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5 Commits

Author SHA1 Message Date
Patrick Devine
c13fb10e19 fix template for cohere2 arch conversion 2025-01-17 21:53:58 -08:00
Patrick Devine
5a3950a2a1 update the cohere2 template 2025-01-17 21:53:58 -08:00
Patrick Devine
2591979d3b gofumpt the linter 2025-01-17 21:53:58 -08:00
Patrick Devine
7571d402fb feed linter 2025-01-17 21:53:57 -08:00
Patrick Devine
453d65a8ab add cohere2 models 2025-01-17 21:53:57 -08:00
883 changed files with 76957 additions and 586298 deletions

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

13
.gitattributes vendored
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@ -7,18 +7,5 @@ llama/**/*.cuh linguist-vendored
llama/**/*.m linguist-vendored
llama/**/*.metal linguist-vendored
ml/backend/**/*.c linguist-vendored
ml/backend/**/*.h linguist-vendored
ml/backend/**/*.cpp linguist-vendored
ml/backend/**/*.hpp linguist-vendored
ml/backend/**/*.cu linguist-vendored
ml/backend/**/*.cuh linguist-vendored
ml/backend/**/*.m linguist-vendored
ml/backend/**/*.metal linguist-vendored
ml/backend/**/CMakeLists.txt linguist-vendored
llama/build-info.cpp linguist-generated
ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-embed.s linguist-generated
* text=auto
*.go text eol=lf

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@ -9,14 +9,6 @@ body:
description: What happened? What did you expect to happen?
validations:
required: true
- type: textarea
id: logs
attributes:
label: Relevant log output
description: Please copy and paste any relevant log output. See [Troubleshooting Guide](https://github.com/ollama/ollama/blob/main/docs/troubleshooting.md#how-to-troubleshoot-issues) for details.
render: shell
validations:
required: false
- type: dropdown
id: os
attributes:

File diff suppressed because it is too large Load Diff

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@ -1,5 +1,11 @@
name: test
env:
ROCM_WINDOWS_URL: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe
MSYS2_URL: https://github.com/msys2/msys2-installer/releases/download/2024-07-27/msys2-x86_64-20240727.exe
CUDA_12_WINDOWS_URL: https://developer.download.nvidia.com/compute/cuda/12.4.0/local_installers/cuda_12.4.0_551.61_windows.exe
CUDA_12_WINDOWS_VER: 12.4
concurrency:
# For PRs, later CI runs preempt previous ones. e.g. a force push on a PR
# cancels running CI jobs and starts all new ones.
@ -21,7 +27,7 @@ jobs:
changes:
runs-on: ubuntu-latest
outputs:
changed: ${{ steps.changes.outputs.changed }}
RUNNERS: ${{ steps.changes.outputs.RUNNERS }}
steps:
- uses: actions/checkout@v4
with:
@ -29,213 +35,309 @@ jobs:
- id: changes
run: |
changed() {
local BASE=${{ github.event.pull_request.base.sha }}
local HEAD=${{ github.event.pull_request.head.sha }}
local MERGE_BASE=$(git merge-base $BASE $HEAD)
git diff-tree -r --no-commit-id --name-only "$MERGE_BASE" "$HEAD" \
git diff-tree -r --no-commit-id --name-only \
$(git merge-base ${{ github.event.pull_request.base.sha }} ${{ github.event.pull_request.head.sha }}) \
${{ github.event.pull_request.head.sha }} \
| xargs python3 -c "import sys; from pathlib import Path; print(any(Path(x).match(glob) for x in sys.argv[1:] for glob in '$*'.split(' ')))"
}
echo changed=$(changed 'llama/llama.cpp/**' 'ml/backend/ggml/ggml/**') | tee -a $GITHUB_OUTPUT
{
echo RUNNERS=$(changed 'llama/**')
} >>$GITHUB_OUTPUT
linux:
runners-linux-cuda:
needs: [changes]
if: needs.changes.outputs.changed == 'True'
if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
strategy:
matrix:
include:
- preset: CPU
- preset: CUDA
container: nvidia/cuda:11.8.0-devel-ubuntu22.04
flags: '-DCMAKE_CUDA_ARCHITECTURES=87'
- preset: ROCm
container: rocm/dev-ubuntu-22.04:6.1.2
extra-packages: rocm-libs
flags: '-DAMDGPU_TARGETS=gfx1010 -DCMAKE_PREFIX_PATH=/opt/rocm'
cuda-version:
- '11.8.0'
runs-on: linux
container: ${{ matrix.container }}
container: nvidia/cuda:${{ matrix.cuda-version }}-devel-ubuntu20.04
steps:
- uses: actions/checkout@v4
- run: |
[ -n "${{ matrix.container }}" ] || sudo=sudo
$sudo apt-get update
$sudo apt-get install -y cmake ccache ${{ matrix.extra-packages }}
apt-get update && apt-get install -y git build-essential curl
env:
DEBIAN_FRONTEND: noninteractive
- uses: actions/cache@v4
- uses: actions/checkout@v4
- uses: actions/setup-go@v4
with:
path: /github/home/.cache/ccache
key: ccache-${{ runner.os }}-${{ runner.arch }}-${{ matrix.preset }}
go-version-file: go.mod
cache: true
- run: go get ./...
- run: |
cmake --preset ${{ matrix.preset }} ${{ matrix.flags }}
cmake --build --preset ${{ matrix.preset }} --parallel
windows:
git config --global --add safe.directory /__w/ollama/ollama
cores=$(grep '^core id' /proc/cpuinfo |sort -u|wc -l)
make -j $cores cuda_v11
runners-linux-rocm:
needs: [changes]
if: needs.changes.outputs.changed == 'True'
if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
strategy:
matrix:
include:
- preset: CPU
- preset: CUDA
install: https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda_11.3.1_465.89_win10.exe
flags: '-DCMAKE_CUDA_ARCHITECTURES=80'
- preset: ROCm
install: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q4-WinSvr2022-For-HIP.exe
flags: '-DAMDGPU_TARGETS=gfx1010'
rocm-version:
- '6.1.2'
runs-on: linux
container: rocm/dev-ubuntu-20.04:${{ matrix.rocm-version }}
steps:
- run: |
apt-get update && apt-get install -y git build-essential curl rocm-libs
env:
DEBIAN_FRONTEND: noninteractive
- uses: actions/checkout@v4
- uses: actions/setup-go@v4
with:
go-version-file: go.mod
cache: true
- run: go get ./...
- run: |
git config --global --add safe.directory /__w/ollama/ollama
cores=$(grep '^core id' /proc/cpuinfo |sort -u|wc -l)
make -j $cores rocm
# ROCm generation step
runners-windows-rocm:
needs: [changes]
if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
runs-on: windows
steps:
- run: |
choco install -y --no-progress ccache ninja
ccache -o cache_dir=${{ github.workspace }}\.ccache
- if: matrix.preset == 'CUDA' || matrix.preset == 'ROCm'
id: cache-install
uses: actions/cache/restore@v4
with:
path: |
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA
C:\Program Files\AMD\ROCm
key: ${{ matrix.install }}
- if: matrix.preset == 'CUDA'
name: Install CUDA ${{ matrix.cuda-version }}
run: |
$ErrorActionPreference = "Stop"
if ("${{ steps.cache-install.outputs.cache-hit }}" -ne 'true') {
Invoke-WebRequest -Uri "${{ matrix.install }}" -OutFile "install.exe"
Start-Process -FilePath .\install.exe -ArgumentList (@("-s", "cudart_11.3", "nvcc_11.3", "cublas_11.3", "cublas_dev_11.3")) -NoNewWindow -Wait
}
$cudaPath = (Resolve-Path "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\*").path
echo "$cudaPath\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
- if: matrix.preset == 'ROCm'
name: Install ROCm ${{ matrix.rocm-version }}
run: |
$ErrorActionPreference = "Stop"
if ("${{ steps.cache-install.outputs.cache-hit }}" -ne 'true') {
Invoke-WebRequest -Uri "${{ matrix.install }}" -OutFile "install.exe"
Start-Process -FilePath .\install.exe -ArgumentList '-install' -NoNewWindow -Wait
}
$hipPath = (Resolve-Path "C:\Program Files\AMD\ROCm\*").path
echo "$hipPath\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
echo "CC=$hipPath\bin\clang.exe" | Out-File -FilePath $env:GITHUB_ENV -Append
echo "CXX=$hipPath\bin\clang++.exe" | Out-File -FilePath $env:GITHUB_ENV -Append
- if: ${{ !cancelled() && steps.cache-install.outputs.cache-hit != 'true' }}
uses: actions/cache/save@v4
with:
path: |
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA
C:\Program Files\AMD\ROCm
key: ${{ matrix.install }}
- uses: actions/checkout@v4
- uses: actions/cache@v4
- uses: actions/setup-go@v5
with:
path: ${{ github.workspace }}\.ccache
key: ccache-${{ runner.os }}-${{ runner.arch }}-${{ matrix.preset }}
- run: |
Import-Module 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
Enter-VsDevShell -VsInstallPath 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise' -SkipAutomaticLocation -DevCmdArguments '-arch=x64 -no_logo'
cmake --preset "${{ matrix.preset }}" ${{ matrix.flags }}
cmake --build --parallel --preset "${{ matrix.preset }}"
env:
CMAKE_GENERATOR: Ninja
go-version-file: go.mod
cache: true
- name: Set make jobs default
run: |
echo "MAKEFLAGS=--jobs=$((Get-ComputerInfo -Property CsProcessors).CsProcessors.NumberOfCores)" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
go_mod_tidy:
runs-on: ubuntu-latest
# ROCM installation steps
- name: 'Cache ROCm installer'
id: cache-rocm
uses: actions/cache@v4
with:
path: rocm-install.exe
key: ${{ env.ROCM_WINDOWS_URL }}
- name: 'Conditionally Download ROCm'
if: steps.cache-rocm.outputs.cache-hit != 'true'
run: |
$ErrorActionPreference = "Stop"
Invoke-WebRequest -Uri "${env:ROCM_WINDOWS_URL}" -OutFile "rocm-install.exe"
- name: 'Install ROCm'
run: |
Start-Process "rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
- name: 'Verify ROCm'
run: |
& 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version
echo "HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path | select -first 1)" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
- name: Add msys paths
run: |
echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
- name: Install msys2 tools
run: |
Start-Process "c:\msys64\usr\bin\pacman.exe" -ArgumentList @("-S", "--noconfirm", "mingw-w64-clang-x86_64-gcc-compat", "mingw-w64-clang-x86_64-clang") -NoNewWindow -Wait
- name: make rocm runner
run: |
import-module 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
Enter-VsDevShell -vsinstallpath 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise' -skipautomaticlocation -DevCmdArguments '-arch=x64 -no_logo'
if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
make -C llama print-HIP_PATH print-HIP_LIB_DIR
make rocm
# CUDA generation step
runners-windows-cuda:
needs: [changes]
if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
runs-on: windows
steps:
- uses: actions/checkout@v4
- name: check that 'go mod tidy' is clean
run: go mod tidy --diff || (echo "Please run 'go mod tidy'." && exit 1)
- uses: actions/setup-go@v5
with:
go-version-file: go.mod
cache: true
- name: Set make jobs default
run: |
echo "MAKEFLAGS=--jobs=$((Get-ComputerInfo -Property CsProcessors).CsProcessors.NumberOfCores)" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
test:
# CUDA installation steps
- name: 'Cache CUDA installer'
id: cache-cuda
uses: actions/cache@v4
with:
path: cuda-install.exe
key: ${{ env.CUDA_12_WINDOWS_URL }}
- name: 'Conditionally Download CUDA'
if: steps.cache-cuda.outputs.cache-hit != 'true'
run: |
$ErrorActionPreference = "Stop"
Invoke-WebRequest -Uri "${env:CUDA_12_WINDOWS_URL}" -OutFile "cuda-install.exe"
- name: 'Install CUDA'
run: |
$subpackages = @("cudart", "nvcc", "cublas", "cublas_dev") | foreach-object {"${_}_${{ env.CUDA_12_WINDOWS_VER }}"}
Start-Process "cuda-install.exe" -ArgumentList (@("-s") + $subpackages) -NoNewWindow -Wait
- name: 'Verify CUDA'
run: |
& (resolve-path "c:\Program Files\NVIDIA*\CUDA\v*\bin\nvcc.exe")[0] --version
$cudaPath=((resolve-path "c:\Program Files\NVIDIA*\CUDA\v*\bin\nvcc.exe")[0].path | split-path | split-path)
$cudaVer=($cudaPath | split-path -leaf ) -replace 'v(\d+).(\d+)', '$1_$2'
echo "$cudaPath\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
echo "CUDA_PATH=$cudaPath" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
echo "CUDA_PATH_V${cudaVer}=$cudaPath" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
echo "CUDA_PATH_VX_Y=CUDA_PATH_V${cudaVer}" | Out-File -FilePath $env:GITHUB_ENV -Encoding utf8 -Append
- name: Add msys paths
run: |
echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
- name: Install msys2 tools
run: |
Start-Process "c:\msys64\usr\bin\pacman.exe" -ArgumentList @("-S", "--noconfirm", "mingw-w64-clang-x86_64-gcc-compat", "mingw-w64-clang-x86_64-clang") -NoNewWindow -Wait
- name: make cuda runner
run: |
import-module 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
Enter-VsDevShell -vsinstallpath 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise' -skipautomaticlocation -DevCmdArguments '-arch=x64 -no_logo'
if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
make cuda_v$(($env:CUDA_PATH | split-path -leaf) -replace 'v(\d+).*', '$1')
runners-cpu:
needs: [changes]
if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
strategy:
matrix:
os: [ubuntu-latest, macos-latest, windows-latest]
os: [ubuntu-latest, macos-latest, windows-2019]
arch: [amd64, arm64]
exclude:
- os: ubuntu-latest
arch: arm64
- os: windows-2019
arch: arm64
runs-on: ${{ matrix.os }}
env:
GOARCH: ${{ matrix.arch }}
ARCH: ${{ matrix.arch }}
CGO_ENABLED: '1'
GOEXPERIMENT: 'synctest'
steps:
- name: checkout
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # 4.2.2
- name: cache restore
uses: actions/cache/restore@1bd1e32a3bdc45362d1e726936510720a7c30a57 # v4.2.0
- uses: actions/checkout@v4
- uses: actions/setup-go@v5
with:
# Note: unlike the other setups, this is only grabbing the mod download
# cache, rather than the whole mod directory, as the download cache
# contains zips that can be unpacked in parallel faster than they can be
# fetched and extracted by tar
path: |
~/.cache/go-build
~/go/pkg/mod/cache
~\AppData\Local\go-build
# NOTE: The -3- here should be incremented when the scheme of data to be
# cached changes (e.g. path above changes).
key: ${{ github.job }}-${{ runner.os }}-${{ matrix.goarch }}-${{ matrix.buildflags }}-go-3-${{ hashFiles('**/go.sum') }}-${{ github.run_id }}
restore-keys: |
${{ github.job }}-${{ runner.os }}-${{ matrix.goarch }}-${{ matrix.buildflags }}-go-3-${{ hashFiles('**/go.sum') }}
${{ github.job }}-${{ runner.os }}-${{ matrix.goarch }}-${{ matrix.buildflags }}-go-3-
- name: Setup Go
uses: actions/setup-go@v5
with:
# The caching strategy of setup-go is less than ideal, and wastes
# time by not saving artifacts due to small failures like the linter
# complaining, etc. This means subsequent have to rebuild their world
# again until all checks pass. For instance, if you mispell a word,
# you're punished until you fix it. This is more hostile than
# helpful.
cache: false
go-version-file: go.mod
# It is tempting to run this in a platform independent way, but the past
# shows this codebase will see introductions of platform specific code
# generation, and so we need to check this per platform to ensure we
# don't abuse go generate on specific platforms.
- name: check that 'go generate' is clean
if: always()
cache: true
- name: Add msys paths
if: ${{ startsWith(matrix.os, 'windows-') }}
run: |
go generate ./...
git diff --name-only --exit-code || (echo "Please run 'go generate ./...'." && exit 1)
echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
- name: Install msys2 tools
if: ${{ startsWith(matrix.os, 'windows-') }}
run: |
Start-Process "c:\msys64\usr\bin\pacman.exe" -ArgumentList @("-S", "--noconfirm", "mingw-w64-clang-x86_64-gcc-compat", "mingw-w64-clang-x86_64-clang") -NoNewWindow -Wait
- name: 'Build Windows Go Runners'
if: ${{ startsWith(matrix.os, 'windows-') }}
run: |
$gopath=(get-command go).source | split-path -parent
$gccpath=(get-command gcc).source | split-path -parent
import-module 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
Enter-VsDevShell -vsinstallpath 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise' -skipautomaticlocation -DevCmdArguments '-arch=x64 -no_logo'
$env:CMAKE_SYSTEM_VERSION="10.0.22621.0"
$env:PATH="$gopath;$gccpath;$env:PATH"
echo $env:PATH
if (!(gcc --version | select-string -quiet clang)) { throw "wrong gcc compiler detected - must be clang" }
make -j 4
- name: 'Build Unix Go Runners'
if: ${{ ! startsWith(matrix.os, 'windows-') }}
run: make -j 4
- run: go build .
- name: go test
if: always()
run: go test -count=1 -benchtime=1x ./...
# TODO(bmizerany): replace this heavy tool with just the
# tools/checks/binaries we want and then make them all run in parallel
# across jobs, not on a single tiny vm on Github Actions.
lint:
strategy:
matrix:
os: [ubuntu-latest, macos-latest, windows-2019]
arch: [amd64, arm64]
exclude:
- os: ubuntu-latest
arch: arm64
- os: windows-2019
arch: arm64
- os: macos-latest
arch: amd64
runs-on: ${{ matrix.os }}
env:
GOARCH: ${{ matrix.arch }}
CGO_ENABLED: '1'
steps:
- uses: actions/checkout@v4
with:
submodules: recursive
- name: Add msys paths
if: ${{ startsWith(matrix.os, 'windows-') }}
run: |
echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
- name: Install msys2 tools
if: ${{ startsWith(matrix.os, 'windows-') }}
run: |
Start-Process "c:\msys64\usr\bin\pacman.exe" -ArgumentList @("-S", "--noconfirm", "mingw-w64-clang-x86_64-gcc-compat", "mingw-w64-clang-x86_64-clang") -NoNewWindow -Wait
- uses: actions/setup-go@v5
with:
go-version-file: go.mod
cache: false
- run: |
case ${{ matrix.arch }} in
amd64) echo ARCH=x86_64 ;;
arm64) echo ARCH=arm64 ;;
esac >>$GITHUB_ENV
shell: bash
- uses: golangci/golangci-lint-action@v6
with:
args: --timeout 10m0s -v
- name: cache save
# Always save the cache, even if the job fails. The artifacts produced
# during the building of test binaries are not all for naught. They can
# be used to speed up subsequent runs.
if: always()
uses: actions/cache/save@1bd1e32a3bdc45362d1e726936510720a7c30a57 # v4.2.0
test:
strategy:
matrix:
os: [ubuntu-latest, macos-latest, windows-2019]
arch: [amd64]
exclude:
- os: ubuntu-latest
arch: arm64
- os: windows-2019
arch: arm64
runs-on: ${{ matrix.os }}
env:
GOARCH: ${{ matrix.arch }}
CGO_ENABLED: '1'
steps:
- uses: actions/checkout@v4
with:
# Note: unlike the other setups, this is only grabbing the mod download
# cache, rather than the whole mod directory, as the download cache
# contains zips that can be unpacked in parallel faster than they can be
# fetched and extracted by tar
path: |
~/.cache/go-build
~/go/pkg/mod/cache
~\AppData\Local\go-build
# NOTE: The -3- here should be incremented when the scheme of data to be
# cached changes (e.g. path above changes).
key: ${{ github.job }}-${{ runner.os }}-${{ matrix.goarch }}-${{ matrix.buildflags }}-go-3-${{ hashFiles('**/go.sum') }}-${{ github.run_id }}
submodules: recursive
- name: Add msys paths
if: ${{ startsWith(matrix.os, 'windows-') }}
run: |
echo "c:\msys64\usr\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
echo "C:\msys64\clang64\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
- name: Install msys2 tools
if: ${{ startsWith(matrix.os, 'windows-') }}
run: |
Start-Process "c:\msys64\usr\bin\pacman.exe" -ArgumentList @("-S", "--noconfirm", "mingw-w64-clang-x86_64-gcc-compat", "mingw-w64-clang-x86_64-clang") -NoNewWindow -Wait
- uses: actions/setup-go@v5
with:
go-version-file: go.mod
cache: true
- run: |
case ${{ matrix.arch }} in
amd64) echo ARCH=amd64 ;;
arm64) echo ARCH=arm64 ;;
esac >>$GITHUB_ENV
shell: bash
- run: go test ./...
patches:
needs: [changes]
if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Verify patches apply cleanly and do not change files
with:
submodules: recursive
- name: Verify patches carry all the changes
run: |
make -f Makefile.sync clean checkout apply-patches sync
git diff --compact-summary --exit-code
make apply-patches sync && git diff --compact-summary --exit-code llama

7
.gitignore vendored
View File

@ -4,13 +4,12 @@
.venv
.swp
dist
build
ollama
.cache
*.exe
.idea
test_data
*.crt
__debug_bin*
llama/build
llama/vendor
/ollama
__debug_bin*
llama/vendor

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@ -6,6 +6,8 @@ linters:
- bidichk
- bodyclose
- containedctx
- contextcheck
- errcheck
- gocheckcompilerdirectives
- gofmt
- gofumpt
@ -19,13 +21,12 @@ linters:
- nolintlint
- nosprintfhostport
- staticcheck
- tenv
- unconvert
- usetesting
- unused
- usestdlibvars
- wastedassign
- whitespace
disable:
- usestdlibvars
- errcheck
linters-settings:
staticcheck:
checks:
@ -38,4 +39,5 @@ severity:
- gofmt
- goimports
- intrange
- usestdlibvars
severity: info

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@ -1,133 +0,0 @@
cmake_minimum_required(VERSION 3.21)
project(Ollama C CXX)
include(CheckLanguage)
find_package(Threads REQUIRED)
set(CMAKE_BUILD_TYPE Release)
set(BUILD_SHARED_LIBS ON)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
set(CMAKE_CXX_EXTENSIONS OFF)
set(GGML_BUILD ON)
set(GGML_SHARED ON)
set(GGML_CCACHE ON)
set(GGML_BACKEND_DL ON)
set(GGML_BACKEND_SHARED ON)
set(GGML_SCHED_MAX_COPIES 4)
set(GGML_LLAMAFILE ON)
set(GGML_CUDA_PEER_MAX_BATCH_SIZE 128)
set(GGML_CUDA_GRAPHS ON)
set(GGML_CUDA_FA ON)
set(GGML_CUDA_COMPRESSION_MODE default)
if((CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_OSX_ARCHITECTURES MATCHES "arm64")
OR (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_SYSTEM_PROCESSOR MATCHES "arm|aarch64|ARM64|ARMv[0-9]+"))
set(GGML_CPU_ALL_VARIANTS ON)
endif()
if (CMAKE_OSX_ARCHITECTURES MATCHES "x86_64")
set(CMAKE_BUILD_RPATH "@loader_path")
set(CMAKE_INSTALL_RPATH "@loader_path")
endif()
set(OLLAMA_BUILD_DIR ${CMAKE_BINARY_DIR}/lib/ollama)
set(OLLAMA_INSTALL_DIR ${CMAKE_INSTALL_PREFIX}/lib/ollama)
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${OLLAMA_BUILD_DIR})
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY_DEBUG ${OLLAMA_BUILD_DIR})
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY_RELEASE ${OLLAMA_BUILD_DIR})
set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${OLLAMA_BUILD_DIR})
set(CMAKE_LIBRARY_OUTPUT_DIRECTORY_DEBUG ${OLLAMA_BUILD_DIR})
set(CMAKE_LIBRARY_OUTPUT_DIRECTORY_RELEASE ${OLLAMA_BUILD_DIR})
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src)
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/include)
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-cpu)
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-cpu/amx)
set(GGML_CPU ON)
add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src)
set_property(TARGET ggml PROPERTY EXCLUDE_FROM_ALL TRUE)
get_target_property(CPU_VARIANTS ggml-cpu MANUALLY_ADDED_DEPENDENCIES)
if(NOT CPU_VARIANTS)
set(CPU_VARIANTS "ggml-cpu")
endif()
install(TARGETS ggml-base ${CPU_VARIANTS}
RUNTIME_DEPENDENCIES
PRE_EXCLUDE_REGEXES ".*"
RUNTIME DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT CPU
LIBRARY DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT CPU
FRAMEWORK DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT CPU
)
check_language(CUDA)
if(CMAKE_CUDA_COMPILER)
if(CMAKE_VERSION VERSION_GREATER_EQUAL "3.24" AND NOT CMAKE_CUDA_ARCHITECTURES)
set(CMAKE_CUDA_ARCHITECTURES "native")
endif()
find_package(CUDAToolkit)
add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-cuda)
set(OLLAMA_CUDA_INSTALL_DIR ${OLLAMA_INSTALL_DIR}/cuda_v${CUDAToolkit_VERSION_MAJOR})
install(TARGETS ggml-cuda
RUNTIME_DEPENDENCIES
DIRECTORIES ${CUDAToolkit_BIN_DIR} ${CUDAToolkit_LIBRARY_DIR}
PRE_INCLUDE_REGEXES cublas cublasLt cudart
PRE_EXCLUDE_REGEXES ".*"
RUNTIME DESTINATION ${OLLAMA_CUDA_INSTALL_DIR} COMPONENT CUDA
LIBRARY DESTINATION ${OLLAMA_CUDA_INSTALL_DIR} COMPONENT CUDA
)
endif()
set(WINDOWS_AMDGPU_TARGETS_EXCLUDE_REGEX "^gfx(906|908|90a|1200|1201):xnack[+-]$"
CACHE STRING
"Regular expression describing AMDGPU_TARGETS not supported on Windows. Override to force building these targets. Default \"^gfx(906|908|90a|1200|1201):xnack[+-]$\"."
)
check_language(HIP)
if(CMAKE_HIP_COMPILER)
set(HIP_PLATFORM "amd")
find_package(hip REQUIRED)
if(NOT AMDGPU_TARGETS)
list(FILTER AMDGPU_TARGETS INCLUDE REGEX "^gfx(900|94[012]|101[02]|1030|110[012]|120[01])$")
elseif(WIN32 AND WINDOWS_AMDGPU_TARGETS_EXCLUDE_REGEX)
list(FILTER AMDGPU_TARGETS EXCLUDE REGEX ${WINDOWS_AMDGPU_TARGETS_EXCLUDE_REGEX})
endif()
if(AMDGPU_TARGETS)
add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-hip)
if (WIN32)
target_compile_definitions(ggml-hip PRIVATE GGML_CUDA_NO_PEER_COPY)
endif()
target_compile_definitions(ggml-hip PRIVATE GGML_HIP_NO_VMM)
set(OLLAMA_HIP_INSTALL_DIR ${OLLAMA_INSTALL_DIR}/rocm)
install(TARGETS ggml-hip
RUNTIME_DEPENDENCIES
DIRECTORIES ${HIP_BIN_INSTALL_DIR} ${HIP_LIB_INSTALL_DIR}
PRE_INCLUDE_REGEXES hipblas rocblas amdhip64 rocsolver amd_comgr hsa-runtime64 rocsparse tinfo rocprofiler-register drm drm_amdgpu numa elf
PRE_EXCLUDE_REGEXES ".*"
POST_EXCLUDE_REGEXES "system32"
RUNTIME DESTINATION ${OLLAMA_HIP_INSTALL_DIR} COMPONENT HIP
LIBRARY DESTINATION ${OLLAMA_HIP_INSTALL_DIR} COMPONENT HIP
)
foreach(HIP_LIB_BIN_INSTALL_DIR IN ITEMS ${HIP_BIN_INSTALL_DIR} ${HIP_LIB_INSTALL_DIR})
if(EXISTS ${HIP_LIB_BIN_INSTALL_DIR}/rocblas)
install(DIRECTORY ${HIP_LIB_BIN_INSTALL_DIR}/rocblas DESTINATION ${OLLAMA_HIP_INSTALL_DIR} COMPONENT HIP)
break()
endif()
endforeach()
endif()
endif()

View File

@ -1,112 +0,0 @@
{
"version": 3,
"configurePresets": [
{
"name": "Default",
"binaryDir": "${sourceDir}/build",
"installDir": "${sourceDir}/dist",
"cacheVariables": {
"CMAKE_BUILD_TYPE": "Release"
}
},
{
"name": "CPU",
"inherits": [ "Default" ]
},
{
"name": "CUDA",
"inherits": [ "Default" ]
},
{
"name": "CUDA 11",
"inherits": [ "CUDA" ],
"cacheVariables": {
"CMAKE_CUDA_ARCHITECTURES": "50;52;53;60;61;70;75;80;86",
"CMAKE_CUDA_FLAGS": "-Wno-deprecated-gpu-targets"
}
},
{
"name": "CUDA 12",
"inherits": [ "CUDA" ],
"cacheVariables": {
"CMAKE_CUDA_ARCHITECTURES": "50;60;61;70;75;80;86;87;89;90;90a;120",
"CMAKE_CUDA_FLAGS": "-Wno-deprecated-gpu-targets"
}
},
{
"name": "JetPack 5",
"inherits": [ "CUDA" ],
"cacheVariables": {
"CMAKE_CUDA_ARCHITECTURES": "72;87"
}
},
{
"name": "JetPack 6",
"inherits": [ "CUDA" ],
"cacheVariables": {
"CMAKE_CUDA_ARCHITECTURES": "87"
}
},
{
"name": "ROCm",
"inherits": [ "Default" ],
"cacheVariables": {
"CMAKE_HIP_PLATFORM": "amd"
}
},
{
"name": "ROCm 6",
"inherits": [ "ROCm" ],
"cacheVariables": {
"AMDGPU_TARGETS": "gfx900;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102;gfx1151;gfx1200;gfx1201;gfx906:xnack-;gfx908:xnack-;gfx90a:xnack+;gfx90a:xnack-"
}
}
],
"buildPresets": [
{
"name": "Default",
"configurePreset": "Default",
"configuration": "Release"
},
{
"name": "CPU",
"configurePreset": "Default",
"targets": [ "ggml-cpu" ]
},
{
"name": "CUDA",
"configurePreset": "CUDA",
"targets": [ "ggml-cuda" ]
},
{
"name": "CUDA 11",
"inherits": [ "CUDA" ],
"configurePreset": "CUDA 11"
},
{
"name": "CUDA 12",
"inherits": [ "CUDA" ],
"configurePreset": "CUDA 12"
},
{
"name": "JetPack 5",
"inherits": [ "CUDA" ],
"configurePreset": "JetPack 5"
},
{
"name": "JetPack 6",
"inherits": [ "CUDA" ],
"configurePreset": "JetPack 6"
},
{
"name": "ROCm",
"configurePreset": "ROCm",
"targets": [ "ggml-hip" ]
},
{
"name": "ROCm 6",
"inherits": [ "ROCm" ],
"configurePreset": "ROCm 6"
}
]
}

View File

@ -6,6 +6,8 @@ Thank you for your interest in contributing to Ollama! Here are a few guidelines
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.
@ -24,64 +26,11 @@ See the [development documentation](./docs/development.md) for instructions on h
* Changes that add significant friction to the user experience
* Changes that create a large future maintenance burden for maintainers and contributors
## Proposing a (non-trivial) change
### Best practices
> By "non-trivial", we mean a change that is not a bug fix or small
> documentation update. If you are unsure, please ask us on our [Discord
> server](https://discord.gg/ollama).
Before opening a non-trivial Pull Request, please open an issue to discuss the change and
get feedback from the maintainers. This helps us understand the context of the
change and how it fits into Ollama's roadmap and prevents us from duplicating
work or you from spending time on a change that we may not be able to accept.
Tips for proposals:
* Explain the problem you are trying to solve, not what you are trying to do.
* Explain why the change is important.
* Explain how the change will be used.
* Explain how the change will be tested.
Additionally, for bonus points: Provide draft documentation you would expect to
see if the change were accepted.
## Pull requests
**Commit messages**
The title should look like:
<package>: <short description>
The package is the most affected Go package. If the change does not affect Go
code, then use the directory name instead. Changes to a single well-known
file in the root directory may use the file name.
The short description should start with a lowercase letter and be a
continuation of the sentence:
"This changes Ollama to..."
Examples:
llm/backend/mlx: support the llama architecture
CONTRIBUTING: provide clairity on good commit messages, and bad
Bad Examples:
feat: add more emoji
fix: was not using famous web framework
chore: generify code
**Tests**
Please include tests. Strive to test behavior, not implementation.
**New dependencies**
Dependencies should be added sparingly. If you are adding a new dependency,
please explain why it is necessary and what other ways you attempted that
did not work without it.
* 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?

View File

@ -1,131 +1,201 @@
# vim: filetype=dockerfile
ARG GOLANG_VERSION=1.22.8
ARG CUDA_VERSION_11=11.3.1
ARG CUDA_VERSION_12=12.4.0
ARG ROCM_VERSION=6.1.2
ARG JETPACK_6=r36.2.0
ARG JETPACK_5=r35.4.1
ARG FLAVOR=${TARGETARCH}
### To create a local image for building linux binaries on mac or windows with efficient incremental builds
#
# docker build --platform linux/amd64 -t builder-amd64 -f Dockerfile --target unified-builder-amd64 .
# docker run --platform linux/amd64 --rm -it -v $(pwd):/go/src/github.com/ollama/ollama/ builder-amd64
#
### Then incremental builds will be much faster in this container
#
# make -j 10 dist
#
FROM --platform=linux/amd64 rocm/dev-centos-7:${ROCM_VERSION}-complete AS unified-builder-amd64
ARG GOLANG_VERSION
ARG CUDA_VERSION_11
ARG CUDA_VERSION_12
COPY ./scripts/rh_linux_deps.sh /
ENV PATH /opt/rh/devtoolset-10/root/usr/bin:/usr/local/cuda/bin:$PATH
ENV LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64
RUN GOLANG_VERSION=${GOLANG_VERSION} sh /rh_linux_deps.sh
RUN yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-rhel7.repo && \
dnf clean all && \
dnf install -y \
zsh \
cuda-toolkit-$(echo ${CUDA_VERSION_11} | cut -f1-2 -d. | sed -e "s/\./-/g") \
cuda-toolkit-$(echo ${CUDA_VERSION_12} | cut -f1-2 -d. | sed -e "s/\./-/g")
# TODO intel oneapi goes here...
ENV GOARCH amd64
ENV CGO_ENABLED 1
WORKDIR /go/src/github.com/ollama/ollama/
ENTRYPOINT [ "zsh" ]
ARG ROCMVERSION=6.3.3
ARG JETPACK5VERSION=r35.4.1
ARG JETPACK6VERSION=r36.4.0
ARG CMAKEVERSION=3.31.2
### To create a local image for building linux binaries on mac or linux/arm64 with efficient incremental builds
# Note: this does not contain jetson variants
#
# docker build --platform linux/arm64 -t builder-arm64 -f Dockerfile --target unified-builder-arm64 .
# docker run --platform linux/arm64 --rm -it -v $(pwd):/go/src/github.com/ollama/ollama/ builder-arm64
#
FROM --platform=linux/arm64 rockylinux:8 AS unified-builder-arm64
ARG GOLANG_VERSION
ARG CUDA_VERSION_11
ARG CUDA_VERSION_12
COPY ./scripts/rh_linux_deps.sh /
RUN GOLANG_VERSION=${GOLANG_VERSION} sh /rh_linux_deps.sh
RUN yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/sbsa/cuda-rhel8.repo && \
dnf config-manager --set-enabled appstream && \
dnf clean all && \
dnf install -y \
zsh \
cuda-toolkit-$(echo ${CUDA_VERSION_11} | cut -f1-2 -d. | sed -e "s/\./-/g") \
cuda-toolkit-$(echo ${CUDA_VERSION_12} | cut -f1-2 -d. | sed -e "s/\./-/g")
ENV PATH /opt/rh/gcc-toolset-10/root/usr/bin:$PATH:/usr/local/cuda/bin
ENV LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64
ENV LIBRARY_PATH=/usr/local/cuda/lib64/stubs:/opt/amdgpu/lib64
ENV GOARCH arm64
ENV CGO_ENABLED 1
WORKDIR /go/src/github.com/ollama/ollama/
ENTRYPOINT [ "zsh" ]
# CUDA v11 requires gcc v10. v10.3 has regressions, so the rockylinux 8.5 AppStream has the latest compatible version
FROM --platform=linux/amd64 rocm/dev-almalinux-8:${ROCMVERSION}-complete AS base-amd64
RUN yum install -y yum-utils \
&& yum-config-manager --add-repo https://dl.rockylinux.org/vault/rocky/8.5/AppStream/\$basearch/os/ \
&& rpm --import https://dl.rockylinux.org/pub/rocky/RPM-GPG-KEY-Rocky-8 \
&& dnf install -y yum-utils ccache gcc-toolset-10-gcc-10.2.1-8.2.el8 gcc-toolset-10-gcc-c++-10.2.1-8.2.el8 gcc-toolset-10-binutils-2.35-11.el8 \
&& yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo
ENV PATH=/opt/rh/gcc-toolset-10/root/usr/bin:$PATH
FROM --platform=linux/arm64 almalinux:8 AS base-arm64
# install epel-release for ccache
RUN yum install -y yum-utils epel-release \
&& dnf install -y clang ccache \
&& yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/sbsa/cuda-rhel8.repo
ENV CC=clang CXX=clang++
FROM base-${TARGETARCH} AS base
ARG CMAKEVERSION
RUN curl -fsSL https://github.com/Kitware/CMake/releases/download/v${CMAKEVERSION}/cmake-${CMAKEVERSION}-linux-$(uname -m).tar.gz | tar xz -C /usr/local --strip-components 1
COPY CMakeLists.txt CMakePresets.json .
COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
ENV LDFLAGS=-s
FROM base AS cpu
RUN dnf install -y gcc-toolset-11-gcc gcc-toolset-11-gcc-c++
ENV PATH=/opt/rh/gcc-toolset-11/root/usr/bin:$PATH
RUN --mount=type=cache,target=/root/.ccache \
cmake --preset 'CPU' \
&& cmake --build --parallel --preset 'CPU' \
&& cmake --install build --component CPU --strip --parallel 8
FROM base AS cuda-11
ARG CUDA11VERSION=11.3
RUN dnf install -y cuda-toolkit-${CUDA11VERSION//./-}
ENV PATH=/usr/local/cuda-11/bin:$PATH
RUN --mount=type=cache,target=/root/.ccache \
cmake --preset 'CUDA 11' \
&& cmake --build --parallel --preset 'CUDA 11' \
&& cmake --install build --component CUDA --strip --parallel 8
FROM base AS cuda-12
ARG CUDA12VERSION=12.8
RUN dnf install -y cuda-toolkit-${CUDA12VERSION//./-}
ENV PATH=/usr/local/cuda-12/bin:$PATH
RUN --mount=type=cache,target=/root/.ccache \
cmake --preset 'CUDA 12' \
&& cmake --build --parallel --preset 'CUDA 12' \
&& cmake --install build --component CUDA --strip --parallel 8
FROM base AS rocm-6
ENV PATH=/opt/rocm/hcc/bin:/opt/rocm/hip/bin:/opt/rocm/bin:/opt/rocm/hcc/bin:$PATH
RUN --mount=type=cache,target=/root/.ccache \
cmake --preset 'ROCm 6' \
&& cmake --build --parallel --preset 'ROCm 6' \
&& cmake --install build --component HIP --strip --parallel 8
FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${JETPACK5VERSION} AS jetpack-5
ARG CMAKEVERSION
RUN apt-get update && apt-get install -y curl ccache \
&& curl -fsSL https://github.com/Kitware/CMake/releases/download/v${CMAKEVERSION}/cmake-${CMAKEVERSION}-linux-$(uname -m).tar.gz | tar xz -C /usr/local --strip-components 1
COPY CMakeLists.txt CMakePresets.json .
COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
RUN --mount=type=cache,target=/root/.ccache \
cmake --preset 'JetPack 5' \
&& cmake --build --parallel --preset 'JetPack 5' \
&& cmake --install build --component CUDA --strip --parallel 8
FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${JETPACK6VERSION} AS jetpack-6
ARG CMAKEVERSION
RUN apt-get update && apt-get install -y curl ccache \
&& curl -fsSL https://github.com/Kitware/CMake/releases/download/v${CMAKEVERSION}/cmake-${CMAKEVERSION}-linux-$(uname -m).tar.gz | tar xz -C /usr/local --strip-components 1
COPY CMakeLists.txt CMakePresets.json .
COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
RUN --mount=type=cache,target=/root/.ccache \
cmake --preset 'JetPack 6' \
&& cmake --build --parallel --preset 'JetPack 6' \
&& cmake --install build --component CUDA --strip --parallel 8
FROM base AS build
WORKDIR /go/src/github.com/ollama/ollama
COPY go.mod go.sum .
RUN curl -fsSL https://golang.org/dl/go$(awk '/^go/ { print $2 }' go.mod).linux-$(case $(uname -m) in x86_64) echo amd64 ;; aarch64) echo arm64 ;; esac).tar.gz | tar xz -C /usr/local
ENV PATH=/usr/local/go/bin:$PATH
RUN go mod download
FROM --platform=linux/amd64 unified-builder-amd64 AS build-amd64
COPY . .
ARG GOFLAGS="'-ldflags=-w -s'"
ENV CGO_ENABLED=1
RUN --mount=type=cache,target=/root/.cache/go-build \
go build -trimpath -buildmode=pie -o /bin/ollama .
ARG OLLAMA_SKIP_CUDA_GENERATE
ARG OLLAMA_SKIP_ROCM_GENERATE
ARG OLLAMA_FAST_BUILD
ARG VERSION
ARG CUSTOM_CPU_FLAGS
RUN --mount=type=cache,target=/root/.ccache \
if grep "^flags" /proc/cpuinfo|grep avx>/dev/null; then \
make -j $(nproc) dist ; \
else \
make -j 5 dist ; \
fi
RUN cd dist/linux-$GOARCH && \
tar -cf - . | pigz --best > ../ollama-linux-$GOARCH.tgz
RUN if [ -z ${OLLAMA_SKIP_ROCM_GENERATE} ] ; then \
cd dist/linux-$GOARCH-rocm && \
tar -cf - . | pigz --best > ../ollama-linux-$GOARCH-rocm.tgz ;\
fi
FROM --platform=linux/amd64 scratch AS amd64
COPY --from=cuda-11 dist/lib/ollama/cuda_v11 /lib/ollama/cuda_v11
COPY --from=cuda-12 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_v12
# Jetsons need to be built in discrete stages
FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${JETPACK_5} AS runners-jetpack5-arm64
ARG GOLANG_VERSION
RUN apt-get update && apt-get install -y git curl ccache && \
curl -s -L https://dl.google.com/go/go${GOLANG_VERSION}.linux-arm64.tar.gz | tar xz -C /usr/local && \
ln -s /usr/local/go/bin/go /usr/local/bin/go && \
ln -s /usr/local/go/bin/gofmt /usr/local/bin/gofmt && \
apt-get clean && rm -rf /var/lib/apt/lists/*
WORKDIR /go/src/github.com/ollama/ollama/
COPY . .
ARG CGO_CFLAGS
ENV GOARCH arm64
ARG VERSION
RUN --mount=type=cache,target=/root/.ccache \
make -j 5 dist_cuda_v11 \
CUDA_ARCHITECTURES="72;87" \
GPU_RUNNER_VARIANT=_jetpack5 \
DIST_LIB_DIR=/go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack5/lib/ollama \
DIST_GPU_RUNNER_DEPS_DIR=/go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack5/lib/ollama/cuda_jetpack5
FROM --platform=linux/arm64 scratch AS arm64
COPY --from=cuda-11 dist/lib/ollama/cuda_v11 /lib/ollama/cuda_v11
COPY --from=cuda-12 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_v12
COPY --from=jetpack-5 dist/lib/ollama/cuda_v11 /lib/ollama/cuda_jetpack5
COPY --from=jetpack-6 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_jetpack6
FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${JETPACK_6} AS runners-jetpack6-arm64
ARG GOLANG_VERSION
RUN apt-get update && apt-get install -y git curl ccache && \
curl -s -L https://dl.google.com/go/go${GOLANG_VERSION}.linux-arm64.tar.gz | tar xz -C /usr/local && \
ln -s /usr/local/go/bin/go /usr/local/bin/go && \
ln -s /usr/local/go/bin/gofmt /usr/local/bin/gofmt && \
apt-get clean && rm -rf /var/lib/apt/lists/*
WORKDIR /go/src/github.com/ollama/ollama/
COPY . .
ARG CGO_CFLAGS
ENV GOARCH arm64
ARG VERSION
RUN --mount=type=cache,target=/root/.ccache \
make -j 5 dist_cuda_v12 \
CUDA_ARCHITECTURES="87" \
GPU_RUNNER_VARIANT=_jetpack6 \
DIST_LIB_DIR=/go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack6/lib/ollama \
DIST_GPU_RUNNER_DEPS_DIR=/go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack6/lib/ollama/cuda_jetpack6
FROM scratch AS rocm
COPY --from=rocm-6 dist/lib/ollama/rocm /lib/ollama/rocm
FROM --platform=linux/arm64 unified-builder-arm64 AS build-arm64
COPY . .
ARG OLLAMA_SKIP_CUDA_GENERATE
ARG OLLAMA_FAST_BUILD
ARG VERSION
RUN --mount=type=cache,target=/root/.ccache \
make -j 5 dist
COPY --from=runners-jetpack5-arm64 /go/src/github.com/ollama/ollama/dist/ dist/
COPY --from=runners-jetpack6-arm64 /go/src/github.com/ollama/ollama/dist/ dist/
RUN cd dist/linux-$GOARCH && \
tar -cf - . | pigz --best > ../ollama-linux-$GOARCH.tgz
RUN cd dist/linux-$GOARCH-jetpack5 && \
tar -cf - . | pigz --best > ../ollama-linux-$GOARCH-jetpack5.tgz
RUN cd dist/linux-$GOARCH-jetpack6 && \
tar -cf - . | pigz --best > ../ollama-linux-$GOARCH-jetpack6.tgz
FROM ${FLAVOR} AS archive
COPY --from=cpu dist/lib/ollama /lib/ollama
COPY --from=build /bin/ollama /bin/ollama
FROM --platform=linux/amd64 scratch AS dist-amd64
COPY --from=build-amd64 /go/src/github.com/ollama/ollama/dist/ollama-linux-*.tgz /
FROM --platform=linux/arm64 scratch AS dist-arm64
COPY --from=build-arm64 /go/src/github.com/ollama/ollama/dist/ollama-linux-*.tgz /
FROM dist-$TARGETARCH AS dist
FROM ubuntu:20.04
RUN apt-get update \
&& apt-get install -y ca-certificates \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*
COPY --from=archive /bin /usr/bin
# For amd64 container images, filter out cuda/rocm to minimize size
FROM build-amd64 AS runners-cuda-amd64
RUN rm -rf \
./dist/linux-amd64/lib/ollama/libggml_hipblas.so \
./dist/linux-amd64/lib/ollama/runners/rocm*
FROM build-amd64 AS runners-rocm-amd64
RUN rm -rf \
./dist/linux-amd64/lib/ollama/libggml_cuda*.so \
./dist/linux-amd64/lib/ollama/libcu*.so* \
./dist/linux-amd64/lib/ollama/runners/cuda*
FROM --platform=linux/amd64 ubuntu:22.04 AS runtime-amd64
RUN apt-get update && \
apt-get install -y ca-certificates && \
apt-get clean && rm -rf /var/lib/apt/lists/*
COPY --from=build-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/bin/ /bin/
COPY --from=runners-cuda-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/lib/ /lib/
FROM --platform=linux/arm64 ubuntu:22.04 AS runtime-arm64
RUN apt-get update && \
apt-get install -y ca-certificates && \
apt-get clean && rm -rf /var/lib/apt/lists/*
COPY --from=build-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/bin/ /bin/
COPY --from=build-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/lib/ /lib/
COPY --from=runners-jetpack5-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack5/lib/ /lib/
COPY --from=runners-jetpack6-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64-jetpack6/lib/ /lib/
# ROCm libraries larger so we keep it distinct from the CPU/CUDA image
FROM --platform=linux/amd64 ubuntu:22.04 AS runtime-rocm
# Frontload the rocm libraries which are large, and rarely change to increase chance of a common layer
# across releases
COPY --from=build-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64-rocm/lib/ /lib/
RUN apt-get update && \
apt-get install -y ca-certificates && \
apt-get clean && rm -rf /var/lib/apt/lists/*
COPY --from=build-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/bin/ /bin/
COPY --from=runners-rocm-amd64 /go/src/github.com/ollama/ollama/dist/linux-amd64/lib/ /lib/
EXPOSE 11434
ENV OLLAMA_HOST 0.0.0.0
ENTRYPOINT ["/bin/ollama"]
CMD ["serve"]
FROM runtime-$TARGETARCH
EXPOSE 11434
ENV OLLAMA_HOST 0.0.0.0
ENV PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
COPY --from=archive /lib/ollama /usr/lib/ollama
ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
ENV NVIDIA_VISIBLE_DEVICES=all
ENV OLLAMA_HOST=0.0.0.0:11434
EXPOSE 11434
ENTRYPOINT ["/bin/ollama"]
CMD ["serve"]

