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 The Librarian
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+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
|
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**Sync Date:** 2025-12-12
|
||||
**Commit:** 948f69330acf96a2310f1b53fdfc211731a386d8
|
||||
**Sync Date:** 2026-08-12
|
||||
**Content:** Paths: docs
|
||||
|
||||
---
|
||||
|
||||
+2
-1
@@ -14,9 +14,10 @@
|
||||
* [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
|
||||
|
||||
* [Troubleshooting Guide](https://docs.ollama.com/troubleshooting)
|
||||
* [FAQ](https://docs.ollama.com/faq#faq)
|
||||
* [FAQ](https://docs.ollama.com/faq)
|
||||
* [Development guide](./development.md)
|
||||
|
||||
+9
-6
@@ -45,7 +45,7 @@ Generate a response for a given prompt with a provided model. This is a streamin
|
||||
- `prompt`: the prompt to generate a response for
|
||||
- `suffix`: the text after the model response
|
||||
- `images`: (optional) a list of base64-encoded images (for multimodal models such as `llava`)
|
||||
- `think`: (for thinking models) should the model think before responding?
|
||||
- `think`: (for thinking models) should the model think before responding? Can be a boolean or a thinking level (`"low"`, `"medium"`, `"high"`, or `"max"`).
|
||||
|
||||
Advanced parameters (optional):
|
||||
|
||||
@@ -388,6 +388,7 @@ curl http://localhost:11434/api/generate -d '{
|
||||
"num_keep": 5,
|
||||
"seed": 42,
|
||||
"num_predict": 100,
|
||||
"draft_num_predict": 4,
|
||||
"top_k": 20,
|
||||
"top_p": 0.9,
|
||||
"min_p": 0.0,
|
||||
@@ -493,7 +494,7 @@ Generate the next message in a chat with a provided model. This is a streaming e
|
||||
- `model`: (required) the [model name](#model-names)
|
||||
- `messages`: the messages of the chat, this can be used to keep a chat memory
|
||||
- `tools`: list of tools in JSON for the model to use if supported
|
||||
- `think`: (for thinking models) should the model think before responding?
|
||||
- `think`: (for thinking models) should the model think before responding? Can be a boolean or a thinking level (`"low"`, `"medium"`, `"high"`, or `"max"`).
|
||||
|
||||
The `message` object has the following fields:
|
||||
|
||||
@@ -895,11 +896,11 @@ curl http://localhost:11434/api/chat -d '{
|
||||
"tool_calls": [
|
||||
{
|
||||
"function": {
|
||||
"name": "get_temperature",
|
||||
"name": "get_weather",
|
||||
"arguments": {
|
||||
"city": "Toronto"
|
||||
}
|
||||
},
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
@@ -907,7 +908,7 @@ curl http://localhost:11434/api/chat -d '{
|
||||
{
|
||||
"role": "tool",
|
||||
"content": "11 degrees celsius",
|
||||
"tool_name": "get_temperature",
|
||||
"tool_name": "get_weather"
|
||||
}
|
||||
],
|
||||
"stream": false,
|
||||
@@ -1178,7 +1179,7 @@ Create a model from:
|
||||
- a safetensors directory; or
|
||||
- a GGUF file.
|
||||
|
||||
If you are creating a model from a safetensors directory or from a GGUF file, you must [create a blob](#create-a-blob) for each of the files and then use the file name and SHA256 digest associated with each blob in the `files` field.
|
||||
If you are creating a model from a safetensors directory or from a GGUF file, you must [push a blob](#push-a-blob) for each of the files and then use the file name and SHA256 digest associated with each blob in the `files` field.
|
||||
|
||||
### Parameters
|
||||
|
||||
@@ -1187,6 +1188,8 @@ If you are creating a model from a safetensors directory or from a GGUF file, yo
|
||||
- `files`: (optional) a dictionary of file names to SHA256 digests of blobs to create the model from
|
||||
- `adapters`: (optional) a dictionary of file names to SHA256 digests of blobs for LORA adapters
|
||||
- `template`: (optional) the prompt template for the model
|
||||
- `renderer`: (optional) the name of the renderer for the model
|
||||
- `parser`: (optional) the name of the parser for the model
|
||||
- `license`: (optional) a string or list of strings containing the license or licenses for the model
|
||||
- `system`: (optional) a string containing the system prompt for the model
|
||||
- `parameters`: (optional) a dictionary of parameters for the model (see [Modelfile](./modelfile.mdx#valid-parameters-and-values) for a list of parameters)
|
||||
|
||||
@@ -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 |
|
||||
+4
-4
@@ -28,9 +28,9 @@ Errors are returned in the `application/json` format with the following structur
|
||||
If an error occurs mid-stream, the error will be returned as an object in the `application/x-ndjson` format with an `error` property. Since the response has already started, the status code of the response will not be changed.
|
||||
|
||||
```json
|
||||
{"model":"gemma3","created_at":"2025-10-26T17:21:21.196249Z","response":" Yes","done":false}
|
||||
{"model":"gemma3","created_at":"2025-10-26T17:21:21.207235Z","response":".","done":false}
|
||||
{"model":"gemma3","created_at":"2025-10-26T17:21:21.219166Z","response":"I","done":false}
|
||||
{"model":"gemma3","created_at":"2025-10-26T17:21:21.231094Z","response":"can","done":false}
|
||||
{"model":"gemma4","created_at":"2025-10-26T17:21:21.196249Z","response":" Yes","done":false}
|
||||
{"model":"gemma4","created_at":"2025-10-26T17:21:21.207235Z","response":".","done":false}
|
||||
{"model":"gemma4","created_at":"2025-10-26T17:21:21.219166Z","response":"I","done":false}
|
||||
{"model":"gemma4","created_at":"2025-10-26T17:21:21.231094Z","response":"can","done":false}
|
||||
{"error":"an error was encountered while running the model"}
|
||||
```
|
||||
|
||||
@@ -2,11 +2,11 @@
|
||||
title: Introduction
|
||||
---
|
||||
|
||||
Ollama's API allows you to run and interact with models programatically.
|
||||
Use Ollama's API to run and interact with models.
|
||||
|
||||
## Get started
|
||||
|
||||
If you're just getting started, follow the [quickstart](/quickstart) documentation to get up and running with Ollama's API.
|
||||
Follow the [quickstart](/quickstart) to install Ollama and make your first request.
|
||||
|
||||
## Base URL
|
||||
|
||||
@@ -16,7 +16,7 @@ After installation, Ollama's API is served by default at:
|
||||
http://localhost:11434/api
|
||||
```
|
||||
|
||||
For running cloud models on **ollama.com**, the same API is available with the following base URL:
|
||||
For running cloud models on [ollama.com](https://ollama.com), the same API is available with the following base URL:
|
||||
|
||||
```
|
||||
https://ollama.com/api
|
||||
@@ -28,7 +28,7 @@ Once Ollama is running, its API is automatically available and can be accessed v
|
||||
|
||||
```shell
|
||||
curl http://localhost:11434/api/generate -d '{
|
||||
"model": "gemma3",
|
||||
"model": "gemma4",
|
||||
"prompt": "Why is the sky blue?"
|
||||
}'
|
||||
```
|
||||
@@ -40,7 +40,7 @@ Ollama has official libraries for Python and JavaScript:
|
||||
- [Python](https://github.com/ollama/ollama-python)
|
||||
- [JavaScript](https://github.com/ollama/ollama-js)
|
||||
|
||||
Several community-maintained libraries are available for Ollama. For a full list, see the [Ollama GitHub repository](https://github.com/ollama/ollama?tab=readme-ov-file#libraries-1).
|
||||
Several community-maintained libraries are available for Ollama. For a full list, see the [Ollama GitHub repository](https://github.com/ollama/ollama?tab=readme-ov-file#libraries--sdks).
|
||||
|
||||
## Versioning
|
||||
|
||||
|
||||
@@ -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"`, `"max"`, `"none"`)
|
||||
- [x] `reasoning`
|
||||
- [x] `effort` (`"high"`, `"medium"`, `"low"`, `"max"`, `"none"`)
|
||||
- [ ] `tool_choice`
|
||||
- [ ] `logit_bias`
|
||||
- [ ] `user`
|
||||
@@ -277,6 +281,8 @@ curl -X POST http://localhost:11434/v1/chat/completions \
|
||||
|
||||
### `/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
|
||||
|
||||
@@ -5,13 +5,13 @@ title: Streaming
|
||||
Certain API endpoints stream responses by default, such as `/api/generate`. These responses are provided in the newline-delimited JSON format (i.e. the `application/x-ndjson` content type). For example:
|
||||
|
||||
```json
|
||||
{"model":"gemma3","created_at":"2025-10-26T17:15:24.097767Z","response":"That","done":false}
|
||||
{"model":"gemma3","created_at":"2025-10-26T17:15:24.109172Z","response":"'","done":false}
|
||||
{"model":"gemma3","created_at":"2025-10-26T17:15:24.121485Z","response":"s","done":false}
|
||||
{"model":"gemma3","created_at":"2025-10-26T17:15:24.132802Z","response":" a","done":false}
|
||||
{"model":"gemma3","created_at":"2025-10-26T17:15:24.143931Z","response":" fantastic","done":false}
|
||||
{"model":"gemma3","created_at":"2025-10-26T17:15:24.155176Z","response":" question","done":false}
|
||||
{"model":"gemma3","created_at":"2025-10-26T17:15:24.166576Z","response":"!","done":true, "done_reason": "stop"}
|
||||
{"model":"gemma4","created_at":"2025-10-26T17:15:24.097767Z","response":"That","done":false}
|
||||
{"model":"gemma4","created_at":"2025-10-26T17:15:24.109172Z","response":"'","done":false}
|
||||
{"model":"gemma4","created_at":"2025-10-26T17:15:24.121485Z","response":"s","done":false}
|
||||
{"model":"gemma4","created_at":"2025-10-26T17:15:24.132802Z","response":" a","done":false}
|
||||
{"model":"gemma4","created_at":"2025-10-26T17:15:24.143931Z","response":" fantastic","done":false}
|
||||
{"model":"gemma4","created_at":"2025-10-26T17:15:24.155176Z","response":" question","done":false}
|
||||
{"model":"gemma4","created_at":"2025-10-26T17:15:24.166576Z","response":"!","done":true, "done_reason": "stop"}
|
||||
```
|
||||
|
||||
## Disabling streaming
|
||||
@@ -19,7 +19,7 @@ Certain API endpoints stream responses by default, such as `/api/generate`. Thes
|
||||
Streaming can be disabled by providing `{"stream": false}` in the request body for any endpoint that support streaming. This will cause responses to be returned in the `application/json` format instead:
|
||||
|
||||
```json
|
||||
{"model":"gemma3","created_at":"2025-10-26T17:15:24.166576Z","response":"That's a fantastic question!","done":true}
|
||||
{"model":"gemma4","created_at":"2025-10-26T17:15:24.166576Z","response":"That's a fantastic question!","done":true}
|
||||
```
|
||||
|
||||
## When to use streaming vs non-streaming
|
||||
|
||||
+1
-1
@@ -19,7 +19,7 @@ For endpoints that return usage metrics, the response body will include the usag
|
||||
|
||||
```json
|
||||
{
|
||||
"model": "gemma3",
|
||||
"model": "gemma4",
|
||||
"created_at": "2025-10-17T23:14:07.414671Z",
|
||||
"response": "Hello! How can I help you today?",
|
||||
"done": true,
|
||||
|
||||
@@ -2,6 +2,10 @@
|
||||
title: Structured Outputs
|
||||
---
|
||||
|
||||
<Note>
|
||||
Ollama's Cloud currently does not support structured outputs.
|
||||
</Note>
|
||||
|
||||
Structured outputs let you enforce a JSON schema on model responses so you can reliably extract structured data, describe images, or keep every reply consistent.
|
||||
|
||||
## Generating structured JSON
|
||||
@@ -96,12 +100,11 @@ Provide a JSON schema to the `format` field.
|
||||
```
|
||||
</Tab>
|
||||
<Tab title="JavaScript">
|
||||
Serialize a Zod schema with `zodToJsonSchema()` and parse the structured response:
|
||||
Serialize a Zod schema with `z.toJSONSchema()` and parse the structured response:
|
||||
|
||||
```javascript
|
||||
import ollama from 'ollama'
|
||||
import { z } from 'zod'
|
||||
import { zodToJsonSchema } from 'zod-to-json-schema'
|
||||
import * as z from 'zod'
|
||||
|
||||
const Country = z.object({
|
||||
name: z.string(),
|
||||
@@ -112,7 +115,7 @@ Provide a JSON schema to the `format` field.
|
||||
const response = await ollama.chat({
|
||||
model: 'gpt-oss',
|
||||
messages: [{ role: 'user', content: 'Tell me about Canada.' }],
|
||||
format: zodToJsonSchema(Country),
|
||||
format: z.toJSONSchema(Country),
|
||||
})
|
||||
|
||||
const country = Country.parse(JSON.parse(response.message.content))
|
||||
@@ -173,7 +176,7 @@ class ImageDescription(BaseModel):
|
||||
text_content: Optional[str] = None
|
||||
|
||||
response = chat(
|
||||
model='gemma3',
|
||||
model='gemma4',
|
||||
messages=[{
|
||||
'role': 'user',
|
||||
'content': 'Describe this photo and list the objects you detect.',
|
||||
|
||||
@@ -16,7 +16,7 @@ Use this capability to audit model steps, animate the model *thinking* in a UI,
|
||||
|
||||
## Enable thinking in API calls
|
||||
|
||||
Set the `think` field on chat or generate requests. Most models accept booleans (`true`/`false`).
