[string]token)
+	if f, err := fsys.Open("tokenizer.json"); errors.Is(err, os.ErrNotExist) {
+	} else if err != nil {
+		return nil, err
+	} else {
+		defer f.Close()
+
+		var tt tokenizer
+		if err := json.NewDecoder(f).Decode(&tt); err != nil {
+			return nil, err
+		}
+
+		for _, t := range tt.AddedTokens {
+			addedTokens[t.Content] = t
+		}
+
+		t.Merges = tt.Model.Merges
+
+		sha256sum := sha256.New()
+		for _, pt := range tt.PreTokenizer.PreTokenizers {
+			switch pt.Type {
+			case "Split":
+				if pt.Pattern.Regex != "" {
+					// create a checksum of all Split pretokenizers which should be sufficient
+					// to identify the pretokenizer
+					sha256sum.Write([]byte(pt.Pattern.Regex))
+				}
+			}
+		}
+
+		switch digest := hex.EncodeToString(sha256sum.Sum(nil)); digest {
+		case "d98f9631be1e9607a9848c26c1f9eac1aa9fc21ac6ba82a2fc0741af9780a48f":
+			t.Pre = "llama-bpe"
+		case "03df5c5863ad70781dcfdef491ead25140f895fe8010964be0daefe27be32b02":
+			t.Pre = "deepseek-llm"
+		case "21cde974d587f0d54dc8d56b183cc1e6239600172035c68fbd6d4b9f8da0576e":
+			t.Pre = "deepseek-coder"
+		case "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855":
+			// noop, empty pretokenizer
+		default:
+			slog.Warn("unknown pretokenizer, using default", "digest", digest)
+		}
+	}
+
+	if f, err := fsys.Open("tokenizer_config.json"); errors.Is(err, os.ErrNotExist) {
+	} else if err != nil {
+		return nil, err
+	} else {
+		defer f.Close()
+
+		var p map[string]json.RawMessage
+		if err := json.NewDecoder(f).Decode(&p); err != nil {
+			return nil, err
+		}
+
+		if template, ok := p["chat_template"]; ok {
+			if err := json.Unmarshal(template, &t.Template); err != nil {
+				return nil, err
+			}
+		}
+
+		for _, st := range specialTokenTypes {
+			sv := SpecialVocabulary{Type: st}
+			if bts, ok := p[fmt.Sprintf("add_%s_token", st)]; ok {
+				if err := json.Unmarshal(bts, &sv.AddToken); err != nil {
+					return nil, err
+				}
+			}
+
+			if bts, ok := p[fmt.Sprintf("%s_token", st)]; ok {
+				var content string
+				if err := json.Unmarshal(bts, &content); err != nil {
+					var mm map[string]any
+					if err := json.Unmarshal(bts, &mm); err != nil {
+						continue
+					}
+
+					content, ok = mm["content"].(string)
+					if !ok {
+						continue
+					}
+				}
+
+				sv.Content = content
+			}
+
+			if id, ok := addedTokens[sv.Content]; ok {
+				sv.ID = id.ID
+				t.SpecialVocabulary = append(t.SpecialVocabulary, &sv)
+			}
+		}
+	}
+
+	return t, nil
+}
+
+type tokenizer struct {
+	Version     string  `json:"version"`
+	AddedTokens []token `json:"added_tokens"`
+	Model       struct {
+		Type   string         `json:"type"`
+		Vocab  map[string]int `json:"vocab"`
+		Merges []string       `json:"merges"`
+	} `json:"model"`
+
+	PreTokenizer struct {
+		PreTokenizers []struct {
+			Type    string `json:"type"`
+			Pattern struct {
+				Regex string `json:"Regex"`
+			} `json:"pattern"`
+		} `json:"pretokenizers"`
+	} `json:"pre_tokenizer"`
+}
+
+type token struct {
+	ID          int    `json:"id"`
+	Content     string `json:"content"`
+	Special     bool   `json:"special"`
+	UserDefined bool
+}
+
+type Vocabulary struct {
+	Model  string
+	Tokens []string
+	Scores []float32
+	Types  []int32
+}
+
+func parseVocabularyFromTokenizer(fsys fs.FS) (*Vocabulary, error) {
+	f, err := fsys.Open("tokenizer.json")
+	if err != nil {
+		return nil, err
+	}
+	defer f.Close()
+
+	var t tokenizer
+	if err := json.NewDecoder(f).Decode(&t); err != nil {
+		return nil, err
+	}
+
+	tokens := make(map[int]token, len(t.Model.Vocab))
+	for k, v := range t.Model.Vocab {
+		tokens[v] = token{
+			ID:      v,
+			Content: k,
+		}
+	}
+
+	for _, token := range t.AddedTokens {
+		token.UserDefined = true
+		tokens[token.ID] = token
+	}
+
+	keys := maps.Keys(tokens)
+	slices.Sort(keys)
+
+	v := Vocabulary{Model: "gpt2"}
+	for _, k := range keys {
+		token := tokens[k]
+		v.Tokens = append(v.Tokens, token.Content)
+		v.Scores = append(v.Scores, float32(token.ID))
+
+		switch {
+		case token.Special:
+			v.Types = append(v.Types, tokenTypeControl)
+		case token.UserDefined:
+			v.Types = append(v.Types, tokenTypeUserDefined)
+		default:
+			v.Types = append(v.Types, tokenTypeNormal)
+		}
+	}
+
+	return &v, nil
+}
+
+func parseVocabulary(fsys fs.FS) (*Vocabulary, error) {
+	patterns := []struct {
+		Pattern string
+		Func    func(fs.FS) (*Vocabulary, error)
+	}{
+		{"tokenizer.model", parseSentencePiece},
+		{"tokenizer.json", parseVocabularyFromTokenizer},
+	}
+
+	for _, pattern := range patterns {
+		if _, err := fs.Stat(fsys, pattern.Pattern); errors.Is(err, os.ErrNotExist) {
+			continue
+		} else if err != nil {
+			return nil, err
+		}
+
+		return pattern.Func(fsys)
+	}
+
+	return nil, errors.New("unknown tensor format")
+}
+
+type SpecialVocabulary struct {
+	Type     string
+	ID       int
+	Content  string
+	AddToken bool
+}
+
+func (sv SpecialVocabulary) Key() string {
+	switch t := sv.Type; t {
+	case "bos", "eos", "cls", "mask":
+		return t
+	case "unk":
+		return "unknown"
+	case "sep":
+		//nolint:misspell // this is an upstream typo
+		return "seperator"
+	case "pad":
+		return "padding"
+	}
+
+	panic("unknown special vocabulary type")
+}

convert/tokenizer_spm.go

@@ -0,0 +1,113 @@
+package convert
+
+import (
+	"cmp"
+	"encoding/json"
+	"errors"
+	"fmt"
+	"io/fs"
+	"os"
+	"slices"
+
+	"google.golang.org/protobuf/proto"
+
+	"github.com/ollama/ollama/convert/sentencepiece"
+)
+
+func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
+	ast, err := parseAdditionalSpecialTokens(fsys)
+	if err != nil {
+		return nil, err
+	}
+
+	bts, err := fs.ReadFile(fsys, "tokenizer.model")
+	if err != nil {
+		return nil, err
+	}
+
+	var spm sentencepiece.ModelProto
+	if err := proto.Unmarshal(bts, &spm); err != nil {
+		return nil, err
+	}
+
+	v := Vocabulary{Model: "llama"}
+	for _, piece := range spm.GetPieces() {
+		v.Tokens = append(v.Tokens, piece.GetPiece())
+		v.Scores = append(v.Scores, piece.GetScore())
+
+		switch t := piece.GetType(); t {
+		case sentencepiece.ModelProto_SentencePiece_UNKNOWN,
+			sentencepiece.ModelProto_SentencePiece_CONTROL,
+			sentencepiece.ModelProto_SentencePiece_UNUSED,
+			sentencepiece.ModelProto_SentencePiece_BYTE:
+			v.Types = append(v.Types, int32(t))
+		default:
+			tt := int32(sentencepiece.ModelProto_SentencePiece_NORMAL)
+			if slices.Contains(ast, piece.GetPiece()) {
+				tt = int32(sentencepiece.ModelProto_SentencePiece_CONTROL)
+			}
+
+			v.Types = append(v.Types, tt)
+		}
+	}
+
+	f, err := fsys.Open("added_tokens.json")
+	if errors.Is(err, os.ErrNotExist) {
+		return &v, nil
+	} else if err != nil {
+		return nil, err
+	}
+	defer f.Close()
+
+	var atm map[string]int
+	if err := json.NewDecoder(f).Decode(&atm); err != nil {
+		return nil, err
+	}
+
+	type t struct {
+		id      int
+		content string
+	}
+
+	var ts []t
+	for content, id := range atm {
+		ts = append(ts, t{id, content})
+	}
+
+	slices.SortFunc(ts, func(i, j t) int {
+		return cmp.Compare(i.id, j.id)
+	})
+
+	n := len(v.Tokens)
+	for i, t := range ts {
+		if t.id != i+n {
+			return nil, fmt.Errorf("invalid token id: %d", t.id)
+		}
+
+		v.Tokens = append(v.Tokens, t.content)
+		v.Scores = append(v.Scores, -1000.0)
+		v.Types = append(v.Types, tokenTypeUserDefined)
+	}
+
+	return &v, nil
+}
+
+func parseAdditionalSpecialTokens(fsys fs.FS) ([]string, error) {
+	f, err := fsys.Open("special_tokens_map.json")
+	if errors.Is(err, os.ErrNotExist) {
+		return nil, nil
+	} else if err != nil {
+		return nil, err
+	}
+	defer f.Close()
+
+	var m struct {
+		AdditionalSpecialTokens []string `json:"additional_special_tokens"`
+	}
+
+	if err := json.NewDecoder(f).Decode(&m); err != nil {
+		return nil, err
+	}
+
+	return m.AdditionalSpecialTokens, nil
+}

docs/README.md

@@ -6,7 +6,7 @@
 * [Importing models](./import.md)
 * [Linux Documentation](./linux.md)
 * [Windows Documentation](./windows.md)
-* [Docker Documentation](https://hub.docker.com/r/ollama/ollama)
+* [Docker Documentation](./docker.md)
 
 ### Reference
 

docs/api.md

@@ -12,12 +12,13 @@
 - [Pull a Model](#pull-a-model)
 - [Push a Model](#push-a-model)
 - [Generate Embeddings](#generate-embeddings)
+- [List Running Models](#list-running-models)
 
 ## Conventions
 
 ### Model names
 
-Model names follow a `model:tag` format, where `model` can have an optional namespace such as `example/model`. Some examples are `orca-mini:3b-q4_1` and `llama2:70b`. The tag is optional and, if not provided, will default to `latest`. The tag is used to identify a specific version.
+Model names follow a `model:tag` format, where `model` can have an optional namespace such as `example/model`. Some examples are `orca-mini:3b-q4_1` and `llama3:70b`. The tag is optional and, if not provided, will default to `latest`. The tag is used to identify a specific version.
 
 ### Durations
 
@@ -25,7 +26,7 @@ All durations are returned in nanoseconds.
 
 ### Streaming responses
 
-Certain endpoints stream responses as JSON objects and can optional return non-streamed responses.
+Certain endpoints stream responses as JSON objects. Streaming can be disabled by providing `{"stream": false}` for these endpoints.
 
 ## Generate a completion
 
@@ -39,6 +40,7 @@ Generate a response for a given prompt with a provided model. This is a streamin
 
 - `model`: (required) the [model name](#model-names)
 - `prompt`: the prompt to generate a response for
+- `suffix`: the text after the model response
 - `images`: (optional) a list of base64-encoded images (for multimodal models such as `llava`)
 
 Advanced parameters (optional):
@@ -56,7 +58,8 @@ Advanced parameters (optional):
 
 Enable JSON mode by setting the `format` parameter to `json`. This will structure the response as a valid JSON object. See the JSON mode [example](#request-json-mode) below.
 
-> Note: it's important to instruct the model to use JSON in the `prompt`. Otherwise, the model may generate large amounts whitespace.
+> [!IMPORTANT]
+> It's important to instruct the model to use JSON in the `prompt`. Otherwise, the model may generate large amounts whitespace.
 
 ### Examples
 
@@ -66,7 +69,7 @@ Enable JSON mode by setting the `format` parameter to `json`. This will structur
 
 ```shell
 curl http://localhost:11434/api/generate -d '{
-  "model": "llama2",
+  "model": "llama3",
   "prompt": "Why is the sky blue?"
 }'
 ```
@@ -77,7 +80,7 @@ A stream of JSON objects is returned:
 
 ```json
 {
-  "model": "llama2",
+  "model": "llama3",
   "created_at": "2023-08-04T08:52:19.385406455-07:00",
   "response": "The",
   "done": false
@@ -90,16 +93,16 @@ The final response in the stream also includes additional data about the generat
 - `load_duration`: time spent in nanoseconds loading the model
 - `prompt_eval_count`: number of tokens in the prompt
 - `prompt_eval_duration`: time spent in nanoseconds evaluating the prompt
-- `eval_count`: number of tokens the response
+- `eval_count`: number of tokens in the response
 - `eval_duration`: time in nanoseconds spent generating the response
 - `context`: an encoding of the conversation used in this response, this can be sent in the next request to keep a conversational memory
 - `response`: empty if the response was streamed, if not streamed, this will contain the full response
 
-To calculate how fast the response is generated in tokens per second (token/s), divide `eval_count` / `eval_duration`.
+To calculate how fast the response is generated in tokens per second (token/s), divide `eval_count` / `eval_duration` * `10^9`.
 