103
Makefile Normal file
View File

@ -0,0 +1,103 @@
# top level makefile for Ollama
include make/common-defs.make
# Determine which if any GPU runners we should build
include make/cuda-v11-defs.make
include make/cuda-v12-defs.make
include make/rocm-defs.make
ifeq ($(CUSTOM_CPU_FLAGS),)
ifeq ($(ARCH),amd64)
RUNNER_TARGETS=cpu
endif
# Without CUSTOM_CPU_FLAGS we default to build both v11 and v12 if present
ifeq ($(OLLAMA_SKIP_CUDA_GENERATE),)
ifneq ($(CUDA_11_COMPILER),)
RUNNER_TARGETS += cuda_v11
endif
ifneq ($(CUDA_12_COMPILER),)
RUNNER_TARGETS += cuda_v12
endif
endif
else # CUSTOM_CPU_FLAGS is set, we'll build only the latest cuda version detected
ifneq ($(CUDA_12_COMPILER),)
RUNNER_TARGETS += cuda_v12
else ifneq ($(CUDA_11_COMPILER),)
RUNNER_TARGETS += cuda_v11
endif
endif
ifeq ($(OLLAMA_SKIP_ROCM_GENERATE),)
ifneq ($(HIP_COMPILER),)
RUNNER_TARGETS += rocm
endif
endif
all: runners exe
dist: $(addprefix dist_, $(RUNNER_TARGETS)) dist_exe
dist_%:
@$(MAKE) --no-print-directory -f make/Makefile.$* dist
runners: $(RUNNER_TARGETS)
$(RUNNER_TARGETS):
@$(MAKE) --no-print-directory -f make/Makefile.$@
exe dist_exe:
@$(MAKE) --no-print-directory -f make/Makefile.ollama $@
help-sync apply-patches create-patches sync sync-clean:
@$(MAKE) --no-print-directory -f make/Makefile.sync $@
test integration lint:
@$(MAKE) --no-print-directory -f make/Makefile.test $@
clean:
rm -rf $(BUILD_DIR) $(DIST_LIB_DIR) $(OLLAMA_EXE) $(DIST_OLLAMA_EXE)
go clean -cache
help:
@echo "The following make targets will help you build Ollama"
@echo ""
@echo " make all # (default target) Build Ollama llm subprocess runners, and the primary ollama executable"
@echo " make runners # Build Ollama llm subprocess runners; after you may use 'go build .' to build the primary ollama exectuable"
@echo " make <runner> # Build specific runners. Enabled: '$(RUNNER_TARGETS)'"
@echo " make dist # Build the runners and primary ollama executable for distribution"
@echo " make help-sync # Help information on vendor update targets"
@echo " make help-runners # Help information on runner targets"
@echo ""
@echo "The following make targets will help you test Ollama"
@echo ""
@echo " make test # Run unit tests"
@echo " make integration # Run integration tests. You must 'make all' first"
@echo " make lint # Run lint and style tests"
@echo ""
@echo "For more information see 'docs/development.md'"
@echo ""
help-runners:
@echo "The following runners will be built based on discovered GPU libraries: '$(RUNNER_TARGETS)'"
@echo ""
@echo "GPU Runner CPU Flags: '$(GPU_RUNNER_CPU_FLAGS)' (Override with CUSTOM_CPU_FLAGS)"
@echo ""
@echo "# CUDA_PATH sets the location where CUDA toolkits are present"
@echo "CUDA_PATH=$(CUDA_PATH)"
@echo " CUDA_11_PATH=$(CUDA_11_PATH)"
@echo " CUDA_11_COMPILER=$(CUDA_11_COMPILER)"
@echo " CUDA_12_PATH=$(CUDA_12_PATH)"
@echo " CUDA_12_COMPILER=$(CUDA_12_COMPILER)"
@echo ""
@echo "# HIP_PATH sets the location where the ROCm toolkit is present"
@echo "HIP_PATH=$(HIP_PATH)"
@echo " HIP_COMPILER=$(HIP_COMPILER)"
.PHONY: all exe dist help help-sync help-runners test integration lint runners clean $(RUNNER_TARGETS)
# Handy debugging for make variables
print-%:
@echo '$*=$($*)'

View File

@ -1,63 +0,0 @@
UPSTREAM=https://github.com/ggerganov/llama.cpp.git
WORKDIR=llama/vendor
FETCH_HEAD=de4c07f93783a1a96456a44dc16b9db538ee1618
.PHONY: help
help:
@echo "Available targets:"
@echo " sync Sync with upstream repositories"
@echo " checkout Checkout upstream repository"
@echo " apply-patches Apply patches to local repository"
@echo " format-patches Format patches from local repository"
@echo " clean Clean local repository"
@echo
@echo "Example:"
@echo " make -f $(lastword $(MAKEFILE_LIST)) clean sync"
.PHONY: sync
sync: llama/build-info.cpp ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-embed.metal
llama/build-info.cpp: llama/build-info.cpp.in llama/llama.cpp
sed -e 's|@FETCH_HEAD@|$(FETCH_HEAD)|' <$< >$@
ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-embed.metal: ml/backend/ggml/ggml
go generate ./$(@D)
.PHONY: llama/llama.cpp
llama/llama.cpp: llama/vendor/
rsync -arvzc -f "merge $@/.rsync-filter" $< $@
.PHONY: ml/backend/ggml/ggml
ml/backend/ggml/ggml: llama/vendor/ggml/
rsync -arvzc -f "merge $@/.rsync-filter" $< $@
PATCHES=$(wildcard llama/patches/*.patch)
PATCHED=$(join $(dir $(PATCHES)), $(addsuffix ed, $(addprefix ., $(notdir $(PATCHES)))))
.PHONY: apply-patches
.NOTPARALLEL:
apply-patches: $(PATCHED)
llama/patches/.%.patched: llama/patches/%.patch
@if git -c user.name=nobody -c 'user.email=<>' -C $(WORKDIR) am -3 $(realpath $<); then touch $@; else git -C $(WORKDIR) am --abort; exit 1; fi
.PHONY: checkout
checkout: $(WORKDIR)
git -C $(WORKDIR) fetch
git -C $(WORKDIR) checkout -f $(FETCH_HEAD)
$(WORKDIR):
git clone $(UPSTREAM) $(WORKDIR)
.PHONE: format-patches
format-patches: llama/patches
git -C $(WORKDIR) format-patch \
--no-signature \
--no-numbered \
--zero-commit \
-o $(realpath $<) \
$(FETCH_HEAD)
.PHONE: clean
clean: checkout
$(RM) llama/patches/.*.patched

144
README.md
View File

@ -1,5 +1,5 @@
<div align="center">
  <a href="https://ollama.com">
  <a href="https://ollama.com" />
<img alt="ollama" height="200px" src="https://github.com/ollama/ollama/assets/3325447/0d0b44e2-8f4a-4e99-9b52-a5c1c741c8f7">
</a>
</div>
@ -18,7 +18,7 @@ Get up and running with large language models.
### Linux
```shell
```
curl -fsSL https://ollama.com/install.sh | sh
```
@ -42,7 +42,7 @@ The official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama) `olla
To run and chat with [Llama 3.2](https://ollama.com/library/llama3.2):
```shell
```
ollama run llama3.2
```
@ -54,15 +54,6 @@ Here are some example models that can be downloaded:
| Model | Parameters | Size | Download |
| ------------------ | ---------- | ----- | -------------------------------- |
| Gemma 3 | 1B | 815MB | `ollama run gemma3:1b` |
| Gemma 3 | 4B | 3.3GB | `ollama run gemma3` |
| Gemma 3 | 12B | 8.1GB | `ollama run gemma3:12b` |
| Gemma 3 | 27B | 17GB | `ollama run gemma3:27b` |
| QwQ | 32B | 20GB | `ollama run qwq` |
| DeepSeek-R1 | 7B | 4.7GB | `ollama run deepseek-r1` |
| DeepSeek-R1 | 671B | 404GB | `ollama run deepseek-r1:671b` |
| Llama 4 | 109B | 67GB | `ollama run llama4:scout` |
| Llama 4 | 400B | 245GB | `ollama run llama4:maverick` |
| Llama 3.3 | 70B | 43GB | `ollama run llama3.3` |
| Llama 3.2 | 3B | 2.0GB | `ollama run llama3.2` |
| Llama 3.2 | 1B | 1.3GB | `ollama run llama3.2:1b` |
@ -71,7 +62,10 @@ Here are some example models that can be downloaded:
| Llama 3.1 | 8B | 4.7GB | `ollama run llama3.1` |
| Llama 3.1 | 405B | 231GB | `ollama run llama3.1:405b` |
| Phi 4 | 14B | 9.1GB | `ollama run phi4` |
| Phi 4 Mini | 3.8B | 2.5GB | `ollama run phi4-mini` |
| Phi 3 Mini | 3.8B | 2.3GB | `ollama run phi3` |
| 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` |
| Moondream 2 | 1.4B | 829MB | `ollama run moondream` |
| Neural Chat | 7B | 4.1GB | `ollama run neural-chat` |
@ -79,7 +73,7 @@ Here are some example models that can be downloaded:
| Code Llama | 7B | 3.8GB | `ollama run codellama` |
| Llama 2 Uncensored | 7B | 3.8GB | `ollama run llama2-uncensored` |
| LLaVA | 7B | 4.5GB | `ollama run llava` |
| Granite-3.3 | 8B | 4.9GB | `ollama run granite3.3` |
| Solar | 10.7B | 6.1GB | `ollama run solar` |
> [!NOTE]
> You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
@ -98,13 +92,13 @@ Ollama supports importing GGUF models in the Modelfile:
2. Create the model in Ollama
```shell
```
ollama create example -f Modelfile
```
3. Run the model
```shell
```
ollama run example
```
@ -116,7 +110,7 @@ See the [guide](docs/import.md) on importing models for more information.
Models from the Ollama library can be customized with a prompt. For example, to customize the `llama3.2` model:
```shell
```
ollama pull llama3.2
```
@ -151,13 +145,13 @@ For more information on working with a Modelfile, see the [Modelfile](docs/model
`ollama create` is used to create a model from a Modelfile.
```shell
```
ollama create mymodel -f ./Modelfile
```
### Pull a model
```shell
```
ollama pull llama3.2
```
@ -165,13 +159,13 @@ ollama pull llama3.2
### Remove a model
```shell
```
ollama rm llama3.2
```
### Copy a model
```shell
```
ollama cp llama3.2 my-model
```
@ -190,39 +184,37 @@ I'm a basic program that prints the famous "Hello, world!" message to the consol
```
ollama run llava "What's in this image? /Users/jmorgan/Desktop/smile.png"
The image features a yellow smiley face, which is likely the central focus of the picture.
```
> **Output**: The image features a yellow smiley face, which is likely the central focus of the picture.
### Pass the prompt as an argument
```shell
ollama run llama3.2 "Summarize this file: $(cat README.md)"
```
> **Output**: Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications.
$ ollama run llama3.2 "Summarize this file: $(cat README.md)"
Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications.
```
### Show model information
```shell
```
ollama show llama3.2
```
### List models on your computer
```shell
```
ollama list
```
### List which models are currently loaded
```shell
```
ollama ps
```
### Stop a model which is currently running
```shell
```
ollama stop llama3.2
```
@ -238,13 +230,13 @@ See the [developer guide](https://github.com/ollama/ollama/blob/main/docs/develo
Next, start the server:
```shell
```
./ollama serve
```
Finally, in a separate shell, run a model:
```shell
```
./ollama run llama3.2
```
@ -254,7 +246,7 @@ Ollama has a REST API for running and managing models.
### Generate a response
```shell
```
curl http://localhost:11434/api/generate -d '{
"model": "llama3.2",
"prompt":"Why is the sky blue?"
@ -263,7 +255,7 @@ curl http://localhost:11434/api/generate -d '{
### Chat with a model
```shell
```
curl http://localhost:11434/api/chat -d '{
"model": "llama3.2",
"messages": [
@ -279,7 +271,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
### Web & Desktop
- [Open WebUI](https://github.com/open-webui/open-webui)
- [SwiftChat (macOS with ReactNative)](https://github.com/aws-samples/swift-chat)
- [Enchanted (macOS native)](https://github.com/AugustDev/enchanted)
- [Hollama](https://github.com/fmaclen/hollama)
- [Lollms-Webui](https://github.com/ParisNeo/lollms-webui)
@ -287,13 +278,12 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [Bionic GPT](https://github.com/bionic-gpt/bionic-gpt)
- [HTML UI](https://github.com/rtcfirefly/ollama-ui)
- [Saddle](https://github.com/jikkuatwork/saddle)
- [TagSpaces](https://www.tagspaces.org) (A platform for file-based apps, [utilizing Ollama](https://docs.tagspaces.org/ai/) for the generation of tags and descriptions)
- [Chatbot UI](https://github.com/ivanfioravanti/chatbot-ollama)
- [Chatbot UI v2](https://github.com/mckaywrigley/chatbot-ui)
- [Typescript UI](https://github.com/ollama-interface/Ollama-Gui?tab=readme-ov-file)
- [Minimalistic React UI for Ollama Models](https://github.com/richawo/minimal-llm-ui)
- [Ollamac](https://github.com/kevinhermawan/Ollamac)
- [big-AGI](https://github.com/enricoros/big-AGI)
- [big-AGI](https://github.com/enricoros/big-AGI/blob/main/docs/config-local-ollama.md)
- [Cheshire Cat assistant framework](https://github.com/cheshire-cat-ai/core)
- [Amica](https://github.com/semperai/amica)
- [chatd](https://github.com/BruceMacD/chatd)
@ -314,8 +304,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [Ollama Basic Chat: Uses HyperDiv Reactive UI](https://github.com/rapidarchitect/ollama_basic_chat)
- [Ollama-chats RPG](https://github.com/drazdra/ollama-chats)
- [IntelliBar](https://intellibar.app/) (AI-powered assistant for macOS)
- [Jirapt](https://github.com/AliAhmedNada/jirapt) (Jira Integration to generate issues, tasks, epics)
- [ojira](https://github.com/AliAhmedNada/ojira) (Jira chrome plugin to easily generate descriptions for tasks)
- [QA-Pilot](https://github.com/reid41/QA-Pilot) (Interactive chat tool that can leverage Ollama models for rapid understanding and navigation of GitHub code repositories)
- [ChatOllama](https://github.com/sugarforever/chat-ollama) (Open Source Chatbot based on Ollama with Knowledge Bases)
- [CRAG Ollama Chat](https://github.com/Nagi-ovo/CRAG-Ollama-Chat) (Simple Web Search with Corrective RAG)
@ -329,14 +317,13 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [RWKV-Runner](https://github.com/josStorer/RWKV-Runner) (RWKV offline LLM deployment tool, also usable as a client for ChatGPT and Ollama)
- [Ollama Grid Search](https://github.com/dezoito/ollama-grid-search) (app to evaluate and compare models)
- [Olpaka](https://github.com/Otacon/olpaka) (User-friendly Flutter Web App for Ollama)
- [Casibase](https://casibase.org) (An open source AI knowledge base and dialogue system combining the latest RAG, SSO, ollama support, and multiple large language models.)
- [OllamaSpring](https://github.com/CrazyNeil/OllamaSpring) (Ollama Client for macOS)
- [LLocal.in](https://github.com/kartikm7/llocal) (Easy to use Electron Desktop Client for Ollama)
- [Shinkai Desktop](https://github.com/dcSpark/shinkai-apps) (Two click install Local AI using Ollama + Files + RAG)
- [AiLama](https://github.com/zeyoyt/ailama) (A Discord User App that allows you to interact with Ollama anywhere in Discord)
- [AiLama](https://github.com/zeyoyt/ailama) (A Discord User App that allows you to interact with Ollama anywhere in discord )
- [Ollama with Google Mesop](https://github.com/rapidarchitect/ollama_mesop/) (Mesop Chat Client implementation with Ollama)
- [R2R](https://github.com/SciPhi-AI/R2R) (Open-source RAG engine)
- [Ollama-Kis](https://github.com/elearningshow/ollama-kis) (A simple easy-to-use GUI with sample custom LLM for Drivers Education)
- [Ollama-Kis](https://github.com/elearningshow/ollama-kis) (A simple easy to use GUI with sample custom LLM for Drivers Education)
- [OpenGPA](https://opengpa.org) (Open-source offline-first Enterprise Agentic Application)
- [Painting Droid](https://github.com/mateuszmigas/painting-droid) (Painting app with AI integrations)
- [Kerlig AI](https://www.kerlig.com/) (AI writing assistant for macOS)
@ -345,16 +332,16 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [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)
- [PyGPT](https://github.com/szczyglis-dev/py-gpt) (AI desktop assistant for Linux, Windows, and Mac)
- [Alpaca](https://github.com/Jeffser/Alpaca) (An Ollama client application for Linux and macOS made with GTK4 and Adwaita)
- [PyGPT](https://github.com/szczyglis-dev/py-gpt) (AI desktop assistant for Linux, Windows and Mac)
- [Alpaca](https://github.com/Jeffser/Alpaca) (An Ollama client application for linux and macos made with GTK4 and Adwaita)
- [AutoGPT](https://github.com/Significant-Gravitas/AutoGPT/blob/master/docs/content/platform/ollama.md) (AutoGPT Ollama integration)
- [Go-CREW](https://www.jonathanhecl.com/go-crew/) (Powerful Offline RAG in Golang)
- [PartCAD](https://github.com/openvmp/partcad/) (CAD model generation with OpenSCAD and CadQuery)
- [Ollama4j Web UI](https://github.com/ollama4j/ollama4j-web-ui) - Java-based Web UI for Ollama built with Vaadin, Spring Boot, and Ollama4j
- [Ollama4j Web UI](https://github.com/ollama4j/ollama4j-web-ui) - Java-based Web UI for Ollama built with Vaadin, Spring Boot and Ollama4j
- [PyOllaMx](https://github.com/kspviswa/pyOllaMx) - macOS application capable of chatting with both Ollama and Apple MLX models.
- [Cline](https://github.com/cline/cline) - Formerly known as Claude Dev is a VSCode extension for multi-file/whole-repo coding
- [Claude Dev](https://github.com/saoudrizwan/claude-dev) - VSCode extension for multi-file/whole-repo coding
- [Cherry Studio](https://github.com/kangfenmao/cherry-studio) (Desktop client with Ollama support)
- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy-focused LLM chat interface with optional encryption)
- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
- [Archyve](https://github.com/nickthecook/archyve) (RAG-enabling document library)
- [crewAI with Mesop](https://github.com/rapidarchitect/ollama-crew-mesop) (Mesop Web Interface to run crewAI with Ollama)
- [Tkinter-based client](https://github.com/chyok/ollama-gui) (Python tkinter-based Client for Ollama)
@ -366,13 +353,12 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [Web management](https://github.com/lemonit-eric-mao/ollama-web-management) (Web management page)
- [Promptery](https://github.com/promptery/promptery) (desktop client for Ollama.)
- [Ollama App](https://github.com/JHubi1/ollama-app) (Modern and easy-to-use multi-platform client for Ollama)
- [chat-ollama](https://github.com/annilq/chat-ollama) (a React Native client for Ollama)
- [SpaceLlama](https://github.com/tcsenpai/spacellama) (Firefox and Chrome extension to quickly summarize web pages with ollama in a sidebar)
- [YouLama](https://github.com/tcsenpai/youlama) (Webapp to quickly summarize any YouTube video, supporting Invidious as well)
- [DualMind](https://github.com/tcsenpai/dualmind) (Experimental app allowing two models to talk to each other in the terminal or in a web interface)
- [ollamarama-matrix](https://github.com/h1ddenpr0cess20/ollamarama-matrix) (Ollama chatbot for the Matrix chat protocol)
- [ollama-chat-app](https://github.com/anan1213095357/ollama-chat-app) (Flutter-based chat app)
- [Perfect Memory AI](https://www.perfectmemory.ai/) (Productivity AI assists personalized by what you have seen on your screen, heard, and said in the meetings)
- [Perfect Memory AI](https://www.perfectmemory.ai/) (Productivity AI assists personalized by what you have seen on your screen, heard and said in the meetings)
- [Hexabot](https://github.com/hexastack/hexabot) (A conversational AI builder)
- [Reddit Rate](https://github.com/rapidarchitect/reddit_analyzer) (Search and Rate Reddit topics with a weighted summation)
- [OpenTalkGpt](https://github.com/adarshM84/OpenTalkGpt) (Chrome Extension to manage open-source models supported by Ollama, create custom models, and chat with models from a user-friendly UI)
@ -383,28 +369,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [Minima](https://github.com/dmayboroda/minima) (RAG with on-premises or fully local workflow)
- [aidful-ollama-model-delete](https://github.com/AidfulAI/aidful-ollama-model-delete) (User interface for simplified model cleanup)
- [Perplexica](https://github.com/ItzCrazyKns/Perplexica) (An AI-powered search engine & an open-source alternative to Perplexity AI)
- [Ollama Chat WebUI for Docker ](https://github.com/oslook/ollama-webui) (Support for local docker deployment, lightweight ollama webui)
- [AI Toolkit for Visual Studio Code](https://aka.ms/ai-tooklit/ollama-docs) (Microsoft-official VSCode extension to chat, test, evaluate models with Ollama support, and use them in your AI applications.)
- [MinimalNextOllamaChat](https://github.com/anilkay/MinimalNextOllamaChat) (Minimal Web UI for Chat and Model Control)
- [Chipper](https://github.com/TilmanGriesel/chipper) AI interface for tinkerers (Ollama, Haystack RAG, Python)
- [ChibiChat](https://github.com/CosmicEventHorizon/ChibiChat) (Kotlin-based Android app to chat with Ollama and Koboldcpp API endpoints)
- [LocalLLM](https://github.com/qusaismael/localllm) (Minimal Web-App to run ollama models on it with a GUI)
- [Ollamazing](https://github.com/buiducnhat/ollamazing) (Web extension to run Ollama models)
- [OpenDeepResearcher-via-searxng](https://github.com/benhaotang/OpenDeepResearcher-via-searxng) (A Deep Research equivalent endpoint with Ollama support for running locally)
- [AntSK](https://github.com/AIDotNet/AntSK) (Out-of-the-box & Adaptable RAG Chatbot)
- [MaxKB](https://github.com/1Panel-dev/MaxKB/) (Ready-to-use & flexible RAG Chatbot)
- [yla](https://github.com/danielekp/yla) (Web interface to freely interact with your customized models)
- [LangBot](https://github.com/RockChinQ/LangBot) (LLM-based instant messaging bots platform, with Agents, RAG features, supports multiple platforms)
- [1Panel](https://github.com/1Panel-dev/1Panel/) (Web-based Linux Server Management Tool)
- [AstrBot](https://github.com/Soulter/AstrBot/) (User-friendly LLM-based multi-platform chatbot with a WebUI, supporting RAG, LLM agents, and plugins integration)
- [Reins](https://github.com/ibrahimcetin/reins) (Easily tweak parameters, customize system prompts per chat, and enhance your AI experiments with reasoning model support.)
- [Flufy](https://github.com/Aharon-Bensadoun/Flufy) (A beautiful chat interface for interacting with Ollama's API. Built with React, TypeScript, and Material-UI.)
- [Ellama](https://github.com/zeozeozeo/ellama) (Friendly native app to chat with an Ollama instance)
- [screenpipe](https://github.com/mediar-ai/screenpipe) Build agents powered by your screen history
- [Ollamb](https://github.com/hengkysteen/ollamb) (Simple yet rich in features, cross-platform built with Flutter and designed for Ollama. Try the [web demo](https://hengkysteen.github.io/demo/ollamb/).)
- [Writeopia](https://github.com/Writeopia/Writeopia) (Text editor with integration with Ollama)
- [AppFlowy](https://github.com/AppFlowy-IO/AppFlowy) (AI collaborative workspace with Ollama, cross-platform and self-hostable)
- [Lumina](https://github.com/cushydigit/lumina.git) (A lightweight, minimal React.js frontend for interacting with Ollama servers)
### Cloud
@ -444,14 +408,10 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [SwollamaCLI](https://github.com/marcusziade/Swollama) bundled with the Swollama Swift package. [Demo](https://github.com/marcusziade/Swollama?tab=readme-ov-file#cli-usage)
- [aichat](https://github.com/sigoden/aichat) All-in-one LLM CLI tool featuring Shell Assistant, Chat-REPL, RAG, AI tools & agents, with access to OpenAI, Claude, Gemini, Ollama, Groq, and more.
- [PowershAI](https://github.com/rrg92/powershai) PowerShell module that brings AI to terminal on Windows, including support for Ollama
- [DeepShell](https://github.com/Abyss-c0re/deepshell) Your self-hosted AI assistant. Interactive Shell, Files and Folders analysis.
- [orbiton](https://github.com/xyproto/orbiton) Configuration-free text editor and IDE with support for tab completion with Ollama.
- [orca-cli](https://github.com/molbal/orca-cli) Ollama Registry CLI Application - Browse, pull, and download models from Ollama Registry in your terminal.
- [GGUF-to-Ollama](https://github.com/jonathanhecl/gguf-to-ollama) - Importing GGUF to Ollama made easy (multiplatform)
### Apple Vision Pro
- [SwiftChat](https://github.com/aws-samples/swift-chat) (Cross-platform AI chat app supporting Apple Vision Pro via "Designed for iPad")
- [Enchanted](https://github.com/AugustDev/enchanted)
### Database
@ -466,15 +426,14 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [Pacman](https://archlinux.org/packages/extra/x86_64/ollama/)
- [Gentoo](https://github.com/gentoo/guru/tree/master/app-misc/ollama)
- [Homebrew](https://formulae.brew.sh/formula/ollama)
- [Helm Chart](https://artifacthub.io/packages/helm/ollama-helm/ollama)
- [Guix channel](https://codeberg.org/tusharhero/ollama-guix)
- [Nix package](https://search.nixos.org/packages?show=ollama&from=0&size=50&sort=relevance&type=packages&query=ollama)
- [Nix package](https://search.nixos.org/packages?channel=24.05&show=ollama&from=0&size=50&sort=relevance&type=packages&query=ollama)
- [Flox](https://flox.dev/blog/ollama-part-one)
### Libraries
- [LangChain](https://python.langchain.com/docs/integrations/chat/ollama/) and [LangChain.js](https://js.langchain.com/docs/integrations/chat/ollama/) with [example](https://js.langchain.com/docs/tutorials/local_rag/)
- [LangChain](https://python.langchain.com/docs/integrations/llms/ollama) and [LangChain.js](https://js.langchain.com/docs/integrations/chat/ollama/) with [example](https://js.langchain.com/docs/tutorials/local_rag/)
- [Firebase Genkit](https://firebase.google.com/docs/genkit/plugins/ollama)
- [crewAI](https://github.com/crewAIInc/crewAI)
- [Yacana](https://remembersoftwares.github.io/yacana/) (User-friendly multi-agent framework for brainstorming and executing predetermined flows with built-in tool integration)
@ -521,23 +480,14 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [Swollama for Swift](https://github.com/marcusziade/Swollama) with [DocC](https://marcusziade.github.io/Swollama/documentation/swollama/)
- [GoLamify](https://github.com/prasad89/golamify)
- [Ollama for Haskell](https://github.com/tusharad/ollama-haskell)
- [multi-llm-ts](https://github.com/nbonamy/multi-llm-ts) (A Typescript/JavaScript library allowing access to different LLM in a unified API)
- [LlmTornado](https://github.com/lofcz/llmtornado) (C# library providing a unified interface for major FOSS & Commercial inference APIs)
- [Ollama for Zig](https://github.com/dravenk/ollama-zig)
- [Abso](https://github.com/lunary-ai/abso) (OpenAI-compatible TypeScript SDK for any LLM provider)
- [Nichey](https://github.com/goodreasonai/nichey) is a Python package for generating custom wikis for your research topic
- [Ollama for D](https://github.com/kassane/ollama-d)
- [OllamaPlusPlus](https://github.com/HardCodeDev777/OllamaPlusPlus) (Very simple C++ library for Ollama)
- [multi-llm-ts](https://github.com/nbonamy/multi-llm-ts) (A Typescript/JavaScript library allowing access to different LLM in unified API)
### Mobile
- [SwiftChat](https://github.com/aws-samples/swift-chat) (Lightning-fast Cross-platform AI chat app with native UI for Android, iOS, and iPad)
- [Enchanted](https://github.com/AugustDev/enchanted)
- [Maid](https://github.com/Mobile-Artificial-Intelligence/maid)
- [Ollama App](https://github.com/JHubi1/ollama-app) (Modern and easy-to-use multi-platform client for Ollama)
- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy-focused LLM chat interface with optional encryption)
- [Ollama Android Chat](https://github.com/sunshine0523/OllamaServer) (No need for Termux, start the Ollama service with one click on an Android device)
- [Reins](https://github.com/ibrahimcetin/reins) (Easily tweak parameters, customize system prompts per chat, and enhance your AI experiments with reasoning model support.)
- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
### Extensions & Plugins
@ -559,7 +509,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [Obsidian Local GPT plugin](https://github.com/pfrankov/obsidian-local-gpt)
- [Open Interpreter](https://docs.openinterpreter.com/language-model-setup/local-models/ollama)
- [Llama Coder](https://github.com/ex3ndr/llama-coder) (Copilot alternative using Ollama)
- [Ollama Copilot](https://github.com/bernardo-bruning/ollama-copilot) (Proxy that allows you to use Ollama as a copilot like GitHub Copilot)
- [Ollama Copilot](https://github.com/bernardo-bruning/ollama-copilot) (Proxy that allows you to use ollama as a copilot like Github copilot)
- [twinny](https://github.com/rjmacarthy/twinny) (Copilot and Copilot chat alternative using Ollama)
- [Wingman-AI](https://github.com/RussellCanfield/wingman-ai) (Copilot code and chat alternative using Ollama and Hugging Face)
- [Page Assist](https://github.com/n4ze3m/page-assist) (Chrome Extension)
@ -569,8 +519,8 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [Discord-Ollama Chat Bot](https://github.com/kevinthedang/discord-ollama) (Generalized TypeScript Discord Bot w/ Tuning Documentation)
- [ChatGPTBox: All in one browser extension](https://github.com/josStorer/chatGPTBox) with [Integrating Tutorial](https://github.com/josStorer/chatGPTBox/issues/616#issuecomment-1975186467)
- [Discord AI chat/moderation bot](https://github.com/rapmd73/Companion) Chat/moderation bot written in python. Uses Ollama to create personalities.
- [Headless Ollama](https://github.com/nischalj10/headless-ollama) (Scripts to automatically install ollama client & models on any OS for apps that depend on ollama server)
- [Terraform AWS Ollama & Open WebUI](https://github.com/xuyangbocn/terraform-aws-self-host-llm) (A Terraform module to deploy on AWS a ready-to-use Ollama service, together with its front-end Open WebUI service.)
- [Headless Ollama](https://github.com/nischalj10/headless-ollama) (Scripts to automatically install ollama client & models on any OS for apps that depends on ollama server)
- [Terraform AWS Ollama & Open WebUI](https://github.com/xuyangbocn/terraform-aws-self-host-llm) (A Terraform module to deploy on AWS a ready-to-use Ollama service, together with its front end Open WebUI service.)
- [node-red-contrib-ollama](https://github.com/jakubburkiewicz/node-red-contrib-ollama)
- [Local AI Helper](https://github.com/ivostoykov/localAI) (Chrome and Firefox extensions that enable interactions with the active tab and customisable API endpoints. Includes secure storage for user prompts.)
- [vnc-lm](https://github.com/jake83741/vnc-lm) (Discord bot for messaging with LLMs through Ollama and LiteLLM. Seamlessly move between local and flagship models.)
@ -581,19 +531,13 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [TextCraft](https://github.com/suncloudsmoon/TextCraft) (Copilot in Word alternative using Ollama)
- [Alfred Ollama](https://github.com/zeitlings/alfred-ollama) (Alfred Workflow)
- [TextLLaMA](https://github.com/adarshM84/TextLLaMA) A Chrome Extension that helps you write emails, correct grammar, and translate into any language
- [Simple-Discord-AI](https://github.com/zyphixor/simple-discord-ai)
- [LLM Telegram Bot](https://github.com/innightwolfsleep/llm_telegram_bot) (telegram bot, primary for RP. Oobabooga-like buttons, [A1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) API integration e.t.c)
- [mcp-llm](https://github.com/sammcj/mcp-llm) (MCP Server to allow LLMs to call other LLMs)
- [UnityCodeLama](https://github.com/HardCodeDev777/UnityCodeLama) (Unity Edtior tool to analyze scripts via Ollama)
### Supported backends
- [llama.cpp](https://github.com/ggerganov/llama.cpp) project founded by Georgi Gerganov.
### Observability
- [Opik](https://www.comet.com/docs/opik/cookbook/ollama) is an open-source platform to debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards. Opik supports native intergration to Ollama.
- [Lunary](https://lunary.ai/docs/integrations/ollama) is the leading open-source LLM observability platform. It provides a variety of enterprise-grade features such as real-time analytics, prompt templates management, PII masking, and comprehensive agent tracing.
- [OpenLIT](https://github.com/openlit/openlit) is an OpenTelemetry-native tool for monitoring Ollama Applications & GPUs using traces and metrics.
- [HoneyHive](https://docs.honeyhive.ai/integrations/ollama) is an AI observability and evaluation platform for AI agents. Use HoneyHive to evaluate agent performance, interrogate failures, and monitor quality in production.
- [Langfuse](https://langfuse.com/docs/integrations/ollama) is an open source LLM observability platform that enables teams to collaboratively monitor, evaluate and debug AI applications.
- [MLflow Tracing](https://mlflow.org/docs/latest/llms/tracing/index.html#automatic-tracing) is an open source LLM observability tool with a convenient API to log and visualize traces, making it easy to debug and evaluate GenAI applications.

