Compare commits

..

2 Commits

Author SHA1 Message Date
Daniel Hiltgen
8de8729e35 Remove llama.cpp submodule and shift new build to top 2024-10-23 22:06:01 -07:00
Daniel Hiltgen
4e988ad5d6 Move Go code out of llm package 2024-10-23 12:38:11 -07:00
1150 changed files with 130630 additions and 918828 deletions

View File

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

13
.gitattributes vendored
View File

@@ -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

View File

@@ -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:

View File

File diff suppressed because it is too large Load Diff

View File

@@ -21,7 +21,7 @@ jobs:
changes:
runs-on: ubuntu-latest
outputs:
changed: ${{ steps.changes.outputs.changed }}
RUNNERS: ${{ steps.changes.outputs.RUNNERS }}
steps:
- uses: actions/checkout@v4
with:
@@ -29,226 +29,255 @@ jobs:
- id: changes
run: |
changed() {
local BASE=${{ 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:13.0.0-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: ${{ needs.changes.outputs.RUNNERS == 'True' }}
strategy:
matrix:
include:
- preset: CPU
- preset: CUDA
install: https://developer.download.nvidia.com/compute/cuda/13.0.0/local_installers/cuda_13.0.0_windows.exe
flags: '-DCMAKE_CUDA_ARCHITECTURES=80'
cuda-components:
- '"cudart"'
- '"nvcc"'
- '"cublas"'
- '"cublas_dev"'
- '"crt"'
- '"nvvm"'
- '"nvptxcompiler"'
cuda-version: '13.0'
- preset: ROCm
install: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q4-WinSvr2022-For-HIP.exe
flags: '-DAMDGPU_TARGETS=gfx1010 -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_C_FLAGS="-parallel-jobs=4 -Wno-ignored-attributes -Wno-deprecated-pragma" -DCMAKE_CXX_FLAGS="-parallel-jobs=4 -Wno-ignored-attributes -Wno-deprecated-pragma"'
rocm-version:
- '6.1.2'
runs-on: linux
container: rocm/dev-ubuntu-20.04:${{ 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"
$subpackages = @(${{ join(matrix.cuda-components, ', ') }}) | Foreach-Object {"${_}_${{ matrix.cuda-version }}"}
Start-Process -FilePath .\install.exe -ArgumentList (@("-s") + $subpackages) -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 $env:GITHUB_ENV -Append
echo "HIPCXX=$hipPath\bin\clang++.exe" | Out-File -FilePath $env:GITHUB_ENV -Append
echo "HIP_PLATFORM=amd" | Out-File -FilePath $env:GITHUB_ENV -Append
echo "CMAKE_PREFIX_PATH=$hipPath" | 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 }}
go-version-file: go.mod
cache: true
- name: 'Install ROCm'
run: |
$ErrorActionPreference = "Stop"
write-host "downloading AMD HIP Installer"
Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
write-host "Installing AMD HIP"
Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
write-host "Completed AMD HIP"
- name: 'Verify ROCm'
run: |
& 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version
- run: go get ./...
- run: |
Import-Module 'C:\Program Files\Microsoft Visual Studio\2022\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
Enter-VsDevShell -VsInstallPath 'C:\Program Files\Microsoft Visual Studio\2022\Enterprise' -SkipAutomaticLocation -DevCmdArguments '-arch=x64 -no_logo'
cmake --preset "${{ matrix.preset }}" ${{ matrix.flags }}
cmake --build --parallel --preset "${{ matrix.preset }}"
env:
CMAKE_GENERATOR: Ninja
$gopath=(get-command go).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'
$env:PATH="$gopath;$env:PATH"
$env:OLLAMA_SKIP_CPU_GENERATE="1"
$env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path)
$cores = (Get-ComputerInfo -Property CsProcessors).CsProcessors.NumberOfCores
write-host $env:HIP_PATH
make -C llama print-HIP_PATH print-HIP_LIB_DIR
make -j $cores rocm
name: make
go_mod_tidy:
runs-on: ubuntu-latest
# 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: 'Install CUDA'
run: |
$ErrorActionPreference = "Stop"
write-host "downloading CUDA Installer"
Invoke-WebRequest -Uri "https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda_11.3.1_465.89_win10.exe" -OutFile "${env:RUNNER_TEMP}\cuda-install.exe"
write-host "Installing CUDA"
Start-Process "${env:RUNNER_TEMP}\cuda-install.exe" -ArgumentList '-s' -NoNewWindow -Wait
write-host "Completed CUDA"
$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" >> $env:GITHUB_PATH
echo "CUDA_PATH=$cudaPath" >> $env:GITHUB_ENV
echo "CUDA_PATH_V${cudaVer}=$cudaPath" >> $env:GITHUB_ENV
echo "CUDA_PATH_VX_Y=CUDA_PATH_V${cudaVer}" >> $env:GITHUB_ENV
- name: 'Verify CUDA'
run: nvcc -V
- run: go get ./...
- name: make
run: |
$gopath=(get-command go).source | split-path -parent
$cudabin=(get-command nvcc).source | split-path
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;$cudabin;$env:PATH"
$env:OLLAMA_SKIP_CPU_GENERATE="1"
$cores = (Get-ComputerInfo -Property CsProcessors).CsProcessors.NumberOfCores
make -j $cores cuda_v11
env:
OLLAMA_SKIP_CPU_GENERATE: '1'
runners-cpu:
needs: [changes]
if: ${{ needs.changes.outputs.RUNNERS == 'True' }}
strategy:
matrix:
os: [ubuntu-latest, macos-latest, windows-2019]
arch: [amd64, arm64]
exclude:
- os: ubuntu-latest
arch: arm64
- os: windows-2019
arch: arm64
runs-on: ${{ matrix.os }}
env:
GOARCH: ${{ matrix.arch }}
ARCH: ${{ matrix.arch }}
CGO_ENABLED: '1'
steps:
- uses: actions/checkout@v4
- uses: actions/setup-go@v5
with:
go-version-file: go.mod
cache: true
- run: go get ./...
- 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
make -j 4
- name: 'Build Unix Go Runners'
if: ${{ ! startsWith(matrix.os, 'windows-') }}
run: make -j 4
- run: go build .
lint:
strategy:
matrix:
os: [ubuntu-latest, macos-latest, windows-2019]
arch: [amd64, arm64]
exclude:
- os: ubuntu-latest
arch: arm64
- os: windows-2019
arch: arm64
- os: macos-latest
arch: amd64
runs-on: ${{ matrix.os }}
env:
GOARCH: ${{ matrix.arch }}
CGO_ENABLED: '1'
steps:
- uses: actions/checkout@v4
with:
submodules: recursive
- 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 8m0s -v
test:
strategy:
matrix:
os: [ubuntu-latest, macos-latest, windows-latest]
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'
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
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
submodules: recursive
- 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()
run: |
go generate ./...
git diff --name-only --exit-code || (echo "Please run 'go generate ./...'." && exit 1)
- 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.
- 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
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 }}
cache: true
- run: |
case ${{ matrix.arch }} in
amd64) echo ARCH=amd64 ;;
arm64) echo ARCH=arm64 ;;
esac >>$GITHUB_ENV
shell: bash
- run: go build
- run: go test -v ./...
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

10
.gitignore vendored
View File

@@ -4,13 +4,15 @@
.venv
.swp
dist
build
ollama
.cache
*.exe
.idea
test_data
*.crt
__debug_bin*
llm/build
build/*/*/*
!build/**/placeholder
llama/build
llama/vendor
/ollama
__debug_bin*
llama/vendor

View File

@@ -6,6 +6,10 @@ linters:
- bidichk
- bodyclose
- containedctx
- contextcheck
- errcheck
- exportloopref
- gci
- gocheckcompilerdirectives
- gofmt
- gofumpt
@@ -19,14 +23,15 @@ linters:
- nolintlint
- nosprintfhostport
- staticcheck
- tenv
- unconvert
- usetesting
- unused
- usestdlibvars
- wastedassign
- whitespace
disable:
- usestdlibvars
- errcheck
linters-settings:
gci:
sections: [standard, default, localmodule]
staticcheck:
checks:
- all
@@ -38,4 +43,5 @@ severity:
- gofmt
- goimports
- intrange
- usestdlibvars
severity: info

10
.prettierrc.json Normal file
View File

@@ -0,0 +1,10 @@
{
"trailingComma": "es5",
"tabWidth": 2,
"useTabs": false,
"semi": false,
"singleQuote": true,
"jsxSingleQuote": true,
"printWidth": 120,
"arrowParens": "avoid"
}

View File

@@ -1,139 +0,0 @@
cmake_minimum_required(VERSION 3.21)
project(Ollama C CXX)
include(CheckLanguage)
include(GNUInstallDirs)
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 ${CMAKE_INSTALL_PREFIX}/lib/ollama/${OLLAMA_RUNNER_DIR})
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(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-cpu/amx)
add_compile_definitions(NDEBUG GGML_VERSION=0x0 GGML_COMMIT=0x0)
set(GGML_CPU ON)
add_subdirectory(${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)
install(TARGETS ggml-cuda
RUNTIME_DEPENDENCIES
DIRECTORIES ${CUDAToolkit_BIN_DIR} ${CUDAToolkit_BIN_DIR}/x64 ${CUDAToolkit_LIBRARY_DIR}
PRE_INCLUDE_REGEXES cublas cublasLt cudart
PRE_EXCLUDE_REGEXES ".*"
RUNTIME DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT CUDA
LIBRARY DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT CUDA
)
endif()
set(WINDOWS_AMDGPU_TARGETS_EXCLUDE_REGEX "^gfx(906|908|90a|1200|1201):xnack[+-]$"
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_DEPENDENCY_SET rocm
RUNTIME DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT HIP
LIBRARY DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT HIP
)
install(RUNTIME_DEPENDENCY_SET rocm
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

@@ -1,127 +0,0 @@
{
"version": 3,
"configurePresets": [
{
"name": "Default",
"binaryDir": "${sourceDir}/build",
"installDir": "${sourceDir}/dist",
"cacheVariables": {
"CMAKE_BUILD_TYPE": "Release",
"CMAKE_MSVC_RUNTIME_LIBRARY": "MultiThreaded"
}
},
{
"name": "CPU",
"inherits": [ "Default" ]
},
{
"name": "CUDA",
"inherits": [ "Default" ]
},
{
"name": "CUDA 11",
"inherits": [ "CUDA" ],
"cacheVariables": {
"CMAKE_CUDA_ARCHITECTURES": "50-virtual;60-virtual;61-virtual;70-virtual;75-virtual;80-virtual;86-virtual;87-virtual;89-virtual;90-virtual",
"CMAKE_CUDA_FLAGS": "-Wno-deprecated-gpu-targets -t 2"
}
},
{
"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 -t 2"
}
},
{
"name": "CUDA 13",
"inherits": [ "CUDA" ],
"cacheVariables": {
"CMAKE_CUDA_ARCHITECTURES": "75-virtual;80-virtual;86-virtual;87-virtual;89-virtual;90-virtual;90a-virtual;100-virtual;110-virtual;120-virtual;121-virtual",
"CMAKE_CUDA_FLAGS": "-t 2"
}
},
{
"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": {
"CMAKE_HIP_FLAGS": "-parallel-jobs=4",
"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": "CUDA 13",
"inherits": [ "CUDA" ],
"configurePreset": "CUDA 13"
},
{
"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.
@@ -24,65 +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 clarity on good commit messages, and bad
docs: simplify manual installation with shorter curl commands
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

@@ -1,155 +1,221 @@
# vim: filetype=dockerfile
# Note: once we have fully transitioned to the Go server, this will replace the old Dockerfile at the top of the tree
ARG GOLANG_VERSION=1.22.5
ARG CMAKE_VERSION=3.22.1
ARG CUDA_VERSION_11=11.3.1
ARG CUDA_V11_ARCHITECTURES="50;52;53;60;61;62;70;72;75;80;86"
ARG CUDA_VERSION_12=12.4.0
ARG CUDA_V12_ARCHITECTURES="60;61;62;70;72;75;80;86;87;89;90;90a"
ARG ROCM_VERSION=6.1.2
ARG FLAVOR=${TARGETARCH}
ARG PARALLEL=8
### 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 $(pwd):/go/src/github.com/ollama/ollama/ builder-amd64
#
### Then incremental builds will be much faster in this container
#
# make -C llama -j 10 && go build -trimpath -o dist/linux-amd64/ollama .
#
FROM --platform=linux/amd64 rocm/dev-centos-7:${ROCM_VERSION}-complete AS unified-builder-amd64
ARG CMAKE_VERSION
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
ENV LIBRARY_PATH=/usr/local/cuda/lib64/stubs:/opt/amdgpu/lib64
RUN CMAKE_VERSION=${CMAKE_VERSION} GOLANG_VERSION=${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-$(echo ${CUDA_VERSION_11} | cut -f1-2 -d. | sed -e "s/\./-/g") \
cuda-$(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 $(pwd):/go/src/github.com/ollama/ollama/ builder-arm64
#
FROM --platform=linux/arm64 rockylinux:8 AS unified-builder-arm64
ARG CMAKE_VERSION
ARG GOLANG_VERSION
ARG CUDA_VERSION_11
ARG CUDA_VERSION_12
COPY ./scripts/rh_linux_deps.sh /
RUN CMAKE_VERSION=${CMAKE_VERSION} 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=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64
ENV LIBRARY_PATH=/usr/local/cuda/lib64/stubs:/opt/amdgpu/lib64
ENV GOARCH amd64
ENV CGO_ENABLED 1
WORKDIR /go/src/github.com/ollama/ollama/
ENTRYPOINT [ "zsh" ]
# We require gcc v10 minimum. v10.3 has regressions, so the rockylinux 8.5 AppStream has the latest compatible version
FROM --platform=linux/amd64 rocm/dev-almalinux-8:${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 \
&& dnf install -y ccache \
&& 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
ARG PARALLEL
RUN --mount=type=cache,target=/root/.ccache \
cmake --preset 'CPU' \
&& cmake --build --parallel ${PARALLEL} --preset 'CPU' \
&& cmake --install build --component CPU --strip --parallel ${PARALLEL}
FROM base AS cuda-11
ARG CUDA11VERSION=11.8
RUN dnf install -y cuda-toolkit-${CUDA11VERSION//./-}
ENV PATH=/usr/local/cuda-11/bin:$PATH
ARG PARALLEL
RUN --mount=type=cache,target=/root/.ccache \
cmake --preset 'CUDA 11' -DOLLAMA_RUNNER_DIR="cuda_v11" \
&& cmake --build --parallel ${PARALLEL} --preset 'CUDA 11' \
&& cmake --install build --component CUDA --strip --parallel ${PARALLEL}
FROM base AS cuda-12
ARG CUDA12VERSION=12.8
RUN dnf install -y cuda-toolkit-${CUDA12VERSION//./-}
ENV PATH=/usr/local/cuda-12/bin:$PATH
ARG PARALLEL
RUN --mount=type=cache,target=/root/.ccache \
cmake --preset 'CUDA 12' -DOLLAMA_RUNNER_DIR="cuda_v12"\
&& cmake --build --parallel ${PARALLEL} --preset 'CUDA 12' \
&& cmake --install build --component CUDA --strip --parallel ${PARALLEL}
FROM base AS cuda-13
ARG CUDA13VERSION=13.0
RUN dnf install -y cuda-toolkit-${CUDA13VERSION//./-}
ENV PATH=/usr/local/cuda-13/bin:$PATH
ARG PARALLEL
RUN --mount=type=cache,target=/root/.ccache \
cmake --preset 'CUDA 13' -DOLLAMA_RUNNER_DIR="cuda_v13" \
&& cmake --build --parallel ${PARALLEL} --preset 'CUDA 13' \
&& cmake --install build --component CUDA --strip --parallel ${PARALLEL}
FROM base AS rocm-6
ENV PATH=/opt/rocm/hcc/bin:/opt/rocm/hip/bin:/opt/rocm/bin:/opt/rocm/hcc/bin:$PATH
ARG PARALLEL
RUN --mount=type=cache,target=/root/.ccache \
cmake --preset 'ROCm 6' \
&& cmake --build --parallel ${PARALLEL} --preset 'ROCm 6' \
&& cmake --install build --component HIP --strip --parallel ${PARALLEL}
FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${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
ARG PARALLEL
RUN --mount=type=cache,target=/root/.ccache \
cmake --preset 'JetPack 5' \
&& cmake --build --parallel ${PARALLEL} --preset 'JetPack 5' \
&& cmake --install build --component CUDA --strip --parallel ${PARALLEL}
FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${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
ARG PARALLEL
RUN --mount=type=cache,target=/root/.ccache \
cmake --preset 'JetPack 6' \
&& cmake --build --parallel ${PARALLEL} --preset 'JetPack 6' \
&& cmake --install build --component CUDA --strip --parallel ${PARALLEL}
FROM base AS build
WORKDIR /go/src/github.com/ollama/ollama
COPY go.mod go.sum .
RUN curl -fsSL https://golang.org/dl/go$(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 runners-amd64
COPY . .
ARG GOFLAGS="'-ldflags=-w -s'"
ENV CGO_ENABLED=1
ARG OLLAMA_SKIP_CUDA_GENERATE
ARG OLLAMA_SKIP_CUDA_11_GENERATE
ARG OLLAMA_SKIP_CUDA_12_GENERATE
ARG OLLAMA_SKIP_ROCM_GENERATE
ARG CUDA_V11_ARCHITECTURES
ARG CUDA_V12_ARCHITECTURES
ARG OLLAMA_FAST_BUILD
RUN --mount=type=cache,target=/root/.ccache \
if grep "^flags" /proc/cpuinfo|grep avx>/dev/null; then \
make -C llama -j $(expr $(nproc) / 2 ) ; \
else \
make -C llama -j 5 ; \
fi
FROM --platform=linux/arm64 unified-builder-arm64 AS runners-arm64
COPY . .
ARG OLLAMA_SKIP_CUDA_GENERATE
ARG OLLAMA_SKIP_CUDA_11_GENERATE
ARG OLLAMA_SKIP_CUDA_12_GENERATE
ARG CUDA_V11_ARCHITECTURES
ARG CUDA_V12_ARCHITECTURES
ARG OLLAMA_FAST_BUILD
RUN --mount=type=cache,target=/root/.ccache \
make -C llama -j 8
# Intermediate stages used for ./scripts/build_linux.sh
FROM --platform=linux/amd64 centos:7 AS builder-amd64
ARG CMAKE_VERSION
ARG GOLANG_VERSION
COPY ./scripts/rh_linux_deps.sh /
RUN CMAKE_VERSION=${CMAKE_VERSION} GOLANG_VERSION=${GOLANG_VERSION} sh /rh_linux_deps.sh
ENV PATH /opt/rh/devtoolset-10/root/usr/bin:$PATH
ENV CGO_ENABLED 1
ENV GOARCH amd64
WORKDIR /go/src/github.com/ollama/ollama
FROM --platform=linux/amd64 builder-amd64 AS build-amd64
COPY . .
COPY --from=runners-amd64 /go/src/github.com/ollama/ollama/dist/ dist/
COPY --from=runners-amd64 /go/src/github.com/ollama/ollama/build/ build/
ARG GOFLAGS
ARG CGO_CFLAGS
ARG CGO_CXXFLAGS
RUN --mount=type=cache,target=/root/.cache/go-build \
go build -trimpath -buildmode=pie -o /bin/ollama .
ARG OLLAMA_SKIP_ROCM_GENERATE
RUN --mount=type=cache,target=/root/.ccache \
go build -trimpath -o dist/linux-amd64/bin/ollama .
RUN cd dist/linux-$GOARCH && \
tar --exclude runners -cf - . | pigz --best > ../ollama-linux-$GOARCH.tgz
RUN if [ -z ${OLLAMA_SKIP_ROCM_GENERATE} ] ; then \
cd dist/linux-$GOARCH-rocm && \
tar -cf - . | pigz --best > ../ollama-linux-$GOARCH-rocm.tgz ;\
fi
FROM --platform=linux/amd64 scratch AS amd64
# COPY --from=cuda-11 dist/lib/ollama/ /lib/ollama/
COPY --from=cuda-12 dist/lib/ollama /lib/ollama/
COPY --from=cuda-13 dist/lib/ollama/ /lib/ollama/
FROM --platform=linux/arm64 rockylinux:8 AS builder-arm64
ARG CMAKE_VERSION
ARG GOLANG_VERSION
COPY ./scripts/rh_linux_deps.sh /
RUN CMAKE_VERSION=${CMAKE_VERSION} GOLANG_VERSION=${GOLANG_VERSION} sh /rh_linux_deps.sh
ENV PATH /opt/rh/gcc-toolset-10/root/usr/bin:$PATH
ENV CGO_ENABLED 1
ENV GOARCH arm64
WORKDIR /go/src/github.com/ollama/ollama
FROM --platform=linux/arm64 scratch AS arm64
# COPY --from=cuda-11 dist/lib/ollama/ /lib/ollama/
COPY --from=cuda-12 dist/lib/ollama /lib/ollama/
COPY --from=cuda-13 dist/lib/ollama/ /lib/ollama/
COPY --from=jetpack-5 dist/lib/ollama /lib/ollama/cuda_jetpack5
COPY --from=jetpack-6 dist/lib/ollama /lib/ollama/cuda_jetpack6
FROM --platform=linux/arm64 builder-arm64 AS build-arm64
COPY . .
COPY --from=runners-arm64 /go/src/github.com/ollama/ollama/dist/ dist/
COPY --from=runners-arm64 /go/src/github.com/ollama/ollama/build/ build/
ARG GOFLAGS
ARG CGO_CFLAGS
RUN --mount=type=cache,target=/root/.ccache \
go build -trimpath -o dist/linux-arm64/bin/ollama .
RUN cd dist/linux-$GOARCH && \
tar --exclude runners -cf - . | pigz --best > ../ollama-linux-$GOARCH.tgz
FROM scratch AS rocm
COPY --from=rocm-6 dist/lib/ollama /lib/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 ${FLAVOR} AS archive
COPY --from=cpu dist/lib/ollama /lib/ollama
COPY --from=build /bin/ollama /bin/ollama
FROM ubuntu:24.04
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
# Optimized container images do not cary nested payloads
FROM --platform=linux/amd64 builder-amd64 AS container-build-amd64
WORKDIR /go/src/github.com/ollama/ollama
COPY . .
ARG GOFLAGS
ARG CGO_CFLAGS
RUN --mount=type=cache,target=/root/.ccache \
go build -trimpath -o dist/linux-amd64/bin/ollama .
FROM --platform=linux/arm64 builder-arm64 AS container-build-arm64
WORKDIR /go/src/github.com/ollama/ollama
COPY . .
ARG GOFLAGS
ARG CGO_CFLAGS
RUN --mount=type=cache,target=/root/.ccache \
go build -trimpath -o dist/linux-arm64/bin/ollama .
# For amd64 container images, filter out cuda/rocm to minimize size
FROM runners-amd64 AS runners-cuda-amd64
RUN rm -rf \
./dist/linux-amd64/lib/ollama/libggml_hipblas.so \
./dist/linux-amd64/lib/ollama/runners/rocm*
FROM runners-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 && \
rm -rf /var/lib/apt/lists/*
COPY --from=container-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 && \
rm -rf /var/lib/apt/lists/*
COPY --from=container-build-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/bin/ /bin/
COPY --from=runners-arm64 /go/src/github.com/ollama/ollama/dist/linux-arm64/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 && \
rm -rf /var/lib/apt/lists/*
COPY --from=container-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"]

4
Makefile Normal file
View File

@@ -0,0 +1,4 @@
GOALS := $(or $(MAKECMDGOALS),all)
.PHONY: $(GOALS)
$(GOALS):
$(MAKE) -C llama $@

View File

@@ -1,72 +0,0 @@
UPSTREAM=https://github.com/ggml-org/llama.cpp.git
WORKDIR=llama/vendor
FETCH_HEAD=e54d41befcc1575f4c898c5ff4ef43970cead75f
.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 $(MAKEFILE_LIST)) clean apply-patches sync"
.PHONY: sync
sync: llama/build-info.cpp ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-embed.metal
llama/build-info.cpp: llama/build-info.cpp.in llama/llama.cpp
sed -e 's|@FETCH_HEAD@|$(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 --delete -f "include LICENSE" -f "merge $@/.rsync-filter" $(addprefix $<,/LICENSE /) $@
.PHONY: ml/backend/ggml/ggml
ml/backend/ggml/ggml: llama/vendor
rsync -arvzc --delete -f "include LICENSE" -f "merge $@/.rsync-filter" $(addprefix $<,/LICENSE /ggml/) $@
PATCHES=$(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 \
echo "Patch failed. Resolve any conflicts then continue."; \
echo "1. Run 'git -C $(WORKDIR) am --continue'"; \
echo "2. Run 'make -f $(lastword $(MAKEFILE_LIST)) format-patches'"; \
echo "3. Run 'make -f $(lastword $(MAKEFILE_LIST)) clean apply-patches'"; \
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
@git -C $(WORKDIR) am --abort || true
$(RM) llama/patches/.*.patched