 ```json
 {
-  "model": "llama2",
+  "model": "llama3",
   "created_at": "2023-08-04T19:22:45.499127Z",
   "response": "",
   "done": true,
@@ -121,7 +124,7 @@ A response can be received in one reply when streaming is off.
 
 ```shell
 curl http://localhost:11434/api/generate -d '{
-  "model": "llama2",
+  "model": "llama3",
   "prompt": "Why is the sky blue?",
   "stream": false
 }'
@@ -133,7 +136,7 @@ If `stream` is set to `false`, the response will be a single JSON object:
 
 ```json
 {
-  "model": "llama2",
+  "model": "llama3",
   "created_at": "2023-08-04T19:22:45.499127Z",
   "response": "The sky is blue because it is the color of the sky.",
   "done": true,
@@ -147,15 +150,51 @@ If `stream` is set to `false`, the response will be a single JSON object:
 }
 ```
 
+#### Request (with suffix)
+
+##### Request
+
+```shell
+curl http://localhost:11434/api/generate -d '{
+  "model": "codellama:code",
+  "prompt": "def compute_gcd(a, b):",
+  "suffix": "    return result",
+  "options": {
+    "temperature": 0
+  },
+  "stream": false
+}'
+```
+
+##### Response
+
+```json
+{
+  "model": "codellama:code",
+  "created_at": "2024-07-22T20:47:51.147561Z",
+  "response": "\n  if a == 0:\n    return b\n  else:\n    return compute_gcd(b % a, a)\n\ndef compute_lcm(a, b):\n  result = (a * b) / compute_gcd(a, b)\n",
+  "done": true,
+  "done_reason": "stop",
+  "context": [...],
+  "total_duration": 1162761250,
+  "load_duration": 6683708,
+  "prompt_eval_count": 17,
+  "prompt_eval_duration": 201222000,
+  "eval_count": 63,
+  "eval_duration": 953997000
+}
+```
+
 #### Request (JSON mode)
 
+> [!IMPORTANT]
 > When `format` is set to `json`, the output will always be a well-formed JSON object. It's important to also instruct the model to respond in JSON.
 
 ##### Request
 
 ```shell
 curl http://localhost:11434/api/generate -d '{
-  "model": "llama2",
+  "model": "llama3",
   "prompt": "What color is the sky at different times of the day? Respond using JSON",
   "format": "json",
   "stream": false
@@ -166,7 +205,7 @@ curl http://localhost:11434/api/generate -d '{
 
 ```json
 {
-  "model": "llama2",
+  "model": "llama3",
   "created_at": "2023-11-09T21:07:55.186497Z",
   "response": "{\n\"morning\": {\n\"color\": \"blue\"\n},\n\"noon\": {\n\"color\": \"blue-gray\"\n},\n\"afternoon\": {\n\"color\": \"warm gray\"\n},\n\"evening\": {\n\"color\": \"orange\"\n}\n}\n",
   "done": true,
@@ -249,7 +288,7 @@ curl http://localhost:11434/api/generate -d '{
 
 #### Request (Reproducible outputs)
 
-For reproducible outputs, set `temperature` to 0 and `seed` to a number:
+For reproducible outputs, set `seed` to a number:
 
 ##### Request
 
@@ -258,8 +297,7 @@ curl http://localhost:11434/api/generate -d '{
   "model": "mistral",
   "prompt": "Why is the sky blue?",
   "options": {
-    "seed": 123,
+    "seed": 123
-    "temperature": 0
   }
 }'
 ```
@@ -289,7 +327,7 @@ If you want to set custom options for the model at runtime rather than in the Mo
 
 ```shell
 curl http://localhost:11434/api/generate -d '{
-  "model": "llama2",
+  "model": "llama3",
   "prompt": "Why is the sky blue?",
   "stream": false,
   "options": {
@@ -298,6 +336,7 @@ curl http://localhost:11434/api/generate -d '{
     "num_predict": 100,
     "top_k": 20,
     "top_p": 0.9,
+    "min_p": 0.0,
     "tfs_z": 0.5,
     "typical_p": 0.7,
     "repeat_last_n": 33,
@@ -313,7 +352,6 @@ curl http://localhost:11434/api/generate -d '{
     "numa": false,
     "num_ctx": 1024,
     "num_batch": 2,
-    "num_gqa": 1,
     "num_gpu": 1,
     "main_gpu": 0,
     "low_vram": false,
@@ -321,8 +359,6 @@ curl http://localhost:11434/api/generate -d '{
     "vocab_only": false,
     "use_mmap": true,
     "use_mlock": false,
-    "rope_frequency_base": 1.1,
-    "rope_frequency_scale": 0.8,
     "num_thread": 8
   }
 }'
@@ -332,7 +368,7 @@ curl http://localhost:11434/api/generate -d '{
 
 ```json
 {
-  "model": "llama2",
+  "model": "llama3",
   "created_at": "2023-08-04T19:22:45.499127Z",
   "response": "The sky is blue because it is the color of the sky.",
   "done": true,
@@ -354,7 +390,7 @@ If an empty prompt is provided, the model will be loaded into memory.
 
 ```shell
 curl http://localhost:11434/api/generate -d '{
-  "model": "llama2"
+  "model": "llama3"
 }'
 ```
 
@@ -364,7 +400,7 @@ A single JSON object is returned:
 
 ```json
 {
-  "model": "llama2",
+  "model": "llama3",
   "created_at": "2023-12-18T19:52:07.071755Z",
   "response": "",
   "done": true
@@ -383,12 +419,14 @@ Generate the next message in a chat with a provided model. This is a streaming e
 
 - `model`: (required) the [model name](#model-names)
 - `messages`: the messages of the chat, this can be used to keep a chat memory
+- `tools`: tools for the model to use if supported. Requires `stream` to be set to `false`
 
 The `message` object has the following fields:
 
-- `role`: the role of the message, either `system`, `user` or `assistant`
+- `role`: the role of the message, either `system`, `user`, `assistant`, or `tool`
 - `content`: the content of the message
 - `images` (optional): a list of images to include in the message (for multimodal models such as `llava`)
+- `tool_calls` (optional): a list of tools the model wants to use
 
 Advanced parameters (optional):
 
@@ -407,7 +445,7 @@ Send a chat message with a streaming response.
 
 ```shell
 curl http://localhost:11434/api/chat -d '{
-  "model": "llama2",
+  "model": "llama3",
   "messages": [
     {
       "role": "user",
@@ -423,7 +461,7 @@ A stream of JSON objects is returned:
 
 ```json
 {
-  "model": "llama2",
+  "model": "llama3",
   "created_at": "2023-08-04T08:52:19.385406455-07:00",
   "message": {
     "role": "assistant",
@@ -438,7 +476,7 @@ Final response:
 
 ```json
 {
-  "model": "llama2",
+  "model": "llama3",
   "created_at": "2023-08-04T19:22:45.499127Z",
   "done": true,
   "total_duration": 4883583458,
@@ -456,7 +494,7 @@ Final response:
 
 ```shell
 curl http://localhost:11434/api/chat -d '{
-  "model": "llama2",
+  "model": "llama3",
   "messages": [
     {
       "role": "user",
@@ -471,7 +509,7 @@ curl http://localhost:11434/api/chat -d '{
 
 ```json
 {
-  "model": "registry.ollama.ai/library/llama2:latest",
+  "model": "registry.ollama.ai/library/llama3:latest",
   "created_at": "2023-12-12T14:13:43.416799Z",
   "message": {
     "role": "assistant",
@@ -495,7 +533,7 @@ Send a chat message with a conversation history. You can use this same approach
 
 ```shell
 curl http://localhost:11434/api/chat -d '{
-  "model": "llama2",
+  "model": "llama3",
   "messages": [
     {
       "role": "user",
@@ -519,7 +557,7 @@ A stream of JSON objects is returned:
 
 ```json
 {
-  "model": "llama2",
+  "model": "llama3",
   "created_at": "2023-08-04T08:52:19.385406455-07:00",
   "message": {
     "role": "assistant",
@@ -533,7 +571,7 @@ Final response:
 
 ```json
 {
-  "model": "llama2",
+  "model": "llama3",
   "created_at": "2023-08-04T19:22:45.499127Z",
   "done": true,
   "total_duration": 8113331500,
@@ -549,7 +587,7 @@ Final response:
 
 ##### Request
 
-Send a chat message with a conversation history.
+Send a chat message with images. The images should be provided as an array, with the individual images encoded in Base64.
 
 ```shell
 curl http://localhost:11434/api/chat -d '{
@@ -591,7 +629,7 @@ curl http://localhost:11434/api/chat -d '{
 
 ```shell
 curl http://localhost:11434/api/chat -d '{
-  "model": "llama2",
+  "model": "llama3",
   "messages": [
     {
       "role": "user",
@@ -609,7 +647,7 @@ curl http://localhost:11434/api/chat -d '{
 
 ```json
 {
-  "model": "registry.ollama.ai/library/llama2:latest",
+  "model": "registry.ollama.ai/library/llama3:latest",
   "created_at": "2023-12-12T14:13:43.416799Z",
   "message": {
     "role": "assistant",
@@ -625,6 +663,79 @@ curl http://localhost:11434/api/chat -d '{
 }
 ```
 
+#### Chat request (with tools)
+
+##### Request
+
+```
+curl http://localhost:11434/api/chat -d '{
+  "model": "llama3.1",
+  "messages": [
+    {
+      "role": "user",
+      "content": "What is the weather today in Paris?"
+    }
+  ],
+  "stream": false,
+  "tools": [
+    {
+      "type": "function",
+      "function": {
+        "name": "get_current_weather",
+        "description": "Get the current weather for a location",
+        "parameters": {
+          "type": "object",
+          "properties": {
+            "location": {
+              "type": "string",
+              "description": "The location to get the weather for, e.g. San Francisco, CA"
+            },
+            "format": {
+              "type": "string",
+              "description": "The format to return the weather in, e.g. 'celsius' or 'fahrenheit'",
+              "enum": ["celsius", "fahrenheit"]
+            }
+          },
+          "required": ["location", "format"]
+        }
+      }
+    }
+  ]
+}'
+```
+
+##### Response
+
+```json
+{
+  "model": "llama3.1",
+  "created_at": "2024-07-22T20:33:28.123648Z",
+  "message": {
+    "role": "assistant",
+    "content": "",
+    "tool_calls": [
+      {
+        "function": {
+          "name": "get_current_weather",
+          "arguments": {
+            "format": "celsius",
+            "location": "Paris, FR"
+          }
+        }
+      }
+    ]
+  },
+  "done_reason": "stop",
+  "done": true,
+  "total_duration": 885095291,
+  "load_duration": 3753500,
+  "prompt_eval_count": 122,
+  "prompt_eval_duration": 328493000,
+  "eval_count": 33,
+  "eval_duration": 552222000
+}
+```
+
 ## Create a Model
 
 ```shell
@@ -651,7 +762,7 @@ Create a new model from a `Modelfile`.
 ```shell
 curl http://localhost:11434/api/create -d '{
   "name": "mario",
-  "modelfile": "FROM llama2\nSYSTEM You are mario from Super Mario Bros."
+  "modelfile": "FROM llama3\nSYSTEM You are mario from Super Mario Bros."
 }'
 ```
 
@@ -758,7 +869,7 @@ A single JSON object will be returned.
       }
     },
     {
-      "name": "llama2:latest",
+      "name": "llama3:latest",
       "modified_at": "2023-12-07T09:32:18.757212583-08:00",
       "size": 3825819519,
       "digest": "fe938a131f40e6f6d40083c9f0f430a515233eb2edaa6d72eb85c50d64f2300e",
@@ -780,11 +891,12 @@ A single JSON object will be returned.
 POST /api/show
 ```
 
-Show information about a model including details, modelfile, template, parameters, license, and system prompt.
+Show information about a model including details, modelfile, template, parameters, license, system prompt.
 
 ### Parameters
 
 - `name`: name of the model to show
+- `verbose`: (optional) if set to `true`, returns full data for verbose response fields
 
 ### Examples
 
@@ -792,7 +904,7 @@ Show information about a model including details, modelfile, template, parameter
 
 ```shell
 curl http://localhost:11434/api/show -d '{
-  "name": "llama2"
+  "name": "llama3"
 }'
 ```
 
@@ -800,15 +912,41 @@ curl http://localhost:11434/api/show -d '{
 
 ```json
 {
-  "modelfile": "# Modelfile generated by \"ollama show\"\n# To build a new Modelfile based on this one, replace the FROM line with:\n# FROM llava:latest\n\nFROM /Users/matt/.ollama/models/blobs/sha256:200765e1283640ffbd013184bf496e261032fa75b99498a9613be4e94d63ad52\nTEMPLATE \"\"\"{{ .System }}\nUSER: {{ .Prompt }}\nASSSISTANT: \"\"\"\nPARAMETER num_ctx 4096\nPARAMETER stop \"\u003c/s\u003e\"\nPARAMETER stop \"USER:\"\nPARAMETER stop \"ASSSISTANT:\"",
+  "modelfile": "# Modelfile generated by \"ollama show\"\n# To build a new Modelfile based on this one, replace the FROM line with:\n# FROM llava:latest\n\nFROM /Users/matt/.ollama/models/blobs/sha256:200765e1283640ffbd013184bf496e261032fa75b99498a9613be4e94d63ad52\nTEMPLATE \"\"\"{{ .System }}\nUSER: {{ .Prompt }}\nASSISTANT: \"\"\"\nPARAMETER num_ctx 4096\nPARAMETER stop \"\u003c/s\u003e\"\nPARAMETER stop \"USER:\"\nPARAMETER stop \"ASSISTANT:\"",
-  "parameters": "num_ctx                        4096\nstop                           \u003c/s\u003e\nstop                           USER:\nstop                           ASSSISTANT:",
+  "parameters": "num_keep                       24\nstop                           \"<|start_header_id|>\"\nstop                           \"<|end_header_id|>\"\nstop                           \"<|eot_id|>\"",
-  "template": "{{ .System }}\nUSER: {{ .Prompt }}\nASSSISTANT: ",
+  "template": "{{ if .System }}<|start_header_id|>system<|end_header_id|>\n\n{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>\n\n{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>\n\n{{ .Response }}<|eot_id|>",
   "details": {
+    "parent_model": "",
     "format": "gguf",
     "family": "llama",
-    "families": ["llama", "clip"],
+    "families": [
-    "parameter_size": "7B",
+      "llama"
+    ],
+    "parameter_size": "8.0B",
     "quantization_level": "Q4_0"
+  },
+  "model_info": {
+    "general.architecture": "llama",
+    "general.file_type": 2,
+    "general.parameter_count": 8030261248,
+    "general.quantization_version": 2,
+    "llama.attention.head_count": 32,
+    "llama.attention.head_count_kv": 8,
+    "llama.attention.layer_norm_rms_epsilon": 0.00001,
+    "llama.block_count": 32,
+    "llama.context_length": 8192,
+    "llama.embedding_length": 4096,
+    "llama.feed_forward_length": 14336,
+    "llama.rope.dimension_count": 128,
+    "llama.rope.freq_base": 500000,
+    "llama.vocab_size": 128256,
+    "tokenizer.ggml.bos_token_id": 128000,
+    "tokenizer.ggml.eos_token_id": 128009,
+    "tokenizer.ggml.merges": [],            // populates if `verbose=true`
+    "tokenizer.ggml.model": "gpt2",
+    "tokenizer.ggml.pre": "llama-bpe",
+    "tokenizer.ggml.token_type": [],        // populates if `verbose=true`
+    "tokenizer.ggml.tokens": []             // populates if `verbose=true`
   }
 }
 ```
@@ -827,8 +965,8 @@ Copy a model. Creates a model with another name from an existing model.
 
 ```shell
 curl http://localhost:11434/api/copy -d '{
-  "source": "llama2",
+  "source": "llama3",
-  "destination": "llama2-backup"
+  "destination": "llama3-backup"
 }'
 ```
 
@@ -854,7 +992,7 @@ Delete a model and its data.
 
