add q4_0_4_4/4_8/8_8

2024-08-01 14:31:36 -07:00
136 changed files with 3028 additions and 4807 deletions
--- a/.gitattributes
+++ b/.gitattributes
@@ -1,3 +1 @@
 llm/ext_server/* linguist-vendored
-* text=auto
-*.go text eol=lf
--- a/.github/workflows/release.yaml
+++ b/.github/workflows/release.yaml
@@ -31,7 +31,7 @@ jobs:
          security set-keychain-settings -lut 3600 build.keychain
      - uses: actions/setup-go@v5
        with:
-          go-version-file: go.mod
+          go-version: "stable"
          cache: true
      - name: Build Darwin
        env:
@@ -87,7 +87,7 @@ jobs:
          write-host "plugin installed"
      - uses: actions/setup-go@v5
        with:
-          go-version-file: go.mod
+          go-version: "stable"
          cache: true
      - run: go get ./...
      - run: |
@@ -141,7 +141,7 @@ jobs:
          write-host "plugin installed"
      - uses: actions/setup-go@v5
        with:
-          go-version-file: go.mod
+          go-version: "stable"
          cache: true
      - name: 'Install ROCm'
        run: |
@@ -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:
@@ -225,13 +218,13 @@ jobs:
          write-host "plugin installed"
      - uses: actions/setup-go@v5
        with:
-          go-version-file: go.mod
+          go-version: "stable"
          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
@@ -314,7 +306,7 @@ jobs:
          write-host "plugin installed"
      - uses: actions/setup-go@v5
        with:
-          go-version-file: go.mod
+          go-version: "stable"
          cache: true
      - run: go get
      - uses: actions/download-artifact@v4
@@ -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
@@ -472,8 +459,7 @@ jobs:
          merge-multiple: true
      - run: |
          ls -lh dist/
-          (cd dist; find . -type f | xargs sha256sum > ../sha256sum.txt)
-          mv sha256sum.txt dist/
+          (cd dist; sha256sum * > sha256sum.txt)
          cat dist/sha256sum.txt
      - name: Create or update Release
        run: |
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -63,7 +63,7 @@ jobs:
      - uses: actions/checkout@v4
      - uses: actions/setup-go@v5
        with:
-          go-version-file: go.mod
+          go-version: "stable"
          cache: true
      - run: go get ./...
      - run: |
@@ -163,7 +163,7 @@ jobs:
      - uses: actions/checkout@v4
      - uses: actions/setup-go@v5
        with:
-          go-version-file: go.mod
+          go-version: "stable"
          cache: true
      - name: 'Install ROCm'
        run: |
@@ -200,7 +200,7 @@ jobs:
      - uses: actions/checkout@v4
      - uses: actions/setup-go@v5
        with:
-          go-version-file: go.mod
+          go-version: "stable"
          cache: true
      - name: 'Install CUDA'
        run: |
@@ -255,7 +255,7 @@ jobs:
          submodules: recursive
      - uses: actions/setup-go@v5
        with:
-          go-version-file: go.mod
+          go-version: "stable"
          cache: false
      - run: |
          case ${{ matrix.arch }} in
@@ -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:
@@ -297,7 +297,7 @@ jobs:
          submodules: recursive
      - uses: actions/setup-go@v5
        with:
-          go-version-file: go.mod
+          go-version: "stable"
          cache: true
      - run: |
          case ${{ matrix.arch }} in
--- a/.golangci.yaml
+++ b/.golangci.yaml
@@ -7,31 +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]
+    - usestdlibvars
 severity:
  default-severity: error
  rules:
--- a/CONTRIBUTING.md
+++ b/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 dorrect missing documentation is helpful, however large documentation additions can be hard to maintain over time.
-
-### Issues that may not be accepted
-
-* Changes that break backwards compatibility in Ollama's API (including the OpenAI-compatible API)
-* Changes that add significant friction to the user experience
-* Changes that create a large future maintenance burden for maintainers and contributors
-
-### Best practices
-
-* Commit messages: please leave both a title and a description in your commit messages. The title should be a short summary of the changes, with a leading word that explains the section of the code being changed (e.g. `api: fix parsing of prompt field`) . In the description, leave a short 2-3 sentences that explain more about the change and its impact.
-* Tests: please add test coverage to changes where possible.
-* Minimize dependencies: avoid adding new dependencies unless absolutely necessary.
-
-## Need help?
-
-If you need help with anything, feel free to reach out to us on our [Discord server](https://discord.gg/ollama).
--- a/131
+++ b/131
@@ -1,9 +1,7 @@
 ARG GOLANG_VERSION=1.22.5
 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/lib/ollama && \
-    (cd /opt/rocm/lib && tar cf - rocblas/library) | (cd ../../dist/linux-amd64/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
@@ -169,36 +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
-COPY --from=build-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/bin/ /bin/
-
+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
-COPY --from=build-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/bin/ /bin/
+COPY --from=build-arm64 /go/src/github.com/ollama/ollama/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

--- a/README.md
+++ b/README.md
@@ -54,7 +54,6 @@ Here are some example models that can be downloaded:
 | Llama 3.1          | 405B       | 231GB | `ollama run llama3.1:405b`     |
 | Phi 3 Mini         | 3.8B       | 2.3GB | `ollama run phi3`              |
 | Phi 3 Medium       | 14B        | 7.9GB | `ollama run phi3:medium`       |
-| Gemma 2            | 2B         | 1.6GB | `ollama run gemma2:2b`         |
 | Gemma 2            | 9B         | 5.5GB | `ollama run gemma2`            |
 | Gemma 2            | 27B        | 16GB  | `ollama run gemma2:27b`        |
 | Mistral            | 7B         | 4.1GB | `ollama run mistral`           |
@@ -301,7 +300,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Sidellama](https://github.com/gyopak/sidellama) (browser-based LLM client)
 - [LLMStack](https://github.com/trypromptly/LLMStack) (No-code multi-agent framework to build LLM agents and workflows)
 - [BoltAI for Mac](https://boltai.com) (AI Chat Client for Mac)
- [Harbor](https://github.com/av/harbor) (Containerized LLM Toolkit with Ollama as default backend)

 ### Terminal

@@ -325,7 +323,6 @@ 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/)

 ### Database

--- a/api/client.go
+++ b/api/client.go
@@ -18,7 +18,6 @@ import (
 	"bytes"
 	"context"
 	"encoding/json"
-	"errors"
 	"fmt"
 	"io"
 	"net/http"
@@ -173,7 +172,7 @@ 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 +297,7 @@ 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 +332,7 @@ 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 {
--- a/api/types.go
+++ b/api/types.go
@@ -231,6 +231,7 @@ 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"`
@@ -504,7 +505,7 @@ 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
 		}

@@ -614,6 +615,7 @@ func DefaultOptions() Options {
 			F16KV:     true,
 			UseMLock:  false,
 			UseMMap:   nil,
+			UseNUMA:   false,
 		},
 	}
 }
--- a/api/types_test.go
+++ b/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]"),
 		},
 	}

--- a/app/lifecycle/getstarted_nonwindows.go
+++ b/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")
 }
--- a/app/lifecycle/getstarted_windows.go
+++ b/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)
 	}
--- a/app/lifecycle/logging.go
+++ b/app/lifecycle/logging.go
@@ -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
--- a/app/lifecycle/logging_nonwindows.go
+++ b/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")
 }
--- a/app/lifecycle/logging_test.go
+++ b/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)
--- a/app/lifecycle/server.go
+++ b/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)
 	}
--- a/app/lifecycle/updater.go
+++ b/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 {
--- a/app/lifecycle/updater_nonwindows.go
+++ b/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")
 }
--- a/app/lifecycle/updater_windows.go
+++ b/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?
--- a/app/ollama.iss
+++ b/app/ollama.iss
@@ -87,11 +87,20 @@ DialogFontSize=12

 [Files]
 Source: ".\app.exe"; DestDir: "{app}"; DestName: "{#MyAppExeName}" ; Flags: ignoreversion 64bit
-Source: "..\ollama.exe"; DestDir: "{app}\bin"; Flags: ignoreversion 64bit
-Source: "..\dist\windows-{#ARCH}\lib\ollama\runners\*"; DestDir: "{app}\lib\ollama\runners"; Flags: ignoreversion 64bit recursesubdirs
+Source: "..\ollama.exe"; DestDir: "{app}"; Flags: ignoreversion 64bit
+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"
@@ -99,7 +108,7 @@ Name: "{userstartup}\{#MyAppName}"; Filename: "{app}\{#MyAppExeName}"; IconFilen
 Name: "{userprograms}\{#MyAppName}"; Filename: "{app}\{#MyAppExeName}"; IconFilename: "{app}\app.ico"

 [Run]
-Filename: "{cmd}"; Parameters: "/C set PATH={app}\bin;%PATH% & ""{app}\{#MyAppExeName}"""; Flags: postinstall nowait runhidden
+Filename: "{cmd}"; Parameters: "/C set PATH={app};%PATH% & ""{app}\{#MyAppExeName}"""; Flags: postinstall nowait runhidden

 [UninstallRun]
 ; Filename: "{cmd}"; Parameters: "/C ""taskkill /im ''{#MyAppExeName}'' /f /t"; Flags: runhidden
@@ -134,8 +143,8 @@ SetupAppRunningError=Another Ollama installer is running.%n%nPlease cancel or fi

 [Registry]
 Root: HKCU; Subkey: "Environment"; \
-    ValueType: expandsz; ValueName: "Path"; ValueData: "{olddata};{app}\bin"; \
-    Check: NeedsAddPath('{app}\bin')
+    ValueType: expandsz; ValueName: "Path"; ValueData: "{olddata};{app}"; \
+    Check: NeedsAddPath('{app}')

 [Code]

--- a/app/tray/tray_nonwindows.go
+++ b/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")
 }
--- a/app/tray/wintray/eventloop.go
+++ b/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()
--- a/app/tray/wintray/menus.go
+++ b/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 {
--- a/app/tray/wintray/tray.go
+++ b/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()
--- a/app/tray/wintray/w32api.go
+++ b/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
--- a/auth/auth.go
+++ b/auth/auth.go
@@ -5,7 +5,6 @@ import (
 	"context"
 	"crypto/rand"
 	"encoding/base64"
-	"errors"
 	"fmt"
 	"io"
 	"log/slog"
@@ -79,7 +78,7 @@ func Sign(ctx context.Context, bts []byte) (string, error) {
 	publicKey := ssh.MarshalAuthorizedKey(privateKey.PublicKey())
 	parts := bytes.Split(publicKey, []byte(" "))
 	if len(parts) < 2 {
-		return "", errors.New("malformed public key")
+		return "", fmt.Errorf("malformed public key")
 	}

 	signedData, err := privateKey.Sign(rand.Reader, bts)
--- a/cmd/cmd.go
+++ b/cmd/cmd.go
@@ -22,7 +22,6 @@ import (
 	"runtime"
 	"slices"
 	"strings"
-	"sync/atomic"
 	"syscall"
 	"time"

@@ -79,7 +78,6 @@ 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 +112,7 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 				path = tempfile
 			}

-			digest, err := createBlob(cmd, client, path, spinner)
+			digest, err := createBlob(cmd, client, path)
 			if err != nil {
 				return err
 			}
@@ -265,20 +263,13 @@ 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) {
+func createBlob(cmd *cobra.Command, client *api.Client, 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
@@ -288,43 +279,13 @@ 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 {
+	if err = client.CreateBlob(cmd.Context(), digest, bin); err != nil {
 		return "", err
 	}
 	return digest, nil
 }

-type progressWriter struct {
-	n atomic.Int64
-}
-
-func (w *progressWriter) Write(p []byte) (n int, err error) {
-	w.n.Add(int64(len(p)))
-	return len(p), nil
-}
-
 func RunHandler(cmd *cobra.Command, args []string) error {
 	interactive := true

@@ -1125,7 +1086,7 @@ func generate(cmd *cobra.Command, opts runOptions) error {
 	return nil
 }

-func RunServer(_ *cobra.Command, _ []string) error {
+func RunServer(cmd *cobra.Command, _ []string) error {
 	if err := initializeKeypair(); err != nil {
 		return err
 	}
@@ -1199,7 +1160,7 @@ 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
--- a/cmd/interactive.go
+++ b/cmd/interactive.go
@@ -604,7 +604,7 @@ func getImageData(filePath string) ([]byte, error) {
 	// Check if the file size exceeds 100MB
 	var maxSize int64 = 100 * 1024 * 1024 // 100MB in bytes
 	if info.Size() > maxSize {
-		return nil, errors.New("file size exceeds maximum limit (100MB)")
+		return nil, fmt.Errorf("file size exceeds maximum limit (100MB)")
 	}

 	buf = make([]byte, info.Size())
--- a/cmd/start_darwin.go
+++ b/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 {
--- a/cmd/start_default.go
+++ b/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")
 }
--- a/cmd/start_windows.go
+++ b/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")
 			}
 		}
 	}
--- a/convert/convert.go
+++ b/convert/convert.go
@@ -1,128 +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 Parameters 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
 }

-func (Parameters) 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 ByteOrder interface {
+	binary.ByteOrder
+	binary.AppendByteOrder
 }

-func (Parameters) specialTokenTypes() []string {
-	return []string{
-		"bos", "eos", "unk", "sep", "pad", "cls", "mask",
-	}
+type ModelArch interface {
+	GetTensors() error
+	LoadVocab() error
+	WriteGGUF(io.WriteSeeker) error
 }

-func (Parameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []llm.Tensor) error {
-	return llm.WriteGGUF(ws, kv, ts)
+type ModelFormat interface {
+	GetLayerName(string) (string, error)
+	GetTensors(string, *Params) ([]llm.Tensor, error)
+	GetParams(string) (*Params, error)
+	GetModelArch(string, string, *Params) (ModelArch, error)
 }

-type Converter 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
-
-	// tensorName returns the LLM tensor name for a specific input name
-	tensorName(string) string
-	// specialTokenTypes returns any special token types the model uses
-	specialTokenTypes() []string
-	writeFile(io.WriteSeeker, llm.KV, []llm.Tensor) error
+type ModelData struct {
+	Path    string
+	Name    string
+	Params  *Params
+	Vocab   *Vocab
+	Tensors []llm.Tensor
+	Format  ModelFormat
 }

-// 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 Convert(fsys fs.FS, ws io.WriteSeeker) error {
-	bts, err := fs.ReadFile(fsys, "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 Parameters
-	if err := json.Unmarshal(bts, &p); err != nil {
-		return err
-	}
-
-	if len(p.Architectures) < 1 {
-		return errors.New("unknown architecture")
-	}
-
-	var conv Converter
-	switch p.Architectures[0] {
-	case "LlamaForCausalLM", "MistralForCausalLM":
-		conv = &llama{}
-	case "MixtralForCausalLM":
-		conv = &mixtral{}
-	case "GemmaForCausalLM":
-		conv = &gemma{}
-	case "Phi3ForCausalLM":
-		conv = &phi3{}
-	default:
-		return errors.New("unsupported architecture")
-	}
-
-	if err := json.Unmarshal(bts, conv); err != nil {
-		return err
-	}
-
-	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)
+	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
 		}
-	} else {
-		slog.Debug("vocabulary", "size", len(t.Vocabulary.Tokens))
 	}

-	ts, err := parseTensors(fsys)
-	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
 }
--- a/convert/convert_gemma.go
+++ b/convert/convert_gemma.go
@@ -1,103 +0,0 @@
-package convert
-
-import (
-	"strings"
-
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type gemma struct {
-	Parameters
-	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 _ Converter = (*gemma)(nil)
-
-func (p *gemma) KV(t *Tokenizer) llm.KV {
-	kv := p.Parameters.KV(t)
-	kv["general.architecture"] = "gemma"
-	kv["general.name"] = "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 *gemma) Tensors(ts []Tensor) []llm.Tensor {
-	var out []llm.Tensor
-	for _, t := range ts {
-		name := p.tensorName(t.Name())
-		if strings.HasSuffix(name, "_norm.weight") {
-			t.SetRepacker(p.addOne)
-		}
-
-		out = append(out, llm.Tensor{
-			Name:     name,
-			Kind:     t.Kind(),
-			Shape:    t.Shape(),
-			WriterTo: t,
-		})
-	}
-
-	return out
-}
-
-func (p *gemma) tensorName(n string) string {
-	return strings.NewReplacer(
-		"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",
-		"block_sparse_moe.gate", "ffn_inp",
-	).Replace(n)
-}
-
-func (*gemma) 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
-}
--- a/convert/convert_llama.go
+++ b/convert/convert_llama.go
@@ -1,179 +0,0 @@
-package convert
-
-import (
-	"cmp"
-	"fmt"
-	"strings"
-
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type llama struct {
-	Parameters
-	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"`
-		Factor float32 `json:"factor"`
-	} `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 _ Converter = (*llama)(nil)
-
-func (p *llama) KV(t *Tokenizer) llm.KV {
-	kv := p.Parameters.KV(t)
-	kv["general.architecture"] = "llama"
-	kv["general.name"] = "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
-	}
-
-	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 *llama) Tensors(ts []Tensor) []llm.Tensor {
-	var out []llm.Tensor
-	for _, t := range ts {
-		name := p.tensorName(t.Name())
-		if strings.HasSuffix(name, "attn_q.weight") ||
-			strings.HasSuffix(name, "attn_k.weight") {
-			t.SetRepacker(p.repack)
-		}
-
-		out = append(out, llm.Tensor{
-			Name:     name,
-			Kind:     t.Kind(),
-			Shape:    t.Shape(),
-			WriterTo: t,
-		})
-	}
-
-	return out
-}
-
-func (p *llama) tensorName(n string) string {
-	return strings.NewReplacer(
-		"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",
-		// mixtral
-		"block_sparse_moe.gate", "ffn_gate_inp",
-	).Replace(n)
-}
-
-func (p *llama) 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, "q_proj.weight") {
-		heads = p.NumAttentionHeads
-	} else if strings.HasSuffix(name, "k_proj.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
-}
--- a/convert/convert_mixtral.go
+++ b/convert/convert_mixtral.go
@@ -1,89 +0,0 @@
-package convert
-
-import (
-	"fmt"
-	"io"
-	"slices"
-	"strings"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type mixtral struct {
-	llama
-	NumLocalExperts    uint32 `json:"num_local_experts"`
-	NumExpertsPerToken uint32 `json:"num_experts_per_tok"`
-}
-
-var _ Converter = (*mixtral)(nil)
-
-func (p *mixtral) KV(t *Tokenizer) llm.KV {
-	kv := p.llama.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 *mixtral) 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.llama.Tensors(ts)...)
-}
-
-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
-}
--- a/convert/convert_phi3.go
+++ b/convert/convert_phi3.go
@@ -1,125 +0,0 @@
-package convert
-
-import (
-	"cmp"
-	"encoding/binary"
-	"io"
-	"math"
-	"strings"
-	"sync"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type phi3 struct {
-	Parameters
-	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 _ Converter = (*phi3)(nil)
-
-func (p *phi3) KV(t *Tokenizer) llm.KV {
-	kv := p.Parameters.KV(t)
-	kv["general.architecture"] = "phi3"
-	kv["general.name"] = "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 *phi3) Tensors(ts []Tensor) []llm.Tensor {
-	var addRopeFactors sync.Once
-
-	out := make([]llm.Tensor, 0, len(ts)+2)
-	for _, t := range ts {
-		name := p.tensorName(t.Name())
-		if strings.HasPrefix(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:     name,
-			Kind:     t.Kind(),
-			Shape:    t.Shape(),
-			WriterTo: t,
-		})
-	}
-
-	return out
-}
-
-func (p *phi3) tensorName(n string) string {
-	return strings.NewReplacer(
-		"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",
-	).Replace(n)
-}
-
-type ropeFactor []float32
-
-func (r ropeFactor) WriteTo(w io.Writer) (int64, error) {
-	err := binary.Write(w, binary.LittleEndian, r)
-	return 0, err
-}
--- a/convert/convert_test.go
+++ b/convert/convert_test.go
@@ -1,35 +1,48 @@
+//go:build slow
+
 package convert

 import (
-	"crypto/sha256"
-	"encoding/hex"
-	"encoding/json"
-	"flag"
-	"fmt"
-	"io"
-	"io/fs"
-	"log/slog"
-	"math"
 	"os"
 	"path/filepath"
-	"slices"
 	"testing"