|
||||
Set the `think` field on chat or generate requests. Most models accept booleans (`true`/`false`) or levels (`low`, `medium`, `high`, `max`), where `max` requests the highest thinking level.
|
||||
|
||||
GPT-OSS instead expects one of `low`, `medium`, or `high` to tune the trace length.
|
||||
|
||||
|
||||
@@ -7,7 +7,7 @@ Vision models accept images alongside text so the model can describe, classify,
|
||||
## Quick start
|
||||
|
||||
```shell
|
||||
ollama run gemma3 ./image.png whats in this image?
|
||||
ollama run gemma4 ./image.png whats in this image?
|
||||
```
|
||||
|
||||
|
||||
@@ -28,7 +28,7 @@ Provide an `images` array. SDKs accept file paths, URLs or raw bytes while the R
|
||||
curl -X POST http://localhost:11434/api/chat \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"model": "gemma3",
|
||||
"model": "gemma4",
|
||||
"messages": [{
|
||||
"role": "user",
|
||||
"content": "What is in this image?",
|
||||
@@ -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">
|
||||
@@ -53,7 +52,7 @@ Provide an `images` array. SDKs accept file paths, URLs or raw bytes while the R
|
||||
# img = Path(path).read_bytes()
|
||||
|
||||
response = chat(
|
||||
model='gemma3',
|
||||
model='gemma4',
|
||||
messages=[
|
||||
{
|
||||
'role': 'user',
|
||||
@@ -72,7 +71,7 @@ Provide an `images` array. SDKs accept file paths, URLs or raw bytes while the R
|
||||
|
||||
const imagePath = '/absolute/path/to/image.jpg'
|
||||
const response = await ollama.chat({
|
||||
model: 'gemma3',
|
||||
model: 'gemma4',
|
||||
messages: [
|
||||
{ role: 'user', content: 'What is in this image?', images: [imagePath] }
|
||||
],
|
||||
|
||||
@@ -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));
|
||||
```
|
||||
|
||||
|
||||
+48
-6
@@ -5,7 +5,49 @@ title: CLI Reference
|
||||
### Run a model
|
||||
|
||||
```
|
||||
ollama run gemma3
|
||||
ollama run gemma4
|
||||
```
|
||||
|
||||
### 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
|
||||
@@ -22,7 +64,7 @@ I'm a basic program that prints the famous "Hello, world!" message to the consol
|
||||
#### Multimodal models
|
||||
|
||||
```
|
||||
ollama run gemma3 "What's in this image? /Users/jmorgan/Desktop/smile.png"
|
||||
ollama run gemma4 "What's in this image? /Users/jmorgan/Desktop/smile.png"
|
||||
```
|
||||
|
||||
### Generate embeddings
|
||||
@@ -40,13 +82,13 @@ echo "Hello world" | ollama run nomic-embed-text
|
||||
### Download a model
|
||||
|
||||
```
|
||||
ollama pull gemma3
|
||||
ollama pull gemma4
|
||||
```
|
||||
|
||||
### Remove a model
|
||||
|
||||
```
|
||||
ollama rm gemma3
|
||||
ollama rm gemma4
|
||||
```
|
||||
|
||||
### List models
|
||||
@@ -72,7 +114,7 @@ ollama signout
|
||||
First, create a `Modelfile`
|
||||
|
||||
```
|
||||
FROM gemma3
|
||||
FROM gemma4
|
||||
SYSTEM """You are a happy cat."""
|
||||
```
|
||||
|
||||
@@ -91,7 +133,7 @@ ollama ps
|
||||
### Stop a running model
|
||||
|
||||
```
|
||||
ollama stop gemma3
|
||||
ollama stop gemma4
|
||||
```
|
||||
|
||||
### Start Ollama
|
||||
|
||||
+64
-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 +226,67 @@ 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) features.
|
||||
|
||||
## Retirements
|
||||
|
||||
Ollama will occasionally deprecate and retire older cloud models as newer and better open-source models are released.
|
||||
Tools and applications relying on Ollama Cloud models may need to be updated to keep working. Impacted users will be
|
||||
notified in advance of model deprecation and retirement. Deprecations will be communicated through email and on the
|
||||
Ollama website.
|
||||
|
||||
Ollama Cloud model retirement does not affect local models.
|
||||
|
||||
### Upcoming retirements
|
||||
|
||||
| Retirement date | Model | Recommended alternative |
|
||||
| --- | --- | --- |
|
||||
| July 31, 2026 | `minimax-m2.5` | `minimax-m2.7` |
|
||||
| July 31, 2026 | `kimi-k2.5` | `kimi-k2.6` |
|
||||
|
||||
### Past retirements
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="July 15, 2026">
|
||||
| Model | Recommended alternative |
|
||||
| --- | --- |
|
||||
| `deepseek-v3.1:671b` | `deepseek-v4-flash` |
|
||||
| `deepseek-v3.2` | `deepseek-v4-flash` |
|
||||
| `devstral-2:123b` | `mistral-large-3:675b` |
|
||||
| `devstral-small-2:24b` | |
|
||||
| `ministral-3:14b` | |
|
||||
| `ministral-3:3b` | |
|
||||
| `ministral-3:8b` | |
|
||||
| `gemini-3-flash-preview` | `minimax-m3` |
|
||||
| `gemma3:12b` | `gemma4:31b` |
|
||||
| `gemma3:27b` | `gemma4:31b` |
|
||||
| `gemma3:4b` | `gemma4:31b` |
|
||||
| `glm-4.7` | `glm-5.2` |
|
||||
| `glm-5` | `glm-5.2` |
|
||||
| `minimax-m2.1` | `minimax-m3` |
|
||||
| `qwen3-coder-next` | `qwen3.5:397b` |
|
||||
| `qwen3-coder:480b` | `qwen3.5:397b` |
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="June 30, 2026">
|
||||
| Model | Recommended alternative |
|
||||
| --- | --- |
|
||||
| `rnj-1:8b` | |
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="June 16, 2026">
|
||||
| Model | Recommended alternative |
|
||||
| --- | --- |
|
||||
| `kimi-k2-thinking` | `kimi-k2.6` |
|
||||
| `kimi-k2:1t` | `kimi-k2.6` |
|
||||
| `minimax-m2` | `minimax-m3` |
|
||||
| `glm-4.6` | `glm-5.1` |
|
||||
| `qwen3-next:80b` | `qwen3.5` |
|
||||
| `qwen3-vl:235b` | `qwen3.5` |
|
||||
| `qwen3-vl:235b-instruct` | `qwen3.5` |
|
||||
| `cogito-2.1:671b` | `deepseek-v4-flash` |
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
@@ -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
|
||||
@@ -34,5 +37,5 @@ ollama ps
|
||||
```
|
||||
```
|
||||
NAME ID SIZE PROCESSOR CONTEXT UNTIL
|
||||
gemma3:latest a2af6cc3eb7f 6.6 GB 100% GPU 65536 2 minutes from now
|
||||
gemma4:latest c6eb396dbd59 9.6 GB 100% GPU 131072 2 minutes from now
|
||||
```
|
||||
|
||||
+97
-89
@@ -3,9 +3,11 @@
|
||||
Install prerequisites:
|
||||
|
||||
- [Go](https://go.dev/doc/install)
|
||||
- C/C++ Compiler e.g. Clang on macOS, [TDM-GCC](https://github.com/jmeubank/tdm-gcc/releases/latest) (Windows amd64) or [llvm-mingw](https://github.com/mstorsjo/llvm-mingw) (Windows arm64), GCC/Clang on Linux.
|
||||
- [CMake](https://cmake.org/download/) 3.24 or newer
|
||||
- C/C++ compiler: Clang on macOS, Visual Studio 2022 C++ tools on Windows, or GCC/Clang on Linux
|
||||
- [Ninja](https://github.com/ninja-build/ninja/releases) in `PATH` is recommended, especially on Windows
|
||||
|
||||
Then build and run Ollama from the root directory of the repository:
|
||||
For pure Go iteration against an existing native payload, run Ollama from the repository root:
|
||||
|
||||
```shell
|
||||
go run . serve
|
||||
@@ -14,50 +16,73 @@ go run . serve
|
||||
> [!NOTE]
|
||||
> Ollama includes native code compiled with CGO. From time to time these data structures can change and CGO can get out of sync resulting in unexpected crashes. You can force a full build of the native code by running `go clean -cache` first.
|
||||
|
||||
## Native build model
|
||||
|
||||
For a fresh checkout, or after changing native code, build from the repository root. On macOS arm64, this builds Metal inference. On all other platforms this builds CPU-only inference. It builds the Go binary at the repository root and installs the native runtime payload under `build/lib/ollama`.
|
||||
|
||||
```shell
|
||||
cmake -B build .
|
||||
cmake --build build --parallel 8
|
||||
./ollama serve
|
||||
```
|
||||
|
||||
To install into a standard prefix layout:
|
||||
|
||||
```shell
|
||||
cmake --install build --prefix /path/to/install
|
||||
```
|
||||
|
||||
On all platforms except macOS arm64, to build GPU backends select the backends explicitly:
|
||||
|
||||
```shell
|
||||
cmake -B build . -DOLLAMA_LLAMA_BACKENDS="cuda_v13;vulkan"
|
||||
cmake --build build --parallel 8
|
||||
```
|
||||
|
||||
Supported backend values are `cuda_v12`, `cuda_v13`, `rocm_v7_1`, `rocm_v7_2`, `vulkan`, `cuda_jetpack5`, and `cuda_jetpack6`.
|
||||
|
||||
Use standard CMake architecture overrides to narrow GPU builds for local hardware:
|
||||
|
||||
```shell
|
||||
# CUDA
|
||||
cmake -B build . -DOLLAMA_LLAMA_BACKENDS=cuda_v13 -DCMAKE_CUDA_ARCHITECTURES=native
|
||||
|
||||
# ROCm / HIP
|
||||
cmake -B build . -DOLLAMA_LLAMA_BACKENDS=rocm_v7_2 -DCMAKE_HIP_ARCHITECTURES=gfx1100
|
||||
```
|
||||
|
||||
You can tune GGML build options by setting `GGML_*` values during configure. For example, to disable CUDA flash attention kernels for local debugging:
|
||||
|
||||
```shell
|
||||
cmake -B build . -DOLLAMA_LLAMA_BACKENDS=cuda_v12 -DGGML_CUDA_FA=OFF
|
||||
```
|
||||
|
||||
## macOS (Apple Silicon)
|
||||
|
||||
macOS Apple Silicon supports Metal which is built-in to the Ollama binary. No additional steps are required.
|
||||
Additional prerequisites:
|
||||
|
||||
## macOS (Intel)
|
||||
|
||||
Install prerequisites:
|
||||
|
||||
- [CMake](https://cmake.org/download/) or `brew install cmake`
|
||||
|
||||
Then, configure and build the project:
|
||||
MLX Metal requires the Metal toolchain. Install [Xcode](https://developer.apple.com/xcode/) first, then:
|
||||
|
||||
```shell
|
||||
cmake -B build
|
||||
cmake --build build
|
||||
```
|
||||
|
||||
Lastly, run Ollama:
|
||||
|
||||
```shell
|
||||
go run . serve
|
||||
xcodebuild -downloadComponent MetalToolchain
|
||||
```
|
||||
|
||||
## Windows
|
||||
|
||||
Install prerequisites:
|
||||
Additional prerequisites:
|
||||
|
||||
- [CMake](https://cmake.org/download/)
|
||||
- [Visual Studio 2022](https://visualstudio.microsoft.com/downloads/) including the Native Desktop Workload
|
||||
- (Optional) AMD GPU support
|
||||
- [ROCm](https://rocm.docs.amd.com/en/latest/)
|
||||
- [Ninja](https://github.com/ninja-build/ninja/releases)
|
||||
- (Optional) NVIDIA GPU support
|
||||
- [CUDA SDK](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=11&target_type=exe_network)
|
||||
- (Optional) VULKAN GPU support
|
||||
- [VULKAN SDK](https://vulkan.lunarg.com/sdk/home) - useful for AMD/Intel GPUs
|
||||
- [CUDA SDK](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&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:
|
||||
|
||||
```shell
|
||||
cmake -B build
|
||||
cmake --build build --config Release
|
||||
```
|
||||
For Ninja builds, run CMake from a Developer PowerShell/Command Prompt or another shell where the Visual Studio compiler is available.
|
||||
|
||||
> Building for Vulkan requires VULKAN_SDK environment variable:
|
||||
>
|
||||
@@ -70,52 +95,64 @@ cmake --build build --config Release
|
||||
> set VULKAN_SDK=C:\VulkanSDK\<version>
|
||||
> ```
|
||||
|
||||
> [!IMPORTANT]
|
||||
> Building for ROCm requires additional flags:
|
||||
> ```
|
||||
> cmake -B build -G Ninja -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++
|
||||
> cmake --build build --config Release
|
||||
> ```
|
||||
|
||||
|
||||
|
||||
Lastly, run Ollama:
|
||||
|
||||
```shell
|
||||
go run . serve
|
||||
```
|
||||
|
||||
## Windows (ARM)
|
||||
|
||||
Windows ARM does not support additional acceleration libraries at this time. Do not use cmake, simply `go run` or `go build`.
