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4
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 The Librarian
|
5fb5e0e5b8 | ||
|
The Librarian
|
1c2aec910d | ||
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The Librarian
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6bfb23ec9f | ||
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The Librarian
|
62c02724f3 |
+2
-2
@@ -4,8 +4,8 @@ This is a mirror of the Ollama repository.
|
||||
|
||||
**Synced from:** https://github.com/ollama/ollama.git
|
||||
**Branch:** main
|
||||
**Commit:** 9f7822851c1f080d7d2a1dbe0e4d51233e5a28bc
|
||||
**Sync Date:** 2025-12-12
|
||||
**Commit:** 9330bb912079ed1ba3c384cc762728700c9e3691
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**Sync Date:** 2026-04-12
|
||||
**Content:** Paths: docs
|
||||
|
||||
---
|
||||
|
||||
@@ -14,6 +14,7 @@
|
||||
* [API Reference](https://docs.ollama.com/api)
|
||||
* [Modelfile Reference](https://docs.ollama.com/modelfile)
|
||||
* [OpenAI Compatibility](https://docs.ollama.com/api/openai-compatibility)
|
||||
* [Anthropic Compatibility](./api/anthropic-compatibility.mdx)
|
||||
|
||||
### Resources
|
||||
|
||||
|
||||
+65
-3
@@ -16,6 +16,7 @@
|
||||
- [Generate Embeddings](#generate-embeddings)
|
||||
- [List Running Models](#list-running-models)
|
||||
- [Version](#version)
|
||||
- [Experimental: Image Generation](#image-generation-experimental)
|
||||
|
||||
## Conventions
|
||||
|
||||
@@ -58,6 +59,15 @@ Advanced parameters (optional):
|
||||
- `keep_alive`: controls how long the model will stay loaded into memory following the request (default: `5m`)
|
||||
- `context` (deprecated): the context parameter returned from a previous request to `/generate`, this can be used to keep a short conversational memory
|
||||
|
||||
Experimental image generation parameters (for image generation models only):
|
||||
|
||||
> [!WARNING]
|
||||
> These parameters are experimental and may change in future versions.
|
||||
|
||||
- `width`: width of the generated image in pixels
|
||||
- `height`: height of the generated image in pixels
|
||||
- `steps`: number of diffusion steps
|
||||
|
||||
#### Structured outputs
|
||||
|
||||
Structured outputs are supported by providing a JSON schema in the `format` parameter. The model will generate a response that matches the schema. See the [structured outputs](#request-structured-outputs) example below.
|
||||
@@ -895,11 +905,11 @@ curl http://localhost:11434/api/chat -d '{
|
||||
"tool_calls": [
|
||||
{
|
||||
"function": {
|
||||
"name": "get_temperature",
|
||||
"name": "get_weather",
|
||||
"arguments": {
|
||||
"city": "Toronto"
|
||||
}
|
||||
},
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
@@ -907,7 +917,7 @@ curl http://localhost:11434/api/chat -d '{
|
||||
{
|
||||
"role": "tool",
|
||||
"content": "11 degrees celsius",
|
||||
"tool_name": "get_temperature",
|
||||
"tool_name": "get_weather"
|
||||
}
|
||||
],
|
||||
"stream": false,
|
||||
@@ -1867,3 +1877,55 @@ curl http://localhost:11434/api/version
|
||||
"version": "0.5.1"
|
||||
}
|
||||
```
|
||||
|
||||
## Experimental Features
|
||||
|
||||
### Image Generation (Experimental)
|
||||
|
||||
> [!WARNING]
|
||||
> Image generation is experimental and may change in future versions.
|
||||
|
||||
Image generation is now supported through the standard `/api/generate` endpoint when using image generation models. The API automatically detects when an image generation model is being used.
|
||||
|
||||
See the [Generate a completion](#generate-a-completion) section for the full API documentation. The experimental image generation parameters (`width`, `height`, `steps`) are documented there.
|
||||
|
||||
#### Example
|
||||
|
||||
##### Request
|
||||
|
||||
```shell
|
||||
curl http://localhost:11434/api/generate -d '{
|
||||
"model": "x/z-image-turbo",
|
||||
"prompt": "a sunset over mountains",
|
||||
"width": 1024,
|
||||
"height": 768
|
||||
}'
|
||||
```
|
||||
|
||||
##### Response (streaming)
|
||||
|
||||
Progress updates during generation:
|
||||
|
||||
```json
|
||||
{
|
||||
"model": "x/z-image-turbo",
|
||||
"created_at": "2024-01-15T10:30:00.000000Z",
|
||||
"completed": 5,
|
||||
"total": 20,
|
||||
"done": false
|
||||
}
|
||||
```
|
||||
|
||||
##### Final Response
|
||||
|
||||
```json
|
||||
{
|
||||
"model": "x/z-image-turbo",
|
||||
"created_at": "2024-01-15T10:30:15.000000Z",
|
||||
"image": "iVBORw0KGgoAAAANSUhEUg...",
|
||||
"done": true,
|
||||
"done_reason": "stop",
|
||||
"total_duration": 15000000000,
|
||||
"load_duration": 2000000000
|
||||
}
|
||||
```
|
||||
|
||||
@@ -0,0 +1,421 @@
|
||||
---
|
||||
title: Anthropic compatibility
|
||||
---
|
||||
|
||||
Ollama provides compatibility with the [Anthropic Messages API](https://docs.anthropic.com/en/api/messages) to help connect existing applications to Ollama, including tools like Claude Code.
|
||||
|
||||
## Usage
|
||||
|
||||
### Environment variables
|
||||
|
||||
To use Ollama with tools that expect the Anthropic API (like Claude Code), set these environment variables:
|
||||
|
||||
```shell
|
||||
export ANTHROPIC_AUTH_TOKEN=ollama # required but ignored
|
||||
export ANTHROPIC_BASE_URL=http://localhost:11434
|
||||
```
|
||||
|
||||
### Simple `/v1/messages` example
|
||||
|
||||
<CodeGroup dropdown>
|
||||
|
||||
```python basic.py
|
||||
import anthropic
|
||||
|
||||
client = anthropic.Anthropic(
|
||||
base_url='http://localhost:11434',
|
||||
api_key='ollama', # required but ignored
|
||||
)
|
||||
|
||||
message = client.messages.create(
|
||||
model='qwen3-coder',
|
||||
max_tokens=1024,
|
||||
messages=[
|
||||
{'role': 'user', 'content': 'Hello, how are you?'}
|
||||
]
|
||||
)
|
||||
print(message.content[0].text)
|
||||
```
|
||||
|
||||
```javascript basic.js
|
||||
import Anthropic from "@anthropic-ai/sdk";
|
||||
|
||||
const anthropic = new Anthropic({
|
||||
baseURL: "http://localhost:11434",
|
||||
apiKey: "ollama", // required but ignored
|
||||
});
|
||||
|
||||
const message = await anthropic.messages.create({
|
||||
model: "qwen3-coder",
|
||||
max_tokens: 1024,
|
||||
messages: [{ role: "user", content: "Hello, how are you?" }],
|
||||
});
|
||||
|
||||
console.log(message.content[0].text);
|
||||
```
|
||||
|
||||
```shell basic.sh
|
||||
curl -X POST http://localhost:11434/v1/messages \
|
||||
-H "Content-Type: application/json" \
|
||||
-H "x-api-key: ollama" \
|
||||
-H "anthropic-version: 2023-06-01" \
|
||||
-d '{
|
||||
"model": "qwen3-coder",
|
||||
"max_tokens": 1024,
|
||||
"messages": [{ "role": "user", "content": "Hello, how are you?" }]
|
||||
}'
|
||||
```
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
### Streaming example
|
||||
|
||||
<CodeGroup dropdown>
|
||||
|
||||
```python streaming.py
|
||||
import anthropic
|
||||
|
||||
client = anthropic.Anthropic(
|
||||
base_url='http://localhost:11434',
|
||||
api_key='ollama',
|
||||
)
|
||||
|
||||
with client.messages.stream(
|
||||
model='qwen3-coder',
|
||||
max_tokens=1024,
|
||||
messages=[{'role': 'user', 'content': 'Count from 1 to 10'}]
|
||||
) as stream:
|
||||
for text in stream.text_stream:
|
||||
print(text, end='', flush=True)
|
||||
```
|
||||
|
||||
```javascript streaming.js
|
||||
import Anthropic from "@anthropic-ai/sdk";
|
||||
|
||||
const anthropic = new Anthropic({
|
||||
baseURL: "http://localhost:11434",
|
||||
apiKey: "ollama",
|
||||
});
|
||||
|
||||
const stream = await anthropic.messages.stream({
|
||||
model: "qwen3-coder",
|
||||
max_tokens: 1024,
|
||||
messages: [{ role: "user", content: "Count from 1 to 10" }],
|
||||
});
|
||||
|
||||
for await (const event of stream) {
|
||||
if (
|
||||
event.type === "content_block_delta" &&
|
||||
event.delta.type === "text_delta"
|
||||
) {
|
||||
process.stdout.write(event.delta.text);
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
```shell streaming.sh
|
||||
curl -X POST http://localhost:11434/v1/messages \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"model": "qwen3-coder",
|
||||
"max_tokens": 1024,
|
||||
"stream": true,
|
||||
"messages": [{ "role": "user", "content": "Count from 1 to 10" }]
|
||||
}'
|
||||
```
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
### Tool calling example
|
||||
|
||||
<CodeGroup dropdown>
|
||||
|
||||
```python tools.py
|
||||
import anthropic
|
||||
|
||||
client = anthropic.Anthropic(
|
||||
base_url='http://localhost:11434',
|
||||
api_key='ollama',
|
||||
)
|
||||
|
||||
message = client.messages.create(
|
||||
model='qwen3-coder',
|
||||
max_tokens=1024,
|
||||
tools=[
|
||||
{
|
||||
'name': 'get_weather',
|
||||
'description': 'Get the current weather in a location',
|
||||
'input_schema': {
|
||||
'type': 'object',
|
||||
'properties': {
|
||||
'location': {
|
||||
'type': 'string',
|
||||
'description': 'The city and state, e.g. San Francisco, CA'
|
||||
}
|
||||
},
|
||||
'required': ['location']
|
||||
}
|
||||
}
|
||||
],
|
||||
messages=[{'role': 'user', 'content': "What's the weather in San Francisco?"}]
|
||||
)
|
||||
|
||||
for block in message.content:
|
||||
if block.type == 'tool_use':
|
||||
print(f'Tool: {block.name}')
|
||||
print(f'Input: {block.input}')
|
||||
```
|
||||
|
||||
```javascript tools.js
|
||||
import Anthropic from "@anthropic-ai/sdk";
|
||||
|
||||
const anthropic = new Anthropic({
|
||||
baseURL: "http://localhost:11434",
|
||||
apiKey: "ollama",
|
||||
});
|
||||
|
||||
const message = await anthropic.messages.create({
|
||||
model: "qwen3-coder",
|
||||
max_tokens: 1024,
|
||||
tools: [
|
||||
{
|
||||
name: "get_weather",
|
||||
description: "Get the current weather in a location",
|
||||
input_schema: {
|
||||
type: "object",
|
||||
properties: {
|
||||
location: {
|
||||
type: "string",
|
||||
description: "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
},
|
||||
required: ["location"],
|
||||
},
|
||||
},
|
||||
],
|
||||
messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
|
||||
});
|
||||
|
||||
for (const block of message.content) {
|
||||
if (block.type === "tool_use") {
|
||||
console.log("Tool:", block.name);
|
||||
console.log("Input:", block.input);
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
```shell tools.sh
|
||||
curl -X POST http://localhost:11434/v1/messages \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"model": "qwen3-coder",
|
||||
"max_tokens": 1024,
|
||||
"tools": [
|
||||
{
|
||||
"name": "get_weather",
|
||||
"description": "Get the current weather in a location",
|
||||
"input_schema": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state"
|
||||
}
|
||||
},
|
||||
"required": ["location"]
|
||||
}
|
||||
}
|
||||
],
|
||||
"messages": [{ "role": "user", "content": "What is the weather in San Francisco?" }]
|
||||
}'
|
||||
```
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
## Using with Claude Code
|
||||
|
||||
[Claude Code](https://code.claude.com/docs/en/overview) can be configured to use Ollama as its backend.
|
||||
|
||||
### Recommended models
|
||||
|
||||
For coding use cases, models like `glm-4.7`, `minimax-m2.1`, and `qwen3-coder` are recommended.
|
||||
|
||||
Download a model before use:
|
||||
|
||||
```shell
|
||||
ollama pull qwen3-coder
|
||||
```
|
||||
> Note: Qwen 3 coder is a 30B parameter model requiring at least 24GB of VRAM to run smoothly. More is required for longer context lengths.
