mirrors/ollama-for-amd
1
0
Fork 0
mirror of https://github.com/likelovewant/ollama-for-amd.git synced 2025-12-21 22:33:56 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'ollama:main' into main

Browse Source
This commit is contained in:
likelovewant
2024-09-27 11:44:48 +08:00
committed by GitHub
parent c8d6d9a010 03608cb46e
commit f6cc192947
39 changed files with 454 additions and 160 deletions
Download Patch File Download Diff File Expand all files Collapse all files

134
.github/workflows/release.yaml vendored
View File

@@ -274,7 +274,134 @@ jobs:
path: dist/deps/* path: dist/deps/*
# Import the prior generation steps and build the final windows assets # windows arm64 generate, go build, and zip file (no installer)
# Output of this build is aggregated into the final x86 build
# for a unified windows installer
windows-arm64:
runs-on: windows-arm64
environment: release
env:
KEY_CONTAINER: ${{ vars.KEY_CONTAINER }}
steps:
# The current Windows arm64 beta image has effectively zero dev tools installed...
- name: Install git and gzip
run: |
Set-ExecutionPolicy Bypass -Scope Process -Force
[System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072
iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))
choco install -y --no-progress git gzip
echo "C:\Program Files\Git\cmd" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
echo "C:\ProgramData\chocolatey\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
- name: Install Visual Studio 2022
run: |
$components = @(
"Microsoft.VisualStudio.Component.CoreEditor",
"Microsoft.VisualStudio.Workload.CoreEditor",
"Microsoft.VisualStudio.Component.Roslyn.Compiler",
"Microsoft.Component.MSBuild",
"Microsoft.VisualStudio.Component.TextTemplating",
"Microsoft.VisualStudio.Component.Debugger.JustInTime",
"Microsoft.VisualStudio.Component.VC.CoreIde",
"Microsoft.VisualStudio.Component.VC.Tools.x86.x64",
"Microsoft.VisualStudio.Component.Windows11SDK.22621",
"Microsoft.VisualStudio.Component.VC.Tools.ARM64EC",
"Microsoft.VisualStudio.Component.VC.Tools.ARM64",
"Microsoft.VisualStudio.Component.VC.ATL",
"Microsoft.VisualStudio.Component.VC.ATL.ARM64",
"Microsoft.VisualStudio.Component.Graphics",
"Microsoft.VisualStudio.Component.VC.Redist.14.Latest",
"Microsoft.VisualStudio.ComponentGroup.NativeDesktop.Core",
"Microsoft.VisualStudio.Component.Windows11Sdk.WindowsPerformanceToolkit",
"Microsoft.VisualStudio.Component.CppBuildInsights",
"Microsoft.VisualStudio.Component.VC.DiagnosticTools",
"Microsoft.VisualStudio.ComponentGroup.WebToolsExtensions.CMake",
"Microsoft.VisualStudio.Component.VC.CMake.Project",
"Microsoft.VisualStudio.Component.VC.ASAN",
"Microsoft.VisualStudio.Component.Vcpkg",
"Microsoft.VisualStudio.Workload.NativeDesktop"
)
$config = @{
"version" = "1.0"
"components" = $components
"extensions" = @()
}
$configPath = "${env:RUNNER_TEMP}\vsconfig"
$config | ConvertTo-Json | Out-File -FilePath $configPath
$bootstrapperFilePath = "${env:RUNNER_TEMP}\vs_community.exe"
write-host "Downloading Visual Studio 2022"
Invoke-WebRequest -Uri "https://aka.ms/vs/17/release/vs_community.exe" -outfile $bootstrapperFilePath
$bootstrapperArgumentList = ('/c', $bootstrapperFilePath, '--config', $configPath, '--quiet', '--wait' )
write-host "Installing Visual Studio 2022"
$process = Start-Process -FilePath cmd.exe -ArgumentList $bootstrapperArgumentList -Wait -PassThru
$exitCode = $process.ExitCode
write-host $exitCode
# pacman in mingw/msys2 is ~broken on windows arm right now - hangs consistently during attempts to install
# so we'll use this alternative GCC binary
- name: Install llvm-mingw GCC
run: |
$gcc_url="https://github.com/mstorsjo/llvm-mingw/releases/download/20240619/llvm-mingw-20240619-ucrt-aarch64.zip"
write-host "Downloading llvm-mingw"
Invoke-WebRequest -Uri "${gcc_url}" -OutFile "${env:RUNNER_TEMP}\gcc.zip"
write-host "Unpacking llvm-mingw"
expand-archive -path "${env:RUNNER_TEMP}\gcc.zip" -destinationpath "c:\"
mv c:\llvm-mingw-* c:\llvm-mingw
echo "c:\llvm-mingw\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
- name: Verify GCC
run: |
echo $env:PATH
gcc --version
- uses: actions/checkout@v4
- name: Set Version
run: |
$ver=${env:GITHUB_REF_NAME}.trim("v")
echo VERSION=$ver | Out-File -FilePath ${env:GITHUB_ENV} -Encoding utf8 -Append
- uses: 'google-github-actions/auth@v2'
with:
project_id: 'ollama'
credentials_json: '${{ secrets.GOOGLE_SIGNING_CREDENTIALS }}'
- run: echo "${{ vars.OLLAMA_CERT }}" | Out-File -FilePath ollama_inc.crt -Encoding utf8
- name: install Windows SDK 8.1 to get signtool
run: |
$ErrorActionPreference = "Stop"
write-host "downloading SDK"
Invoke-WebRequest -Uri "https://go.microsoft.com/fwlink/p/?LinkId=323507" -OutFile "${env:RUNNER_TEMP}\sdksetup.exe"
Start-Process "${env:RUNNER_TEMP}\sdksetup.exe" -ArgumentList @("/q") -NoNewWindow -Wait
write-host "Win SDK 8.1 installed"
gci -path 'C:\Program Files (x86)\Windows Kits\' -r -fi 'signtool.exe'
- name: install signing plugin
run: |
$ErrorActionPreference = "Stop"
write-host "downloading plugin"
Invoke-WebRequest -Uri "https://github.com/GoogleCloudPlatform/kms-integrations/releases/download/cng-v1.0/kmscng-1.0-windows-amd64.zip" -OutFile "${env:RUNNER_TEMP}\plugin.zip"
Expand-Archive -Path "${env:RUNNER_TEMP}\plugin.zip" -DestinationPath ${env:RUNNER_TEMP}\plugin\
write-host "Installing plugin"
& "${env:RUNNER_TEMP}\plugin\*\kmscng.msi" /quiet
write-host "plugin installed"
- uses: actions/setup-go@v5
with:
go-version-file: go.mod
cache: true
- run: go get ./...
- run: |
$gopath=(get-command go).source | split-path -parent
$gccpath=(get-command gcc).source | split-path -parent
& "C:\Program Files\Microsoft Visual Studio\2022\Community\Common7\Tools\Launch-VsDevShell.ps1"
cd $env:GITHUB_WORKSPACE
$env:CMAKE_SYSTEM_VERSION="10.0.22621.0"
$env:PATH="$gopath;$gccpath;$env:PATH;C:\Program Files\Microsoft Visual Studio\2022\Community\Common7\IDE\CommonExtensions\Microsoft\CMake\CMake\bin"
echo $env:PATH
$env:ARCH="arm64"
.\scripts\build_windows.ps1 buildOllama buildApp gatherDependencies distZip
name: 'Windows Build'
- uses: actions/upload-artifact@v4
with:
name: windows-arm64
path: |
dist/windows-arm64/**
dist/windows-arm64-app.exe
dist/ollama-windows-arm64.zip
# Import the prior generation steps plus the full arm64 build, and build the final windows assets
build-windows: build-windows:
environment: release environment: release
runs-on: windows runs-on: windows
@@ -282,6 +409,7 @@ jobs:
- generate-windows-cuda - generate-windows-cuda
- generate-windows-rocm - generate-windows-rocm
- generate-windows-cpu - generate-windows-cpu
- windows-arm64
env: env:
KEY_CONTAINER: ${{ vars.KEY_CONTAINER }} KEY_CONTAINER: ${{ vars.KEY_CONTAINER }}
steps: steps:
@@ -339,6 +467,10 @@ jobs:
- uses: actions/download-artifact@v4 - uses: actions/download-artifact@v4
with: with:
name: generate-windows-rocm name: generate-windows-rocm
- uses: actions/download-artifact@v4
with:
name: windows-arm64
path: dist
- run: dir build - run: dir build
- run: | - run: |
$gopath=(get-command go).source | split-path -parent $gopath=(get-command go).source | split-path -parent