-	"golang.org/x/exp/maps"
-
 	"github.com/ollama/ollama/llm"
 )

-func convertFull(t *testing.T, fsys fs.FS) (*os.File, llm.KV, llm.Tensors) {
+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 := Convert(fsys, f); err != nil {
+	if err := arch.WriteGGUF(f); err != nil {
 		t.Fatal(err)
 	}

@@ -37,93 +50,53 @@ 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()
-}
-
-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())
+	return m.KV(), m.Tensors()
 }

 func TestConvertFull(t *testing.T) {
-	cases := []string{
-		"Meta-Llama-3-8B-Instruct",
-		"Mistral-7B-Instruct-v0.2",
-		"Mixtral-8x7B-Instruct-v0.1",
-		"gemma-2b-it",
-		// microsoft/Phi-3-mini-128-instruct@d548c233192db00165d842bf8edff054bb3212f8
-		"Phi-3-mini-128k-instruct",
+	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 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 := 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)
-					}
+			kv, tensors := convertFull(t, p)

-					actual[k] = fmt.Sprintf("%x", sha256.Sum256(bts))
-				}
+			if kv.Architecture() != tt.arch {
+				t.Fatalf("expected llama, got %s", kv.Architecture())
 			}

-			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))
+			if kv.FileType().String() != "F16" {
+				t.Fatalf("expected F16, got %s", kv.FileType())
 			}

-			expectFile, err := os.Open(filepath.Join("testdata", fmt.Sprintf("%s.json", tt)))
-			if err != nil {
-				t.Fatal(err)
+			if len(tensors) != tt.tensors {
+				t.Fatalf("expected %d tensors, got %d", tt.tensors, len(tensors))
 			}

-			var expect map[string]string
-			if err := json.NewDecoder(expectFile).Decode(&expect); err != nil {
-				t.Fatal(err)
-			}
-
-			keys := maps.Keys(expect)
-			slices.Sort(keys)
-			for _, k := range keys {
-				if v, ok := actual[k]; !ok {
-					t.Errorf("missing %s", k)
-				} else if v != expect[k] {
-					t.Errorf("unexpected %s: want %s, got %s", k, expect[k], v)
-				}
+			layers := tensors.Layers()
+			if len(layers) != tt.layers {
+				t.Fatalf("expected %d layers, got %d", tt.layers, len(layers))
 			}
 		})
 	}
--- a/convert/fs.go
+++ b/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)
-}
--- a/convert/gemma.go
+++ b/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)
+}
--- a/convert/llama.go
+++ b/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
+}
--- a/convert/mistral.go
+++ b/convert/mistral.go
@@ -0,0 +1,84 @@
+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),
+	}
+
+	if m.Params.HeadDimension > 0 {
+		kv["llama.attention.key_length"] = uint32(m.Params.HeadDimension)
+		kv["llama.attention.value_length"] = uint32(m.Params.HeadDimension)
+	}
+
+	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)
+}
--- a/convert/mixtral.go
+++ b/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)
+}
--- a/convert/reader.go
+++ b/convert/reader.go
@@ -1,82 +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, ".block_sparse_moe.gate.weight") {
-		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) ([]Tensor, error) {
-	patterns := []struct {
-		Pattern string
-		Func    func(fs.FS, ...string) ([]Tensor, error)
-	}{
-		{"model-*-of-*.safetensors", parseSafetensors},
-		{"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, matches...)
-		}
-	}
-
-	return nil, errors.New("unknown tensor format")
-}
--- a/convert/reader_safetensors.go
+++ b/convert/reader_safetensors.go
@@ -1,150 +0,0 @@
-package convert
-
-import (
-	"bytes"
-	"encoding/binary"
-	"encoding/json"
-	"fmt"
-	"io"
-	"io/fs"
-	"slices"
-
-	"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, ps ...string) ([]Tensor, error) {
-	var ts []Tensor
-	for _, p := range ps {
-		f, err := fsys.Open(p)
-		if err != nil {
-			return nil, err
-		}
-		defer f.Close()
-
-		var n int64
-		if err := binary.Read(f, binary.LittleEndian, &n); err != nil {
-			return nil, err
-		}
-
-		b := bytes.NewBuffer(make([]byte, 0, n))
-		if _, err = io.CopyN(b, f, n); err != nil {
-			return nil, err
-		}
-
-		var headers map[string]safetensorMetadata
-		if err := json.NewDecoder(b).Decode(&headers); err != nil {
-			return nil, err
-		}
-
-		keys := maps.Keys(headers)
-		slices.Sort(keys)
-
-		for _, key := range keys {
-			if value := headers[key]; value.Type != "" {
-				ts = append(ts, safetensor{
-					fs:     fsys,
-					path:   p,
-					dtype:  value.Type,
-					offset: safetensorsPad(n, value.Offsets[0]),
-					size:   safetensorsPad(n, value.Offsets[1]) - safetensorsPad(n, value.Offsets[0]),
-					tensorBase: &tensorBase{
-						name:  key,
-						shape: value.Shape,
-					},
-				})
-			}
-		}
-	}
-
-	return ts, nil
-}
-
-// safetensorsPad returns the padded size of the safetensors file given a length n and offset s
-func safetensorsPad(n, offset int64) int64 {
-	return 8 + n + offset
-}
-
-type safetensor struct {
-	fs     fs.FS
-	path   string
-	dtype  string
-	offset int64
-	size   int64
-	*tensorBase
-}
-
-func (st safetensor) WriteTo(w io.Writer) (int64, error) {
-	f, err := st.fs.Open(st.path)
-	if err != nil {
-		return 0, err
-	}
-	defer f.Close()
-
-	if seeker, ok := f.(io.Seeker); ok {
-		if _, err := seeker.Seek(st.offset, io.SeekStart); err != nil {
-			return 0, err
-		}
-	} else {
-		if _, err := io.CopyN(io.Discard, f, st.offset); err != nil {
-			return 0, err
-		}
-	}
-
-	var f32s []float32
-	switch st.dtype {
-	case "F32":
-		f32s = make([]float32, st.size/4)
-		if err = binary.Read(f, binary.LittleEndian, f32s); err != nil {
-			return 0, err
-		}
-	case "F16":
-		u16s := make([]uint16, st.size/2)
-		if err = binary.Read(f, binary.LittleEndian, u16s); err != nil {
-			return 0, err
-		}
-
-		f32s = make([]float32, len(u16s))
-		for i := range u16s {
-			f32s[i] = float16.Frombits(u16s[i]).Float32()
-		}
-
-	case "BF16":
-		u8s := make([]uint8, st.size)
-		if err = binary.Read(f, binary.LittleEndian, u8s); err != nil {
-			return 0, err
-		}
-
-		f32s = bfloat16.DecodeFloat32(u8s)
-	default:
-		return 0, fmt.Errorf("unknown data type: %s", st.dtype)
-	}
-
-	if st.repacker != nil {
-		f32s, err = st.repacker(st.Name(), f32s, st.Shape())
-		if err != nil {
-			return 0, err
-		}
-	}
-
-	switch st.Kind() {
-	case tensorKindF32:
-		return 0, binary.Write(w, binary.LittleEndian, f32s)
-	case tensorKindF16:
-		f16s := make([]uint16, len(f32s))
-		for i := range f32s {
-			f16s[i] = float16.Fromfloat32(f32s[i]).Bits()
-		}
-
-		return 0, binary.Write(w, binary.LittleEndian, f16s)
-	default:
-		return 0, fmt.Errorf("unknown storage type: %d", st.Kind())
-	}
-}
--- a/convert/reader_torch.go
+++ b/convert/reader_torch.go
@@ -1,47 +0,0 @@
-package convert
-
-import (
-	"io"
-	"io/fs"
-
-	"github.com/nlpodyssey/gopickle/pytorch"
-	"github.com/nlpodyssey/gopickle/types"
-)
-
-func parseTorch(fsys fs.FS, 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:  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
-}
--- a/convert/safetensors.go
+++ b/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")
+}
--- a/convert/testdata/Meta-Llama-3-8B-Instruct.json
+++ b/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",
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-  "output_norm.weight": "7cc5b7ce10e5082000fa00bfa68af8c7c5da218e59e2c41cf2f1499d40ca229e"
-}
--- a/convert/testdata/Mistral-7B-Instruct-v0.2.json
+++ b/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",
-  "tokenizer.ggml.token_type": "6040635e6bd38d98af06698feb75c1802bad35180ee6ae0a503e38c0f60fd71e",
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-  "blk.27.ffn_up.weight": "1eae3088a75629571fdbf6a20f141bc2bb2ed3f5ba2b9fd1d949f80695e442a1",
-  "blk.28.attn_k.weight": "c4e80af714962d6f9040d2c09f316f4a1cbc3a2e994e19902d7c653cf3c73dba",
-  "blk.28.attn_norm.weight": "c1ecf85dedc1c83d5d402bb7c94fb8b9c11f1a3e5f64e7680f80912d4a560794",
-  "blk.28.attn_output.weight": "72ba47c061b21f5ebc5213a455eaf6fc49c8f8e04ff9ce37e6ed4921b629161d",
-  "blk.28.attn_q.weight": "c4abc47234307f44b8ca789aa6668e298158fa4b459b2c1e84bd581806591cc1",
-  "blk.28.attn_v.weight": "aeba950799d4950e491ad0fcbe30334e39b8975177990a2cb339031c45ac153c",
-  "blk.28.ffn_down.weight": "4e84ce382a37b994fb8608df451a60040559e3f4f3241c3b3cb8989a3ed50d83",
-  "blk.28.ffn_gate.weight": "04df157acdc8e8534ad60acc2d2a4dd3a7a6610f6382535ec728994fa6f83f83",
-  "blk.28.ffn_norm.weight": "4d0386dae2bd1c1a9d0f9730718333e3a486c3bc6a5c5d482193c75d39832c80",
-  "blk.28.ffn_up.weight": "fec60bb0a3daf182a14bd8311fe6dd1e3fd020c5fc273e2549cdb1a2d6b79b05",
-  "blk.29.attn_k.weight": "b0532a263aa5a4e2a7a80adc83fc5dec974493bd18da7f953e7ebfc3f3a19aae",
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-  "blk.29.attn_q.weight": "d482d45386d4ad3394f08e9dff233ee3a70d0427d65c0b8fa05905da7e25ca53",
-  "blk.29.attn_v.weight": "cd3b5a6e2852da796902930a6a84bc87fc6a7c7bf51f8fc23758d12a39013b36",
-  "blk.29.ffn_down.weight": "5b3dba6f9753bd1b1ebcba65ef5373dd62c38e755c44b7231b95d93d45761f89",
-  "blk.29.ffn_gate.weight": "8610d9d2db15c256243ffcca3ffd31786d0ada0af0e7c7aa3fd20524370ab036",
-  "blk.29.ffn_norm.weight": "1a2ef2d38b7ac3e51190b9ccb8b6552ba83ab290e523356a7f851ddb35dedca2",
-  "blk.29.ffn_up.weight": "a5fdd15811bde16dc27677cf1a4c97daab4c28cb12a9530f1a0e573134fdb69c",
-  "blk.30.attn_k.weight": "1efeb0b5f4b45a85cdf47300f892ac77ac1f38000ec3653565d1303d1fb8c743",
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-  "blk.30.attn_output.weight": "3a0fd89de2d274614750345d827a9c886a4f97b343a13cdf680390505df596a3",
-  "blk.30.attn_q.weight": "711e113362bdb067db843c66236704eb1cd3fc5f40e3767143e96d510686ef4e",
-  "blk.30.attn_v.weight": "82b12a9a74fd3d91b73cc2e841e2b3f0a5197ccd2998afa17020995f880d2267",
-  "blk.30.ffn_down.weight": "af9f4b1287c0d824ae22d6e335d19e04a70135b835be7caa2435f1d85e931993",
-  "blk.30.ffn_gate.weight": "e2ab3e6f15f5c50fca66c084cb6a57a2b6b82406d65150e82ea0437b93dd9a46",
-  "blk.30.ffn_norm.weight": "c1b9c325c83f00e177386a4d7e769945f2995e60950c4a576c0a2c4ab9703d04",
-  "blk.30.ffn_up.weight": "9b94a21efd419715d82071b490d3b635cf1e8da080620dcc39e5bde976d7e9a6",
-  "blk.31.attn_k.weight": "0db0d82e3ddcc2c06209f5f013e1d72a84a996c40bf00186be485b909cc268e8",
-  "blk.31.attn_norm.weight": "2b8b7239471f57140c5cdfe06bd224a4f6326282f99736e44fba4c7b120ac101",
-  "blk.31.attn_output.weight": "a310b048840cc3ff2be4b84796340e8e2cdf05ec89d14bd3655c109b2bfa9fcd",
-  "blk.31.attn_q.weight": "f45e0cd95645175ea82813455356d171838539bc3f7676d877c698f2af0a0eda",
-  "blk.31.attn_v.weight": "8bde008e809112aa7e7c23e9c3099087bcc557313b01306c87efa0a4a30805ba",
-  "blk.31.ffn_down.weight": "8266fec7e203fbfad7033120861e44984581ff8b6851d01dfb7b81c5d8fa90ec",
-  "blk.31.ffn_gate.weight": "b73bc0aa5baf006d9ef6403104891b8133671b0992398fe038380b67e0d7e2cf",
-  "blk.31.ffn_norm.weight": "9c62cc27a7b6017c1df8ad49bff249a8245e8895c6754f402cd44623fda83268",
-  "blk.31.ffn_up.weight": "5b970a4694ea3171a0167f6e1636d9f00268bc1c9640430ffc35218494884adb",
-  "output.weight": "74fa0ef08c57a30e633e7117b1e9c805f833e2e5e21434bc79ddf9c92c6d7330",
-  "output_norm.weight": "59b8a59fd3fbf39353506116e43e5e76edd0cbf2a2873d869da4cf27a04997c3"
-}
--- a/convert/testdata/Mixtral-8x7B-Instruct-v0.1.json
+++ b/convert/testdata/Mixtral-8x7B-Instruct-v0.1.json
@@ -1,348 +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.rope.dimension_count": "128",
-  "llama.rope.freq_base": "1e+06",
-  "llama.attention.head_count": "32",
-  "llama.attention.head_count_kv": "8",
-  "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",
-  "tokenizer.ggml.scores": "e3d3eea80bb41a1213f2d0aa3e8a38581d1f19323be77dbd779c9c7e3b72e676",
-  "tokenizer.ggml.token_type": "6040635e6bd38d98af06698feb75c1802bad35180ee6ae0a503e38c0f60fd71e",
-  "tokenizer.ggml.tokens": "604ac4bfbd019e430d7b6cdf18c6c0cd5b967900601f0307f714ec7773aa5ca6",
-  "token_embd.weight": "1d1d1d39a867d5a4bfb32792a47247d2638c10c95a6259391d02843583505cc4",
-  "blk.0.ffn_gate_exps.weight": "2e5cd43ac3f26c44f071926ff6c3f239ecc52a34bc9a5b5906d3d4c1bf2fbbfa",
-  "blk.0.ffn_down_exps.weight": "a4dfc7e7c96e7402eb70279601675b956bb7331da8101e63fe5c0a611b6972e5",
-  "blk.0.ffn_up_exps.weight": "2d5d87b378b2319c344ed2c642598b6f7cb6beeb582a8ea51abc9ae690d473c3",
-  "blk.0.ffn_gate_inp.weight": "a46aaf5aba7401ce6e41f158242b4879d34901661f3ede85496cbd0ce79d6314",
-  "blk.0.attn_norm.weight": "3fe37d913bdd2b65076bcdd6efe64a37b0b03cacbb1b80b9f7089068aa35f38c",
-  "blk.0.ffn_norm.weight": "5e14308a3c894734eb204c8f558bdc817e94bbd5b4e9cb4094e91ba388c8f7f2",
-  "blk.0.attn_k.weight": "73d943dcac0911e87bd771f4aa1c901e1bfe1aed293af06e1a67812159859f67",
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-  "blk.0.attn_q.weight": "d6fd7403c873d49c05f6f03208f30d99ad34cb3b71c9990c47334d502a8e4c7b",
-  "blk.0.attn_v.weight": "cf17cf64b2d683bd9de6cebaf60e5c264df6fdc38fe719dde9d54c80334f6366",
-  "blk.1.ffn_gate_inp.weight": "0d524de81cd915816b4e714bf595ad6946a9130b3de731cd89428b2781230809",
-  "blk.1.attn_k.weight": "2ea47f412992b374c70674730fe84700e0c8cce177086ce9b6635e42408964bd",
-  "blk.1.attn_output.weight": "b4b2520794d54113e86c8ff678eacfc62e35be4395a594a6c8c22b4383ebcc0c",
-  "blk.1.attn_q.weight": "5db930c98c4f91f6eab57eb974c72210b158e366d23d6d2890b2759c053bee33",
-  "blk.1.attn_v.weight": "079bdde09668394bf7af9f8bc175017b4f48f0ab64e6dd855a4d7561d1693c0f",
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-  "blk.1.ffn_norm.weight": "405a455fa7d1ec36894652ceb554bbcb09a07fd6405f42741e66dc4a4665c19c",
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-  "output.weight": "16101cbb74b54cda9ebc07ca3c762e3263a56efb3cc011156184b95807d7cf13",
-  "output_norm.weight": "d7aa61585baedd60157aafe157930785742c55989c288573566a971b02423564"
-}
--- a/convert/testdata/Phi-3-mini-128k-instruct.json
+++ b/convert/testdata/Phi-3-mini-128k-instruct.json
@@ -1,225 +0,0 @@
-{
-  "general.architecture": "phi3",
-  "general.file_type": "1",
-  "general.quantization_version": "2",
-  "phi3.block_count": "32",
-  "phi3.context_length": "131072",
-  "phi3.embedding_length": "3072",
-  "phi3.feed_forward_length": "8192",
-  "phi3.rope.scaling.original_context_length": "4096",
-  "phi3.rope.dimension_count": "96",
-  "phi3.rope.freq_base": "10000",
-  "phi3.rope.scaling.attn_factor": "1.1902381",
-  "phi3.attention.head_count": "32",
-  "phi3.attention.head_count_kv": "32",
-  "phi3.attention.layer_norm_rms_epsilon": "1e-05",
-  "phi3.attention.sliding_window": "262144",
-  "tokenizer.ggml.model": "llama",
-  "tokenizer.ggml.pre": "default",
-  "tokenizer.ggml.add_bos_token": "false",
-  "tokenizer.ggml.add_eos_token": "false",
-  "tokenizer.ggml.bos_token_id": "1",
-  "tokenizer.ggml.eos_token_id": "32000",
-  "tokenizer.ggml.unknown_token_id": "0",
-  "tokenizer.ggml.padding_token_id": "32000",
-  "tokenizer.ggml.scores": "6e37bcde2adc7e350e87c496eddd7a2124329c1dc66c5bf3ad3997253e4f7a62",
-  "tokenizer.ggml.token_type": "b6ecf55ec64ee67d87750bdb8d757a2c58bf78377e9f4219f5689a6c4dea57ce",
-  "tokenizer.ggml.tokens": "d168da3ddd3eee820916945fcb9baf24dd3cde42f606cffa2d19e7c8a8743918",
-  "blk.0.attn_norm.weight": "216aeb2c9e0c271f899e1ef2a63cceeb8f41e97642e84fada54b1d3c1c11cf25",
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-  "blk.2.attn_output.weight": "bc5491ea54e0db81462d7d9b7d25cbdda380c2db8de041bd1c4ab7b76a1d19c3",
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-}
--- a/convert/testdata/gemma-2b-it.json
+++ b/convert/testdata/gemma-2b-it.json
@@ -1,188 +0,0 @@
-{
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-  "general.file_type": "1",
-  "general.quantization_version": "2",
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-  "tokenizer.ggml.model": "llama",
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-  "blk.12.ffn_gate.weight": "3462a2d8f6b4743b25e24da51b90018ac2858d05ac7e582bcb69063cfdac1104",
-  "blk.12.ffn_norm.weight": "1f96392c1faa34e34ae5dea55a6a86c5aa4c79758952075d53d28de89dd88456",
-  "blk.12.ffn_up.weight": "d22eacc612a7411953d948483c5fb201e11722955ee0754da866e7bec578ac6d",
-  "blk.13.attn_k.weight": "5864977e6b733ea942647d6feed5c76156c48c200649c22e4e11b9e5860e57f3",
-  "blk.13.attn_norm.weight": "87e053535144723db4145aa5402acc54331b7696752d852bb9fc542ff33f0fb5",
-  "blk.13.attn_output.weight": "078145f5ad83f8b14f97a869346f7fd1583b24d1e3edadaa95d3da4242973f8f",
-  "blk.13.attn_q.weight": "3b8caf35504cbc4d1a7dd6e011a95760703b7f71e2218b030b1254f811362dd7",
-  "blk.13.attn_v.weight": "4fdf8365a603e043e5b40c4a21c84ac167f9be62794178f9d8a608dfe5653bf9",
-  "blk.13.ffn_down.weight": "a07d3abbfcacf48ba028df2cab895be32cc15022d23389a745286e79c1b1d1fd",
-  "blk.13.ffn_gate.weight": "1d2ab39666aa2909acc96787432a3ed13b19d25170f74665fadff9b17bbaffb1",
-  "blk.13.ffn_norm.weight": "4f2e809fda5f3eadf52578ee50e0ba36e53be91e55dce418c12dfe595f5f18e7",
-  "blk.13.ffn_up.weight": "8783d2720c2c37ca176a5801e0b3ef1f9cc9cf3ef1cd37af423aaf6b2a27e2bd",
-  "blk.14.attn_k.weight": "ce9428e2b55d43ae0c6690dbd56182f99adc427694ba8236b405cc8ea5035e86",
-  "blk.14.attn_norm.weight": "6abb35f9db8251d6ae954bda147c6ada2371b0574d11702e828f3c6ac99b7cc0",
-  "blk.14.attn_output.weight": "fe3880916d0ceb5bff672c88bbefb7060a545be609bf049beb2024b38221836d",
-  "blk.14.attn_q.weight": "7c8ad81be6f4a350931fd108b5f7c9e366e8c26ef62d1d85ffef5dca8fd893f8",
-  "blk.14.attn_v.weight": "e4bdedffacbebe38567a0734dfd67db90e911d9a9669fcde9a7c4ad8a0066c52",
-  "blk.14.ffn_down.weight": "ef6694dff1e05820aac0cd2b22f39ac7788b4967afc9250775575554c66aab2c",
-  "blk.14.ffn_gate.weight": "db63c4179e2db704bc505e2b4696e055b593e295a1b7c4c586fc793bdd5aab19",
-  "blk.14.ffn_norm.weight": "2796a62d832a9710148f95d533320492a33e712b2e5218659c548705bd11684d",
-  "blk.14.ffn_up.weight": "3f78c78d8c2d54df45f799d4ff902316628af296834afe4ceed63d4a324ff03e",
-  "blk.15.attn_k.weight": "6e810ee3859e07695645ee0c9a5efc7962668984a5f0a9325f47e462743b447c",
-  "blk.15.attn_norm.weight": "0956b576ae96db0b28cb09f761f801cfd9281432284664f0fe181c8d9c55d1ec",
-  "blk.15.attn_output.weight": "03a17f7e94208177aace5cc41b7f54670ba57873b7274ff6e23caf58cce110ca",
-  "blk.15.attn_q.weight": "b8edafe7d2216a6f8b4ae4905a906475490e6ea418f6e1d3cec563dbdc6fab91",
-  "blk.15.attn_v.weight": "f8ae8cae0f4cfa34a459824eba57350c3c248104ba5607e7d9dc7d7c39aaf4a6",
-  "blk.15.ffn_down.weight": "8d02eb439da852246d2ca67e9b7b6de0b090b80744355e64728a23e41926505b",
-  "blk.15.ffn_gate.weight": "ed5bf361c67db8731f186b775826f21c33bdb521111fd2d922539719a770239f",
-  "blk.15.ffn_norm.weight": "5942ca3c73209ac9a0c8bfd9b4aab7f7be7aee9aa12d9c35833493b44af76767",
-  "blk.15.ffn_up.weight": "f4bebf4ad99ec5f911327dec347be6c595814885309c7bc5647ce28c7f4d1cf5",
-  "blk.16.attn_k.weight": "756a534c19364448e0958b8948fe33891c6ccda0fbb4dfa2024e1f532a87804b",
-  "blk.16.attn_norm.weight": "386b7b9e4e6509f6af9c022d942b6c6c6cc136aeed8751ecb037c74d7c4bfb93",
-  "blk.16.attn_output.weight": "3ba1a766a25830b84d7c22178203635f9c5624caad290bc5e5d73da5d5e7a2ec",
-  "blk.16.attn_q.weight": "d39b0c91e1fda7685d50a0f7cc8d18c44b5bdc90a142c7fda0bc329cca1afa74",
-  "blk.16.attn_v.weight": "98b33fcb0ee3483cff1b06ecb44d7b7ffb4d34c268248e4d73dfdf82b2065b2f",
-  "blk.16.ffn_down.weight": "14006f5e4acb2f9416271ae562e299359cd2585739c7fc77ccbca54495563948",
-  "blk.16.ffn_gate.weight": "12f8abae2d301d8f88bedb6af98b1daecc7b0b8d05148594f931f30958d77aca",
-  "blk.16.ffn_norm.weight": "129a15a046ee96d06de288bd43c80f77a6b0fb3a159c7367154c6e4aaf362672",
-  "blk.16.ffn_up.weight": "b4a5911a45f3871ef1d4efb7dc7108645a564b70f818eccf45beebef2e844ee9",
-  "blk.17.attn_k.weight": "5e1bfcff0146ebdde3817b656952892eb671e14e75afc92fa53f84f8eecbec4c",
-  "blk.17.attn_norm.weight": "60bc988fab7c4b29ee9de599df41a8de00caa94fcd74677da011fac82f60f465",
-  "blk.17.attn_output.weight": "ba49b40d6a0b5685f749c24b0edbed3adc44dbe13b5d5e5fa1e56169fc746555",
-  "blk.17.attn_q.weight": "82bb415d24efcd14d03ace03f907bb70db6a204c76a0bdd1892e0fba165db87d",
-  "blk.17.attn_v.weight": "73dbe54beb91a899884e275ea81ffc5187a20cb7d5b68d5c299b783096999d94",
-  "blk.17.ffn_down.weight": "7c086166241e0664f8963fd1ca4ed74c737abfb2525ec20f8435821ff50158f3",
-  "blk.17.ffn_gate.weight": "51a32f78244d42a539f619c5ce661db9e6cf41636280a826d439b5444edcd28c",
-  "blk.17.ffn_norm.weight": "c4bb247fccd1ecc84875028af63dd20aaf5cbd17eb94a9bc36679c09285dccab",
-  "blk.17.ffn_up.weight": "b5886182790bc6fbadd63de9bc4ffee416f3b69a66280d197ab8c18edf769abf",
-  "output_norm.weight": "481f3097d0a20412e35b3a739b1b958487bcd41ff67744baa3c9acbddd2ee4d4"
-}
--- a/convert/tokenizer.go
+++ b/convert/tokenizer.go
@@ -3,150 +3,19 @@ package convert
 import (
 	"cmp"
 	"crypto/sha256"
-	"encoding/hex"
 	"encoding/json"
-	"errors"
 	"fmt"
-	"io/fs"
 	"log/slog"
 	"os"
 	"slices"
-)