|
||||
|
||||
```shell
|
||||
ollama pull glm-4.7:cloud
|
||||
```
|
||||
|
||||
### Quick setup
|
||||
|
||||
```shell
|
||||
ollama launch claude
|
||||
```
|
||||
|
||||
This will prompt you to select a model, configure Claude Code automatically, and launch it. To configure without launching:
|
||||
|
||||
```shell
|
||||
ollama launch claude --config
|
||||
```
|
||||
|
||||
### Manual setup
|
||||
|
||||
Set the environment variables and run Claude Code:
|
||||
|
||||
```shell
|
||||
ANTHROPIC_AUTH_TOKEN=ollama ANTHROPIC_BASE_URL=http://localhost:11434 claude --model qwen3-coder
|
||||
```
|
||||
|
||||
Or set the environment variables in your shell profile:
|
||||
|
||||
```shell
|
||||
export ANTHROPIC_AUTH_TOKEN=ollama
|
||||
export ANTHROPIC_BASE_URL=http://localhost:11434
|
||||
```
|
||||
|
||||
Then run Claude Code with any Ollama model:
|
||||
|
||||
```shell
|
||||
claude --model qwen3-coder
|
||||
```
|
||||
|
||||
## Endpoints
|
||||
|
||||
### `/v1/messages`
|
||||
|
||||
#### Supported features
|
||||
|
||||
- [x] Messages
|
||||
- [x] Streaming
|
||||
- [x] System prompts
|
||||
- [x] Multi-turn conversations
|
||||
- [x] Vision (images)
|
||||
- [x] Tools (function calling)
|
||||
- [x] Tool results
|
||||
- [x] Thinking/extended thinking
|
||||
|
||||
#### Supported request fields
|
||||
|
||||
- [x] `model`
|
||||
- [x] `max_tokens`
|
||||
- [x] `messages`
|
||||
- [x] Text `content`
|
||||
- [x] Image `content` (base64)
|
||||
- [x] Array of content blocks
|
||||
- [x] `tool_use` blocks
|
||||
- [x] `tool_result` blocks
|
||||
- [x] `thinking` blocks
|
||||
- [x] `system` (string or array)
|
||||
- [x] `stream`
|
||||
- [x] `temperature`
|
||||
- [x] `top_p`
|
||||
- [x] `top_k`
|
||||
- [x] `stop_sequences`
|
||||
- [x] `tools`
|
||||
- [x] `thinking`
|
||||
- [ ] `tool_choice`
|
||||
- [ ] `metadata`
|
||||
|
||||
#### Supported response fields
|
||||
|
||||
- [x] `id`
|
||||
- [x] `type`
|
||||
- [x] `role`
|
||||
- [x] `model`
|
||||
- [x] `content` (text, tool_use, thinking blocks)
|
||||
- [x] `stop_reason` (end_turn, max_tokens, tool_use)
|
||||
- [x] `usage` (input_tokens, output_tokens)
|
||||
|
||||
#### Streaming events
|
||||
|
||||
- [x] `message_start`
|
||||
- [x] `content_block_start`
|
||||
- [x] `content_block_delta` (text_delta, input_json_delta, thinking_delta)
|
||||
- [x] `content_block_stop`
|
||||
- [x] `message_delta`
|
||||
- [x] `message_stop`
|
||||
- [x] `ping`
|
||||
- [x] `error`
|
||||
|
||||
## Models
|
||||
|
||||
Ollama supports both local and cloud models.
|
||||
|
||||
### Local models
|
||||
|
||||
Pull a local model before use:
|
||||
|
||||
```shell
|
||||
ollama pull qwen3-coder
|
||||
```
|
||||
|
||||
Recommended local models:
|
||||
- `qwen3-coder` - Excellent for coding tasks
|
||||
- `gpt-oss:20b` - Strong general-purpose model
|
||||
|
||||
### Cloud models
|
||||
|
||||
Cloud models are available immediately without pulling:
|
||||
|
||||
- `glm-4.7:cloud` - High-performance cloud model
|
||||
- `minimax-m2.1:cloud` - Fast cloud model
|
||||
|
||||
### Default model names
|
||||
|
||||
For tooling that relies on default Anthropic model names such as `claude-3-5-sonnet`, use `ollama cp` to copy an existing model name:
|
||||
|
||||
```shell
|
||||
ollama cp qwen3-coder claude-3-5-sonnet
|
||||
```
|
||||
|
||||
Afterwards, this new model name can be specified in the `model` field:
|
||||
|
||||
```shell
|
||||
curl http://localhost:11434/v1/messages \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"model": "claude-3-5-sonnet",
|
||||
"max_tokens": 1024,
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Hello!"
|
||||
}
|
||||
]
|
||||
}'
|
||||
```
|
||||
|
||||
## Differences from the Anthropic API
|
||||
|
||||
### Behavior differences
|
||||
|
||||
- API key is accepted but not validated
|
||||
- `anthropic-version` header is accepted but not used
|
||||
- Token counts are approximations based on the underlying model's tokenizer
|
||||
|
||||
### Not supported
|
||||
|
||||
The following Anthropic API features are not currently supported:
|
||||
|
||||
| Feature | Description |
|
||||
|---------|-------------|
|
||||
| `/v1/messages/count_tokens` | Token counting endpoint |
|
||||
| `tool_choice` | Forcing specific tool use or disabling tools |
|
||||
| `metadata` | Request metadata (user_id) |
|
||||
| Prompt caching | `cache_control` blocks for caching prefixes |
|
||||
| Batches API | `/v1/messages/batches` for async batch processing |
|
||||
| Citations | `citations` content blocks |
|
||||
| PDF support | `document` content blocks with PDF files |
|
||||
| Server-sent errors | `error` events during streaming (errors return HTTP status) |
|
||||
|
||||
### Partial support
|
||||
|
||||
| Feature | Status |
|
||||
|---------|--------|
|
||||
| Image content | Base64 images supported; URL images not supported |
|
||||
| Extended thinking | Basic support; `budget_tokens` accepted but not enforced |
|
||||
@@ -6,7 +6,7 @@ Ollama provides compatibility with parts of the [OpenAI API](https://platform.op
|
||||
|
||||
## Usage
|
||||
|
||||
### Simple `v1/chat/completions` example
|
||||
### Simple `/v1/chat/completions` example
|
||||
|
||||
<CodeGroup dropdown>
|
||||
|
||||
@@ -57,7 +57,7 @@ curl -X POST http://localhost:11434/v1/chat/completions \
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
### Simple `v1/responses` example
|
||||
### Simple `/v1/responses` example
|
||||
|
||||
<CodeGroup dropdown>
|
||||
|
||||
@@ -103,7 +103,7 @@ curl -X POST http://localhost:11434/v1/responses \
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
### v1/chat/completions with vision example
|
||||
### `/v1/chat/completions` with vision example
|
||||
|
||||
<CodeGroup dropdown>
|
||||
|
||||
@@ -184,6 +184,7 @@ curl -X POST http://localhost:11434/v1/chat/completions \
|
||||
- [x] Reproducible outputs
|
||||
- [x] Vision
|
||||
- [x] Tools
|
||||
- [x] Reasoning/thinking control (for thinking models)
|
||||
- [ ] Logprobs
|
||||
|
||||
#### Supported request fields
|
||||
@@ -207,6 +208,9 @@ curl -X POST http://localhost:11434/v1/chat/completions \
|
||||
- [x] `top_p`
|
||||
- [x] `max_tokens`
|
||||
- [x] `tools`
|
||||
- [x] `reasoning_effort` (`"high"`, `"medium"`, `"low"`, `"none"`)
|
||||
- [x] `reasoning`
|
||||
- [x] `effort` (`"high"`, `"medium"`, `"low"`, `"none"`)
|
||||
- [ ] `tool_choice`
|
||||
- [ ] `logit_bias`
|
||||
- [ ] `user`
|
||||
@@ -275,8 +279,77 @@ curl -X POST http://localhost:11434/v1/chat/completions \
|
||||
- [x] `dimensions`
|
||||
- [ ] `user`
|
||||
|
||||
### `/v1/images/generations` (experimental)
|
||||
|
||||
> Note: This endpoint is experimental and may change or be removed in future versions.
|
||||
|
||||
Generate images using image generation models.
|
||||
|
||||
<CodeGroup dropdown>
|
||||
|
||||
```python images.py
|
||||
from openai import OpenAI
|
||||
|
||||
client = OpenAI(
|
||||
base_url='http://localhost:11434/v1/',
|
||||
api_key='ollama', # required but ignored
|
||||
)
|
||||
|
||||
response = client.images.generate(
|
||||
model='x/z-image-turbo',
|
||||
prompt='A cute robot learning to paint',
|
||||
size='1024x1024',
|
||||
response_format='b64_json',
|
||||
)
|
||||
print(response.data[0].b64_json[:50] + '...')
|
||||
```
|
||||
|
||||
```javascript images.js
|
||||
import OpenAI from "openai";
|
||||
|
||||
const openai = new OpenAI({
|
||||
baseURL: "http://localhost:11434/v1/",
|
||||
apiKey: "ollama", // required but ignored
|
||||
});
|
||||
|
||||
const response = await openai.images.generate({
|
||||
model: "x/z-image-turbo",
|
||||
prompt: "A cute robot learning to paint",
|
||||
size: "1024x1024",
|
||||
response_format: "b64_json",
|
||||
});
|
||||
|
||||
console.log(response.data[0].b64_json.slice(0, 50) + "...");
|
||||
```
|
||||
|
||||
```shell images.sh
|
||||
curl -X POST http://localhost:11434/v1/images/generations \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"model": "x/z-image-turbo",
|
||||
"prompt": "A cute robot learning to paint",
|
||||
"size": "1024x1024",
|
||||
"response_format": "b64_json"
|
||||
}'
|
||||
```
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
#### Supported request fields
|
||||
|
||||
- [x] `model`
|
||||
- [x] `prompt`
|
||||
- [x] `size` (e.g. "1024x1024")
|
||||
- [x] `response_format` (only `b64_json` supported)
|
||||
- [ ] `n`
|
||||
- [ ] `quality`
|
||||
- [ ] `style`
|
||||
- [ ] `user`
|
||||
|
||||
### `/v1/responses`
|
||||
|
||||
> Note: Added in Ollama v0.13.3
|
||||
|
||||
Ollama supports the [OpenAI Responses API](https://platform.openai.com/docs/api-reference/responses). Only the non-stateful flavor is supported (i.e., there is no `previous_response_id` or `conversation` support).
|
||||
|
||||
#### Supported features
|
||||
|
||||
@@ -36,7 +36,6 @@ Provide an `images` array. SDKs accept file paths, URLs or raw bytes while the R
|
||||
}],
|
||||
"stream": false
|
||||
}'
|
||||
"
|
||||
```
|
||||
</Tab>
|
||||
<Tab title="Python">
|
||||
|
||||
@@ -110,7 +110,7 @@ More Ollama [Python example](https://github.com/ollama/ollama-python/blob/main/e
|
||||
import { Ollama } from "ollama";
|
||||
|
||||
const client = new Ollama();
|
||||
const results = await client.webSearch({ query: "what is ollama?" });
|
||||
const results = await client.webSearch("what is ollama?");
|
||||
console.log(JSON.stringify(results, null, 2));
|
||||
```
|
||||
|
||||
@@ -213,7 +213,7 @@ models](https://ollama.com/models)\n\nAvailable for macOS, Windows, and Linux',
|
||||
import { Ollama } from "ollama";
|
||||
|
||||
const client = new Ollama();
|
||||
const fetchResult = await client.webFetch({ url: "https://ollama.com" });
|
||||
const fetchResult = await client.webFetch("https://ollama.com");
|
||||
console.log(JSON.stringify(fetchResult, null, 2));
|
||||
```
|
||||
|
||||
|
||||
@@ -8,6 +8,48 @@ title: CLI Reference
|
||||
ollama run gemma3
|
||||
```
|
||||
|
||||
### Launch integrations
|
||||
|
||||
```
|
||||
ollama launch
|
||||
```
|
||||
|
||||
Configure and launch external applications to use Ollama models. This provides an interactive way to set up and start integrations with supported apps.
|
||||
|
||||
#### Supported integrations
|
||||
|
||||
- **OpenCode** - Open-source coding assistant
|
||||
- **Claude Code** - Anthropic's agentic coding tool
|
||||
- **Codex** - OpenAI's coding assistant
|
||||
- **VS Code** - Microsoft's IDE with built-in AI chat
|
||||
- **Droid** - Factory's AI coding agent
|
||||
|
||||
#### Examples
|
||||
|
||||
Launch an integration interactively:
|
||||
|
||||
```
|
||||
ollama launch
|
||||
```
|
||||
|
||||
Launch a specific integration:
|
||||
|
||||
```
|
||||
ollama launch claude
|
||||
```
|
||||
|
||||
Launch with a specific model:
|
||||
|
||||
```
|
||||
ollama launch claude --model qwen3.5
|
||||
```
|
||||
|
||||
Configure without launching:
|
||||
|
||||
```
|
||||
ollama launch droid --config
|
||||
```
|
||||
|
||||
#### Multiline input
|
||||
|
||||
For multiline input, you can wrap text with `"""`:
|
||||
|
||||
+4
-2
@@ -3,8 +3,6 @@ title: Cloud
|
||||
sidebarTitle: Cloud
|
||||
---
|
||||
|
||||
<Info>Ollama's cloud is currently in preview.</Info>
|
||||
|
||||
## Cloud Models
|
||||
|
||||
Ollama's cloud models are a new kind of model in Ollama that can run without a powerful GPU. Instead, cloud models are automatically offloaded to Ollama's cloud service while offering the same capabilities as local models, making it possible to keep using your local tools while running larger models that wouldn't fit on a personal computer.