 ```shell
 curl -X DELETE http://localhost:11434/api/delete -d '{
-  "name": "llama2:13b"
+  "name": "llama3:13b"
 }'
 ```
 
@@ -882,7 +1020,7 @@ Download a model from the ollama library. Cancelled pulls are resumed from where
 
 ```shell
 curl http://localhost:11434/api/pull -d '{
-  "name": "llama2"
+  "name": "llama3"
 }'
 ```
 
@@ -1001,6 +1139,121 @@ If `stream` is set to `false`, then the response is a single JSON object:
 
 ## Generate Embeddings
 
+```shell
+POST /api/embed
+```
+
+Generate embeddings from a model
+
+### Parameters
+
+- `model`: name of model to generate embeddings from
+- `input`: text or list of text to generate embeddings for
+
+Advanced parameters:
+
+- `truncate`: truncates the end of each input to fit within context length. Returns error if `false` and context length is exceeded. Defaults to `true`
+- `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.md#valid-parameters-and-values) such as `temperature`
+- `keep_alive`: controls how long the model will stay loaded into memory following the request (default: `5m`)
+
+### Examples
+
+#### Request
+
+```shell
+curl http://localhost:11434/api/embed -d '{
+  "model": "all-minilm",
+  "input": "Why is the sky blue?"
+}'
+```
+
+#### Response
+
+```json
+{
+  "model": "all-minilm",
+  "embeddings": [[
+    0.010071029, -0.0017594862, 0.05007221, 0.04692972, 0.054916814,
+    0.008599704, 0.105441414, -0.025878139, 0.12958129, 0.031952348
+  ]],
+  "total_duration": 14143917,
+  "load_duration": 1019500,
+  "prompt_eval_count": 8
+}
+```
+
+#### Request (Multiple input)
+
+```shell
+curl http://localhost:11434/api/embed -d '{
+  "model": "all-minilm",
+  "input": ["Why is the sky blue?", "Why is the grass green?"]
+}'
+```
+
+#### Response
+
+```json
+{
+  "model": "all-minilm",
+  "embeddings": [[
+    0.010071029, -0.0017594862, 0.05007221, 0.04692972, 0.054916814,
+    0.008599704, 0.105441414, -0.025878139, 0.12958129, 0.031952348
+  ],[
+    -0.0098027075, 0.06042469, 0.025257962, -0.006364387, 0.07272725,
+    0.017194884, 0.09032035, -0.051705178, 0.09951512, 0.09072481
+  ]]
+}
+```
+
+## List Running Models
+```shell
+GET /api/ps
+```
+
+List models that are currently loaded into memory.
+
+#### Examples
+
+### Request
+
+```shell
+curl http://localhost:11434/api/ps
+```
+
+#### Response
+
+A single JSON object will be returned.
+
+```json
+{
+  "models": [
+    {
+      "name": "mistral:latest",
+      "model": "mistral:latest",
+      "size": 5137025024,
+      "digest": "2ae6f6dd7a3dd734790bbbf58b8909a606e0e7e97e94b7604e0aa7ae4490e6d8",
+      "details": {
+        "parent_model": "",
+        "format": "gguf",
+        "family": "llama",
+        "families": [
+          "llama"
+        ],
+        "parameter_size": "7.2B",
+        "quantization_level": "Q4_0"
+      },
+      "expires_at": "2024-06-04T14:38:31.83753-07:00",
+      "size_vram": 5137025024
+    }
+  ]
+}
+```
+
+## Generate Embedding
+
+> Note: this endpoint has been superseded by `/api/embed`
+
 ```shell
 POST /api/embeddings
 ```

docs/development.md

@@ -6,6 +6,8 @@ Install required tools:
 - go version 1.22 or higher
 - gcc version 11.4.0 or higher
 
+### MacOS
+
 ```bash
 brew install go cmake gcc
 ```
@@ -51,7 +53,7 @@ Typically the build scripts will auto-detect CUDA, however, if your Linux distro
 or installation approach uses unusual paths, you can specify the location by
 specifying an environment variable `CUDA_LIB_DIR` to the location of the shared
 libraries, and `CUDACXX` to the location of the nvcc compiler. You can customize
-set set of target CUDA architectues by setting `CMAKE_CUDA_ARCHITECTURES` (e.g. "50;60;70")
+a set of target CUDA architectures by setting `CMAKE_CUDA_ARCHITECTURES` (e.g. "50;60;70")
 
 Then generate dependencies:
 
@@ -102,7 +104,7 @@ like to use. For example, to compile an optimized binary for an Intel i9-9880H,
 you might use:
 
 ```
-OLLAMA_CUSTOM_CPU_DEFS="-DLLAMA_AVX=on -DLLAMA_AVX2=on -DLLAMA_F16C=on -DLLAMA_FMA=on" go generate ./...
+OLLAMA_CUSTOM_CPU_DEFS="-DGGML_AVX=on -DGGML_AVX2=on -DGGML_F16C=on -DGGML_FMA=on" go generate ./...
 go build .
 ```
 
@@ -112,15 +114,18 @@ If you have Docker available, you can build linux binaries with `./scripts/build
 
 ### Windows
 
-Note: The windows build for Ollama is still under development.
+Note: The Windows build for Ollama is still under development.
 
-Install required tools:
+First, install required tools:
 
 - MSVC toolchain - C/C++ and cmake as minimal requirements
 - Go version 1.22 or higher
 - MinGW (pick one variant) with GCC.
   - [MinGW-w64](https://www.mingw-w64.org/)
   - [MSYS2](https://www.msys2.org/)
+- The `ThreadJob` Powershell module: `Install-Module -Name ThreadJob -Scope CurrentUser`
+
+Then, build the `ollama` binary:
 
 ```powershell
 $env:CGO_ENABLED="1"

docs/docker.md

@@ -0,0 +1,71 @@
+# Ollama Docker image
+
+### CPU only
+
+```bash
+docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
+```
+
+### Nvidia GPU
+Install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installation).
+
+#### Install with Apt
+1.  Configure the repository
+```bash
+curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey \
+    | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
+curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list \
+    | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' \
+    | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
+sudo apt-get update
+```
+2.  Install the NVIDIA Container Toolkit packages
+```bash
+sudo apt-get install -y nvidia-container-toolkit
+```
+
+#### Install with Yum or Dnf
+1.  Configure the repository
+
+```bash
+curl -s -L https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo \
+    | sudo tee /etc/yum.repos.d/nvidia-container-toolkit.repo
+```
+
+2. Install the NVIDIA Container Toolkit packages
+
+```bash
+sudo yum install -y nvidia-container-toolkit
+```
+
+#### Configure Docker to use Nvidia driver
+```
+sudo nvidia-ctk runtime configure --runtime=docker
+sudo systemctl restart docker
+```
+
+#### Start the container
+
+```bash
+docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
+```
+
+### AMD GPU
+
+To run Ollama using Docker with AMD GPUs, use the `rocm` tag and the following command:
+
+```
+docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama:rocm
+```
+
+### Run model locally
+
+Now you can run a model:
+
+```
+docker exec -it ollama ollama run llama3.1
+```
+
+### Try different models
+
+More models can be found on the [Ollama library](https://ollama.com/library).

docs/faq.md

@@ -6,7 +6,7 @@ Ollama on macOS and Windows will automatically download updates. Click on the ta
 
 On Linux, re-run the install script:
 
-```
+```shell
 curl -fsSL https://ollama.com/install.sh | sh
 ```
 
@@ -30,9 +30,9 @@ To change this when using `ollama run`, use `/set parameter`:
 
 When using the API, specify the `num_ctx` parameter:
 
-```
+```shell
 curl http://localhost:11434/api/generate -d '{
-  "model": "llama2",
+  "model": "llama3",
   "prompt": "Why is the sky blue?",
   "options": {
     "num_ctx": 4096
@@ -40,6 +40,21 @@ curl http://localhost:11434/api/generate -d '{
 }'
 ```
 
+## How can I tell if my model was loaded onto the GPU?
+
+Use the `ollama ps` command to see what models are currently loaded into memory.
+
+```shell
+ollama ps
+NAME      	ID          	SIZE 	PROCESSOR	UNTIL
+llama3:70b	bcfb190ca3a7	42 GB	100% GPU 	4 minutes from now
+```
+
+The `Processor` column will show which memory the model was loaded in to:
+* `100% GPU` means the model was loaded entirely into the GPU
+* `100% CPU` means the model was loaded entirely in system memory
+* `48%/52% CPU/GPU` means the model was loaded partially onto both the GPU and into system memory
+
 ## How do I configure Ollama server?
 
 Ollama server can be configured with environment variables.
@@ -80,18 +95,51 @@ If Ollama is run as a systemd service, environment variables should be set using
 
 ### Setting environment variables on Windows
 
-On windows, Ollama inherits your user and system environment variables.
+On Windows, Ollama inherits your user and system environment variables.
 
-1. First Quit Ollama by clicking on it in the task bar
+1. First Quit Ollama by clicking on it in the task bar.
 
-2. Edit system environment variables from the control panel
+2. Start the Settings (Windows 11) or Control Panel (Windows 10) application and search for _environment variables_.
 
-3. Edit or create New variable(s) for your user account for `OLLAMA_HOST`, `OLLAMA_MODELS`, etc.
+3. Click on _Edit environment variables for your account_.
 
-4. Click OK/Apply to save 
+4. Edit or create a new variable for your user account for `OLLAMA_HOST`, `OLLAMA_MODELS`, etc.
 
-5. Run `ollama` from a new terminal window 
+5. Click OK/Apply to save.
 
+6. Start the Ollama application from the Windows Start menu.
+
+## How do I use Ollama behind a proxy?
+
+Ollama pulls models from the Internet and may require a proxy server to access the models. Use `HTTPS_PROXY` to redirect outbound requests through the proxy. Ensure the proxy certificate is installed as a system certificate. Refer to the section above for how to use environment variables on your platform.
+
+> [!NOTE]
+> Avoid setting `HTTP_PROXY`. Ollama does not use HTTP for model pulls, only HTTPS. Setting `HTTP_PROXY` may interrupt client connections to the server.
+
+### How do I use Ollama behind a proxy in Docker?
+
+The Ollama Docker container image can be configured to use a proxy by passing `-e HTTPS_PROXY=https://proxy.example.com` when starting the container.
+
+Alternatively, the Docker daemon can be configured to use a proxy. Instructions are available for Docker Desktop on [macOS](https://docs.docker.com/desktop/settings/mac/#proxies), [Windows](https://docs.docker.com/desktop/settings/windows/#proxies), and [Linux](https://docs.docker.com/desktop/settings/linux/#proxies), and Docker [daemon with systemd](https://docs.docker.com/config/daemon/systemd/#httphttps-proxy).
+
+Ensure the certificate is installed as a system certificate when using HTTPS. This may require a new Docker image when using a self-signed certificate.
+
+```dockerfile
+FROM ollama/ollama
+COPY my-ca.pem /usr/local/share/ca-certificates/my-ca.crt
+RUN update-ca-certificates
+```
+
+Build and run this image:
+
+```shell
+docker build -t ollama-with-ca .
+docker run -d -e HTTPS_PROXY=https://my.proxy.example.com -p 11434:11434 ollama-with-ca
+```
+
+## Does Ollama send my prompts and answers back to ollama.com?
+
+No. Ollama runs locally, and conversation data does not leave your machine.
 
 ## How can I expose Ollama on my network?
 
@@ -118,7 +166,7 @@ server {
 
 Ollama can be accessed using a range of tools for tunneling tools. For example with Ngrok:
 
-```
+```shell
 ngrok http 11434 --host-header="localhost:11434"
 ```
 
@@ -126,7 +174,7 @@ ngrok http 11434 --host-header="localhost:11434"
 
 To use Ollama with Cloudflare Tunnel, use the `--url` and `--http-host-header` flags:
 
-```
+```shell
 cloudflared tunnel --url http://localhost:11434 --http-host-header="localhost:11434"
 ```
 
@@ -140,7 +188,7 @@ Refer to the section [above](#how-do-i-configure-ollama-server) for how to set e
 
 - macOS: `~/.ollama/models`
 - Linux: `/usr/share/ollama/.ollama/models`
-- Windows: `C:\Users\<username>\.ollama\models`
+- Windows: `C:\Users\%username%\.ollama\models`
 
 ### How do I set them to a different location?
 
@@ -148,39 +196,10 @@ If a different directory needs to be used, set the environment variable `OLLAMA_
 
 Refer to the section [above](#how-do-i-configure-ollama-server) for how to set environment variables on your platform.
 
-## Does Ollama send my prompts and answers back to ollama.com?
-
-No. Ollama runs locally, and conversation data does not leave your machine.
-
 ## How can I use Ollama in Visual Studio Code?
 
 There is already a large collection of plugins available for VSCode as well as other editors that leverage Ollama. See the list of [extensions & plugins](https://github.com/ollama/ollama#extensions--plugins) at the bottom of the main repository readme.
 
-## How do I use Ollama behind a proxy?
-
-Ollama is compatible with proxy servers if `HTTP_PROXY` or `HTTPS_PROXY` are configured. When using either variables, ensure it is set where `ollama serve` can access the values. When using `HTTPS_PROXY`, ensure the proxy certificate is installed as a system certificate. Refer to the section above for how to use environment variables on your platform.
-
-### How do I use Ollama behind a proxy in Docker?
-
-The Ollama Docker container image can be configured to use a proxy by passing `-e HTTPS_PROXY=https://proxy.example.com` when starting the container.
-
-Alternatively, the Docker daemon can be configured to use a proxy. Instructions are available for Docker Desktop on [macOS](https://docs.docker.com/desktop/settings/mac/#proxies), [Windows](https://docs.docker.com/desktop/settings/windows/#proxies), and [Linux](https://docs.docker.com/desktop/settings/linux/#proxies), and Docker [daemon with systemd](https://docs.docker.com/config/daemon/systemd/#httphttps-proxy).
-
-Ensure the certificate is installed as a system certificate when using HTTPS. This may require a new Docker image when using a self-signed certificate.
-
-```dockerfile
-FROM ollama/ollama
-COPY my-ca.pem /usr/local/share/ca-certificates/my-ca.crt
-RUN update-ca-certificates
-```
-
-Build and run this image:
-
-```shell
-docker build -t ollama-with-ca .
-docker run -d -e HTTPS_PROXY=https://my.proxy.example.com -p 11434:11434 ollama-with-ca
-```
-
 ## How do I use Ollama with GPU acceleration in Docker?
 
 The Ollama Docker container can be configured with GPU acceleration in Linux or Windows (with WSL2). This requires the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit). See [ollama/ollama](https://hub.docker.com/r/ollama/ollama) for more details.
@@ -195,7 +214,7 @@ Open `Control Panel > Networking and Internet > View network status and tasks` a
 Click on `Configure` and open the `Advanced` tab. Search through each of the properties until you find `Large Send Offload Version 2 (IPv4)` and `Large Send Offload Version 2 (IPv6)`. *Disable* both of these
 properties.
 
-## How can I pre-load a model to get faster response times?
+## How can I preload a model into Ollama to get faster response times?
 
 If you are using the API you can preload a model by sending the Ollama server an empty request. This works with both the `/api/generate` and `/api/chat` API endpoints.
 