View File

@ -10,7 +10,7 @@
// repository].
//
// [the API documentation]: https://github.com/ollama/ollama/blob/main/docs/api.md
// [in the GitHub repository]: https://github.com/ollama/ollama/tree/main/api/examples
// [in the GitHub repository]: https://github.com/ollama/ollama/tree/main/examples
package api
import (
@ -132,7 +132,7 @@ func (c *Client) do(ctx context.Context, method, path string, reqData, respData
const maxBufferSize = 512 * format.KiloByte
func (c *Client) stream(ctx context.Context, method, path string, data any, fn func([]byte) error) error {
var buf io.Reader
var buf *bytes.Buffer
if data != nil {
bts, err := json.Marshal(data)
if err != nil {

View File

@ -1,12 +1,6 @@
package api
import (
"encoding/json"
"fmt"
"net/http"
"net/http/httptest"
"net/url"
"strings"
"testing"
)
@ -49,206 +43,3 @@ func TestClientFromEnvironment(t *testing.T) {
})
}
}
// testError represents an internal error type with status code and message
// this is used since the error response from the server is not a standard error struct
type testError struct {
message string
statusCode int
}
func (e testError) Error() string {
return e.message
}
func TestClientStream(t *testing.T) {
testCases := []struct {
name string
responses []any
wantErr string
}{
{
name: "immediate error response",
responses: []any{
testError{
message: "test error message",
statusCode: http.StatusBadRequest,
},
},
wantErr: "test error message",
},
{
name: "error after successful chunks, ok response",
responses: []any{
ChatResponse{Message: Message{Content: "partial response 1"}},
ChatResponse{Message: Message{Content: "partial response 2"}},
testError{
message: "mid-stream error",
statusCode: http.StatusOK,
},
},
wantErr: "mid-stream error",
},
{
name: "successful stream completion",
responses: []any{
ChatResponse{Message: Message{Content: "chunk 1"}},
ChatResponse{Message: Message{Content: "chunk 2"}},
ChatResponse{
Message: Message{Content: "final chunk"},
Done: true,
DoneReason: "stop",
},
},
},
}
for _, tc := range testCases {
t.Run(tc.name, func(t *testing.T) {
ts := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
flusher, ok := w.(http.Flusher)
if !ok {
t.Fatal("expected http.Flusher")
}
w.Header().Set("Content-Type", "application/x-ndjson")
for _, resp := range tc.responses {
if errResp, ok := resp.(testError); ok {
w.WriteHeader(errResp.statusCode)
err := json.NewEncoder(w).Encode(map[string]string{
"error": errResp.message,
})
if err != nil {
t.Fatal("failed to encode error response:", err)
}
return
}
if err := json.NewEncoder(w).Encode(resp); err != nil {
t.Fatalf("failed to encode response: %v", err)
}
flusher.Flush()
}
}))
defer ts.Close()
client := NewClient(&url.URL{Scheme: "http", Host: ts.Listener.Addr().String()}, http.DefaultClient)
var receivedChunks []ChatResponse
err := client.stream(t.Context(), http.MethodPost, "/v1/chat", nil, func(chunk []byte) error {
var resp ChatResponse
if err := json.Unmarshal(chunk, &resp); err != nil {
return fmt.Errorf("failed to unmarshal chunk: %w", err)
}
receivedChunks = append(receivedChunks, resp)
return nil
})
if tc.wantErr != "" {
if err == nil {
t.Fatal("expected error but got nil")
}
if !strings.Contains(err.Error(), tc.wantErr) {
t.Errorf("expected error containing %q, got %v", tc.wantErr, err)
}
return
}
if err != nil {
t.Errorf("unexpected error: %v", err)
}
})
}
}
func TestClientDo(t *testing.T) {
testCases := []struct {
name string
response any
wantErr string
}{
{
name: "immediate error response",
response: testError{
message: "test error message",
statusCode: http.StatusBadRequest,
},
wantErr: "test error message",
},
{
name: "server error response",
response: testError{
message: "internal error",
statusCode: http.StatusInternalServerError,
},
wantErr: "internal error",
},
{
name: "successful response",
response: struct {
ID string `json:"id"`
Success bool `json:"success"`
}{
ID: "msg_123",
Success: true,
},
},
}
for _, tc := range testCases {
t.Run(tc.name, func(t *testing.T) {
ts := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
if errResp, ok := tc.response.(testError); ok {
w.WriteHeader(errResp.statusCode)
err := json.NewEncoder(w).Encode(map[string]string{
"error": errResp.message,
})
if err != nil {
t.Fatal("failed to encode error response:", err)
}
return
}
w.Header().Set("Content-Type", "application/json")
if err := json.NewEncoder(w).Encode(tc.response); err != nil {
t.Fatalf("failed to encode response: %v", err)
}
}))
defer ts.Close()
client := NewClient(&url.URL{Scheme: "http", Host: ts.Listener.Addr().String()}, http.DefaultClient)
var resp struct {
ID string `json:"id"`
Success bool `json:"success"`
}
err := client.do(t.Context(), http.MethodPost, "/v1/messages", nil, &resp)
if tc.wantErr != "" {
if err == nil {
t.Fatalf("got nil, want error %q", tc.wantErr)
}
if err.Error() != tc.wantErr {
t.Errorf("error message mismatch: got %q, want %q", err.Error(), tc.wantErr)
}
return
}
if err != nil {
t.Fatalf("got error %q, want nil", err)
}
if expectedResp, ok := tc.response.(struct {
ID string `json:"id"`
Success bool `json:"success"`
}); ok {
if resp.ID != expectedResp.ID {
t.Errorf("response ID mismatch: got %q, want %q", resp.ID, expectedResp.ID)
}
if resp.Success != expectedResp.Success {
t.Errorf("response Success mismatch: got %v, want %v", resp.Success, expectedResp.Success)
}
}
})
}
}

View File

@ -2,10 +2,9 @@
Run the examples in this directory with:
```shell
```
go run example_name/main.go
```
## Chat - Chat with a model
- [chat/main.go](chat/main.go)

View File

@ -10,9 +10,6 @@ import (
"strconv"
"strings"
"time"
"github.com/ollama/ollama/envconfig"
"github.com/ollama/ollama/types/model"
)
// StatusError is an error with an HTTP status code and message.
@ -76,13 +73,13 @@ type GenerateRequest struct {
// this request.
KeepAlive *Duration `json:"keep_alive,omitempty"`
// Images is an optional list of raw image bytes accompanying this
// Images is an optional list of base64-encoded images accompanying this
// request, for multimodal models.
Images []ImageData `json:"images,omitempty"`
// Options lists model-specific options. For example, temperature can be
// set through this field, if the model supports it.
Options map[string]any `json:"options"`
Options map[string]interface{} `json:"options"`
}
// ChatRequest describes a request sent by [Client.Chat].
@ -107,7 +104,7 @@ type ChatRequest struct {
Tools `json:"tools,omitempty"`
// Options lists model-specific options.
Options map[string]any `json:"options"`
Options map[string]interface{} `json:"options"`
}
type Tools []Tool
@ -163,65 +160,19 @@ func (t *ToolCallFunctionArguments) String() string {
type Tool struct {
Type string `json:"type"`
Items any `json:"items,omitempty"`
Function ToolFunction `json:"function"`
}
// PropertyType can be either a string or an array of strings
type PropertyType []string
// UnmarshalJSON implements the json.Unmarshaler interface
func (pt *PropertyType) UnmarshalJSON(data []byte) error {
// Try to unmarshal as a string first
var s string
if err := json.Unmarshal(data, &s); err == nil {
*pt = []string{s}
return nil
}
// If that fails, try to unmarshal as an array of strings
var a []string
if err := json.Unmarshal(data, &a); err != nil {
return err
}
*pt = a
return nil
}
// MarshalJSON implements the json.Marshaler interface
func (pt PropertyType) MarshalJSON() ([]byte, error) {
if len(pt) == 1 {
// If there's only one type, marshal as a string
return json.Marshal(pt[0])
}
// Otherwise marshal as an array
return json.Marshal([]string(pt))
}
// String returns a string representation of the PropertyType
func (pt PropertyType) String() string {
if len(pt) == 0 {
return ""
}
if len(pt) == 1 {
return pt[0]
}
return fmt.Sprintf("%v", []string(pt))
}
type ToolFunction struct {
Name string `json:"name"`
Description string `json:"description"`
Parameters struct {
Type string `json:"type"`
Defs any `json:"$defs,omitempty"`
Items any `json:"items,omitempty"`
Required []string `json:"required"`
Properties map[string]struct {
Type PropertyType `json:"type"`
Items any `json:"items,omitempty"`
Description string `json:"description"`
Enum []any `json:"enum,omitempty"`
Type string `json:"type"`
Description string `json:"description"`
Enum []string `json:"enum,omitempty"`
} `json:"properties"`
} `json:"parameters"`
}
@ -271,6 +222,9 @@ type Options struct {
RepeatPenalty float32 `json:"repeat_penalty,omitempty"`
PresencePenalty float32 `json:"presence_penalty,omitempty"`
FrequencyPenalty float32 `json:"frequency_penalty,omitempty"`
Mirostat int `json:"mirostat,omitempty"`
MirostatTau float32 `json:"mirostat_tau,omitempty"`
MirostatEta float32 `json:"mirostat_eta,omitempty"`
Stop []string `json:"stop,omitempty"`
}
@ -280,7 +234,12 @@ type Runner struct {
NumBatch int `json:"num_batch,omitempty"`
NumGPU int `json:"num_gpu,omitempty"`
MainGPU int `json:"main_gpu,omitempty"`
LowVRAM bool `json:"low_vram,omitempty"`
F16KV bool `json:"f16_kv,omitempty"` // Deprecated: This option is ignored
LogitsAll bool `json:"logits_all,omitempty"`
VocabOnly bool `json:"vocab_only,omitempty"`
UseMMap *bool `json:"use_mmap,omitempty"`
UseMLock bool `json:"use_mlock,omitempty"`
NumThread int `json:"num_thread,omitempty"`
}
@ -299,7 +258,7 @@ type EmbedRequest struct {
Truncate *bool `json:"truncate,omitempty"`
// Options lists model-specific options.
Options map[string]any `json:"options"`
Options map[string]interface{} `json:"options"`
}
// EmbedResponse is the response from [Client.Embed].
@ -325,7 +284,7 @@ type EmbeddingRequest struct {
KeepAlive *Duration `json:"keep_alive,omitempty"`
// Options lists model-specific options.
Options map[string]any `json:"options"`
Options map[string]interface{} `json:"options"`
}
// EmbeddingResponse is the response from [Client.Embeddings].
@ -371,7 +330,7 @@ type ShowRequest struct {
Template string `json:"template"`
Verbose bool `json:"verbose"`
Options map[string]any `json:"options"`
Options map[string]interface{} `json:"options"`
// Deprecated: set the model name with Model instead
Name string `json:"name"`
@ -379,18 +338,16 @@ type ShowRequest struct {
// ShowResponse is the response returned from [Client.Show].
type ShowResponse struct {
License string `json:"license,omitempty"`
Modelfile string `json:"modelfile,omitempty"`
Parameters string `json:"parameters,omitempty"`
Template string `json:"template,omitempty"`
System string `json:"system,omitempty"`
Details ModelDetails `json:"details,omitempty"`
Messages []Message `json:"messages,omitempty"`
ModelInfo map[string]any `json:"model_info,omitempty"`
ProjectorInfo map[string]any `json:"projector_info,omitempty"`
Tensors []Tensor `json:"tensors,omitempty"`
Capabilities []model.Capability `json:"capabilities,omitempty"`
ModifiedAt time.Time `json:"modified_at,omitempty"`
License string `json:"license,omitempty"`
Modelfile string `json:"modelfile,omitempty"`
Parameters string `json:"parameters,omitempty"`
Template string `json:"template,omitempty"`
System string `json:"system,omitempty"`
Details ModelDetails `json:"details,omitempty"`
Messages []Message `json:"messages,omitempty"`
ModelInfo map[string]any `json:"model_info,omitempty"`
ProjectorInfo map[string]any `json:"projector_info,omitempty"`
ModifiedAt time.Time `json:"modified_at,omitempty"`
}
// CopyRequest is the request passed to [Client.Copy].
@ -402,9 +359,9 @@ type CopyRequest struct {
// PullRequest is the request passed to [Client.Pull].
type PullRequest struct {
Model string `json:"model"`
Insecure bool `json:"insecure,omitempty"` // Deprecated: ignored
Username string `json:"username"` // Deprecated: ignored
Password string `json:"password"` // Deprecated: ignored
Insecure bool `json:"insecure,omitempty"`
Username string `json:"username"`
Password string `json:"password"`
Stream *bool `json:"stream,omitempty"`
// Deprecated: set the model name with Model instead
@ -463,6 +420,13 @@ type ProcessModelResponse struct {
SizeVRAM int64 `json:"size_vram"`
}
type RetrieveModelResponse struct {
Id string `json:"id"`
Object string `json:"object"`
Created int64 `json:"created"`
OwnedBy string `json:"owned_by"`
}
type TokenResponse struct {
Token string `json:"token"`
}
@ -501,13 +465,6 @@ type ModelDetails struct {
QuantizationLevel string `json:"quantization_level"`
}
// Tensor describes the metadata for a given tensor.
type Tensor struct {
Name string `json:"name"`
Type string `json:"type"`
Shape []uint64 `json:"shape"`
}
func (m *Metrics) Summary() {
if m.TotalDuration > 0 {
fmt.Fprintf(os.Stderr, "total duration: %v\n", m.TotalDuration)
@ -536,7 +493,7 @@ func (m *Metrics) Summary() {
}
}
func (opts *Options) FromMap(m map[string]any) error {
func (opts *Options) FromMap(m map[string]interface{}) error {
valueOpts := reflect.ValueOf(opts).Elem() // names of the fields in the options struct
typeOpts := reflect.TypeOf(opts).Elem() // types of the fields in the options struct
@ -593,12 +550,12 @@ func (opts *Options) FromMap(m map[string]any) error {
}
field.SetString(val)
case reflect.Slice:
// JSON unmarshals to []any, not []string
val, ok := val.([]any)
// JSON unmarshals to []interface{}, not []string
val, ok := val.([]interface{})
if !ok {
return fmt.Errorf("option %q must be of type array", key)
}
// convert []any to []string
// convert []interface{} to []string
slice := make([]string, len(val))
for i, item := range val {
str, ok := item.(string)
@ -645,14 +602,19 @@ func DefaultOptions() Options {
RepeatPenalty: 1.1,
PresencePenalty: 0.0,
FrequencyPenalty: 0.0,
Mirostat: 0,
MirostatTau: 5.0,
MirostatEta: 0.1,
Seed: -1,
Runner: Runner{
// options set when the model is loaded
NumCtx: int(envconfig.ContextLength()),
NumCtx: 2048,
NumBatch: 512,
NumGPU: -1, // -1 here indicates that NumGPU should be set dynamically
NumThread: 0, // let the runtime decide
LowVRAM: false,
UseMLock: false,
UseMMap: nil,
},
}
@ -700,7 +662,7 @@ func (d *Duration) UnmarshalJSON(b []byte) (err error) {
}
// FormatParams converts specified parameter options to their correct types
func FormatParams(params map[string][]string) (map[string]any, error) {
func FormatParams(params map[string][]string) (map[string]interface{}, error) {
opts := Options{}
valueOpts := reflect.ValueOf(&opts).Elem() // names of the fields in the options struct
typeOpts := reflect.TypeOf(opts) // types of the fields in the options struct
@ -714,7 +676,7 @@ func FormatParams(params map[string][]string) (map[string]any, error) {
}
}
out := make(map[string]any)
out := make(map[string]interface{})
// iterate params and set values based on json struct tags
for key, vals := range params {
if opt, ok := jsonOpts[key]; !ok {

View File

@ -134,7 +134,7 @@ func TestUseMmapParsingFromJSON(t *testing.T) {
for _, test := range tests {
t.Run(test.name, func(t *testing.T) {
var oMap map[string]any
var oMap map[string]interface{}
err := json.Unmarshal([]byte(test.req), &oMap)
require.NoError(t, err)
opts := DefaultOptions()
@ -231,144 +231,3 @@ func TestMessage_UnmarshalJSON(t *testing.T) {
}
}
}
func TestToolFunction_UnmarshalJSON(t *testing.T) {
tests := []struct {
name string
input string
wantErr string
}{
{
name: "valid enum with same types",
input: `{
"name": "test",
"description": "test function",
"parameters": {
"type": "object",
"required": ["test"],
"properties": {
"test": {
"type": "string",
"description": "test prop",
"enum": ["a", "b", "c"]
}
}
}
}`,
wantErr: "",
},
{
name: "empty enum array",
input: `{
"name": "test",
"description": "test function",
"parameters": {
"type": "object",
"required": ["test"],
"properties": {
"test": {
"type": "string",
"description": "test prop",
"enum": []
}
}
}
}`,
wantErr: "",
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
var tf ToolFunction
err := json.Unmarshal([]byte(tt.input), &tf)
if tt.wantErr != "" {
require.Error(t, err)
assert.Contains(t, err.Error(), tt.wantErr)
} else {
require.NoError(t, err)
}
})
}
}
func TestPropertyType_UnmarshalJSON(t *testing.T) {
tests := []struct {
name string
input string
expected PropertyType
}{
{
name: "string type",
input: `"string"`,
expected: PropertyType{"string"},
},
{
name: "array of types",
input: `["string", "number"]`,
expected: PropertyType{"string", "number"},
},
{
name: "array with single type",
input: `["string"]`,
expected: PropertyType{"string"},
},
}
for _, test := range tests {
t.Run(test.name, func(t *testing.T) {
var pt PropertyType
if err := json.Unmarshal([]byte(test.input), &pt); err != nil {
t.Errorf("Unexpected error: %v", err)
}
if len(pt) != len(test.expected) {
t.Errorf("Length mismatch: got %v, expected %v", len(pt), len(test.expected))
}
for i, v := range pt {
if v != test.expected[i] {
t.Errorf("Value mismatch at index %d: got %v, expected %v", i, v, test.expected[i])
}
}
})
}
}
func TestPropertyType_MarshalJSON(t *testing.T) {
tests := []struct {
name string
input PropertyType
expected string
}{
{
name: "single type",
input: PropertyType{"string"},
expected: `"string"`,
},
{
name: "multiple types",
input: PropertyType{"string", "number"},
expected: `["string","number"]`,
},
{
name: "empty type",
input: PropertyType{},
expected: `[]`,
},
}
for _, test := range tests {
t.Run(test.name, func(t *testing.T) {
data, err := json.Marshal(test.input)
if err != nil {
t.Errorf("Unexpected error: %v", err)
}
if string(data) != test.expected {
t.Errorf("Marshaled data mismatch: got %v, expected %v", string(data), test.expected)
}
})
}
}

View File

@ -17,6 +17,6 @@ If you want to build the installer, youll need to install
In the top directory of this repo, run the following powershell script
to build the ollama CLI, ollama app, and ollama installer.
```powershell
```
powershell -ExecutionPolicy Bypass -File .\scripts\build_windows.ps1
```

View File

@ -4,14 +4,20 @@ import (
"fmt"
"log/slog"
"os"
"path/filepath"
"strconv"
"strings"
"github.com/ollama/ollama/envconfig"
"github.com/ollama/ollama/logutil"
)
func InitLogging() {
level := slog.LevelInfo
if envconfig.Debug() {
level = slog.LevelDebug
}
var logFile *os.File
var err error
// Detect if we're a GUI app on windows, and if not, send logs to console
@ -27,8 +33,20 @@ func InitLogging() {
return
}
}
handler := slog.NewTextHandler(logFile, &slog.HandlerOptions{
Level: level,
AddSource: true,
ReplaceAttr: func(_ []string, attr slog.Attr) slog.Attr {
if attr.Key == slog.SourceKey {
source := attr.Value.Any().(*slog.Source)
source.File = filepath.Base(source.File)
}
return attr
},
})
slog.SetDefault(slog.New(handler))
slog.SetDefault(logutil.NewLogger(logFile, envconfig.LogLevel()))
slog.Info("ollama app started")
}

View File

@ -1,178 +0,0 @@
package benchmark
import (
"context"
"flag"
"fmt"
"testing"
"time"
"github.com/ollama/ollama/api"
)
// Command line flags
var modelFlag string
func init() {
flag.StringVar(&modelFlag, "m", "", "Name of the model to benchmark")
flag.Lookup("m").DefValue = "model"
}
// modelName returns the model name from flags, failing the test if not set
func modelName(b *testing.B) string {
if modelFlag == "" {
b.Fatal("Error: -m flag is required for benchmark tests")
}
return modelFlag
}
type TestCase struct {
name string
prompt string
maxTokens int
}
// runGenerateBenchmark contains the common generate and metrics logic
func runGenerateBenchmark(b *testing.B, ctx context.Context, client *api.Client, req *api.GenerateRequest) {
start := time.Now()
var ttft time.Duration
var metrics api.Metrics
err := client.Generate(ctx, req, func(resp api.GenerateResponse) error {
if ttft == 0 && resp.Response != "" {
ttft = time.Since(start)
}
if resp.Done {
metrics = resp.Metrics
}
return nil
})
// Report custom metrics as part of the benchmark results
b.ReportMetric(float64(ttft.Milliseconds()), "ttft_ms")
b.ReportMetric(float64(metrics.LoadDuration.Milliseconds()), "load_ms")
// Token throughput metrics
promptThroughput := float64(metrics.PromptEvalCount) / metrics.PromptEvalDuration.Seconds()
genThroughput := float64(metrics.EvalCount) / metrics.EvalDuration.Seconds()
b.ReportMetric(promptThroughput, "prompt_tok/s")
b.ReportMetric(genThroughput, "gen_tok/s")
// Token counts
b.ReportMetric(float64(metrics.PromptEvalCount), "prompt_tokens")
b.ReportMetric(float64(metrics.EvalCount), "gen_tokens")
if err != nil {
b.Fatal(err)
}
}
// BenchmarkColdStart runs benchmarks with model loading from cold state
func BenchmarkColdStart(b *testing.B) {
client := setup(b)
tests := []TestCase{
{"short_prompt", "Write a long story", 100},
{"medium_prompt", "Write a detailed economic analysis", 500},
{"long_prompt", "Write a comprehensive AI research paper", 1000},
}
m := modelName(b)
for _, tt := range tests {
b.Run(fmt.Sprintf("%s/cold/%s", m, tt.name), func(b *testing.B) {
ctx := b.Context()
// Set number of tokens as our throughput metric
b.SetBytes(int64(tt.maxTokens))
for b.Loop() {
b.StopTimer()
// Ensure model is unloaded before each iteration
unload(client, m, b)
b.StartTimer()
req := &api.GenerateRequest{
Model: m,
Prompt: tt.prompt,
Options: map[string]any{"num_predict": tt.maxTokens, "temperature": 0.1},
}
runGenerateBenchmark(b, ctx, client, req)
}
})
}
}
// BenchmarkWarmStart runs benchmarks with pre-loaded model
func BenchmarkWarmStart(b *testing.B) {
client := setup(b)
tests := []TestCase{
{"short_prompt", "Write a long story", 100},
{"medium_prompt", "Write a detailed economic analysis", 500},
{"long_prompt", "Write a comprehensive AI research paper", 1000},
}
m := modelName(b)
for _, tt := range tests {
b.Run(fmt.Sprintf("%s/warm/%s", m, tt.name), func(b *testing.B) {
ctx := b.Context()
// Pre-warm the model
warmup(client, m, tt.prompt, b)
// Set number of tokens as our throughput metric
b.SetBytes(int64(tt.maxTokens))
for b.Loop() {
req := &api.GenerateRequest{
Model: m,
Prompt: tt.prompt,
Options: map[string]any{"num_predict": tt.maxTokens, "temperature": 0.1},
}
runGenerateBenchmark(b, ctx, client, req)
}
})
}
}
// setup verifies server and model availability
func setup(b *testing.B) *api.Client {
client, err := api.ClientFromEnvironment()
if err != nil {
b.Fatal(err)
}
if _, err := client.Show(b.Context(), &api.ShowRequest{Model: modelName(b)}); err != nil {
b.Fatalf("Model unavailable: %v", err)
}
return client
}
// warmup ensures the model is loaded and warmed up
func warmup(client *api.Client, model string, prompt string, b *testing.B) {
for range 3 {
err := client.Generate(
context.Background(),
&api.GenerateRequest{
Model: model,
Prompt: prompt,
Options: map[string]any{"num_predict": 50, "temperature": 0.1},
},
func(api.GenerateResponse) error { return nil },
)
if err != nil {
b.Logf("Error during model warm-up: %v", err)
}
}
}
// unload forces model unloading using KeepAlive: 0 parameter
func unload(client *api.Client, model string, b *testing.B) {
req := &api.GenerateRequest{
Model: model,
KeepAlive: &api.Duration{Duration: 0},
}
if err := client.Generate(context.Background(), req, func(api.GenerateResponse) error { return nil }); err != nil {
b.Logf("Unload error: %v", err)
}
time.Sleep(1 * time.Second)
}

View File

@ -18,8 +18,6 @@ import (
"os/signal"
"path/filepath"
"runtime"
"slices"
"sort"
"strconv"
"strings"
"sync/atomic"
@ -31,18 +29,17 @@ import (
"github.com/olekukonko/tablewriter"
"github.com/spf13/cobra"
"golang.org/x/crypto/ssh"
"golang.org/x/sync/errgroup"
"golang.org/x/term"
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/envconfig"
"github.com/ollama/ollama/format"
"github.com/ollama/ollama/llama"
"github.com/ollama/ollama/llama/runner"
"github.com/ollama/ollama/parser"
"github.com/ollama/ollama/progress"
"github.com/ollama/ollama/runner"
"github.com/ollama/ollama/server"
"github.com/ollama/ollama/types/model"
"github.com/ollama/ollama/types/syncmap"
"github.com/ollama/ollama/version"
)
@ -108,7 +105,7 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
}
spinner.Stop()
req.Model = args[0]
req.Name = args[0]
quantize, _ := cmd.Flags().GetString("quantize")
if quantize != "" {
req.Quantize = quantize
@ -119,54 +116,34 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
return err
}
var g errgroup.Group
g.SetLimit(max(runtime.GOMAXPROCS(0)-1, 1))
files := syncmap.NewSyncMap[string, string]()
for f, digest := range req.Files {
g.Go(func() error {
if len(req.Files) > 0 {
fileMap := map[string]string{}
for f, digest := range req.Files {
if _, err := createBlob(cmd, client, f, digest, p); err != nil {
return err
}
// TODO: this is incorrect since the file might be in a subdirectory
// instead this should take the path relative to the model directory
// but the current implementation does not allow this
files.Store(filepath.Base(f), digest)
return nil
})
fileMap[filepath.Base(f)] = digest
}
req.Files = fileMap
}
adapters := syncmap.NewSyncMap[string, string]()
for f, digest := range req.Adapters {
g.Go(func() error {
if len(req.Adapters) > 0 {
fileMap := map[string]string{}
for f, digest := range req.Adapters {
if _, err := createBlob(cmd, client, f, digest, p); err != nil {
return err
}
// TODO: same here
adapters.Store(filepath.Base(f), digest)
return nil
})
fileMap[filepath.Base(f)] = digest
}
req.Adapters = fileMap
}
if err := g.Wait(); err != nil {
return err
}
req.Files = files.Items()
req.Adapters = adapters.Items()
bars := make(map[string]*progress.Bar)
fn := func(resp api.ProgressResponse) error {
if resp.Digest != "" {
bar, ok := bars[resp.Digest]
if !ok {
msg := resp.Status
if msg == "" {
msg = fmt.Sprintf("pulling %s...", resp.Digest[7:19])
}
bar = progress.NewBar(msg, resp.Total, resp.Completed)
bar = progress.NewBar(fmt.Sprintf("pulling %s...", resp.Digest[7:19]), resp.Total, resp.Completed)
bars[resp.Digest] = bar
p.Add(resp.Digest, bar)
}
@ -235,7 +212,7 @@ func createBlob(cmd *cobra.Command, client *api.Client, path string, digest stri
}
}()
if err := client.CreateBlob(cmd.Context(), digest, io.TeeReader(bin, &pw)); err != nil {
if err = client.CreateBlob(cmd.Context(), digest, io.TeeReader(bin, &pw)); err != nil {
return "", err
}
return digest, nil
@ -279,7 +256,6 @@ func StopHandler(cmd *cobra.Command, args []string) error {
if strings.Contains(err.Error(), "not found") {
return fmt.Errorf("couldn't find model \"%s\" to stop", args[0])
}
return err
}
return nil
}
@ -290,7 +266,7 @@ func RunHandler(cmd *cobra.Command, args []string) error {
opts := runOptions{
Model: args[0],
WordWrap: os.Getenv("TERM") == "xterm-256color",
Options: map[string]any{},
Options: map[string]interface{}{},
}
format, err := cmd.Flags().GetString("format")
@ -362,21 +338,7 @@ func RunHandler(cmd *cobra.Command, args []string) error {
return err
}
opts.MultiModal = slices.Contains(info.Capabilities, model.CapabilityVision)
// TODO: remove the projector info and vision info checks below,
// these are left in for backwards compatibility with older servers
// that don't have the capabilities field in the model info
if len(info.ProjectorInfo) != 0 {
opts.MultiModal = true
}
for k := range info.ModelInfo {
if strings.Contains(k, ".vision.") {
opts.MultiModal = true
break
}
}
opts.MultiModal = len(info.ProjectorInfo) != 0
opts.ParentModel = info.Details.ParentModel
if interactive {
@ -597,9 +559,8 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
parameters, errParams := cmd.Flags().GetBool("parameters")
system, errSystem := cmd.Flags().GetBool("system")
template, errTemplate := cmd.Flags().GetBool("template")
verbose, errVerbose := cmd.Flags().GetBool("verbose")
for _, boolErr := range []error{errLicense, errModelfile, errParams, errSystem, errTemplate, errVerbose} {
for _, boolErr := range []error{errLicense, errModelfile, errParams, errSystem, errTemplate} {
if boolErr != nil {
return errors.New("error retrieving flags")
}
@ -637,7 +598,7 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
return errors.New("only one of '--license', '--modelfile', '--parameters', '--system', or '--template' can be specified")
}
req := api.ShowRequest{Name: args[0], Verbose: verbose}
req := api.ShowRequest{Name: args[0]}
resp, err := client.Show(cmd.Context(), &req)
if err != nil {
return err
@ -660,10 +621,10 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
return nil
}
return showInfo(resp, verbose, os.Stdout)
return showInfo(resp, os.Stdout)
}
func showInfo(resp *api.ShowResponse, verbose bool, w io.Writer) error {
func showInfo(resp *api.ShowResponse, w io.Writer) error {
tableRender := func(header string, rows func() [][]string) {
fmt.Fprintln(w, " ", header)
table := tablewriter.NewWriter(w)
@ -697,15 +658,6 @@ func showInfo(resp *api.ShowResponse, verbose bool, w io.Writer) error {
return
})
if len(resp.Capabilities) > 0 {
tableRender("Capabilities", func() (rows [][]string) {
for _, capability := range resp.Capabilities {
rows = append(rows, []string{"", capability.String()})
}
return
})
}
if resp.ProjectorInfo != nil {
tableRender("Projector", func() (rows [][]string) {
arch := resp.ProjectorInfo["general.architecture"].(string)
@ -729,47 +681,6 @@ func showInfo(resp *api.ShowResponse, verbose bool, w io.Writer) error {
})
}
if resp.ModelInfo != nil && verbose {
tableRender("Metadata", func() (rows [][]string) {
keys := make([]string, 0, len(resp.ModelInfo))
for k := range resp.ModelInfo {
keys = append(keys, k)
}
sort.Strings(keys)
for _, k := range keys {
var v string
switch vData := resp.ModelInfo[k].(type) {
case bool:
v = fmt.Sprintf("%t", vData)
case string:
v = vData
case float64:
v = fmt.Sprintf("%g", vData)
case []any:
n := 3
if len(vData) < n {
n = len(vData)
}
v = fmt.Sprintf("%v", vData[:n])
default:
v = fmt.Sprintf("%T", vData)
}
rows = append(rows, []string{"", k, v})
}
return
})
}
if len(resp.Tensors) > 0 && verbose {
tableRender("Tensors", func() (rows [][]string) {
for _, t := range resp.Tensors {
rows = append(rows, []string{"", t.Name, t.Type, fmt.Sprint(t.Shape)})
}
return
})
}
head := func(s string, n int) (rows [][]string) {
scanner := bufio.NewScanner(strings.NewReader(s))
for scanner.Scan() && (len(rows) < n || n < 0) {
@ -830,38 +741,13 @@ func PullHandler(cmd *cobra.Command, args []string) error {
fn := func(resp api.ProgressResponse) error {
if resp.Digest != "" {
if resp.Completed == 0 {
// This is the initial status update for the
// layer, which the server sends before
// beginning the download, for clients to
// compute total size and prepare for
// downloads, if needed.
//
// Skipping this here to avoid showing a 0%
// progress bar, which *should* clue the user
// into the fact that many things are being
// downloaded and that the current active
// download is not that last. However, in rare
// cases it seems to be triggering to some, and
// it isn't worth explaining, so just ignore
// and regress to the old UI that keeps giving
// you the "But wait, there is more!" after
// each "100% done" bar, which is "better."
return nil
}
if spinner != nil {
spinner.Stop()
}
bar, ok := bars[resp.Digest]
if !ok {
name, isDigest := strings.CutPrefix(resp.Digest, "sha256:")
name = strings.TrimSpace(name)
if isDigest {
name = name[:min(12, len(name))]
}
bar = progress.NewBar(fmt.Sprintf("pulling %s:", name), resp.Total, resp.Completed)
bar = progress.NewBar(fmt.Sprintf("pulling %s...", resp.Digest[7:19]), resp.Total, resp.Completed)
bars[resp.Digest] = bar
p.Add(resp.Digest, bar)
}
@ -881,7 +767,11 @@ func PullHandler(cmd *cobra.Command, args []string) error {
}
request := api.PullRequest{Name: args[0], Insecure: insecure}
return client.Pull(cmd.Context(), &request, fn)
if err := client.Pull(cmd.Context(), &request, fn); err != nil {
return err
}
return nil
}
type generateContextKey string
@ -895,7 +785,7 @@ type runOptions struct {
Format string
System string
Images []api.ImageData
Options map[string]any
Options map[string]interface{}
MultiModal bool
KeepAlive *api.Duration
}
@ -1297,7 +1187,6 @@ func NewCLI() *cobra.Command {
showCmd.Flags().Bool("parameters", false, "Show parameters of a model")
showCmd.Flags().Bool("template", false, "Show template of a model")
showCmd.Flags().Bool("system", false, "Show system message of a model")
showCmd.Flags().BoolP("verbose", "v", false, "Show detailed model information")
runCmd := &cobra.Command{
Use: "run MODEL [PROMPT]",
@ -1382,6 +1271,7 @@ func NewCLI() *cobra.Command {
runnerCmd := &cobra.Command{
Use: "runner",
Short: llama.PrintSystemInfo(),
Hidden: true,
RunE: func(cmd *cobra.Command, args []string) error {
return runner.Execute(os.Args[1:])
@ -1424,6 +1314,7 @@ func NewCLI() *cobra.Command {
envVars["OLLAMA_NOPRUNE"],
envVars["OLLAMA_ORIGINS"],
envVars["OLLAMA_SCHED_SPREAD"],
envVars["OLLAMA_TMPDIR"],
envVars["OLLAMA_FLASH_ATTENTION"],
envVars["OLLAMA_KV_CACHE_TYPE"],
envVars["OLLAMA_LLM_LIBRARY"],

View File

@ -2,6 +2,7 @@ package cmd
import (
"bytes"
"context"
"encoding/json"
"io"
"net/http"
@ -9,13 +10,11 @@ import (
"os"
"strings"
"testing"
"time"
"github.com/google/go-cmp/cmp"
"github.com/spf13/cobra"
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/types/model"
)
func TestShowInfo(t *testing.T) {
@ -27,7 +26,7 @@ func TestShowInfo(t *testing.T) {
ParameterSize: "7B",
QuantizationLevel: "FP16",
},
}, false, &b); err != nil {
}, &b); err != nil {
t.Fatal(err)
}
@ -57,7 +56,7 @@ func TestShowInfo(t *testing.T) {
ParameterSize: "7B",
QuantizationLevel: "FP16",
},
}, false, &b); err != nil {
}, &b); err != nil {
t.Fatal(err)
}
@ -68,60 +67,6 @@ func TestShowInfo(t *testing.T) {
embedding length 0
quantization FP16
`
if diff := cmp.Diff(expect, b.String()); diff != "" {
t.Errorf("unexpected output (-want +got):\n%s", diff)
}
})
t.Run("verbose model", func(t *testing.T) {
var b bytes.Buffer
if err := showInfo(&api.ShowResponse{
Details: api.ModelDetails{
Family: "test",
ParameterSize: "8B",
QuantizationLevel: "FP16",
},
Parameters: `
stop up`,
ModelInfo: map[string]any{
"general.architecture": "test",
"general.parameter_count": float64(8_000_000_000),
"some.true_bool": true,
"some.false_bool": false,
"test.context_length": float64(1000),
"test.embedding_length": float64(11434),
},
Tensors: []api.Tensor{
{Name: "blk.0.attn_k.weight", Type: "BF16", Shape: []uint64{42, 3117}},
{Name: "blk.0.attn_q.weight", Type: "FP16", Shape: []uint64{3117, 42}},
},
}, true, &b); err != nil {
t.Fatal(err)
}
expect := ` Model
architecture test
parameters 8B
context length 1000
embedding length 11434
quantization FP16
Parameters
stop up
Metadata
general.architecture test
general.parameter_count 8e+09
some.false_bool false
some.true_bool true
test.context_length 1000
test.embedding_length 11434
Tensors
blk.0.attn_k.weight BF16 [42 3117]
blk.0.attn_q.weight FP16 [3117 42]
`
if diff := cmp.Diff(expect, b.String()); diff != "" {
t.Errorf("unexpected output (-want +got):\n%s", diff)
@ -143,7 +88,7 @@ func TestShowInfo(t *testing.T) {
stop you
stop up
temperature 99`,
}, false, &b); err != nil {
}, &b); err != nil {
t.Fatal(err)
}
@ -180,7 +125,7 @@ func TestShowInfo(t *testing.T) {
"clip.vision.embedding_length": float64(0),
"clip.vision.projection_dim": float64(0),
},
}, false, &b); err != nil {
}, &b); err != nil {
t.Fatal(err)
}
@ -213,7 +158,7 @@ func TestShowInfo(t *testing.T) {
Ahoy, matey!
Weigh anchor!
`,
}, false, &b); err != nil {
}, &b); err != nil {
t.Fatal(err)
}
@ -242,7 +187,7 @@ Weigh anchor!
QuantizationLevel: "FP16",
},
License: license,
}, false, &b); err != nil {
}, &b); err != nil {
t.Fatal(err)
}
@ -260,34 +205,6 @@ Weigh anchor!
t.Errorf("unexpected output (-want +got):\n%s", diff)
}
})
t.Run("capabilities", func(t *testing.T) {
var b bytes.Buffer
if err := showInfo(&api.ShowResponse{
Details: api.ModelDetails{
Family: "test",
ParameterSize: "7B",
QuantizationLevel: "FP16",
},
Capabilities: []model.Capability{model.CapabilityVision, model.CapabilityTools},
}, false, &b); err != nil {
t.Fatal(err)
}
expect := " Model\n" +
" architecture test \n" +
" parameters 7B \n" +
" quantization FP16 \n" +
"\n" +
" Capabilities\n" +
" vision \n" +
" tools \n" +
"\n"
if diff := cmp.Diff(expect, b.String()); diff != "" {
t.Errorf("unexpected output (-want +got):\n%s", diff)
}
})
}
func TestDeleteHandler(t *testing.T) {
@ -336,7 +253,7 @@ func TestDeleteHandler(t *testing.T) {
t.Cleanup(mockServer.Close)
cmd := &cobra.Command{}
cmd.SetContext(t.Context())
cmd.SetContext(context.TODO())
if err := DeleteHandler(cmd, []string{"test-model"}); err != nil {
t.Fatalf("DeleteHandler failed: %v", err)
}
@ -398,6 +315,11 @@ func TestGetModelfileName(t *testing.T) {
var expectedFilename string
if tt.fileExists {
tempDir, err := os.MkdirTemp("", "modelfiledir")
defer os.RemoveAll(tempDir)
if err != nil {
t.Fatalf("temp modelfile dir creation failed: %v", err)
}
var fn string
if tt.modelfileName != "" {
fn = tt.modelfileName
@ -405,11 +327,10 @@ func TestGetModelfileName(t *testing.T) {
fn = "Modelfile"
}
tempFile, err := os.CreateTemp(t.TempDir(), fn)
tempFile, err := os.CreateTemp(tempDir, fn)
if err != nil {
t.Fatalf("temp modelfile creation failed: %v", err)
}
defer tempFile.Close()
expectedFilename = tempFile.Name()
err = cmd.Flags().Set("file", expectedFilename)
@ -524,7 +445,7 @@ func TestPushHandler(t *testing.T) {
cmd := &cobra.Command{}
cmd.Flags().Bool("insecure", false, "")
cmd.SetContext(t.Context())
cmd.SetContext(context.TODO())
// Redirect stderr to capture progress output
oldStderr := os.Stderr
@ -569,96 +490,6 @@ func TestPushHandler(t *testing.T) {
}
}
func TestListHandler(t *testing.T) {
tests := []struct {
name string
args []string
serverResponse []api.ListModelResponse
expectedError string
expectedOutput string
}{
{
name: "list all models",
args: []string{},
serverResponse: []api.ListModelResponse{
{Name: "model1", Digest: "sha256:abc123", Size: 1024, ModifiedAt: time.Now().Add(-24 * time.Hour)},
{Name: "model2", Digest: "sha256:def456", Size: 2048, ModifiedAt: time.Now().Add(-48 * time.Hour)},
},
expectedOutput: "NAME ID SIZE MODIFIED \n" +
"model1 sha256:abc12 1.0 KB 24 hours ago \n" +
"model2 sha256:def45 2.0 KB 2 days ago \n",
},
{
name: "filter models by prefix",
args: []string{"model1"},
serverResponse: []api.ListModelResponse{
{Name: "model1", Digest: "sha256:abc123", Size: 1024, ModifiedAt: time.Now().Add(-24 * time.Hour)},
{Name: "model2", Digest: "sha256:def456", Size: 2048, ModifiedAt: time.Now().Add(-24 * time.Hour)},
},
expectedOutput: "NAME ID SIZE MODIFIED \n" +
"model1 sha256:abc12 1.0 KB 24 hours ago \n",
},
{
name: "server error",
args: []string{},
expectedError: "server error",
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
mockServer := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
if r.URL.Path != "/api/tags" || r.Method != http.MethodGet {
t.Errorf("unexpected request to %s %s", r.Method, r.URL.Path)
http.Error(w, "not found", http.StatusNotFound)
return
}
if tt.expectedError != "" {
http.Error(w, tt.expectedError, http.StatusInternalServerError)
return
}
response := api.ListResponse{Models: tt.serverResponse}
if err := json.NewEncoder(w).Encode(response); err != nil {
t.Fatal(err)
}
}))
defer mockServer.Close()
t.Setenv("OLLAMA_HOST", mockServer.URL)
cmd := &cobra.Command{}
cmd.SetContext(t.Context())
// Capture stdout
oldStdout := os.Stdout
r, w, _ := os.Pipe()
os.Stdout = w
err := ListHandler(cmd, tt.args)
// Restore stdout and get output
w.Close()
os.Stdout = oldStdout
output, _ := io.ReadAll(r)
if tt.expectedError == "" {
if err != nil {
t.Errorf("expected no error, got %v", err)
}
if got := string(output); got != tt.expectedOutput {
t.Errorf("expected output:\n%s\ngot:\n%s", tt.expectedOutput, got)
}
} else {
if err == nil || !strings.Contains(err.Error(), tt.expectedError) {
t.Errorf("expected error containing %q, got %v", tt.expectedError, err)
}
}
})
}
}
func TestCreateHandler(t *testing.T) {
tests := []struct {
name string
@ -684,7 +515,7 @@ func TestCreateHandler(t *testing.T) {
return
}
if req.Model != "test-model" {
if req.Name != "test-model" {
t.Errorf("expected model name 'test-model', got %s", req.Name)
}
@ -724,7 +555,7 @@ func TestCreateHandler(t *testing.T) {
}))
t.Setenv("OLLAMA_HOST", mockServer.URL)
t.Cleanup(mockServer.Close)
tempFile, err := os.CreateTemp(t.TempDir(), "modelfile")
tempFile, err := os.CreateTemp("", "modelfile")
if err != nil {
t.Fatal(err)
}
@ -744,7 +575,7 @@ func TestCreateHandler(t *testing.T) {
}
cmd.Flags().Bool("insecure", false, "")
cmd.SetContext(t.Context())
cmd.SetContext(context.TODO())
// Redirect stderr to capture progress output
oldStderr := os.Stderr
@ -785,132 +616,3 @@ func TestCreateHandler(t *testing.T) {
})
}
}
func TestNewCreateRequest(t *testing.T) {
tests := []struct {
name string
from string
opts runOptions
expected *api.CreateRequest
}{
{
"basic test",
"newmodel",
runOptions{
Model: "mymodel",
ParentModel: "",
Prompt: "You are a fun AI agent",
Messages: []api.Message{},
WordWrap: true,
},
&api.CreateRequest{
From: "mymodel",
Model: "newmodel",
},
},
{
"parent model test",
"newmodel",
runOptions{
Model: "mymodel",
ParentModel: "parentmodel",
Messages: []api.Message{},
WordWrap: true,
},
&api.CreateRequest{
From: "parentmodel",
Model: "newmodel",
},
},
{
"parent model as filepath test",
"newmodel",
runOptions{
Model: "mymodel",
ParentModel: "/some/file/like/etc/passwd",
Messages: []api.Message{},
WordWrap: true,
},
&api.CreateRequest{
From: "mymodel",
Model: "newmodel",
},
},
{
"parent model as windows filepath test",
"newmodel",
runOptions{
Model: "mymodel",
ParentModel: "D:\\some\\file\\like\\etc\\passwd",
Messages: []api.Message{},
WordWrap: true,
},
&api.CreateRequest{
From: "mymodel",
Model: "newmodel",
},
},
{
"options test",
"newmodel",
runOptions{
Model: "mymodel",
ParentModel: "parentmodel",
Options: map[string]any{
"temperature": 1.0,
},
},
&api.CreateRequest{
From: "parentmodel",
Model: "newmodel",
Parameters: map[string]any{
"temperature": 1.0,
},
},
},
{
"messages test",
"newmodel",
runOptions{
Model: "mymodel",
ParentModel: "parentmodel",
System: "You are a fun AI agent",
Messages: []api.Message{
{
Role: "user",
Content: "hello there!",
},
{
Role: "assistant",
Content: "hello to you!",
},
},
WordWrap: true,
},
&api.CreateRequest{
From: "parentmodel",
Model: "newmodel",
System: "You are a fun AI agent",
Messages: []api.Message{
{
Role: "user",
Content: "hello there!",
},
{
Role: "assistant",
Content: "hello to you!",
},
},
},
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
actual := NewCreateRequest(tt.from, tt.opts)
if !cmp.Equal(actual, tt.expected) {
t.Errorf("expected output %#v, got %#v", tt.expected, actual)
}
})
}
}

View File

@ -18,7 +18,6 @@ import (
"github.com/ollama/ollama/envconfig"
"github.com/ollama/ollama/readline"
"github.com/ollama/ollama/types/errtypes"
"github.com/ollama/ollama/types/model"
)
type MultilineState int
@ -44,7 +43,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
fmt.Fprintln(os.Stderr, "Use \"\"\" to begin a multi-line message.")
if opts.MultiModal {
fmt.Fprintf(os.Stderr, "Use %s to include .jpg, .png, or .webp images.\n", filepath.FromSlash("/path/to/file"))
fmt.Fprintf(os.Stderr, "Use %s to include .jpg or .png images.\n", filepath.FromSlash("/path/to/file"))
}
fmt.Fprintln(os.Stderr, "")
@ -196,10 +195,6 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
opts.Messages = []api.Message{}
fmt.Printf("Loading model '%s'\n", opts.Model)
if err := loadOrUnloadModel(cmd, &opts); err != nil {
if strings.Contains(err.Error(), "not found") {
fmt.Printf("error: %v\n", err)
continue
}
return err
}
continue
@ -348,7 +343,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
switch args[1] {
case "info":
_ = showInfo(resp, false, os.Stderr)
_ = showInfo(resp, os.Stderr)
case "license":
if resp.License == "" {
fmt.Println("No license was specified for this model.")
@ -460,16 +455,9 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
}
func NewCreateRequest(name string, opts runOptions) *api.CreateRequest {
parentModel := opts.ParentModel
modelName := model.ParseName(parentModel)
if !modelName.IsValid() {
parentModel = ""
}
req := &api.CreateRequest{
Model: name,
From: cmp.Or(parentModel, opts.Model),
Name: name,
From: cmp.Or(opts.ParentModel, opts.Model),
}
if opts.System != "" {
@ -503,7 +491,6 @@ func normalizeFilePath(fp string) string {
"\\\\", "\\", // Escaped backslash
"\\*", "*", // Escaped asterisk
"\\?", "?", // Escaped question mark
"\\~", "~", // Escaped tilde
).Replace(fp)
}
@ -511,7 +498,7 @@ func extractFileNames(input string) []string {
// Regex to match file paths starting with optional drive letter, / ./ \ or .\ and include escaped or unescaped spaces (\ or %20)
// and followed by more characters and a file extension
// This will capture non filename strings, but we'll check for file existence to remove mismatches
regexPattern := `(?:[a-zA-Z]:)?(?:\./|/|\\)[\S\\ ]+?\.(?i:jpg|jpeg|png|webp)\b`
regexPattern := `(?:[a-zA-Z]:)?(?:\./|/|\\)[\S\\ ]+?\.(?i:jpg|jpeg|png)\b`
re := regexp.MustCompile(regexPattern)
return re.FindAllString(input, -1)
@ -531,8 +518,6 @@ func extractFileData(input string) (string, []api.ImageData, error) {
return "", imgs, err
}
fmt.Fprintf(os.Stderr, "Added image '%s'\n", nfp)
input = strings.ReplaceAll(input, "'"+nfp+"'", "")
input = strings.ReplaceAll(input, "'"+fp+"'", "")
input = strings.ReplaceAll(input, fp, "")
imgs = append(imgs, data)
}
@ -553,7 +538,7 @@ func getImageData(filePath string) ([]byte, error) {
}
contentType := http.DetectContentType(buf)
allowedTypes := []string{"image/jpeg", "image/jpg", "image/png", "image/webp"}
allowedTypes := []string{"image/jpeg", "image/jpg", "image/png"}
if !slices.Contains(allowedTypes, contentType) {
return nil, fmt.Errorf("invalid image type: %s", contentType)
}