|
||||
Windows ARM does not support additional acceleration libraries at this time.
|
||||
|
||||
## Linux
|
||||
|
||||
Install prerequisites:
|
||||
Additional prerequisites:
|
||||
|
||||
- [CMake](https://cmake.org/download/) or `sudo apt install cmake` or `sudo dnf install cmake`
|
||||
- (Optional) AMD GPU support
|
||||
- [ROCm](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/quick-start.html)
|
||||
- (Optional) NVIDIA GPU support
|
||||
- [CUDA SDK](https://developer.nvidia.com/cuda-downloads)
|
||||
- (Optional) VULKAN GPU support
|
||||
- [VULKAN SDK](https://vulkan.lunarg.com/sdk/home) - useful for AMD/Intel GPUs
|
||||
- (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.
|
||||
|
||||
## MLX Engine (Optional)
|
||||
|
||||
Then, configure and build the project:
|
||||
The MLX engine enables running safetensor based models. On macOS arm64, MLX is enabled by default. On other platforms, MLX backends are selected with `OLLAMA_MLX_BACKENDS`.
|
||||
|
||||
### CUDA
|
||||
|
||||
Requires CUDA 13+ and [cuDNN](https://developer.nvidia.com/cudnn) 9+.
|
||||
|
||||
```shell
|
||||
cmake -B build
|
||||
cmake --build build
|
||||
cmake -B build . -DOLLAMA_MLX_BACKENDS=cuda_v13
|
||||
cmake --build build --parallel 8
|
||||
```
|
||||
|
||||
Lastly, run Ollama:
|
||||
### 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
|
||||
go run . serve
|
||||
export OLLAMA_MLX_SOURCE=/path/to/mlx
|
||||
export OLLAMA_MLX_C_SOURCE=/path/to/mlx-c
|
||||
```
|
||||
|
||||
On macOS arm64:
|
||||
|
||||
```shell
|
||||
OLLAMA_MLX_SOURCE=../mlx OLLAMA_MLX_C_SOURCE=../mlx-c cmake -B build .
|
||||
cmake --build build --parallel 8
|
||||
```
|
||||
|
||||
For CUDA:
|
||||
|
||||
```powershell
|
||||
$env:OLLAMA_MLX_SOURCE="../mlx"
|
||||
$env:OLLAMA_MLX_C_SOURCE="../mlx-c"
|
||||
cmake -B build . -DOLLAMA_MLX_BACKENDS=cuda_v13
|
||||
cmake --build build --parallel 8
|
||||
```
|
||||
|
||||
## Docker
|
||||
@@ -138,42 +175,13 @@ To run tests, use `go test`:
|
||||
go test ./...
|
||||
```
|
||||
|
||||
> NOTE: In rare circumstances, you may need to change a package using the new
|
||||
> "synctest" package in go1.24.
|
||||
>
|
||||
> If you do not have the "synctest" package enabled, you will not see build or
|
||||
> test failures resulting from your change(s), if any, locally, but CI will
|
||||
> break.
|
||||
>
|
||||
> If you see failures in CI, you can either keep pushing changes to see if the
|
||||
> CI build passes, or you can enable the "synctest" package locally to see the
|
||||
> failures before pushing.
|
||||
>
|
||||
> To enable the "synctest" package for testing, run the following command:
|
||||
>
|
||||
> ```shell
|
||||
> GOEXPERIMENT=synctest go test ./...
|
||||
> ```
|
||||
>
|
||||
> If you wish to enable synctest for all go commands, you can set the
|
||||
> `GOEXPERIMENT` environment variable in your shell profile or by using:
|
||||
>
|
||||
> ```shell
|
||||
> go env -w GOEXPERIMENT=synctest
|
||||
> ```
|
||||
>
|
||||
> Which will enable the "synctest" package for all go commands without needing
|
||||
> to set it for all shell sessions.
|
||||
>
|
||||
> The synctest package is not required for production builds.
|
||||
|
||||
## Library detection
|
||||
|
||||
Ollama looks for acceleration libraries in the following paths relative to the `ollama` executable:
|
||||
Ollama looks for native helper binaries and acceleration libraries in installed and local development layouts:
|
||||
|
||||
* `./lib/ollama` (Windows)
|
||||
* `../lib/ollama` (Linux)
|
||||
* `.` (macOS)
|
||||
* `build/lib/ollama` (for development)
|
||||
* `../lib/ollama` for standard installs where `ollama` is under `bin/`
|
||||
* `./lib/ollama` for Windows release-style payloads and local dist output
|
||||
* `.` for macOS release artifacts that colocate helpers with `ollama`
|
||||
* `build/lib/ollama` and `dist/<platform>/lib/ollama` for local development builds
|
||||
|
||||
If the libraries are not found, Ollama will not run with any acceleration libraries.
|
||||
|
||||
+6
-3
@@ -70,12 +70,16 @@ docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 114
|
||||
|
||||
## Vulkan Support
|
||||
|
||||
Vulkan is bundled into the `ollama/ollama` image.
|
||||
Vulkan is bundled into the `ollama/ollama` image and is enabled by default when
|
||||
the container can access the GPU devices.
|
||||
|
||||
```shell
|
||||
docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 11434:11434 -e OLLAMA_VULKAN=1 --name ollama ollama/ollama
|
||||
docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
|
||||
```
|
||||
|
||||
Use `OLLAMA_VULKAN=0` to disable Vulkan, or `GGML_VK_VISIBLE_DEVICES=<ids>` to
|
||||
select specific Vulkan devices.
|
||||
|
||||
|
||||
## Run model locally
|
||||
|
||||
@@ -88,4 +92,3 @@ docker exec -it ollama ollama run llama3.2
|
||||
## Try different models
|
||||
|
||||
More models can be found on the [Ollama library](https://ollama.com/library).
|
||||
|
||||
|
||||
+110
-19
@@ -32,7 +32,9 @@
|
||||
"codeblocks": "system"
|
||||
},
|
||||
"contextual": {
|
||||
"options": ["copy"]
|
||||
"options": [
|
||||
"copy"
|
||||
]
|
||||
},
|
||||
"navbar": {
|
||||
"links": [
|
||||
@@ -52,10 +54,32 @@
|
||||
"display": "simple"
|
||||
},
|
||||
"examples": {
|
||||
"languages": ["curl"]
|
||||
"languages": [
|
||||
"curl"
|
||||
]
|
||||
}
|
||||
},
|
||||
"redirects": [
|
||||
{
|
||||
"source": "/development.md",
|
||||
"destination": "/development"
|
||||
},
|
||||
{
|
||||
"source": "/api/openai-compatibility.mdx",
|
||||
"destination": "/api/openai-compatibility"
|
||||
},
|
||||
{
|
||||
"source": "/gpu.mdx",
|
||||
"destination": "/gpu"
|
||||
},
|
||||
{
|
||||
"source": "/integrations/vscode.mdx",
|
||||
"destination": "/integrations/vscode"
|
||||
},
|
||||
{
|
||||
"source": "/troubleshooting.mdx",
|
||||
"destination": "/troubleshooting"
|
||||
},
|
||||
{
|
||||
"source": "/openai",
|
||||
"destination": "/api/openai-compatibility"
|
||||
@@ -67,12 +91,20 @@
|
||||
{
|
||||
"source": "/api",
|
||||
"destination": "/api/introduction"
|
||||
},
|
||||
{
|
||||
"source": "/integrations/clawdbot",
|
||||
"destination": "/integrations/openclaw"
|
||||
},
|
||||
{
|
||||
"source": "/integrations/poolside",
|
||||
"destination": "/integrations/pool"
|
||||
}
|
||||
],
|
||||
"navigation": {
|
||||
"tabs": [
|
||||
{
|
||||
"tab": "Documentation",
|
||||
"tab": "Guide",
|
||||
"groups": [
|
||||
{
|
||||
"group": "Get started",
|
||||
@@ -94,21 +126,6 @@
|
||||
"/capabilities/web-search"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Integrations",
|
||||
"pages": [
|
||||
"/integrations/vscode",
|
||||
"/integrations/jetbrains",
|
||||
"/integrations/codex",
|
||||
"/integrations/cline",
|
||||
"/integrations/droid",
|
||||
"/integrations/goose",
|
||||
"/integrations/zed",
|
||||
"/integrations/roo-code",
|
||||
"/integrations/n8n",
|
||||
"/integrations/xcode"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "More information",
|
||||
"pages": [
|
||||
@@ -127,6 +144,79 @@
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"tab": "Integrations",
|
||||
"groups": [
|
||||
{
|
||||
"group": "Integrations",
|
||||
"pages": [
|
||||
"/integrations/index"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Assistants",
|
||||
"expanded": true,
|
||||
"pages": [
|
||||
"/integrations/openclaw",
|
||||
"/integrations/hermes",
|
||||
"/integrations/hermes-desktop"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Coding",
|
||||
"expanded": true,
|
||||
"pages": [
|
||||
"/integrations/claude-code",
|
||||
"/integrations/opencode",
|
||||
"/integrations/cline-cli",
|
||||
"/integrations/codex-app",
|
||||
"/integrations/codex",
|
||||
"/integrations/copilot-cli",
|
||||
"/integrations/droid",
|
||||
"/integrations/goose",
|
||||
"/integrations/oh-my-pi",
|
||||
"/integrations/pi",
|
||||
"/integrations/pool"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "IDEs & Editors",
|
||||
"expanded": true,
|
||||
"pages": [
|
||||
"/integrations/vscode",
|
||||
"/integrations/cline",
|
||||
"/integrations/jetbrains",
|
||||
"/integrations/roo-code",
|
||||
"/integrations/xcode",
|
||||
"/integrations/zed"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Chat & RAG",
|
||||
"pages": [
|
||||
"/integrations/onyx"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Automation",
|
||||
"pages": [
|
||||
"/integrations/n8n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Notebooks",
|
||||
"pages": [
|
||||
"/integrations/marimo"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Assistant Sandboxing",
|
||||
"pages": [
|
||||
"/integrations/nemoclaw"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"tab": "API Reference",
|
||||
"openapi": "/openapi.yaml",
|
||||
@@ -139,7 +229,8 @@
|
||||
"/api/streaming",
|
||||
"/api/usage",
|
||||
"/api/errors",
|
||||
"/api/openai-compatibility"
|
||||
"/api/openai-compatibility",
|
||||
"/api/anthropic-compatibility"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
+1
-1
@@ -11,4 +11,4 @@ Ollama JavaScript examples at [ollama-js/examples](https://github.com/ollama/oll
|
||||
|
||||
|
||||
## OpenAI compatibility examples
|
||||
Ollama OpenAI compatibility examples at [ollama/examples/openai](../docs/openai.md)
|
||||
Ollama OpenAI compatibility examples at [ollama/examples/openai](./api/openai-compatibility.mdx)
|
||||
|
||||
+33
-13
@@ -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:
|
||||
|
||||
@@ -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 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"
|
||||
@@ -228,7 +248,7 @@ Refer to the section [above](#how-do-i-configure-ollama-server) for how to set e
|
||||
|
||||
## How can I use Ollama in Visual Studio Code?
|
||||
|
||||
There is already a large collection of plugins available for VS Code as well as other editors that leverage Ollama. See the list of [extensions & plugins](https://github.com/ollama/ollama#extensions--plugins) at the bottom of the main repository readme.
|
||||
Install the [Ollama extension](https://marketplace.visualstudio.com/items?itemName=Ollama.ollama) to use Ollama models in VS Code Chat. See the [VS Code integration guide](./integrations/vscode.mdx) for setup and troubleshooting.
|
||||
|
||||
## How do I use Ollama with GPU acceleration in Docker?
|
||||
|
||||
@@ -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?
|
||||
|
||||
@@ -323,7 +343,7 @@ When loading a new model, Ollama evaluates the required VRAM for the model again
|
||||
|
||||
## How can I enable Flash Attention?
|
||||
|
||||
Flash Attention is a feature of most modern models that can significantly reduce memory usage as the context size grows. To enable Flash Attention, set the `OLLAMA_FLASH_ATTENTION` environment variable to `1` when starting the Ollama server.
|
||||
Flash Attention is a feature of most modern models that can significantly reduce memory usage as the context size grows. Ollama uses Flash Attention automatically when the selected backend and devices support it. To force Flash Attention on, set `OLLAMA_FLASH_ATTENTION=1` when starting the Ollama server. To disable it, set `OLLAMA_FLASH_ATTENTION=0`.
|
||||
|
||||
## How can I set the quantization type for the K/V cache?
|
||||
|
||||
@@ -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.
|
||||
|
||||
+34
-24
@@ -3,13 +3,15 @@ title: Hardware support
|
||||
---
|
||||
|
||||
## Nvidia
|
||||
Ollama supports Nvidia GPUs with compute capability 5.0+ and driver version 531 and newer.
|
||||
Ollama supports Nvidia GPUs with compute capability 5.0+ and driver version 550 and newer.
|
||||
Nvidia GPUs with compute capability 5.0 through 6.2 require driver version 570 or newer.
|
||||
|
||||
Check your compute compatibility to see if your card is supported:
|
||||
[https://developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus)
|
||||
|
||||
| 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 +35,7 @@ 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 [development](./development.md).
|
||||
|
||||
### GPU Selection
|
||||
|
||||
@@ -54,26 +56,32 @@ 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
|
||||
|
||||
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 | `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` |
|
||||
| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| 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` |
|
||||
| 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
|
||||
|
||||
With ROCm v6.1, the following GPUs are supported on Windows.