|
||||
@@ -228,3 +226,7 @@ curl https://ollama.com/api/chat \
|
||||
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
## Local only
|
||||
|
||||
Ollama can run in local-only mode by [disabling Ollama's cloud](./faq#how-do-i-disable-ollama-cloud) features.
|
||||
@@ -5,10 +5,13 @@ title: Context length
|
||||
Context length is the maximum number of tokens that the model has access to in memory.
|
||||
|
||||
<Note>
|
||||
The default context length in Ollama is 4096 tokens.
|
||||
Ollama defaults to the following context lengths based on VRAM:
|
||||
- < 24 GiB VRAM: 4k context
|
||||
- 24-48 GiB VRAM: 32k context
|
||||
- >= 48 GiB VRAM: 256k context
|
||||
</Note>
|
||||
|
||||
Tasks which require large context like web search, agents, and coding tools should be set to at least 32000 tokens.
|
||||
Tasks which require large context like web search, agents, and coding tools should be set to at least 64000 tokens.
|
||||
|
||||
## Setting context length
|
||||
|
||||
@@ -24,7 +27,7 @@ Change the slider in the Ollama app under settings to your desired context lengt
|
||||
### CLI
|
||||
If editing the context length for Ollama is not possible, the context length can also be updated when serving Ollama.
|
||||
```
|
||||
OLLAMA_CONTEXT_LENGTH=32000 ollama serve
|
||||
OLLAMA_CONTEXT_LENGTH=64000 ollama serve
|
||||
```
|
||||
|
||||
### Check allocated context length and model offloading
|
||||
|
||||
@@ -51,6 +51,9 @@ Install prerequisites:
|
||||
- [CUDA SDK](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=11&target_type=exe_network)
|
||||
- (Optional) VULKAN GPU support
|
||||
- [VULKAN SDK](https://vulkan.lunarg.com/sdk/home) - useful for AMD/Intel GPUs
|
||||
- (Optional) MLX engine support
|
||||
- [CUDA 13+ SDK](https://developer.nvidia.com/cuda-downloads)
|
||||
- [cuDNN 9+](https://developer.nvidia.com/cudnn)
|
||||
|
||||
Then, configure and build the project:
|
||||
|
||||
@@ -101,6 +104,10 @@ Install prerequisites:
|
||||
- (Optional) VULKAN GPU support
|
||||
- [VULKAN SDK](https://vulkan.lunarg.com/sdk/home) - useful for AMD/Intel GPUs
|
||||
- Or install via package manager: `sudo apt install vulkan-sdk` (Ubuntu/Debian) or `sudo dnf install vulkan-sdk` (Fedora/CentOS)
|
||||
- (Optional) MLX engine support
|
||||
- [CUDA 13+ SDK](https://developer.nvidia.com/cuda-downloads)
|
||||
- [cuDNN 9+](https://developer.nvidia.com/cudnn)
|
||||
- OpenBLAS/LAPACK: `sudo apt install libopenblas-dev liblapack-dev liblapacke-dev` (Ubuntu/Debian)
|
||||
> [!IMPORTANT]
|
||||
> Ensure prerequisites are in `PATH` before running CMake.
|
||||
|
||||
@@ -118,6 +125,67 @@ Lastly, run Ollama:
|
||||
go run . serve
|
||||
```
|
||||
|
||||
## MLX Engine (Optional)
|
||||
|
||||
The MLX engine enables running safetensor based models. It requires building the [MLX](https://github.com/ml-explore/mlx) and [MLX-C](https://github.com/ml-explore/mlx-c) shared libraries separately via CMake. On MacOS, MLX leverages the Metal library to run on the GPU, and on Windows and Linux, runs on NVIDIA GPUs via CUDA v13.
|
||||
|
||||
### macOS (Apple Silicon)
|
||||
|
||||
Requires the Metal toolchain. Install [Xcode](https://developer.apple.com/xcode/) first, then:
|
||||
|
||||
```shell
|
||||
xcodebuild -downloadComponent MetalToolchain
|
||||
```
|
||||
|
||||
Verify it's installed correctly (should print "no input files"):
|
||||
|
||||
```shell
|
||||
xcrun metal
|
||||
```
|
||||
|
||||
Then build:
|
||||
|
||||
```shell
|
||||
cmake -B build --preset MLX
|
||||
cmake --build build --preset MLX --parallel
|
||||
cmake --install build --component MLX
|
||||
```
|
||||
|
||||
> [!NOTE]
|
||||
> Without the Metal toolchain, cmake will silently complete with Metal disabled. Check the cmake output for `Setting MLX_BUILD_METAL=OFF` which indicates the toolchain is missing.
|
||||
|
||||
### Windows / Linux (CUDA)
|
||||
|
||||
Requires CUDA 13+ and [cuDNN](https://developer.nvidia.com/cudnn) 9+.
|
||||
|
||||
```shell
|
||||
cmake -B build --preset "MLX CUDA 13"
|
||||
cmake --build build --target mlx --target mlxc --config Release --parallel
|
||||
cmake --install build --component MLX --strip
|
||||
```
|
||||
|
||||
### Local MLX source overrides
|
||||
|
||||
To build against a local checkout of MLX and/or MLX-C (useful for development), set environment variables before running CMake:
|
||||
|
||||
```shell
|
||||
export OLLAMA_MLX_SOURCE=/path/to/mlx
|
||||
export OLLAMA_MLX_C_SOURCE=/path/to/mlx-c
|
||||
```
|
||||
|
||||
For example, using the helper scripts with local mlx and mlx-c repos:
|
||||
```shell
|
||||
OLLAMA_MLX_SOURCE=../mlx OLLAMA_MLX_C_SOURCE=../mlx-c ./scripts/build_linux.sh
|
||||
|
||||
OLLAMA_MLX_SOURCE=../mlx OLLAMA_MLX_C_SOURCE=../mlx-c ./scripts/build_darwin.sh
|
||||
```
|
||||
|
||||
```powershell
|
||||
$env:OLLAMA_MLX_SOURCE="../mlx"
|
||||
$env:OLLAMA_MLX_C_SOURCE="../mlx-c"
|
||||
./scripts/build_darwin.ps1
|
||||
```
|
||||
|
||||
## Docker
|
||||
|
||||
```shell
|
||||
|
||||
+69
-13
@@ -32,7 +32,9 @@
|
||||
"codeblocks": "system"
|
||||
},
|
||||
"contextual": {
|
||||
"options": ["copy"]
|
||||
"options": [
|
||||
"copy"
|
||||
]
|
||||
},
|
||||
"navbar": {
|
||||
"links": [
|
||||
@@ -52,7 +54,9 @@
|
||||
"display": "simple"
|
||||
},
|
||||
"examples": {
|
||||
"languages": ["curl"]
|
||||
"languages": [
|
||||
"curl"
|
||||
]
|
||||
}
|
||||
},
|
||||
"redirects": [
|
||||
@@ -67,6 +71,10 @@
|
||||
{
|
||||
"source": "/api",
|
||||
"destination": "/api/introduction"
|
||||
},
|
||||
{
|
||||
"source": "/integrations/clawdbot",
|
||||
"destination": "/integrations/openclaw"
|
||||
}
|
||||
],
|
||||
"navigation": {
|
||||
@@ -97,22 +105,69 @@
|
||||
{
|
||||
"group": "Integrations",
|
||||
"pages": [
|
||||
"/integrations/vscode",
|
||||
"/integrations/jetbrains",
|
||||
"/integrations/codex",
|
||||
"/integrations/cline",
|
||||
"/integrations/droid",
|
||||
"/integrations/goose",
|
||||
"/integrations/zed",
|
||||
"/integrations/roo-code",
|
||||
"/integrations/n8n",
|
||||
"/integrations/xcode"
|
||||
"/integrations/index",
|
||||
{
|
||||
"group": "Assistants",
|
||||
"expanded": true,
|
||||
"pages": [
|
||||
"/integrations/openclaw",
|
||||
"/integrations/hermes"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Coding",
|
||||
"expanded": true,
|
||||
"pages": [
|
||||
"/integrations/claude-code",
|
||||
"/integrations/codex",
|
||||
"/integrations/opencode",
|
||||
"/integrations/droid",
|
||||
"/integrations/goose",
|
||||
"/integrations/pi"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "IDEs & Editors",
|
||||
"expanded": true,
|
||||
"pages": [
|
||||
"/integrations/cline",
|
||||
"/integrations/jetbrains",
|
||||
"/integrations/roo-code",
|
||||
"/integrations/vscode",
|
||||
"/integrations/xcode",
|
||||
"/integrations/zed"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Chat & RAG",
|
||||
"pages": [
|
||||
"/integrations/onyx"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Automation",
|
||||
"pages": [
|
||||
"/integrations/n8n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Notebooks",
|
||||
"pages": [
|
||||
"/integrations/marimo"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "More information",
|
||||
"pages": [
|
||||
"/cli",
|
||||
{
|
||||
"group": "Assistant Sandboxing",
|
||||
"pages": [
|
||||
"/integrations/nemoclaw"
|
||||
]
|
||||
},
|
||||
"/modelfile",
|
||||
"/context-length",
|
||||
"/linux",
|
||||
@@ -139,7 +194,8 @@
|
||||
"/api/streaming",
|
||||
"/api/usage",
|
||||
"/api/errors",
|
||||
"/api/openai-compatibility"
|
||||
"/api/openai-compatibility",
|
||||
"/api/anthropic-compatibility"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
+32
-12
@@ -14,15 +14,15 @@ curl -fsSL https://ollama.com/install.sh | sh
|
||||
|
||||
## How can I view the logs?
|
||||
|
||||
Review the [Troubleshooting](./troubleshooting.md) docs for more about using logs.
|
||||
Review the [Troubleshooting](./troubleshooting.mdx) docs for more about using logs.
|
||||
|
||||
## Is my GPU compatible with Ollama?
|
||||
|
||||
Please refer to the [GPU docs](./gpu.md).
|
||||
Please refer to the [GPU docs](./gpu.mdx).
|
||||
|
||||
## How can I specify the context window size?
|
||||
|
||||
By default, Ollama uses a context window size of 2048 tokens.
|
||||
By default, Ollama uses a context window size of 4096 tokens.
|
||||
|
||||
This can be overridden with the `OLLAMA_CONTEXT_LENGTH` environment variable. For example, to set the default context window to 8K, use:
|
||||
|
||||
@@ -66,7 +66,7 @@ llama3:70b bcfb190ca3a7 42 GB 100% GPU 4 minutes from now
|
||||
```
|
||||
</Info>
|
||||
|
||||
The `Processor` column will show which memory the model was loaded in to:
|
||||
The `Processor` column will show which memory the model was loaded into:
|
||||
|
||||
- `100% GPU` means the model was loaded entirely into the GPU
|
||||
- `100% CPU` means the model was loaded entirely in system memory
|
||||
@@ -158,7 +158,27 @@ docker run -d -e HTTPS_PROXY=https://my.proxy.example.com -p 11434:11434 ollama-
|
||||
|
||||
## Does Ollama send my prompts and answers back to ollama.com?
|
||||
|
||||
No. Ollama runs locally, and conversation data does not leave your machine.
|
||||
Ollama runs locally. We don't see your prompts or data when you run locally. When using cloud-hosted models, we process your prompts and responses to provide the service but do not store or log that content and never train on it. We collect basic account info and limited usage metadata to provide the service that does not include prompt or response content. We don't sell your data. You can delete your account anytime.
|
||||
|
||||
## How do I disable Ollama's cloud features?
|
||||
|
||||
Ollama can run in local only mode by disabling Ollama's cloud features. By turning off Ollama's cloud features, you will lose the ability to use Ollama's cloud models and web search.
|
||||
|
||||
Set `disable_ollama_cloud` in `~/.ollama/server.json`:
|
||||
|
||||
```json
|
||||
{
|
||||
"disable_ollama_cloud": true
|
||||
}
|
||||
```
|
||||
|
||||
You can also set the environment variable:
|
||||
|
||||
```shell
|
||||
OLLAMA_NO_CLOUD=1
|
||||
```
|
||||
|
||||
Restart Ollama after changing configuration. Once disabled, Ollama's logs will show `Ollama cloud disabled: true`.
