revert other pr change

.gitattributes

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

.github/workflows/release.yaml

@@ -187,13 +187,6 @@ 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:
@@ -227,11 +220,11 @@ jobs:
         with:
           go-version-file: go.mod
           cache: true
-      - name: 'Install CUDA ${{ matrix.cuda.version }}'
+      - name: 'Install CUDA'
         run: |
           $ErrorActionPreference = "Stop"
           write-host "downloading CUDA Installer"
-          Invoke-WebRequest -Uri "${{ matrix.cuda.url }}" -OutFile "${env:RUNNER_TEMP}\cuda-install.exe"
+          Invoke-WebRequest -Uri "https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda_11.3.1_465.89_win10.exe" -OutFile "${env:RUNNER_TEMP}\cuda-install.exe"
           write-host "Installing CUDA"
           Start-Process "${env:RUNNER_TEMP}\cuda-install.exe" -ArgumentList '-s' -NoNewWindow -Wait
           write-host "Completed CUDA"
@@ -263,16 +256,15 @@ jobs:
           cp "${NVIDIA_DIR}\cublasLt64_*.dll" "dist\deps\"
       - uses: actions/upload-artifact@v4
         with:
-          name: generate-windows-cuda-${{ matrix.cuda.version }}
+          name: generate-windows-cuda
           path: |
             llm/build/**/bin/*
             dist/windows-amd64/**
       - uses: actions/upload-artifact@v4
         with:
-          name: windows-cuda-deps-${{ matrix.cuda.version }}
+          name: windows-cuda-deps
           path: dist/deps/*
 
-
   # Import the prior generation steps and build the final windows assets
   build-windows:
     environment: release
@@ -322,16 +314,10 @@ jobs:
           name: generate-windows-cpu
       - uses: actions/download-artifact@v4
         with:
-          name: generate-windows-cuda-11
+          name: generate-windows-cuda
       - uses: actions/download-artifact@v4
         with:
-          name: generate-windows-cuda-12
-      - uses: actions/download-artifact@v4
-        with:
-          name: windows-cuda-deps-11
-      - uses: actions/download-artifact@v4
-        with:
-          name: windows-cuda-deps-12
+          name: windows-cuda-deps
       - uses: actions/download-artifact@v4
         with:
           name: windows-rocm-deps
@@ -377,6 +363,7 @@ jobs:
       - run: |
           ./scripts/build_linux.sh
           ./scripts/build_docker.sh
+          mv dist/deps/* dist/
       - uses: actions/upload-artifact@v4
         with:
           name: dist-linux-amd64
@@ -472,10 +459,7 @@ jobs:
           merge-multiple: true
       - run: |
           ls -lh dist/
-          (cd dist; find . -type f | xargs sha256sum > ../sha256sum.txt)
-          mv sha256sum.txt dist/
-          mv dist/linux-???64 .
-          mv dist/linux-amd64-rocm .
+          (cd dist; sha256sum * > sha256sum.txt)
           cat dist/sha256sum.txt
       - name: Create or update Release
         run: |

.github/workflows/test.yaml

@@ -273,7 +273,7 @@ jobs:
         if: ${{ startsWith(matrix.os, 'macos-') }}
       - uses: golangci/golangci-lint-action@v6
         with:
-          args: --timeout 8m0s -v
+          args: --timeout 8m0s -v ${{ startsWith(matrix.os, 'windows-') && '' || '--disable gofmt --disable goimports' }}
   test:
     strategy:
       matrix:

.golangci.yaml

@@ -7,35 +7,22 @@ linters:
     - bodyclose
     - containedctx
     - contextcheck
-    - errcheck
     - exportloopref
-    - gci
     - gocheckcompilerdirectives
-    - gofmt
-    - gofumpt
-    - gosimple
-    - govet
-    - ineffassign
+    # conditionally enable this on linux/macos
+    # - gofmt
+    # - goimports
     - intrange
-    - makezero
     - misspell
     - nilerr
     - nolintlint
     - nosprintfhostport
-    - staticcheck
-    - tenv
+    - testifylint
     - unconvert
     - unused
-    - usestdlibvars
     - wastedassign
     - whitespace
-linters-settings:
-  gci:
-    sections: [standard, default, localmodule]
-  staticcheck:
-    checks:
-      - all
-      - -SA1019 # omit Deprecated check
+    - usestdlibvars
 severity:
   default-severity: error
   rules:

CONTRIBUTING.md

@@ -1,37 +0,0 @@
-# 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 correct 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

@@ -1,9 +1,7 @@
-ARG GOLANG_VERSION=1.22.5
+ARG GOLANG_VERSION=1.22.1
 ARG CMAKE_VERSION=3.22.1
-ARG CUDA_VERSION_11=11.3.1
-ARG CUDA_V11_ARCHITECTURES="50;52;53;60;61;62;70;72;75;80;86"
-ARG CUDA_VERSION_12=12.4.0
-ARG CUDA_V12_ARCHITECTURES="60;61;62;70;72;75;80;86;87;89;90;90a"
+# this CUDA_VERSION corresponds with the one specified in docs/gpu.md
+ARG CUDA_VERSION=11.3.1
 ARG ROCM_VERSION=6.1.2
 
 # Copy the minimal context we need to run the generate scripts
@@ -12,7 +10,7 @@ COPY .git .git
 COPY .gitmodules .gitmodules
 COPY llm llm
 
-FROM --platform=linux/amd64 nvidia/cuda:$CUDA_VERSION_11-devel-centos7 AS cuda-11-build-amd64
+FROM --platform=linux/amd64 nvidia/cuda:$CUDA_VERSION-devel-centos7 AS cuda-build-amd64
 ARG CMAKE_VERSION
 COPY ./scripts/rh_linux_deps.sh /
 RUN CMAKE_VERSION=${CMAKE_VERSION} sh /rh_linux_deps.sh
@@ -20,34 +18,9 @@ ENV PATH /opt/rh/devtoolset-10/root/usr/bin:$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_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
+RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_SKIP_CPU_GENERATE=1 sh gen_linux.sh
 
-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:$CUDA_VERSION_11-devel-rockylinux8 AS cuda-11-build-server-arm64
+FROM --platform=linux/arm64 nvidia/cuda:$CUDA_VERSION-devel-rockylinux8 AS cuda-build-arm64
 ARG CMAKE_VERSION
 COPY ./scripts/rh_linux_deps.sh /
 RUN CMAKE_VERSION=${CMAKE_VERSION} sh /rh_linux_deps.sh
@@ -55,32 +28,7 @@ ENV PATH /opt/rh/gcc-toolset-10/root/usr/bin:$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_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
-
+RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_SKIP_CPU_GENERATE=1 sh gen_linux.sh
 
 FROM --platform=linux/amd64 rocm/dev-centos-7:${ROCM_VERSION}-complete AS rocm-build-amd64
 ARG CMAKE_VERSION
@@ -92,11 +40,15 @@ 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
-ENV GOARCH amd64
-RUN --mount=type=cache,target=/root/.ccache \
-    OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_SKIP_CPU_GENERATE=1 bash gen_linux.sh
-RUN mkdir -p ../../dist/linux-amd64-rocm/lib/ollama && \
-    (cd /opt/rocm/lib && tar cf - rocblas/library) | (cd ../../dist/linux-amd64-rocm/lib/ollama && tar xf - )
+RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_SKIP_CPU_GENERATE=1 sh gen_linux.sh
+RUN mkdir /tmp/scratch && \
+    for dep in $(zcat /go/src/github.com/ollama/ollama/llm/build/linux/x86_64/rocm*/bin/deps.txt.gz) ; do \
+        cp ${dep} /tmp/scratch/ || exit 1 ; \
+    done && \
+    (cd /opt/rocm/lib && tar cf - rocblas/library) | (cd /tmp/scratch/ && tar xf - ) && \
+    mkdir -p /go/src/github.com/ollama/ollama/dist/deps/ && \
+    (cd /tmp/scratch/ && tar czvf /go/src/github.com/ollama/ollama/dist/deps/ollama-linux-amd64-rocm.tgz . )
+
 
 FROM --platform=linux/amd64 centos:7 AS cpu-builder-amd64
 ARG CMAKE_VERSION
@@ -107,21 +59,16 @@ 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 --mount=type=cache,target=/root/.ccache \
-    OLLAMA_CPU_TARGET="static" bash gen_linux.sh
+RUN OLLAMA_CPU_TARGET="static" sh gen_linux.sh
 FROM --platform=linux/amd64 cpu-builder-amd64 AS cpu-build-amd64
-RUN --mount=type=cache,target=/root/.ccache \
-    OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu" bash gen_linux.sh
+RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu" sh gen_linux.sh
 FROM --platform=linux/amd64 cpu-builder-amd64 AS cpu_avx-build-amd64
-RUN --mount=type=cache,target=/root/.ccache \
-    OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu_avx" bash gen_linux.sh
+RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu_avx" sh gen_linux.sh
 FROM --platform=linux/amd64 cpu-builder-amd64 AS cpu_avx2-build-amd64
-RUN --mount=type=cache,target=/root/.ccache \
-    OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu_avx2" bash gen_linux.sh
+RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu_avx2" sh gen_linux.sh
 
 FROM --platform=linux/arm64 rockylinux:8 AS cpu-builder-arm64
 ARG CMAKE_VERSION
@@ -132,15 +79,12 @@ 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 --mount=type=cache,target=/root/.ccache \
-    OLLAMA_CPU_TARGET="static" bash gen_linux.sh
+RUN OLLAMA_CPU_TARGET="static" sh gen_linux.sh
 FROM --platform=linux/arm64 cpu-builder-arm64 AS cpu-build-arm64
-RUN --mount=type=cache,target=/root/.ccache \
-    OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu" bash gen_linux.sh
+RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu" sh gen_linux.sh
 
 
 # Intermediate stage used for ./scripts/build_linux.sh
@@ -151,16 +95,12 @@ 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-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=cuda-build-amd64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
 COPY --from=rocm-build-amd64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
+COPY --from=rocm-build-amd64 /go/src/github.com/ollama/ollama/dist/deps/ ./dist/deps/
 ARG GOFLAGS
 ARG CGO_CFLAGS
-RUN --mount=type=cache,target=/root/.ccache \
-    go build -trimpath -o dist/linux-amd64/bin/ollama .
+RUN go build -trimpath .
 
 # Intermediate stage used for ./scripts/build_linux.sh
 FROM --platform=linux/arm64 cpu-build-arm64 AS build-arm64
@@ -169,38 +109,23 @@ 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-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/
+COPY --from=cuda-build-arm64 /go/src/github.com/ollama/ollama/llm/build/linux/ llm/build/linux/
 ARG GOFLAGS
 ARG CGO_CFLAGS
-RUN --mount=type=cache,target=/root/.ccache \
-    go build -trimpath -o dist/linux-arm64/bin/ollama .
-
-# 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*
+RUN go build -trimpath .
 
 # 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 && \
-    apt-get clean && rm -rf /var/lib/apt/lists/*
-COPY --from=build-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/bin/ /bin/
-
+RUN apt-get update && apt-get install -y ca-certificates
+COPY --from=build-amd64 /go/src/github.com/ollama/ollama/ollama /bin/ollama
 FROM --platform=linux/arm64 ubuntu:22.04 as runtime-arm64
-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 && \
-    apt-get clean && rm -rf /var/lib/apt/lists/*
-COPY --from=build-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/bin/ /bin/
+RUN apt-get update && apt-get install -y ca-certificates
+COPY --from=build-arm64 /go/src/github.com/ollama/ollama/ollama /bin/ollama
 
 # Radeon images are much larger so we keep it distinct from the CPU/CUDA image
 FROM --platform=linux/amd64 rocm/dev-centos-7:${ROCM_VERSION}-complete as runtime-rocm
 RUN update-pciids
-COPY --from=build-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/bin/ /bin/
-RUN ln -s /opt/rocm/lib /lib/ollama
+COPY --from=build-amd64 /go/src/github.com/ollama/ollama/ollama /bin/ollama
 EXPOSE 11434
 ENV OLLAMA_HOST 0.0.0.0
 

README.md

@@ -35,10 +35,10 @@ The official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama) `olla
 
 ## Quickstart
 
-To run and chat with [Llama 3.1](https://ollama.com/library/llama3.1):
+To run and chat with [Llama 3](https://ollama.com/library/llama3):
 
 ```
-ollama run llama3.1
+ollama run llama3
 ```
 
 ## Model library
@@ -49,12 +49,10 @@ Here are some example models that can be downloaded:
 
 | Model              | Parameters | Size  | Download                       |
 | ------------------ | ---------- | ----- | ------------------------------ |
-| Llama 3.1          | 8B         | 4.7GB | `ollama run llama3.1`          |
-| Llama 3.1          | 70B        | 40GB  | `ollama run llama3.1:70b`      |
-| Llama 3.1          | 405B       | 231GB | `ollama run llama3.1:405b`     |
+| Llama 3            | 8B         | 4.7GB | `ollama run llama3`            |
+| Llama 3            | 70B        | 40GB  | `ollama run llama3:70b`        |
 | 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`           |
@@ -66,8 +64,7 @@ Here are some example models that can be downloaded:
 | LLaVA              | 7B         | 4.5GB | `ollama run llava`             |
 | Solar              | 10.7B      | 6.1GB | `ollama run solar`             |
 
-> [!NOTE]
-> You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
+> 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
 
@@ -99,16 +96,16 @@ See the [guide](docs/import.md) on importing models for more information.
 
 ### Customize a prompt
 
-Models from the Ollama library can be customized with a prompt. For example, to customize the `llama3.1` model:
+Models from the Ollama library can be customized with a prompt. For example, to customize the `llama3` model:
 
 ```
-ollama pull llama3.1
+ollama pull llama3
 ```
 
 Create a `Modelfile`:
 
 ```
-FROM llama3.1
+FROM llama3
 
 # set the temperature to 1 [higher is more creative, lower is more coherent]
 PARAMETER temperature 1
@@ -143,7 +140,7 @@ ollama create mymodel -f ./Modelfile
 ### Pull a model
 
 ```
-ollama pull llama3.1
+ollama pull llama3
 ```
 
 > This command can also be used to update a local model. Only the diff will be pulled.
@@ -151,13 +148,13 @@ ollama pull llama3.1
 ### Remove a model
 
 ```
-ollama rm llama3.1
+ollama rm llama3
 ```
 
 ### Copy a model
 
 ```
-ollama cp llama3.1 my-model
+ollama cp llama3 my-model
 ```
 
 ### Multiline input
@@ -174,21 +171,21 @@ I'm a basic program that prints the famous "Hello, world!" message to the consol
 ### Multimodal models
 
 ```
-ollama run llava "What's in this image? /Users/jmorgan/Desktop/smile.png"
+>>> 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 the prompt as an argument
 
 ```
-$ ollama run llama3.1 "Summarize this file: $(cat README.md)"
+$ ollama run llama3 "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
+ollama show llama3
 ```
 
 ### List models on your computer
@@ -216,7 +213,7 @@ Next, start the server:
 Finally, in a separate shell, run a model:
 
 ```
-./ollama run llama3.1
+./ollama run llama3
 ```
 