-const (
-	_ int32 = iota
-	tokenTypeNormal
-	tokenTypeUnknown
-	tokenTypeControl
-	tokenTypeUserDefined
-	tokenTypeUnused
-	tokenTypeByte
+	"golang.org/x/exp/maps"
 )

 type Tokenizer struct {
-	*Vocabulary
-	SpecialVocabulary []*SpecialVocabulary
-	Merges            []string
-
-	Pre      string
-	Template string
-}
-
-func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error) {
-	v, err := parseVocabulary(fsys)
-	if err != nil {
-		return nil, err
-	}
-
-	t := &Tokenizer{
-		Vocabulary: v,
-		Pre:        "default",
-	}
-
-	addedTokens := make(map[string]token)
-	if f, err := fsys.Open("tokenizer.json"); errors.Is(err, os.ErrNotExist) {
-	} else if err != nil {
-		return nil, err
-	} else {
-		defer f.Close()
-
-		var tt tokenizer
-		if err := json.NewDecoder(f).Decode(&tt); err != nil {
-			return nil, err
-		}
-
-		for _, t := range tt.AddedTokens {
-			addedTokens[t.Content] = t
-		}
-
-		t.Merges = tt.Model.Merges
-
-		sha256sum := sha256.New()
-		for _, pt := range tt.PreTokenizer.PreTokenizers {
-			switch pt.Type {
-			case "Split":
-				if pt.Pattern.Regex != "" {
-					// create a checksum of all Split pretokenizers which should be sufficient
-					// to identify the pretokenizer
-					sha256sum.Write([]byte(pt.Pattern.Regex))
-				}
-			}
-		}
-
-		switch digest := hex.EncodeToString(sha256sum.Sum(nil)); digest {
-		case "d98f9631be1e9607a9848c26c1f9eac1aa9fc21ac6ba82a2fc0741af9780a48f":
-			t.Pre = "llama-bpe"
-		case "03df5c5863ad70781dcfdef491ead25140f895fe8010964be0daefe27be32b02":
-			t.Pre = "deepseek-llm"
-		case "21cde974d587f0d54dc8d56b183cc1e6239600172035c68fbd6d4b9f8da0576e":
-			t.Pre = "deepseek-coder"
-		case "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855":
-			// noop, empty pretokenizer
-		default:
-			slog.Warn("unknown pretokenizer, using default", "digest", digest)
-		}
-	}
-
-	if f, err := fsys.Open("tokenizer_config.json"); errors.Is(err, os.ErrNotExist) {
-	} else if err != nil {
-		return nil, err
-	} else {
-		defer f.Close()
-
-		var p map[string]json.RawMessage
-		if err := json.NewDecoder(f).Decode(&p); err != nil {
-			return nil, err
-		}
-
-		if template, ok := p["chat_template"]; ok {
-			if err := json.Unmarshal(template, &t.Template); err != nil {
-				return nil, err
-			}
-		}
-
-		for _, st := range specialTokenTypes {
-			sv := SpecialVocabulary{Type: st}
-			if bts, ok := p[fmt.Sprintf("add_%s_token", st)]; ok {
-				if err := json.Unmarshal(bts, &sv.AddToken); err != nil {
-					return nil, err
-				}
-			}
-
-			if bts, ok := p[fmt.Sprintf("%s_token", st)]; ok {
-				var content string
-				if err := json.Unmarshal(bts, &content); err != nil {
-					var mm map[string]any
-					if err := json.Unmarshal(bts, &mm); err != nil {
-						continue
-					}
-
-					content, ok = mm["content"].(string)
-					if !ok {
-						continue
-					}
-				}
-
-				sv.Content = content
-			}
-
-			if id, ok := addedTokens[sv.Content]; ok {
-				sv.ID = id.ID
-				t.SpecialVocabulary = append(t.SpecialVocabulary, &sv)
-			}
-		}
-	}
-
-	return t, nil
-}
-
-type tokenizer struct {
-	Version     string  `json:"version"`
-	AddedTokens []token `json:"added_tokens"`
-	Model       struct {
-		Type   string         `json:"type"`
-		Vocab  map[string]int `json:"vocab"`
-		Merges []string       `json:"merges"`
-	} `json:"model"`
+	Version     string         `json:"version"`
+	AddedTokens []Token        `json:"added_tokens"`
+	Model       TokenizerModel `json:"model"`

 	PreTokenizer struct {
 		PreTokenizers []struct {
@@ -158,108 +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
 	}

-	var tokens []token
+	tokens = make([]Token, t.maxID()+1)
 	for k, v := range t.Model.Vocab {
-		tokens = append(tokens, token{
-			ID:      v,
-			Content: k,
-		})
+		tokens[v] = Token{ID: v, Content: k, Special: false, UserDefined: false}
 	}

-	for _, t := range t.AddedTokens {
-		t.UserDefined = true
-		tokens = append(tokens, t)
+	for _, v := range t.AddedTokens {
+		v.UserDefined = true
+		tokens[v.ID] = v
 	}

-	slices.SortFunc(tokens, func(i, j token) int {
-		return cmp.Compare(i.ID, j.ID)
-	})
-
-	v := Vocabulary{Model: "gpt2"}
-	for _, t := range tokens {
-		v.Tokens = append(v.Tokens, t.Content)
-		v.Scores = append(v.Scores, float32(t.ID))
-
-		switch {
-		case t.Special:
-			v.Types = append(v.Types, tokenTypeControl)
-		case t.UserDefined:
-			v.Types = append(v.Types, tokenTypeUserDefined)
-		default:
-			v.Types = append(v.Types, tokenTypeNormal)
+	sha256sum := sha256.New()
+	for _, pt := range t.PreTokenizer.PreTokenizers {
+		if pt.Type == "Split" && pt.Pattern.Regex != "" {
+			sha256sum.Write([]byte(pt.Pattern.Regex))
 		}
 	}

-	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},
+	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"
 	}

-	for _, pattern := range patterns {
-		if _, err := fs.Stat(fsys, pattern.Pattern); errors.Is(err, os.ErrNotExist) {
-			continue
-		} else if err != nil {
-			return nil, err
-		}
-
-		return pattern.Func(fsys)
-	}
-
-	return nil, errors.New("unknown tensor format")
-}
-
-type SpecialVocabulary struct {
-	Type     string
-	ID       int
-	Content  string
-	AddToken bool
-}
-
-func (sv SpecialVocabulary) Key() string {
-	switch t := sv.Type; t {
-	case "bos", "eos", "cls", "mask":
-		return t
-	case "unk":
-		return "unknown"
-	case "sep":
-		//nolint:misspell // this is an upstream typo
-		return "seperator"
-	case "pad":
-		return "padding"
-	}
-
-	panic("unknown special vocabulary type")
+	return pre, tokens, t.Model.Merges, nil
 }
--- a/convert/tokenizer_spm.go
+++ b/convert/tokenizer_spm.go
@@ -1,83 +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) {
-	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:
-			v.Types = append(v.Types, int32(sentencepiece.ModelProto_SentencePiece_NORMAL))
-		}
-	}
-
-	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
-}
--- a/convert/torch.go
+++ b/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")
+}
--- a/docs/api.md
+++ b/docs/api.md
@@ -669,7 +669,7 @@ curl http://localhost:11434/api/chat -d '{

 ```
 curl http://localhost:11434/api/chat -d '{
-  "model": "llama3.1",
+  "model": "mistral",
  "messages": [
    {
      "role": "user",
@@ -708,7 +708,7 @@ curl http://localhost:11434/api/chat -d '{

 ```json
 {
-  "model": "llama3.1",
+  "model": "mistral:7b-instruct-v0.3-q4_K_M",
  "created_at": "2024-07-22T20:33:28.123648Z",
  "message": {
    "role": "assistant",
@@ -1175,10 +1175,7 @@ curl http://localhost:11434/api/embed -d '{
  "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
+  ]]
 }
 ```

--- a/docs/docker.md
+++ b/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 llama3.1
+```
+
+### Try different models
+
+More models can be found on the [Ollama library](https://ollama.com/library).
--- a/docs/import.md
+++ b/docs/import.md
@@ -16,9 +16,7 @@ If the model being imported is one of these architectures, it can be imported di

 - LlamaForCausalLM
 - MistralForCausalLM
- - MixtralForCausalLM
 - GemmaForCausalLM
- - Phi3ForCausalLM

 ```dockerfile
 FROM /path/to/safetensors/directory
--- a/docs/linux.md
+++ b/docs/linux.md
@@ -20,12 +20,13 @@ GPU.

 ## Manual install

-### Download `ollama`
+### Download the `ollama` binary

-Download and extract the Linux package:
+Ollama is distributed as a self-contained binary. Download it to a directory in your PATH:

 ```bash
-curl -fsSL https://ollama.com/download/ollama-linux-amd64.tgz | sudo tar zx -C /usr
+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)
@@ -95,7 +96,8 @@ curl -fsSL https://ollama.com/install.sh | sh
 Or by downloading the ollama binary:

 ```bash
-curl -fsSL https://ollama.com/download/ollama-linux-amd64.tgz | sudo tar zx -C /usr
+sudo curl -L https://ollama.com/download/ollama-linux-amd64 -o /usr/bin/ollama
+sudo chmod +x /usr/bin/ollama
 ```

 ## Installing specific versions
--- a/docs/openai.md
+++ b/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
 - [ ] 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`
@@ -208,67 +103,6 @@ curl http://localhost:11434/v1/embeddings \
 - [ ] `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`:
--- a/docs/template.md
+++ b/docs/template.md
@@ -112,9 +112,15 @@ Keep the following tips and best practices in mind when working with Go template
 ChatML is a popular template format. It can be used for models such as Databrick's DBRX, Intel's Neural Chat, and Microsoft's Orca 2.