|
||||
Ollama requires an AMD ROCm v7 / HIP7-capable driver stack on Windows.
|
||||
|
||||
| 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` |
|
||||
| AMD Radeon PRO | `W7900` `W7800` `W7700` `W7600` `W7500` `W6900X` `W6800X Duo` `W6800X` `W6800` `V620` |
|
||||
| AMD Radeon RX | `7900 XTX` `7900 XT` `7900 GRE` `7800 XT` `7700 XT` `7600 XT` `7600` |
|
||||
| AMD Radeon PRO | `W7900` `W7800` `W7700` `W7600` `W7500` |
|
||||
|
||||
### Overrides on Linux
|
||||
|
||||
@@ -96,17 +104,17 @@ 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 |
|
||||
| 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.
|
||||
| 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.
|
||||
@@ -132,12 +140,9 @@ Ollama supports GPU acceleration on Apple devices via the Metal API.
|
||||
|
||||
## Vulkan GPU Support
|
||||
|
||||
> [!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)
|
||||
|
||||
Additional GPU support on Windows and Linux is provided via
|
||||
[Vulkan](https://www.vulkan.org/). On Windows most GPU vendors drivers come
|
||||
[Vulkan](https://www.vulkan.org/). Vulkan is enabled by default when the
|
||||
backend is installed. On Windows most GPU vendors drivers come
|
||||
bundled with Vulkan support and require no additional setup steps. Most Linux
|
||||
distributions require installing additional components, and you may have
|
||||
multiple options for Vulkan drivers between Mesa and GPU Vendor specific packages
|
||||
@@ -161,6 +166,11 @@ 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 `OLLAMA_VULKAN=0` or `GGML_VK_VISIBLE_DEVICES=-1`.
|
||||
|
||||
On mixed iGPU/dGPU systems where the Vulkan iGPU is unstable, keep Vulkan
|
||||
enabled and set `GGML_VK_VISIBLE_DEVICES` to the discrete GPU index. For
|
||||
example, use `GGML_VK_VISIBLE_DEVICES=1` when `Vulkan1` is the discrete
|
||||
GPU.
|
||||
|
||||
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|
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@@ -0,0 +1 @@
|
||||
<svg height="24" style="flex:none;line-height:1" viewBox="-1 -1 26 26" width="24" xmlns="http://www.w3.org/2000/svg"><title>Claude Code</title><path clip-rule="evenodd" d="M20.998 10.949H24v3.102h-3v3.028h-1.487V20H18v-2.921h-1.487V20H15v-2.921H9V20H7.488v-2.921H6V20H4.487v-2.921H3V14.05H0V10.95h3V5h17.998v5.949zM6 10.949h1.488V8.102H6v2.847zm10.51 0H18V8.102h-1.49v2.847z" fill="#D97757" fill-rule="evenodd"></path></svg>
|
||||
|
After Width: | Height: | Size: 425 B |
@@ -0,0 +1,181 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<svg xmlns="http://www.w3.org/2000/svg" width="1000" height="1000" viewBox="-25 -25 1050 1050"><circle cx="500.0" cy="500.0" r="500.0" fill="white"/><g transform="translate(50.0 50.0) scale(0.9375)"><g transform="translate(0.000000,960.000000) scale(0.100000,-0.100000)"
|
||||
fill="black" stroke="none">
|
||||
<path d="M4485 9589 c-248 -27 -432 -60 -730 -130 -458 -108 -798 -230 -1207
|
||||
-435 -533 -267 -1072 -675 -1358 -1030 -205 -255 -442 -748 -535 -1114 -108
|
||||
-426 -97 -870 29 -1160 73 -169 236 -369 381 -467 139 -94 425 -206 425 -167
|
||||
0 3 -26 32 -58 63 -74 71 -147 182 -184 278 -16 41 -27 77 -25 79 2 3 31 -28
|
||||
63 -68 91 -114 153 -177 231 -235 40 -29 77 -62 83 -73 7 -13 4 -85 -10 -242
|
||||
-10 -123 -24 -281 -30 -353 -6 -71 -29 -296 -51 -500 -135 -1262 -202 -1568
|
||||
-378 -1733 -69 -64 -105 -77 -216 -77 -132 0 -188 20 -271 97 -110 103 -165
|
||||
248 -167 438 -1 123 12 191 60 318 20 51 33 95 29 99 -10 10 -92 -74 -136
|
||||
-137 -153 -223 -204 -504 -146 -790 19 -91 97 -252 161 -333 112 -141 316
|
||||
-237 504 -237 180 0 419 118 591 290 81 81 107 115 196 255 50 78 54 56 13
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||||
<g>
|
||||
<path fill-rule="evenodd" class="s50" d="m83.5 394.5q1.88-0.06 3 1.5-2.25 0.88-3-1.5z"/>
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||||
</g>
|
||||
<g>
|
||||
<path fill-rule="evenodd" class="s51" d="m258.5 392.5q3.51 0.41 0 2.5-2.33 1.93-5 2 2.61-2.28 5-4.5z"/>
|
||||
</g>
|
||||
<g>
|
||||
<path fill-rule="evenodd" class="s52" d="m111.5 392.5q0.09-0.81 1-1 1.48 4.9 1 10-1-4.5-2-9z"/>
|
||||
</g>
|
||||
</svg>
|
||||
|
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|
||||
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|
||||
<rect width="512" height="512" fill="#131010"></rect>
|
||||
<path d="M320 224V352H192V224H320Z" fill="#5A5858"></path>
|
||||
<path fill-rule="evenodd" clip-rule="evenodd" d="M384 416H128V96H384V416ZM320 160H192V352H320V160Z" fill="white"></path>
|
||||
</svg><style>@media (prefers-color-scheme: light) { :root { filter: none; } }
|
||||
@media (prefers-color-scheme: dark) { :root { filter: none; } }
|
||||
</style></svg>
|
||||
|
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||||
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|
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+4
-14
@@ -4,10 +4,10 @@ title: Importing a Model
|
||||
|
||||
## Table of Contents
|
||||
|
||||
- [Importing a Safetensors adapter](#Importing-a-fine-tuned-adapter-from-Safetensors-weights)
|
||||
- [Importing a Safetensors model](#Importing-a-model-from-Safetensors-weights)
|
||||
- [Importing a GGUF file](#Importing-a-GGUF-based-model-or-adapter)
|
||||
- [Sharing models on ollama.com](#Sharing-your-model-on-ollamacom)
|
||||
- [Importing a Safetensors adapter](#importing-a-fine-tuned-adapter-from-safetensors-weights)
|
||||
- [Importing a Safetensors model](#importing-a-model-from-safetensors-weights)
|
||||
- [Importing a GGUF file](#importing-a-gguf-based-model-or-adapter)
|
||||
- [Sharing models on ollama.com](#sharing-your-model-on-ollama-com)
|
||||
|
||||
## Importing a fine tuned adapter from Safetensors weights
|
||||
|
||||
@@ -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
|
||||
|
||||
|
||||
+28
-30
@@ -1,47 +1,45 @@
|
||||
---
|
||||
title: Ollama's documentation
|
||||
title: Ollama documentation
|
||||
sidebarTitle: Welcome
|
||||
---
|
||||
|
||||
<img src="/images/welcome.png" noZoom className="rounded-3xl" />
|
||||
Start building with open models.
|
||||
|
||||
[Ollama](https://ollama.com) is the easiest way to get up and running with large language models such as gpt-oss, Gemma 3, DeepSeek-R1, Qwen3 and more.
|
||||
Follow the [quickstart](/quickstart), then choose a model, integration, or API.
|
||||
|
||||
## Models
|
||||
|
||||
Run models locally or use larger models in Ollama's cloud.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Quickstart" icon="rocket" href="/quickstart">
|
||||
Get up and running with your first model
|
||||
<Card title="Browse models" icon="star" href="https://ollama.com/search">
|
||||
Find models for chat, coding, vision, embeddings, and reasoning.
|
||||
</Card>
|
||||
<Card
|
||||
title="Download Ollama"
|
||||
icon="download"
|
||||
href="https://ollama.com/download"
|
||||
>
|
||||
Download Ollama on macOS, Windows or Linux
|
||||
</Card>
|
||||
<Card title="Cloud" icon="cloud" href="/cloud">
|
||||
Ollama's cloud models offer larger models with better performance.
|
||||
</Card>
|
||||
<Card title="API reference" icon="terminal" href="/api">
|
||||
View Ollama's API reference
|
||||
|
||||
<Card title="Cloud models" icon="cloud" href="/cloud">
|
||||
Run larger models on Ollama's Cloud without the download.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
## Libraries
|
||||
## Next steps
|
||||
|
||||
Connect Ollama to an app, or build with the API.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card
|
||||
title="Ollama's Python Library"
|
||||
icon="python"
|
||||
href="https://github.com/ollama/ollama-python"
|
||||
>
|
||||
The official library for using Ollama with Python
|
||||
<Card title="Integrations" icon="plug" href="/integrations">
|
||||
Connect Ollama to an app, editor, or agent.
|
||||
</Card>
|
||||
|
||||
<Card title="Ollama's JavaScript library" icon="js" href="https://github.com/ollama/ollama-js">
|
||||
The official library for using Ollama with JavaScript or TypeScript.
|
||||
<Card title="First API request" icon="code" href="/api/introduction">
|
||||
Learn the local and cloud base URLs, then send a request with `curl`.
|
||||
</Card>
|
||||
<Card title="Community libraries" icon="github" href="https://github.com/ollama/ollama?tab=readme-ov-file#libraries-1">
|
||||
View a list of 20+ community-supported libraries for Ollama
|
||||
|
||||
<Card title="Python library" icon="python" href="https://github.com/ollama/ollama-python">
|
||||
Use Ollama from Python.
|
||||
</Card>
|
||||
|
||||
<Card title="JavaScript library" icon="js" href="https://github.com/ollama/ollama-js">
|
||||
Use Ollama from JavaScript or TypeScript.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
@@ -49,10 +47,10 @@ sidebarTitle: Welcome
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Discord" icon="discord" href="https://discord.gg/ollama">
|
||||
Join our Discord community
|
||||
Join the Ollama Discord.
|
||||
</Card>
|
||||
|
||||
<Card title="Reddit" icon="reddit" href="https://reddit.com/r/ollama">
|
||||
Join our Reddit community
|
||||
Join the Ollama subreddit.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
@@ -0,0 +1,192 @@
|
||||
---
|
||||
title: Claude Code
|
||||
---
|
||||
|
||||
[Claude Code](https://code.claude.com/docs/en/overview) is an agentic coding tool that reads your codebase, edits files, and runs commands.
|
||||
|
||||
Ollama connects Claude Code to local and cloud models through its Anthropic-compatible API.
|
||||
|
||||
## Get started
|
||||
|
||||
Launch Claude Code with Ollama:
|
||||
|
||||
```shell
|
||||
ollama launch claude
|
||||
```
|
||||
|
||||
## Capabilities
|
||||
|
||||
<div className="capability-list capability-list-full">
|
||||
<div className="capability-list-grid">
|
||||
<div className="capability-list-item">
|
||||
<div className="capability-list-icon"><Icon icon="comment" /></div>
|
||||
<div>
|
||||
<div className="capability-list-heading">Chat</div>
|
||||
<div className="capability-list-copy">Ask questions about a repository or task</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="capability-list-item">
|
||||
<div className="capability-list-icon"><Icon icon="terminal" /></div>
|
||||
<div>
|
||||
<div className="capability-list-heading">Command line</div>
|
||||
<div className="capability-list-copy">Run commands with Claude Code's permission flow</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="capability-list-item">
|
||||
<div className="capability-list-icon"><Icon icon="code" /></div>
|
||||
<div>
|
||||
<div className="capability-list-heading">Tool calling</div>
|
||||
<div className="capability-list-copy">Use tools with compatible models</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="capability-list-item">
|
||||
<div className="capability-list-icon"><Icon icon="file-pen" /></div>
|
||||
<div>
|
||||
<div className="capability-list-heading">File edits</div>
|
||||
<div className="capability-list-copy">Read and edit files in your project</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="capability-list-item">
|
||||
<div className="capability-list-icon"><Icon icon="users" /></div>
|
||||
<div>
|
||||
<div className="capability-list-heading">Subagents</div>
|
||||
<div className="capability-list-copy">Split work across tasks</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="capability-list-item">
|
||||
<div className="capability-list-icon"><Icon icon="globe" /></div>
|
||||
<div>
|
||||
<div className="capability-list-heading">Web search</div>
|
||||
<div className="capability-list-copy">Search the web through Ollama</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="capability-list-item">
|
||||
<div className="capability-list-icon"><Icon icon="file-text" /></div>
|
||||
<div>
|
||||
<div className="capability-list-heading">Web fetch</div>
|
||||
<div className="capability-list-copy">Fetch and summarize web pages</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="capability-list-item">
|
||||
<div className="capability-list-icon"><Icon icon="image" /></div>
|
||||
<div>
|
||||
<div className="capability-list-heading">Vision</div>
|
||||
<div className="capability-list-copy">Send images and screenshots</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="capability-list-item">
|
||||
<div className="capability-list-icon"><Icon icon="brain" /></div>
|
||||
<div>
|
||||
<div className="capability-list-heading">Thinking</div>
|
||||
<div className="capability-list-copy">Use thinking controls with compatible models</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
|
||||
## Models
|
||||
|
||||
Choose a model with enough context for your repository.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Cloud models" icon="cloud" href="https://ollama.com/search?c=cloud">
|
||||
Use larger models without downloading them.