 ## REST API
@@ -227,7 +224,7 @@ Ollama has a REST API for running and managing models.
 
 ```
 curl http://localhost:11434/api/generate -d '{
-  "model": "llama3.1",
+  "model": "llama3",
   "prompt":"Why is the sky blue?"
 }'
 ```
@@ -236,7 +233,7 @@ curl http://localhost:11434/api/generate -d '{
 
 ```
 curl http://localhost:11434/api/chat -d '{
-  "model": "llama3.1",
+  "model": "llama3",
   "messages": [
     { "role": "user", "content": "why is the sky blue?" }
   ]
@@ -295,23 +292,10 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Olpaka](https://github.com/Otacon/olpaka) (User-friendly Flutter Web App for Ollama)
 - [OllamaSpring](https://github.com/CrazyNeil/OllamaSpring) (Ollama Client for macOS)
 - [LLocal.in](https://github.com/kartikm7/llocal) (Easy to use Electron Desktop Client for Ollama)
-- [AiLama](https://github.com/zeyoyt/ailama) (A Discord User App that allows you to interact with Ollama anywhere in discord )
 - [Ollama with Google Mesop](https://github.com/rapidarchitect/ollama_mesop/) (Mesop Chat Client implementation with Ollama)
-- [Painting Droid](https://github.com/mateuszmigas/painting-droid) (Painting app with AI integrations)
 - [Kerlig AI](https://www.kerlig.com/) (AI writing assistant for macOS)
 - [AI Studio](https://github.com/MindWorkAI/AI-Studio)
 - [Sidellama](https://github.com/gyopak/sidellama) (browser-based LLM client)
-- [LLMStack](https://github.com/trypromptly/LLMStack) (No-code multi-agent framework to build LLM agents and workflows)
-- [BoltAI for Mac](https://boltai.com) (AI Chat Client for Mac)
-- [Harbor](https://github.com/av/harbor) (Containerized LLM Toolkit with Ollama as default backend)
-- [Go-CREW](https://www.jonathanhecl.com/go-crew/) (Powerful Offline RAG in Golang)
-- [PartCAD](https://github.com/openvmp/partcad/) (CAD model generation with OpenSCAD and CadQuery)
-- [Ollama4j Web UI](https://github.com/ollama4j/ollama4j-web-ui) - Java-based Web UI for Ollama built with Vaadin, Spring Boot and Ollama4j
-- [PyOllaMx](https://github.com/kspviswa/pyOllaMx) - macOS application capable of chatting with both Ollama and Apple MLX models.
-- [Claude Dev](https://github.com/saoudrizwan/claude-dev) - VSCode extension for multi-file/whole-repo coding
-- [Cherry Studio](https://github.com/kangfenmao/cherry-studio) (Desktop client with Ollama support)
-- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
-- [Archyve](https://github.com/nickthecook/archyve) (RAG-enabling document library)
 
 ### Terminal
 
@@ -335,10 +319,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [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/)
-
-### Apple Vision Pro
-- [Enchanted](https://github.com/AugustDev/enchanted)
 
 ### Database
 
@@ -348,27 +328,22 @@ See the [API documentation](./docs/api.md) for all endpoints.
 ### Package managers
 
 - [Pacman](https://archlinux.org/packages/extra/x86_64/ollama/)
-- [Gentoo](https://github.com/gentoo/guru/tree/master/app-misc/ollama)
 - [Helm Chart](https://artifacthub.io/packages/helm/ollama-helm/ollama)
 - [Guix channel](https://codeberg.org/tusharhero/ollama-guix)
-- [Nix package](https://search.nixos.org/packages?channel=24.05&show=ollama&from=0&size=50&sort=relevance&type=packages&query=ollama)
-- [Flox](https://flox.dev/blog/ollama-part-one)
 
 ### Libraries
 
 - [LangChain](https://python.langchain.com/docs/integrations/llms/ollama) and [LangChain.js](https://js.langchain.com/docs/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)
-- [OllamaFarm for Go](https://github.com/presbrey/ollamafarm)
 - [OllamaSharp for .NET](https://github.com/awaescher/OllamaSharp)
 - [Ollama for Ruby](https://github.com/gbaptista/ollama-ai)
 - [Ollama-rs for Rust](https://github.com/pepperoni21/ollama-rs)
 - [Ollama-hpp for C++](https://github.com/jmont-dev/ollama-hpp)
-- [Ollama4j for Java](https://github.com/ollama4j/ollama4j)
+- [Ollama4j for Java](https://github.com/amithkoujalgi/ollama4j)
 - [ModelFusion Typescript Library](https://modelfusion.dev/integration/model-provider/ollama)
 - [OllamaKit for Swift](https://github.com/kevinhermawan/OllamaKit)
 - [Ollama for Dart](https://github.com/breitburg/dart-ollama)
@@ -385,16 +360,11 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Portkey](https://portkey.ai/docs/welcome/integration-guides/ollama)
 - [PromptingTools.jl](https://github.com/svilupp/PromptingTools.jl) with an [example](https://svilupp.github.io/PromptingTools.jl/dev/examples/working_with_ollama)
 - [LlamaScript](https://github.com/Project-Llama/llamascript)
-- [Gollm](https://docs.gollm.co/examples/ollama-example)
-- [Ollamaclient for Golang](https://github.com/xyproto/ollamaclient)
-- [High-level function abstraction in Go](https://gitlab.com/tozd/go/fun)
-- [Ollama PHP](https://github.com/ArdaGnsrn/ollama-php)
 
 ### Mobile
 
 - [Enchanted](https://github.com/AugustDev/enchanted)
 - [Maid](https://github.com/Mobile-Artificial-Intelligence/maid)
-- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
 
 ### Extensions & Plugins
 
@@ -417,16 +387,13 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [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 Hugging Face)
+- [Wingman-AI](https://github.com/RussellCanfield/wingman-ai) (Copilot code and chat alternative using Ollama and HuggingFace)
 - [Page Assist](https://github.com/n4ze3m/page-assist) (Chrome Extension)
-- [Plasmoid Ollama Control](https://github.com/imoize/plasmoid-ollamacontrol) (KDE Plasma extension that allows you to quickly manage/control Ollama model)
 - [AI Telegram Bot](https://github.com/tusharhero/aitelegrambot) (Telegram bot using Ollama in backend)
 - [AI ST Completion](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (Sublime Text 4 AI assistant plugin with Ollama support)
 - [Discord-Ollama Chat Bot](https://github.com/kevinthedang/discord-ollama) (Generalized TypeScript Discord Bot w/ Tuning Documentation)
 - [Discord AI chat/moderation bot](https://github.com/rapmd73/Companion) Chat/moderation bot written in python. Uses Ollama to create personalities.
 - [Headless Ollama](https://github.com/nischalj10/headless-ollama) (Scripts to automatically install ollama client & models on any OS for apps that depends on ollama server)
-- [vnc-lm](https://github.com/jk011ru/vnc-lm) (A containerized Discord bot with support for attachments and web links)
-- [LSP-AI](https://github.com/SilasMarvin/lsp-ai) (Open-source language server for AI-powered functionality)
 
 ### Supported backends
 

SECURITY.md

@@ -1,25 +0,0 @@
-# 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

@@ -17,14 +17,18 @@ import (
 	"bufio"
 	"bytes"
 	"context"
+	"encoding/base64"
 	"encoding/json"
-	"errors"
 	"fmt"
 	"io"
+	"net"
 	"net/http"
 	"net/url"
 	"runtime"
+	"strings"
+	"time"
 
+	"github.com/ollama/ollama/auth"
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
 	"github.com/ollama/ollama/version"
@@ -63,8 +67,13 @@ func checkError(resp *http.Response, body []byte) error {
 // If the variable is not specified, a default ollama host and port will be
 // used.
 func ClientFromEnvironment() (*Client, error) {
+	ollamaHost := envconfig.Host
+
 	return &Client{
-		base: envconfig.Host(),
+		base: &url.URL{
+			Scheme: ollamaHost.Scheme,
+			Host:   net.JoinHostPort(ollamaHost.Host, ollamaHost.Port),
+		},
 		http: http.DefaultClient,
 	}, nil
 }
@@ -173,7 +182,7 @@ func (c *Client) stream(ctx context.Context, method, path string, data any, fn f
 		}
 
 		if errorResponse.Error != "" {
-			return errors.New(errorResponse.Error)
+			return fmt.Errorf(errorResponse.Error)
 		}
 
 		if response.StatusCode >= http.StatusBadRequest {
@@ -298,7 +307,7 @@ func (c *Client) List(ctx context.Context) (*ListResponse, error) {
 	return &lr, nil
 }
 
-// ListRunning lists running models.
+// List 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 {
@@ -333,7 +342,7 @@ 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
+// Hearbeat 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 {
@@ -378,3 +387,16 @@ func (c *Client) Version(ctx context.Context) (string, error) {
 
 	return version.Version, nil
 }
+
+func Authorization(ctx context.Context, request *http.Request) (string, error) {
+	data := []byte(fmt.Sprintf("%s,%s,%d", request.Method, request.URL.RequestURI(), time.Now().Unix()))
+
+	token, err := auth.Sign(ctx, data)
+	if err != nil {
+		return "", err
+	}
+
+	// interleave request data into the token
+	key, sig, _ := strings.Cut(token, ":")
+	return fmt.Sprintf("%s:%s:%s", key, base64.StdEncoding.EncodeToString(data), sig), nil
+}

api/client_test.go

@@ -2,6 +2,8 @@ package api
 
 import (
 	"testing"
+
+	"github.com/ollama/ollama/envconfig"
 )
 
 func TestClientFromEnvironment(t *testing.T) {
@@ -31,6 +33,7 @@ func TestClientFromEnvironment(t *testing.T) {
 	for k, v := range testCases {
 		t.Run(k, func(t *testing.T) {
 			t.Setenv("OLLAMA_HOST", v.value)
+			envconfig.LoadConfig()
 
 			client, err := ClientFromEnvironment()
 			if err != v.err {

api/types.go

@@ -114,11 +114,6 @@ func (t Tools) String() string {
 	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.
@@ -214,7 +209,6 @@ 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"`
@@ -231,6 +225,7 @@ 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"`
 	NumBatch  int   `json:"num_batch,omitempty"`
 	NumGPU    int   `json:"num_gpu,omitempty"`
@@ -266,10 +261,6 @@ type EmbedRequest struct {
 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].
@@ -296,17 +287,15 @@ type EmbeddingResponse struct {
 // CreateRequest is the request passed to [Client.Create].
 type CreateRequest struct {
 	Model     string `json:"model"`
+	Path      string `json:"path"`
 	Modelfile string `json:"modelfile"`
 	Stream    *bool  `json:"stream,omitempty"`
 	Quantize  string `json:"quantize,omitempty"`
 
-	// Deprecated: set the model name with Model instead
+	// Name is deprecated, see Model
 	Name string `json:"name"`
 
-	// Deprecated: set the file content with Modelfile instead
-	Path string `json:"path"`
-
-	// Deprecated: use Quantize instead
+	// Quantization is deprecated, see Quantize
 	Quantization string `json:"quantization,omitempty"`
 }
 
@@ -314,7 +303,7 @@ type CreateRequest struct {
 type DeleteRequest struct {
 	Model string `json:"model"`
 
-	// Deprecated: set the model name with Model instead
+	// Name is deprecated, see Model
 	Name string `json:"name"`
 }
 
@@ -329,7 +318,7 @@ type ShowRequest struct {
 
 	Options map[string]interface{} `json:"options"`
 
-	// Deprecated: set the model name with Model instead
+	// Name is deprecated, see Model
 	Name string `json:"name"`
 }
 
@@ -361,7 +350,7 @@ type PullRequest struct {
 	Password string `json:"password"`
 	Stream   *bool  `json:"stream,omitempty"`
 
-	// Deprecated: set the model name with Model instead
+	// Name is deprecated, see Model
 	Name string `json:"name"`
 }
 
@@ -382,7 +371,7 @@ type PushRequest struct {
 	Password string `json:"password"`
 	Stream   *bool  `json:"stream,omitempty"`
 
-	// Deprecated: set the model name with Model instead
+	// Name is deprecated, see Model
 	Name string `json:"name"`
 }
 
@@ -506,7 +495,7 @@ func (opts *Options) FromMap(m map[string]interface{}) error {
 	for key, val := range m {
 		opt, ok := jsonOpts[key]
 		if !ok {
-			slog.Warn("invalid option provided", "option", key)
+			slog.Warn("invalid option provided", "option", opt.Name)
 			continue
 		}
 
@@ -616,6 +605,7 @@ func DefaultOptions() Options {
 			F16KV:     true,
 			UseMLock:  false,
 			UseMMap:   nil,
+			UseNUMA:   false,
 		},
 	}
 }

api/types_test.go

@@ -2,7 +2,7 @@ package api
 
 import (
 	"encoding/json"
-	"errors"
+	"fmt"
 	"math"
 	"testing"
 	"time"
@@ -192,7 +192,7 @@ func TestUseMmapFormatParams(t *testing.T) {
 				"use_mmap": {"foo"},
 			},
 			exp: nil,
-			err: errors.New("invalid bool value [foo]"),
+			err: fmt.Errorf("invalid bool value [foo]"),
 		},
 	}
 

app/lifecycle/getstarted_nonwindows.go

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

app/lifecycle/getstarted_windows.go

@@ -34,6 +34,7 @@ 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

@@ -14,7 +14,7 @@ import (
 func InitLogging() {
 	level := slog.LevelInfo
 
-	if envconfig.Debug() {
+	if envconfig.Debug {
 		level = slog.LevelDebug
 	}
 
@@ -27,7 +27,7 @@ func InitLogging() {
 		// TODO - write one-line to the app.log file saying we're running in console mode to help avoid confusion
 	} else {
 		rotateLogs(AppLogFile)
-		logFile, err = os.OpenFile(AppLogFile, os.O_APPEND|os.O_WRONLY|os.O_CREATE, 0o755)
+		logFile, err = os.OpenFile(AppLogFile, os.O_APPEND|os.O_WRONLY|os.O_CREATE, 0755)
 		if err != nil {
 			slog.Error(fmt.Sprintf("failed to create server log %v", err))
 			return

app/lifecycle/logging_nonwindows.go

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

app/lifecycle/logging_test.go

@@ -17,7 +17,7 @@ func TestRotateLogs(t *testing.T) {
 	// No log exists
 	rotateLogs(logFile)
 
-	require.NoError(t, os.WriteFile(logFile, []byte("1"), 0o644))
+	require.NoError(t, os.WriteFile(logFile, []byte("1"), 0644))
 	assert.FileExists(t, logFile)
 	// First rotation
 	rotateLogs(logFile)
@@ -32,7 +32,7 @@ func TestRotateLogs(t *testing.T) {
 	assert.NoFileExists(t, logFile)
 
 	for i := 2; i <= LogRotationCount+1; i++ {
-		require.NoError(t, os.WriteFile(logFile, []byte(strconv.Itoa(i)), 0o644))
+		require.NoError(t, os.WriteFile(logFile, []byte(strconv.Itoa(i)), 0644))
 		assert.FileExists(t, logFile)
 		rotateLogs(logFile)
 		assert.NoFileExists(t, logFile)

app/lifecycle/server.go

@@ -55,7 +55,7 @@ func start(ctx context.Context, command string) (*exec.Cmd, error) {
 	}
 
 	rotateLogs(ServerLogFile)
-	logFile, err := os.OpenFile(ServerLogFile, os.O_APPEND|os.O_WRONLY|os.O_CREATE, 0o755)
+	logFile, err := os.OpenFile(ServerLogFile, os.O_APPEND|os.O_WRONLY|os.O_CREATE, 0755)
 	if err != nil {
 		return nil, fmt.Errorf("failed to create server log: %w", err)
 	}

app/lifecycle/updater.go

@@ -15,7 +15,6 @@ import (
 	"path"
 	"path/filepath"
 	"runtime"
-	"strconv"
 	"strings"
 	"time"
 
@@ -47,7 +46,7 @@ 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", strconv.FormatInt(time.Now().Unix(), 10))
+	query.Add("ts", fmt.Sprintf("%d", time.Now().Unix()))
 
 	nonce, err := auth.NewNonce(rand.Reader, 16)
 	if err != nil {

app/lifecycle/updater_nonwindows.go

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

app/lifecycle/updater_windows.go

@@ -2,7 +2,6 @@ package lifecycle
 
 import (
 	"context"
-	"errors"
 	"fmt"
 	"log/slog"
 	"os"
@@ -16,7 +15,7 @@ func DoUpgrade(cancel context.CancelFunc, done chan int) error {
 		return fmt.Errorf("failed to lookup downloads: %s", err)
 	}
 	if len(files) == 0 {
-		return errors.New("no update downloads found")
+		return fmt.Errorf("no update downloads found")
 	} else if len(files) > 1 {
 		// Shouldn't happen
 		slog.Warn(fmt.Sprintf("multiple downloads found, using first one %v", files))
@@ -65,7 +64,7 @@ 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 errors.New("installer process did not start")
+		return fmt.Errorf("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

@@ -88,10 +88,19 @@ DialogFontSize=12
 [Files]
 Source: ".\app.exe"; DestDir: "{app}"; DestName: "{#MyAppExeName}" ; Flags: ignoreversion 64bit
 Source: "..\ollama.exe"; DestDir: "{app}"; Flags: ignoreversion 64bit
-Source: "..\dist\windows-{#ARCH}\lib\ollama\runners\*"; DestDir: "{app}\lib\ollama\runners"; Flags: ignoreversion 64bit recursesubdirs
+Source: "..\dist\windows-{#ARCH}\ollama_runners\*"; DestDir: "{app}\ollama_runners"; Flags: ignoreversion 64bit recursesubdirs
 Source: "..\dist\ollama_welcome.ps1"; DestDir: "{app}"; Flags: ignoreversion
 Source: ".\assets\app.ico"; DestDir: "{app}"; Flags: ignoreversion
-Source: "..\dist\windows-amd64\lib\ollama\*"; DestDir: "{app}\lib\ollama\"; Flags: ignoreversion recursesubdirs
+#if DirExists("..\dist\windows-amd64\cuda")
+  Source: "..\dist\windows-amd64\cuda\*"; DestDir: "{app}\cuda\"; Flags: ignoreversion recursesubdirs
+#endif
+#if DirExists("..\dist\windows-amd64\oneapi")
+  Source: "..\dist\windows-amd64\oneapi\*"; DestDir: "{app}\oneapi\"; Flags: ignoreversion recursesubdirs
+#endif
+#if DirExists("..\dist\windows-amd64\rocm")
+  Source: "..\dist\windows-amd64\rocm\*"; DestDir: "{app}\rocm\"; Flags: ignoreversion recursesubdirs
+#endif
+
 
 [Icons]
 Name: "{group}\{#MyAppName}"; Filename: "{app}\{#MyAppExeName}"; IconFilename: "{app}\app.ico"
@@ -129,7 +138,7 @@ 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 llama3.1
+;FinishedLabel=%nRun this command in a PowerShell or cmd terminal.%n%n%n    ollama run llama3
 ;ClickFinish=%n
 
 [Registry]

app/ollama_welcome.ps1

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

app/tray/tray_nonwindows.go

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

app/tray/wintray/eventloop.go

@@ -11,7 +11,9 @@ import (
 	"golang.org/x/sys/windows"
 )
 
-var quitOnce sync.Once
+var (
+	quitOnce sync.Once
+)
 
 func (t *winTray) Run() {
 	nativeLoop()

app/tray/wintray/menus.go

@@ -11,12 +11,12 @@ import (
 )
 
 const (
-	updateAvailableMenuID = 1
-	updateMenuID          = updateAvailableMenuID + 1
-	separatorMenuID       = updateMenuID + 1
-	diagLogsMenuID        = separatorMenuID + 1
-	diagSeparatorMenuID   = diagLogsMenuID + 1
-	quitMenuID            = diagSeparatorMenuID + 1
+	updatAvailableMenuID = 1
+	updateMenuID         = updatAvailableMenuID + 1
+	separatorMenuID      = updateMenuID + 1
+	diagLogsMenuID       = separatorMenuID + 1
+	diagSeparatorMenuID  = diagLogsMenuID + 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(updateAvailableMenuID, 0, updateAvailableMenuTitle, true); err != nil {
+		if err := t.addOrUpdateMenuItem(updatAvailableMenuID, 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

@@ -11,12 +11,10 @@ import (
 	"path/filepath"
 	"sort"
 	"sync"
-	"syscall"
 	"unsafe"
 
-	"golang.org/x/sys/windows"
-
 	"github.com/ollama/ollama/app/tray/commontray"
+	"golang.org/x/sys/windows"
 )
 
 // Helpful sources: https://github.com/golang/exp/blob/master/shiny/driver/internal/win32
@@ -416,7 +414,7 @@ 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, 0o644); err != nil {
+		if err := os.WriteFile(iconFilePath, iconBytes, 0644); err != nil {
 			return "", err
 		}
 	}
@@ -434,12 +432,7 @@ func (t *winTray) setIcon(src string) error {
 	t.muNID.Lock()
 	defer t.muNID.Unlock()
 	t.nid.Icon = h
-	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.Flags |= NIF_ICON
 	t.nid.Size = uint32(unsafe.Sizeof(*t.nid))
 
 	return t.nid.modify()

app/tray/wintray/w32api.go

@@ -61,7 +61,6 @@ 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

@@ -3,50 +3,68 @@ package auth
 import (
 	"bytes"
 	"context"
+	"crypto/ed25519"
 	"crypto/rand"
 	"encoding/base64"
+	"encoding/pem"
 	"errors"
 	"fmt"
 	"io"
 	"log/slog"
 	"os"
 	"path/filepath"
-	"strings"
 
 	"golang.org/x/crypto/ssh"
 )
 
 const defaultPrivateKey = "id_ed25519"
 
-func keyPath() (string, error) {
+func privateKey() (ssh.Signer, error) {
 	home, err := os.UserHomeDir()
 	if err != nil {
-		return "", err
+		return nil, err
 	}
 
-	return filepath.Join(home, ".ollama", defaultPrivateKey), nil
+	keyPath := filepath.Join(home, ".ollama", defaultPrivateKey)
+	privateKeyFile, err := os.ReadFile(keyPath)
+	if errors.Is(err, os.ErrNotExist) {
+		err := initializeKeypair()
+		if err != nil {
+			return nil, err
+		}
+
+		return privateKey()
+	} else if err != nil {
+		slog.Info(fmt.Sprintf("Failed to load private key: %v", err))
+		return nil, err
+	}
+
+	return ssh.ParsePrivateKey(privateKeyFile)
 }
 
-func GetPublicKey() (string, error) {
-	keyPath, err := keyPath()
+func GetPublicKey() (ssh.PublicKey, error) {
+	// try to read pubkey first
+	home, err := os.UserHomeDir()
 	if err != nil {
-		return "", err
+		return nil, err
 	}
 
-	privateKeyFile, err := os.ReadFile(keyPath)
-	if err != nil {
-		slog.Info(fmt.Sprintf("Failed to load private key: %v", err))
-		return "", err
+	pubkeyPath := filepath.Join(home, ".ollama", defaultPrivateKey+".pub")
+	pubKeyFile, err := os.ReadFile(pubkeyPath)
+	if errors.Is(err, os.ErrNotExist) {
+		// try from privateKey
+		privateKey, err := privateKey()
+		if err != nil {
+			return nil, fmt.Errorf("failed to read public key: %w", err)
+		}
+
+		return privateKey.PublicKey(), nil
+	} else if err != nil {
+		return nil, fmt.Errorf("failed to read public key: %w", err)
 	}
 
-	privateKey, err := ssh.ParsePrivateKey(privateKeyFile)
-	if err != nil {
-		return "", err
-	}
-
-	publicKey := ssh.MarshalAuthorizedKey(privateKey.PublicKey())
-
-	return strings.TrimSpace(string(publicKey)), nil
+	pubKey, _, _, _, err := ssh.ParseAuthorizedKey(pubKeyFile)
+	return pubKey, err
 }
 
 func NewNonce(r io.Reader, length int) (string, error) {
@@ -59,27 +77,22 @@ func NewNonce(r io.Reader, length int) (string, error) {
 }
 
 func Sign(ctx context.Context, bts []byte) (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)
+	privateKey, err := privateKey()
 	if err != nil {
 		return "", err
 	}
 
 	// get the pubkey, but remove the type
-	publicKey := ssh.MarshalAuthorizedKey(privateKey.PublicKey())
-	parts := bytes.Split(publicKey, []byte(" "))
+	publicKey, err := GetPublicKey()
+	if err != nil {
+		return "", err
+	}
+
+	publicKeyBytes := ssh.MarshalAuthorizedKey(publicKey)
+
+	parts := bytes.Split(publicKeyBytes, []byte(" "))
 	if len(parts) < 2 {
-		return "", errors.New("malformed public key")
+		return "", fmt.Errorf("malformed public key")
 	}
 
 	signedData, err := privateKey.Sign(rand.Reader, bts)
@@ -90,3 +103,49 @@ func Sign(ctx context.Context, bts []byte) (string, error) {
 	// signature is <pubkey>:<signature>
 	return fmt.Sprintf("%s:%s", bytes.TrimSpace(parts[1]), base64.StdEncoding.EncodeToString(signedData.Blob)), nil
 }
+
+func initializeKeypair() error {
+	home, err := os.UserHomeDir()
+	if err != nil {
+		return err
+	}
+
+	privKeyPath := filepath.Join(home, ".ollama", "id_ed25519")