288
README.md
View File

@@ -1,24 +1,24 @@
<div align="center">
  <a href="https://ollama.com">
<img alt="ollama" width="240" src="https://github.com/ollama/ollama/assets/3325447/0d0b44e2-8f4a-4e99-9b52-a5c1c741c8f7">
</a>
 <img alt="ollama" height="200px" src="https://github.com/ollama/ollama/assets/3325447/0d0b44e2-8f4a-4e99-9b52-a5c1c741c8f7">
</div>
# Ollama
[![Discord](https://dcbadge.vercel.app/api/server/ollama?style=flat&compact=true)](https://discord.gg/ollama)
Get up and running with large language models.
### macOS
[Download](https://ollama.com/download/Ollama.dmg)
[Download](https://ollama.com/download/Ollama-darwin.zip)
### Windows
### Windows preview
[Download](https://ollama.com/download/OllamaSetup.exe)
### Linux
```shell
```
curl -fsSL https://ollama.com/install.sh | sh
```
@@ -33,17 +33,12 @@ The official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama) `olla
- [ollama-python](https://github.com/ollama/ollama-python)
- [ollama-js](https://github.com/ollama/ollama-js)
### Community
- [Discord](https://discord.gg/ollama)
- [Reddit](https://reddit.com/r/ollama)
## Quickstart
To run and chat with [Gemma 3](https://ollama.com/library/gemma3):
To run and chat with [Llama 3.2](https://ollama.com/library/llama3.2):
```shell
ollama run gemma3
```
ollama run llama3.2
```
## Model library
@@ -52,34 +47,26 @@ Ollama supports a list of models available on [ollama.com/library](https://ollam
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` |
| Llama 3.2 Vision | 11B | 7.9GB | `ollama run llama3.2-vision` |
| Llama 3.2 Vision | 90B | 55GB | `ollama run llama3.2-vision:90b` |
| Llama 3.1 | 8B | 4.7GB | `ollama run llama3.1` |
| Llama 3.1 | 405B | 231GB | `ollama run llama3.1:405b` |
| Phi 4 | 14B | 9.1GB | `ollama run phi4` |
| Phi 4 Mini | 3.8B | 2.5GB | `ollama run phi4-mini` |
| Mistral | 7B | 4.1GB | `ollama run mistral` |
| Moondream 2 | 1.4B | 829MB | `ollama run moondream` |
| Neural Chat | 7B | 4.1GB | `ollama run neural-chat` |
| Starling | 7B | 4.1GB | `ollama run starling-lm` |
| Code Llama | 7B | 3.8GB | `ollama run codellama` |
| Llama 2 Uncensored | 7B | 3.8GB | `ollama run llama2-uncensored` |
| LLaVA | 7B | 4.5GB | `ollama run llava` |
| Granite-3.3 | 8B | 4.9GB | `ollama run granite3.3` |
| Model | Parameters | Size | Download |
| ------------------ | ---------- | ----- | ------------------------------ |
| Llama 3.2 | 3B | 2.0GB | `ollama run llama3.2` |
| Llama 3.2 | 1B | 1.3GB | `ollama run llama3.2:1b` |
| Llama 3.1 | 8B | 4.7GB | `ollama run llama3.1` |
| Llama 3.1 | 70B | 40GB | `ollama run llama3.1:70b` |
| Llama 3.1 | 405B | 231GB | `ollama run llama3.1:405b` |
| Phi 3 Mini | 3.8B | 2.3GB | `ollama run phi3` |
| Phi 3 Medium | 14B | 7.9GB | `ollama run phi3:medium` |
| Gemma 2 | 2B | 1.6GB | `ollama run gemma2:2b` |
| Gemma 2 | 9B | 5.5GB | `ollama run gemma2` |
| Gemma 2 | 27B | 16GB | `ollama run gemma2:27b` |
| Mistral | 7B | 4.1GB | `ollama run mistral` |
| Moondream 2 | 1.4B | 829MB | `ollama run moondream` |
| Neural Chat | 7B | 4.1GB | `ollama run neural-chat` |
| Starling | 7B | 4.1GB | `ollama run starling-lm` |
| Code Llama | 7B | 3.8GB | `ollama run codellama` |
| Llama 2 Uncensored | 7B | 3.8GB | `ollama run llama2-uncensored` |
| LLaVA | 7B | 4.5GB | `ollama run llava` |
| Solar | 10.7B | 6.1GB | `ollama run solar` |
> [!NOTE]
> You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
@@ -98,17 +85,17 @@ 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
```
### Import from Safetensors
### Import from PyTorch or Safetensors
See the [guide](docs/import.md) on importing models for more information.
@@ -116,7 +103,7 @@ 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
```
@@ -143,7 +130,7 @@ ollama run mario
Hello! It's your friend Mario.
```
For more information on working with a Modelfile, see the [Modelfile](docs/modelfile.md) documentation.
For more examples, see the [examples](examples) directory. For more information on working with a Modelfile, see the [Modelfile](docs/modelfile.md) documentation.
## CLI Reference
@@ -151,13 +138,13 @@ 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 +152,13 @@ ollama pull llama3.2
### Remove a model
```shell
```
ollama rm llama3.2
```
### Copy a model
```shell
```
ollama cp llama3.2 my-model
```
@@ -190,39 +177,37 @@ 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 +223,13 @@ 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 +239,7 @@ 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 +248,7 @@ 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 +264,6 @@ 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 +271,12 @@ 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)
@@ -313,10 +296,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [AnythingLLM (Docker + MacOs/Windows/Linux native app)](https://github.com/Mintplex-Labs/anything-llm)
- [Ollama Basic Chat: Uses HyperDiv Reactive UI](https://github.com/rapidarchitect/ollama_basic_chat)
- [Ollama-chats RPG](https://github.com/drazdra/ollama-chats)
- [IntelliBar](https://intellibar.app/) (AI-powered assistant for macOS)
- [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)
- [QA-Pilot](https://github.com/reid41/QA-Pilot) (Chat with Code Repository)
- [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)
- [RAGFlow](https://github.com/infiniflow/ragflow) (Open-source Retrieval-Augmented Generation engine based on deep document understanding)
@@ -326,18 +306,11 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [Ollama RAG Chatbot](https://github.com/datvodinh/rag-chatbot.git) (Local Chat with multiple PDFs using Ollama and RAG)
- [BrainSoup](https://www.nurgo-software.com/products/brainsoup) (Flexible native client with RAG & multi-agent automation)
- [macai](https://github.com/Renset/macai) (macOS client for Ollama, ChatGPT, and other compatible API back-ends)
- [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)
- [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)
- [AI Studio](https://github.com/MindWorkAI/AI-Studio)
@@ -345,89 +318,25 @@ 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)
- [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)
- [LLMChat](https://github.com/trendy-design/llmchat) (Privacy focused, 100% local, intuitive all-in-one chat interface)
- [Local Multimodal AI Chat](https://github.com/Leon-Sander/Local-Multimodal-AI-Chat) (Ollama-based LLM Chat with support for multiple features, including PDF RAG, voice chat, image-based interactions, and integration with OpenAI.)
- [ARGO](https://github.com/xark-argo/argo) (Locally download and run Ollama and Huggingface models with RAG and deep research on Mac/Windows/Linux)
- [OrionChat](https://github.com/EliasPereirah/OrionChat) - OrionChat is a web interface for chatting with different AI providers
- [ARGO](https://github.com/xark-argo/argo) (Locally download and run Ollama and Huggingface models with RAG on Mac/Windows/Linux)
- [G1](https://github.com/bklieger-groq/g1) (Prototype of using prompting strategies to improve the LLM's reasoning through o1-like reasoning chains.)
- [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)
- [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)
- [VT](https://github.com/vinhnx/vt.ai) (A minimal multimodal AI chat app, with dynamic conversation routing. Supports local models via Ollama)
- [Nosia](https://github.com/nosia-ai/nosia) (Easy to install and use RAG platform based on Ollama)
- [Witsy](https://github.com/nbonamy/witsy) (An AI Desktop application available for Mac/Windows/Linux)
- [Abbey](https://github.com/US-Artificial-Intelligence/abbey) (A configurable AI interface server with notebooks, document storage, and YouTube support)
- [Minima](https://github.com/dmayboroda/minima) (RAG with on-premises or fully local workflow)
- [aidful-ollama-model-delete](https://github.com/AidfulAI/aidful-ollama-model-delete) (User interface for simplified model cleanup)
- [Perplexica](https://github.com/ItzCrazyKns/Perplexica) (An AI-powered search engine & an open-source alternative to Perplexity AI)
- [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)
- [Tiny Notepad](https://pypi.org/project/tiny-notepad) (A lightweight, notepad-like interface to chat with ollama available on PyPI)
- [macLlama (macOS native)](https://github.com/hellotunamayo/macLlama) (A native macOS GUI application for interacting with Ollama models, featuring a chat interface.)
- [GPTranslate](https://github.com/philberndt/GPTranslate) (A fast and lightweight, AI powered desktop translation application written with Rust and Tauri. Features real-time translation with OpenAI/Azure/Ollama.)
- [ollama launcher](https://github.com/NGC13009/ollama-launcher) (A launcher for Ollama, aiming to provide users with convenient functions such as ollama server launching, management, or configuration.)
- [ai-hub](https://github.com/Aj-Seven/ai-hub) (AI Hub supports multiple models via API keys and Chat support via Ollama API.)
- [Mayan EDMS](https://gitlab.com/mayan-edms/mayan-edms) (Open source document management system to organize, tag, search, and automate your files with powerful Ollama driven workflows.)
- [Serene Pub](https://github.com/doolijb/serene-pub) (Beginner friendly, open source AI Roleplaying App for Windows, Mac OS and Linux. Search, download and use models with Ollama all inside the app.)
- [Andes](https://github.com/aqerd/andes) (A Visual Studio Code extension that provides a local UI interface for Ollama models)
- [Clueless](https://github.com/KashyapTan/clueless) (Open Source & Local Cluely: A desktop application LLM assistant to help you talk to anything on your screen using locally served Ollama models. Also undetectable to screenshare)
- [ollama-co2](https://github.com/carbonatedWaterOrg/ollama-co2) (FastAPI web interface for monitoring and managing local and remote Ollama servers with real-time model monitoring and concurrent downloads)
### Cloud
- [Google Cloud](https://cloud.google.com/run/docs/tutorials/gpu-gemma2-with-ollama)
- [Fly.io](https://fly.io/docs/python/do-more/add-ollama/)
- [Koyeb](https://www.koyeb.com/deploy/ollama)
### Terminal
- [oterm](https://github.com/ggozad/oterm)
- [Ellama Emacs client](https://github.com/s-kostyaev/ellama)
- [Emacs client](https://github.com/zweifisch/ollama)
- [neollama](https://github.com/paradoxical-dev/neollama) UI client for interacting with models from within Neovim
- [gen.nvim](https://github.com/David-Kunz/gen.nvim)
- [ollama.nvim](https://github.com/nomnivore/ollama.nvim)
- [ollero.nvim](https://github.com/marco-souza/ollero.nvim)
@@ -437,7 +346,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [Oatmeal](https://github.com/dustinblackman/oatmeal)
- [cmdh](https://github.com/pgibler/cmdh)
- [ooo](https://github.com/npahlfer/ooo)
- [shell-pilot](https://github.com/reid41/shell-pilot)(Interact with models via pure shell scripts on Linux or macOS)
- [shell-pilot](https://github.com/reid41/shell-pilot)
- [tenere](https://github.com/pythops/tenere)
- [llm-ollama](https://github.com/taketwo/llm-ollama) for [Datasette's LLM CLI](https://llm.datasette.io/en/stable/).
- [typechat-cli](https://github.com/anaisbetts/typechat-cli)
@@ -445,58 +354,35 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [tlm](https://github.com/yusufcanb/tlm)
- [podman-ollama](https://github.com/ericcurtin/podman-ollama)
- [gollama](https://github.com/sammcj/gollama)
- [ParLlama](https://github.com/paulrobello/parllama)
- [Ollama eBook Summary](https://github.com/cognitivetech/ollama-ebook-summary/)
- [Ollama Mixture of Experts (MOE) in 50 lines of code](https://github.com/rapidarchitect/ollama_moe)
- [vim-intelligence-bridge](https://github.com/pepo-ec/vim-intelligence-bridge) Simple interaction of "Ollama" with the Vim editor
- [x-cmd ollama](https://x-cmd.com/mod/ollama)
- [bb7](https://github.com/drunkwcodes/bb7)
- [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)
- [AWS-Strands-With-Ollama](https://github.com/rapidarchitect/ollama_strands) - AWS Strands Agents with Ollama Examples
- [ollama-multirun](https://github.com/attogram/ollama-multirun) - A bash shell script to run a single prompt against any or all of your locally installed ollama models, saving the output and performance statistics as easily navigable web pages. ([Demo](https://attogram.github.io/ai_test_zone/))
- [ollama-bash-toolshed](https://github.com/attogram/ollama-bash-toolshed) - Bash scripts to chat with tool using models. Add new tools to your shed with ease. Runs on Ollama.
### 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
- [pgai](https://github.com/timescale/pgai) - PostgreSQL as a vector database (Create and search embeddings from Ollama models using pgvector)
- [Get started guide](https://github.com/timescale/pgai/blob/main/docs/vectorizer-quick-start.md)
- [MindsDB](https://github.com/mindsdb/mindsdb/blob/staging/mindsdb/integrations/handlers/ollama_handler/README.md) (Connects Ollama models with nearly 200 data platforms and apps)
- [chromem-go](https://github.com/philippgille/chromem-go/blob/v0.5.0/embed_ollama.go) with [example](https://github.com/philippgille/chromem-go/tree/v0.5.0/examples/rag-wikipedia-ollama)
- [Kangaroo](https://github.com/dbkangaroo/kangaroo) (AI-powered SQL client and admin tool for popular databases)
### Package managers
- [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)
- [Spring AI](https://github.com/spring-projects/spring-ai) with [reference](https://docs.spring.io/spring-ai/reference/api/chat/ollama-chat.html) and [example](https://github.com/tzolov/ollama-tools)
- [LangChainGo](https://github.com/tmc/langchaingo/) with [example](https://github.com/tmc/langchaingo/tree/main/examples/ollama-completion-example)
- [LangChain4j](https://github.com/langchain4j/langchain4j) with [example](https://github.com/langchain4j/langchain4j-examples/tree/main/ollama-examples/src/main/java)
- [LangChainRust](https://github.com/Abraxas-365/langchain-rust) with [example](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/llm_ollama.rs)
- [LangChain for .NET](https://github.com/tryAGI/LangChain) with [example](https://github.com/tryAGI/LangChain/blob/main/examples/LangChain.Samples.OpenAI/Program.cs)
- [LLPhant](https://github.com/theodo-group/LLPhant?tab=readme-ov-file#ollama)
- [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/llm/ollama/) and [LlamaIndexTS](https://ts.llamaindex.ai/modules/llms/available_llms/ollama)
- [LiteLLM](https://github.com/BerriAI/litellm)
- [OllamaFarm for Go](https://github.com/presbrey/ollamafarm)
@@ -521,46 +407,25 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [Portkey](https://portkey.ai/docs/welcome/integration-guides/ollama)
- [PromptingTools.jl](https://github.com/svilupp/PromptingTools.jl) with an [example](https://svilupp.github.io/PromptingTools.jl/dev/examples/working_with_ollama)
- [LlamaScript](https://github.com/Project-Llama/llamascript)
- [llm-axe](https://github.com/emirsahin1/llm-axe) (Python Toolkit for Building LLM Powered Apps)
- [Gollm](https://docs.gollm.co/examples/ollama-example)
- [Gollama for Golang](https://github.com/jonathanhecl/gollama)
- [Ollamaclient for Golang](https://github.com/xyproto/ollamaclient)
- [High-level function abstraction in Go](https://gitlab.com/tozd/go/fun)
- [Ollama PHP](https://github.com/ArdaGnsrn/ollama-php)
- [Agents-Flex for Java](https://github.com/agents-flex/agents-flex) with [example](https://github.com/agents-flex/agents-flex/tree/main/agents-flex-llm/agents-flex-llm-ollama/src/test/java/com/agentsflex/llm/ollama)
- [Parakeet](https://github.com/parakeet-nest/parakeet) is a GoLang library, made to simplify the development of small generative AI applications with Ollama.
- [Haverscript](https://github.com/andygill/haverscript) with [examples](https://github.com/andygill/haverscript/tree/main/examples)
- [Ollama for Swift](https://github.com/mattt/ollama-swift)
- [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)
- [any-llm](https://github.com/mozilla-ai/any-llm) (A single interface to use different llm providers by [mozilla.ai](https://www.mozilla.ai/))
- [any-agent](https://github.com/mozilla-ai/any-agent) (A single interface to use and evaluate different agent frameworks by [mozilla.ai](https://www.mozilla.ai/))
- [Neuro SAN](https://github.com/cognizant-ai-lab/neuro-san-studio) (Data-driven multi-agent orchestration framework) with [example](https://github.com/cognizant-ai-lab/neuro-san-studio/blob/main/docs/user_guide.md#ollama)
### 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
- [Raycast extension](https://github.com/MassimilianoPasquini97/raycast_ollama)
- [Discollama](https://github.com/mxyng/discollama) (Discord bot inside the Ollama discord channel)
- [Continue](https://github.com/continuedev/continue)
- [Vibe](https://github.com/thewh1teagle/vibe) (Transcribe and analyze meetings with Ollama)
- [Obsidian Ollama plugin](https://github.com/hinterdupfinger/obsidian-ollama)
- [Logseq Ollama plugin](https://github.com/omagdy7/ollama-logseq)
- [NotesOllama](https://github.com/andersrex/notesollama) (Apple Notes Ollama plugin)
@@ -575,7 +440,7 @@ 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)
@@ -583,37 +448,14 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [AI Telegram Bot](https://github.com/tusharhero/aitelegrambot) (Telegram bot using Ollama in backend)
- [AI ST Completion](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (Sublime Text 4 AI assistant plugin with Ollama support)
- [Discord-Ollama Chat Bot](https://github.com/kevinthedang/discord-ollama) (Generalized TypeScript Discord Bot w/ Tuning Documentation)
- [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.)
- [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.)
- [Headless Ollama](https://github.com/nischalj10/headless-ollama) (Scripts to automatically install ollama client & models on any OS for apps that depends on ollama server)
- [vnc-lm](https://github.com/jk011ru/vnc-lm) (A containerized Discord bot with support for attachments and web links)
- [LSP-AI](https://github.com/SilasMarvin/lsp-ai) (Open-source language server for AI-powered functionality)
- [QodeAssist](https://github.com/Palm1r/QodeAssist) (AI-powered coding assistant plugin for Qt Creator)
- [Obsidian Quiz Generator plugin](https://github.com/ECuiDev/obsidian-quiz-generator)
- [AI Summmary Helper plugin](https://github.com/philffm/ai-summary-helper)
- [TextCraft](https://github.com/suncloudsmoon/TextCraft) (Copilot in Word alternative using Ollama)
- [Alfred Ollama](https://github.com/zeitlings/alfred-ollama) (Alfred Workflow)
- [TextLLaMA](https://github.com/adarshM84/TextLLaMA) A Chrome Extension that helps you write emails, correct grammar, and translate into any language
- [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)
- [SimpleOllamaUnity](https://github.com/HardCodeDev777/SimpleOllamaUnity) (Unity Engine extension for communicating with Ollama in a few lines of code. Also works at runtime)
- [UnityCodeLama](https://github.com/HardCodeDev777/UnityCodeLama) (Unity Edtior tool to analyze scripts via Ollama)
- [NativeMind](https://github.com/NativeMindBrowser/NativeMindExtension) (Private, on-device AI Assistant, no cloud dependencies)
- [GMAI - Gradle Managed AI](https://gmai.premex.se/) (Gradle plugin for automated Ollama lifecycle management during build phases)
- [NOMYO Router](https://github.com/nomyo-ai/nomyo-router) (A transparent Ollama proxy with model deployment aware routing which auto-manages multiple Ollama instances in a given network)
### Supported backends
- [llama.cpp](https://github.com/ggml-org/llama.cpp) project founded by Georgi Gerganov.
- [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 (
@@ -24,10 +24,7 @@ import (
"net/http"
"net/url"
"runtime"
"strconv"
"time"
"github.com/ollama/ollama/auth"
"github.com/ollama/ollama/envconfig"
"github.com/ollama/ollama/format"
"github.com/ollama/ollama/version"
@@ -58,7 +55,7 @@ func checkError(resp *http.Response, body []byte) error {
// ClientFromEnvironment creates a new [Client] using configuration from the
// environment variable OLLAMA_HOST, which points to the network host and
// port on which the ollama service is listening. The format of this variable
// port on which the ollama service is listenting. The format of this variable
// is:
//
// <scheme>://<host>:<port>
@@ -79,14 +76,6 @@ func NewClient(base *url.URL, http *http.Client) *Client {
}
}
func getAuthorizationToken(ctx context.Context, challenge string) (string, error) {
token, err := auth.Sign(ctx, []byte(challenge))
if err != nil {
return "", err
}
return token, nil
}
func (c *Client) do(ctx context.Context, method, path string, reqData, respData any) error {
var reqBody io.Reader
var data []byte
@@ -108,21 +97,6 @@ func (c *Client) do(ctx context.Context, method, path string, reqData, respData
}
requestURL := c.base.JoinPath(path)
var token string
if envconfig.UseAuth() || c.base.Hostname() == "ollama.com" {
now := strconv.FormatInt(time.Now().Unix(), 10)
chal := fmt.Sprintf("%s,%s?ts=%s", method, path, now)
token, err = getAuthorizationToken(ctx, chal)
if err != nil {
return err
}
q := requestURL.Query()
q.Set("ts", now)
requestURL.RawQuery = q.Encode()
}
request, err := http.NewRequestWithContext(ctx, method, requestURL.String(), reqBody)
if err != nil {
return err
@@ -132,10 +106,6 @@ func (c *Client) do(ctx context.Context, method, path string, reqData, respData
request.Header.Set("Accept", "application/json")
request.Header.Set("User-Agent", fmt.Sprintf("ollama/%s (%s %s) Go/%s", version.Version, runtime.GOARCH, runtime.GOOS, runtime.Version()))
if token != "" {
request.Header.Set("Authorization", token)
}
respObj, err := c.http.Do(request)
if err != nil {
return err
@@ -162,7 +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 {
@@ -173,22 +143,6 @@ func (c *Client) stream(ctx context.Context, method, path string, data any, fn f
}
requestURL := c.base.JoinPath(path)
var token string
if envconfig.UseAuth() || c.base.Hostname() == "ollama.com" {
var err error
now := strconv.FormatInt(time.Now().Unix(), 10)
chal := fmt.Sprintf("%s,%s?ts=%s", method, path, now)
token, err = getAuthorizationToken(ctx, chal)
if err != nil {
return err
}
q := requestURL.Query()
q.Set("ts", now)
requestURL.RawQuery = q.Encode()
}
request, err := http.NewRequestWithContext(ctx, method, requestURL.String(), buf)
if err != nil {
return err
@@ -198,10 +152,6 @@ func (c *Client) stream(ctx context.Context, method, path string, data any, fn f
request.Header.Set("Accept", "application/x-ndjson")
request.Header.Set("User-Agent", fmt.Sprintf("ollama/%s (%s %s) Go/%s", version.Version, runtime.GOARCH, runtime.GOOS, runtime.Version()))
if token != "" {
request.Header.Set("Authorization", token)
}
response, err := c.http.Do(request)
if err != nil {
return err
@@ -222,17 +172,11 @@ func (c *Client) stream(ctx context.Context, method, path string, data any, fn f
return fmt.Errorf("unmarshal: %w", err)
}
if response.StatusCode == http.StatusUnauthorized {
pubKey, pkErr := auth.GetPublicKey()
if pkErr != nil {
return pkErr
}
return AuthorizationError{
StatusCode: response.StatusCode,
Status: response.Status,
PublicKey: pubKey,
}
} else if response.StatusCode >= http.StatusBadRequest {
if errorResponse.Error != "" {
return errors.New(errorResponse.Error)
}
if response.StatusCode >= http.StatusBadRequest {
return StatusError{
StatusCode: response.StatusCode,
Status: response.Status,
@@ -240,10 +184,6 @@ func (c *Client) stream(ctx context.Context, method, path string, data any, fn f
}
}
if errorResponse.Error != "" {
return errors.New(errorResponse.Error)
}
if err := fn(bts); err != nil {
return err
}
@@ -438,16 +378,3 @@ func (c *Client) Version(ctx context.Context) (string, error) {
return version.Version, nil
}
// Signout will disconnect an ollama instance from ollama.com
func (c *Client) Signout(ctx context.Context, encodedKey string) error {
return c.do(ctx, http.MethodDelete, fmt.Sprintf("/api/user/keys/%s", encodedKey), nil, nil)
}
func (c *Client) Whoami(ctx context.Context) (*UserResponse, error) {
var resp UserResponse
if err := c.do(ctx, http.MethodPost, "/api/me", nil, &resp); err != nil {
return nil, err
}
return &resp, nil
}

View File

@@ -1,12 +1,6 @@
package api
import (
"encoding/json"
"fmt"
"net/http"
"net/http/httptest"
"net/url"
"strings"
"testing"
)
@@ -49,216 +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: "http status error takes precedence over general error",
responses: []any{
testError{
message: "custom error message",
statusCode: http.StatusInternalServerError,
},
},
wantErr: "500",
},
{
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

@@ -1,18 +0,0 @@
# Ollama API Examples
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)
## Generate - Generate text from a model
- [generate/main.go](generate/main.go)
- [generate-streaming/main.go](generate-streaming/main.go)
## Pull - Pull a model
- [pull-progress/main.go](pull-progress/main.go)

View File

@@ -10,14 +10,9 @@ import (
"strconv"
"strings"
"time"
"github.com/google/uuid"
"github.com/ollama/ollama/envconfig"
"github.com/ollama/ollama/types/model"
)
// StatusError is an error with an HTTP status code and message.
// StatusError is an error with and HTTP status code.
type StatusError struct {
StatusCode int
Status string
@@ -38,19 +33,6 @@ func (e StatusError) Error() string {
}
}
type AuthorizationError struct {
StatusCode int
Status string
PublicKey string `json:"public_key"`
}
func (e AuthorizationError) Error() string {
if e.Status != "" {
return e.Status
}
return "something went wrong, please see the ollama server logs for details"
}
// ImageData represents the raw binary data of an image file.
type ImageData []byte
@@ -75,7 +57,7 @@ type GenerateRequest struct {
Template string `json:"template"`
// Context is the context parameter returned from a previous call to
// [Client.Generate]. It can be used to keep a short conversational memory.
// Generate call. It can be used to keep a short conversational memory.
Context []int `json:"context,omitempty"`
// Stream specifies whether the response is streaming; it is true by default.
@@ -85,30 +67,19 @@ type GenerateRequest struct {
Raw bool `json:"raw,omitempty"`
// Format specifies the format to return a response in.
Format json.RawMessage `json:"format,omitempty"`
Format string `json:"format"`
// KeepAlive controls how long the model will stay loaded in memory following
// this request.
KeepAlive *Duration `json:"keep_alive,omitempty"`
// Images is an optional list of 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"`
// Think controls whether thinking/reasoning models will think before
// responding. Can be a boolean (true/false) or a string ("high", "medium", "low")
// for supported models. Needs to be a pointer so we can distinguish between false
// (request that thinking _not_ be used) and unset (use the old behavior
// before this option was introduced)
Think *ThinkValue `json:"think,omitempty"`
// DebugRenderOnly is a debug option that, when set to true, returns the rendered
// template instead of calling the model.
DebugRenderOnly bool `json:"_debug_render_only,omitempty"`
Options map[string]interface{} `json:"options"`
}
// ChatRequest describes a request sent by [Client.Chat].
@@ -119,30 +90,21 @@ type ChatRequest struct {
// Messages is the messages of the chat - can be used to keep a chat memory.
Messages []Message `json:"messages"`
// Stream enables streaming of returned responses; true by default.
// Stream enable streaming of returned response; true by default.
Stream *bool `json:"stream,omitempty"`
// Format is the format to return the response in (e.g. "json").
Format json.RawMessage `json:"format,omitempty"`
Format string `json:"format"`
// KeepAlive controls how long the model will stay loaded into memory
// following the request.
// followin the request.
KeepAlive *Duration `json:"keep_alive,omitempty"`
// Tools is an optional list of tools the model has access to.
Tools `json:"tools,omitempty"`
// Options lists model-specific options.
Options map[string]any `json:"options"`
// Think controls whether thinking/reasoning models will think before
// responding. Can be a boolean (true/false) or a string ("high", "medium", "low")
// for supported models.
Think *ThinkValue `json:"think,omitempty"`
// DebugRenderOnly is a debug option that, when set to true, returns the rendered
// template instead of calling the model.
DebugRenderOnly bool `json:"_debug_render_only,omitempty"`
Options map[string]interface{} `json:"options"`
}
type Tools []Tool
@@ -161,14 +123,10 @@ func (t Tool) String() string {
// role ("system", "user", or "assistant"), the content and an optional list
// of images.
type Message struct {
Role string `json:"role"`
Content string `json:"content"`
// Thinking contains the text that was inside thinking tags in the
// original model output when ChatRequest.Think is enabled.
Thinking string `json:"thinking,omitempty"`
Role string `json:"role"`
Content string `json:"content"`
Images []ImageData `json:"images,omitempty"`
ToolCalls []ToolCall `json:"tool_calls,omitempty"`
ToolName string `json:"tool_name,omitempty"`
}
func (m *Message) UnmarshalJSON(b []byte) error {
@@ -188,7 +146,6 @@ type ToolCall struct {
}
type ToolCallFunction struct {
Index int `json:"index,omitempty"`
Name string `json:"name"`
Arguments ToolCallFunctionArguments `json:"arguments"`
}
@@ -202,122 +159,21 @@ 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 ToolProperty struct {
AnyOf []ToolProperty `json:"anyOf,omitempty"`
Type PropertyType `json:"type"`
Items any `json:"items,omitempty"`
Description string `json:"description"`
Enum []any `json:"enum,omitempty"`
}
// ToTypeScriptType converts a ToolProperty to a TypeScript type string
func (tp ToolProperty) ToTypeScriptType() string {
if len(tp.AnyOf) > 0 {
var types []string
for _, anyOf := range tp.AnyOf {
types = append(types, anyOf.ToTypeScriptType())
}
return strings.Join(types, " | ")
}
if len(tp.Type) == 0 {
return "any"
}
if len(tp.Type) == 1 {
return mapToTypeScriptType(tp.Type[0])
}
var types []string
for _, t := range tp.Type {
types = append(types, mapToTypeScriptType(t))
}
return strings.Join(types, " | ")
}
// mapToTypeScriptType maps JSON Schema types to TypeScript types
func mapToTypeScriptType(jsonType string) string {
switch jsonType {
case "string":
return "string"
case "number", "integer":
return "number"
case "boolean":
return "boolean"
case "array":
return "any[]"
case "object":
return "Record<string, any>"
case "null":
return "null"
default:
return "any"
}
}
type ToolFunctionParameters struct {
Type string `json:"type"`
Defs any `json:"$defs,omitempty"`
Items any `json:"items,omitempty"`
Required []string `json:"required"`
Properties map[string]ToolProperty `json:"properties"`
}
func (t *ToolFunctionParameters) String() string {
bts, _ := json.Marshal(t)
return string(bts)
}
type ToolFunction struct {
Name string `json:"name"`
Description string `json:"description"`
Parameters ToolFunctionParameters `json:"parameters"`
Name string `json:"name"`
Description string `json:"description"`
Parameters struct {
Type string `json:"type"`
Required []string `json:"required"`
Properties map[string]struct {
Type string `json:"type"`
Description string `json:"description"`
Enum []string `json:"enum,omitempty"`
} `json:"properties"`
} `json:"parameters"`
}
func (t *ToolFunction) String() string {
@@ -328,38 +184,16 @@ func (t *ToolFunction) String() string {
// ChatResponse is the response returned by [Client.Chat]. Its fields are
// similar to [GenerateResponse].
type ChatResponse struct {
// Model is the model name that generated the response.
Model string `json:"model"`
Model string `json:"model"`
CreatedAt time.Time `json:"created_at"`
Message Message `json:"message"`
DoneReason string `json:"done_reason,omitempty"`
// RemoteModel is the name of the upstream model that generated the response.
RemoteModel string `json:"remote_model,omitempty"`
// RemoteHost is the URL of the upstream Ollama host that generated the response.
RemoteHost string `json:"remote_host,omitempty"`
// CreatedAt is the timestamp of the response.
CreatedAt time.Time `json:"created_at"`
// Message contains the message or part of a message from the model.
Message Message `json:"message"`
// Done specifies if the response is complete.
Done bool `json:"done"`
// DoneReason is the reason the model stopped generating text.
DoneReason string `json:"done_reason,omitempty"`
DebugInfo *DebugInfo `json:"_debug_info,omitempty"`
Metrics
}
// DebugInfo contains debug information for template rendering
type DebugInfo struct {
RenderedTemplate string `json:"rendered_template"`
ImageCount int `json:"image_count,omitempty"`
}
type Metrics struct {
TotalDuration time.Duration `json:"total_duration,omitempty"`
LoadDuration time.Duration `json:"load_duration,omitempty"`
@@ -369,8 +203,8 @@ type Metrics struct {
EvalDuration time.Duration `json:"eval_duration,omitempty"`
}
// Options specified in [GenerateRequest]. If you add a new option here, also
// add it to the API docs.