|
||||
|
||||
## How can I expose Ollama on my network?
|
||||
|
||||
@@ -183,7 +203,7 @@ server {
|
||||
|
||||
## How can I use Ollama with ngrok?
|
||||
|
||||
Ollama can be accessed using a range of tools for tunneling tools. For example with Ngrok:
|
||||
Ollama can be accessed using a range of tunneling apps. For example with Ngrok:
|
||||
|
||||
```shell
|
||||
ngrok http 11434 --host-header="localhost:11434"
|
||||
@@ -240,7 +260,7 @@ GPU acceleration is not available for Docker Desktop in macOS due to the lack of
|
||||
|
||||
This can impact both installing Ollama, as well as downloading models.
|
||||
|
||||
Open `Control Panel > Networking and Internet > View network status and tasks` and click on `Change adapter settings` on the left panel. Find the `vEthernel (WSL)` adapter, right click and select `Properties`.
|
||||
Open `Control Panel > Networking and Internet > View network status and tasks` and click on `Change adapter settings` on the left panel. Find the `vEthernet (WSL)` adapter, right click and select `Properties`.
|
||||
Click on `Configure` and open the `Advanced` tab. Search through each of the properties until you find `Large Send Offload Version 2 (IPv4)` and `Large Send Offload Version 2 (IPv6)`. _Disable_ both of these
|
||||
properties.
|
||||
|
||||
@@ -299,7 +319,7 @@ The `keep_alive` API parameter with the `/api/generate` and `/api/chat` API endp
|
||||
|
||||
## How do I manage the maximum number of requests the Ollama server can queue?
|
||||
|
||||
If too many requests are sent to the server, it will respond with a 503 error indicating the server is overloaded. You can adjust how many requests may be queue by setting `OLLAMA_MAX_QUEUE`.
|
||||
If too many requests are sent to the server, it will respond with a 503 error indicating the server is overloaded. You can adjust how many requests may be queued by setting `OLLAMA_MAX_QUEUE`.
|
||||
|
||||
## How does Ollama handle concurrent requests?
|
||||
|
||||
@@ -312,10 +332,10 @@ Parallel request processing for a given model results in increasing the context
|
||||
The following server settings may be used to adjust how Ollama handles concurrent requests on most platforms:
|
||||
|
||||
- `OLLAMA_MAX_LOADED_MODELS` - The maximum number of models that can be loaded concurrently provided they fit in available memory. The default is 3 \* the number of GPUs or 3 for CPU inference.
|
||||
- `OLLAMA_NUM_PARALLEL` - The maximum number of parallel requests each model will process at the same time. The default will auto-select either 4 or 1 based on available memory.
|
||||
- `OLLAMA_NUM_PARALLEL` - The maximum number of parallel requests each model will process at the same time, default 1. Required RAM will scale by `OLLAMA_NUM_PARALLEL` * `OLLAMA_CONTEXT_LENGTH`.
|
||||
- `OLLAMA_MAX_QUEUE` - The maximum number of requests Ollama will queue when busy before rejecting additional requests. The default is 512
|
||||
|
||||
Note: Windows with Radeon GPUs currently default to 1 model maximum due to limitations in ROCm v5.7 for available VRAM reporting. Once ROCm v6.2 is available, Windows Radeon will follow the defaults above. You may enable concurrent model loads on Radeon on Windows, but ensure you don't load more models than will fit into your GPUs VRAM.
|
||||
Note: Windows with Radeon GPUs currently default to 1 model maximum due to limitations in ROCm v5.7 for available VRAM reporting. Once ROCm v6.2 is available, Windows Radeon will follow the defaults above. You may enable concurrent model loads on Radeon on Windows, but ensure you don't load more models than will fit into your GPU's VRAM.
|
||||
|
||||
## How does Ollama load models on multiple GPUs?
|
||||
|
||||
@@ -382,7 +402,7 @@ ollama signin
|
||||
Replace <username> with your actual Windows user name.
|
||||
</Note>
|
||||
|
||||
## How can I stop Ollama from starting when I login to my computer
|
||||
## How can I stop Ollama from starting when I login to my computer?
|
||||
|
||||
Ollama for Windows and macOS register as a login item during installation. You can disable this if you prefer not to have Ollama automatically start. Ollama will respect this setting across upgrades, unless you uninstall the application.
|
||||
|
||||
@@ -390,4 +410,4 @@ Ollama for Windows and macOS register as a login item during installation. You
|
||||
- In `Task Manager` go to the `Startup apps` tab, search for `ollama` then click `Disable`
|
||||
|
||||
**MacOS**
|
||||
- Open `Settings` and search for "Login Items", find the `Ollama` entry under "Allow in the Background`, then click the slider to disable.
|
||||
- Open `Settings` and search for "Login Items", find the `Ollama` entry under `Allow in the Background`, then click the slider to disable.
|
||||
|
||||
+28
-18
@@ -10,6 +10,7 @@ Check your compute compatibility to see if your card is supported:
|
||||
|
||||
| Compute Capability | Family | Cards |
|
||||
| ------------------ | ------------------- | ------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| 12.1 | NVIDIA | `GB10 (DGX Spark)` |
|
||||
| 12.0 | GeForce RTX 50xx | `RTX 5060` `RTX 5060 Ti` `RTX 5070` `RTX 5070 Ti` `RTX 5080` `RTX 5090` |
|
||||
| | NVIDIA Professional | `RTX PRO 4000 Blackwell` `RTX PRO 4500 Blackwell` `RTX PRO 5000 Blackwell` `RTX PRO 6000 Blackwell` |
|
||||
| 9.0 | NVIDIA | `H200` `H100` |
|
||||
@@ -33,7 +34,7 @@ Check your compute compatibility to see if your card is supported:
|
||||
| 5.0 | GeForce GTX | `GTX 750 Ti` `GTX 750` `NVS 810` |
|
||||
| | Quadro | `K2200` `K1200` `K620` `M1200` `M520` `M5000M` `M4000M` `M3000M` `M2000M` `M1000M` `K620M` `M600M` `M500M` |
|
||||
|
||||
For building locally to support older GPUs, see [developer.md](./development.md#linux-cuda-nvidia)
|
||||
For building locally to support older GPUs, see [developer](./development#linux-cuda-nvidia)
|
||||
|
||||
### GPU Selection
|
||||
|
||||
@@ -54,17 +55,23 @@ sudo modprobe nvidia_uvm`
|
||||
|
||||
Ollama supports the following AMD GPUs via the ROCm library:
|
||||
|
||||
> [!NOTE]
|
||||
> **NOTE:**
|
||||
> Additional AMD GPU support is provided by the Vulkan Library - see below.
|
||||
|
||||
|
||||
### Linux Support
|
||||
|
||||
| Family | Cards and accelerators |
|
||||
| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| AMD Radeon RX | `7900 XTX` `7900 XT` `7900 GRE` `7800 XT` `7700 XT` `7600 XT` `7600` `6950 XT` `6900 XTX` `6900XT` `6800 XT` `6800` `Vega 64` |
|
||||
| AMD Radeon PRO | `W7900` `W7800` `W7700` `W7600` `W7500` `W6900X` `W6800X Duo` `W6800X` `W6800` `V620` `V420` `V340` `V320` `Vega II Duo` `Vega II` `SSG` |
|
||||
| AMD Instinct | `MI300X` `MI300A` `MI300` `MI250X` `MI250` `MI210` `MI200` `MI100` `MI60` |
|
||||
Ollama requires the AMD ROCm v7 driver on Linux. You can install or upgrade
|
||||
using the `amdgpu-install` utility from
|
||||
[AMD's ROCm documentation](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/).
|
||||
|
||||
| Family | Cards and accelerators |
|
||||
| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| AMD Radeon RX | `9070 XT` `9070 GRE` `9070` `9060 XT` `9060 XT LP` `9060` `7900 XTX` `7900 XT` `7900 GRE` `7800 XT` `7700 XT` `7700` `7600 XT` `7600` `6950 XT` `6900 XTX` `6900XT` `6800 XT` `6800` `5700 XT` `5700` `5600 XT` `5500 XT` |
|
||||
| AMD Radeon AI PRO | `R9700` `R9600D` |
|
||||
| AMD Radeon PRO | `W7900` `W7800` `W7700` `W7600` `W7500` `W6900X` `W6800X Duo` `W6800X` `W6800` `V620` |
|
||||
| AMD Ryzen AI | `Ryzen AI Max+ 395` `Ryzen AI Max 390` `Ryzen AI Max 385` `Ryzen AI 9 HX 475` `Ryzen AI 9 HX 470` `Ryzen AI 9 465` `Ryzen AI 9 HX 375` `Ryzen AI 9 HX 370` `Ryzen AI 9 365` |
|
||||
| AMD Instinct | `MI350X` `MI300X` `MI300A` `MI250X` `MI250` `MI210` `MI100` |
|
||||
|
||||
### Windows Support
|
||||
|
||||
@@ -96,17 +103,20 @@ This table shows some example GPUs that map to these LLVM targets:
|
||||
| **LLVM Target** | **An Example GPU** |
|
||||
|-----------------|---------------------|
|
||||
| gfx908 | Radeon Instinct MI100 |
|
||||
| gfx90a | Radeon Instinct MI210 |
|
||||
| gfx940 | Radeon Instinct MI300 |
|
||||
| gfx941 | |
|
||||
| gfx942 | |
|
||||
| gfx90a | Radeon Instinct MI210/MI250 |
|
||||
| gfx942 | Radeon Instinct MI300X/MI300A |
|
||||
| gfx950 | Radeon Instinct MI350X |
|
||||
| gfx1010 | Radeon RX 5700 XT |
|
||||
| gfx1012 | Radeon RX 5500 XT |
|
||||
| gfx1030 | Radeon PRO V620 |
|
||||
| gfx1100 | Radeon PRO W7900 |
|
||||
| gfx1101 | Radeon PRO W7700 |
|
||||
| gfx1102 | Radeon RX 7600 |
|
||||
|
||||
AMD is working on enhancing ROCm v6 to broaden support for families of GPUs in a
|
||||
future release which should increase support for more GPUs.
|
||||
| gfx1103 | Radeon 780M |
|
||||
| gfx1150 | Ryzen AI 9 HX 375 |
|
||||
| gfx1151 | Ryzen AI Max+ 395 |
|
||||
| gfx1200 | Radeon RX 9070 |
|
||||
| gfx1201 | Radeon RX 9070 XT |
|
||||
|
||||
Reach out on [Discord](https://discord.gg/ollama) or file an
|
||||
[issue](https://github.com/ollama/ollama/issues) for additional help.
|
||||
@@ -132,9 +142,9 @@ Ollama supports GPU acceleration on Apple devices via the Metal API.
|
||||
|
||||
## Vulkan GPU Support
|
||||
|
||||
> [!NOTE]
|
||||
> **NOTE:**
|
||||
> Vulkan is currently an Experimental feature. To enable, you must set OLLAMA_VULKAN=1 for the Ollama server as
|
||||
described in the [FAQ](faq.md#how-do-i-configure-ollama-server)
|
||||
described in the [FAQ](faq#how-do-i-configure-ollama-server)
|
||||
|
||||
Additional GPU support on Windows and Linux is provided via
|
||||
[Vulkan](https://www.vulkan.org/). On Windows most GPU vendors drivers come
|
||||
@@ -161,6 +171,6 @@ sudo setcap cap_perfmon+ep /usr/local/bin/ollama
|
||||
|
||||
To select specific Vulkan GPU(s), you can set the environment variable
|
||||
`GGML_VK_VISIBLE_DEVICES` to one or more numeric IDs on the Ollama server as
|
||||
described in the [FAQ](faq.md#how-do-i-configure-ollama-server). If you
|
||||
described in the [FAQ](faq#how-do-i-configure-ollama-server). If you
|
||||
encounter any problems with Vulkan based GPUs, you can disable all Vulkan GPUs
|
||||
by setting `GGML_VK_VISIBLE_DEVICES=-1`
|
||||
by setting `GGML_VK_VISIBLE_DEVICES=-1`
|
||||
|
||||
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@@ -134,22 +134,12 @@ success
|
||||
|
||||
### Supported Quantizations
|
||||
|
||||
- `q4_0`
|
||||
- `q4_1`
|
||||
- `q5_0`
|
||||
- `q5_1`
|
||||
- `q8_0`
|
||||
|
||||
#### K-means Quantizations
|
||||
|
||||
- `q3_K_S`
|
||||
- `q3_K_M`
|
||||
- `q3_K_L`
|
||||
- `q4_K_S`
|
||||
- `q4_K_M`
|
||||
- `q5_K_S`
|
||||
- `q5_K_M`
|
||||
- `q6_K`
|
||||
|
||||
## Sharing your model on ollama.com
|
||||
|
||||
|
||||
+1
-1
@@ -9,7 +9,7 @@ sidebarTitle: Welcome
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Quickstart" icon="rocket" href="/quickstart">
|
||||
Get up and running with your first model
|
||||
Get up and running with your first model or integrate Ollama with your favorite tools
|
||||
</Card>
|
||||
<Card
|
||||
title="Download Ollama"
|
||||
|
||||
@@ -0,0 +1,136 @@
|
||||
---
|
||||
title: Claude Code
|
||||
---
|
||||
|
||||
Claude Code is Anthropic's agentic coding tool that can read, modify, and execute code in your working directory.