+	pubKeyPath := filepath.Join(home, ".ollama", "id_ed25519.pub")
+
+	_, err = os.Stat(privKeyPath)
+	if errors.Is(err, os.ErrNotExist) {
+		fmt.Printf("Couldn't find '%s'. Generating new private key.\n", privKeyPath)
+		cryptoPublicKey, cryptoPrivateKey, err := ed25519.GenerateKey(rand.Reader)
+		if err != nil {
+			return err
+		}
+
+		privateKeyBytes, err := ssh.MarshalPrivateKey(cryptoPrivateKey, "")
+		if err != nil {
+			return err
+		}
+
+		if err := os.MkdirAll(filepath.Dir(privKeyPath), 0o755); err != nil {
+			return fmt.Errorf("could not create directory %w", err)
+		}
+
+		if err := os.WriteFile(privKeyPath, pem.EncodeToMemory(privateKeyBytes), 0o600); err != nil {
+			return err
+		}
+
+		sshPublicKey, err := ssh.NewPublicKey(cryptoPublicKey)
+		if err != nil {
+			return err
+		}
+
+		publicKeyBytes := ssh.MarshalAuthorizedKey(sshPublicKey)
+
+		if err := os.WriteFile(pubKeyPath, publicKeyBytes, 0o644); err != nil {
+			return err
+		}
+
+		fmt.Printf("Your new public key is: \n\n%s\n", publicKeyBytes)
+	}
+	return nil
+}

cmd/cmd.go

@@ -4,10 +4,7 @@ import (
 	"archive/zip"
 	"bytes"
 	"context"
-	"crypto/ed25519"
-	"crypto/rand"
 	"crypto/sha256"
-	"encoding/pem"
 	"errors"
 	"fmt"
 	"io"
@@ -15,6 +12,7 @@ import (
 	"math"
 	"net"
 	"net/http"
+	"net/url"
 	"os"
 	"os/signal"
 	"path/filepath"
@@ -22,7 +20,6 @@ import (
 	"runtime"
 	"slices"
 	"strings"
-	"sync/atomic"
 	"syscall"
 	"time"
 
@@ -79,7 +76,6 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 	status := "transferring model data"
 	spinner := progress.NewSpinner(status)
 	p.Add(status, spinner)
-	defer p.Stop()
 
 	for i := range modelfile.Commands {
 		switch modelfile.Commands[i].Name {
@@ -114,7 +110,7 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 				path = tempfile
 			}
 
-			digest, err := createBlob(cmd, client, path, spinner)
+			digest, err := createBlob(cmd, path)
 			if err != nil {
 				return err
 			}
@@ -204,12 +200,6 @@ func tempZipFiles(path string) (string, error) {
 		// safetensors files might be unresolved git lfs references; skip if they are
 		// covers model-x-of-y.safetensors, model.fp32-x-of-y.safetensors, model.safetensors
 		files = append(files, st...)
-	} else if st, _ := glob(filepath.Join(path, "adapters.safetensors"), "application/octet-stream"); len(st) > 0 {
-		// covers adapters.safetensors
-		files = append(files, st...)
-	} else if st, _ := glob(filepath.Join(path, "adapter_model.safetensors"), "application/octet-stream"); len(st) > 0 {
-		// covers adapter_model.safetensors
-		files = append(files, st...)
 	} else if pt, _ := glob(filepath.Join(path, "pytorch_model*.bin"), "application/zip"); len(pt) > 0 {
 		// pytorch files might also be unresolved git lfs references; skip if they are
 		// covers pytorch_model-x-of-y.bin, pytorch_model.fp32-x-of-y.bin, pytorch_model.bin
@@ -229,14 +219,6 @@ func tempZipFiles(path string) (string, error) {
 	}
 	files = append(files, js...)
 
-	// bert models require a nested config.json
-	// TODO(mxyng): merge this with the glob above
-	js, err = glob(filepath.Join(path, "**/*.json"), "text/plain")
-	if err != nil {
-		return "", err
-	}
-	files = append(files, js...)
-
 	if tks, _ := glob(filepath.Join(path, "tokenizer.model"), "application/octet-stream"); len(tks) > 0 {
 		// add tokenizer.model if it exists, tokenizer.json is automatically picked up by the previous glob
 		// tokenizer.model might be a unresolved git lfs reference; error if it is
@@ -266,11 +248,6 @@ func tempZipFiles(path string) (string, error) {
 			return "", err
 		}
 
-		zfi.Name, err = filepath.Rel(path, file)
-		if err != nil {
-			return "", err
-		}
-
 		zf, err := zipfile.CreateHeader(zfi)
 		if err != nil {
 			return "", err
@@ -284,20 +261,15 @@ func tempZipFiles(path string) (string, error) {
 	return tempfile.Name(), nil
 }
 
-func createBlob(cmd *cobra.Command, client *api.Client, path string, spinner *progress.Spinner) (string, error) {
+var ErrBlobExists = errors.New("blob exists")
+
+func createBlob(cmd *cobra.Command, path string) (string, error) {
 	bin, err := os.Open(path)
 	if err != nil {
 		return "", err
 	}
 	defer bin.Close()
 
-	// Get file info to retrieve the size
-	fileInfo, err := bin.Stat()
-	if err != nil {
-		return "", err
-	}
-	fileSize := fileInfo.Size()
-
 	hash := sha256.New()
 	if _, err := io.Copy(hash, bin); err != nil {
 		return "", err
@@ -307,41 +279,64 @@ func createBlob(cmd *cobra.Command, client *api.Client, path string, spinner *pr
 		return "", err
 	}
 
-	var pw progressWriter
-	status := "transferring model data 0%"
-	spinner.SetMessage(status)
-
-	done := make(chan struct{})
-	defer close(done)
-
-	go func() {
-		ticker := time.NewTicker(60 * time.Millisecond)
-		defer ticker.Stop()
-		for {
-			select {
-			case <-ticker.C:
-				spinner.SetMessage(fmt.Sprintf("transferring model data %d%%", int(100*pw.n.Load()/fileSize)))
-			case <-done:
-				spinner.SetMessage("transferring model data 100%")
-				return
-			}
-		}
-	}()
-
 	digest := fmt.Sprintf("sha256:%x", hash.Sum(nil))
-	if err = client.CreateBlob(cmd.Context(), digest, io.TeeReader(bin, &pw)); err != nil {
+
+	// Use our new CreateBlob request which will include the file path
+	// The server checks for that file and if the server is local, it will copy the file over
+	// If the local copy fails, the server will continue to the default local copy
+	// If that fails, it will continue with the server POST
+	err = CreateBlob(cmd.Context(), path, digest, bin)
+	if errors.Is(err, ErrBlobExists) {
+		return digest, nil
+	}
+
+	if err != nil {
 		return "", err
 	}
+
 	return digest, nil
 }
 
-type progressWriter struct {
-	n atomic.Int64
-}
+func CreateBlob(ctx context.Context, src, digest string, r *os.File) (error) {
+	ollamaHost := envconfig.Host
 
-func (w *progressWriter) Write(p []byte) (n int, err error) {
-	w.n.Add(int64(len(p)))
-	return len(p), nil
+	client := http.DefaultClient
+	base := &url.URL{
+		Scheme: ollamaHost.Scheme,
+		Host:   net.JoinHostPort(ollamaHost.Host, ollamaHost.Port),
+	}
+
+	path := fmt.Sprintf("/api/blobs/%s", digest)
+	requestURL := base.JoinPath(path)
+	request, err := http.NewRequestWithContext(ctx, http.MethodPost, requestURL.String(), r)
+	if err != nil {
+		return err
+	}
+
+	authz, err := api.Authorization(ctx, request)
+	if err != nil {
+		return err
+	}
+
+	request.Header.Set("Authorization", authz)
+	request.Header.Set("User-Agent", fmt.Sprintf("ollama/%s (%s %s) Go/%s", version.Version, runtime.GOARCH, runtime.GOOS, runtime.Version()))
+	request.Header.Set("X-Ollama-File", src)
+
+	resp, err := client.Do(request)
+	if err != nil {
+		return err
+	}
+	defer resp.Body.Close()
+
+	if resp.StatusCode == http.StatusCreated {
+		return nil
+	}
+
+	if resp.StatusCode == http.StatusOK {
+		return ErrBlobExists
+	}
+
+	return err
 }
 
 func RunHandler(cmd *cobra.Command, args []string) error {
@@ -420,24 +415,9 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 
 	opts.MultiModal = slices.Contains(info.Details.Families, "clip")
 	opts.ParentModel = info.Details.ParentModel
+	opts.Messages = append(opts.Messages, info.Messages...)
 
 	if interactive {
-		if err := loadModel(cmd, &opts); err != nil {
-			return err
-		}
-
-		for _, msg := range info.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 generateInteractive(cmd, opts)
 	}
 	return generate(cmd, opts)
@@ -452,11 +432,12 @@ func errFromUnknownKey(unknownKeyErr error) error {
 	if len(matches) > 0 {
 		serverPubKey := matches[0]
 
-		localPubKey, err := auth.GetPublicKey()
+		publicKey, err := auth.GetPublicKey()
 		if err != nil {
 			return unknownKeyErr
 		}
 
+		localPubKey := strings.TrimSpace(string(ssh.MarshalAuthorizedKey(publicKey)))
 		if runtime.GOOS == "linux" && serverPubKey != localPubKey {
 			// try the ollama service public key
 			svcPubKey, err := os.ReadFile("/usr/share/ollama/.ollama/id_ed25519.pub")
@@ -726,17 +707,14 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
 }
 
 func showInfo(resp *api.ShowResponse) {
+	arch := resp.ModelInfo["general.architecture"].(string)
+
 	modelData := [][]string{
+		{"arch", arch},
 		{"parameters", resp.Details.ParameterSize},
 		{"quantization", resp.Details.QuantizationLevel},
-	}
-	if resp.ModelInfo != nil {
-		arch := resp.ModelInfo["general.architecture"].(string)
-		modelData = append(modelData,
-			[]string{"arch", arch},
-			[]string{"context length", fmt.Sprintf("%v", resp.ModelInfo[fmt.Sprintf("%s.context_length", arch)].(float64))},
-			[]string{"embedding length", fmt.Sprintf("%v", resp.ModelInfo[fmt.Sprintf("%s.embedding_length", arch)].(float64))},
-		)
+		{"context length", fmt.Sprintf("%v", resp.ModelInfo[fmt.Sprintf("%s.context_length", arch)].(float64))},
+		{"embedding length", fmt.Sprintf("%v", resp.ModelInfo[fmt.Sprintf("%s.embedding_length", arch)].(float64))},
 	}
 
 	mainTableData := [][]string{
@@ -1147,12 +1125,12 @@ func generate(cmd *cobra.Command, opts runOptions) error {
 	return nil
 }
 
-func RunServer(_ *cobra.Command, _ []string) error {
-	if err := initializeKeypair(); err != nil {
+func RunServer(cmd *cobra.Command, _ []string) error {
+	if _, err := auth.GetPublicKey(); err != nil {
 		return err
 	}
 
-	ln, err := net.Listen("tcp", envconfig.Host().Host)
+	ln, err := net.Listen("tcp", net.JoinHostPort(envconfig.Host.Host, envconfig.Host.Port))
 	if err != nil {
 		return err
 	}
@@ -1165,52 +1143,6 @@ func RunServer(_ *cobra.Command, _ []string) error {
 	return err
 }
 
-func initializeKeypair() error {
-	home, err := os.UserHomeDir()
-	if err != nil {
-		return err
-	}
-
-	privKeyPath := filepath.Join(home, ".ollama", "id_ed25519")
-	pubKeyPath := filepath.Join(home, ".ollama", "id_ed25519.pub")
-
-	_, err = os.Stat(privKeyPath)
-	if os.IsNotExist(err) {
-		fmt.Printf("Couldn't find '%s'. Generating new private key.\n", privKeyPath)
-		cryptoPublicKey, cryptoPrivateKey, err := ed25519.GenerateKey(rand.Reader)
-		if err != nil {
-			return err
-		}
-
-		privateKeyBytes, err := ssh.MarshalPrivateKey(cryptoPrivateKey, "")
-		if err != nil {
-			return err
-		}
-
-		if err := os.MkdirAll(filepath.Dir(privKeyPath), 0o755); err != nil {
-			return fmt.Errorf("could not create directory %w", err)
-		}
-
-		if err := os.WriteFile(privKeyPath, pem.EncodeToMemory(privateKeyBytes), 0o600); err != nil {
-			return err
-		}
-
-		sshPublicKey, err := ssh.NewPublicKey(cryptoPublicKey)
-		if err != nil {
-			return err
-		}
-
-		publicKeyBytes := ssh.MarshalAuthorizedKey(sshPublicKey)
-
-		if err := os.WriteFile(pubKeyPath, publicKeyBytes, 0o644); err != nil {
-			return err
-		}
-
-		fmt.Printf("Your new public key is: \n\n%s\n", publicKeyBytes)
-	}
-	return nil
-}
-
 func checkServerHeartbeat(cmd *cobra.Command, _ []string) error {
 	client, err := api.ClientFromEnvironment()
 	if err != nil {
@@ -1221,7 +1153,7 @@ func checkServerHeartbeat(cmd *cobra.Command, _ []string) error {
 			return err
 		}
 		if err := startApp(cmd.Context(), client); err != nil {
-			return errors.New("could not connect to ollama app, is it running?")
+			return fmt.Errorf("could not connect to ollama app, is it running?")
 		}
 	}
 	return nil
@@ -1417,12 +1349,9 @@ func NewCLI() *cobra.Command {
 				envVars["OLLAMA_NUM_PARALLEL"],
 				envVars["OLLAMA_NOPRUNE"],
 				envVars["OLLAMA_ORIGINS"],
-				envVars["OLLAMA_SCHED_SPREAD"],
 				envVars["OLLAMA_TMPDIR"],
 				envVars["OLLAMA_FLASH_ATTENTION"],
 				envVars["OLLAMA_LLM_LIBRARY"],
-				envVars["OLLAMA_GPU_OVERHEAD"],
-				envVars["OLLAMA_LOAD_TIMEOUT"],
 			})
 		default:
 			appendEnvDocs(cmd, envs)

cmd/interactive.go

@@ -1,7 +1,6 @@
 package cmd
 
 import (
-	"cmp"
 	"errors"
 	"fmt"
 	"io"
@@ -10,14 +9,13 @@ import (
 	"path/filepath"
 	"regexp"
 	"slices"
+	"sort"
 	"strings"
 
 	"github.com/spf13/cobra"
-	"golang.org/x/exp/maps"
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/envconfig"
-	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/progress"
 	"github.com/ollama/ollama/readline"
 	"github.com/ollama/ollama/types/errtypes"
@@ -48,10 +46,29 @@ func loadModel(cmd *cobra.Command, opts *runOptions) error {
 		KeepAlive: opts.KeepAlive,
 	}
 
-	return client.Chat(cmd.Context(), chatReq, func(api.ChatResponse) error { return nil })
+	return client.Chat(cmd.Context(), chatReq, func(resp api.ChatResponse) error {
+		p.StopAndClear()
+		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
+	})
 }
 
 func generateInteractive(cmd *cobra.Command, opts runOptions) error {
+	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")
@@ -121,7 +138,6 @@ 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")
@@ -141,7 +157,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 		return err
 	}
 
-	if envconfig.NoHistory() {
+	if envconfig.NoHistory {
 		scanner.HistoryDisable()
 	}
 
@@ -359,9 +375,9 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 					return err
 				}
 				req := &api.ShowRequest{
-					Name:    opts.Model,
-					System:  opts.System,
-					Options: opts.Options,
+					Name:     opts.Model,
+					System:   opts.System,
+					Options:  opts.Options,
 				}
 				resp, err := client.Show(cmd.Context(), req)
 				if err != nil {
@@ -490,35 +506,31 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 }
 
 func buildModelfile(opts runOptions) string {
-	var f parser.File
-	f.Commands = append(f.Commands, parser.Command{Name: "model", Args: cmp.Or(opts.ParentModel, opts.Model)})
-
+	var mf strings.Builder
+	model := opts.ParentModel
+	if model == "" {
+		model = opts.Model
+	}
+	fmt.Fprintf(&mf, "FROM %s\n", model)
 	if opts.System != "" {
-		f.Commands = append(f.Commands, parser.Command{Name: "system", Args: opts.System})
+		fmt.Fprintf(&mf, "SYSTEM \"\"\"%s\"\"\"\n", opts.System)
 	}
 
-	keys := maps.Keys(opts.Options)
-	slices.Sort(keys)
+	keys := make([]string, 0)
+	for k := range opts.Options {
+		keys = append(keys, k)
+	}
+	sort.Strings(keys)
 	for _, k := range keys {
-		v := opts.Options[k]
-		var cmds []parser.Command
-		switch t := v.(type) {
-		case []string:
-			for _, s := range t {
-				cmds = append(cmds, parser.Command{Name: k, Args: s})
-			}
-		default:
-			cmds = append(cmds, parser.Command{Name: k, Args: fmt.Sprintf("%v", t)})
-		}
-
-		f.Commands = append(f.Commands, cmds...)
+		fmt.Fprintf(&mf, "PARAMETER %s %v\n", k, opts.Options[k])
 	}
+	fmt.Fprintln(&mf)
 
 	for _, msg := range opts.Messages {
-		f.Commands = append(f.Commands, parser.Command{Name: "message", Args: fmt.Sprintf("%s: %s", msg.Role, msg.Content)})
+		fmt.Fprintf(&mf, "MESSAGE %s \"\"\"%s\"\"\"\n", msg.Role, msg.Content)
 	}
 
-	return f.String()
+	return mf.String()
 }
 
 func normalizeFilePath(fp string) string {
@@ -604,7 +616,7 @@ 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, errors.New("file size exceeds maximum limit (100MB)")
+		return nil, fmt.Errorf("file size exceeds maximum limit (100MB)")
 	}
 
 	buf = make([]byte, info.Size())

cmd/interactive_test.go

@@ -1,10 +1,12 @@
 package cmd
 
 import (
+	"bytes"
 	"testing"
+	"text/template"
 
-	"github.com/google/go-cmp/cmp"
 	"github.com/stretchr/testify/assert"
+	"github.com/stretchr/testify/require"
 
 	"github.com/ollama/ollama/api"
 )
@@ -55,53 +57,58 @@ d:\path with\spaces\seven.svg inbetween7 c:\users\jdoe\eight.png inbetween8
 
 func TestModelfileBuilder(t *testing.T) {
 	opts := runOptions{
-		Model:  "hork",
-		System: "You are part horse and part shark, but all hork. Do horklike things",
+		Model:    "hork",
+		System:   "You are part horse and part shark, but all hork. Do horklike things",
 		Messages: []api.Message{
 			{Role: "user", Content: "Hey there hork!"},
 			{Role: "assistant", Content: "Yes it is true, I am half horse, half shark."},
 		},
-		Options: map[string]any{
-			"temperature":      0.9,
-			"seed":             42,
-			"penalize_newline": false,
-			"stop":             []string{"hi", "there"},
-		},
+		Options: map[string]interface{}{},
 	}
 
-	t.Run("model", func(t *testing.T) {
-		expect := `FROM hork
-SYSTEM You are part horse and part shark, but all hork. Do horklike things
+	opts.Options["temperature"] = 0.9
+	opts.Options["seed"] = 42
+	opts.Options["penalize_newline"] = false
+	opts.Options["stop"] = []string{"hi", "there"}
+
+	mf := buildModelfile(opts)
+	expectedModelfile := `FROM {{.Model}}
+SYSTEM """{{.System}}"""
 PARAMETER penalize_newline false
 PARAMETER seed 42
-PARAMETER stop hi
-PARAMETER stop there
+PARAMETER stop [hi there]
 PARAMETER temperature 0.9
-MESSAGE user Hey there hork!
-MESSAGE assistant Yes it is true, I am half horse, half shark.
+
+MESSAGE user """Hey there hork!"""
+MESSAGE assistant """Yes it is true, I am half horse, half shark."""
 `
 
-		actual := buildModelfile(opts)
-		if diff := cmp.Diff(expect, actual); diff != "" {
-			t.Errorf("mismatch (-want +got):\n%s", diff)
-		}
-	})
+	tmpl, err := template.New("").Parse(expectedModelfile)
+	require.NoError(t, err)
 
-	t.Run("parent model", func(t *testing.T) {
-		opts.ParentModel = "horseshark"
-		expect := `FROM horseshark
-SYSTEM You are part horse and part shark, but all hork. Do horklike things
+	var buf bytes.Buffer
+	err = tmpl.Execute(&buf, opts)
+	require.NoError(t, err)
+	assert.Equal(t, buf.String(), mf)
+
+	opts.ParentModel = "horseshark"
+	mf = buildModelfile(opts)
+	expectedModelfile = `FROM {{.ParentModel}}
+SYSTEM """{{.System}}"""
 PARAMETER penalize_newline false
 PARAMETER seed 42
-PARAMETER stop hi
-PARAMETER stop there
+PARAMETER stop [hi there]
 PARAMETER temperature 0.9
-MESSAGE user Hey there hork!
-MESSAGE assistant Yes it is true, I am half horse, half shark.
+
+MESSAGE user """Hey there hork!"""
+MESSAGE assistant """Yes it is true, I am half horse, half shark."""
 `
-		actual := buildModelfile(opts)
-		if diff := cmp.Diff(expect, actual); diff != "" {
-			t.Errorf("mismatch (-want +got):\n%s", diff)
-		}
-	})
+
+	tmpl, err = template.New("").Parse(expectedModelfile)
+	require.NoError(t, err)
+
+	var parentBuf bytes.Buffer
+	err = tmpl.Execute(&parentBuf, opts)
+	require.NoError(t, err)
+	assert.Equal(t, parentBuf.String(), mf)
 }

cmd/start_darwin.go

@@ -2,7 +2,7 @@ package cmd
 
 import (
 	"context"
-	"errors"
+	"fmt"
 	"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 errors.New("could not find ollama app")
+		return fmt.Errorf("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"
-	"errors"
+	"fmt"
 
 	"github.com/ollama/ollama/api"
 )
 
 func startApp(ctx context.Context, client *api.Client) error {
-	return errors.New("could not connect to ollama server, run 'ollama serve' to start it")
+	return fmt.Errorf("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 errors.New("could not locate ollama app")
+				return fmt.Errorf("could not locate ollama app")
 			}
 		}
 	}

convert/convert.go

@@ -1,228 +1,200 @@
 package convert
 
 import (
+	"cmp"
+	"encoding/binary"
 	"encoding/json"
-	"errors"
 	"fmt"
 	"io"
-	"io/fs"
 	"log/slog"
+	"os"
+	"path/filepath"
+	"slices"
 	"strings"
 
+	"google.golang.org/protobuf/proto"
+
+	"github.com/ollama/ollama/convert/sentencepiece"
 	"github.com/ollama/ollama/llm"
 )
 
-type ModelParameters struct {
-	Architectures []string `json:"architectures"`
-	VocabSize     uint32   `json:"vocab_size"`
+const (
+	_ int32 = iota
+	tokenTypeNormal
+	tokenTypeUnknown
+	tokenTypeControl
+	tokenTypeUserDefined
+	tokenTypeUnused
+	tokenTypeByte
+)
+
+type Params struct {
+	Architectures     []string `json:"architectures"`
+	VocabSize         int      `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"`
+	RopeFrequencyBase float64  `json:"rope_theta"`
+
+	Experts     int `json:"num_local_experts"`
+	ExpertsUsed int `json:"num_experts_per_tok"`
+
+	PreTokenizer string
+
+	ByteOrder
 }
 
-type AdapterParameters struct {
-	Alpha          uint32 `json:"lora_alpha"`
-	LoraLayers     uint32 `json:"lora_layers"`
-	LoraParameters struct {
-		Rank  uint32  `json:"rank"`
-		Alpha float32 `json:"alpha"`
-		Scale float32 `json:"scale"`
-	} `json:"lora_parameters"`
+type ByteOrder interface {
+	binary.ByteOrder
+	binary.AppendByteOrder
 }
 
-func (ModelParameters) KV(t *Tokenizer) llm.KV {
-	kv := llm.KV{
-		"general.file_type":            uint32(1),
-		"general.quantization_version": uint32(2),
-		"tokenizer.ggml.pre":           t.Pre,
-		"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 {
+	GetTensors() error
+	LoadVocab() error
+	WriteGGUF(io.WriteSeeker) error
 }
 
-func (p AdapterParameters) KV() llm.KV {
-	var alpha float32
-	if p.LoraParameters.Alpha == 0 {
-		alpha = float32(p.Alpha)
-	} else {
-		alpha = p.LoraParameters.Alpha
-	}
-
-	kv := llm.KV{
-		"adapter.lora.alpha": alpha,
-		"adapter.type":       "lora",
-		"general.file_type":  uint32(1),
-		"general.type":       "adapter",
-		"general.version":    "v0.2",
-	}
-
-	return kv
+type ModelFormat interface {
+	GetLayerName(string) (string, error)
+	GetTensors(string, *Params) ([]llm.Tensor, error)
+	GetParams(string) (*Params, error)
+	GetModelArch(string, string, *Params) (ModelArch, error)
 }
 
-func (ModelParameters) specialTokenTypes() []string {
-	return []string{
-		"bos", "eos", "unk", "sep", "pad", "cls", "mask",
-	}
+type ModelData struct {
+	Path    string
+	Name    string
+	Params  *Params
+	Vocab   *Vocab
+	Tensors []llm.Tensor
+	Format  ModelFormat
 }
 
-func (ModelParameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []llm.Tensor) error {
-	return llm.WriteGGUF(ws, kv, ts)
-}
-
-func (AdapterParameters) writeFile(ws io.WriteSeeker, kv 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")
+func GetModelFormat(dirname string) (ModelFormat, error) {
+	files, err := filepath.Glob(filepath.Join(dirname, "*"))
 	if err != nil {
-		return err
+		return nil, err
 	}
 
-	var p AdapterParameters
-	if err := json.Unmarshal(bts, &p); err != nil {
-		return err
-	}
-
-	arch, ok := baseKV["general.architecture"]
-	if !ok {
-		return errors.New("architecture not set for the base model")
-	}
-