// Options specified in [GenerateRequest], if you add a new option here add it
// to the API docs also.
type Options struct {
Runner
@@ -381,12 +215,17 @@ type Options struct {
TopK int `json:"top_k,omitempty"`
TopP float32 `json:"top_p,omitempty"`
MinP float32 `json:"min_p,omitempty"`
TFSZ float32 `json:"tfs_z,omitempty"`
TypicalP float32 `json:"typical_p,omitempty"`
RepeatLastN int `json:"repeat_last_n,omitempty"`
Temperature float32 `json:"temperature,omitempty"`
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"`
PenalizeNewline bool `json:"penalize_newline,omitempty"`
Stop []string `json:"stop,omitempty"`
}
@@ -396,7 +235,12 @@ 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"`
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"`
}
@@ -412,14 +256,10 @@ type EmbedRequest struct {
// this request.
KeepAlive *Duration `json:"keep_alive,omitempty"`
// Truncate truncates the input to fit the model's max sequence length.
Truncate *bool `json:"truncate,omitempty"`
// Dimensions truncates the output embedding to the specified dimension.
Dimensions int `json:"dimensions,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].
@@ -445,7 +285,7 @@ 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].
@@ -455,50 +295,17 @@ type EmbeddingResponse struct {
// CreateRequest is the request passed to [Client.Create].
type CreateRequest struct {
// Model is the model name to create.
Model string `json:"model"`
// Stream specifies whether the response is streaming; it is true by default.
Stream *bool `json:"stream,omitempty"`
// Quantize is the quantization format for the model; leave blank to not change the quantization level.
Quantize string `json:"quantize,omitempty"`
// From is the name of the model or file to use as the source.
From string `json:"from,omitempty"`
// RemoteHost is the URL of the upstream ollama API for the model (if any).
RemoteHost string `json:"remote_host,omitempty"`
// Files is a map of files include when creating the model.
Files map[string]string `json:"files,omitempty"`
// Adapters is a map of LoRA adapters to include when creating the model.
Adapters map[string]string `json:"adapters,omitempty"`
// Template is the template used when constructing a request to the model.
Template string `json:"template,omitempty"`
// License is a string or list of strings for licenses.
License any `json:"license,omitempty"`
// System is the system prompt for the model.
System string `json:"system,omitempty"`
// Parameters is a map of hyper-parameters which are applied to the model.
Parameters map[string]any `json:"parameters,omitempty"`
// Messages is a list of messages added to the model before chat and generation requests.
Messages []Message `json:"messages,omitempty"`
Renderer string `json:"renderer,omitempty"`
Parser string `json:"parser,omitempty"`
// Info is a map of additional information for the model
Info map[string]any `json:"info,omitempty"`
Model string `json:"model"`
Modelfile string `json:"modelfile"`
Stream *bool `json:"stream,omitempty"`
Quantize string `json:"quantize,omitempty"`
// Deprecated: set the model name with Model instead
Name string `json:"name"`
// Deprecated: set the file content with Modelfile instead
Path string `json:"path"`
// Deprecated: use Quantize instead
Quantization string `json:"quantization,omitempty"`
}
@@ -520,7 +327,7 @@ 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"`
@@ -528,22 +335,16 @@ 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"`
Renderer string `json:"renderer,omitempty"`
Parser string `json:"parser,omitempty"`
Details ModelDetails `json:"details,omitempty"`
Messages []Message `json:"messages,omitempty"`
RemoteModel string `json:"remote_model,omitempty"`
RemoteHost string `json:"remote_host,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].
@@ -555,9 +356,9 @@ 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
@@ -597,26 +398,30 @@ type ProcessResponse struct {
// ListModelResponse is a single model description in [ListResponse].
type ListModelResponse struct {
Name string `json:"name"`
Model string `json:"model"`
RemoteModel string `json:"remote_model,omitempty"`
RemoteHost string `json:"remote_host,omitempty"`
ModifiedAt time.Time `json:"modified_at"`
Size int64 `json:"size"`
Digest string `json:"digest"`
Details ModelDetails `json:"details,omitempty"`
Name string `json:"name"`
Model string `json:"model"`
ModifiedAt time.Time `json:"modified_at"`
Size int64 `json:"size"`
Digest string `json:"digest"`
Details ModelDetails `json:"details,omitempty"`
}
// ProcessModelResponse is a single model description in [ProcessResponse].
type ProcessModelResponse struct {
Name string `json:"name"`
Model string `json:"model"`
Size int64 `json:"size"`
Digest string `json:"digest"`
Details ModelDetails `json:"details,omitempty"`
ExpiresAt time.Time `json:"expires_at"`
SizeVRAM int64 `json:"size_vram"`
ContextLength int `json:"context_length"`
Name string `json:"name"`
Model string `json:"model"`
Size int64 `json:"size"`
Digest string `json:"digest"`
Details ModelDetails `json:"details,omitempty"`
ExpiresAt time.Time `json:"expires_at"`
SizeVRAM int64 `json:"size_vram"`
}
type RetrieveModelResponse struct {
Id string `json:"id"`
Object string `json:"object"`
Created int64 `json:"created"`
OwnedBy string `json:"owned_by"`
}
type TokenResponse struct {
@@ -628,22 +433,12 @@ type GenerateResponse struct {
// Model is the model name that generated the response.
Model string `json:"model"`
// RemoteModel is the name of the upstream model that generated the response.
RemoteModel string `json:"remote_model,omitempty"`
// RemoteHost is the URL of the upstream Ollama host that generated the response.
RemoteHost string `json:"remote_host,omitempty"`
// CreatedAt is the timestamp of the response.
CreatedAt time.Time `json:"created_at"`
// Response is the textual response itself.
Response string `json:"response"`
// Thinking contains the text that was inside thinking tags in the
// original model output when ChatRequest.Think is enabled.
Thinking string `json:"thinking,omitempty"`
// Done specifies if the response is complete.
Done bool `json:"done"`
@@ -655,10 +450,6 @@ type GenerateResponse struct {
Context []int `json:"context,omitempty"`
Metrics
ToolCalls []ToolCall `json:"tool_calls,omitempty"`
DebugInfo *DebugInfo `json:"_debug_info,omitempty"`
}
// ModelDetails provides details about a model.
@@ -671,25 +462,6 @@ type ModelDetails struct {
QuantizationLevel string `json:"quantization_level"`
}
// UserResponse provides information about a user.
type UserResponse struct {
ID uuid.UUID `json:"id"`
Email string `json:"email"`
Name string `json:"name"`
Bio string `json:"bio,omitempty"`
AvatarURL string `json:"avatarurl,omitempty"`
FirstName string `json:"firstname,omitempty"`
LastName string `json:"lastname,omitempty"`
Plan string `json:"plan,omitempty"`
}
// 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)
@@ -718,7 +490,7 @@ 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
@@ -775,12 +547,12 @@ 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)
@@ -822,131 +594,32 @@ func DefaultOptions() Options {
Temperature: 0.8,
TopK: 40,
TopP: 0.9,
TFSZ: 1.0,
TypicalP: 1.0,
RepeatLastN: 64,
RepeatPenalty: 1.1,
PresencePenalty: 0.0,
FrequencyPenalty: 0.0,
Mirostat: 0,
MirostatTau: 5.0,
MirostatEta: 0.1,
PenalizeNewline: true,
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,
F16KV: true,
UseMLock: false,
UseMMap: nil,
},
}
}
// ThinkValue represents a value that can be a boolean or a string ("high", "medium", "low")
type ThinkValue struct {
// Value can be a bool or string
Value interface{}
}
// IsValid checks if the ThinkValue is valid
func (t *ThinkValue) IsValid() bool {
if t == nil || t.Value == nil {
return true // nil is valid (means not set)
}
switch v := t.Value.(type) {
case bool:
return true
case string:
return v == "high" || v == "medium" || v == "low"
default:
return false
}
}
// IsBool returns true if the value is a boolean
func (t *ThinkValue) IsBool() bool {
if t == nil || t.Value == nil {
return false
}
_, ok := t.Value.(bool)
return ok
}
// IsString returns true if the value is a string
func (t *ThinkValue) IsString() bool {
if t == nil || t.Value == nil {
return false
}
_, ok := t.Value.(string)
return ok
}
// Bool returns the value as a bool (true if enabled in any way)
func (t *ThinkValue) Bool() bool {
if t == nil || t.Value == nil {
return false
}
switch v := t.Value.(type) {
case bool:
return v
case string:
// Any string value ("high", "medium", "low") means thinking is enabled
return v == "high" || v == "medium" || v == "low"
default:
return false
}
}
// String returns the value as a string
func (t *ThinkValue) String() string {
if t == nil || t.Value == nil {
return ""
}
switch v := t.Value.(type) {
case string:
return v
case bool:
if v {
return "medium" // Default level when just true
}
return ""
default:
return ""
}
}
// UnmarshalJSON implements json.Unmarshaler
func (t *ThinkValue) UnmarshalJSON(data []byte) error {
// Try to unmarshal as bool first
var b bool
if err := json.Unmarshal(data, &b); err == nil {
t.Value = b
return nil
}
// Try to unmarshal as string
var s string
if err := json.Unmarshal(data, &s); err == nil {
// Validate string values
if s != "high" && s != "medium" && s != "low" {
return fmt.Errorf("invalid think value: %q (must be \"high\", \"medium\", \"low\", true, or false)", s)
}
t.Value = s
return nil
}
return fmt.Errorf("think must be a boolean or string (\"high\", \"medium\", \"low\")")
}
// MarshalJSON implements json.Marshaler
func (t *ThinkValue) MarshalJSON() ([]byte, error) {
if t == nil || t.Value == nil {
return []byte("null"), nil
}
return json.Marshal(t.Value)
}
type Duration struct {
time.Duration
}
@@ -971,7 +644,7 @@ func (d *Duration) UnmarshalJSON(b []byte) (err error) {
if t < 0 {
d.Duration = time.Duration(math.MaxInt64)
} else {
d.Duration = time.Duration(t * float64(time.Second))
d.Duration = time.Duration(int(t) * int(time.Second))
}
case string:
d.Duration, err = time.ParseDuration(t)
@@ -989,7 +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
@@ -1003,7 +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

@@ -17,11 +17,6 @@ func TestKeepAliveParsingFromJSON(t *testing.T) {
req string
exp *Duration
}{
{
name: "Unset",
req: `{ }`,
exp: nil,
},
{
name: "Positive Integer",
req: `{ "keep_alive": 42 }`,
@@ -30,7 +25,7 @@ func TestKeepAliveParsingFromJSON(t *testing.T) {
{
name: "Positive Float",
req: `{ "keep_alive": 42.5 }`,
exp: &Duration{42500 * time.Millisecond},
exp: &Duration{42 * time.Second},
},
{
name: "Positive Integer String",
@@ -139,7 +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()
@@ -236,255 +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)
}
})
}
}
func TestThinking_UnmarshalJSON(t *testing.T) {
tests := []struct {
name string
input string
expectedThinking *ThinkValue
expectedError bool
}{
{
name: "true",
input: `{ "think": true }`,
expectedThinking: &ThinkValue{Value: true},
},
{
name: "false",
input: `{ "think": false }`,
expectedThinking: &ThinkValue{Value: false},
},
{
name: "unset",
input: `{ }`,
expectedThinking: nil,
},
{
name: "string_high",
input: `{ "think": "high" }`,
expectedThinking: &ThinkValue{Value: "high"},
},
{
name: "string_medium",
input: `{ "think": "medium" }`,
expectedThinking: &ThinkValue{Value: "medium"},
},
{
name: "string_low",
input: `{ "think": "low" }`,
expectedThinking: &ThinkValue{Value: "low"},
},
{
name: "invalid_string",
input: `{ "think": "invalid" }`,
expectedThinking: nil,
expectedError: true,
},
}
for _, test := range tests {
t.Run(test.name, func(t *testing.T) {
var req GenerateRequest
err := json.Unmarshal([]byte(test.input), &req)
if test.expectedError {
require.Error(t, err)
} else {
require.NoError(t, err)
if test.expectedThinking == nil {
assert.Nil(t, req.Think)
} else {
require.NotNil(t, req.Think)
assert.Equal(t, test.expectedThinking.Value, req.Think.Value)
}
}
})
}
}
func TestToolFunctionParameters_String(t *testing.T) {
tests := []struct {
name string
params ToolFunctionParameters
expected string
}{
{
name: "simple object with string property",
params: ToolFunctionParameters{
Type: "object",
Required: []string{"name"},
Properties: map[string]ToolProperty{
"name": {
Type: PropertyType{"string"},
Description: "The name of the person",
},
},
},
expected: `{"type":"object","required":["name"],"properties":{"name":{"type":"string","description":"The name of the person"}}}`,
},
{
name: "marshal failure returns empty string",
params: ToolFunctionParameters{
Type: "object",
Defs: func() any {
// Create a cycle that will cause json.Marshal to fail
type selfRef struct {
Self *selfRef
}
s := &selfRef{}
s.Self = s
return s
}(),
Properties: map[string]ToolProperty{},
},
expected: "",
},
}
for _, test := range tests {
t.Run(test.name, func(t *testing.T) {
result := test.params.String()
assert.Equal(t, test.expected, result)
})
}
}

View File

@@ -1,142 +0,0 @@
package api
import (
"testing"
)
func TestToolParameterToTypeScriptType(t *testing.T) {
tests := []struct {
name string
param ToolProperty
expected string
}{
{
name: "single string type",
param: ToolProperty{
Type: PropertyType{"string"},
},
expected: "string",
},
{
name: "single number type",
param: ToolProperty{
Type: PropertyType{"number"},
},
expected: "number",
},
{
name: "integer maps to number",
param: ToolProperty{
Type: PropertyType{"integer"},
},
expected: "number",
},
{
name: "boolean type",
param: ToolProperty{
Type: PropertyType{"boolean"},
},
expected: "boolean",
},
{
name: "array type",
param: ToolProperty{
Type: PropertyType{"array"},
},
expected: "any[]",
},
{
name: "object type",
param: ToolProperty{
Type: PropertyType{"object"},
},
expected: "Record<string, any>",
},
{
name: "null type",
param: ToolProperty{
Type: PropertyType{"null"},
},
expected: "null",
},
{
name: "multiple types as union",
param: ToolProperty{
Type: PropertyType{"string", "number"},
},
expected: "string | number",
},
{
name: "string or null union",
param: ToolProperty{
Type: PropertyType{"string", "null"},
},
expected: "string | null",
},
{
name: "anyOf with single types",
param: ToolProperty{
AnyOf: []ToolProperty{
{Type: PropertyType{"string"}},
{Type: PropertyType{"number"}},
},
},
expected: "string | number",
},
{
name: "anyOf with multiple types in each branch",
param: ToolProperty{
AnyOf: []ToolProperty{
{Type: PropertyType{"string", "null"}},
{Type: PropertyType{"number"}},
},
},
expected: "string | null | number",
},
{
name: "nested anyOf",
param: ToolProperty{
AnyOf: []ToolProperty{
{Type: PropertyType{"boolean"}},
{
AnyOf: []ToolProperty{
{Type: PropertyType{"string"}},
{Type: PropertyType{"number"}},
},
},
},
},
expected: "boolean | string | number",
},
{
name: "empty type returns any",
param: ToolProperty{
Type: PropertyType{},
},
expected: "any",
},
{
name: "unknown type maps to any",
param: ToolProperty{
Type: PropertyType{"unknown_type"},
},
expected: "any",
},
{
name: "multiple types including array",
param: ToolProperty{
Type: PropertyType{"string", "array", "null"},
},
expected: "string | any[] | null",
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
result := tt.param.ToTypeScriptType()
if result != tt.expected {
t.Errorf("ToTypeScriptType() = %q, want %q", result, tt.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

@@ -11,12 +11,10 @@ import (
"github.com/ollama/ollama/app/store"
"github.com/ollama/ollama/app/tray"
"github.com/ollama/ollama/envconfig"
)
func Run() {
InitLogging()
slog.Info("app config", "env", envconfig.Values())
ctx, cancel := context.WithCancel(context.Background())
var done chan int

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

@@ -36,13 +36,8 @@ func init() {
ServerLogFile = filepath.Join(AppDataDir, "server.log")
UpgradeLogFile = filepath.Join(AppDataDir, "upgrade.log")
exe, err := os.Executable()
if err != nil {
slog.Warn("error discovering executable directory", "error", err)
AppDir = filepath.Join(localAppData, "Programs", "Ollama")
} else {
AppDir = filepath.Dir(exe)
}
// Executables are stored in APPDATA
AppDir = filepath.Join(localAppData, "Programs", "Ollama")
// Make sure we have PATH set correctly for any spawned children
paths := strings.Split(os.Getenv("PATH"), ";")
@@ -69,7 +64,7 @@ func init() {
}
// Make sure our logging dir exists
_, err = os.Stat(AppDataDir)
_, err := os.Stat(AppDataDir)
if errors.Is(err, os.ErrNotExist) {
if err := os.MkdirAll(AppDataDir, 0o755); err != nil {
slog.Error(fmt.Sprintf("create ollama dir %s: %v", AppDataDir, err))

View File

@@ -18,17 +18,11 @@ func getCLIFullPath(command string) string {
var cmdPath string
appExe, err := os.Executable()
if err == nil {
// Check both the same location as the tray app, as well as ./bin
cmdPath = filepath.Join(filepath.Dir(appExe), command)
_, err := os.Stat(cmdPath)
if err == nil {
return cmdPath
}
cmdPath = filepath.Join(filepath.Dir(appExe), "bin", command)
_, err = os.Stat(cmdPath)
if err == nil {
return cmdPath
}
}
cmdPath, err = exec.LookPath(command)
if err == nil {

View File

@@ -26,15 +26,19 @@ func DoUpgrade(cancel context.CancelFunc, done chan int) error {
slog.Info("starting upgrade with " + installerExe)
slog.Info("upgrade log file " + UpgradeLogFile)
// make the upgrade show progress, but non interactive
// When running in debug mode, we'll be "verbose" and let the installer pop up and prompt
installArgs := []string{
"/CLOSEAPPLICATIONS", // Quit the tray app if it's still running
"/LOG=" + filepath.Base(UpgradeLogFile), // Only relative seems reliable, so set pwd
"/FORCECLOSEAPPLICATIONS", // Force close the tray app - might be needed
"/SP", // Skip the "This will install... Do you wish to continue" prompt
"/NOCANCEL", // Disable the ability to cancel upgrade mid-flight to avoid partially installed upgrades
"/SILENT",
}
// make the upgrade as quiet as possible (no GUI, no prompts)
installArgs = append(installArgs,
"/SP", // Skip the "This will install... Do you wish to continue" prompt
"/SUPPRESSMSGBOXES",
"/SILENT",
"/VERYSILENT",
)
// Safeguard in case we have requests in flight that need to drain...
slog.Info("Waiting for server to shutdown")

View File

@@ -53,8 +53,8 @@ RestartIfNeededByRun=no
; https://jrsoftware.org/ishelp/index.php?topic=setup_wizardimagefile
WizardSmallImageFile=.\assets\setup.bmp
; Ollama requires Windows 10 22H2 or newer for proper unicode rendering
; TODO: consider setting this to 10.0.19045
; TODO verifty actual min windows version...
; OG Win 10
MinVersion=10.0.10240
; First release that supports WinRT UI Composition for win32 apps
@@ -97,6 +97,7 @@ Source: "..\dist\windows-amd64\lib\ollama\*"; DestDir: "{app}\lib\ollama\"; Chec
Source: "..\dist\windows-arm64\vc_redist.arm64.exe"; DestDir: "{tmp}"; Check: IsArm64() and vc_redist_needed(); Flags: deleteafterinstall
Source: "..\dist\windows-arm64-app.exe"; DestDir: "{app}"; DestName: "{#MyAppExeName}" ;Check: IsArm64(); Flags: ignoreversion 64bit
Source: "..\dist\windows-arm64\ollama.exe"; DestDir: "{app}"; Check: IsArm64(); Flags: ignoreversion 64bit
Source: "..\dist\windows-arm64\lib\ollama\*"; DestDir: "{app}\lib\ollama\"; Check: IsArm64(); Flags: ignoreversion 64bit recursesubdirs
#endif
Source: "..\dist\ollama_welcome.ps1"; DestDir: "{app}"; Flags: ignoreversion
@@ -135,7 +136,7 @@ Type: filesandordirs; Name: "{%TEMP}\ollama*"
Type: filesandordirs; Name: "{%LOCALAPPDATA}\Programs\Ollama"
[Messages]
WizardReady=Ollama
WizardReady=Ollama Windows Preview
ReadyLabel1=%nLet's get you up and running with your own large language models.
SetupAppRunningError=Another Ollama installer is running.%n%nPlease cancel or finish the other installer, then click OK to continue with this install, or Cancel to exit.

View File

@@ -64,7 +64,7 @@ func initStore() {
slog.Debug(fmt.Sprintf("unexpected error searching for store: %s", err))
}
slog.Debug("initializing new store")
store.ID = uuid.NewString()
store.ID = uuid.New().String()
writeStore(getStorePath())
}

View File

@@ -98,7 +98,7 @@ func (t *winTray) wndProc(hWnd windows.Handle, message uint32, wParam, lParam ui
}
err = t.wcex.unregister()
if err != nil {
slog.Error(fmt.Sprintf("failed to unregister window %s", err))
slog.Error(fmt.Sprintf("failed to uregister windo %s", err))
}
case WM_DESTROY:
// same as WM_ENDSESSION, but throws 0 exit code after all

View File

@@ -11,13 +11,12 @@ import (
)
const (
_ = iota
updateAvailableMenuID
updateMenuID
separatorMenuID
diagLogsMenuID
diagSeparatorMenuID
quitMenuID
updateAvailableMenuID = 1
updateMenuID = updateAvailableMenuID + 1
separatorMenuID = updateMenuID + 1
diagLogsMenuID = separatorMenuID + 1
diagSeparatorMenuID = diagLogsMenuID + 1
quitMenuID = diagSeparatorMenuID + 1
)
func (t *winTray) initMenus() error {
@@ -39,7 +38,7 @@ func (t *winTray) UpdateAvailable(ver string) error {
if err := t.addOrUpdateMenuItem(updateAvailableMenuID, 0, updateAvailableMenuTitle, true); err != nil {
return fmt.Errorf("unable to create menu entries %w", err)
}
if err := t.addOrUpdateMenuItem(updateMenuID, 0, updateMenuTitle, false); err != nil {
if err := t.addOrUpdateMenuItem(updateMenuID, 0, updateMenutTitle, false); err != nil {
return fmt.Errorf("unable to create menu entries %w", err)
}
if err := t.addSeparatorMenuItem(separatorMenuID, 0); err != nil {

View File

@@ -10,6 +10,6 @@ const (
quitMenuTitle = "Quit Ollama"
updateAvailableMenuTitle = "An update is available"
updateMenuTitle = "Restart to update"
updateMenutTitle = "Restart to update"
diagLogsMenuTitle = "View logs"
)

View File

@@ -361,7 +361,7 @@ func (t *winTray) showMenu() error {
boolRet, _, err = pTrackPopupMenu.Call(
uintptr(t.menus[0]),
TPM_BOTTOMALIGN|TPM_LEFTALIGN|TPM_RIGHTBUTTON,
TPM_BOTTOMALIGN|TPM_LEFTALIGN,
uintptr(p.X),
uintptr(p.Y),
0,

View File

@@ -67,7 +67,6 @@ const (
SW_HIDE = 0
TPM_BOTTOMALIGN = 0x0020
TPM_LEFTALIGN = 0x0000
TPM_RIGHTBUTTON = 0x0002
WM_CLOSE = 0x0010
WM_USER = 0x0400
WS_CAPTION = 0x00C00000

View File

@@ -19,31 +19,6 @@ import (
const defaultPrivateKey = "id_ed25519"
func keyPath() (string, error) {
fileIsReadable := func(fp string) bool {
info, err := os.Stat(fp)
if err != nil {
return false
}
// Check that it's a regular file, not a directory or other file type
if !info.Mode().IsRegular() {
return false
}
// Try to open it to check readability
file, err := os.Open(fp)
if err != nil {
return false
}
file.Close()
return true
}
systemPath := filepath.Join("/usr/share/ollama/.ollama", defaultPrivateKey)
if fileIsReadable(systemPath) {
return systemPath, nil
}
home, err := os.UserHomeDir()
if err != nil {
return "", err

View File

@@ -0,0 +1 @@
This is here to make sure the build/ directory exists for the go:embed command

View File

@@ -0,0 +1 @@
This is here to make sure the build/ directory exists for the go:embed command

View File

@@ -0,0 +1,8 @@
package build
import "embed"
// Darwin payloads separated by architecture to avoid duplicate payloads when cross compiling
//go:embed darwin/amd64/*
var EmbedFS embed.FS

View File

@@ -0,0 +1,8 @@
package build
import "embed"
// Darwin payloads separated by architecture to avoid duplicate payloads when cross compiling
//go:embed darwin/arm64/*
var EmbedFS embed.FS

6
build/embed_linux.go Normal file
View File

@@ -0,0 +1,6 @@
package build
import "embed"
//go:embed linux/*
var EmbedFS embed.FS

8
build/embed_unused.go Normal file
View File

@@ -0,0 +1,8 @@
//go:build !linux && !darwin
package build
import "embed"
// unused on windows
var EmbedFS embed.FS

View File

@@ -0,0 +1 @@
This is here to make sure the build/ directory exists for the go:embed command

View File

@@ -0,0 +1 @@
This is here to make sure the build/ directory exists for the go:embed command

View File

File diff suppressed because it is too large Load Diff

View File

@@ -2,21 +2,19 @@ package cmd
import (
"bytes"
"context"
"encoding/json"
"fmt"
"io"
"net/http"
"net/http/httptest"
"os"
"path/filepath"
"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) {
@@ -28,7 +26,7 @@ func TestShowInfo(t *testing.T) {
ParameterSize: "7B",
QuantizationLevel: "FP16",
},
}, false, &b); err != nil {
}, &b); err != nil {
t.Fatal(err)
}
@@ -58,7 +56,7 @@ func TestShowInfo(t *testing.T) {
ParameterSize: "7B",
QuantizationLevel: "FP16",
},
}, false, &b); err != nil {
}, &b); err != nil {
t.Fatal(err)
}
@@ -69,60 +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)
@@ -144,7 +88,7 @@ func TestShowInfo(t *testing.T) {
stop you
stop up
temperature 99`,
}, false, &b); err != nil {
}, &b); err != nil {
t.Fatal(err)
}
@@ -181,7 +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)
}
@@ -214,7 +158,7 @@ func TestShowInfo(t *testing.T) {
Ahoy, matey!
Weigh anchor!
`,
}, false, &b); err != nil {
}, &b); err != nil {
t.Fatal(err)
}
@@ -226,7 +170,6 @@ Weigh anchor!
System
You are a pirate!
Ahoy, matey!
...
`
if diff := cmp.Diff(expect, b.String()); diff != "" {
@@ -236,15 +179,19 @@ Weigh anchor!
t.Run("license", func(t *testing.T) {
var b bytes.Buffer
license := "MIT License\nCopyright (c) Ollama\n"
license, err := os.ReadFile(filepath.Join("..", "LICENSE"))
if err != nil {
t.Fatal(err)
}
if err := showInfo(&api.ShowResponse{
Details: api.ModelDetails{
Family: "test",
ParameterSize: "7B",
QuantizationLevel: "FP16",
},
License: license,
}, false, &b); err != nil {
License: string(license),
}, &b); err != nil {
t.Fatal(err)
}
@@ -262,34 +209,6 @@ 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) {
@@ -305,8 +224,6 @@ func TestDeleteHandler(t *testing.T) {
w.WriteHeader(http.StatusOK)
} else {
w.WriteHeader(http.StatusNotFound)
errPayload := `{"error":"model '%s' not found"}`
w.Write([]byte(fmt.Sprintf(errPayload, req.Name)))
}
return
}
@@ -340,7 +257,7 @@ 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)
}
@@ -349,7 +266,7 @@ func TestDeleteHandler(t *testing.T) {
}
err := DeleteHandler(cmd, []string{"test-model-not-found"})
if err == nil || !strings.Contains(err.Error(), "model 'test-model-not-found' not found") {
if err == nil || !strings.Contains(err.Error(), "unable to stop existing running model \"test-model-not-found\"") {
t.Fatalf("DeleteHandler failed: expected error about stopping non-existent model, got %v", err)
}
}
@@ -380,7 +297,7 @@ func TestGetModelfileName(t *testing.T) {
name: "modelfile specified, no modelfile exists",
modelfileName: "crazyfile",
fileExists: false,
expectedName: "",
expectedName: "crazyfile",
expectedErr: os.ErrNotExist,
},
{
@@ -402,6 +319,11 @@ 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
@@ -409,11 +331,10 @@ 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)
@@ -421,8 +342,8 @@ func TestGetModelfileName(t *testing.T) {
t.Fatalf("couldn't set file flag: %v", err)
}
} else {
expectedFilename = tt.expectedName
if tt.modelfileName != "" {
expectedFilename = tt.modelfileName
err := cmd.Flags().Set("file", tt.modelfileName)
if err != nil {
t.Fatalf("couldn't set file flag: %v", err)
@@ -448,474 +369,3 @@ func TestGetModelfileName(t *testing.T) {
})
}
}
func TestPushHandler(t *testing.T) {
tests := []struct {
name string
modelName string
serverResponse map[string]func(w http.ResponseWriter, r *http.Request)
expectedError string
expectedOutput string
}{
{
name: "successful push",
modelName: "test-model",
serverResponse: map[string]func(w http.ResponseWriter, r *http.Request){
"/api/push": func(w http.ResponseWriter, r *http.Request) {
if r.Method != http.MethodPost {
t.Errorf("expected POST request, got %s", r.Method)
}
var req api.PushRequest
if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
http.Error(w, err.Error(), http.StatusBadRequest)
return
}
if req.Name != "test-model" {
t.Errorf("expected model name 'test-model', got %s", req.Name)
}
// Simulate progress updates
responses := []api.ProgressResponse{
{Status: "preparing manifest"},
{Digest: "sha256:abc123456789", Total: 100, Completed: 50},
{Digest: "sha256:abc123456789", Total: 100, Completed: 100},
}
for _, resp := range responses {
if err := json.NewEncoder(w).Encode(resp); err != nil {
http.Error(w, err.Error(), http.StatusInternalServerError)
return
}
w.(http.Flusher).Flush()
}
},
},
expectedOutput: "\nYou can find your model at:\n\n\thttps://ollama.com/test-model\n",
},
{
name: "unauthorized push",
modelName: "unauthorized-model",
serverResponse: map[string]func(w http.ResponseWriter, r *http.Request){
"/api/push": func(w http.ResponseWriter, r *http.Request) {
w.Header().Set("Content-Type", "application/json")
w.WriteHeader(http.StatusUnauthorized)
err := json.NewEncoder(w).Encode(map[string]string{
"error": "403: {\"errors\":[{\"code\":\"ACCESS DENIED\", \"message\":\"access denied\"}]}",
})
if err != nil {
t.Fatal(err)
}
},
},
expectedError: "you are not authorized to push to this namespace, create the model under a namespace you own",
},
}
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 handler, ok := tt.serverResponse[r.URL.Path]; ok {
handler(w, r)
return
}
http.Error(w, "not found", http.StatusNotFound)
}))
defer mockServer.Close()
t.Setenv("OLLAMA_HOST", mockServer.URL)
initializeKeypair()
cmd := &cobra.Command{}
cmd.Flags().Bool("insecure", false, "")
cmd.SetContext(t.Context())
// Redirect stderr to capture progress output
oldStderr := os.Stderr
r, w, _ := os.Pipe()
os.Stderr = w
// Capture stdout for the "Model pushed" message
oldStdout := os.Stdout
outR, outW, _ := os.Pipe()
os.Stdout = outW
err := PushHandler(cmd, []string{tt.modelName})
// Restore stderr
w.Close()
os.Stderr = oldStderr
// drain the pipe
if _, err := io.ReadAll(r); err != nil {
t.Fatal(err)
}
// Restore stdout and get output
outW.Close()
os.Stdout = oldStdout
stdout, _ := io.ReadAll(outR)
if tt.expectedError == "" {
if err != nil {
t.Errorf("expected no error, got %v", err)
}
if tt.expectedOutput != "" {
if got := string(stdout); got != tt.expectedOutput {
t.Errorf("expected output %q, got %q", tt.expectedOutput, got)
}
}
} else {
if err == nil || !strings.Contains(err.Error(), tt.expectedError) {
t.Errorf("expected error containing %q, got %v", tt.expectedError, err)
}
}
})
}
}
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
modelName string
modelFile string
serverResponse map[string]func(w http.ResponseWriter, r *http.Request)
expectedError string
expectedOutput string
}{
{
name: "successful create",
modelName: "test-model",
modelFile: "FROM foo",
serverResponse: map[string]func(w http.ResponseWriter, r *http.Request){
"/api/create": func(w http.ResponseWriter, r *http.Request) {
if r.Method != http.MethodPost {
t.Errorf("expected POST request, got %s", r.Method)
}
req := api.CreateRequest{}
if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
http.Error(w, err.Error(), http.StatusBadRequest)
return
}
if req.Model != "test-model" {
t.Errorf("expected model name 'test-model', got %s", req.Name)
}
if req.From != "foo" {
t.Errorf("expected from 'foo', got %s", req.From)
}
responses := []api.ProgressResponse{
{Status: "using existing layer sha256:56bb8bd477a519ffa694fc449c2413c6f0e1d3b1c88fa7e3c9d88d3ae49d4dcb"},
{Status: "writing manifest"},
{Status: "success"},
}
for _, resp := range responses {
if err := json.NewEncoder(w).Encode(resp); err != nil {
http.Error(w, err.Error(), http.StatusInternalServerError)
return
}
w.(http.Flusher).Flush()
}
},
},
expectedOutput: "",
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
mockServer := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
handler, ok := tt.serverResponse[r.URL.Path]
if !ok {
t.Errorf("unexpected request to %s", r.URL.Path)
http.Error(w, "not found", http.StatusNotFound)
return
}
handler(w, r)
}))
t.Setenv("OLLAMA_HOST", mockServer.URL)
t.Cleanup(mockServer.Close)
tempFile, err := os.CreateTemp(t.TempDir(), "modelfile")
if err != nil {
t.Fatal(err)
}
defer os.Remove(tempFile.Name())
if _, err := tempFile.WriteString(tt.modelFile); err != nil {
t.Fatal(err)
}
if err := tempFile.Close(); err != nil {
t.Fatal(err)
}
cmd := &cobra.Command{}
cmd.Flags().String("file", "", "")
if err := cmd.Flags().Set("file", tempFile.Name()); err != nil {
t.Fatal(err)
}
cmd.Flags().Bool("insecure", false, "")
cmd.SetContext(t.Context())
// Redirect stderr to capture progress output
oldStderr := os.Stderr
r, w, _ := os.Pipe()
os.Stderr = w
// Capture stdout for the "Model pushed" message
oldStdout := os.Stdout
outR, outW, _ := os.Pipe()
os.Stdout = outW
err = CreateHandler(cmd, []string{tt.modelName})
// Restore stderr
w.Close()
os.Stderr = oldStderr
// drain the pipe
if _, err := io.ReadAll(r); err != nil {
t.Fatal(err)
}
// Restore stdout and get output
outW.Close()
os.Stdout = oldStdout
stdout, _ := io.ReadAll(outR)
if tt.expectedError == "" {
if err != nil {
t.Errorf("expected no error, got %v", err)
}
if tt.expectedOutput != "" {
if got := string(stdout); got != tt.expectedOutput {
t.Errorf("expected output %q, got %q", tt.expectedOutput, got)
}
}
}
})
}
}
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

@@ -13,12 +13,13 @@ import (
"strings"
"github.com/spf13/cobra"
"golang.org/x/exp/maps"
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/envconfig"
"github.com/ollama/ollama/parser"
"github.com/ollama/ollama/readline"
"github.com/ollama/ollama/types/errtypes"
"github.com/ollama/ollama/types/model"
)