@@ -209,6 +228,11 @@ To use the chat completions endpoint, use:
 curl http://localhost:11434/api/chat -d '{"model": "mistral"}'
 ```
 
+To preload a model using the CLI, use the command:
+```shell
+ollama run llama3.1 ""
+```
+
 ## How do I keep a model loaded in memory or make it unload immediately?
 
 By default models are kept in memory for 5 minutes before being unloaded. This allows for quicker response times if you are making numerous requests to the LLM. You may, however, want to free up the memory before the 5 minutes have elapsed or keep the model loaded indefinitely. Use the `keep_alive` parameter with either the `/api/generate` and `/api/chat` API endpoints to control how long the model is left in memory.
@@ -221,10 +245,38 @@ The `keep_alive` parameter can be set to:
 
 For example, to preload a model and leave it in memory use:
 ```shell
-curl http://localhost:11434/api/generate -d '{"model": "llama2", "keep_alive": -1}'
+curl http://localhost:11434/api/generate -d '{"model": "llama3", "keep_alive": -1}'
 ```
 
 To unload the model and free up memory use:
 ```shell
-curl http://localhost:11434/api/generate -d '{"model": "llama2", "keep_alive": 0}'
+curl http://localhost:11434/api/generate -d '{"model": "llama3", "keep_alive": 0}'
 ```
+
+Alternatively, you can change the amount of time all models are loaded into memory by setting the `OLLAMA_KEEP_ALIVE` environment variable when starting the Ollama server. The `OLLAMA_KEEP_ALIVE` variable uses the same parameter types as the `keep_alive` parameter types mentioned above. Refer to section explaining [how to configure the Ollama server](#how-do-i-configure-ollama-server) to correctly set the environment variable.
+
+If you wish to override the `OLLAMA_KEEP_ALIVE` setting, use the `keep_alive` API parameter with the `/api/generate` or `/api/chat` API endpoints.
+
+## How do I manage the maximum number of requests the Ollama server can queue?
+
+If too many requests are sent to the server, it will respond with a 503 error indicating the server is overloaded.  You can adjust how many requests may be queue by setting `OLLAMA_MAX_QUEUE`.
+
+## How does Ollama handle concurrent requests?
+
+Ollama supports two levels of concurrent processing.  If your system has sufficient available memory (system memory when using CPU inference, or VRAM for GPU inference) then multiple models can be loaded at the same time.  For a given model, if there is sufficient available memory when the model is loaded, it is configured to allow parallel request processing.
+
+If there is insufficient available memory to load a new model request while one or more models are already loaded, all new requests will be queued until the new model can be loaded.  As prior models become idle, one or more will be unloaded to make room for the new model.  Queued requests will be processed in order.  When using GPU inference new models must be able to completely fit in VRAM to allow concurrent model loads.
+
+Parallel request processing for a given model results in increasing the context size by the number of parallel requests.  For example, a 2K context with 4 parallel requests will result in an 8K context and additional memory allocation.
+
+The following server settings may be used to adjust how Ollama handles concurrent requests on most platforms:
+
+- `OLLAMA_MAX_LOADED_MODELS` - The maximum number of models that can be loaded concurrently provided they fit in available memory.  The default is 3 * the number of GPUs or 3 for CPU inference.
+- `OLLAMA_NUM_PARALLEL` - The maximum number of parallel requests each model will process at the same time.  The default will auto-select either 4 or 1 based on available memory.
+- `OLLAMA_MAX_QUEUE` - The maximum number of requests Ollama will queue when busy before rejecting additional requests. The default is 512
+
+Note: Windows with Radeon GPUs currently default to 1 model maximum due to limitations in ROCm v5.7 for available VRAM reporting.  Once ROCm v6.2 is available, Windows Radeon will follow the defaults above.  You may enable concurrent model loads on Radeon on Windows, but ensure you don't load more models than will fit into your GPUs VRAM.
+
+## How does Ollama load models on multiple GPUs?
+
+Installing multiple GPUs of the same brand can be a great way to increase your available VRAM to load larger models.  When you load a new model, Ollama evaluates the required VRAM for the model against what is currently available.  If the model will entirely fit on any single GPU, Ollama will load the model on that GPU.  This typically provides the best performance as it reduces the amount of data transfering across the PCI bus during inference.  If the model does not fit entirely on one GPU, then it will be spread across all the available GPUs.

docs/gpu.md

@@ -8,7 +8,7 @@ Check your compute compatibility to see if your card is supported:
 | Compute Capability | Family              | Cards                                                                                                       |
 | ------------------ | ------------------- | ----------------------------------------------------------------------------------------------------------- |
 | 9.0                | NVIDIA              | `H100`                                                                                                      |
-| 8.9                | GeForce RTX 40xx    | `RTX 4090` `RTX 4080` `RTX 4070 Ti` `RTX 4060 Ti`                                                           |
+| 8.9                | GeForce RTX 40xx    | `RTX 4090` `RTX 4080 SUPER` `RTX 4080` `RTX 4070 Ti SUPER` `RTX 4070 Ti` `RTX 4070 SUPER` `RTX 4070` `RTX 4060 Ti` `RTX 4060`  |
 |                    | NVIDIA Professional | `L4` `L40` `RTX 6000`                                                                                       |
 | 8.6                | GeForce RTX 30xx    | `RTX 3090 Ti` `RTX 3090` `RTX 3080 Ti` `RTX 3080` `RTX 3070 Ti` `RTX 3070` `RTX 3060 Ti` `RTX 3060`         |
 |                    | NVIDIA Professional | `A40` `RTX A6000` `RTX A5000` `RTX A4000` `RTX A3000` `RTX A2000` `A10` `A16` `A2`                          |
@@ -18,7 +18,7 @@ Check your compute compatibility to see if your card is supported:
 |                    | Quadro              | `RTX 8000` `RTX 6000` `RTX 5000` `RTX 4000`                                                                 |
 | 7.0                | NVIDIA              | `TITAN V` `V100` `Quadro GV100`                                                                             |
 | 6.1                | NVIDIA TITAN        | `TITAN Xp` `TITAN X`                                                                                        |
-|                    | GeForce GTX         | `GTX 1080 Ti` `GTX 1080` `GTX 1070 Ti` `GTX 1070` `GTX 1060` `GTX 1050`                                     |
+|                    | GeForce GTX         | `GTX 1080 Ti` `GTX 1080` `GTX 1070 Ti` `GTX 1070` `GTX 1060` `GTX 1050 Ti` `GTX 1050`                       |
 |                    | Quadro              | `P6000` `P5200` `P4200` `P3200` `P5000` `P4000` `P3000` `P2200` `P2000` `P1000` `P620` `P600` `P500` `P520` |
 |                    | Tesla               | `P40` `P4`                                                                                                  |
 | 6.0                | NVIDIA              | `Tesla P100` `Quadro GP100`                                                                                 |
@@ -46,13 +46,24 @@ sudo modprobe nvidia_uvm`
 
 ## AMD Radeon
 Ollama supports the following AMD GPUs:
+
+### Linux Support
 | Family         | Cards and accelerators                                                                                                               |
 | -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
 | AMD Radeon RX  | `7900 XTX` `7900 XT` `7900 GRE` `7800 XT` `7700 XT` `7600 XT` `7600` `6950 XT` `6900 XTX` `6900XT` `6800 XT` `6800` `Vega 64` `Vega 56`    |
 | AMD Radeon PRO | `W7900` `W7800` `W7700` `W7600` `W7500` `W6900X` `W6800X Duo` `W6800X` `W6800` `V620` `V420` `V340` `V320` `Vega II Duo` `Vega II` `VII` `SSG` |
 | AMD Instinct   | `MI300X` `MI300A` `MI300` `MI250X` `MI250` `MI210` `MI200` `MI100` `MI60` `MI50`                                                               |
 
-### Overrides
+### Windows Support
+With ROCm v6.1, the following GPUs are supported on Windows.
+
+| Family         | Cards and accelerators                                                                                                               |
+| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
+| AMD Radeon RX  | `7900 XTX` `7900 XT` `7900 GRE` `7800 XT` `7700 XT` `7600 XT` `7600` `6950 XT` `6900 XTX` `6900XT` `6800 XT` `6800`    |
+| AMD Radeon PRO | `W7900` `W7800` `W7700` `W7600` `W7500` `W6900X` `W6800X Duo` `W6800X` `W6800` `V620` |
+
+
+### Overrides on Linux
 Ollama leverages the AMD ROCm library, which does not support all AMD GPUs. In
 some cases you can force the system to try to use a similar LLVM target that is
 close.  For example The Radeon RX 5400 is `gfx1034` (also known as 10.3.4)
@@ -63,7 +74,7 @@ would set `HSA_OVERRIDE_GFX_VERSION="10.3.0"` as an environment variable for the
 server.  If you have an unsupported AMD GPU you can experiment using the list of
 supported types below.
 
-At this time, the known supported GPU types are the following LLVM Targets.
+At this time, the known supported GPU types on linux are the following LLVM Targets.
 This table shows some example GPUs that map to these LLVM targets:
 | **LLVM Target** | **An Example GPU** |
 |-----------------|---------------------|

docs/import.md

@@ -1,168 +1,186 @@
-# Import a model
+# Importing a model
 
-This guide walks through importing a GGUF, PyTorch or Safetensors model.
+## Table of Contents
 
-## Importing (GGUF)
+  * [Importing a Safetensors adapter](#Importing-a-fine-tuned-adapter-from-Safetensors-weights)
+  * [Importing a Safetensors model](#Importing-a-model-from-Safetensors-weights)
+  * [Importing a GGUF file](#Importing-a-GGUF-based-model-or-adapter)
+  * [Sharing models on ollama.com](#Sharing-your-model-on-ollamacom)
 
-### Step 1: Write a `Modelfile`
+## Importing a fine tuned adapter from Safetensors weights
 
-Start by creating a `Modelfile`. This file is the blueprint for your model, specifying weights, parameters, prompt templates and more.
+First, create a `Modelfile` with a `FROM` command pointing at the base model you used for fine tuning, and an `ADAPTER` command which points to the directory with your Safetensors adapter:
 
-```
+```dockerfile
-FROM ./mistral-7b-v0.1.Q4_0.gguf
+FROM <base model name>
+ADAPTER /path/to/safetensors/adapter/directory
 ```
 
-(Optional) many chat models require a prompt template in order to answer correctly. A default prompt template can be specified with the `TEMPLATE` instruction in the `Modelfile`:
+Make sure that you use the same base model in the `FROM` command as you used to create the adapter otherwise you will get erratic results. Most frameworks use different quantization methods, so it's best to use non-quantized (i.e. non-QLoRA) adapters. If your adapter is in the same directory as your `Modelfile`, use `ADAPTER .` to specify the adapter path.
 
-```
+Now run `ollama create` from the directory where the `Modelfile` was created:
-FROM ./mistral-7b-v0.1.Q4_0.gguf
+
-TEMPLATE "[INST] {{ .Prompt }} [/INST]"
+```bash
+ollama create my-model
 ```
 
-### Step 2: Create the Ollama model
+Lastly, test the model:
 
-Finally, create a model from your `Modelfile`:
+```bash
-
+ollama run my-model
-```
-ollama create example -f Modelfile
 ```
 
-### Step 3: Run your model
+Ollama supports importing adapters based on several different model architectures including:
 
-Next, test the model with `ollama run`:
+  * Llama (including Llama 2, Llama 3, and Llama 3.1);
+  * Mistral (including Mistral 1, Mistral 2, and Mixtral); and
+  * Gemma (including Gemma 1 and Gemma 2)
 
-```
+You can create the adapter using a fine tuning framework or tool which can output adapters in the Safetensors format, such as:
-ollama run example "What is your favourite condiment?"
+
+  * Hugging Face [fine tuning framework] (https://huggingface.co/docs/transformers/en/training)
+  * [Unsloth](https://github.com/unslothai/unsloth)
+  * [MLX](https://github.com/ml-explore/mlx)
+
+
+## Importing a model from Safetensors weights
+
+First, create a `Modelfile` with a `FROM` command which points to the directory containing your Safetensors weights:
+
+```dockerfile
+FROM /path/to/safetensors/directory
 ```
 
-## Importing (PyTorch & Safetensors)
+If you create the Modelfile in the same directory as the weights, you can use the command `FROM .`.
 
-> Importing from PyTorch and Safetensors is a longer process than importing from GGUF. Improvements that make it easier are a work in progress.
+Now run the `ollama create` command from the directory where you created the `Modelfile`:
-
-### Setup
-
-First, clone the `ollama/ollama` repo:
-
-```
-git clone git@github.com:ollama/ollama.git ollama
-cd ollama
-```
-
-and then fetch its `llama.cpp` submodule:
 
 ```shell
-git submodule init
+ollama create my-model
-git submodule update llm/llama.cpp
 ```
 
-Next, install the Python dependencies:
+Lastly, test the model:
 
-```
+```shell
-python3 -m venv llm/llama.cpp/.venv
+ollama run my-model
-source llm/llama.cpp/.venv/bin/activate
-pip install -r llm/llama.cpp/requirements.txt
 ```
 
-Then build the `quantize` tool:
+Ollama supports importing models for several different architectures including:
 
-```
+  * Llama (including Llama 2, Llama 3, and Llama 3.1);
-make -C llm/llama.cpp quantize
+  * Mistral (including Mistral 1, Mistral 2, and Mixtral);
+  * Gemma (including Gemma 1 and Gemma 2); and
+  * Phi3
+
+This includes importing foundation models as well as any fine tuned models which which have been _fused_ with a foundation model.
+
+
+## Importing a GGUF based model or adapter
+
+If you have a GGUF based model or adapter it is possible to import it into Ollama. You can obtain a GGUF model or adapter by:
+
+  * converting a Safetensors model with the `convert_hf_to_gguf.py` from Llama.cpp; 
+  * converting a Safetensors adapter with the `convert_lora_to_gguf.py` from Llama.cpp; or
+  * downloading a model or adapter from a place such as HuggingFace
+
+To import a GGUF model, create a `Modelfile` containg:
+
+```dockerfile
+FROM /path/to/file.gguf
 ```
 
-### Clone the HuggingFace repository (optional)
+For a GGUF adapter, create the `Modelfile` with:
 
-If the model is currently hosted in a HuggingFace repository, first clone that repository to download the raw model.
+```dockerfile
-
+FROM <model name>
-Install [Git LFS](https://docs.github.com/en/repositories/working-with-files/managing-large-files/installing-git-large-file-storage), verify it's installed, and then clone the model's repository:
+ADAPTER /path/to/file.gguf
-
-```
-git lfs install
-git clone https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1 model
 ```
 
-### Convert the model
+When importing a GGUF adapter, it's important to use the same base model as the base model that the adapter was created with. You can use:
 
-> Note: some model architectures require using specific convert scripts. For example, Qwen models require running `convert-hf-to-gguf.py` instead of `convert.py`
+ * a model from Ollama
+ * a GGUF file
+ * a Safetensors based model 
 
-```
+Once you have created your `Modelfile`, use the `ollama create` command to build the model.
-python llm/llama.cpp/convert.py ./model --outtype f16 --outfile converted.bin
+
+```shell
+ollama create my-model
 ```
 
-### Quantize the model
+## Quantizing a Model
 
-```
+Quantizing a model allows you to run models faster and with less memory consumption but at reduced accuracy. This allows you to run a model on more modest hardware.
-llm/llama.cpp/quantize converted.bin quantized.bin q4_0
+
+Ollama can quantize FP16 and FP32 based models into different quantization levels using the `-q/--quantize` flag with the `ollama create` command.
+
+First, create a Modelfile with the FP16 or FP32 based model you wish to quantize.
+
+```dockerfile
+FROM /path/to/my/gemma/f16/model
 ```
 
-### Step 3: Write a `Modelfile`
+Use `ollama create` to then create the quantized model.
 