 ```gotmpl
+{{- if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}
 {{- range .Messages }}<|im_start|>{{ .Role }}
 {{ .Content }}<|im_end|>
 {{ end }}<|im_start|>assistant
+{{ else }}
+{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
 ```

 ### Example Tools
--- a/docs/troubleshooting.md
+++ b/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:
--- a/envconfig/config.go
+++ b/envconfig/config.go
@@ -174,7 +174,7 @@ func RunnersDir() (p string) {

 	defer func() {
 		if p == "" {
-			slog.Error("unable to locate llm runner directory. Set OLLAMA_RUNNERS_DIR to the location of 'ollama/runners'")
+			slog.Error("unable to locate llm runner directory. Set OLLAMA_RUNNERS_DIR to the location of 'ollama_runners'")
 		}
 	}()

@@ -190,17 +190,17 @@ func RunnersDir() (p string) {
 	}

 	var paths []string
-	for _, root := range []string{filepath.Dir(exe), filepath.Join(filepath.Dir(exe), ".."), cwd} {
+	for _, root := range []string{filepath.Dir(exe), cwd} {
 		paths = append(paths,
 			root,
-			filepath.Join(root, runtime.GOOS+"-"+runtime.GOARCH),
-			filepath.Join(root, "dist", runtime.GOOS+"-"+runtime.GOARCH),
+			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 _, path := range paths {
-		candidate := filepath.Join(path, "lib", "ollama", "runners")
+		candidate := filepath.Join(path, "ollama_runners")
 		if _, err := os.Stat(candidate); err == nil {
 			p = candidate
 			break
--- a/format/format.go
+++ b/format/format.go
@@ -3,7 +3,6 @@ package format
 import (
 	"fmt"
 	"math"
-	"strconv"
 )

 const (
@@ -29,6 +28,6 @@ func HumanNumber(b uint64) string {
 	case b >= Thousand:
 		return fmt.Sprintf("%.0fK", float64(b)/Thousand)
 	default:
-		return strconv.FormatUint(b, 10)
+		return fmt.Sprintf("%d", b)
 	}
 }
--- a/go.mod
+++ b/go.mod
@@ -1,6 +1,6 @@
 module github.com/ollama/ollama

-go 1.22.5
+go 1.22.0

 require (
 	github.com/containerd/console v1.0.3
--- a/gpu/amd_common.go
+++ b/gpu/amd_common.go
@@ -3,7 +3,7 @@
 package gpu

 import (
-	"errors"
+	"fmt"
 	"log/slog"
 	"os"
 	"path/filepath"
@@ -54,7 +54,7 @@ func commonAMDValidateLibDir() (string, error) {
 	// Installer payload location if we're running the installed binary
 	exe, err := os.Executable()
 	if err == nil {
-		rocmTargetDir := filepath.Join(filepath.Dir(exe), "..", "lib", "ollama")
+		rocmTargetDir := filepath.Join(filepath.Dir(exe), "rocm")
 		if rocmLibUsable(rocmTargetDir) {
 			slog.Debug("detected ROCM next to ollama executable " + rocmTargetDir)
 			return rocmTargetDir, nil
@@ -95,5 +95,5 @@ func commonAMDValidateLibDir() (string, error) {
 		}
 	}

-	return "", errors.New("no suitable rocm found, falling back to CPU")
+	return "", fmt.Errorf("no suitable rocm found, falling back to CPU")
 }
--- a/gpu/amd_hip_windows.go
+++ b/gpu/amd_hip_windows.go
@@ -1,7 +1,6 @@
 package gpu

 import (
-	"errors"
 	"fmt"
 	"log/slog"
 	"syscall"
@@ -77,7 +76,7 @@ func (hl *HipLib) Release() {

 func (hl *HipLib) AMDDriverVersion() (driverMajor, driverMinor int, err error) {
 	if hl.dll == 0 {
-		return 0, 0, errors.New("dll has been unloaded")
+		return 0, 0, fmt.Errorf("dll has been unloaded")
 	}
 	var version int
 	status, _, err := syscall.SyscallN(hl.hipDriverGetVersion, uintptr(unsafe.Pointer(&version)))
@@ -111,7 +110,7 @@ func (hl *HipLib) HipGetDeviceCount() int {

 func (hl *HipLib) HipSetDevice(device int) error {
 	if hl.dll == 0 {
-		return errors.New("dll has been unloaded")
+		return fmt.Errorf("dll has been unloaded")
 	}
 	status, _, err := syscall.SyscallN(hl.hipSetDevice, uintptr(device))
 	if status != hipSuccess {
@@ -122,7 +121,7 @@ func (hl *HipLib) HipSetDevice(device int) error {

 func (hl *HipLib) HipGetDeviceProperties(device int) (*hipDevicePropMinimal, error) {
 	if hl.dll == 0 {
-		return nil, errors.New("dll has been unloaded")
+		return nil, fmt.Errorf("dll has been unloaded")
 	}
 	var props hipDevicePropMinimal
 	status, _, err := syscall.SyscallN(hl.hipGetDeviceProperties, uintptr(unsafe.Pointer(&props)), uintptr(device))
@@ -135,7 +134,7 @@ func (hl *HipLib) HipGetDeviceProperties(device int) (*hipDevicePropMinimal, err
 // free, total, err
 func (hl *HipLib) HipMemGetInfo() (uint64, uint64, error) {
 	if hl.dll == 0 {
-		return 0, 0, errors.New("dll has been unloaded")
+		return 0, 0, fmt.Errorf("dll has been unloaded")
 	}
 	var totalMemory uint64
 	var freeMemory uint64
--- a/gpu/amd_linux.go
+++ b/gpu/amd_linux.go
@@ -393,7 +393,7 @@ func AMDValidateLibDir() (string, error) {

 	// If we still haven't found a usable rocm, the user will have to install it on their own
 	slog.Warn("amdgpu detected, but no compatible rocm library found.  Either install rocm v6, or follow manual install instructions at https://github.com/ollama/ollama/blob/main/docs/linux.md#manual-install")
-	return "", errors.New("no suitable rocm found, falling back to CPU")
+	return "", fmt.Errorf("no suitable rocm found, falling back to CPU")
 }

 func AMDDriverVersion() (driverMajor, driverMinor int, err error) {
--- a/gpu/amd_windows.go
+++ b/gpu/amd_windows.go
@@ -2,7 +2,7 @@ package gpu

 import (
 	"bytes"
-	"errors"
+	"fmt"
 	"log/slog"
 	"os"
 	"path/filepath"
@@ -85,7 +85,7 @@ func AMDGetGPUInfo() []RocmGPUInfo {
 		n = bytes.IndexByte(props.GcnArchName[:], 0)
 		gfx := string(props.GcnArchName[:n])
 		slog.Debug("hip device", "id", i, "name", name, "gfx", gfx)
-		// slog.Info(fmt.Sprintf("[%d] Integrated: %d", i, props.iGPU)) // DOESN'T REPORT CORRECTLY!  Always 0
+		//slog.Info(fmt.Sprintf("[%d] Integrated: %d", i, props.iGPU)) // DOESN'T REPORT CORRECTLY!  Always 0
 		// TODO  Why isn't props.iGPU accurate!?
 		if strings.EqualFold(name, iGPUName) {
 			slog.Info("unsupported Radeon iGPU detected skipping", "id", i, "name", name, "gfx", gfx)
@@ -153,7 +153,7 @@ func AMDValidateLibDir() (string, error) {
 	// Installer payload (if we're running from some other location)
 	localAppData := os.Getenv("LOCALAPPDATA")
 	appDir := filepath.Join(localAppData, "Programs", "Ollama")
-	rocmTargetDir := filepath.Join(appDir, "..", "lib", "ollama")
+	rocmTargetDir := filepath.Join(appDir, "rocm")
 	if rocmLibUsable(rocmTargetDir) {
 		slog.Debug("detected ollama installed ROCm at " + rocmTargetDir)
 		return rocmTargetDir, nil
@@ -161,7 +161,7 @@ func AMDValidateLibDir() (string, error) {

 	// Should not happen on windows since we include it in the installer, but stand-alone binary might hit this
 	slog.Warn("amdgpu detected, but no compatible rocm library found.  Please install ROCm")
-	return "", errors.New("no suitable rocm found, falling back to CPU")
+	return "", fmt.Errorf("no suitable rocm found, falling back to CPU")
 }

 func (gpus RocmGPUInfoList) RefreshFreeMemory() error {
--- a/gpu/assets.go
+++ b/gpu/assets.go
@@ -42,16 +42,20 @@ func PayloadsDir() (string, error) {
 				return "", fmt.Errorf("failed to generate tmp dir: %w", err)
 			}
 		} else {
-			err = os.MkdirAll(tmpDir, 0o755)
+			err = os.MkdirAll(tmpDir, 0755)
 			if err != nil {
 				return "", fmt.Errorf("failed to generate tmp dir %s: %w", tmpDir, err)
 			}
 		}

 		// Track our pid so we can clean up orphaned tmpdirs
-		n := filepath.Join(tmpDir, "ollama.pid")
-		if err := os.WriteFile(n, []byte(strconv.Itoa(os.Getpid())), 0o644); err != nil {
-			return "", fmt.Errorf("failed to write pid file %s: %w", n, err)
+		pidFilePath := filepath.Join(tmpDir, "ollama.pid")
+		pidFile, err := os.OpenFile(pidFilePath, os.O_CREATE|os.O_TRUNC|os.O_WRONLY, os.ModePerm)
+		if err != nil {
+			return "", err
+		}
+		if _, err := pidFile.Write([]byte(fmt.Sprint(os.Getpid()))); err != nil {
+			return "", err
 		}

 		// We create a distinct subdirectory for payloads within the tmpdir
@@ -63,44 +67,37 @@ func PayloadsDir() (string, error) {

 // Best effort to clean up prior tmpdirs
 func cleanupTmpDirs() {
-	matches, err := filepath.Glob(filepath.Join(os.TempDir(), "ollama*", "ollama.pid"))
+	dirs, err := filepath.Glob(filepath.Join(os.TempDir(), "ollama*"))
 	if err != nil {
 		return
 	}
-
-	for _, match := range matches {
-		raw, err := os.ReadFile(match)
-		if errors.Is(err, os.ErrNotExist) {
-			slog.Debug("not a ollama runtime directory, skipping", "path", match)
+	for _, d := range dirs {
+		info, err := os.Stat(d)
+		if err != nil || !info.IsDir() {
 			continue
-		} else if err != nil {
-			slog.Warn("could not read ollama.pid, skipping", "path", match, "error", err)
+		}
+		raw, err := os.ReadFile(filepath.Join(d, "ollama.pid"))
+		if err != nil {
+			slog.Warn("failed to read ollama.pid", "path", d, "error", err)
+			// No pid, ignore this tmpdir
 			continue
 		}

 		pid, err := strconv.Atoi(string(raw))
 		if err != nil {
-			slog.Warn("invalid pid, skipping", "path", match, "error", err)
+			slog.Warn("failed to parse pid", "path", d, "error", err)
 			continue
 		}

-		p, err := os.FindProcess(pid)
-		if err == nil && !errors.Is(p.Signal(syscall.Signal(0)), os.ErrProcessDone) {
-			slog.Warn("process still running, skipping", "pid", pid, "path", match)
+		proc, err := os.FindProcess(pid)
+		if err == nil && !errors.Is(proc.Signal(syscall.Signal(0)), os.ErrProcessDone) {
+			slog.Warn("found running ollama", "pid", pid, "path", d)
+			// Another running ollama, ignore this tmpdir
 			continue
 		}

-		if err := os.Remove(match); err != nil {
-			slog.Warn("could not cleanup stale pidfile", "path", match, "error", err)
-		}
-
-		runners := filepath.Join(filepath.Dir(match), "runners")
-		if err := os.RemoveAll(runners); err != nil {
-			slog.Warn("could not cleanup stale runners", "path", runners, "error", err)
-		}
-
-		if err := os.Remove(filepath.Dir(match)); err != nil {
-			slog.Warn("could not cleanup stale tmpdir", "path", filepath.Dir(match), "error", err)
+		if err := os.Remove(d); err != nil {
+			slog.Warn("unable to cleanup stale tmpdir", "path", d, "error", err)
 		}
 	}
 }
--- a/gpu/cpu_common.go
+++ b/gpu/cpu_common.go
@@ -1,11 +1,6 @@
 package gpu

 import (
-	"os"
-	"path/filepath"
-	"runtime"
-	"strings"
-
 	"golang.org/x/sys/cpu"
 )

@@ -19,19 +14,3 @@ func GetCPUCapability() CPUCapability {
 	// else LCD
 	return CPUCapabilityNone
 }
-
-func IsNUMA() bool {
-	if runtime.GOOS != "linux" {
-		// numa support in llama.cpp is linux only
-		return false
-	}
-	ids := map[string]interface{}{}
-	packageIds, _ := filepath.Glob("/sys/devices/system/cpu/cpu*/topology/physical_package_id")
-	for _, packageId := range packageIds {
-		id, err := os.ReadFile(packageId)
-		if err == nil {
-			ids[strings.TrimSpace(string(id))] = struct{}{}
-		}
-	}
-	return len(ids) > 1
-}
--- a/gpu/cuda_common.go
+++ b/gpu/cuda_common.go
@@ -4,17 +4,9 @@ package gpu

 import (
 	"log/slog"
-	"os"
-	"regexp"
-	"runtime"
-	"strconv"
 	"strings"
 )

-// Jetson devices have JETSON_JETPACK="x.y.z" factory set to the Jetpack version installed.
-// Included to drive logic for reducing Ollama-allocated overhead on L4T/Jetson devices.
-var CudaTegra string = os.Getenv("JETSON_JETPACK")
-
 func cudaGetVisibleDevicesEnv(gpuInfo []GpuInfo) (string, string) {
 	ids := []string{}
 	for _, info := range gpuInfo {
@@ -27,38 +19,3 @@ func cudaGetVisibleDevicesEnv(gpuInfo []GpuInfo) (string, string) {
 	}
 	return "CUDA_VISIBLE_DEVICES", strings.Join(ids, ",")
 }
-
-func cudaVariant(gpuInfo CudaGPUInfo) string {
-	if runtime.GOARCH == "arm64" && runtime.GOOS == "linux" {
-		if CudaTegra != "" {
-			ver := strings.Split(CudaTegra, ".")
-			if len(ver) > 0 {
-				return "jetpack" + ver[0]
-			}
-		} else if data, err := os.ReadFile("/etc/nv_tegra_release"); err == nil {
-			r := regexp.MustCompile(` R(\d+) `)
-			m := r.FindSubmatch(data)
-			if len(m) != 2 {
-				slog.Info("Unexpected format for /etc/nv_tegra_release.  Set JETSON_JETPACK to select version")
-			} else {
-				if l4t, err := strconv.Atoi(string(m[1])); err == nil {
-					// Note: mapping from L4t -> JP is inconsistent (can't just subtract 30)
-					// https://developer.nvidia.com/embedded/jetpack-archive
-					switch l4t {
-					case 35:
-						return "jetpack5"
-					case 36:
-						return "jetpack6"
-					default:
-						slog.Info("unsupported L4T version", "nv_tegra_release", string(data))
-					}
-				}
-			}
-		}
-	}
-
-	if gpuInfo.computeMajor < 6 || gpuInfo.DriverMajor < 12 {
-		return "v11"
-	}
-	return "v12"
-}
--- a/gpu/gpu.go
+++ b/gpu/gpu.go
@@ -7,9 +7,9 @@ package gpu
 #cgo windows LDFLAGS: -lpthread

 #include "gpu_info.h"
+
 */
 import "C"
-
 import (
 	"fmt"
 	"log/slog"
@@ -64,8 +64,13 @@ var RocmComputeMin = 9
 // TODO find a better way to detect iGPU instead of minimum memory
 const IGPUMemLimit = 1 * format.GibiByte // 512G is what they typically report, so anything less than 1G must be iGPU

+// Jetson devices have JETSON_JETPACK="x.y.z" factory set to the Jetpack version installed.
+// Included to drive logic for reducing Ollama-allocated overhead on L4T/Jetson devices.
+var CudaTegra string = os.Getenv("JETSON_JETPACK")
+
 // Note: gpuMutex must already be held
 func initCudaHandles() *cudaHandles {
+
 	// TODO - if the ollama build is CPU only, don't do these checks as they're irrelevant and confusing

 	cHandles := &cudaHandles{}
@@ -206,16 +211,14 @@ func GetGPUInfo() GpuInfoList {
 		if err != nil {
 			slog.Warn("error looking up system memory", "error", err)
 		}
-		cpus = []CPUInfo{
-			{
-				GpuInfo: GpuInfo{
-					memInfo: mem,
-					Library: "cpu",
-					Variant: cpuCapability.String(),
-					ID:      "0",
-				},
+		cpus = []CPUInfo{CPUInfo{
+			GpuInfo: GpuInfo{
+				memInfo: mem,
+				Library: "cpu",
+				Variant: cpuCapability,
+				ID:      "0",
 			},
-		}
+		}}

 		// Fallback to CPU mode if we're lacking required vector extensions on x86
 		if cpuCapability < GPURunnerCPUCapability && runtime.GOARCH == "amd64" {
@@ -225,7 +228,11 @@ func GetGPUInfo() GpuInfoList {
 			return GpuInfoList{cpus[0].GpuInfo}
 		}

-		depPath := LibraryDir()
+		// On windows we bundle the nvidia library one level above the runner dir
+		depPath := ""
+		if runtime.GOOS == "windows" && envconfig.RunnersDir() != "" {
+			depPath = filepath.Join(filepath.Dir(envconfig.RunnersDir()), "cuda")
+		}

 		// Load ALL libraries
 		cHandles = initCudaHandles()
@@ -261,23 +268,11 @@ func GetGPUInfo() GpuInfoList {
 				gpuInfo.FreeMemory = uint64(memInfo.free)
 				gpuInfo.ID = C.GoString(&memInfo.gpu_id[0])
 				gpuInfo.Compute = fmt.Sprintf("%d.%d", memInfo.major, memInfo.minor)
-				gpuInfo.computeMajor = int(memInfo.major)
-				gpuInfo.computeMinor = int(memInfo.minor)
 				gpuInfo.MinimumMemory = cudaMinimumMemory
-				variant := cudaVariant(gpuInfo)
-				if depPath != "" {
-					gpuInfo.DependencyPath = depPath
-					// Check for variant specific directory
-					if variant != "" {
-						if _, err := os.Stat(filepath.Join(depPath, "cuda_"+variant)); err == nil {
-							gpuInfo.DependencyPath = filepath.Join(depPath, "cuda_"+variant)
-						}
-					}
-				}
+				gpuInfo.DependencyPath = depPath
 				gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
 				gpuInfo.DriverMajor = driverMajor
 				gpuInfo.DriverMinor = driverMinor
-				gpuInfo.Variant = variant

 				// query the management library as well so we can record any skew between the two
 				// which represents overhead on the GPU we must set aside on subsequent updates
@@ -309,34 +304,38 @@ func GetGPUInfo() GpuInfoList {
 		// Intel
 		if envconfig.IntelGPU() {
 			oHandles = initOneAPIHandles()
-			if oHandles != nil && oHandles.oneapi != nil {
-				for d := range oHandles.oneapi.num_drivers {
-					if oHandles.oneapi == nil {
-						// shouldn't happen
-						slog.Warn("nil oneapi handle with driver count", "count", int(oHandles.oneapi.num_drivers))
-						continue
-					}
-					devCount := C.oneapi_get_device_count(*oHandles.oneapi, C.int(d))
-					for i := range devCount {
-						gpuInfo := OneapiGPUInfo{
-							GpuInfo: GpuInfo{
-								Library: "oneapi",
-							},
-							driverIndex: int(d),
-							gpuIndex:    int(i),
-						}
-						// TODO - split bootstrapping from updating free memory
-						C.oneapi_check_vram(*oHandles.oneapi, C.int(d), i, &memInfo)
-						// TODO - convert this to MinimumMemory based on testing...
-						var totalFreeMem float64 = float64(memInfo.free) * 0.95 // work-around: leave some reserve vram for mkl lib used in ggml-sycl backend.
-						memInfo.free = C.uint64_t(totalFreeMem)
-						gpuInfo.TotalMemory = uint64(memInfo.total)
-						gpuInfo.FreeMemory = uint64(memInfo.free)
-						gpuInfo.ID = C.GoString(&memInfo.gpu_id[0])
-						gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