type MultilineState int
@@ -44,7 +45,7 @@ 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, "")
@@ -62,8 +63,6 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
fmt.Fprintln(os.Stderr, " /set noformat Disable formatting")
fmt.Fprintln(os.Stderr, " /set verbose Show LLM stats")
fmt.Fprintln(os.Stderr, " /set quiet Disable LLM stats")
fmt.Fprintln(os.Stderr, " /set think Enable thinking")
fmt.Fprintln(os.Stderr, " /set nothink Disable thinking")
fmt.Fprintln(os.Stderr, "")
}
@@ -130,7 +129,6 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
var sb strings.Builder
var multiline MultilineState
var thinkExplicitlySet bool = opts.Think != nil
for {
line, err := scanner.Readline()
@@ -198,19 +196,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
opts.Model = args[1]
opts.Messages = []api.Message{}
fmt.Printf("Loading model '%s'\n", opts.Model)
opts.Think, err = inferThinkingOption(nil, &opts, thinkExplicitlySet)
if err != nil {
return err
}
if err := loadOrUnloadModel(cmd, &opts); err != nil {
if strings.Contains(err.Error(), "not found") {
fmt.Printf("error: %v\n", err)
continue
}
if strings.Contains(err.Error(), "does not support thinking") {
fmt.Printf("error: %v\n", err)
continue
}
return err
}
continue
@@ -227,7 +213,10 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
return err
}
req := NewCreateRequest(args[1], opts)
req := &api.CreateRequest{
Name: args[1],
Modelfile: buildModelfile(opts),
}
fn := func(resp api.ProgressResponse) error { return nil }
err = client.Create(cmd.Context(), req, fn)
if err != nil {
@@ -271,35 +260,6 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
return err
}
fmt.Println("Set 'quiet' mode.")
case "think":
thinkValue := api.ThinkValue{Value: true}
var maybeLevel string
if len(args) > 2 {
maybeLevel = args[2]
}
if maybeLevel != "" {
// TODO(drifkin): validate the level, could be model dependent
// though... It will also be validated on the server once a call is
// made.
thinkValue.Value = maybeLevel
}
opts.Think = &thinkValue
thinkExplicitlySet = true
if client, err := api.ClientFromEnvironment(); err == nil {
ensureThinkingSupport(cmd.Context(), client, opts.Model)
}
if maybeLevel != "" {
fmt.Printf("Set 'think' mode to '%s'.\n", maybeLevel)
} else {
fmt.Println("Set 'think' mode.")
}
case "nothink":
opts.Think = &api.ThinkValue{Value: false}
thinkExplicitlySet = true
if client, err := api.ClientFromEnvironment(); err == nil {
ensureThinkingSupport(cmd.Context(), client, opts.Model)
}
fmt.Println("Set 'nothink' mode.")
case "format":
if len(args) < 3 || args[2] != "json" {
fmt.Println("Invalid or missing format. For 'json' mode use '/set format json'")
@@ -359,6 +319,8 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
opts.Messages = append(opts.Messages, newMessage)
}
fmt.Println("Set system message.")
sb.Reset()
sb.Reset()
continue
default:
@@ -388,7 +350,7 @@ 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.")
@@ -398,21 +360,18 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
case "modelfile":
fmt.Println(resp.Modelfile)
case "parameters":
fmt.Println("Model defined parameters:")
if resp.Parameters == "" {
fmt.Println(" No additional parameters were specified for this model.")
fmt.Println("No parameters were specified for this model.")
} else {
for _, l := range strings.Split(resp.Parameters, "\n") {
fmt.Printf(" %s\n", l)
if len(opts.Options) > 0 {
fmt.Println("User defined parameters:")
for k, v := range opts.Options {
fmt.Printf("%-*s %v\n", 30, k, v)
}
fmt.Println()
}
}
fmt.Println()
if len(opts.Options) > 0 {
fmt.Println("User defined parameters:")
for k, v := range opts.Options {
fmt.Printf(" %-*s %v\n", 30, k, v)
}
fmt.Println()
fmt.Println("Model defined parameters:")
fmt.Println(resp.Parameters)
}
case "system":
switch {
@@ -491,12 +450,6 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
assistant, err := chat(cmd, opts)
if err != nil {
if strings.Contains(err.Error(), "does not support thinking") ||
strings.Contains(err.Error(), "invalid think value") {
fmt.Printf("error: %v\n", err)
sb.Reset()
continue
}
return err
}
if assistant != nil {
@@ -508,32 +461,36 @@ 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),
}
func buildModelfile(opts runOptions) string {
var f parser.File
f.Commands = append(f.Commands, parser.Command{Name: "model", Args: cmp.Or(opts.ParentModel, opts.Model)})
if opts.System != "" {
req.System = opts.System
f.Commands = append(f.Commands, parser.Command{Name: "system", Args: opts.System})
}
if len(opts.Options) > 0 {
req.Parameters = opts.Options
keys := maps.Keys(opts.Options)
slices.Sort(keys)
for _, k := range keys {
v := opts.Options[k]
var cmds []parser.Command
switch t := v.(type) {
case []string:
for _, s := range t {
cmds = append(cmds, parser.Command{Name: k, Args: s})
}
default:
cmds = append(cmds, parser.Command{Name: k, Args: fmt.Sprintf("%v", t)})
}
f.Commands = append(f.Commands, cmds...)
}
if len(opts.Messages) > 0 {
req.Messages = opts.Messages
for _, msg := range opts.Messages {
f.Commands = append(f.Commands, parser.Command{Name: "message", Args: fmt.Sprintf("%s: %s", msg.Role, msg.Content)})
}
return req
return f.String()
}
func normalizeFilePath(fp string) string {
@@ -552,7 +509,6 @@ func normalizeFilePath(fp string) string {
"\\\\", "\\", // Escaped backslash
"\\*", "*", // Escaped asterisk
"\\?", "?", // Escaped question mark
"\\~", "~", // Escaped tilde
).Replace(fp)
}
@@ -560,7 +516,7 @@ 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|svg)\b`
re := regexp.MustCompile(regexPattern)
return re.FindAllString(input, -1)
@@ -580,8 +536,6 @@ 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)
}
@@ -602,7 +556,7 @@ 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

@@ -1,86 +1,107 @@
package cmd
import (
"os"
"path/filepath"
"testing"
"github.com/google/go-cmp/cmp"
"github.com/stretchr/testify/assert"
"github.com/ollama/ollama/api"
)
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`
./relative\ path/one.png inbetween1 ./not a valid two.jpg inbetween2
/unescaped space /three.jpeg inbetween3 /valid\ path/dir/four.png "./quoted with spaces/five.svg`
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.Contains(t, res[4], "five.svg")
assert.NotContains(t, res[4], '"')
assert.NotContains(t, res, "inbetween1")
assert.NotContains(t, res, "./1.svg")
assert.NotContains(t, res, "inbtween")
// Windows style paths
input = ` some preamble
c:/users/jdoe/one.png inbetween1 c:/program files/someplace/two.jpg inbetween2
/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
./relative\ path/five.svg inbetween5 "./relative with/spaces/six.png inbetween6
d:\path with\spaces\seven.svg inbetween7 c:\users\jdoe\eight.png inbetween8
d:\program files\someplace\nine.png inbetween9 "E:\program files\someplace\ten.svg some ending
`
res = extractFileNames(input)
assert.Len(t, res, 13)
assert.NotContains(t, res, "inbetween2")
assert.Len(t, res, 10)
assert.NotContains(t, res, "inbtween")
assert.Contains(t, res[0], "one.png")
assert.Contains(t, res[0], "c:")
assert.Contains(t, res[1], "two.jpg")
assert.Contains(t, res[1], "c:")
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[4], "five.svg")
assert.Contains(t, res[5], "six.png")
assert.Contains(t, res[6], "seven.JPEG")
assert.Contains(t, res[6], "seven.svg")
assert.Contains(t, res[6], "d:")
assert.Contains(t, res[7], "eight.png")
assert.Contains(t, res[7], "c:")
assert.Contains(t, res[8], "nine.png")
assert.Contains(t, res[8], "d:")
assert.Contains(t, res[9], "ten.PNG")
assert.Contains(t, res[9], "ten.svg")
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)
func TestModelfileBuilder(t *testing.T) {
opts := runOptions{
Model: "hork",
System: "You are part horse and part shark, but all hork. Do horklike things",
Messages: []api.Message{
{Role: "user", Content: "Hey there hork!"},
{Role: "assistant", Content: "Yes it is true, I am half horse, half shark."},
},
Options: map[string]any{
"temperature": 0.9,
"seed": 42,
"penalize_newline": false,
"stop": []string{"hi", "there"},
},
}
input := "before '" + fp + "' after"
cleaned, imgs, err := extractFileData(input)
assert.NoError(t, err)
assert.Len(t, imgs, 1)
assert.Equal(t, cleaned, "before after")
t.Run("model", func(t *testing.T) {
expect := `FROM hork
SYSTEM You are part horse and part shark, but all hork. Do horklike things
PARAMETER penalize_newline false
PARAMETER seed 42
PARAMETER stop hi
PARAMETER stop there
PARAMETER temperature 0.9
MESSAGE user Hey there hork!
MESSAGE assistant Yes it is true, I am half horse, half shark.
`
actual := buildModelfile(opts)
if diff := cmp.Diff(expect, actual); diff != "" {
t.Errorf("mismatch (-want +got):\n%s", diff)
}
})
t.Run("parent model", func(t *testing.T) {
opts.ParentModel = "horseshark"
expect := `FROM horseshark
SYSTEM You are part horse and part shark, but all hork. Do horklike things
PARAMETER penalize_newline false
PARAMETER seed 42
PARAMETER stop hi
PARAMETER stop there
PARAMETER temperature 0.9
MESSAGE user Hey there hork!
MESSAGE assistant Yes it is true, I am half horse, half shark.
`
actual := buildModelfile(opts)
if diff := cmp.Diff(expect, actual); diff != "" {
t.Errorf("mismatch (-want +got):\n%s", diff)
}
})
}

View File

@@ -1,15 +0,0 @@
package main
import (
"fmt"
"os"
"github.com/ollama/ollama/runner"
)
func main() {
if err := runner.Execute(os.Args[1:]); err != nil {
fmt.Fprintf(os.Stderr, "error: %s\n", err)
os.Exit(1)
}
}

View File

@@ -5,7 +5,7 @@ import (
"errors"
"os"
"os/exec"
"regexp"
"strings"
"github.com/ollama/ollama/api"
)
@@ -19,12 +19,11 @@ func startApp(ctx context.Context, client *api.Client) error {
if err != nil {
return err
}
r := regexp.MustCompile(`^.*/Ollama\s?\d*.app`)
m := r.FindStringSubmatch(link)
if len(m) != 1 {
if !strings.Contains(link, "Ollama.app") {
return errors.New("could not find ollama app")
}
if err := exec.Command("/usr/bin/open", "-j", "-a", m[0], "--args", "--fast-startup").Run(); err != nil {
path := strings.Split(link, "Ollama.app")
if err := exec.Command("/usr/bin/open", "-a", path[0]+"Ollama.app").Run(); err != nil {
return err
}
return waitForServer(ctx, client)

View File

@@ -4,27 +4,17 @@ import (
"context"
"errors"
"fmt"
"log/slog"
"os"
"os/exec"
"path"
"path/filepath"
"strings"
"syscall"
"unsafe"
"github.com/ollama/ollama/api"
"golang.org/x/sys/windows"
)
const (
Installer = "OllamaSetup.exe"
)
func startApp(ctx context.Context, client *api.Client) error {
if len(isProcRunning(Installer)) > 0 {
return fmt.Errorf("upgrade in progress...")
}
// log.Printf("XXX Attempting to find and start ollama app")
AppName := "ollama app.exe"
exe, err := os.Executable()
if err != nil {
@@ -45,11 +35,14 @@ func startApp(ctx context.Context, client *api.Client) error {
}
}
}
// log.Printf("XXX attempting to start app %s", appExe)
cmd_path := "c:\\Windows\\system32\\cmd.exe"
cmd := exec.Command(cmd_path, "/c", appExe, "--hide", "--fast-startup")
cmd := exec.Command(cmd_path, "/c", appExe)
// TODO - these hide flags aren't working - still pops up a command window for some reason
cmd.SysProcAttr = &syscall.SysProcAttr{CreationFlags: 0x08000000, HideWindow: true}
// TODO this didn't help either...
cmd.Stdin = strings.NewReader("")
cmd.Stdout = os.Stdout
cmd.Stderr = os.Stderr
@@ -63,50 +56,3 @@ func startApp(ctx context.Context, client *api.Client) error {
}
return waitForServer(ctx, client)
}
func isProcRunning(procName string) []uint32 {
pids := make([]uint32, 2048)
var ret uint32
if err := windows.EnumProcesses(pids, &ret); err != nil || ret == 0 {
slog.Debug("failed to check for running installers", "error", err)
return nil
}
if ret > uint32(len(pids)) {
pids = make([]uint32, ret+10)
if err := windows.EnumProcesses(pids, &ret); err != nil || ret == 0 {
slog.Debug("failed to check for running installers", "error", err)
return nil
}
}
if ret < uint32(len(pids)) {
pids = pids[:ret]
}
var matches []uint32
for _, pid := range pids {
if pid == 0 {
continue
}
hProcess, err := windows.OpenProcess(windows.PROCESS_QUERY_INFORMATION|windows.PROCESS_VM_READ, false, pid)
if err != nil {
continue
}
defer windows.CloseHandle(hProcess)
var module windows.Handle
var cbNeeded uint32
cb := (uint32)(unsafe.Sizeof(module))
if err := windows.EnumProcessModules(hProcess, &module, cb, &cbNeeded); err != nil {
continue
}
var sz uint32 = 1024 * 8
moduleName := make([]uint16, sz)
cb = uint32(len(moduleName)) * (uint32)(unsafe.Sizeof(uint16(0)))
if err := windows.GetModuleBaseName(hProcess, module, &moduleName[0], cb); err != nil && err != syscall.ERROR_INSUFFICIENT_BUFFER {
continue
}
exeFile := path.Base(strings.ToLower(syscall.UTF16ToString(moduleName)))
if strings.EqualFold(exeFile, procName) {
matches = append(matches, pid)
}
}
return matches
}

View File

@@ -1,63 +0,0 @@
package cmd
import (
"encoding/json"
"io"
"net/http"
"net/http/httptest"
"os"
"strings"
"testing"
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/types/model"
)
// Test that a warning is printed when thinking is requested but not supported.
func TestWarnMissingThinking(t *testing.T) {
cases := []struct {
capabilities []model.Capability
expectWarn bool
}{
{capabilities: []model.Capability{model.CapabilityThinking}, expectWarn: false},
{capabilities: []model.Capability{}, expectWarn: true},
}
for _, tc := range cases {
srv := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
if r.URL.Path != "/api/show" || r.Method != http.MethodPost {
t.Fatalf("unexpected request to %s %s", r.URL.Path, r.Method)
}
var req api.ShowRequest
if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
t.Fatalf("decode request: %v", err)
}
resp := api.ShowResponse{Capabilities: tc.capabilities}
if err := json.NewEncoder(w).Encode(resp); err != nil {
t.Fatalf("encode response: %v", err)
}
}))
defer srv.Close()
t.Setenv("OLLAMA_HOST", srv.URL)
client, err := api.ClientFromEnvironment()
if err != nil {
t.Fatal(err)
}
oldStderr := os.Stderr
r, w, _ := os.Pipe()
os.Stderr = w
ensureThinkingSupport(t.Context(), client, "m")
w.Close()
os.Stderr = oldStderr
out, _ := io.ReadAll(r)
warned := strings.Contains(string(out), "warning:")
if tc.expectWarn && !warned {
t.Errorf("expected warning, got none")
}
if !tc.expectWarn && warned {
t.Errorf("did not expect warning, got: %s", string(out))
}
}
}

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/fileutils"
)
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) fileutils.KV {
kv := fileutils.KV{
"general.file_type": uint32(1),
"general.quantization_version": uint32(2),
"tokenizer.ggml.pre": t.Pre,
@@ -53,17 +47,14 @@ func (ModelParameters) KV(t *Tokenizer) ggml.KV {
}
for _, sv := range t.SpecialVocabulary {
kv[fmt.Sprintf("tokenizer.ggml.add_%s_token", sv.Key())] = sv.AddToken
kv[fmt.Sprintf("tokenizer.ggml.%s_token_id", sv.Key())] = uint32(sv.ID)
if len(sv.IDs) > 0 {
kv[fmt.Sprintf("tokenizer.ggml.%s_token_ids", sv.Key())] = sv.IDs
}
kv[fmt.Sprintf("tokenizer.ggml.add_%s_token", sv.Key())] = sv.AddToken
}
return kv
}
func (p AdapterParameters) KV() ggml.KV {
func (p AdapterParameters) KV() fileutils.KV {
var alpha float32
if p.LoraParameters.Alpha == 0 {
alpha = float32(p.Alpha)
@@ -71,7 +62,7 @@ func (p AdapterParameters) KV() ggml.KV {
alpha = p.LoraParameters.Alpha
}
kv := ggml.KV{
kv := fileutils.KV{
"adapter.lora.alpha": alpha,
"adapter.type": "lora",
"general.file_type": uint32(1),
@@ -88,17 +79,27 @@ func (ModelParameters) specialTokenTypes() []string {
}
}
func (ModelParameters) writeFile(ws io.WriteSeeker, kv fileutils.KV, ts []fileutils.Tensor) error {
return fileutils.WriteGGUF(ws, kv, ts)
}
func (AdapterParameters) writeFile(ws io.WriteSeeker, kv fileutils.KV, ts []fileutils.Tensor) error {
return fileutils.WriteGGUF(ws, kv, ts)
}
type ModelConverter interface {
// KV maps parameters to LLM key-values
KV(*Tokenizer) ggml.KV
KV(*Tokenizer) fileutils.KV
// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
Tensors([]Tensor) []*ggml.Tensor
Tensors([]Tensor) []fileutils.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, fileutils.KV, []fileutils.Tensor) error
}
type moreParser interface {
@@ -107,15 +108,17 @@ type moreParser interface {
type AdapterConverter interface {
// KV maps parameters to LLM key-values
KV(ggml.KV) ggml.KV
KV(fileutils.KV) fileutils.KV
// Tensors maps input tensors to LLM tensors. Adapter specific modifications can be done here.
Tensors([]Tensor) []*ggml.Tensor
Tensors([]Tensor) []fileutils.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, fileutils.KV, []fileutils.Tensor) error
}
func ConvertAdapter(fsys fs.FS, f *os.File, baseKV ggml.KV) error {
func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV fileutils.KV) error {
bts, err := fs.ReadFile(fsys, "adapter_config.json")
if err != nil {
return err
@@ -150,14 +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
@@ -174,38 +177,20 @@ 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 "Gemma3nForConditionalGeneration":
conv = &gemma3nModel{}
case "Phi3ForCausalLM":
conv = &phi3Model{}
case "Qwen2ForCausalLM":
conv = &qwen2Model{}
case "Qwen2_5_VLForConditionalGeneration":
conv = &qwen25VLModel{}
case "BertModel":
conv = &bertModel{}
case "CohereForCausalLM":
conv = &commandrModel{}
case "GptOssForCausalLM":
conv = &gptossModel{}
default:
return fmt.Errorf("unsupported architecture %q", p.Architectures[0])
return errors.New("unsupported architecture")
}
if err := json.Unmarshal(bts, conv); err != nil {
@@ -223,22 +208,17 @@ 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))
}
@@ -248,13 +228,5 @@ 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/fileutils"
)
type bertModel struct {
@@ -28,7 +28,6 @@ type bertModel struct {
LayerNormEPS float32 `json:"layer_norm_eps"`
LayerNormEpsilon float32 `json:"layer_norm_epsilon"`
NormEpsilon float32 `json:"norm_epsilon"`
normalizeEmbeddings bool
PoolingType uint32
}
@@ -55,11 +54,9 @@ func (p *bertModel) parseMore(fsys fs.FS) error {
var pooling string
for _, m := range modules {
switch m.Type {
case "sentence_transformers.models.Pooling":
if m.Type == "sentence_transformers.models.Pooling" {
pooling = m.Path
case "sentence_transformers.models.Normalize":
p.normalizeEmbeddings = true
break
}
}
@@ -88,12 +85,11 @@ func (p *bertModel) parseMore(fsys fs.FS) error {
return nil
}
func (p *bertModel) KV(t *Tokenizer) ggml.KV {
func (p *bertModel) KV(t *Tokenizer) fileutils.KV {
kv := p.ModelParameters.KV(t)
kv["general.architecture"] = "bert"
kv["bert.attention.causal"] = false
kv["bert.pooling_type"] = p.PoolingType
kv["bert.normalize_embeddings"] = p.normalizeEmbeddings
kv["bert.block_count"] = cmp.Or(p.NLayers, p.NumHiddenLayers, p.NLayer)
@@ -136,8 +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) []fileutils.Tensor {
var out []fileutils.Tensor
for _, t := range ts {
if slices.Contains([]string{
"embeddings.position_ids",
@@ -147,7 +143,7 @@ func (p *bertModel) Tensors(ts []Tensor) []*ggml.Tensor {
continue
}
out = append(out, &ggml.Tensor{
out = append(out, fileutils.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),

View File

@@ -1,76 +0,0 @@
package convert
import (
"cmp"
"github.com/ollama/ollama/fs/ggml"
)
type commandrModel 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"`
}
var _ ModelConverter = (*commandrModel)(nil)
func (p *commandrModel) KV(t *Tokenizer) ggml.KV {
kv := p.ModelParameters.KV(t)
kv["general.architecture"] = "command-r"
kv["general.name"] = "command-r"
kv["command-r.context_length"] = cmp.Or(p.MaxLength, p.MaxPositionEmbeddings, p.NCtx)
kv["command-r.embedding_length"] = p.HiddenSize
kv["command-r.block_count"] = p.HiddenLayers
kv["command-r.feed_forward_length"] = p.IntermediateSize
kv["command-r.attention.head_count"] = p.NumAttentionHeads
kv["command-r.attention.head_count_kv"] = p.NumKeyValueHeads
kv["command-r.attention.layer_norm_epsilon"] = p.LayerNormEPS
kv["command-r.rope.freq_base"] = p.RopeTheta
kv["command-r.max_position_embeddings"] = cmp.Or(p.MaxLength, p.MaxPositionEmbeddings)
kv["command-r.logit_scale"] = p.LogitScale
kv["command-r.rope.scaling.type"] = "none"
return kv
}
func (p *commandrModel) Tensors(ts []Tensor) []*ggml.Tensor {
var out []*ggml.Tensor
for _, t := range ts {
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),
WriterTo: t,
})
}
return out
}
func (p *commandrModel) 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

@@ -6,7 +6,7 @@ import (
"github.com/pdevine/tensor"
"github.com/pdevine/tensor/native"
"github.com/ollama/ollama/fs/ggml"
"github.com/ollama/ollama/fileutils"
)
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) fileutils.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) []fileutils.Tensor {
var out []fileutils.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, fileutils.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/fileutils"
)
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) fileutils.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/fileutils"
)
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 fileutils.KV) fileutils.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) []fileutils.Tensor {
var out []fileutils.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, fileutils.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

@@ -1,165 +0,0 @@
package convert
import (
"slices"
"strings"
"github.com/ollama/ollama/fs/ggml"
"github.com/pdevine/tensor"
"github.com/pdevine/tensor/native"
"gonum.org/v1/gonum/stat/distuv"
)
type gemma3nModel struct {
ModelParameters
TextModel struct {
ActivationSparsityPattern []float32 `json:"activation_sparsity_pattern"`
AltupActiveIdx uint32 `json:"altup_active_idx"`
AltupCoefClip float32 `json:"altup_coef_clip"`
AltupCorrectScale bool `json:"altup_correct_scale"`
AltupLRMultiplier float32 `json:"altup_lr_multiplier"`
AltupNumInputs uint32 `json:"altup_num_inputs"`
HeadDim uint32 `json:"head_dim"`
HiddenSize uint32 `json:"hidden_size"`
HiddenSizePerLayerInput uint32 `json:"hidden_size_per_layer_input"`
IntermediateSize uint32 `json:"intermediate_size"`
MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
NumAttentionHeads uint32 `json:"num_attention_heads"`
NumHiddenLayers uint32 `json:"num_hidden_layers"`
NumKeyValueHeads uint32 `json:"num_key_value_heads"`
NumKVSharedLayers uint32 `json:"num_kv_shared_layers"`
RMSNormEPS float32 `json:"rms_norm_eps"`
RopeLocalBaseFreq float32 `json:"rope_local_base_freq"`
RopeTheta float32 `json:"rope_theta"`
SlidingWindow uint32 `json:"sliding_window"`
LayerTypes []string `json:"layer_types"`
} `json:"text_config"`
VisionModel struct{} `json:"vision_config"`
}
func (m *gemma3nModel) KV(t *Tokenizer) ggml.KV {
kv := m.ModelParameters.KV(t)
kv["general.architecture"] = "gemma3n"
kv["gemma3n.activation_sparsity_scale"] = slices.Collect(func(yield func(float32) bool) {
norm := distuv.Normal{Mu: 0, Sigma: 1}
for _, v := range m.TextModel.ActivationSparsityPattern {
if !yield(float32(norm.Quantile(float64(v)))) {
break
}
}
})
kv["gemma3n.altup.active_idx"] = m.TextModel.AltupActiveIdx
kv["gemma3n.altup.correct_scale"] = m.TextModel.AltupCorrectScale
kv["gemma3n.altup.lr_multiplier"] = m.TextModel.AltupLRMultiplier
kv["gemma3n.altup.num_inputs"] = m.TextModel.AltupNumInputs
kv["gemma3n.attention.head_count_kv"] = m.TextModel.NumKeyValueHeads
kv["gemma3n.attention.head_count"] = m.TextModel.NumAttentionHeads
kv["gemma3n.attention.layer_norm_rms_epsilon"] = m.TextModel.RMSNormEPS
kv["gemma3n.attention.sliding_window"] = m.TextModel.SlidingWindow
kv["gemma3n.attention.sliding_window_pattern"] = slices.Collect(func(yield func(bool) bool) {
for _, t := range m.TextModel.LayerTypes {
if !yield(t == "sliding_attention") {
break
}
}
})
kv["gemma3n.attention.shared_kv_layers"] = m.TextModel.NumKVSharedLayers
kv["gemma3n.block_count"] = m.TextModel.NumHiddenLayers
kv["gemma3n.context_length"] = m.TextModel.MaxPositionEmbeddings
kv["gemma3n.embedding_length_per_layer_input"] = m.TextModel.HiddenSizePerLayerInput
kv["gemma3n.embedding_length"] = m.TextModel.HiddenSize
kv["gemma3n.feed_forward_length"] = m.TextModel.IntermediateSize
kv["gemma3n.head_dim"] = m.TextModel.HeadDim
kv["gemma3n.rope.freq_base_local"] = m.TextModel.RopeLocalBaseFreq
kv["gemma3n.rope.freq_base"] = m.TextModel.RopeTheta
return kv
}
func (m *gemma3nModel) Tensors(ts []Tensor) []*ggml.Tensor {
out, ts := mergeTensors(ts,
merge{"altup_proj.*.weight", "altup_proj.weight"},
merge{"altup_unembd_proj.*.weight", "altup_unembd_proj.weight"},
)
for _, t := range ts {
switch {
case strings.Contains(t.Name(), "audio_tower"),
strings.Contains(t.Name(), "embed_audio"),
strings.Contains(t.Name(), "vision_tower"),
strings.Contains(t.Name(), "embed_vision"):
// TODO: handle audio and vision towers
continue
case strings.Contains(t.Name(), "altup_predict_coef"),
strings.Contains(t.Name(), "altup_correct_coef"):
if m.TextModel.AltupCoefClip > 0 {
t.SetRepacker(func(name string, data []float32, shape []uint64) (_ []float32, err 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 = tensor.Clamp(t, -m.TextModel.AltupCoefClip, m.TextModel.AltupCoefClip)
if err != nil {
return nil, err
}
if err := t.Reshape(t.Shape().TotalSize()); err != nil {
return nil, err
}
return native.VectorF32(t.(*tensor.Dense))
})
}
}
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),
WriterTo: t,
})
}
return out
}
func (m *gemma3nModel) Replacements() []string {
return []string{
"model.language_model.embed_tokens_per_layer", "per_layer_token_embd",
"model.language_model.embed_tokens", "token_embd",
"model.language_model.per_layer_model_projection", "per_layer_model_proj",
"model.language_model.per_layer_projection_norm", "per_layer_proj_norm", "model.language_model.altup_projections", "altup_proj",
"model.language_model.altup_unembed_projections", "altup_unembd_proj",
"model.language_model.norm", "output_norm",
"model.language_model.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",
"post_attention_layernorm", "post_attention_norm",
"pre_feedforward_layernorm", "ffn_norm",
"mlp.gate_proj", "ffn_gate",
"mlp.up_proj", "ffn_up",
"mlp.down_proj", "ffn_down",
"post_feedforward_layernorm", "post_ffw_norm",
"per_layer_input_gate", "inp_gate",
"per_layer_projection", "proj",
"post_per_layer_input_norm", "post_norm",
"altup.", "altup_",
"modality_router", "router",
"prediction_coefs", "predict_coef",
"correction_coefs", "correct_coef",
"correct_output_scale", "correct_scale.weight",
"laurel.", "laurel_",
"linear_left", "l",
"linear_right", "r",
"post_laurel_norm", "post_norm",
}
}

View File

@@ -1,223 +0,0 @@
package convert
import (
"bytes"
"cmp"
"encoding/binary"
"io"
"slices"
"strings"
"github.com/ollama/ollama/fs/ggml"
"github.com/pdevine/tensor"
"github.com/pdevine/tensor/native"
)
type gptossModel struct {
ModelParameters
HiddenLayers uint32 `json:"num_hidden_layers"`
MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
HiddenSize uint32 `json:"hidden_size"`
IntermediateSize uint32 `json:"intermediate_size"`
AttentionHeads uint32 `json:"num_attention_heads"`
KeyValueHeads uint32 `json:"num_key_value_heads"`
HeadDim uint32 `json:"head_dim"`
Experts uint32 `json:"num_experts"`
LocalExperts uint32 `json:"num_local_experts"`
ExpertsPerToken uint32 `json:"experts_per_token"`
RMSNormEpsilon float32 `json:"rms_norm_eps"`
InitialContextLength uint32 `json:"initial_context_length"`
RopeTheta float32 `json:"rope_theta"`
RopeScalingFactor float32 `json:"rope_scaling_factor"`
RopeScaling struct {
Factor float32 `json:"factor"`
} `json:"rope_scaling"`
SlidingWindow uint32 `json:"sliding_window"`
}
var _ ModelConverter = (*gptossModel)(nil)
func (m *gptossModel) KV(t *Tokenizer) ggml.KV {
kv := m.ModelParameters.KV(t)
kv["general.architecture"] = "gptoss"
kv["general.file_type"] = uint32(4)
kv["gptoss.context_length"] = cmp.Or(m.MaxPositionEmbeddings, uint32(m.RopeScalingFactor*float32(m.InitialContextLength)))
kv["gptoss.block_count"] = m.HiddenLayers
kv["gptoss.embedding_length"] = m.HiddenSize
kv["gptoss.feed_forward_length"] = m.IntermediateSize
kv["gptoss.expert_count"] = cmp.Or(m.Experts, m.LocalExperts)
kv["gptoss.expert_used_count"] = m.ExpertsPerToken
kv["gptoss.attention.head_count"] = m.AttentionHeads
kv["gptoss.attention.head_count_kv"] = m.KeyValueHeads
kv["gptoss.attention.key_length"] = m.HeadDim
kv["gptoss.attention.value_length"] = m.HeadDim
kv["gptoss.attention.layer_norm_rms_epsilon"] = cmp.Or(m.RMSNormEpsilon, 1e-5)
kv["gptoss.attention.sliding_window"] = m.SlidingWindow
kv["gptoss.rope.freq_base"] = m.RopeTheta
kv["gptoss.rope.scaling.factor"] = cmp.Or(m.RopeScalingFactor, m.RopeScaling.Factor)
kv["gptoss.rope.scaling.original_context_length"] = m.InitialContextLength
kv["tokenizer.ggml.bos_token_id"] = uint32(199998) // <|startoftext|>
kv["tokenizer.ggml.add_bos_token"] = false
kv["tokenizer.ggml.eos_token_id"] = uint32(199999) // <|endoftext|>
kv["tokenizer.ggml.eos_token_ids"] = []int32{
199999, /* <|endoftext|> */
200002, /* <|return|> */
200012, /* <|call|> */
}
kv["tokenizer.ggml.add_eos_token"] = false
return kv
}
func (m *gptossModel) Tensors(ts []Tensor) []*ggml.Tensor {
var out []*ggml.Tensor
mxfp4s := make(map[string]*mxfp4)
for _, t := range ts {
if strings.HasSuffix(t.Name(), ".blocks") || strings.HasSuffix(t.Name(), ".scales") {
dot := strings.LastIndex(t.Name(), ".")
name, suffix := t.Name()[:dot], t.Name()[dot+1:]
if _, ok := mxfp4s[name]; !ok {
mxfp4s[name] = &mxfp4{}
}
switch suffix {
case "blocks":
mxfp4s[name].blocks = t
case "scales":
mxfp4s[name].scales = t
}
} else {
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),
WriterTo: t,
})
}
}
for name, mxfp4 := range mxfp4s {
dims := mxfp4.blocks.Shape()
if !strings.HasSuffix(name, ".weight") {
name += ".weight"
}
out = append(out, &ggml.Tensor{
Name: name,
Kind: uint32(ggml.TensorTypeMXFP4),
Shape: []uint64{dims[0], dims[1], dims[2] * dims[3] * 2},
WriterTo: mxfp4,
})
}
return out
}
func (m *gptossModel) Replacements() []string {
var replacements []string
if m.MaxPositionEmbeddings > 0 {
// hf flavored model
replacements = []string{
"lm_head", "output",
"model.embed_tokens", "token_embd",
"model.layers", "blk",
"input_layernorm", "attn_norm",
"self_attn.q_proj", "attn_q",
"self_attn.k_proj", "attn_k",
"self_attn.v_proj", "attn_v",
"self_attn.o_proj", "attn_out",
"self_attn.sinks", "attn_sinks",
"post_attention_layernorm", "ffn_norm",
"mlp.router", "ffn_gate_inp",
"mlp.experts.gate_up_proj_", "ffn_gate_up_exps.",
"mlp.experts.down_proj_", "ffn_down_exps.",
"model.norm", "output_norm",
}
} else {
replacements = []string{
// noop replacements so other replacements will not be applied
".blocks", ".blocks",
".scales", ".scales",
// real replacements
"block", "blk",
"attn.norm", "attn_norm",
"attn.qkv", "attn_qkv",
"attn.sinks", "attn_sinks",
"attn.out", "attn_out",
"mlp.norm", "ffn_norm",
"mlp.gate", "ffn_gate_inp",
"mlp.mlp1_", "ffn_gate_up_exps.",
"mlp.mlp2_", "ffn_down_exps.",
"embedding", "token_embd",
"norm", "output_norm",
"unembedding", "output",
"scale", "weight",
}
}
return replacements
}
type mxfp4 struct {
blocks, scales Tensor
}
func (m *mxfp4) WriteTo(w io.Writer) (int64, error) {
var b bytes.Buffer
if _, err := m.blocks.WriteTo(&b); err != nil {
return 0, err
}
blocksDims := make([]int, len(m.blocks.Shape()))
for i, d := range m.blocks.Shape() {
blocksDims[i] = int(d)
}
bts := b.Bytes()
var tmp [16]byte
for i := 0; i < b.Len(); i += 16 {
for j := range 8 {
// transform a1b2c3 ... x7y8z9 -> 71xa82yb93zc
a, b := bts[i+j], bts[i+j+8]
tmp[2*j+0] = (a & 0x0F) | (b << 4)
tmp[2*j+1] = (a >> 4) | (b & 0xF0)
}
copy(bts[i:i+16], tmp[:])
}
var blocks tensor.Tensor = tensor.New(tensor.WithShape(blocksDims...), tensor.WithBacking(bts))
var s bytes.Buffer
if _, err := m.scales.WriteTo(&s); err != nil {
return 0, err
}
scalesDims := slices.Repeat([]int{1}, len(m.blocks.Shape()))
for i, d := range m.scales.Shape() {
scalesDims[i] = int(d)
}
var scales tensor.Tensor = tensor.New(tensor.WithShape(scalesDims...), tensor.WithBacking(s.Bytes()))
out, err := tensor.Concat(3, scales, blocks)
if err != nil {
return 0, err
}
out = tensor.Materialize(out)
if err := out.Reshape(out.Shape().TotalSize()); err != nil {
return 0, err
}
u8s, err := native.VectorU8(out.(*tensor.Dense))
if err != nil {
return 0, err
}
if err := binary.Write(w, binary.LittleEndian, u8s); err != nil {
return 0, err
}
return int64(len(u8s)), nil
}

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/fileutils"
)
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) fileutils.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) []fileutils.Tensor {
var out []fileutils.Tensor
if p.RopeScaling.factors != nil {
out = append(out, &ggml.Tensor{
out = append(out, fileutils.Tensor{
Name: "rope_freqs.weight",
Kind: 0,
Shape: []uint64{uint64(len(p.RopeScaling.factors))},
@@ -139,14 +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") ||
strings.HasSuffix(t.Name(), "attn_q_proj.weight") || strings.HasSuffix(t.Name(), "attn_k_proj.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, fileutils.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),
@@ -182,9 +174,9 @@ func (p *llamaModel) repack(name string, data []float32, shape []uint64) ([]floa
}
var heads uint32
if strings.HasSuffix(name, "attn_q.weight") || strings.HasSuffix(name, "attn_q_proj.weight") {