View File

@ -1,8 +1,6 @@
package cmd
import (
"os"
"path/filepath"
"testing"
"github.com/stretchr/testify/assert"
@ -12,17 +10,14 @@ func TestExtractFilenames(t *testing.T) {
// Unix style paths
input := ` some preamble
./relative\ path/one.png inbetween1 ./not a valid two.jpg inbetween2 ./1.svg
/unescaped space /three.jpeg inbetween3 /valid\ path/dir/four.png "./quoted with spaces/five.JPG
/unescaped space /six.webp inbetween6 /valid\ path/dir/seven.WEBP`
/unescaped space /three.jpeg inbetween3 /valid\ path/dir/four.png "./quoted with spaces/five.JPG`
res := extractFileNames(input)
assert.Len(t, res, 7)
assert.Len(t, res, 5)
assert.Contains(t, res[0], "one.png")
assert.Contains(t, res[1], "two.jpg")
assert.Contains(t, res[2], "three.jpeg")
assert.Contains(t, res[3], "four.png")
assert.Contains(t, res[4], "five.JPG")
assert.Contains(t, res[5], "six.webp")
assert.Contains(t, res[6], "seven.WEBP")
assert.NotContains(t, res[4], '"')
assert.NotContains(t, res, "inbetween1")
assert.NotContains(t, res, "./1.svg")
@ -33,12 +28,10 @@ func TestExtractFilenames(t *testing.T) {
/absolute/nospace/three.jpeg inbetween3 /absolute/with space/four.png inbetween4
./relative\ path/five.JPG inbetween5 "./relative with/spaces/six.png inbetween6
d:\path with\spaces\seven.JPEG inbetween7 c:\users\jdoe\eight.png inbetween8
d:\program files\someplace\nine.png inbetween9 "E:\program files\someplace\ten.PNG
c:/users/jdoe/eleven.webp inbetween11 c:/program files/someplace/twelve.WebP inbetween12
d:\path with\spaces\thirteen.WEBP some ending
d:\program files\someplace\nine.png inbetween9 "E:\program files\someplace\ten.PNG some ending
`
res = extractFileNames(input)
assert.Len(t, res, 13)
assert.Len(t, res, 10)
assert.NotContains(t, res, "inbetween2")
assert.Contains(t, res[0], "one.png")
assert.Contains(t, res[0], "c:")
@ -56,31 +49,4 @@ d:\path with\spaces\thirteen.WEBP some ending
assert.Contains(t, res[8], "d:")
assert.Contains(t, res[9], "ten.PNG")
assert.Contains(t, res[9], "E:")
assert.Contains(t, res[10], "eleven.webp")
assert.Contains(t, res[10], "c:")
assert.Contains(t, res[11], "twelve.WebP")
assert.Contains(t, res[11], "c:")
assert.Contains(t, res[12], "thirteen.WEBP")
assert.Contains(t, res[12], "d:")
}
// Ensure that file paths wrapped in single quotes are removed with the quotes.
func TestExtractFileDataRemovesQuotedFilepath(t *testing.T) {
dir := t.TempDir()
fp := filepath.Join(dir, "img.jpg")
data := make([]byte, 600)
copy(data, []byte{
0xff, 0xd8, 0xff, 0xe0, 0x00, 0x10, 'J', 'F', 'I', 'F',
0x00, 0x01, 0x01, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0xff, 0xd9,
})
if err := os.WriteFile(fp, data, 0o600); err != nil {
t.Fatalf("failed to write test image: %v", err)
}
input := "before '" + fp + "' after"
cleaned, imgs, err := extractFileData(input)
assert.NoError(t, err)
assert.Len(t, imgs, 1)
assert.Equal(t, cleaned, "before after")
}

View File

@ -4,7 +4,7 @@ import (
"fmt"
"os"
"github.com/ollama/ollama/runner"
"github.com/ollama/ollama/llama/runner"
)
func main() {

View File

@ -1,26 +1,20 @@
package convert
import (
"cmp"
"encoding/json"
"errors"
"fmt"
"io"
"io/fs"
"log/slog"
"os"
"slices"
"strings"
"github.com/ollama/ollama/fs/ggml"
"github.com/ollama/ollama/llm"
)
type ModelParameters struct {
Architectures []string `json:"architectures"`
VocabSize uint32 `json:"vocab_size"`
TextModel struct {
VocabSize uint32 `json:"vocab_size"`
} `json:"text_config"`
}
type AdapterParameters struct {
@ -33,8 +27,8 @@ type AdapterParameters struct {
} `json:"lora_parameters"`
}
func (ModelParameters) KV(t *Tokenizer) ggml.KV {
kv := ggml.KV{
func (ModelParameters) KV(t *Tokenizer) llm.KV {
kv := llm.KV{
"general.file_type": uint32(1),
"general.quantization_version": uint32(2),
"tokenizer.ggml.pre": t.Pre,
@ -60,7 +54,7 @@ func (ModelParameters) KV(t *Tokenizer) ggml.KV {
return kv
}
func (p AdapterParameters) KV() ggml.KV {
func (p AdapterParameters) KV() llm.KV {
var alpha float32
if p.LoraParameters.Alpha == 0 {
alpha = float32(p.Alpha)
@ -68,7 +62,7 @@ func (p AdapterParameters) KV() ggml.KV {
alpha = p.LoraParameters.Alpha
}
kv := ggml.KV{
kv := llm.KV{
"adapter.lora.alpha": alpha,
"adapter.type": "lora",
"general.file_type": uint32(1),
@ -85,17 +79,27 @@ func (ModelParameters) specialTokenTypes() []string {
}
}
func (ModelParameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []llm.Tensor) error {
return llm.WriteGGUF(ws, kv, ts)
}
func (AdapterParameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []llm.Tensor) error {
return llm.WriteGGUF(ws, kv, ts)
}
type ModelConverter interface {
// KV maps parameters to LLM key-values
KV(*Tokenizer) ggml.KV
KV(*Tokenizer) llm.KV
// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
Tensors([]Tensor) []*ggml.Tensor
Tensors([]Tensor) []llm.Tensor
// Replacements returns a list of string pairs to replace in tensor names.
// See [strings.Replacer](https://pkg.go.dev/strings#Replacer) for details
Replacements() []string
// specialTokenTypes returns any special token types the model uses
specialTokenTypes() []string
// writeFile writes the model to the provided io.WriteSeeker
writeFile(io.WriteSeeker, llm.KV, []llm.Tensor) error
}
type moreParser interface {
@ -104,15 +108,17 @@ type moreParser interface {
type AdapterConverter interface {
// KV maps parameters to LLM key-values
KV(ggml.KV) ggml.KV
KV(llm.KV) llm.KV
// Tensors maps input tensors to LLM tensors. Adapter specific modifications can be done here.
Tensors([]Tensor) []*ggml.Tensor
Tensors([]Tensor) []llm.Tensor
// Replacements returns a list of string pairs to replace in tensor names.
// See [strings.Replacer](https://pkg.go.dev/strings#Replacer) for details
Replacements() []string
writeFile(io.WriteSeeker, llm.KV, []llm.Tensor) error
}
func ConvertAdapter(fsys fs.FS, f *os.File, baseKV ggml.KV) error {
func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV llm.KV) error {
bts, err := fs.ReadFile(fsys, "adapter_config.json")
if err != nil {
return err
@ -147,14 +153,14 @@ func ConvertAdapter(fsys fs.FS, f *os.File, baseKV ggml.KV) error {
return err
}
return writeFile(f, conv.KV(baseKV), conv.Tensors(ts))
return conv.writeFile(ws, conv.KV(baseKV), conv.Tensors(ts))
}
// Convert writes an Ollama compatible model to the provided io.WriteSeeker based on configurations
// and files it finds in the input path.
// Supported input model formats include safetensors.
// Supported input tokenizers files include tokenizer.json (preferred) and tokenizer.model.
func ConvertModel(fsys fs.FS, f *os.File) error {
func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
bts, err := fs.ReadFile(fsys, "config.json")
if err != nil {
return err
@ -171,34 +177,26 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
var conv ModelConverter
switch p.Architectures[0] {
case "LlamaForCausalLM":
case "LlamaForCausalLM", "MistralForCausalLM":
conv = &llamaModel{}
case "MllamaForConditionalGeneration":
conv = &mllamaModel{}
case "Llama4ForConditionalGeneration":
conv = &llama4Model{}
case "Mistral3ForConditionalGeneration":
conv = &mistral3Model{}
case "MixtralForCausalLM":
conv = &mixtralModel{}
case "GemmaForCausalLM":
conv = &gemmaModel{}
case "Gemma2ForCausalLM":
conv = &gemma2Model{}
case "Gemma3ForCausalLM", "Gemma3ForConditionalGeneration":
conv = &gemma3Model{Architecture: p.Architectures[0]}
case "Phi3ForCausalLM":
conv = &phi3Model{}
case "Qwen2ForCausalLM":
conv = &qwen2Model{}
case "Qwen2_5_VLForConditionalGeneration":
conv = &qwen25VLModel{}
case "BertModel":
conv = &bertModel{}
case "CohereForCausalLM":
conv = &commandrModel{}
case "Cohere2ForCausalLM":
conv = &cohere2Model{}
default:
return fmt.Errorf("unsupported architecture %q", p.Architectures[0])
return errors.New("unsupported architecture")
}
if err := json.Unmarshal(bts, conv); err != nil {
@ -216,22 +214,17 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
return err
}
vocabSize := int(cmp.Or(p.VocabSize, p.TextModel.VocabSize))
vocabSize := int(p.VocabSize)
switch {
case vocabSize == 0:
slog.Debug("vocabulary size was not explicitly set by the model", "default size", len(t.Vocabulary.Tokens))
case vocabSize > len(t.Vocabulary.Tokens):
slog.Debug("vocabulary is smaller than expected, padding with dummy tokens", "expect", vocabSize, "actual", len(t.Vocabulary.Tokens))
slog.Warn("vocabulary is smaller than expected, padding with dummy tokens", "expect", 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)
}
case vocabSize < len(t.Vocabulary.Tokens):
slog.Debug("vocabulary is larger than expected", "want", vocabSize, "got", len(t.Vocabulary.Tokens))
p.VocabSize = uint32(len(t.Vocabulary.Tokens))
p.TextModel.VocabSize = uint32(len(t.Vocabulary.Tokens))
return fmt.Errorf("vocabulary is larger than expected '%d' instead of '%d'", len(t.Vocabulary.Tokens), vocabSize)
default:
slog.Debug("vocabulary", "size", len(t.Vocabulary.Tokens))
}
@ -241,13 +234,5 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
return err
}
return writeFile(f, conv.KV(t), conv.Tensors(ts))
}
func writeFile(f *os.File, kv ggml.KV, ts []*ggml.Tensor) error {
for i := range ts {
ts[i].Shape = slices.Clone(ts[i].Shape)
slices.Reverse(ts[i].Shape)
}
return ggml.WriteGGUF(f, kv, ts)
return conv.writeFile(ws, conv.KV(t), conv.Tensors(ts))
}

View File

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

View File

@ -0,0 +1,85 @@
package convert
import (
"cmp"
"github.com/ollama/ollama/llm"
)
type cohere2Model struct {
ModelParameters
MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
HiddenSize uint32 `json:"hidden_size"`
HiddenLayers uint32 `json:"num_hidden_layers"`
IntermediateSize uint32 `json:"intermediate_size"`
NumAttentionHeads uint32 `json:"num_attention_heads"`
NumKeyValueHeads uint32 `json:"num_key_value_heads"`
LayerNormEPS float32 `json:"layer_norm_eps"`
RopeTheta float32 `json:"rope_theta"`
UseQKNorm bool `json:"use_qk_norm"`
MaxLength uint32 `json:"model_max_length"`
LogitScale float32 `json:"logit_scale"`
NCtx uint32 `json:"n_ctx"`
SlidingWindow uint32 `json:"sliding_window"`
HeadDim uint32 `json:"head_dim"`
RotaryPct float32 `json:"rotary_pct"`
VocabSize uint32 `json:"vocab_size"`
}
var _ ModelConverter = (*cohere2Model)(nil)
func (p *cohere2Model) KV(t *Tokenizer) llm.KV {
kv := p.ModelParameters.KV(t)
kv["general.architecture"] = "cohere2"
kv["general.name"] = "C4Ai Command R7B"
kv["cohere2.context_length"] = cmp.Or(p.MaxLength, p.MaxPositionEmbeddings, p.NCtx)
kv["cohere2.embedding_length"] = p.HiddenSize
kv["cohere2.block_count"] = p.HiddenLayers
kv["cohere2.feed_forward_length"] = p.IntermediateSize
kv["cohere2.attention.head_count"] = p.NumAttentionHeads
kv["cohere2.attention.head_count_kv"] = p.NumKeyValueHeads
kv["cohere2.attention.key_length"] = p.HeadDim
kv["cohere2.attention.layer_norm_epsilon"] = p.LayerNormEPS
kv["cohere2.attention.sliding_window"] = p.SlidingWindow
kv["cohere2.attention.value_length"] = p.HeadDim
kv["cohere2.max_position_embeddings"] = cmp.Or(p.MaxLength, p.MaxPositionEmbeddings)
kv["cohere2.logit_scale"] = p.LogitScale
kv["cohere2.rope.dimension_count"] = uint32(p.RotaryPct * float32(p.HiddenSize/p.NumAttentionHeads))
kv["cohere2.rope.freq_base"] = p.RopeTheta
kv["cohere2.rope.scaling.type"] = "none"
kv["cohere2.vocab_size"] = p.VocabSize
return kv
}
func (p *cohere2Model) Tensors(ts []Tensor) []llm.Tensor {
var out []llm.Tensor
for _, t := range ts {
out = append(out, llm.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),
WriterTo: t,
})
}
return out
}
func (p *cohere2Model) Replacements() []string {
return []string{
"self_attn.q_norm", "attn_q_norm",
"self_attn.k_norm", "attn_k_norm",
"model.layers", "blk",
"input_layernorm", "attn_norm",
"mlp.down_proj", "ffn_down",
"mlp.gate_proj", "ffn_gate",
"mlp.up_proj", "ffn_up",
"self_attn.k_proj", "attn_k",
"self_attn.o_proj", "attn_output",
"self_attn.q_proj", "attn_q",
"self_attn.v_proj", "attn_v",
"model.norm", "output_norm",
"model.embed_tokens", "token_embd",
}
}

View File

@ -3,7 +3,7 @@ package convert
import (
"cmp"
"github.com/ollama/ollama/fs/ggml"
"github.com/ollama/ollama/llm"
)
type commandrModel struct {
@ -24,7 +24,7 @@ type commandrModel struct {
var _ ModelConverter = (*commandrModel)(nil)
func (p *commandrModel) KV(t *Tokenizer) ggml.KV {
func (p *commandrModel) KV(t *Tokenizer) llm.KV {
kv := p.ModelParameters.KV(t)
kv["general.architecture"] = "command-r"
kv["general.name"] = "command-r"
@ -43,10 +43,10 @@ func (p *commandrModel) KV(t *Tokenizer) ggml.KV {
return kv
}
func (p *commandrModel) Tensors(ts []Tensor) []*ggml.Tensor {
var out []*ggml.Tensor
func (p *commandrModel) Tensors(ts []Tensor) []llm.Tensor {
var out []llm.Tensor
for _, t := range ts {
out = append(out, &ggml.Tensor{
out = append(out, llm.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),

View File

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

View File

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

View File

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

View File

@ -1,142 +0,0 @@
package convert
import (
"cmp"
"github.com/ollama/ollama/fs/ggml"
)
type gemma3Model struct {
gemmaModel
Architecture string
TextModel struct {
HeadDim uint32 `json:"head_dim"`
HiddenSize uint32 `json:"hidden_size"`
HiddenLayers uint32 `json:"num_hidden_layers"`
IntermediateSize uint32 `json:"intermediate_size"`
SlidingWindow uint32 `json:"sliding_window"`
} `json:"text_config"`
VisionModel struct {
NumAttentionHeads uint32 `json:"num_attention_heads"` // attention.head_count 16
LayerNormEpsilon float32 `json:"layer_norm_eps"` // attention.layer_norm_epsilon 1e-05
NumHiddenLayers uint32 `json:"num_hidden_layers"` // block_count 32
HiddenSize uint32 `json:"hidden_size"` // embedding_length 1280
IntermediateSize uint32 `json:"intermediate_size"` // feed_forward_length 5120
ImageSize uint32 `json:"image_size"` // image_size 560
NumChannels uint32 `json:"num_channels"` // num_channels 3
PatchSize uint32 `json:"patch_size"` // patch_size 14
} `json:"vision_config"`
MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
NumAttentionHeads uint32 `json:"num_attention_heads"`
NumKeyValueHeads uint32 `json:"num_key_value_heads"`
RMSNormEPS float32 `json:"rms_norm_eps"`
HeadDim uint32 `json:"head_dim"`
FinalLogitSoftcap float32 `json:"final_logit_softcapping"`
RopeLocalTheta float32 `json:"rope_local_base_freq"`
RopeGlobalTheta float32 `json:"rope_global_base_freq"`
SlidingWindow uint32 `json:"sliding_window"`
MultiModalTokensPerImage uint32 `json:"mm_tokens_per_image"`
}
const (
gemma4BLayerCount = 34
gemma12BLayerCount = 48
gemma27BLayerCount = 62
)
func (p *gemma3Model) KV(t *Tokenizer) ggml.KV {
kv := p.ModelParameters.KV(t)
kv["general.architecture"] = "gemma3"
numBlocks := cmp.Or(p.HiddenLayers, p.TextModel.HiddenLayers)
kv["gemma3.block_count"] = numBlocks
var (
numHeads uint32
numKVHeads uint32
)
switch numBlocks {
case gemma4BLayerCount:
numHeads = 8
numKVHeads = 4
case gemma12BLayerCount:
numHeads = 16
numKVHeads = 8
case gemma27BLayerCount:
numHeads = 32
numKVHeads = 16
default:
numHeads = p.NumAttentionHeads
numKVHeads = p.NumKeyValueHeads
}
kv["gemma3.attention.head_count"] = numHeads
kv["gemma3.attention.head_count_kv"] = numKVHeads
switch p.Architecture {
case "Gemma3ForCausalLM":
kv["gemma3.context_length"] = p.MaxPositionEmbeddings
kv["gemma3.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
kv["gemma3.attention.key_length"] = p.HeadDim
kv["gemma3.attention.value_length"] = p.HeadDim
kv["gemma3.attention.sliding_window"] = p.SlidingWindow
kv["gemma3.final_logit_softcapping"] = cmp.Or(p.FinalLogitSoftcap, 30)
kv["gemma3.rope.local.freq_base"] = cmp.Or(p.RopeLocalTheta, 10000.0)
kv["gemma3.rope.global.freq_base"] = cmp.Or(p.RopeGlobalTheta, 1000000.0)
kv["gemma3.embedding_length"] = p.HiddenSize
kv["gemma3.feed_forward_length"] = p.IntermediateSize
default:
kv["gemma3.context_length"] = cmp.Or(p.MaxPositionEmbeddings, 131072)
kv["gemma3.embedding_length"] = p.TextModel.HiddenSize
kv["gemma3.feed_forward_length"] = p.TextModel.IntermediateSize
kv["gemma3.attention.sliding_window"] = p.TextModel.SlidingWindow
kv["gemma3.vision.block_count"] = p.VisionModel.NumHiddenLayers
kv["gemma3.vision.embedding_length"] = p.VisionModel.HiddenSize
kv["gemma3.vision.feed_forward_length"] = p.VisionModel.IntermediateSize
kv["gemma3.vision.image_size"] = p.VisionModel.ImageSize
kv["gemma3.vision.patch_size"] = p.VisionModel.PatchSize
kv["gemma3.vision.num_channels"] = cmp.Or(p.VisionModel.NumChannels, 3)
kv["gemma3.vision.attention.head_count"] = p.VisionModel.NumAttentionHeads
kv["gemma3.vision.attention.layer_norm_epsilon"] = cmp.Or(p.VisionModel.LayerNormEpsilon, 1e-6)
kv["gemma3.attention.key_length"] = cmp.Or(p.TextModel.HeadDim, 256)
kv["gemma3.attention.value_length"] = cmp.Or(p.TextModel.HeadDim, 256)
}
if p.MultiModalTokensPerImage > 0 {
kv["gemma3.mm.tokens_per_image"] = p.MultiModalTokensPerImage
}
return kv
}
func (p *gemma3Model) Replacements() []string {
return []string{
"lm_head", "output",
"model.embed_tokens", "token_embd",
"model.norm", "output_norm",
"vision_tower.vision_model.embeddings", "v",
"vision_tower.vision_model", "v",
"vision_model.vision_model.embeddings", "v",
"vision_model.vision_model", "v",
"language_model.", "",
"model.layers", "blk",
"encoder.layers", "blk",
"input_layernorm", "attn_norm",
"self_attn.q_proj", "attn_q",
"self_attn.q_norm", "attn_q_norm",
"self_attn.k_proj", "attn_k",
"self_attn.k_norm", "attn_k_norm",
"self_attn.v_proj", "attn_v",
"self_attn.o_proj", "attn_output",
"self_attn.out_proj", "attn_output",
"mlp.gate_proj", "ffn_gate",
"mlp.down_proj", "ffn_down",
"mlp.up_proj", "ffn_up",
"post_attention_layernorm", "post_attention_norm",
"pre_feedforward_layernorm", "ffn_norm",
"post_feedforward_layernorm", "post_ffw_norm",
"input_projection_weight", "input_projection.weight",
"multi_modal_projector", "mm",
}
}

View File

@ -9,7 +9,7 @@ import (
"github.com/pdevine/tensor"
"github.com/pdevine/tensor/native"
"github.com/ollama/ollama/fs/ggml"
"github.com/ollama/ollama/llm"
)
type llamaModel struct {
@ -28,12 +28,12 @@ type llamaModel struct {
NumKeyValueHeads uint32 `json:"num_key_value_heads"`
RopeTheta float32 `json:"rope_theta"`
RopeScaling struct {
Type string `json:"type"`
RopeType string `json:"rope_type"`
Factor float32 `json:"factor"`
LowFrequencyFactor float32 `json:"low_freq_factor"`
HighFrequencyFactor float32 `json:"high_freq_factor"`
OriginalMaxPositionEmbeddings uint32 `json:"original_max_position_embeddings"`
Type string `json:"type"`
RopeType string `json:"rope_type"`
Factor float32 `json:"factor"`
LowFrequencyFactor float32 `json:"low_freq_factor"`
HighFrequencyFactor float32 `json:"high_freq_factor"`
OriginalMaxPositionalEmbeddings uint32 `json:"original_max_positional_embeddings"`
factors ropeFactor
} `json:"rope_scaling"`
@ -42,13 +42,11 @@ type llamaModel struct {
LayerNormEpsilon float32 `json:"layer_norm_epsilon"`
NormEpsilon float32 `json:"norm_epsilon"`
HeadDim uint32 `json:"head_dim"`
skipRepack bool
}
var _ ModelConverter = (*llamaModel)(nil)
func (p *llamaModel) KV(t *Tokenizer) ggml.KV {
func (p *llamaModel) KV(t *Tokenizer) llm.KV {
kv := p.ModelParameters.KV(t)
kv["general.architecture"] = "llama"
kv["llama.vocab_size"] = p.VocabSize
@ -72,10 +70,6 @@ func (p *llamaModel) KV(t *Tokenizer) ggml.KV {
kv["llama.rope.dimension_count"] = p.HiddenSize / headCount
}
if p.HeadDim > 0 {
kv["llama.attention.head_dim"] = p.HeadDim
}
if p.RopeTheta > 0 {
kv["llama.rope.freq_base"] = p.RopeTheta
}
@ -90,7 +84,7 @@ func (p *llamaModel) KV(t *Tokenizer) ggml.KV {
factorLow := cmp.Or(p.RopeScaling.LowFrequencyFactor, 1.0)
factorHigh := cmp.Or(p.RopeScaling.HighFrequencyFactor, 4.0)
original := cmp.Or(p.RopeScaling.OriginalMaxPositionEmbeddings, 8192)
original := cmp.Or(p.RopeScaling.OriginalMaxPositionalEmbeddings, 8192)
lambdaLow := float32(original) / factorLow
lambdaHigh := float32(original) / factorHigh
@ -126,11 +120,11 @@ func (p *llamaModel) KV(t *Tokenizer) ggml.KV {
return kv
}
func (p *llamaModel) Tensors(ts []Tensor) []*ggml.Tensor {
var out []*ggml.Tensor
func (p *llamaModel) Tensors(ts []Tensor) []llm.Tensor {
var out []llm.Tensor
if p.RopeScaling.factors != nil {
out = append(out, &ggml.Tensor{
out = append(out, llm.Tensor{
Name: "rope_freqs.weight",
Kind: 0,
Shape: []uint64{uint64(len(p.RopeScaling.factors))},
@ -139,13 +133,12 @@ func (p *llamaModel) Tensors(ts []Tensor) []*ggml.Tensor {
}
for _, t := range ts {
if strings.HasSuffix(t.Name(), "attn_q.weight") || strings.HasSuffix(t.Name(), "attn_k.weight") {
if !p.skipRepack {
t.SetRepacker(p.repack)
}
if strings.HasSuffix(t.Name(), "attn_q.weight") ||
strings.HasSuffix(t.Name(), "attn_k.weight") {
t.SetRepacker(p.repack)
}
out = append(out, &ggml.Tensor{
out = append(out, llm.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),

View File

@ -1,169 +0,0 @@
package convert
import (
"slices"
"strings"
"github.com/pdevine/tensor"
"github.com/pdevine/tensor/native"
"github.com/ollama/ollama/fs/ggml"
)
type llama4Model struct {
ModelParameters
TextModel struct {
llamaModel
NumExpertsPerToken uint32 `json:"num_experts_per_tok"`
NumLocalExperts uint32 `json:"num_local_experts"`
InterleaveMOELayerStep uint32 `json:"interleave_moe_layer_step"`
UseQKNorm bool `json:"use_qk_norm"`
IntermediateSizeMLP uint32 `json:"intermediate_size_mlp"`
AttentionChunkSize uint32 `json:"attention_chunk_size"`
} `json:"text_config"`
VisionModel struct {
NumHiddenLayers uint32 `json:"num_hidden_layers"`
HiddenSize uint32 `json:"hidden_size"`
IntermediateSize uint32 `json:"intermediate_size"`
NumAttentionHeads uint32 `json:"num_attention_heads"`
ImageSize uint32 `json:"image_size"`
PatchSize uint32 `json:"patch_size"`
RopeTheta float32 `json:"rope_theta"`
NormEpsilon float32 `json:"norm_eps"`
PixelShuffleRatio float32 `json:"pixel_shuffle_ratio"`
} `json:"vision_config"`
}
// KV implements ModelConverter.
func (p *llama4Model) KV(t *Tokenizer) ggml.KV {
kv := p.ModelParameters.KV(t)
kv["general.architecture"] = "llama4"
for k, v := range p.TextModel.KV(t) {
if strings.HasPrefix(k, "llama.") {
kv[strings.ReplaceAll(k, "llama.", "llama4.")] = v
}
}
kv["llama4.feed_forward_length"] = p.TextModel.IntermediateSizeMLP
kv["llama4.expert_feed_forward_length"] = p.TextModel.IntermediateSize
kv["llama4.expert_count"] = p.TextModel.NumLocalExperts
kv["llama4.expert_used_count"] = p.TextModel.NumExpertsPerToken
kv["llama4.interleave_moe_layer_step"] = p.TextModel.InterleaveMOELayerStep
kv["llama4.use_qk_norm"] = p.TextModel.UseQKNorm
kv["llama4.attention.chunk_size"] = p.TextModel.AttentionChunkSize
kv["llama4.vision.block_count"] = p.VisionModel.NumHiddenLayers
kv["llama4.vision.embedding_length"] = p.VisionModel.HiddenSize
kv["llama4.vision.feed_forward_length"] = p.VisionModel.IntermediateSize
kv["llama4.vision.attention.head_count"] = p.VisionModel.NumAttentionHeads
kv["llama4.vision.image_size"] = p.VisionModel.ImageSize
kv["llama4.vision.patch_size"] = p.VisionModel.PatchSize
kv["llama4.vision.rope.freq_base"] = p.VisionModel.RopeTheta
kv["llama4.vision.layer_norm_epsilon"] = p.VisionModel.NormEpsilon
kv["llama4.vision.pixel_shuffle_ratio"] = p.VisionModel.PixelShuffleRatio
return kv
}
// Replacements implements ModelConverter.
func (p *llama4Model) Replacements() []string {
return append(
p.TextModel.Replacements(),
"language_model.", "",
"vision_model", "v",
"multi_modal_projector", "mm",
"feed_forward.down_proj", "ffn_down",
"feed_forward.up_proj", "ffn_up",
"feed_forward.gate_proj", "ffn_gate",
"feed_forward.", "ffn_",
"shared_expert.down_proj", "down_shexp",
"shared_expert.gate_proj", "gate_shexp",
"shared_expert.up_proj", "up_shexp",
"experts.down_proj", "down_exps.weight",
"experts.gate_up_proj", "gate_up_exps.weight",
"router", "gate_inp",
"patch_embedding.linear", "patch_embedding",
)
}
// Tensors implements ModelConverter.
func (p *llama4Model) Tensors(ts []Tensor) []*ggml.Tensor {
var out []*ggml.Tensor
var textTensors []Tensor
for _, t := range ts {
if strings.HasPrefix(t.Name(), "v.") || strings.HasPrefix(t.Name(), "mm.") {
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),
WriterTo: t,
})
} else if strings.Contains(t.Name(), "ffn_gate_up_exps") {
// gate and up projectors are fused
// dims[1], dims[2] must be swapped
// [experts, hidden_size, intermediate_size * 2] --> [experts, intermediate_size, hidden_size]
halfDim := int(t.Shape()[2]) / 2
newShape := slices.Clone(t.Shape())
newShape[1], newShape[2] = newShape[2]/2, newShape[1]
for i, name := range []string{"ffn_gate_exps", "ffn_up_exps"} {
// clone tensor since we need separate repackers
tt := t.Clone()
tt.SetRepacker(p.repack(nil, nil, tensor.S(i*halfDim, (i+1)*halfDim)))
out = append(out, &ggml.Tensor{
Name: strings.ReplaceAll(tt.Name(), "ffn_gate_up_exps", name),
Kind: tt.Kind(),
Shape: newShape,
WriterTo: tt,
})
}
} else if strings.Contains(t.Name(), "ffn_down_exps") {
// dims[1], dims[2] must be swapped
// [experts, intermediate_size, hidden_size] --> [experts, hidden_size, intermediate_size]
t.SetRepacker(p.repack())
newShape := slices.Clone(t.Shape())
newShape[1], newShape[2] = newShape[2], newShape[1]
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: newShape,
WriterTo: t,
})
} else {
textTensors = append(textTensors, t)
}
}
p.TextModel.skipRepack = true
out = append(out, p.TextModel.Tensors(textTensors)...)
return out
}
func (p *llama4Model) repack(slice ...tensor.Slice) Repacker {
return func(name string, data []float32, shape []uint64) ([]float32, error) {
dims := make([]int, len(shape))
for i, dim := range shape {
dims[i] = int(dim)
}
var t tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
t, err := t.Slice(slice...)
if err != nil {
return nil, err
}
if err := t.T(0, 2, 1); err != nil {
return nil, err
}
t = tensor.Materialize(t)
// flatten tensor so it can be return as a vector
if err := t.Reshape(t.Shape().TotalSize()); err != nil {
return nil, err
}
return native.VectorF32(t.(*tensor.Dense))
}
}

View File

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

View File

@ -1,190 +0,0 @@
package convert
import (
"cmp"
"fmt"
"strings"
"github.com/pdevine/tensor"
"github.com/pdevine/tensor/native"
"github.com/ollama/ollama/fs/ggml"
)
type mistral3Model struct {
ModelParameters
ImageTokenIndex uint32 `json:"image_token_index"`
SpatialMergeSize uint32 `json:"spatial_merge_size"`
VisionFeatureLayer int32 `json:"vision_feature_layer"`
TextModel struct {
NumHiddenLayers uint32 `json:"num_hidden_layers"`
MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
HiddenSize uint32 `json:"hidden_size"`
IntermediateSize uint32 `json:"intermediate_size"`
NumAttentionHeads uint32 `json:"num_attention_heads"`
NumKeyValueHeads uint32 `json:"num_key_value_heads"`
RopeTheta float32 `json:"rope_theta"`
RMSNormEPS float32 `json:"rms_norm_eps"`
HeadDim uint32 `json:"head_dim"`
SlidingWindow *uint32 `json:"sliding_window"`
HiddenAct string `json:"hidden_act"`
VocabSize uint32 `json:"vocab_size"`
} `json:"text_config"`
VisionModel struct {
NumAttentionHeads uint32 `json:"num_attention_heads"`
NumHiddenLayers uint32 `json:"num_hidden_layers"`
HiddenSize uint32 `json:"hidden_size"`
IntermediateSize uint32 `json:"intermediate_size"`
ImageSize uint32 `json:"image_size"`
NumChannels uint32 `json:"num_channels"`
PatchSize uint32 `json:"patch_size"`
HeadDim uint32 `json:"head_dim"`
HiddenAct string `json:"hidden_act"`
RopeTheta float32 `json:"rope_theta"`
} `json:"vision_config"`
MultiModalProjectorBias bool `json:"multimodal_projector_bias"`
ProjectorHiddenAct string `json:"projector_hidden_act"`
}
func (p *mistral3Model) KV(t *Tokenizer) ggml.KV {
kv := p.ModelParameters.KV(t)
kv["general.architecture"] = "mistral3"
kv["mistral3.vocab_size"] = p.TextModel.VocabSize
// Text configuration
kv["mistral3.block_count"] = p.TextModel.NumHiddenLayers
kv["mistral3.context_length"] = p.TextModel.MaxPositionEmbeddings
kv["mistral3.embedding_length"] = p.TextModel.HiddenSize
kv["mistral3.feed_forward_length"] = p.TextModel.IntermediateSize
kv["mistral3.attention.head_count"] = p.TextModel.NumAttentionHeads
kv["mistral3.attention.head_count_kv"] = p.TextModel.NumKeyValueHeads
kv["mistral3.attention.layer_norm_rms_epsilon"] = p.TextModel.RMSNormEPS
kv["mistral3.attention.key_length"] = p.TextModel.HeadDim
kv["mistral3.attention.value_length"] = p.TextModel.HeadDim
kv["mistral3.rope.dimension_count"] = p.TextModel.HiddenSize / p.TextModel.NumHiddenLayers
kv["mistral3.rope.freq_base"] = p.TextModel.RopeTheta
// Vision configuration
kv["mistral3.vision.block_count"] = p.VisionModel.NumHiddenLayers
kv["mistral3.vision.embedding_length"] = p.VisionModel.HiddenSize
kv["mistral3.vision.feed_forward_length"] = p.VisionModel.IntermediateSize
kv["mistral3.vision.attention.head_count"] = p.VisionModel.NumAttentionHeads
kv["mistral3.vision.attention.key_length"] = p.VisionModel.HeadDim
kv["mistral3.vision.image_size"] = p.VisionModel.ImageSize
kv["mistral3.vision.patch_size"] = p.VisionModel.PatchSize
kv["mistral3.vision.num_channels"] = p.VisionModel.NumChannels
// kv["mistral3.vision.attention.layer_norm_epsilon"] = 1e-05 // Default value
kv["mistral3.vision.rope.freq_base"] = p.VisionModel.RopeTheta
// Multimodal configuration
kv["mistral3.image_token_index"] = p.ImageTokenIndex
kv["mistral3.spatial_merge_size"] = p.SpatialMergeSize
kv["mistral3.mm.projector_bias"] = p.MultiModalProjectorBias
if p.ProjectorHiddenAct != "" {
kv["mistral3.mm.projector_hidden_act"] = p.ProjectorHiddenAct
}
return kv
}
func (p *mistral3Model) Tensors(ts []Tensor) []*ggml.Tensor {
var out []*ggml.Tensor
for _, t := range ts {
if !strings.HasPrefix(t.Name(), "v.") {
if strings.HasSuffix(t.Name(), ".attn_q.weight") ||
strings.HasSuffix(t.Name(), ".attn_k.weight") {
t.SetRepacker(p.repack)
}
}
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),
WriterTo: t,
})
}
return out
}
func (p *mistral3Model) Replacements() []string {
return []string{
"language_model.model.norm", "output_norm",
"language_model.model.", "",
"language_model.", "",
"layers", "blk",
"transformer.layers", "blk",
"vision_tower", "v",
"ln_pre", "encoder_norm",
"input_layernorm", "attn_norm",
"post_attention_layernorm", "ffn_norm",
"embed_tokens", "token_embd",
"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.down_proj", "ffn_down",
"mlp.gate_proj", "ffn_gate",
"mlp.up_proj", "ffn_up",
"attention.q_proj", "attn_q",
"attention.k_proj", "attn_k",
"attention.v_proj", "attn_v",
"attention.o_proj", "attn_output",
"attention_norm", "attn_norm",
"feed_forward.gate_proj", "ffn_gate",
"feed_forward.down_proj", "ffn_down",
"feed_forward.up_proj", "ffn_up",
"multi_modal_projector", "mm",
"ffn_norm", "ffn_norm",
"lm_head", "output",
}
}
func (p *mistral3Model) repack(name string, data []float32, shape []uint64) ([]float32, error) {
var dims []int
for _, dim := range shape {
dims = append(dims, int(dim))
}
var heads uint32
if strings.HasSuffix(name, ".attn_q.weight") {
heads = p.TextModel.NumAttentionHeads
} else if strings.HasSuffix(name, ".attn_k.weight") {
heads = cmp.Or(p.TextModel.NumKeyValueHeads, p.TextModel.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
}

View File

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

View File

@ -1,160 +0,0 @@
package convert
import (
"strings"
"github.com/ollama/ollama/fs/ggml"
"github.com/pdevine/tensor"
"github.com/pdevine/tensor/native"
)
type mllamaModel struct {
ModelParameters
TextModel struct {
llamaModel
CrossAttentionLayers []int32 `json:"cross_attention_layers"`
} `json:"text_config"`
VisionModel struct {
NumHiddenLayers uint32 `json:"num_hidden_layers"`
NumGlobalLayers uint32 `json:"num_global_layers"`
IntermediateLayersIndices []int32 `json:"intermediate_layers_indices"`
HiddenSize uint32 `json:"hidden_size"`
IntermediateSize uint32 `json:"intermediate_size"`
AttentionHeads uint32 `json:"attention_heads"`
ImageSize uint32 `json:"image_size"`
PatchSize uint32 `json:"patch_size"`
NumChannels uint32 `json:"num_channels"`
MaxNumTiles uint32 `json:"max_num_tiles"`
NormEpsilon float32 `json:"norm_eps"`
RopeTheta float32 `json:"rope.freq_base"`
} `json:"vision_config"`
}
func (m *mllamaModel) KV(t *Tokenizer) ggml.KV {
kv := m.ModelParameters.KV(t)
kv["general.architecture"] = "mllama"
for k, v := range m.TextModel.KV(t) {
if strings.HasPrefix(k, "llama.") {
kv[strings.ReplaceAll(k, "llama.", "mllama.")] = v
}
}
kv["mllama.attention.cross_attention_layers"] = m.TextModel.CrossAttentionLayers
kv["mllama.vision.block_count"] = m.VisionModel.NumHiddenLayers
kv["mllama.vision.global.block_count"] = m.VisionModel.NumGlobalLayers
kv["mllama.vision.intermediate_layers_indices"] = m.VisionModel.IntermediateLayersIndices
kv["mllama.vision.embedding_length"] = m.VisionModel.HiddenSize
kv["mllama.vision.feed_forward_length"] = m.VisionModel.IntermediateSize
kv["mllama.vision.attention.head_count"] = m.VisionModel.AttentionHeads
kv["mllama.vision.attention.layer_norm_epsilon"] = m.VisionModel.NormEpsilon
kv["mllama.vision.image_size"] = m.VisionModel.ImageSize
kv["mllama.vision.patch_size"] = m.VisionModel.PatchSize
kv["mllama.vision.max_num_tiles"] = m.VisionModel.MaxNumTiles
kv["mllama.vision.num_channels"] = m.VisionModel.NumChannels
return kv
}
func (m *mllamaModel) Replacements() []string {
return append(
m.TextModel.Replacements(),
"language_model.", "",
"gate_attn", "attn_gate",
"gate_ffn", "ffn_gate",
"cross_attn.", "cross_attn_",
"vision_model", "v",
"class_embedding", "class_embd",
"patch_embedding", "patch_embd",
"gated_positional_embedding.tile_embedding", "tile_position_embd",
"gated_positional_embedding.embedding", "position_embd.weight",
"gated_positional_embedding", "position_embd",
"embedding.weight", "weight",
"pre_tile_positional_embedding", "pre_tile_position_embd",
"post_tile_positional_embedding", "post_tile_position_embd",
"layernorm_pre", "pre_ln",
"layernorm_post", "post_ln",
"global_transformer.layers", "global.blk",
"transformer.layers", "blk",
"mlp.fc1", "ffn_up",
"mlp.fc2", "ffn_down",
"multi_modal_projector", "mm.0",
)
}
func (m *mllamaModel) Tensors(ts []Tensor) []*ggml.Tensor {
var out []*ggml.Tensor
var text []Tensor
for _, t := range ts {
if t.Name() == "v.position_embd.gate" {
for _, name := range []string{"v.position_embd.gate", "v.tile_position_embd.gate"} {
tt := t.Clone()
tt.SetRepacker(m.repack(name))
out = append(out, &ggml.Tensor{
Name: name,
Kind: t.Kind(),
Shape: t.Shape(),
WriterTo: tt,
})
}
} else if t.Name() == "v.pre_tile_position_embd.gate" || t.Name() == "v.post_tile_position_embd.gate" {
t.SetRepacker(m.repack(t.Name()))
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),
WriterTo: t,
})
} else if strings.HasPrefix(t.Name(), "v.") || strings.HasPrefix(t.Name(), "mm.") {
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),
WriterTo: t,
})
} else {
text = append(text, t)
}
}
return append(out, m.TextModel.Tensors(text)...)
}
func (m *mllamaModel) repack(name string) Repacker {
return func(_ string, data []float32, shape []uint64) (_ []float32, err error) {
dims := make([]int, len(shape))
for i, dim := range shape {
dims[i] = int(dim)
}
var t tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
t, err = tensor.Tanh(t)
if err != nil {
return nil, err
}
if name == "v.position_embd.gate" {
t, err = tensor.Sub(float32(1), t)
if err != nil {
return nil, err
}
}
t = tensor.Materialize(t)
// flatten tensor so it can be return as a vector
if err := t.Reshape(t.Shape().TotalSize()); err != nil {
return nil, err
}
return native.VectorF32(t.(*tensor.Dense))
}
}