|
||||
</Card>
|
||||
|
||||
<Card title="Local models" icon="hard-drive" href="https://ollama.com/search?c=tools">
|
||||
Choose a model and set a 64k+ context window.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
<Note>For larger repositories, set the [context length](/context-length) to 64k or higher.</Note>
|
||||
|
||||
## More features
|
||||
|
||||
### Run without interaction
|
||||
|
||||
Use `--yes` for scripts, Docker, or CI:
|
||||
|
||||
```shell
|
||||
ollama launch claude --model gemma4:cloud --yes -- -p "how does this repository work?"
|
||||
```
|
||||
|
||||
The `--yes` flag skips selectors, pulls the model when needed, and requires `--model`. Arguments after `--` are passed directly to Claude Code.
|
||||
|
||||
### Web search
|
||||
|
||||
Use Ollama's web search API from Claude Code.
|
||||
|
||||
See [Web search](/capabilities/web-search) for setup and usage.
|
||||
|
||||
### Scheduled tasks with `/loop`
|
||||
|
||||
Use `/loop` inside Claude Code to run a prompt or slash command on a schedule:
|
||||
|
||||
```text
|
||||
/loop <interval> <prompt or /command>
|
||||
```
|
||||
|
||||
Examples:
|
||||
|
||||
```text
|
||||
/loop 30m Check my open PRs and summarize their status
|
||||
/loop 1h Research the latest AI news and summarize key developments
|
||||
/loop 15m Check for new GitHub issues and triage by priority
|
||||
```
|
||||
|
||||
### Telegram
|
||||
|
||||
Connect a Telegram bot to your Claude Code session. Install the [Telegram plugin](https://github.com/anthropics/claude-plugins-official), create a bot with [@BotFather](https://t.me/BotFather), then launch Claude Code:
|
||||
|
||||
```shell
|
||||
ollama launch claude -- --channels plugin:telegram@claude-plugins-official
|
||||
```
|
||||
|
||||
Claude Code prompts 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 an isolated environment.
|
||||
|
||||
See the [plugin README](https://github.com/anthropics/claude-plugins-official/tree/main/external_plugins/telegram) for setup instructions.
|
||||
|
||||
## Manual setup
|
||||
|
||||
<p className="manual-step-title">1. Install Claude Code</p>
|
||||
|
||||
<CodeGroup>
|
||||
|
||||
```shell macOS / Linux
|
||||
curl -fsSL https://claude.ai/install.sh | bash
|
||||
```
|
||||
|
||||
```powershell Windows
|
||||
irm https://claude.ai/install.ps1 | iex
|
||||
```
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
<p className="manual-step-title">2. Set the environment variables</p>
|
||||
|
||||
```shell
|
||||
export ANTHROPIC_AUTH_TOKEN=ollama
|
||||
export ANTHROPIC_API_KEY=""
|
||||
export ANTHROPIC_BASE_URL=http://localhost:11434
|
||||
```
|
||||
|
||||
<p className="manual-step-title">3. Run Claude Code</p>
|
||||
|
||||
```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 kimi-k2.7-code:cloud
|
||||
```
|
||||
@@ -0,0 +1,13 @@
|
||||
---
|
||||
title: Claude Desktop
|
||||
---
|
||||
|
||||
Claude Desktop is no longer supported by `ollama launch`.
|
||||
|
||||
Existing installations can be restored to the usual Claude profile:
|
||||
|
||||
```shell
|
||||
ollama launch claude-desktop --restore
|
||||
```
|
||||
|
||||
Use [Claude Code](/integrations/claude-code) for Anthropic-compatible coding workflows with Ollama.
|
||||
@@ -0,0 +1,98 @@
|
||||
---
|
||||
title: Cline CLI
|
||||
---
|
||||
|
||||
Cline CLI is an autonomous coding agent for interactive terminal sessions.
|
||||
|
||||
<img
|
||||
src="/images/cline-cli.png"
|
||||
alt="Cline CLI launched with Ollama selected as the provider"
|
||||
style={{ borderRadius: "12px" }}
|
||||
/>
|
||||
|
||||
## Install
|
||||
|
||||
Install the [Cline CLI](https://docs.cline.bot/usage/cli-overview). For the IDE extension, see [Cline](/integrations/cline).
|
||||
|
||||
```bash
|
||||
npm install -g cline
|
||||
```
|
||||
|
||||
<Note>If Cline CLI is not installed and `npm` is available, `ollama launch cline` will prompt to install `cline@latest`.</Note>
|
||||
|
||||
## Usage with Ollama
|
||||
|
||||
### Quick setup
|
||||
|
||||
```bash
|
||||
ollama launch cline
|
||||
```
|
||||
|
||||
When launched through `ollama launch cline`, Ollama sets Cline's provider to Ollama, points it at the local Ollama endpoint, and selects the model you choose.
|
||||
|
||||
To configure without launching:
|
||||
|
||||
```shell
|
||||
ollama launch cline --config
|
||||
```
|
||||
|
||||
### Run directly with a model
|
||||
|
||||
```shell
|
||||
ollama launch cline --model qwen3.5
|
||||
```
|
||||
|
||||
To use a cloud model:
|
||||
|
||||
```shell
|
||||
ollama launch cline --model kimi-k2.6:cloud
|
||||
```
|
||||
|
||||
|
||||
### Pass a prompt to Cline
|
||||
|
||||
Arguments after `--` are passed directly to Cline:
|
||||
|
||||
```shell
|
||||
ollama launch cline -- "summarize this repository"
|
||||
```
|
||||
|
||||
To open Cline's Kanban board:
|
||||
|
||||
```shell
|
||||
ollama launch cline -- kanban
|
||||
```
|
||||
|
||||
<img
|
||||
src="/images/cline-kanban.png"
|
||||
alt="Cline Kanban board opened from the CLI"
|
||||
style={{ borderRadius: "12px" }}
|
||||
/>
|
||||
|
||||
### Manual setup
|
||||
|
||||
To configure Cline CLI manually, first make sure Ollama is running and the model you want to use is available:
|
||||
|
||||
```shell
|
||||
ollama pull qwen3.5
|
||||
```
|
||||
|
||||
Then run Cline's interactive auth flow:
|
||||
|
||||
```shell
|
||||
cline auth
|
||||
```
|
||||
|
||||
Select Ollama as the provider, use `http://localhost:11434` as the base URL if prompted, and choose a model such as `qwen3.5` or `kimi-k2.6:cloud`.
|
||||
|
||||
To check the current Cline configuration:
|
||||
|
||||
```shell
|
||||
cline config
|
||||
```
|
||||
|
||||
To start an interactive session:
|
||||
|
||||
```shell
|
||||
cline
|
||||
```
|
||||
@@ -0,0 +1,82 @@
|
||||
---
|
||||
title: Codex App
|
||||
---
|
||||
|
||||
Codex App is OpenAI's desktop coding agent for macOS and Windows. Ollama configures the app to use Ollama's OpenAI-compatible endpoint, so Codex can work with local models and Ollama Cloud models in the desktop app.
|
||||
|
||||
<img
|
||||
src="/images/codex-app-home.png"
|
||||
alt="Codex App with Ollama selected"
|
||||
style={{ borderRadius: "12px" }}
|
||||
/>
|
||||
|
||||
## Install
|
||||
|
||||
Install the [Codex App](https://developers.openai.com/codex/quickstart/) for macOS or Windows.
|
||||
|
||||
<Note>Codex App support is available in Ollama v0.24.0 and newer.</Note>
|
||||
|
||||
|
||||
## Quick setup
|
||||
|
||||
```shell
|
||||
ollama launch codex-app
|
||||
```
|
||||
|
||||
Once Codex App opens, start a task or open a repository as usual.
|
||||
|
||||
## Built-in browser
|
||||
|
||||
Codex App can open local servers and sites in its built-in browser. Annotate directly on the page to request changes.
|
||||
|
||||
<img
|
||||
src="/images/codex-app-annotate.png"
|
||||
alt="Codex App browser annotations"
|
||||
style={{ borderRadius: "12px" }}
|
||||
/>
|
||||
|
||||
## Review mode
|
||||
|
||||
Use review mode to inspect code changes, leave comments, and iterate on fixes without leaving the app.
|
||||
|
||||
<img
|
||||
src="/images/codex-app-review.png"
|
||||
alt="Codex App review comments"
|
||||
style={{ borderRadius: "12px" }}
|
||||
/>
|
||||
|
||||
### Run directly with a model
|
||||
|
||||
```shell
|
||||
ollama launch codex-app --model kimi-k2.6:cloud
|
||||
```
|
||||
|
||||
Use a local model by passing its model name:
|
||||
|
||||
```shell
|
||||
ollama launch codex-app --model gemma4:31b
|
||||
```
|
||||
|
||||
Running `ollama launch codex-app` is persistent and will have your model selected next time you open Codex.
|
||||
|
||||
|
||||
### Restore Codex App
|
||||
|
||||
To switch Codex App back to the profile you were using before `ollama launch codex-app`, run:
|
||||
|
||||
```shell
|
||||
ollama launch codex-app --restore
|
||||
```
|
||||
|
||||
Ollama restores Codex App's settings and configs. If Codex App is open, Ollama asks before restarting it.
|
||||
|
||||
|
||||
The Codex CLI profile managed by `ollama launch codex` is left separate from the Codex App profile.
|
||||
|
||||
Before overwriting Codex App config files, Ollama Launch saves backups under `~/.ollama/backup/codex-app/`. On Windows, `~` resolves to your user profile directory.
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
If Codex App does not open after setup, open Codex manually once and run `ollama launch codex-app` again.
|
||||
|
||||
If Codex App is already running and does not switch models, allow Ollama to restart it when prompted, or quit Codex App and run `ollama launch codex-app` again.
|
||||
+40
-18
@@ -1,11 +1,11 @@
|
||||
---
|
||||
title: Codex
|
||||
title: Codex CLI
|
||||
---
|
||||
|
||||
|
||||
## Install
|
||||
|
||||
Install the [Codex CLI](https://developers.openai.com/codex/cli/):
|
||||
Install the [Codex CLI](https://developers.openai.com/codex/cli/). For the desktop app, see [Codex App](/integrations/codex-app).
|
||||
|
||||
```
|
||||
npm install -g @openai/codex
|
||||
@@ -13,7 +13,30 @@ 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
|
||||
```
|
||||
|
||||
When launched through `ollama launch codex`, Ollama refreshes the model catalog
|
||||
and uses a dedicated Codex profile for that session.
|
||||
|
||||
To configure without launching:
|
||||
|
||||
```shell
|
||||
ollama launch codex --config
|
||||
```
|
||||
|
||||
To remove the Ollama launch profile and generated model catalog:
|
||||
|
||||
```shell
|
||||
ollama launch codex --restore
|
||||
```
|
||||
|
||||
### Manual setup
|
||||
|
||||
To use `codex` with Ollama, use the `--oss` flag:
|
||||
|
||||
@@ -21,36 +44,35 @@ 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 Codex CLI configuration, create `~/.codex/ollama-launch.config.toml`:
|
||||
|
||||
```toml
|
||||
model = "gpt-oss:120b"
|
||||
model_provider = "ollama"
|
||||
model_provider = "ollama-launch"
|
||||
model_catalog_json = "/Users/you/.codex/model.json"
|
||||
|
||||
[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/"
|
||||
wire_api = "responses"
|
||||
```
|
||||
|
||||
Run `codex` in a new terminal to load the new settings.
|
||||
Then run:
|
||||
|
||||
```
|
||||
codex --profile ollama-launch
|
||||
```
|
||||
|
||||
@@ -0,0 +1,93 @@
|
||||
---
|
||||
title: Copilot CLI
|
||||
---
|
||||
|
||||
GitHub Copilot CLI is GitHub's AI coding agent for the terminal. It can understand your codebase, make edits, run commands, and help you build software faster.
|
||||
|
||||
Open models can be used with Copilot CLI through Ollama, enabling you to use models such as `qwen3.5`, `glm-5.1:cloud`, `kimi-k2.5:cloud`.
|
||||
|
||||
## Install
|
||||
|
||||
Install [Copilot CLI](https://github.com/features/copilot/cli/):
|
||||
|
||||
<CodeGroup>
|
||||
|
||||
```shell macOS / Linux (Homebrew)
|
||||
brew install copilot-cli
|
||||
```
|
||||
|
||||
```shell npm (all platforms)
|
||||
npm install -g @github/copilot
|
||||
```
|
||||
|
||||
```shell macOS / Linux (script)
|
||||
curl -fsSL https://gh.io/copilot-install | bash
|
||||
```
|
||||
|
||||
```powershell Windows (WinGet)
|
||||
winget install GitHub.Copilot
|
||||
```
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
## Usage with Ollama
|
||||
|
||||
### Quick setup
|
||||
|
||||
```shell
|
||||
ollama launch copilot
|
||||
```
|
||||
|
||||
### Run directly with a model
|
||||
|
||||
```shell
|
||||
ollama launch copilot --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 Copilot CLI without interaction for use in Docker, CI/CD, or scripts:
|
||||
|
||||
```shell
|
||||
ollama launch copilot --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 Copilot CLI.
|
||||
|
||||
## Manual setup
|
||||
|
||||
Copilot CLI connects to Ollama using the OpenAI-compatible API via environment variables.