-Next, create a `Modelfile` for your model:
+```shell
-
+$ ollama create --quantize q4_K_M mymodel
-```
+transferring model data
-FROM quantized.bin
+quantizing F16 model to Q4_K_M
-TEMPLATE "[INST] {{ .Prompt }} [/INST]"
+creating new layer sha256:735e246cc1abfd06e9cdcf95504d6789a6cd1ad7577108a70d9902fef503c1bd
+creating new layer sha256:0853f0ad24e5865173bbf9ffcc7b0f5d56b66fd690ab1009867e45e7d2c4db0f
+writing manifest
+success
 ```
 
-### Step 4: Create the Ollama model
+### Supported Quantizations
 
-Finally, create a model from your `Modelfile`:
+- `q4_0`
+- `q4_1`
+- `q5_0`
+- `q5_1`
+- `q8_0`
 
-```
+#### K-means Quantizations
-ollama create example -f Modelfile
-```
 
-### Step 5: Run your model
-
-Next, test the model with `ollama run`:
-
-```
-ollama run example "What is your favourite condiment?"
-```
-
-## Publishing your model (optional – early alpha)
-
-Publishing models is in early alpha. If you'd like to publish your model to share with others, follow these steps:
-
-1. Create [an account](https://ollama.com/signup)
-2. Copy your Ollama public key:
-  - macOS: `cat ~/.ollama/id_ed25519.pub`
-  - Windows: `type %USERPROFILE%\.ollama\id_ed25519.pub`
-  - Linux: `cat /usr/share/ollama/.ollama/id_ed25519.pub`
-3. Add your public key to your [Ollama account](https://ollama.com/settings/keys)
-
-Next, copy your model to your username's namespace:
-
-```
-ollama cp example <your username>/example
-```
-
-Then push the model:
-
-```
-ollama push <your username>/example
-```
-
-After publishing, your model will be available at `https://ollama.com/<your username>/example`.
-
-## Quantization reference
-
-The quantization options are as follow (from highest highest to lowest levels of quantization). Note: some architectures such as Falcon do not support K quants.
-
-- `q2_K`
-- `q3_K`
 - `q3_K_S`
 - `q3_K_M`
 - `q3_K_L`
-- `q4_0` (recommended)
-- `q4_1`
-- `q4_K`
 - `q4_K_S`
 - `q4_K_M`
-- `q5_0`
-- `q5_1`
-- `q5_K`
 - `q5_K_S`
 - `q5_K_M`
 - `q6_K`
-- `q8_0`
+
-- `f16`
+
+## Sharing your model on ollama.com
+
+You can share any model you have created by pushing it to [ollama.com](https://ollama.com) so that other users can try it out.
+
+First, use your browser to go to the [Ollama Sign-Up](https://ollama.com/signup) page. If you already have an account, you can skip this step.
+
+![Sign-Up](images/signup.png)
+
+The `Username` field will be used as part of your model's name (e.g. `jmorganca/mymodel`), so make sure you are comfortable with the username that you have selected.
+
+Now that you have created an account and are signed-in, go to the [Ollama Keys Settings](https://ollama.com/settings/keys) page.
+
+Follow the directions on the page to determine where your Ollama Public Key is located.
+
+![Ollama Key](images/ollama-keys.png)
+
+Click on the `Add Ollama Public Key` button, and copy and paste the contents of your Ollama Public Key into the text field.
+
+To push a model to [ollama.com](https://ollama.com), first make sure that it is named correctly with your username. You may have to use the `ollama cp` command to copy
+your model to give it the correct name. Once you're happy with your model's name, use the `ollama push` command to push it to [ollama.com](https://ollama.com).
+
+```shell
+ollama cp mymodel myuser/mymodel
+ollama push myuser/mymodel
+```
+
+Once your model has been pushed, other users can pull and run it by using the command:
+
+```shell
+ollama run myuser/mymodel
+```
+

docs/linux.md

@@ -20,13 +20,12 @@ GPU.
 
 ## Manual install
 
-### Download the `ollama` binary
+### Download `ollama`
 
-Ollama is distributed as a self-contained binary. Download it to a directory in your PATH:
+Download and extract the Linux package:
 
 ```bash
-sudo curl -L https://ollama.com/download/ollama-linux-amd64 -o /usr/bin/ollama
+curl -fsSL https://ollama.com/download/ollama-linux-amd64.tgz | sudo tar zx -C /usr
-sudo chmod +x /usr/bin/ollama
 ```
 
 ### Adding Ollama as a startup service (recommended)
@@ -96,8 +95,17 @@ curl -fsSL https://ollama.com/install.sh | sh
 Or by downloading the ollama binary:
 
 ```bash
-sudo curl -L https://ollama.com/download/ollama-linux-amd64 -o /usr/bin/ollama
+curl -fsSL https://ollama.com/download/ollama-linux-amd64.tgz | sudo tar zx -C /usr
-sudo chmod +x /usr/bin/ollama
+```
+
+## Installing specific versions
+
+Use `OLLAMA_VERSION` environment variable with the install script to install a specific version of Ollama, including pre-releases. You can find the version numbers in the [releases page](https://github.com/ollama/ollama/releases). 
+
+For example:
+
+```
+curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.1.32 sh
 ```
 
 ## Viewing logs
@@ -105,7 +113,7 @@ sudo chmod +x /usr/bin/ollama
 To view logs of Ollama running as a startup service, run:
 
 ```bash
-journalctl -u ollama
+journalctl -e -u ollama
 ```
 
 ## Uninstall

docs/modelfile.md

@@ -1,6 +1,7 @@
 # Ollama Model File
 
-> Note: `Modelfile` syntax is in development
+> [!NOTE]
+> `Modelfile` syntax is in development
 
 A model file is the blueprint to create and share models with Ollama.
 
@@ -10,7 +11,7 @@ A model file is the blueprint to create and share models with Ollama.
 - [Examples](#examples)
 - [Instructions](#instructions)
   - [FROM (Required)](#from-required)
-    - [Build from llama2](#build-from-llama2)
+    - [Build from llama3](#build-from-llama3)
     - [Build from a bin file](#build-from-a-bin-file)
   - [PARAMETER](#parameter)
     - [Valid Parameters and Values](#valid-parameters-and-values)
@@ -48,7 +49,7 @@ INSTRUCTION arguments
 An example of a `Modelfile` creating a mario blueprint:
 
 ```modelfile
-FROM llama2
+FROM llama3
 # sets the temperature to 1 [higher is more creative, lower is more coherent]
 PARAMETER temperature 1
 # sets the context window size to 4096, this controls how many tokens the LLM can use as context to generate the next token
@@ -67,33 +68,25 @@ To use this:
 
 More examples are available in the [examples directory](../examples).
 
-### `Modelfile`s in [ollama.com/library][1]
+To view the Modelfile of a given model, use the `ollama show --modelfile` command.
-
-There are two ways to view `Modelfile`s underlying the models in [ollama.com/library][1]:
-
-- Option 1: view a details page from a model's tags page:
-  1.  Go to a particular model's tags (e.g. https://ollama.com/library/llama2/tags)
-  2.  Click on a tag (e.g. https://ollama.com/library/llama2:13b)
-  3.  Scroll down to "Layers"
-      - Note: if the [`FROM` instruction](#from-required) is not present,
-        it means the model was created from a local file
-- Option 2: use `ollama show` to print the `Modelfile` for any local models like so:
 
   ```bash
-  > ollama show --modelfile llama2:13b
+  > ollama show --modelfile llama3
   # Modelfile generated by "ollama show"
   # To build a new Modelfile based on this one, replace the FROM line with:
-  # FROM llama2:13b
+  # FROM llama3:latest
+  FROM /Users/pdevine/.ollama/models/blobs/sha256-00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29
+  TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>
 
-  FROM /root/.ollama/models/blobs/sha256:123abc
+  {{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
-  TEMPLATE """[INST] {{ if .System }}<<SYS>>{{ .System }}<</SYS>>
 
-  {{ end }}{{ .Prompt }} [/INST] """
+  {{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
-  SYSTEM """"""
+
-  PARAMETER stop [INST]
+  {{ .Response }}<|eot_id|>"""
-  PARAMETER stop [/INST]
+  PARAMETER stop "<|start_header_id|>"
-  PARAMETER stop <<SYS>>
+  PARAMETER stop "<|end_header_id|>"
-  PARAMETER stop <</SYS>>
+  PARAMETER stop "<|eot_id|>"
+  PARAMETER stop "<|reserved_special_token"
   ```
 
 ## Instructions
@@ -106,10 +99,10 @@ The `FROM` instruction defines the base model to use when creating a model.
 FROM <model name>:<tag>
 ```
 
-#### Build from llama2
+#### Build from llama3
 
 ```modelfile
-FROM llama2
+FROM llama3
 ```
 
 A list of available base models:
@@ -139,9 +132,6 @@ PARAMETER <parameter> <parametervalue>
 | mirostat_eta   | Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1)                        | float      | mirostat_eta 0.1     |
 | mirostat_tau   | Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0)                                                                                                         | float      | mirostat_tau 5.0     |
 | num_ctx        | Sets the size of the context window used to generate the next token. (Default: 2048)                                                                                                                                                                    | int        | num_ctx 4096         |
-| num_gqa        | The number of GQA groups in the transformer layer. Required for some models, for example it is 8 for llama2:70b                                                                                                                                         | int        | num_gqa 1            |
-| num_gpu        | The number of layers to send to the GPU(s). On macOS it defaults to 1 to enable metal support, 0 to disable.                                                                                                                                            | int        | num_gpu 50           |
-| num_thread     | Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). | int        | num_thread 8         |
 | repeat_last_n  | Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)                                                                                                                                           | int        | repeat_last_n 64     |
 | repeat_penalty | Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)                                                                     | float      | repeat_penalty 1.1   |
 | temperature    | The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)                                                                                                                                     | float      | temperature 0.7      |
@@ -151,6 +141,7 @@ PARAMETER <parameter> <parametervalue>
 | num_predict    | Maximum number of tokens to predict when generating text. (Default: 128, -1 = infinite generation, -2 = fill context)                                                                                                                                   | int        | num_predict 42       |
 | top_k          | Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)                                                                        | int        | top_k 40             |
 | top_p          | Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)                                                                 | float      | top_p 0.9            |
+| min_p          | Alternative to the top_p, and aims to ensure a balance of quality and variety. The parameter *p* represents the minimum probability for a token to be considered, relative to the probability of the most likely token. For example, with *p*=0.05 and the most likely token having a probability of 0.9, logits with a value less than 0.045 are filtered out. (Default: 0.0) | float      | min_p 0.05            |
 
 ### TEMPLATE
 

docs/openai.md

@@ -25,7 +25,38 @@ chat_completion = client.chat.completions.create(
             'content': 'Say this is a test',
         }
     ],
-    model='llama2',
+    model='llama3',
+)
+
+response = client.chat.completions.create(
+    model="llava",
+    messages=[
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "What's in this image?"},
+                {
+                    "type": "image_url",
+                    "image_url": "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",
+                },
+            ],
+        }
+    ],
+    max_tokens=300,
+)
+
+completion = client.completions.create(
+    model="llama3",
+    prompt="Say this is a test",
+)
+
+list_completion = client.models.list()
+
+model = client.models.retrieve("llama3")
+
+embeddings = client.embeddings.create(
+    model="all-minilm",
+    input=["why is the sky blue?", "why is the grass green?"],
 )
 ```
 
@@ -42,18 +73,48 @@ const openai = new OpenAI({
 })
 
 const chatCompletion = await openai.chat.completions.create({
-  messages: [{ role: 'user', content: 'Say this is a test' }],
+    messages: [{ role: 'user', content: 'Say this is a test' }],
-  model: 'llama2',
+    model: 'llama3',
+})
+
+const response = await openai.chat.completions.create({
+    model: "llava",
+    messages: [
+        {
+        role: "user",
+        content: [
+            { type: "text", text: "What's in this image?" },
+            {
+            type: "image_url",
+            image_url: "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",
+            },
+        ],
+        },
+    ],
+})
+
+const completion = await openai.completions.create({
+    model: "llama3",
+    prompt: "Say this is a test.",
+})
+
+const listCompletion = await openai.models.list()
+
+const model = await openai.models.retrieve("llama3")
+
+const embedding = await openai.embeddings.create({
+  model: "all-minilm",
+  input: ["why is the sky blue?", "why is the grass green?"],
 })
 ```
 
 ### `curl`
 
-```
+``` shell
 curl http://localhost:11434/v1/chat/completions \
     -H "Content-Type: application/json" \
     -d '{
-        "model": "llama2",
+        "model": "llama3",
         "messages": [
             {
                 "role": "system",
@@ -65,6 +126,48 @@ curl http://localhost:11434/v1/chat/completions \
             }
         ]
     }'
+
+curl http://localhost:11434/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "llava",
+    "messages": [
+      {
+        "role": "user",
+        "content": [
+          {
+            "type": "text",
+            "text": "What'\''s in this image?"
+          },
+          {
+            "type": "image_url",
+            "image_url": {
+               "url": "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"
+            }
+          }
+        ]
+      }
+    ],
+    "max_tokens": 300
+  }'
+
+curl http://localhost:11434/v1/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "llama3",
+        "prompt": "Say this is a test"
+    }'
+
+curl http://localhost:11434/v1/models
+
+curl http://localhost:11434/v1/models/llama3
+
+curl http://localhost:11434/v1/embeddings \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "all-minilm",
+        "input": ["why is the sky blue?", "why is the grass green?"]
+    }'
 ```
 
 ## Endpoints
@@ -77,8 +180,8 @@ curl http://localhost:11434/v1/chat/completions \
 - [x] Streaming
 - [x] JSON mode
 - [x] Reproducible outputs
-- [ ] Vision
+- [x] Vision
-- [ ] Function calling
+- [x] Tools (streaming support coming soon)
 - [ ] Logprobs
 
 #### Supported request fields
@@ -86,7 +189,10 @@ curl http://localhost:11434/v1/chat/completions \
 - [x] `model`
 - [x] `messages`
   - [x] Text `content`
-  - [ ] Array of `content` parts
+  - [x] Image `content`
+    - [x] Base64 encoded image
+    - [ ] Image URL
+  - [x] Array of `content` parts
 - [x] `frequency_penalty`
 - [x] `presence_penalty`
 - [x] `response_format`
@@ -96,24 +202,79 @@ curl http://localhost:11434/v1/chat/completions \
 - [x] `temperature`
 - [x] `top_p`
 - [x] `max_tokens`
-- [ ] `logit_bias`
+- [x] `tools`
-- [ ] `tools`
 - [ ] `tool_choice`
+- [ ] `logit_bias`
+- [ ] `user`
+- [ ] `n`
+
+### `/v1/completions`
+
+#### Supported features
+
+- [x] Completions
+- [x] Streaming
+- [x] JSON mode
+- [x] Reproducible outputs
+- [ ] Logprobs
+
+#### Supported request fields
+
+- [x] `model`
+- [x] `prompt`
+- [x] `frequency_penalty`
+- [x] `presence_penalty`
+- [x] `seed`
+- [x] `stop`
+- [x] `stream`
+- [x] `temperature`
+- [x] `top_p`
+- [x] `max_tokens`
+- [x] `suffix`
+- [ ] `best_of`
+- [ ] `echo`
+- [ ] `logit_bias`
 - [ ] `user`
 - [ ] `n`
 
 #### Notes
 
-- Setting `seed` will always set `temperature` to `0`
+- `prompt` currently only accepts a string
-- `finish_reason` will always be `stop`
+
-- `usage.prompt_tokens` will be 0 for completions where prompt evaluation is cached
+### `/v1/models`
+
+#### Notes
+
+- `created` corresponds to when the model was last modified
+- `owned_by` corresponds to the ollama username, defaulting to `"library"`
+
+### `/v1/models/{model}`
+
+#### Notes
+
+- `created` corresponds to when the model was last modified
+- `owned_by` corresponds to the ollama username, defaulting to `"library"`
+
+### `/v1/embeddings`
+
+#### Supported request fields
+
+- [x] `model`
+- [x] `input`
+  - [x] string
+  - [x] array of strings
+  - [ ] array of tokens
+  - [ ] array of token arrays
+- [ ] `encoding format`
+- [ ] `dimensions`
+- [ ] `user`
 