|
||||
|
||||
Open models can be used with Claude Code through Ollama's Anthropic-compatible API, enabling you to use models such as `qwen3.5`, `glm-5:cloud`, `kimi-k2.5:cloud`.
|
||||
|
||||
|
||||
|
||||
## Install
|
||||
|
||||
Install [Claude Code](https://code.claude.com/docs/en/overview):
|
||||
|
||||
<CodeGroup>
|
||||
|
||||
```shell macOS / Linux
|
||||
curl -fsSL https://claude.ai/install.sh | bash
|
||||
```
|
||||
|
||||
```powershell Windows
|
||||
irm https://claude.ai/install.ps1 | iex
|
||||
```
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
## Usage with Ollama
|
||||
|
||||
### Quick setup
|
||||
|
||||
```shell
|
||||
ollama launch claude
|
||||
```
|
||||
|
||||
### Run directly with a model
|
||||
```shell
|
||||
ollama launch claude --model kimi-k2.5:cloud
|
||||
```
|
||||
|
||||
## Recommended Models
|
||||
|
||||
- `kimi-k2.5:cloud`
|
||||
- `glm-5:cloud`
|
||||
- `minimax-m2.7:cloud`
|
||||
- `qwen3.5:cloud`
|
||||
- `glm-4.7-flash`
|
||||
- `qwen3.5`
|
||||
|
||||
Cloud models are also available at [ollama.com/search?c=cloud](https://ollama.com/search?c=cloud).
|
||||
|
||||
## Non-interactive (headless) mode
|
||||
|
||||
Run Claude Code without interaction for use in Docker, CI/CD, or scripts:
|
||||
|
||||
```shell
|
||||
ollama launch claude --model kimi-k2.5:cloud --yes -- -p "how does this repository work?"
|
||||
```
|
||||
|
||||
The `--yes` flag auto-pulls the model, skips selectors, and requires `--model` to be specified. Arguments after `--` are passed directly to Claude Code.
|
||||
|
||||
## Web search
|
||||
|
||||
Claude Code can search the web through Ollama's web search API. See the [web search documentation](/capabilities/web-search) for setup and usage.
|
||||
|
||||
## Scheduled Tasks with `/loop`
|
||||
|
||||
The `/loop` command runs a prompt or slash command on a recurring schedule inside Claude Code. This is useful for automating repetitive tasks like checking PRs, running research, or setting reminders.
|
||||
|
||||
```
|
||||
/loop <interval> <prompt or /command>
|
||||
```
|
||||
|
||||
### Examples
|
||||
|
||||
**Check in on your PRs**
|
||||
|
||||
```
|
||||
/loop 30m Check my open PRs and summarize their status
|
||||
```
|
||||
|
||||
**Automate research tasks**
|
||||
|
||||
```
|
||||
/loop 1h Research the latest AI news and summarize key developments
|
||||
```
|
||||
|
||||
**Automate bug reporting and triaging**
|
||||
|
||||
```
|
||||
/loop 15m Check for new GitHub issues and triage by priority
|
||||
```
|
||||
|
||||
**Set reminders**
|
||||
|
||||
```
|
||||
/loop 1h Remind me to review the deploy status
|
||||
```
|
||||
|
||||
## Telegram
|
||||
|
||||
Chat with Claude Code from Telegram by connecting a bot to your session. Install the [Telegram plugin](https://github.com/anthropics/claude-plugins-official), create a bot via [@BotFather](https://t.me/BotFather), then launch with the channel flag:
|
||||
|
||||
```shell
|
||||
ollama launch claude -- --channels plugin:telegram@claude-plugins-official
|
||||
```
|
||||
|
||||
Claude Code will prompt for permission on most actions. To allow the bot to work autonomously, configure [permission rules](https://code.claude.com/docs/en/permissions) or pass `--dangerously-skip-permissions` in isolated environments.
|
||||
|
||||
See the [plugin README](https://github.com/anthropics/claude-plugins-official/tree/main/external_plugins/telegram) for full setup instructions including pairing and access control.
|
||||
|
||||
## Manual setup
|
||||
|
||||
Claude Code connects to Ollama using the Anthropic-compatible API.
|
||||
|
||||
1. Set the environment variables:
|
||||
|
||||
```shell
|
||||
export ANTHROPIC_AUTH_TOKEN=ollama
|
||||
export ANTHROPIC_API_KEY=""
|
||||
export ANTHROPIC_BASE_URL=http://localhost:11434
|
||||
```
|
||||
|
||||
2. Run Claude Code with an Ollama model:
|
||||
|
||||
```shell
|
||||
claude --model qwen3.5
|
||||
```
|
||||
|
||||
Or run with environment variables inline:
|
||||
|
||||
```shell
|
||||
ANTHROPIC_AUTH_TOKEN=ollama ANTHROPIC_BASE_URL=http://localhost:11434 ANTHROPIC_API_KEY="" claude --model glm-5:cloud
|
||||
```
|
||||
|
||||
**Note:** Claude Code requires a large context window. We recommend at least 64k tokens. See the [context length documentation](/context-length) for how to adjust context length in Ollama.
|
||||
|
||||
+35
-18
@@ -13,7 +13,21 @@ npm install -g @openai/codex
|
||||
|
||||
## Usage with Ollama
|
||||
|
||||
<Note>Codex requires a larger context window. It is recommended to use a context window of at least 32K tokens.</Note>
|
||||
<Note>Codex requires a larger context window. It is recommended to use a context window of at least 64k tokens.</Note>
|
||||
|
||||
### Quick setup
|
||||
|
||||
```
|
||||
ollama launch codex
|
||||
```
|
||||
|
||||
To configure without launching:
|
||||
|
||||
```shell
|
||||
ollama launch codex --config
|
||||
```
|
||||
|
||||
### Manual setup
|
||||
|
||||
To use `codex` with Ollama, use the `--oss` flag:
|
||||
|
||||
@@ -21,36 +35,39 @@ To use `codex` with Ollama, use the `--oss` flag:
|
||||
codex --oss
|
||||
```
|
||||
|
||||
### Changing Models
|
||||
|
||||
By default, codex will use the local `gpt-oss:20b` model. However, you can specify a different model with the `-m` flag:
|
||||
To use a specific model, pass the `-m` flag:
|
||||
|
||||
```
|
||||
codex --oss -m gpt-oss:120b
|
||||
```
|
||||
|
||||
### Cloud Models
|
||||
To use a cloud model:
|
||||
|
||||
```
|
||||
codex --oss -m gpt-oss:120b-cloud
|
||||
```
|
||||
|
||||
### Profile-based setup
|
||||
|
||||
## Connecting to ollama.com
|
||||
|
||||
|
||||
Create an [API key](https://ollama.com/settings/keys) from ollama.com and export it as `OLLAMA_API_KEY`.
|
||||
|
||||
To use ollama.com directly, edit your `~/.codex/config.toml` file to point to ollama.com.
|
||||
For a persistent configuration, add an Ollama provider and profiles to `~/.codex/config.toml`:
|
||||
|
||||
```toml
|
||||
model = "gpt-oss:120b"
|
||||
model_provider = "ollama"
|
||||
|
||||
[model_providers.ollama]
|
||||
[model_providers.ollama-launch]
|
||||
name = "Ollama"
|
||||
base_url = "https://ollama.com/v1"
|
||||
env_key = "OLLAMA_API_KEY"
|
||||
base_url = "http://localhost:11434/v1"
|
||||
|
||||
[profiles.ollama-launch]
|
||||
model = "gpt-oss:120b"
|
||||
model_provider = "ollama-launch"
|
||||
|
||||
[profiles.ollama-cloud]
|
||||
model = "gpt-oss:120b-cloud"
|
||||
model_provider = "ollama-launch"
|
||||
```
|
||||
|
||||
Run `codex` in a new terminal to load the new settings.
|
||||
Then run:
|
||||
|
||||
```
|
||||
codex --profile ollama-launch
|
||||
codex --profile ollama-cloud
|
||||
```
|
||||
|
||||
@@ -11,10 +11,24 @@ Install the [Droid CLI](https://factory.ai/):
|
||||
curl -fsSL https://app.factory.ai/cli | sh
|
||||
```
|
||||
|
||||
<Note>Droid requires a larger context window. It is recommended to use a context window of at least 32K tokens. See [Context length](/context-length) for more information.</Note>
|
||||
<Note>Droid requires a larger context window. It is recommended to use a context window of at least 64k tokens. See [Context length](/context-length) for more information.</Note>
|
||||
|
||||
## Usage with Ollama
|
||||
|
||||
### Quick setup
|
||||
|
||||
```bash
|
||||
ollama launch droid
|
||||
```
|
||||
|
||||
To configure without launching:
|
||||
|
||||
```shell
|
||||
ollama launch droid --config
|
||||
```
|
||||
|
||||
### Manual setup
|
||||
|
||||
Add a local configuration block to `~/.factory/config.json`:
|
||||
|
||||
```json
|
||||
@@ -73,4 +87,4 @@ Add the cloud configuration block to `~/.factory/config.json`:
|
||||
}
|
||||
```
|
||||
|
||||
Run `droid` in a new terminal to load the new settings.
|
||||
Run `droid` in a new terminal to load the new settings.
|
||||
|
||||
@@ -0,0 +1,111 @@
|
||||
---
|
||||
title: Hermes Agent
|
||||
---
|
||||
|
||||
Hermes Agent is a self-improving AI agent built by Nous Research. It features automatic skill creation, cross-session memory, and connects messaging platforms (Telegram, Discord, Slack, WhatsApp, Signal, Email) to models through a unified gateway.
|
||||
|
||||
## Quick start
|
||||
|
||||
### Pull a model
|
||||
|
||||
Before running the setup wizard, make sure you have a model available. Hermes will auto-detect models downloaded through Ollama.
|
||||
|
||||
```bash
|
||||
ollama pull kimi-k2.5:cloud
|
||||
```
|
||||
|
||||
See [Recommended models](#recommended-models) for more options.
|
||||
|
||||
### Install
|
||||
|
||||
```bash
|
||||
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
|
||||
```
|
||||
|
||||
### Set up
|
||||
|
||||
After installation, Hermes launches the setup wizard automatically. Choose **Quick setup**:
|
||||
|
||||
```
|
||||
How would you like to set up Hermes?
|
||||
|
||||
→ Quick setup — provider, model & messaging (recommended)
|
||||
Full setup — configure everything
|
||||
```
|
||||
|
||||
### Connect to Ollama
|
||||
|
||||
1. Select **More providers...**
|
||||
2. Select **Custom endpoint (enter URL manually)**
|
||||
3. Set the API base URL to the Ollama OpenAI-compatible endpoint:
|
||||
|
||||
```
|
||||
API base URL [e.g. https://api.example.com/v1]: http://127.0.0.1:11434/v1
|
||||
```
|
||||
|
||||
4. Leave the API key blank (not required for local Ollama):
|
||||
|
||||
```
|
||||
API key [optional]:
|
||||
```
|
||||
|
||||
5. Hermes auto-detects downloaded models, confirm the one you want:
|
||||
|
||||
```
|
||||
Verified endpoint via http://127.0.0.1:11434/v1/models (1 model(s) visible)
|
||||
Detected model: kimi-k2.5:cloud
|
||||
Use this model? [Y/n]:
|
||||
```
|
||||
|
||||
6. Leave context length blank to auto-detect:
|
||||
|
||||
```
|
||||
Context length in tokens [leave blank for auto-detect]:
|
||||
```
|
||||
|
||||
### Connect messaging
|
||||
|
||||
Optionally connect a messaging platform during setup:
|
||||
|
||||
```
|
||||
Connect a messaging platform? (Telegram, Discord, etc.)
|
||||
|
||||
→ Set up messaging now (recommended)
|
||||
Skip — set up later with 'hermes setup gateway'
|
||||
```
|
||||
|
||||
### Launch
|
||||
|
||||
```
|
||||
Launch hermes chat now? [Y/n]: Y
|
||||
```
|
||||
|
||||
## Recommended models
|
||||
|
||||
**Cloud models**:
|
||||
|
||||
- `kimi-k2.5:cloud` — Multimodal reasoning with subagents
|
||||
- `qwen3.5:cloud` — Reasoning, coding, and agentic tool use with vision
|
||||
- `glm-5.1:cloud` — Reasoning and code generation
|
||||
- `minimax-m2.7:cloud` — Fast, efficient coding and real-world productivity
|
||||
|
||||
**Local models:**
|
||||
|
||||
- `gemma4` — Reasoning and code generation locally (~16 GB VRAM)
|
||||
- `qwen3.5` — Reasoning, coding, and visual understanding locally (~11 GB VRAM)
|
||||
|
||||
More models at [ollama.com/search](https://ollama.com/models).