|
||||
|
||||
1. Set the environment variables:
|
||||
|
||||
```shell
|
||||
export COPILOT_PROVIDER_BASE_URL=http://localhost:11434/v1
|
||||
export COPILOT_PROVIDER_API_KEY=
|
||||
export COPILOT_PROVIDER_WIRE_API=responses
|
||||
export COPILOT_MODEL=qwen3.5
|
||||
```
|
||||
|
||||
1. Run Copilot CLI:
|
||||
|
||||
```shell
|
||||
copilot
|
||||
```
|
||||
|
||||
Or run with environment variables inline:
|
||||
|
||||
```shell
|
||||
COPILOT_PROVIDER_BASE_URL=http://localhost:11434/v1 COPILOT_PROVIDER_API_KEY= COPILOT_PROVIDER_WIRE_API=responses COPILOT_MODEL=glm-5:cloud copilot
|
||||
```
|
||||
|
||||
**Note:** Copilot 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.
|
||||
@@ -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
|
||||
|
||||
@@ -4,7 +4,7 @@ title: Goose
|
||||
|
||||
## Goose Desktop
|
||||
|
||||
Install [Goose](https://block.github.io/goose/docs/getting-started/installation/) Desktop.
|
||||
Install [Goose](https://goose-docs.ai/docs/getting-started/installation/) Desktop.
|
||||
|
||||
### Usage with Ollama
|
||||
1. In Goose, open **Settings** → **Configure Provider**.
|
||||
@@ -27,7 +27,7 @@ Install [Goose](https://block.github.io/goose/docs/getting-started/installation/
|
||||
|
||||
## Goose CLI
|
||||
|
||||
Install [Goose](https://block.github.io/goose/docs/getting-started/installation/) CLI
|
||||
Install [Goose](https://goose-docs.ai/docs/getting-started/installation/) CLI
|
||||
|
||||
### Usage with Ollama
|
||||
1. Run `goose configure`
|
||||
|
||||
@@ -0,0 +1,38 @@
|
||||
---
|
||||
title: Hermes Desktop
|
||||
---
|
||||
|
||||
Hermes Desktop is a native AI assistant app by Nous Research. It provides a desktop chat interface for Hermes Agent, an AI agent that can work with models, run tools, manage projects, use memory and skills, and connect to messaging gateways.
|
||||
|
||||
|
||||
|
||||
## Quick start
|
||||
|
||||
```bash
|
||||
ollama launch hermes-desktop
|
||||
```
|
||||
|
||||
Ollama handles the setup flow automatically:
|
||||
|
||||
1. **Install** - If Hermes isn't installed, Ollama prompts to install the Hermes command-line agent. On first desktop launch, Hermes builds its packaged desktop app.
|
||||
2. **Model** - Pick a model from the selector
|
||||
3. **Configure** - Ollama configures Hermes Desktop to use your selected Ollama model
|
||||
4. **Launch** - Ollama opens Hermes Desktop
|
||||
|
||||
## Run directly with a model
|
||||
|
||||
```bash
|
||||
ollama launch hermes-desktop --model <model>
|
||||
```
|
||||
|
||||
Run `ollama launch hermes-desktop` again to switch models later.
|
||||
|
||||
## Install Hermes Desktop directly
|
||||
|
||||
On macOS and Windows, the Hermes Desktop installer is the recommended upstream installation path. It installs the desktop app and Hermes Agent together. If you prefer the command line, `ollama launch hermes-desktop` remains the explicit Ollama-managed path and uses the same Hermes configuration, sessions, skills, and memory as the CLI.
|
||||
|
||||
To force Hermes to rebuild its packaged desktop app:
|
||||
|
||||
```bash
|
||||
ollama launch hermes-desktop -- --force-build
|
||||
```
|
||||
@@ -0,0 +1,116 @@
|
||||
---
|
||||
title: Hermes Agent
|
||||
---
|
||||
|
||||
Hermes Agent is a self-improving AI agent built by Nous Research. It features automatic skill creation, cross-session memory, and 70+ skills that it ships with by default.
|
||||
|
||||
|
||||
|
||||
## Quick start
|
||||
|
||||
```bash
|
||||
ollama launch hermes
|
||||
```
|
||||
|
||||
Ollama handles everything automatically:
|
||||
|
||||
1. **Install** — If Hermes isn't installed, Ollama prompts to install the Hermes command-line agent
|
||||
2. **Model** — Pick a model from the selector (local or cloud)
|
||||
3. **Onboarding** — Ollama configures the Ollama provider, points Hermes at `http://127.0.0.1:11434/v1`, and sets your model as the primary
|
||||
4. **Gateway** — Optionally connects a messaging platform (Telegram, Discord, Slack, WhatsApp, Signal, Email) and launches the Hermes chat
|
||||
|
||||
## Recommended models
|
||||
|
||||
**Cloud models**:
|
||||
|
||||
- `kimi-k2.5:cloud` — Multimodal reasoning with subagents
|
||||
- `glm-5.1:cloud` — Reasoning and code generation
|
||||
- `qwen3.5:cloud` — Reasoning, coding, and agentic tool use with vision
|
||||
- `minimax-m2.7:cloud` — Fast, efficient coding and real-world productivity
|
||||
|
||||
**Local models:**
|
||||
|
||||
- `gemma4` — Reasoning and code generation locally (~16 GB VRAM)
|
||||
- `qwen3.6` — Reasoning, coding, and visual understanding locally (~24 GB VRAM)
|
||||
|
||||
More models at [ollama.com/search](https://ollama.com/search?c=cloud).
|
||||
|
||||
## Connect messaging apps
|
||||
|
||||
Link Telegram, Discord, Slack, WhatsApp, Signal, or Email to chat with your models from anywhere:
|
||||
|
||||
```bash
|
||||
hermes gateway setup
|
||||
```
|
||||
|
||||
## Reconfigure
|
||||
|
||||
Use Hermes's model picker to change providers or models later:
|
||||
|
||||
```bash
|
||||
hermes model
|
||||
```
|
||||
|
||||
## Manual setup
|
||||
|
||||
If you'd rather drive Hermes's own wizard instead of `ollama launch hermes`, install it directly:
|
||||
|
||||
```bash
|
||||
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
|
||||
```
|
||||
|
||||
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 gateway setup'
|
||||
```
|
||||
|
||||
### Launch
|
||||
|
||||
```
|
||||
Launch hermes chat now? [Y/n]: Y
|
||||
```
|
||||
@@ -0,0 +1,44 @@
|
||||
---
|
||||
title: Overview
|
||||
---
|
||||
|
||||
Use Ollama from coding agents, personal assistants, and editors.
|
||||
|
||||
Run `ollama launch` to see the latest integrations you can run from the terminal.
|
||||
|
||||
## Code in the terminal
|
||||
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Claude Code" icon="/images/launch-icons/claude-code.svg" href="/integrations/claude-code">
|
||||
Terminal coding agent with tools, vision, web search, and long context.
|
||||
</Card>
|
||||
|
||||
<Card title="OpenCode" icon="/images/launch-icons/opencode.svg" href="/integrations/opencode">
|
||||
Open-source coding agent that edits, runs, and iterates on code.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
## Connect an assistant
|
||||
|
||||
Assistants with memory, skills, and messaging app access.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="OpenClaw" icon="/images/launch-icons/openclaw.svg" href="/integrations/openclaw">
|
||||
Personal assistant for messaging apps and everyday tasks.
|
||||
</Card>
|
||||
|
||||
<Card title="Hermes Agent" icon="/images/launch-icons/hermes-agent.svg" href="/integrations/hermes">
|
||||
Open-source agent with self-improving skills, memory, and messaging.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
## Work in your editor
|
||||
|
||||
Use Ollama models inside your editor.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="VS Code" icon="/images/launch-icons/vscode.svg" href="/integrations/vscode">
|
||||
Use Ollama models in VS Code Chat.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
@@ -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 +1,35 @@
|
||||
---
|
||||
title: Oh My Pi
|
||||
---
|
||||
|
||||
Oh My Pi (OMP) is a terminal coding agent with IDE-style tools built in. It combines chat, project context, structured code edits, language server support, debugging tools, browser access, plugins, and subagents in one terminal workflow.
|
||||
|
||||
Ollama can configure OMP to use Ollama as its model provider and launch an interactive session.
|
||||
|
||||
|
||||
|
||||
## Quick setup
|
||||
|
||||
```bash
|
||||
ollama launch omp
|
||||
```
|
||||
|
||||
This configures Ollama as a provider, sets up web search tools, and starts OMP.
|
||||
|
||||
### Run directly with a model
|
||||
|
||||
```shell
|
||||
ollama launch omp --model <model>
|
||||
```
|
||||
|
||||
## Plugins
|
||||
|
||||
OMP supports plugins for extra tools and capabilities. When launching OMP through Ollama, the Ollama web search plugin is managed automatically.
|
||||
|
||||
## Manual setup
|
||||
|
||||
Install OMP from [omp.sh](https://omp.sh), then run:
|
||||
|
||||
```bash
|
||||
ollama launch omp --config
|
||||
```
|
||||
@@ -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 +1,95 @@
|
||||
---
|
||||
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 enables OpenClaw's bundled Ollama web search
|
||||
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 bundled Ollama `web_search` provider that lets local or cloud-backed Ollama setups search the web through the configured Ollama host.
|
||||
|
||||
```bash
|
||||
ollama launch openclaw
|
||||
```
|
||||
|
||||
Ollama web search is enabled automatically when launching OpenClaw through Ollama. To configure it manually:
|
||||
|
||||
```bash
|
||||
openclaw configure --section web
|
||||
```
|
||||
|
||||
<Note>Ollama 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 +1,145 @@
|
||||
---
|
||||
title: OpenCode
|
||||
---
|
||||
|
||||
[OpenCode](https://opencode.ai) is an open-source coding agent that runs in your terminal, reads your project, edits files, and runs commands.
|
||||
|
||||
Ollama configures OpenCode to use local and cloud models.
|
||||
|
||||
## Get started
|
||||
|
||||
Launch OpenCode with Ollama:
|
||||
|
||||
```shell
|
||||
ollama launch opencode
|
||||
```
|
||||
|
||||
## Capabilities
|
||||
|
||||
<div className="capability-list capability-list-full">
|
||||
<div className="capability-list-grid">
|
||||
<div className="capability-list-item">
|
||||
<div className="capability-list-icon"><Icon icon="comment" /></div>
|
||||
<div>
|
||||
<div className="capability-list-heading">Chat</div>
|
||||
<div className="capability-list-copy">Ask questions about a repository or task</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="capability-list-item">
|
||||
<div className="capability-list-icon"><Icon icon="terminal" /></div>
|
||||
<div>
|
||||
<div className="capability-list-heading">Command line</div>
|
||||
<div className="capability-list-copy">Run commands from your working directory</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="capability-list-item">
|
||||
<div className="capability-list-icon"><Icon icon="code" /></div>
|
||||
<div>
|
||||
<div className="capability-list-heading">File edits</div>
|
||||
<div className="capability-list-copy">Read and edit files in your project</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="capability-list-item">
|
||||
<div className="capability-list-icon"><Icon icon="users" /></div>
|
||||
<div>
|
||||
<div className="capability-list-heading">Subagents</div>
|
||||
<div className="capability-list-copy">Split work across tasks</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="capability-list-item">
|
||||
<div className="capability-list-icon"><Icon icon="file-text" /></div>
|
||||
<div>
|
||||
<div className="capability-list-heading">Web fetch</div>
|
||||
<div className="capability-list-copy">Fetch and summarize web pages</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="capability-list-item">
|
||||
<div className="capability-list-icon"><Icon icon="image" /></div>
|
||||
<div>
|
||||
<div className="capability-list-heading">Vision</div>
|
||||
<div className="capability-list-copy">Send images and screenshots</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
## Models
|
||||
|
||||
Choose a model with enough context for your repository.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Cloud models" icon="cloud" href="https://ollama.com/search?c=cloud">
|
||||
Use larger models without downloading them.
|
||||
</Card>
|
||||
|
||||
<Card title="Local models" icon="hard-drive" href="https://ollama.com/search?c=tools">
|
||||
Choose a model and set a 64k+ context window.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
<Note>OpenCode requires a context length of 64k or higher. See [Context length](/context-length) for more information.</Note>
|
||||
|
||||
## Manual setup
|
||||
|
||||
<p className="manual-step-title">1. Install OpenCode</p>
|
||||
|
||||
<CodeGroup>
|
||||
|
||||
```shell macOS / Linux
|
||||
curl -fsSL https://opencode.ai/install | bash
|
||||
```
|
||||
|
||||
```powershell Windows
|
||||
npm install -g opencode-ai
|
||||
```
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
<p className="manual-step-title">2. Configure Ollama as a provider</p>
|
||||
|
||||
Add an Ollama provider to `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": {
|
||||
"qwen3.5": {
|
||||
"name": "qwen3.5"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
<p className="manual-step-title">3. Run OpenCode</p>
|
||||
|
||||
```shell
|
||||
opencode
|
||||
```
|
||||
|
||||
To configure OpenCode with Ollama without starting an interactive session:
|
||||
|
||||
```shell
|
||||
ollama launch opencode --config
|
||||
```
|
||||
|
||||
### Configuration precedence
|
||||
|
||||
`ollama launch opencode` starts OpenCode with an inline config for the selected Ollama model. It does not overwrite `~/.config/opencode/opencode.json`; existing OpenCode settings still apply.