 ## Models
 
 Before using a model, pull it locally `ollama pull`:
 
 ```shell
-ollama pull llama2
+ollama pull llama3
 ```
 
 ### Default model names
@@ -121,7 +282,7 @@ ollama pull llama2
 For tooling that relies on default OpenAI model names such as `gpt-3.5-turbo`, use `ollama cp` to copy an existing model name to a temporary name:
 
 ```
-ollama cp llama2 gpt-3.5-turbo
+ollama cp llama3 gpt-3.5-turbo
 ```
 
 Afterwards, this new model name can be specified the `model` field:

docs/template.md

@@ -0,0 +1,167 @@
+# Template
+
+Ollama provides a powerful templating engine backed by Go's built-in templating engine to construct prompts for your large language model. This feature is a valuable tool to get the most out of your models.
+
+## Basic Template Structure
+
+A basic Go template consists of three main parts:
+
+* **Layout**: The overall structure of the template.
+* **Variables**: Placeholders for dynamic data that will be replaced with actual values when the template is rendered.
+* **Functions**: Custom functions or logic that can be used to manipulate the template's content.
+
+Here's an example of a simple chat template:
+
+```gotmpl
+{{- range .Messages }}
+{{ .Role }}: {{ .Content }}
+{{- end }}
+```
+
+In this example, we have:
+
+* A basic messages structure (layout)
+* Three variables: `Messages`, `Role`, and `Content` (variables)
+* A custom function (action) that iterates over an array of items (`range .Messages`) and displays each item
+
+## Adding templates to your model
+
+By default, models imported into Ollama have a default template of `{{ .Prompt }}`, i.e. user inputs are sent verbatim to the LLM. This is appropriate for text or code completion models but lacks essential markers for chat or instruction models.
+
+Omitting a template in these models puts the responsibility of correctly templating input onto the user. Adding a template allows users to easily get the best results from the model.
+
+To add templates in your model, you'll need to add a `TEMPLATE` command to the Modelfile. Here's an example using Meta's Llama 3.
+
+```dockerfile
+FROM llama3
+
+TEMPLATE """{{- if .System }}<|start_header_id|>system<|end_header_id|>
+
+{{ .System }}<|eot_id|>
+{{- end }}
+{{- range .Messages }}<|start_header_id|>{{ .Role }}<|end_header_id|>
+
+{{ .Content }}<|eot_id|>
+{{- end }}<|start_header_id|>assistant<|end_header_id|>
+
+"""
+```
+
+## Variables
+
+`System` (string): system prompt
+
+`Prompt` (string): user prompt
+
+`Response` (string): assistant response
+
+`Suffix` (string): text inserted after the assistant's response
+
+`Messages` (list): list of messages
+
+`Messages[].Role` (string): role which can be one of `system`, `user`, `assistant`, or `tool`
+
+`Messages[].Content` (string):  message content
+
+`Messages[].ToolCalls` (list): list of tools the model wants to call
+
+`Messages[].ToolCalls[].Function` (object): function to call
+
+`Messages[].ToolCalls[].Function.Name` (string): function name
+
+`Messages[].ToolCalls[].Function.Arguments` (map): mapping of argument name to argument value
+
+`Tools` (list): list of tools the model can access
+
+`Tools[].Type` (string): schema type. `type` is always `function`
+
+`Tools[].Function` (object): function definition
+
+`Tools[].Function.Name` (string): function name
+
+`Tools[].Function.Description` (string): function description
+
+`Tools[].Function.Parameters` (object): function parameters
+
+`Tools[].Function.Parameters.Type` (string): schema type. `type` is always `object`
+
+`Tools[].Function.Parameters.Required` (list): list of required properties
+
+`Tools[].Function.Parameters.Properties` (map): mapping of property name to property definition
+
+`Tools[].Function.Parameters.Properties[].Type` (string): property type
+
+`Tools[].Function.Parameters.Properties[].Description` (string): property description
+
+`Tools[].Function.Parameters.Properties[].Enum` (list): list of valid values
+
+## Tips and Best Practices
+
+Keep the following tips and best practices in mind when working with Go templates:
+
+* **Be mindful of dot**: Control flow structures like `range` and `with` changes the value `.`
+* **Out-of-scope variables**: Use `$.` to reference variables not currently in scope, starting from the root
+* **Whitespace control**: Use `-` to trim leading (`{{-`) and trailing (`-}}`) whitespace
+
+## Examples
+
+### Example Messages
+
+#### ChatML
+
+ChatML is a popular template format. It can be used for models such as Databrick's DBRX, Intel's Neural Chat, and Microsoft's Orca 2.
+
+```gotmpl
+{{- range .Messages }}<|im_start|>{{ .Role }}
+{{ .Content }}<|im_end|>
+{{ end }}<|im_start|>assistant
+```
+
+### Example Tools
+
+Tools support can be added to a model by adding a `{{ .Tools }}` node to the template. This feature is useful for models trained to call external tools and can a powerful tool for retrieving real-time data or performing complex tasks.
+
+#### Mistral
+
+Mistral v0.3 and Mixtral 8x22B supports tool calling.
+
+```gotmpl
+{{- range $index, $_ := .Messages }}
+{{- if eq .Role "user" }}
+{{- if and (le (len (slice $.Messages $index)) 2) $.Tools }}[AVAILABLE_TOOLS] {{ json $.Tools }}[/AVAILABLE_TOOLS]
+{{- end }}[INST] {{ if and (eq (len (slice $.Messages $index)) 1) $.System }}{{ $.System }}
+
+{{ end }}{{ .Content }}[/INST]
+{{- else if eq .Role "assistant" }}
+{{- if .Content }} {{ .Content }}</s>
+{{- else if .ToolCalls }}[TOOL_CALLS] [
+{{- range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ json .Function.Arguments }}}
+{{- end }}]</s>
+{{- end }}
+{{- else if eq .Role "tool" }}[TOOL_RESULTS] {"content": {{ .Content }}}[/TOOL_RESULTS]
+{{- end }}
+{{- end }}
+```
+
+### Example Fill-in-Middle
+
+Fill-in-middle support can be added to a model by adding a `{{ .Suffix }}` node to the template. This feature is useful for models that are trained to generate text in the middle of user input, such as code completion models.
+
+#### CodeLlama
+
+CodeLlama [7B](https://ollama.com/library/codellama:7b-code) and [13B](https://ollama.com/library/codellama:13b-code) code completion models support fill-in-middle.
+
+```gotmpl
+<PRE> {{ .Prompt }} <SUF>{{ .Suffix }} <MID>
+```
+
+> [!NOTE]
+> CodeLlama 34B and 70B code completion and all instruct and Python fine-tuned models do not support fill-in-middle.
+
+#### Codestral
+
+Codestral [22B](https://ollama.com/library/codestral:22b) supports fill-in-middle.
+
+```gotmpl
+[SUFFIX]{{ .Suffix }}[PREFIX] {{ .Prompt }}
+```

docs/troubleshooting.md

@@ -9,7 +9,7 @@ cat ~/.ollama/logs/server.log
 On **Linux** systems with systemd, the logs can be found with this command:
 
 ```shell
-journalctl -u ollama
+journalctl -u ollama --no-pager
 ```
 
 When you run Ollama in a **container**, the logs go to stdout/stderr in the container:
@@ -21,8 +21,8 @@ docker logs <container-name>
 
 If manually running `ollama serve` in a terminal, the logs will be on that terminal.
 
-When you run Ollama on **Windows**, there are a few different locations.  You can view them in the explorer window by hitting `<cmd>+R` and type in:
+When you run Ollama on **Windows**, there are a few different locations. You can view them in the explorer window by hitting `<cmd>+R` and type in:
-- `explorer %LOCALAPPDATA%\Ollama` to view logs
+- `explorer %LOCALAPPDATA%\Ollama` to view logs.  The most recent server logs will be in `server.log` and older logs will be in `server-#.log` 
 - `explorer %LOCALAPPDATA%\Programs\Ollama` to browse the binaries (The installer adds this to your user PATH)
 - `explorer %HOMEPATH%\.ollama` to browse where models and configuration is stored
 - `explorer %TEMP%` where temporary executable files are stored in one or more `ollama*` directories
@@ -37,16 +37,9 @@ Join the [Discord](https://discord.gg/ollama) for help interpreting the logs.
 
 ## LLM libraries
 
-Ollama includes multiple LLM libraries compiled for different GPUs and CPU
+Ollama includes multiple LLM libraries compiled for different GPUs and CPU vector features. Ollama tries to pick the best one based on the capabilities of your system. If this autodetection has problems, or you run into other problems (e.g. crashes in your GPU) you can workaround this by forcing a specific LLM library. `cpu_avx2` will perform the best, followed by `cpu_avx` an the slowest but most compatible is `cpu`. Rosetta emulation under MacOS will work with the `cpu` library. 
-vector features.  Ollama tries to pick the best one based on the capabilities of
-your system.  If this autodetection has problems, or you run into other problems
-(e.g. crashes in your GPU) you can workaround this by forcing a specific LLM
-library.  `cpu_avx2` will perform the best, followed by `cpu_avx` an the slowest
-but most compatible is `cpu`.  Rosetta emulation under MacOS will work with the
-`cpu` library. 
 
-In the server log, you will see a message that looks something like this (varies
+In the server log, you will see a message that looks something like this (varies from release to release):
-from release to release):
 
 ```
 Dynamic LLM libraries [rocm_v6 cpu cpu_avx cpu_avx2 cuda_v11 rocm_v5]
@@ -54,9 +47,7 @@ Dynamic LLM libraries [rocm_v6 cpu cpu_avx cpu_avx2 cuda_v11 rocm_v5]
 
 **Experimental LLM Library Override**
 
-You can set OLLAMA_LLM_LIBRARY to any of the available LLM libraries to bypass
+You can set OLLAMA_LLM_LIBRARY to any of the available LLM libraries to bypass autodetection, so for example, if you have a CUDA card, but want to force the CPU LLM library with AVX2 vector support, use:
-autodetection, so for example, if you have a CUDA card, but want to force the
-CPU LLM library with AVX2 vector support, use:
 
 ```
 OLLAMA_LLM_LIBRARY="cpu_avx2" ollama serve
@@ -64,14 +55,12 @@ OLLAMA_LLM_LIBRARY="cpu_avx2" ollama serve
 
 You can see what features your CPU has with the following.
 ```
-cat /proc/cpuinfo| grep flags  | head -1
+cat /proc/cpuinfo| grep flags | head -1
 ```
 
 ## Installing older or pre-release versions on Linux
 
-If you run into problems on Linux and want to install an older version, or you'd
+If you run into problems on Linux and want to install an older version, or you'd like to try out a pre-release before it's officially released, you can tell the install script which version to install.
-like to try out a pre-release before it's officially released, you can tell the
-install script which version to install.
 
 ```sh
 curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION="0.1.29" sh
@@ -79,7 +68,29 @@ curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION="0.1.29" sh
 
 ## Linux tmp noexec 
 
-If your system is configured with the "noexec" flag where Ollama stores its
+If your system is configured with the "noexec" flag where Ollama stores its temporary executable files, you can specify an alternate location by setting OLLAMA_TMPDIR to a location writable by the user ollama runs as. For example OLLAMA_TMPDIR=/usr/share/ollama/
-temporary executable files, you can specify an alternate location by setting
+
-OLLAMA_TMPDIR to a location writable by the user ollama runs as.  For example
+## NVIDIA GPU Discovery
-OLLAMA_TMPDIR=/usr/share/ollama/
+
+When Ollama starts up, it takes inventory of the GPUs present in the system to determine compatibility and how much VRAM is available.  Sometimes this discovery can fail to find your GPUs.  In general, running the latest driver will yield the best results.
+
+### Linux NVIDIA Troubleshooting
+
+If you are using a container to run Ollama, make sure you've set up the container runtime first as described in [docker.md](./docker.md)
+
+Sometimes the Ollama can have difficulties initializing the GPU. When you check the server logs, this can show up as various error codes, such as "3" (not initialized), "46" (device unavailable), "100" (no device), "999" (unknown), or others. The following troubleshooting techniques may help resolve the problem
+
+- If you are using a container, is the container runtime working?  Try `docker run --gpus all ubuntu nvidia-smi` - if this doesn't work, Ollama wont be able to see your NVIDIA GPU.
+- Is the uvm driver loaded? `sudo nvidia-modprobe -u`
+- Try reloading the nvidia_uvm driver - `sudo rmmod nvidia_uvm` then `sudo modprobe nvidia_uvm`
+- Try rebooting
+- Make sure you're running the latest nvidia drivers
+
+If none of those resolve the problem, gather additional information and file an issue:
+- Set `CUDA_ERROR_LEVEL=50` and try again to get more diagnostic logs
+- Check dmesg for any errors `sudo dmesg | grep -i nvrm` and `sudo dmesg | grep -i nvidia`
+
+
+## Windows Terminal Errors
+
+Older versions of Windows 10 (e.g., 21H1) are known to have a bug where the standard terminal program does not display control characters correctly.  This can result in a long string of strings like `←[?25h←[?25l` being displayed, sometimes erroring with `The parameter is incorrect`  To resolve this problem, please update to Win 10 22H1 or newer.

docs/tutorials/langchainjs.md

@@ -5,25 +5,25 @@ In this tutorial, we are going to use JavaScript with LangChain and Ollama to le
 To get started, let's just use **LangChain** to ask a simple question to a model. To do this with JavaScript, we need to install **LangChain**:
 
 ```bash
-npm install langchain
+npm install @langchain/community
 ```
 
 Now we can start building out our JavaScript:
 
 ```javascript
-import { Ollama } from "langchain/llms/ollama";
+import { Ollama } from "@langchain/community/llms/ollama";
 
 const ollama = new Ollama({
   baseUrl: "http://localhost:11434",
-  model: "llama2",
+  model: "llama3.1",
 });
 
-const answer = await ollama.call(`why is the sky blue?`);
+const answer = await ollama.invoke(`why is the sky blue?`);
 
 console.log(answer);
 ```
 
-That will get us the same thing as if we ran `ollama run llama2 "why is the sky blue"` in the terminal. But we want to load a document from the web to ask a question against. **Cheerio** is a great library for ingesting a webpage, and **LangChain** uses it in their **CheerioWebBaseLoader**. So let's install **Cheerio** and build that part of the app.
+That will get us the same thing as if we ran `ollama run llama3.1 "why is the sky blue"` in the terminal. But we want to load a document from the web to ask a question against. **Cheerio** is a great library for ingesting a webpage, and **LangChain** uses it in their **CheerioWebBaseLoader**. So let's install **Cheerio** and build that part of the app.
 