-	var conv AdapterConverter
-	switch arch {
-	case "llama":
-		conv = &llamaAdapter{}
-	case "gemma2":
-		conv = &gemma2Adapter{}
-	default:
-		return errors.New("unsupported architecture")
-	}
-
-	ts, err := parseTensors(fsys, strings.NewReplacer(conv.Replacements()...))
-	if err != nil {
-		return err
-	}
-
-	if err := json.Unmarshal(bts, conv); err != nil {
-		return err
-	}
-
-	return conv.writeFile(ws, conv.KV(baseKV), conv.Tensors(ts))
-}
-
-// Convert writes an Ollama compatible model to the provided io.WriteSeeker based on configurations
-// and files it finds in the input path.
-// Supported input model formats include safetensors.
-// Supported input tokenizers files include tokenizer.json (preferred) and tokenizer.model.
-func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
-	bts, err := fs.ReadFile(fsys, "config.json")
-	if err != nil {
-		return err
-	}
-
-	var p ModelParameters
-	if err := json.Unmarshal(bts, &p); err != nil {
-		return err
-	}
-
-	if len(p.Architectures) < 1 {
-		return errors.New("unknown architecture")
-	}
-
-	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
+	for _, fn := range files {
+		if strings.HasSuffix(fn, ".safetensors") {
+			return &SafetensorFormat{}, nil
+		} else if strings.HasSuffix(fn, ".bin") || strings.HasSuffix(fn, ".pth") {
+			slog.Debug("model is torch")
+			return &TorchFormat{}, nil
 		}
 	}
 
-	t, err := parseTokenizer(fsys, conv.specialTokenTypes())
-	if err != nil {
-		return err
-	}
-
-	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))
-	}
-
-	ts, err := parseTensors(fsys, strings.NewReplacer(conv.Replacements()...))
-	if err != nil {
-		return err
-	}
-
-	return conv.writeFile(ws, conv.KV(t), conv.Tensors(ts))
+	return nil, fmt.Errorf("couldn't determine model format")
+}
+
+// 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
+	Merges []string
+}
+
+func LoadSentencePieceTokens(dirpath string, params *Params) (*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, tokenTypeUserDefined)
+	}
+	slog.Info(fmt.Sprintf("vocab size w/ extra tokens: %d", len(v.Tokens)))
+
+	if params.VocabSize > len(v.Tokens) {
+		missingTokens := params.VocabSize - len(v.Tokens)
+		slog.Warn(fmt.Sprintf("vocab is missing %d tokens", missingTokens))
+		for cnt := range missingTokens {
+			v.Tokens = append(v.Tokens, fmt.Sprintf("<dummy%05d>", cnt+1))
+			v.Scores = append(v.Scores, -1)
+			v.Types = append(v.Types, tokenTypeUserDefined)
+		}
+	}
+
+	return v, nil
 }

convert/convert_bert.go

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

convert/convert_gemma.go

@@ -1,100 +0,0 @@
-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

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

convert/convert_gemma2_adapter.go

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

convert/convert_llama.go

@@ -1,213 +0,0 @@
-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

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

convert/convert_mixtral.go

@@ -1,94 +0,0 @@
-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

@@ -1,123 +0,0 @@
-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

@@ -1,44 +1,48 @@
+//go:build slow
+
 package convert
 
 import (
-	"bytes"
-	"crypto/sha256"
-	"encoding/binary"
-	"encoding/hex"
-	"encoding/json"
-	"flag"
-	"fmt"
-	"io"
-	"io/fs"
-	"log/slog"
-	"math"
 	"os"
 	"path/filepath"
-	"slices"
-	"strings"
 	"testing"
 
-	"golang.org/x/exp/maps"
-
 	"github.com/ollama/ollama/llm"
 )
 
-type tensorData struct {
-	Offsets []int  `json:"data_offsets"`
-	Type    string `json:"dtype"`
-	Shape   []int  `json:"shape"`
-}
-
-func convertFull(t *testing.T, fsys fs.FS) (*os.File, llm.KV, llm.Tensors) {
+func convertFull(t *testing.T, p string) (llm.KV, llm.Tensors) {
 	t.Helper()
 
+	mf, err := GetModelFormat(p)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	params, err := mf.GetParams(p)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	arch, err := mf.GetModelArch("", p, params)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	if err := arch.LoadVocab(); err != nil {
+		t.Fatal(err)
+	}
+
+	if err := arch.GetTensors(); err != nil {
+		t.Fatal(err)
+	}
+
 	f, err := os.CreateTemp(t.TempDir(), "f16")
 	if err != nil {
 		t.Fatal(err)
 	}
 	defer f.Close()
 
-	if err := ConvertModel(fsys, f); err != nil {
+	if err := arch.WriteGGUF(f); err != nil {
 		t.Fatal(err)
 	}
 
@@ -46,431 +50,54 @@ func convertFull(t *testing.T, fsys fs.FS) (*os.File, llm.KV, llm.Tensors) {
 	if err != nil {
 		t.Fatal(err)
 	}
-	t.Cleanup(func() { r.Close() })
+	defer r.Close()
 
-	m, _, err := llm.DecodeGGML(r, math.MaxInt)
+	m, _, err := llm.DecodeGGML(r)
 	if err != nil {
 		t.Fatal(err)
 	}
 
-	if _, err := r.Seek(0, io.SeekStart); err != nil {
-		t.Fatal(err)
-	}
-
-	return r, m.KV(), m.Tensors()
+	return 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))
-		}
+func TestConvertFull(t *testing.T) {
+	cases := []struct {
+		path    string
+		arch    string
+		tensors int
+		layers  int
+	}{
+		{"Meta-Llama-3-8B-Instruct", "llama", 291, 35},
+		{"Mistral-7B-Instruct-v0.2", "llama", 291, 35},
+		{"Mixtral-8x7B-Instruct-v0.1", "llama", 291, 35},
+		{"gemma-2b-it", "gemma", 164, 20},
 	}
 
-	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 TestConvertModel(t *testing.T) {
-	cases := []string{
-		"Meta-Llama-3-8B-Instruct",
-		"Meta-Llama-3.1-8B-Instruct",
-		"Mistral-7B-Instruct-v0.2",
-		"Mixtral-8x7B-Instruct-v0.1",
-		"gemma-2b-it",
-		"gemma-2-2b-it",
-		// microsoft/Phi-3-mini-128-instruct@d548c233192db00165d842bf8edff054bb3212f8
-		"Phi-3-mini-128k-instruct",
-		"all-MiniLM-L6-v2",
-		"gemma-2-9b-it",
-	}
-
-	for i := range cases {
-		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 {
+	for _, tt := range cases {
+		t.Run(tt.path, func(t *testing.T) {
+			p := filepath.Join("testdata", tt.path)
+			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)
+			kv, tensors := convertFull(t, p)
 
-			expectFile, err := os.Open(filepath.Join("testdata", fmt.Sprintf("%s.json", tt)))
-			if err != nil {
-				t.Fatal(err)
+			if kv.Architecture() != tt.arch {
+				t.Fatalf("expected llama, got %s", kv.Architecture())
 			}
 
-			var expect map[string]string
-			if err := json.NewDecoder(expectFile).Decode(&expect); err != nil {
-				t.Fatal(err)
+			if kv.FileType().String() != "F16" {
+				t.Fatalf("expected F16, got %s", kv.FileType())
 			}
 
-			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)
-				}
+			if len(tensors) != tt.tensors {
+				t.Fatalf("expected %d tensors, got %d", tt.tensors, len(tensors))
+			}
+
+			layers := tensors.Layers()
+			if len(layers) != tt.layers {
+				t.Fatalf("expected %d layers, got %d", tt.layers, len(layers))
 			}
 		})
 	}
 }
-
-func TestConvertInvalidTensorNames(t *testing.T) {
-	f, err := os.CreateTemp(t.TempDir(), "testmodel")
-	if err != nil {
-		t.Fatal(err)
-	}
-	defer f.Close()
-
-	tempDir := t.TempDir()
-
-	td := map[string]*tensorData{}
-	offset := 4096
-
-	td["model.layers.0.self_attn.q_proj.weight"] = &tensorData{
-		Offsets: []int{0, offset},
-		Type:    "F32",
-		Shape:   []int{4096, 4096},
-	}
-	td["blk.0.attn_q.weight"] = &tensorData{
-		Offsets: []int{offset, offset * 2},
-		Type:    "F32",
-		Shape:   []int{4096, 4096},
-	}
-	generateSafetensorTestData(t, tempDir, td)
-
-	err = ConvertModel(os.DirFS(tempDir), f)
-	if err == nil || !strings.HasPrefix(err.Error(), "duplicate tensor name") {
-		t.Errorf("expected error but didn't get one")
-	}
-}
-
-func TestConvertInvalidDatatype(t *testing.T) {
-	f, err := os.CreateTemp(t.TempDir(), "testmodel")
-	if err != nil {
-		t.Fatal(err)
-	}
-	defer f.Close()
-
-	tempDir := t.TempDir()
-
-	td := map[string]*tensorData{}
-	offset := 4096 * 14336
-
-	td["model.layers.0.mlp.down_proj.weight"] = &tensorData{
-		Offsets: []int{0, offset},
-		Type:    "I8",
-		Shape:   []int{4096, 14336},
-	}
-	td["model.layers.0.mlp.down_proj.weight_format"] = &tensorData{
-		Offsets: []int{offset, offset},
-		Type:    "U8",
-		Shape:   []int{},
-	}
-	generateSafetensorTestData(t, tempDir, td)
-
-	err = ConvertModel(os.DirFS(tempDir), f)
-	if err == nil || err.Error() != "unsupported safetensors model" {
-		t.Errorf("expected error but didn't get one")
-	}
-}
-
-func generateSafetensorTestData(t *testing.T, tempDir string, tensorData map[string]*tensorData) {
-	data, err := json.Marshal(tensorData)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	var buf bytes.Buffer
-
-	l := int64(len(data))
-	err = binary.Write(&buf, binary.LittleEndian, l)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	_, err = buf.Write(data)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	fdata, err := os.Create(filepath.Join(tempDir, "model-00001-of-00001.safetensors"))
-	if err != nil {
-		t.Fatal(err)
-	}
-	defer fdata.Close()
-
-	_, err = fdata.Write(buf.Bytes())
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	configData := `
-{
-  "architectures": [
-    "LlamaForCausalLM"
-  ]
-}
-`
-
-	f, err := os.Create(filepath.Join(tempDir, "config.json"))
-	if err != nil {
-		t.Fatal(err)
-	}
-	defer f.Close()
-
-	_, err = f.WriteString(configData)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	tokenizerData := `
-{
-}
-`
-
-	f, err = os.Create(filepath.Join(tempDir, "tokenizer.json"))
-	if err != nil {
-		t.Fatal(err)
-	}
-	defer f.Close()
-
-	_, err = f.WriteString(tokenizerData)
-	if err != nil {
-		t.Fatal(err)
-	}
-}
-
-func TestConvertAdapter(t *testing.T) {
-	type AdapterCase struct {
-		Name     string
-		BaseKV   map[string]any
-		Expected map[string]string
-	}
-
-	cases := []AdapterCase{
-		{
-			Name: "discollama",
-			BaseKV: map[string]any{
-				"general.architecture":          "llama",
-				"llama.attention.head_count":    uint32(32),
-				"llama.attention.head_count_kv": uint32(8),
-			},
-			Expected: map[string]string{
-				"general.architecture":          "llama",
-				"general.file_type":             "1",
-				"general.parameter_count":       "106496",
-				"general.type":                  "adapter",
-				"general.version":               "v0.2",
-				"adapter.lora.alpha":            "16",
-				"adapter.type":                  "lora",
-				"llama.attention.head_count":    "32",
-				"llama.attention.head_count_kv": "8",
-				"blk.31.attn_q.weight.lora_a":   "0eb3318b02cd313429bcc7621b539fdbb10240fea190c56c9e5f93fcd37a4e50",
-				"blk.31.attn_q.weight.lora_b":   "0eb3318b02cd313429bcc7621b539fdbb10240fea190c56c9e5f93fcd37a4e50",
-				"blk.31.attn_v.weight.lora_a":   "0eb3318b02cd313429bcc7621b539fdbb10240fea190c56c9e5f93fcd37a4e50",
-				"blk.31.attn_v.weight.lora_b":   "071dcafe89df065d6e1c935ecb8fdf6479b3c202eb912e7da938597673ff5857",
-			},
-		},
-	}
-
-	for _, c := range cases {
-		t.Run(c.Name, func(t *testing.T) {
-			t.Parallel()
-
-			f, err := os.CreateTemp(t.TempDir(), "f16")
-			if err != nil {
-				t.Fatal(err)
-			}
-			defer f.Close()
-
-			tempDir := t.TempDir()
-			generateLoraTestData(t, tempDir)
-
-			if err = ConvertAdapter(os.DirFS(tempDir), f, c.BaseKV); err != nil {
-				t.Fatal(err)
-			}
-
-			r, err := os.Open(f.Name())
-			if err != nil {
-				t.Fatal(err)
-			}
-			defer r.Close()
-
-			m, _, err := llm.DecodeGGML(r, math.MaxInt)
-			if err != nil {
-				t.Fatal(err)
-			}
-
-			if _, err := r.Seek(0, io.SeekStart); err != nil {
-				t.Fatal(err)
-			}
-
-			actual := generateResultsJSON(t, r, m.KV(), m.Tensors())
-
-			keys := maps.Keys(c.Expected)
-			slices.Sort(keys)
-			for _, k := range keys {
-				if v, ok := actual[k]; !ok {
-					t.Errorf("missing %s", k)
-				} else if v != c.Expected[k] {
-					t.Errorf("unexpected %s: want %s, got %s", k, c.Expected[k], v)
-				}
-			}
-		})
-	}
-}
-
-func generateLoraTestData(t *testing.T, tempDir string) {
-	offset := 4096 * 8 * 4
-
-	td := map[string]*tensorData{"__metadata__": nil}
-	td["model.layers.31.self_attn.q_proj.lora_a"] = &tensorData{
-		Offsets: []int{0, offset},
-		Type:    "F32",
-		Shape:   []int{4096, 8},
-	}
-	td["model.layers.31.self_attn.q_proj.lora_b"] = &tensorData{
-		Offsets: []int{offset, offset * 2},
-		Type:    "F32",
-		Shape:   []int{8, 4096},
-	}
-	td["model.layers.31.self_attn.v_proj.lora_a"] = &tensorData{
-		Offsets: []int{offset * 2, offset * 3},
-		Type:    "F32",
-		Shape:   []int{4096, 8},
-	}
-	td["model.layers.31.self_attn.v_proj.lora_b"] = &tensorData{
-		Offsets: []int{offset * 3, offset*3 + 8*1024*4},
-		Type:    "F32",
-		Shape:   []int{8, 1024},
-	}
-
-	data, err := json.Marshal(td)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	var buf bytes.Buffer
-
-	l := int64(len(data))
-	err = binary.Write(&buf, binary.LittleEndian, l)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	_, err = buf.Write(data)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	// write some data for the tensors
-
-	ones := make([]float32, 4096*8)
-	for i := range ones {
-		ones[i] = float32(1)
-	}
-
-	for range 3 {
-		err = binary.Write(&buf, binary.LittleEndian, ones)
-		if err != nil {
-			t.Fatal(err)
-		}
-	}
-
-	ones = make([]float32, 1024*8)
-	for i := range ones {
-		ones[i] = float32(1)
-	}
-
-	err = binary.Write(&buf, binary.LittleEndian, ones)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	fdata, err := os.Create(filepath.Join(tempDir, "adapters.safetensors"))
-	if err != nil {
-		t.Fatal(err)
-	}
-	defer fdata.Close()
-
-	_, err = fdata.Write(buf.Bytes())
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	configData := `
-{
-    "adapter_path": "adapters-test",
-    "batch_size": 8,
-    "config": "config-tiny.json",
-    "data": "../discollama-completion",
-    "grad_checkpoint": null,
-    "iters": 1000,
-    "learning_rate": 1e-05,
-    "lora_layers": 1,
-    "lora_parameters": {
-        "rank": 8,
-        "alpha": 16,
-        "dropout": 0.0,
-        "scale": 2.0
-    },
-    "lr_schedule": null,
-    "max_seq_length": 2048,
-    "model": "/Users/pdevine/git/Meta-Llama-3-8B-Instruct",
-    "resume_adapter_file": null,
-    "save_every": 100,
-    "seed": 0,
-    "steps_per_eval": 200,
-    "steps_per_report": 10,
-    "test": false,
-    "test_batches": 500,
-    "train": true,
-    "use_dora": false,
-    "val_batches": 25
-}
-`
-	f, err := os.Create(filepath.Join(tempDir, "adapter_config.json"))
-	if err != nil {
-		t.Fatal(err)
-	}
-	defer f.Close()
-
-	_, err = f.WriteString(configData)
-	if err != nil {
-		t.Fatal(err)
-	}
-}

convert/fs.go

@@ -1,58 +0,0 @@
-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

@@ -0,0 +1,102 @@
+package convert
+
+import (
+	"fmt"
+	"io"
+	"log/slog"
+	"strings"
+
+	"github.com/pdevine/tensor"
+	"github.com/pdevine/tensor/native"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type GemmaModel struct {
+	ModelData
+}
+
+func addOnes(data []float32, vectorSize int) ([]float32, error) {
+	n := tensor.New(tensor.WithShape(vectorSize), tensor.WithBacking(data))
+	ones := tensor.Ones(tensor.Float32, vectorSize)
+
+	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
+}
+
+func (m *GemmaModel) GetTensors() error {
+	t, err := m.Format.GetTensors(m.Path, m.Params)
+	if err != nil {
+		return err
+	}
+
+	slog.Debug(fmt.Sprintf("Total tensors: %d", len(t)))
+	for _, l := range t {
+		if strings.HasSuffix(l.Name, "norm.weight") {
+			wt := l.WriterTo.(safetensorWriterTo)
+			wt.repacker = m.Repack
+			l.WriterTo = wt
+		}
+		m.Tensors = append(m.Tensors, l)
+	}
+
+	return nil
+}
+
+func (m *GemmaModel) LoadVocab() error {
+	v, err := LoadSentencePieceTokens(m.Path, m.Params)
+	if err != nil {
+		return err
+	}
+	m.Vocab = v
+	return nil
+}
+
+func (m *GemmaModel) Repack(_ string, data []float32, shape []uint64) ([]float32, error) {
+	return addOnes(data, int(shape[0]))
+}
+
+func (m *GemmaModel) WriteGGUF(ws io.WriteSeeker) 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,
+	}
+
+	return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors)
+}

convert/llama.go

@@ -0,0 +1,159 @@
+package convert
+
+import (
+	"cmp"
+	"errors"
+	"fmt"
+	"io"
+	"os"
+	"path/filepath"
+	"regexp"
+	"strings"
+
+	"github.com/pdevine/tensor"
+	"github.com/pdevine/tensor/native"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type LlamaModel struct {
+	ModelData
+}
+
+func (m *LlamaModel) GetTensors() error {
+	t, err := m.Format.GetTensors(m.Path, m.Params)
+	if err != nil {
+		return err
+	}
+
+	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 {
+			switch m.Format.(type) {
+			case *TorchFormat:
+				wt := l.WriterTo.(torchWriterTo)
+				wt.repacker = m.Repack
+				l.WriterTo = wt
+			case *SafetensorFormat:
+				wt := l.WriterTo.(safetensorWriterTo)
+				wt.repacker = m.Repack
+				l.WriterTo = wt
+			}
+		}
+		m.Tensors = append(m.Tensors, l)
+	}
+
+	return nil
+}
+
+func (m *LlamaModel) LoadVocab() (err error) {
+	pre, ts, merges, err := parseTokens(filepath.Join(m.Path, "tokenizer.json"))
+	if errors.Is(err, os.ErrNotExist) {
+		return nil
+	} else if err != nil {
+		return err
+	}
+
+	m.Vocab = &Vocab{}
+	for _, t := range ts {
+		m.Vocab.Tokens = append(m.Vocab.Tokens, t.Content)
+		m.Vocab.Types = append(m.Vocab.Types, t.Type())
+	}
+
+	m.Vocab.Merges = merges
+	m.Params.PreTokenizer = pre
+	return nil
+}
+
+func (m *LlamaModel) WriteGGUF(ws io.WriteSeeker) error {
+	kv := llm.KV{
+		"general.architecture":                   "llama",
+		"general.name":                           m.Name,
+		"llama.vocab_size":                       uint32(len(m.Vocab.Tokens)),
+		"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.freq_base":                   float32(m.Params.RopeFrequencyBase),
+		"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":                   "gpt2",
+
+		"tokenizer.ggml.pre":        m.Params.PreTokenizer,
+		"tokenizer.ggml.tokens":     m.Vocab.Tokens,
+		"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.unknown_token_id": uint32(0),
+	}
+
+	if len(m.Vocab.Merges) > 0 {
+		kv["tokenizer.ggml.merges"] = m.Vocab.Merges
+	} else {
+		kv["tokenizer.ggml.scores"] = m.Vocab.Scores
+	}
+
+	return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors)
+}
+
+func (m *LlamaModel) Repack(name string, data []float32, shape []uint64) ([]float32, error) {
+	return llamaRepack(name, m.Params, data, shape)
+}
+
+func llamaRepack(name string, params *Params, data []float32, shape []uint64) ([]float32, error) {
+	var dims []int
+	for _, dim := range shape {
+		if dim != 0 {
+			dims = append(dims, int(dim))
+		}
+	}
+
+	var heads int
+	switch {
+	case strings.HasSuffix(name, "attn_q.weight"):
+		heads = params.AttentionHeads
+	case strings.HasSuffix(name, "attn_k.weight"):
+		heads = cmp.Or(params.KeyValHeads, params.AttentionHeads)
+	default:
+		return nil, fmt.Errorf("unknown tensor name: %s", name)
+	}
+
+	n := tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
+	if err := n.Reshape(append([]int{heads, 2, dims[0] / 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/mistral.go

@@ -0,0 +1,79 @@
+package convert
+
+import (
+	"io"
+	"regexp"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type MistralModel struct {
+	ModelData
+}
+
+func (m *MistralModel) GetTensors() error {
+	t, err := m.Format.GetTensors(m.Path, m.Params)
+	if err != nil {
+		return err
+	}
+
+	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.repacker = m.Repack
+			l.WriterTo = wt
+		}
+		m.Tensors = append(m.Tensors, l)
+	}
+
+	return nil
+}
+
+func (m *MistralModel) LoadVocab() error {
+	v, err := LoadSentencePieceTokens(m.Path, m.Params)
+	if err != nil {
+		return err
+	}
+	m.Vocab = v
+	return nil
+}
+
+func (m *MistralModel) WriteGGUF(ws io.WriteSeeker) 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),
+	}
+
+	return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors)
+}
+
+func (m *MistralModel) Repack(name string, data []float32, shape []uint64) ([]float32, error) {
+	return llamaRepack(name, m.Params, data, shape)
+}

convert/mixtral.go

@@ -0,0 +1,87 @@
+package convert
+
+import (
+	"io"
+	"regexp"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type MixtralModel struct {
+	ModelData
+}
+
+func (m *MixtralModel) GetTensors() error {
+	t, err := m.Format.GetTensors(m.Path, m.Params)
+	if err != nil {
+		return err
+	}
+
+	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.repacker = m.Repack
+			l.WriterTo = wt
+		}
+		m.Tensors = append(m.Tensors, l)
+	}
+
+	return nil
+}
+
+func (m *MixtralModel) LoadVocab() error {
+	v, err := LoadSentencePieceTokens(m.Path, m.Params)
+	if err != nil {
+		return err
+	}
+	m.Vocab = v
+	return nil
+}
+
+func (m *MixtralModel) WriteGGUF(ws io.WriteSeeker) error {
+	kv := llm.KV{
+		"general.architecture":          "llama",
+		"general.name":                  m.Name,
+		"llama.block_count":             uint32(m.Params.HiddenLayers),
+		"llama.context_length":          uint32(m.Params.ContextSize),
+		"llama.embedding_length":        uint32(m.Params.HiddenSize),
+		"llama.feed_forward_length":     uint32(m.Params.IntermediateSize),
+		"llama.attention.head_count":    uint32(m.Params.AttentionHeads),
+		"llama.attention.head_count_kv": uint32(m.Params.KeyValHeads),
+
+		"llama.rope.freq_base":                   float32(m.Params.RopeFrequencyBase),
+		"llama.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
+
+		"llama.expert_count":      uint32(m.Params.Experts),
+		"llama.expert_used_count": uint32(m.Params.ExpertsUsed),
+
+		"llama.vocab_size":           uint32(len(m.Vocab.Tokens)),
+		"llama.rope.dimension_count": uint32(m.Params.HiddenSize / m.Params.AttentionHeads),
+
+		"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.unknown_token_id": uint32(0),
+		"tokenizer.ggml.add_bos_token":    true,
+		"tokenizer.ggml.add_eos_token":    false,
+	}
+
+	return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors)
+}
+
+func (m *MixtralModel) Repack(name string, data []float32, shape []uint64) ([]float32, error) {
+	return llamaRepack(name, m.Params, data, shape)
+}

convert/reader.go

@@ -1,86 +0,0 @@
-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

@@ -1,163 +0,0 @@
-package convert