|
||||
|
||||
## Configure later
|
||||
|
||||
Re-run the setup wizard at any time:
|
||||
|
||||
```bash
|
||||
hermes setup
|
||||
```
|
||||
|
||||
To configure just messaging:
|
||||
|
||||
```bash
|
||||
hermes setup gateway
|
||||
```
|
||||
@@ -0,0 +1,52 @@
|
||||
---
|
||||
title: Overview
|
||||
---
|
||||
|
||||
Ollama integrates with a wide range of tools.
|
||||
|
||||
## Coding Agents
|
||||
|
||||
Coding assistants that can read, modify, and execute code in your projects.
|
||||
|
||||
- [Claude Code](/integrations/claude-code)
|
||||
- [Codex](/integrations/codex)
|
||||
- [OpenCode](/integrations/opencode)
|
||||
- [Droid](/integrations/droid)
|
||||
- [Goose](/integrations/goose)
|
||||
- [Pi](/integrations/pi)
|
||||
|
||||
## Assistants
|
||||
|
||||
AI assistants that help with everyday tasks.
|
||||
|
||||
- [OpenClaw](/integrations/openclaw)
|
||||
- [Hermes Agent](/integrations/hermes)
|
||||
|
||||
## IDEs & Editors
|
||||
|
||||
Native integrations for popular development environments.
|
||||
|
||||
- [VS Code](/integrations/vscode)
|
||||
- [Cline](/integrations/cline)
|
||||
- [Roo Code](/integrations/roo-code)
|
||||
- [JetBrains](/integrations/jetbrains)
|
||||
- [Xcode](/integrations/xcode)
|
||||
- [Zed](/integrations/zed)
|
||||
|
||||
## Chat & RAG
|
||||
|
||||
Chat interfaces and retrieval-augmented generation platforms.
|
||||
|
||||
- [Onyx](/integrations/onyx)
|
||||
|
||||
## Automation
|
||||
|
||||
Workflow automation platforms with AI integration.
|
||||
|
||||
- [n8n](/integrations/n8n)
|
||||
|
||||
## Notebooks
|
||||
|
||||
Interactive computing environments with AI capabilities.
|
||||
|
||||
- [marimo](/integrations/marimo)
|
||||
@@ -0,0 +1,73 @@
|
||||
---
|
||||
title: marimo
|
||||
---
|
||||
|
||||
## Install
|
||||
|
||||
Install [marimo](https://marimo.io). You can use `pip` or `uv` for this. You
|
||||
can also use `uv` to create a sandboxed environment for marimo by running:
|
||||
|
||||
```
|
||||
uvx marimo edit --sandbox notebook.py
|
||||
```
|
||||
|
||||
## Usage with Ollama
|
||||
|
||||
1. In marimo, go to the user settings and go to the AI tab. From here
|
||||
you can find and configure Ollama as an AI provider. For local use you
|
||||
would typically point the base url to `http://localhost:11434/v1`.
|
||||
|
||||
<div style={{ display: 'flex', justifyContent: 'center' }}>
|
||||
<img
|
||||
src="/images/marimo-settings.png"
|
||||
alt="Ollama settings in marimo"
|
||||
width="50%"
|
||||
/>
|
||||
</div>
|
||||
|
||||
2. Once the AI provider is set up, you can turn on/off specific AI models you'd like to access.
|
||||
|
||||
<div style={{ display: 'flex', justifyContent: 'center' }}>
|
||||
<img
|
||||
src="/images/marimo-models.png"
|
||||
alt="Selecting an Ollama model"
|
||||
width="50%"
|
||||
/>
|
||||
</div>
|
||||
|
||||
3. You can also add a model to the list of available models by scrolling to the bottom and using the UI there.
|
||||
|
||||
<div style={{ display: 'flex', justifyContent: 'center' }}>
|
||||
<img
|
||||
src="/images/marimo-add-model.png"
|
||||
alt="Adding a new Ollama model"
|
||||
width="50%"
|
||||
/>
|
||||
</div>
|
||||
|
||||
4. Once configured, you can now use Ollama for AI chats in marimo.
|
||||
|
||||
<div style={{ display: 'flex', justifyContent: 'center' }}>
|
||||
<img
|
||||
src="/images/marimo-chat.png"
|
||||
alt="Configure code completion"
|
||||
width="50%"
|
||||
/>
|
||||
</div>
|
||||
|
||||
4. Alternatively, you can now use Ollama for **inline code completion** in marimo. This can be configured in the "AI Features" tab.
|
||||
|
||||
<div style={{ display: 'flex', justifyContent: 'center' }}>
|
||||
<img
|
||||
src="/images/marimo-code-completion.png"
|
||||
alt="Configure code completion"
|
||||
width="50%"
|
||||
/>
|
||||
</div>
|
||||
|
||||
|
||||
## Connecting to ollama.com
|
||||
|
||||
1. Sign in to ollama cloud via `ollama signin`
|
||||
2. In the ollama model settings add a model that ollama hosts, like `gpt-oss:120b`.
|
||||
3. You can now refer to this model in marimo!
|
||||
@@ -0,0 +1,67 @@
|
||||
---
|
||||
title: NemoClaw
|
||||
---
|
||||
|
||||
NemoClaw is NVIDIA's open source security stack for [OpenClaw](/integrations/openclaw). It wraps OpenClaw with the NVIDIA OpenShell runtime to provide kernel-level sandboxing, network policy controls, and audit trails for AI agents.
|
||||
|
||||
## Quick start
|
||||
|
||||
Pull a model:
|
||||
|
||||
```bash
|
||||
ollama pull nemotron-3-nano:30b
|
||||
```
|
||||
|
||||
Run the installer:
|
||||
|
||||
```bash
|
||||
curl -fsSL https://www.nvidia.com/nemoclaw.sh | \
|
||||
NEMOCLAW_NON_INTERACTIVE=1 \
|
||||
NEMOCLAW_PROVIDER=ollama \
|
||||
NEMOCLAW_MODEL=nemotron-3-nano:30b \
|
||||
bash
|
||||
```
|
||||
|
||||
Connect to your sandbox:
|
||||
|
||||
```bash
|
||||
nemoclaw my-assistant connect
|
||||
```
|
||||
|
||||
Open the TUI:
|
||||
|
||||
```bash
|
||||
openclaw tui
|
||||
```
|
||||
|
||||
<Note>Ollama support in NemoClaw is still experimental.</Note>
|
||||
|
||||
## Platform support
|
||||
|
||||
| Platform | Runtime | Status |
|
||||
|----------|---------|--------|
|
||||
| Linux (Ubuntu 22.04+) | Docker | Primary |
|
||||
| macOS (Apple Silicon) | Colima or Docker Desktop | Supported |
|
||||
| Windows | WSL2 with Docker Desktop | Supported |
|
||||
|
||||
CMD and PowerShell are not supported on Windows — WSL2 is required.
|
||||
|
||||
<Note>Ollama must be installed and running before the installer runs. When running inside WSL2 or a container, ensure Ollama is reachable from the sandbox (e.g. `OLLAMA_HOST=0.0.0.0`).</Note>
|
||||
|
||||
## System requirements
|
||||
|
||||
- CPU: 4 vCPU minimum
|
||||
- RAM: 8 GB minimum (16 GB recommended)
|
||||
- Disk: 20 GB free (40 GB recommended for local models)
|
||||
- Node.js 20+ and npm 10+
|
||||
- Container runtime (Docker preferred)
|
||||
|
||||
## Recommended models
|
||||
|
||||
- `nemotron-3-super:cloud` — Strong reasoning and coding
|
||||
- `qwen3.5:cloud` — 397B; reasoning and code generation
|
||||
- `nemotron-3-nano:30b` — Recommended local model; fits in 24 GB VRAM
|
||||
- `qwen3.5:27b` — Fast local reasoning (~18 GB VRAM)
|
||||
- `glm-4.7-flash` — Reasoning and code generation (~25 GB VRAM)
|
||||
|
||||
More models at [ollama.com/search](https://ollama.com/search).
|
||||
@@ -0,0 +1,63 @@
|
||||
---
|
||||
title: Onyx
|
||||
---
|
||||
|
||||
## Overview
|
||||
[Onyx](http://onyx.app/) is a self-hostable Chat UI that integrates with all Ollama models. Features include:
|
||||
- Creating custom Agents
|
||||
- Web search
|
||||
- Deep Research
|
||||
- RAG over uploaded documents and connected apps
|
||||
- Connectors to applications like Google Drive, Email, Slack, etc.
|
||||
- MCP and OpenAPI Actions support
|
||||
- Image generation
|
||||
- User/Groups management, RBAC, SSO, etc.
|
||||
|
||||
Onyx can be deployed for single users or large organizations.
|
||||
|
||||
## Install Onyx
|
||||
|
||||
Deploy Onyx with the [quickstart guide](https://docs.onyx.app/deployment/getting_started/quickstart).
|
||||
|
||||
<Info>
|
||||
Resourcing/scaling docs [here](https://docs.onyx.app/deployment/getting_started/resourcing).
|
||||
</Info>
|
||||
|
||||
## Usage with Ollama
|
||||
|
||||
1. Login to your Onyx deployment (create an account first).
|
||||
<div style={{ display: 'flex', justifyContent: 'center' }}>
|
||||
<img
|
||||
src="/images/onyx-login.png"
|
||||
alt="Onyx Login Page"
|
||||
width="75%"
|
||||
/>
|
||||
</div>
|
||||
2. In the set-up process select `Ollama` as the LLM provider.
|
||||
<div style={{ display: 'flex', justifyContent: 'center' }}>
|
||||
<img
|
||||
src="/images/onyx-ollama-llm.png"
|
||||
alt="Onyx Set Up Form"
|
||||
width="75%"
|
||||
/>
|
||||
</div>
|
||||
3. Provide your **Ollama API URL** and select your models.
|
||||
<Note>If you're running Onyx in Docker, to access your computer's local network use `http://host.docker.internal` instead of `http://127.0.0.1`.</Note>
|
||||
<div style={{ display: 'flex', justifyContent: 'center' }}>
|
||||
<img
|
||||
src="/images/onyx-ollama-form.png"
|
||||
alt="Selecting Ollama Models"
|
||||
width="75%"
|
||||
/>
|
||||
</div>
|
||||
|
||||
You can also easily connect up Onyx Cloud with the `Ollama Cloud` tab of the setup.
|
||||
|
||||
## Send your first query
|
||||
<div style={{ display: 'flex', justifyContent: 'center' }}>
|
||||
<img
|
||||
src="/images/onyx-query.png"
|
||||
alt="Onyx Query Example"
|
||||
width="75%"
|
||||
/>
|
||||
</div>
|
||||
@@ -0,0 +1,96 @@
|
||||
---
|
||||
title: OpenClaw
|
||||
---
|
||||
|
||||
OpenClaw is a personal AI assistant that runs on your own devices. It bridges messaging services (WhatsApp, Telegram, Slack, Discord, iMessage, and more) to AI coding agents through a centralized gateway.
|
||||
|
||||
## Quick start
|
||||
|
||||
```bash
|
||||
ollama launch openclaw
|
||||
```
|
||||
|
||||
Ollama handles everything automatically:
|
||||
|
||||
1. **Install** — If OpenClaw isn't installed, Ollama prompts to install it via npm
|
||||
2. **Security** — On the first launch, a security notice explains the risks of tool access
|
||||
3. **Model** — Pick a model from the selector (local or cloud)
|
||||
4. **Onboarding** — Ollama configures the provider, installs the gateway daemon, sets your model as the primary, and installs the web search and fetch plugin
|
||||
5. **Gateway** — Starts in the background and opens the OpenClaw TUI
|
||||
|
||||
<Note>OpenClaw requires a larger context window. It is recommended to use a context window of at least 64k tokens if using local models. See [Context length](/context-length) for more information.</Note>
|
||||
|
||||
<Note>Previously known as Clawdbot. `ollama launch clawdbot` still works as an alias.</Note>
|
||||
|
||||
## Web search and fetch
|
||||
|
||||
OpenClaw ships with a web search and fetch plugin that gives local or cloud models the ability to search the web and extract readable page content.
|
||||
|
||||
```bash
|
||||
ollama launch openclaw
|
||||
```
|
||||
|
||||
Web search and fetch is enabled automatically when launching OpenClaw through Ollama. To install the plugin directly:
|
||||
|
||||
```bash
|
||||
openclaw plugins install @ollama/openclaw-web-search
|
||||
```
|
||||
|
||||
<Note>Web search for local models requires `ollama signin`.</Note>
|
||||
|
||||
## Configure without launching
|
||||
|
||||
To change the model without starting the gateway and TUI:
|
||||
|
||||
```bash
|
||||
ollama launch openclaw --config
|
||||
```
|
||||
|
||||
To use a specific model directly:
|
||||
|
||||
```bash
|
||||
ollama launch openclaw --model kimi-k2.5:cloud
|
||||
```
|
||||
|
||||
If the gateway is already running, it restarts automatically to pick up the new model.