33
README.md
View File

@@ -53,10 +53,10 @@ The official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama) `olla
## Quickstart ## Quickstart
To run and chat with [Llama 3.1](https://ollama.com/library/llama3.1): To run and chat with [Llama 3.2](https://ollama.com/library/llama3.2):
``` ```
ollama run llama3.1 ollama run llama3.2
``` ```
## Model library ## Model library
@@ -67,6 +67,8 @@ Here are some example models that can be downloaded:
| Model | Parameters | Size | Download | | 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 | 8B | 4.7GB | `ollama run llama3.1` |
| Llama 3.1 | 70B | 40GB | `ollama run llama3.1:70b` | | Llama 3.1 | 70B | 40GB | `ollama run llama3.1:70b` |
| Llama 3.1 | 405B | 231GB | `ollama run llama3.1:405b` | | Llama 3.1 | 405B | 231GB | `ollama run llama3.1:405b` |
@@ -117,16 +119,16 @@ See the [guide](docs/import.md) on importing models for more information.
### Customize a prompt ### Customize a prompt
Models from the Ollama library can be customized with a prompt. For example, to customize the `llama3.1` model: Models from the Ollama library can be customized with a prompt. For example, to customize the `llama3.2` model:
``` ```
ollama pull llama3.1 ollama pull llama3.2
``` ```
Create a `Modelfile`: Create a `Modelfile`:
``` ```
FROM llama3.1 FROM llama3.2
# set the temperature to 1 [higher is more creative, lower is more coherent] # set the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1 PARAMETER temperature 1
@@ -161,7 +163,7 @@ ollama create mymodel -f ./Modelfile
### Pull a model ### Pull a model
``` ```
ollama pull llama3.1 ollama pull llama3.2
``` ```
> This command can also be used to update a local model. Only the diff will be pulled. > This command can also be used to update a local model. Only the diff will be pulled.
@@ -169,13 +171,13 @@ ollama pull llama3.1
### Remove a model ### Remove a model
``` ```
ollama rm llama3.1 ollama rm llama3.2
``` ```
### Copy a model ### Copy a model
``` ```
ollama cp llama3.1 my-model ollama cp llama3.2 my-model
``` ```
### Multiline input ### Multiline input
@@ -199,14 +201,14 @@ The image features a yellow smiley face, which is likely the central focus of th
### Pass the prompt as an argument ### Pass the prompt as an argument
``` ```
$ ollama run llama3.1 "Summarize this file: $(cat README.md)" $ 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. 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 ### Show model information
``` ```
ollama show llama3.1 ollama show llama3.2
``` ```
### List models on your computer ### List models on your computer
@@ -224,7 +226,7 @@ ollama ps
### Stop a model which is currently running ### Stop a model which is currently running
``` ```
ollama stop llama3.1 ollama stop llama3.2
``` ```
### Start Ollama ### Start Ollama
@@ -246,7 +248,7 @@ Next, start the server:
Finally, in a separate shell, run a model: Finally, in a separate shell, run a model:
``` ```
./ollama run llama3.1 ./ollama run llama3.2
``` ```
## REST API ## REST API
@@ -257,7 +259,7 @@ Ollama has a REST API for running and managing models.
``` ```
curl http://localhost:11434/api/generate -d '{ curl http://localhost:11434/api/generate -d '{
"model": "llama3.1", "model": "llama3.2",
"prompt":"Why is the sky blue?" "prompt":"Why is the sky blue?"
}' }'
``` ```
@@ -266,7 +268,7 @@ curl http://localhost:11434/api/generate -d '{
``` ```
curl http://localhost:11434/api/chat -d '{ curl http://localhost:11434/api/chat -d '{
"model": "llama3.1", "model": "llama3.2",
"messages": [ "messages": [
{ "role": "user", "content": "why is the sky blue?" } { "role": "user", "content": "why is the sky blue?" }
] ]
@@ -343,6 +345,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [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) - [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) - [crewAI with Mesop](https://github.com/rapidarchitect/ollama-crew-mesop) (Mesop Web Interface to run crewAI with Ollama)
- [LLMChat](https://github.com/trendy-design/llmchat) (Privacy focused, 100% local, intuitive all-in-one chat interface)
### Terminal ### Terminal
@@ -395,7 +398,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [LangChainGo](https://github.com/tmc/langchaingo/) with [example](https://github.com/tmc/langchaingo/tree/main/examples/ollama-completion-example) - [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) - [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) - [LangChainRust](https://github.com/Abraxas-365/langchain-rust) with [example](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/llm_ollama.rs)
- [LlamaIndex](https://gpt-index.readthedocs.io/en/stable/examples/llm/ollama.html) - [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) - [LiteLLM](https://github.com/BerriAI/litellm)
- [OllamaFarm for Go](https://github.com/presbrey/ollamafarm) - [OllamaFarm for Go](https://github.com/presbrey/ollamafarm)
- [OllamaSharp for .NET](https://github.com/awaescher/OllamaSharp) - [OllamaSharp for .NET](https://github.com/awaescher/OllamaSharp)

63
app/ollama.iss
View File

@@ -28,8 +28,8 @@ AppPublisher={#MyAppPublisher}
AppPublisherURL={#MyAppURL} AppPublisherURL={#MyAppURL}
AppSupportURL={#MyAppURL} AppSupportURL={#MyAppURL}
AppUpdatesURL={#MyAppURL} AppUpdatesURL={#MyAppURL}
ArchitecturesAllowed=x64 arm64 ArchitecturesAllowed=x64compatible arm64
ArchitecturesInstallIn64BitMode=x64 arm64 ArchitecturesInstallIn64BitMode=x64compatible arm64
DefaultDirName={localappdata}\Programs\{#MyAppName} DefaultDirName={localappdata}\Programs\{#MyAppName}
DefaultGroupName={#MyAppName} DefaultGroupName={#MyAppName}
DisableProgramGroupPage=yes DisableProgramGroupPage=yes
@@ -48,6 +48,7 @@ OutputDir=..\dist\
SetupLogging=yes SetupLogging=yes
CloseApplications=yes CloseApplications=yes
RestartApplications=no RestartApplications=no
RestartIfNeededByRun=no
; https://jrsoftware.org/ishelp/index.php?topic=setup_wizardimagefile ; https://jrsoftware.org/ishelp/index.php?topic=setup_wizardimagefile
WizardSmallImageFile=.\assets\setup.bmp WizardSmallImageFile=.\assets\setup.bmp
@@ -86,12 +87,21 @@ Name: "english"; MessagesFile: "compiler:Default.isl"
DialogFontSize=12 DialogFontSize=12
[Files] [Files]
Source: ".\app.exe"; DestDir: "{app}"; DestName: "{#MyAppExeName}" ; Flags: ignoreversion 64bit #if DirExists("..\dist\windows-amd64")
Source: "..\ollama.exe"; DestDir: "{app}"; Flags: ignoreversion 64bit Source: "..\dist\windows-amd64-app.exe"; DestDir: "{app}"; DestName: "{#MyAppExeName}" ;Check: not IsArm64(); Flags: ignoreversion 64bit
Source: "..\dist\windows-{#ARCH}\lib\ollama\runners\*"; DestDir: "{app}\lib\ollama\runners"; Flags: ignoreversion 64bit recursesubdirs Source: "..\dist\windows-amd64\ollama.exe"; DestDir: "{app}"; Check: not IsArm64(); Flags: ignoreversion 64bit
Source: "..\dist\windows-amd64\lib\ollama\*"; DestDir: "{app}\lib\ollama\"; Check: not IsArm64(); Flags: ignoreversion 64bit recursesubdirs
#endif
#if DirExists("..\dist\windows-arm64")
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 Source: "..\dist\ollama_welcome.ps1"; DestDir: "{app}"; Flags: ignoreversion
Source: ".\assets\app.ico"; DestDir: "{app}"; Flags: ignoreversion Source: ".\assets\app.ico"; DestDir: "{app}"; Flags: ignoreversion
Source: "..\dist\windows-amd64\lib\ollama\*"; DestDir: "{app}\lib\ollama\"; Flags: ignoreversion recursesubdirs
[Icons] [Icons]
Name: "{group}\{#MyAppName}"; Filename: "{app}\{#MyAppExeName}"; IconFilename: "{app}\app.ico" Name: "{group}\{#MyAppName}"; Filename: "{app}\{#MyAppExeName}"; IconFilename: "{app}\app.ico"
@@ -99,6 +109,9 @@ Name: "{userstartup}\{#MyAppName}"; Filename: "{app}\{#MyAppExeName}"; IconFilen
Name: "{userprograms}\{#MyAppName}"; Filename: "{app}\{#MyAppExeName}"; IconFilename: "{app}\app.ico" Name: "{userprograms}\{#MyAppName}"; Filename: "{app}\{#MyAppExeName}"; IconFilename: "{app}\app.ico"
[Run] [Run]
#if DirExists("..\dist\windows-arm64")
Filename: "{tmp}\vc_redist.arm64.exe"; Parameters: "/install /passive /norestart"; Check: IsArm64() and vc_redist_needed(); StatusMsg: "Installing VC++ Redistributables..."; Flags: waituntilterminated
#endif
Filename: "{cmd}"; Parameters: "/C set PATH={app};%PATH% & ""{app}\{#MyAppExeName}"""; Flags: postinstall nowait runhidden Filename: "{cmd}"; Parameters: "/C set PATH={app};%PATH% & ""{app}\{#MyAppExeName}"""; Flags: postinstall nowait runhidden
[UninstallRun] [UninstallRun]
@@ -129,7 +142,7 @@ SetupAppRunningError=Another Ollama installer is running.%n%nPlease cancel or fi
;FinishedHeadingLabel=Run your first model ;FinishedHeadingLabel=Run your first model
;FinishedLabel=%nRun this command in a PowerShell or cmd terminal.%n%n%n ollama run llama3.1 ;FinishedLabel=%nRun this command in a PowerShell or cmd terminal.%n%n%n ollama run llama3.2
;ClickFinish=%n ;ClickFinish=%n
[Registry] [Registry]
@@ -154,3 +167,39 @@ begin
{ Pos() returns 0 if not found } { Pos() returns 0 if not found }
Result := Pos(';' + ExpandConstant(Param) + ';', ';' + OrigPath + ';') = 0; Result := Pos(';' + ExpandConstant(Param) + ';', ';' + OrigPath + ';') = 0;
end; end;
{ --- VC Runtime libraries discovery code - Only install vc_redist if it isn't already installed ----- }
const VCRTL_MIN_V1 = 14;
const VCRTL_MIN_V2 = 40;
const VCRTL_MIN_V3 = 33807;
const VCRTL_MIN_V4 = 0;
// check if the minimum required vc redist is installed (by looking the registry)
function vc_redist_needed (): Boolean;
var
sRegKey: string;
v1: Cardinal;
v2: Cardinal;
v3: Cardinal;
v4: Cardinal;
begin
sRegKey := 'SOFTWARE\WOW6432Node\Microsoft\VisualStudio\14.0\VC\Runtimes\arm64';
if (RegQueryDWordValue (HKEY_LOCAL_MACHINE, sRegKey, 'Major', v1) and
RegQueryDWordValue (HKEY_LOCAL_MACHINE, sRegKey, 'Minor', v2) and
RegQueryDWordValue (HKEY_LOCAL_MACHINE, sRegKey, 'Bld', v3) and
RegQueryDWordValue (HKEY_LOCAL_MACHINE, sRegKey, 'RBld', v4)) then
begin
Log ('VC Redist version: ' + IntToStr (v1) +
'.' + IntToStr (v2) + '.' + IntToStr (v3) +
'.' + IntToStr (v4));
{ Version info was found. Return true if later or equal to our
minimal required version RTL_MIN_Vx }
Result := not (
(v1 > VCRTL_MIN_V1) or ((v1 = VCRTL_MIN_V1) and
((v2 > VCRTL_MIN_V2) or ((v2 = VCRTL_MIN_V2) and
((v3 > VCRTL_MIN_V3) or ((v3 = VCRTL_MIN_V3) and
(v4 >= VCRTL_MIN_V4)))))));
end
else
Result := TRUE;
end;

2
app/ollama_welcome.ps1
View File

@@ -4,5 +4,5 @@ write-host "Welcome to Ollama!"
write-host "" write-host ""
write-host "Run your first model:" write-host "Run your first model:"
write-host "" write-host ""
write-host "`tollama run llama3.1" write-host "`tollama run llama3.2"
write-host "" write-host ""

64
docs/api.md
View File

@@ -69,7 +69,7 @@ Enable JSON mode by setting the `format` parameter to `json`. This will structur
```shell ```shell
curl http://localhost:11434/api/generate -d '{ curl http://localhost:11434/api/generate -d '{
"model": "llama3.1", "model": "llama3.2",
"prompt": "Why is the sky blue?" "prompt": "Why is the sky blue?"
}' }'
``` ```
@@ -80,7 +80,7 @@ A stream of JSON objects is returned:
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at": "2023-08-04T08:52:19.385406455-07:00", "created_at": "2023-08-04T08:52:19.385406455-07:00",
"response": "The", "response": "The",
"done": false "done": false
@@ -102,7 +102,7 @@ To calculate how fast the response is generated in tokens per second (token/s),
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at": "2023-08-04T19:22:45.499127Z", "created_at": "2023-08-04T19:22:45.499127Z",
"response": "", "response": "",
"done": true, "done": true,
@@ -124,7 +124,7 @@ A response can be received in one reply when streaming is off.
```shell ```shell
curl http://localhost:11434/api/generate -d '{ curl http://localhost:11434/api/generate -d '{
"model": "llama3.1", "model": "llama3.2",
"prompt": "Why is the sky blue?", "prompt": "Why is the sky blue?",
"stream": false "stream": false
}' }'
@@ -136,7 +136,7 @@ If `stream` is set to `false`, the response will be a single JSON object:
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at": "2023-08-04T19:22:45.499127Z", "created_at": "2023-08-04T19:22:45.499127Z",
"response": "The sky is blue because it is the color of the sky.", "response": "The sky is blue because it is the color of the sky.",
"done": true, "done": true,
@@ -194,7 +194,7 @@ curl http://localhost:11434/api/generate -d '{
```shell ```shell
curl http://localhost:11434/api/generate -d '{ curl http://localhost:11434/api/generate -d '{
"model": "llama3.1", "model": "llama3.2",
"prompt": "What color is the sky at different times of the day? Respond using JSON", "prompt": "What color is the sky at different times of the day? Respond using JSON",
"format": "json", "format": "json",
"stream": false "stream": false
@@ -205,7 +205,7 @@ curl http://localhost:11434/api/generate -d '{
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at": "2023-11-09T21:07:55.186497Z", "created_at": "2023-11-09T21:07:55.186497Z",