-						gpuInfo.DependencyPath = depPath
-						oneapiGPUs = append(oneapiGPUs, gpuInfo)
+			// On windows we bundle the oneapi library one level above the runner dir
+			depPath = ""
+			if runtime.GOOS == "windows" && envconfig.RunnersDir() != "" {
+				depPath = filepath.Join(filepath.Dir(envconfig.RunnersDir()), "oneapi")
+			}
+
+			for d := range oHandles.oneapi.num_drivers {
+				if oHandles.oneapi == nil {
+					// shouldn't happen
+					slog.Warn("nil oneapi handle with driver count", "count", int(oHandles.oneapi.num_drivers))
+					continue
+				}
+				devCount := C.oneapi_get_device_count(*oHandles.oneapi, C.int(d))
+				for i := range devCount {
+					gpuInfo := OneapiGPUInfo{
+						GpuInfo: GpuInfo{
+							Library: "oneapi",
+						},
+						driverIndex: int(d),
+						gpuIndex:    int(i),
 					}
+					// TODO - split bootstrapping from updating free memory
+					C.oneapi_check_vram(*oHandles.oneapi, C.int(d), i, &memInfo)
+					// TODO - convert this to MinimumMemory based on testing...
+					var totalFreeMem float64 = float64(memInfo.free) * 0.95 // work-around: leave some reserve vram for mkl lib used in ggml-sycl backend.
+					memInfo.free = C.uint64_t(totalFreeMem)
+					gpuInfo.TotalMemory = uint64(memInfo.total)
+					gpuInfo.FreeMemory = uint64(memInfo.free)
+					gpuInfo.ID = C.GoString(&memInfo.gpu_id[0])
+					gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
+					gpuInfo.DependencyPath = depPath
+					oneapiGPUs = append(oneapiGPUs, gpuInfo)
 				}
 			}
 		}
@@ -464,12 +463,10 @@ func GetGPUInfo() GpuInfoList {
 func FindGPULibs(baseLibName string, defaultPatterns []string) []string {
 	// Multiple GPU libraries may exist, and some may not work, so keep trying until we exhaust them
 	var ldPaths []string
+	var patterns []string
 	gpuLibPaths := []string{}
 	slog.Debug("Searching for GPU library", "name", baseLibName)

-	// Start with our bundled libraries
-	patterns := []string{filepath.Join(LibraryDir(), baseLibName)}
-
 	switch runtime.GOOS {
 	case "windows":
 		ldPaths = strings.Split(os.Getenv("PATH"), ";")
@@ -478,14 +475,13 @@ func FindGPULibs(baseLibName string, defaultPatterns []string) []string {
 	default:
 		return gpuLibPaths
 	}
-
-	// Then with whatever we find in the PATH/LD_LIBRARY_PATH
+	// Start with whatever we find in the PATH/LD_LIBRARY_PATH
 	for _, ldPath := range ldPaths {
 		d, err := filepath.Abs(ldPath)
 		if err != nil {
 			continue
 		}
-		patterns = append(patterns, filepath.Join(d, baseLibName))
+		patterns = append(patterns, filepath.Join(d, baseLibName+"*"))
 	}
 	patterns = append(patterns, defaultPatterns...)
 	slog.Debug("gpu library search", "globs", patterns)
@@ -641,31 +637,3 @@ func (l GpuInfoList) GetVisibleDevicesEnv() (string, string) {
 		return "", ""
 	}
 }
-
-func LibraryDir() string {
-	// On Windows/linux we bundle the dependencies at the same level as the executable
-	appExe, err := os.Executable()
-	if err != nil {
-		slog.Warn("failed to lookup executable path", "error", err)
-	}
-	cwd, err := os.Getwd()
-	if err != nil {
-		slog.Warn("failed to lookup working directory", "error", err)
-	}
-	// Scan for any of our dependeices, and pick first match
-	for _, root := range []string{filepath.Dir(appExe), filepath.Join(filepath.Dir(appExe), ".."), cwd} {
-		libDep := filepath.Join("lib", "ollama")
-		if _, err := os.Stat(filepath.Join(root, libDep)); err == nil {
-			return filepath.Join(root, libDep)
-		}
-		// Developer mode, local build
-		if _, err := os.Stat(filepath.Join(root, runtime.GOOS+"-"+runtime.GOARCH, libDep)); err == nil {
-			return filepath.Join(root, runtime.GOOS+"-"+runtime.GOARCH, libDep)
-		}
-		if _, err := os.Stat(filepath.Join(root, "dist", runtime.GOOS+"-"+runtime.GOARCH, libDep)); err == nil {
-			return filepath.Join(root, "dist", runtime.GOOS+"-"+runtime.GOARCH, libDep)
-		}
-	}
-	slog.Warn("unable to locate gpu dependency libraries")
-	return ""
-}
--- a/gpu/gpu_darwin.go
+++ b/gpu/gpu_darwin.go
@@ -8,7 +8,6 @@ package gpu
 #include "gpu_info_darwin.h"
 */
 import "C"
-
 import (
 	"runtime"

@@ -25,7 +24,7 @@ func GetGPUInfo() GpuInfoList {
 		return []GpuInfo{
 			{
 				Library: "cpu",
-				Variant: GetCPUCapability().String(),
+				Variant: GetCPUCapability(),
 				memInfo: mem,
 			},
 		}
@@ -48,7 +47,7 @@ func GetCPUInfo() GpuInfoList {
 	return []GpuInfo{
 		{
 			Library: "cpu",
-			Variant: GetCPUCapability().String(),
+			Variant: GetCPUCapability(),
 			memInfo: mem,
 		},
 	}
--- a/gpu/gpu_info.h
+++ b/gpu/gpu_info.h
@@ -67,4 +67,4 @@ void cpu_check_ram(mem_info_t *resp);
 #include "gpu_info_oneapi.h"

 #endif  // __GPU_INFO_H__
-#endif  // __APPLE__
+#endif  // __APPLE__
--- a/gpu/gpu_linux.go
+++ b/gpu/gpu_linux.go
@@ -43,12 +43,10 @@ var OneapiGlobs = []string{
 	"/usr/lib*/libze_intel_gpu.so*",
 }

-var (
-	CudartMgmtName = "libcudart.so*"
-	NvcudaMgmtName = "libcuda.so*"
-	NvmlMgmtName   = "" // not currently wired on linux
-	OneapiMgmtName = "libze_intel_gpu.so*"
-)
+var CudartMgmtName = "libcudart.so*"
+var NvcudaMgmtName = "libcuda.so*"
+var NvmlMgmtName = "" // not currently wired on linux
+var OneapiMgmtName = "libze_intel_gpu.so"

 func GetCPUMem() (memInfo, error) {
 	var mem memInfo
--- a/gpu/gpu_windows.go
+++ b/gpu/gpu_windows.go
@@ -40,12 +40,10 @@ var OneapiGlobs = []string{
 	"c:\\Windows\\System32\\DriverStore\\FileRepository\\*\\ze_intel_gpu64.dll",
 }

-var (
-	CudartMgmtName = "cudart64_*.dll"
-	NvcudaMgmtName = "nvcuda.dll"
-	NvmlMgmtName   = "nvml.dll"
-	OneapiMgmtName = "ze_intel_gpu64.dll"
-)
+var CudartMgmtName = "cudart64_*.dll"
+var NvcudaMgmtName = "nvcuda.dll"
+var NvmlMgmtName = "nvml.dll"
+var OneapiMgmtName = "ze_intel_gpu64.dll"

 func GetCPUMem() (memInfo, error) {
 	memStatus := MEMORYSTATUSEX{length: sizeofMemoryStatusEx}
--- a/gpu/types.go
+++ b/gpu/types.go
@@ -19,7 +19,7 @@ type GpuInfo struct {
 	Library string `json:"library,omitempty"`

 	// Optional variant to select (e.g. versions, cpu feature flags)
-	Variant string `json:"variant"`
+	Variant CPUCapability `json:"variant"`

 	// MinimumMemory represents the minimum memory required to use the GPU
 	MinimumMemory uint64 `json:"-"`
@@ -53,10 +53,8 @@ type CPUInfo struct {

 type CudaGPUInfo struct {
 	GpuInfo
-	OSOverhead   uint64 // Memory overhead between the driver library and management library
-	index        int    //nolint:unused,nolintlint
-	computeMajor int    //nolint:unused,nolintlint
-	computeMinor int    //nolint:unused,nolintlint
+	OSOverhead uint64 // Memory overhead between the driver library and management library
+	index      int    //nolint:unused,nolintlint
 }
 type CudaGPUInfoList []CudaGPUInfo

@@ -83,8 +81,8 @@ func (l GpuInfoList) ByLibrary() []GpuInfoList {
 	for _, info := range l {
 		found := false
 		requested := info.Library
-		if info.Variant != CPUCapabilityNone.String() {
-			requested += "_" + info.Variant
+		if info.Variant != CPUCapabilityNone {
+			requested += "_" + info.Variant.String()
 		}
 		for i, lib := range libs {
 			if lib == requested {
@@ -107,7 +105,6 @@ func (l GpuInfoList) LogDetails() {
 		slog.Info("inference compute",
 			"id", g.ID,
 			"library", g.Library,
-			"variant", g.Variant,
 			"compute", g.Compute,
 			"driver", fmt.Sprintf("%d.%d", g.DriverMajor, g.DriverMinor),
 			"name", g.Name,
--- a/integration/concurrency_test.go
+++ b/integration/concurrency_test.go
@@ -5,7 +5,6 @@ package integration
 import (
 	"context"
 	"log/slog"
-	"os"
 	"strconv"
 	"sync"
 	"testing"
@@ -14,6 +13,7 @@ import (
 	"github.com/stretchr/testify/require"

 	"github.com/ollama/ollama/api"
+	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
 )

@@ -41,8 +41,8 @@ func TestMultiModelConcurrency(t *testing.T) {
 			},
 		}
 		resp = [2][]string{
-			{"sunlight"},
-			{"england", "english", "massachusetts", "pilgrims", "british"},
+			[]string{"sunlight"},
+			[]string{"england", "english", "massachusetts", "pilgrims", "british"},
 		}
 	)
 	var wg sync.WaitGroup
@@ -71,11 +71,12 @@ func TestIntegrationConcurrentPredictOrcaMini(t *testing.T) {
 	reqLimit := len(req)
 	iterLimit := 5

-	if s := os.Getenv("OLLAMA_MAX_VRAM"); s != "" {
-		maxVram, err := strconv.ParseUint(s, 10, 64)
+	vram := os.Getenv("OLLAMA_MAX_VRAM") // TODO - discover actual VRAM
+	if vram != "" {
+		max, err := strconv.ParseUint(vram, 10, 64)
 		require.NoError(t, err)
 		// Don't hammer on small VRAM cards...
-		if maxVram < 4*format.GibiByte {
+		if max < 4*1024*1024*1024 {
 			reqLimit = min(reqLimit, 2)
 			iterLimit = 2
 		}
@@ -232,12 +233,12 @@ func TestMultiModelStress(t *testing.T) {
 	consumed := uint64(256 * format.MebiByte) // Assume some baseline usage
 	for i := 0; i < len(req); i++ {
 		// Always get at least 2 models, but dont' overshoot VRAM too much or we'll take too long
-		if i > 1 && consumed > maxVram {
-			slog.Info("achieved target vram exhaustion", "count", i, "vram", format.HumanBytes2(maxVram), "models", format.HumanBytes2(consumed))
+		if i > 1 && consumed > vram {
+			slog.Info("achieved target vram exhaustion", "count", i, "vram", format.HumanBytes2(vram), "models", format.HumanBytes2(consumed))
 			break
 		}
 		consumed += chosenModels[i].size
-		slog.Info("target vram", "count", i, "vram", format.HumanBytes2(maxVram), "models", format.HumanBytes2(consumed))
+		slog.Info("target vram", "count", i, "vram", format.HumanBytes2(vram), "models", format.HumanBytes2(consumed))

 		wg.Add(1)
 		go func(i int) {
--- a/integration/llm_test.go
+++ b/integration/llm_test.go
@@ -35,8 +35,8 @@ var (
 		},
 	}
 	resp = [2][]string{
-		{"sunlight"},
-		{"england", "english", "massachusetts", "pilgrims"},
+		[]string{"sunlight"},
+		[]string{"england", "english", "massachusetts", "pilgrims"},
 	}
 )

--- a/integration/max_queue_test.go
+++ b/integration/max_queue_test.go
@@ -29,7 +29,7 @@ func TestMaxQueue(t *testing.T) {
 	// Also note that by default Darwin can't sustain > ~128 connections without adjusting limits
 	threadCount := 32
 	if maxQueue := envconfig.MaxQueue(); maxQueue != 0 {
-		threadCount = int(maxQueue)
+		threadCount = maxQueue
 	} else {
 		t.Setenv("OLLAMA_MAX_QUEUE", strconv.Itoa(threadCount))
 	}
--- a/integration/utils_test.go
+++ b/integration/utils_test.go
@@ -162,7 +162,7 @@ func PullIfMissing(ctx context.Context, client *api.Client, modelName string) er
 	fn := func(resp api.ProgressResponse) error {
 		// fmt.Print(".")
 		if !stallTimer.Reset(stallDuration) {
-			return errors.New("stall was detected, aborting status reporting")
+			return fmt.Errorf("stall was detected, aborting status reporting")
 		}
 		return nil
 	}
@@ -180,7 +180,7 @@ func PullIfMissing(ctx context.Context, client *api.Client, modelName string) er

 	select {
 	case <-stallTimer.C:
-		return errors.New("download stalled")
+		return fmt.Errorf("download stalled")
 	case <-done:
 		return pullError
 	}
@@ -243,7 +243,7 @@ func DoGenerate(ctx context.Context, t *testing.T, client *api.Client, genReq ap
 		// fmt.Print(".")
 		buf.Write([]byte(response.Response))
 		if !stallTimer.Reset(streamTimeout) {
-			return errors.New("stall was detected while streaming response, aborting")
+			return fmt.Errorf("stall was detected while streaming response, aborting")
 		}
 		return nil
 	}
@@ -334,10 +334,10 @@ func GenerateRequests() ([]api.GenerateRequest, [][]string) {
 			},
 		},
 		[][]string{
-			{"sunlight"},
-			{"soil", "organic", "earth", "black", "tan"},
-			{"england", "english", "massachusetts", "pilgrims", "british"},
-			{"fourth", "july", "declaration", "independence"},
-			{"nitrogen", "oxygen", "carbon", "dioxide"},
+			[]string{"sunlight"},
+			[]string{"soil", "organic", "earth", "black", "tan"},
+			[]string{"england", "english", "massachusetts", "pilgrims", "british"},
+			[]string{"fourth", "july", "declaration", "independence"},
+			[]string{"nitrogen", "oxygen", "carbon", "dioxide"},
 		}
 }
--- a/llm/ext_server/CMakeLists.txt
+++ b/llm/ext_server/CMakeLists.txt
@@ -1,14 +1,13 @@
-set(TARGET ollama_llama_server)
-option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
-set(LLAMA_SERVER_LDFLAGS $ENV{LLAMA_SERVER_LDFLAGS})
-include_directories(${CMAKE_CURRENT_SOURCE_DIR})
-add_executable(${TARGET} server.cpp utils.hpp json.hpp httplib.h)
-install(TARGETS ${TARGET} RUNTIME)
-target_compile_definitions(${TARGET} PRIVATE
-    SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
-)
-target_link_libraries(${TARGET} PRIVATE ggml llama common llava ${CMAKE_THREAD_LIBS_INIT} ${LLAMA_SERVER_LDFLAGS})
-if (WIN32)
-    TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
-endif()
+set(TARGET ollama_llama_server)
+option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
+include_directories(${CMAKE_CURRENT_SOURCE_DIR})
+add_executable(${TARGET} server.cpp utils.hpp json.hpp httplib.h)
+install(TARGETS ${TARGET} RUNTIME)
+target_compile_definitions(${TARGET} PRIVATE
+    SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
+)
+target_link_libraries(${TARGET} PRIVATE ggml llama common llava ${CMAKE_THREAD_LIBS_INIT})
+if (WIN32)
+    TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
+endif()
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
--- a/llm/ext_server/server.cpp
+++ b/llm/ext_server/server.cpp
@@ -44,7 +44,6 @@
 #include <errhandlingapi.h>
 #endif

-#include <algorithm>
 #include <cstddef>
 #include <thread>
 #include <chrono>
@@ -403,9 +402,7 @@ struct llama_server_context
            }
        }

-        auto init_result = llama_init_from_gpt_params(params);
-        model = init_result.model;
-        ctx = init_result.context;
+        std::tie(model, ctx) = llama_init_from_gpt_params(params);
        if (model == nullptr)
        {
            LOG_ERROR("unable to load model", {{"model", params.model}});
@@ -1224,6 +1221,7 @@ struct llama_server_context
                res.result_json = json
                {
                    {"embedding", std::vector<float>(embd, embd + n_embd)},
+                    {"timings",             slot.get_formated_timings()},
                };
            }
        }
@@ -2422,10 +2420,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, g
                invalid_param = true;
                break;
            }
-            params.lora_adapters.push_back({
-                std::string(argv[i]),
-                1.0,
-            });
+            params.lora_adapter.emplace_back(argv[i], 1.0f);
            params.use_mmap = false;
        }
        else if (arg == "--lora-scaled")
@@ -2441,10 +2436,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, g
                invalid_param = true;
                break;
            }
-            params.lora_adapters.push_back({
-                lora_adapter,
-                std::stof(argv[i])
-            });
+            params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i]));
            params.use_mmap = false;
        }
        else if (arg == "-v" || arg == "--verbose")
@@ -3192,17 +3184,37 @@ int main(int argc, char **argv) {
                    prompt = "";
                }

+                if (prompt.size() == 1) {
+                    prompt = prompt[0];
+                }
+
                // create and queue the task
-                const int task_id = llama.queue_tasks.get_new_id();
-                llama.queue_results.add_waiting_task_id(task_id);
-                llama.request_completion(task_id, {{"prompt", prompt}}, true, -1);
+                json responses;
+                {
+                    const int id_task = llama.queue_tasks.get_new_id();
+                    llama.queue_results.add_waiting_task_id(id_task);
+                    llama.request_completion(id_task, {{"prompt", prompt}}, true, -1);

-                // get the result
-                task_result result = llama.queue_results.recv(task_id);
-                llama.queue_results.remove_waiting_task_id(task_id);
+                    // get the result
+                    task_result result = llama.queue_results.recv(id_task);
+                    llama.queue_results.remove_waiting_task_id(id_task);
+                    if (result.error) {
+                        return res.set_content(result.result_json.dump(), "application/json; charset=utf-8");
+                    }

-                // send the result
-                return res.set_content(result.result_json.dump(), "application/json; charset=utf-8");
+                    responses = result.result_json.value("results", std::vector<json>{result.result_json});