-
-import (
-	"bytes"
-	"encoding/binary"
-	"encoding/json"
-	"errors"
-	"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)
-
-		names := make(map[string]struct{}, len(keys))
-
-		for _, key := range keys {
-			if value := headers[key]; value.Type != "" {
-				// bitsandbytes quantized models are unsupported
-				if len(value.Shape) == 0 {
-					return nil, errors.New("unsupported safetensors model")
-				}
-				ggufName := replacer.Replace(key)
-				if _, ok := names[ggufName]; ok {
-					return nil, fmt.Errorf("duplicate tensor name '%s' was found for this model", ggufName)
-				}
-				names[ggufName] = struct{}{}
-				ts = append(ts, safetensor{
-					fs:     fsys,
-					path:   p,
-					dtype:  value.Type,
-					offset: safetensorsPad(n, value.Offsets[0]),
-					size:   safetensorsPad(n, value.Offsets[1]) - safetensorsPad(n, value.Offsets[0]),
-					tensorBase: &tensorBase{
-						name:  ggufName,
-						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

@@ -1,48 +0,0 @@
-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/safetensors.go

@@ -0,0 +1,309 @@
+package convert
+
+import (
+	"bytes"
+	"encoding/binary"
+	"encoding/json"
+	"fmt"
+	"io"
+	"os"
+	"path/filepath"
+	"regexp"
+	"slices"
+	"strings"
+
+	"github.com/d4l3k/go-bfloat16"
+	"github.com/x448/float16"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type safetensorWriterTo struct {
+	t *llm.Tensor
+
+	params *Params
+	bo     ByteOrder
+
+	filename string
+	dtype    string
+
+	offset, size int64
+	repacker     func(string, []float32, []uint64) ([]float32, error)
+}
+
+type safetensorMetadata struct {
+	Type    string   `json:"dtype"`
+	Shape   []uint64 `json:"shape"`
+	Offsets []int64  `json:"data_offsets"`
+}
+
+type SafetensorFormat struct{}
+
+func (m *SafetensorFormat) GetTensors(dirpath string, params *Params) ([]llm.Tensor, error) {
+	var tensors []llm.Tensor
+	matches, err := filepath.Glob(filepath.Join(dirpath, "*.safetensors"))
+	if err != nil {
+		return nil, err
+	}
+
+	var offset uint64
+	for _, f := range matches {
+		var t []llm.Tensor
+		var err error
+		t, offset, err = m.readTensors(f, offset, params)
+		if err != nil {
+			return nil, err
+		}
+
+		tensors = append(tensors, t...)
+	}
+	return tensors, nil
+}
+
+func (m *SafetensorFormat) readTensors(fn string, offset uint64, params *Params) ([]llm.Tensor, uint64, error) {
+	f, err := os.Open(fn)
+	if err != nil {
+		return nil, 0, err
+	}
+	defer f.Close()
+
+	var n int64
+	if err := binary.Read(f, binary.LittleEndian, &n); err != nil {
+		return nil, 0, err
+	}
+
+	b := bytes.NewBuffer(make([]byte, 0, n))
+	if _, err = io.CopyN(b, f, n); err != nil {
+		return nil, 0, err
+	}
+
+	var headers map[string]safetensorMetadata
+	if err := json.NewDecoder(b).Decode(&headers); err != nil {
+		return nil, 0, err
+	}
+
+	var keys []string
+	for key := range headers {
+		if !strings.HasSuffix(key, "self_attn.rotary_embd.inv_freq") {
+			keys = append(keys, key)
+		}
+	}
+
+	slices.Sort(keys)
+
+	var tensors []llm.Tensor
+	for _, key := range keys {
+		value := headers[key]
+
+		var kind uint32
+		switch len(value.Shape) {
+		case 0:
+			// valuedata
+			continue
+		case 2:
+			kind = 1
+		}
+
+		name, err := m.GetLayerName(key)
+		if err != nil {
+			return nil, 0, err
+		}
+
+		shape := make([]uint64, len(value.Shape))
+		copy(shape, value.Shape)
+
+		pad := func(s int64) int64 {
+			return 8 + n + s
+		}
+
+		t := llm.Tensor{
+			Name:   name,
+			Kind:   kind,
+			Offset: offset,
+			Shape:  shape,
+		}
+
+		t.WriterTo = safetensorWriterTo{
+			t:        &t,
+			params:   params,
+			bo:       params.ByteOrder,
+			filename: fn,
+			dtype:    value.Type,
+			offset:   pad(value.Offsets[0]),
+			size:     pad(value.Offsets[1]) - pad(value.Offsets[0]),
+		}
+
+		offset += t.Size()
+		tensors = append(tensors, t)
+	}
+
+	return tensors, offset, nil
+}
+
+func (m *SafetensorFormat) GetParams(dirpath string) (*Params, error) {
+	f, err := os.Open(filepath.Join(dirpath, "config.json"))
+	if err != nil {
+		return nil, err
+	}
+	defer f.Close()
+
+	var params Params
+
+	if err := json.NewDecoder(f).Decode(&params); err != nil {
+		return nil, err
+	}
+
+	params.ByteOrder = binary.LittleEndian
+	return &params, nil
+}
+
+func (m *SafetensorFormat) GetLayerName(n string) (string, error) {
+	directMap := map[string]string{
+		"model.embed_tokens.weight": "token_embd.weight",
+		"lm_head.weight":            "output.weight",
+		"model.norm.weight":         "output_norm.weight",
+	}
+
+	tMap := map[string]string{
+		"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",
+		"model.layers.(\\d+).block_sparse_moe.gate.weight":              "blk.$1.ffn_gate_inp.weight",
+		"model.layers.(\\d+).block_sparse_moe.experts.(\\d+).w1.weight": "blk.$1.ffn_gate.$2.weight",
+		"model.layers.(\\d+).block_sparse_moe.experts.(\\d+).w2.weight": "blk.$1.ffn_down.$2.weight",
+		"model.layers.(\\d+).block_sparse_moe.experts.(\\d+).w3.weight": "blk.$1.ffn_up.$2.weight",
+	}
+
+	v, ok := directMap[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)
+}
+
+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(r.offset, io.SeekStart); err != nil {
+		return 0, err
+	}
+
+	var f32s []float32
+	switch r.dtype {
+	case "F32":
+		f32s = make([]float32, r.size/4)
+		if err = binary.Read(f, r.bo, f32s); err != nil {
+			return 0, err
+		}
+	case "F16":
+		u16s := make([]uint16, r.size/2)
+		if err = binary.Read(f, r.bo, u16s); err != nil {
+			return 0, err
+		}
+
+		for _, b := range u16s {
+			f32s = append(f32s, float16.Frombits(b).Float32())
+		}
+
+	case "BF16":
+		u8s := make([]uint8, r.size)
+		if err = binary.Read(f, r.bo, u8s); err != nil {
+			return 0, err
+		}
+
+		f32s = bfloat16.DecodeFloat32(u8s)
+	default:
+		return 0, fmt.Errorf("unknown data type: %s", r.dtype)
+	}
+
+	if r.repacker != nil {
+		f32s, err = r.repacker(r.t.Name, f32s, r.t.Shape)
+		if err != nil {
+			return 0, err
+		}
+	}
+
+	switch r.t.Kind {
+	case 0:
+		return 0, binary.Write(w, r.bo, f32s)
+	case 1:
+		f16s := make([]uint16, len(f32s))
+		for i := range f32s {
+			f16s[i] = float16.Fromfloat32(f32s[i]).Bits()
+		}
+
+		return 0, binary.Write(w, r.bo, f16s)
+	default:
+		return 0, fmt.Errorf("unknown storage type: %d", r.t.Kind)
+	}
+}
+
+func (m *SafetensorFormat) GetModelArch(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 "LlamaForCausalLM":
+			return &LlamaModel{
+				ModelData{
+					Name:   name,
+					Path:   dirPath,
+					Params: params,
+					Format: m,
+				},
+			}, nil
+		case "MistralForCausalLM":
+			return &MistralModel{
+				ModelData{
+					Name:   name,
+					Path:   dirPath,
+					Params: params,
+					Format: m,
+				},
+			}, nil
+		case "MixtralForCausalLM":
+			return &MixtralModel{
+				ModelData{
+					Name:   name,
+					Path:   dirPath,
+					Params: params,
+					Format: m,
+				},
+			}, nil
+		case "GemmaForCausalLM":
+			return &GemmaModel{
+				ModelData{
+					Name:   name,
+					Path:   dirPath,
+					Params: params,
+					Format: m,
+				},
+			}, nil
+		default:
+			return nil, fmt.Errorf("Models based on '%s' are not yet supported", params.Architectures[0])
+		}
+	}
+
+	return nil, fmt.Errorf("Unknown error")
+}

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

@@ -1,313 +0,0 @@
-{
-  "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",
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-  "output_norm.weight": "7cc5b7ce10e5082000fa00bfa68af8c7c5da218e59e2c41cf2f1499d40ca229e"
-}

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

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

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

@@ -1,313 +0,0 @@
-{
-  "general.architecture": "llama",
-  "general.file_type": "1",
-  "general.quantization_version": "2",
-  "llama.block_count": "32",
-  "llama.context_length": "32768",
-  "llama.embedding_length": "4096",
-  "llama.feed_forward_length": "14336",
-  "llama.attention.head_count": "32",
-  "llama.attention.head_count_kv": "8",
-  "llama.attention.layer_norm_rms_epsilon": "1e-05",
-  "llama.rope.dimension_count": "128",
-  "tokenizer.ggml.model": "llama",
-  "tokenizer.ggml.add_bos_token": "true",
-  "tokenizer.ggml.add_eos_token": "false",
-  "tokenizer.ggml.bos_token_id": "1",
-  "tokenizer.ggml.eos_token_id": "2",
-  "tokenizer.ggml.unknown_token_id": "0",
-  "tokenizer.ggml.scores": "e3d3eea80bb41a1213f2d0aa3e8a38581d1f19323be77dbd779c9c7e3b72e676",
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-}

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

@@ -1,348 +0,0 @@
-{
-  "general.architecture": "llama",
-  "general.file_type": "1",
-  "general.quantization_version": "2",
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-  "llama.context_length": "32768",
-  "llama.embedding_length": "4096",
-  "llama.feed_forward_length": "14336",
-  "llama.rope.dimension_count": "128",
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-  "llama.attention.head_count": "32",
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-  "llama.attention.layer_norm_rms_epsilon": "1e-05",
-  "llama.expert_count": "8",
-  "llama.expert_used_count": "2",
-  "tokenizer.ggml.model": "llama",
-  "tokenizer.ggml.add_bos_token": "true",
-  "tokenizer.ggml.add_eos_token": "false",
-  "tokenizer.ggml.bos_token_id": "1",
-  "tokenizer.ggml.eos_token_id": "2",
-  "tokenizer.ggml.unknown_token_id": "0",
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-}

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

@@ -1,225 +0,0 @@
-{
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-  "general.file_type": "1",
-  "general.quantization_version": "2",
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convert/testdata/all-MiniLM-L6-v2.json

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

@@ -1,312 +0,0 @@
-{
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-}

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

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

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

@@ -1,188 +0,0 @@
-{
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-  "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

@@ -1,12 +1,10 @@
 package convert
 
 import (
+	"cmp"
 	"crypto/sha256"
-	"encoding/hex"
 	"encoding/json"
-	"errors"
 	"fmt"
-	"io/fs"
 	"log/slog"
 	"os"
 	"slices"
@@ -14,152 +12,10 @@ import (
 	"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 {
-			var s []struct {
-				Name     string `json:"name"`
-				Template string `json:"template"`
-			}
-			if err := json.Unmarshal(template, &t.Template); err == nil {
-				// noop
-			} else if err := json.Unmarshal(template, &s); err == nil {
-				for _, e := range s {
-					if e.Name == "default" {
-						t.Template = e.Template
-						break
-					}
-				}
-			} else {
-				return nil, fmt.Errorf("invalid chat_template: %w", err)
-			}
-		}
-
-		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 {
-	AddedTokens []token `json:"added_tokens"`
-	Model       struct {
-		Type   string         `json:"type"`
-		Vocab  map[string]int `json:"vocab"`
-		Merges []string       `json:"merges"`
-	} `json:"model"`
+	Version     string         `json:"version"`
+	AddedTokens []Token        `json:"added_tokens"`
+	Model       TokenizerModel `json:"model"`
 
 	PreTokenizer struct {
 		PreTokenizers []struct {
@@ -171,108 +27,80 @@ type tokenizer struct {
 	} `json:"pre_tokenizer"`
 }
 
-type token struct {
+type TokenizerModel struct {
+	Type   string         `json:"type"`
+	Vocab  map[string]int `json:"vocab"`
+	Merges []string       `json:"merges"`
+	Tokens []Token
+}
+
+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 (t *Token) Type() int32 {
+	switch {
+	case t.Special:
+		return tokenTypeControl
+	case t.UserDefined:
+		return tokenTypeUserDefined
+	default:
+		return tokenTypeNormal
+	}
 }
 
-func parseVocabularyFromTokenizer(fsys fs.FS) (*Vocabulary, error) {
-	f, err := fsys.Open("tokenizer.json")
+func (t *Tokenizer) maxID() int {
+	return max(
+		slices.Max(maps.Values(t.Model.Vocab)),
+		slices.MaxFunc(t.AddedTokens, func(a, b Token) int {
+			return cmp.Compare(a.ID, b.ID)
+		}).ID,
+	)
+}
+
+func parseTokens(dirpath string) (pre string, tokens []Token, merges []string, err error) {
+	f, err := os.Open(dirpath)
 	if err != nil {
-		return nil, err
+		panic(err)
 	}
 	defer f.Close()
 
-	var t tokenizer
+	var t Tokenizer
 	if err := json.NewDecoder(f).Decode(&t); err != nil {
-		return nil, err
+		return "", nil, nil, err
 	}
 
-	tokens := make(map[int]token, len(t.Model.Vocab))
+	tokens = make([]Token, t.maxID()+1)
 	for k, v := range t.Model.Vocab {
-		tokens[v] = token{
-			ID:      v,
-			Content: k,
+		tokens[v] = Token{ID: v, Content: k, Special: false, UserDefined: false}
+	}
+
+	for _, v := range t.AddedTokens {
+		v.UserDefined = true
+		tokens[v.ID] = v
+	}
+
+	sha256sum := sha256.New()
+	for _, pt := range t.PreTokenizer.PreTokenizers {
+		if pt.Type == "Split" && pt.Pattern.Regex != "" {
+			sha256sum.Write([]byte(pt.Pattern.Regex))
 		}
 	}
 
-	for _, token := range t.AddedTokens {
-		token.UserDefined = true
-		tokens[token.ID] = token
+	switch digest := fmt.Sprintf("%x", sha256sum.Sum(nil)); digest {
+	case "d98f9631be1e9607a9848c26c1f9eac1aa9fc21ac6ba82a2fc0741af9780a48f":
+		pre = "llama-bpe"
+	case "03df5c5863ad70781dcfdef491ead25140f895fe8010964be0daefe27be32b02":
+		pre = "deepseek-llm"
+	case "21cde974d587f0d54dc8d56b183cc1e6239600172035c68fbd6d4b9f8da0576e":
+		pre = "deepseek-coder"
+	default:
+		slog.Warn("unknown pretokenizer, using default", "digest", digest)
+		pre = "default"
 	}
 
-	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 tokenizer 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")
+	return pre, tokens, t.Model.Merges, nil
 }

convert/tokenizer_spm.go

@@ -1,113 +0,0 @@
-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
-}

convert/tokenizer_test.go

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

convert/torch.go

@@ -0,0 +1,287 @@
+package convert
+
+import (
+	"encoding/binary"
+	"encoding/json"
+	"fmt"
+	"io"
+	"log/slog"
+	"os"
+	"path/filepath"
+	"regexp"
+	"strings"
+
+	"github.com/nlpodyssey/gopickle/pytorch"
+	"github.com/nlpodyssey/gopickle/types"
+	"github.com/x448/float16"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type torchWriterTo struct {
+	t *llm.Tensor
+
+	params *Params
+	bo     ByteOrder
+
+	storage  pytorch.StorageInterface
+	repacker func(string, []float32, []uint64) ([]float32, error)
+}
+
+type TorchFormat struct{}
+
+func (tf *TorchFormat) GetTensors(dirpath string, params *Params) ([]llm.Tensor, error) {
+	slog.Debug("getting torch tensors")
+
+	var files []string
+	if pt, _ := filepath.Glob(filepath.Join(dirpath, "consolidated*.pth")); len(pt) > 0 {
+		files = append(files, pt...)
+	} else if pt, _ := filepath.Glob(filepath.Join(dirpath, "pytorch_model*.pth")); len(pt) > 0 {
+		files = append(files, pt...)
+	}
+
+	var offset uint64
+	var tensors []llm.Tensor
+	for _, fn := range files {
+		m, err := pytorch.Load(fn)
+		if err != nil {
+			slog.Error(fmt.Sprintf("error unpickling: %q", err))
+			return []llm.Tensor{}, err
+		}
+
+		for _, k := range m.(*types.Dict).Keys() {
+			if strings.HasSuffix(k.(string), "self_attn.rotary_emb.inv_freq") {
+				continue
+			}
+
+			t, _ := m.(*types.Dict).Get(k)
+			tshape := t.(*pytorch.Tensor).Size
+
+			var size uint64
+			var kind uint32
+			switch len(tshape) {
+			case 0:
+				continue
+			case 1:
+				// convert to float32
+				kind = 0
+				size = uint64(tshape[0] * 4)
+			case 2:
+				// convert to float16
+				kind = 1
+				size = uint64(tshape[0] * tshape[1] * 2)
+			}
+
+			ggufName, err := tf.GetLayerName(k.(string))
+			if err != nil {
+				slog.Error(err.Error())
+				return nil, err
+			}
+			slog.Debug(fmt.Sprintf("'%35s': '%30s' %10d [%#v]", k.(string), ggufName, size, tshape))
+
+			shape := []uint64{0, 0, 0, 0}
+			for i := range tshape {
+				shape[i] = uint64(tshape[i])
+			}
+
+			tensor := llm.Tensor{
+				Name:   ggufName,
+				Kind:   kind,
+				Offset: offset, // calculate the offset
+				Shape:  shape,
+			}
+
+			tensor.WriterTo = torchWriterTo{
+				t:       &tensor,
+				params:  params,
+				bo:      params.ByteOrder,
+				storage: t.(*pytorch.Tensor).Source,
+			}
+
+			tensors = append(tensors, tensor)
+			offset += size
+		}
+	}
+
+	return tensors, nil
+}
+
+func getAltParams(dirpath string) (*Params, error) {
+	f, err := os.Open(filepath.Join(dirpath, "params.json"))
+	if err != nil {
+		slog.Error("no params.json")
+		return nil, err
+	}
+	defer f.Close()
+
+	type TorchParams struct {
+		HiddenSize     int     `json:"dim"`
+		AttentionHeads int     `json:"n_heads"`
+		KeyValHeads    int     `json:"n_kv_heads"`
+		HiddenLayers   int     `json:"n_layers"`
+		RopeTheta      float64 `json:"rope_theta"`
+		NormEPS        float64 `json:"norm_eps"`
+	}
+
+	var tparams TorchParams
+
+	d := json.NewDecoder(f)
+	err = d.Decode(&tparams)
+	if err != nil {
+		return nil, err
+	}
+
+	params := &Params{
+		Architectures:  []string{"LlamaForCausalLM"},
+		HiddenSize:     tparams.HiddenSize,
+		AttentionHeads: tparams.AttentionHeads,
+		KeyValHeads:    tparams.KeyValHeads,
+		HiddenLayers:   tparams.HiddenLayers,
+		NormEPS:        tparams.NormEPS,
+	}
+
+	switch {
+	case tparams.RopeTheta == 1000000:
+		// Codellama
+		params.ContextSize = 16384
+	case tparams.NormEPS == 1e-06:
+		// llama2
+		slog.Debug("Found llama2 - setting context size to 4096")
+		params.ContextSize = 4096
+	default:
+		params.ContextSize = 2048
+	}
+
+	params.ByteOrder = binary.LittleEndian
+	return params, nil
+}
+
+func (m *TorchFormat) GetParams(dirpath string) (*Params, error) {
+	f, err := os.Open(filepath.Join(dirpath, "config.json"))
+	if err != nil {
+		if os.IsNotExist(err) {
+			// try params.json instead
+			return getAltParams(dirpath)
+		} else {
+			return nil, err
+		}
+	}
+
+	var params Params
+	d := json.NewDecoder(f)
+	err = d.Decode(&params)
+	if err != nil {
+		return nil, err
+	}
+
+	params.ByteOrder = binary.LittleEndian
+	return &params, nil
+}
+
+func (m *TorchFormat) GetLayerName(n string) (string, error) {
+	directMap := map[string]string{
+		"tok_embeddings.weight":     "token_embd.weight",
+		"output.weight":             "output.weight",
+		"norm.weight":               "output_norm.weight",
+		"rope.freqs":                "rope_freqs.weight",
+		"model.embed_tokens.weight": "token_embd.weight",
+		"lm_head.weight":            "output.weight",
+		"model.norm.weight":         "output_norm.weight",
+	}
+
+	lMap := map[string]string{
+		"layers.(\\d+).attention_norm.weight":                 "blk.$1.attn_norm.weight",
+		"layers.(\\d+).attention_output_norm.weight":          "blk.$1.attn_norm.weight",
+		"layers.(\\d+).feed_forward.w2.weight":                "blk.$1.ffn_down.weight",
+		"layers.(\\d+).feed_forward.w1.weight":                "blk.$1.ffn_gate.weight",
+		"layers.(\\d+).feed_forward.w3.weight":                "blk.$1.ffn_up.weight",
+		"layers.(\\d+).ffn_norm.weight":                       "blk.$1.ffn_norm.weight",
+		"layers.(\\d+).attention.wk.weight":                   "blk.$1.attn_k.weight",
+		"layers.(\\d+).attention.wo.weight":                   "blk.$1.attn_output.weight",
+		"layers.(\\d+).attention.wq.weight":                   "blk.$1.attn_q.weight",
+		"layers.(\\d+).attention.wv.weight":                   "blk.$1.attn_v.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",
+	}
+
+	v, ok := directMap[n]
+	if ok {
+		return v, nil
+	}
+
+	// quick hack to rename the layers to gguf format
+	for k, v := range lMap {
+		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)
+}
+
+func (r torchWriterTo) WriteTo(w io.Writer) (n int64, err error) {
+	var f32s []float32
+	switch s := r.storage.(type) {
+	case *pytorch.FloatStorage:
+		f32s = s.Data
+	case *pytorch.HalfStorage:
+		f32s = s.Data
+	case *pytorch.BFloat16Storage:
+		f32s = s.Data
+	default:
+		return 0, fmt.Errorf("unknown data type: %T", s)
+	}
+
+	if r.repacker != nil {
+		f32s, err = r.repacker(r.t.Name, f32s, r.t.Shape)
+		if err != nil {
+			return 0, err
+		}
+	}
+
+	switch r.t.Kind {
+	case 0:
+		return 0, binary.Write(w, r.bo, f32s)
+	case 1:
+		f16s := make([]uint16, len(f32s))
+		for i := range f32s {
+			f16s[i] = float16.Fromfloat32(f32s[i]).Bits()
+		}
+
+		return 0, binary.Write(w, r.bo, f16s)
+	default:
+		return 0, fmt.Errorf("unknown storage type: %d", r.t.Kind)
+	}
+}
+
+func (m *TorchFormat) GetModelArch(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 "LlamaForCausalLM":
+			return &LlamaModel{
+				ModelData{
+					Name:   name,
+					Path:   dirPath,
+					Params: params,
+					Format: m,
+				},
+			}, nil
+		default:
+			return nil, fmt.Errorf("Models based on '%s' are not yet supported", params.Architectures[0])
+		}
+	}
+
+	return nil, fmt.Errorf("Unknown error")
+}

docs/api.md

@@ -40,7 +40,6 @@ 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):
@@ -58,8 +57,7 @@ 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.
 
-> [!IMPORTANT]
-> It's important to instruct the model to use JSON in the `prompt`. Otherwise, the model may generate large amounts whitespace.
+> Note: it's important to instruct the model to use JSON in the `prompt`. Otherwise, the model may generate large amounts whitespace.
 
 ### Examples
 
@@ -150,44 +148,8 @@ 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
@@ -336,7 +298,6 @@ 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,
@@ -419,14 +380,12 @@ 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`, `assistant`, or `tool`
+- `role`: the role of the message, either `system`, `user` or `assistant`
 - `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):
 
@@ -587,7 +546,7 @@ Final response:
 
 ##### Request
 
-Send a chat message with images. The images should be provided as an array, with the individual images encoded in Base64.
+Send a chat message with a conversation history.
 