"response": "{\n\"morning\": {\n\"color\": \"blue\"\n},\n\"noon\": {\n\"color\": \"blue-gray\"\n},\n\"afternoon\": {\n\"color\": \"warm gray\"\n},\n\"evening\": {\n\"color\": \"orange\"\n}\n}\n", "response": "{\n\"morning\": {\n\"color\": \"blue\"\n},\n\"noon\": {\n\"color\": \"blue-gray\"\n},\n\"afternoon\": {\n\"color\": \"warm gray\"\n},\n\"evening\": {\n\"color\": \"orange\"\n}\n}\n",
"done": true, "done": true,
@@ -327,7 +327,7 @@ If you want to set custom options for the model at runtime rather than in the Mo
```shell ```shell
curl http://localhost:11434/api/generate -d '{ curl http://localhost:11434/api/generate -d '{
"model": "llama3.1", "model": "llama3.2",
"prompt": "Why is the sky blue?", "prompt": "Why is the sky blue?",
"stream": false, "stream": false,
"options": { "options": {
@@ -368,7 +368,7 @@ curl http://localhost:11434/api/generate -d '{
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at": "2023-08-04T19:22:45.499127Z", "created_at": "2023-08-04T19:22:45.499127Z",
"response": "The sky is blue because it is the color of the sky.", "response": "The sky is blue because it is the color of the sky.",
"done": true, "done": true,
@@ -390,7 +390,7 @@ If an empty prompt is provided, the model will be loaded into memory.
```shell ```shell
curl http://localhost:11434/api/generate -d '{ curl http://localhost:11434/api/generate -d '{
"model": "llama3.1" "model": "llama3.2"
}' }'
``` ```
@@ -400,7 +400,7 @@ A single JSON object is returned:
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at": "2023-12-18T19:52:07.071755Z", "created_at": "2023-12-18T19:52:07.071755Z",
"response": "", "response": "",
"done": true "done": true
@@ -415,7 +415,7 @@ If an empty prompt is provided and the `keep_alive` parameter is set to `0`, a m
```shell ```shell
curl http://localhost:11434/api/generate -d '{ curl http://localhost:11434/api/generate -d '{
"model": "llama3.1", "model": "llama3.2",
"keep_alive": 0 "keep_alive": 0
}' }'
``` ```
@@ -426,7 +426,7 @@ A single JSON object is returned:
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at": "2024-09-12T03:54:03.516566Z", "created_at": "2024-09-12T03:54:03.516566Z",
"response": "", "response": "",
"done": true, "done": true,
@@ -472,7 +472,7 @@ Send a chat message with a streaming response.
```shell ```shell
curl http://localhost:11434/api/chat -d '{ curl http://localhost:11434/api/chat -d '{
"model": "llama3.1", "model": "llama3.2",
"messages": [ "messages": [
{ {
"role": "user", "role": "user",
@@ -488,7 +488,7 @@ A stream of JSON objects is returned:
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at": "2023-08-04T08:52:19.385406455-07:00", "created_at": "2023-08-04T08:52:19.385406455-07:00",
"message": { "message": {
"role": "assistant", "role": "assistant",
@@ -503,7 +503,7 @@ Final response:
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at": "2023-08-04T19:22:45.499127Z", "created_at": "2023-08-04T19:22:45.499127Z",
"done": true, "done": true,
"total_duration": 4883583458, "total_duration": 4883583458,
@@ -521,7 +521,7 @@ Final response:
```shell ```shell
curl http://localhost:11434/api/chat -d '{ curl http://localhost:11434/api/chat -d '{
"model": "llama3.1", "model": "llama3.2",
"messages": [ "messages": [
{ {
"role": "user", "role": "user",
@@ -536,7 +536,7 @@ curl http://localhost:11434/api/chat -d '{
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at": "2023-12-12T14:13:43.416799Z", "created_at": "2023-12-12T14:13:43.416799Z",
"message": { "message": {
"role": "assistant", "role": "assistant",
@@ -560,7 +560,7 @@ Send a chat message with a conversation history. You can use this same approach
```shell ```shell
curl http://localhost:11434/api/chat -d '{ curl http://localhost:11434/api/chat -d '{
"model": "llama3.1", "model": "llama3.2",
"messages": [ "messages": [
{ {
"role": "user", "role": "user",
@@ -584,7 +584,7 @@ A stream of JSON objects is returned:
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at": "2023-08-04T08:52:19.385406455-07:00", "created_at": "2023-08-04T08:52:19.385406455-07:00",
"message": { "message": {
"role": "assistant", "role": "assistant",
@@ -598,7 +598,7 @@ Final response:
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at": "2023-08-04T19:22:45.499127Z", "created_at": "2023-08-04T19:22:45.499127Z",
"done": true, "done": true,
"total_duration": 8113331500, "total_duration": 8113331500,
@@ -656,7 +656,7 @@ curl http://localhost:11434/api/chat -d '{
```shell ```shell
curl http://localhost:11434/api/chat -d '{ curl http://localhost:11434/api/chat -d '{
"model": "llama3.1", "model": "llama3.2",
"messages": [ "messages": [
{ {
"role": "user", "role": "user",
@@ -674,7 +674,7 @@ curl http://localhost:11434/api/chat -d '{
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at": "2023-12-12T14:13:43.416799Z", "created_at": "2023-12-12T14:13:43.416799Z",
"message": { "message": {
"role": "assistant", "role": "assistant",
@@ -696,7 +696,7 @@ curl http://localhost:11434/api/chat -d '{
``` ```
curl http://localhost:11434/api/chat -d '{ curl http://localhost:11434/api/chat -d '{
"model": "llama3.1", "model": "llama3.2",
"messages": [ "messages": [
{ {
"role": "user", "role": "user",
@@ -735,7 +735,7 @@ curl http://localhost:11434/api/chat -d '{
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at": "2024-07-22T20:33:28.123648Z", "created_at": "2024-07-22T20:33:28.123648Z",
"message": { "message": {
"role": "assistant", "role": "assistant",
@@ -771,7 +771,7 @@ If the messages array is empty, the model will be loaded into memory.
``` ```
curl http://localhost:11434/api/chat -d '{ curl http://localhost:11434/api/chat -d '{
"model": "llama3.1", "model": "llama3.2",
"messages": [] "messages": []
}' }'
``` ```
@@ -779,7 +779,7 @@ curl http://localhost:11434/api/chat -d '{
##### Response ##### Response
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at":"2024-09-12T21:17:29.110811Z", "created_at":"2024-09-12T21:17:29.110811Z",
"message": { "message": {
"role": "assistant", "role": "assistant",
@@ -798,7 +798,7 @@ If the messages array is empty and the `keep_alive` parameter is set to `0`, a m
``` ```
curl http://localhost:11434/api/chat -d '{ curl http://localhost:11434/api/chat -d '{
"model": "llama3.1", "model": "llama3.2",
"messages": [], "messages": [],
"keep_alive": 0 "keep_alive": 0
}' }'
@@ -810,7 +810,7 @@ A single JSON object is returned:
```json ```json
{ {
"model": "llama3.1", "model": "llama3.2",
"created_at":"2024-09-12T21:33:17.547535Z", "created_at":"2024-09-12T21:33:17.547535Z",
"message": { "message": {
"role": "assistant", "role": "assistant",
@@ -989,7 +989,7 @@ Show information about a model including details, modelfile, template, parameter
```shell ```shell
curl http://localhost:11434/api/show -d '{ curl http://localhost:11434/api/show -d '{
"name": "llama3.1" "name": "llama3.2"
}' }'
``` ```
@@ -1050,7 +1050,7 @@ Copy a model. Creates a model with another name from an existing model.
```shell ```shell
curl http://localhost:11434/api/copy -d '{ curl http://localhost:11434/api/copy -d '{
"source": "llama3.1", "source": "llama3.2",
"destination": "llama3-backup" "destination": "llama3-backup"
}' }'
``` ```
@@ -1105,7 +1105,7 @@ Download a model from the ollama library. Cancelled pulls are resumed from where
```shell ```shell
curl http://localhost:11434/api/pull -d '{ curl http://localhost:11434/api/pull -d '{
"name": "llama3.1" "name": "llama3.2"
}' }'
``` ```

19
docs/development.md
View File

@@ -148,3 +148,22 @@ In addition to the common Windows development tools described above, install AMD
- [Strawberry Perl](https://strawberryperl.com/) - [Strawberry Perl](https://strawberryperl.com/)
Lastly, add `ninja.exe` included with MSVC to the system path (e.g. `C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\CommonExtensions\Microsoft\CMake\Ninja`). Lastly, add `ninja.exe` included with MSVC to the system path (e.g. `C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\Common7\IDE\CommonExtensions\Microsoft\CMake\Ninja`).
#### Windows arm64
The default `Developer PowerShell for VS 2022` may default to x86 which is not what you want. To ensure you get an arm64 development environment, start a plain PowerShell terminal and run:
```powershell
import-module 'C:\\Program Files\\Microsoft Visual Studio\\2022\\Community\\Common7\\Tools\\Microsoft.VisualStudio.DevShell.dll'
Enter-VsDevShell -Arch arm64 -vsinstallpath 'C:\\Program Files\\Microsoft Visual Studio\\2022\\Community' -skipautomaticlocation
```
You can confirm with `write-host $env:VSCMD_ARG_TGT_ARCH`
Follow the instructions at https://www.msys2.org/wiki/arm64/ to set up an arm64 msys2 environment. Ollama requires gcc and mingw32-make to compile, which is not currently available on Windows arm64, but a gcc compatibility adapter is available via `mingw-w64-clang-aarch64-gcc-compat`. At a minimum you will need to install the following:
```
pacman -S mingw-w64-clang-aarch64-clang mingw-w64-clang-aarch64-gcc-compat mingw-w64-clang-aarch64-make make
```
You will need to ensure your PATH includes go, cmake, gcc and clang mingw32-make to build ollama from source. (typically `C:\msys64\clangarm64\bin\`)

2
docs/docker.md
View File

@@ -63,7 +63,7 @@ docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 114
Now you can run a model: Now you can run a model:
``` ```
docker exec -it ollama ollama run llama3.1 docker exec -it ollama ollama run llama3.2
``` ```
### Try different models ### Try different models

10
docs/faq.md
View File

@@ -32,7 +32,7 @@ When using the API, specify the `num_ctx` parameter:
```shell ```shell
curl http://localhost:11434/api/generate -d '{ curl http://localhost:11434/api/generate -d '{
"model": "llama3.1", "model": "llama3.2",
"prompt": "Why is the sky blue?", "prompt": "Why is the sky blue?",
"options": { "options": {
"num_ctx": 4096 "num_ctx": 4096
@@ -232,7 +232,7 @@ curl http://localhost:11434/api/chat -d '{"model": "mistral"}'
To preload a model using the CLI, use the command: To preload a model using the CLI, use the command:
```shell ```shell
ollama run llama3.1 "" ollama run llama3.2 ""
``` ```
## How do I keep a model loaded in memory or make it unload immediately? ## How do I keep a model loaded in memory or make it unload immediately?
@@ -240,7 +240,7 @@ ollama run llama3.1 ""
By default models are kept in memory for 5 minutes before being unloaded. This allows for quicker response times if you're making numerous requests to the LLM. If you want to immediately unload a model from memory, use the `ollama stop` command: By default models are kept in memory for 5 minutes before being unloaded. This allows for quicker response times if you're making numerous requests to the LLM. If you want to immediately unload a model from memory, use the `ollama stop` command:
```shell ```shell
ollama stop llama3.1 ollama stop llama3.2
``` ```
If you're using the API, use the `keep_alive` parameter with the `/api/generate` and `/api/chat` endpoints to set the amount of time that a model stays in memory. The `keep_alive` parameter can be set to: If you're using the API, use the `keep_alive` parameter with the `/api/generate` and `/api/chat` endpoints to set the amount of time that a model stays in memory. The `keep_alive` parameter can be set to:
@@ -251,12 +251,12 @@ If you're using the API, use the `keep_alive` parameter with the `/api/generate`
For example, to preload a model and leave it in memory use: For example, to preload a model and leave it in memory use:
```shell ```shell
curl http://localhost:11434/api/generate -d '{"model": "llama3.1", "keep_alive": -1}' curl http://localhost:11434/api/generate -d '{"model": "llama3.2", "keep_alive": -1}'
``` ```
To unload the model and free up memory use: To unload the model and free up memory use:
```shell ```shell
curl http://localhost:11434/api/generate -d '{"model": "llama3.1", "keep_alive": 0}' curl http://localhost:11434/api/generate -d '{"model": "llama3.2", "keep_alive": 0}'
``` ```
Alternatively, you can change the amount of time all models are loaded into memory by setting the `OLLAMA_KEEP_ALIVE` environment variable when starting the Ollama server. The `OLLAMA_KEEP_ALIVE` variable uses the same parameter types as the `keep_alive` parameter types mentioned above. Refer to the section explaining [how to configure the Ollama server](#how-do-i-configure-ollama-server) to correctly set the environment variable. Alternatively, you can change the amount of time all models are loaded into memory by setting the `OLLAMA_KEEP_ALIVE` environment variable when starting the Ollama server. The `OLLAMA_KEEP_ALIVE` variable uses the same parameter types as the `keep_alive` parameter types mentioned above. Refer to the section explaining [how to configure the Ollama server](#how-do-i-configure-ollama-server) to correctly set the environment variable.