+                    json embeddings = json::array();
+
+                    int prompt_n = 0;
+                    for (auto & elem : responses) {
+                        embeddings.push_back(elem.at("embedding"));
+                        prompt_n += elem.at("timings").at("prompt_n").get<int>();
+                    }
+
+                    // send the result
+                    json embedding_res = json{{"embedding", embeddings}, {"prompt_n", prompt_n}};
+                    return res.set_content(embedding_res.dump(), "application/json; charset=utf-8");
+                }
            });

    // GG: if I put the main loop inside a thread, it crashes on the first request when build in Debug!?
--- a/llm/filetype.go
+++ b/llm/filetype.go
@@ -37,6 +37,9 @@ const (
 	fileTypeIQ2_M
 	fileTypeIQ1_M
 	fileTypeBF16
+	fileTypeQ4_0_4_4
+	fileTypeQ4_0_4_8
+	fileTypeQ4_0_8_8

 	fileTypeUnknown
 )
@@ -103,6 +106,12 @@ func ParseFileType(s string) (fileType, error) {
 		return fileTypeIQ1_M, nil
 	case "BF16":
 		return fileTypeBF16, nil
+	case "Q4_0_4_4":
+		return fileTypeQ4_0_4_4, nil
+	case "Q4_0_4_8":
+		return fileTypeQ4_0_4_8, nil
+	case "Q4_0_8_8":
+		return fileTypeQ4_0_8_8, nil
 	default:
 		return fileTypeUnknown, fmt.Errorf("unknown fileType: %s", s)
 	}
@@ -170,6 +179,12 @@ func (t fileType) String() string {
 		return "IQ1_M"
 	case fileTypeBF16:
 		return "BF16"
+	case fileTypeQ4_0_4_4:
+		return "Q4_0_4_4"
+	case fileTypeQ4_0_4_8:
+		return "Q4_0_4_8"
+	case fileTypeQ4_0_8_8:
+		return "Q4_0_8_8"
 	default:
 		return "unknown"
 	}
--- a/llm/generate/gen_common.sh
+++ b/llm/generate/gen_common.sh
@@ -9,14 +9,11 @@ init_vars() {
        ARCH="arm64"
        ;;
    *)
-        echo "GOARCH must be set"
-        echo "this script is meant to be run from within go generate"
-        exit 1
-        ;;
+        ARCH=$(uname -m | sed -e "s/aarch64/arm64/g")
    esac

    LLAMACPP_DIR=../llama.cpp
-    CMAKE_DEFS="-DCMAKE_SKIP_RPATH=on"
+    CMAKE_DEFS=""
    CMAKE_TARGETS="--target ollama_llama_server"
    if echo "${CGO_CFLAGS}" | grep -- '-g' >/dev/null; then
        CMAKE_DEFS="-DCMAKE_BUILD_TYPE=RelWithDebInfo -DCMAKE_VERBOSE_MAKEFILE=on -DLLAMA_GPROF=on -DLLAMA_SERVER_VERBOSE=on ${CMAKE_DEFS}"
@@ -30,7 +27,6 @@ init_vars() {
        WHOLE_ARCHIVE="-Wl,-force_load"
        NO_WHOLE_ARCHIVE=""
        GCC_ARCH="-arch ${ARCH}"
-        DIST_BASE=../../dist/darwin-${GOARCH}/
        ;;
    "Linux")
        LIB_EXT="so"
@@ -39,7 +35,6 @@ init_vars() {

        # Cross compiling not supported on linux - Use docker
        GCC_ARCH=""
-        DIST_BASE=../../dist/linux-${GOARCH}/
        ;;
    *)
        ;;
@@ -47,7 +42,6 @@ init_vars() {
    if [ -z "${CMAKE_CUDA_ARCHITECTURES}" ] ; then
        CMAKE_CUDA_ARCHITECTURES="50;52;61;70;75;80"
    fi
-    GZIP=$(which pigz 2>/dev/null || echo "gzip")
 }

 git_module_setup() {
@@ -91,36 +85,26 @@ build() {

 compress() {
    echo "Compressing payloads to reduce overall binary size..."
+    pids=""
    rm -rf ${BUILD_DIR}/bin/*.gz
    for f in ${BUILD_DIR}/bin/* ; do
-        ${GZIP} -n --best -f ${f} &
-        compress_pids+=" $!"
+        gzip -n --best -f ${f} &
+        pids+=" $!"
    done
    # check for lib directory
    if [ -d ${BUILD_DIR}/lib ]; then
        for f in ${BUILD_DIR}/lib/* ; do
-            ${GZIP} -n --best -f ${f} &
-            compress_pids+=" $!"
+            gzip -n --best -f ${f} &
+            pids+=" $!"
        done
    fi
    echo
-}
-
-wait_for_compress() {
-    for pid in ${compress_pids}; do
+    for pid in ${pids}; do
        wait $pid
    done
    echo "Finished compression"
 }

-install() {
-    echo "Installing libraries to bin dir ${BUILD_DIR}/bin/"
-    for lib in $(find ${BUILD_DIR} -name \*.${LIB_EXT}); do
-        rm -f "${BUILD_DIR}/bin/$(basename ${lib})"
-        cp -af "${lib}" "${BUILD_DIR}/bin/"
-    done
-}
-
 # Keep the local tree clean after we're done with the build
 cleanup() {
    (cd ${LLAMACPP_DIR}/ && git checkout CMakeLists.txt)
--- a/llm/generate/gen_darwin.sh
+++ b/llm/generate/gen_darwin.sh
@@ -6,7 +6,6 @@

 set -ex
 set -o pipefail
-compress_pids=""
 echo "Starting darwin generate script"
 source $(dirname $0)/gen_common.sh
 init_vars
@@ -99,5 +98,4 @@ case "${GOARCH}" in
 esac

 cleanup
-wait_for_compress
 echo "go generate completed.  LLM runners: $(cd ${BUILD_DIR}/..; echo *)"
--- a/llm/generate/gen_linux.sh
+++ b/llm/generate/gen_linux.sh
@@ -13,7 +13,6 @@

 set -ex
 set -o pipefail
-compress_pids=""

 # See https://llvm.org/docs/AMDGPUUsage.html#processors for reference
 amdGPUs() {
@@ -52,7 +51,7 @@ if [ -z "${CUDACXX}" ]; then
        export CUDACXX=$(command -v nvcc)
    fi
 fi
-COMMON_CMAKE_DEFS="-DCMAKE_SKIP_RPATH=on -DBUILD_SHARED_LIBS=on -DCMAKE_POSITION_INDEPENDENT_CODE=on -DGGML_NATIVE=off -DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_OPENMP=off"
+COMMON_CMAKE_DEFS="-DBUILD_SHARED_LIBS=off -DCMAKE_POSITION_INDEPENDENT_CODE=on -DGGML_NATIVE=off -DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_OPENMP=off"
 source $(dirname $0)/gen_common.sh
 init_vars
 git_module_setup
@@ -78,11 +77,10 @@ if [ -z "${OLLAMA_SKIP_CPU_GENERATE}" ]; then
    if [ -n "${OLLAMA_CUSTOM_CPU_DEFS}" ]; then
        init_vars
        echo "OLLAMA_CUSTOM_CPU_DEFS=\"${OLLAMA_CUSTOM_CPU_DEFS}\""
-        CMAKE_DEFS="${OLLAMA_CUSTOM_CPU_DEFS} -DBUILD_SHARED_LIBS=on -DCMAKE_POSITION_INDEPENDENT_CODE=on ${CMAKE_DEFS}"
+        CMAKE_DEFS="${OLLAMA_CUSTOM_CPU_DEFS} -DBUILD_SHARED_LIBS=off -DCMAKE_POSITION_INDEPENDENT_CODE=on ${CMAKE_DEFS}"
        BUILD_DIR="../build/linux/${ARCH}/cpu"
        echo "Building custom CPU"
        build
-        install
        compress
    else
        # Darwin Rosetta x86 emulation does NOT support AVX, AVX2, AVX512
@@ -95,7 +93,7 @@ if [ -z "${OLLAMA_SKIP_CPU_GENERATE}" ]; then
        # -DGGML_AVX512_VBMI -- 2018 Intel Cannon Lake
        # -DGGML_AVX512_VNNI -- 2021 Intel Alder Lake

-        COMMON_CPU_DEFS="-DBUILD_SHARED_LIBS=on -DCMAKE_POSITION_INDEPENDENT_CODE=on -DGGML_NATIVE=off -DGGML_OPENMP=off"
+        COMMON_CPU_DEFS="-DBUILD_SHARED_LIBS=off -DCMAKE_POSITION_INDEPENDENT_CODE=on -DGGML_NATIVE=off -DGGML_OPENMP=off"
        if [ -z "${OLLAMA_CPU_TARGET}" -o "${OLLAMA_CPU_TARGET}" = "cpu" ]; then
            #
            # CPU first for the default library, set up as lowest common denominator for maximum compatibility (including Rosetta)
@@ -105,7 +103,6 @@ if [ -z "${OLLAMA_SKIP_CPU_GENERATE}" ]; then
            BUILD_DIR="../build/linux/${ARCH}/cpu"
            echo "Building LCD CPU"
            build
-            install
            compress
        fi

@@ -123,7 +120,6 @@ if [ -z "${OLLAMA_SKIP_CPU_GENERATE}" ]; then
                BUILD_DIR="../build/linux/${ARCH}/cpu_avx"
                echo "Building AVX CPU"
                build
-                install
                compress
            fi

@@ -137,7 +133,6 @@ if [ -z "${OLLAMA_SKIP_CPU_GENERATE}" ]; then
                BUILD_DIR="../build/linux/${ARCH}/cpu_avx2"
                echo "Building AVX2 CPU"
                build
-                install
                compress
            fi
        fi
@@ -165,7 +160,7 @@ if [ -z "${OLLAMA_SKIP_CUDA_GENERATE}" -a -d "${CUDA_LIB_DIR}" ]; then
    echo "CUDA libraries detected - building dynamic CUDA library"
    init_vars
    CUDA_MAJOR=$(ls "${CUDA_LIB_DIR}"/libcudart.so.* | head -1 | cut -f3 -d. || true)
-    if [ -n "${CUDA_MAJOR}" -a -z "${CUDA_VARIANT}" ]; then
+    if [ -n "${CUDA_MAJOR}" ]; then
        CUDA_VARIANT=_v${CUDA_MAJOR}
    fi
    if [ "${ARCH}" == "arm64" ]; then
@@ -183,19 +178,29 @@ if [ -z "${OLLAMA_SKIP_CUDA_GENERATE}" -a -d "${CUDA_LIB_DIR}" ]; then
        CMAKE_CUDA_DEFS="-DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES} ${OLLAMA_CUSTOM_CUDA_DEFS}"
        echo "Building custom CUDA GPU"
    else
-        CMAKE_CUDA_DEFS="-DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES}"
+        CMAKE_CUDA_DEFS="-DGGML_CUDA=on -DCMAKE_CUDA_FLAGS=-t8 -DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES}"
    fi
-    export CUDAFLAGS="-t8"
-    CMAKE_DEFS="${COMMON_CMAKE_DEFS} ${CMAKE_DEFS} ${ARM64_DEFS} ${CMAKE_CUDA_DEFS} -DGGML_STATIC=off"
+    CMAKE_DEFS="${COMMON_CMAKE_DEFS} ${CMAKE_DEFS} ${ARM64_DEFS} ${CMAKE_CUDA_DEFS}"
    BUILD_DIR="../build/linux/${ARCH}/cuda${CUDA_VARIANT}"
-    export LLAMA_SERVER_LDFLAGS="-L${CUDA_LIB_DIR} -lcudart -lcublas -lcublasLt -lcuda"
-    CUDA_DIST_DIR="${CUDA_DIST_DIR:-${DIST_BASE}/lib/ollama}"
+    EXTRA_LIBS="-L${CUDA_LIB_DIR} -lcudart -lcublas -lcublasLt -lcuda"
    build
-    install
-    echo "Installing CUDA dependencies in ${CUDA_DIST_DIR}"
-    mkdir -p "${CUDA_DIST_DIR}"
-    for lib in ${CUDA_LIB_DIR}/libcudart.so* ${CUDA_LIB_DIR}/libcublas.so* ${CUDA_LIB_DIR}/libcublasLt.so* ; do
-        cp -a "${lib}" "${CUDA_DIST_DIR}"
+
+    # Carry the CUDA libs as payloads to help reduce dependency burden on users
+    #
+    # TODO - in the future we may shift to packaging these separately and conditionally
+    #        downloading them in the install script.
+    DEPS="$(ldd ${BUILD_DIR}/bin/ollama_llama_server )"
+    for lib in libcudart.so libcublas.so libcublasLt.so ; do
+        DEP=$(echo "${DEPS}" | grep ${lib} | cut -f1 -d' ' | xargs || true)
+        if [ -n "${DEP}" -a -e "${CUDA_LIB_DIR}/${DEP}" ]; then
+            cp "${CUDA_LIB_DIR}/${DEP}" "${BUILD_DIR}/bin/"
+        elif [ -e "${CUDA_LIB_DIR}/${lib}.${CUDA_MAJOR}" ]; then
+            cp "${CUDA_LIB_DIR}/${lib}.${CUDA_MAJOR}" "${BUILD_DIR}/bin/"
+        elif [ -e "${CUDART_LIB_DIR}/${lib}" ]; then
+            cp -d ${CUDART_LIB_DIR}/${lib}* "${BUILD_DIR}/bin/"
+        else
+            cp -d "${CUDA_LIB_DIR}/${lib}*" "${BUILD_DIR}/bin/"
+        fi
    done
    compress

@@ -213,24 +218,21 @@ if [ -z "${OLLAMA_SKIP_ONEAPI_GENERATE}" -a -d "${ONEAPI_ROOT}" ]; then
    CC=icx
    CMAKE_DEFS="${COMMON_CMAKE_DEFS} ${CMAKE_DEFS} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL=ON -DGGML_SYCL_F16=OFF"
    BUILD_DIR="../build/linux/${ARCH}/oneapi"
-    ONEAPI_DIST_DIR="${DIST_BASE}/lib/ollama"
-    export LLAMA_SERVER_LDFLAGS="-fsycl -lOpenCL -lmkl_core -lmkl_sycl_blas -lmkl_intel_ilp64 -lmkl_tbb_thread -ltbb"
+    EXTRA_LIBS="-fsycl -Wl,-rpath,${ONEAPI_ROOT}/compiler/latest/lib,-rpath,${ONEAPI_ROOT}/mkl/latest/lib,-rpath,${ONEAPI_ROOT}/tbb/latest/lib,-rpath,${ONEAPI_ROOT}/compiler/latest/opt/oclfpga/linux64/lib -lOpenCL -lmkl_core -lmkl_sycl_blas -lmkl_intel_ilp64 -lmkl_tbb_thread -ltbb"
    DEBUG_FLAGS="" # icx compiles with -O0 if we pass -g, so we must remove it
    build

    # copy oneAPI dependencies
-    mkdir -p "${ONEAPI_DIST_DIR}"
    for dep in $(ldd "${BUILD_DIR}/bin/ollama_llama_server" | grep "=>" | cut -f2 -d= | cut -f2 -d' ' | grep -e sycl -e mkl -e tbb); do
-        cp -a "${dep}" "${ONEAPI_DIST_DIR}"
+        cp "${dep}" "${BUILD_DIR}/bin/"
    done
-    cp "${ONEAPI_ROOT}/compiler/latest/lib/libOpenCL.so" "${ONEAPI_DIST_DIR}"
-    cp "${ONEAPI_ROOT}/compiler/latest/lib/libimf.so" "${ONEAPI_DIST_DIR}"
-    cp "${ONEAPI_ROOT}/compiler/latest/lib/libintlc.so.5" "${ONEAPI_DIST_DIR}"
-    cp "${ONEAPI_ROOT}/compiler/latest/lib/libirng.so" "${ONEAPI_DIST_DIR}"
-    cp "${ONEAPI_ROOT}/compiler/latest/lib/libpi_level_zero.so" "${ONEAPI_DIST_DIR}"
-    cp "${ONEAPI_ROOT}/compiler/latest/lib/libsvml.so" "${ONEAPI_DIST_DIR}"
-    cp "${ONEAPI_ROOT}/compiler/latest/lib/libur_loader.so.0" "${ONEAPI_DIST_DIR}"
-    install
+    cp "${ONEAPI_ROOT}/compiler/latest/lib/libOpenCL.so" "${BUILD_DIR}/bin/"
+    cp "${ONEAPI_ROOT}/compiler/latest/lib/libimf.so" "${BUILD_DIR}/bin/"
+    cp "${ONEAPI_ROOT}/compiler/latest/lib/libintlc.so.5" "${BUILD_DIR}/bin/"
+    cp "${ONEAPI_ROOT}/compiler/latest/lib/libirng.so" "${BUILD_DIR}/bin/"
+    cp "${ONEAPI_ROOT}/compiler/latest/lib/libpi_level_zero.so" "${BUILD_DIR}/bin/"
+    cp "${ONEAPI_ROOT}/compiler/latest/lib/libsvml.so" "${BUILD_DIR}/bin/"
+    cp "${ONEAPI_ROOT}/compiler/latest/lib/libur_loader.so.0" "${BUILD_DIR}/bin/"
    compress
 fi

@@ -260,21 +262,23 @@ if [ -z "${OLLAMA_SKIP_ROCM_GENERATE}" -a -d "${ROCM_PATH}" ]; then
        echo "Building custom ROCM GPU"
    fi
    BUILD_DIR="../build/linux/${ARCH}/rocm${ROCM_VARIANT}"
-    ROCM_DIST_DIR="${DIST_BASE}/lib/ollama"
-    # TODO figure out how to disable runpath (rpath)
-    # export CMAKE_HIP_FLAGS="-fno-rtlib-add-rpath" # doesn't work
-    export LLAMA_SERVER_LDFLAGS="-L${ROCM_PATH}/lib -L/opt/amdgpu/lib/x86_64-linux-gnu/ -lhipblas -lrocblas -lamdhip64 -lrocsolver -lamd_comgr -lhsa-runtime64 -lrocsparse -ldrm -ldrm_amdgpu"
+    EXTRA_LIBS="-L${ROCM_PATH}/lib -L/opt/amdgpu/lib/x86_64-linux-gnu/ -Wl,-rpath,\$ORIGIN/../../rocm/ -lhipblas -lrocblas -lamdhip64 -lrocsolver -lamd_comgr -lhsa-runtime64 -lrocsparse -ldrm -ldrm_amdgpu"
    build

-    # copy the ROCM dependencies
-    mkdir -p "${ROCM_DIST_DIR}"
-    for dep in $(ldd "${BUILD_DIR}/bin/ollama_llama_server" | grep "=>" | cut -f2 -d= | cut -f2 -d' ' | grep -v "${ARCH}/rocm${ROCM_VARIANT}" | grep -e rocm -e amdgpu -e libtinfo ); do
-        cp -a "${dep}"* "${ROCM_DIST_DIR}"
+    # Record the ROCM dependencies
+    rm -f "${BUILD_DIR}/bin/deps.txt"
+    touch "${BUILD_DIR}/bin/deps.txt"
+    for dep in $(ldd "${BUILD_DIR}/bin/ollama_llama_server" | grep "=>" | cut -f2 -d= | cut -f2 -d' ' | grep -e rocm -e amdgpu -e libtinfo ); do
+        echo "${dep}" >> "${BUILD_DIR}/bin/deps.txt"
    done
-    install
+    # bomb out if for some reason we didn't get a few deps
+    if [ $(cat "${BUILD_DIR}/bin/deps.txt" | wc -l ) -lt 8 ] ; then
+        cat "${BUILD_DIR}/bin/deps.txt"
+        echo "ERROR: deps file short"
+        exit 1
+    fi
    compress
 fi

 cleanup
-wait_for_compress
 echo "go generate completed.  LLM runners: $(cd ${BUILD_DIR}/..; echo *)"
--- a/llm/generate/gen_windows.ps1
+++ b/llm/generate/gen_windows.ps1
@@ -35,7 +35,7 @@ function init_vars {
        )
    $script:commonCpuDefs = @("-DCMAKE_POSITION_INDEPENDENT_CODE=on")
    $script:ARCH = $Env:PROCESSOR_ARCHITECTURE.ToLower()
-    $script:DIST_BASE = "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\runners"
+    $script:DIST_BASE = "${script:SRC_DIR}\dist\windows-${script:ARCH}\ollama_runners"
    md "$script:DIST_BASE" -ea 0 > $null
    if ($env:CGO_CFLAGS -contains "-g") {
        $script:cmakeDefs += @("-DCMAKE_VERBOSE_MAKEFILE=on", "-DLLAMA_SERVER_VERBOSE=on", "-DCMAKE_BUILD_TYPE=RelWithDebInfo")
@@ -117,7 +117,7 @@ function build {
    if ($cmakeDefs -contains "-G") {
        $extra=@("-j8")
    } else {
-        $extra= @("--", "/maxCpuCount:8")
+        $extra= @("--", "/p:CL_MPcount=8")
    }
    write-host "building with: cmake --build $script:buildDir --config $script:config $($script:cmakeTargets | ForEach-Object { `"--target`", $_ }) $extra"
    & cmake --build $script:buildDir --config $script:config ($script:cmakeTargets | ForEach-Object { "--target", $_ }) $extra
@@ -261,7 +261,7 @@ function build_cuda() {
    if ((-not "${env:OLLAMA_SKIP_CUDA_GENERATE}") -and ("${script:CUDA_LIB_DIR}")) {
        # Then build cuda as a dynamically loaded library
        $nvcc = "$script:CUDA_LIB_DIR\nvcc.exe"
-        $script:CUDA_VERSION=((get-item ($nvcc | split-path | split-path)).Basename -Split "\.")[0]
+        $script:CUDA_VERSION=(get-item ($nvcc | split-path | split-path)).Basename
        if ($null -ne $script:CUDA_VERSION) {
            $script:CUDA_VARIANT="_"+$script:CUDA_VERSION
        }
@@ -273,9 +273,9 @@ function build_cuda() {
            "-DGGML_CUDA=ON",
            "-DGGML_AVX=on",
            "-DGGML_AVX2=off",
-            "-DCMAKE_CUDA_FLAGS=-t6",
-            "-DCMAKE_CUDA_ARCHITECTURES=${script:CMAKE_CUDA_ARCHITECTURES}",
-            "-DCMAKE_CUDA_COMPILER_TOOLKIT_ROOT=$env:CUDA_PATH"
+            "-DCUDAToolkit_INCLUDE_DIR=$script:CUDA_INCLUDE_DIR",
+            "-DCMAKE_CUDA_FLAGS=-t8",
+            "-DCMAKE_CUDA_ARCHITECTURES=${script:CMAKE_CUDA_ARCHITECTURES}"
            )
        if ($null -ne $env:OLLAMA_CUSTOM_CUDA_DEFS) {
            write-host "OLLAMA_CUSTOM_CUDA_DEFS=`"${env:OLLAMA_CUSTOM_CUDA_DEFS}`""
@@ -286,11 +286,12 @@ function build_cuda() {
        sign
        install

-        md "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\" -ea 0 > $null
-        write-host "copying CUDA dependencies to ${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
-        cp "${script:CUDA_LIB_DIR}\cudart64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