 ```bash
 npm install cheerio

docs/tutorials/langchainpy.md

@@ -12,15 +12,17 @@ So let's figure out how we can use **LangChain** with Ollama to ask our question
 
 Let's start by asking a simple question that we can get an answer to from the **Llama2** model using **Ollama**. First, we need to install the **LangChain** package:
 
-`pip install langchain`
+`pip install langchain_community`
 
 Then we can create a model and ask the question:
 
 ```python
-from langchain.llms import Ollama
+from langchain_community.llms import Ollama
-ollama = Ollama(base_url='http://localhost:11434',
+ollama = Ollama(
-model="llama2")
+    base_url='http://localhost:11434',
-print(ollama("why is the sky blue"))
+    model="llama3"
+)
+print(ollama.invoke("why is the sky blue"))
 ```
 
 Notice that we are defining the model and the base URL for Ollama.
@@ -43,7 +45,7 @@ all_splits = text_splitter.split_documents(data)
 ```
 
 It's split up, but we have to find the relevant splits and then submit those to the model. We can do this by creating embeddings and storing them in a vector database. We can use Ollama directly to instantiate an embedding model. We will use ChromaDB in this example for a vector database. `pip install chromadb`
-
+We also need to pull embedding model: `ollama pull nomic-embed-text`
 ```python
 from langchain.embeddings import OllamaEmbeddings
 from langchain.vectorstores import Chroma
@@ -66,7 +68,8 @@ The next thing is to send the question and the relevant parts of the docs to the
 ```python
 from langchain.chains import RetrievalQA
 qachain=RetrievalQA.from_chain_type(ollama, retriever=vectorstore.as_retriever())
-qachain.invoke({"query": question})
+res = qachain.invoke({"query": question})
+print(res['result'])
 ```
 
 The answer received from this chain was:

docs/tutorials/nvidia-jetson.md

@@ -1,38 +1,15 @@
 # Running Ollama on NVIDIA Jetson Devices
 
-With some minor configuration, Ollama runs well on [NVIDIA Jetson Devices](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/). The following has been tested on [JetPack 5.1.2](https://developer.nvidia.com/embedded/jetpack).
+Ollama runs well on [NVIDIA Jetson Devices](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/) and should run out of the box with the standard installation instructions. 
 
-NVIDIA Jetson devices are Linux-based embedded AI computers that are purpose-built for AI applications.
+The following has been tested on [JetPack 5.1.2](https://developer.nvidia.com/embedded/jetpack), but should also work on JetPack 6.0.
-
-Jetsons have an integrated GPU that is wired directly to the memory controller of the machine. For this reason, the `nvidia-smi` command is unrecognized, and Ollama proceeds to operate in "CPU only"
-mode. This can be verified by using a monitoring tool like jtop.
-
-In order to address this, we simply pass the path to the Jetson's pre-installed CUDA libraries into `ollama serve` (while in a tmux session). We then hardcode the num_gpu parameters into a cloned
-version of our target model.
-
-Prerequisites:
-
-- curl
-- tmux
-
-Here are the steps:
 
 - Install Ollama via standard Linux command (ignore the 404 error): `curl https://ollama.com/install.sh | sh`
-- Stop the Ollama service: `sudo systemctl stop ollama`
-- Start Ollama serve in a tmux session called ollama_jetson and reference the CUDA libraries path: `tmux has-session -t ollama_jetson 2>/dev/null || tmux new-session -d -s ollama_jetson 
-'LD_LIBRARY_PATH=/usr/local/cuda/lib64 ollama serve'`
 - Pull the model you want to use (e.g. mistral): `ollama pull mistral`
-- Create a new Modelfile specifically for enabling GPU support on the Jetson: `touch ModelfileMistralJetson`
+- Start an interactive session: `ollama run mistral`
-- In the ModelfileMistralJetson file, specify the FROM model and the num_gpu PARAMETER as shown below:
-
-```
-FROM mistral
-PARAMETER num_gpu 999
-```
-
-- Create a new model from your Modelfile: `ollama create mistral-jetson -f ./ModelfileMistralJetson`
-- Run the new model: `ollama run mistral-jetson`
-
-If you run a monitoring tool like jtop you should now see that Ollama is using the Jetson's integrated GPU.
 
 And that's it!
+
+# Running Ollama in Docker
+
+When running GPU accelerated applications in Docker, it is highly recommended to use [dusty-nv jetson-containers repo](https://github.com/dusty-nv/jetson-containers).

docs/windows.md

@@ -14,34 +14,50 @@ As this is a preview release, you should expect a few bugs here and there.  If
 you run into a problem you can reach out on
 [Discord](https://discord.gg/ollama), or file an
 [issue](https://github.com/ollama/ollama/issues).
-Logs will often be helpful in dianosing the problem (see
+Logs will often be helpful in diagnosing the problem (see
 [Troubleshooting](#troubleshooting) below)
 
 ## System Requirements
 
-* Windows 10 or newer, Home or Pro
+* Windows 10 22H2 or newer, Home or Pro
 * NVIDIA 452.39 or newer Drivers if you have an NVIDIA card
 * AMD Radeon Driver https://www.amd.com/en/support if you have a Radeon card
 
+Ollama uses unicode characters for progress indication, which may render as unknown squares in some older terminal fonts in Windows 10. If you see this, try changing your terminal font settings.
+
 ## API Access
 
 Here's a quick example showing API access from `powershell`
 ```powershell
-(Invoke-WebRequest -method POST -Body '{"model":"llama2", "prompt":"Why is the sky blue?", "stream": false}' -uri http://localhost:11434/api/generate ).Content | ConvertFrom-json
+(Invoke-WebRequest -method POST -Body '{"model":"llama3", "prompt":"Why is the sky blue?", "stream": false}' -uri http://localhost:11434/api/generate ).Content | ConvertFrom-json
 ```
 
 ## Troubleshooting
 
 While we're in preview, `OLLAMA_DEBUG` is always enabled, which adds
-a "view logs" menu item to the app, and increses logging for the GUI app and
+a "view logs" menu item to the app, and increases logging for the GUI app and
 server.
 
 Ollama on Windows stores files in a few different locations.  You can view them in
 the explorer window by hitting `<cmd>+R` and type in:
 - `explorer %LOCALAPPDATA%\Ollama` contains logs, and downloaded updates
-    - *app.log* contains logs from the GUI application
+    - *app.log* contains most resent logs from the GUI application
-    - *server.log* contains the server logs
+    - *server.log* contains the most recent server logs
     - *upgrade.log* contains log output for upgrades
 - `explorer %LOCALAPPDATA%\Programs\Ollama` contains the binaries (The installer adds this to your user PATH)
 - `explorer %HOMEPATH%\.ollama` contains models and configuration
 - `explorer %TEMP%` contains temporary executable files in one or more `ollama*` directories
+
+
+## Standalone CLI
+
+The easiest way to install Ollama on Windows is to use the `OllamaSetup.exe`
+installer. It installs in your account without requiring Administrator rights.
+We update Ollama regularly to support the latest models, and this installer will
+help you keep up to date.
+
+If you'd like to install or integrate Ollama as a service, a standalone
+`ollama-windows-amd64.zip` zip file is available containing only the Ollama CLI
+and GPU library dependencies for Nvidia and AMD. This allows for embedding
+Ollama in existing applications, or running it as a system service via `ollama
+serve` with tools such as [NSSM](https://nssm.cc/).

envconfig/config.go

@@ -0,0 +1,284 @@
+package envconfig
+
+import (
+	"fmt"
+	"log/slog"
+	"math"
+	"net"
+	"net/url"
+	"os"
+	"path/filepath"
+	"runtime"
+	"strconv"
+	"strings"
+	"time"
+)
+
+// Host returns the scheme and host. Host can be configured via the OLLAMA_HOST environment variable.
+// Default is scheme "http" and host "127.0.0.1:11434"
+func Host() *url.URL {
+	defaultPort := "11434"
+
+	s := strings.TrimSpace(Var("OLLAMA_HOST"))
+	scheme, hostport, ok := strings.Cut(s, "://")
+	switch {
+	case !ok:
+		scheme, hostport = "http", s
+	case scheme == "http":
+		defaultPort = "80"
+	case scheme == "https":
+		defaultPort = "443"
+	}
+
+	// trim trailing slashes
+	hostport = strings.TrimRight(hostport, "/")
+
+	host, port, err := net.SplitHostPort(hostport)
+	if err != nil {
+		host, port = "127.0.0.1", defaultPort
+		if ip := net.ParseIP(strings.Trim(hostport, "[]")); ip != nil {
+			host = ip.String()
+		} else if hostport != "" {
+			host = hostport
+		}
+	}
+
+	if n, err := strconv.ParseInt(port, 10, 32); err != nil || n > 65535 || n < 0 {
+		slog.Warn("invalid port, using default", "port", port, "default", defaultPort)
+		return &url.URL{
+			Scheme: scheme,
+			Host:   net.JoinHostPort(host, defaultPort),
+		}
+	}
+
+	return &url.URL{
+		Scheme: scheme,
+		Host:   net.JoinHostPort(host, port),
+	}
+}
+
+// Origins returns a list of allowed origins. Origins can be configured via the OLLAMA_ORIGINS environment variable.
+func Origins() (origins []string) {
+	if s := Var("OLLAMA_ORIGINS"); s != "" {
+		origins = strings.Split(s, ",")
+	}
+
+	for _, origin := range []string{"localhost", "127.0.0.1", "0.0.0.0"} {
+		origins = append(origins,
+			fmt.Sprintf("http://%s", origin),
+			fmt.Sprintf("https://%s", origin),
+			fmt.Sprintf("http://%s", net.JoinHostPort(origin, "*")),
+			fmt.Sprintf("https://%s", net.JoinHostPort(origin, "*")),
+		)
+	}
+
+	origins = append(origins,
+		"app://*",
+		"file://*",
+		"tauri://*",
+	)
+
+	return origins
+}
+
+// Models returns the path to the models directory. Models directory can be configured via the OLLAMA_MODELS environment variable.
+// Default is $HOME/.ollama/models
+func Models() string {
+	if s := Var("OLLAMA_MODELS"); s != "" {
+		return s
+	}
+
+	home, err := os.UserHomeDir()
+	if err != nil {
+		panic(err)
+	}
+
+	return filepath.Join(home, ".ollama", "models")
+}
+
+// KeepAlive returns the duration that models stay loaded in memory. KeepAlive can be configured via the OLLAMA_KEEP_ALIVE environment variable.
+// Negative values are treated as infinite. Zero is treated as no keep alive.
+// Default is 5 minutes.
+func KeepAlive() (keepAlive time.Duration) {
+	keepAlive = 5 * time.Minute
+	if s := Var("OLLAMA_KEEP_ALIVE"); s != "" {
+		if d, err := time.ParseDuration(s); err == nil {
+			keepAlive = d
+		} else if n, err := strconv.ParseInt(s, 10, 64); err == nil {
+			keepAlive = time.Duration(n) * time.Second
+		}
+	}
+
+	if keepAlive < 0 {
+		return time.Duration(math.MaxInt64)
+	}
+
+	return keepAlive
+}
+
+func Bool(k string) func() bool {
+	return func() bool {
+		if s := Var(k); s != "" {
+			b, err := strconv.ParseBool(s)
+			if err != nil {
+				return true
+			}
+
+			return b
+		}
+
+		return false
+	}
+}
+
+var (
+	// Debug enabled additional debug information.
+	Debug = Bool("OLLAMA_DEBUG")
+	// FlashAttention enables the experimental flash attention feature.
+	FlashAttention = Bool("OLLAMA_FLASH_ATTENTION")
+	// NoHistory disables readline history.
+	NoHistory = Bool("OLLAMA_NOHISTORY")
+	// NoPrune disables pruning of model blobs on startup.
+	NoPrune = Bool("OLLAMA_NOPRUNE")
+	// SchedSpread allows scheduling models across all GPUs.
+	SchedSpread = Bool("OLLAMA_SCHED_SPREAD")
+	// IntelGPU enables experimental Intel GPU detection.
+	IntelGPU = Bool("OLLAMA_INTEL_GPU")
+)
+
+func String(s string) func() string {
+	return func() string {
+		return Var(s)
+	}
+}
+
+var (
+	LLMLibrary = String("OLLAMA_LLM_LIBRARY")
+	TmpDir     = String("OLLAMA_TMPDIR")
+
+	CudaVisibleDevices    = String("CUDA_VISIBLE_DEVICES")
+	HipVisibleDevices     = String("HIP_VISIBLE_DEVICES")
+	RocrVisibleDevices    = String("ROCR_VISIBLE_DEVICES")
+	GpuDeviceOrdinal      = String("GPU_DEVICE_ORDINAL")
+	HsaOverrideGfxVersion = String("HSA_OVERRIDE_GFX_VERSION")
+)
+
+func RunnersDir() (p string) {
+	if p := Var("OLLAMA_RUNNERS_DIR"); p != "" {
+		return p
+	}
+
+	if runtime.GOOS != "windows" {
+		return
+	}
+
+	defer func() {
+		if p == "" {
+			slog.Error("unable to locate llm runner directory. Set OLLAMA_RUNNERS_DIR to the location of 'ollama/runners'")
+		}
+	}()
+
+	// On Windows we do not carry the payloads inside the main executable
+	exe, err := os.Executable()
+	if err != nil {
+		return
+	}
+
+	cwd, err := os.Getwd()
+	if err != nil {
+		return
+	}
+
+	var paths []string
+	for _, root := range []string{filepath.Dir(exe), filepath.Join(filepath.Dir(exe), ".."), cwd} {
+		paths = append(paths,
+			root,
+			filepath.Join(root, runtime.GOOS+"-"+runtime.GOARCH),
+			filepath.Join(root, "dist", runtime.GOOS+"-"+runtime.GOARCH),
+		)
+	}
+
+	// Try a few variations to improve developer experience when building from source in the local tree
+	for _, path := range paths {
+		candidate := filepath.Join(path, "lib", "ollama", "runners")
+		if _, err := os.Stat(candidate); err == nil {
+			p = candidate
+			break
+		}
+	}
+
+	return p
+}
+
+func Uint(key string, defaultValue uint) func() uint {
+	return func() uint {
+		if s := Var(key); s != "" {
+			if n, err := strconv.ParseUint(s, 10, 64); err != nil {
+				slog.Warn("invalid environment variable, using default", "key", key, "value", s, "default", defaultValue)
+			} else {
+				return uint(n)
+			}
+		}
+
+		return defaultValue
+	}
+}
+
+var (
+	// NumParallel sets the number of parallel model requests. NumParallel can be configured via the OLLAMA_NUM_PARALLEL environment variable.
+	NumParallel = Uint("OLLAMA_NUM_PARALLEL", 0)
+	// MaxRunners sets the maximum number of loaded models. MaxRunners can be configured via the OLLAMA_MAX_LOADED_MODELS environment variable.
+	MaxRunners = Uint("OLLAMA_MAX_LOADED_MODELS", 0)
+	// MaxQueue sets the maximum number of queued requests. MaxQueue can be configured via the OLLAMA_MAX_QUEUE environment variable.
+	MaxQueue = Uint("OLLAMA_MAX_QUEUE", 512)
+	// MaxVRAM sets a maximum VRAM override in bytes. MaxVRAM can be configured via the OLLAMA_MAX_VRAM environment variable.
+	MaxVRAM = Uint("OLLAMA_MAX_VRAM", 0)
+)
+
+type EnvVar struct {
+	Name        string
+	Value       any
+	Description string
+}
+
+func AsMap() map[string]EnvVar {
+	ret := map[string]EnvVar{
+		"OLLAMA_DEBUG":             {"OLLAMA_DEBUG", Debug(), "Show additional debug information (e.g. OLLAMA_DEBUG=1)"},
+		"OLLAMA_FLASH_ATTENTION":   {"OLLAMA_FLASH_ATTENTION", FlashAttention(), "Enabled flash attention"},
+		"OLLAMA_HOST":              {"OLLAMA_HOST", Host(), "IP Address for the ollama server (default 127.0.0.1:11434)"},
+		"OLLAMA_KEEP_ALIVE":        {"OLLAMA_KEEP_ALIVE", KeepAlive(), "The duration that models stay loaded in memory (default \"5m\")"},
+		"OLLAMA_LLM_LIBRARY":       {"OLLAMA_LLM_LIBRARY", LLMLibrary(), "Set LLM library to bypass autodetection"},
+		"OLLAMA_MAX_LOADED_MODELS": {"OLLAMA_MAX_LOADED_MODELS", MaxRunners(), "Maximum number of loaded models per GPU"},
+		"OLLAMA_MAX_QUEUE":         {"OLLAMA_MAX_QUEUE", MaxQueue(), "Maximum number of queued requests"},
+		"OLLAMA_MODELS":            {"OLLAMA_MODELS", Models(), "The path to the models directory"},
+		"OLLAMA_NOHISTORY":         {"OLLAMA_NOHISTORY", NoHistory(), "Do not preserve readline history"},
+		"OLLAMA_NOPRUNE":           {"OLLAMA_NOPRUNE", NoPrune(), "Do not prune model blobs on startup"},
+		"OLLAMA_NUM_PARALLEL":      {"OLLAMA_NUM_PARALLEL", NumParallel(), "Maximum number of parallel requests"},
+		"OLLAMA_ORIGINS":           {"OLLAMA_ORIGINS", Origins(), "A comma separated list of allowed origins"},
+		"OLLAMA_RUNNERS_DIR":       {"OLLAMA_RUNNERS_DIR", RunnersDir(), "Location for runners"},
+		"OLLAMA_SCHED_SPREAD":      {"OLLAMA_SCHED_SPREAD", SchedSpread(), "Always schedule model across all GPUs"},
+		"OLLAMA_TMPDIR":            {"OLLAMA_TMPDIR", TmpDir(), "Location for temporary files"},
+	}
+	if runtime.GOOS != "darwin" {
+		ret["CUDA_VISIBLE_DEVICES"] = EnvVar{"CUDA_VISIBLE_DEVICES", CudaVisibleDevices(), "Set which NVIDIA devices are visible"}
+		ret["HIP_VISIBLE_DEVICES"] = EnvVar{"HIP_VISIBLE_DEVICES", HipVisibleDevices(), "Set which AMD devices are visible"}
+		ret["ROCR_VISIBLE_DEVICES"] = EnvVar{"ROCR_VISIBLE_DEVICES", RocrVisibleDevices(), "Set which AMD devices are visible"}
+		ret["GPU_DEVICE_ORDINAL"] = EnvVar{"GPU_DEVICE_ORDINAL", GpuDeviceOrdinal(), "Set which AMD devices are visible"}
+		ret["HSA_OVERRIDE_GFX_VERSION"] = EnvVar{"HSA_OVERRIDE_GFX_VERSION", HsaOverrideGfxVersion(), "Override the gfx used for all detected AMD GPUs"}
+		ret["OLLAMA_INTEL_GPU"] = EnvVar{"OLLAMA_INTEL_GPU", IntelGPU(), "Enable experimental Intel GPU detection"}
+	}
+	return ret
+}
+
+func Values() map[string]string {
+	vals := make(map[string]string)
+	for k, v := range AsMap() {
+		vals[k] = fmt.Sprintf("%v", v.Value)
+	}
+	return vals
+}
+
+// Var returns an environment variable stripped of leading and trailing quotes or spaces
+func Var(key string) string {
+	return strings.Trim(strings.TrimSpace(os.Getenv(key)), "\"'")
+}