8
docs/modelfile.md
View File

@@ -50,7 +50,7 @@ INSTRUCTION arguments
An example of a `Modelfile` creating a mario blueprint: An example of a `Modelfile` creating a mario blueprint:
```modelfile ```modelfile
FROM llama3.1 FROM llama3.2
# sets the temperature to 1 [higher is more creative, lower is more coherent] # sets the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1 PARAMETER temperature 1
# sets the context window size to 4096, this controls how many tokens the LLM can use as context to generate the next token # sets the context window size to 4096, this controls how many tokens the LLM can use as context to generate the next token
@@ -72,10 +72,10 @@ More examples are available in the [examples directory](../examples).
To view the Modelfile of a given model, use the `ollama show --modelfile` command. To view the Modelfile of a given model, use the `ollama show --modelfile` command.
```bash ```bash
> ollama show --modelfile llama3.1 > ollama show --modelfile llama3.2
# Modelfile generated by "ollama show" # Modelfile generated by "ollama show"
# To build a new Modelfile based on this one, replace the FROM line with: # To build a new Modelfile based on this one, replace the FROM line with:
# FROM llama3.1:latest # FROM llama3.2:latest
FROM /Users/pdevine/.ollama/models/blobs/sha256-00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29 FROM /Users/pdevine/.ollama/models/blobs/sha256-00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29
TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|> TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>
@@ -103,7 +103,7 @@ FROM <model name>:<tag>
#### Build from existing model #### Build from existing model
```modelfile ```modelfile
FROM llama3.1 FROM llama3.2
``` ```
A list of available base models: A list of available base models:

22
docs/openai.md
View File

@@ -25,7 +25,7 @@ chat_completion = client.chat.completions.create(
'content': 'Say this is a test', 'content': 'Say this is a test',
} }
], ],
model='llama3.1', model='llama3.2',
) )
response = client.chat.completions.create( response = client.chat.completions.create(
@@ -46,13 +46,13 @@ response = client.chat.completions.create(
) )
completion = client.completions.create( completion = client.completions.create(
model="llama3.1", model="llama3.2",
prompt="Say this is a test", prompt="Say this is a test",
) )
list_completion = client.models.list() list_completion = client.models.list()
model = client.models.retrieve("llama3.1") model = client.models.retrieve("llama3.2")
embeddings = client.embeddings.create( embeddings = client.embeddings.create(
model="all-minilm", model="all-minilm",
@@ -74,7 +74,7 @@ const openai = new OpenAI({
const chatCompletion = await openai.chat.completions.create({ const chatCompletion = await openai.chat.completions.create({
messages: [{ role: 'user', content: 'Say this is a test' }], messages: [{ role: 'user', content: 'Say this is a test' }],
model: 'llama3.1', model: 'llama3.2',
}) })
const response = await openai.chat.completions.create({ const response = await openai.chat.completions.create({
@@ -94,13 +94,13 @@ const response = await openai.chat.completions.create({
}) })
const completion = await openai.completions.create({ const completion = await openai.completions.create({
model: "llama3.1", model: "llama3.2",
prompt: "Say this is a test.", prompt: "Say this is a test.",
}) })
const listCompletion = await openai.models.list() const listCompletion = await openai.models.list()
const model = await openai.models.retrieve("llama3.1") const model = await openai.models.retrieve("llama3.2")
const embedding = await openai.embeddings.create({ const embedding = await openai.embeddings.create({
model: "all-minilm", model: "all-minilm",
@@ -114,7 +114,7 @@ const embedding = await openai.embeddings.create({
curl http://localhost:11434/v1/chat/completions \ curl http://localhost:11434/v1/chat/completions \
-H "Content-Type: application/json" \ -H "Content-Type: application/json" \
-d '{ -d '{
"model": "llama3.1", "model": "llama3.2",
"messages": [ "messages": [
{ {
"role": "system", "role": "system",
@@ -154,13 +154,13 @@ curl http://localhost:11434/v1/chat/completions \
curl http://localhost:11434/v1/completions \ curl http://localhost:11434/v1/completions \
-H "Content-Type: application/json" \ -H "Content-Type: application/json" \
-d '{ -d '{
"model": "llama3.1", "model": "llama3.2",
"prompt": "Say this is a test" "prompt": "Say this is a test"
}' }'
curl http://localhost:11434/v1/models curl http://localhost:11434/v1/models
curl http://localhost:11434/v1/models/llama3.1 curl http://localhost:11434/v1/models/llama3.2
curl http://localhost:11434/v1/embeddings \ curl http://localhost:11434/v1/embeddings \
-H "Content-Type: application/json" \ -H "Content-Type: application/json" \
@@ -274,7 +274,7 @@ curl http://localhost:11434/v1/embeddings \
Before using a model, pull it locally `ollama pull`: Before using a model, pull it locally `ollama pull`:
```shell ```shell
ollama pull llama3.1 ollama pull llama3.2
``` ```
### Default model names ### Default model names
@@ -282,7 +282,7 @@ ollama pull llama3.1
For tooling that relies on default OpenAI model names such as `gpt-3.5-turbo`, use `ollama cp` to copy an existing model name to a temporary name: For tooling that relies on default OpenAI model names such as `gpt-3.5-turbo`, use `ollama cp` to copy an existing model name to a temporary name:
``` ```
ollama cp llama3.1 gpt-3.5-turbo ollama cp llama3.2 gpt-3.5-turbo
``` ```
Afterwards, this new model name can be specified the `model` field: Afterwards, this new model name can be specified the `model` field:

2
docs/template.md
View File

@@ -33,7 +33,7 @@ Omitting a template in these models puts the responsibility of correctly templat
To add templates in your model, you'll need to add a `TEMPLATE` command to the Modelfile. Here's an example using Meta's Llama 3. To add templates in your model, you'll need to add a `TEMPLATE` command to the Modelfile. Here's an example using Meta's Llama 3.
```dockerfile ```dockerfile
FROM llama3.1 FROM llama3.2
TEMPLATE """{{- if .System }}<|start_header_id|>system<|end_header_id|> TEMPLATE """{{- if .System }}<|start_header_id|>system<|end_header_id|>

4
docs/tutorials/langchainjs.md
View File

@@ -15,7 +15,7 @@ import { Ollama } from "@langchain/community/llms/ollama";
const ollama = new Ollama({ const ollama = new Ollama({
baseUrl: "http://localhost:11434", baseUrl: "http://localhost:11434",
model: "llama3.1", model: "llama3.2",
}); });
const answer = await ollama.invoke(`why is the sky blue?`); const answer = await ollama.invoke(`why is the sky blue?`);
@@ -23,7 +23,7 @@ const answer = await ollama.invoke(`why is the sky blue?`);
console.log(answer); console.log(answer);
``` ```
That will get us the same thing as if we ran `ollama run llama3.1 "why is the sky blue"` in the terminal. But we want to load a document from the web to ask a question against. **Cheerio** is a great library for ingesting a webpage, and **LangChain** uses it in their **CheerioWebBaseLoader**. So let's install **Cheerio** and build that part of the app. That will get us the same thing as if we ran `ollama run llama3.2 "why is the sky blue"` in the terminal. But we want to load a document from the web to ask a question against. **Cheerio** is a great library for ingesting a webpage, and **LangChain** uses it in their **CheerioWebBaseLoader**. So let's install **Cheerio** and build that part of the app.
```bash ```bash
npm install cheerio npm install cheerio

2
docs/windows.md
View File

@@ -29,7 +29,7 @@ Ollama uses unicode characters for progress indication, which may render as unkn
Here's a quick example showing API access from `powershell` Here's a quick example showing API access from `powershell`
```powershell ```powershell
(Invoke-WebRequest -method POST -Body '{"model":"llama3.1", "prompt":"Why is the sky blue?", "stream": false}' -uri http://localhost:11434/api/generate ).Content | ConvertFrom-json (Invoke-WebRequest -method POST -Body '{"model":"llama3.2", "prompt":"Why is the sky blue?", "stream": false}' -uri http://localhost:11434/api/generate ).Content | ConvertFrom-json
``` ```
## Troubleshooting ## Troubleshooting

2
examples/go-chat/main.go
View File

@@ -35,7 +35,7 @@ func main() {
ctx := context.Background() ctx := context.Background()
req := &api.ChatRequest{ req := &api.ChatRequest{
Model: "llama3.1", Model: "llama3.2",
Messages: messages, Messages: messages,
} }

4
examples/langchain-python-rag-document/README.md
View File

@@ -4,10 +4,10 @@ This example provides an interface for asking questions to a PDF document.
## Setup ## Setup
1. Ensure you have the `llama3.1` model installed: 1. Ensure you have the `llama3.2` model installed:
``` ```
ollama pull llama3.1 ollama pull llama3.2
``` ```
2. Install the Python Requirements. 2. Install the Python Requirements.

2
examples/langchain-python-rag-document/main.py
View File

@@ -51,7 +51,7 @@ while True:
template=template, template=template,
) )
llm = Ollama(model="llama3.1", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()])) llm = Ollama(model="llama3.2", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))
qa_chain = RetrievalQA.from_chain_type( qa_chain = RetrievalQA.from_chain_type(
llm, llm,
retriever=vectorstore.as_retriever(), retriever=vectorstore.as_retriever(),

4
examples/langchain-python-rag-websummary/README.md
View File

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

2
examples/langchain-python-rag-websummary/main.py
View File

@@ -5,7 +5,7 @@ from langchain.chains.summarize import load_summarize_chain
loader = WebBaseLoader("https://ollama.com/blog/run-llama2-uncensored-locally") loader = WebBaseLoader("https://ollama.com/blog/run-llama2-uncensored-locally")
docs = loader.load() docs = loader.load()
llm = Ollama(model="llama3.1") llm = Ollama(model="llama3.2")
chain = load_summarize_chain(llm, chain_type="stuff") chain = load_summarize_chain(llm, chain_type="stuff")
result = chain.invoke(docs) result = chain.invoke(docs)

4
examples/langchain-python-simple/README.md
View File

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

2
examples/langchain-python-simple/main.py
View File

@@ -1,6 +1,6 @@
from langchain.llms import Ollama from langchain.llms import Ollama
input = input("What is your question?") input = input("What is your question?")
llm = Ollama(model="llama3.1") llm = Ollama(model="llama3.2")
res = llm.predict(input) res = llm.predict(input)
print (res) print (res)

2
examples/modelfile-mario/Modelfile
View File

@@ -1,4 +1,4 @@
FROM llama3.1 FROM llama3.2
PARAMETER temperature 1 PARAMETER temperature 1
SYSTEM """ SYSTEM """
You are Mario from super mario bros, acting as an assistant. You are Mario from super mario bros, acting as an assistant.

6
examples/modelfile-mario/readme.md
View File

@@ -2,12 +2,12 @@
# Example character: Mario # Example character: Mario
This example shows how to create a basic character using Llama3.1 as the base model. This example shows how to create a basic character using Llama 3.2 as the base model.
To run this example: To run this example:
1. Download the Modelfile 1. Download the Modelfile
2. `ollama pull llama3.1` to get the base model used in the model file. 2. `ollama pull llama3.2` to get the base model used in the model file.
3. `ollama create NAME -f ./Modelfile` 3. `ollama create NAME -f ./Modelfile`
4. `ollama run NAME` 4. `ollama run NAME`
@@ -18,7 +18,7 @@ Ask it some questions like "Who are you?" or "Is Peach in trouble again?"
What the model file looks like: What the model file looks like:
``` ```
FROM llama3.1 FROM llama3.2
PARAMETER temperature 1 PARAMETER temperature 1
SYSTEM """ SYSTEM """
You are Mario from Super Mario Bros, acting as an assistant. You are Mario from Super Mario Bros, acting as an assistant.

6
examples/python-grounded-factuality-rag-check/README.md
View File

@@ -1,14 +1,14 @@
# RAG Hallucination Checker using Bespoke-Minicheck # RAG Hallucination Checker using Bespoke-Minicheck
This example allows the user to ask questions related to a document, which can be specified via an article url. Relevant chunks are retreived from the document and given to `llama3.1` as context to answer the question. Then each sentence in the answer is checked against the retrieved chunks using `bespoke-minicheck` to ensure that the answer does not contain hallucinations. This example allows the user to ask questions related to a document, which can be specified via an article url. Relevant chunks are retreived from the document and given to `llama3.2` as context to answer the question. Then each sentence in the answer is checked against the retrieved chunks using `bespoke-minicheck` to ensure that the answer does not contain hallucinations.
## Running the Example ## Running the Example
1. Ensure `all-minilm` (embedding) `llama3.1` (chat) and `bespoke-minicheck` (check) models installed: 1. Ensure `all-minilm` (embedding) `llama3.2` (chat) and `bespoke-minicheck` (check) models installed:
```bash ```bash
ollama pull all-minilm ollama pull all-minilm
ollama pull llama3.1 ollama pull llama3.2
ollama pull bespoke-minicheck ollama pull bespoke-minicheck
``` ```

4
examples/python-grounded-factuality-rag-check/main.py
View File

@@ -9,7 +9,7 @@ import nltk
warnings.filterwarnings( warnings.filterwarnings(
"ignore", category=FutureWarning, module="transformers.tokenization_utils_base" "ignore", category=FutureWarning, module="transformers.tokenization_utils_base"
) )
nltk.download("punkt", quiet=True) nltk.download("punkt_tab", quiet=True)
def getArticleText(url): def getArticleText(url):
@@ -119,7 +119,7 @@ if __name__ == "__main__":
system_prompt = f"Only use the following information to answer the question. Do not use anything else: {sourcetext}" system_prompt = f"Only use the following information to answer the question. Do not use anything else: {sourcetext}"
ollama_response = ollama.generate( ollama_response = ollama.generate(
model="llama3.1", model="llama3.2",
prompt=question, prompt=question,
system=system_prompt, system=system_prompt,
options={"stream": False}, options={"stream": False},

2
examples/python-json-datagenerator/predefinedschema.py
View File

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

2
examples/python-json-datagenerator/randomaddresses.py
View File

@@ -12,7 +12,7 @@ countries = [
"France", "France",
] ]
country = random.choice(countries) country = random.choice(countries)
model = "llama3.1" model = "llama3.2"
prompt = f"generate one realistically believable sample data set of a persons first name, last name, address in {country}, and phone number. Do not use common names. Respond using JSON. Key names should have no backslashes, values should use plain ascii with no special characters." prompt = f"generate one realistically believable sample data set of a persons first name, last name, address in {country}, and phone number. Do not use common names. Respond using JSON. Key names should have no backslashes, values should use plain ascii with no special characters."

4
examples/python-json-datagenerator/readme.md
View File

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

2
examples/python-simplechat/client.py
View File

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

4
examples/python-simplechat/readme.md
View File

@@ -4,10 +4,10 @@ The **chat** endpoint is one of two ways to generate text from an LLM with Ollam
## Running the Example ## Running the Example
1. Ensure you have the `llama3.1` model installed: 1. Ensure you have the `llama3.2` model installed:
```bash ```bash
ollama pull llama3.1 ollama pull llama3.2
``` ```
2. Install the Python Requirements. 2. Install the Python Requirements.