 ```shell
 curl http://localhost:11434/api/chat -d '{
@@ -663,79 +622,6 @@ 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
@@ -1140,7 +1026,7 @@ If `stream` is set to `false`, then the response is a single JSON object:
 ## Generate Embeddings
 
 ```shell
-POST /api/embed
+POST /api/embeddings
 ```
 
 Generate embeddings from a model
@@ -1148,11 +1034,10 @@ Generate embeddings from a model
 ### Parameters
 
 - `model`: name of model to generate embeddings from
-- `input`: text or list of text to generate embeddings for
+- `prompt`: 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`)
 
@@ -1161,9 +1046,9 @@ Advanced parameters:
 #### Request
 
 ```shell
-curl http://localhost:11434/api/embed -d '{
+curl http://localhost:11434/api/embeddings -d '{
   "model": "all-minilm",
-  "input": "Why is the sky blue?"
+  "prompt": "Here is an article about llamas..."
 }'
 ```
 
@@ -1171,38 +1056,10 @@ curl http://localhost:11434/api/embed -d '{
 
 ```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
-  ]]
+  "embedding": [
+    0.5670403838157654, 0.009260174818336964, 0.23178744316101074, -0.2916173040866852, -0.8924556970596313,
+    0.8785552978515625, -0.34576427936553955, 0.5742510557174683, -0.04222835972905159, -0.137906014919281
+  ]
 }
 ```
 
@@ -1249,45 +1106,3 @@ A single JSON object will be returned.
   ]
 }
 ```
-
-## Generate Embedding
-
-> Note: this endpoint has been superseded by `/api/embed`
-
-```shell
-POST /api/embeddings
-```
-
-Generate embeddings from a model
-
-### Parameters
-
-- `model`: name of model to generate embeddings from
-- `prompt`: text to generate embeddings for
-
-Advanced parameters:
-
-- `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/embeddings -d '{
-  "model": "all-minilm",
-  "prompt": "Here is an article about llamas..."
-}'
-```
-
-#### Response
-
-```json
-{
-  "embedding": [
-    0.5670403838157654, 0.009260174818336964, 0.23178744316101074, -0.2916173040866852, -0.8924556970596313,
-    0.8785552978515625, -0.34576427936553955, 0.5742510557174683, -0.04222835972905159, -0.137906014919281
-  ]
-}
-```

docs/docker.md

@@ -1,71 +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).
+# 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
+```
+
+### Try different models
+
+More models can be found on the [Ollama library](https://ollama.com/library).

docs/faq.md

@@ -111,10 +111,7 @@ On Windows, Ollama inherits your user and system environment variables.
 
 ## How do I use Ollama behind a proxy?
 
-Ollama pulls models from the Internet and may require a proxy server to access the models. Use `HTTPS_PROXY` to redirect outbound requests through the proxy. Ensure the proxy certificate is installed as a system certificate. Refer to the section above for how to use environment variables on your platform.
-
-> [!NOTE]
-> Avoid setting `HTTP_PROXY`. Ollama does not use HTTP for model pulls, only HTTPS. Setting `HTTP_PROXY` may interrupt client connections to the server.
+Ollama is compatible with proxy servers if `HTTP_PROXY` or `HTTPS_PROXY` are configured. When using either variables, ensure it is set where `ollama serve` can access the values. When using `HTTPS_PROXY`, ensure the proxy certificate is installed as a system certificate. Refer to the section above for how to use environment variables on your platform.
 
 ### How do I use Ollama behind a proxy in Docker?
 
@@ -194,8 +191,6 @@ Refer to the section [above](#how-do-i-configure-ollama-server) for how to set e
 
 If a different directory needs to be used, set the environment variable `OLLAMA_MODELS` to the chosen directory.
 
-> Note: on Linux using the standard installer, the `ollama` user needs read and write access to the specified directory. To assign the directory to the `ollama` user run `sudo chown -R ollama:ollama <directory>`.
-
 Refer to the section [above](#how-do-i-configure-ollama-server) for how to set environment variables on your platform.
 
 ## How can I use Ollama in Visual Studio Code?
@@ -232,7 +227,7 @@ curl http://localhost:11434/api/chat -d '{"model": "mistral"}'
 
 To preload a model using the CLI, use the command:
 ```shell
-ollama run llama3.1 ""
+ollama run llama3 ""
 ```
 
 ## How do I keep a model loaded in memory or make it unload immediately?
@@ -277,8 +272,4 @@ The following server settings may be used to adjust how Ollama handles concurren
 - `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.
+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.

docs/gpu.md

@@ -10,7 +10,7 @@ Check your compute compatibility to see if your card is supported:
 | 9.0                | NVIDIA              | `H100`                                                                                                      |
 | 8.9                | GeForce RTX 40xx    | `RTX 4090` `RTX 4080 SUPER` `RTX 4080` `RTX 4070 Ti SUPER` `RTX 4070 Ti` `RTX 4070 SUPER` `RTX 4070` `RTX 4060 Ti` `RTX 4060`  |
 |                    | NVIDIA Professional | `L4` `L40` `RTX 6000`                                                                                       |
-| 8.6                | GeForce RTX 30xx    | `RTX 3090 Ti` `RTX 3090` `RTX 3080 Ti` `RTX 3080` `RTX 3070 Ti` `RTX 3070` `RTX 3060 Ti` `RTX 3060` `RTX 3050 Ti` `RTX 3050`   |
+| 8.6                | GeForce RTX 30xx    | `RTX 3090 Ti` `RTX 3090` `RTX 3080 Ti` `RTX 3080` `RTX 3070 Ti` `RTX 3070` `RTX 3060 Ti` `RTX 3060`         |
 |                    | NVIDIA Professional | `A40` `RTX A6000` `RTX A5000` `RTX A4000` `RTX A3000` `RTX A2000` `A10` `A16` `A2`                          |
 | 8.0                | NVIDIA              | `A100` `A30`                                                                                                |
 | 7.5                | GeForce GTX/RTX     | `GTX 1650 Ti` `TITAN RTX` `RTX 2080 Ti` `RTX 2080` `RTX 2070` `RTX 2060`                                    |

docs/import.md

@@ -1,129 +1,42 @@
-# Importing a model
+# Import
 
-## Table of Contents
+GGUF models and select Safetensors models can be imported directly into Ollama.
 
-  * [Importing a Safetensors adapter](#Importing-a-fine-tuned-adapter-from-Safetensors-weights)
-  * [Importing a Safetensors model](#Importing-a-model-from-Safetensors-weights)
-  * [Importing a GGUF file](#Importing-a-GGUF-based-model-or-adapter)
-  * [Sharing models on ollama.com](#Sharing-your-model-on-ollamacom)
+## Import GGUF
 
-## Importing a fine tuned adapter from Safetensors weights
-
-First, create a `Modelfile` with a `FROM` command pointing at the base model you used for fine tuning, and an `ADAPTER` command which points to the directory with your Safetensors adapter:
-
-```dockerfile
-FROM <base model name>
-ADAPTER /path/to/safetensors/adapter/directory
-```
-
-Make sure that you use the same base model in the `FROM` command as you used to create the adapter otherwise you will get erratic results. Most frameworks use different quantization methods, so it's best to use non-quantized (i.e. non-QLoRA) adapters. If your adapter is in the same directory as your `Modelfile`, use `ADAPTER .` to specify the adapter path.
-
-Now run `ollama create` from the directory where the `Modelfile` was created:
-
-```bash
-ollama create my-model
-```
-
-Lastly, test the model:
-
-```bash
-ollama run my-model
-```
-
-Ollama supports importing adapters based on several different model architectures including:
-
-  * Llama (including Llama 2, Llama 3, 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:
-
-  * Hugging Face [fine tuning framework] (https://huggingface.co/docs/transformers/en/training)
-  * [Unsloth](https://github.com/unslothai/unsloth)
-  * [MLX](https://github.com/ml-explore/mlx)
-
-
-## Importing a model from Safetensors weights
-
-First, create a `Modelfile` with a `FROM` command which points to the directory containing your Safetensors weights:
-
-```dockerfile
-FROM /path/to/safetensors/directory
-```
-
-If you create the Modelfile in the same directory as the weights, you can use the command `FROM .`.
-
-Now run the `ollama create` command from the directory where you created the `Modelfile`:
-
-```shell
-ollama create my-model
-```
-
-Lastly, test the model:
-
-```shell
-ollama run my-model
-```
-
-Ollama supports importing models for several different architectures including:
-
-  * Llama (including Llama 2, Llama 3, and Llama 3.1);
-  * 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:
+A binary GGUF file can be imported directly into Ollama through a Modelfile.
 
 ```dockerfile
 FROM /path/to/file.gguf
 ```
 
-For a GGUF adapter, create the `Modelfile` with:
+## Import Safetensors
+
+If the model being imported is one of these architectures, it can be imported directly into Ollama through a Modelfile:
+
+ - LlamaForCausalLM
+ - MistralForCausalLM
+ - GemmaForCausalLM
 
 ```dockerfile
-FROM <model name>
-ADAPTER /path/to/file.gguf
+FROM /path/to/safetensors/directory
 ```
 
-When importing a GGUF adapter, it's important to use the same base model as the base model that the adapter was created with. You can use:
+For architectures not directly convertable by Ollama, see llama.cpp's [guide](https://github.com/ggerganov/llama.cpp/blob/master/README.md#prepare-and-quantize) on conversion. After conversion, see [Import GGUF](#import-gguf).
 
- * a model from Ollama
- * a GGUF file
- * a Safetensors based model 
+## Automatic Quantization
 
-Once you have created your `Modelfile`, use the `ollama create` command to build the model.
+> [!NOTE]
+> Automatic quantization requires v0.1.35 or higher.
 
-```shell
-ollama create my-model
-```
-
-## Quantizing a Model
-
-Quantizing a model allows you to run models faster and with less memory consumption but at reduced accuracy. This allows you to run a model on more modest hardware.
-
-Ollama can quantize FP16 and FP32 based models into different quantization levels using the `-q/--quantize` flag with the `ollama create` command.
-
-First, create a Modelfile with the FP16 or FP32 based model you wish to quantize.
+Ollama is capable of quantizing FP16 or FP32 models to any of the supported quantizations with the `-q/--quantize` flag in `ollama create`.
 
 ```dockerfile
 FROM /path/to/my/gemma/f16/model
 ```
 
-Use `ollama create` to then create the quantized model.
-
 ```shell
-$ ollama create --quantize q4_K_M mymodel
+$ ollama create -q Q4_K_M mymodel
 transferring model data
 quantizing F16 model to Q4_K_M
 creating new layer sha256:735e246cc1abfd06e9cdcf95504d6789a6cd1ad7577108a70d9902fef503c1bd
@@ -134,53 +47,42 @@ success
 
 ### Supported Quantizations
 
-- `q4_0`
-- `q4_1`
-- `q5_0`
-- `q5_1`
-- `q8_0`
+- `Q4_0`
+- `Q4_1`
+- `Q5_0`
+- `Q5_1`
+- `Q8_0`
 
 #### K-means Quantizations
 
-- `q3_K_S`
-- `q3_K_M`
-- `q3_K_L`
-- `q4_K_S`
-- `q4_K_M`
-- `q5_K_S`
-- `q5_K_M`
-- `q6_K`
+- `Q3_K_S`
+- `Q3_K_M`
+- `Q3_K_L`
+- `Q4_K_S`
+- `Q4_K_M`
+- `Q5_K_S`
+- `Q5_K_M`
+- `Q6_K`
 
+## Template Detection
 
-## Sharing your model on ollama.com
+> [!NOTE]
+> Template detection requires v0.1.42 or higher.
 
-You can share any model you have created by pushing it to [ollama.com](https://ollama.com) so that other users can try it out.
+Ollama uses model metadata, specifically `tokenizer.chat_template`, to automatically create a template appropriate for the model you're importing.
 