envconfig/config_test.go

@@ -0,0 +1,235 @@
+package envconfig
+
+import (
+	"math"
+	"testing"
+	"time"
+
+	"github.com/google/go-cmp/cmp"
+)
+
+func TestHost(t *testing.T) {
+	cases := map[string]struct {
+		value  string
+		expect string
+	}{
+		"empty":               {"", "127.0.0.1:11434"},
+		"only address":        {"1.2.3.4", "1.2.3.4:11434"},
+		"only port":           {":1234", ":1234"},
+		"address and port":    {"1.2.3.4:1234", "1.2.3.4:1234"},
+		"hostname":            {"example.com", "example.com:11434"},
+		"hostname and port":   {"example.com:1234", "example.com:1234"},
+		"zero port":           {":0", ":0"},
+		"too large port":      {":66000", ":11434"},
+		"too small port":      {":-1", ":11434"},
+		"ipv6 localhost":      {"[::1]", "[::1]:11434"},
+		"ipv6 world open":     {"[::]", "[::]:11434"},
+		"ipv6 no brackets":    {"::1", "[::1]:11434"},
+		"ipv6 + port":         {"[::1]:1337", "[::1]:1337"},
+		"extra space":         {" 1.2.3.4 ", "1.2.3.4:11434"},
+		"extra quotes":        {"\"1.2.3.4\"", "1.2.3.4:11434"},
+		"extra space+quotes":  {" \" 1.2.3.4 \" ", "1.2.3.4:11434"},
+		"extra single quotes": {"'1.2.3.4'", "1.2.3.4:11434"},
+		"http":                {"http://1.2.3.4", "1.2.3.4:80"},
+		"http port":           {"http://1.2.3.4:4321", "1.2.3.4:4321"},
+		"https":               {"https://1.2.3.4", "1.2.3.4:443"},
+		"https port":          {"https://1.2.3.4:4321", "1.2.3.4:4321"},
+	}
+
+	for name, tt := range cases {
+		t.Run(name, func(t *testing.T) {
+			t.Setenv("OLLAMA_HOST", tt.value)
+			if host := Host(); host.Host != tt.expect {
+				t.Errorf("%s: expected %s, got %s", name, tt.expect, host.Host)
+			}
+		})
+	}
+}
+
+func TestOrigins(t *testing.T) {
+	cases := []struct {
+		value  string
+		expect []string
+	}{
+		{"", []string{
+			"http://localhost",
+			"https://localhost",
+			"http://localhost:*",
+			"https://localhost:*",
+			"http://127.0.0.1",
+			"https://127.0.0.1",
+			"http://127.0.0.1:*",
+			"https://127.0.0.1:*",
+			"http://0.0.0.0",
+			"https://0.0.0.0",
+			"http://0.0.0.0:*",
+			"https://0.0.0.0:*",
+			"app://*",
+			"file://*",
+			"tauri://*",
+		}},
+		{"http://10.0.0.1", []string{
+			"http://10.0.0.1",
+			"http://localhost",
+			"https://localhost",
+			"http://localhost:*",
+			"https://localhost:*",
+			"http://127.0.0.1",
+			"https://127.0.0.1",
+			"http://127.0.0.1:*",
+			"https://127.0.0.1:*",
+			"http://0.0.0.0",
+			"https://0.0.0.0",
+			"http://0.0.0.0:*",
+			"https://0.0.0.0:*",
+			"app://*",
+			"file://*",
+			"tauri://*",
+		}},
+		{"http://172.16.0.1,https://192.168.0.1", []string{
+			"http://172.16.0.1",
+			"https://192.168.0.1",
+			"http://localhost",
+			"https://localhost",
+			"http://localhost:*",
+			"https://localhost:*",
+			"http://127.0.0.1",
+			"https://127.0.0.1",
+			"http://127.0.0.1:*",
+			"https://127.0.0.1:*",
+			"http://0.0.0.0",
+			"https://0.0.0.0",
+			"http://0.0.0.0:*",
+			"https://0.0.0.0:*",
+			"app://*",
+			"file://*",
+			"tauri://*",
+		}},
+		{"http://totally.safe,http://definitely.legit", []string{
+			"http://totally.safe",
+			"http://definitely.legit",
+			"http://localhost",
+			"https://localhost",
+			"http://localhost:*",
+			"https://localhost:*",
+			"http://127.0.0.1",
+			"https://127.0.0.1",
+			"http://127.0.0.1:*",
+			"https://127.0.0.1:*",
+			"http://0.0.0.0",
+			"https://0.0.0.0",
+			"http://0.0.0.0:*",
+			"https://0.0.0.0:*",
+			"app://*",
+			"file://*",
+			"tauri://*",
+		}},
+	}
+	for _, tt := range cases {
+		t.Run(tt.value, func(t *testing.T) {
+			t.Setenv("OLLAMA_ORIGINS", tt.value)
+
+			if diff := cmp.Diff(Origins(), tt.expect); diff != "" {
+				t.Errorf("%s: mismatch (-want +got):\n%s", tt.value, diff)
+			}
+		})
+	}
+}
+
+func TestBool(t *testing.T) {
+	cases := map[string]bool{
+		"":      false,
+		"true":  true,
+		"false": false,
+		"1":     true,
+		"0":     false,
+		// invalid values
+		"random":    true,
+		"something": true,
+	}
+
+	for k, v := range cases {
+		t.Run(k, func(t *testing.T) {
+			t.Setenv("OLLAMA_BOOL", k)
+			if b := Bool("OLLAMA_BOOL")(); b != v {
+				t.Errorf("%s: expected %t, got %t", k, v, b)
+			}
+		})
+	}
+}
+
+func TestUint(t *testing.T) {
+	cases := map[string]uint{
+		"0":    0,
+		"1":    1,
+		"1337": 1337,
+		// default values
+		"":       11434,
+		"-1":     11434,
+		"0o10":   11434,
+		"0x10":   11434,
+		"string": 11434,
+	}
+
+	for k, v := range cases {
+		t.Run(k, func(t *testing.T) {
+			t.Setenv("OLLAMA_UINT", k)
+			if i := Uint("OLLAMA_UINT", 11434)(); i != v {
+				t.Errorf("%s: expected %d, got %d", k, v, i)
+			}
+		})
+	}
+}
+
+func TestKeepAlive(t *testing.T) {
+	cases := map[string]time.Duration{
+		"":       5 * time.Minute,
+		"1s":     time.Second,
+		"1m":     time.Minute,
+		"1h":     time.Hour,
+		"5m0s":   5 * time.Minute,
+		"1h2m3s": 1*time.Hour + 2*time.Minute + 3*time.Second,
+		"0":      time.Duration(0),
+		"60":     60 * time.Second,
+		"120":    2 * time.Minute,
+		"3600":   time.Hour,
+		"-0":     time.Duration(0),
+		"-1":     time.Duration(math.MaxInt64),
+		"-1m":    time.Duration(math.MaxInt64),
+		// invalid values
+		" ":   5 * time.Minute,
+		"???": 5 * time.Minute,
+		"1d":  5 * time.Minute,
+		"1y":  5 * time.Minute,
+		"1w":  5 * time.Minute,
+	}
+
+	for tt, expect := range cases {
+		t.Run(tt, func(t *testing.T) {
+			t.Setenv("OLLAMA_KEEP_ALIVE", tt)
+			if actual := KeepAlive(); actual != expect {
+				t.Errorf("%s: expected %s, got %s", tt, expect, actual)
+			}
+		})
+	}
+}
+
+func TestVar(t *testing.T) {
+	cases := map[string]string{
+		"value":       "value",
+		" value ":     "value",
+		" 'value' ":   "value",
+		` "value" `:   "value",
+		" ' value ' ": " value ",
+		` " value " `: " value ",
+	}
+
+	for k, v := range cases {
+		t.Run(k, func(t *testing.T) {
+			t.Setenv("OLLAMA_VAR", k)
+			if s := Var("OLLAMA_VAR"); s != v {
+				t.Errorf("%s: expected %q, got %q", k, v, s)
+			}
+		})
+	}
+}

examples/bash-comparemodels/README.md

@@ -1,10 +0,0 @@
-# Bash Shell examples
-
-When calling `ollama`, you can pass it a file to run all the prompts in the file, one after the other:
-
-`ollama run llama2 < sourcequestions.txt`
-
-This concept is used in the following example.
-
-## Compare Models
-`comparemodels.sh` is a script that runs all the questions in `sourcequestions.txt` using any 4 models you choose that you have already pulled from the Ollama library or have created locally.

examples/bash-comparemodels/comparemodels.sh

@@ -1,64 +0,0 @@
-#! /usr/bin/env bash
-# Compare multiple models by running them with the same questions
-
-NUMBEROFCHOICES=4
-SELECTIONS=()
-declare -a SUMS=()
-
-# Get the list of models
-CHOICES=$(ollama list | awk '{print $1}')
-
-# Select which models to run as a comparison
-echo "Select $NUMBEROFCHOICES models to compare:"
-select ITEM in $CHOICES; do
-    if [[ -n $ITEM ]]; then
-        echo "You have selected $ITEM"
-        SELECTIONS+=("$ITEM")
-        ((COUNT++))
-        if [[ $COUNT -eq $NUMBEROFCHOICES ]]; then
-            break
-        fi
-    else
-        echo "Invalid selection"
-    fi
-done
-
-# Loop through each of the selected models
-for ITEM in "${SELECTIONS[@]}"; do
-    echo "--------------------------------------------------------------"
-    echo "Loading the model $ITEM into memory"
-    ollama run "$ITEM" ""
-    echo "--------------------------------------------------------------"
-    echo "Running the questions through the model $ITEM"
-    COMMAND_OUTPUT=$(ollama run "$ITEM" --verbose < sourcequestions.txt 2>&1| tee /dev/stderr)
-
-    # eval duration is sometimes listed in seconds and sometimes in milliseconds. 
-    # Add up the values for each model
-    SUM=$(echo "$COMMAND_OUTPUT" | awk '
-    /eval duration:/ {
-        value = $3
-        if (index(value, "ms") > 0) {
-            gsub("ms", "", value)
-            value /= 1000
-        } else {
-            gsub("s", "", value)
-        }
-        sum += value
-    }
-    END { print sum }')
-
-
-    SUMS+=("All questions for $ITEM completed in $SUM seconds")
-done
-
-echo ""
-echo "--------------------------------------------------------------"
-echo -e "Sums of eval durations for each run:"
-for val in "${SUMS[@]}"; do
-    echo "$val"
-done
-
-echo "--------------------------------------------------------------"
-echo "Comparison complete. Now you can decide"
-echo "which model is best."
-echo "--------------------------------------------------------------"

examples/bash-comparemodels/sourcequestions.txt

@@ -1,7 +0,0 @@
-Why is the sky blue
-What is a black hole
-Explain the big bang theory like I am 5?
-What is the quickest way to win a game of Monopoly with 3 others?
-Why does a vacuum bottle keep my coffee hot and my milkshake cold?
-What is the difference between a meteor, a meteorite, and a meteoroid?
-Create an array with 5 items and print to the console. Do this in Python, C#, Typescript, and Rust.

examples/flyio/.gitignore

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

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`.

examples/go-chat/main.go

@@ -0,0 +1,51 @@
+package main
+
+import (
+	"context"
+	"fmt"
+	"log"
+
+	"github.com/ollama/ollama/api"
+)
+
+func main() {
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	messages := []api.Message{
+		api.Message{
+			Role:    "system",
+			Content: "Provide very brief, concise responses",
+		},
+		api.Message{
+			Role:    "user",
+			Content: "Name some unusual animals",
+		},
+		api.Message{
+			Role:    "assistant",
+			Content: "Monotreme, platypus, echidna",
+		},
+		api.Message{
+			Role:    "user",
+			Content: "which of these is the most dangerous?",
+		},
+	}
+
+	ctx := context.Background()
+	req := &api.ChatRequest{
+		Model:    "llama3.1",
+		Messages: messages,
+	}
+
+	respFunc := func(resp api.ChatResponse) error {
+		fmt.Print(resp.Message.Content)
+		return nil
+	}
+
+	err = client.Chat(ctx, req, respFunc)
+	if err != nil {
+		log.Fatal(err)
+	}
+}