2
examples/typescript-simplechat/client.ts
View File

@@ -1,6 +1,6 @@
import * as readline from "readline"; import * as readline from "readline";
const model = "llama3.1"; const model = "llama3.2";
type Message = { type Message = {
role: "assistant" | "user" | "system"; role: "assistant" | "user" | "system";
content: string; content: string;

13
gpu/gpu.go
View File

@@ -205,13 +205,16 @@ func GetGPUInfo() GpuInfoList {
if err != nil { if err != nil {
slog.Warn("error looking up system memory", "error", err) slog.Warn("error looking up system memory", "error", err)
} }
depPath := LibraryDir()
cpus = []CPUInfo{ cpus = []CPUInfo{
{ {
GpuInfo: GpuInfo{ GpuInfo: GpuInfo{
memInfo: mem, memInfo: mem,
Library: "cpu", Library: "cpu",
Variant: cpuCapability.String(), Variant: cpuCapability.String(),
ID: "0", ID: "0",
DependencyPath: depPath,
}, },
}, },
} }
@@ -224,8 +227,6 @@ func GetGPUInfo() GpuInfoList {
return GpuInfoList{cpus[0].GpuInfo} return GpuInfoList{cpus[0].GpuInfo}
} }
depPath := LibraryDir()
// Load ALL libraries // Load ALL libraries
cHandles = initCudaHandles() cHandles = initCudaHandles()

1
llm/ext_server/CMakeLists.txt vendored
View File

@@ -10,5 +10,6 @@ target_compile_definitions(${TARGET} PRIVATE
target_link_libraries(${TARGET} PRIVATE ggml llama common llava ${CMAKE_THREAD_LIBS_INIT} ${LLAMA_SERVER_LDFLAGS}) target_link_libraries(${TARGET} PRIVATE ggml llama common llava ${CMAKE_THREAD_LIBS_INIT} ${LLAMA_SERVER_LDFLAGS})
if (WIN32) if (WIN32)
TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32) TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
target_link_options(${TARGET} PRIVATE -municode -Wl,/subsystem:console)
endif() endif()
target_compile_features(${TARGET} PRIVATE cxx_std_11) target_compile_features(${TARGET} PRIVATE cxx_std_11)

66
llm/generate/gen_windows.ps1
View File

@@ -37,6 +37,19 @@ function amdGPUs {
function init_vars { function init_vars {
write-host "Checking for cmake..."
get-command cmake
write-host "Checking for ninja..."
$d=(get-command -ea 'silentlycontinue' ninja).path
if ($null -eq $d) {
$MSVC_INSTALL=(Get-CimInstance MSFT_VSInstance -Namespace root/cimv2/vs)[0].InstallLocation
$matches=(gci -path $MSVC_INSTALL -r -fi ninja.exe)
if ($matches.count -eq 0) {
throw "Unable to locate ninja"
}
$ninjaDir=($matches[0].FullName | split-path -parent)
$env:PATH="$env:PATH;$ninjaDir"
}
if (!$script:SRC_DIR) { if (!$script:SRC_DIR) {
$script:SRC_DIR = $(resolve-path "..\..\") $script:SRC_DIR = $(resolve-path "..\..\")
} }
@@ -163,7 +176,7 @@ function cleanup {
} }
# Checkout each file # Checkout each file
foreach ($file in $filePaths) { foreach ($file in $filePaths) {
git -C "${script:llamacppDir}" checkout $file git -C "${script:llamacppDir}" checkout $file
} }
git -C "${script:llamacppDir}" checkout CMakeLists.txt git -C "${script:llamacppDir}" checkout CMakeLists.txt
@@ -180,12 +193,12 @@ function build_static() {
if ((-not "${env:OLLAMA_SKIP_STATIC_GENERATE}") -and ((-not "${env:OLLAMA_CPU_TARGET}") -or ("${env:OLLAMA_CPU_TARGET}" -eq "static"))) { if ((-not "${env:OLLAMA_SKIP_STATIC_GENERATE}") -and ((-not "${env:OLLAMA_CPU_TARGET}") -or ("${env:OLLAMA_CPU_TARGET}" -eq "static"))) {
# GCC build for direct linking into the Go binary # GCC build for direct linking into the Go binary
init_vars init_vars
# cmake will silently fallback to msvc compilers if mingw isn't in the path, so detect and fail fast
# as we need this to be compiled by gcc for golang to be able to link with itx # cmake will silently fallback to msvc compilers if gcc isn't in the path, so detect and fail fast
write-host "Checking for MinGW..." # as we need this to be compiled by gcc for golang to be able to link with it
# error action ensures we exit on failure write-host "Checking for gcc..."
get-command gcc get-command gcc
get-command mingw32-make get-command mingw32-make
$oldTargets = $script:cmakeTargets $oldTargets = $script:cmakeTargets
$script:cmakeTargets = @("llama", "ggml") $script:cmakeTargets = @("llama", "ggml")
$script:cmakeDefs = @( $script:cmakeDefs = @(
@@ -209,11 +222,10 @@ function build_static() {
} }
} }
function build_cpu($gen_arch) { function build_cpu_x64 {
if ((-not "${env:OLLAMA_SKIP_CPU_GENERATE}" ) -and ((-not "${env:OLLAMA_CPU_TARGET}") -or ("${env:OLLAMA_CPU_TARGET}" -eq "cpu"))) { if ((-not "${env:OLLAMA_SKIP_CPU_GENERATE}" ) -and ((-not "${env:OLLAMA_CPU_TARGET}") -or ("${env:OLLAMA_CPU_TARGET}" -eq "cpu"))) {
# remaining llama.cpp builds use MSVC
init_vars init_vars
$script:cmakeDefs = $script:commonCpuDefs + @("-A", $gen_arch, "-DGGML_AVX=off", "-DGGML_AVX2=off", "-DGGML_AVX512=off", "-DGGML_FMA=off", "-DGGML_F16C=off") + $script:cmakeDefs $script:cmakeDefs = $script:commonCpuDefs + @("-A", "x64", "-DGGML_AVX=off", "-DGGML_AVX2=off", "-DGGML_AVX512=off", "-DGGML_FMA=off", "-DGGML_F16C=off") + $script:cmakeDefs
$script:buildDir="../build/windows/${script:ARCH}/cpu" $script:buildDir="../build/windows/${script:ARCH}/cpu"
$script:distDir="$script:DIST_BASE\cpu" $script:distDir="$script:DIST_BASE\cpu"
write-host "Building LCD CPU" write-host "Building LCD CPU"
@@ -225,6 +237,32 @@ function build_cpu($gen_arch) {
} }
} }
function build_cpu_arm64 {
if ((-not "${env:OLLAMA_SKIP_CPU_GENERATE}" ) -and ((-not "${env:OLLAMA_CPU_TARGET}") -or ("${env:OLLAMA_CPU_TARGET}" -eq "cpu"))) {
init_vars
write-host "Checking for clang..."
get-command clang
$env:CFLAGS="-march=armv8.7-a -fvectorize -ffp-model=fast -fno-finite-math-only"
$env:CXXFLAGS="$env:CFLAGS"
$env:LDFLAGS="-static-libstdc++"
$script:cmakeDefs = $script:commonCpuDefs + @(
"-DCMAKE_VERBOSE_MAKEFILE=on",
"-DCMAKE_C_COMPILER=clang.exe",
"-DCMAKE_CXX_COMPILER=clang++.exe",
"-DMSVC_RUNTIME_LIBRARY=MultiThreaded"
) + $script:cmakeDefs
$script:buildDir="../build/windows/${script:ARCH}/cpu"
$script:distDir="$script:DIST_BASE\cpu"
write-host "Building LCD CPU"
build
sign
install
} else {
write-host "Skipping CPU generation step as requested"
}
}
function build_cpu_avx() { function build_cpu_avx() {
if ((-not "${env:OLLAMA_SKIP_CPU_GENERATE}" ) -and ((-not "${env:OLLAMA_CPU_TARGET}") -or ("${env:OLLAMA_CPU_TARGET}" -eq "cpu_avx"))) { if ((-not "${env:OLLAMA_SKIP_CPU_GENERATE}" ) -and ((-not "${env:OLLAMA_CPU_TARGET}") -or ("${env:OLLAMA_CPU_TARGET}" -eq "cpu_avx"))) {
init_vars init_vars
@@ -349,7 +387,7 @@ function build_rocm() {
$script:buildDir="../build/windows/${script:ARCH}/rocm$script:ROCM_VARIANT" $script:buildDir="../build/windows/${script:ARCH}/rocm$script:ROCM_VARIANT"
$script:distDir="$script:DIST_BASE\rocm$script:ROCM_VARIANT" $script:distDir="$script:DIST_BASE\rocm$script:ROCM_VARIANT"
$script:cmakeDefs += @( $script:cmakeDefs += @(
"-G", "Ninja", "-G", "Ninja",
"-DCMAKE_C_COMPILER=clang.exe", "-DCMAKE_C_COMPILER=clang.exe",
"-DCMAKE_CXX_COMPILER=clang++.exe", "-DCMAKE_CXX_COMPILER=clang++.exe",
"-DGGML_HIPBLAS=on", "-DGGML_HIPBLAS=on",
@@ -398,9 +436,9 @@ if ($($args.count) -eq 0) {
apply_patches apply_patches
build_static build_static
if ($script:ARCH -eq "arm64") { if ($script:ARCH -eq "arm64") {
build_cpu("ARM64") build_cpu_arm64
} else { # amd64 } else { # amd64
build_cpu("x64") build_cpu_x64
build_cpu_avx build_cpu_avx
build_cpu_avx2 build_cpu_avx2
build_cuda build_cuda
@@ -414,5 +452,5 @@ if ($($args.count) -eq 0) {
for ( $i = 0; $i -lt $args.count; $i++ ) { for ( $i = 0; $i -lt $args.count; $i++ ) {
write-host "performing $($args[$i])" write-host "performing $($args[$i])"
& $($args[$i]) & $($args[$i])
} }
} }