-First, use your browser to go to the [Ollama Sign-Up](https://ollama.com/signup) page. If you already have an account, you can skip this step.
-
-<img src="images/signup.png" alt="Sign-Up" width="40%">
-
-The `Username` field will be used as part of your model's name (e.g. `jmorganca/mymodel`), so make sure you are comfortable with the username that you have selected.
-
-Now that you have created an account and are signed-in, go to the [Ollama Keys Settings](https://ollama.com/settings/keys) page.
-
-Follow the directions on the page to determine where your Ollama Public Key is located.
-
-<img src="images/ollama-keys.png" alt="Ollama Keys" width="80%">
-
-Click on the `Add Ollama Public Key` button, and copy and paste the contents of your Ollama Public Key into the text field.
-
-To push a model to [ollama.com](https://ollama.com), first make sure that it is named correctly with your username. You may have to use the `ollama cp` command to copy
-your model to give it the correct name. Once you're happy with your model's name, use the `ollama push` command to push it to [ollama.com](https://ollama.com).
-
-```shell
-ollama cp mymodel myuser/mymodel
-ollama push myuser/mymodel
+```dockerfile
+FROM /path/to/my/gemma/model
 ```
 
-Once your model has been pushed, other users can pull and run it by using the command:
-
 ```shell
-ollama run myuser/mymodel
+$ ollama create mymodel
+transferring model data
+using autodetected template gemma-instruct
+creating new layer sha256:baa2a0edc27d19cc6b7537578a9a7ba1a4e3214dc185ed5ae43692b319af7b84
+creating new layer sha256:ba66c3309914dbef07e5149a648fd1877f030d337a4f240d444ea335008943cb
+writing manifest
+success
 ```
 
+Defining a template in the Modelfile will disable this feature which may be useful if you want to use a different template than the autodetected one.

docs/linux.md

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

docs/modelfile.md

@@ -1,7 +1,6 @@
 # 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.
 
@@ -11,9 +10,8 @@ A model file is the blueprint to create and share models with Ollama.
 - [Examples](#examples)
 - [Instructions](#instructions)
   - [FROM (Required)](#from-required)
-    - [Build from llama3.1](#build-from-llama31)
-    - [Build from a Safetensors model](#build-from-a-safetensors-model)
-    - [Build from a GGUF file](#build-from-a-gguf-file)
+    - [Build from llama3](#build-from-llama3)
+    - [Build from a bin file](#build-from-a-bin-file)
   - [PARAMETER](#parameter)
     - [Valid Parameters and Values](#valid-parameters-and-values)
   - [TEMPLATE](#template)
@@ -100,39 +98,22 @@ The `FROM` instruction defines the base model to use when creating a model.
 FROM <model name>:<tag>
 ```
 
-#### Build from llama3.1
+#### Build from llama3
 
 ```modelfile
-FROM llama3.1
+FROM llama3
 ```
 
 A list of available base models:
 <https://github.com/ollama/ollama#model-library>
-Additional models can be found at:
-<https://ollama.com/library>
 
-#### Build from a Safetensors model
+#### Build from a `bin` file
 
 ```modelfile
-FROM <model directory>
+FROM ./ollama-model.bin
 ```
 
-The model directory should contain the Safetensors weights for a supported architecture.
-
-Currently supported model architectures:
-  * Llama (including Llama 2, Llama 3, and Llama 3.1)
-  * Mistral (including Mistral 1, Mistral 2, and Mixtral)
-  * Gemma (including Gemma 1 and Gemma 2)
-  * Phi3
-
-#### Build from a GGUF file
-
-```modelfile
-FROM ./ollama-model.gguf
-```
-
-The GGUF file location should be specified as an absolute path or relative to the `Modelfile` location.
-
+This bin file location should be specified as an absolute path or relative to the `Modelfile` location.
 
 ### PARAMETER
 
@@ -159,7 +140,6 @@ 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
 
@@ -192,23 +172,10 @@ SYSTEM """<system message>"""
 
 ### ADAPTER
 
-The `ADAPTER` instruction specifies a fine tuned LoRA adapter that should apply to the base model. The value of the adapter should be an absolute path or a path relative to the Modelfile. The base model should be specified with a `FROM` instruction. If the base model is not the same as the base model that the adapter was tuned from the behaviour will be erratic.
-
-#### Safetensor adapter
+The `ADAPTER` instruction is an optional instruction that specifies any LoRA adapter that should apply to the base model. The value of this instruction should be an absolute path or a path relative to the Modelfile and the file must be in a GGML file format. The adapter should be tuned from the base model otherwise the behaviour is undefined.
 
 ```modelfile
-ADAPTER <path to safetensor adapter>
-```
-
-Currently supported Safetensor adapters:
-  * Llama (including Llama 2, Llama 3, and Llama 3.1)
-  * Mistral (including Mistral 1, Mistral 2, and Mixtral)
-  * Gemma (including Gemma 1 and Gemma 2)
-
-#### GGUF adapter
-
-```modelfile
-ADAPTER ./ollama-lora.gguf
+ADAPTER ./ollama-lora.bin
 ```
 
 ### LICENSE

docs/openai.md

@@ -27,37 +27,6 @@ chat_completion = client.chat.completions.create(
     ],
     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?"],
-)
 ```
 
 ### OpenAI JavaScript library
@@ -73,44 +42,14 @@ const openai = new OpenAI({
 })
 
 const chatCompletion = await openai.chat.completions.create({
-    messages: [{ role: 'user', content: 'Say this is a test' }],
-    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?"],
+  messages: [{ role: 'user', content: 'Say this is a test' }],
+  model: 'llama3',
 })
 ```
 
 ### `curl`
 
-``` shell
+```
 curl http://localhost:11434/v1/chat/completions \
     -H "Content-Type: application/json" \
     -d '{
@@ -127,47 +66,6 @@ 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": "iVBORw0KGgoAAAANSUhEUgAAAG0AAABmCAYAAADBPx+VAAAACXBIWXMAAAsTAAALEwEAmpwYAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAA3VSURBVHgB7Z27r0zdG8fX743i1bi1ikMoFMQloXRpKFFIqI7LH4BEQ+NWIkjQuSWCRIEoULk0gsK1kCBI0IhrQVT7tz/7zZo888yz1r7MnDl7z5xvsjkzs2fP3uu71nNfa7lkAsm7d++Sffv2JbNmzUqcc8m0adOSzZs3Z+/XES4ZckAWJEGWPiCxjsQNLWmQsWjRIpMseaxcuTKpG/7HP27I8P79e7dq1ars/yL4/v27S0ejqwv+cUOGEGGpKHR37tzJCEpHV9tnT58+dXXCJDdECBE2Ojrqjh071hpNECjx4cMHVycM1Uhbv359B2F79+51586daxN/+pyRkRFXKyRDAqxEp4yMlDDzXG1NPnnyJKkThoK0VFd1ELZu3TrzXKxKfW7dMBQ6bcuWLW2v0VlHjx41z717927ba22U9APcw7Nnz1oGEPeL3m3p2mTAYYnFmMOMXybPPXv2bNIPpFZr1NHn4HMw0KRBjg9NuRw95s8PEcz/6DZELQd/09C9QGq5RsmSRybqkwHGjh07OsJSsYYm3ijPpyHzoiacg35MLdDSIS/O1yM778jOTwYUkKNHWUzUWaOsylE00MyI0fcnOwIdjvtNdW/HZwNLGg+sR1kMepSNJXmIwxBZiG8tDTpEZzKg0GItNsosY8USkxDhD0Rinuiko2gfL/RbiD2LZAjU9zKQJj8RDR0vJBR1/Phx9+PHj9Z7REF4nTZkxzX4LCXHrV271qXkBAPGfP/atWvu/PnzHe4C97F48eIsRLZ9+3a3f/9+87dwP1JxaF7/3r17ba+5l4EcaVo0lj3SBq5kGTJSQmLWMjgYNei2GPT1MuMqGTDEFHzeQSP2wi/jGnkmPJ/nhccs44jvDAxpVcxnq0F6eT8h4ni/iIWpR5lPyA6ETkNXoSukvpJAD3AsXLiwpZs49+fPn5ke4j10TqYvegSfn0OnafC+Tv9ooA/JPkgQysqQNBzagXY55nO/oa1F7qvIPWkRL12WRpMWUvpVDYmxAPehxWSe8ZEXL20sadYIozfmNch4QJPAfeJgW3rNsnzphBKNJM2KKODo1rVOMRYik5ETy3ix4qWNI81qAAirizgMIc+yhTytx0JWZuNI03qsrgWlGtwjoS9XwgUhWGyhUaRZZQNNIEwCiXD16tXcAHUs79co0vSD8rrJCIW98pzvxpAWyyo3HYwqS0+H0BjStClcZJT5coMm6D2LOF8TolGJtK9fvyZpyiC5ePFi9nc/oJU4eiEP0jVoAnHa9wyJycITMP78+eMeP37sXrx44d6+fdt6f82aNdkx1pg9e3Zb5W+RSRE+n+VjksQWifvVaTKFhn5O8my63K8Qabdv33b379/PiAP//vuvW7BggZszZ072/+TJk91YgkafPn166zXB1rQHFvouAWHq9z3SEevSUerqCn2/dDCeta2jxYbr69evk4MHDyY7d+7MjhMnTiTPnz9Pfv/+nfQT2ggpO2dMF8cghuoM7Ygj5iWCqRlGFml0QC/ftGmTmzt3rmsaKDsgBSPh0/8yPeLLBihLkOKJc0jp8H8vUzcxIA1k6QJ/c78tWEyj5P3o4u9+jywNPdJi5rAH9x0KHcl4Hg570eQp3+vHXGyrmEeigzQsQsjavXt38ujRo44LQuDDhw+TW7duRS1HGgMxhNXHgflaNTOsHyKvHK5Ijo2jbFjJBQK9YwFd6RVMzfgRBmEfP37suBBm/p49e1qjEP2mwTViNRo0VJWH1deMXcNK08uUjVUu7s/zRaL+oLNxz1bpANco4npUgX4G2eFbpDFyQoQxojBCpEGSytmOH8qrH5Q9vuzD6ofQylkCUmh8DBAr+q8JCyVNtWQIidKQE9wNtLSQnS4jDSsxNHogzFuQBw4cyM61UKVsjfr3ooBkPSqqQHesUPWVtzi9/vQi1T+rJj7WiTz4Pt/l3LxUkr5P2VYZaZ4URpsE+st/dujQoaBBYokbrz/8TJNQYLSonrPS9kUaSkPeZyj1AWSj+d+VBoy1pIWVNed8P0Ll/ee5HdGRhrHhR5GGN0r4LGZBaj8oFDJitBTJzIZgFcmU0Y8ytWMZMzJOaXUSrUs5RxKnrxmbb5YXO9VGUhtpXldhEUogFr3IzIsvlpmdosVcGVGXFWp2oU9kLFL3dEkSz6NHEY1sjSRdIuDFWEhd8KxFqsRi1uM/nz9/zpxnwlESONdg6dKlbsaMGS4EHFHtjFIDHwKOo46l4TxSuxgDzi+rE2jg+BaFruOX4HXa0Nnf1lwAPufZeF8/r6zD97WK2qFnGjBxTw5qNGPxT+5T/r7/7RawFC3j4vTp09koCxkeHjqbHJqArmH5UrFKKksnxrK7FuRIs8STfBZv+luugXZ2pR/pP9Ois4z+TiMzUUkUjD0iEi1fzX8GmXyuxUBRcaUfykV0YZnlJGKQpOiGB76x5GeWkWWJc3mOrK6S7xdND+W5N6XyaRgtWJFe13GkaZnKOsYqGdOVVVbGupsyA/l7emTLHi7vwTdirNEt0qxnzAvBFcnQF16xh/TMpUuXHDowhlA9vQVraQhkudRdzOnK+04ZSP3DUhVSP61YsaLtd/ks7ZgtPcXqPqEafHkdqa84X6aCeL7YWlv6edGFHb+ZFICPlljHhg0bKuk0CSvVznWsotRu433alNdFrqG45ejoaPCaUkWERpLXjzFL2Rpllp7PJU2a/v7Ab8N05/9t27Z16KUqoFGsxnI9EosS2niSYg9SpU6B4JgTrvVW1flt1sT+0ADIJU2maXzcUTraGCRaL1Wp9rUMk16PMom8QhruxzvZIegJjFU7LLCePfS8uaQdPny4jTTL0dbee5mYokQsXTIWNY46kuMbnt8Kmec+LGWtOVIl9cT1rCB0V8WqkjAsRwta93TbwNYoGKsUSChN44lgBNCoHLHzquYKrU6qZ8lolCIN0Rh6cP0Q3U6I6IXILYOQI513hJaSKAorFpuHXJNfVlpRtmYBk1Su1obZr5dnKAO+L10Hrj3WZW+E3qh6IszE37F6EB+68mGpvKm4eb9bFrlzrok7fvr0Kfv727dvWRmdVTJHw0qiiCUSZ6wCK+7XL/AcsgNyL74DQQ730sv78Su7+t/A36MdY0sW5o40ahslXr58aZ5HtZB8GH64m9EmMZ7FpYw4T6QnrZfgenrhFxaSiSGXtPnz57e9TkNZLvTjeqhr734CNtrK41L40sUQckmj1lGKQ0rC37x544r8eNXRpnVE3ZZY7zXo8NomiO0ZUCj2uHz58rbXoZ6gc0uA+F6ZeKS/jhRDUq8MKrTho9fEkihMmhxtBI1DxKFY9XLpVcSkfoi8JGnToZO5sU5aiDQIW716ddt7ZLYtMQlhECdBGXZZMWldY5BHm5xgAroWj4C0hbYkSc/jBmggIrXJWlZM6pSETsEPGqZOndr2uuuR5rF169a2HoHPdurUKZM4CO1WTPqaDaAd+GFGKdIQkxAn9RuEWcTRyN2KSUgiSgF5aWzPTeA/lN5rZubMmR2bE4SIC4nJoltgAV/dVefZm72AtctUCJU2CMJ327hxY9t7EHbkyJFseq+EJSY16RPo3Dkq1kkr7+q0bNmyDuLQcZBEPYmHVdOBiJyIlrRDq41YPWfXOxUysi5fvtyaj+2BpcnsUV/oSoEMOk2CQGlr4ckhBwaetBhjCwH0ZHtJROPJkyc7UjcYLDjmrH7ADTEBXFfOYmB0k9oYBOjJ8b4aOYSe7QkKcYhFlq3QYLQhSidNmtS2RATwy8YOM3EQJsUjKiaWZ+vZToUQgzhkHXudb/PW5YMHD9yZM2faPsMwoc7RciYJXbGuBqJ1UIGKKLv915jsvgtJxCZDubdXr165mzdvtr1Hz5LONA8jrUwKPqsmVesKa49S3Q4WxmRPUEYdTjgiUcfUwLx589ySJUva3oMkP6IYddq6HMS4o55xBJBUeRjzfa4Zdeg56QZ43LhxoyPo7Lf1kNt7oO8wWAbNwaYjIv5lhyS7kRf96dvm5Jah8vfvX3flyhX35cuX6HfzFHOToS1H4BenCaHvO8pr8iDuwoUL7tevX+b5ZdbBair0xkFIlFDlW4ZknEClsp/TzXyAKVOmmHWFVSbDNw1l1+4f90U6IY/q4V27dpnE9bJ+v87QEydjqx/UamVVPRG+mwkNTYN+9tjkwzEx+atCm/X9WvWtDtAb68Wy9LXa1UmvCDDIpPkyOQ5ZwSzJ4jMrvFcr0rSjOUh+GcT4LSg5ugkW1Io0/SCDQBojh0hPlaJdah+tkVYrnTZowP8iq1F1TgMBBauufyB33x1v+NWFYmT5KmppgHC+NkAgbmRkpD3yn9QIseXymoTQFGQmIOKTxiZIWpvAatenVqRVXf2nTrAWMsPnKrMZHz6bJq5jvce6QK8J1cQNgKxlJapMPdZSR64/UivS9NztpkVEdKcrs5alhhWP9NeqlfWopzhZScI6QxseegZRGeg5a8C3Re1Mfl1ScP36ddcUaMuv24iOJtz7sbUjTS4qBvKmstYJoUauiuD3k5qhyr7QdUHMeCgLa1Ear9NquemdXgmum4fvJ6w1lqsuDhNrg1qSpleJK7K3TF0Q2jSd94uSZ60kK1e3qyVpQK6PVWXp2/FC3mp6jBhKKOiY2h3gtUV64TWM6wDETRPLDfSakXmH3w8g9Jlug8ZtTt4kVF0kLUYYmCCtD/DrQ5YhMGbA9L3ucdjh0y8kOHW5gU/VEEmJTcL4Pz/f7mgoAbYkAAAAAElFTkSuQmCC"
-            }
-          }
-        ]
-      }
-    ],
-    "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
@@ -180,8 +78,8 @@ curl http://localhost:11434/v1/embeddings \
 - [x] Streaming
 - [x] JSON mode
 - [x] Reproducible outputs
-- [x] Vision
-- [x] Tools (streaming support coming soon)
+- [ ] Vision
+- [ ] Function calling
 - [ ] Logprobs
 
 #### Supported request fields
@@ -189,10 +87,7 @@ curl http://localhost:11434/v1/embeddings \
 - [x] `model`
 - [x] `messages`
   - [x] Text `content`
-  - [x] Image `content`
-    - [x] Base64 encoded image
-    - [ ] Image URL
-  - [x] Array of `content` parts
+  - [ ] Array of `content` parts
 - [x] `frequency_penalty`
 - [x] `presence_penalty`
 - [x] `response_format`
@@ -202,73 +97,12 @@ curl http://localhost:11434/v1/embeddings \
 - [x] `temperature`
 - [x] `top_p`
 - [x] `max_tokens`
-- [x] `tools`
+- [ ] `logit_bias`
+- [ ] `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
-
-- `prompt` currently only accepts a string
-
-### `/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`:
@@ -300,28 +134,3 @@ curl http://localhost:11434/v1/chat/completions \
         ]
     }'
 ```
-
-### Setting the context size
-
-The OpenAI API does not have a way of setting the context size for a model. If you need to change the context size, create a `Modelfile` which looks like:
-
-```modelfile
-FROM <some model>
-PARAMETER num_ctx <context size>
-```
-
-Use the `ollama create mymodel` command to create a new model with the updated context size. Call the API with the updated model name:
-
-```shell
-curl http://localhost:11434/v1/chat/completions \
-    -H "Content-Type: application/json" \
-    -d '{
-        "model": "mymodel",
-        "messages": [
-            {
-                "role": "user",
-                "content": "Hello!"
-            }
-        ]
-    }'
-```

docs/template.md

@@ -1,167 +0,0 @@
-# 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 --no-pager
+journalctl -u ollama
 ```
 
 When you run Ollama in a **container**, the logs go to stdout/stderr in the container:

docs/tutorials/langchainjs.md

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

@@ -23,8 +23,6 @@ Logs will often be helpful in diagnosing the problem (see
 * 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`
@@ -48,9 +46,6 @@ the explorer window by hitting `<cmd>+R` and type in:
 - `explorer %HOMEPATH%\.ollama` contains models and configuration
 - `explorer %TEMP%` contains temporary executable files in one or more `ollama*` directories
 
-## Uninstall
-
-The Ollama Windows installer registers an Uninstaller application.  Under `Add or remove programs` in Windows Settings, you can uninstall Ollama.
 