View File

@ -8,7 +8,7 @@ import (
"strings"
"sync"
"github.com/ollama/ollama/fs/ggml"
"github.com/ollama/ollama/llm"
)
type phi3Model struct {
@ -37,7 +37,7 @@ type phi3Model struct {
var _ ModelConverter = (*phi3Model)(nil)
func (p *phi3Model) KV(t *Tokenizer) ggml.KV {
func (p *phi3Model) KV(t *Tokenizer) llm.KV {
kv := p.ModelParameters.KV(t)
kv["general.architecture"] = "phi3"
kv["phi3.context_length"] = p.MaxPositionEmbeddings
@ -68,19 +68,19 @@ func (p *phi3Model) KV(t *Tokenizer) ggml.KV {
return kv
}
func (p *phi3Model) Tensors(ts []Tensor) []*ggml.Tensor {
func (p *phi3Model) Tensors(ts []Tensor) []llm.Tensor {
var addRopeFactors sync.Once
out := make([]*ggml.Tensor, 0, len(ts)+2)
out := make([]llm.Tensor, 0, len(ts)+2)
for _, t := range ts {
if strings.HasPrefix(t.Name(), "blk.0.") {
addRopeFactors.Do(func() {
out = append(out, &ggml.Tensor{
out = append(out, llm.Tensor{
Name: "rope_factors_long.weight",
Kind: 0,
Shape: []uint64{uint64(len(p.RopeScaling.LongFactor))},
WriterTo: p.RopeScaling.LongFactor,
}, &ggml.Tensor{
}, llm.Tensor{
Name: "rope_factors_short.weight",
Kind: 0,
Shape: []uint64{uint64(len(p.RopeScaling.ShortFactor))},
@ -89,7 +89,7 @@ func (p *phi3Model) Tensors(ts []Tensor) []*ggml.Tensor {
})
}
out = append(out, &ggml.Tensor{
out = append(out, llm.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),
@ -118,5 +118,6 @@ func (p *phi3Model) Replacements() []string {
type ropeFactor []float32
func (r ropeFactor) WriteTo(w io.Writer) (int64, error) {
return 0, binary.Write(w, binary.LittleEndian, r)
err := binary.Write(w, binary.LittleEndian, r)
return 0, err
}

View File

@ -1,6 +1,6 @@
package convert
import "github.com/ollama/ollama/fs/ggml"
import "github.com/ollama/ollama/llm"
type qwen2Model struct {
ModelParameters
@ -15,14 +15,13 @@ type qwen2Model struct {
Type string `json:"type"`
Factor ropeFactor `json:"factor"`
OriginalMaxPositionEmbeddings uint32 `json:"original_max_position_embeddings"`
MropeSection []int32 `json:"mrope_section"`
} `json:"rope_scaling"`
RMSNormEPS float32 `json:"rms_norm_eps"`
}
var _ ModelConverter = (*qwen2Model)(nil)
func (q *qwen2Model) KV(t *Tokenizer) ggml.KV {
func (q *qwen2Model) KV(t *Tokenizer) llm.KV {
kv := q.ModelParameters.KV(t)
kv["general.architecture"] = "qwen2"
kv["qwen2.block_count"] = q.HiddenLayers
@ -40,18 +39,16 @@ func (q *qwen2Model) KV(t *Tokenizer) ggml.KV {
case "yarn":
kv["qwen2.rope.scaling.type"] = q.RopeScaling.Type
kv["qwen2.rope.scaling.factor"] = q.RopeScaling.Factor
case "mrope", "default":
kv["qwen2.rope.mrope_section"] = q.RopeScaling.MropeSection
default:
panic("unknown rope scaling type")
}
return kv
}
func (q *qwen2Model) Tensors(ts []Tensor) []*ggml.Tensor {
var out []*ggml.Tensor
func (q *qwen2Model) Tensors(ts []Tensor) []llm.Tensor {
var out []llm.Tensor
for _, t := range ts {
out = append(out, &ggml.Tensor{
out = append(out, llm.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),

View File

@ -1,102 +0,0 @@
package convert
import (
"cmp"
"slices"
"strings"
"github.com/ollama/ollama/fs/ggml"
)
type qwen25VLModel struct {
qwen2Model
VisionModel struct {
Depth uint32 `json:"depth"`
HiddenSize uint32 `json:"hidden_size"`
NumHeads uint32 `json:"num_heads"`
InChannels uint32 `json:"in_chans"`
PatchSize uint32 `json:"patch_size"`
SpatialMergeSize uint32 `json:"spatial_merge_size"`
SpatialPatchSize uint32 `json:"spatial_patch_size"`
WindowSize uint32 `json:"window_size"`
RMSNormEps float32 `json:"layer_norm_epsilon"`
RopeTheta float32 `json:"rope_theta"`
FullAttentionBlocks []int32 `json:"fullatt_block_indexes"`
TemporalPatchSize uint32 `json:"temporal_patch_size"`
} `json:"vision_config"`
}
var _ ModelConverter = (*qwen25VLModel)(nil)
func (q *qwen25VLModel) KV(t *Tokenizer) ggml.KV {
kv := q.ModelParameters.KV(t)
kv["general.architecture"] = "qwen25vl"
for k, v := range q.qwen2Model.KV(t) {
if strings.HasPrefix(k, "qwen2.") {
kv[strings.Replace(k, "qwen2.", "qwen25vl.", 1)] = v
}
}
if q.VisionModel.FullAttentionBlocks == nil {
kv["qwen25vl.vision.fullatt_block_indexes"] = []int32{7, 15, 23, 31}
}
kv["qwen25vl.vision.block_count"] = cmp.Or(q.VisionModel.Depth, 32)
kv["qwen25vl.vision.embedding_length"] = q.VisionModel.HiddenSize
kv["qwen25vl.vision.attention.head_count"] = cmp.Or(q.VisionModel.NumHeads, 16)
kv["qwen25vl.vision.num_channels"] = q.VisionModel.InChannels
kv["qwen25vl.vision.patch_size"] = cmp.Or(q.VisionModel.PatchSize, 14)
kv["qwen25vl.vision.spatial_merge_size"] = cmp.Or(q.VisionModel.SpatialMergeSize, 2)
kv["qwen25vl.vision.spatial_patch_size"] = q.VisionModel.SpatialPatchSize
kv["qwen25vl.vision.window_size"] = cmp.Or(q.VisionModel.WindowSize, 112)
kv["qwen25vl.vision.attention.layer_norm_epsilon"] = cmp.Or(q.VisionModel.RMSNormEps, 1e-6)
kv["qwen25vl.vision.rope.freq_base"] = cmp.Or(q.VisionModel.RopeTheta, 1e4)
kv["qwen25vl.vision.fullatt_block_indexes"] = q.VisionModel.FullAttentionBlocks
kv["qwen25vl.vision.temporal_patch_size"] = cmp.Or(q.VisionModel.TemporalPatchSize, 2)
return kv
}
func (q *qwen25VLModel) Tensors(ts []Tensor) []*ggml.Tensor {
var out []*ggml.Tensor
for _, t := range ts {
if strings.Contains(t.Name(), "patch_embed.proj") {
for t := range splitDim(t, 2,
strings.NewReplacer("patch_embed.proj", "patch_embd_0"),
strings.NewReplacer("patch_embed.proj", "patch_embd_1"),
) {
t.Shape = slices.DeleteFunc(t.Shape, func(i uint64) bool { return i == 1 })
out = append(out, t)
}
} else if strings.Contains(t.Name(), "attn.qkv") {
out = append(out, slices.Collect(splitDim(t, 0,
strings.NewReplacer("attn.qkv", "attn_q"),
strings.NewReplacer("attn.qkv", "attn_k"),
strings.NewReplacer("attn.qkv", "attn_v"),
))...)
} else {
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),
WriterTo: t,
})
}
}
return out
}
func (p *qwen25VLModel) Replacements() []string {
return append(
p.qwen2Model.Replacements(),
"visual", "v",
"blocks", "blk",
"attn.proj", "attn_out",
"norm1", "ln1",
"norm2", "ln2",
)
}

View File

@ -11,6 +11,7 @@ import (
"io"
"io/fs"
"log/slog"
"math"
"os"
"path/filepath"
"slices"
@ -19,7 +20,7 @@ import (
"golang.org/x/exp/maps"
"github.com/ollama/ollama/fs/ggml"
"github.com/ollama/ollama/llm"
)
type tensorData struct {
@ -28,7 +29,9 @@ type tensorData struct {
Shape []int `json:"shape"`
}
func convertFull(t *testing.T, fsys fs.FS) (*os.File, ggml.KV, ggml.Tensors) {
var generate string
func convertFull(t *testing.T, fsys fs.FS) (*os.File, llm.KV, *llm.Tensors) {
t.Helper()
f, err := os.CreateTemp(t.TempDir(), "f16")
@ -47,7 +50,7 @@ func convertFull(t *testing.T, fsys fs.FS) (*os.File, ggml.KV, ggml.Tensors) {
}
t.Cleanup(func() { r.Close() })
m, _, err := ggml.Decode(r, -1)
m, _, err := llm.DecodeGGML(r, math.MaxInt)
if err != nil {
t.Fatal(err)
}
@ -59,7 +62,7 @@ func convertFull(t *testing.T, fsys fs.FS) (*os.File, ggml.KV, ggml.Tensors) {
return r, m.KV(), m.Tensors()
}
func generateResultsJSON(t *testing.T, f *os.File, kv ggml.KV, tensors ggml.Tensors) map[string]string {
func generateResultsJSON(t *testing.T, f *os.File, kv llm.KV, tensors *llm.Tensors) map[string]string {
actual := make(map[string]string)
for k, v := range kv {
if s, ok := v.(json.Marshaler); !ok {
@ -74,7 +77,7 @@ func generateResultsJSON(t *testing.T, f *os.File, kv ggml.KV, tensors ggml.Tens
}
}
for _, tensor := range tensors.Items() {
for _, tensor := range tensors.Items {
sha256sum := sha256.New()
sr := io.NewSectionReader(f, int64(tensors.Offset+tensor.Offset), int64(tensor.Size()))
if _, err := io.Copy(sha256sum, sr); err != nil {
@ -90,6 +93,7 @@ func generateResultsJSON(t *testing.T, f *os.File, kv ggml.KV, tensors ggml.Tens
func TestMain(m *testing.M) {
var level slog.Level
flag.TextVar(&level, "level", slog.LevelInfo, "log level")
flag.StringVar(&generate, "generate", "", "generate model data")
flag.Parse()
slog.SetLogLoggerLevel(level)
os.Exit(m.Run())
@ -109,6 +113,7 @@ func TestConvertModel(t *testing.T) {
"gemma-2-9b-it",
"Qwen2.5-0.5B-Instruct",
"c4ai-command-r-v01",
"c4ai-command-r7b-12-2024",
}
for i := range cases {
@ -126,11 +131,23 @@ func TestConvertModel(t *testing.T) {
f, kv, tensors := convertFull(t, os.DirFS(p))
actual := generateResultsJSON(t, f, kv, tensors)
if generate != "" && generate == tt {
outFile := filepath.Join("testdata", fmt.Sprintf("%s.json", tt))
data, err := json.MarshalIndent(actual, "", " ")
if err != nil {
t.Fatal(err)
}
if err := os.WriteFile(outFile, data, 0o644); err != nil {
t.Fatal(err)
}
t.Logf("Generated expected results for %s", tt)
return
}
expectFile, err := os.Open(filepath.Join("testdata", fmt.Sprintf("%s.json", tt)))
if err != nil {
t.Fatal(err)
}
defer expectFile.Close()
var expect map[string]string
if err := json.NewDecoder(expectFile).Decode(&expect); err != nil {
@ -332,7 +349,7 @@ func TestConvertAdapter(t *testing.T) {
}
defer r.Close()
m, _, err := ggml.Decode(r, -1)
m, _, err := llm.DecodeGGML(r, math.MaxInt)
if err != nil {
t.Fatal(err)
}

58
convert/fs.go Normal file
View File

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

View File

@ -11,15 +11,14 @@ type Tensor interface {
Name() string
Shape() []uint64
Kind() uint32
SetRepacker(Repacker)
SetRepacker(repacker)
WriteTo(io.Writer) (int64, error)
Clone() Tensor
}
type tensorBase struct {
name string
shape []uint64
repacker Repacker
name string
shape []uint64
repacker
}
func (t tensorBase) Name() string {
@ -37,11 +36,7 @@ const (
func (t tensorBase) Kind() uint32 {
if strings.HasSuffix(t.name, ".ffn_gate_inp.weight") ||
t.name == "token_types.weight" ||
t.name == "v.positional_embedding_vlm" ||
t.name == "v.tile_position_embd.weight" ||
t.name == "v.pre_tile_position_embd.weight" ||
t.name == "v.post_tile_position_embd.weight" {
t.name == "token_types.weight" {
// these tensors are always F32
return 0
}
@ -56,18 +51,21 @@ func (t tensorBase) Kind() uint32 {
}
}
func (t *tensorBase) SetRepacker(fn Repacker) {
func (t *tensorBase) SetRepacker(fn repacker) {
t.repacker = fn
}
type Repacker func(string, []float32, []uint64) ([]float32, error)
type repacker func(string, []float32, []uint64) ([]float32, error)
func parseTensors(fsys fs.FS, replacer *strings.Replacer) ([]Tensor, error) {
patterns := []struct {
Pattern string
Func func(fs.FS, *strings.Replacer, ...string) ([]Tensor, error)
}{
{"*.safetensors", parseSafetensors},
{"model-*-of-*.safetensors", parseSafetensors},
{"model.safetensors", parseSafetensors},
{"adapters.safetensors", parseSafetensors},
{"adapter_model.safetensors", parseSafetensors},
{"pytorch_model-*-of-*.bin", parseTorch},
{"pytorch_model.bin", parseTorch},
{"consolidated.*.pth", parseTorch},

View File

@ -94,21 +94,6 @@ type safetensor struct {
*tensorBase
}
func (st safetensor) Clone() Tensor {
return &safetensor{
fs: st.fs,
path: st.path,
dtype: st.dtype,
offset: st.offset,
size: st.size,
tensorBase: &tensorBase{
name: st.name,
repacker: st.repacker,
shape: slices.Clone(st.shape),
},
}
}
func (st safetensor) WriteTo(w io.Writer) (int64, error) {
f, err := st.fs.Open(st.path)
if err != nil {

View File

@ -43,17 +43,6 @@ type torch struct {
*tensorBase
}
func (t torch) Clone() Tensor {
return torch{
storage: t.storage,
tensorBase: &tensorBase{
name: t.name,
shape: t.shape,
repacker: t.repacker,
},
}
}
func (pt torch) WriteTo(w io.Writer) (int64, error) {
return 0, nil
}

View File

@ -1360,7 +1360,7 @@ func file_sentencepiece_model_proto_rawDescGZIP() []byte {
var file_sentencepiece_model_proto_enumTypes = make([]protoimpl.EnumInfo, 2)
var file_sentencepiece_model_proto_msgTypes = make([]protoimpl.MessageInfo, 6)
var file_sentencepiece_model_proto_goTypes = []any{
var file_sentencepiece_model_proto_goTypes = []interface{}{
(TrainerSpec_ModelType)(0), // 0: sentencepiece.TrainerSpec.ModelType
(ModelProto_SentencePiece_Type)(0), // 1: sentencepiece.ModelProto.SentencePiece.Type
(*TrainerSpec)(nil), // 2: sentencepiece.TrainerSpec
@ -1392,7 +1392,7 @@ func file_sentencepiece_model_proto_init() {
return
}
if !protoimpl.UnsafeEnabled {
file_sentencepiece_model_proto_msgTypes[0].Exporter = func(v any, i int) any {
file_sentencepiece_model_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*TrainerSpec); i {
case 0:
return &v.state
@ -1406,7 +1406,7 @@ func file_sentencepiece_model_proto_init() {
return nil
}
}
file_sentencepiece_model_proto_msgTypes[1].Exporter = func(v any, i int) any {
file_sentencepiece_model_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*NormalizerSpec); i {
case 0:
return &v.state
@ -1420,7 +1420,7 @@ func file_sentencepiece_model_proto_init() {
return nil
}
}
file_sentencepiece_model_proto_msgTypes[2].Exporter = func(v any, i int) any {
file_sentencepiece_model_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*SelfTestData); i {
case 0:
return &v.state
@ -1434,7 +1434,7 @@ func file_sentencepiece_model_proto_init() {
return nil
}
}
file_sentencepiece_model_proto_msgTypes[3].Exporter = func(v any, i int) any {
file_sentencepiece_model_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ModelProto); i {
case 0:
return &v.state
@ -1448,7 +1448,7 @@ func file_sentencepiece_model_proto_init() {
return nil
}
}
file_sentencepiece_model_proto_msgTypes[4].Exporter = func(v any, i int) any {
file_sentencepiece_model_proto_msgTypes[4].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*SelfTestData_Sample); i {
case 0:
return &v.state
@ -1460,7 +1460,7 @@ func file_sentencepiece_model_proto_init() {
return nil
}
}
file_sentencepiece_model_proto_msgTypes[5].Exporter = func(v any, i int) any {
file_sentencepiece_model_proto_msgTypes[5].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*ModelProto_SentencePiece); i {
case 0:
return &v.state

View File

@ -1,56 +0,0 @@
package convert
import (
"iter"
"slices"
"strings"
"github.com/ollama/ollama/fs/ggml"
"github.com/pdevine/tensor"
"github.com/pdevine/tensor/native"
)
// splitDim splits a tensor along a specified dimension into multiple tensors. The dimension
// is split evenly based on the number of replacers provided.
func splitDim(t Tensor, dim int, replacers ...*strings.Replacer) iter.Seq[*ggml.Tensor] {
return func(yield func(*ggml.Tensor) bool) {
for i, replacer := range replacers {
shape := slices.Clone(t.Shape())
shape[dim] = shape[dim] / uint64(len(replacers))
slice := slices.Repeat([]tensor.Slice{nil}, len(shape))
slice[dim] = tensor.S(i*int(shape[dim]), (i+1)*int(shape[dim]))
tt := t.Clone()
tt.SetRepacker(func(_ string, data []float32, shape []uint64) ([]float32, error) {
dims := make([]int, len(shape))
for i := range shape {
dims[i] = int(shape[i])
}
var t tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
t, err := t.Slice(slice...)
if err != nil {
return nil, err
}
t = tensor.Materialize(t)
// flatten tensor so it can be written as a vector
if err := t.Reshape(t.Shape().TotalSize()); err != nil {
return nil, err
}
return native.VectorF32(t.(*tensor.Dense))
})
if !yield(&ggml.Tensor{
Name: replacer.Replace(t.Name()),
Kind: t.Kind(),
Shape: shape,
WriterTo: tt,
}) {
break
}
}
}
}

File diff suppressed because one or more lines are too long

View File

@ -6,9 +6,7 @@ import (
"errors"
"fmt"
"io/fs"
"log/slog"
"os"
"reflect"
"slices"
"google.golang.org/protobuf/proto"
@ -17,8 +15,6 @@ import (
)
func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
slog.Debug("using spm vocabulary")
ast, err := parseAdditionalSpecialTokens(fsys)
if err != nil {
return nil, err
@ -47,19 +43,10 @@ func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
v.Types = append(v.Types, int32(t))
default:
tt := int32(sentencepiece.ModelProto_SentencePiece_NORMAL)
// temporary fix to handle gemma3 broken configs
if slices.Contains([]string{"<end_of_turn>", "<start_of_turn>"}, piece.GetPiece()) {
if slices.Contains(ast, piece.GetPiece()) {
tt = int32(sentencepiece.ModelProto_SentencePiece_CONTROL)
}
for _, t := range ast {
if t.Content == piece.GetPiece() {
tt = int32(sentencepiece.ModelProto_SentencePiece_CONTROL)
break
}
}
v.Types = append(v.Types, tt)
}
}
@ -91,16 +78,10 @@ func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
return cmp.Compare(i.id, j.id)
})
for _, t := range ts {
if t.id < len(v.Tokens) {
if v.Tokens[t.id] == t.content {
slog.Warn("tokenizer", "duplicate token", t.content, "id", t.id)
continue
}
return nil, fmt.Errorf("token mismatch: %s != %s at pos [%d]", t.content, v.Tokens[t.id], t.id)
}
if t.id != len(v.Tokens) {
return nil, fmt.Errorf("invalid token id: [%d] as pos [%d]", t.id, len(v.Tokens))
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)
@ -111,15 +92,7 @@ func parseSentencePiece(fsys fs.FS) (*Vocabulary, error) {
return &v, nil
}
type specialToken struct {
Content string `json:"content"`
Lstrip bool `json:"lstrip"`
Normalized bool `json:"normalized"`
Rstrip bool `json:"rstrip"`
SingleWord bool `json:"single_word"`
}
func parseAdditionalSpecialTokens(fsys fs.FS) ([]specialToken, error) {
func parseAdditionalSpecialTokens(fsys fs.FS) ([]string, error) {
f, err := fsys.Open("special_tokens_map.json")
if errors.Is(err, os.ErrNotExist) {
return nil, nil
@ -129,43 +102,12 @@ func parseAdditionalSpecialTokens(fsys fs.FS) ([]specialToken, error) {
defer f.Close()
var m struct {
AdditionalSpecialTokens any `json:"additional_special_tokens"`
AdditionalSpecialTokens []string `json:"additional_special_tokens"`
}
if err := json.NewDecoder(f).Decode(&m); err != nil {
return nil, err
}
var ast []specialToken
switch st := m.AdditionalSpecialTokens.(type) {
case []string:
for _, s := range st {
ast = append(ast, specialToken{Content: s})
}
case []any:
for _, s := range st {
// marshal and unmarshal the object to get the special token
tMap := s.(map[string]any)
data, err := json.Marshal(tMap)
if err != nil {
return nil, err
}
var token specialToken
err = json.Unmarshal(data, &token)
if err != nil {
return nil, err
}
ast = append(ast, token)
}
default:
slog.Warn("special token", "unknown token", reflect.TypeOf(st))
}
slog.Debug("spm tokenizer", "additional tokens", ast)
return ast, nil
return m.AdditionalSpecialTokens, nil
}

View File

@ -9,6 +9,8 @@ import (
"path/filepath"
"runtime"
"strings"
"github.com/ollama/ollama/envconfig"
)
// Determine if the given ROCm lib directory is usable by checking for existence of some glob patterns
@ -39,10 +41,13 @@ func commonAMDValidateLibDir() (string, error) {
// Favor our bundled version
// Installer payload location if we're running the installed binary
rocmTargetDir := filepath.Join(LibOllamaPath, "rocm")
if rocmLibUsable(rocmTargetDir) {
slog.Debug("detected ROCM next to ollama executable " + rocmTargetDir)
return rocmTargetDir, nil
exe, err := os.Executable()
if err == nil {
rocmTargetDir := filepath.Join(filepath.Dir(exe), envconfig.LibRelativeToExe(), "lib", "ollama")
if rocmLibUsable(rocmTargetDir) {
slog.Debug("detected ROCM next to ollama executable " + rocmTargetDir)
return rocmTargetDir, nil
}
}
// Prefer explicit HIP env var

View File

@ -77,7 +77,8 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
gfxOverride := envconfig.HsaOverrideGfxVersion()
var supported []string
var libDir string
depPaths := LibraryDirs()
libDir := ""
// The amdgpu driver always exposes the host CPU(s) first, but we have to skip them and subtract
// from the other IDs to get alignment with the HIP libraries expectations (zero is the first GPU, not the CPU)
@ -352,8 +353,9 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
})
return nil, err
}
depPaths = append(depPaths, libDir)
}
gpuInfo.DependencyPath = []string{libDir}
gpuInfo.DependencyPath = depPaths
if gfxOverride == "" {
// Only load supported list once

View File

@ -5,6 +5,7 @@ import (
"errors"
"fmt"
"log/slog"
"os"
"path/filepath"
"slices"
"strconv"
@ -49,13 +50,14 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
slog.Info(err.Error())
return nil, err
}
depPaths := LibraryDirs()
libDir, err := AMDValidateLibDir()
if err != nil {
err = fmt.Errorf("unable to verify rocm library: %w", err)
slog.Warn(err.Error())
return nil, err
}
depPaths = append(depPaths, libDir)
var supported []string
gfxOverride := envconfig.HsaOverrideGfxVersion()
@ -111,7 +113,7 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
UnreliableFreeMemory: true,
ID: strconv.Itoa(i), // TODO this is probably wrong if we specify visible devices
DependencyPath: []string{libDir},
DependencyPath: depPaths,
MinimumMemory: rocmMinimumMemory,
Name: name,
Compute: gfx,
@ -162,7 +164,9 @@ func AMDValidateLibDir() (string, error) {
}
// Installer payload (if we're running from some other location)
rocmTargetDir := filepath.Join(LibOllamaPath, "rocm")
localAppData := os.Getenv("LOCALAPPDATA")
appDir := filepath.Join(localAppData, "Programs", "Ollama")
rocmTargetDir := filepath.Join(appDir, envconfig.LibRelativeToExe(), "lib", "ollama")
if rocmLibUsable(rocmTargetDir) {
slog.Debug("detected ollama installed ROCm at " + rocmTargetDir)
return rocmTargetDir, nil

View File

@ -12,7 +12,7 @@ func IsNUMA() bool {
// numa support in llama.cpp is linux only
return false
}
ids := map[string]any{}
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)

View File

@ -57,8 +57,7 @@ func cudaVariant(gpuInfo CudaGPUInfo) string {
}
}
// driver 12.0 has problems with the cuda v12 library, so run v11 on those older drivers
if gpuInfo.DriverMajor < 12 || (gpuInfo.DriverMajor == 12 && gpuInfo.DriverMinor == 0) {
if gpuInfo.computeMajor < 6 || gpuInfo.DriverMajor < 12 || (gpuInfo.DriverMajor == 12 && gpuInfo.DriverMinor == 0) {
return "v11"
}
return "v12"

View File

@ -23,6 +23,7 @@ import (
"github.com/ollama/ollama/envconfig"
"github.com/ollama/ollama/format"
"github.com/ollama/ollama/runners"
)
type cudaHandles struct {
@ -100,7 +101,15 @@ func initCudaHandles() *cudaHandles {
// Aligned with driver, we can't carry as payloads
nvcudaMgmtPatterns := NvcudaGlobs
cudartMgmtPatterns = append(cudartMgmtPatterns, filepath.Join(LibOllamaPath, "cuda_v*", CudartMgmtName))
if runtime.GOOS == "windows" {
localAppData := os.Getenv("LOCALAPPDATA")
cudartMgmtPatterns = []string{filepath.Join(localAppData, "Programs", "Ollama", CudartMgmtName)}
}
libDirs := LibraryDirs()
for _, d := range libDirs {
cudartMgmtPatterns = append(cudartMgmtPatterns, filepath.Join(d, CudartMgmtName))
}
cudartMgmtPatterns = append(cudartMgmtPatterns, CudartGlobs...)
if len(NvmlGlobs) > 0 {
@ -231,7 +240,7 @@ func GetGPUInfo() GpuInfoList {
if err != nil {
slog.Warn("error looking up system memory", "error", err)
}
depPaths := LibraryDirs()
details, err := GetCPUDetails()
if err != nil {
slog.Warn("failed to lookup CPU details", "error", err)
@ -239,9 +248,11 @@ func GetGPUInfo() GpuInfoList {
cpus = []CPUInfo{
{
GpuInfo: GpuInfo{
memInfo: mem,
Library: "cpu",
ID: "0",
memInfo: mem,
Library: "cpu",
Variant: runners.GetCPUCapability().String(),
ID: "0",
DependencyPath: depPaths,
},
CPUs: details,
},
@ -283,13 +294,17 @@ func GetGPUInfo() GpuInfoList {
gpuInfo.DriverMajor = driverMajor
gpuInfo.DriverMinor = driverMinor
variant := cudaVariant(gpuInfo)
// Start with our bundled libraries
if variant != "" {
variantPath := filepath.Join(LibOllamaPath, "cuda_"+variant)
if _, err := os.Stat(variantPath); err == nil {
// Put the variant directory first in the search path to avoid runtime linking to the wrong library
gpuInfo.DependencyPath = append([]string{variantPath}, gpuInfo.DependencyPath...)
if depPaths != nil {
gpuInfo.DependencyPath = depPaths
// Check for variant specific directory
if variant != "" {
for _, d := range depPaths {
if _, err := os.Stat(filepath.Join(d, "cuda_"+variant)); err == nil {
// Put the variant directory first in the search path to avoid runtime linking to the wrong library
gpuInfo.DependencyPath = append([]string{filepath.Join(d, "cuda_"+variant)}, gpuInfo.DependencyPath...)
break
}
}
}
}
gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
@ -361,7 +376,7 @@ func GetGPUInfo() GpuInfoList {
gpuInfo.FreeMemory = uint64(memInfo.free)
gpuInfo.ID = C.GoString(&memInfo.gpu_id[0])
gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
gpuInfo.DependencyPath = []string{LibOllamaPath}
gpuInfo.DependencyPath = depPaths
oneapiGPUs = append(oneapiGPUs, gpuInfo)
}
}
@ -497,30 +512,33 @@ func GetGPUInfo() GpuInfoList {
func FindGPULibs(baseLibName string, defaultPatterns []string) []string {
// Multiple GPU libraries may exist, and some may not work, so keep trying until we exhaust them
var ldPaths []string
gpuLibPaths := []string{}
slog.Debug("Searching for GPU library", "name", baseLibName)
// search our bundled libraries first
patterns := []string{filepath.Join(LibOllamaPath, baseLibName)}
var ldPaths []string
switch runtime.GOOS {
case "windows":
ldPaths = strings.Split(os.Getenv("PATH"), string(os.PathListSeparator))
case "linux":
ldPaths = strings.Split(os.Getenv("LD_LIBRARY_PATH"), string(os.PathListSeparator))
// Start with our bundled libraries
patterns := []string{}
for _, d := range LibraryDirs() {
patterns = append(patterns, filepath.Join(d, baseLibName))
}
// then search the system's LD_LIBRARY_PATH
for _, p := range ldPaths {
p, err := filepath.Abs(p)
switch runtime.GOOS {
case "windows":
ldPaths = strings.Split(os.Getenv("PATH"), ";")
case "linux":
ldPaths = strings.Split(os.Getenv("LD_LIBRARY_PATH"), ":")
default:
return gpuLibPaths
}
// Then 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(p, baseLibName))
patterns = append(patterns, filepath.Join(d, baseLibName))
}
// finally, search the default patterns provided by the caller
patterns = append(patterns, defaultPatterns...)
slog.Debug("gpu library search", "globs", patterns)
for _, pattern := range patterns {
@ -670,7 +688,7 @@ func loadOneapiMgmt(oneapiLibPaths []string) (int, *C.oneapi_handle_t, string, e
}
func getVerboseState() C.uint16_t {
if envconfig.LogLevel() < slog.LevelInfo {
if envconfig.Debug() {
return C.uint16_t(1)
}
return C.uint16_t(0)
@ -697,6 +715,28 @@ func (l GpuInfoList) GetVisibleDevicesEnv() (string, string) {
}
}
func LibraryDirs() []string {
// dependencies can exist wherever we found the runners (e.g. build tree for developers) and relative to the executable
// This can be simplified once we no longer carry runners as payloads
paths := []string{}
appExe, err := os.Executable()
if err != nil {
slog.Warn("failed to lookup executable path", "error", err)
} else {
appRelative := filepath.Join(filepath.Dir(appExe), envconfig.LibRelativeToExe(), "lib", "ollama")
if _, err := os.Stat(appRelative); err == nil {
paths = append(paths, appRelative)
}
}
rDir := runners.Locate()
if err != nil {
slog.Warn("unable to locate gpu dependency libraries", "error", err)
} else {
paths = append(paths, filepath.Dir(rDir))
}
return paths
}
func GetSystemInfo() SystemInfo {
gpus := GetGPUInfo()
gpuMutex.Lock()

View File

@ -15,6 +15,7 @@ import (
"syscall"
"github.com/ollama/ollama/format"
"github.com/ollama/ollama/runners"
)
const (
@ -27,6 +28,7 @@ func GetGPUInfo() GpuInfoList {
return []GpuInfo{
{
Library: "cpu",
Variant: runners.GetCPUCapability().String(),
memInfo: mem,
},
}
@ -49,6 +51,7 @@ func GetCPUInfo() GpuInfoList {
return []GpuInfo{
{
Library: "cpu",
Variant: runners.GetCPUCapability().String(),
memInfo: mem,
},
}

View File

@ -27,14 +27,12 @@
#endif
#ifndef LOG
#define LOG(verbose, ...) \
do { \
if (verbose) { \
fprintf(stderr, __VA_ARGS__); \
} \
} while (0)
#endif
#ifdef __cplusplus
extern "C" {

View File

@ -1,7 +1,6 @@
#ifndef __APPLE__ // TODO - maybe consider nvidia support on intel macs?
#include <string.h>
#include <inttypes.h>
#include "gpu_info_cudart.h"
void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp) {
@ -59,7 +58,7 @@ void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp) {
LOG(resp->ch.verbose, "cudaSetDevice err: %d\n", ret);
UNLOAD_LIBRARY(resp->ch.handle);
resp->ch.handle = NULL;
if (ret == CUDART_ERROR_INSUFFICIENT_DRIVER) {
if (ret == CUDA_ERROR_INSUFFICIENT_DRIVER) {
resp->err = strdup("your nvidia driver is too old or missing. If you have a CUDA GPU please upgrade to run ollama");
return;
}
@ -169,9 +168,9 @@ void cudart_bootstrap(cudart_handle_t h, int i, mem_info_t *resp) {
resp->free = memInfo.free;
resp->used = memInfo.used;
LOG(h.verbose, "[%s] CUDA totalMem %" PRId64 "\n", resp->gpu_id, resp->total);
LOG(h.verbose, "[%s] CUDA freeMem %" PRId64 "\n", resp->gpu_id, resp->free);
LOG(h.verbose, "[%s] CUDA usedMem %" PRId64 "\n", resp->gpu_id, resp->used);
LOG(h.verbose, "[%s] CUDA totalMem %lu\n", resp->gpu_id, resp->total);
LOG(h.verbose, "[%s] CUDA freeMem %lu\n", resp->gpu_id, resp->free);
LOG(h.verbose, "[%s] CUDA usedMem %lu\n", resp->gpu_id, resp->used);
LOG(h.verbose, "[%s] Compute Capability %d.%d\n", resp->gpu_id, resp->major, resp->minor);
}
@ -181,4 +180,4 @@ void cudart_release(cudart_handle_t h) {
h.handle = NULL;
}
#endif // __APPLE__
#endif // __APPLE__

View File

@ -1,7 +1,6 @@
#ifndef __APPLE__ // TODO - maybe consider nvidia support on intel macs?
#include <string.h>
#include <inttypes.h>
#include "gpu_info_nvcuda.h"
void nvcuda_init(char *nvcuda_lib_path, nvcuda_init_resp_t *resp) {
@ -194,8 +193,8 @@ void nvcuda_bootstrap(nvcuda_handle_t h, int i, mem_info_t *resp) {
resp->total = memInfo.total;
resp->free = memInfo.free;
LOG(h.verbose, "[%s] CUDA totalMem %" PRId64 "mb\n", resp->gpu_id, resp->total / 1024 / 1024);
LOG(h.verbose, "[%s] CUDA freeMem %" PRId64 "mb\n", resp->gpu_id, resp->free / 1024 / 1024);
LOG(h.verbose, "[%s] CUDA totalMem %lu mb\n", resp->gpu_id, resp->total / 1024 / 1024);
LOG(h.verbose, "[%s] CUDA freeMem %lu mb\n", resp->gpu_id, resp->free / 1024 / 1024);
LOG(h.verbose, "[%s] Compute Capability %d.%d\n", resp->gpu_id, resp->major, resp->minor);
@ -248,4 +247,4 @@ void nvcuda_release(nvcuda_handle_t h) {
h.handle = NULL;
}
#endif // __APPLE__
#endif // __APPLE__

View File

@ -111,7 +111,6 @@ func GetCPUDetails() ([]CPU, error) {
if err != nil {
return nil, err
}
defer file.Close()
return linuxCPUDetails(file)
}
@ -169,11 +168,13 @@ func linuxCPUDetails(file io.Reader) ([]CPU, error) {
for id, s := range socketByID {
s.CoreCount = len(coreBySocket[id])
s.ThreadCount = 0
for _, tc := range threadsByCoreBySocket[id] {
s.ThreadCount += tc
}
// This only works if HT is enabled, consider a more reliable model, maybe cache size comparisons?
efficiencyCoreCount := 0
for _, threads := range threadsByCoreBySocket[id] {
s.ThreadCount += threads
if threads == 1 {
efficiencyCoreCount++
}

View File

@ -1,56 +0,0 @@
package discover
import (
"os"
"path/filepath"
"runtime"
)
// LibPath is a path to lookup dynamic libraries
// in development it's usually 'build/lib/ollama'
// in distribution builds it's 'lib/ollama' on Windows
// '../lib/ollama' on Linux and the executable's directory on macOS
// note: distribution builds, additional GPU-specific libraries are
// found in subdirectories of the returned path, such as
// 'cuda_v11', 'cuda_v12', 'rocm', etc.
var LibOllamaPath string = func() string {
exe, err := os.Executable()
if err != nil {
return ""
}
if eval, err := filepath.EvalSymlinks(exe); err == nil {
exe = eval
}
var libPath string
switch runtime.GOOS {
case "windows":
libPath = filepath.Join(filepath.Dir(exe), "lib", "ollama")
case "linux":
libPath = filepath.Join(filepath.Dir(exe), "..", "lib", "ollama")
case "darwin":
libPath = filepath.Dir(exe)
}
cwd, err := os.Getwd()
if err != nil {
return ""
}
paths := []string{
libPath,
// build paths for development
filepath.Join(filepath.Dir(exe), "build", "lib", "ollama"),
filepath.Join(cwd, "build", "lib", "ollama"),
}
for _, p := range paths {
if _, err := os.Stat(p); err == nil {
return p
}
}
return filepath.Dir(exe)
}()

View File

@ -5,6 +5,7 @@ import (
"log/slog"
"github.com/ollama/ollama/format"
"github.com/ollama/ollama/runners"
)
type memInfo struct {
@ -106,7 +107,7 @@ func (l GpuInfoList) ByLibrary() []GpuInfoList {
for _, info := range l {
found := false
requested := info.Library
if info.Variant != "" {
if info.Variant != runners.CPUCapabilityNone.String() {
requested += "_" + info.Variant
}
for i, lib := range libs {