2
llm/llm.go
View File

@@ -5,7 +5,7 @@ package llm
// #cgo darwin,arm64 LDFLAGS: -L${SRCDIR}/build/darwin/arm64_static -L${SRCDIR}/build/darwin/arm64_static/src -L${SRCDIR}/build/darwin/arm64_static/ggml/src -framework Accelerate -framework Metal // #cgo darwin,arm64 LDFLAGS: -L${SRCDIR}/build/darwin/arm64_static -L${SRCDIR}/build/darwin/arm64_static/src -L${SRCDIR}/build/darwin/arm64_static/ggml/src -framework Accelerate -framework Metal
// #cgo darwin,amd64 LDFLAGS: -L${SRCDIR}/build/darwin/x86_64_static -L${SRCDIR}/build/darwin/x86_64_static/src -L${SRCDIR}/build/darwin/x86_64_static/ggml/src // #cgo darwin,amd64 LDFLAGS: -L${SRCDIR}/build/darwin/x86_64_static -L${SRCDIR}/build/darwin/x86_64_static/src -L${SRCDIR}/build/darwin/x86_64_static/ggml/src
// #cgo windows,amd64 LDFLAGS: -static-libstdc++ -static-libgcc -static -L${SRCDIR}/build/windows/amd64_static -L${SRCDIR}/build/windows/amd64_static/src -L${SRCDIR}/build/windows/amd64_static/ggml/src // #cgo windows,amd64 LDFLAGS: -static-libstdc++ -static-libgcc -static -L${SRCDIR}/build/windows/amd64_static -L${SRCDIR}/build/windows/amd64_static/src -L${SRCDIR}/build/windows/amd64_static/ggml/src
// #cgo windows,arm64 LDFLAGS: -static-libstdc++ -static-libgcc -static -L${SRCDIR}/build/windows/arm64_static -L${SRCDIR}/build/windows/arm64_static/src -L${SRCDIR}/build/windows/arm64_static/ggml/src // #cgo windows,arm64 LDFLAGS: -lllama -lggml -static-libstdc++ -static-libgcc -static -L${SRCDIR}/build/windows/arm64_static -L${SRCDIR}/build/windows/arm64_static/src -L${SRCDIR}/build/windows/arm64_static/ggml/src
// #cgo linux,amd64 LDFLAGS: -L${SRCDIR}/build/linux/x86_64_static -L${SRCDIR}/build/linux/x86_64_static/src -L${SRCDIR}/build/linux/x86_64_static/ggml/src // #cgo linux,amd64 LDFLAGS: -L${SRCDIR}/build/linux/x86_64_static -L${SRCDIR}/build/linux/x86_64_static/src -L${SRCDIR}/build/linux/x86_64_static/ggml/src
// #cgo linux,arm64 LDFLAGS: -L${SRCDIR}/build/linux/arm64_static -L${SRCDIR}/build/linux/arm64_static/src -L${SRCDIR}/build/linux/arm64_static/ggml/src // #cgo linux,arm64 LDFLAGS: -L${SRCDIR}/build/linux/arm64_static -L${SRCDIR}/build/linux/arm64_static/src -L${SRCDIR}/build/linux/arm64_static/ggml/src
// #include <stdlib.h> // #include <stdlib.h>

10
llm/llm_windows.go
View File

@@ -4,7 +4,10 @@ import (
"syscall" "syscall"
) )
const CREATE_DEFAULT_ERROR_MODE = 0x04000000 const (
CREATE_DEFAULT_ERROR_MODE = 0x04000000
ABOVE_NORMAL_PRIORITY_CLASS = 0x00008000
)
var LlamaServerSysProcAttr = &syscall.SysProcAttr{ var LlamaServerSysProcAttr = &syscall.SysProcAttr{
// Wire up the default error handling logic If for some reason a DLL is // Wire up the default error handling logic If for some reason a DLL is
@@ -12,5 +15,8 @@ var LlamaServerSysProcAttr = &syscall.SysProcAttr{
// the user can either fix their PATH, or report a bug. Without this // the user can either fix their PATH, or report a bug. Without this
// setting, the process exits immediately with a generic exit status but no // setting, the process exits immediately with a generic exit status but no
// way to (easily) figure out what the actual missing DLL was. // way to (easily) figure out what the actual missing DLL was.
CreationFlags: CREATE_DEFAULT_ERROR_MODE, //
// Setting Above Normal priority class ensures when running as a "background service"
// with "programs" given best priority, we aren't starved of cpu cycles
CreationFlags: CREATE_DEFAULT_ERROR_MODE | ABOVE_NORMAL_PRIORITY_CLASS,
} }

2
macapp/src/app.tsx
View File

@@ -19,7 +19,7 @@ export default function () {
const [step, setStep] = useState<Step>(Step.WELCOME) const [step, setStep] = useState<Step>(Step.WELCOME)
const [commandCopied, setCommandCopied] = useState<boolean>(false) const [commandCopied, setCommandCopied] = useState<boolean>(false)
const command = 'ollama run llama3.1' const command = 'ollama run llama3.2'
return ( return (
<div className='drag'> <div className='drag'>