 ## Standalone CLI
 

envconfig/config.go

@@ -1,11 +1,11 @@
 package envconfig
 
 import (
+	"errors"
 	"fmt"
 	"log/slog"
 	"math"
 	"net"
-	"net/url"
 	"os"
 	"path/filepath"
 	"runtime"
@@ -14,260 +14,64 @@ import (
 	"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"
-	}
-
-	hostport, path, _ := strings.Cut(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)
-		port = defaultPort
-	}
-
-	return &url.URL{
-		Scheme: scheme,
-		Host:   net.JoinHostPort(host, port),
-		Path:   path,
-	}
+type OllamaHost struct {
+	Scheme string
+	Host   string
+	Port   string
 }
 
-// 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
+func (o OllamaHost) String() string {
+	return fmt.Sprintf("%s://%s:%s", o.Scheme, o.Host, o.Port)
 }
 
-// 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
-}
-
-// LoadTimeout returns the duration for stall detection during model loads. LoadTimeout can be configured via the OLLAMA_LOAD_TIMEOUT environment variable.
-// Zero or Negative values are treated as infinite.
-// Default is 5 minutes.
-func LoadTimeout() (loadTimeout time.Duration) {
-	loadTimeout = 5 * time.Minute
-	if s := Var("OLLAMA_LOAD_TIMEOUT"); s != "" {
-		if d, err := time.ParseDuration(s); err == nil {
-			loadTimeout = d
-		} else if n, err := strconv.ParseInt(s, 10, 64); err == nil {
-			loadTimeout = time.Duration(n) * time.Second
-		}
-	}
-
-	if loadTimeout <= 0 {
-		return time.Duration(math.MaxInt64)
-	}
-
-	return loadTimeout
-}
-
-func Bool(k string) func() bool {
-	return func() bool {
-		if s := Var(k); s != "" {
-			b, err := strconv.ParseBool(s)
-			if err != nil {
-				return true
-			}
-
-			return b
-		}
-
-		return false
-	}
-}
+var ErrInvalidHostPort = errors.New("invalid port specified in OLLAMA_HOST")
 
 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")
+	// Set via OLLAMA_ORIGINS in the environment
+	AllowOrigins []string
+	// Set via OLLAMA_DEBUG in the environment
+	Debug bool
+	// Experimental flash attention
+	FlashAttention bool
+	// Set via OLLAMA_HOST in the environment
+	Host *OllamaHost
+	// Set via OLLAMA_KEEP_ALIVE in the environment
+	KeepAlive time.Duration
+	// Set via OLLAMA_LLM_LIBRARY in the environment
+	LLMLibrary string
+	// Set via OLLAMA_MAX_LOADED_MODELS in the environment
+	MaxRunners int
+	// Set via OLLAMA_MAX_QUEUE in the environment
+	MaxQueuedRequests int
+	// Set via OLLAMA_MODELS in the environment
+	ModelsDir string
+	// Set via OLLAMA_NOHISTORY in the environment
+	NoHistory bool
+	// Set via OLLAMA_NOPRUNE in the environment
+	NoPrune bool
+	// Set via OLLAMA_NUM_PARALLEL in the environment
+	NumParallel int
+	// Set via OLLAMA_RUNNERS_DIR in the environment
+	RunnersDir string
+	// Set via OLLAMA_SCHED_SPREAD in the environment
+	SchedSpread bool
+	// Set via OLLAMA_TMPDIR in the environment
+	TmpDir string
+	// Set via OLLAMA_INTEL_GPU in the environment
+	IntelGpu bool
+
+	// Set via CUDA_VISIBLE_DEVICES in the environment
+	CudaVisibleDevices string
+	// Set via HIP_VISIBLE_DEVICES in the environment
+	HipVisibleDevices string
+	// Set via ROCR_VISIBLE_DEVICES in the environment
+	RocrVisibleDevices string
+	// Set via GPU_DEVICE_ORDINAL in the environment
+	GpuDeviceOrdinal string
+	// Set via HSA_OVERRIDE_GFX_VERSION in the environment
+	HsaOverrideGfxVersion string
 )
 
-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), LibRelativeToExe()), 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)
-)
-
-func Uint64(key string, defaultValue uint64) func() uint64 {
-	return func() uint64 {
-		if s := Var(key); s != "" {
-			if n, err := strconv.ParseUint(s, 10, 64); err != nil {
-				slog.Warn("invalid environment variable, using default", "key", key, "value", s, "default", defaultValue)
-			} else {
-				return n
-			}
-		}
-
-		return defaultValue
-	}
-}
-
-// Set aside VRAM per GPU
-var GpuOverhead = Uint64("OLLAMA_GPU_OVERHEAD", 0)
-
 type EnvVar struct {
 	Name        string
 	Value       any
@@ -276,31 +80,29 @@ type EnvVar struct {
 
 func AsMap() map[string]EnvVar {
 	ret := map[string]EnvVar{
-		"OLLAMA_DEBUG":             {"OLLAMA_DEBUG", Debug(), "Show additional debug information (e.g. OLLAMA_DEBUG=1)"},
-		"OLLAMA_FLASH_ATTENTION":   {"OLLAMA_FLASH_ATTENTION", FlashAttention(), "Enabled flash attention"},
-		"OLLAMA_GPU_OVERHEAD":      {"OLLAMA_GPU_OVERHEAD", GpuOverhead(), "Reserve a portion of VRAM per GPU (bytes)"},
-		"OLLAMA_HOST":              {"OLLAMA_HOST", Host(), "IP Address for the ollama server (default 127.0.0.1:11434)"},
-		"OLLAMA_KEEP_ALIVE":        {"OLLAMA_KEEP_ALIVE", KeepAlive(), "The duration that models stay loaded in memory (default \"5m\")"},
-		"OLLAMA_LLM_LIBRARY":       {"OLLAMA_LLM_LIBRARY", LLMLibrary(), "Set LLM library to bypass autodetection"},
-		"OLLAMA_LOAD_TIMEOUT":      {"OLLAMA_LOAD_TIMEOUT", LoadTimeout(), "How long to allow model loads to stall before giving up (default \"5m\")"},
-		"OLLAMA_MAX_LOADED_MODELS": {"OLLAMA_MAX_LOADED_MODELS", MaxRunners(), "Maximum number of loaded models per GPU"},
-		"OLLAMA_MAX_QUEUE":         {"OLLAMA_MAX_QUEUE", MaxQueue(), "Maximum number of queued requests"},
-		"OLLAMA_MODELS":            {"OLLAMA_MODELS", Models(), "The path to the models directory"},
-		"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"},
+		"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", MaxQueuedRequests, "Maximum number of queued requests"},
+		"OLLAMA_MODELS":            {"OLLAMA_MODELS", ModelsDir, "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", AllowOrigins, "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"}
+		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
 }
@@ -313,16 +115,246 @@ func Values() map[string]string {
 	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)), "\"'")
+var defaultAllowOrigins = []string{
+	"localhost",
+	"127.0.0.1",
+	"0.0.0.0",
 }
 
-// On windows, we keep the binary at the top directory, but
-// other platforms use a "bin" directory, so this returns ".."
-func LibRelativeToExe() string {
-	if runtime.GOOS == "windows" {
-		return "."
-	}
-	return ".."
+// Clean quotes and spaces from the value
+func clean(key string) string {
+	return strings.Trim(os.Getenv(key), "\"' ")
+}
+
+func init() {
+	// default values
+	NumParallel = 0 // Autoselect
+	MaxRunners = 0  // Autoselect
+	MaxQueuedRequests = 512
+	KeepAlive = 5 * time.Minute
+
+	LoadConfig()
+}
+
+func LoadConfig() {
+	if debug := clean("OLLAMA_DEBUG"); debug != "" {
+		d, err := strconv.ParseBool(debug)
+		if err == nil {
+			Debug = d
+		} else {
+			Debug = true
+		}
+	}
+
+	if fa := clean("OLLAMA_FLASH_ATTENTION"); fa != "" {
+		d, err := strconv.ParseBool(fa)
+		if err == nil {
+			FlashAttention = d
+		}
+	}
+
+	RunnersDir = clean("OLLAMA_RUNNERS_DIR")
+	if runtime.GOOS == "windows" && RunnersDir == "" {
+		// On Windows we do not carry the payloads inside the main executable
+		appExe, err := os.Executable()
+		if err != nil {
+			slog.Error("failed to lookup executable path", "error", err)
+		}
+
+		cwd, err := os.Getwd()
+		if err != nil {
+			slog.Error("failed to lookup working directory", "error", err)
+		}
+
+		var paths []string
+		for _, root := range []string{filepath.Dir(appExe), cwd} {
+			paths = append(paths,
+				root,
+				filepath.Join(root, "windows-"+runtime.GOARCH),
+				filepath.Join(root, "dist", "windows-"+runtime.GOARCH),
+			)
+		}
+
+		// Try a few variations to improve developer experience when building from source in the local tree
+		for _, p := range paths {
+			candidate := filepath.Join(p, "ollama_runners")
+			_, err := os.Stat(candidate)
+			if err == nil {
+				RunnersDir = candidate
+				break
+			}
+		}
+		if RunnersDir == "" {
+			slog.Error("unable to locate llm runner directory.  Set OLLAMA_RUNNERS_DIR to the location of 'ollama_runners'")
+		}
+	}
+
+	TmpDir = clean("OLLAMA_TMPDIR")
+
+	LLMLibrary = clean("OLLAMA_LLM_LIBRARY")
+
+	if onp := clean("OLLAMA_NUM_PARALLEL"); onp != "" {
+		val, err := strconv.Atoi(onp)
+		if err != nil {
+			slog.Error("invalid setting, ignoring", "OLLAMA_NUM_PARALLEL", onp, "error", err)
+		} else {
+			NumParallel = val
+		}
+	}
+
+	if nohistory := clean("OLLAMA_NOHISTORY"); nohistory != "" {
+		NoHistory = true
+	}
+
+	if spread := clean("OLLAMA_SCHED_SPREAD"); spread != "" {
+		s, err := strconv.ParseBool(spread)
+		if err == nil {
+			SchedSpread = s
+		} else {
+			SchedSpread = true
+		}
+	}
+
+	if noprune := clean("OLLAMA_NOPRUNE"); noprune != "" {
+		NoPrune = true
+	}
+
+	if origins := clean("OLLAMA_ORIGINS"); origins != "" {
+		AllowOrigins = strings.Split(origins, ",")
+	}
+	for _, allowOrigin := range defaultAllowOrigins {
+		AllowOrigins = append(AllowOrigins,
+			fmt.Sprintf("http://%s", allowOrigin),
+			fmt.Sprintf("https://%s", allowOrigin),
+			fmt.Sprintf("http://%s", net.JoinHostPort(allowOrigin, "*")),
+			fmt.Sprintf("https://%s", net.JoinHostPort(allowOrigin, "*")),
+		)
+	}
+
+	AllowOrigins = append(AllowOrigins,
+		"app://*",
+		"file://*",
+		"tauri://*",
+	)
+
+	maxRunners := clean("OLLAMA_MAX_LOADED_MODELS")
+	if maxRunners != "" {
+		m, err := strconv.Atoi(maxRunners)
+		if err != nil {
+			slog.Error("invalid setting, ignoring", "OLLAMA_MAX_LOADED_MODELS", maxRunners, "error", err)
+		} else {
+			MaxRunners = m
+		}
+	}
+
+	if onp := os.Getenv("OLLAMA_MAX_QUEUE"); onp != "" {
+		p, err := strconv.Atoi(onp)
+		if err != nil || p <= 0 {
+			slog.Error("invalid setting, ignoring", "OLLAMA_MAX_QUEUE", onp, "error", err)
+		} else {
+			MaxQueuedRequests = p
+		}
+	}
+
+	ka := clean("OLLAMA_KEEP_ALIVE")
+	if ka != "" {
+		loadKeepAlive(ka)
+	}
+
+	var err error
+	ModelsDir, err = getModelsDir()
+	if err != nil {
+		slog.Error("invalid setting", "OLLAMA_MODELS", ModelsDir, "error", err)
+	}
+
+	Host, err = getOllamaHost()
+	if err != nil {
+		slog.Error("invalid setting", "OLLAMA_HOST", Host, "error", err, "using default port", Host.Port)
+	}
+
+	if set, err := strconv.ParseBool(clean("OLLAMA_INTEL_GPU")); err == nil {
+		IntelGpu = set
+	}
+
+	CudaVisibleDevices = clean("CUDA_VISIBLE_DEVICES")
+	HipVisibleDevices = clean("HIP_VISIBLE_DEVICES")
+	RocrVisibleDevices = clean("ROCR_VISIBLE_DEVICES")
+	GpuDeviceOrdinal = clean("GPU_DEVICE_ORDINAL")
+	HsaOverrideGfxVersion = clean("HSA_OVERRIDE_GFX_VERSION")
+}
+
+func getModelsDir() (string, error) {
+	if models, exists := os.LookupEnv("OLLAMA_MODELS"); exists {
+		return models, nil
+	}
+	home, err := os.UserHomeDir()
+	if err != nil {
+		return "", err
+	}
+	return filepath.Join(home, ".ollama", "models"), nil
+}
+
+func getOllamaHost() (*OllamaHost, error) {
+	defaultPort := "11434"
+
+	hostVar := os.Getenv("OLLAMA_HOST")
+	hostVar = strings.TrimSpace(strings.Trim(strings.TrimSpace(hostVar), "\"'"))
+
+	scheme, hostport, ok := strings.Cut(hostVar, "://")
+	switch {
+	case !ok:
+		scheme, hostport = "http", hostVar
+	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 portNum, err := strconv.ParseInt(port, 10, 32); err != nil || portNum > 65535 || portNum < 0 {
+		return &OllamaHost{
+			Scheme: scheme,
+			Host:   host,
+			Port:   defaultPort,
+		}, ErrInvalidHostPort
+	}
+
+	return &OllamaHost{
+		Scheme: scheme,
+		Host:   host,
+		Port:   port,
+	}, nil
+}
+
+func loadKeepAlive(ka string) {
+	v, err := strconv.Atoi(ka)
+	if err != nil {
+		d, err := time.ParseDuration(ka)
+		if err == nil {
+			if d < 0 {
+				KeepAlive = time.Duration(math.MaxInt64)
+			} else {
+				KeepAlive = d
+			}
+		}
+	} else {
+		d := time.Duration(v) * time.Second
+		if d < 0 {
+			KeepAlive = time.Duration(math.MaxInt64)
+		} else {
+			KeepAlive = d
+		}
+	}
 }

envconfig/config_test.go

@@ -1,269 +1,87 @@
 package envconfig
 
 import (
+	"fmt"
 	"math"
+	"net"
 	"testing"
 	"time"
 
-	"github.com/google/go-cmp/cmp"
+	"github.com/stretchr/testify/assert"
+	"github.com/stretchr/testify/require"
 )
 
-func TestHost(t *testing.T) {
-	cases := map[string]struct {
+func TestConfig(t *testing.T) {
+	Debug = false // Reset whatever was loaded in init()
+	t.Setenv("OLLAMA_DEBUG", "")
+	LoadConfig()
+	require.False(t, Debug)
+	t.Setenv("OLLAMA_DEBUG", "false")
+	LoadConfig()
+	require.False(t, Debug)
+	t.Setenv("OLLAMA_DEBUG", "1")
+	LoadConfig()
+	require.True(t, Debug)
+	t.Setenv("OLLAMA_FLASH_ATTENTION", "1")
+	LoadConfig()
+	require.True(t, FlashAttention)
+	t.Setenv("OLLAMA_KEEP_ALIVE", "")
+	LoadConfig()
+	require.Equal(t, 5*time.Minute, KeepAlive)
+	t.Setenv("OLLAMA_KEEP_ALIVE", "3")
+	LoadConfig()
+	require.Equal(t, 3*time.Second, KeepAlive)
+	t.Setenv("OLLAMA_KEEP_ALIVE", "1h")
+	LoadConfig()
+	require.Equal(t, 1*time.Hour, KeepAlive)
+	t.Setenv("OLLAMA_KEEP_ALIVE", "-1s")
+	LoadConfig()
+	require.Equal(t, time.Duration(math.MaxInt64), KeepAlive)
+	t.Setenv("OLLAMA_KEEP_ALIVE", "-1")
+	LoadConfig()
+	require.Equal(t, time.Duration(math.MaxInt64), KeepAlive)
+}
+
+func TestClientFromEnvironment(t *testing.T) {
+	type testCase struct {
 		value  string
 		expect string
-	}{
-		"empty":               {"", "http://127.0.0.1:11434"},
-		"only address":        {"1.2.3.4", "http://1.2.3.4:11434"},
-		"only port":           {":1234", "http://:1234"},
-		"address and port":    {"1.2.3.4:1234", "http://1.2.3.4:1234"},
-		"hostname":            {"example.com", "http://example.com:11434"},
-		"hostname and port":   {"example.com:1234", "http://example.com:1234"},
-		"zero port":           {":0", "http://:0"},
-		"too large port":      {":66000", "http://:11434"},
-		"too small port":      {":-1", "http://:11434"},
-		"ipv6 localhost":      {"[::1]", "http://[::1]:11434"},
-		"ipv6 world open":     {"[::]", "http://[::]:11434"},
-		"ipv6 no brackets":    {"::1", "http://[::1]:11434"},
-		"ipv6 + port":         {"[::1]:1337", "http://[::1]:1337"},
-		"extra space":         {" 1.2.3.4 ", "http://1.2.3.4:11434"},
-		"extra quotes":        {"\"1.2.3.4\"", "http://1.2.3.4:11434"},
-		"extra space+quotes":  {" \" 1.2.3.4 \" ", "http://1.2.3.4:11434"},
-		"extra single quotes": {"'1.2.3.4'", "http://1.2.3.4:11434"},
-		"http":                {"http://1.2.3.4", "http://1.2.3.4:80"},
-		"http port":           {"http://1.2.3.4:4321", "http://1.2.3.4:4321"},
-		"https":               {"https://1.2.3.4", "https://1.2.3.4:443"},
-		"https port":          {"https://1.2.3.4:4321", "https://1.2.3.4:4321"},
-		"proxy path":          {"https://example.com/ollama", "https://example.com:443/ollama"},
+		err    error
 	}
 
-	for name, tt := range cases {
-		t.Run(name, func(t *testing.T) {
-			t.Setenv("OLLAMA_HOST", tt.value)
-			if host := Host(); host.String() != tt.expect {
-				t.Errorf("%s: expected %s, got %s", name, tt.expect, host.String())
-			}
-		})
-	}
-}
-
-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,
+	hostTestCases := map[string]*testCase{
+		"empty":               {value: "", expect: "127.0.0.1:11434"},
+		"only address":        {value: "1.2.3.4", expect: "1.2.3.4:11434"},
+		"only port":           {value: ":1234", expect: ":1234"},
+		"address and port":    {value: "1.2.3.4:1234", expect: "1.2.3.4:1234"},
+		"hostname":            {value: "example.com", expect: "example.com:11434"},
+		"hostname and port":   {value: "example.com:1234", expect: "example.com:1234"},
+		"zero port":           {value: ":0", expect: ":0"},
+		"too large port":      {value: ":66000", err: ErrInvalidHostPort},
+		"too small port":      {value: ":-1", err: ErrInvalidHostPort},
+		"ipv6 localhost":      {value: "[::1]", expect: "[::1]:11434"},
+		"ipv6 world open":     {value: "[::]", expect: "[::]:11434"},
+		"ipv6 no brackets":    {value: "::1", expect: "[::1]:11434"},
+		"ipv6 + port":         {value: "[::1]:1337", expect: "[::1]:1337"},
+		"extra space":         {value: " 1.2.3.4 ", expect: "1.2.3.4:11434"},
+		"extra quotes":        {value: "\"1.2.3.4\"", expect: "1.2.3.4:11434"},
+		"extra space+quotes":  {value: " \" 1.2.3.4 \" ", expect: "1.2.3.4:11434"},
+		"extra single quotes": {value: "'1.2.3.4'", expect: "1.2.3.4:11434"},
 	}
 
-	for k, v := range cases {
+	for k, v := range hostTestCases {
 		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 TestLoadTimeout(t *testing.T) {
-	defaultTimeout := 5 * time.Minute
-	cases := map[string]time.Duration{
-		"":       defaultTimeout,
-		"1s":     time.Second,
-		"1m":     time.Minute,
-		"1h":     time.Hour,
-		"5m0s":   defaultTimeout,
-		"1h2m3s": 1*time.Hour + 2*time.Minute + 3*time.Second,
-		"0":      time.Duration(math.MaxInt64),
-		"60":     60 * time.Second,
-		"120":    2 * time.Minute,
-		"3600":   time.Hour,
-		"-0":     time.Duration(math.MaxInt64),
-		"-1":     time.Duration(math.MaxInt64),
-		"-1m":    time.Duration(math.MaxInt64),
-		// invalid values
-		" ":   defaultTimeout,
-		"???": defaultTimeout,
-		"1d":  defaultTimeout,
-		"1y":  defaultTimeout,
-		"1w":  defaultTimeout,
-	}
-
-	for tt, expect := range cases {
-		t.Run(tt, func(t *testing.T) {
-			t.Setenv("OLLAMA_LOAD_TIMEOUT", tt)
-			if actual := LoadTimeout(); actual != expect {
-				t.Errorf("%s: expected %s, got %s", tt, expect, actual)
-			}
-		})
-	}
-}
-
-func TestVar(t *testing.T) {
-	cases := map[string]string{
-		"value":       "value",
-		" 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)
+			t.Setenv("OLLAMA_HOST", v.value)
+			LoadConfig()
+
+			oh, err := getOllamaHost()
+			if err != v.err {
+				t.Fatalf("expected %s, got %s", v.err, err)
+			}
+
+			if err == nil {
+				host := net.JoinHostPort(oh.Host, oh.Port)
+				assert.Equal(t, v.expect, host, fmt.Sprintf("%s: expected %s, got %s", k, v.expect, host))
 			}
 		})
 	}

examples/go-chat/main.go

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

examples/go-generate-streaming/main.go

@@ -16,7 +16,7 @@ func main() {
 
 	// By default, GenerateRequest is streaming.
 	req := &api.GenerateRequest{
-		Model:  "gemma2",
+		Model:  "gemma",
 		Prompt: "how many planets are there?",
 	}
 

examples/go-generate/main.go

@@ -15,7 +15,7 @@ func main() {
 	}
 
 	req := &api.GenerateRequest{
-		Model:  "gemma2",
+		Model:  "gemma",
 		Prompt: "how many planets are there?",
 
 		// set streaming to false

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

@@ -4,14 +4,6 @@ This example provides an interface for asking questions to a PDF document.
 
 ## Setup
 
-1. Ensure you have the `llama3.1` model installed:
-
-```
-ollama pull llama3.1
-```
-
-2. Install the Python Requirements.
-
 ```
 pip install -r requirements.txt
 ```

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

@@ -51,7 +51,7 @@ while True:
         template=template,
     )
 
-    llm = Ollama(model="llama3.1", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))
+    llm = Ollama(model="llama3:8b", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))
     qa_chain = RetrievalQA.from_chain_type(
         llm,
         retriever=vectorstore.as_retriever(),

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

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

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

@@ -5,8 +5,8 @@ from langchain.chains.summarize import load_summarize_chain
 loader = WebBaseLoader("https://ollama.com/blog/run-llama2-uncensored-locally")
 docs = loader.load()
 
-llm = Ollama(model="llama3.1")
+llm = Ollama(model="llama3")
 chain = load_summarize_chain(llm, chain_type="stuff")
 
-result = chain.invoke(docs)
+result = chain.invoke(docs) 
 print(result)

examples/langchain-python-simple/README.md

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

examples/langchain-python-simple/main.py

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

examples/modelfile-mario/Modelfile

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

examples/modelfile-mario/readme.md

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

examples/python-dockerit/dockerit.py

@@ -4,7 +4,7 @@ imageName = input("Enter the name of the image: ")
 client = docker.from_env()
 s = requests.Session()
 output=""
-with s.post('http://localhost:11434/api/generate', json={'model': 'mattw/dockerit', 'prompt': inputDescription}, stream=True) as r:
+with s.post('http://localhost:11434/api/generate', json={'model': 'dockerit', 'prompt': inputDescription}, stream=True) as r:
   for line in r.iter_lines():
     if line:
       j = json.loads(line)

examples/python-json-datagenerator/predefinedschema.py

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

examples/python-json-datagenerator/randomaddresses.py

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

examples/python-json-datagenerator/readme.md

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

examples/python-simplechat/client.py

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