if strings.HasSuffix(name, "attn_q.weight") {
heads = p.NumAttentionHeads
} else if strings.HasSuffix(name, "attn_k.weight") || strings.HasSuffix(name, "attn_k_proj.weight") {
} else if strings.HasSuffix(name, "attn_k.weight") {
heads = cmp.Or(p.NumKeyValueHeads, p.NumAttentionHeads)
} else {
return nil, fmt.Errorf("unknown tensor for repack: %s", name)

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/fileutils"
)
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 fileutils.KV) fileutils.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) []fileutils.Tensor {
var out []fileutils.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, fileutils.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

@@ -2,8 +2,11 @@ package convert
import (
"fmt"
"io"
"slices"
"strings"
"github.com/ollama/ollama/fs/ggml"
"github.com/ollama/ollama/fileutils"
)
type mixtralModel struct {
@@ -12,7 +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) fileutils.KV {
kv := p.llamaModel.KV(t)
if p.NumLocalExperts > 0 {
@@ -26,39 +29,66 @@ func (p *mixtralModel) KV(t *Tokenizer) ggml.KV {
return kv
}
func (p *mixtralModel) Tensors(ts []Tensor) []*ggml.Tensor {
merges := make([]merge, 0, p.NumHiddenLayers*6)
for i := range p.NumHiddenLayers {
merges = append(merges, merge{
fmt.Sprintf("blk.%d.*.w1.weight", i),
fmt.Sprintf("blk.%d.ffn_gate_exps.weight", i),
}, merge{
fmt.Sprintf("blk.%d.*.w1.bias", i),
fmt.Sprintf("blk.%d.ffn_gate_exps.bias", i),
}, merge{
fmt.Sprintf("blk.%d.*.w2.weight", i),
fmt.Sprintf("blk.%d.ffn_up_exps.weight", i),
}, merge{
fmt.Sprintf("blk.%d.*.w2.bias", i),
fmt.Sprintf("blk.%d.ffn_up_exps.bias", i),
}, merge{
fmt.Sprintf("blk.%d.*.w3.weight", i),
fmt.Sprintf("blk.%d.ffn_down_exps.weight", i),
}, merge{
fmt.Sprintf("blk.%d.*.w3.bias", i),
fmt.Sprintf("blk.%d.ffn_down_exps.bias", i),
func (p *mixtralModel) Tensors(ts []Tensor) []fileutils.Tensor {
oldnew := []string{
"model.layers", "blk",
"w1", "ffn_gate_exps",
"w2", "ffn_down_exps",
"w3", "ffn_up_exps",
}
for i := range p.NumLocalExperts {
oldnew = append(oldnew, fmt.Sprintf(".block_sparse_moe.experts.%d.", i), ".")
}
// group experts of the same layer (model.layers.%d) and type (w[123]) into a single tensor
namer := strings.NewReplacer(oldnew...)
experts := make(map[string]experts)
// merge experts into a single tensor while removing them from ts
ts = slices.DeleteFunc(ts, func(t Tensor) bool {
if !strings.Contains(t.Name(), ".block_sparse_moe.experts.") {
return false
}
name := namer.Replace(t.Name())
experts[name] = append(experts[name], t)
return true
})
var out []fileutils.Tensor
for n, e := range experts {
// TODO(mxyng): sanity check experts
out = append(out, fileutils.Tensor{
Name: n,
Kind: e[0].Kind(),
Shape: append([]uint64{uint64(len(e))}, e[0].Shape()...),
WriterTo: e,
})
}
out, ts := mergeTensors(ts, merges...)
return append(out, p.llamaModel.Tensors(ts)...)
}
func (p *mixtralModel) Replacements() []string {
return append(
p.llamaModel.Replacements(),
"model.layers", "blk",
"block_sparse_moe.gate", "ffn_gate_inp",
"block_sparse_moe.experts.", ".",
)
}
type experts []Tensor
func (e experts) WriteTo(w io.Writer) (int64, error) {
// TODO(mxyng): experts _should_ be numerically sorted by expert but this should check
for _, t := range e {
// the canonical merged experts tensor stacks all experts along a new, 0 axis,
// e.g. `tensor.Stack(0, e[0], e[1:]...)`, which requires allocating temporary buffers
// this accomplishes the same thing by writing each expert tensor in sequence
if _, err := t.WriteTo(w); err != nil {
return 0, err
}
}
return 0, nil
}

View File

@@ -1,179 +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 !strings.HasPrefix(t.Name(), "v.") && !strings.HasPrefix(t.Name(), "mm.") {
text = append(text, t)
} else 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()))
} else if strings.HasSuffix(t.Name(), "attn_q.weight") || strings.HasSuffix(t.Name(), "attn_k.weight") {
t.SetRepacker(m.repack(t.Name()))
} else if strings.HasSuffix(t.Name(), "attn_gate") || strings.HasSuffix(t.Name(), "ffn_gate") {
t.SetRepacker(m.repack(t.Name()))
}
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),
WriterTo: 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))
if strings.HasSuffix(name, "attn_q.weight") || strings.HasSuffix(name, "attn_k.weight") {
heads := m.VisionModel.AttentionHeads
if err := t.Reshape(append([]int{int(heads), 2, dims[0] / int(heads) / 2}, dims[1:]...)...); err != nil {
return nil, err
}
if err := t.T(0, 2, 1, 3); err != nil {
return nil, err
}
if err := t.Reshape(dims...); err != nil {
return nil, err
}
if err := t.Transpose(); err != nil {
return nil, err
}
} else {
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/fileutils"
)
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) fileutils.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) []fileutils.Tensor {
var addRopeFactors sync.Once
out := make([]*ggml.Tensor, 0, len(ts)+2)
out := make([]fileutils.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, fileutils.Tensor{
Name: "rope_factors_long.weight",
Kind: 0,
Shape: []uint64{uint64(len(p.RopeScaling.LongFactor))},
WriterTo: p.RopeScaling.LongFactor,
}, &ggml.Tensor{
}, fileutils.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, fileutils.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

@@ -1,81 +0,0 @@
package convert
import "github.com/ollama/ollama/fs/ggml"
type qwen2Model struct {
ModelParameters
MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
HiddenSize uint32 `json:"hidden_size"`
HiddenLayers uint32 `json:"num_hidden_layers"`
IntermediateSize uint32 `json:"intermediate_size"`
NumAttentionHeads uint32 `json:"num_attention_heads"`
NumKeyValueHeads uint32 `json:"num_key_value_heads"`
RopeTheta float32 `json:"rope_theta"`
RopeScaling struct {
Type string `json:"type"`
Factor ropeFactor `json:"factor"`
OriginalMaxPositionEmbeddings uint32 `json:"original_max_position_embeddings"`
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 {
kv := q.ModelParameters.KV(t)
kv["general.architecture"] = "qwen2"
kv["qwen2.block_count"] = q.HiddenLayers
kv["qwen2.context_length"] = q.MaxPositionEmbeddings
kv["qwen2.embedding_length"] = q.HiddenSize
kv["qwen2.feed_forward_length"] = q.IntermediateSize
kv["qwen2.attention.head_count"] = q.NumAttentionHeads
kv["qwen2.attention.head_count_kv"] = q.NumKeyValueHeads
kv["qwen2.rope.freq_base"] = q.RopeTheta
kv["qwen2.attention.layer_norm_rms_epsilon"] = q.RMSNormEPS
switch q.RopeScaling.Type {
case "":
// no scaling
case "yarn":
kv["qwen2.rope.scaling.type"] = q.RopeScaling.Type
kv["qwen2.rope.scaling.factor"] = q.RopeScaling.Factor
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
for _, t := range ts {
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),
WriterTo: t,
})
}
return out
}
func (p *qwen2Model) Replacements() []string {
return []string{
"lm_head", "output",
"model.embed_tokens", "token_embd",
"model.layers", "blk",
"input_layernorm", "attn_norm",
"self_attn.k_proj", "attn_k",
"self_attn.v_proj", "attn_v",
"self_attn.q_proj", "attn_q",
"self_attn.o_proj", "attn_output",
"mlp.down_proj", "ffn_down",
"mlp.gate_proj", "ffn_gate",
"mlp.up_proj", "ffn_up",
"post_attention_layernorm", "ffn_norm",
"model.norm", "output_norm",
}
}

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,
split{Replacer: strings.NewReplacer("patch_embed.proj", "patch_embd_0")},
split{Replacer: 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,
split{Replacer: strings.NewReplacer("attn.qkv", "attn_q")},
split{Replacer: strings.NewReplacer("attn.qkv", "attn_k")},
split{Replacer: 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

@@ -11,14 +11,16 @@ import (
"io"
"io/fs"
"log/slog"
"maps"
"math"
"os"
"path/filepath"
"slices"
"strings"
"testing"
"github.com/ollama/ollama/fs/ggml"
"golang.org/x/exp/maps"
"github.com/ollama/ollama/fileutils"
)
type tensorData struct {
@@ -27,7 +29,7 @@ type tensorData struct {
Shape []int `json:"shape"`
}
func convertFull(t *testing.T, fsys fs.FS) (*os.File, ggml.KV, ggml.Tensors) {
func convertFull(t *testing.T, fsys fs.FS) (*os.File, fileutils.KV, *fileutils.Tensors) {
t.Helper()
f, err := os.CreateTemp(t.TempDir(), "f16")
@@ -46,7 +48,7 @@ func convertFull(t *testing.T, fsys fs.FS) (*os.File, ggml.KV, ggml.Tensors) {
}
t.Cleanup(func() { r.Close() })
m, err := ggml.Decode(r, -1)
m, _, err := fileutils.DecodeGGML(r, math.MaxInt)
if err != nil {
t.Fatal(err)
}
@@ -58,7 +60,7 @@ func convertFull(t *testing.T, fsys fs.FS) (*os.File, ggml.KV, ggml.Tensors) {
return r, m.KV(), m.Tensors()
}
func generateResultsJSON(t *testing.T, f *os.File, kv ggml.KV, tensors ggml.Tensors) map[string]string {
func generateResultsJSON(t *testing.T, f *os.File, kv fileutils.KV, tensors *fileutils.Tensors) map[string]string {
actual := make(map[string]string)
for k, v := range kv {
if s, ok := v.(json.Marshaler); !ok {
@@ -73,7 +75,7 @@ func generateResultsJSON(t *testing.T, f *os.File, kv ggml.KV, tensors ggml.Tens
}
}
for _, tensor := range tensors.Items() {
for _, tensor := range tensors.Items {
sha256sum := sha256.New()
sr := io.NewSectionReader(f, int64(tensors.Offset+tensor.Offset), int64(tensor.Size()))
if _, err := io.Copy(sha256sum, sr); err != nil {
@@ -106,8 +108,6 @@ func TestConvertModel(t *testing.T) {
"Phi-3-mini-128k-instruct",
"all-MiniLM-L6-v2",
"gemma-2-9b-it",
"Qwen2.5-0.5B-Instruct",
"c4ai-command-r-v01",
}
for i := range cases {
@@ -129,14 +129,15 @@ func TestConvertModel(t *testing.T) {
if err != nil {
t.Fatal(err)
}
defer expectFile.Close()
var expect map[string]string
if err := json.NewDecoder(expectFile).Decode(&expect); err != nil {
t.Fatal(err)
}
for _, k := range slices.Sorted(maps.Keys(expect)) {
keys := maps.Keys(expect)
slices.Sort(keys)
for _, k := range keys {
if v, ok := actual[k]; !ok {
t.Errorf("missing %s", k)
} else if v != expect[k] {
@@ -329,7 +330,7 @@ func TestConvertAdapter(t *testing.T) {
}
defer r.Close()
m, err := ggml.Decode(r, -1)
m, _, err := fileutils.DecodeGGML(r, math.MaxInt)
if err != nil {
t.Fatal(err)
}
@@ -340,7 +341,9 @@ func TestConvertAdapter(t *testing.T) {
actual := generateResultsJSON(t, r, m.KV(), m.Tensors())
for _, k := range slices.Sorted(maps.Keys(c.Expected)) {
keys := maps.Keys(c.Expected)
slices.Sort(keys)
for _, k := range keys {
if v, ok := actual[k]; !ok {
t.Errorf("missing %s", k)
} else if v != c.Expected[k] {

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 {
@@ -31,46 +30,42 @@ func (t tensorBase) Shape() []uint64 {
}
const (
tensorKindFP32 uint32 = iota
tensorKindFP16
tensorKindBF16 = 30
tensorKindMXFP4 = 39
tensorKindF32 uint32 = iota
tensorKindF16
)
func (t tensorBase) Kind() uint32 {
if strings.HasSuffix(t.name, ".ffn_gate_inp.weight") ||
strings.HasSuffix(t.name, ".bias") ||
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 tensorKindFP32
return 0
}
switch len(t.shape) {
case 0:
panic("invalid tensor shape")
case 1:
return tensorKindFP32
return tensorKindF32
default:
return tensorKindFP16
return tensorKindF16
}
}
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

@@ -1,7 +1,6 @@
package convert
import (
"bufio"
"bytes"
"encoding/binary"
"encoding/json"
@@ -9,12 +8,12 @@ import (
"fmt"
"io"
"io/fs"
"maps"
"slices"
"strings"
"github.com/d4l3k/go-bfloat16"
"github.com/x448/float16"
"golang.org/x/exp/maps"
)
type safetensorMetadata struct {
@@ -47,7 +46,8 @@ func parseSafetensors(fsys fs.FS, replacer *strings.Replacer, ps ...string) ([]T
return nil, err
}
keys := slices.Sorted(maps.Keys(headers))
keys := maps.Keys(headers)
slices.Sort(keys)
names := make(map[string]struct{}, len(keys))
@@ -94,30 +94,6 @@ type safetensor struct {
*tensorBase
}
func (st safetensor) Kind() uint32 {
kind := st.tensorBase.Kind()
if !strings.HasPrefix(st.name, "v.") && st.dtype == "BF16" && kind != tensorKindFP32 {
kind = tensorKindBF16
}
return kind
}
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 {
@@ -125,41 +101,26 @@ func (st safetensor) WriteTo(w io.Writer) (int64, error) {
}
defer f.Close()
r, err := func() (io.Reader, error) {
if readerAt, ok := f.(io.ReaderAt); ok {
return io.NewSectionReader(readerAt, st.offset, st.size), nil
} else if seeker, ok := f.(io.Seeker); ok {
_, err := seeker.Seek(st.offset, io.SeekStart)
return f, err
} else {
_, err := io.CopyN(io.Discard, f, st.offset)
return f, err
if seeker, ok := f.(io.Seeker); ok {
if _, err := seeker.Seek(st.offset, io.SeekStart); err != nil {
return 0, err
}
} else {
if _, err := io.CopyN(io.Discard, f, st.offset); err != nil {
return 0, err
}
}()
if err != nil {
return 0, err
}
br := bufio.NewReaderSize(r, min(32<<10, int(st.size)))
// special case when input and output are same type and the
// tensor doesn't need repacking
if (st.repacker == nil) &&
((st.dtype == "F32" && st.Kind() == tensorKindFP32) ||
(st.dtype == "F16" && st.Kind() == tensorKindFP16) ||
(st.dtype == "U8")) {
return io.CopyN(w, br, st.size)
}
var f32s []float32
switch st.dtype {
case "F32":
f32s = make([]float32, st.size/4)
if err = binary.Read(br, binary.LittleEndian, f32s); err != nil {
if err = binary.Read(f, binary.LittleEndian, f32s); err != nil {
return 0, err
}
case "F16":
u16s := make([]uint16, st.size/2)
if err = binary.Read(br, binary.LittleEndian, u16s); err != nil {
if err = binary.Read(f, binary.LittleEndian, u16s); err != nil {
return 0, err
}
@@ -170,7 +131,7 @@ func (st safetensor) WriteTo(w io.Writer) (int64, error) {
case "BF16":
u8s := make([]uint8, st.size)
if err = binary.Read(br, binary.LittleEndian, u8s); err != nil {
if err = binary.Read(f, binary.LittleEndian, u8s); err != nil {
return 0, err
}
@@ -187,18 +148,15 @@ func (st safetensor) WriteTo(w io.Writer) (int64, error) {
}
switch st.Kind() {
case tensorKindFP32:
return int64(len(f32s) * 4), binary.Write(w, binary.LittleEndian, f32s)
case tensorKindFP16:
case tensorKindF32:
return 0, binary.Write(w, binary.LittleEndian, f32s)
case tensorKindF16:
f16s := make([]uint16, len(f32s))
for i := range f32s {
f16s[i] = float16.Fromfloat32(f32s[i]).Bits()
}
return int64(len(f16s) * 2), binary.Write(w, binary.LittleEndian, f16s)
case tensorKindBF16:
u8s := bfloat16.EncodeFloat32(f32s)
return int64(len(u8s)), binary.Write(w, binary.LittleEndian, u8s)
return 0, binary.Write(w, binary.LittleEndian, f16s)
default:
return 0, fmt.Errorf("unknown storage type: %d", st.Kind())
}

View File

@@ -1,294 +0,0 @@
package convert
import (
"bytes"
"encoding/binary"
"os"
"path/filepath"
"testing"
"github.com/d4l3k/go-bfloat16"
"github.com/google/go-cmp/cmp"
"github.com/x448/float16"
)
func TestSafetensors(t *testing.T) {
t.Parallel()
root, err := os.OpenRoot(t.TempDir())
if err != nil {
t.Fatal(err)
}
defer root.Close()
cases := []struct {
name,
dtype string
offset,
size int64
shape []uint64
setup func(*testing.T, *os.File)
want []byte
}{
{
name: "fp32-fp32",
dtype: "F32",
size: 32 * 4, // 32 floats, each 4 bytes
shape: []uint64{32},
setup: func(t *testing.T, f *os.File) {
f32s := make([]float32, 32)
for i := range f32s {
f32s[i] = float32(i)
}
if err := binary.Write(f, binary.LittleEndian, f32s); err != nil {
t.Fatal(err)
}
},
want: []byte{
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x3f, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x40, 0x40,
0x00, 0x00, 0x80, 0x40, 0x00, 0x00, 0xa0, 0x40, 0x00, 0x00, 0xc0, 0x40, 0x00, 0x00, 0xe0, 0x40,
0x00, 0x00, 0x00, 0x41, 0x00, 0x00, 0x10, 0x41, 0x00, 0x00, 0x20, 0x41, 0x00, 0x00, 0x30, 0x41,
0x00, 0x00, 0x40, 0x41, 0x00, 0x00, 0x50, 0x41, 0x00, 0x00, 0x60, 0x41, 0x00, 0x00, 0x70, 0x41,
0x00, 0x00, 0x80, 0x41, 0x00, 0x00, 0x88, 0x41, 0x00, 0x00, 0x90, 0x41, 0x00, 0x00, 0x98, 0x41,
0x00, 0x00, 0xa0, 0x41, 0x00, 0x00, 0xa8, 0x41, 0x00, 0x00, 0xb0, 0x41, 0x00, 0x00, 0xb8, 0x41,
0x00, 0x00, 0xc0, 0x41, 0x00, 0x00, 0xc8, 0x41, 0x00, 0x00, 0xd0, 0x41, 0x00, 0x00, 0xd8, 0x41,
0x00, 0x00, 0xe0, 0x41, 0x00, 0x00, 0xe8, 0x41, 0x00, 0x00, 0xf0, 0x41, 0x00, 0x00, 0xf8, 0x41,
},
},
{
name: "fp32-fp16",
dtype: "F32",
size: 32 * 4, // 32 floats, each 4 bytes
shape: []uint64{16, 2},
setup: func(t *testing.T, f *os.File) {
f32s := make([]float32, 32)
for i := range f32s {
f32s[i] = float32(i)
}
if err := binary.Write(f, binary.LittleEndian, f32s); err != nil {
t.Fatal(err)
}
},
want: []byte{
0x00, 0x00, 0x00, 0x3c, 0x00, 0x40, 0x00, 0x42, 0x00, 0x44, 0x00, 0x45, 0x00, 0x46, 0x00, 0x47,
0x00, 0x48, 0x80, 0x48, 0x00, 0x49, 0x80, 0x49, 0x00, 0x4a, 0x80, 0x4a, 0x00, 0x4b, 0x80, 0x4b,
0x00, 0x4c, 0x40, 0x4c, 0x80, 0x4c, 0xc0, 0x4c, 0x00, 0x4d, 0x40, 0x4d, 0x80, 0x4d, 0xc0, 0x4d,
0x00, 0x4e, 0x40, 0x4e, 0x80, 0x4e, 0xc0, 0x4e, 0x00, 0x4f, 0x40, 0x4f, 0x80, 0x4f, 0xc0, 0x4f,
},
},
{
name: "fp16-fp16",
dtype: "F16",
size: 32 * 2, // 32 floats, each 2 bytes
shape: []uint64{16, 2},
setup: func(t *testing.T, f *os.File) {
u16s := make([]uint16, 32)
for i := range u16s {
u16s[i] = float16.Fromfloat32(float32(i)).Bits()
}
if err := binary.Write(f, binary.LittleEndian, u16s); err != nil {
t.Fatal(err)
}
},
want: []byte{
0x00, 0x00, 0x00, 0x3c, 0x00, 0x40, 0x00, 0x42, 0x00, 0x44, 0x00, 0x45, 0x00, 0x46, 0x00, 0x47,
0x00, 0x48, 0x80, 0x48, 0x00, 0x49, 0x80, 0x49, 0x00, 0x4a, 0x80, 0x4a, 0x00, 0x4b, 0x80, 0x4b,
0x00, 0x4c, 0x40, 0x4c, 0x80, 0x4c, 0xc0, 0x4c, 0x00, 0x4d, 0x40, 0x4d, 0x80, 0x4d, 0xc0, 0x4d,
0x00, 0x4e, 0x40, 0x4e, 0x80, 0x4e, 0xc0, 0x4e, 0x00, 0x4f, 0x40, 0x4f, 0x80, 0x4f, 0xc0, 0x4f,
},
},
{
name: "fp16-fp32",
dtype: "F16",
size: 32 * 2, // 32 floats, each 2 bytes
shape: []uint64{32},
setup: func(t *testing.T, f *os.File) {
u16s := make([]uint16, 32)
for i := range u16s {
u16s[i] = float16.Fromfloat32(float32(i)).Bits()
}
if err := binary.Write(f, binary.LittleEndian, u16s); err != nil {
t.Fatal(err)
}
},
want: []byte{
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x3f, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x40, 0x40,
0x00, 0x00, 0x80, 0x40, 0x00, 0x00, 0xa0, 0x40, 0x00, 0x00, 0xc0, 0x40, 0x00, 0x00, 0xe0, 0x40,
0x00, 0x00, 0x00, 0x41, 0x00, 0x00, 0x10, 0x41, 0x00, 0x00, 0x20, 0x41, 0x00, 0x00, 0x30, 0x41,
0x00, 0x00, 0x40, 0x41, 0x00, 0x00, 0x50, 0x41, 0x00, 0x00, 0x60, 0x41, 0x00, 0x00, 0x70, 0x41,
0x00, 0x00, 0x80, 0x41, 0x00, 0x00, 0x88, 0x41, 0x00, 0x00, 0x90, 0x41, 0x00, 0x00, 0x98, 0x41,
0x00, 0x00, 0xa0, 0x41, 0x00, 0x00, 0xa8, 0x41, 0x00, 0x00, 0xb0, 0x41, 0x00, 0x00, 0xb8, 0x41,
0x00, 0x00, 0xc0, 0x41, 0x00, 0x00, 0xc8, 0x41, 0x00, 0x00, 0xd0, 0x41, 0x00, 0x00, 0xd8, 0x41,
0x00, 0x00, 0xe0, 0x41, 0x00, 0x00, 0xe8, 0x41, 0x00, 0x00, 0xf0, 0x41, 0x00, 0x00, 0xf8, 0x41,
},
},
{
name: "bf16-bf16",
dtype: "BF16",
size: 32 * 2, // 32 brain floats, each 2 bytes
shape: []uint64{16, 2},
setup: func(t *testing.T, f *os.File) {
f32s := make([]float32, 32)
for i := range f32s {
f32s[i] = float32(i)
}
if err := binary.Write(f, binary.LittleEndian, bfloat16.EncodeFloat32(f32s)); err != nil {
t.Fatal(err)
}
},
want: []byte{
0x00, 0x00, 0x80, 0x3f, 0x00, 0x40, 0x40, 0x40, 0x80, 0x40, 0xa0, 0x40, 0xc0, 0x40, 0xe0, 0x40,
0x00, 0x41, 0x10, 0x41, 0x20, 0x41, 0x30, 0x41, 0x40, 0x41, 0x50, 0x41, 0x60, 0x41, 0x70, 0x41,
0x80, 0x41, 0x88, 0x41, 0x90, 0x41, 0x98, 0x41, 0xa0, 0x41, 0xa8, 0x41, 0xb0, 0x41, 0xb8, 0x41,
0xc0, 0x41, 0xc8, 0x41, 0xd0, 0x41, 0xd8, 0x41, 0xe0, 0x41, 0xe8, 0x41, 0xf0, 0x41, 0xf8, 0x41,
},
},
{
name: "bf16-fp32",
dtype: "BF16",
size: 32 * 2, // 32 brain floats, each 2 bytes
shape: []uint64{32},
setup: func(t *testing.T, f *os.File) {
f32s := make([]float32, 32)
for i := range f32s {
f32s[i] = float32(i)
}
if err := binary.Write(f, binary.LittleEndian, bfloat16.EncodeFloat32(f32s)); err != nil {
t.Fatal(err)
}
},
want: []byte{
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x3f, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x40, 0x40,
0x00, 0x00, 0x80, 0x40, 0x00, 0x00, 0xa0, 0x40, 0x00, 0x00, 0xc0, 0x40, 0x00, 0x00, 0xe0, 0x40,
0x00, 0x00, 0x00, 0x41, 0x00, 0x00, 0x10, 0x41, 0x00, 0x00, 0x20, 0x41, 0x00, 0x00, 0x30, 0x41,
0x00, 0x00, 0x40, 0x41, 0x00, 0x00, 0x50, 0x41, 0x00, 0x00, 0x60, 0x41, 0x00, 0x00, 0x70, 0x41,
0x00, 0x00, 0x80, 0x41, 0x00, 0x00, 0x88, 0x41, 0x00, 0x00, 0x90, 0x41, 0x00, 0x00, 0x98, 0x41,
0x00, 0x00, 0xa0, 0x41, 0x00, 0x00, 0xa8, 0x41, 0x00, 0x00, 0xb0, 0x41, 0x00, 0x00, 0xb8, 0x41,
0x00, 0x00, 0xc0, 0x41, 0x00, 0x00, 0xc8, 0x41, 0x00, 0x00, 0xd0, 0x41, 0x00, 0x00, 0xd8, 0x41,
0x00, 0x00, 0xe0, 0x41, 0x00, 0x00, 0xe8, 0x41, 0x00, 0x00, 0xf0, 0x41, 0x00, 0x00, 0xf8, 0x41,
},
},
{
name: "u8-u8",
dtype: "U8",
size: 32, // 32 brain floats, each 1 bytes
shape: []uint64{32},
setup: func(t *testing.T, f *os.File) {
u8s := make([]uint8, 32)
for i := range u8s {
u8s[i] = uint8(i)
}
if err := binary.Write(f, binary.LittleEndian, u8s); err != nil {
t.Fatal(err)
}
},
want: []byte{
0x00, 0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07, 0x08, 0x09, 0x0a, 0x0b, 0x0c, 0x0d, 0x0e, 0x0f,
0x10, 0x11, 0x12, 0x13, 0x14, 0x15, 0x16, 0x17, 0x18, 0x19, 0x1a, 0x1b, 0x1c, 0x1d, 0x1e, 0x1f,
},
},
}
for _, tt := range cases {
t.Run(tt.name, func(t *testing.T) {
path := filepath.Base(t.Name())
st := safetensor{
fs: root.FS(),
path: path,
dtype: tt.dtype,
offset: tt.offset,
size: tt.size,
tensorBase: &tensorBase{
name: tt.name,
shape: tt.shape,
},
}
f, err := root.Create(path)
if err != nil {
t.Fatal(err)
}
defer f.Close()
tt.setup(t, f)
var b bytes.Buffer
if _, err := st.WriteTo(&b); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(tt.want, b.Bytes()); diff != "" {
t.Errorf("safetensor.WriteTo() mismatch (-want +got):\n%s", diff)
}
})
}
}
func TestSafetensorKind(t *testing.T) {
tests := []struct {
name string
st safetensor
expected uint32
}{
{
name: "BF16 dtype with non-v. prefix and non-FP32 base kind should return BF16",
st: safetensor{
tensorBase: &tensorBase{
name: "weight.matrix",
shape: []uint64{10, 10}, // will default to FP16
},
dtype: "BF16",
},
expected: tensorKindBF16,
},
{
name: "BF16 dtype with v. prefix should return base kind",
st: safetensor{
tensorBase: &tensorBase{
name: "v.weight.matrix",
shape: []uint64{10, 10}, // will default to FP16
},
dtype: "BF16",
},
expected: tensorKindFP16,
},
{
name: "BF16 dtype with FP32 base kind should return FP32",
st: safetensor{
tensorBase: &tensorBase{
name: "weight.matrix",
shape: []uint64{10}, // will default to FP32
},
dtype: "BF16",
},
expected: tensorKindFP32,
},
{
name: "Non-BF16 dtype should return base kind",
st: safetensor{
tensorBase: &tensorBase{
name: "weight.matrix",
shape: []uint64{10, 10}, // will default to FP16
},
dtype: "FP16",
},
expected: tensorKindFP16,
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
result := tt.st.Kind()
if result != tt.expected {
t.Errorf("Kind() = %d, expected %d", result, tt.expected)
}
})
}
}

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

@@ -331,7 +331,7 @@ type TrainerSpec struct {
// Reserved special meta tokens.
// * -1 is not used.
// * unk_id must not be -1.
// Id must start with 0 and be contiguous.
// Id must starts with 0 and be contigous.
UnkId *int32 `protobuf:"varint,40,opt,name=unk_id,json=unkId,def=0" json:"unk_id,omitempty"` // <unk>
BosId *int32 `protobuf:"varint,41,opt,name=bos_id,json=bosId,def=1" json:"bos_id,omitempty"` // <s>
EosId *int32 `protobuf:"varint,42,opt,name=eos_id,json=eosId,def=2" json:"eos_id,omitempty"` // </s>
@@ -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

@@ -213,7 +213,7 @@ message TrainerSpec {
// Reserved special meta tokens.
// * -1 is not used.
// * unk_id must not be -1.
// Id must start with 0 and be contiguous.
// Id must starts with 0 and be contigous.
optional int32 unk_id = 40 [default = 0]; // <unk>
optional int32 bos_id = 41 [default = 1]; // <s>
optional int32 eos_id = 42 [default = 2]; // </s>

View File

@@ -1,129 +0,0 @@
package convert
import (
"cmp"
"io"
"iter"
"path"
"slices"
"strings"
"github.com/pdevine/tensor"
"github.com/pdevine/tensor/native"
"github.com/ollama/ollama/fs/ggml"
)
type split struct {
*strings.Replacer
dim int
// fn is an optional function to apply to the tensor after slicing
fn func(tensor.Tensor) (tensor.Tensor, error)
}
// splitDim splits a tensor along a specified dimension into multiple tensors. The dimension
// is split evenly based on the number of replacers provided unless a specific count is given.
func splitDim(t Tensor, dim int, splits ...split) iter.Seq[*ggml.Tensor] {
return func(yield func(*ggml.Tensor) bool) {
var offset int
for _, split := range splits {
t := t.Clone()
shape := slices.Clone(t.Shape())
shape[dim] = cmp.Or(uint64(split.dim), shape[dim]/uint64(len(splits)))
slice := slices.Repeat([]tensor.Slice{nil}, len(shape))
slice[dim] = tensor.S(offset, offset+int(shape[dim]))
offset += int(shape[dim])
t.SetRepacker(func(_ string, data []float32, shape []uint64) ([]float32, error) {
dims := make([]int, len(shape))
for i := range shape {
dims[i] = int(shape[i])
}
var tt tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
tt, err := tt.Slice(slice...)
if err != nil {
return nil, err
}
tt = tensor.Materialize(tt)
if split.fn != nil {
tt, err = split.fn(tt)
if err != nil {
return nil, err
}
}
// flatten tensor so it can be written as a vector
if err := tt.Reshape(tt.Shape().TotalSize()); err != nil {
return nil, err
}
return native.VectorF32(tt.(*tensor.Dense))
})
if !yield(&ggml.Tensor{
Name: split.Replace(t.Name()),
Kind: t.Kind(),
Shape: shape,
WriterTo: t,
}) {
break
}
}
}
}
type merge struct {
pattern, name string
}
// mergeTensors merges tensors that match a given pattern into a single tensor.
func mergeTensors(unmatched []Tensor, merges ...merge) (out []*ggml.Tensor, _ []Tensor) {
var matched []Tensor
for i := range merges {
matched, unmatched = slicesSplitFunc(unmatched, func(t Tensor) bool {
matched, _ := path.Match(merges[i].pattern, t.Name())
return matched
})
if len(matched) > 0 {
out = append(out, &ggml.Tensor{
Name: merges[i].name,
Kind: matched[0].Kind(),
Shape: append([]uint64{uint64(len(matched))}, matched[0].Shape()...),
WriterTo: mergeGroup(matched),
})
}
}
return out, unmatched
}
// slicesSplitFunc splits a slice into two slices based on a predicate function.
func slicesSplitFunc[S ~[]E, E comparable](s S, fn func(e E) bool) (matched, unmatched S) {
for _, e := range s {
if fn(e) {
matched = append(matched, e)
} else {
unmatched = append(unmatched, e)
}
}
return matched, unmatched
}
type mergeGroup []Tensor
func (g mergeGroup) WriteTo(w io.Writer) (int64, error) {
for _, t := range g {
if _, err := t.WriteTo(w); err != nil {
return 0, err
}
}
return 0, nil
}

View File

@@ -1,953 +0,0 @@
package convert
import (
"bytes"
"encoding/binary"
"io"
"iter"
"slices"
"strings"
"testing"
"github.com/google/go-cmp/cmp"
"github.com/ollama/ollama/fs/ggml"
"github.com/pdevine/tensor"
)
type fakeTensor struct {
name string
shape []uint64
data []float32
repacker Repacker
}
func (f fakeTensor) Name() string {
return f.name
}
func (f fakeTensor) Shape() []uint64 {
return f.shape
}
func (f fakeTensor) Kind() uint32 {
return 0
}
func (f *fakeTensor) SetRepacker(fn Repacker) {
f.repacker = fn
}
func (f fakeTensor) Clone() Tensor {
return &fakeTensor{
name: f.name,
shape: slices.Clone(f.shape),
data: slices.Clone(f.data),
repacker: f.repacker,
}
}
func (f fakeTensor) WriteTo(w io.Writer) (n int64, err error) {
data := f.data
if f.repacker != nil {
data, err = f.repacker(f.name, data, f.shape)
if err != nil {
return 0, err
}
}
if err := binary.Write(w, binary.LittleEndian, data); err != nil {
return 0, err
}
return int64(len(data) * 4), nil
}
func mul(shape []uint64) int {
n := 1
for _, dim := range shape {
n *= int(dim)
}
return n
}
func TestSplitDim(t *testing.T) {
t.Run("2d", func(t *testing.T) {
r := fakeTensor{
name: "a.b",
shape: []uint64{3, 4},
data: []float32{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11},
}
t.Run("no split", func(t *testing.T) {
for tt := range splitDim(&r, 0, split{Replacer: strings.NewReplacer("a", "x")}) {
if tt.Name != "x.b" {
t.Fatalf("expected name 'x', got '%s'", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{3, 4}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
})
t.Run("even split", func(t *testing.T) {
next, stop := iter.Pull(splitDim(&r, 1,
split{Replacer: strings.NewReplacer("a", "x")},
split{Replacer: strings.NewReplacer("b", "y")},
))
defer stop()
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "x.b" {
t.Fatal("expected name 'x.b', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{3, 2}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{0, 1, 4, 5, 8, 9}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "a.y" {
t.Fatal("expected name 'a.y', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{3, 2}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{2, 3, 6, 7, 10, 11}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
})
t.Run("uneven split", func(t *testing.T) {
next, stop := iter.Pull(splitDim(&r, 0,
split{Replacer: strings.NewReplacer("a", "x"), dim: 2},
split{Replacer: strings.NewReplacer("b", "y"), dim: 1},
))
defer stop()
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "x.b" {
t.Fatal("expected name 'x.b', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{2, 4}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{0, 1, 2, 3, 4, 5, 6, 7}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "a.y" {
t.Fatal("expected name 'a.y', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{1, 4}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{8, 9, 10, 11}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
})
t.Run("three way split", func(t *testing.T) {
next, stop := iter.Pull(splitDim(&r, 0,
split{Replacer: strings.NewReplacer("a", "x"), dim: 1},
split{Replacer: strings.NewReplacer("b", "y"), dim: 1},
split{Replacer: strings.NewReplacer("b", "z"), dim: 1},
))
defer stop()
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "x.b" {
t.Fatal("expected name 'x.b', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{1, 4}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{0, 1, 2, 3}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "a.y" {
t.Fatal("expected name 'x.b', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{1, 4}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{4, 5, 6, 7}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "a.z" {
t.Fatal("expected name 'x.b', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{1, 4}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{8, 9, 10, 11}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
})
t.Run("uneven three way split", func(t *testing.T) {
next, stop := iter.Pull(splitDim(&r, 1,
split{Replacer: strings.NewReplacer("a", "x"), dim: 2},
split{Replacer: strings.NewReplacer("b", "y"), dim: 1},
split{Replacer: strings.NewReplacer("b", "z"), dim: 1},
))
defer stop()
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "x.b" {
t.Fatal("expected name 'x.b', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{3, 2}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{0, 1, 4, 5, 8, 9}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "a.y" {
t.Fatal("expected name 'x.b', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{3, 1}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{2, 6, 10}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "a.z" {
t.Fatal("expected name 'x.b', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{3, 1}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{3, 7, 11}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
})
t.Run("split with transpose", func(t *testing.T) {
next, stop := iter.Pull(splitDim(&r, 1,
split{Replacer: strings.NewReplacer("a", "x")},
split{Replacer: strings.NewReplacer("b", "y"), fn: func(tt tensor.Tensor) (tensor.Tensor, error) {
return tensor.Transpose(tt, 1, 0)
}},
))
defer stop()
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "x.b" {
t.Fatal("expected name 'x.b', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{3, 2}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{0, 1, 4, 5, 8, 9}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "a.y" {
t.Fatal("expected name 'a.y', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{3, 2}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{2, 6, 10, 3, 7, 11}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
})
})
t.Run("3d", func(t *testing.T) {
r := fakeTensor{
name: "a.b",
shape: []uint64{3, 4, 2},
data: []float32{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23},
}
t.Run("no split", func(t *testing.T) {
for tt := range splitDim(&r, 0, split{Replacer: strings.NewReplacer("a", "x")}) {
if tt.Name != "x.b" {
t.Fatalf("expected name 'x', got '%s'", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{3, 4, 2}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
})
t.Run("even split", func(t *testing.T) {
next, stop := iter.Pull(splitDim(&r, 1,
split{Replacer: strings.NewReplacer("a", "x")},
split{Replacer: strings.NewReplacer("b", "y")},
))
defer stop()
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "x.b" {
t.Fatal("expected name 'x.b', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{3, 2, 2}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "a.y" {
t.Fatal("expected name 'a.y', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{3, 2, 2}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
})
t.Run("uneven split", func(t *testing.T) {