93
scripts/build_windows.ps1
View File

@@ -7,12 +7,22 @@
$ErrorActionPreference = "Stop" $ErrorActionPreference = "Stop"
function checkEnv() { function checkEnv() {
$script:ARCH = $Env:PROCESSOR_ARCHITECTURE.ToLower() if ($null -ne $env:ARCH ) {
$script:TARGET_ARCH=$Env:PROCESSOR_ARCHITECTURE.ToLower() $script:ARCH = $env:ARCH
} else {
$arch=([System.Runtime.InteropServices.RuntimeInformation]::OSArchitecture)
if ($null -ne $arch) {
$script:ARCH = ($arch.ToString().ToLower()).Replace("x64", "amd64")
} else {
write-host "WARNING: old powershell detected, assuming amd64 architecture - set `$env:ARCH to override"
$script:ARCH="amd64"
}
}
$script:TARGET_ARCH=$script:ARCH
Write-host "Building for ${script:TARGET_ARCH}" Write-host "Building for ${script:TARGET_ARCH}"
write-host "Locating required tools and paths" write-host "Locating required tools and paths"
$script:SRC_DIR=$PWD $script:SRC_DIR=$PWD
if (!$env:VCToolsRedistDir) { if ($null -eq $env:VCToolsRedistDir) {
$MSVC_INSTALL=(Get-CimInstance MSFT_VSInstance -Namespace root/cimv2/vs)[0].InstallLocation $MSVC_INSTALL=(Get-CimInstance MSFT_VSInstance -Namespace root/cimv2/vs)[0].InstallLocation
$env:VCToolsRedistDir=(get-item "${MSVC_INSTALL}\VC\Redist\MSVC\*")[0] $env:VCToolsRedistDir=(get-item "${MSVC_INSTALL}\VC\Redist\MSVC\*")[0]
} }
@@ -28,9 +38,12 @@ function checkEnv() {
$script:CUDA_DIRS=$cudaList $script:CUDA_DIRS=$cudaList
} }
$script:INNO_SETUP_DIR=(get-item "C:\Program Files*\Inno Setup*\")[0] $inoSetup=(get-item "C:\Program Files*\Inno Setup*\")
if ($inoSetup.length -gt 0) {
$script:INNO_SETUP_DIR=$inoSetup[0]
}
$script:DEPS_DIR="${script:SRC_DIR}\dist\windows-${script:TARGET_ARCH}" $script:DIST_DIR="${script:SRC_DIR}\dist\windows-${script:TARGET_ARCH}"
$env:CGO_ENABLED="1" $env:CGO_ENABLED="1"
Write-Output "Checking version" Write-Output "Checking version"
if (!$env:VERSION) { if (!$env:VERSION) {
@@ -67,7 +80,6 @@ function checkEnv() {
function buildOllama() { function buildOllama() {
write-host "Building ollama CLI"
if ($null -eq ${env:OLLAMA_SKIP_GENERATE}) { if ($null -eq ${env:OLLAMA_SKIP_GENERATE}) {
Remove-Item -ea 0 -recurse -force -path "${script:SRC_DIR}\dist\windows-${script:ARCH}" Remove-Item -ea 0 -recurse -force -path "${script:SRC_DIR}\dist\windows-${script:ARCH}"
@@ -75,15 +87,16 @@ function buildOllama() {
# which targets to build # which targets to build
# Start by skipping CUDA to build everything else # Start by skipping CUDA to build everything else
pwsh -Command { $env:OLLAMA_SKIP_CUDA_GENERATE="1"; & go generate ./... } write-host "Building ollama runners"
powershell -Command { $env:OLLAMA_SKIP_CUDA_GENERATE="1"; & go generate ./... }
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)} if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
# Then skip everyhting else and build all the CUDA variants # Then skip everyhting else and build all the CUDA variants
foreach ($env:CUDA_LIB_DIR in $script:CUDA_DIRS) { foreach ($env:CUDA_LIB_DIR in $script:CUDA_DIRS) {
write-host "Building CUDA ${env:CUDA_LIB_DIR}" write-host "Building CUDA ${env:CUDA_LIB_DIR} runner"
if ($env:CUDA_LIB_DIR.Contains("v12")) { if ($env:CUDA_LIB_DIR.Contains("v12")) {
pwsh -Command { powershell -Command {
$env:OLLAMA_SKIP_CUDA_GENERATE="" $env:OLLAMA_SKIP_CUDA_GENERATE=""
$env:OLLAMA_SKIP_STATIC_GENERATE="1" $env:OLLAMA_SKIP_STATIC_GENERATE="1"
$env:OLLAMA_SKIP_CPU_GENERATE="1" $env:OLLAMA_SKIP_CPU_GENERATE="1"
@@ -96,7 +109,7 @@ function buildOllama() {
& go generate ./... & go generate ./...
} }
} else { } else {
pwsh -Command { powershell -Command {
$env:OLLAMA_SKIP_CUDA_GENERATE="" $env:OLLAMA_SKIP_CUDA_GENERATE=""
$env:OLLAMA_SKIP_STATIC_GENERATE="1" $env:OLLAMA_SKIP_STATIC_GENERATE="1"
$env:OLLAMA_SKIP_CPU_GENERATE="1" $env:OLLAMA_SKIP_CPU_GENERATE="1"
@@ -115,6 +128,7 @@ function buildOllama() {
} else { } else {
write-host "Skipping generate step with OLLAMA_SKIP_GENERATE set" write-host "Skipping generate step with OLLAMA_SKIP_GENERATE set"
} }
write-host "Building ollama CLI"
& go build -trimpath -ldflags "-s -w -X=github.com/ollama/ollama/version.Version=$script:VERSION -X=github.com/ollama/ollama/server.mode=release" . & go build -trimpath -ldflags "-s -w -X=github.com/ollama/ollama/version.Version=$script:VERSION -X=github.com/ollama/ollama/server.mode=release" .
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)} if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
if ("${env:KEY_CONTAINER}") { if ("${env:KEY_CONTAINER}") {
@@ -130,34 +144,50 @@ function buildApp() {
write-host "Building Ollama App" write-host "Building Ollama App"
cd "${script:SRC_DIR}\app" cd "${script:SRC_DIR}\app"
& windres -l 0 -o ollama.syso ollama.rc & windres -l 0 -o ollama.syso ollama.rc
& go build -trimpath -ldflags "-s -w -H windowsgui -X=github.com/ollama/ollama/version.Version=$script:VERSION -X=github.com/ollama/ollama/server.mode=release" . & go build -trimpath -ldflags "-s -w -H windowsgui -X=github.com/ollama/ollama/version.Version=$script:VERSION -X=github.com/ollama/ollama/server.mode=release" -o "${script:SRC_DIR}\dist\windows-${script:TARGET_ARCH}-app.exe" .
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)} if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
if ("${env:KEY_CONTAINER}") { if ("${env:KEY_CONTAINER}") {
& "${script:SignTool}" sign /v /fd sha256 /t http://timestamp.digicert.com /f "${script:OLLAMA_CERT}" ` & "${script:SignTool}" sign /v /fd sha256 /t http://timestamp.digicert.com /f "${script:OLLAMA_CERT}" `
/csp "Google Cloud KMS Provider" /kc ${env:KEY_CONTAINER} app.exe /csp "Google Cloud KMS Provider" /kc ${env:KEY_CONTAINER} "${script:SRC_DIR}\dist\windows-${script:TARGET_ARCH}-app.exe"
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)} if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
} }
} }
function gatherDependencies() { function gatherDependencies() {
write-host "Gathering runtime dependencies" if ($null -eq $env:VCToolsRedistDir) {
write-error "Unable to locate VC Install location - please use a Developer shell"
exit 1
}
write-host "Gathering runtime dependencies from $env:VCToolsRedistDir"
cd "${script:SRC_DIR}" cd "${script:SRC_DIR}"
md "${script:DEPS_DIR}\lib\ollama" -ea 0 > $null md "${script:DIST_DIR}\lib\ollama" -ea 0 > $null
# TODO - this varies based on host build system and MSVC version - drive from dumpbin output # TODO - this varies based on host build system and MSVC version - drive from dumpbin output
# currently works for Win11 + MSVC 2019 + Cuda V11 # currently works for Win11 + MSVC 2019 + Cuda V11
cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\msvcp140*.dll" "${script:DEPS_DIR}\lib\ollama\" if ($script:TARGET_ARCH -eq "amd64") {
cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\vcruntime140.dll" "${script:DEPS_DIR}\lib\ollama\" $depArch="x64"
cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\vcruntime140_1.dll" "${script:DEPS_DIR}\lib\ollama\" } else {
foreach ($part in $("runtime", "stdio", "filesystem", "math", "convert", "heap", "string", "time", "locale", "environment")) { $depArch=$script:TARGET_ARCH
cp "$env:VCToolsRedistDir\..\..\..\Tools\Llvm\x64\bin\api-ms-win-crt-${part}*.dll" "${script:DEPS_DIR}\lib\ollama\" }
if ($depArch -eq "amd64") {
cp "${env:VCToolsRedistDir}\${depArch}\Microsoft.VC*.CRT\msvcp140*.dll" "${script:DIST_DIR}\lib\ollama\"
cp "${env:VCToolsRedistDir}\${depArch}\Microsoft.VC*.CRT\vcruntime140.dll" "${script:DIST_DIR}\lib\ollama\"
cp "${env:VCToolsRedistDir}\${depArch}\Microsoft.VC*.CRT\vcruntime140_1.dll" "${script:DIST_DIR}\lib\ollama\"
$llvmCrtDir="$env:VCToolsRedistDir\..\..\..\Tools\Llvm\${depArch}\bin"
foreach ($part in $("runtime", "stdio", "filesystem", "math", "convert", "heap", "string", "time", "locale", "environment")) {
write-host "cp ${llvmCrtDir}\api-ms-win-crt-${part}*.dll ${script:DIST_DIR}\lib\ollama\"
cp "${llvmCrtDir}\api-ms-win-crt-${part}*.dll" "${script:DIST_DIR}\lib\ollama\"
}
} else {
# Carying the dll's doesn't seem to work, so use the redist installer
copy-item -path "${env:VCToolsRedistDir}\vc_redist.arm64.exe" -destination "${script:DIST_DIR}" -verbose
} }
cp "${script:SRC_DIR}\app\ollama_welcome.ps1" "${script:SRC_DIR}\dist\" cp "${script:SRC_DIR}\app\ollama_welcome.ps1" "${script:SRC_DIR}\dist\"
if ("${env:KEY_CONTAINER}") { if ("${env:KEY_CONTAINER}") {
write-host "about to sign" write-host "about to sign"
foreach ($file in (get-childitem "${script:DEPS_DIR}\lib\ollama\cu*.dll") + @("${script:SRC_DIR}\dist\ollama_welcome.ps1")){ foreach ($file in (get-childitem "${script:DIST_DIR}\lib\ollama\cu*.dll") + @("${script:SRC_DIR}\dist\ollama_welcome.ps1")){
write-host "signing $file" write-host "signing $file"
& "${script:SignTool}" sign /v /fd sha256 /t http://timestamp.digicert.com /f "${script:OLLAMA_CERT}" ` & "${script:SignTool}" sign /v /fd sha256 /t http://timestamp.digicert.com /f "${script:OLLAMA_CERT}" `
/csp "Google Cloud KMS Provider" /kc ${env:KEY_CONTAINER} $file /csp "Google Cloud KMS Provider" /kc ${env:KEY_CONTAINER} $file
@@ -167,6 +197,10 @@ function gatherDependencies() {
} }
function buildInstaller() { function buildInstaller() {
if ($null -eq ${script:INNO_SETUP_DIR}) {
write-host "Inno Setup not present, skipping installer build"
return
}
write-host "Building Ollama Installer" write-host "Building Ollama Installer"
cd "${script:SRC_DIR}\app" cd "${script:SRC_DIR}\app"
$env:PKG_VERSION=$script:PKG_VERSION $env:PKG_VERSION=$script:PKG_VERSION
@@ -183,13 +217,20 @@ function distZip() {
Compress-Archive -Path "${script:SRC_DIR}\dist\windows-${script:TARGET_ARCH}\*" -DestinationPath "${script:SRC_DIR}\dist\ollama-windows-${script:TARGET_ARCH}.zip" -Force Compress-Archive -Path "${script:SRC_DIR}\dist\windows-${script:TARGET_ARCH}\*" -DestinationPath "${script:SRC_DIR}\dist\ollama-windows-${script:TARGET_ARCH}.zip" -Force
} }
checkEnv
try { try {
checkEnv if ($($args.count) -eq 0) {
buildOllama buildOllama
buildApp buildApp
gatherDependencies gatherDependencies
buildInstaller buildInstaller
distZip distZip
} else {
for ( $i = 0; $i -lt $args.count; $i++ ) {
write-host "performing $($args[$i])"
& $($args[$i])
}
}
} catch { } catch {
write-host "Build Failed" write-host "Build Failed"
write-host $_ write-host $_

4
server/images.go
View File

@@ -1025,6 +1025,8 @@ func makeRequestWithRetry(ctx context.Context, method string, requestURL *url.UR
switch { switch {
case resp.StatusCode == http.StatusUnauthorized: case resp.StatusCode == http.StatusUnauthorized:
resp.Body.Close()
// Handle authentication error with one retry // Handle authentication error with one retry
challenge := parseRegistryChallenge(resp.Header.Get("www-authenticate")) challenge := parseRegistryChallenge(resp.Header.Get("www-authenticate"))
token, err := getAuthorizationToken(ctx, challenge) token, err := getAuthorizationToken(ctx, challenge)
@@ -1040,8 +1042,10 @@ func makeRequestWithRetry(ctx context.Context, method string, requestURL *url.UR
} }
} }
case resp.StatusCode == http.StatusNotFound: case resp.StatusCode == http.StatusNotFound:
resp.Body.Close()
return nil, os.ErrNotExist return nil, os.ErrNotExist
case resp.StatusCode >= http.StatusBadRequest: case resp.StatusCode >= http.StatusBadRequest:
defer resp.Body.Close()
responseBody, err := io.ReadAll(resp.Body) responseBody, err := io.ReadAll(resp.Body)
if err != nil { if err != nil {
return nil, fmt.Errorf("%d: %s", resp.StatusCode, err) return nil, fmt.Errorf("%d: %s", resp.StatusCode, err)

4
server/sched_test.go
View File

@@ -354,7 +354,7 @@ func TestRequestsMultipleLoadedModels(t *testing.T) {
} }
func TestGetRunner(t *testing.T) { func TestGetRunner(t *testing.T) {
ctx, done := context.WithTimeout(context.Background(), 100*time.Millisecond) ctx, done := context.WithTimeout(context.Background(), 200*time.Millisecond)
defer done() defer done()
a := newScenarioRequest(t, ctx, "ollama-model-1a", 10, &api.Duration{Duration: 2 * time.Millisecond}) a := newScenarioRequest(t, ctx, "ollama-model-1a", 10, &api.Duration{Duration: 2 * time.Millisecond})
@@ -395,7 +395,7 @@ func TestGetRunner(t *testing.T) {
slog.Info("c") slog.Info("c")
successCh1c, errCh1c := s.GetRunner(c.ctx, c.req.model, c.req.opts, c.req.sessionDuration) successCh1c, errCh1c := s.GetRunner(c.ctx, c.req.model, c.req.opts, c.req.sessionDuration)
// Starts in pending channel, then should be quickly processsed to return an error // Starts in pending channel, then should be quickly processsed to return an error
time.Sleep(20 * time.Millisecond) // Long enough for the "a" model to expire and unload time.Sleep(50 * time.Millisecond) // Long enough for the "a" model to expire and unload
require.Empty(t, successCh1c) require.Empty(t, successCh1c)
s.loadedMu.Lock() s.loadedMu.Lock()
require.Empty(t, s.loaded) require.Empty(t, s.loaded)