View File

@ -19,7 +19,7 @@
### Model names
Model names follow a `model:tag` format, where `model` can have an optional namespace such as `example/model`. Some examples are `orca-mini:3b-q8_0` and `llama3:70b`. The tag is optional and, if not provided, will default to `latest`. The tag is used to identify a specific version.
Model names follow a `model:tag` format, where `model` can have an optional namespace such as `example/model`. Some examples are `orca-mini:3b-q4_1` and `llama3:70b`. The tag is optional and, if not provided, will default to `latest`. The tag is used to identify a specific version.
### Durations
@ -31,7 +31,7 @@ Certain endpoints stream responses as JSON objects. Streaming can be disabled by
## Generate a completion
```
```shell
POST /api/generate
```
@ -173,7 +173,7 @@ curl http://localhost:11434/api/generate -d '{
##### Response
```json5
```json
{
"model": "codellama:code",
"created_at": "2024-07-22T20:47:51.147561Z",
@ -306,7 +306,7 @@ curl http://localhost:11434/api/generate -d '{
#### Response
```json
```
{
"model": "llava",
"created_at": "2023-11-03T15:36:02.583064Z",
@ -394,6 +394,9 @@ curl http://localhost:11434/api/generate -d '{
"repeat_penalty": 1.2,
"presence_penalty": 1.5,
"frequency_penalty": 1.0,
"mirostat": 1,
"mirostat_tau": 0.8,
"mirostat_eta": 0.6,
"penalize_newline": true,
"stop": ["\n", "user:"],
"numa": false,
@ -401,7 +404,10 @@ curl http://localhost:11434/api/generate -d '{
"num_batch": 2,
"num_gpu": 1,
"main_gpu": 0,
"low_vram": false,
"vocab_only": false,
"use_mmap": true,
"use_mlock": false,
"num_thread": 8
}
}'
@ -479,7 +485,7 @@ A single JSON object is returned:
## Generate a chat completion
```
```shell
POST /api/chat
```
@ -489,14 +495,14 @@ Generate the next message in a chat with a provided model. This is a streaming e
- `model`: (required) the [model name](#model-names)
- `messages`: the messages of the chat, this can be used to keep a chat memory
- `tools`: list of tools in JSON for the model to use if supported
- `tools`: tools for the model to use if supported. Requires `stream` to be set to `false`
The `message` object has the following fields:
- `role`: the role of the message, either `system`, `user`, `assistant`, or `tool`
- `content`: the content of the message
- `images` (optional): a list of images to include in the message (for multimodal models such as `llava`)
- `tool_calls` (optional): a list of tools in JSON that the model wants to use
- `tool_calls` (optional): a list of tools the model wants to use
Advanced parameters (optional):
@ -552,10 +558,6 @@ Final response:
{
"model": "llama3.2",
"created_at": "2023-08-04T19:22:45.499127Z",
"message": {
"role": "assistant",
"content": ""
},
"done": true,
"total_duration": 4883583458,
"load_duration": 1334875,
@ -793,7 +795,7 @@ curl http://localhost:11434/api/chat -d '{
##### Request
```shell
```
curl http://localhost:11434/api/chat -d '{
"model": "llama3.2",
"messages": [
@ -868,7 +870,7 @@ If the messages array is empty, the model will be loaded into memory.
##### Request
```shell
```
curl http://localhost:11434/api/chat -d '{
"model": "llama3.2",
"messages": []
@ -876,7 +878,6 @@ curl http://localhost:11434/api/chat -d '{
```
##### Response
```json
{
"model": "llama3.2",
@ -896,7 +897,7 @@ If the messages array is empty and the `keep_alive` parameter is set to `0`, a m
##### Request
```shell
```
curl http://localhost:11434/api/chat -d '{
"model": "llama3.2",
"messages": [],
@ -923,7 +924,7 @@ A single JSON object is returned:
## Create a Model
```
```shell
POST /api/create
```
@ -952,8 +953,19 @@ If you are creating a model from a safetensors directory or from a GGUF file, yo
| Type | Recommended |
| --- | :-: |
| q2_K | |
| q3_K_L | |
| q3_K_M | |
| q3_K_S | |
| q4_0 | |
| q4_1 | |
| q4_K_M | * |
| q4_K_S | |
| q5_0 | |
| q5_1 | |
| q5_K_M | |
| q5_K_S | |
| q6_K | |
| q8_0 | * |
### Examples
@ -998,8 +1010,8 @@ Quantize a non-quantized model.
```shell
curl http://localhost:11434/api/create -d '{
"model": "llama3.2:quantized",
"from": "llama3.2:3b-instruct-fp16",
"model": "llama3.1:quantized",
"from": "llama3.1:8b-instruct-fp16",
"quantize": "q4_K_M"
}'
```
@ -1008,15 +1020,13 @@ curl http://localhost:11434/api/create -d '{
A stream of JSON objects is returned:
```json
{"status":"quantizing F16 model to Q4_K_M","digest":"0","total":6433687776,"completed":12302}
{"status":"quantizing F16 model to Q4_K_M","digest":"0","total":6433687776,"completed":6433687552}
{"status":"verifying conversion"}
{"status":"creating new layer sha256:fb7f4f211b89c6c4928ff4ddb73db9f9c0cfca3e000c3e40d6cf27ddc6ca72eb"}
{"status":"using existing layer sha256:966de95ca8a62200913e3f8bfbf84c8494536f1b94b49166851e76644e966396"}
{"status":"using existing layer sha256:fcc5a6bec9daf9b561a68827b67ab6088e1dba9d1fa2a50d7bbcc8384e0a265d"}
{"status":"using existing layer sha256:a70ff7e570d97baaf4e62ac6e6ad9975e04caa6d900d3742d37698494479e0cd"}
```
{"status":"quantizing F16 model to Q4_K_M"}
{"status":"creating new layer sha256:667b0c1932bc6ffc593ed1d03f895bf2dc8dc6df21db3042284a6f4416b06a29"}
{"status":"using existing layer sha256:11ce4ee3e170f6adebac9a991c22e22ab3f8530e154ee669954c4bc73061c258"}
{"status":"using existing layer sha256:0ba8f0e314b4264dfd19df045cde9d4c394a52474bf92ed6a3de22a4ca31a177"}
{"status":"using existing layer sha256:56bb8bd477a519ffa694fc449c2413c6f0e1d3b1c88fa7e3c9d88d3ae49d4dcb"}
{"status":"creating new layer sha256:455f34728c9b5dd3376378bfb809ee166c145b0b4c1f1a6feca069055066ef9a"}
{"status":"writing manifest"}
{"status":"success"}
```
@ -1041,7 +1051,7 @@ curl http://localhost:11434/api/create -d '{
A stream of JSON objects is returned:
```json
```
{"status":"parsing GGUF"}
{"status":"using existing layer sha256:432f310a77f4650a88d0fd59ecdd7cebed8d684bafea53cbff0473542964f0c3"}
{"status":"writing manifest"}
@ -1108,7 +1118,7 @@ Return 200 OK if the blob exists, 404 Not Found if it does not.
## Push a Blob
```
```shell
POST /api/blobs/:digest
```
@ -1132,7 +1142,7 @@ Return 201 Created if the blob was successfully created, 400 Bad Request if the
## List Local Models
```
```shell
GET /api/tags
```
@ -1154,37 +1164,29 @@ A single JSON object will be returned.
{
"models": [
{
"name": "deepseek-r1:latest",
"model": "deepseek-r1:latest",
"modified_at": "2025-05-10T08:06:48.639712648-07:00",
"size": 4683075271,
"digest": "0a8c266910232fd3291e71e5ba1e058cc5af9d411192cf88b6d30e92b6e73163",
"name": "codellama:13b",
"modified_at": "2023-11-04T14:56:49.277302595-07:00",
"size": 7365960935,
"digest": "9f438cb9cd581fc025612d27f7c1a6669ff83a8bb0ed86c94fcf4c5440555697",
"details": {
"parent_model": "",
"format": "gguf",
"family": "qwen2",
"families": [
"qwen2"
],
"parameter_size": "7.6B",
"quantization_level": "Q4_K_M"
"family": "llama",
"families": null,
"parameter_size": "13B",
"quantization_level": "Q4_0"
}
},
{
"name": "llama3.2:latest",
"model": "llama3.2:latest",
"modified_at": "2025-05-04T17:37:44.706015396-07:00",
"size": 2019393189,
"digest": "a80c4f17acd55265feec403c7aef86be0c25983ab279d83f3bcd3abbcb5b8b72",
"name": "llama3:latest",
"modified_at": "2023-12-07T09:32:18.757212583-08:00",
"size": 3825819519,
"digest": "fe938a131f40e6f6d40083c9f0f430a515233eb2edaa6d72eb85c50d64f2300e",
"details": {
"parent_model": "",
"format": "gguf",
"family": "llama",
"families": [
"llama"
],
"parameter_size": "3.2B",
"quantization_level": "Q4_K_M"
"families": null,
"parameter_size": "7B",
"quantization_level": "Q4_0"
}
}
]
@ -1193,7 +1195,7 @@ A single JSON object will be returned.
## Show Model Information
```
```shell
POST /api/show
```
@ -1210,13 +1212,13 @@ Show information about a model including details, modelfile, template, parameter
```shell
curl http://localhost:11434/api/show -d '{
"model": "llava"
"model": "llama3.2"
}'
```
#### Response
```json5
```json
{
"modelfile": "# Modelfile generated by \"ollama show\"\n# To build a new Modelfile based on this one, replace the FROM line with:\n# FROM llava:latest\n\nFROM /Users/matt/.ollama/models/blobs/sha256:200765e1283640ffbd013184bf496e261032fa75b99498a9613be4e94d63ad52\nTEMPLATE \"\"\"{{ .System }}\nUSER: {{ .Prompt }}\nASSISTANT: \"\"\"\nPARAMETER num_ctx 4096\nPARAMETER stop \"\u003c/s\u003e\"\nPARAMETER stop \"USER:\"\nPARAMETER stop \"ASSISTANT:\"",
"parameters": "num_keep 24\nstop \"<|start_header_id|>\"\nstop \"<|end_header_id|>\"\nstop \"<|eot_id|>\"",
@ -1253,17 +1255,13 @@ curl http://localhost:11434/api/show -d '{
"tokenizer.ggml.pre": "llama-bpe",
"tokenizer.ggml.token_type": [], // populates if `verbose=true`
"tokenizer.ggml.tokens": [] // populates if `verbose=true`
},
"capabilities": [
"completion",
"vision"
],
}
}
```
## Copy a Model
```
```shell
POST /api/copy
```
@ -1286,7 +1284,7 @@ Returns a 200 OK if successful, or a 404 Not Found if the source model doesn't e
## Delete a Model
```
```shell
DELETE /api/delete
```
@ -1312,7 +1310,7 @@ Returns a 200 OK if successful, 404 Not Found if the model to be deleted doesn't
## Pull a Model
```
```shell
POST /api/pull
```
@ -1384,7 +1382,7 @@ if `stream` is set to false, then the response is a single JSON object:
## Push a Model
```
```shell
POST /api/push
```
@ -1449,7 +1447,7 @@ If `stream` is set to `false`, then the response is a single JSON object:
## Generate Embeddings
```
```shell
POST /api/embed
```
@ -1517,7 +1515,7 @@ curl http://localhost:11434/api/embed -d '{
```
## List Running Models
```
```shell
GET /api/ps
```
@ -1564,7 +1562,7 @@ A single JSON object will be returned.
> Note: this endpoint has been superseded by `/api/embed`
```
```shell
POST /api/embeddings
```
@ -1604,7 +1602,7 @@ curl http://localhost:11434/api/embeddings -d '{
## Version
```
```shell
GET /api/version
```

View File

@ -1,59 +0,0 @@
# Benchmark
Go benchmark tests that measure end-to-end performance of a running Ollama server. Run these tests to evaluate model inference performance on your hardware and measure the impact of code changes.
## When to use
Run these benchmarks when:
- Making changes to the model inference engine
- Modifying model loading/unloading logic
- Changing prompt processing or token generation code
- Implementing a new model architecture
- Testing performance across different hardware setups
## Prerequisites
- Ollama server running locally with `ollama serve` on `127.0.0.1:11434`
## Usage and Examples
>[!NOTE]
>All commands must be run from the root directory of the Ollama project.
Basic syntax:
```bash
go test -bench=. ./benchmark/... -m $MODEL_NAME
```
Required flags:
- `-bench=.`: Run all benchmarks
- `-m`: Model name to benchmark
Optional flags:
- `-count N`: Number of times to run the benchmark (useful for statistical analysis)
- `-timeout T`: Maximum time for the benchmark to run (e.g. "10m" for 10 minutes)
Common usage patterns:
Single benchmark run with a model specified:
```bash
go test -bench=. ./benchmark/... -m llama3.3
```
## Output metrics
The benchmark reports several key metrics:
- `gen_tok/s`: Generated tokens per second
- `prompt_tok/s`: Prompt processing tokens per second
- `ttft_ms`: Time to first token in milliseconds
- `load_ms`: Model load time in milliseconds
- `gen_tokens`: Total tokens generated
- `prompt_tokens`: Total prompt tokens processed
Each benchmark runs two scenarios:
- Cold start: Model is loaded from disk for each test
- Warm start: Model is pre-loaded in memory
Three prompt lengths are tested for each scenario:
- Short prompt (100 tokens)
- Medium prompt (500 tokens)
- Long prompt (1000 tokens)

View File

@ -1,159 +1,165 @@
# Development
Install prerequisites:
Install required tools:
- [Go](https://go.dev/doc/install)
- C/C++ Compiler e.g. Clang on macOS, [TDM-GCC](https://github.com/jmeubank/tdm-gcc/releases/latest) (Windows amd64) or [llvm-mingw](https://github.com/mstorsjo/llvm-mingw) (Windows arm64), GCC/Clang on Linux.
- go version 1.22 or higher
- OS specific C/C++ compiler (see below)
- GNU Make
Then build and run Ollama from the root directory of the repository:
```shell
go run . serve
## Overview
Ollama uses a mix of Go and C/C++ code to interface with GPUs. The C/C++ code is compiled with both CGO and GPU library specific compilers. A set of GNU Makefiles are used to compile the project. GPU Libraries are auto-detected based on the typical environment variables used by the respective libraries, but can be overridden if necessary. The default make target will build the runners and primary Go Ollama application that will run within the repo directory. Throughout the examples below `-j 5` is suggested for 5 parallel jobs to speed up the build. You can adjust the job count based on your CPU Core count to reduce build times. If you want to relocate the built binaries, use the `dist` target and recursively copy the files in `./dist/$OS-$ARCH/` to your desired location. To learn more about the other make targets use `make help`
Once you have built the GPU/CPU runners, you can compile the main application with `go build .`
### MacOS
[Download Go](https://go.dev/dl/)
```bash
make -j 5
```
## macOS (Apple Silicon)
Now you can run `ollama`:
macOS Apple Silicon supports Metal which is built-in to the Ollama binary. No additional steps are required.
## macOS (Intel)
Install prerequisites:
- [CMake](https://cmake.org/download/) or `brew install cmake`
Then, configure and build the project:
```shell
cmake -B build
cmake --build build
```bash
./ollama
```
Lastly, run Ollama:
#### Xcode 15 warnings
```shell
go run . serve
If you are using Xcode newer than version 14, you may see a warning during `go build` about `ld: warning: ignoring duplicate libraries: '-lobjc'` due to Golang issue https://github.com/golang/go/issues/67799 which can be safely ignored. You can suppress the warning with `export CGO_LDFLAGS="-Wl,-no_warn_duplicate_libraries"`
### Linux
#### Linux CUDA (NVIDIA)
_Your operating system distribution may already have packages for NVIDIA CUDA. Distro packages are often preferable, but instructions are distro-specific. Please consult distro-specific docs for dependencies if available!_
Install `make`, `gcc` and `golang` as well as [NVIDIA CUDA](https://developer.nvidia.com/cuda-downloads)
development and runtime packages.
Typically the makefile will auto-detect CUDA, however, if your Linux distro
or installation approach uses alternative paths, you can specify the location by
overriding `CUDA_PATH` to the location of the CUDA toolkit. You can customize
a set of target CUDA architectures by setting `CUDA_ARCHITECTURES` (e.g. `CUDA_ARCHITECTURES=50;60;70`)
```
make -j 5
```
## Windows
If both v11 and v12 tookkits are detected, runners for both major versions will be built by default. You can build just v12 with `make cuda_v12`
Install prerequisites:
#### Older Linux CUDA (NVIDIA)
- [CMake](https://cmake.org/download/)
- [Visual Studio 2022](https://visualstudio.microsoft.com/downloads/) including the Native Desktop Workload
- (Optional) AMD GPU support
- [ROCm](https://rocm.docs.amd.com/en/latest/)
- [Ninja](https://github.com/ninja-build/ninja/releases)
- (Optional) NVIDIA GPU support
- [CUDA SDK](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=11&target_type=exe_network)
To support older GPUs with Compute Capability 3.5 or 3.7, you will need to use an older version of the Driver from [Unix Driver Archive](https://www.nvidia.com/en-us/drivers/unix/) (tested with 470) and [CUDA Toolkit Archive](https://developer.nvidia.com/cuda-toolkit-archive) (tested with cuda V11). When you build Ollama, you will need to set two make variable to adjust the minimum compute capability Ollama supports via `make -j 5 CUDA_ARCHITECTURES="35;37;50;52" EXTRA_GOLDFLAGS="\"-X=github.com/ollama/ollama/discover.CudaComputeMajorMin=3\" \"-X=github.com/ollama/ollama/discover.CudaComputeMinorMin=5\""`. To find the Compute Capability of your older GPU, refer to [GPU Compute Capability](https://developer.nvidia.com/cuda-gpus).
Then, configure and build the project:
#### Linux ROCm (AMD)
```shell
cmake -B build
cmake --build build --config Release
_Your operating system distribution may already have packages for AMD ROCm. Distro packages are often preferable, but instructions are distro-specific. Please consult distro-specific docs for dependencies if available!_
Install [ROCm](https://rocm.docs.amd.com/en/latest/) development packages first, as well as `make`, `gcc`, and `golang`.
Typically the build scripts will auto-detect ROCm, however, if your Linux distro
or installation approach uses unusual paths, you can specify the location by
specifying an environment variable `HIP_PATH` to the location of the ROCm
install (typically `/opt/rocm`). You can also customize
the AMD GPU targets by setting HIP_ARCHS (e.g. `HIP_ARCHS=gfx1101;gfx1102`)
```
make -j 5
```
> [!IMPORTANT]
> Building for ROCm requires additional flags:
> ```
> cmake -B build -G Ninja -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++
> cmake --build build --config Release
> ```
ROCm requires elevated privileges to access the GPU at runtime. On most distros you can add your user account to the `render` group, or run as root.
#### Containerized Linux Build
Lastly, run Ollama:
If you have Docker and buildx available, you can build linux binaries with `./scripts/build_linux.sh` which has the CUDA and ROCm dependencies included. The resulting artifacts are placed in `./dist` and by default the script builds both arm64 and amd64 binaries. If you want to build only amd64, you can build with `PLATFORM=linux/amd64 ./scripts/build_linux.sh`
```shell
go run . serve
### Windows
The following tools are required as a minimal development environment to build CPU inference support.
- Go version 1.22 or higher
- https://go.dev/dl/
- Git
- https://git-scm.com/download/win
- clang with gcc compat and Make. There are multiple options on how to go about installing these tools on Windows. We have verified the following, but others may work as well:
- [MSYS2](https://www.msys2.org/)
- After installing, from an MSYS2 terminal, run `pacman -S mingw-w64-clang-x86_64-gcc-compat mingw-w64-clang-x86_64-clang make` to install the required tools
- Assuming you used the default install prefix for msys2 above, add `C:\msys64\clang64\bin` and `c:\msys64\usr\bin` to your environment variable `PATH` where you will perform the build steps below (e.g. system-wide, account-level, powershell, cmd, etc.)
> [!NOTE]
> Due to bugs in the GCC C++ library for unicode support, Ollama should be built with clang on windows.
```
make -j 5
```
## Windows (ARM)
#### GPU Support
Windows ARM does not support additional acceleration libraries at this time. Do not use cmake, simply `go run` or `go build`.
The GPU tools require the Microsoft native build tools. To build either CUDA or ROCm, you must first install MSVC via Visual Studio:
## Linux
- Make sure to select `Desktop development with C++` as a Workload during the Visual Studio install
- You must complete the Visual Studio install and run it once **BEFORE** installing CUDA or ROCm for the tools to properly register
- Add the location of the **64 bit (x64)** compiler (`cl.exe`) to your `PATH`
- Note: the default Developer Shell may configure the 32 bit (x86) compiler which will lead to build failures. Ollama requires a 64 bit toolchain.
Install prerequisites:
#### Windows CUDA (NVIDIA)
- [CMake](https://cmake.org/download/) or `sudo apt install cmake` or `sudo dnf install cmake`
- (Optional) AMD GPU support
- [ROCm](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/quick-start.html)
- (Optional) NVIDIA GPU support
- [CUDA SDK](https://developer.nvidia.com/cuda-downloads)
In addition to the common Windows development tools and MSVC described above:
> [!IMPORTANT]
> Ensure prerequisites are in `PATH` before running CMake.
- [NVIDIA CUDA](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html)
#### Windows ROCm (AMD Radeon)
Then, configure and build the project:
In addition to the common Windows development tools and MSVC described above:
```shell
cmake -B build
cmake --build build
- [AMD HIP](https://www.amd.com/en/developer/resources/rocm-hub/hip-sdk.html)
#### Windows arm64
The default `Developer PowerShell for VS 2022` may default to x86 which is not what you want. To ensure you get an arm64 development environment, start a plain PowerShell terminal and run:
```powershell
import-module 'C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\Tools\\Microsoft.VisualStudio.DevShell.dll'
Enter-VsDevShell -Arch arm64 -vsinstallpath 'C:\\Program Files\\Microsoft Visual Studio\\2022\\Community' -skipautomaticlocation
```
Lastly, run Ollama:
You can confirm with `write-host $env:VSCMD_ARG_TGT_ARCH`
```shell
go run . serve
Follow the instructions at https://www.msys2.org/wiki/arm64/ to set up an arm64 msys2 environment. Ollama requires gcc and mingw32-make to compile, which is not currently available on Windows arm64, but a gcc compatibility adapter is available via `mingw-w64-clang-aarch64-gcc-compat`. At a minimum you will need to install the following:
```
pacman -S mingw-w64-clang-aarch64-clang mingw-w64-clang-aarch64-gcc-compat mingw-w64-clang-aarch64-make make
```
## Docker
You will need to ensure your PATH includes go, cmake, gcc and clang mingw32-make to build ollama from source. (typically `C:\msys64\clangarm64\bin\`)
```shell
docker build .
## Advanced CPU Vector Settings
On x86, running `make` will compile several CPU runners which can run on different CPU families. At runtime, Ollama will auto-detect the best variation to load. If GPU libraries are present at build time, Ollama also compiles GPU runners with the `AVX` CPU vector feature enabled. This provides a good performance balance when loading large models that split across GPU and CPU with broad compatibility. Some users may prefer no vector extensions (e.g. older Xeon/Celeron processors, or hypervisors that mask the vector features) while other users may prefer turning on many more vector extensions to further improve performance for split model loads.
To customize the set of CPU vector features enabled for a CPU runner and all GPU runners, use CUSTOM_CPU_FLAGS during the build.
To build without any vector flags:
```
make CUSTOM_CPU_FLAGS=""
```
### ROCm
```shell
docker build --build-arg FLAVOR=rocm .
To build with both AVX and AVX2:
```
make CUSTOM_CPU_FLAGS=avx,avx2
```
## Running tests
To build with AVX512 features turned on:
To run tests, use `go test`:
```shell
go test ./...
```
make CUSTOM_CPU_FLAGS=avx,avx2,avx512,avx512vbmi,avx512vnni,avx512bf16
```
> NOTE: In rare cirumstances, you may nedd to change a package using the new
> "synctest" package in go1.24.
>
> If you do not have the "synctest" package enabled, you will not see build or
> test failures resulting from your change(s), if any, locally, but CI will
> break.
>
> If you see failures in CI, you can either keep pushing changes to see if the
> CI build passes, or you can enable the "synctest" package locally to see the
> failures before pushing.
>
> To enable the "synctest" package for testing, run the following command:
>
> ```shell
> GOEXPERIMENT=synctest go test ./...
> ```
>
> If you wish to enable synctest for all go commands, you can set the
> `GOEXPERIMENT` environment variable in your shell profile or by using:
>
> ```shell
> go env -w GOEXPERIMENT=synctest
> ```
>
> Which will enable the "synctest" package for all go commands without needing
> to set it for all shell sessions.
>
> The synctest package is not required for production builds.
## Library detection
Ollama looks for acceleration libraries in the following paths relative to the `ollama` executable:
* `./lib/ollama` (Windows)
* `../lib/ollama` (Linux)
* `.` (macOS)
* `build/lib/ollama` (for development)
If the libraries are not found, Ollama will not run with any acceleration libraries.
> [!NOTE]
> If you are experimenting with different flags, make sure to do a `make clean` between each change to ensure everything is rebuilt with the new compiler flags

View File

@ -2,7 +2,7 @@
### CPU only
```shell
```bash
docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
```
@ -11,46 +11,42 @@ Install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-
#### Install with Apt
1. Configure the repository
```shell
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
```
```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
```shell
sudo apt-get install -y nvidia-container-toolkit
```
```bash
sudo apt-get install -y nvidia-container-toolkit
```
#### Install with Yum or Dnf
1. Configure the repository
```shell
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
```
```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
```shell
sudo yum install -y nvidia-container-toolkit
```
```bash
sudo yum install -y nvidia-container-toolkit
```
#### Configure Docker to use Nvidia driver
```shell
```
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
```
#### Start the container
```shell
```bash
docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
```
@ -61,7 +57,7 @@ docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ol
To run Ollama using Docker with AMD GPUs, use the `rocm` tag and the following command:
```shell
```
docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama:rocm
```
@ -69,7 +65,7 @@ docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 114
Now you can run a model:
```shell
```
docker exec -it ollama ollama run llama3.2
```

View File

@ -20,17 +20,11 @@ Please refer to the [GPU docs](./gpu.md).
## How can I specify the context window size?
By default, Ollama uses a context window size of 4096 tokens.
This can be overridden with the `OLLAMA_CONTEXT_LENGTH` environment variable. For example, to set the default context window to 8K, use:
```shell
OLLAMA_CONTEXT_LENGTH=8192 ollama serve
```
By default, Ollama uses a context window size of 2048 tokens.
To change this when using `ollama run`, use `/set parameter`:
```shell
```
/set parameter num_ctx 4096
```
@ -52,15 +46,10 @@ Use the `ollama ps` command to see what models are currently loaded into memory.
```shell
ollama ps
NAME ID SIZE PROCESSOR UNTIL
llama3:70b bcfb190ca3a7 42 GB 100% GPU 4 minutes from now
```
> **Output**:
>
> ```
> NAME ID SIZE PROCESSOR UNTIL
> llama3:70b bcfb190ca3a7 42 GB 100% GPU 4 minutes from now
> ```
The `Processor` column will show which memory the model was loaded in to:
* `100% GPU` means the model was loaded entirely into the GPU
* `100% CPU` means the model was loaded entirely in system memory
@ -77,7 +66,7 @@ If Ollama is run as a macOS application, environment variables should be set usi
1. For each environment variable, call `launchctl setenv`.
```bash
launchctl setenv OLLAMA_HOST "0.0.0.0:11434"
launchctl setenv OLLAMA_HOST "0.0.0.0"
```
2. Restart Ollama application.
@ -92,14 +81,14 @@ If Ollama is run as a systemd service, environment variables should be set using
```ini
[Service]
Environment="OLLAMA_HOST=0.0.0.0:11434"
Environment="OLLAMA_HOST=0.0.0.0"
```
3. Save and exit.
4. Reload `systemd` and restart Ollama:
```shell
```bash
systemctl daemon-reload
systemctl restart ollama
```
@ -193,13 +182,6 @@ cloudflared tunnel --url http://localhost:11434 --http-host-header="localhost:11
Ollama allows cross-origin requests from `127.0.0.1` and `0.0.0.0` by default. Additional origins can be configured with `OLLAMA_ORIGINS`.
For browser extensions, you'll need to explicitly allow the extension's origin pattern. Set `OLLAMA_ORIGINS` to include `chrome-extension://*`, `moz-extension://*`, and `safari-web-extension://*` if you wish to allow all browser extensions access, or specific extensions as needed:
```
# Allow all Chrome, Firefox, and Safari extensions
OLLAMA_ORIGINS=chrome-extension://*,moz-extension://*,safari-web-extension://* ollama serve
```
Refer to the section [above](#how-do-i-configure-ollama-server) for how to set environment variables on your platform.
## Where are models stored?
@ -239,19 +221,16 @@ properties.
If you are using the API you can preload a model by sending the Ollama server an empty request. This works with both the `/api/generate` and `/api/chat` API endpoints.
To preload the mistral model using the generate endpoint, use:
```shell
curl http://localhost:11434/api/generate -d '{"model": "mistral"}'
```
To use the chat completions endpoint, use:
```shell
curl http://localhost:11434/api/chat -d '{"model": "mistral"}'
```
To preload a model using the CLI, use the command:
```shell
ollama run llama3.2 ""
```
@ -271,13 +250,11 @@ If you're using the API, use the `keep_alive` parameter with the `/api/generate`
* '0' which will unload the model immediately after generating a response
For example, to preload a model and leave it in memory use:
```shell
curl http://localhost:11434/api/generate -d '{"model": "llama3.2", "keep_alive": -1}'
```
To unload the model and free up memory use:
```shell
curl http://localhost:11434/api/generate -d '{"model": "llama3.2", "keep_alive": 0}'
```

View File

@ -7,7 +7,7 @@ Check your compute compatibility to see if your card is supported:
| Compute Capability | Family | Cards |
| ------------------ | ------------------- | ----------------------------------------------------------------------------------------------------------- |
| 9.0 | NVIDIA | `H200` `H100` |
| 9.0 | NVIDIA | `H100` |
| 8.9 | GeForce RTX 40xx | `RTX 4090` `RTX 4080 SUPER` `RTX 4080` `RTX 4070 Ti SUPER` `RTX 4070 Ti` `RTX 4070 SUPER` `RTX 4070` `RTX 4060 Ti` `RTX 4060` |
| | NVIDIA Professional | `L4` `L40` `RTX 6000` |
| 8.6 | GeForce RTX 30xx | `RTX 3090 Ti` `RTX 3090` `RTX 3080 Ti` `RTX 3080` `RTX 3070 Ti` `RTX 3070` `RTX 3060 Ti` `RTX 3060` `RTX 3050 Ti` `RTX 3050` |
@ -38,7 +38,7 @@ Numeric IDs may be used, however ordering may vary, so UUIDs are more reliable.
You can discover the UUID of your GPUs by running `nvidia-smi -L` If you want to
ignore the GPUs and force CPU usage, use an invalid GPU ID (e.g., "-1")
### Linux Suspend Resume
### Laptop Suspend Resume
On linux, after a suspend/resume cycle, sometimes Ollama will fail to discover
your NVIDIA GPU, and fallback to running on the CPU. You can workaround this

View File

@ -20,13 +20,13 @@ Make sure that you use the same base model in the `FROM` command as you used to
Now run `ollama create` from the directory where the `Modelfile` was created:
```shell
```bash
ollama create my-model
```
Lastly, test the model:
```shell
```bash
ollama run my-model
```

View File

@ -75,7 +75,7 @@ RestartSec=3
Environment="PATH=$PATH"
[Install]
WantedBy=multi-user.target
WantedBy=default.target
```
Then start the service:
@ -119,7 +119,7 @@ sudo systemctl status ollama
To customize the installation of Ollama, you can edit the systemd service file or the environment variables by running:
```shell
```
sudo systemctl edit ollama
```
@ -152,7 +152,7 @@ Use `OLLAMA_VERSION` environment variable with the install script to install a s
For example:
```shell
curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.5.7 sh
curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.3.9 sh
```
## Viewing logs
@ -186,9 +186,3 @@ sudo rm -r /usr/share/ollama
sudo userdel ollama
sudo groupdel ollama
```
Remove installed libraries:
```shell
sudo rm -rf /usr/local/lib/ollama
```

View File

@ -28,7 +28,7 @@ A model file is the blueprint to create and share models with Ollama.
The format of the `Modelfile`:
```
```modelfile
# comment
INSTRUCTION arguments
```
@ -49,7 +49,7 @@ INSTRUCTION arguments
An example of a `Modelfile` creating a mario blueprint:
```
```modelfile
FROM llama3.2
# sets the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1
@ -67,32 +67,28 @@ To use this:
3. `ollama run choose-a-model-name`
4. Start using the model!
More examples are available in the [examples directory](../examples).
To view the Modelfile of a given model, use the `ollama show --modelfile` command.
```shell
ollama show --modelfile llama3.2
```
```bash
> ollama show --modelfile llama3.2
# Modelfile generated by "ollama show"
# To build a new Modelfile based on this one, replace the FROM line with:
# FROM llama3.2:latest
FROM /Users/pdevine/.ollama/models/blobs/sha256-00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29
TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>
> **Output**:
>
> ```
> # Modelfile generated by "ollama show"
> # To build a new Modelfile based on this one, replace the FROM line with:
> # FROM llama3.2:latest
> FROM /Users/pdevine/.ollama/models/blobs/sha256-00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29
> TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>
>
> {{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
>
> {{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
>
> {{ .Response }}<|eot_id|>"""
> PARAMETER stop "<|start_header_id|>"
> PARAMETER stop "<|end_header_id|>"
> PARAMETER stop "<|eot_id|>"
> PARAMETER stop "<|reserved_special_token"
> ```
{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
{{ .Response }}<|eot_id|>"""
PARAMETER stop "<|start_header_id|>"
PARAMETER stop "<|end_header_id|>"
PARAMETER stop "<|eot_id|>"
PARAMETER stop "<|reserved_special_token"
```
## Instructions
@ -100,13 +96,13 @@ ollama show --modelfile llama3.2
The `FROM` instruction defines the base model to use when creating a model.
```
```modelfile
FROM <model name>:<tag>
```
#### Build from existing model
```
```modelfile
FROM llama3.2
```
@ -117,7 +113,7 @@ Additional models can be found at:
#### Build from a Safetensors model
```
```modelfile
FROM <model directory>
```
@ -131,7 +127,7 @@ Currently supported model architectures:
#### Build from a GGUF file
```
```modelfile
FROM ./ollama-model.gguf
```
@ -142,7 +138,7 @@ The GGUF file location should be specified as an absolute path or relative to th
The `PARAMETER` instruction defines a parameter that can be set when the model is run.
```
```modelfile
PARAMETER <parameter> <parametervalue>
```
@ -150,12 +146,16 @@ PARAMETER <parameter> <parametervalue>
| Parameter | Description | Value Type | Example Usage |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | -------------------- |
| mirostat | Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) | int | mirostat 0 |
| mirostat_eta | Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1) | float | mirostat_eta 0.1 |
| mirostat_tau | Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0) | float | mirostat_tau 5.0 |
| num_ctx | Sets the size of the context window used to generate the next token. (Default: 2048) | int | num_ctx 4096 |
| repeat_last_n | Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx) | int | repeat_last_n 64 |
| repeat_penalty | Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1) | float | repeat_penalty 1.1 |
| temperature | The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8) | float | temperature 0.7 |
| seed | Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. (Default: 0) | int | seed 42 |
| stop | Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return. Multiple stop patterns may be set by specifying multiple separate `stop` parameters in a modelfile. | string | stop "AI assistant:" |
| tfs_z | Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (default: 1) | float | tfs_z 1 |
| num_predict | Maximum number of tokens to predict when generating text. (Default: -1, infinite generation) | int | num_predict 42 |
| top_k | Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40) | int | top_k 40 |
| top_p | Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9) | float | top_p 0.9 |
@ -186,7 +186,7 @@ TEMPLATE """{{ if .System }}<|im_start|>system
The `SYSTEM` instruction specifies the system message to be used in the template, if applicable.
```
```modelfile
SYSTEM """<system message>"""
```
@ -196,7 +196,7 @@ The `ADAPTER` instruction specifies a fine tuned LoRA adapter that should apply
#### Safetensor adapter
```
```modelfile
ADAPTER <path to safetensor adapter>
```
@ -207,7 +207,7 @@ Currently supported Safetensor adapters:
#### GGUF adapter
```
```modelfile
ADAPTER ./ollama-lora.gguf
```
@ -215,7 +215,7 @@ ADAPTER ./ollama-lora.gguf
The `LICENSE` instruction allows you to specify the legal license under which the model used with this Modelfile is shared or distributed.
```
```modelfile
LICENSE """
<license text>
"""
@ -225,7 +225,7 @@ LICENSE """
The `MESSAGE` instruction allows you to specify a message history for the model to use when responding. Use multiple iterations of the MESSAGE command to build up a conversation which will guide the model to answer in a similar way.
```
```modelfile
MESSAGE <role> <message>
```
@ -240,7 +240,7 @@ MESSAGE <role> <message>
#### Example conversation
```
```modelfile
MESSAGE user Is Toronto in Canada?
MESSAGE assistant yes
MESSAGE user Is Sacramento in Canada?

View File

@ -1,7 +1,6 @@
# OpenAI compatibility
> [!NOTE]
> OpenAI compatibility is experimental and is subject to major adjustments including breaking changes. For fully-featured access to the Ollama API, see the Ollama [Python library](https://github.com/ollama/ollama-python), [JavaScript library](https://github.com/ollama/ollama-js) and [REST API](https://github.com/ollama/ollama/blob/main/docs/api.md).
> **Note:** OpenAI compatibility is experimental and is subject to major adjustments including breaking changes. For fully-featured access to the Ollama API, see the Ollama [Python library](https://github.com/ollama/ollama-python), [JavaScript library](https://github.com/ollama/ollama-js) and [REST API](https://github.com/ollama/ollama/blob/main/docs/api.md).
Ollama provides experimental compatibility with parts of the [OpenAI API](https://platform.openai.com/docs/api-reference) to help connect existing applications to Ollama.
@ -60,10 +59,8 @@ embeddings = client.embeddings.create(
input=["why is the sky blue?", "why is the grass green?"],
)
```
#### Structured outputs
```python
```py
from pydantic import BaseModel
from openai import OpenAI
@ -147,7 +144,7 @@ const embedding = await openai.embeddings.create({
### `curl`
```shell
``` shell
curl http://localhost:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
@ -322,7 +319,7 @@ ollama pull llama3.2
For tooling that relies on default OpenAI model names such as `gpt-3.5-turbo`, use `ollama cp` to copy an existing model name to a temporary name:
```shell
```
ollama cp llama3.2 gpt-3.5-turbo
```
@ -346,7 +343,7 @@ curl http://localhost:11434/v1/chat/completions \
The OpenAI API does not have a way of setting the context size for a model. If you need to change the context size, create a `Modelfile` which looks like:
```
```modelfile
FROM <some model>
PARAMETER num_ctx <context size>
```

View File

@ -12,7 +12,7 @@ A basic Go template consists of three main parts:
Here's an example of a simple chat template:
```go
```gotmpl
{{- range .Messages }}
{{ .Role }}: {{ .Content }}
{{- end }}
@ -162,6 +162,6 @@ CodeLlama [7B](https://ollama.com/library/codellama:7b-code) and [13B](https://o
Codestral [22B](https://ollama.com/library/codestral:22b) supports fill-in-middle.
```go
```gotmpl
[SUFFIX]{{ .Suffix }}[PREFIX] {{ .Prompt }}
```

View File

@ -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 --follow --pager-end
journalctl -u ollama --no-pager
```
When you run Ollama in a **container**, the logs go to stdout/stderr in the container:
@ -17,7 +17,6 @@ When you run Ollama in a **container**, the logs go to stdout/stderr in the cont
```shell
docker logs <container-name>
```
(Use `docker ps` to find the container name)
If manually running `ollama serve` in a terminal, the logs will be on that terminal.
@ -26,9 +25,9 @@ When you run Ollama on **Windows**, there are a few different locations. You can
- `explorer %LOCALAPPDATA%\Ollama` to view logs. The most recent server logs will be in `server.log` and older logs will be in `server-#.log`
- `explorer %LOCALAPPDATA%\Programs\Ollama` to browse the binaries (The installer adds this to your user PATH)
- `explorer %HOMEPATH%\.ollama` to browse where models and configuration is stored
- `explorer %TEMP%` where temporary executable files are stored in one or more `ollama*` directories
To enable additional debug logging to help troubleshoot problems, first **Quit the running app from the tray menu** then in a powershell terminal
```powershell
$env:OLLAMA_DEBUG="1"
& "ollama app.exe"
@ -50,13 +49,12 @@ Dynamic LLM libraries [rocm_v6 cpu cpu_avx cpu_avx2 cuda_v11 rocm_v5]
You can set OLLAMA_LLM_LIBRARY to any of the available LLM libraries to bypass autodetection, so for example, if you have a CUDA card, but want to force the CPU LLM library with AVX2 vector support, use:
```shell
```
OLLAMA_LLM_LIBRARY="cpu_avx2" ollama serve
```
You can see what features your CPU has with the following.
```shell
```
cat /proc/cpuinfo| grep flags | head -1
```
@ -64,13 +62,13 @@ cat /proc/cpuinfo| grep flags | head -1
If you run into problems on Linux and want to install an older version, or you'd like to try out a pre-release before it's officially released, you can tell the install script which version to install.
```shell
curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.5.7 sh
```sh
curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION="0.1.29" sh
```
## Linux docker
## Linux tmp noexec
If Ollama initially works on the GPU in a docker container, but then switches to running on CPU after some period of time with errors in the server log reporting GPU discovery failures, this can be resolved by disabling systemd cgroup management in Docker. Edit `/etc/docker/daemon.json` on the host and add `"exec-opts": ["native.cgroupdriver=cgroupfs"]` to the docker configuration.
If your system is configured with the "noexec" flag where Ollama stores its temporary executable files, you can specify an alternate location by setting OLLAMA_TMPDIR to a location writable by the user ollama runs as. For example OLLAMA_TMPDIR=/usr/share/ollama/
## NVIDIA GPU Discovery
@ -99,6 +97,8 @@ On linux, AMD GPU access typically requires `video` and/or `render` group member
When running in a container, in some Linux distributions and container runtimes, the ollama process may be unable to access the GPU. Use `ls -lnd /dev/kfd /dev/dri /dev/dri/*` on the host system to determine the **numeric** group IDs on your system, and pass additional `--group-add ...` arguments to the container so it can access the required devices. For example, in the following output `crw-rw---- 1 0 44 226, 0 Sep 16 16:55 /dev/dri/card0` the group ID column is `44`
If Ollama initially works on the GPU in a docker container, but then switches to running on CPU after some period of time with errors in the server log reporting GPU discovery failures, this can be resolved by disabling systemd cgroup management in Docker. Edit `/etc/docker/daemon.json` on the host and add `"exec-opts": ["native.cgroupdriver=cgroupfs"]` to the docker configuration.
If you are experiencing problems getting Ollama to correctly discover or use your GPU for inference, the following may help isolate the failure.
- `AMD_LOG_LEVEL=3` Enable info log levels in the AMD HIP/ROCm libraries. This can help show more detailed error codes that can help troubleshoot problems
- `OLLAMA_DEBUG=1` During GPU discovery additional information will be reported

View File

@ -47,7 +47,6 @@ If Ollama is already running, Quit the tray application and relaunch it from the
## API Access
Here's a quick example showing API access from `powershell`
```powershell
(Invoke-WebRequest -method POST -Body '{"model":"llama3.2", "prompt":"Why is the sky blue?", "stream": false}' -uri http://localhost:11434/api/generate ).Content | ConvertFrom-json
```
@ -55,13 +54,14 @@ Here's a quick example showing API access from `powershell`
## Troubleshooting
Ollama on Windows stores files in a few different locations. You can view them in
the explorer window by hitting `<Ctrl>+R` and type in:
the explorer window by hitting `<cmd>+R` and type in:
- `explorer %LOCALAPPDATA%\Ollama` contains logs, and downloaded updates
- *app.log* contains most resent logs from the GUI application
- *server.log* contains the most recent server logs
- *upgrade.log* contains log output for upgrades
- `explorer %LOCALAPPDATA%\Programs\Ollama` contains the binaries (The installer adds this to your user PATH)
- `explorer %HOMEPATH%\.ollama` contains models and configuration
- `explorer %TEMP%` contains temporary executable files in one or more `ollama*` directories
## Uninstall
@ -80,11 +80,9 @@ help you keep up to date.
If you'd like to install or integrate Ollama as a service, a standalone
`ollama-windows-amd64.zip` zip file is available containing only the Ollama CLI
and GPU library dependencies for Nvidia. If you have an AMD GPU, also download
and extract the additional ROCm package `ollama-windows-amd64-rocm.zip` into the
same directory. This allows for embedding Ollama in existing applications, or
running it as a system service via `ollama serve` with tools such as
[NSSM](https://nssm.cc/).
and GPU library dependencies for Nvidia and AMD. This allows for embedding
Ollama in existing applications, or running it as a system service via `ollama
serve` with tools such as [NSSM](https://nssm.cc/).
> [!NOTE]
> If you are upgrading from a prior version, you should remove the old directories first.