next, stop := iter.Pull(splitDim(&r, 0,
split{Replacer: strings.NewReplacer("a", "x"), dim: 2},
split{Replacer: strings.NewReplacer("b", "y"), dim: 1},
))
defer stop()
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "x.b" {
t.Fatal("expected name 'x.b', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{2, 4, 2}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "a.y" {
t.Fatal("expected name 'a.y', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{1, 4, 2}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{16, 17, 18, 19, 20, 21, 22, 23}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
})
t.Run("three way split", func(t *testing.T) {
next, stop := iter.Pull(splitDim(&r, 0,
split{Replacer: strings.NewReplacer("a", "x"), dim: 1},
split{Replacer: strings.NewReplacer("b", "y"), dim: 1},
split{Replacer: strings.NewReplacer("b", "z"), dim: 1},
))
defer stop()
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "x.b" {
t.Fatal("expected name 'x.b', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{1, 4, 2}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{0, 1, 2, 3, 4, 5, 6, 7}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "a.y" {
t.Fatal("expected name 'x.b', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{1, 4, 2}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{8, 9, 10, 11, 12, 13, 14, 15}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "a.z" {
t.Fatal("expected name 'x.b', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{1, 4, 2}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{16, 17, 18, 19, 20, 21, 22, 23}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
})
t.Run("uneven three way split", func(t *testing.T) {
next, stop := iter.Pull(splitDim(&r, 1,
split{Replacer: strings.NewReplacer("a", "x"), dim: 2},
split{Replacer: strings.NewReplacer("b", "y"), dim: 1},
split{Replacer: strings.NewReplacer("b", "z"), dim: 1},
))
defer stop()
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "x.b" {
t.Fatal("expected name 'x.b', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{3, 2, 2}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "a.y" {
t.Fatal("expected name 'x.b', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{3, 1, 2}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{4, 5, 12, 13, 20, 21}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
{
tt, ok := next()
if !ok {
t.Fatal("expected at least one split")
}
if tt.Name != "a.z" {
t.Fatal("expected name 'x.b', got", tt.Name)
}
if diff := cmp.Diff(tt.Shape, []uint64{3, 1, 2}); diff != "" {
t.Errorf("unexpected shape (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := tt.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, mul(tt.Shape))
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(f32s, []float32{6, 7, 14, 15, 22, 23}); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
})
})
}
func TestMerge(t *testing.T) {
unmatched := []Tensor{
&fakeTensor{
name: "a.0.b",
shape: []uint64{5, 2},
data: []float32{10, 11, 12, 13, 14, 15, 16, 17, 18, 19},
},
&fakeTensor{
name: "a.1.b",
shape: []uint64{5, 2},
data: []float32{20, 21, 22, 23, 24, 25, 26, 27, 28, 29},
},
&fakeTensor{
name: "c.0.d",
shape: []uint64{5, 2},
data: []float32{30, 31, 32, 33, 34, 35, 36, 37, 38, 39},
},
&fakeTensor{
name: "c.1.d",
shape: []uint64{5, 2},
data: []float32{40, 41, 42, 43, 44, 45, 46, 47, 48, 49},
},
&fakeTensor{
name: "e.0.f",
shape: []uint64{5, 2},
data: []float32{50, 51, 52, 53, 54, 55, 56, 57, 58, 59},
},
}
checkMatched := func(t *testing.T, n int, matched []*ggml.Tensor) {
for i := range n {
got := matched[i]
if diff := cmp.Diff([]uint64{2, 5, 2}, got.Shape); diff != "" {
t.Errorf("unexpected (-want +got):\n%s", diff)
}
var b bytes.Buffer
if _, err := got.WriteTo(&b); err != nil {
t.Fatal(err)
}
f32s := make([]float32, 20)
if err := binary.Read(&b, binary.LittleEndian, &f32s); err != nil {
t.Fatal(err)
}
offset := 10 + (i * 20)
want := make([]float32, 20)
for j := range 20 {
want[j] = float32(offset + j)
}
if diff := cmp.Diff(want, f32s); diff != "" {
t.Errorf("unexpected data (-want +got):\n%s", diff)
}
}
}
t.Run("single merge", func(t *testing.T) {
matched, unmatched := mergeTensors(unmatched, merge{"a.*.b", "a.b"})
if len(unmatched) != 3 {
t.Error("expected 3 remaining tensors, got", len(unmatched))
}
if len(matched) != 1 {
t.Error("expected 1 merged tensor, got", len(matched))
}
checkMatched(t, 1, matched)
})
t.Run("multiple merges", func(t *testing.T) {
matched, unmatched := mergeTensors(unmatched, merge{"a.*.b", "a.b"}, merge{"c.*.d", "c.d"})
if len(unmatched) != 1 {
t.Error("expected 1 remaining tensors, got", len(unmatched))
}
if len(matched) != 2 {
t.Error("expected 2 merged tensor, got", len(matched))
}
checkMatched(t, 2, matched)
})
t.Run("no match", func(t *testing.T) {
matched, unmatched := mergeTensors(unmatched, merge{"x.*.y", "x.y"})
if len(unmatched) != 5 {
t.Error("expected 5 remaining tensors, got", len(unmatched))
}
if len(matched) != 0 {
t.Error("expected no merged tensors, got", len(matched))
}
})
}

View File

@@ -1,314 +0,0 @@
{
"general.architecture": "qwen2",
"general.file_type": "1",
"general.parameter_count": "494032768",
"general.quantization_version": "2",
"output_norm.weight": "93a01a6db3419e85320a244bbf8ae81c43033b1d10c342bea3797ff2ce348390",
"qwen2.attention.head_count": "14",
"qwen2.attention.head_count_kv": "2",
"qwen2.attention.layer_norm_rms_epsilon": "1e-06",
"qwen2.block_count": "24",
"qwen2.context_length": "32768",
"qwen2.embedding_length": "896",
"qwen2.feed_forward_length": "4864",
"qwen2.rope.freq_base": "1e+06",
"token_embd.weight": "d74257dc547b48be5ae7b93f1c9af072c0c42dbbb85503078e25c59cd09e68d0",
"tokenizer.ggml.add_eos_token": "false",
"tokenizer.ggml.add_padding_token": "false",
"tokenizer.ggml.eos_token_id": "151645",
"tokenizer.ggml.merges": "6b1b1c58f1223d74f9095929d3e6416cdd74784440221a5507b87b8197f2bfd2",
"tokenizer.ggml.model": "gpt2",
"tokenizer.ggml.padding_token_id": "151643",
"tokenizer.ggml.pre": "qwen2",
"tokenizer.ggml.scores": "94e247e531e8b0fa3d248f3de09c9beae0c87da8106208a8edfaac0b8ec4b53d",
"tokenizer.ggml.token_type": "b178dbc9d1b2e08f84d02918e00fc2de2619a250e6c188c91a6605f701860055",
"tokenizer.ggml.tokens": "1d93f6679b23a1152b725f7f473792d54d53c1040c5250d3e46b42f81e0a1a34",
"blk.0.attn_k.bias": "5ce6617845f66c34515978d23d52e729c298d8bffa28c356a0428bef17142cf1",
"blk.0.attn_k.weight": "a960832a9e0e83e4d95402e5d1a01cc74300fcca0c381237162126330e1a7af8",
"blk.0.attn_norm.weight": "32c7d51cd0958f1f1771174192db341f9770516d7595a2f0fd18a4d78bd5aba3",
"blk.0.attn_output.weight": "c67e6e7e868354a11bf9121c70ee56c140b20eec611a8955e7dfe54a21d40a98",
"blk.0.attn_q.bias": "3e9e994eb1f03bccfc82f8bb3c324c920d42d547e07de5be83be12c428645063",
"blk.0.attn_q.weight": "dc12132f789b97cfa1e3f5775ceb835247fa67aa47400fd09c8f9f3769208583",
"blk.0.attn_v.bias": "a3fd0757b31fdc78af5ec320332d239c1a79d34e8804df06c5454e86955e8cc9",
"blk.0.attn_v.weight": "f43094a2134c7ee2dcc52aac3c8b7d9d64fb0295a8adb94cabfd49213f017b84",
"blk.0.ffn_down.weight": "18c2aec92db14f21976838a8c35d5575f80d0e4b1e05ccc0d8388d5877e80147",
"blk.0.ffn_gate.weight": "a3a1c4ef38f8f750eabadfe3d83bbb0f77941eec1cc1a388e51852e99c8691f6",
"blk.0.ffn_norm.weight": "b59b779c42d44b5c4cec41e39b4eb61e0491a07c1b3e946ccb5b8d5c657eda3f",
"blk.0.ffn_up.weight": "db64f09987ea59449e90abae5a2ffcc20efd9203f0eebec77a6aacb5809d6cff",
"blk.1.attn_k.bias": "a5c8c5671703ec0aa0143ff70a20ffdd67b5d5790ca1dfa5bba4e87e4071ed9f",
"blk.1.attn_k.weight": "835c7c7cc95b3cb2e55bd9cac585aa0760a033896621d3e06421f3378c540f7d",
"blk.1.attn_norm.weight": "f4c36fb6c14fce721fab0de78cc118d6f66e3a3d3ea0017bb14aade24c3c5434",
"blk.1.attn_output.weight": "cc1e80310c97cef068e48e40b7096f32fa2138519d6209c6a1a9994985999016",
"blk.1.attn_q.bias": "bc332780e66b0aac80ec5e63ac32344919a840db2fcc8f87bcef16a43a54138e",
"blk.1.attn_q.weight": "d766f06c925cce38d4b31b2165b3448e1fb49a7d561985f95d9cd2fcba52367a",
"blk.1.attn_v.bias": "9f486626fb6ed9ac84970a71e9b9818dd2758501fd3f61bb1c08540dcc7a8631",
"blk.1.attn_v.weight": "e873d1e5bd4f4d6abfd47c0f55119c2c111105838753ee273a03c5ccea25ce5c",
"blk.1.ffn_down.weight": "b3ce82b093f187344de04284b1783a452de1b72640914609b8f830dc81580521",
"blk.1.ffn_gate.weight": "5cd44ad237edaca525a28a3ac13975d1b565f576d6a8003237a341ae0d156f2e",
"blk.1.ffn_norm.weight": "4ac774ee8afaee119610c46aa1ff89fc6c9084a29d226075bc4aa4d2f15f746c",
"blk.1.ffn_up.weight": "042d81ab5f1983d85c81213232f3bfc05a9302d9dfaa98d931ebba326b6058b8",
"blk.10.attn_k.bias": "767ecfeacd60a2c2221ac4d76c357190849dd9cdf64ced418d9d0c7949101401",
"blk.10.attn_k.weight": "a9f3df343227537636be8202303453086375091944e498bad11e0b91e45e8c71",
"blk.10.attn_norm.weight": "01acd0e7b3e363f873dbfde6f0995ffcce83f5aaa10ff91c31dbf775035f6d5a",
"blk.10.attn_output.weight": "a531fe660769604ab869f01b203eb115e025cad4c0baeacdd1bcca99cf6d0264",
"blk.10.attn_q.bias": "356a02c9163dd660c1340fbe1e049b335ac6178891e00996131bba9ab4cb3e59",
"blk.10.attn_q.weight": "81be0cfb227339d83f954cd8dcf35828441211c6e1d184060e3eb76085041e2f",
"blk.10.attn_v.bias": "ed0450653284b62f8bf2c2db19c0ff7a6cf3cda1324d0a044c5e3db7bb692bd3",
"blk.10.attn_v.weight": "c1247ff7092babd2ed979883095b9aa022b2996cab1c77fb9e6176ddc1498d16",
"blk.10.ffn_down.weight": "fda7544965dc9af874f1062c22151c6cefc8ba08cbe15dc67aa89979e77b2de4",
"blk.10.ffn_gate.weight": "9f2632b1dee7304d10c70bd38d85bb1f148a628a8468f894f57975b8a2f1d945",
"blk.10.ffn_norm.weight": "94f8cbd6b17a4d5aabd93fa32930a687db3b11f086142f1cd71c535c11adcad4",
"blk.10.ffn_up.weight": "8dc2f8db0474939a277a3d89db34c3bcc3381cfea57bd05a8426a164634d9112",
"blk.11.attn_k.bias": "3b8e5a662b19411e3f6530714b766aad2ee41eebc8161bec9db0bc82d383a6e0",
"blk.11.attn_k.weight": "2c29f1ed1ce53ce9604e9ea3663c2c373157e909a0d6064a8920005f6d15dad9",
"blk.11.attn_norm.weight": "48f68a99c3da4ab4c9e492677b606d1b8e0e3de1fdbf6a977523f97b8c21ec31",
"blk.11.attn_output.weight": "5859f3838a94898b020c23040941ed88f4fcb132db400d0849f30a01f62c0f1c",
"blk.11.attn_q.bias": "c5ad89a5628f2bd81252ef44ef6bbcbff15c33ad16fba66435509b959c2af6d3",
"blk.11.attn_q.weight": "d102104e5d61c1e3219564f1d0149fd593db6c6daa9f3872460c84403323cfef",
"blk.11.attn_v.bias": "8653f7d48c5f75a5b55630819f99ecf01c932f12d33fd1a3ee634613e70edde8",
"blk.11.attn_v.weight": "e0a7c7d89b9f2d0d781ce85330022229126e130a8600a09d4a5f920f0bbd50b2",
"blk.11.ffn_down.weight": "4a22b3361eba8bbe1d9a6fda1812618e894c49f13bcacb505defa9badb6b96a6",
"blk.11.ffn_gate.weight": "484698b206760d3fd8df68b252a3c5bae65c8bf6392fb53a5261b021b6f39144",
"blk.11.ffn_norm.weight": "da69e96338cbe30882cf5a9544004387f5bbc0bcb6038e61ba2baabbd2623bac",
"blk.11.ffn_up.weight": "26ec74f1f504d1281715680dfbcc321db4e9900c53932fa40955daceb891b9aa",
"blk.12.attn_k.bias": "f94b49ec3e498f14f6bc3ebefe1f82018935bbe594df03253bfffae36bc20751",
"blk.12.attn_k.weight": "ae6323d0bbcfcea01f598d308993d1a7530317e78c1f64923e36d4b1649e9e73",
"blk.12.attn_norm.weight": "3784536a7611a839a42a29a5cc538c74ee4f9793092e5efe1b227b48f8c4d37f",
"blk.12.attn_output.weight": "46826c00b066829355db78293ab216e890f5eaaed3a70499ee68785189a6b0d9",
"blk.12.attn_q.bias": "b14db2d327ce0deec97beda7d3965a56c43e1e63dc9181840fb176b114cf643a",
"blk.12.attn_q.weight": "30f67df52ced06f76b6c85531657584276a454d6ec9bb7d0c7d2ca8f067f5551",
"blk.12.attn_v.bias": "57ab4b7e43f4fc5853bca7bfbb2702f8c2c391a49252a760abbb7b26330dc4aa",
"blk.12.attn_v.weight": "3ccd9da0cfe241cd33a63310f3ca6d81c5bc5a50d200bfea6612ac376166aca2",
"blk.12.ffn_down.weight": "a095774413198a83c549ce132d7c9684c0baef33145eaa889be370ef9c881c81",
"blk.12.ffn_gate.weight": "bb3b2bbdfb065d2a0a795909c53beec327781a4a7e974bf9f99c436cea459991",
"blk.12.ffn_norm.weight": "3b486c6cd97eb4b17967d9d6c0cc3821a1a6ad73d96b4d8fbf980101b32b8dab",
"blk.12.ffn_up.weight": "d020b82dd39a5d5a9d3881397bf53a567790a07f395284e6eb0f5fe0fef53de3",
"blk.13.attn_k.bias": "69381f8254586eba3623eceb18697fe79f9b4d8f2c30136acb10d5926e3ba1d0",
"blk.13.attn_k.weight": "c4d7a31495d71269f81b586203a50abea3a9e2985667faf258c9306ec6030f1d",
"blk.13.attn_norm.weight": "907da11075d16eda668dabe548af3cfd794df26b8ab53939af1344d91bec6fba",
"blk.13.attn_output.weight": "ca01cf6d2b8ece2fb3b0f56f1eb76194471ac27b54fe264f99c909f5eb7fef4a",
"blk.13.attn_q.bias": "2f5ecebafe03b1d485b93c41cff756ca57fb65b02e9d8336f14a3d26ab5d159a",
"blk.13.attn_q.weight": "f557f8acad7f0fa62da06b5da134182fe04a5bed8bdb269e316f970c9cc440fb",
"blk.13.attn_v.bias": "a492a88ae131e95714b092545a8752eaea7c7d2f9cb77852628ca8296c415525",
"blk.13.attn_v.weight": "d1220b1fe9f1cc0a5a88ee239d65fec900f5eaf6c448b6c2cbe74c81e15ed333",
"blk.13.ffn_down.weight": "53184e33440b49848a896304eb16a983efbc6b8bee0b93de8c8de716e1585fcb",
"blk.13.ffn_gate.weight": "684bf8896f148c851506c62717e45c426921b93c10d536ecdeb0fb28259a106d",
"blk.13.ffn_norm.weight": "6cb4e547ad8665eb7c174855c08afe1e5490fece66122522c1e9e8132d9064eb",
"blk.13.ffn_up.weight": "c64107897e38c06727075aba4ea7940b2cdd0e278b5c555dffb2790ef553bb57",
"blk.14.attn_k.bias": "2814ca9b160b16ae39557c9b629482fbe3a7592d372c1e1bf1ac59a2d578fde1",
"blk.14.attn_k.weight": "3377177396463afba667742972920ebb45dfdc37e9950e1f0e1d60a2f936b27d",
"blk.14.attn_norm.weight": "5cae870477d51dd35a6d22aaeacfce4dff218ffba693820ede6a4e11f02afd6d",
"blk.14.attn_output.weight": "3cfe9ccf3d48ae9e95b93a132a1c6240189a277d764f58590fb36fdbb714cad0",
"blk.14.attn_q.bias": "6a75acc2f090b2e67bfc26f7fca080ae8bd7c7aa090ec252e694be66b8b8f038",
"blk.14.attn_q.weight": "5ef45c86d7dda1df585aa1b827b89823adf679a6bb9c164bd0f97b2aa6eb96f1",
"blk.14.attn_v.bias": "5534480443e10ed72c31a917f3d104b0f49df5e6dbfa58d0eb5e7318120e3aee",
"blk.14.attn_v.weight": "58f45cf3240c4623626ec415c7d5441eaa8d2fb184f101aba973f222989422d1",
"blk.14.ffn_down.weight": "2dc82a0f20c05b77512458738130d8d05ce150cc078680ae7ee6dd7ed68d955d",
"blk.14.ffn_gate.weight": "d4a6c6f0fcccddfd1fcaa074846622f4a74cb22b9a654ab497abdc1d0dde9450",
"blk.14.ffn_norm.weight": "777e444932a0212ff3feac98442444e17bd8a98cb758ea3356697d0846d12c56",
"blk.14.ffn_up.weight": "6b75f6bd00195198447b69a417ed9d98f8ca28b3cb8be82f4bad908be0777d57",
"blk.15.attn_k.bias": "2d07211a58e6c2f23aa3a6dc03c80a7d135dfb28726b60b0e0fdd0f35ea5c37b",
"blk.15.attn_k.weight": "e77f3c0075a1810e70df956cc51fd08612f576cc09b6de8708dcae5daedb0739",
"blk.15.attn_norm.weight": "379a10d90609a5d5ba67d633803eda1424fc61ba5cca8d3bffe70c8b18b58ebf",
"blk.15.attn_output.weight": "402751c12ee9dbc9db5e3bf66a7b23ebe7d36c0500e0be67be4c8b1c4357fa62",
"blk.15.attn_q.bias": "acb37fc409ee725ceedf7a3a41b40106086abc47b76780728f781942c5120208",
"blk.15.attn_q.weight": "89cd3047a09b46ed2bb57c69dd687f67a1f0235149b30376fa31b525898e4a55",
"blk.15.attn_v.bias": "f081a37289cbe811978feb4da3ef543bdeb7355414d476f44e09b498da10cb2c",
"blk.15.attn_v.weight": "8404f242a11e6d512c9ead9b2f083cda031e9b269f8a0a83f57ee4c56934764e",
"blk.15.ffn_down.weight": "93438f43ee8cc4f1a7fd3840a6afdd5f02123e76db4f0d9474430c0100d148fc",
"blk.15.ffn_gate.weight": "ff935a2698843e87fad9dbf7125f53e460190ec71ee128b650b3fc027fe37bfc",
"blk.15.ffn_norm.weight": "4be80f199841cba831982e988451e1833c3c938a4d6ca1169319087bf0bd723e",
"blk.15.ffn_up.weight": "ee9ba63c66d71053e33551ddd519878bb30b88eeb03cfe047119c5c4000fb0a6",
"blk.16.attn_k.bias": "3f5fbabed4510c620b99d9d542739295fa6a262a7157f3a00a4889253f8341b8",
"blk.16.attn_k.weight": "8ca6eb139b281c257324cddea97a8e9aa7c048b53075cf00153123b967c27ee5",
"blk.16.attn_norm.weight": "290157f005e5aa7dddf4bd60100e7ee7b0baa7f11ec5c2cea5e0ead2aad3a4c6",
"blk.16.attn_output.weight": "b1f4d80a7447f08f1c331712527f750d00147f35c042442ade96fd029dadc5a1",
"blk.16.attn_q.bias": "e3e4e442ad4416791b468cad8de0d0d2d68c7e7df8d06002f4d49b4da9cb25e4",
"blk.16.attn_q.weight": "cc7392fa5bb1107d3816e7e7363de252d37efd4165d065e258806291ce0a147b",
"blk.16.attn_v.bias": "a7629830f2f6293e018916849614636d40b1bcd11245f75dbc34d38abae8f324",
"blk.16.attn_v.weight": "b6c7856c7d594437630929c8cf3b31d476e817875daf1095334ec08e40c5e355",
"blk.16.ffn_down.weight": "f9c0a777a00170990a4982d5a06717511bf9b0dd08aeaab64d9040d59bcbebba",
"blk.16.ffn_gate.weight": "ed88f11bc3176c9f22004e3559ccb9830a278b75edd05e11971d51c014bd5cd2",
"blk.16.ffn_norm.weight": "ab24abdcc4957895e434c6bb3a5237a71ff5044efb9f76c1a9e76e280c128410",
"blk.16.ffn_up.weight": "99f594dc8db37f554efa606e71d215fbc3907aa464a54038d6e40e9229a547ff",
"blk.17.attn_k.bias": "f236625676f9b2faa6781c7184d12d84c089c130d2a9350a6cf70210990f6bf1",
"blk.17.attn_k.weight": "c2a4f20cd3e98538308a13afe9cc5880bdd90d543449c6072dedd694b511ee1a",
"blk.17.attn_norm.weight": "5a9da4ee168311f487a79fc9d065a035432c6cafa8adb963a84954cf32f57a2a",
"blk.17.attn_output.weight": "d5df7031e354186ce65dc09d6f8a92eb721c0319816f8596b0c8a5d148ed0a2a",
"blk.17.attn_q.bias": "3212d5eeaa7ed7fac93cc99e16544de93c01bb681ae9391256ed4a8671fc6b00",
"blk.17.attn_q.weight": "d18cd9aa7ee10c551cb705549fa1ae974aea233f86471c9a19022dc29b63d0d5",
"blk.17.attn_v.bias": "a74ad11a1f8357742f80e2a0c0b3a2578fc8bbaf14c8223000767e07a5d79703",
"blk.17.attn_v.weight": "da18ac0e90884436a1cb0ad6a067f97a37f321b03c70b8b03bf481339fef5c80",
"blk.17.ffn_down.weight": "81a8a5d7a194fb53d976558e0347efbe9fdb1effffde9634c70162e1a20eff51",
"blk.17.ffn_gate.weight": "72870d83ab62f2dcd45f593924e291a45e4ae1b87f804b5b88aa34cfd76dd15e",
"blk.17.ffn_norm.weight": "cae39ac69b9bdaeefab7533796fdf11dbb7a4bdbdeed601e20f209503aafe008",
"blk.17.ffn_up.weight": "e7cb40b0842468507cec0e502bbed8a86428b51d439e3466bc12f44b2754e28f",
"blk.18.attn_k.bias": "8bfc02b94f9587aa125e2d8bbc2b15f0a5eb8f378d8b3e64a8150ae0a8ca3df2",
"blk.18.attn_k.weight": "434bc3b3332ea48afee890aa689eb458a75c50bc783492b0cbf64d42db40e8ad",
"blk.18.attn_norm.weight": "d6ffc09396c42a70d1f0e97d81113eee704d3bfc9eeae2bed022075a5dd08075",
"blk.18.attn_output.weight": "133f001f81f3b082468a7de67cb2e7a76508fce34bcc4dee7f0858e06eee082c",
"blk.18.attn_q.bias": "758d0e28bf5e660b3090aafb70e2a3191b4f3bb218d65e9139a086ceacaf599f",
"blk.18.attn_q.weight": "12d7b86fc1b09b9fa7f8b7ed43d8a410892cec8672d0c752f8346f6193343696",
"blk.18.attn_v.bias": "9efd15bab0519462431d6c6e8a5b7dd4e151dc449468097ee0ddca369c0ecc2e",
"blk.18.attn_v.weight": "f631231a79d4a2e9730fb2e386d8c18621eb3fb7900fbfdff5e6d52cc42db122",
"blk.18.ffn_down.weight": "874a2dddf456f3ab56b958b0860d71c8c680a6f89322c9bf6b2f32a113592300",
"blk.18.ffn_gate.weight": "4549ef8976c345a511df4a7133bdaf6fe387335f52dfd8a4605a8ae3f728c403",
"blk.18.ffn_norm.weight": "80c258a2536a860e19bfcbd9f29afa13214fbb4c34bde0d4da51287d354e9a59",
"blk.18.ffn_up.weight": "8b03308a581457a3c038b7a086f3cdf14941d7ad4107c4bd6d9d6b062fd00d73",
"blk.19.attn_k.bias": "e77f7b0c8e3e0a9b0d61918cd88371047752a1b02b1576936f4ec807d4d870ee",
"blk.19.attn_k.weight": "a2a318e93355230c0d0f95c441b080bf9c4914507255f363fb67a5e771d4d1e6",
"blk.19.attn_norm.weight": "9a4bdeb3970be21ac74a94c2c81eb36986533db81b78db6edec48d9802910d59",
"blk.19.attn_output.weight": "2369b103dd3947e2cef02b2669b405af5957fb3a7f9d0ff40646078c4b4317ad",
"blk.19.attn_q.bias": "e20bf427bef69059ae84a5d9f98f7d688489627f198fb6153def018ff9fd2e34",
"blk.19.attn_q.weight": "45a3bb3bdfd2f29dd76e5f78ddae73678b9a2a85dfaf609e460240ef5b7be2ad",
"blk.19.attn_v.bias": "a441f58a3e02ed86ee1819eefc9bd4e8b70d11b864a929d58a2c2ac0aeb8203d",
"blk.19.attn_v.weight": "30b0b04480c510450a7abb2ce9fa05c65b150a3cc4dc76f8916bf8d013f1b6be",
"blk.19.ffn_down.weight": "eebb9ab8fdb6a6efcfff8cf383adac9ec2d64aeeff703d16ed60d3621f86c395",
"blk.19.ffn_gate.weight": "3fef1493029298378886586478410b3d2e4e879f6aa83c07e210a7ce6481817f",
"blk.19.ffn_norm.weight": "e1be99ea1e8fb9678f7b8ba200f3f37e03878f3574d65d57bcd3a9fd796e2112",
"blk.19.ffn_up.weight": "f07cf25e09394fb69fe3ef324bdc0df9a4cecf3dc53070b8acc39e6d1689bf82",
"blk.2.attn_k.bias": "b29baa8221f125eff6b8ac1a950fa1d7cfc1bce7bdc636bf3df7d4065ab6466c",
"blk.2.attn_k.weight": "4bd0c179bced8bc37a09f5748c394e0cf50273942fb38a866e5cf50b6c96c437",
"blk.2.attn_norm.weight": "07b3edc6a6325c3428aa12f29bcae0be0de363ce61a6af487bc5c93fb8c468d9",
"blk.2.attn_output.weight": "056b5b31dbc81087c81b9d41c25960aa66c7190004c842ba343979644d7f4d88",
"blk.2.attn_q.bias": "479b6212401e097767c9d52b12a1adb8961c0fce9fcaaab81f202a9d85744376",
"blk.2.attn_q.weight": "f89196076f446a6dd8a9eee017f303504f9c03094c326449cee5a7fc0a97fade",
"blk.2.attn_v.bias": "ef9b1b986dbd9d7291027a88b67dc31434435b20e76e4f1e9d6273ebd31224f0",
"blk.2.attn_v.weight": "9322f4f00e85f8c0936845c51ca64b202a93df104f36886986a8452a8e4967a5",
"blk.2.ffn_down.weight": "7beac0d2440dc49af33ededb85a6cc3ba23ab33ad3ffa5760714b2ef84d94f6e",
"blk.2.ffn_gate.weight": "818a93864a5890c1f4dc66429004fad07645a50142350e9bff9a68fe24608a52",
"blk.2.ffn_norm.weight": "152c924d5514942ad274aafb8cc91b35c1db3627c3d973d92f60ff75f3daf9ba",
"blk.2.ffn_up.weight": "9c9579e600f209546db6015c9acfeda4f51b6d3cca6e8db4d20a04285fe61a37",
"blk.20.attn_k.bias": "fd22bfeffb63d818ce2ff1ea2ace0db5d940f7a9489b6bfc1ec4a5398848d7fe",
"blk.20.attn_k.weight": "f74439bc74c2f9252130c9c28384fd7352368b58bb7ce3f2444cf0288dfff861",
"blk.20.attn_norm.weight": "5c15d2613df87be6495fb7546b7dcedd2801d12fa5ecc02c877df889330e8f37",
"blk.20.attn_output.weight": "6731a39286a67f6859832f96695732e579e14e0c36956eccd1edce3db11595b8",
"blk.20.attn_q.bias": "04466e5a3f454a19b9b433fc2585396feac780027ece7ccb4e4bb3e406fc14d8",
"blk.20.attn_q.weight": "ead4c71daaeb17bf20d014a34c88b97f238456488e815ae0f281a5daf6fc99b8",
"blk.20.attn_v.bias": "adcc848e043025de9bd55ccb14dd8fb6343e8b5185ed07e12964be41d0faf99f",
"blk.20.attn_v.weight": "81bfc23f83526386a4761c2c16b6a93cd0bbf9d846c1a51b82c71f1474a465f1",
"blk.20.ffn_down.weight": "9bf660af3bafad919d03173c89a65fc9c89440a76c42c9e55e4d171076f3c17f",
"blk.20.ffn_gate.weight": "c04b4f3ccce44917ee228b998e2c19dd702aef10a43413afb152e808b5ac5c42",
"blk.20.ffn_norm.weight": "3d5b555d7746a71220143c6b8fff5ce4eb63283d9d9c772f1233d848f69f4ff4",
"blk.20.ffn_up.weight": "d7a196505c39e5469dfc7c6958bdbb54e93629ac1a047a6663ed96b318753094",
"blk.21.attn_k.bias": "4db1f48e5c6a3bc5720a5da813bbef08283e6269e12d83f8a9c54e52715d8011",
"blk.21.attn_k.weight": "c687b2f0e132a5e220a2a059b61aa2a537f37d8a674d7709f87880637b263b31",
"blk.21.attn_norm.weight": "ec23b0ff847a4b45585ab8e04f10fc20bb1637c5f1fbcdc4d73f336bcb5d1bd0",
"blk.21.attn_output.weight": "01255390576316c1731ef201e32c6e934eba356c28438cd06d9027ac6a3ff84f",
"blk.21.attn_q.bias": "3098f37205a15418e1681e407c82b7ce7c6fda6c6826b0590a13e1b68a38a1ea",
"blk.21.attn_q.weight": "30ea62cbb702a5359229dc96819df17ee535e2e9988d044b005c73ea536e1005",
"blk.21.attn_v.bias": "7bbedb2c22a04737f21993115701d4a06b985b7ca3b64681f53cd1be8d7ea39e",
"blk.21.attn_v.weight": "e11905e63579e36fbee978062af7599339ae29633765a4835628d79a795ec8df",
"blk.21.ffn_down.weight": "84def2ffd8aca766f9ce12ed9ac76919ab81eb34bdeae44fa4224417c38af527",
"blk.21.ffn_gate.weight": "4e99f05377b4a0b8d875045530a5c59dee6a46ac8a45597f6579f6fdfa800787",
"blk.21.ffn_norm.weight": "af48f13d03fba38ff8794a5f5005e666e501f971ca2e30bbded2777a8096f37d",
"blk.21.ffn_up.weight": "a29541c39a6acbc364be86994632a5bf55d701027cb7f23320f8c6d55ee42c91",
"blk.22.attn_k.bias": "c97f84db6c75422df6ef5768676d4e9abefaa3b8337aa2730ff260f8fc350480",
"blk.22.attn_k.weight": "af9a0c56f68779513e95be11611b7be6175ddae27d48bee9dd72fdbf05f6cbfa",
"blk.22.attn_norm.weight": "1c7518eb5bcff4a202c6f4a2827f14abd76f9bcc64ce75fe9db60b69437a5c9c",
"blk.22.attn_output.weight": "1abcf1f3caa2f59dd018646b93f9cf8fd30d49e98a473e6a8704419a751be46f",
"blk.22.attn_q.bias": "7221e01cb692faf2f7f8c2eb6e2fac38a1b751a9c9fdb6a21a0a936eb0bf4b96",
"blk.22.attn_q.weight": "faaf8fb7b6c19f343d47f3ea6b57151fb46c787e0b3bd2c292fd327d3d4d8e35",
"blk.22.attn_v.bias": "3ec05942e82d735de99dfd0d8228d8425e63e2fc584da98b3326bdef89ecb2e5",
"blk.22.attn_v.weight": "42e7b0ad06db76227837da9d4e74b2db97f3df4050ecb3a87cb9b55e08dfcb42",
"blk.22.ffn_down.weight": "87ef98ad2d0e824b0fa5ad8aa18787162922e527c9b1b721a99bc07d3bf97c82",
"blk.22.ffn_gate.weight": "562d6e5a1654b03aaa0e33864d23c10297fd4bcaa72d30fac69fb771ee1df9d6",
"blk.22.ffn_norm.weight": "f8a405dee467749d59427ce05cdd4b9c11bb18934a89258ea461f013b7d251f5",
"blk.22.ffn_up.weight": "90e1f4ae4062649d4d838399eb353e8bb8d56a49982b6a7f64aa3945377f7187",
"blk.23.attn_k.bias": "9ad22178a85f3be7e25f5aff462f31627466364f2f5e92f265cc91db0da9a8a8",
"blk.23.attn_k.weight": "d813beffb10f03278f5b58eea0f9d73cdcb7b5b4045ae025c379592e854f7dfd",
"blk.23.attn_norm.weight": "f583c9836044bdb056d6f8911088ac28add68e500043ae1f97b5d9158fe3d769",
"blk.23.attn_output.weight": "02789911ac3b97f6b761e958b7dd6dc7da61a46a1be92bd0b346039ca7ecd2b2",
"blk.23.attn_q.bias": "38c4970fb9b4f7e4a139258a45639d848653814b4bc89ea9849709b13f16414b",
"blk.23.attn_q.weight": "eb694be9a5ab5858b8dab064ee4cce247dc757424e65282989bd4d015b8580ce",
"blk.23.attn_v.bias": "0a25f6533aa7e7a152a4b198cf6c411c2408a34afa4f161bb4d5ffba2f74e33f",
"blk.23.attn_v.weight": "187e1bac6b70f74e6364de226565aa8275ee2854d09cbe5895451a689596049e",
"blk.23.ffn_down.weight": "88880dd9ba7ee80ade972927f810b5d2c30a69520c615190b27f9daabc0a8c5a",
"blk.23.ffn_gate.weight": "5abec63197935ab3eb8e6de0a5307396ec46cdb1cc5de25d87c845f3c4a3e887",
"blk.23.ffn_norm.weight": "60e1f5e6310c3a531c554a6bb7cd883aed58db1e51853f739436ea461c1843d7",
"blk.23.ffn_up.weight": "3d7f502771743f4a634188dfcd8b8a384fb07467ca8528366aee59ddb25b7bce",
"blk.3.attn_k.bias": "0b6b442ebbac29c8c4b67e8e3876d0382dd2dc52efdf4ab0ebbc6f71b6252393",
"blk.3.attn_k.weight": "480f40584fbda692c26f2cee45f5923780b236f8b4e8ec7bbee0237777a0918d",
"blk.3.attn_norm.weight": "39872be2af31bc9cd6b583ebba6fb759f621d586d66e5a2fc0b85991615a8923",
"blk.3.attn_output.weight": "924b2c80d8513bf637f8ebb3756a340d9cf2243de723fd08d7f5dccd46b3f8b6",
"blk.3.attn_q.bias": "863c9d848156847a3fe9bbc44415a4395245b5d13e95673c014fdb71e494ab0a",
"blk.3.attn_q.weight": "bff73ee5de92fba8f6c089bbb19ce57e17ab3c9c29295712804bb752711b882e",
"blk.3.attn_v.bias": "e1b6fea126e86189112fcdfee79ffc66a087461527bc9c2dc52dc80f3b7de95e",
"blk.3.attn_v.weight": "7812b7f5133636f06cdbb4dcc48ef7803206538641b6c960777b37f60a8e6752",
"blk.3.ffn_down.weight": "00b393d6a7e3ad9b5224211ccdbc54a96aae151f24ed631764ac224972a6bc82",
"blk.3.ffn_gate.weight": "cfd63fa3a038af05dc53c6eeb3c192f1602f26ff24cb840bcf1510fcb37b5513",
"blk.3.ffn_norm.weight": "7389fc240a282949580ea2f5b0d7973ac79f32f76dc0155b537bb6b751f8e27a",
"blk.3.ffn_up.weight": "2a945f47090df9cb16f92f1f06c520f156f8e232182eaaed09f257b8947a2a62",
"blk.4.attn_k.bias": "62533c31f0de498187593f238c6597503fef2a92e920cd540a96bc5311b3b2a0",
"blk.4.attn_k.weight": "93e829868bffd980a8e589b9c4566cd81e6ce4296a5f357a2ae93febe1284156",
"blk.4.attn_norm.weight": "9e0aaa4bbdd1389890f8abec20533f3ab16d61b872b1a8dbd623023921c660a9",
"blk.4.attn_output.weight": "74467d6f44357d67f452ac49da861468b38e98057017bd38bc9a449f9d3538e6",
"blk.4.attn_q.bias": "8e6d9026fd69b314c1773c5946be2e11daf806ef22a5d91d744344fd30c58c59",
"blk.4.attn_q.weight": "e5bfbafd94a4d530f3769f5edbba8cc08d9b5bee8f66ebf4cb54e69bc0b7f63b",
"blk.4.attn_v.bias": "20c570f92022d9905eb85c0e41d1fdb30db22007a9628b51f512f8268d6c34a2",
"blk.4.attn_v.weight": "9638d459d61da03c9dd34dad985e03c43b4f8a5bc9701a82153478329b0517e0",
"blk.4.ffn_down.weight": "9d91b06e89d52f4365dece7eaeec50f81e52cb2407b333248a81e6e2f84c05b8",
"blk.4.ffn_gate.weight": "bf6350a79c6a6ee9146edfd788b88d4a4c2b54db1aa0adcc1464dbba8a84b646",
"blk.4.ffn_norm.weight": "11a70a6b9f7ce336292f4e3a2c6c92d366d4ee4306ad4fdb1870fde107e9cc31",
"blk.4.ffn_up.weight": "64f23f493d02b147a72a59605e6b7dd1c4c74f6813a38a2a60818bd66f697347",
"blk.5.attn_k.bias": "f6c2c279c0ed686f298ad1e5514b5cd882199341f896abbb2c2129d4c64ce9c5",
"blk.5.attn_k.weight": "0e682f75870abf9efaca10dac5f04c580f42820ecf4e234d43af967019acb86f",
"blk.5.attn_norm.weight": "01efae7653705e741932fcd79dff3be643d7e97f4b5719b887835dffe44b3a82",
"blk.5.attn_output.weight": "69e841d00d196acc489cd70bc5ffbbb63530ac5fabb169d40c4fb3a32ebb8ed8",
"blk.5.attn_q.bias": "f3304d76ccd44fed887565857c8e513b1211d89a5d3e81782de507ab3f6fc045",
"blk.5.attn_q.weight": "98612a6b7920a247853ada95c240807d4ca8e43604279e7a2fc9bb265ae40469",
"blk.5.attn_v.bias": "39940a9b353ceed3edfd4a39b985c9520490aa1b9f11749c94fdf6d879d1a259",
"blk.5.attn_v.weight": "839f84b828cf83aecf479a0dc7bc86cce05145ef77dcf29916dc3e0680f5b665",
"blk.5.ffn_down.weight": "1f48cbb0960f15e06ab8a3754ade792995a655856389ddbca629c07e89d1b114",
"blk.5.ffn_gate.weight": "33d8219fce3189e1aab376039896eebd4ad36ebd26a8278cd19b26e4357e4f81",
"blk.5.ffn_norm.weight": "0f4a0f83d37127fa4483f2905cb4f38ef6ddc71584b6cb05632c62a9af313dda",
"blk.5.ffn_up.weight": "22a64a11e5f0a1ff45ca327bf9e1efa258f085ff6a96edc398b7474f725b4514",
"blk.6.attn_k.bias": "baa91df99d4df2d25e8d590bca4e334b97f2d9aa3df8e748fedc8a6188499111",
"blk.6.attn_k.weight": "121f3b9f4b9491996499392e2688a929cafe102a67920b4cb2a039349c43d8eb",
"blk.6.attn_norm.weight": "b4cf987e923d71f2f84c58d20ea8af7576b225bf61952145b489fdd395e3d411",
"blk.6.attn_output.weight": "a112642150a138d54b2a4038042fd33619035a35694771e966f3575856c635d6",
"blk.6.attn_q.bias": "a97ea10469cdfa3fdddf8bad6de683ef99f6170eb8d29d15dcf6bf4bce37c5a3",
"blk.6.attn_q.weight": "d80c787019317a87361de6bbc7df6701357216bdd9b404522cede34a719a5500",
"blk.6.attn_v.bias": "d846269db9cd77ae28da26ba0914cace1b6754bd5301af9c44607085dfcbd2d7",
"blk.6.attn_v.weight": "06567c433e8a391647633291b50828a076ad7c2436106bb9278c60a3f8fccb3b",
"blk.6.ffn_down.weight": "f15f66f56b3c474eac8c6315c5fff07c3e29c6e483d7efd4d303c7f43814be91",
"blk.6.ffn_gate.weight": "47768f89c6da8eefb29adb766ff4eb38c9dfd79320bbc1386248319fcbcf567f",
"blk.6.ffn_norm.weight": "7f8195e6b148212967145fc9d86ce36b699cff0de026042245c2d344f1ef8510",
"blk.6.ffn_up.weight": "53d7707ae4347aadb445289f9f87a008b72df5cb855b00080a605442fdd8edf3",
"blk.7.attn_k.bias": "63e274df3217dde25b8369a383e480fe4f6b403a74385f15ac0b5db71dce2744",
"blk.7.attn_k.weight": "f6fce88602f5945eee09767acbcad387d132614e6da39ae359f2bbf380d94b1f",
"blk.7.attn_norm.weight": "bbf5dc7336c0f9a511afef6bf5efeffd78f1b83940850c3eb7eb20c621b75656",
"blk.7.attn_output.weight": "d9fb907a138396a859cecbfcb377927308dc93c24c7fb52dba5eb59265feadec",
"blk.7.attn_q.bias": "f02ba1318346af77e309f40aee716e2de7ee8cab67e67b17636db9bf40894fb0",
"blk.7.attn_q.weight": "54a691e824be287a61c35c172edc01922ed792d2addeee029afc17ba6c7e11b9",
"blk.7.attn_v.bias": "3a4f182f51e84ce862d558fb2751b91802b65d74596bb14d624808513a8a83ec",
"blk.7.attn_v.weight": "a142fe6e106d3ab484e2dc6f9c72b8fc0a385279dde08deb1ad1fd05ac25deb1",
"blk.7.ffn_down.weight": "8daf7e8c430d183a4d6ab3eb575fafa4b5e31689f68b290c8b370411ad9d0f12",
"blk.7.ffn_gate.weight": "a2a786b45eb660994254b48e2aaf22f3e9821cfb383dee0ba04cc4350a2f8e72",
"blk.7.ffn_norm.weight": "73828bbc8c9610cc139fcf03e96272648cdc291263251fe3a67367408deb69e1",
"blk.7.ffn_up.weight": "e85dd0f63fed449ce16893c5795ea6a050a2d7a66d9534410a227e22c905dafa",
"blk.8.attn_k.bias": "91a752a6e2c364e5ee6a015770fe289aece4911ae6c6bbfe74ac52f465465f93",
"blk.8.attn_k.weight": "99c069e92c43a2efb74e23188256b3cabbbe06399878e681ce203a05d5da378a",
"blk.8.attn_norm.weight": "c76d36d3cc06aa2a9edb1abf9f602bb7ed61ac9d61f8ef7ed736a1e619abe717",
"blk.8.attn_output.weight": "ee5ff156a2625e1f203f65e69b514f9df04bd9a5e82b28e3876e16cf1c6f65c5",
"blk.8.attn_q.bias": "8fbd868a93b330c8b0418b488c5301f42a7eb0c58445a4e515d56777f1d96ed5",
"blk.8.attn_q.weight": "9f20ef86e80098ba52a3a31ebcc315bea3a614dac9cba7ac1db02f156db9b577",
"blk.8.attn_v.bias": "c4813571d5d618742183a7890c0b89cd7f18e210c758f63aad564659bc38a26d",
"blk.8.attn_v.weight": "ea88e1a4cf8bd56e9a88ada427d2b0cd352234827640757ee2a9ed594fb67a53",
"blk.8.ffn_down.weight": "b0d1a7495811580b189aaa3e20ea871d6d01ed7b6c23e59825078ef786944ff2",
"blk.8.ffn_gate.weight": "0a17c0caa0b06721c49b59b2a63a5dcbf744dd1cffa55962b404ba910c658a62",
"blk.8.ffn_norm.weight": "f15f109d4a8e9d1ff7c71fa5bc6373df7ee80c5f7d1de3fa0d4849d747e36bcb",
"blk.8.ffn_up.weight": "bbf4c5c4c5c8a0f9ae8b88e3cc8b86f81b98148722d5a350995af176c0b774f2",
"blk.9.attn_k.bias": "a7f60d962686b8ca60f69643e0e0fa8614688be738fb0b1c6bd54de35c2beb5e",
"blk.9.attn_k.weight": "dd80ce4adb00e338fc04b307e4c18a27071f4ba4397184a24d765e6e4a268ef4",
"blk.9.attn_norm.weight": "721e6487547e2b3986ab4b4e2500ceade59d908bccf4436e1e8031f246deb2bd",
"blk.9.attn_output.weight": "5a800af39107b363861e5f5173483cdcd644d8ac3b0c8a443b9c759d71285db8",
"blk.9.attn_q.bias": "0a19b4925ea8ca8067acc909b058adc327de3874cfc94cc9eb4a106d3f370123",
"blk.9.attn_q.weight": "93e84906684c0c7ede79967236d9fc8344da84a9f1daa04e8295c2c9b6b26a24",
"blk.9.attn_v.bias": "615421f812f821e230ecde4e6da35d868823248355ce7e4e51e2d650ead565f9",
"blk.9.attn_v.weight": "7f4913e289aefd9ceecbdaf9767b1e95303f5d59dd67ecb2cc15768477f4d08e",
"blk.9.ffn_down.weight": "95d1b3933221e87dc4af70dd566daec9498bf358070b8d26f1fc70766a84a152",
"blk.9.ffn_gate.weight": "530f2d04f6a1fbffaaa5f2fbc3a328ebed7b330e3af14b4fc7d8a51b13ad8d42",
"blk.9.ffn_norm.weight": "28077de416217ea1df94b96017bef4cc562ab62e51b1a03a671c70abc29ce52a",
"blk.9.ffn_up.weight": "b87b6190778aaee4695938e24ac6c90dbbee6dce7c5c2ab5bc26ba4564581822"
}

View File

@@ -1,344 +0,0 @@
{
"general.architecture": "command-r",
"general.name": "command-r",
"command-r.attention.head_count": "64",
"command-r.attention.head_count_kv": "64",
"command-r.attention.layer_norm_epsilon": "1e-05",