|
||||
|
||||
Models defined only in `opencode.json` do not appear in the `ollama launch` model picker.
|
||||
|
||||
See OpenCode's [config precedence](https://opencode.ai/docs/config/#precedence-order).
|
||||
@@ -0,0 +1,109 @@
|
||||
---
|
||||
title: Pi
|
||||
---
|
||||
|
||||
Pi is a minimal and extensible coding agent.
|
||||
|
||||
## Quick setup
|
||||
|
||||
```bash
|
||||
ollama launch pi
|
||||
```
|
||||
|
||||
This installs Pi if needed, 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
|
||||
|
||||
### Install
|
||||
|
||||
Install [Pi](https://github.com/earendil-works/pi):
|
||||
|
||||
```bash
|
||||
npm install -g @earendil-works/pi-coding-agent
|
||||
```
|
||||
|
||||
|
||||
|
||||
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"
|
||||
}
|
||||
```
|
||||
@@ -0,0 +1,54 @@
|
||||
---
|
||||
title: Pool
|
||||
---
|
||||
|
||||
Pool is Poolside's software agent for the terminal, built for enterprise development workflows.
|
||||
|
||||
## Install
|
||||
|
||||
Install [Pool](https://github.com/poolsideai/pool):
|
||||
|
||||
## Usage with Ollama
|
||||
|
||||
### Quick setup
|
||||
|
||||
```shell
|
||||
ollama launch pool
|
||||
```
|
||||
|
||||
### Run directly with a model
|
||||
|
||||
```shell
|
||||
ollama launch pool --model kimi-k2.6:cloud
|
||||
```
|
||||
|
||||
### Pass arguments through to Pool
|
||||
|
||||
Arguments after `--` are passed directly to Pool:
|
||||
|
||||
```shell
|
||||
ollama launch pool -- --help
|
||||
```
|
||||
|
||||
## Manual setup
|
||||
|
||||
Pool connects to Ollama using the OpenAI-compatible API via environment variables.
|
||||
|
||||
1. Set the environment variables:
|
||||
|
||||
```shell
|
||||
export POOLSIDE_STANDALONE_BASE_URL=http://localhost:11434/v1
|
||||
export POOLSIDE_API_KEY=ollama
|
||||
```
|
||||
|
||||
2. Run Pool with an Ollama model:
|
||||
|
||||
```shell
|
||||
pool -m kimi-k2.6:cloud
|
||||
```
|
||||
|
||||
Or run with environment variables inline:
|
||||
|
||||
```shell
|
||||
POOLSIDE_STANDALONE_BASE_URL=http://localhost:11434/v1 POOLSIDE_API_KEY=ollama pool -m kimi-k2.6:cloud
|
||||
```
|
||||
@@ -2,33 +2,57 @@
|
||||
title: VS Code
|
||||
---
|
||||
|
||||
## Install
|
||||
Use Ollama models in VS Code Chat with the [Ollama extension](https://marketplace.visualstudio.com/items?itemName=Ollama.ollama).
|
||||
|
||||
Install [VS Code](https://code.visualstudio.com/download).
|
||||
## Requirements
|
||||
|
||||
## Usage with Ollama
|
||||
- [Visual Studio Code 1.127 or newer](https://code.visualstudio.com/download)
|
||||
- Ollama installed and running
|
||||
- At least one local or cloud model available in Ollama
|
||||
|
||||
1. Open Copilot side bar found in top right window
|
||||
<div style={{ display: "flex", justifyContent: "center" }}>
|
||||
<img
|
||||
src="/images/vscode-sidebar.png"
|
||||
alt="VS Code chat Sidebar"
|
||||
width="75%"
|
||||
/>
|
||||
</div>
|
||||
2. Select the model dropdown > **Manage models**
|
||||
<div style={{ display: "flex", justifyContent: "center" }}>
|
||||
<img
|
||||
src="/images/vscode-models.png"
|
||||
alt="VS Code model picker"
|
||||
width="75%"
|
||||
/>
|
||||
</div>
|
||||
3. Enter **Ollama** under **Provider Dropdown** and select desired models (e.g `qwen3, qwen3-coder:480b-cloud`)
|
||||
<div style={{ display: "flex", justifyContent: "center" }}>
|
||||
<img
|
||||
src="/images/vscode-model-options.png"
|
||||
alt="VS Code model options dropdown"
|
||||
width="75%"
|
||||
/>
|
||||
</div>
|
||||
Earlier VS Code versions do not reliably cancel requests from language model providers.
|
||||
|
||||
Ollama 0.17.6 or newer is recommended for cloud model sign-in and richer model metadata. Older versions may still work with local models.
|
||||
|
||||
## Install the extension
|
||||
|
||||
1. Install the [Ollama extension](https://marketplace.visualstudio.com/items?itemName=Ollama.ollama) from the VS Code Marketplace.
|
||||
2. Open Chat in VS Code.
|
||||
3. Open the model picker at the bottom of the chat input.
|
||||
4. Choose a model from the **Ollama** section.
|
||||
|
||||
The extension discovers models from `http://127.0.0.1:11434` by default.
|
||||
|
||||
## Add a model
|
||||
|
||||
Pull a local model:
|
||||
|
||||
```shell
|
||||
ollama pull qwen3.6
|
||||
```
|
||||
|
||||
To use a cloud model, pull it and sign in:
|
||||
|
||||
```shell
|
||||
ollama pull kimi-k2.6:cloud
|
||||
ollama signin
|
||||
```
|
||||
|
||||
Local models do not require sign-in.
|
||||
|
||||
## Context length
|
||||
|
||||
VS Code may show a model's maximum supported context length even when Ollama allocates a smaller context at runtime.
|
||||
|
||||
For local models, open Ollama **Settings**, set the context length to at least 64k, reload the VS Code window, and resend your prompt. See [Context length](/context-length) for more information.
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
If Ollama models do not appear in the model picker:
|
||||
|
||||
1. Make sure Ollama is running.
|
||||
2. Run `ollama list` and confirm that models are available.
|
||||
3. Run **Ollama: Refresh Models** from the Command Palette.
|
||||
4. Run **Ollama: Diagnose Models** and check the **Ollama** output channel.
|
||||
|
||||
If a cloud model asks you to sign in, run `ollama signin`.
|
||||
|
||||
+10
-10
@@ -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
|
||||
|
||||
@@ -116,7 +116,7 @@ sudo systemctl status ollama
|
||||
While AMD has contributed the `amdgpu` driver upstream to the official linux
|
||||
kernel source, the version is older and may not support all ROCm features. We
|
||||
recommend you install the latest driver from
|
||||
https://www.amd.com/en/support/linux-drivers for best support of your Radeon
|
||||
https://www.amd.com/en/support/download/linux-drivers.html for best support of your Radeon
|
||||
GPU.
|
||||
</Note>
|
||||
|
||||
@@ -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
|
||||
|
||||
+14
-6
@@ -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
|
||||
|
||||
@@ -106,12 +107,8 @@ FROM <model name>:<tag>
|
||||
FROM llama3.2
|
||||
```
|
||||
|
||||
<Card title="Base Models" href="https://github.com/ollama/ollama#model-library">
|
||||
A list of available base models
|
||||
</Card>
|
||||
|
||||
<Card title="Base Models" href="https://ollama.com/library">
|
||||
Additional models can be found at
|
||||
<Card title="Model library" href="https://ollama.com/library">
|
||||
Browse available models
|
||||
</Card>
|
||||
|
||||
#### Build from a Safetensors model
|
||||
@@ -156,6 +153,7 @@ PARAMETER <parameter> <parametervalue>
|
||||
| seed | Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. (Default: 0) | int | seed 42 |
|
||||
| stop | Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return. Multiple stop patterns may be set by specifying multiple separate `stop` parameters in a modelfile. | string | stop "AI assistant:" |
|
||||
| num_predict | Maximum number of tokens to predict when generating text. (Default: -1, infinite generation) | int | num_predict 42 |
|
||||
| draft_num_predict | Maximum number of speculative draft tokens to predict per step when a draft model is available. Separate draft models default to 4; embedded MTP tensors require setting this parameter. Set to 0 to disable speculative drafting. | int | draft_num_predict 4 |
|
||||
| top_k | Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40) | int | top_k 40 |
|
||||
| top_p | Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9) | float | top_p 0.9 |
|
||||
| min_p | Alternative to the top*p, and aims to ensure a balance of quality and variety. The parameter \_p* represents the minimum probability for a token to be considered, relative to the probability of the most likely token. For example, with _p_=0.05 and the most likely token having a probability of 0.9, logits with a value less than 0.045 are filtered out. (Default: 0.0) | float | min_p 0.05 |
|
||||
@@ -248,6 +246,16 @@ 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.
|
||||
|
||||
+109
-69
@@ -99,8 +99,8 @@ components:
|
||||
oneOf:
|
||||
- type: boolean
|
||||
- type: string
|
||||
enum: [high, medium, low]
|
||||
description: When true, returns separate thinking output in addition to content. Can be a boolean (true/false) or a string ("high", "medium", "low") for supported models.
|
||||
enum: [high, medium, low, max]
|
||||
description: When true, returns separate thinking output in addition to content. Can be a boolean (true/false) or a string ("high", "medium", "low", "max") for supported models, with "max" requesting the highest thinking level.
|
||||
raw:
|
||||
type: boolean
|
||||
description: When true, returns the raw response from the model without any prompt templating
|
||||
@@ -291,8 +291,8 @@ components:
|
||||
oneOf:
|
||||
- type: boolean
|
||||
- type: string
|
||||
enum: [high, medium, low]
|
||||
description: When true, returns separate thinking output in addition to content. Can be a boolean (true/false) or a string ("high", "medium", "low") for supported models.
|
||||
enum: [high, medium, low, max]
|
||||
description: When true, returns separate thinking output in addition to content. Can be a boolean (true/false) or a string ("high", "medium", "low", "max") for supported models, with "max" requesting the highest thinking level.
|
||||
keep_alive:
|
||||
oneOf:
|
||||
- type: string
|
||||
@@ -483,6 +483,12 @@ components:
|
||||
template:
|
||||
type: string
|
||||
description: Prompt template to use for the model
|
||||
renderer:
|
||||
type: string
|
||||
description: Name of the renderer for the model
|
||||
parser:
|
||||
type: string
|
||||
description: Name of the parser for the model
|
||||
license:
|
||||
oneOf:
|
||||
- type: string
|
||||
@@ -596,6 +602,15 @@ 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 +651,9 @@ components:
|
||||
Ps:
|
||||
type: object
|
||||
properties:
|
||||
name:
|
||||
type: string
|
||||
description: Name of the running model
|
||||
model:
|
||||
type: string
|
||||
description: Name of the running model
|
||||
@@ -782,14 +800,14 @@ paths:
|
||||
label: Default
|
||||
source: |
|
||||
curl http://localhost:11434/api/generate -d '{
|
||||
"model": "gemma3",
|
||||
"model": "gemma4",
|
||||
"prompt": "Why is the sky blue?"
|
||||
}'
|
||||
- lang: bash
|
||||
label: Non-streaming
|
||||
source: |
|
||||
curl http://localhost:11434/api/generate -d '{
|
||||
"model": "gemma3",
|
||||
"model": "gemma4",
|
||||
"prompt": "Why is the sky blue?",
|
||||
"stream": false
|
||||
}'
|
||||
@@ -797,7 +815,7 @@ paths:
|
||||
label: With options
|
||||
source: |
|
||||
curl http://localhost:11434/api/generate -d '{
|
||||
"model": "gemma3",
|
||||
"model": "gemma4",
|
||||
"prompt": "Why is the sky blue?",
|
||||
"options": {
|
||||
"temperature": 0.8,
|
||||
@@ -809,7 +827,7 @@ paths:
|
||||
label: Structured outputs
|
||||
source: |
|
||||
curl http://localhost:11434/api/generate -d '{
|
||||
"model": "gemma3",
|
||||
"model": "gemma4",
|
||||
"prompt": "What are the populations of the United States and Canada?",
|
||||
"stream": false,
|
||||
"format": {
|
||||
@@ -834,7 +852,7 @@ paths:
|
||||
label: With images
|
||||
source: |
|
||||
curl http://localhost:11434/api/generate -d '{
|
||||
"model": "gemma3",
|
||||
"model": "gemma4",
|
||||
"prompt": "What is in this picture?",
|
||||
"images": ["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"]
|
||||
}'
|
||||
@@ -842,13 +860,13 @@ paths:
|
||||
label: Load model
|
||||
source: |
|
||||
curl http://localhost:11434/api/generate -d '{
|
||||
"model": "gemma3"
|
||||
"model": "gemma4"
|
||||
}'
|
||||
- lang: bash
|
||||
label: Unload model
|
||||
source: |
|
||||
curl http://localhost:11434/api/generate -d '{
|
||||
"model": "gemma3",
|
||||
"model": "gemma4",
|
||||
"keep_alive": 0
|
||||
}'
|
||||
requestBody:
|
||||
@@ -858,7 +876,7 @@ paths:
|
||||
schema:
|
||||
$ref: "#/components/schemas/GenerateRequest"
|
||||
example:
|
||||
model: gemma3
|
||||
model: gemma4
|
||||
prompt: Why is the sky blue?