View File

@ -53,8 +53,8 @@ func Host() *url.URL {
}
}
// AllowedOrigins returns a list of allowed origins. AllowedOrigins can be configured via the OLLAMA_ORIGINS environment variable.
func AllowedOrigins() (origins []string) {
// Origins returns a list of allowed origins. Origins can be configured via the OLLAMA_ORIGINS environment variable.
func Origins() (origins []string) {
if s := Var("OLLAMA_ORIGINS"); s != "" {
origins = strings.Split(s, ",")
}
@ -73,7 +73,6 @@ func AllowedOrigins() (origins []string) {
"file://*",
"tauri://*",
"vscode-webview://*",
"vscode-file://*",
)
return origins
@ -149,22 +148,9 @@ func Bool(k string) func() bool {
}
}
// LogLevel returns the log level for the application.
// Values are 0 or false INFO (Default), 1 or true DEBUG, 2 TRACE
func LogLevel() slog.Level {
level := slog.LevelInfo
if s := Var("OLLAMA_DEBUG"); s != "" {
if b, _ := strconv.ParseBool(s); b {
level = slog.LevelDebug
} else if i, _ := strconv.ParseInt(s, 10, 64); i != 0 {
level = slog.Level(i * -4)
}
}
return level
}
var (
// Debug enabled additional debug information.
Debug = Bool("OLLAMA_DEBUG")
// FlashAttention enables the experimental flash attention feature.
FlashAttention = Bool("OLLAMA_FLASH_ATTENTION")
// KvCacheType is the quantization type for the K/V cache.
@ -179,10 +165,6 @@ var (
IntelGPU = Bool("OLLAMA_INTEL_GPU")
// MultiUserCache optimizes prompt caching for multi-user scenarios
MultiUserCache = Bool("OLLAMA_MULTIUSER_CACHE")
// Enable the new Ollama engine
NewEngine = Bool("OLLAMA_NEW_ENGINE")
// ContextLength sets the default context length
ContextLength = Uint("OLLAMA_CONTEXT_LENGTH", 4096)
)
func String(s string) func() string {
@ -222,6 +204,8 @@ var (
MaxRunners = Uint("OLLAMA_MAX_LOADED_MODELS", 0)
// MaxQueue sets the maximum number of queued requests. MaxQueue can be configured via the OLLAMA_MAX_QUEUE environment variable.
MaxQueue = Uint("OLLAMA_MAX_QUEUE", 512)
// MaxVRAM sets a maximum VRAM override in bytes. MaxVRAM can be configured via the OLLAMA_MAX_VRAM environment variable.
MaxVRAM = Uint("OLLAMA_MAX_VRAM", 0)
)
func Uint64(key string, defaultValue uint64) func() uint64 {
@ -249,7 +233,7 @@ type EnvVar struct {
func AsMap() map[string]EnvVar {
ret := map[string]EnvVar{
"OLLAMA_DEBUG": {"OLLAMA_DEBUG", LogLevel(), "Show additional debug information (e.g. OLLAMA_DEBUG=1)"},
"OLLAMA_DEBUG": {"OLLAMA_DEBUG", Debug(), "Show additional debug information (e.g. OLLAMA_DEBUG=1)"},
"OLLAMA_FLASH_ATTENTION": {"OLLAMA_FLASH_ATTENTION", FlashAttention(), "Enabled flash attention"},
"OLLAMA_KV_CACHE_TYPE": {"OLLAMA_KV_CACHE_TYPE", KvCacheType(), "Quantization type for the K/V cache (default: f16)"},
"OLLAMA_GPU_OVERHEAD": {"OLLAMA_GPU_OVERHEAD", GpuOverhead(), "Reserve a portion of VRAM per GPU (bytes)"},
@ -263,11 +247,9 @@ func AsMap() map[string]EnvVar {
"OLLAMA_NOHISTORY": {"OLLAMA_NOHISTORY", NoHistory(), "Do not preserve readline history"},
"OLLAMA_NOPRUNE": {"OLLAMA_NOPRUNE", NoPrune(), "Do not prune model blobs on startup"},
"OLLAMA_NUM_PARALLEL": {"OLLAMA_NUM_PARALLEL", NumParallel(), "Maximum number of parallel requests"},
"OLLAMA_ORIGINS": {"OLLAMA_ORIGINS", AllowedOrigins(), "A comma separated list of allowed origins"},
"OLLAMA_ORIGINS": {"OLLAMA_ORIGINS", Origins(), "A comma separated list of allowed origins"},
"OLLAMA_SCHED_SPREAD": {"OLLAMA_SCHED_SPREAD", SchedSpread(), "Always schedule model across all GPUs"},
"OLLAMA_MULTIUSER_CACHE": {"OLLAMA_MULTIUSER_CACHE", MultiUserCache(), "Optimize prompt caching for multi-user scenarios"},
"OLLAMA_CONTEXT_LENGTH": {"OLLAMA_CONTEXT_LENGTH", ContextLength(), "Context length to use unless otherwise specified (default: 4096)"},
"OLLAMA_NEW_ENGINE": {"OLLAMA_NEW_ENGINE", NewEngine(), "Enable the new Ollama engine"},
// Informational
"HTTP_PROXY": {"HTTP_PROXY", String("HTTP_PROXY")(), "HTTP proxy"},
@ -306,3 +288,12 @@ func Values() map[string]string {
func Var(key string) string {
return strings.Trim(strings.TrimSpace(os.Getenv(key)), "\"'")
}
// On windows, we keep the binary at the top directory, but
// other platforms use a "bin" directory, so this returns ".."
func LibRelativeToExe() string {
if runtime.GOOS == "windows" {
return "."
}
return ".."
}

View File

@ -1,13 +1,11 @@
package envconfig
import (
"log/slog"
"math"
"testing"
"time"
"github.com/google/go-cmp/cmp"
"github.com/ollama/ollama/logutil"
)
func TestHost(t *testing.T) {
@ -71,7 +69,6 @@ func TestOrigins(t *testing.T) {
"file://*",
"tauri://*",
"vscode-webview://*",
"vscode-file://*",
}},
{"http://10.0.0.1", []string{
"http://10.0.0.1",
@ -91,7 +88,6 @@ func TestOrigins(t *testing.T) {
"file://*",
"tauri://*",
"vscode-webview://*",
"vscode-file://*",
}},
{"http://172.16.0.1,https://192.168.0.1", []string{
"http://172.16.0.1",
@ -112,7 +108,6 @@ func TestOrigins(t *testing.T) {
"file://*",
"tauri://*",
"vscode-webview://*",
"vscode-file://*",
}},
{"http://totally.safe,http://definitely.legit", []string{
"http://totally.safe",
@ -133,14 +128,13 @@ func TestOrigins(t *testing.T) {
"file://*",
"tauri://*",
"vscode-webview://*",
"vscode-file://*",
}},
}
for _, tt := range cases {
t.Run(tt.value, func(t *testing.T) {
t.Setenv("OLLAMA_ORIGINS", tt.value)
if diff := cmp.Diff(AllowedOrigins(), tt.expect); diff != "" {
if diff := cmp.Diff(Origins(), tt.expect); diff != "" {
t.Errorf("%s: mismatch (-want +got):\n%s", tt.value, diff)
}
})
@ -278,50 +272,3 @@ func TestVar(t *testing.T) {
})
}
}
func TestContextLength(t *testing.T) {
cases := map[string]uint{
"": 4096,
"2048": 2048,
}
for k, v := range cases {
t.Run(k, func(t *testing.T) {
t.Setenv("OLLAMA_CONTEXT_LENGTH", k)
if i := ContextLength(); i != v {
t.Errorf("%s: expected %d, got %d", k, v, i)
}
})
}
}
func TestLogLevel(t *testing.T) {
cases := map[string]slog.Level{
// Default to INFO
"": slog.LevelInfo,
"false": slog.LevelInfo,
"f": slog.LevelInfo,
"0": slog.LevelInfo,
// True values enable Debug
"true": slog.LevelDebug,
"t": slog.LevelDebug,
// Positive values increase verbosity
"1": slog.LevelDebug,
"2": logutil.LevelTrace,
// Negative values decrease verbosity
"-1": slog.LevelWarn,
"-2": slog.LevelError,
}
for k, v := range cases {
t.Run(k, func(t *testing.T) {
t.Setenv("OLLAMA_DEBUG", k)
if i := LogLevel(); i != v {
t.Errorf("%s: expected %d, got %d", k, v, i)
}
})
}
}

View File

@ -40,6 +40,8 @@ func HumanBytes(b int64) string {
}
switch {
case value >= 100:
return fmt.Sprintf("%d %s", int(value), unit)
case value >= 10:
return fmt.Sprintf("%d %s", int(value), unit)
case value != math.Trunc(value):

View File

@ -1,91 +0,0 @@
package format
import (
"testing"
)
func TestHumanBytes(t *testing.T) {
type testCase struct {
input int64
expected string
}
tests := []testCase{
// Test bytes (B)
{0, "0 B"},
{1, "1 B"},
{999, "999 B"},
// Test kilobytes (KB)
{1000, "1 KB"},
{1500, "1.5 KB"},
{999999, "999 KB"},
// Test megabytes (MB)
{1000000, "1 MB"},
{1500000, "1.5 MB"},
{999999999, "999 MB"},
// Test gigabytes (GB)
{1000000000, "1 GB"},
{1500000000, "1.5 GB"},
{999999999999, "999 GB"},
// Test terabytes (TB)
{1000000000000, "1 TB"},
{1500000000000, "1.5 TB"},
{1999999999999, "2.0 TB"},
// Test fractional values
{1234, "1.2 KB"},
{1234567, "1.2 MB"},
{1234567890, "1.2 GB"},
}
for _, tc := range tests {
t.Run(tc.expected, func(t *testing.T) {
result := HumanBytes(tc.input)
if result != tc.expected {
t.Errorf("Expected %s, got %s", tc.expected, result)
}
})
}
}
func TestHumanBytes2(t *testing.T) {
type testCase struct {
input uint64
expected string
}
tests := []testCase{
// Test bytes (B)
{0, "0 B"},
{1, "1 B"},
{1023, "1023 B"},
// Test kibibytes (KiB)
{1024, "1.0 KiB"},
{1536, "1.5 KiB"},
{1048575, "1024.0 KiB"},
// Test mebibytes (MiB)
{1048576, "1.0 MiB"},
{1572864, "1.5 MiB"},
{1073741823, "1024.0 MiB"},
// Test gibibytes (GiB)
{1073741824, "1.0 GiB"},
{1610612736, "1.5 GiB"},
{2147483648, "2.0 GiB"},
}
for _, tc := range tests {
t.Run(tc.expected, func(t *testing.T) {
result := HumanBytes2(tc.input)
if result != tc.expected {
t.Errorf("Expected %s, got %s", tc.expected, result)
}
})
}
}

View File

@ -12,9 +12,6 @@ func TestHumanNumber(t *testing.T) {
testCases := []testCase{
{0, "0"},
{999, "999"},
{1000, "1K"},
{1001, "1K"},
{1000000, "1M"},
{125000000, "125M"},
{500500000, "500.50M"},

View File

@ -5,7 +5,7 @@ import (
"time"
)
func assertEqual(t *testing.T, a any, b any) {
func assertEqual(t *testing.T, a interface{}, b interface{}) {
if a != b {
t.Errorf("Assert failed, expected %v, got %v", b, a)
}

View File

@ -1,13 +0,0 @@
package fs
type Config interface {
Architecture() string
String(string, ...string) string
Uint(string, ...uint32) uint32
Float(string, ...float32) float32
Bool(string, ...bool) bool
Strings(string, ...[]string) []string
Ints(string, ...[]int32) []int32
Floats(string, ...[]float32) []float32
}

View File

@ -1,713 +0,0 @@
package ggml
import (
"encoding/binary"
"errors"
"fmt"
"io"
"log/slog"
"math"
"slices"
"strings"
"github.com/ollama/ollama/fs/util/bufioutil"
)
type GGML struct {
container
model
}
type model interface {
KV() KV
Tensors() Tensors
}
type KV map[string]any
func (kv KV) Architecture() string {
return kv.String("general.architecture", "unknown")
}
func (kv KV) Kind() string {
return kv.String("general.type", "unknown")
}
func (kv KV) ParameterCount() uint64 {
return keyValue(kv, "general.parameter_count", uint64(0))
}
func (kv KV) FileType() FileType {
if t := kv.Uint("general.file_type"); t > 0 {
return FileType(t)
}
return FileTypeUnknown
}
func (kv KV) BlockCount() uint64 {
return uint64(kv.Uint("block_count"))
}
func (kv KV) EmbeddingLength() uint64 {
return uint64(kv.Uint("embedding_length"))
}
func (kv KV) HeadCount() uint64 {
return uint64(kv.Uint("attention.head_count"))
}
func (kv KV) HeadCountKV() uint64 {
return uint64(kv.Uint("attention.head_count_kv", 1))
}
func (kv KV) EmbeddingHeadCount() uint64 {
if heads := kv.HeadCount(); heads > 0 {
return kv.EmbeddingLength() / heads
}
return 0
}
func (kv KV) EmbeddingHeadCountK() uint64 {
return uint64(kv.Uint("attention.key_length", uint32(kv.EmbeddingHeadCount())))
}
func (kv KV) EmbeddingHeadCountV() uint64 {
return uint64(kv.Uint("attention.value_length", uint32(kv.EmbeddingHeadCount())))
}
func (kv KV) GQA() uint64 {
return kv.HeadCount() / kv.HeadCountKV()
}
func (kv KV) ContextLength() uint64 {
return uint64(kv.Uint("context_length"))
}
func (kv KV) ChatTemplate() string {
return kv.String("tokenizer.chat_template")
}
func (kv KV) String(key string, defaultValue ...string) string {
return keyValue(kv, key, append(defaultValue, "")...)
}
func (kv KV) Uint(key string, defaultValue ...uint32) uint32 {
return keyValue(kv, key, append(defaultValue, 0)...)
}
func (kv KV) Float(key string, defaultValue ...float32) float32 {
return keyValue(kv, key, append(defaultValue, 0)...)
}
func (kv KV) Bool(key string, defaultValue ...bool) bool {
return keyValue(kv, key, append(defaultValue, false)...)
}
func (kv KV) Strings(key string, defaultValue ...[]string) []string {
return keyValue(kv, key, &array[string]{values: append(defaultValue, []string(nil))[0]}).values
}
func (kv KV) Ints(key string, defaultValue ...[]int32) []int32 {
return keyValue(kv, key, &array[int32]{values: append(defaultValue, []int32(nil))[0]}).values
}
func (kv KV) Uints(key string, defaultValue ...[]uint32) []uint32 {
return keyValue(kv, key, &array[uint32]{values: append(defaultValue, []uint32(nil))[0]}).values
}
func (kv KV) Floats(key string, defaultValue ...[]float32) []float32 {
return keyValue(kv, key, &array[float32]{values: append(defaultValue, []float32(nil))[0]}).values
}
func (kv KV) OllamaEngineRequired() bool {
return slices.Contains([]string{
"gemma3",
"mistral3",
"llama4",
"mllama",
"qwen25vl",
}, kv.Architecture())
}
type valueTypes interface {
uint8 | int8 | uint16 | int16 |
uint32 | int32 | uint64 | int64 |
string | float32 | float64 | bool
}
type arrayValueTypes interface {
*array[uint8] | *array[int8] | *array[uint16] | *array[int16] |
*array[uint32] | *array[int32] | *array[uint64] | *array[int64] |
*array[string] | *array[float32] | *array[float64] | *array[bool]
}
func keyValue[T valueTypes | arrayValueTypes](kv KV, key string, defaultValue ...T) T {
if !strings.HasPrefix(key, "tokenizer.") && !strings.HasPrefix(key, "general.") {
key = kv.Architecture() + "." + key
}
if val, ok := kv[key]; ok {
return val.(T)
}
slog.Debug("key not found", "key", key, "default", defaultValue[0])
return defaultValue[0]
}
type Tensors struct {
items []*Tensor
Offset uint64
}
func (s Tensors) Items(prefix ...string) []*Tensor {
if len(prefix) == 0 {
return s.items
}
var items []*Tensor
for _, t := range s.items {
if strings.HasPrefix(t.Name, prefix[0]) {
items = append(items, t)
}
}
return items
}
func (ts Tensors) GroupLayers() map[string]Layer {
layers := make(map[string]Layer)
for _, t := range ts.items {
parts := strings.Split(t.Name, ".")
if index := slices.IndexFunc(parts, func(s string) bool { return s == "blk" || s == "mm" }); index != -1 {
if len(parts) > index+2 {
// blk and mm should have a number after them, join it
parts = append(
[]string{strings.Join(parts[:index+2], ".")},
parts[index+2:]...)
}
}
if _, ok := layers[parts[0]]; !ok {
layers[parts[0]] = make(Layer)
}
layers[parts[0]][strings.Join(parts[1:], ".")] = t
}
return layers
}
type Layer map[string]*Tensor
func (l Layer) Size() (size uint64) {
for _, t := range l {
size += t.Size()
}
return size
}
type Tensor struct {
Name string `json:"name"`
Kind uint32 `json:"kind"`
Offset uint64 `json:"-"`
// Shape is the number of elements in each dimension
Shape []uint64 `json:"shape"`
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 {
return (TensorType)(t.Kind).BlockSize()
}
func (t TensorType) BlockSize() uint64 {
switch t {
case
0, // F32
1, // F16
24, // I8
25, // I16
26, // I32
27, // I64
28, // F64
30: // BF16
return 1
case
2, // Q4_0
3, // Q4_1
6, // Q5_0
7, // Q5_1
8, // Q8_0
9, // Q8_1
20: // IQ4_NL
return 32
default:
return 256
}
}
func (t Tensor) typeSize() uint64 {
return TensorType(t.Kind).TypeSize()
}
func (t TensorType) TypeSize() uint64 {
blockSize := t.BlockSize()
switch t {
case TensorTypeF32:
return 4
case TensorTypeF16:
return 2
case TensorTypeQ4_0:
return 2 + blockSize/2
case TensorTypeQ4_1:
return 2 + 2 + blockSize/2
case TensorTypeQ5_0:
return 2 + 4 + blockSize/2
case TensorTypeQ5_1:
return 2 + 2 + 4 + blockSize/2
case TensorTypeQ8_0:
return 2 + blockSize
case TensorTypeQ8_1:
return 2 + 2 + blockSize
case TensorTypeQ2_K:
return blockSize/16 + blockSize/4 + 2 + 2
case TensorTypeQ3_K:
return blockSize/8 + blockSize/4 + 12 + 2
case TensorTypeQ4_K:
return 2 + 2 + 12 + blockSize/2
case TensorTypeQ5_K:
return 2 + 2 + 12 + blockSize/8 + blockSize/2
case TensorTypeQ6_K:
return blockSize/2 + blockSize/4 + blockSize/16 + 2
case TensorTypeQ8_K:
return 4 + blockSize + 2*blockSize/16
case tensorTypeIQ2_XXS:
return 2 + 2*blockSize/8
case tensorTypeIQ2_XS:
return 2 + 2*blockSize/8 + blockSize/32
case tensorTypeIQ3_XXS:
return 2 + blockSize/4 + blockSize/8
case tensorTypeIQ1_S:
return 2 + blockSize/8 + blockSize/16
case tensorTypeIQ4_NL:
return 2 + blockSize/2
case tensorTypeIQ3_S:
return 2 + blockSize/4 + blockSize/8 + blockSize/32 + 4
case tensorTypeIQ2_S:
return 2 + blockSize/4 + blockSize/16
case tensorTypeIQ4_XS:
return 2 + 2 + blockSize/2 + blockSize/64
case TensorTypeI8:
return 1
case TensorTypeI16:
return 2
case TensorTypeI32:
return 4
case TensorTypeI64:
return 8
case TensorTypeF64:
return 8
case tensorTypeIQ1_M:
return blockSize/8 + blockSize/16 + blockSize/32
case TensorTypeBF16:
return 2
default:
return 0
}
}
func (t Tensor) Elements() uint64 {
var count uint64 = 1
for _, n := range t.Shape {
count *= n
}
return count
}
func (t Tensor) Size() uint64 {
return t.Elements() * t.typeSize() / t.blockSize()
}
func (t Tensor) Type() string {
return TensorType(t.Kind).String()
}
type container interface {
Name() string
Decode(io.ReadSeeker) (model, error)
}
const (
// Magic constant for `ggml` files (unversioned).
FILE_MAGIC_GGML = 0x67676d6c
// Magic constant for `ggml` files (versioned, ggmf).
FILE_MAGIC_GGMF = 0x67676d66
// Magic constant for `ggml` files (versioned, ggjt).
FILE_MAGIC_GGJT = 0x67676a74
// Magic constant for `ggla` files (LoRA adapter).
FILE_MAGIC_GGLA = 0x67676C61
// Magic constant for `gguf` files (versioned, gguf)
FILE_MAGIC_GGUF_LE = 0x46554747
FILE_MAGIC_GGUF_BE = 0x47475546
)
var ErrUnsupportedFormat = errors.New("unsupported model format")
func DetectContentType(b []byte) string {
switch binary.LittleEndian.Uint32(b[:4]) {
case FILE_MAGIC_GGML:
return "ggml"
case FILE_MAGIC_GGMF:
return "ggmf"
case FILE_MAGIC_GGJT:
return "ggjt"
case FILE_MAGIC_GGLA:
return "ggla"
case FILE_MAGIC_GGUF_LE, FILE_MAGIC_GGUF_BE:
return "gguf"
default:
return ""
}
}
// Decode decodes a GGML model from the given reader.
//
// It collects array values for arrays with a size less than or equal to
// maxArraySize. If the maxArraySize is negative, all arrays are collected.
func Decode(rs io.ReadSeeker, maxArraySize int) (*GGML, int64, error) {
rs = bufioutil.NewBufferedSeeker(rs, 32<<10)
var magic uint32
if err := binary.Read(rs, binary.LittleEndian, &magic); err != nil {
return nil, 0, err
}
var c container
switch magic {
case FILE_MAGIC_GGUF_LE:
c = &containerGGUF{ByteOrder: binary.LittleEndian, maxArraySize: maxArraySize}
case FILE_MAGIC_GGUF_BE:
c = &containerGGUF{ByteOrder: binary.BigEndian, maxArraySize: maxArraySize}
default:
return nil, 0, errors.New("invalid file magic")
}
model, err := c.Decode(rs)
if err != nil {
return nil, 0, err
}
offset, err := rs.Seek(0, io.SeekCurrent)
if err != nil {
return nil, 0, err
}
// final model type
return &GGML{
container: c,
model: model,
}, offset, nil
}
func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType string) (kv []uint64, partialOffload, fullOffload uint64) {
embedding := f.KV().EmbeddingLength()
heads := f.KV().HeadCount()
headsKV := f.KV().HeadCountKV()
vocab := uint64(f.KV()["tokenizer.ggml.tokens"].(*array[string]).size)
embeddingHeads := f.KV().EmbeddingHeadCount()
embeddingHeadsK := f.KV().EmbeddingHeadCountK()
embeddingHeadsV := f.KV().EmbeddingHeadCountV()
layers := f.Tensors().GroupLayers()
bytesPerElement := kvCacheBytesPerElement(kvCacheType)
kv = make([]uint64, f.KV().BlockCount())
for i := range kv {
kv[i] = uint64(float64(context*(embeddingHeadsK+embeddingHeadsV)*headsKV) * bytesPerElement)
}
switch f.KV().Architecture() {
case "llama", "llama4":
fullOffload = max(
4*batch*(1+4*embedding+context*(1+heads)),
4*batch*(embedding+vocab),
)
partialOffload = 4 * batch * embedding
partialOffload += max(
4*batch*(1+embedding+max(context, embedding))+embedding*embedding*9/16+4*context*(batch*heads+embeddingHeads*headsKV),
4*batch*(embedding+vocab)+embedding*vocab*105/128,
)
if ffnGateExpsWeight, ok := layers["blk.0"]["ffn_gate_exps.weight"]; ok {
// mixtral 8x22b
ff := uint64(f.KV().Uint("feed_forward_length"))
partialOffload = max(
3*ffnGateExpsWeight.Size()+4*batch*(2*ff+headsKV+embedding+context+embeddingHeads*headsKV),
4*(context*batch*heads+context*embeddingHeads*headsKV+batch*1024+embeddingHeads*headsKV*batch),
)
} else if ffnGateWeight, ok := layers["blk.0"]["ffn_gate.0.weight"]; ok {
// mixtral 8x7b
ffnGateWeight1 := ffnGateWeight.Shape[1]
fullOffload = 4 * batch * (2 + 3*embedding + context*(1+heads) + 2*headsKV + ffnGateWeight1)
partialOffload = max(
4*batch*(3+embeddingHeads*headsKV+embedding+context*(1+heads)+ffnGateWeight1)+(embedding*embedding+3*embedding*headsKV*ffnGateWeight1)*9/16,
4*batch*(1+2*embedding+context*(1+heads))+embedding*(6*context*headsKV/heads+embedding*9/16),
)
}
case "mllama":
var visionTokens, tiles uint64 = 1601, 4
crossAttentionLayers := f.KV().Ints("attention.cross_attention_layers")
for i := range kv {
if slices.Contains(crossAttentionLayers, int32(i)) {
kv[i] = headsKV * (embeddingHeadsK + embeddingHeadsV) *
4 * // sizeof(float32)
visionTokens *
tiles
}
}
fullOffload = max(
4*batch*(2+3*embedding+embeddingHeadsK*heads+context*(1+heads)),
// vocab graph
4*batch*(embedding+vocab),
)
var ropeFreqsCount uint64
if ropeFreqs, ok := f.Tensors().GroupLayers()["rope_freqs"]; ok {
if ropeFreqsWeights, ok := ropeFreqs["weights"]; ok {
ropeFreqsCount = ropeFreqsWeights.Elements()
}
}
partialOffload = max(
4*(batch*
(2*embedding+1+context*(1+heads)+embeddingHeadsK*heads)+
ropeFreqsCount+
embeddingHeadsK*context*headsKV),
// vocab graph
4*batch*(embedding+vocab)+embedding*vocab*105/128,
)
case "gemma", "gemma2", "gemma3":
fullOffload = max(
4*batch*(embedding+vocab),
4*batch*(2+context+context*heads+2*embedding+2*embeddingHeadsK*heads),
)
partialOffload = max(
4*embedding*batch+embedding*vocab*105/128+4*vocab*batch,
4*batch*(2*embedding+1+2*embeddingHeadsK*heads+context+context*heads)+
4*embeddingHeadsK*context*8+
embedding*embeddingHeadsK*heads*9/16,
)
// Gemma2 also has sliding window attention but we only have an optimized implementation in the Ollama
// engine. Gemma3 always uses the Ollama engine.
if f.KV().Architecture() == "gemma3" {
const gemma3GlobalCacheCount = 6
slidingWindow := (uint64(numParallel) * uint64(f.KV().Uint("attention.sliding_window"))) + batch
for i := range kv {
// Every 6th layer is a global layer, which is the full context size that has already been set. The other
// layers are the smaller local (sliding) layers.
if (i+1)%gemma3GlobalCacheCount != 0 {
kv[i] = uint64(float64(slidingWindow*(embeddingHeadsK+embeddingHeadsV)*headsKV) * bytesPerElement)
}
}
}
case "command-r":
fullOffload = max(
4*batch*(embedding+vocab),
4*batch*(2+4*embedding+context*(1+heads)),
)
partialOffload = max(
4*batch*(embedding+vocab)+embedding*vocab*105/128,
4*batch*(1+2*embedding+context*(1+heads))+4*embedding*context+embedding*embedding*9/16,
)
case "qwen2":
fullOffload = max(
4*batch*(embedding+vocab),
4*batch*(1+2*embedding+context+context*heads),
)
partialOffload = max(
4*batch*(embedding+vocab)+embedding*vocab*105/128,
4*(batch*(1+2*embedding+context*(1+heads))+embedding*(1+context)),
)
case "phi2":
fullOffload = max(
4*batch*(embedding+vocab),
4*batch*(1+4*embedding+context+context*heads),
)
partialOffload = max(
4*batch*(2*embedding+vocab)+embedding*vocab*105/128,
4*batch*(2+3*embedding+context+context*heads),
)
case "stablelm":
fullOffload = 4 * batch * (context*(1+heads) + 3*embedding + 2)
partialOffload = max(
4*batch*(vocab+2*embedding),
fullOffload,
)
case "deepseek2":
fullOffload = max(
4*batch*(3*embedding+vocab),
4*batch*(3*embedding+2+context*(1+headsKV)+2*embeddingHeadsK*headsKV),
)
partialOffload = max(
4*batch*(3*embedding+vocab)+embedding*vocab*105/128,
4*batch*(2*embedding+1+2*embeddingHeadsK*headsKV+context+context*headsKV)+4*embeddingHeadsK*context*headsKV+embedding*embeddingHeadsK*headsKV*9/16,
)
case "chatglm":
fullOffload = 4 * batch * (embedding + vocab)
partialOffload = 4*batch*(embedding+vocab) + embedding*vocab*105/128
if qkvBias, ok := layers["blk.0"]["attn_qkv.bias"]; ok {
fullOffload = max(
fullOffload,
4*batch*(2+
2*embedding+
context+
context*heads+
embeddingHeadsK*heads+
qkvBias.Shape[0]),
)
partialOffload = max(
partialOffload,
4*batch*(1+
2*embedding+
embeddingHeadsK*heads+
context+
context*heads)+
4*embeddingHeadsK*context+
4*context*embeddingHeadsK+
4*qkvBias.Shape[0],
)
}
}
return
}
func (llm GGML) VisionGraphSize() (weights, graphSize uint64) {
if llm.KV().Uint("vision.block_count") == 0 {
return
}
for name, layer := range llm.Tensors().GroupLayers() {
if name == "v" || strings.HasPrefix(name, "v.") {
for _, tensor := range layer {
weights += tensor.Size()
}
}
}
imageSize := uint64(llm.KV().Uint("vision.image_size"))
patchSize := uint64(llm.KV().Uint("vision.patch_size"))
if patchSize == 0 {
slog.Warn("unknown patch size for vision model")
return
}
numChannels := uint64(llm.KV().Uint("vision.num_channels"))
numPatches := (imageSize / patchSize) * (imageSize / patchSize)
if _, ok := llm.Tensors().GroupLayers()["v"]["class_embd"]; ok {
numPatches++
}
headCount := uint64(llm.KV().Uint("vision.attention.head_count"))
embeddingLength := uint64(llm.KV().Uint("vision.embedding_length"))
switch llm.KV().Architecture() {
case "mllama":
numPaddedPatches := numPatches + 8 - (numPatches%8)%8
maxNumTiles := uint64(llm.KV().Uint("vision.max_num_tiles"))
graphSize = 4 * (8 +
imageSize*imageSize*numChannels*maxNumTiles +
embeddingLength*numPatches*maxNumTiles +
9*embeddingLength*numPaddedPatches*maxNumTiles +
numPaddedPatches*maxNumTiles*numPaddedPatches*maxNumTiles*headCount)
case "gemma3", "mistral3":
graphSize = 4 * (imageSize*imageSize*numChannels +
embeddingLength*patchSize +
numPatches*numPatches*headCount)
case "qwen25vl":
maxPixels := uint64(llm.KV().Uint("vision.max_pixels", 28*28*1280))
mergeSize := uint64(llm.KV().Uint("vision.spatial_merge_size", 2))
temporalPatchSize := uint64(2)
// Calculate max possible patches based on max_pixels
maxHeight := uint64(math.Sqrt(float64(maxPixels)))
maxWidth := maxPixels / maxHeight
maxGridHeight := maxHeight / patchSize
maxGridWidth := maxWidth / patchSize
// Account for merged patches (2x2 grid)
numPatches := (maxGridHeight * maxGridWidth) / (mergeSize * mergeSize)
// Calculate graph size based on typical operations in ProcessImage and createPatches
graphSize = 4 * (maxPixels*numChannels + // Original image storage
// Normalized pixels
maxPixels*numChannels +
// Patches storage (numPatches * channels * temporalPatchSize * patchSize^2)
numPatches*numChannels*temporalPatchSize*patchSize*patchSize +
// Self-attention calculations (similar to other architectures)
numPatches*numPatches*headCount +
// Additional buffer for processing
embeddingLength*numPatches)
case "llama4":
// vision graph is computed independently in the same schedule
// and is negligible compared to the worst case text graph
}
return weights, graphSize
}
// SupportsKVCacheType checks if the requested cache type is supported
func (f GGML) SupportsKVCacheType(cacheType string) bool {
return slices.Contains([]string{"f16", "q8_0", "q4_0"}, cacheType)
}
// SupportsFlashAttention checks if the model supports flash attention
func (f GGML) SupportsFlashAttention() bool {
_, isEmbedding := f.KV()[fmt.Sprintf("%s.pooling_type", f.KV().Architecture())]
if isEmbedding {
return false
}
// Check head counts match and are non-zero
headCountK := f.KV().EmbeddingHeadCountK()
headCountV := f.KV().EmbeddingHeadCountV()
return headCountK != 0 && headCountV != 0 && headCountK == headCountV
}
// kvCacheBytesPerElement returns the number of bytes per element for a given KV cache type
func kvCacheBytesPerElement(cacheType string) float64 {
switch cacheType {
case "q8_0":
return 1 // 1/2 of fp16
case "q4_0":
return 0.5 // 1/4 of fp16
default:
return 2 // f16 (default)
}
}

View File

@ -1,271 +0,0 @@
package ggml
import (
"maps"
"math"
"slices"
"strconv"
"strings"
"testing"
"github.com/google/go-cmp/cmp"
)
func TestTensorLayers(t *testing.T) {
tensors := make(map[string]*Tensor)
for _, name := range []string{
"token_embd.weight",
"blk.0.attn_k.weight",
"blk.0.attn_output.weight",
"blk.0.attn_q.weight",
"blk.0.attn_v.weight",
"blk.0.attn_norm.weight",
"blk.0.ffn_down.weight",
"blk.0.ffn_gate.weight",
"blk.0.ffn_up.weight",
"blk.0.ffn_norm.weight",
"output_norm.weight",
"mm.0.bias",
"mm.0.weight",
"v.blk.0.attn_k.weight",
"v.blk.0.attn_output.weight",
"v.blk.0.attn_q.weight",
"v.blk.0.attn_v.weight",
"v.blk.0.attn_norm.weight",
"v.blk.0.ffn_down.weight",
"v.blk.0.ffn_gate.weight",
"v.blk.0.ffn_up.weight",
"v.blk.0.ffn_norm.weight",
"v.patch_embd.weight",
"v.position_embd.gate",
"v.position_embd.weight",
} {
tensors[name] = &Tensor{Name: name}
}
cases := []struct {
name string
items []*Tensor
want map[string]Layer
}{
{
name: "text",
items: slices.Collect(func(yield func(*Tensor) bool) {
for k, v := range tensors {
if !strings.HasPrefix(k, "mm.") && !strings.HasPrefix(k, "v.") {
if !yield(v) {
return
}
}
}
}),
want: map[string]Layer{
"blk.0": {
"attn_k.weight": tensors["blk.0.attn_k.weight"],
"attn_q.weight": tensors["blk.0.attn_q.weight"],
"attn_v.weight": tensors["blk.0.attn_v.weight"],
"attn_output.weight": tensors["blk.0.attn_output.weight"],
"attn_norm.weight": tensors["blk.0.attn_norm.weight"],
"ffn_down.weight": tensors["blk.0.ffn_down.weight"],
"ffn_gate.weight": tensors["blk.0.ffn_gate.weight"],
"ffn_up.weight": tensors["blk.0.ffn_up.weight"],
"ffn_norm.weight": tensors["blk.0.ffn_norm.weight"],
},
"token_embd": {"weight": tensors["token_embd.weight"]},
"output_norm": {"weight": tensors["output_norm.weight"]},
},
},
{
name: "vision",
items: slices.Collect(func(yield func(*Tensor) bool) {
for k, v := range tensors {
if strings.HasPrefix(k, "mm.") || strings.HasPrefix(k, "v.") {
if !yield(v) {
return
}
}
}
}),
want: map[string]Layer{
"mm.0": {
"bias": tensors["mm.0.bias"],
"weight": tensors["mm.0.weight"],
},
"v.blk.0": {
"attn_k.weight": tensors["v.blk.0.attn_k.weight"],
"attn_q.weight": tensors["v.blk.0.attn_q.weight"],
"attn_v.weight": tensors["v.blk.0.attn_v.weight"],
"attn_output.weight": tensors["v.blk.0.attn_output.weight"],
"attn_norm.weight": tensors["v.blk.0.attn_norm.weight"],
"ffn_down.weight": tensors["v.blk.0.ffn_down.weight"],
"ffn_gate.weight": tensors["v.blk.0.ffn_gate.weight"],
"ffn_up.weight": tensors["v.blk.0.ffn_up.weight"],
"ffn_norm.weight": tensors["v.blk.0.ffn_norm.weight"],
},
"v": {
"patch_embd.weight": tensors["v.patch_embd.weight"],
"position_embd.gate": tensors["v.position_embd.gate"],
"position_embd.weight": tensors["v.position_embd.weight"],
},
},
},
{
name: "vision and text",
items: slices.Collect(maps.Values(tensors)),
want: map[string]Layer{
"blk.0": {
"attn_k.weight": tensors["blk.0.attn_k.weight"],
"attn_q.weight": tensors["blk.0.attn_q.weight"],
"attn_v.weight": tensors["blk.0.attn_v.weight"],
"attn_output.weight": tensors["blk.0.attn_output.weight"],
"attn_norm.weight": tensors["blk.0.attn_norm.weight"],
"ffn_down.weight": tensors["blk.0.ffn_down.weight"],
"ffn_gate.weight": tensors["blk.0.ffn_gate.weight"],
"ffn_up.weight": tensors["blk.0.ffn_up.weight"],
"ffn_norm.weight": tensors["blk.0.ffn_norm.weight"],
},
"token_embd": {"weight": tensors["token_embd.weight"]},
"output_norm": {"weight": tensors["output_norm.weight"]},
"mm.0": {
"bias": tensors["mm.0.bias"],
"weight": tensors["mm.0.weight"],
},
"v.blk.0": {
"attn_k.weight": tensors["v.blk.0.attn_k.weight"],
"attn_q.weight": tensors["v.blk.0.attn_q.weight"],
"attn_v.weight": tensors["v.blk.0.attn_v.weight"],
"attn_output.weight": tensors["v.blk.0.attn_output.weight"],
"attn_norm.weight": tensors["v.blk.0.attn_norm.weight"],
"ffn_down.weight": tensors["v.blk.0.ffn_down.weight"],
"ffn_gate.weight": tensors["v.blk.0.ffn_gate.weight"],
"ffn_up.weight": tensors["v.blk.0.ffn_up.weight"],
"ffn_norm.weight": tensors["v.blk.0.ffn_norm.weight"],
},
"v": {
"patch_embd.weight": tensors["v.patch_embd.weight"],
"position_embd.gate": tensors["v.position_embd.gate"],
"position_embd.weight": tensors["v.position_embd.weight"],
},
},
},
}
for _, tt := range cases {
t.Run(tt.name, func(t *testing.T) {
got := Tensors{items: tt.items}.GroupLayers()
if diff := cmp.Diff(got, tt.want); diff != "" {
t.Errorf("unexpected layers (-got +want):\n%s", diff)
}
})
}
}
// ref: https://github.com/ggml-org/llama.cpp/blob/a82c9e7c23ef6db48cebfa194dc9cebbc4ac3552/ggml/src/ggml.c#L572
func TestTensorTypes(t *testing.T) {
cases := []struct {
kind uint32
blockSize uint64
typeSize uint64
}{
{0, 1, 4},
{1, 1, 2},
{2, 32, 18},
{3, 32, 20},
{6, 32, 22},
{7, 32, 24},
{8, 32, 34},
{9, 32, 36},
{10, 256, 84},
{11, 256, 110},
{12, 256, 144},
{13, 256, 176},
{14, 256, 210},
{15, 256, 292},
{16, 256, 66},
{17, 256, 74},
{18, 256, 98},
{19, 256, 50},
{20, 32, 18},
{21, 256, 110},
{22, 256, 82},
{23, 256, 136},
{24, 1, 1},
{25, 1, 2},
{26, 1, 4},
{27, 1, 8},
{28, 1, 8},
{29, 256, 56},
{30, 1, 2},
}
for _, tt := range cases {
t.Run(strconv.Itoa(int(tt.kind)), func(t *testing.T) {
tensor := Tensor{Kind: tt.kind}
if tensor.blockSize() != tt.blockSize {
t.Errorf("unexpected block size: got=%d want=%d", tensor.blockSize(), tt.blockSize)
}
if tensor.typeSize() != tt.typeSize {
t.Errorf("unexpected type size: got=%d want=%d", tensor.typeSize(), tt.typeSize)
}
})
}
}
func TestKeyValue(t *testing.T) {
kv := KV{
"general.architecture": "test",
"test.strings": &array[string]{size: 3, values: []string{"a", "b", "c"}},
"test.float32s": &array[float32]{size: 3, values: []float32{1.0, 2.0, 3.0}},
"test.int32s": &array[int32]{size: 3, values: []int32{1, 2, 3}},
"test.uint32s": &array[uint32]{size: 3, values: []uint32{1, 2, 3}},
}
if diff := cmp.Diff(kv.Strings("strings"), []string{"a", "b", "c"}); diff != "" {
t.Errorf("unexpected strings (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Strings("nonexistent.strings"), []string(nil)); diff != "" {
t.Errorf("unexpected strings (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Strings("default.strings", []string{"ollama"}), []string{"ollama"}); diff != "" {
t.Errorf("unexpected strings (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Floats("float32s"), []float32{1.0, 2.0, 3.0}); diff != "" {
t.Errorf("unexpected float32s (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Floats("nonexistent.float32s"), []float32(nil)); diff != "" {
t.Errorf("unexpected float32s (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Floats("default.float32s", []float32{math.MaxFloat32}), []float32{math.MaxFloat32}); diff != "" {
t.Errorf("unexpected float32s (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Ints("int32s"), []int32{1, 2, 3}); diff != "" {
t.Errorf("unexpected int8s (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Ints("nonexistent.int32s"), []int32(nil)); diff != "" {
t.Errorf("unexpected int8s (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Ints("default.int32s", []int32{math.MaxInt32}), []int32{math.MaxInt32}); diff != "" {
t.Errorf("unexpected int8s (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Uints("uint32s"), []uint32{1, 2, 3}); diff != "" {
t.Errorf("unexpected uint8s (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Uints("nonexistent.uint32s"), []uint32(nil)); diff != "" {
t.Errorf("unexpected uint8s (-got +want):\n%s", diff)
}
if diff := cmp.Diff(kv.Uints("default.uint32s", []uint32{math.MaxUint32}), []uint32{math.MaxUint32}); diff != "" {
t.Errorf("unexpected uint8s (-got +want):\n%s", diff)
}
}

View File

@ -1,63 +0,0 @@
package ggml
import (
"bytes"
"os"
"slices"
"testing"
"github.com/google/go-cmp/cmp"
)
func TestWriteGGUF(t *testing.T) {
w, err := os.CreateTemp(t.TempDir(), "*.bin")
if err != nil {
t.Fatal(err)
}
defer w.Close()
if err := WriteGGUF(w, KV{
"general.alignment": uint32(16),
}, []*Tensor{
{Name: "test.0", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
{Name: "test.1", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
{Name: "test.2", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
{Name: "test.3", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
{Name: "test.4", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
{Name: "test.5", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
}); err != nil {
t.Fatal(err)
}
r, err := os.Open(w.Name())
if err != nil {
t.Fatal(err)
}
defer r.Close()
ff, _, err := Decode(r, 0)
if err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(ff.KV(), KV{
"general.alignment": uint32(16),
"general.parameter_count": uint64(36),
}); diff != "" {
t.Errorf("Mismatch (-want +got):\n%s", diff)
}
if diff := cmp.Diff(ff.Tensors(), Tensors{
Offset: 336,
items: []*Tensor{
{Name: "test.0", Offset: 0, Shape: []uint64{2, 3}},
{Name: "test.1", Offset: 32, Shape: []uint64{2, 3}},
{Name: "test.2", Offset: 64, Shape: []uint64{2, 3}},
{Name: "test.3", Offset: 96, Shape: []uint64{2, 3}},
{Name: "test.4", Offset: 128, Shape: []uint64{2, 3}},
{Name: "test.5", Offset: 160, Shape: []uint64{2, 3}},
},
}, cmp.AllowUnexported(Tensors{})); diff != "" {
t.Errorf("Mismatch (-want +got):\n%s", diff)
}
}

View File

@ -1,318 +0,0 @@
package ggml
import (
"fmt"
"log/slog"
"strings"
)
// FileType is the Go equivalent to llama_ftype used for gguf file typing
type FileType uint32
const (
FileTypeF32 FileType = iota
FileTypeF16
fileTypeQ4_0
fileTypeQ4_1
fileTypeQ4_1_F16 // unused by GGML
fileTypeQ4_2 // unused by GGML
fileTypeQ4_3 // unused by GGML
FileTypeQ8_0
fileTypeQ5_0
fileTypeQ5_1
fileTypeQ2_K
fileTypeQ3_K_S
fileTypeQ3_K_M
fileTypeQ3_K_L
FileTypeQ4_K_S
FileTypeQ4_K_M
fileTypeQ5_K_S
fileTypeQ5_K_M
fileTypeQ6_K
fileTypeIQ2_XXS
fileTypeIQ2_XS
fileTypeQ2_K_S
fileTypeIQ3_XS
fileTypeIQ3_XXS
fileTypeIQ1_S
fileTypeIQ4_NL
fileTypeIQ3_S
fileTypeIQ3_M
fileTypeIQ2_S
fileTypeIQ2_M
fileTypeIQ4_XS
fileTypeIQ1_M
FileTypeBF16
fileTypeQ4_0_4_4 // unused by GGML
fileTypeQ4_0_4_8 // unused by GGML
fileTypeQ4_0_8_8 // unused by GGML
fileTypeTQ1_0
fileTypeTQ2_0
FileTypeUnknown = 1024
)
// ParseFileType parses the provided GGUF file type
// Only Ollama supported types are considered valid
func ParseFileType(s string) (FileType, error) {
switch s {
case "F32":
return FileTypeF32, nil
case "F16":
return FileTypeF16, nil
case "Q8_0":
return FileTypeQ8_0, nil
case "Q4_K_S":
return FileTypeQ4_K_S, nil
case "Q4_K_M", "Q4_K":
return FileTypeQ4_K_M, nil
case "BF16":
return FileTypeBF16, nil
default:
supportedFileTypes := []FileType{
FileTypeF32,
FileTypeF16,
FileTypeQ4_K_S,
FileTypeQ4_K_M,
FileTypeQ8_0,
// fsggml.FileTypeBF16, // TODO
}
strs := make([]string, len(supportedFileTypes))
for i := range supportedFileTypes {
strs[i] = supportedFileTypes[i].String()
}
return FileTypeUnknown, fmt.Errorf("unsupported quantization type %s - supported types are %s", s, strings.Join(strs, ", "))
}
}
func (t FileType) String() string {
// Note: this routine will return a broader set of file types for existing models
switch t {
case FileTypeF32:
return "F32"
case FileTypeF16:
return "F16"
case fileTypeQ4_0:
return "Q4_0"
case fileTypeQ4_1:
return "Q4_1"
case FileTypeQ8_0:
return "Q8_0"
case fileTypeQ5_0:
return "Q5_0"
case fileTypeQ5_1:
return "Q5_1"
case fileTypeQ2_K:
return "Q2_K"
case fileTypeQ3_K_S:
return "Q3_K_S"
case fileTypeQ3_K_M:
return "Q3_K_M"
case fileTypeQ3_K_L:
return "Q3_K_L"
case FileTypeQ4_K_S:
return "Q4_K_S"
case FileTypeQ4_K_M:
return "Q4_K_M"
case fileTypeQ5_K_S:
return "Q5_K_S"
case fileTypeQ5_K_M:
return "Q5_K_M"
case fileTypeQ6_K:
return "Q6_K"
case fileTypeQ2_K_S:
return "Q2_K_S"
case FileTypeBF16:
return "BF16"
default:
return "unknown"
}
}
func (t FileType) Value() uint32 {
return uint32(t)
}
func (ftype FileType) ToTensorType() TensorType {
switch ftype {
case FileTypeF32:
return TensorTypeF32
case FileTypeF16:
return TensorTypeF16
case fileTypeQ4_0:
return TensorTypeQ4_0
case fileTypeQ4_1:
return TensorTypeQ4_1
case FileTypeQ8_0:
return TensorTypeQ8_0
case fileTypeQ5_0:
return TensorTypeQ5_0
case fileTypeQ5_1:
return TensorTypeQ5_1
case fileTypeQ2_K:
return TensorTypeQ2_K
case fileTypeQ3_K_S:
return TensorTypeQ3_K
case fileTypeQ3_K_M:
return TensorTypeQ3_K
case fileTypeQ3_K_L:
return TensorTypeQ3_K
case FileTypeQ4_K_S:
return TensorTypeQ4_K
case FileTypeQ4_K_M:
return TensorTypeQ4_K
case fileTypeQ5_K_S:
return TensorTypeQ5_K
case fileTypeQ5_K_M:
return TensorTypeQ5_K
case fileTypeQ6_K:
return TensorTypeQ6_K
case fileTypeQ2_K_S:
return TensorTypeQ2_K
case FileTypeBF16:
return TensorTypeBF16
default:
slog.Warn("unsupported file type", "type", ftype)
return 0 // F32
}
}
// TensorType is equivalent to ggml_type for individual tensor types
// Note: these are not the same as FileType
type TensorType uint32
const (
TensorTypeF32 TensorType = iota
TensorTypeF16
TensorTypeQ4_0
TensorTypeQ4_1
tensorTypeQ4_2 // unused by GGML
tensorTypeQ4_3 // unused by GGML
TensorTypeQ5_0
TensorTypeQ5_1
TensorTypeQ8_0
TensorTypeQ8_1
TensorTypeQ2_K
TensorTypeQ3_K
TensorTypeQ4_K
TensorTypeQ5_K
TensorTypeQ6_K
TensorTypeQ8_K
tensorTypeIQ2_XXS // not supported by ollama
tensorTypeIQ2_XS // not supported by ollama
tensorTypeIQ3_XXS // not supported by ollama
tensorTypeIQ1_S // not supported by ollama
tensorTypeIQ4_NL // not supported by ollama
tensorTypeIQ3_S // not supported by ollama
tensorTypeIQ2_S // not supported by ollama
tensorTypeIQ4_XS // not supported by ollama
TensorTypeI8
TensorTypeI16
TensorTypeI32
TensorTypeI64
TensorTypeF64
tensorTypeIQ1_M // not supported by ollama
TensorTypeBF16
tensorTypeQ4_0_4_4 // unused by GGML
tensorTypeQ4_0_4_8 // unused by GGML
tensorTypeQ4_0_8_8 // unused by GGML
tensorTypeTQ1_0 // not supported by ollama
tensorTypeTQ2_0 // not supported by ollama
tensorTypeIQ4_NL_4_4 // unused by GGML
tensorTypeIQ4_NL_4_8 // unused by GGML
tensorTypeIQ4_NL_8_8 // unused by GGML
)
// ParseFileType parses the provided GGUF file type
// Only Ollama supported types are considered valid
func ParseTensorType(s string) (TensorType, error) {
switch s {
case "F32":
return TensorTypeF32, nil
case "F16":
return TensorTypeF16, nil
case "Q4_0":
return TensorTypeQ4_0, nil
case "Q4_1":
return TensorTypeQ4_1, nil
case "Q5_0":
return TensorTypeQ5_0, nil
case "Q5_1":
return TensorTypeQ5_1, nil
case "Q8_0":
return TensorTypeQ8_0, nil
case "Q8_1":
return TensorTypeQ8_1, nil
case "Q2_K":
return TensorTypeQ2_K, nil
case "Q3_K":
return TensorTypeQ3_K, nil
case "Q4_K":
return TensorTypeQ4_K, nil
case "Q5_K":
return TensorTypeQ5_K, nil
case "Q6_K":
return TensorTypeQ6_K, nil
case "Q8_K":
return TensorTypeQ8_K, nil
case "F64":
return TensorTypeF64, nil
case "BF16":
return TensorTypeBF16, nil
default:
return 0, fmt.Errorf("unsupported quantization type %s", s)
}
}
func (t TensorType) IsQuantized() bool {
switch t {
case TensorTypeF32, TensorTypeF16, TensorTypeBF16:
return false
default:
return true
}
}
func (t TensorType) RowSize(ne uint64) uint64 {
return t.TypeSize() * ne / t.BlockSize()
}
func (t TensorType) String() string {
switch t {
case TensorTypeF32:
return "F32"
case TensorTypeF16:
return "F16"
case TensorTypeQ4_0:
return "Q4_0"
case TensorTypeQ4_1:
return "Q4_1"
case TensorTypeQ5_0:
return "Q5_0"
case TensorTypeQ5_1:
return "Q5_1"
case TensorTypeQ8_0:
return "Q8_0"
case TensorTypeQ8_1:
return "Q8_1"
case TensorTypeQ2_K:
return "Q2_K"
case TensorTypeQ3_K:
return "Q3_K"
case TensorTypeQ4_K:
return "Q4_K"
case TensorTypeQ5_K:
return "Q5_K"
case TensorTypeQ6_K:
return "Q6_K"
case TensorTypeQ8_K:
return "Q8_K"
case TensorTypeF64:
return "F64"
case TensorTypeBF16:
return "BF16"
default:
return "unknown"
}
}