"command-r.block_count": "40",
"command-r.context_length": "131072",
"command-r.embedding_length": "8192",
"command-r.feed_forward_length": "22528",
"command-r.logit_scale": "0.0625",
"command-r.rope.freq_base": "8e+06",
"command-r.rope.scaling.type": "none",
"tokenizer.ggml.add_bos_token": "true",
"tokenizer.ggml.add_eos_token": "false",
"tokenizer.ggml.bos_token_id": "5",
"tokenizer.ggml.eos_token_id": "255001",
"tokenizer.ggml.merges": "902a060cac8884a5793d2a857dd2e53a259de46c8d08c4deb243c239671e1350",
"tokenizer.ggml.model": "gpt2",
"tokenizer.ggml.padding_token_id": "0",
"tokenizer.ggml.token_type": "b7a352ccd1c99d4413bcf452c2db707b0526d0e1216616b865560fab80296462",
"tokenizer.ggml.tokens": "815ac90ff23565081522d7258f46648c8a0619eb847a9c7c31b238a9b984e4ae",
"blk.0.attn_k.weight": "6fcfdb466f9ceb1229404ce4ec4e480751b8d00da12707a11783dad7256cb864",
"blk.0.attn_norm.weight": "6063317f731371864049c7704a70772f1eb632194201ebdc2ed0f8e483507c72",
"blk.0.attn_output.weight": "920f49716a1e2fc73b6794ec777947f1c122701e63ed302422ac89e90f06e9da",
"blk.0.attn_q.weight": "ddbcd7cde197e632564ac58e4f25d9e3a8ca52917329eeb6081eb41a797932ab",
"blk.0.attn_v.weight": "318fc02a189d87420f0cbf57f47f11e00c21ec1ed472ce0a2a895b44f7fa0fca",
"blk.0.ffn_down.weight": "aa71975b6eb1f4c77b03d2ac4a194cf8d95718efac741bb12f0f3ff79a27f9bc",
"blk.0.ffn_gate.weight": "42967702fa0bc738b88dc50007ace26dbe74a5a9e0978124dd093f818241a9e1",
"blk.0.ffn_up.weight": "5282c8788b086bd30f46525e7995a17464882a72703fd27165491afdd8bfd4af",
"blk.1.attn_k.weight": "cd248882e64fd2c3402c44790ebe12440133dc671b6893fdad0564c461973adc",
"blk.1.attn_norm.weight": "ba84e1c8fd30af6ec94208db4078befac8c921aad3acb887812887f3282ea2be",
"blk.1.attn_output.weight": "2efa3ef7c5666ccceb05e339b83ad680cc0d2c3ec78203f5da5959f23a80e14f",
"blk.1.attn_q.weight": "5106f2e255358a1303c22e8b5f0ec044852bb30a866c52cabefd30017a7a6b7d",
"blk.1.attn_v.weight": "a211a634a1a5df1d5f973645438be0461dd922210f9747c6b04e386c7f1ebe95",
"blk.1.ffn_down.weight": "37093afe48d32c578ec956c9ed85242cd000d6aa979e60526aafa10c822dbb10",
"blk.1.ffn_gate.weight": "469860819e9159caefb1aad0bc66db790f3393f05fd87b08e52256a7ed256543",
"blk.1.ffn_up.weight": "736742c97d35d1a011f9cafd3c0ce947ad559bb2fba6da73c816f6bfd0fa9aeb",
"blk.2.attn_k.weight": "92c219d92804d832ab404bd6dc7339c90877bb7cf405dd030c121f8b27757739",
"blk.2.attn_norm.weight": "61e4466069474b76b6d1e702566420eb669faf3556b00ff7b824784aca13a2d6",
"blk.2.attn_output.weight": "d2fb38a2b2171fd91caf037faa585a62225819aa232d86fd4f7f9d2c3c8a45e9",
"blk.2.attn_q.weight": "f6faf5cc6844e3daa4f9f68d90f5458c64879de68a7728860e38374e30c3429d",
"blk.2.attn_v.weight": "f340ef8f7341d987a6f37c0e9afe0aef5be67be00c0ce5f57612daf73319cce1",
"blk.2.ffn_down.weight": "c7be61a701d779860b621b143fb6365b607bf99ec7c0f153b07908ac8120885a",
"blk.2.ffn_gate.weight": "b64f0878187bd3392abfa4c3e8ad2f8b4c133903e54246747ff8f3b4639ad83e",
"blk.2.ffn_up.weight": "50b11c712652e90ee7428dbb45cffebb80662ac982bc72bd9eafff361b5eb5a8",
"blk.3.attn_k.weight": "2b7bcbe9ee5c9c630c8c8d7483887e78b73581016f4cbb6933db2a147a25f431",
"blk.3.attn_norm.weight": "0181dac7f4eee7252980323e8032cf339bef2046ce0a16c0fd72af7c98a8a37b",
"blk.3.attn_output.weight": "aef8843b636ce231da9e7c9acbee197883cc15df0e2887709324c6a50f16da7b",
"blk.3.attn_q.weight": "55404130fa10e81322d33eb378aa0de31a92990ce7730f1338c0ace0406bb1b1",
"blk.3.attn_v.weight": "76f7fb8040d82b957d689ce34fea2302a6640ad5bbaa0052ad2b7ebce270c33d",
"blk.3.ffn_down.weight": "648628933eff3b357c3729c33c5b1ae51c28e59b9c19acd1601a2ff7c5d5d9a5",
"blk.3.ffn_gate.weight": "6a588885d16e98d5f50ebed05af089154f680085ca9c97691e5b489088630a4a",
"blk.3.ffn_up.weight": "e12455a1d702f4986e1a663493e3d5102b367af74d45557522002a35d63ecac2",
"blk.4.attn_k.weight": "40d943380a8a85e4eab147934bf6e16f23cc8ab753f6636526382c074d182288",
"blk.4.attn_norm.weight": "4ab2c098983d4599fe540eef624c4df954adb7473faebda7471ef0ba4134814c",
"blk.4.attn_output.weight": "d14b91e40f58bf4a3c8c2eca0b12bb541de406574af39027d56f6c588a147082",
"blk.4.attn_q.weight": "e1224960a3562107488589f883fa32414bae41712fa8dbd47c5f3e3a7801452f",
"blk.4.attn_v.weight": "063f297bc4aa6e709fc32c4c32e35af7d07d80e83cb939b76adbba858006c03d",
"blk.4.ffn_down.weight": "f88a18020c5e1caaa29596895eb348e76ee5bfad27ed57651a86cd8cd1f9b5aa",
"blk.4.ffn_gate.weight": "48e7e1eed3fb52e92e61d3557dd0ec002418327090e034ce4322fd68542266f8",
"blk.4.ffn_up.weight": "1ca8a7aa17355b6ce0d9ad5539fdad3899fa47fd359c285fbfb31f19f47bf073",
"blk.5.attn_k.weight": "2bdf15f8e73d068d972380f25d207004cf0bf3b5bfa46946803ba6fba07d9175",
"blk.5.attn_norm.weight": "60448d7cde6e1b6467aa31bdea012e39cdb08c88081cee7d102dca4f93f766ef",
"blk.5.attn_output.weight": "f9f687d7c457537f9fca8a4087a59f1c3bebfaf5537b94e42c831a13224f7799",
"blk.5.attn_q.weight": "987db7a2ad68657a92625e1980effbb1f79697c2183f2b9f3b3a0570c51b0ab9",
"blk.5.attn_v.weight": "cf696891148f3e4783ad1d20f93462ae091eb8651c656bba9b662253b6263e02",
"blk.5.ffn_down.weight": "c0662b0bd0929136005fb9d691fdd9b2c33867d9ce9622339a6a456b720b059a",
"blk.5.ffn_gate.weight": "200bbdfab615d7a3a84719b6ced7751e3ce52757ef212d96f87798bc1de5e987",
"blk.5.ffn_up.weight": "df5d23e7e035fb1b9d163da7ddfdfe38da6a37e86e96534dc02ad20f011b55b3",
"blk.6.attn_k.weight": "c0dae2d272a7c5a2fa004bbb8475dbab362fc1f6d008e73d5a4434a9382ac6ba",
"blk.6.attn_norm.weight": "51c57ac8b55e04354d5dca6bb9c0cf4177639d3b038e80209e33036209688f64",
"blk.6.attn_output.weight": "229d97892c62f85bcdf431675250e01c976ad69ffa450b01fb543bf88f14a2fb",
"blk.6.attn_q.weight": "c20e49621821bd46ed156e6823864a5bda4f317750e71ab8dc54e44eb48cf7c2",
"blk.6.attn_v.weight": "53ceb1a2ee43fce3c7b5b33c58a9fc5ee7f44dc1c6f29bc9dbefc37582102dc9",
"blk.6.ffn_down.weight": "7923c943b7629d560a032d1efa210d1d75c6692140f1be94464ee7ed24f44ed0",
"blk.6.ffn_gate.weight": "57593d350361af753a6a39f53b066282634c0fb44f396f6f2966a574b01d8f8c",
"blk.6.ffn_up.weight": "327b6a7a387098b8899d3ded04a4d4e7c658ca61b80d4e7b17594be232721602",
"blk.7.attn_k.weight": "9ca48b87a10116fd8868e62b76f211d4bb91f166096be9061439ee2e1c3a5c20",
"blk.7.attn_norm.weight": "cd56cfcc4e2ad6b96e23ea7b0d32b4caf236107d99a0b22c56760b62e63c8cfd",
"blk.7.attn_output.weight": "7352b509a03cae2491ffc060e577d189341a0f861233f18c96f9d275dc4234bf",
"blk.7.attn_q.weight": "2b3791c8c008c33ddbe12bedba8191322ceea2dcce5cf0eb7a93d40ad254e672",
"blk.7.attn_v.weight": "3ae721d52466487a3d48150581e57f6d64ea1e83ab929f23b28c3d777422eeb6",
"blk.7.ffn_down.weight": "3b6fa8ececdb3c34af3a5363863d6f94289c1c95bf47fce3a3ddcf184c5f0848",
"blk.7.ffn_gate.weight": "dbd7df6c5ae5eb4adb859f0d36453813a4e289a359a1ba8f72d67fcbf21c3e22",
"blk.7.ffn_up.weight": "de68380a334b4c5cfd4c318b0e9854aec59bd79aa0f0c30af3f56414f83482b0",
"blk.8.attn_k.weight": "7303c4e4480abc72a7ee271811311199245fb5c2ea27a2bd3b8cad3a53a03c27",
"blk.8.attn_norm.weight": "2e3d1921898d1b943ce1a1b6818546c8b471d6d542da24f51a8b514b8c3dd4ef",
"blk.8.attn_output.weight": "30421520887b66bf97a18dbcdc283bc8d0b60590b612fd638a319a6eae923227",
"blk.8.attn_q.weight": "73e064d5433c9b500068a1c31744dbd53f4ade298fb450a0e8c97f62cf1f8a8d",
"blk.8.attn_v.weight": "27e21f8b9a9a8533e8178ca34a72aa1d786393d57302b7806dcdf3e51de511a8",
"blk.8.ffn_down.weight": "bf694bd8e00047982108000e7b3dee7b225db8b19abc595e5697b6bbefd92e7c",
"blk.8.ffn_gate.weight": "d55fdbf8606d9141b774b0500c58944fd1253b9e69d1f765eaa9a680b9f2ca40",
"blk.8.ffn_up.weight": "1ae3f580655e7c8e8dd6c34fa4ac574fdfc5e3f1a8536da0c5442d3a2976f0e7",
"blk.9.attn_k.weight": "b18080626012d8aabcf78542d6c7bf31c712bf55a70172fbfe173fcf34481036",
"blk.9.attn_norm.weight": "2e3620620dc09998c6d3063a7d5de5433fbbae8c11e5b00d13f145d39140e162",
"blk.9.attn_output.weight": "69c3c0e27ef1c0fc933eeb7b612b70909f18cde238873c0d576a2ba9714ef174",
"blk.9.attn_q.weight": "68330e5aa28a28873c9a6e67f032186ef651df2df5844e0f27094ba349fbe4ab",
"blk.9.attn_v.weight": "3df8d45a102be082d0793a51cb82aa62a43cd0e9d047ba4115ca0f2414b39325",
"blk.9.ffn_down.weight": "1d6cc162b73745b135b4f040a0aac3c06d5135a3dc5b2421e7ee2af48662fd7f",
"blk.9.ffn_gate.weight": "034a9d40fb1e32b534b45f4bccd65cbe43c4a6a3f5d01132bd245ca0005de5fc",
"blk.9.ffn_up.weight": "c838c38d0e1a0ac0da17eb2a66023ed31929f07d8fcfe1cc546df26096c91f0c",
"blk.10.attn_k.weight": "a78507cb72f744b86ceaa032596e74e5571c822d0226d334881169addb32cbd5",
"blk.10.attn_norm.weight": "35f48d0b28ee0e6b4cad4e983925737562d64824be5b168b3e26df3d6b260cf1",
"blk.10.attn_output.weight": "53712db06796de39b131323e7abf9a58551b6d52da6db66a471580386d396252",
"blk.10.attn_q.weight": "efe08429ba196026b81cd1c471e1c7418afd9e966659feb3936b674aa0803b58",
"blk.10.attn_v.weight": "7ec6055e134f89da0cbe79ec9f13ef2e442ac584b1f03c3e13e7d0cdad0078bd",
"blk.10.ffn_down.weight": "37e66af4bcd1f3079e841e892255b8255070655901864ea3a8c602a7f681a640",
"blk.10.ffn_gate.weight": "1825282bc34830d371c6edcc3c1e73e6ecc1e10f4aea0122dbb7acc1d6f7b1bc",
"blk.10.ffn_up.weight": "819b3b276a4d4c14a35ed6682d5ef18a5e8ed468e5ce3f12e8c75ec18ac20ec4",
"blk.11.attn_k.weight": "5327e6a2af82dfff0619a14971f5864a15553c36fead84e1af42c7630f2729c6",
"blk.11.attn_norm.weight": "fec363b3c4a43036d2c635fb8aa9e122dd87ee79811839f2f6cd955be3373e7b",
"blk.11.attn_output.weight": "ccf7b38f18ee8798b8a6a35018e2df3eb3e007de62876befb68025dd66c79763",
"blk.11.attn_q.weight": "da8c4a1c824ffe174e39f126cd72f7ef83c56aff1259d452a1212de80f98f5e9",
"blk.11.attn_v.weight": "d17ae6bb77f03982b55d341eb67acb5969e9ad3da5994b96eafc09793dcfe3a0",
"blk.11.ffn_down.weight": "a6bac521e2791345f22c57205fa1c2f2f687794dfd24d0e98d50ae0d0eb6088a",
"blk.11.ffn_gate.weight": "5ed902c488cb51ba5635f3df08258c5f84f31a679a00211ea5f9d8b824ef6d9d",
"blk.11.ffn_up.weight": "ee9f1437eb890d2cf9df2574afa1cecf20aafdd847cd75b152d7eb74419afd34",
"blk.12.attn_k.weight": "5a069c06e1019b0f889088e67458f7a11ec77fa190ada6069e46211f62219947",
"blk.12.attn_norm.weight": "194d7e5fcc8c49aea62daf1940532419cf3c505afdce6be377286b677db5db8f",
"blk.12.attn_output.weight": "6534995fd4d6fecb55e317add4b1723aba4d825e1e9471d0b08813dfdc247176",
"blk.12.attn_q.weight": "4ab51ca519b5995581fa34f846276feca3b907ef2b51f192f6cc0b3263c3f5a2",
"blk.12.attn_v.weight": "5652ca3fa81ef9a1ac1543d71fc6813f8517f8ec54b25c701f6f98061614830f",
"blk.12.ffn_down.weight": "4b2c263f54c88516b8eb273bb8d9615b01c5c8b484dc70358adb91b50b300edd",
"blk.12.ffn_gate.weight": "8f50c3c3e3e8568991d6c1b0e74b500cf4f208e7700bbb8e87c3f6a6d359b6b5",
"blk.12.ffn_up.weight": "1c1a581fec1fbe959e1427fa513f400100b5e1ee9d83932630be9905fb49c231",
"blk.13.attn_k.weight": "efd7a38c46f08d8376d82974f33c644e3a02220e142d63b1704718699a8a884c",
"blk.13.attn_norm.weight": "d28fa4f1bd75abbd063b0e622e08f579c89cd0c0c5ce63c1952ec9f944f8ee13",
"blk.13.attn_output.weight": "71e0068a639288718bdb70a6cfdefd50bc8b3ec3993347a65129e70001ca5827",
"blk.13.attn_q.weight": "b97077adc92cff07a2e07d80ee38f214ad8713571c69cd5c70ebd43dc501ac87",
"blk.13.attn_v.weight": "79b3e2749ab4b459c81e96e322b215f1e8af645eb346e176c326bd00cf6ed2fd",
"blk.13.ffn_down.weight": "9f8687d11effa1db7cfecf7bec5631734bcf2962aad74a9f519144491e08ec85",
"blk.13.ffn_gate.weight": "7d14dfa0543852e7777fe8fff29ca533744cbcf1ebcf10067e5adfc4eb345e65",
"blk.13.ffn_up.weight": "852b9527b97fdab211ff3f832a660ee1d93ccb56906144c50f01319a6e8ee615",
"blk.14.attn_k.weight": "79e926b20f36f66d58226cb358881f2f68ae7b468787d33cafae5110287a14a0",
"blk.14.attn_norm.weight": "97d481b63deb0df6142c2c6cd23043720c62eb609e390f47a7113751c79974ec",
"blk.14.attn_output.weight": "aa6e94d7176d5c79fbb89b96e5f13ce75702ce3dd23ee52986446da436a6c3d6",
"blk.14.attn_q.weight": "214becb6d1bb460da9fb8ace0f99b9a5afa9edf7aa7acc19606c7401b11d6305",
"blk.14.attn_v.weight": "488b0e6d7f1a7a2ed0972aaa6d10ef9c775ee5373460324efcf5b3e3da9311df",
"blk.14.ffn_down.weight": "29c7ad16cf9542e30996a1a01ab95b844533b28051f04cc7949c371afb796471",
"blk.14.ffn_gate.weight": "b7ef208f2b054803665b377f5a5980c122c026841809cf855c6ba06d1c3a885a",
"blk.14.ffn_up.weight": "76a5cc28100748d79c4398ce7b9176aab4d661548b6293a82f99144812e5b70e",
"blk.15.attn_k.weight": "a6b8f9e98ab878fa7ebc5d080978ebf2d050acc2ab2fa8ea9188eb10e27702c8",
"blk.15.attn_norm.weight": "a26d07a9752d6dccb68e3a8a2a49fd0752cdd0a415e05547819bc37d9ba63d5e",
"blk.15.attn_output.weight": "c63616c69048ccbee801e05be4f56d21fda21aa0cc470f41d57c31b4d9283a4d",
"blk.15.attn_q.weight": "fd595a67bf96c6ba16eb148a9d02fa52fa3c1d33ed10be28a08f851409fd6e64",
"blk.15.attn_v.weight": "1c5c9d33fa07c05d5f4ed0032c6c4aa83d863f0d31c94a66109d239dcd03cea3",
"blk.15.ffn_down.weight": "585ea62ab8aff7d7d212ea5c1a03226fda6b68370c890b776834af70c948dcbc",
"blk.15.ffn_gate.weight": "a13c63f86f879b03a573d5dd2a25cfd1f4dc73e8132e6454ecc23e538b4cdf6f",
"blk.15.ffn_up.weight": "f7112450f57c12fcd511f049e0dc0b541625a107a7901c3261ed9e984299f65c",
"blk.16.attn_k.weight": "2d2c8b11dd71fba6d1c106aa1673c113a5448653cca7eab897c8739212ed5003",
"blk.16.attn_norm.weight": "95c2ec7be9469690e18a9a1779684acb3e9da44b13e263a0da840305646fbf8a",
"blk.16.attn_output.weight": "31a65046e677f54dae654ded4e733479fcc0f7283d83076b7dc7cbcae8528230",
"blk.16.attn_q.weight": "bfc6292b9c6d49b7118d08060242a138182eb182d136ba5dfaf469437c16081d",
"blk.16.attn_v.weight": "68f81d037340217d87c7853ff4d6edfbc46d9e827ee6d5bff7c3f6238e3a95ad",
"blk.16.ffn_down.weight": "bbd6629691950cef4d5113e1c6670e91b216a9b872cb92cee02dfda4d6c4f7b8",
"blk.16.ffn_gate.weight": "63cb56f282b7401ed6c76e5bb6fdf1bf68a64f9af0c82c014209b55bcb5191d0",
"blk.16.ffn_up.weight": "b54f39a2541063cbfb6f713aa81c3b69a04100e999aa2ebbeec195dc382eceec",
"blk.17.attn_k.weight": "3d9ba49799cc56664ec30a002bcad61eb651294212a68c3ddb573eb042aef5a4",
"blk.17.attn_norm.weight": "42ee0db4b9d63257bca0012a30b12737ead1caafeb5ed3d93c8f48ffec4b46de",
"blk.17.attn_output.weight": "a38fd100f05c9041c592bc739e287de0b10d08ef2bda41a879225bdca9002f71",
"blk.17.attn_q.weight": "8a3bee285b0180a9eb35662e449ee4cbe16d992bdd48fb3a94bc4a347728cfa2",
"blk.17.attn_v.weight": "d7f8f1b8b863494ed4392a1656775912e9b264ad36016547b12e832a1d6757d6",
"blk.17.ffn_down.weight": "bb7ee58f61da8630972e25b621996fbe8ec06f4dc9ab1e268ab5b120c526ca28",
"blk.17.ffn_gate.weight": "6b652dbf167fee09a45ebfd78d500ff6548fb2756dbe5343ffec3f7e6207179f",
"blk.17.ffn_up.weight": "3b67f727e55e742715de978fab80457781e7a3762bc48f79d13b45dcb8de664c",
"blk.18.attn_k.weight": "ff7fe57c57b90c6fcc0aefc39ec24593c3a7d1ea1c23770480075a015450e0f5",
"blk.18.attn_norm.weight": "1d40faca082d2633ef0ccf19e121870dd6c7c3e2154607c7f3543fa96e99cb2d",
"blk.18.attn_output.weight": "9adfecaaa397a92db4687efd5fcabfa0daef9e6b0493763b7ff5ebc185c43a6c",
"blk.18.attn_q.weight": "ad1803eb9b291948639277afe981e666b07167eb3fcae903ba5b73bf86d8f50b",
"blk.18.attn_v.weight": "308cf23399adccf27401a4ab60d74dac6fb9d4cd4b9c5940d9145118d1881b34",
"blk.18.ffn_down.weight": "7de4ac9a561fb580619b745687dfd7ca8a69ef70471dee978741b80e9ff7bead",
"blk.18.ffn_gate.weight": "0c66970f696b33bd5ee8f1f2fbcb41fd78fa5ccabdc927e11a4d5a4089f19c69",
"blk.18.ffn_up.weight": "66a42e988e8a1f468fabf976c48e9e4bb045eaac6916ef16555ac101cd674abc",
"blk.19.attn_k.weight": "a928ab50390bacbcebe2e4b66922498134ce22d7b93beaa87d6cf4ab52eb7174",
"blk.19.attn_norm.weight": "b4a02c55b46c2a96aec9c64a254087cf48e6c1d4b6f31782c77a46fc4daebad1",
"blk.19.attn_output.weight": "b768319c641dff1eac5d1f8ceb960c9899c795bf2b24c1d6bf70aa24fda45f77",
"blk.19.attn_q.weight": "79ef3f57d187d3954a26362096e1b6c222d76f537dff73e034d6e9999935b8bc",
"blk.19.attn_v.weight": "ce13d6b13e24fcb2d5bc6a2662e5bd295b31b12db10a6d0307f86cf29b8d5001",
"blk.19.ffn_down.weight": "cf90d7e2137482cfd50934a8223ad774621d08554969da80a9712df5e6227eb0",
"blk.19.ffn_gate.weight": "71ce30150f003b6eeb3bf7464e05b6ae615f135110d8e47f0a47fd973e537c0f",
"blk.19.ffn_up.weight": "7f92aca0cc29866633feec701ec01a85a8ee2fd4e2b9630173a6cffb1d9d50ee",
"blk.20.attn_k.weight": "a2df23159d6fb74ef28e14b61028fe8b00a693a2fc9234a980be74f20b958682",
"blk.20.attn_norm.weight": "c6cd5f1b096fc5efa4eb59ca1c8c4bd28730f3dcedd59a63601663eccc6724ed",
"blk.20.attn_output.weight": "896a8a166d0f006d4b09867ae4345426303cbc3fb13a18d3d4e1bde00f16dbdf",
"blk.20.attn_q.weight": "01eb79588fe61baea0da43e99f4dc5939590e1bafd01e12dadb8326f102bfea2",
"blk.20.attn_v.weight": "bd39630fdd5a7c859ac1addaf53e63faf524c3f32f5f4896d86b6e746b1d5c06",
"blk.20.ffn_down.weight": "0304a5d39957a0e3f031c4bcc4549a135d396c8d97c8d276fd1c823ce86560c2",
"blk.20.ffn_gate.weight": "117b79d595b1dca0c8b37586beaecc4d84411507276212dc286cde7fc36c9bef",
"blk.20.ffn_up.weight": "6e799346db145c125f01783539749d3828fcc451cd4f10c5352f047a47e28714",
"blk.21.attn_k.weight": "1c37e4c0664147e775bb006b226b9553e3421140cd96288ea755f81731ab80ba",
"blk.21.attn_norm.weight": "00ae783a29000ccda5e4bdbff03df0752fb82805dc3f9b987500ebd80714476e",
"blk.21.attn_output.weight": "7588b84f9fb19f15095b5265c60b4a4e7ae74bcc47d4607dfa5d0bfab6f136cb",
"blk.21.attn_q.weight": "a65f1c0dd06d45bb97532d3e932689c1eecfe7359089b39174a96a149335cbc1",
"blk.21.attn_v.weight": "4220b77e7d5e8709b4eef33a679b5dad11f297085ef44c9977f9e54ef08f7a2d",
"blk.21.ffn_down.weight": "b8c082a0530d4b5328e67db0df84c5498f2af956de23c639fa0198ffea853950",
"blk.21.ffn_gate.weight": "cd1b656ee72d00e9835ef667c19ef89a88de261eb8eb7c0e936e0f9ddf83ef9f",
"blk.21.ffn_up.weight": "dc445f73e36ec7a3bd86884186b728f8e0187f32848c3b8b69d4d41f8571bf31",
"blk.22.attn_k.weight": "e37cf0b893ec8b9ee8c78dd139b8d9c45cb997a3bc0c3d93a70ca1c3f6af8859",
"blk.22.attn_norm.weight": "248a27838d3c46cc03a5c312facc84e2e0e2c990ef8401e93da25918497f88d1",
"blk.22.attn_output.weight": "fc191a18f6d18332c66761f7ab28008bfe295dd1f5c8741a2488442f9e00d0f5",
"blk.22.attn_q.weight": "4b193a2ab8bc2b085db18f2bf3eeba26e02b537b2cdd738160c8f14b165d0f5a",
"blk.22.attn_v.weight": "7a60ce5ccac7e045e55ba1e1e85bd2a0f93f8c781daee96c5223665e22f0c666",
"blk.22.ffn_down.weight": "e0a34fb4244e2c7168f3dbaa1904c15d339ec39999cdf27128bbaf619ee0a237",
"blk.22.ffn_gate.weight": "8bac872d4b8549c8812f927efa309f1792b524f33601095fff61b826de5a5615",
"blk.22.ffn_up.weight": "b67fa2b94dd901b6ec64c0853ce8ca2d86fe9cb1cc6d2f15fbbbe0e691c0c648",
"blk.23.attn_k.weight": "2c32e66ad01942b819ac09a197c71579fe66f02226a264fdd72ad1e02c67a27e",
"blk.23.attn_norm.weight": "825fdc94deb439cb93c713eeb077c1052b90ed658d6d464fc4ad3d611e911d48",
"blk.23.attn_output.weight": "95ca6707a95b8750b0c7c5d379d368f0f2e7ebef631954e7d4d8ec0f41f13a3a",
"blk.23.attn_q.weight": "6eccc84faca5fac015d1b26e2854501edcfd292a302228fe14cf99f5eb59a34b",
"blk.23.attn_v.weight": "b343ac3d226040f1033ee049668aa1d89b1774bc18431965682e5dbdce78ccdc",
"blk.23.ffn_down.weight": "9fc599befea8d3b1e342d564a110074f66d2542df406c4b90b6bdc5828fbb2b2",
"blk.23.ffn_gate.weight": "488556c1b0c9f0b20b0c99b4bac2e0f4046b81edb601d7b91e7e5b3bab47d667",
"blk.23.ffn_up.weight": "1088e291d7008dd9c7c2dd6830af686a8a84b724d123a016209bd5156d6898f1",
"blk.24.attn_k.weight": "a923fbe35e61e009a53927d7828818e0592bb737d6a1106c4b0b5a1efc367e07",
"blk.24.attn_norm.weight": "9b51aaaa939cefafdd9b13a7e5b74ac7fa2d603427e55a16a909d6f3f353750a",
"blk.24.attn_output.weight": "1beb2baba56f8409466434b037771248c2f620ec5f53e15f44c271d5a2d9ecf4",
"blk.24.attn_q.weight": "4b0194fe5bfae0c6bf6131dcf8cb6e2b994f6ea10b27cb03574f0f4f8cc0c950",
"blk.24.attn_v.weight": "6ac34b1ab0f66226d85bca1194a7c212cd93d384ecbc8b8395de48aec0970a61",
"blk.24.ffn_down.weight": "5508f74cb732a662c2936b32ac5e90742d172b9f961a747b0e5cba0e5906a89d",
"blk.24.ffn_gate.weight": "095e39b8584403835f9bb1ac33e0e81f54175575e4800273d281b845bff381e7",
"blk.24.ffn_up.weight": "2d43ec21637dda12973de367b0113ee9840b0d815bf6fce042f7c3f270b0b530",
"blk.25.attn_k.weight": "9e2aee029f3d2c7f67dfc7926e72c8228fb978382c8e5a4701bbf82c93801419",
"blk.25.attn_norm.weight": "220cd7164fb4cdbe22d26058e4153b26c27c7b5ce2bec8e95bf2c0ea08d23103",
"blk.25.attn_output.weight": "a17f4a5dc6aa51f03dbd75602d98e9491767c205cdc2c3a5f8667fc54bbf7c64",
"blk.25.attn_q.weight": "f60827496835c440c794bf57ce9780704d10a59d8229886bf75ebb18900ba4ef",
"blk.25.attn_v.weight": "9cac217e9e9f4f4c85f14ee51165a77c580165bd4a34b202389169bbe61a1ced",
"blk.25.ffn_down.weight": "a0f36949b663e80849581dfb71e7babcc73580793bbcb0c80ab26d5a6e000359",
"blk.25.ffn_gate.weight": "df4d1be4d50d6afe5ad3ef0d0e0fac76a33e85c963dea769641d612dd53e7d13",
"blk.25.ffn_up.weight": "992da76be762632e25ebc5ef4d03728eece1b43f7c4e31827df19ca724aea694",
"blk.26.attn_k.weight": "34199ff856ac32a500c754539d070258574192a34ecba87a182897cb59fdff52",
"blk.26.attn_norm.weight": "a8e9dfb2dae5d22b5c0aec5f3675991c0e3c3e6a44153db2579136b73f456e00",
"blk.26.attn_output.weight": "1c4f257ffb0d7db0f11cfb275e38b4af736917b43ad82de1badce3f1d227da4d",
"blk.26.attn_q.weight": "33d55786274c2e718cf61e8fbecf3dfa5ee0c208f0b716d42b061f55459acb3c",
"blk.26.attn_v.weight": "684b636939cd4ffcfec5a6238a0790ffa43d853c95783af9b9e8275e74071a7a",
"blk.26.ffn_down.weight": "89d0bf066db154e6d312b5433aed1714f6a28b40f4c52e3e1530ee07703303c8",
"blk.26.ffn_gate.weight": "393d649bebe5e2940e1b043649f6c860b4b8b9f380f30e9da1744a830f358156",
"blk.26.ffn_up.weight": "179edc85ababd9d8440cc6093eecd1004290aa1cb96434b26ecf7585b6cca17b",
"blk.27.attn_k.weight": "334841445a7f1e14731b08f56eb0b1f0938c63823d28bc6d078c4c5f05b36f19",
"blk.27.attn_norm.weight": "57344471bbda2e9deffdfdb2dd05a07aa47f8761e24de53525588639145bf551",
"blk.27.attn_output.weight": "506126af9ee54b535d49f97e36f630e74834f480329f098d6d62e96246d8d65a",
"blk.27.attn_q.weight": "dd984df1acb4783849e25ba7ae378bfd385cd9efc540fb798cd5bdd873f0118f",
"blk.27.attn_v.weight": "b4b3fe9a4455d34c297ff20a2f537b647cef424741d840a747b265f23d320ac0",
"blk.27.ffn_down.weight": "621fdb185ba0d35ba5476dae73d2c81ec1482a0e878d5bfd5c3b29fe837af013",
"blk.27.ffn_gate.weight": "e4fbab45f2ec506fa374103251a0bdb7baa6f576080bdd796f3e9db92098e08f",
"blk.27.ffn_up.weight": "a0c57e463e988002bbd6a6c6792baa21a65e6f89ae303a2c301951b0ae6e4bbe",
"blk.28.attn_k.weight": "bac36cbd52ec5056841663865e1291ddab4b47ef9a2544dd285d4503bfb0e4a0",
"blk.28.attn_norm.weight": "5774a9df2bbb2e86d1f70179c7b92d81e1f401160148b3328fb64db6646a5425",
"blk.28.attn_output.weight": "e8712622d1569557000c75f26c3f55fad267fd300463c2c2cfe3afbfa1c8f908",
"blk.28.attn_q.weight": "11677751fddee52cc739699c02836f7be54d96038be4240be5d4f53d00161608",
"blk.28.attn_v.weight": "e5ee459b8958d65e1445997b9aa1e90e2f5d17761ebcf5357313119a45322507",
"blk.28.ffn_down.weight": "3934518f9f85292da8475fe38a8edcbfc4e24ac56c351b472d6351f98750871e",
"blk.28.ffn_gate.weight": "6ba735d57e98d0847e487f25ffaa25256deaa8abec76f428cb70bd9774279d83",
"blk.28.ffn_up.weight": "977fae6e1e5353114fc645dd98429464749758765cbc6e6457593d596e57850c",
"blk.29.attn_k.weight": "8122a457307d580ad6f1e0acea09a2f593d97f595ba0d6737f5fea16d2433642",
"blk.29.attn_norm.weight": "d626f721e05aa1202439b01027031d4caf1adace61ed37870a277cb6297c77cc",
"blk.29.attn_output.weight": "7fb7122ab1b6b1e6615ca746897da27bc52c92cb70d3147183cdde61795b72b3",
"blk.29.attn_q.weight": "be43e94ff6b6e391024dc824101efa0ddf4005d5b002ac26cb03765c0c73c2fa",
"blk.29.attn_v.weight": "af93c85ebff908f74f9935b81bde0516ca487c84139868a1ce079c3ae20036b1",
"blk.29.ffn_down.weight": "39dae12340ed3120bd19c495fe0872b559613641e41fde69d02d8631900b84c0",
"blk.29.ffn_gate.weight": "36fd482439840ef197c9f3b8905d86acfcea49bcf018544106ca465d4bf8d5c7",
"blk.29.ffn_up.weight": "5243fbdfdc1e2a1dd84b6210a9869d18a014db9088897e345240cdc99990bd5d",
"blk.30.attn_k.weight": "948f263616bd3788b2b968baafd69b9c5bd1b77578665f096c4b7e247b4cea42",
"blk.30.attn_norm.weight": "e168df981e744874ff303faf2eb470e5f6868c2040ba5f383f6c5148669975e7",
"blk.30.attn_output.weight": "4cf0ccca04b792573b756655a24fc89cfb1f272da8305633f0bc66ef14990b93",
"blk.30.attn_q.weight": "21e07d6cba6c50d65350289258209717174a13c42be57e8141d69712cbaf32c1",
"blk.30.attn_v.weight": "65a8ca29c7237b3182ccf03e2fc94e84f9a53d0e160fb679ab401c853170dd9c",
"blk.30.ffn_down.weight": "8b00500a6d00d84058f6658ee1d6f06fb4fcae2f90d4341792259362923b3c13",
"blk.30.ffn_gate.weight": "5bc0e19ab7a31b50ac2118ad1b36e31055271a322cd8ff661d47c3ac0210703c",
"blk.30.ffn_up.weight": "f37a0561955725bd59ee2d064fa9f4e00a12a1b620b624db3bc3add5330bc321",
"blk.31.attn_k.weight": "9a5663edda227f5d87533897146764f8e8a7481b9e71fae197c39204f8463221",
"blk.31.attn_norm.weight": "060a4f438a1ee5e220b5b5278ad2f5c085a428bf38c515766781815597c87529",
"blk.31.attn_output.weight": "6ada5d3cad9dea4780ffbb43302bb6ccc2f24eddd0fc4f5f84c9ce0fc0c6e5dd",
"blk.31.attn_q.weight": "bb5d08c08603907981ad388d5d8b70fcc9b98034ba264b8474c8890cc0297af0",
"blk.31.attn_v.weight": "e01b4252ea9c6a889c32b21144b441a347464d04536ef4f6572425be55759796",
"blk.31.ffn_down.weight": "8ba4d679c36e93ba65ba03180385ef35ea86b3b7cdf2fded9df59369f1c09630",
"blk.31.ffn_gate.weight": "e5b41dc93645f8b5e8eebae3ada3ea43a18f97ce2654228655170b07b463ccb0",
"blk.31.ffn_up.weight": "25b88cdddc8b547af294ed107d3d1312e90b983cae87936fa6062ecd8ea02539",
"blk.32.attn_k.weight": "4bcf86dc0858c8ca2fbdf6aa76674d43eb698f78979fdc1a38f556a7af1facc4",
"blk.32.attn_norm.weight": "cdcc12f3b8b9773c6722736bfb748a2729230b21478cbcc4104859d3148df815",
"blk.32.attn_output.weight": "d43f1196822995ed89a9365c97054753a8b30ce20b6e273c8edcc42673a1e141",
"blk.32.attn_q.weight": "ebf2972bb3865cbc5be4840113a322089752038344beab2a0122c7cb4fb399b6",
"blk.32.attn_v.weight": "714db81704ff34fa137512903c1013acee7877467473e46600728b9240582eb7",
"blk.32.ffn_down.weight": "2cde3da1258bb170a79d5d3cdfe10c86a71eb34b77da46b74c5ed71e7f4fe274",
"blk.32.ffn_gate.weight": "c7e1ed792532613ff9d4e5834b6536e2e0f47df2303bc0fdaa90aac0c1f4e8db",
"blk.32.ffn_up.weight": "d8d6f13fe66a716e28f79101a29817f0c0d6f99969a6f017d51bafd1a16c600c",
"blk.33.attn_k.weight": "a0a28f6cbca88da00cab2ca37094d9b0503bf9defdae77b91895b911c408cbb6",
"blk.33.attn_norm.weight": "0251200c24cc8445607ace6dc8c5aa0566567997262b7cca53a11ac23cc564b2",
"blk.33.attn_output.weight": "b2423205bdf6a1096d43c44d8d12f1a84fcd4e1bb70fcf6dc8542b8b8a71a13c",
"blk.33.attn_q.weight": "00b425c3ef71065ce5e0234e702bf38143b4952da78a85f52ab2c2e3073d97ab",
"blk.33.attn_v.weight": "035edd2335df816c42c765a5e66b9d9b9e15a822a8dc1863508145499c942c14",
"blk.33.ffn_down.weight": "4894a923a3db75bae4496ba3ce5f28796ad31fe33996a066271fb8654964310e",
"blk.33.ffn_gate.weight": "8f6c819b8bbfbe3357fae89e1ac5a3d58be85b3b04be3bacf7b62775869046ff",
"blk.33.ffn_up.weight": "257c3544b5b544fd5d839665bf5caf107a329b59dbc3751efcaa24ae63c56179",
"blk.34.attn_k.weight": "b6cd8bba892e38dac4a2ebc3ba1bce49e71b967fc436fde30c6d76f54a18935f",
"blk.34.attn_norm.weight": "2b3c8e60a064cba9955752bbbbdd92c71ba5c2f1bd721097bdbe88b5abc68787",
"blk.34.attn_output.weight": "8cc272551c9aaca9db5a660c6927bab94a0243d74a30b2bc165f06bd577714ea",
"blk.34.attn_q.weight": "74b561eb4792484e6a94b58fe2583848c3ae28ff2f1bf3d02939a0cfdfa49990",
"blk.34.attn_v.weight": "dba19e24ff05154dc5a1f55c023729303a583d13d68732ce22ea74d4410dc8f0",
"blk.34.ffn_down.weight": "76eca5dfeb274c35774e0bf9f22ee420ed9085c8e99aa2cd5a236e4918b44c61",
"blk.34.ffn_gate.weight": "9af0862d5fcbc24732846488e653db8242a467765c0cdbc00332b3a40256b4a6",
"blk.34.ffn_up.weight": "2a03126bf73587eaba99ece2066103d12e47bcd4ce30ff6c17b2f383b81d40df",
"blk.35.attn_k.weight": "52513fc0cd4e997a842729af7d21dd09399bce0a339558374738be266d0fa2f0",
"blk.35.attn_norm.weight": "e5281fa911964263ccf1630b14762edbd41d0b9472d6ec695fc600fed4892c35",
"blk.35.attn_output.weight": "b391d6705d5dc6f48326b5fd16573f679edf64109d86fb729a498819676590ca",
"blk.35.attn_q.weight": "d16446921966db9b0e0539626ad22a2511ace780e59379d6a4162d8c5441440b",
"blk.35.attn_v.weight": "9d8cdf23ffdb0c5c74106843390b94b24c9f33ef0eb9998d39f78c73390101ea",
"blk.35.ffn_down.weight": "938eb6301f7bbf162d7dd965682a5ed11d0a4a530c6fedd7e5469ce80012fc17",
"blk.35.ffn_gate.weight": "5ad84f5a0c8edcfea1ecf1a3e3d21d85ceda0c4ad9e3c6ca68885eeff8ed3c2f",
"blk.35.ffn_up.weight": "1c4330d9dc71bf4c98812c34356c51f520f47610a534152aa6d29284b758090d",
"blk.36.attn_k.weight": "ef720655e5ca2465f13db2dfc4732fb4ef2c9d53acde52f514fd4f301e974081",
"blk.36.attn_norm.weight": "88f4b9310b3c8c2644e3029160cd35678c79dfa59280430e03f5c29a6fe84a58",
"blk.36.attn_output.weight": "aec6f915fffd7bb72cd783273e871b4f09605950089d45e72059d1316b6c4b01",
"blk.36.attn_q.weight": "72f9408a2405d42f8db6ce5fcf1d26a3660b6f225fc60e77d0277109cfcb82ed",
"blk.36.attn_v.weight": "0f3b3d851dc44b3893ef53f6cca5b4acc9658bacfe1cc2d13c3d704ddd409b67",
"blk.36.ffn_down.weight": "470aec48ce8c5129a6654d9fd26fcae72776f9fc1429a8bb05818072a876475d",
"blk.36.ffn_gate.weight": "7f5f296d09cf55679767b5d15de3eff489c456782119f25204be4b1647f18dcf",
"blk.36.ffn_up.weight": "b7ef74a1f7ffb4982711d93f1787be3a70edc3d2358d5203c41d8900508037d4",
"blk.37.attn_k.weight": "c4ffa5412e4ff2dcfe1aed991c1f54169fd171a4c7638e4b9f21a1ca64c5e1d6",
"blk.37.attn_norm.weight": "4eb6c888d841cccfacf5b963f8611120f6ff24b84af0b5714fd9ab36dcda422f",
"blk.37.attn_output.weight": "db2a7bbf9682f9f6eea672dae8e150738f1bf74dbc80edc7022017a3f040c8ac",
"blk.37.attn_q.weight": "e38c0462aff139afcbab289189823527e453abc9e541154adde5e7af88cacf0b",
"blk.37.attn_v.weight": "952eb2492ed452a72f96bcc12d4b2affad9dfdf46ee39ce4a5d7b57a5dc301e5",
"blk.37.ffn_down.weight": "25f23a8fbc44febf6dc4848fd7fe03a580e2822bd3b3b5a51f4990826bfe3e4e",
"blk.37.ffn_gate.weight": "707da5eb40118b035305d3262444382351f170a20a537386a70e90c5a83a7817",
"blk.37.ffn_up.weight": "d2d2ba5cfc4ef47338dd7384219e22bf030a5a2209e0354d88f5bbaaafd20e87",
"blk.38.attn_k.weight": "abc4bb189dedf7ce661e79028427623a4f91ac091c2cd60e31b58bc62b1cda71",
"blk.38.attn_norm.weight": "9f4803a7d03fd40fcb83d85f84eb1d5682ea4e5bb084f210c02850675d804c3d",
"blk.38.attn_output.weight": "77cb66007f1a41df7135d0e7f900ceb499c2f667dfc3f1a6ac01a3203bbd3ccf",
"blk.38.attn_q.weight": "d94a8b26cd375bf2bcaa76597e314aa8268ee50a479d00931e5e0e021feadb5d",
"blk.38.attn_v.weight": "660c907888bc5016dc69b7d35fe6f55c7ded697c93be0e2d332a2f17aff88758",
"blk.38.ffn_down.weight": "6f06173bae5b00ffaf88ef383619a8b9c6a8d0d5c6494695d17f6c1de1a68a13",
"blk.38.ffn_gate.weight": "89f99be149d03f116527bfcabe073c50001c874de40fb6e817f6619027f3cd05",
"blk.38.ffn_up.weight": "8d57557c8d5e2d2688b73f01dddf1ce8d5194990cda6358153320aea88aac7f8",
"blk.39.attn_k.weight": "21be09c988b46c8393e6c2ec9230f3b5136eb7607dd1953ba92d0811c2f0dd75",
"blk.39.attn_norm.weight": "ba7c1912dd1c4e2d16917201f62396fd0600e4a451137eaddff255548c209abd",
"blk.39.attn_output.weight": "acfaf4abb3fd27fd899b5563c3877f176b597d8f6cdb2f2fd3f3a0bd4da15ed6",
"blk.39.attn_q.weight": "e8adbc140d4c8f0db2a27ca584c5531d5b1e080555fe627e34d80d0814a92bed",
"blk.39.attn_v.weight": "92f96b0e1f724e73a0f90a76c145654418844c04a6d4b14c05eb5af8a62bf8dc",
"blk.39.ffn_down.weight": "4d9ee7c65fc16fe95d10c47b79ac6a525741947600a64b5fcea5d300a82c50de",
"blk.39.ffn_gate.weight": "7e18507989f39b32191133d2657c2ee3b74f42f070579204d727eb72215793d1",
"blk.39.ffn_up.weight": "22cda752269c9757ba918abede1df95bb0f83a5c772dea13c8deea3d5f2723d9",
"output_norm.weight": "2858cf0e39d32caf52b7861378ace076000241e147f10b9eb21d8a5cd149e3cb"
}

View File

@@ -8,10 +8,10 @@ import (
"fmt"
"io/fs"
"log/slog"
"maps"
"os"
"slices"
"strings"
"golang.org/x/exp/maps"
)
const (
@@ -60,25 +60,7 @@ func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error)
addedTokens[t.Content] = t
}
if len(tt.Model.Merges) == 0 {
// noop; merges is empty
} else if err := json.Unmarshal(tt.Model.Merges, &t.Merges); err == nil {
// noop; merges is []string
} else if merges, err := func() ([][]string, error) {
var merges [][]string
if err := json.Unmarshal(tt.Model.Merges, &merges); err != nil {
return nil, err
}
return merges, nil
}(); err == nil {
t.Merges = make([]string, len(merges))
for i := range merges {
t.Merges[i] = strings.Join(merges[i], " ")
}
} else {
return nil, fmt.Errorf("could not parse tokenizer merges. expected []string or [][]string: %w", err)
}
t.Merges = tt.Model.Merges
sha256sum := sha256.New()
for _, pt := range tt.PreTokenizer.PreTokenizers {
@@ -99,8 +81,6 @@ func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error)
t.Pre = "deepseek-llm"
case "21cde974d587f0d54dc8d56b183cc1e6239600172035c68fbd6d4b9f8da0576e":
t.Pre = "deepseek-coder"
case "1ff7f41064896984db5d1bb6ff64fa4bc29007d08c1b439e505b7392777a319e":
t.Pre = "qwen2"
case "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855":
// noop, empty pretokenizer
default:
@@ -109,7 +89,6 @@ func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error)
}
if f, err := fsys.Open("tokenizer_config.json"); errors.Is(err, os.ErrNotExist) {
// noop
} else if err != nil {
return nil, err
} else {
@@ -171,43 +150,15 @@ func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error)
}
}
if f, err := fsys.Open("generation_config.json"); errors.Is(err, os.ErrNotExist) {
} else if err != nil {
return nil, err
} else {
defer f.Close()
var p map[string]json.RawMessage
if err := json.NewDecoder(f).Decode(&p); err != nil {
return nil, err
}
for _, st := range specialTokenTypes {
if bts, ok := p[fmt.Sprintf("%s_token_id", st)]; ok {
var ids []int32
if err := json.Unmarshal(bts, &ids); err != nil {
// value is not a list so the existing ID is used
continue
}
if i := slices.IndexFunc(t.SpecialVocabulary, func(sv *SpecialVocabulary) bool {
return sv.Type == st
}); i >= 0 {
t.SpecialVocabulary[i].IDs = ids
}
}
}
}
return t, nil
}
type tokenizer struct {
AddedTokens []token `json:"added_tokens"`
Model struct {
Type string `json:"type"`