|
||||
|
||||
## Recommended models
|
||||
|
||||
**Cloud models**:
|
||||
|
||||
- `kimi-k2.5:cloud` — Multimodal reasoning with subagents
|
||||
- `qwen3.5:cloud` — Reasoning, coding, and agentic tool use with vision
|
||||
- `glm-5.1:cloud` — Reasoning and code generation
|
||||
- `minimax-m2.7:cloud` — Fast, efficient coding and real-world productivity
|
||||
|
||||
**Local models:**
|
||||
|
||||
- `gemma4` — Reasoning and code generation locally (~16 GB VRAM)
|
||||
- `qwen3.5` — Reasoning, coding, and visual understanding locally (~11 GB VRAM)
|
||||
|
||||
More models at [ollama.com/search](https://ollama.com/search?c=cloud).
|
||||
|
||||
## Non-interactive (headless) mode
|
||||
|
||||
Run OpenClaw without interaction for use in Docker, CI/CD, or scripts:
|
||||
|
||||
```bash
|
||||
ollama launch openclaw --model kimi-k2.5:cloud --yes
|
||||
```
|
||||
|
||||
The `--yes` flag auto-pulls the model, skips selectors, and requires `--model` to be specified.
|
||||
|
||||
## Connect messaging apps
|
||||
|
||||
```bash
|
||||
openclaw configure --section channels
|
||||
```
|
||||
|
||||
Link WhatsApp, Telegram, Slack, Discord, or iMessage to chat with your local models from anywhere.
|
||||
|
||||
## Stopping the gateway
|
||||
|
||||
```bash
|
||||
openclaw gateway stop
|
||||
```
|
||||
|
||||
@@ -0,0 +1,106 @@
|
||||
---
|
||||
title: OpenCode
|
||||
---
|
||||
|
||||
OpenCode is an open-source AI coding assistant that runs in your terminal.
|
||||
|
||||
## Install
|
||||
|
||||
Install the [OpenCode CLI](https://opencode.ai):
|
||||
|
||||
```bash
|
||||
curl -fsSL https://opencode.ai/install | bash
|
||||
```
|
||||
|
||||
<Note>OpenCode requires a larger context window. It is recommended to use a context window of at least 64k tokens. See [Context length](/context-length) for more information.</Note>
|
||||
|
||||
## Usage with Ollama
|
||||
|
||||
### Quick setup
|
||||
|
||||
```bash
|
||||
ollama launch opencode
|
||||
```
|
||||
|
||||
To configure without launching:
|
||||
|
||||
```shell
|
||||
ollama launch opencode --config
|
||||
```
|
||||
|
||||
### Manual setup
|
||||
|
||||
Add a configuration block to `~/.config/opencode/opencode.json`:
|
||||
|
||||
```json
|
||||
{
|
||||
"$schema": "https://opencode.ai/config.json",
|
||||
"provider": {
|
||||
"ollama": {
|
||||
"npm": "@ai-sdk/openai-compatible",
|
||||
"name": "Ollama",
|
||||
"options": {
|
||||
"baseURL": "http://localhost:11434/v1"
|
||||
},
|
||||
"models": {
|
||||
"qwen3-coder": {
|
||||
"name": "qwen3-coder"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Cloud Models
|
||||
|
||||
`glm-4.7:cloud` is the recommended model for use with OpenCode.
|
||||
|
||||
Add the cloud configuration to `~/.config/opencode/opencode.json`:
|
||||
|
||||
```json
|
||||
{
|
||||
"$schema": "https://opencode.ai/config.json",
|
||||
"provider": {
|
||||
"ollama": {
|
||||
"npm": "@ai-sdk/openai-compatible",
|
||||
"name": "Ollama",
|
||||
"options": {
|
||||
"baseURL": "http://localhost:11434/v1"
|
||||
},
|
||||
"models": {
|
||||
"glm-4.7:cloud": {
|
||||
"name": "glm-4.7:cloud"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Connecting to ollama.com
|
||||
|
||||
1. Create an [API key](https://ollama.com/settings/keys) from ollama.com and export it as `OLLAMA_API_KEY`.
|
||||
2. Update `~/.config/opencode/opencode.json` to point to ollama.com:
|
||||
|
||||
```json
|
||||
{
|
||||
"$schema": "https://opencode.ai/config.json",
|
||||
"provider": {
|
||||
"ollama": {
|
||||
"npm": "@ai-sdk/openai-compatible",
|
||||
"name": "Ollama Cloud",
|
||||
"options": {
|
||||
"baseURL": "https://ollama.com/v1"
|
||||
},
|
||||
"models": {
|
||||
"glm-4.7:cloud": {
|
||||
"name": "glm-4.7:cloud"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Run `opencode` in a new terminal to load the new settings.
|
||||
@@ -0,0 +1,109 @@
|
||||
---
|
||||
title: Pi
|
||||
---
|
||||
|
||||
Pi is a minimal and extensible coding agent.
|
||||
|
||||
## Install
|
||||
|
||||
Install [Pi](https://github.com/badlogic/pi-mono):
|
||||
|
||||
```bash
|
||||
npm install -g @mariozechner/pi-coding-agent
|
||||
```
|
||||
|
||||
## Usage with Ollama
|
||||
|
||||
### Quick setup
|
||||
|
||||
```bash
|
||||
ollama launch pi
|
||||
```
|
||||
|
||||
This installs Pi, configures Ollama as a provider including web tools, and drops you into an interactive session.
|
||||
|
||||
To configure without launching:
|
||||
|
||||
```shell
|
||||
ollama launch pi --config
|
||||
```
|
||||
|
||||
### Run directly with a model
|
||||
|
||||
```shell
|
||||
ollama launch pi --model qwen3.5:cloud
|
||||
```
|
||||
|
||||
Cloud models are also available at [ollama.com](https://ollama.com/search?c=cloud).
|
||||
|
||||
## Extensions
|
||||
|
||||
Pi ships with four core tools: `read`, `write`, `edit`, and `bash`. All other capabilities are added through its extension system.
|
||||
|
||||
On-demand capability packages invoked via `/skill:name` commands.
|
||||
|
||||
Install from npm or git:
|
||||
|
||||
```bash
|
||||
pi install npm:@foo/some-tools
|
||||
pi install git:github.com/user/repo@v1
|
||||
```
|
||||
|
||||
See all packages at [pi.dev](https://pi.dev/packages)
|
||||
|
||||
### Web search
|
||||
|
||||
Pi can use web search and fetch tools via the `@ollama/pi-web-search` package.
|
||||
|
||||
When launching Pi through Ollama, package install/update is managed automatically.
|
||||
To install manually:
|
||||
|
||||
```bash
|
||||
pi install npm:@ollama/pi-web-search
|
||||
```
|
||||
|
||||
### Autoresearch with `pi-autoresearch`
|
||||
|
||||
[pi-autoresearch](https://github.com/davebcn87/pi-autoresearch) brings autonomous experiment loops to Pi. Inspired by Karpathy's autoresearch, it turns any measurable metric into an optimization target: test speed, bundle size, build time, model training loss, Lighthouse scores.
|
||||
|
||||
```bash
|
||||
pi install https://github.com/davebcn87/pi-autoresearch
|
||||
```
|
||||
|
||||
Tell Pi what to optimize. It runs experiments, benchmarks each one, keeps improvements, reverts regressions, and repeats — all autonomously. A built-in dashboard tracks every run with confidence scoring to distinguish real gains from benchmark noise.
|
||||
|
||||
```bash
|
||||
/autoresearch optimize unit test runtime
|
||||
```
|
||||
|
||||
Each kept experiment is automatically committed. Each failed one is reverted. When you're done, Pi can group improvements into independent branches for clean review and merge.
|
||||
|
||||
## Manual setup
|
||||
|
||||
Add a configuration block to `~/.pi/agent/models.json`:
|
||||
|
||||
```json
|
||||
{
|
||||
"providers": {
|
||||
"ollama": {
|
||||
"baseUrl": "http://localhost:11434/v1",
|
||||
"api": "openai-completions",
|
||||
"apiKey": "ollama",
|
||||
"models": [
|
||||
{
|
||||
"id": "qwen3-coder"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Update `~/.pi/agent/settings.json` to set the default provider:
|
||||
|
||||
```json
|
||||
{
|
||||
"defaultProvider": "ollama",
|
||||
"defaultModel": "qwen3-coder"
|
||||
}
|
||||
```
|
||||
@@ -2,33 +2,84 @@
|
||||
title: VS Code
|
||||
---
|
||||
|
||||
## Install
|
||||
VS Code includes built-in AI chat through GitHub Copilot Chat. Ollama models can be used directly in the Copilot Chat model picker.
|
||||
|
||||
Install [VS Code](https://code.visualstudio.com/download).
|
||||
|
||||
## Usage with Ollama
|
||||
|
||||
|
||||
1. Open Copilot side bar found in top right window
|
||||
|
||||
## Prerequisites
|
||||
|
||||
- Ollama v0.18.3+
|
||||
- [VS Code 1.113+](https://code.visualstudio.com/download)
|
||||
- [GitHub Copilot Chat extension 0.41.0+](https://marketplace.visualstudio.com/items?itemName=GitHub.copilot-chat)
|
||||
|
||||
<Note> VS Code requires you to be logged in to use its model selector, even for custom models. This doesn't require a paid GitHub Copilot account; GitHub Copilot Free will enable model selection for custom models.</Note>
|
||||
|
||||
## Quick setup
|
||||
|
||||
```shell
|
||||
ollama launch vscode
|
||||
```
|
||||
|
||||
Recommended models will be shown after running the command. See the latest models at [ollama.com](https://ollama.com/search?c=tools).
|
||||
|
||||
Make sure **Local** is selected at the bottom of the Copilot Chat panel to use your Ollama models.
|
||||
<div style={{ display: "flex", justifyContent: "center" }}>
|
||||
<img
|
||||
src="/images/local.png"
|
||||
alt="Ollama Local Models"
|
||||
width="60%"
|
||||
style={{ borderRadius: "4px", marginTop: "10px", marginBottom: "10px" }}
|
||||
/>
|
||||
</div>
|
||||
|
||||
|
||||
## Run directly with a model
|
||||
|
||||
```shell
|
||||
ollama launch vscode --model qwen3.5:cloud
|
||||
```
|
||||
Cloud models are also available at [ollama.com](https://ollama.com/search?c=cloud).
|
||||
|
||||
## Manual setup
|
||||
|
||||
To configure Ollama manually without `ollama launch`:
|
||||
|
||||
1. Open the **Copilot Chat** side bar from the top right corner
|
||||
<div style={{ display: "flex", justifyContent: "center" }}>
|
||||
<img
|
||||
src="/images/vscode-sidebar.png"
|
||||
alt="VS Code chat Sidebar"
|
||||
width="75%"
|
||||
style={{ borderRadius: "4px" }}
|
||||
/>
|
||||
</div>
|
||||
2. Select the model dropdown > **Manage models**
|
||||
2. Click the **settings gear icon** (<Icon icon="gear" />) to bring up the Language Models window
|
||||
<div style={{ display: "flex", justifyContent: "center" }}>
|
||||
<img
|
||||
src="/images/vscode-models.png"
|
||||
src="/images/vscode-other-models.png"
|
||||
alt="VS Code model picker"
|
||||
width="75%"
|
||||
style={{ borderRadius: "4px" }}
|
||||
/>
|
||||
</div>
|
||||
3. Enter **Ollama** under **Provider Dropdown** and select desired models (e.g `qwen3, qwen3-coder:480b-cloud`)
|
||||
3. Click **Add Models** and select **Ollama** to load all your Ollama models into VS Code
|
||||
<div style={{ display: "flex", justifyContent: "center" }}>
|
||||
<img
|
||||
src="/images/vscode-model-options.png"
|
||||
alt="VS Code model options dropdown"
|
||||
src="/images/vscode-add-ollama.png"
|
||||
alt="VS Code model options dropdown to add ollama models"
|
||||
width="75%"
|
||||
style={{ borderRadius: "4px" }}
|
||||
/>
|
||||
</div>
|
||||
|
||||
4. Click the **Unhide** button in the model picker to show your Ollama models
|
||||
<div style={{ display: "flex", justifyContent: "center" }}>
|
||||
<img
|
||||
src="/images/vscode-unhide.png"
|
||||
alt="VS Code unhide models button"
|
||||
width="75%"
|
||||
style={{ borderRadius: "4px" }}
|
||||
/>
|
||||
</div>
|
||||
|
||||
+9
-9
@@ -20,8 +20,8 @@ curl -fsSL https://ollama.com/install.sh | sh
|
||||
Download and extract the package:
|
||||
|
||||
```shell
|
||||
curl -fsSL https://ollama.com/download/ollama-linux-amd64.tgz \
|
||||
| sudo tar zx -C /usr
|
||||
curl -fsSL https://ollama.com/download/ollama-linux-amd64.tar.zst \
|
||||
| sudo tar x -C /usr
|
||||
```
|
||||
|
||||
Start Ollama:
|
||||
@@ -41,8 +41,8 @@ ollama -v
|
||||
If you have an AMD GPU, also download and extract the additional ROCm package:
|
||||
|
||||
```shell
|
||||
curl -fsSL https://ollama.com/download/ollama-linux-amd64-rocm.tgz \
|
||||
| sudo tar zx -C /usr
|
||||
curl -fsSL https://ollama.com/download/ollama-linux-amd64-rocm.tar.zst \
|
||||
| sudo tar x -C /usr
|
||||
```
|
||||
|
||||
### ARM64 install
|
||||
@@ -50,8 +50,8 @@ curl -fsSL https://ollama.com/download/ollama-linux-amd64-rocm.tgz \
|
||||
Download and extract the ARM64-specific package:
|
||||
|
||||
```shell
|
||||
curl -fsSL https://ollama.com/download/ollama-linux-arm64.tgz \
|
||||
| sudo tar zx -C /usr
|
||||
curl -fsSL https://ollama.com/download/ollama-linux-arm64.tar.zst \
|
||||
| sudo tar x -C /usr
|
||||
```
|
||||
|
||||
### Adding Ollama as a startup service (recommended)
|
||||
@@ -101,7 +101,7 @@ nvidia-smi
|
||||
|
||||
### Install AMD ROCm drivers (optional)
|
||||
|
||||
[Download and Install](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html) ROCm v6.