18
go.mod
View File

@ -1,6 +1,6 @@
module github.com/ollama/ollama
go 1.24.0
go 1.23.4
require (
github.com/containerd/console v1.0.3
@ -11,20 +11,18 @@ require (
github.com/spf13/cobra v1.7.0
github.com/stretchr/testify v1.9.0
github.com/x448/float16 v0.8.4
golang.org/x/sync v0.12.0
golang.org/x/sync v0.10.0
)
require (
github.com/agnivade/levenshtein v1.1.1
github.com/d4l3k/go-bfloat16 v0.0.0-20211005043715-690c3bdd05f1
github.com/dlclark/regexp2 v1.11.4
github.com/emirpasic/gods/v2 v2.0.0-alpha
github.com/google/go-cmp v0.6.0
github.com/mattn/go-runewidth v0.0.14
github.com/nlpodyssey/gopickle v0.3.0
github.com/pdevine/tensor v0.0.0-20240510204454-f88f4562727c
golang.org/x/image v0.22.0
golang.org/x/tools v0.30.0
)
require (
@ -70,12 +68,12 @@ require (
github.com/twitchyliquid64/golang-asm v0.15.1 // indirect
github.com/ugorji/go/codec v1.2.12 // indirect
golang.org/x/arch v0.8.0 // indirect
golang.org/x/crypto v0.36.0
golang.org/x/exp v0.0.0-20250218142911-aa4b98e5adaa
golang.org/x/net v0.38.0 // indirect
golang.org/x/sys v0.31.0
golang.org/x/term v0.30.0
golang.org/x/text v0.23.0
golang.org/x/crypto v0.31.0
golang.org/x/exp v0.0.0-20231110203233-9a3e6036ecaa
golang.org/x/net v0.25.0 // indirect
golang.org/x/sys v0.28.0
golang.org/x/term v0.27.0
golang.org/x/text v0.21.0
google.golang.org/protobuf v1.34.1
gopkg.in/yaml.v3 v3.0.1 // indirect
)

32
go.sum
View File

@ -42,8 +42,6 @@ github.com/davecgh/go-spew v1.1.1 h1:vj9j/u1bqnvCEfJOwUhtlOARqs3+rkHYY13jYWTU97c
github.com/davecgh/go-spew v1.1.1/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
github.com/dgryski/trifles v0.0.0-20200323201526-dd97f9abfb48 h1:fRzb/w+pyskVMQ+UbP35JkH8yB7MYb4q/qhBarqZE6g=
github.com/dgryski/trifles v0.0.0-20200323201526-dd97f9abfb48/go.mod h1:if7Fbed8SFyPtHLHbg49SI7NAdJiC5WIA09pe59rfAA=
github.com/dlclark/regexp2 v1.11.4 h1:rPYF9/LECdNymJufQKmri9gV604RvvABwgOA8un7yAo=
github.com/dlclark/regexp2 v1.11.4/go.mod h1:DHkYz0B9wPfa6wondMfaivmHpzrQ3v9q8cnmRbL6yW8=
github.com/emirpasic/gods/v2 v2.0.0-alpha h1:dwFlh8pBg1VMOXWGipNMRt8v96dKAIvBehtCt6OtunU=
github.com/emirpasic/gods/v2 v2.0.0-alpha/go.mod h1:W0y4M2dtBB9U5z3YlghmpuUhiaZT2h6yoeE+C1sCp6A=
github.com/envoyproxy/go-control-plane v0.9.0/go.mod h1:YTl/9mNaCwkRvm6d1a2C3ymFceY/DCBVvsKhRF0iEA4=
@ -214,16 +212,16 @@ golang.org/x/crypto v0.0.0-20190308221718-c2843e01d9a2/go.mod h1:djNgcEr1/C05ACk
golang.org/x/crypto v0.0.0-20190510104115-cbcb75029529/go.mod h1:yigFU9vqHzYiE8UmvKecakEJjdnWj3jj499lnFckfCI=
golang.org/x/crypto v0.0.0-20191011191535-87dc89f01550/go.mod h1:yigFU9vqHzYiE8UmvKecakEJjdnWj3jj499lnFckfCI=
golang.org/x/crypto v0.0.0-20200622213623-75b288015ac9/go.mod h1:LzIPMQfyMNhhGPhUkYOs5KpL4U8rLKemX1yGLhDgUto=
golang.org/x/crypto v0.36.0 h1:AnAEvhDddvBdpY+uR+MyHmuZzzNqXSe/GvuDeob5L34=
golang.org/x/crypto v0.36.0/go.mod h1:Y4J0ReaxCR1IMaabaSMugxJES1EpwhBHhv2bDHklZvc=
golang.org/x/crypto v0.31.0 h1:ihbySMvVjLAeSH1IbfcRTkD/iNscyz8rGzjF/E5hV6U=
golang.org/x/crypto v0.31.0/go.mod h1:kDsLvtWBEx7MV9tJOj9bnXsPbxwJQ6csT/x4KIN4Ssk=
golang.org/x/exp v0.0.0-20180321215751-8460e604b9de/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
golang.org/x/exp v0.0.0-20180807140117-3d87b88a115f/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
golang.org/x/exp v0.0.0-20190121172915-509febef88a4/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
golang.org/x/exp v0.0.0-20190125153040-c74c464bbbf2/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
golang.org/x/exp v0.0.0-20190306152737-a1d7652674e8/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
golang.org/x/exp v0.0.0-20191002040644-a1355ae1e2c3/go.mod h1:NOZ3BPKG0ec/BKJQgnvsSFpcKLM5xXVWnvZS97DWHgE=
golang.org/x/exp v0.0.0-20250218142911-aa4b98e5adaa h1:t2QcU6V556bFjYgu4L6C+6VrCPyJZ+eyRsABUPs1mz4=
golang.org/x/exp v0.0.0-20250218142911-aa4b98e5adaa/go.mod h1:BHOTPb3L19zxehTsLoJXVaTktb06DFgmdW6Wb9s8jqk=
golang.org/x/exp v0.0.0-20231110203233-9a3e6036ecaa h1:FRnLl4eNAQl8hwxVVC17teOw8kdjVDVAiFMtgUdTSRQ=
golang.org/x/exp v0.0.0-20231110203233-9a3e6036ecaa/go.mod h1:zk2irFbV9DP96SEBUUAy67IdHUaZuSnrz1n472HUCLE=
golang.org/x/image v0.0.0-20180708004352-c73c2afc3b81/go.mod h1:ux5Hcp/YLpHSI86hEcLt0YII63i6oz57MZXIpbrjZUs=
golang.org/x/image v0.0.0-20190227222117-0694c2d4d067/go.mod h1:kZ7UVZpmo3dzQBMxlp+ypCbDeSB+sBbTgSJuh5dn5js=
golang.org/x/image v0.0.0-20190802002840-cff245a6509b/go.mod h1:FeLwcggjj3mMvU+oOTbSwawSJRM1uh48EjtB4UJZlP0=
@ -257,8 +255,8 @@ golang.org/x/net v0.0.0-20200822124328-c89045814202/go.mod h1:/O7V0waA8r7cgGh81R
golang.org/x/net v0.0.0-20201021035429-f5854403a974/go.mod h1:sp8m0HH+o8qH0wwXwYZr8TS3Oi6o0r6Gce1SSxlDquU=
golang.org/x/net v0.0.0-20210405180319-a5a99cb37ef4/go.mod h1:p54w0d4576C0XHj96bSt6lcn1PtDYWL6XObtHCRCNQM=
golang.org/x/net v0.0.0-20210614182718-04defd469f4e/go.mod h1:9nx3DQGgdP8bBQD5qxJ1jj9UTztislL4KSBs9R2vV5Y=
golang.org/x/net v0.38.0 h1:vRMAPTMaeGqVhG5QyLJHqNDwecKTomGeqbnfZyKlBI8=
golang.org/x/net v0.38.0/go.mod h1:ivrbrMbzFq5J41QOQh0siUuly180yBYtLp+CKbEaFx8=
golang.org/x/net v0.25.0 h1:d/OCCoBEUq33pjydKrGQhw7IlUPI2Oylr+8qLx49kac=
golang.org/x/net v0.25.0/go.mod h1:JkAGAh7GEvH74S6FOH42FLoXpXbE/aqXSrIQjXgsiwM=
golang.org/x/oauth2 v0.0.0-20180821212333-d2e6202438be/go.mod h1:N/0e6XlmueqKjAGxoOufVs8QHGRruUQn6yWY3a++T0U=
golang.org/x/oauth2 v0.0.0-20200107190931-bf48bf16ab8d/go.mod h1:gOpvHmFTYa4IltrdGE7lF6nIHvwfUNPOp7c8zoXwtLw=
golang.org/x/sync v0.0.0-20180314180146-1d60e4601c6f/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
@ -268,8 +266,8 @@ golang.org/x/sync v0.0.0-20190423024810-112230192c58/go.mod h1:RxMgew5VJxzue5/jJ
golang.org/x/sync v0.0.0-20190911185100-cd5d95a43a6e/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sync v0.0.0-20201020160332-67f06af15bc9/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sync v0.0.0-20210220032951-036812b2e83c/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sync v0.12.0 h1:MHc5BpPuC30uJk597Ri8TV3CNZcTLu6B6z4lJy+g6Jw=
golang.org/x/sync v0.12.0/go.mod h1:1dzgHSNfp02xaA81J2MS99Qcpr2w7fw1gpm99rleRqA=
golang.org/x/sync v0.10.0 h1:3NQrjDixjgGwUOCaF8w2+VYHv0Ve/vGYSbdkTa98gmQ=
golang.org/x/sync v0.10.0/go.mod h1:Czt+wKu1gCyEFDUtn0jG5QVvpJ6rzVqr5aXyt9drQfk=
golang.org/x/sys v0.0.0-20180830151530-49385e6e1522/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
golang.org/x/sys v0.0.0-20190215142949-d0b11bdaac8a/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
golang.org/x/sys v0.0.0-20190312061237-fead79001313/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
@ -285,17 +283,17 @@ golang.org/x/sys v0.0.0-20210510120138-977fb7262007/go.mod h1:oPkhp1MJrh7nUepCBc
golang.org/x/sys v0.0.0-20210630005230-0f9fa26af87c/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.5.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.6.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.31.0 h1:ioabZlmFYtWhL+TRYpcnNlLwhyxaM9kWTDEmfnprqik=
golang.org/x/sys v0.31.0/go.mod h1:BJP2sWEmIv4KK5OTEluFJCKSidICx8ciO85XgH3Ak8k=
golang.org/x/sys v0.28.0 h1:Fksou7UEQUWlKvIdsqzJmUmCX3cZuD2+P3XyyzwMhlA=
golang.org/x/sys v0.28.0/go.mod h1:/VUhepiaJMQUp4+oa/7Zr1D23ma6VTLIYjOOTFZPUcA=
golang.org/x/term v0.0.0-20201126162022-7de9c90e9dd1/go.mod h1:bj7SfCRtBDWHUb9snDiAeCFNEtKQo2Wmx5Cou7ajbmo=
golang.org/x/term v0.30.0 h1:PQ39fJZ+mfadBm0y5WlL4vlM7Sx1Hgf13sMIY2+QS9Y=
golang.org/x/term v0.30.0/go.mod h1:NYYFdzHoI5wRh/h5tDMdMqCqPJZEuNqVR5xJLd/n67g=
golang.org/x/term v0.27.0 h1:WP60Sv1nlK1T6SupCHbXzSaN0b9wUmsPoRS9b61A23Q=
golang.org/x/term v0.27.0/go.mod h1:iMsnZpn0cago0GOrHO2+Y7u7JPn5AylBrcoWkElMTSM=
golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
golang.org/x/text v0.3.3/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
golang.org/x/text v0.3.5/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
golang.org/x/text v0.3.6/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
golang.org/x/text v0.23.0 h1:D71I7dUrlY+VX0gQShAThNGHFxZ13dGLBHQLVl1mJlY=
golang.org/x/text v0.23.0/go.mod h1:/BLNzu4aZCJ1+kcD0DNRotWKage4q2rGVAg4o22unh4=
golang.org/x/text v0.21.0 h1:zyQAAkrwaneQ066sspRyJaG9VNi/YJ1NfzcGB3hZ/qo=
golang.org/x/text v0.21.0/go.mod h1:4IBbMaMmOPCJ8SecivzSH54+73PCFmPWxNTLm+vZkEQ=
golang.org/x/tools v0.0.0-20180525024113-a5b4c53f6e8b/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
golang.org/x/tools v0.0.0-20190114222345-bf090417da8b/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
@ -309,8 +307,6 @@ golang.org/x/tools v0.0.0-20200130002326-2f3ba24bd6e7/go.mod h1:TB2adYChydJhpapK
golang.org/x/tools v0.0.0-20200619180055-7c47624df98f/go.mod h1:EkVYQZoAsY45+roYkvgYkIh4xh/qjgUK9TdY2XT94GE=
golang.org/x/tools v0.0.0-20210106214847-113979e3529a/go.mod h1:emZCQorbCU4vsT4fOWvOPXz4eW1wZW4PmDk9uLelYpA=
golang.org/x/tools v0.1.4/go.mod h1:o0xws9oXOQQZyjljx8fwUC0k7L1pTE6eaCbjGeHmOkk=
golang.org/x/tools v0.30.0 h1:BgcpHewrV5AUp2G9MebG4XPFI1E2W41zU1SaqVA9vJY=
golang.org/x/tools v0.30.0/go.mod h1:c347cR/OJfw5TI+GfX7RUPNMdDRRbjvYTS0jPyvsVtY=
golang.org/x/xerrors v0.0.0-20190717185122-a985d3407aa7/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
golang.org/x/xerrors v0.0.0-20191011141410-1b5146add898/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
golang.org/x/xerrors v0.0.0-20191204190536-9bdfabe68543/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=

View File

@ -1,412 +0,0 @@
//go:build integration
package integration
import (
"bytes"
"context"
"fmt"
"math/rand"
"strings"
"testing"
"time"
"github.com/ollama/ollama/api"
)
func TestAPIGenerate(t *testing.T) {
initialTimeout := 60 * time.Second
streamTimeout := 30 * time.Second
ctx, cancel := context.WithTimeout(context.Background(), 1*time.Minute)
defer cancel()
// Set up the test data
req := api.GenerateRequest{
Model: smol,
Prompt: "why is the sky blue? be brief",
Options: map[string]interface{}{
"temperature": 0,
"seed": 123,
},
}
anyResp := []string{"rayleigh", "scattering"}
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
if err := PullIfMissing(ctx, client, req.Model); err != nil {
t.Fatalf("pull failed %s", err)
}
tests := []struct {
name string
stream bool
}{
{
name: "stream",
stream: true,
},
{
name: "no_stream",
stream: false,
},
}
for _, test := range tests {
t.Run(test.name, func(t *testing.T) {
stallTimer := time.NewTimer(initialTimeout)
var buf bytes.Buffer
fn := func(response api.GenerateResponse) error {
// Fields that must always be present
if response.Model == "" {
t.Errorf("response missing model: %#v", response)
}
if response.Done {
// Required fields for final updates:
if response.DoneReason == "" && *req.Stream {
// TODO - is the lack of done reason on non-stream a bug?
t.Errorf("final response missing done_reason: %#v", response)
}
if response.Metrics.TotalDuration == 0 {
t.Errorf("final response missing total_duration: %#v", response)
}
if response.Metrics.LoadDuration == 0 {
t.Errorf("final response missing load_duration: %#v", response)
}
if response.Metrics.PromptEvalDuration == 0 {
t.Errorf("final response missing prompt_eval_duration: %#v", response)
}
if response.Metrics.EvalCount == 0 {
t.Errorf("final response missing eval_count: %#v", response)
}
if response.Metrics.EvalDuration == 0 {
t.Errorf("final response missing eval_duration: %#v", response)
}
if len(response.Context) == 0 {
t.Errorf("final response missing context: %#v", response)
}
// Note: caching can result in no prompt eval count, so this can't be verified reliably
// if response.Metrics.PromptEvalCount == 0 {
// t.Errorf("final response missing prompt_eval_count: %#v", response)
// }
} // else incremental response, nothing to check right now...
buf.Write([]byte(response.Response))
if !stallTimer.Reset(streamTimeout) {
return fmt.Errorf("stall was detected while streaming response, aborting")
}
return nil
}
done := make(chan int)
var genErr error
go func() {
req.Stream = &test.stream
req.Options["seed"] = rand.Int() // bust cache for prompt eval results
genErr = client.Generate(ctx, &req, fn)
done <- 0
}()
select {
case <-stallTimer.C:
if buf.Len() == 0 {
t.Errorf("generate never started. Timed out after :%s", initialTimeout.String())
} else {
t.Errorf("generate stalled. Response so far:%s", buf.String())
}
case <-done:
if genErr != nil {
t.Fatalf("failed with %s request prompt %s ", req.Model, req.Prompt)
}
// Verify the response contains the expected data
response := buf.String()
atLeastOne := false
for _, resp := range anyResp {
if strings.Contains(strings.ToLower(response), resp) {
atLeastOne = true
break
}
}
if !atLeastOne {
t.Errorf("none of %v found in %s", anyResp, response)
}
case <-ctx.Done():
t.Error("outer test context done while waiting for generate")
}
})
}
// Validate PS while we're at it...
resp, err := client.ListRunning(ctx)
if err != nil {
t.Fatalf("list models API error: %s", err)
}
if resp == nil || len(resp.Models) == 0 {
t.Fatalf("list models API returned empty list while model should still be loaded")
}
// Find the model we just loaded and verify some attributes
found := false
for _, model := range resp.Models {
if strings.Contains(model.Name, req.Model) {
found = true
if model.Model == "" {
t.Errorf("model field omitted: %#v", model)
}
if model.Size == 0 {
t.Errorf("size omitted: %#v", model)
}
if model.Digest == "" {
t.Errorf("digest omitted: %#v", model)
}
verifyModelDetails(t, model.Details)
var nilTime time.Time
if model.ExpiresAt == nilTime {
t.Errorf("expires_at omitted: %#v", model)
}
// SizeVRAM could be zero.
}
}
if !found {
t.Errorf("unable to locate running model: %#v", resp)
}
}
func TestAPIChat(t *testing.T) {
initialTimeout := 60 * time.Second
streamTimeout := 30 * time.Second
ctx, cancel := context.WithTimeout(context.Background(), 1*time.Minute)
defer cancel()
// Set up the test data
req := api.ChatRequest{
Model: smol,
Messages: []api.Message{
{
Role: "user",
Content: "why is the sky blue? be brief",
},
},
Options: map[string]interface{}{
"temperature": 0,
"seed": 123,
},
}
anyResp := []string{"rayleigh", "scattering"}
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
if err := PullIfMissing(ctx, client, req.Model); err != nil {
t.Fatalf("pull failed %s", err)
}
tests := []struct {
name string
stream bool
}{
{
name: "stream",
stream: true,
},
{
name: "no_stream",
stream: false,
},
}
for _, test := range tests {
t.Run(test.name, func(t *testing.T) {
stallTimer := time.NewTimer(initialTimeout)
var buf bytes.Buffer
fn := func(response api.ChatResponse) error {
// Fields that must always be present
if response.Model == "" {
t.Errorf("response missing model: %#v", response)
}
if response.Done {
// Required fields for final updates:
var nilTime time.Time
if response.CreatedAt == nilTime {
t.Errorf("final response missing total_duration: %#v", response)
}
if response.DoneReason == "" {
t.Errorf("final response missing done_reason: %#v", response)
}
if response.Metrics.TotalDuration == 0 {
t.Errorf("final response missing total_duration: %#v", response)
}
if response.Metrics.LoadDuration == 0 {
t.Errorf("final response missing load_duration: %#v", response)
}
if response.Metrics.PromptEvalDuration == 0 {
t.Errorf("final response missing prompt_eval_duration: %#v", response)
}
if response.Metrics.EvalCount == 0 {
t.Errorf("final response missing eval_count: %#v", response)
}
if response.Metrics.EvalDuration == 0 {
t.Errorf("final response missing eval_duration: %#v", response)
}
if response.Metrics.PromptEvalCount == 0 {
t.Errorf("final response missing prompt_eval_count: %#v", response)
}
} // else incremental response, nothing to check right now...
buf.Write([]byte(response.Message.Content))
if !stallTimer.Reset(streamTimeout) {
return fmt.Errorf("stall was detected while streaming response, aborting")
}
return nil
}
done := make(chan int)
var genErr error
go func() {
req.Stream = &test.stream
req.Options["seed"] = rand.Int() // bust cache for prompt eval results
genErr = client.Chat(ctx, &req, fn)
done <- 0
}()
select {
case <-stallTimer.C:
if buf.Len() == 0 {
t.Errorf("chat never started. Timed out after :%s", initialTimeout.String())
} else {
t.Errorf("chat stalled. Response so far:%s", buf.String())
}
case <-done:
if genErr != nil {
t.Fatalf("failed with %s request prompt %v", req.Model, req.Messages)
}
// Verify the response contains the expected data
response := buf.String()
atLeastOne := false
for _, resp := range anyResp {
if strings.Contains(strings.ToLower(response), resp) {
atLeastOne = true
break
}
}
if !atLeastOne {
t.Errorf("none of %v found in %s", anyResp, response)
}
case <-ctx.Done():
t.Error("outer test context done while waiting for chat")
}
})
}
}
func TestAPIListModels(t *testing.T) {
ctx, cancel := context.WithTimeout(context.Background(), 10*time.Second)
defer cancel()
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
// Make sure we have at least one model so an empty list can be considered a failure
if err := PullIfMissing(ctx, client, smol); err != nil {
t.Fatalf("pull failed %s", err)
}
resp, err := client.List(ctx)
if err != nil {
t.Fatalf("unable to list models: %s", err)
}
if len(resp.Models) == 0 {
t.Fatalf("list should not be empty")
}
model := resp.Models[0]
if model.Name == "" {
t.Errorf("first model name empty: %#v", model)
}
var nilTime time.Time
if model.ModifiedAt == nilTime {
t.Errorf("first model modified_at empty: %#v", model)
}
if model.Size == 0 {
t.Errorf("first model size empty: %#v", model)
}
if model.Digest == "" {
t.Errorf("first model digest empty: %#v", model)
}
verifyModelDetails(t, model.Details)
}
func verifyModelDetails(t *testing.T, details api.ModelDetails) {
if details.Format == "" {
t.Errorf("first model details.format empty: %#v", details)
}
if details.Family == "" {
t.Errorf("first model details.family empty: %#v", details)
}
if details.ParameterSize == "" {
t.Errorf("first model details.parameter_size empty: %#v", details)
}
if details.QuantizationLevel == "" {
t.Errorf("first model details.quantization_level empty: %#v", details)
}
}
func TestAPIShowModel(t *testing.T) {
modelName := "llama3.2"
ctx, cancel := context.WithTimeout(context.Background(), 1*time.Minute)
defer cancel()
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
if err := PullIfMissing(ctx, client, modelName); err != nil {
t.Fatalf("pull failed %s", err)
}
resp, err := client.Show(ctx, &api.ShowRequest{Name: modelName})
if err != nil {
t.Fatalf("unable to show model: %s", err)
}
if resp.License == "" {
t.Errorf("%s missing license: %#v", modelName, resp)
}
if resp.Modelfile == "" {
t.Errorf("%s missing modelfile: %#v", modelName, resp)
}
if resp.Parameters == "" {
t.Errorf("%s missing parameters: %#v", modelName, resp)
}
if resp.Template == "" {
t.Errorf("%s missing template: %#v", modelName, resp)
}
// llama3 omits system
verifyModelDetails(t, resp.Details)
// llama3 ommits messages
if len(resp.ModelInfo) == 0 {
t.Errorf("%s missing model_info: %#v", modelName, resp)
}
// llama3 omits projectors
var nilTime time.Time
if resp.ModifiedAt == nilTime {
t.Errorf("%s missing modified_at: %#v", modelName, resp)
}
}
func TestAPIEmbeddings(t *testing.T) {
ctx, cancel := context.WithTimeout(context.Background(), 1*time.Minute)
defer cancel()
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
req := api.EmbeddingRequest{
Model: "orca-mini",
Prompt: "why is the sky blue?",
Options: map[string]interface{}{
"temperature": 0,
"seed": 123,
},
}
if err := PullIfMissing(ctx, client, req.Model); err != nil {
t.Fatalf("pull failed %s", err)
}
resp, err := client.Embeddings(ctx, &req)
if err != nil {
t.Fatalf("embeddings call failed %s", err)
}
if len(resp.Embedding) == 0 {
t.Errorf("zero length embedding response")
}
}

View File

@ -14,15 +14,15 @@ import (
"github.com/stretchr/testify/require"
)
func TestBlueSky(t *testing.T) {
func TestOrcaMiniBlueSky(t *testing.T) {
ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
defer cancel()
// Set up the test data
req := api.GenerateRequest{
Model: smol,
Model: "orca-mini",
Prompt: "why is the sky blue?",
Stream: &stream,
Options: map[string]any{
Options: map[string]interface{}{
"temperature": 0,
"seed": 123,
},
@ -31,7 +31,6 @@ func TestBlueSky(t *testing.T) {
}
func TestUnicode(t *testing.T) {
skipUnderMinVRAM(t, 6)
ctx, cancel := context.WithTimeout(context.Background(), 3*time.Minute)
defer cancel()
// Set up the test data
@ -40,7 +39,7 @@ func TestUnicode(t *testing.T) {
Model: "deepseek-coder-v2:16b-lite-instruct-q2_K",
Prompt: "天空为什么是蓝色的?",
Stream: &stream,
Options: map[string]any{
Options: map[string]interface{}{
"temperature": 0,
"seed": 123,
// Workaround deepseek context shifting bug
@ -62,7 +61,7 @@ func TestExtendedUnicodeOutput(t *testing.T) {
Model: "gemma2:2b",
Prompt: "Output some smily face emoji",
Stream: &stream,
Options: map[string]any{
Options: map[string]interface{}{
"temperature": 0,
"seed": 123,
},
@ -94,10 +93,10 @@ func TestUnicodeModelDir(t *testing.T) {
defer cancel()
req := api.GenerateRequest{
Model: smol,
Model: "orca-mini",
Prompt: "why is the sky blue?",
Stream: &stream,
Options: map[string]any{
Options: map[string]interface{}{
"temperature": 0,
"seed": 123,
},

View File

@ -21,11 +21,11 @@ func TestMultiModelConcurrency(t *testing.T) {
var (
req = [2]api.GenerateRequest{
{
Model: "llama3.2:1b",
Model: "orca-mini",
Prompt: "why is the ocean blue?",
Stream: &stream,
KeepAlive: &api.Duration{Duration: 10 * time.Second},
Options: map[string]any{
Options: map[string]interface{}{
"seed": 42,
"temperature": 0.0,
},
@ -34,7 +34,7 @@ func TestMultiModelConcurrency(t *testing.T) {
Prompt: "what is the origin of the us thanksgiving holiday?",
Stream: &stream,
KeepAlive: &api.Duration{Duration: 10 * time.Second},
Options: map[string]any{
Options: map[string]interface{}{
"seed": 42,
"temperature": 0.0,
},
@ -67,7 +67,7 @@ func TestMultiModelConcurrency(t *testing.T) {
wg.Wait()
}
func TestIntegrationConcurrentPredict(t *testing.T) {
func TestIntegrationConcurrentPredictOrcaMini(t *testing.T) {
req, resp := GenerateRequests()
reqLimit := len(req)
iterLimit := 5
@ -117,9 +117,6 @@ func TestMultiModelStress(t *testing.T) {
if err != nil {
t.Fatal(err)
}
if maxVram < 2*format.GibiByte {
t.Skip("VRAM less than 2G, skipping model stress tests")
}
type model struct {
name string
@ -128,8 +125,8 @@ func TestMultiModelStress(t *testing.T) {
smallModels := []model{
{
name: "llama3.2:1b",
size: 2876 * format.MebiByte,
name: "orca-mini",
size: 2992 * format.MebiByte,
},
{
name: "phi",

View File

@ -23,7 +23,7 @@ func TestLongInputContext(t *testing.T) {
Model: "llama2",
Prompt: "Oh, dont speak to me of Austria. Perhaps I dont understand things, but Austria never has wished, and does not wish, for war. She is betraying us! Russia alone must save Europe. Our gracious sovereign recognizes his high vocation and will be true to it. That is the one thing I have faith in! Our good and wonderful sovereign has to perform the noblest role on earth, and he is so virtuous and noble that God will not forsake him. He will fulfill his vocation and crush the hydra of revolution, which has become more terrible than ever in the person of this murderer and villain! We alone must avenge the blood of the just one.... Whom, I ask you, can we rely on?... England with her commercial spirit will not and cannot understand the Emperor Alexanders loftiness of soul. She has refused to evacuate Malta. She wanted to find, and still seeks, some secret motive in our actions. What answer did Novosíltsev get? None. The English have not understood and cannot understand the self-abnegation of our Emperor who wants nothing for himself, but only desires the good of mankind. And what have they promised? Nothing! And what little they have promised they will not perform! Prussia has always declared that Buonaparte is invincible, and that all Europe is powerless before him.... And I dont believe a word that Hardenburg says, or Haugwitz either. This famous Prussian neutrality is just a trap. I have faith only in God and the lofty destiny of our adored monarch. He will save Europe! What country is this referring to?",
Stream: &stream,
Options: map[string]any{
Options: map[string]interface{}{
"temperature": 0,
"seed": 123,
"num_ctx": 128,
@ -50,7 +50,7 @@ func TestContextExhaustion(t *testing.T) {
Model: "llama2",
Prompt: "Write me a story with a ton of emojis?",
Stream: &stream,
Options: map[string]any{
Options: map[string]interface{}{
"temperature": 0,
"seed": 123,
"num_ctx": 128,

View File

@ -34,15 +34,13 @@ func cosineSimilarity[V float32 | float64](v1, v2 []V) V {
func TestAllMiniLMEmbeddings(t *testing.T) {
ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
defer cancel()
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
req := api.EmbeddingRequest{
Model: "all-minilm",
Prompt: "why is the sky blue?",
}
res, err := embeddingTestHelper(ctx, client, t, req)
res, err := embeddingTestHelper(ctx, t, req)
if err != nil {
t.Fatalf("error: %v", err)
@ -64,15 +62,13 @@ func TestAllMiniLMEmbeddings(t *testing.T) {
func TestAllMiniLMEmbed(t *testing.T) {
ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
defer cancel()
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
req := api.EmbedRequest{
Model: "all-minilm",
Input: "why is the sky blue?",
}
res, err := embedTestHelper(ctx, client, t, req)
res, err := embedTestHelper(ctx, t, req)
if err != nil {
t.Fatalf("error: %v", err)
@ -102,15 +98,13 @@ func TestAllMiniLMEmbed(t *testing.T) {
func TestAllMiniLMBatchEmbed(t *testing.T) {
ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
defer cancel()
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
req := api.EmbedRequest{
Model: "all-minilm",
Input: []string{"why is the sky blue?", "why is the grass green?"},
}
res, err := embedTestHelper(ctx, client, t, req)
res, err := embedTestHelper(ctx, t, req)
if err != nil {
t.Fatalf("error: %v", err)
@ -150,8 +144,6 @@ func TestAllMiniLMBatchEmbed(t *testing.T) {
func TestAllMiniLMEmbedTruncate(t *testing.T) {
ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
defer cancel()
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
truncTrue, truncFalse := true, false
@ -190,7 +182,7 @@ func TestAllMiniLMEmbedTruncate(t *testing.T) {
res := make(map[string]*api.EmbedResponse)
for _, req := range reqs {
response, err := embedTestHelper(ctx, client, t, req.Request)
response, err := embedTestHelper(ctx, t, req.Request)
if err != nil {
t.Fatalf("error: %v", err)
}
@ -206,7 +198,7 @@ func TestAllMiniLMEmbedTruncate(t *testing.T) {
}
// check that truncate set to false returns an error if context length is exceeded
_, err := embedTestHelper(ctx, client, t, api.EmbedRequest{
_, err := embedTestHelper(ctx, t, api.EmbedRequest{
Model: "all-minilm",
Input: "why is the sky blue?",
Truncate: &truncFalse,
@ -218,7 +210,9 @@ func TestAllMiniLMEmbedTruncate(t *testing.T) {
}
}
func embeddingTestHelper(ctx context.Context, client *api.Client, t *testing.T, req api.EmbeddingRequest) (*api.EmbeddingResponse, error) {
func embeddingTestHelper(ctx context.Context, t *testing.T, req api.EmbeddingRequest) (*api.EmbeddingResponse, error) {
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
if err := PullIfMissing(ctx, client, req.Model); err != nil {
t.Fatalf("failed to pull model %s: %v", req.Model, err)
}
@ -232,7 +226,9 @@ func embeddingTestHelper(ctx context.Context, client *api.Client, t *testing.T,
return response, nil
}
func embedTestHelper(ctx context.Context, client *api.Client, t *testing.T, req api.EmbedRequest) (*api.EmbedResponse, error) {
func embedTestHelper(ctx context.Context, t *testing.T, req api.EmbedRequest) (*api.EmbedResponse, error) {
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
if err := PullIfMissing(ctx, client, req.Model); err != nil {
t.Fatalf("failed to pull model %s: %v", req.Model, err)
}

View File

@ -12,63 +12,14 @@ import (
"github.com/stretchr/testify/require"
)
func TestVisionModels(t *testing.T) {
skipUnderMinVRAM(t, 6)
type testCase struct {
model string
}
testCases := []testCase{
{
model: "llava:7b",
},
{
model: "llama3.2-vision",
},
{
model: "gemma3",
},
}
for _, v := range testCases {
t.Run(v.model, func(t *testing.T) {
image, err := base64.StdEncoding.DecodeString(imageEncoding)
require.NoError(t, err)
req := api.GenerateRequest{
Model: v.model,
Prompt: "what does the text in this image say?",
Stream: &stream,
Options: map[string]any{
"seed": 42,
"temperature": 0.0,
},
Images: []api.ImageData{
image,
},
}
ctx, cancel := context.WithTimeout(context.Background(), 5*time.Minute)
defer cancel()
client, _, cleanup := InitServerConnection(ctx, t)
// Note: sometimes it returns "the ollamas" sometimes "the ollams"
resp := "the ollam"
defer cleanup()
require.NoError(t, PullIfMissing(ctx, client, req.Model))
// llava models on CPU can be quite slow to start
DoGenerate(ctx, t, client, req, []string{resp}, 240*time.Second, 30*time.Second)
})
}
}
func TestIntegrationSplitBatch(t *testing.T) {
func TestIntegrationLlava(t *testing.T) {
image, err := base64.StdEncoding.DecodeString(imageEncoding)
require.NoError(t, err)
req := api.GenerateRequest{
Model: "gemma3:4b",
// Fill up a chunk of the batch so the image will partially spill over into the next one
System: "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed aliquet, justo in malesuada lobortis, odio ligula volutpat quam, quis faucibus ipsum magna quis sapien. Aliquam in venenatis diam, eu viverra magna. Phasellus imperdiet hendrerit volutpat. Vivamus sem ex, facilisis placerat felis non, dictum elementum est. Phasellus aliquam imperdiet lacus, eget placerat ligula sodales vel. Pellentesque nec auctor mi. Curabitur arcu nisi, faucibus eget nunc id, viverra interdum mi. Curabitur ornare ipsum ex, ac euismod ex aliquam in. Vestibulum id magna at purus accumsan fermentum. Proin scelerisque posuere nunc quis interdum. Maecenas sed mollis nisl. Etiam vitae ipsum interdum, placerat est quis, tincidunt velit. Nullam tempor nibh non lorem volutpat efficitur. Cras laoreet diam imperdiet ipsum auctor bibendum. Suspendisse ultrices urna sed metus sagittis suscipit. Quisque ullamcorper aliquam nibh ut mollis. Aenean dapibus mauris pharetra, venenatis elit ac, hendrerit odio. Cras vestibulum erat tempor, lobortis justo eu, lobortis ipsum. Nam laoreet dapibus sem. Proin vel diam ultrices, elementum ante et, ornare lectus. Proin eu accumsan nisl. Praesent ac ex vitae ipsum vulputate tristique facilisis sit amet lacus. Nullam faucibus magna a pellentesque pretium. Nunc lacinia ullamcorper sollicitudin. Donec vitae accumsan turpis, sed porttitor est. Donec porttitor mi vitae augue faucibus, vel mollis diam tincidunt.",
Model: "llava:7b",
Prompt: "what does the text in this image say?",
Stream: &stream,
Options: map[string]any{
Options: map[string]interface{}{
"seed": 42,
"temperature": 0.0,
},
@ -88,6 +39,33 @@ func TestIntegrationSplitBatch(t *testing.T) {
DoGenerate(ctx, t, client, req, []string{resp}, 120*time.Second, 30*time.Second)
}
func TestIntegrationMllama(t *testing.T) {
image, err := base64.StdEncoding.DecodeString(imageEncoding)
require.NoError(t, err)
req := api.GenerateRequest{
// TODO fix up once we publish the final image
Model: "x/llama3.2-vision",
Prompt: "what does the text in this image say?",
Stream: &stream,
Options: map[string]interface{}{
"seed": 42,
"temperature": 0.0,
},
Images: []api.ImageData{
image,
},
}
resp := "the ollamas"
ctx, cancel := context.WithTimeout(context.Background(), 5*time.Minute)
defer cancel()
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
require.NoError(t, PullIfMissing(ctx, client, req.Model))
// mllama models on CPU can be quite slow to start,
DoGenerate(ctx, t, client, req, []string{resp}, 240*time.Second, 30*time.Second)
}
const imageEncoding = `iVBORw0KGgoAAAANSUhEUgAAANIAAAB4CAYAAACHHqzKAAAAAXNSR0IArs4c6QAAAIRlWElmTU0AKgAAAAgABQESAAMAAAABAAEAAAEaAAUAAAABAAAASgEb
AAUAAAABAAAAUgEoAAMAAAABAAIAAIdpAAQAAAABAAAAWgAAAAAAAABIAAAAAQAAAEgAAAABAAOgAQADAAAAAQABAACgAgAEAAAAAQAAANKgAwAEAAAAAQAA
AHgAAAAAXdsepgAAAAlwSFlzAAALEwAACxMBAJqcGAAAAVlpVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6

View File

@ -17,30 +17,30 @@ var (
stream = false
req = [2]api.GenerateRequest{
{
Model: smol,
Model: "orca-mini",
Prompt: "why is the ocean blue?",
Stream: &stream,
Options: map[string]any{
Options: map[string]interface{}{
"seed": 42,
"temperature": 0.0,
},
}, {
Model: smol,
Model: "orca-mini",
Prompt: "what is the origin of the us thanksgiving holiday?",
Stream: &stream,
Options: map[string]any{
Options: map[string]interface{}{
"seed": 42,
"temperature": 0.0,
},
},
}
resp = [2][]string{
{"sunlight", "scattering", "interact"},
{"sunlight"},
{"england", "english", "massachusetts", "pilgrims"},
}
)
func TestIntegrationSimple(t *testing.T) {
func TestIntegrationSimpleOrcaMini(t *testing.T) {
ctx, cancel := context.WithTimeout(context.Background(), time.Second*120)
defer cancel()
GenerateTestHelper(ctx, t, req[0], resp[0])

View File

@ -30,9 +30,9 @@ func TestMaxQueue(t *testing.T) {
t.Setenv("OLLAMA_MAX_QUEUE", strconv.Itoa(threadCount))
req := api.GenerateRequest{
Model: smol,
Model: "orca-mini",
Prompt: "write a long historical fiction story about christopher columbus. use at least 10 facts from his actual journey",
Options: map[string]any{
Options: map[string]interface{}{
"seed": 42,
"temperature": 0.0,
},
@ -52,8 +52,8 @@ func TestMaxQueue(t *testing.T) {
embedCtx := ctx
var genwg sync.WaitGroup
genwg.Add(1)
go func() {
genwg.Add(1)
defer genwg.Done()
slog.Info("Starting generate request")
DoGenerate(ctx, t, client, req, resp, 45*time.Second, 5*time.Second)
@ -61,7 +61,7 @@ func TestMaxQueue(t *testing.T) {
}()
// Give the generate a chance to get started before we start hammering on embed requests
time.Sleep(10 * time.Millisecond)
time.Sleep(5 * time.Millisecond)
threadCount += 10 // Add a few extra to ensure we push the queue past its limit
busyCount := 0
@ -71,8 +71,8 @@ func TestMaxQueue(t *testing.T) {
counterMu := sync.Mutex{}
var embedwg sync.WaitGroup
for i := 0; i < threadCount; i++ {
embedwg.Add(1)
go func(i int) {
embedwg.Add(1)
defer embedwg.Done()
slog.Info("embed started", "id", i)
embedReq := api.EmbeddingRequest{

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