Vocab map[string]int `json:"vocab"`
Merges json.RawMessage `json:"merges"`
Type string `json:"type"`
Vocab map[string]int `json:"vocab"`
Merges []string `json:"merges"`
} `json:"model"`
PreTokenizer struct {
@@ -259,8 +210,11 @@ func parseVocabularyFromTokenizer(fsys fs.FS) (*Vocabulary, error) {
tokens[token.ID] = token
}
keys := maps.Keys(tokens)
slices.Sort(keys)
v := Vocabulary{Model: "gpt2"}
for _, k := range slices.Sorted(maps.Keys(tokens)) {
for _, k := range keys {
token := tokens[k]
v.Tokens = append(v.Tokens, token.Content)
v.Scores = append(v.Scores, float32(token.ID))
@@ -305,9 +259,6 @@ type SpecialVocabulary struct {
ID int
Content string
AddToken bool
// IDs is populated by generation_config.json
IDs []int32
}
func (sv SpecialVocabulary) Key() string {

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

@@ -191,123 +191,6 @@ func TestParseTokenizer(t *testing.T) {
Pre: "default",
},
},
{
name: "list string merges",
fsys: createTokenizerFS(t, t.TempDir(), map[string]io.Reader{
"tokenizer.json": strings.NewReader(`{
"model": {
"merges": [
"a b",
"c d",
"e f"
]
}
}`),
}),
want: &Tokenizer{
Vocabulary: &Vocabulary{
Model: "gpt2",
},
Merges: []string{
"a b",
"c d",
"e f",
},
Pre: "default",
},
},
{
name: "list list string merges",
fsys: createTokenizerFS(t, t.TempDir(), map[string]io.Reader{
"tokenizer.json": strings.NewReader(`{
"model": {
"merges": [
[
"a", "b"
],
[
"c", "d"
],
[
"e", "f"
]
]
}
}`),
}),
want: &Tokenizer{
Vocabulary: &Vocabulary{
Model: "gpt2",
},
Merges: []string{
"a b",
"c d",
"e f",
},
Pre: "default",
},
},
{
name: "generation config eos token ids",
fsys: createTokenizerFS(t, t.TempDir(), map[string]io.Reader{
"tokenizer.json": strings.NewReader(`{
"added_tokens": [
{
"id": 0,
"content": "<bos>",
"special": true
},
{
"id": 1,
"content": "<eos>",
"special": true
},
{
"id": 2,
"content": "<eot>",
"special": true
},
{
"id": 3,
"content": "<eom>",
"special": true
}
],
"model": {
"vocab": {
"<bos>": 0,
"<eos>": 1,
"<eot>": 2,
"<eom>": 3
}
}
}`),
"tokenizer_config.json": strings.NewReader(`{
"add_bos_token": true,
"add_eos_token": false,
"bos_token": "<bos>",
"eos_token": "<eos>"
}`),
"generation_config.json": strings.NewReader(`{
"bos_token_id": 0,
"eos_token_id": [1, 2, 3]
}`),
}),
specialTokenTypes: []string{"pad", "eos", "bos", "unk"},
want: &Tokenizer{
Vocabulary: &Vocabulary{
Model: "gpt2",
Tokens: []string{"<bos>", "<eos>", "<eot>", "<eom>"},
Scores: []float32{0, 1, 2, 3},
Types: []int32{3, 3, 3, 3},
},
SpecialVocabulary: []*SpecialVocabulary{
{Type: "eos", Content: "<eos>", ID: 1, IDs: []int32{1, 2, 3}, AddToken: false},
{Type: "bos", Content: "<bos>", ID: 0, AddToken: true},
},
Pre: "default",
},
},
}
for _, tt := range cases {

3
discover/README.md Normal file
View File

@@ -0,0 +1,3 @@
# `discover`
This package is responsible for discovering information about the system and the capabilities to run LLM. This includes GPU and CPU discovery so the optimal runner can be chosen for a given model. The ollama scheduler relies on up-to-date available memory information, so this package provides the ability to refresh free memory as efficiently as possible.

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
@@ -35,14 +37,30 @@ func GetSupportedGFX(libDir string) ([]string, error) {
return ret, nil
}
func rocmGetVisibleDevicesEnv(gpuInfo []GpuInfo) (string, string) {
ids := []string{}
for _, info := range gpuInfo {
if info.Library != "rocm" {
// TODO shouldn't happen if things are wired correctly...
slog.Debug("rocmGetVisibleDevicesEnv skipping over non-rocm device", "library", info.Library)
continue
}
ids = append(ids, info.ID)
}
return "HIP_VISIBLE_DEVICES", strings.Join(ids, ",")
}
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

@@ -64,7 +64,7 @@ func NewHipLib() (*HipLib, error) {
return hl, nil
}
// The hip library only evaluates the ROCR_VISIBLE_DEVICES variable at startup
// The hip library only evaluates the HIP_VISIBLE_DEVICES variable at startup
// so we have to unload/reset the library after we do our initial discovery
// to make sure our updates to that variable are processed by llama.cpp
func (hl *HipLib) Release() {

View File

@@ -58,26 +58,29 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
driverMajor, driverMinor, err := AMDDriverVersion()
if err != nil {
// TODO - if we see users crash and burn with the upstreamed kernel this can be adjusted to hard-fail rocm support and fallback to CPU
slog.Warn("ollama recommends running the https://www.amd.com/en/support/download/linux-drivers.html", "error", err)
slog.Warn("ollama recommends running the https://www.amd.com/en/support/linux-drivers", "error", err)
}
// Determine if the user has already pre-selected which GPUs to look at, then ignore the others
var visibleDevices []string
hipVD := envconfig.HipVisibleDevices() // zero based index only
rocrVD := envconfig.RocrVisibleDevices() // zero based index or UUID
rocrVD := envconfig.RocrVisibleDevices() // zero based index or UUID, but consumer cards seem to not support UUID
gpuDO := envconfig.GpuDeviceOrdinal() // zero based index
switch {
case rocrVD != "":
visibleDevices = strings.Split(rocrVD, ",")
// TODO is this priorty order right?
case hipVD != "":
visibleDevices = strings.Split(hipVD, ",")
case rocrVD != "":
visibleDevices = strings.Split(rocrVD, ",")
// TODO - since we don't yet support UUIDs, consider detecting and reporting here
// all our test systems show GPU-XX indicating UUID is not supported
case gpuDO != "":
visibleDevices = strings.Split(gpuDO, ",")
}
gfxOverride := envconfig.HsaOverrideGfxVersion()
var supported []string
var libDir string
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)
@@ -96,8 +99,7 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
}
return a < b
})
gpuCount := 0
gpuOrdinalID := 0
cpuCount := 0
for _, match := range matches {
slog.Debug("evaluating amdgpu node " + match)
fp, err := os.Open(match)
@@ -106,6 +108,11 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
continue
}
defer fp.Close()
nodeID, err := strconv.Atoi(filepath.Base(filepath.Dir(match)))
if err != nil {
slog.Debug("failed to parse node ID", "error", err)
continue
}
scanner := bufio.NewScanner(fp)
isCPU := false
@@ -179,13 +186,18 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
// do reliably report VRAM usage.
if isCPU {
cpuCount++
continue
}
// Skip over any GPUs that are masked
if major == 0 && minor == 0 && patch == 0 {
slog.Debug("skipping gpu with gfx000")
continue
// CPUs are always first in the list
gpuID := nodeID - cpuCount
// Shouldn't happen, but just in case...
if gpuID < 0 {
err := fmt.Errorf("unexpected amdgpu sysfs data resulted in negative GPU ID, please set OLLAMA_DEBUG=1 and report an issue")
slog.Error(err.Error())
return nil, err
}
// Look up the memory for the current node
@@ -261,14 +273,6 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
name = fmt.Sprintf("%04x:%04x", vendor, device)
}
// Favor UUIDs if available to reduce possibility of getting the numeric IDs wrong
var ID string
if uniqueID != 0 {
ID = fmt.Sprintf("GPU-%016x", uniqueID)
} else {
ID = strconv.Itoa(gpuOrdinalID)
}
gpuInfo := RocmGPUInfo{
GpuInfo: GpuInfo{
Library: "rocm",
@@ -276,8 +280,7 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
TotalMemory: totalMemory,
FreeMemory: (totalMemory - usedMemory),
},
ID: ID,
filterID: gpuOrdinalID,
ID: strconv.Itoa(gpuID),
Name: name,
Compute: fmt.Sprintf("gfx%d%x%x", major, minor, patch),
MinimumMemory: rocmMinimumMemory,
@@ -285,24 +288,45 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
DriverMinor: driverMinor,
},
usedFilepath: usedFile,
index: gpuCount,
}
// Keep track of numeric IDs based on valid GPUs
gpuCount += 1
// iGPU detection, remove this check once we can support an iGPU variant of the rocm library
if totalMemory < IGPUMemLimit {
reason := "unsupported Radeon iGPU detected skipping"
slog.Info(reason, "id", gpuID, "total", format.HumanBytes2(totalMemory))
unsupportedGPUs = append(unsupportedGPUs, UnsupportedGPUInfo{
GpuInfo: gpuInfo.GpuInfo,
Reason: reason,
})
continue
}
if int(major) < RocmComputeMin {
reason := fmt.Sprintf("amdgpu too old gfx%d%x%x", major, minor, patch)
slog.Warn(reason, "gpu", gpuID)
unsupportedGPUs = append(unsupportedGPUs, UnsupportedGPUInfo{
GpuInfo: gpuInfo.GpuInfo,
Reason: reason,
})
continue
}
slog.Debug("amdgpu memory", "gpu", gpuID, "total", format.HumanBytes2(totalMemory))
slog.Debug("amdgpu memory", "gpu", gpuID, "available", format.HumanBytes2(totalMemory-usedMemory))
// If the user wants to filter to a subset of devices, filter out if we aren't a match
if len(visibleDevices) > 0 {
include := false
for _, visible := range visibleDevices {
if (uniqueID != 0 && visible == gpuInfo.ID) || visible == strconv.Itoa(gpuInfo.index) {
if visible == gpuInfo.ID {
include = true
break
}
}
if !include {
reason := "filtering out device per user request"
slog.Info(reason, "id", gpuInfo.ID, "index", gpuInfo.index, "visible_devices", visibleDevices)
slog.Info(reason, "id", gpuInfo.ID, "visible_devices", visibleDevices)
unsupportedGPUs = append(unsupportedGPUs, UnsupportedGPUInfo{
GpuInfo: gpuInfo.GpuInfo,
Reason: reason,
@@ -312,37 +336,6 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
}
}
// Ordinal IDs are based on the visible GPUs
gpuOrdinalID += 1
// iGPU detection, remove this check once we can support an iGPU variant of the rocm library
if totalMemory < IGPUMemLimit {
reason := "unsupported Radeon iGPU detected skipping"
slog.Info(reason, "id", gpuInfo.ID, "total", format.HumanBytes2(totalMemory))
unsupportedGPUs = append(unsupportedGPUs, UnsupportedGPUInfo{
GpuInfo: gpuInfo.GpuInfo,
Reason: reason,
})
continue
}
minVer, err := strconv.Atoi(RocmComputeMajorMin)
if err != nil {
slog.Error("invalid RocmComputeMajorMin setting", "value", RocmComputeMajorMin, "error", err)
}
if int(major) < minVer {
reason := fmt.Sprintf("amdgpu too old gfx%d%x%x", major, minor, patch)
slog.Warn(reason, "gpu", gpuInfo.ID)
unsupportedGPUs = append(unsupportedGPUs, UnsupportedGPUInfo{
GpuInfo: gpuInfo.GpuInfo,
Reason: reason,
})
continue
}
slog.Debug("amdgpu memory", "gpu", gpuInfo.ID, "total", format.HumanBytes2(totalMemory))
slog.Debug("amdgpu memory", "gpu", gpuInfo.ID, "available", format.HumanBytes2(totalMemory-usedMemory))
// Final validation is gfx compatibility - load the library if we haven't already loaded it
// even if the user overrides, we still need to validate the library
if libDir == "" {
@@ -357,7 +350,7 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
return nil, err
}
}
gpuInfo.DependencyPath = []string{libDir}
gpuInfo.DependencyPath = libDir
if gfxOverride == "" {
// Only load supported list once
@@ -395,7 +388,7 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
// Check for env var workarounds
if name == "1002:687f" { // Vega RX 56
gpuInfo.EnvWorkarounds = append(gpuInfo.EnvWorkarounds, "HSA_ENABLE_SDMA=0")
gpuInfo.EnvWorkarounds = append(gpuInfo.EnvWorkarounds, [2]string{"HSA_ENABLE_SDMA", "0"})
}
// The GPU has passed all the verification steps and is supported
@@ -523,27 +516,3 @@ func verifyKFDDriverAccess() error {
fd.Close()
return nil
}
func rocmGetVisibleDevicesEnv(gpuInfo []GpuInfo) string {
ids := []string{}
for _, info := range gpuInfo {
if info.Library != "rocm" {
continue
}
// If the devices requires a numeric ID, for filtering purposes, we use the unfiltered ID number
if _, err := strconv.Atoi(info.ID); err == nil {
ids = append(ids, fmt.Sprintf("%d", info.filterID))
} else {
ids = append(ids, info.ID)
}
}
if len(ids) == 0 {
return ""
}
// There are 3 potential env vars to use to select GPUs.
// ROCR_VISIBLE_DEVICES supports UUID or numeric so is our preferred on linux
// GPU_DEVICE_ORDINAL supports numeric IDs only
// HIP_VISIBLE_DEVICES supports numeric IDs only
return "ROCR_VISIBLE_DEVICES=" + strings.Join(ids, ",")
}

View File

@@ -5,6 +5,7 @@ import (
"errors"
"fmt"
"log/slog"
"os"
"path/filepath"
"slices"
"strconv"
@@ -42,14 +43,13 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
slog.Debug("error looking up amd driver version", "error", err)
}
// Note: the HIP library automatically handles subsetting to any *_VISIBLE_DEVICES the user specified
// Note: the HIP library automatically handles subsetting to any HIP_VISIBLE_DEVICES the user specified
count := hl.HipGetDeviceCount()
if count == 0 {
err := fmt.Errorf("no compatible amdgpu devices detected")
slog.Info(err.Error())
return nil, err
}
libDir, err := AMDValidateLibDir()
if err != nil {
err = fmt.Errorf("unable to verify rocm library: %w", err)
@@ -111,8 +111,7 @@ func AMDGetGPUInfo() ([]RocmGPUInfo, error) {
UnreliableFreeMemory: true,
ID: strconv.Itoa(i), // TODO this is probably wrong if we specify visible devices
filterID: i,
DependencyPath: []string{libDir},
DependencyPath: libDir,
MinimumMemory: rocmMinimumMemory,
Name: name,
Compute: gfx,
@@ -163,7 +162,9 @@ 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
@@ -181,7 +182,7 @@ func (gpus RocmGPUInfoList) RefreshFreeMemory() error {
hl, err := NewHipLib()
if err != nil {
slog.Debug(err.Error())
return err
return nil
}
defer hl.Release()
@@ -200,27 +201,3 @@ func (gpus RocmGPUInfoList) RefreshFreeMemory() error {
}
return nil
}
func rocmGetVisibleDevicesEnv(gpuInfo []GpuInfo) string {
ids := []string{}
for _, info := range gpuInfo {
if info.Library != "rocm" {
continue
}
// If the devices requires a numeric ID, for filtering purposes, we use the unfiltered ID number
if _, err := strconv.Atoi(info.ID); err == nil {
ids = append(ids, fmt.Sprintf("%d", info.filterID))
} else {
ids = append(ids, info.ID)
}
}
if len(ids) == 0 {
return ""
}
// There are 3 potential env vars to use to select GPUs.
// ROCR_VISIBLE_DEVICES supports UUID or numeric but does not work on Windows
// HIP_VISIBLE_DEVICES supports numeric IDs only
// GPU_DEVICE_ORDINAL supports numeric IDs only
return "HIP_VISIBLE_DEVICES=" + strings.Join(ids, ",")
}

View File

@@ -5,14 +5,27 @@ import (
"path/filepath"
"runtime"
"strings"
"golang.org/x/sys/cpu"
)
func GetCPUCapability() CPUCapability {
if cpu.X86.HasAVX2 {
return CPUCapabilityAVX2
}
if cpu.X86.HasAVX {
return CPUCapabilityAVX
}
// else LCD
return CPUCapabilityNone
}
func IsNUMA() bool {
if runtime.GOOS != "linux" {
// 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

@@ -3,7 +3,6 @@
package discover
import (
"fmt"
"log/slog"
"os"
"regexp"
@@ -16,7 +15,20 @@ import (
// Included to drive logic for reducing Ollama-allocated overhead on L4T/Jetson devices.
var CudaTegra string = os.Getenv("JETSON_JETPACK")
func cudaVariant(gpuInfos []CudaGPUInfo) string {
func cudaGetVisibleDevicesEnv(gpuInfo []GpuInfo) (string, string) {
ids := []string{}
for _, info := range gpuInfo {
if info.Library != "cuda" {
// TODO shouldn't happen if things are wired correctly...
slog.Debug("cudaGetVisibleDevicesEnv skipping over non-cuda device", "library", info.Library)
continue
}
ids = append(ids, info.ID)
}
return "CUDA_VISIBLE_DEVICES", strings.Join(ids, ",")
}
func cudaVariant(gpuInfo CudaGPUInfo) string {
if runtime.GOARCH == "arm64" && runtime.GOOS == "linux" {
if CudaTegra != "" {
ver := strings.Split(CudaTegra, ".")
@@ -45,20 +57,8 @@ func cudaVariant(gpuInfos []CudaGPUInfo) string {
}
}
// Check GPU compute capability FIRST, lowest common denominator if multi-gpu
for _, gpuInfo := range gpuInfos {
if gpuInfo.computeMajor < 7 || (gpuInfo.computeMajor == 7 && gpuInfo.computeMinor < 5) {
// GPU is Pascal or older (CC <= 7.4) - use CUDA v12 (supports CC 6.1)
return "v12"
}
if gpuInfo.computeMajor < 6 || gpuInfo.DriverMajor < 12 || (gpuInfo.DriverMajor == 12 && gpuInfo.DriverMinor == 0) {
return "v11"
}
// GPU is Turing or newer (CC >= 7.5) - can use newer CUDA
if len(gpuInfos) > 0 && gpuInfos[0].DriverMajor < 13 {
// The detected driver is older than 580 (Aug 2025)
// Warn if their CC is compatible with v13 and they should upgrade their driver to get better performance
slog.Warn("old CUDA driver detected - please upgrade to a newer driver for best performance", "version", fmt.Sprintf("%d.%d", gpuInfos[0].DriverMajor, gpuInfos[0].DriverMinor))
return "v12"
}
return "v13"
return "v12"
}

View File

@@ -16,7 +16,6 @@ import (
"os"
"path/filepath"
"runtime"
"strconv"
"strings"
"sync"
"unsafe"
@@ -46,6 +45,7 @@ const (
var (
gpuMutex sync.Mutex
bootstrapped bool
cpuCapability CPUCapability
cpus []CPUInfo
cudaGPUs []CudaGPUInfo
nvcudaLibPath string
@@ -64,13 +64,9 @@ var (
)
// With our current CUDA compile flags, older than 5.0 will not work properly
// (string values used to allow ldflags overrides at build time)
var (
CudaComputeMajorMin = "5"
CudaComputeMinorMin = "0"
)
var CudaComputeMin = [2]C.int{5, 0}
var RocmComputeMajorMin = "9"
var RocmComputeMin = 9
// TODO find a better way to detect iGPU instead of minimum memory
const IGPUMemLimit = 1 * format.GibiByte // 512G is what they typically report, so anything less than 1G must be iGPU
@@ -100,7 +96,15 @@ 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)}
}
libDir := LibraryDir()
if libDir != "" {
cudartMgmtPatterns = []string{filepath.Join(libDir, CudartMgmtName)}
}
cudartMgmtPatterns = append(cudartMgmtPatterns, CudartGlobs...)
if len(NvmlGlobs) > 0 {
@@ -215,23 +219,16 @@ func GetGPUInfo() GpuInfoList {
if !bootstrapped {
slog.Info("looking for compatible GPUs")
cudaComputeMajorMin, err := strconv.Atoi(CudaComputeMajorMin)
if err != nil {
slog.Error("invalid CudaComputeMajorMin setting", "value", CudaComputeMajorMin, "error", err)
}
cudaComputeMinorMin, err := strconv.Atoi(CudaComputeMinorMin)
if err != nil {
slog.Error("invalid CudaComputeMinorMin setting", "value", CudaComputeMinorMin, "error", err)
}
bootstrapErrors = []error{}
needRefresh = false
cpuCapability = GetCPUCapability()
var memInfo C.mem_info_t
mem, err := GetCPUMem()
if err != nil {
slog.Warn("error looking up system memory", "error", err)
}
depPath := LibraryDir()
details, err := GetCPUDetails()
if err != nil {
slog.Warn("failed to lookup CPU details", "error", err)
@@ -239,14 +236,26 @@ func GetGPUInfo() GpuInfoList {
cpus = []CPUInfo{
{
GpuInfo: GpuInfo{
memInfo: mem,
Library: "cpu",
ID: "0",
memInfo: mem,
Library: "cpu",
Variant: cpuCapability.String(),
ID: "0",
DependencyPath: depPath,
},
CPUs: details,
},
}
// Fallback to CPU mode if we're lacking required vector extensions on x86
if cpuCapability < GPURunnerCPUCapability && runtime.GOARCH == "amd64" {
err := fmt.Errorf("CPU does not have minimum vector extensions, GPU inference disabled. Required:%s Detected:%s", GPURunnerCPUCapability, cpuCapability)
slog.Warn(err.Error())
bootstrapErrors = append(bootstrapErrors, err)
bootstrapped = true
// No need to do any GPU discovery, since we can't run on them
return GpuInfoList{cpus[0].GpuInfo}
}
// Load ALL libraries
cHandles = initCudaHandles()
@@ -263,8 +272,6 @@ func GetGPUInfo() GpuInfoList {
var driverMinor int
if cHandles.cudart != nil {
C.cudart_bootstrap(*cHandles.cudart, C.int(i), &memInfo)
driverMajor = int(cHandles.cudart.driver_major)
driverMinor = int(cHandles.cudart.driver_minor)
} else {
C.nvcuda_bootstrap(*cHandles.nvcuda, C.int(i), &memInfo)
driverMajor = int(cHandles.nvcuda.driver_major)
@@ -284,10 +291,20 @@ func GetGPUInfo() GpuInfoList {
gpuInfo.MinimumMemory = cudaMinimumMemory
gpuInfo.DriverMajor = driverMajor
gpuInfo.DriverMinor = driverMinor
variant := cudaVariant(gpuInfo)
if depPath != "" {
gpuInfo.DependencyPath = depPath
// Check for variant specific directory
if variant != "" {
if _, err := os.Stat(filepath.Join(depPath, "cuda_"+variant)); err == nil {
gpuInfo.DependencyPath = filepath.Join(depPath, "cuda_"+variant)
}
}
}
gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
gpuInfo.Variant = variant
if int(memInfo.major) < cudaComputeMajorMin || (int(memInfo.major) == cudaComputeMajorMin && int(memInfo.minor) < cudaComputeMinorMin) {
if memInfo.major < CudaComputeMin[0] || (memInfo.major == CudaComputeMin[0] && memInfo.minor < CudaComputeMin[1]) {
unsupportedGPUs = append(unsupportedGPUs,
UnsupportedGPUInfo{
GpuInfo: gpuInfo.GpuInfo,
@@ -299,9 +316,7 @@ func GetGPUInfo() GpuInfoList {
// query the management library as well so we can record any skew between the two
// which represents overhead on the GPU we must set aside on subsequent updates
if cHandles.nvml != nil {
uuid := C.CString(gpuInfo.ID)
defer C.free(unsafe.Pointer(uuid))
C.nvml_get_free(*cHandles.nvml, uuid, &memInfo.free, &memInfo.total, &memInfo.used)
C.nvml_get_free(*cHandles.nvml, C.int(gpuInfo.index), &memInfo.free, &memInfo.total, &memInfo.used)
if memInfo.err != nil {
slog.Warn("error looking up nvidia GPU memory", "error", C.GoString(memInfo.err))
C.free(unsafe.Pointer(memInfo.err))
@@ -323,24 +338,6 @@ func GetGPUInfo() GpuInfoList {
// TODO potentially sort on our own algorithm instead of what the underlying GPU library does...
cudaGPUs = append(cudaGPUs, gpuInfo)
}
// Second pass on NVIDIA GPUs to set lowest common denominator variant and DependencyPaths
variant := cudaVariant(cudaGPUs)
var variantPath string
// Start with our bundled libraries
if variant != "" {
variantPath = filepath.Join(LibOllamaPath, "cuda_"+variant)
if _, err := os.Stat(variantPath); err != nil {
variantPath = ""
}
}
for i := range cudaGPUs {
cudaGPUs[i].Variant = variant
if variantPath != "" {
// Put the variant directory first in the search path to avoid runtime linking to the wrong library
cudaGPUs[i].DependencyPath = append([]string{variantPath}, cudaGPUs[i].DependencyPath...)
}
}
}
// Intel
@@ -371,7 +368,7 @@ 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 = depPath
oneapiGPUs = append(oneapiGPUs, gpuInfo)
}
}
@@ -379,15 +376,6 @@ func GetGPUInfo() GpuInfoList {
}
rocmGPUs, err = AMDGetGPUInfo()
// The ID field is used in context of the filtered set of GPUS
// so we have to replace any of these numeric IDs with their
// placement in this set of GPUs
for i := range rocmGPUs {
if _, err := strconv.Atoi(rocmGPUs[i].ID); err == nil {
rocmGPUs[i].ID = strconv.Itoa(i)
}
}
if err != nil {
bootstrapErrors = append(bootstrapErrors, err)
}
@@ -395,8 +383,6 @@ func GetGPUInfo() GpuInfoList {
if len(cudaGPUs) == 0 && len(rocmGPUs) == 0 && len(oneapiGPUs) == 0 {
slog.Info("no compatible GPUs were discovered")
}
// TODO verify we have runners for the discovered GPUs, filter out any that aren't supported with good error messages
}
// For detected GPUs, load library if not loaded
@@ -431,9 +417,7 @@ func GetGPUInfo() GpuInfoList {
}
for i, gpu := range cudaGPUs {
if cHandles.nvml != nil {
uuid := C.CString(gpu.ID)
defer C.free(unsafe.Pointer(uuid))
C.nvml_get_free(*cHandles.nvml, uuid, &memInfo.free, &memInfo.total, &memInfo.used)
C.nvml_get_free(*cHandles.nvml, C.int(gpu.index), &memInfo.free, &memInfo.total, &memInfo.used)
} else if cHandles.cudart != nil {
C.cudart_bootstrap(*cHandles.cudart, C.int(gpu.index), &memInfo)
} else if cHandles.nvcuda != nil {
@@ -516,33 +500,34 @@ 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)}
// Start with our bundled libraries
patterns := []string{filepath.Join(LibraryDir(), baseLibName)}
var ldPaths []string
switch runtime.GOOS {
case "windows":
ldPaths = strings.Split(os.Getenv("PATH"), string(os.PathListSeparator))
ldPaths = strings.Split(os.Getenv("PATH"), ";")
case "linux":
ldPaths = strings.Split(os.Getenv("LD_LIBRARY_PATH"), string(os.PathListSeparator))
ldPaths = strings.Split(os.Getenv("LD_LIBRARY_PATH"), ":")
default:
return gpuLibPaths
}
// then search the system's LD_LIBRARY_PATH
for _, p := range ldPaths {
p, err := filepath.Abs(p)
// 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 {
// Nvidia PhysX known to return bogus results
if strings.Contains(pattern, "PhysX") {
slog.Debug("skipping PhysX cuda library path", "path", pattern)
@@ -689,7 +674,7 @@ 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)
@@ -697,16 +682,51 @@ func getVerboseState() C.uint16_t {
// Given the list of GPUs this instantiation is targeted for,
// figure out the visible devices environment variable
func (l GpuInfoList) GetVisibleDevicesEnv() []string {
//
// If different libraries are detected, the first one is what we use
func (l GpuInfoList) GetVisibleDevicesEnv() (string, string) {
if len(l) == 0 {
return nil
return "", ""
}
vd := []string{}
// Only filter the AMD GPUs at this level, let all NVIDIA devices through
if tmp := rocmGetVisibleDevicesEnv(l); tmp != "" {
vd = append(vd, tmp)
switch l[0].Library {
case "cuda":
return cudaGetVisibleDevicesEnv(l)
case "rocm":
return rocmGetVisibleDevicesEnv(l)
case "oneapi":
return oneapiGetVisibleDevicesEnv(l)
default:
slog.Debug("no filter required for library " + l[0].Library)
return "", ""
}
return vd
}
func LibraryDir() string {
// On Windows/linux we bundle the dependencies at the same level as the executable
appExe, err := os.Executable()
if err != nil {
slog.Warn("failed to lookup executable path", "error", err)
}
cwd, err := os.Getwd()
if err != nil {
slog.Warn("failed to lookup working directory", "error", err)
}
// Scan for any of our dependeices, and pick first match
for _, root := range []string{filepath.Dir(appExe), filepath.Join(filepath.Dir(appExe), envconfig.LibRelativeToExe()), cwd} {
libDep := filepath.Join("lib", "ollama")
if _, err := os.Stat(filepath.Join(root, libDep)); err == nil {
return filepath.Join(root, libDep)
}
// Developer mode, local build
if _, err := os.Stat(filepath.Join(root, runtime.GOOS+"-"+runtime.GOARCH, libDep)); err == nil {
return filepath.Join(root, runtime.GOOS+"-"+runtime.GOARCH, libDep)
}
if _, err := os.Stat(filepath.Join(root, "dist", runtime.GOOS+"-"+runtime.GOARCH, libDep)); err == nil {
return filepath.Join(root, "dist", runtime.GOOS+"-"+runtime.GOARCH, libDep)
}
}
slog.Warn("unable to locate gpu dependency libraries")
return ""
}
func GetSystemInfo() SystemInfo {

View File

@@ -27,6 +27,7 @@ func GetGPUInfo() GpuInfoList {
return []GpuInfo{
{
Library: "cpu",
Variant: GetCPUCapability().String(),
memInfo: mem,
},
}
@@ -49,6 +50,7 @@ func GetCPUInfo() GpuInfoList {
return []GpuInfo{
{
Library: "cpu",
Variant: GetCPUCapability().String(),
memInfo: mem,
},
}
@@ -62,9 +64,9 @@ func GetCPUMem() (memInfo, error) {
}, nil
}
func (l GpuInfoList) GetVisibleDevicesEnv() []string {
func (l GpuInfoList) GetVisibleDevicesEnv() (string, string) {
// No-op on darwin
return nil
return "", ""
}
func GetSystemInfo() SystemInfo {

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;
}
@@ -69,15 +68,18 @@ void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp) {
}
int version = 0;
cudartDriverVersion_t driverVersion;
driverVersion.major = 0;
driverVersion.minor = 0;
// Report driver version if we're in verbose mode, ignore errors
ret = (*resp->ch.cudaDriverGetVersion)(&version);
if (ret != CUDART_SUCCESS) {
LOG(resp->ch.verbose, "cudaDriverGetVersion failed: %d\n", ret);
} else {
resp->ch.driver_major = version / 1000;
resp->ch.driver_minor = (version - (resp->ch.driver_major * 1000)) / 10;
LOG(resp->ch.verbose, "CUDA driver version: %d-%d\n", resp->ch.driver_major, resp->ch.driver_minor);
driverVersion.major = version / 1000;
driverVersion.minor = (version - (driverVersion.major * 1000)) / 10;
LOG(resp->ch.verbose, "CUDA driver version: %d-%d\n", driverVersion.major, driverVersion.minor);
}
ret = (*resp->ch.cudaGetDeviceCount)(&resp->num_devices);
@@ -166,9 +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);
}
@@ -178,4 +180,4 @@ void cudart_release(cudart_handle_t h) {
h.handle = NULL;
}
#endif // __APPLE__
#endif // __APPLE__

View File

@@ -29,6 +29,11 @@ typedef struct cudartMemory_st {
size_t used;
} cudartMemory_t;
typedef struct cudartDriverVersion {
int major;
int minor;
} cudartDriverVersion_t;
typedef struct cudaUUID {
unsigned char bytes[16];
} cudaUUID_t;
@@ -118,8 +123,6 @@ typedef struct cudaDeviceProp {
typedef struct cudart_handle {
void *handle;
uint16_t verbose;
int driver_major;
int driver_minor;
cudartReturn_t (*cudaSetDevice)(int device);
cudartReturn_t (*cudaDeviceSynchronize)(void);
cudartReturn_t (*cudaDeviceReset)(void);

View File

@@ -1,11 +1,9 @@
#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) {
LOG(resp->ch.verbose, "initializing %s\n", nvcuda_lib_path);
CUresult ret;
resp->err = NULL;
resp->num_devices = 0;
@@ -59,10 +57,8 @@ void nvcuda_init(char *nvcuda_lib_path, nvcuda_init_resp_t *resp) {
resp->cudaErr = -1;
return;
}
LOG(resp->ch.verbose, "dlsym: %s - %p\n", l[i].s, *l[i].p);
}
LOG(resp->ch.verbose, "calling cuInit\n");
ret = (*resp->ch.cuInit)(0);
if (ret != CUDA_SUCCESS) {
LOG(resp->ch.verbose, "cuInit err: %d\n", ret);
@@ -79,18 +75,15 @@ void nvcuda_init(char *nvcuda_lib_path, nvcuda_init_resp_t *resp) {
resp->ch.driver_minor = 0;
// Report driver version if we're in verbose mode, ignore errors
LOG(resp->ch.verbose, "calling cuDriverGetVersion\n");
ret = (*resp->ch.cuDriverGetVersion)(&version);
if (ret != CUDA_SUCCESS) {
LOG(resp->ch.verbose, "cuDriverGetVersion failed: %d\n", ret);
} else {
LOG(resp->ch.verbose, "raw version 0x%x\n", version);
resp->ch.driver_major = version / 1000;
resp->ch.driver_minor = (version - (resp->ch.driver_major * 1000)) / 10;
LOG(resp->ch.verbose, "CUDA driver version: %d.%d\n", resp->ch.driver_major, resp->ch.driver_minor);
}
LOG(resp->ch.verbose, "calling cuDeviceGetCount\n");
ret = (*resp->ch.cuDeviceGetCount)(&resp->num_devices);
if (ret != CUDA_SUCCESS) {
LOG(resp->ch.verbose, "cuDeviceGetCount err: %d\n", ret);
@@ -101,7 +94,6 @@ void nvcuda_init(char *nvcuda_lib_path, nvcuda_init_resp_t *resp) {
resp->cudaErr = ret;
return;
}
LOG(resp->ch.verbose, "device count %d\n", resp->num_devices);
}
const int buflen = 256;
@@ -194,8 +186,8 @@ 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 +240,4 @@ void nvcuda_release(nvcuda_handle_t h) {
h.handle = NULL;
}
#endif // __APPLE__
#endif // __APPLE__

View File

@@ -17,7 +17,7 @@ void nvml_init(char *nvml_lib_path, nvml_init_resp_t *resp) {
} l[] = {
{"nvmlInit_v2", (void *)&resp->ch.nvmlInit_v2},
{"nvmlShutdown", (void *)&resp->ch.nvmlShutdown},
{"nvmlDeviceGetHandleByUUID", (void *)&resp->ch.nvmlDeviceGetHandleByUUID},
{"nvmlDeviceGetHandleByIndex", (void *)&resp->ch.nvmlDeviceGetHandleByIndex},
{"nvmlDeviceGetMemoryInfo", (void *)&resp->ch.nvmlDeviceGetMemoryInfo},
{NULL, NULL},
};
@@ -67,20 +67,20 @@ void nvml_init(char *nvml_lib_path, nvml_init_resp_t *resp) {
}
void nvml_get_free(nvml_handle_t h, char *uuid, uint64_t *free, uint64_t *total, uint64_t *used) {
void nvml_get_free(nvml_handle_t h, int device_id, uint64_t *free, uint64_t *total, uint64_t *used) {
nvmlDevice_t device;
nvmlMemory_t memInfo = {0};
nvmlReturn_t ret;
ret = (*h.nvmlDeviceGetHandleByUUID)((const char *)(uuid), &device);
ret = (*h.nvmlDeviceGetHandleByIndex)(device_id, &device);
if (ret != NVML_SUCCESS) {
LOG(1, "unable to get device handle %s: %d", uuid, ret);
LOG(1, "unable to get device handle %d: %d", device_id, ret);
*free = 0;
return;
}
ret = (*h.nvmlDeviceGetMemoryInfo)(device, &memInfo);
if (ret != NVML_SUCCESS) {
LOG(1, "device memory info lookup failure %s: %d", uuid, ret);
LOG(1, "device memory info lookup failure %d: %d", device_id, ret);
*free = 0;
return;
}

View File

@@ -25,7 +25,7 @@ typedef struct nvml_handle {
uint16_t verbose;
nvmlReturn_t (*nvmlInit_v2)(void);
nvmlReturn_t (*nvmlShutdown)(void);
nvmlReturn_t (*nvmlDeviceGetHandleByUUID)(const char *, nvmlDevice_t *);
nvmlReturn_t (*nvmlDeviceGetHandleByIndex)(unsigned int, nvmlDevice_t *);
nvmlReturn_t (*nvmlDeviceGetMemoryInfo)(nvmlDevice_t, nvmlMemory_t *);
} nvml_handle_t;
@@ -41,7 +41,7 @@ typedef struct nvml_compute_capability {
} nvml_compute_capability_t;
void nvml_init(char *nvml_lib_path, nvml_init_resp_t *resp);
void nvml_get_free(nvml_handle_t ch, char *uuid, uint64_t *free, uint64_t *total, uint64_t *used);
void nvml_get_free(nvml_handle_t ch, int device_id, uint64_t *free, uint64_t *total, uint64_t *used);
void nvml_release(nvml_handle_t ch);
#endif // __GPU_INFO_NVML_H__

View File

@@ -3,11 +3,9 @@ package discover
import (
"bufio"
"fmt"
"io"
"os"
"reflect"
"regexp"
"sort"
"strings"
"github.com/ollama/ollama/format"
@@ -111,11 +109,6 @@ func GetCPUDetails() ([]CPU, error) {
if err != nil {
return nil, err
}
defer file.Close()
return linuxCPUDetails(file)
}
func linuxCPUDetails(file io.Reader) ([]CPU, error) {
reColumns := regexp.MustCompile("\t+: ")
scanner := bufio.NewScanner(file)
cpuInfos := []linuxCpuInfo{}
@@ -138,9 +131,6 @@ func linuxCPUDetails(file io.Reader) ([]CPU, error) {
cpu = &linuxCpuInfo{}
}
}
if cpu.ID != "" {
cpuInfos = append(cpuInfos, *cpu)
}
// Process the sockets/cores/threads
socketByID := map[string]*CPU{}
@@ -169,11 +159,13 @@ 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++
}
@@ -185,14 +177,10 @@ func linuxCPUDetails(file io.Reader) ([]CPU, error) {
s.EfficiencyCoreCount = efficiencyCoreCount
}
}
keys := make([]string, 0, len(socketByID))
result := make([]CPU, 0, len(socketByID))
for k := range socketByID {
keys = append(keys, k)
}
sort.Strings(keys)
for _, k := range keys {
result = append(result, *socketByID[k])
result := []CPU{}
for _, c := range socketByID {
result = append(result, *c)
}
return result, nil
}

Some files were not shown because too many files have changed in this diff Show More