|
||||
[Download and Install](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html) ROCm v7.
|
||||
|
||||
### Start Ollama
|
||||
|
||||
@@ -146,8 +146,8 @@ curl -fsSL https://ollama.com/install.sh | sh
|
||||
Or by re-downloading Ollama:
|
||||
|
||||
```shell
|
||||
curl -fsSL https://ollama.com/download/ollama-linux-amd64.tgz \
|
||||
| sudo tar zx -C /usr
|
||||
curl -fsSL https://ollama.com/download/ollama-linux-amd64.tar.zst \
|
||||
| sudo tar x -C /usr
|
||||
```
|
||||
|
||||
## Installing specific versions
|
||||
|
||||
@@ -41,6 +41,7 @@ INSTRUCTION arguments
|
||||
| [`ADAPTER`](#adapter) | Defines the (Q)LoRA adapters to apply to the model. |
|
||||
| [`LICENSE`](#license) | Specifies the legal license. |
|
||||
| [`MESSAGE`](#message) | Specify message history. |
|
||||
| [`REQUIRES`](#requires) | Specify the minimum version of Ollama required by the model. |
|
||||
|
||||
## Examples
|
||||
|
||||
@@ -248,6 +249,16 @@ MESSAGE user Is Ontario in Canada?
|
||||
MESSAGE assistant yes
|
||||
```
|
||||
|
||||
### REQUIRES
|
||||
|
||||
The `REQUIRES` instruction allows you to specify the minimum version of Ollama required by the model.
|
||||
|
||||
```
|
||||
REQUIRES <version>
|
||||
```
|
||||
|
||||
The version should be a valid Ollama version (e.g. 0.14.0).
|
||||
|
||||
## Notes
|
||||
|
||||
- the **`Modelfile` is not case sensitive**. In the examples, uppercase instructions are used to make it easier to distinguish it from arguments.
|
||||
|
||||
+15
-1
@@ -596,6 +596,15 @@ components:
|
||||
name:
|
||||
type: string
|
||||
description: Model name
|
||||
model:
|
||||
type: string
|
||||
description: Model name
|
||||
remote_model:
|
||||
type: string
|
||||
description: Name of the upstream model, if the model is remote
|
||||
remote_host:
|
||||
type: string
|
||||
description: URL of the upstream Ollama host, if the model is remote
|
||||
modified_at:
|
||||
type: string
|
||||
description: Last modified timestamp in ISO 8601 format
|
||||
@@ -636,6 +645,9 @@ components:
|
||||
Ps:
|
||||
type: object
|
||||
properties:
|
||||
name:
|
||||
type: string
|
||||
description: Name of the running model
|
||||
model:
|
||||
type: string
|
||||
description: Name of the running model
|
||||
@@ -1137,6 +1149,7 @@ paths:
|
||||
example:
|
||||
models:
|
||||
- name: "gemma3"
|
||||
model: "gemma3"
|
||||
modified_at: "2025-10-03T23:34:03.409490317-07:00"
|
||||
size: 3338801804
|
||||
digest: "a2af6cc3eb7fa8be8504abaf9b04e88f17a119ec3f04a3addf55f92841195f5a"
|
||||
@@ -1168,7 +1181,8 @@ paths:
|
||||
$ref: "#/components/schemas/PsResponse"
|
||||
example:
|
||||
models:
|
||||
- model: "gemma3"
|
||||
- name: "gemma3"
|
||||
model: "gemma3"
|
||||
size: 6591830464
|
||||
digest: "a2af6cc3eb7fa8be8504abaf9b04e88f17a119ec3f04a3addf55f92841195f5a"
|
||||
details:
|
||||
|
||||
+37
-73
@@ -2,7 +2,7 @@
|
||||
title: Quickstart
|
||||
---
|
||||
|
||||
This quickstart will walk your through running your first model with Ollama. To get started, download Ollama on macOS, Windows or Linux.
|
||||
Ollama is available on macOS, Windows, and Linux.
|
||||
|
||||
<a
|
||||
href="https://ollama.com/download"
|
||||
@@ -12,92 +12,56 @@ This quickstart will walk your through running your first model with Ollama. To
|
||||
Download Ollama
|
||||
</a>
|
||||
|
||||
## Run a model
|
||||
## Get Started
|
||||
|
||||
<Tabs>
|
||||
<Tab title="CLI">
|
||||
Open a terminal and run the command:
|
||||
Run `ollama` in your terminal to open the interactive menu:
|
||||
|
||||
```
|
||||
ollama run gemma3
|
||||
```
|
||||
```sh
|
||||
ollama
|
||||
```
|
||||
|
||||
</Tab>
|
||||
<Tab title="cURL">
|
||||
```
|
||||
ollama pull gemma3
|
||||
```
|
||||
Navigate with `↑/↓`, press `enter` to launch, `→` to change model, and `esc` to quit.
|
||||
|
||||
Lastly, chat with the model:
|
||||
The menu provides quick access to:
|
||||
- **Run a model** - Start an interactive chat
|
||||
- **Launch tools** - Claude Code, Codex, OpenClaw, and more
|
||||
- **Additional integrations** - Available under "More..."
|
||||
|
||||
```shell
|
||||
curl http://localhost:11434/api/chat -d '{
|
||||
"model": "gemma3",
|
||||
"messages": [{
|
||||
"role": "user",
|
||||
"content": "Hello there!"
|
||||
}],
|
||||
"stream": false
|
||||
}'
|
||||
```
|
||||
## Assistants
|
||||
|
||||
</Tab>
|
||||
<Tab title="Python">
|
||||
Start by downloading a model:
|
||||
Launch [OpenClaw](/integrations/openclaw), a personal AI with 100+ skills:
|
||||
|
||||
```
|
||||
ollama pull gemma3
|
||||
```
|
||||
```sh
|
||||
ollama launch openclaw
|
||||
```
|
||||
|
||||
Then install Ollama's Python library:
|
||||
## Coding
|
||||
|
||||
```
|
||||
pip install ollama
|
||||
```
|
||||
Launch [Claude Code](/integrations/claude-code) and other coding tools with Ollama models:
|
||||
|
||||
Lastly, chat with the model:
|
||||
```sh
|
||||
ollama launch claude
|
||||
```
|
||||
|
||||
```python
|
||||
from ollama import chat
|
||||
from ollama import ChatResponse
|
||||
```sh
|
||||
ollama launch codex
|
||||
```
|
||||
|
||||
response: ChatResponse = chat(model='gemma3', messages=[
|
||||
{
|
||||
'role': 'user',
|
||||
'content': 'Why is the sky blue?',
|
||||
},
|
||||
])
|
||||
print(response['message']['content'])
|
||||
# or access fields directly from the response object
|
||||
print(response.message.content)
|
||||
```
|
||||
```sh
|
||||
ollama launch opencode
|
||||
```
|
||||
|
||||
</Tab>
|
||||
<Tab title="JavaScript">
|
||||
Start by downloading a model:
|
||||
See [integrations](/integrations) for all supported tools.
|
||||
|
||||
```
|
||||
ollama pull gemma3
|
||||
```
|
||||
## API
|
||||
|
||||
Then install the Ollama JavaScript library:
|
||||
```
|
||||
npm i ollama
|
||||
```
|
||||
Use the [API](/api) to integrate Ollama into your applications:
|
||||
|
||||
Lastly, chat with the model:
|
||||
```sh
|
||||
curl http://localhost:11434/api/chat -d '{
|
||||
"model": "gemma3",
|
||||
"messages": [{ "role": "user", "content": "Hello!" }]
|
||||
}'
|
||||
```
|
||||
|
||||
```shell
|
||||
import ollama from 'ollama'
|
||||
|
||||
const response = await ollama.chat({
|
||||
model: 'gemma3',
|
||||
messages: [{ role: 'user', content: 'Why is the sky blue?' }],
|
||||
})
|
||||
console.log(response.message.content)
|
||||
```
|
||||
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
See a full list of available models [here](https://ollama.com/models).
|
||||
See the [API documentation](/api) for Python, JavaScript, and other integrations.
|
||||
|
||||
@@ -1,3 +0,0 @@
|
||||
# Troubleshooting
|
||||
|
||||
For troubleshooting, see [https://docs.ollama.com/troubleshooting](https://docs.ollama.com/troubleshooting)
|
||||
@@ -87,7 +87,7 @@ When Ollama starts up, it takes inventory of the GPUs present in the system to d
|
||||
|
||||
### Linux NVIDIA Troubleshooting
|
||||
|
||||
If you are using a container to run Ollama, make sure you've set up the container runtime first as described in [docker.md](./docker.md)
|
||||
If you are using a container to run Ollama, make sure you've set up the container runtime first as described in [docker](./docker)
|
||||
|
||||
Sometimes the Ollama can have difficulties initializing the GPU. When you check the server logs, this can show up as various error codes, such as "3" (not initialized), "46" (device unavailable), "100" (no device), "999" (unknown), or others. The following troubleshooting techniques may help resolve the problem
|
||||
|
||||
@@ -114,6 +114,25 @@ If you are experiencing problems getting Ollama to correctly discover or use you
|
||||
- `OLLAMA_DEBUG=1` During GPU discovery additional information will be reported
|
||||
- Check dmesg for any errors from amdgpu or kfd drivers `sudo dmesg | grep -i amdgpu` and `sudo dmesg | grep -i kfd`
|
||||
|
||||
### AMD Driver Version Mismatch
|
||||
|
||||
If your AMD GPU is not detected on Linux and the server logs contain messages like:
|
||||
|
||||
```
|
||||
msg="failure during GPU discovery" ... error="failed to finish discovery before timeout"
|
||||
msg="bootstrap discovery took" duration=30s ...
|
||||
```
|
||||
|
||||
This typically means the system's AMD GPU driver is too old. Ollama bundles
|
||||
ROCm 7 linux libraries which require a compatible ROCm 7 kernel driver. If the
|
||||
system is running an older driver (ROCm 6.x or earlier), GPU initialization
|
||||
will hang during device discovery and eventually time out, causing Ollama to
|
||||
fall back to CPU.
|
||||
|
||||
To resolve this, upgrade to the ROCm v7 driver using the `amdgpu-install`
|
||||
utility from [AMD's ROCm documentation](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/).
|
||||
After upgrading, reboot and restart Ollama.
|
||||
|
||||
## Multiple AMD GPUs
|
||||
|
||||
If you experience gibberish responses when models load across multiple AMD GPUs on Linux, see the following guide.
|
||||
|
||||
+7
-3
@@ -80,9 +80,13 @@ help you keep up to date.
|
||||
|
||||
If you'd like to install or integrate Ollama as a service, a standalone
|
||||
`ollama-windows-amd64.zip` zip file is available containing only the Ollama CLI
|
||||
and GPU library dependencies for Nvidia. If you have an AMD GPU, also download
|
||||
and extract the additional ROCm package `ollama-windows-amd64-rocm.zip` into the
|
||||
same directory. This allows for embedding Ollama in existing applications, or
|
||||
and GPU library dependencies for Nvidia. Depending on your hardware, you may also
|
||||
need to download and extract additional packages into the same directory:
|
||||
|
||||
- **AMD GPU**: `ollama-windows-amd64-rocm.zip`
|
||||
- **MLX (CUDA)**: `ollama-windows-amd64-mlx.zip`
|
||||
|
||||
This allows for embedding Ollama in existing applications, or
|
||||
running it as a system service via `ollama serve` with tools such as
|
||||
[NSSM](https://nssm.cc/).
|
||||
|
||||
|
||||
Reference in New Issue
Block a user