-        cp "${script:CUDA_LIB_DIR}\cublas64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
-        cp "${script:CUDA_LIB_DIR}\cublasLt64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
+        rm -ea 0 -recurse -force -path "${script:SRC_DIR}\dist\windows-${script:ARCH}\cuda\"
+        md "${script:SRC_DIR}\dist\windows-${script:ARCH}\cuda\" -ea 0 > $null
+        write-host "copying CUDA dependencies to ${script:SRC_DIR}\dist\windows-${script:ARCH}\cuda\"
+        cp "${script:CUDA_LIB_DIR}\cudart64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\cuda\"
+        cp "${script:CUDA_LIB_DIR}\cublas64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\cuda\"
+        cp "${script:CUDA_LIB_DIR}\cublasLt64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\cuda\"
    } else {
        write-host "Skipping CUDA generation step"
    }
@@ -324,17 +325,18 @@ function build_oneapi() {
    sign
    install

-    md "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\" -ea 0 > $null
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\libirngmd.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\libmmd.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_level_zero.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_unified_runtime.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_win_proxy_loader.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\svml_dispmd.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\sycl7.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
-    cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_core.2.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
-    cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_sycl_blas.4.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
-    cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_tbb_thread.2.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
+    rm -ea 0 -recurse -force -path "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    md "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\" -ea 0 > $null
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\libirngmd.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\libmmd.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_level_zero.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_unified_runtime.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_win_proxy_loader.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\svml_dispmd.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\sycl7.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_core.2.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_sycl_blas.4.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_tbb_thread.2.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
  } else {
    Write-Host "Skipping oneAPI generation step"
  }
@@ -384,11 +386,12 @@ function build_rocm() {
        sign
        install

-        md "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\rocblas\library\" -ea 0 > $null
-        cp "${env:HIP_PATH}\bin\hipblas.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
-        cp "${env:HIP_PATH}\bin\rocblas.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
+        rm -ea 0 -recurse -force -path "${script:SRC_DIR}\dist\windows-${script:ARCH}\rocm\"
+        md "${script:SRC_DIR}\dist\windows-${script:ARCH}\rocm\rocblas\library\" -ea 0 > $null
+        cp "${env:HIP_PATH}\bin\hipblas.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\rocm\"
+        cp "${env:HIP_PATH}\bin\rocblas.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\rocm\"
        # amdhip64.dll dependency comes from the driver and must be installed on the host to use AMD GPUs
-        cp "${env:HIP_PATH}\bin\rocblas\library\*" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\rocblas\library\"
+        cp "${env:HIP_PATH}\bin\rocblas\library\*" "${script:SRC_DIR}\dist\windows-${script:ARCH}\rocm\rocblas\library\"
    } else {
        write-host "Skipping ROCm generation step"
    }
--- a/llm/ggla.go
+++ b/llm/ggla.go
@@ -36,8 +36,6 @@ type ggla struct {

 	kv      KV
 	tensors []*Tensor
-
-	tensorOffset uint64
 }

 func newGGLA(container *containerGGLA) *ggla {
@@ -52,10 +50,7 @@ func (llm *ggla) KV() KV {
 }

 func (llm *ggla) Tensors() Tensors {
-	return Tensors{
-		Items:  llm.tensors,
-		Offset: llm.tensorOffset,
-	}
+	return llm.tensors
 }

 func (llm *ggla) decode(rs io.ReadSeeker) (retErr error) {
@@ -71,13 +66,6 @@ func (llm *ggla) decode(rs io.ReadSeeker) (retErr error) {
 	}
 	llm.kv["alpha"] = alpha

-	offset, err := rs.Seek(0, io.SeekCurrent)
-	if err != nil {
-		return err
-	}
-
-	llm.tensorOffset = uint64(offset)
-
 	for {
 		var dims uint32
 		if err := binary.Read(rs, binary.LittleEndian, &dims); err != nil {
--- a/llm/ggml.go
+++ b/llm/ggml.go
@@ -112,14 +112,11 @@ func (kv KV) ChatTemplate() string {
 	return s
 }

-type Tensors struct {
-	Items  []*Tensor
-	Offset uint64
-}
+type Tensors []*Tensor

 func (ts Tensors) Layers() map[string]Layer {
 	layers := make(map[string]Layer)
-	for _, t := range ts.Items {
+	for _, t := range ts {
 		parts := strings.Split(t.Name, ".")
 		if parts[0] == "blk" {
 			// join first and second part, e.g. blk.%d
@@ -157,19 +154,29 @@ type Tensor struct {
 	io.WriterTo `json:"-"`
 }

-func (t Tensor) block() (n int) {
-	if _, err := fmt.Sscanf(t.Name, "blk.%d.", &n); err != nil {
-		return -1
-	}
-
-	return
-}
-
 func (t Tensor) blockSize() uint64 {
 	switch t.Kind {
-	case 0, 1, 24, 25, 26, 27, 28, 30: // F32, F16, I8, I16, I32, I64, F64, BF16
+	case 0, // F32
+		1,  // F16
+		24, // I8
+		25, // I16
+		26, // I32
+		27, // I64
+		28, // F64
+		30: // BF16
 		return 1
-	case 2, 3, 4, 5, 6, 7, 8, 9, 20: // Q4_0, Q4_1, Q5_0, Q5_1, Q8_0, Q8_1, IQ4_NL
+	case 2, // Q4_0
+		3,  // Q4_1
+		4,  // Q5_0
+		5,  // Q5_1
+		6,  // Q8_0
+		7,  // Q8_1
+		8,  // IQ4_NL
+		9,  // IQ4_NL
+		20, // IQ4_NL
+		31, // Q4_0_4_4
+		32, // Q4_0_4_8
+		33: // Q4_0_8_8
 		return 32
 	default: // All others
 		return 256
@@ -180,11 +187,17 @@ func (t Tensor) typeSize() uint64 {
 	blockSize := t.blockSize()

 	switch t.Kind {
-	case 0: // FP32
+	case 0, // FP32
+		26: // I32
 		return 4
-	case 1: // FP16
+	case 1, // FP16
+		25, // I16
+		30: // BF16
 		return 2
-	case 2: // Q4_0
+	case 2, // Q4_0
+		31, // Q4_0_4_4
+		32, // Q4_0_4_8
+		33: // Q4_0_8_8
 		return 2 + blockSize/2
 	case 3: // Q4_1
 		return 2 + 2 + blockSize/2
@@ -226,10 +239,6 @@ func (t Tensor) typeSize() uint64 {
 		return 2 + 2 + blockSize/2 + blockSize/64
 	case 24: // I8
 		return 1
-	case 25: // I16
-		return 2
-	case 26: // I32
-		return 4
 	case 27: // I64
 		return 8
 	case 28: // F64
--- a/llm/gguf.go
+++ b/llm/gguf.go
@@ -2,16 +2,11 @@ package llm

 import (
 	"bytes"
-	"cmp"
 	"encoding/binary"
 	"encoding/json"
 	"fmt"
 	"io"
-	"log/slog"
-	"slices"
 	"strings"
-
-	"golang.org/x/exp/maps"
 )

 type containerGGUF struct {
@@ -93,8 +88,7 @@ type gguf struct {
 	kv      KV
 	tensors []*Tensor

-	parameters   uint64
-	tensorOffset uint64
+	parameters uint64

 	scratch [16 << 10]byte
 }
@@ -106,15 +100,16 @@ func newGGUF(container *containerGGUF) *gguf {
 	}
 }

+func NewGGUFV3(bo binary.ByteOrder) *gguf {
+	return newGGUF(&containerGGUF{ByteOrder: bo, Version: 3})
+}
+
 func (llm *gguf) KV() KV {
 	return llm.kv
 }

 func (llm *gguf) Tensors() Tensors {
-	return Tensors{
-		Items:  llm.tensors,
-		Offset: llm.tensorOffset,
-	}
+	return llm.tensors
 }

 func (llm *gguf) numTensor() uint64 {
@@ -204,7 +199,7 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
 			return fmt.Errorf("failed to read tensor dimensions: %w", err)
 		}

-		shape := make([]uint64, dims)
+		shape := [4]uint64{1, 1, 1, 1}
 		for i := 0; uint32(i) < dims; i++ {
 			shape[i], err = readGGUF[uint64](llm, rs)
 			if err != nil {
@@ -241,21 +236,13 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
 		alignment = 32
 	}

-	offset, err := rs.Seek(0, io.SeekCurrent)
-	if err != nil {
-		return err
-	}
-
-	padding := ggufPadding(offset, int64(alignment))
-	llm.tensorOffset = uint64(offset + padding)
-
 	for _, tensor := range llm.tensors {
 		offset, err := rs.Seek(0, io.SeekCurrent)
 		if err != nil {
 			return fmt.Errorf("failed to get current offset: %w", err)
 		}

-		padding := ggufPadding(offset, int64(alignment))
+		padding := llm.padding(offset, int64(alignment))
 		if _, err := rs.Seek(padding, io.SeekCurrent); err != nil {
 			return fmt.Errorf("failed to seek to init padding: %w", err)
 		}
@@ -274,12 +261,12 @@ func readGGUF[T any](llm *gguf, r io.Reader) (T, error) {
 	return t, err
 }

-func writeGGUF[V any](w io.Writer, t uint32, v V) error {
-	if err := binary.Write(w, binary.LittleEndian, t); err != nil {
+func writeGGUF[V any](llm *gguf, w io.Writer, t uint32, v V) error {
+	if err := binary.Write(w, llm.ByteOrder, t); err != nil {
 		return err
 	}

-	return binary.Write(w, binary.LittleEndian, v)
+	return binary.Write(w, llm.ByteOrder, v)
 }

 func readGGUFV1String(llm *gguf, r io.Reader) (string, error) {
@@ -343,12 +330,12 @@ func readGGUFString(llm *gguf, r io.Reader) (string, error) {
 	return string(buf), nil
 }

-func writeGGUFString(w io.Writer, s string) error {
-	if err := binary.Write(w, binary.LittleEndian, ggufTypeString); err != nil {
+func writeGGUFString(llm *gguf, w io.Writer, s string) error {
+	if err := binary.Write(w, llm.ByteOrder, ggufTypeString); err != nil {
 		return err
 	}

-	if err := binary.Write(w, binary.LittleEndian, uint64(len(s))); err != nil {
+	if err := binary.Write(w, llm.ByteOrder, uint64(len(s))); err != nil {
 		return err
 	}

@@ -489,71 +476,21 @@ func readGGUFArray(llm *gguf, r io.Reader) (*array, error) {
 	return a, nil
 }

-// writeGGUFArray writes a slice s of type E to the write with a gguf type of t
-func writeGGUFArray[S ~[]E, E any](w io.Writer, t uint32, s S) error {
-	if err := binary.Write(w, binary.LittleEndian, ggufTypeArray); err != nil {
+func writeGGUFArray[S ~[]E, E any](llm *gguf, w io.Writer, t uint32, s S) error {
+	if err := binary.Write(w, llm.ByteOrder, ggufTypeArray); err != nil {
 		return err
 	}

-	if err := binary.Write(w, binary.LittleEndian, t); err != nil {
+	if err := binary.Write(w, llm.ByteOrder, t); err != nil {
 		return err
 	}

-	if err := binary.Write(w, binary.LittleEndian, uint64(len(s))); err != nil {
+	if err := binary.Write(w, llm.ByteOrder, uint64(len(s))); err != nil {
 		return err
 	}

-	return binary.Write(w, binary.LittleEndian, s)
-}
-
-func WriteGGUF(ws io.WriteSeeker, kv KV, ts []Tensor) error {
-	if err := binary.Write(ws, binary.LittleEndian, []byte("GGUF")); err != nil {
-		return err
-	}
-
-	if err := binary.Write(ws, binary.LittleEndian, uint32(3)); err != nil {
-		return err
-	}
-
-	if err := binary.Write(ws, binary.LittleEndian, uint64(len(ts))); err != nil {
-		return err
-	}
-
-	if err := binary.Write(ws, binary.LittleEndian, uint64(len(kv))); err != nil {
-		return err
-	}
-
-	keys := maps.Keys(kv)
-	slices.Sort(keys)
-
-	for _, key := range keys {
-		if err := ggufWriteKV(ws, key, kv[key]); err != nil {
-			return err
-		}
-	}
-
-	slices.SortStableFunc(ts, func(a, b Tensor) int {
-		if i, j := a.block(), b.block(); i < 0 && j > 0 {
-			return 1
-		} else if i > 0 && j < 0 {
-			return -1
-		} else {
-			return cmp.Compare(i, j)
-		}
-	})
-
-	var s uint64
-	for _, t := range ts {
-		t.Offset = s
-		if err := ggufWriteTensorInfo(ws, t); err != nil {
-			return err
-		}
-		s += t.Size()
-	}
-
-	var alignment int64 = 32
-	for _, t := range ts {
-		if err := ggufWriteTensor(ws, t, alignment); err != nil {
+	for _, e := range s {
+		if err := binary.Write(w, llm.ByteOrder, e); err != nil {
 			return err
 		}
 	}
@@ -561,102 +498,201 @@ func WriteGGUF(ws io.WriteSeeker, kv KV, ts []Tensor) error {
 	return nil
 }

-func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
-	slog.Debug(k, "type", fmt.Sprintf("%T", v))
-	if err := binary.Write(ws, binary.LittleEndian, uint64(len(k))); err != nil {
-		return err
-	}
+var ggufKVOrder = map[string][]string{
+	"llama": {
+		"general.architecture",
+		"general.name",
+		"llama.vocab_size",
+		"llama.context_length",
+		"llama.embedding_length",
+		"llama.block_count",
+		"llama.feed_forward_length",
+		"llama.attention.head_count",
+		"llama.attention.head_count_kv",
+		"llama.attention.layer_norm_rms_epsilon",
+		"llama.rope.freq_base",
+		"llama.rope.dimension_count",
+		"llama.expert_count",
+		"llama.expert_used_count",
+		"gemma.context_length",
+		"gemma.embedding_length",
+		"gemma.block_count",
+		"gemma.feed_forward_length",
+		"gemma.attention.head_count",
+		"gemma.attention.head_count_kv",
+		"gemma.attention.layer_norm_rms_epsilon",
+		"gemma.attention.key_length",
+		"gemma.attention.value_length",
+		"general.file_type",
+		"tokenizer.ggml.pre",
+		"tokenizer.ggml.model",
+		"tokenizer.ggml.tokens",
+		"tokenizer.ggml.scores",
+		"tokenizer.ggml.merges",
+		"tokenizer.ggml.token_type",
+		"tokenizer.ggml.bos_token_id",
+		"tokenizer.ggml.eos_token_id",
+		"tokenizer.ggml.unknown_token_id",
+		"tokenizer.ggml.padding_token_id",
+		"tokenizer.ggml.add_bos_token",
+		"tokenizer.ggml.add_eos_token",
+		"tokenizer.chat_template",
+		"bert.pooling_type",
+	},
+}

-	if err := binary.Write(ws, binary.LittleEndian, []byte(k)); err != nil {
-		return err
-	}
-
-	var err error
-	switch v := v.(type) {
-	case uint32:
-		err = writeGGUF(ws, ggufTypeUint32, v)
-	case float32:
-		err = writeGGUF(ws, ggufTypeFloat32, v)
-	case bool:
-		err = writeGGUF(ws, ggufTypeBool, v)
-	case string:
-		err = writeGGUFString(ws, v)
-	case []int32:
-		err = writeGGUFArray(ws, ggufTypeInt32, v)
-	case []uint32:
-		err = writeGGUFArray(ws, ggufTypeUint32, v)
-	case []float32:
-		err = writeGGUFArray(ws, ggufTypeFloat32, v)
-	case []string:
-		if err := binary.Write(ws, binary.LittleEndian, ggufTypeArray); err != nil {
-			return err
-		}
-
-		if err := binary.Write(ws, binary.LittleEndian, ggufTypeString); err != nil {
-			return err
-		}
-
-		if err := binary.Write(ws, binary.LittleEndian, uint64(len(v))); err != nil {
-			return err
-		}
-
-		for _, e := range v {
-			if err := binary.Write(ws, binary.LittleEndian, uint64(len(e))); err != nil {
-				return err
-			}
-
-			if err := binary.Write(ws, binary.LittleEndian, []byte(e)); err != nil {
-				return err
-			}
-		}
+func (llm *gguf) Encode(ws io.WriteSeeker, kv KV, tensors []Tensor) error {
+	switch llm.Version {
+	case 3:
+		llm.V3.NumTensor = uint64(len(tensors))
+		llm.V3.NumKV = uint64(len(kv))
 	default:
-		return fmt.Errorf("improper type for '%s'", k)
+		return fmt.Errorf("not implemented: ggufv%d", llm.Version)
 	}

-	return err
-}
-
-func ggufWriteTensorInfo(ws io.WriteSeeker, t Tensor) error {
-	slog.Debug(t.Name, "kind", t.Kind, "shape", t.Shape, "offset", t.Offset)
-	if err := binary.Write(ws, binary.LittleEndian, uint64(len(t.Name))); err != nil {
+	if err := binary.Write(ws, llm.ByteOrder, []byte("GGUF")); err != nil {
 		return err
 	}

-	if err := binary.Write(ws, binary.LittleEndian, []byte(t.Name)); err != nil {
+	if err := binary.Write(ws, llm.ByteOrder, llm.Version); err != nil {
 		return err
 	}

-	if err := binary.Write(ws, binary.LittleEndian, uint32(len(t.Shape))); err != nil {
+	if err := binary.Write(ws, llm.ByteOrder, llm.numTensor()); err != nil {
 		return err
 	}

-	for i := range len(t.Shape) {
-		if err := binary.Write(ws, binary.LittleEndian, t.Shape[len(t.Shape)-i-1]); err != nil {
+	if err := binary.Write(ws, llm.ByteOrder, llm.numKV()); err != nil {
+		return err
+	}
+
+	kvCheck := make(map[string]bool)
+	for k := range kv {
+		kvCheck[k] = false
+	}
+
+	for _, k := range ggufKVOrder["llama"] {
+		v, ok := kv[k]
+		if !ok {
+			continue
+		}
+		kvCheck[k] = true
+
+		if err := binary.Write(ws, llm.ByteOrder, uint64(len(k))); err != nil {
+			return err
+		}
+
+		if err := binary.Write(ws, llm.ByteOrder, []byte(k)); err != nil {
+			return err
+		}
+
+		var err error
+		switch v := v.(type) {
+		case uint32:
+			err = writeGGUF(llm, ws, ggufTypeUint32, v)
+		case float32:
+			err = writeGGUF(llm, ws, ggufTypeFloat32, v)
+		case bool:
+			err = writeGGUF(llm, ws, ggufTypeBool, v)
+		case string:
+			err = writeGGUFString(llm, ws, v)
+		case []int32:
+			err = writeGGUFArray(llm, ws, ggufTypeInt32, v)
+		case []uint32:
+			err = writeGGUFArray(llm, ws, ggufTypeUint32, v)
+		case []float32:
+			err = writeGGUFArray(llm, ws, ggufTypeFloat32, v)
+		case []string:
+			if err := binary.Write(ws, llm.ByteOrder, ggufTypeArray); err != nil {
+				return err
+			}
+
+			if err := binary.Write(ws, llm.ByteOrder, ggufTypeString); err != nil {
+				return err
+			}
+
+			if err := binary.Write(ws, llm.ByteOrder, uint64(len(v))); err != nil {
+				return err
+			}
+
+			for _, e := range v {
+				if err := binary.Write(ws, llm.ByteOrder, uint64(len(e))); err != nil {
+					return err
+				}
+
+				if err := binary.Write(ws, llm.ByteOrder, []byte(e)); err != nil {
+					return err
+				}
+			}
+		default:
+			return fmt.Errorf("improper type for '%s'", k)
+		}
+		if err != nil {
 			return err
 		}
 	}

-	if err := binary.Write(ws, binary.LittleEndian, t.Kind); err != nil {
-		return err
+	for k, v := range kvCheck {
+		if !v {
+			return fmt.Errorf("Didn't know how to write kv %s", k)
+		}
 	}

-	return binary.Write(ws, binary.LittleEndian, t.Offset)
+	for _, tensor := range tensors {
+		if err := binary.Write(ws, llm.ByteOrder, uint64(len(tensor.Name))); err != nil {
+			return err
+		}
+
+		if err := binary.Write(ws, llm.ByteOrder, []byte(tensor.Name)); err != nil {
+			return err
+		}
+
+		var dims int
+		for cnt := range len(tensor.Shape) {
+			if tensor.Shape[cnt] > 0 {
+				dims++
+			}
+		}
+
+		if err := binary.Write(ws, llm.ByteOrder, uint32(dims)); err != nil {
+			return err
+		}
+
+		for i := range dims {
+			if err := binary.Write(ws, llm.ByteOrder, tensor.Shape[dims-1-i]); err != nil {
+				return err
+			}
+		}
+
+		if err := binary.Write(ws, llm.ByteOrder, tensor.Kind); err != nil {
+			return err
+		}
+
+		if err := binary.Write(ws, llm.ByteOrder, tensor.Offset); err != nil {
+			return err
+		}
+	}
+
+	var alignment int64 = 32
+	for _, tensor := range tensors {
+		offset, err := ws.Seek(0, io.SeekCurrent)
+		if err != nil {
+			return err
+		}
+
+		padding := llm.padding(offset, alignment)
+		if err := binary.Write(ws, llm.ByteOrder, bytes.Repeat([]byte{0}, int(padding))); err != nil {
+			return err
+		}
+
+		if _, err := tensor.WriteTo(ws); err != nil {
+			return err
+		}
+	}
+
+	return nil
 }

-func ggufWriteTensor(ws io.WriteSeeker, t Tensor, alignment int64) error {
-	offset, err := ws.Seek(0, io.SeekCurrent)
-	if err != nil {
-		return err
-	}
-
-	if err := binary.Write(ws, binary.LittleEndian, bytes.Repeat([]byte{0}, int(ggufPadding(offset, alignment)))); err != nil {
-		return err
-	}
-
-	_, err = t.WriteTo(ws)
-	return err
-}
-
-func ggufPadding(offset, align int64) int64 {
+func (gguf) padding(offset, align int64) int64 {
 	return (align - offset%align) % align
 }
--- a/llm/llama.cpp
+++ b/llm/llama.cpp
--- a/llm/llm.go
+++ b/llm/llm.go
@@ -11,9 +11,8 @@ package llm
 // #include <stdlib.h>
 // #include "llama.h"
 import "C"
-
 import (
-	"errors"
+	"fmt"
 	"unsafe"
 )

@@ -34,7 +33,7 @@ func Quantize(infile, outfile string, ftype fileType) error {
 	params.ftype = ftype.Value()

 	if rc := C.llama_model_quantize(cinfile, coutfile, &params); rc != 0 {
-		return errors.New("failed to quantize model. This model architecture may not be supported, or you may need to upgrade Ollama to the latest version")
+		return fmt.Errorf("failed to quantize model. This model architecture may not be supported, or you may need to upgrade Ollama to the latest version")
 	}

 	return nil
--- a/llm/memory_test.go
+++ b/llm/memory_test.go
@@ -2,15 +2,15 @@ package llm

 import (
 	"bytes"
+	"encoding/binary"
 	"fmt"
 	"os"
 	"testing"

-	"github.com/stretchr/testify/assert"
-	"github.com/stretchr/testify/require"
-
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/gpu"
+	"github.com/stretchr/testify/assert"
+	"github.com/stretchr/testify/require"
 )

 func TestEstimateGPULayers(t *testing.T) {
@@ -20,6 +20,7 @@ func TestEstimateGPULayers(t *testing.T) {
 	f, err := os.CreateTemp(t.TempDir(), modelName)
 	require.NoError(t, err)
 	defer f.Close()
+	gguf := NewGGUFV3(binary.LittleEndian)
 	inputLayerCount := 5

 	tensors := []Tensor{
@@ -31,7 +32,7 @@ func TestEstimateGPULayers(t *testing.T) {
 		{Name: "output.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
 	}
 	assert.Len(t, tensors, inputLayerCount+1)
-	err = WriteGGUF(f, KV{
+	err = gguf.Encode(f, KV{
 		"general.architecture":          "llama",
 		"general.name":                  "name",
 		"llama.context_length":          uint32(32),
--- a/llm/patches/09-lora.diff
+++ b/llm/patches/09-lora.diff
@@ -1,32 +1,40 @@
 diff --git a/common/common.cpp b/common/common.cpp
-index 2e8374d5..70d0afde 100644
+index dbb724fb..c26fe6ee 100644
 --- a/common/common.cpp
 +++ b/common/common.cpp
-@@ -2110,9 +2110,21 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
-         loaded_la.adapter = llama_lora_adapter_init(model, la.path.c_str());
-         if (loaded_la.adapter == nullptr) {
-             fprintf(stderr, "%s: error: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
+@@ -2087,14 +2087,27 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
+     for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) {
+         const std::string & lora_adapter = std::get<0>(params.lora_adapter[i]);
+         float lora_scale = std::get<1>(params.lora_adapter[i]);
+
+        // try to load as gguf
+         auto adapter = llama_lora_adapter_init(model, lora_adapter.c_str());
+         if (adapter == nullptr) {
+-            fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
 -            llama_free(lctx);
 -            llama_free_model(model);
-            return iparams;
+-            return std::make_tuple(nullptr, nullptr);
+            fprintf(stderr, "%s: error: failed to apply lora adapter, trying ggla\n", __func__);
 +
 +            // if that fails, try loading as ggla for compatibility
 +            int err = llama_model_apply_lora_from_file(model,
-+                                                    la.path.c_str(),
-+                                                    la.scale,
+                                                    lora_adapter.c_str(),
+                                                    lora_scale,
 +                                                    nullptr,
 +                                                    params.n_threads);
 +            if (err != 0) {
 +                fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
 +                llama_free(lctx);
 +                llama_free_model(model);
-+                return iparams;
-+            } else {
-+                break;
+                return std::make_tuple(nullptr, nullptr);
 +            }
+        } else {
+            llama_lora_adapter_set(lctx, adapter, lora_scale);
         }
-         iparams.lora_adapters.push_back(loaded_la); // copy to list of loaded adapters
+-        llama_lora_adapter_set(lctx, adapter, lora_scale);
     }
+ 
+     if (params.ignore_eos) {
 diff --git a/include/llama.h b/include/llama.h
 index 93fd77ca..b0fb37a6 100644
 --- a/include/llama.h
@@ -347,4 +355,4 @@ index 80a0dd0f..9d7b0e17 100644
 +        return 1;
 +    }
 +}
-\ No newline at end of file
+\ No newline at end of file
--- a/llm/patches/10-params.diff
+++ b/llm/patches/10-params.diff
@@ -0,0 +1,20 @@
+diff --git a/src/llama.cpp b/src/llama.cpp
+index a207451f..fba6b175 100644
+--- a/src/llama.cpp
+++ b/src/llama.cpp
+@@ -4969,6 +4969,7 @@ static void llm_load_hparams(
+                 hparams.attn_soft_cap = true;
+ 
+                 switch (hparams.n_layer) {
+                    case 26: model.type = e_model::MODEL_2B; break;
+                     case 42: model.type = e_model::MODEL_9B; break;
+                     case 46: model.type = e_model::MODEL_27B; break;
+                     default: model.type = e_model::MODEL_UNKNOWN;
+@@ -11736,6 +11737,7 @@ struct llm_build_context {
+ 
+                 // ref: https://github.com/google/gemma_pytorch/commit/03e657582d17cb5a8617ebf333c1c16f3694670e
+                 switch (model.type) {
+                    case e_model::MODEL_2B: Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k))); break;
+                     case e_model::MODEL_9B:  Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));   break;
+                     case e_model::MODEL_27B: Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd / n_head))); break;
+                     default: GGML_ABORT("fatal error");
--- a/llm/payload.go
+++ b/llm/payload.go
@@ -82,8 +82,8 @@ func serversForGpu(info gpu.GpuInfo) []string {
 	// glob workDir for files that start with ollama_
 	availableServers := getAvailableServers()
 	requested := info.Library
-	if info.Variant != gpu.CPUCapabilityNone.String() {
-		requested += "_" + info.Variant
+	if info.Variant != gpu.CPUCapabilityNone {
+		requested += "_" + info.Variant.String()
 	}

 	servers := []string{}
--- a/llm/server.go
+++ b/llm/server.go
@@ -33,7 +33,7 @@ type LlamaServer interface {
 	Ping(ctx context.Context) error
 	WaitUntilRunning(ctx context.Context) error
 	Completion(ctx context.Context, req CompletionRequest, fn func(CompletionResponse)) error
-	Embedding(ctx context.Context, input string) ([]float32, error)
+	Embed(ctx context.Context, input []string) (*EmbedResponse, error)
 	Tokenize(ctx context.Context, content string) ([]int, error)
 	Detokenize(ctx context.Context, tokens []int) (string, error)
 	Close() error
@@ -44,12 +44,11 @@ type LlamaServer interface {

 // llmServer is an instance of the llama.cpp server
 type llmServer struct {
-	port        int
-	cmd         *exec.Cmd
-	done        chan error // Channel to signal when the process exits
-	status      *StatusWriter
-	options     api.Options
-	numParallel int
+	port    int
+	cmd     *exec.Cmd
+	done    chan error // Channel to signal when the process exits
+	status  *StatusWriter
+	options api.Options

 	estimate    MemoryEstimate
 	totalLayers uint64
@@ -125,9 +124,8 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 		}
 	}

-	// On linux and windows, over-allocating CPU memory will almost always result in an error
-	// Darwin has fully dynamic swap so has no direct concept of free swap space
-	if runtime.GOOS != "darwin" {
+	// On linux, over-allocating CPU memory will almost always result in an error
+	if runtime.GOOS == "linux" {
 		systemMemoryRequired := estimate.TotalSize - estimate.VRAMSize
 		available := systemFreeMemory + systemSwapFreeMemory
 		if systemMemoryRequired > available {
@@ -186,15 +184,15 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr

 	params := []string{
 		"--model", model,
-		"--ctx-size", strconv.Itoa(opts.NumCtx),
-		"--batch-size", strconv.Itoa(opts.NumBatch),
+		"--ctx-size", fmt.Sprintf("%d", opts.NumCtx),
+		"--batch-size", fmt.Sprintf("%d", opts.NumBatch),
 		"--embedding",
 	}

 	params = append(params, "--log-disable")

 	if opts.NumGPU >= 0 {
-		params = append(params, "--n-gpu-layers", strconv.Itoa(opts.NumGPU))
+		params = append(params, "--n-gpu-layers", fmt.Sprintf("%d", opts.NumGPU))
 	}

 	if envconfig.Debug() {
@@ -202,7 +200,7 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 	}

 	if opts.MainGPU > 0 {
-		params = append(params, "--main-gpu", strconv.Itoa(opts.MainGPU))
+		params = append(params, "--main-gpu", fmt.Sprintf("%d", opts.MainGPU))
 	}

 	if len(adapters) > 0 {
@@ -216,7 +214,7 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 	}

 	if opts.NumThread > 0 {
-		params = append(params, "--threads", strconv.Itoa(opts.NumThread))
+		params = append(params, "--threads", fmt.Sprintf("%d", opts.NumThread))
 	}

 	if !opts.F16KV {
@@ -258,17 +256,11 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 		params = append(params, "--mlock")
 	}

-	if gpu.IsNUMA() {
-		numaMode := "distribute"
-		if runtime.GOOS == "linux" {
-			if _, err := exec.LookPath("numactl"); err == nil {
-				numaMode = "numactl"
-			}
-		}
-		params = append(params, "--numa", numaMode)
+	if opts.UseNUMA {
+		params = append(params, "--numa")
 	}

-	params = append(params, "--parallel", strconv.Itoa(numParallel))
+	params = append(params, "--parallel", fmt.Sprintf("%d", numParallel))

 	if estimate.TensorSplit != "" {
 		params = append(params, "--tensor-split", estimate.TensorSplit)
@@ -306,18 +298,20 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 		if runtime.GOOS == "windows" {
 			pathEnv = "PATH"
 		}
-		// Start with the server directory for the LD_LIBRARY_PATH/PATH
-		libraryPaths := []string{dir}
+		// prepend the server directory to LD_LIBRARY_PATH/PATH and the parent dir for common dependencies
+		libraryPaths := []string{dir, filepath.Dir(dir)}

 		if libraryPath, ok := os.LookupEnv(pathEnv); ok {
-			// favor our bundled library dependencies over system libraries
+			// Append our runner directory to the path
+			// This will favor system libraries over our bundled library dependencies
 			libraryPaths = append(libraryPaths, filepath.SplitList(libraryPath)...)
 		}

 		// Note: we always put the dependency path first
-		// since this was the exact version we compiled/linked against
+		// since this was the exact version we verified for AMD GPUs
+		// and we favor what the user had in their path
 		if gpus[0].DependencyPath != "" {
-			// assume gpus from the same library have the same dependency path
+			// TODO refine for multi-gpu support
 			libraryPaths = append([]string{gpus[0].DependencyPath}, libraryPaths...)
 		}

@@ -343,7 +337,6 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 			status:      NewStatusWriter(os.Stderr),
 			options:     opts,
 			estimate:    estimate,
-			numParallel: numParallel,
 			sem:         semaphore.NewWeighted(int64(numParallel)),
 			totalLayers: ggml.KV().BlockCount() + 1,
 			gpus:        gpus,
@@ -432,7 +425,7 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 				if strings.Contains(s.status.LastErrMsg, "unknown model") {
 					s.status.LastErrMsg = "this model is not supported by your version of Ollama. You may need to upgrade"
 				}
-				s.done <- errors.New(s.status.LastErrMsg)
+				s.done <- fmt.Errorf(s.status.LastErrMsg)
 			} else {
 				s.done <- err
 			}
@@ -881,15 +874,16 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu
 	return nil
 }

-type EmbeddingRequest struct {
-	Content string `json:"content"`
+type EmbedRequest struct {
+	Content []string `json:"content"`
 }

-type EmbeddingResponse struct {
-	Embedding []float32 `json:"embedding"`
+type EmbedResponse struct {
+	Embedding       [][]float32 `json:"embedding"`
+	PromptEvalCount int         `json:"prompt_n"`
 }

-func (s *llmServer) Embedding(ctx context.Context, input string) ([]float32, error) {
+func (s *llmServer) Embed(ctx context.Context, input []string) (*EmbedResponse, error) {
 	if err := s.sem.Acquire(ctx, 1); err != nil {
 		slog.Error("Failed to acquire semaphore", "error", err)
 		return nil, err
@@ -904,18 +898,18 @@ func (s *llmServer) Embedding(ctx context.Context, input string) ([]float32, err
 		return nil, fmt.Errorf("unexpected server status: %s", status.ToString())
 	}

-	data, err := json.Marshal(EmbeddingRequest{Content: input})
+	data, err := json.Marshal(EmbedRequest{Content: input})
 	if err != nil {
 		return nil, fmt.Errorf("error marshaling embed data: %w", err)
 	}

-	r, err := http.NewRequestWithContext(ctx, http.MethodPost, fmt.Sprintf("http://127.0.0.1:%d/embedding", s.port), bytes.NewBuffer(data))
+	req, err := http.NewRequestWithContext(ctx, http.MethodPost, fmt.Sprintf("http://127.0.0.1:%d/embedding", s.port), bytes.NewBuffer(data))
 	if err != nil {
 		return nil, fmt.Errorf("error creating embed request: %w", err)
 	}
-	r.Header.Set("Content-Type", "application/json")
+	req.Header.Set("Content-Type", "application/json")

-	resp, err := http.DefaultClient.Do(r)
+	resp, err := http.DefaultClient.Do(req)
 	if err != nil {
 		return nil, fmt.Errorf("do embedding request: %w", err)
 	}
@@ -931,12 +925,12 @@ func (s *llmServer) Embedding(ctx context.Context, input string) ([]float32, err
 		return nil, fmt.Errorf("%s", body)
 	}

-	var e EmbeddingResponse
+	var e EmbedResponse
 	if err := json.Unmarshal(body, &e); err != nil {
 		return nil, fmt.Errorf("unmarshal tokenize response: %w", err)
 	}

-	return e.Embedding, nil
+	return &e, nil
 }

 type TokenizeRequest struct {
--- a/llm/status.go
+++ b/llm/status.go
@@ -26,7 +26,6 @@ var errorPrefixes = []string{
 	"cudaMalloc failed",
 	"\"ERR\"",
 	"error loading model",
-	"GGML_ASSERT",
 }

 func (w *StatusWriter) Write(b []byte) (int, error) {
--- a/main.go
+++ b/main.go
@@ -3,9 +3,8 @@ package main
 import (
 	"context"

-	"github.com/spf13/cobra"
-
 	"github.com/ollama/ollama/cmd"
+	"github.com/spf13/cobra"
 )

 func main() {
--- a/Show More
+++ b/Show More