|
||||
responses:
|
||||
"200":
|
||||
@@ -868,7 +886,7 @@ paths:
|
||||
schema:
|
||||
$ref: "#/components/schemas/GenerateResponse"
|
||||
example:
|
||||
model: "gemma3"
|
||||
model: "gemma4"
|
||||
created_at: "2025-10-17T23:14:07.414671Z"
|
||||
response: "Hello! How can I help you today?"
|
||||
done: true
|
||||
@@ -894,7 +912,7 @@ paths:
|
||||
label: Default
|
||||
source: |
|
||||
curl http://localhost:11434/api/chat -d '{
|
||||
"model": "gemma3",
|
||||
"model": "gemma4",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
@@ -906,7 +924,7 @@ paths:
|
||||
label: Non-streaming
|
||||
source: |
|
||||
curl http://localhost:11434/api/chat -d '{
|
||||
"model": "gemma3",
|
||||
"model": "gemma4",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
@@ -919,7 +937,7 @@ paths:
|
||||
label: Structured outputs
|
||||
source: |
|
||||
curl -X POST http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{
|
||||
"model": "gemma3",
|
||||
"model": "gemma4",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
@@ -999,7 +1017,7 @@ paths:
|
||||
label: Images
|
||||
source: |
|
||||
curl http://localhost:11434/api/chat -d '{
|
||||
"model": "gemma3",
|
||||
"model": "gemma4",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
@@ -1024,7 +1042,7 @@ paths:
|
||||
schema:
|
||||
$ref: "#/components/schemas/ChatResponse"
|
||||
example:
|
||||
model: "gemma3"
|
||||
model: "gemma4"
|
||||
created_at: "2025-10-17T23:14:07.414671Z"
|
||||
message:
|
||||
role: "assistant"
|
||||
@@ -1136,16 +1154,17 @@ paths:
|
||||
$ref: "#/components/schemas/ListResponse"
|
||||
example:
|
||||
models:
|
||||
- name: "gemma3"
|
||||
- name: "gemma4"
|
||||
model: "gemma4"
|
||||
modified_at: "2025-10-03T23:34:03.409490317-07:00"
|
||||
size: 3338801804
|
||||
digest: "a2af6cc3eb7fa8be8504abaf9b04e88f17a119ec3f04a3addf55f92841195f5a"
|
||||
size: 9608350245
|
||||
digest: "c6eb396dbd5992bbe3f5cdb947e8bbc0ee413d7c17e2beaae69f5d569cf982eb"
|
||||
details:
|
||||
format: "gguf"
|
||||
family: "gemma"
|
||||
family: "gemma4"
|
||||
families:
|
||||
- "gemma"
|
||||
parameter_size: "4.3B"
|
||||
- "gemma4"
|
||||
parameter_size: "8.0B"
|
||||
quantization_level: "Q4_K_M"
|
||||
/api/ps:
|
||||
get:
|
||||
@@ -1168,16 +1187,17 @@ paths:
|
||||
$ref: "#/components/schemas/PsResponse"
|
||||
example:
|
||||
models:
|
||||
- model: "gemma3"
|
||||
- name: "gemma4"
|
||||
model: "gemma4"
|
||||
size: 6591830464
|
||||
digest: "a2af6cc3eb7fa8be8504abaf9b04e88f17a119ec3f04a3addf55f92841195f5a"
|
||||
digest: "c6eb396dbd5992bbe3f5cdb947e8bbc0ee413d7c17e2beaae69f5d569cf982eb"
|
||||
details:
|
||||
parent_model: ""
|
||||
format: "gguf"
|
||||
family: "gemma3"
|
||||
family: "gemma4"
|
||||
families:
|
||||
- "gemma3"
|
||||
parameter_size: "4.3B"
|
||||
- "gemma4"
|
||||
parameter_size: "8.0B"
|
||||
quantization_level: "Q4_K_M"
|
||||
expires_at: "2025-10-17T16:47:07.93355-07:00"
|
||||
size_vram: 5333539264
|
||||
@@ -1191,13 +1211,13 @@ paths:
|
||||
label: Default
|
||||
source: |
|
||||
curl http://localhost:11434/api/show -d '{
|
||||
"model": "gemma3"
|
||||
"model": "gemma4"
|
||||
}'
|
||||
- lang: bash
|
||||
label: Verbose
|
||||
source: |
|
||||
curl http://localhost:11434/api/show -d '{
|
||||
"model": "gemma3",
|
||||
"model": "gemma4",
|
||||
"verbose": true
|
||||
}'
|
||||
requestBody:
|
||||
@@ -1207,7 +1227,7 @@ paths:
|
||||
schema:
|
||||
$ref: "#/components/schemas/ShowRequest"
|
||||
example:
|
||||
model: gemma3
|
||||
model: gemma4
|
||||
responses:
|
||||
"200":
|
||||
description: Model information
|
||||
@@ -1225,44 +1245,64 @@ paths:
|
||||
details:
|
||||
parent_model: ""
|
||||
format: "gguf"
|
||||
family: "gemma3"
|
||||
family: "gemma4"
|
||||
families:
|
||||
- "gemma3"
|
||||
parameter_size: "4.3B"
|
||||
- "gemma4"
|
||||
parameter_size: "8.0B"
|
||||
quantization_level: "Q4_K_M"
|
||||
model_info:
|
||||
gemma3.attention.head_count: 8
|
||||
gemma3.attention.head_count_kv: 4
|
||||
gemma3.attention.key_length: 256
|
||||
gemma3.attention.sliding_window: 1024
|
||||
gemma3.attention.value_length: 256
|
||||
gemma3.block_count: 34
|
||||
gemma3.context_length: 131072
|
||||
gemma3.embedding_length: 2560
|
||||
gemma3.feed_forward_length: 10240
|
||||
gemma3.mm.tokens_per_image: 256
|
||||
gemma3.vision.attention.head_count: 16
|
||||
gemma3.vision.attention.layer_norm_epsilon: 0.000001
|
||||
gemma3.vision.block_count: 27
|
||||
gemma3.vision.embedding_length: 1152
|
||||
gemma3.vision.feed_forward_length: 4304
|
||||
gemma3.vision.image_size: 896
|
||||
gemma3.vision.num_channels: 3
|
||||
gemma3.vision.patch_size: 14
|
||||
general.architecture: "gemma3"
|
||||
gemma4.attention.head_count: 8
|
||||
gemma4.attention.head_count_kv: 2
|
||||
gemma4.attention.key_length: 512
|
||||
gemma4.attention.key_length_swa: 256
|
||||
gemma4.attention.layer_norm_rms_epsilon: 0.000001
|
||||
gemma4.attention.shared_kv_layers: 18
|
||||
gemma4.attention.sliding_window: 512
|
||||
gemma4.attention.value_length: 512
|
||||
gemma4.attention.value_length_swa: 256
|
||||
gemma4.audio.attention.head_count: 8
|
||||
gemma4.audio.attention.layer_norm_epsilon: 0.000001
|
||||
gemma4.audio.block_count: 12
|
||||
gemma4.audio.conv_kernel_size: 5
|
||||
gemma4.audio.embedding_length: 1024
|
||||
gemma4.audio.feed_forward_length: 4096
|
||||
gemma4.block_count: 42
|
||||
gemma4.context_length: 131072
|
||||
gemma4.embedding_length: 2560
|
||||
gemma4.embedding_length_per_layer_input: 256
|
||||
gemma4.feed_forward_length: 10240
|
||||
gemma4.final_logit_softcapping: 30
|
||||
gemma4.rope.dimension_count: 512
|
||||
gemma4.rope.dimension_count_swa: 256
|
||||
gemma4.rope.freq_base: 1000000
|
||||
gemma4.rope.freq_base_swa: 10000
|
||||
gemma4.vision.attention.head_count: 12
|
||||
gemma4.vision.attention.layer_norm_epsilon: 0.000001
|
||||
gemma4.vision.block_count: 16
|
||||
gemma4.vision.embedding_length: 768
|
||||
gemma4.vision.feed_forward_length: 3072
|
||||
gemma4.vision.num_channels: 3
|
||||
gemma4.vision.patch_size: 16
|
||||
gemma4.vision.projector.scale_factor: 3
|
||||
general.architecture: "gemma4"
|
||||
general.file_type: 15
|
||||
general.parameter_count: 4299915632
|
||||
general.quantization_version: 2
|
||||
tokenizer.ggml.add_bos_token: true
|
||||
tokenizer.ggml.add_bos_token: false
|
||||
tokenizer.ggml.add_eos_token: false
|
||||
tokenizer.ggml.add_mask_token: false
|
||||
tokenizer.ggml.add_padding_token: false
|
||||
tokenizer.ggml.add_unknown_token: false
|
||||
tokenizer.ggml.bos_token_id: 2
|
||||
tokenizer.ggml.eos_token_id: 1
|
||||
tokenizer.ggml.eos_token_ids:
|
||||
- 1
|
||||
- 106
|
||||
- 50
|
||||
tokenizer.ggml.mask_token_id: 4
|
||||
tokenizer.ggml.merges: null
|
||||
tokenizer.ggml.model: "llama"
|
||||
tokenizer.ggml.padding_token_id: 0
|
||||
tokenizer.ggml.pre: "default"
|
||||
tokenizer.ggml.pre: "gemma4"
|
||||
tokenizer.ggml.scores: null
|
||||
tokenizer.ggml.token_type: null
|
||||
tokenizer.ggml.tokens: null
|
||||
@@ -1278,7 +1318,7 @@ paths:
|
||||
label: Default
|
||||
source: |
|
||||
curl http://localhost:11434/api/create -d '{
|
||||
"from": "gemma3",
|
||||
"from": "gemma4",
|
||||
"model": "alpaca",
|
||||
"system": "You are Alpaca, a helpful AI assistant. You only answer with Emojis."
|
||||
}'
|
||||
@@ -1287,7 +1327,7 @@ paths:
|
||||
source: |
|
||||
curl http://localhost:11434/api/create -d '{
|
||||
"model": "ollama",
|
||||
"from": "gemma3",
|
||||
"from": "gemma4",
|
||||
"system": "You are Ollama the llama."
|
||||
}'
|
||||
- lang: bash
|
||||
@@ -1306,7 +1346,7 @@ paths:
|
||||
$ref: "#/components/schemas/CreateRequest"
|
||||
example:
|
||||
model: mario
|
||||
from: gemma3
|
||||
from: gemma4
|
||||
system: "You are Mario from Super Mario Bros."
|
||||
responses:
|
||||
"200":
|
||||
@@ -1333,8 +1373,8 @@ paths:
|
||||
label: Copy a model to a new name
|
||||
source: |
|
||||
curl http://localhost:11434/api/copy -d '{
|
||||
"source": "gemma3",
|
||||
"destination": "gemma3-backup"
|
||||
"source": "gemma4",
|
||||
"destination": "gemma4-backup"
|
||||
}'
|
||||
requestBody:
|
||||
required: true
|
||||
@@ -1343,8 +1383,8 @@ paths:
|
||||
schema:
|
||||
$ref: "#/components/schemas/CopyRequest"
|
||||
example:
|
||||
source: gemma3
|
||||
destination: gemma3-backup
|
||||
source: gemma4
|
||||
destination: gemma4-backup
|
||||
responses:
|
||||
"200":
|
||||
description: Model successfully copied
|
||||
@@ -1359,13 +1399,13 @@ paths:
|
||||
label: Default
|
||||
source: |
|
||||
curl http://localhost:11434/api/pull -d '{
|
||||
"model": "gemma3"
|
||||
"model": "gemma4"
|
||||
}'
|
||||
- lang: bash
|
||||
label: Non-streaming
|
||||
source: |
|
||||
curl http://localhost:11434/api/pull -d '{
|
||||
"model": "gemma3",
|
||||
"model": "gemma4",
|
||||
"stream": false
|
||||
}'
|
||||
requestBody:
|
||||
@@ -1375,7 +1415,7 @@ paths:
|
||||
schema:
|
||||
$ref: "#/components/schemas/PullRequest"
|
||||
example:
|
||||
model: gemma3
|
||||
model: gemma4
|
||||
responses:
|
||||
"200":
|
||||
description: Pull status updates.
|
||||
@@ -1443,7 +1483,7 @@ paths:
|
||||
label: Delete model
|
||||
source: |
|
||||
curl -X DELETE http://localhost:11434/api/delete -d '{
|
||||
"model": "gemma3"
|
||||
"model": "gemma4"
|
||||
}'
|
||||
requestBody:
|
||||
required: true
|
||||
@@ -1452,7 +1492,7 @@ paths:
|
||||
schema:
|
||||
$ref: "#/components/schemas/DeleteRequest"
|
||||
example:
|
||||
model: gemma3
|
||||
model: gemma4
|
||||
responses:
|
||||
"200":
|
||||
description: Model successfully deleted
|
||||
|
||||
+34
-77
@@ -2,7 +2,11 @@
|
||||
title: Quickstart
|
||||
---
|
||||
|
||||
This quickstart will walk your through running your first model with Ollama. To get started, download Ollama on macOS, Windows or Linux.
|
||||
Install Ollama and get your first response.
|
||||
|
||||
## 1. Download Ollama
|
||||
|
||||
Ollama runs on macOS, Windows, and Linux.
|
||||
|
||||
<a
|
||||
href="https://ollama.com/download"
|
||||
@@ -12,92 +16,45 @@ This quickstart will walk your through running your first model with Ollama. To
|
||||
Download Ollama
|
||||
</a>
|
||||
|
||||
## Run a model
|
||||
## 2. Open the menu
|
||||
|
||||
<Tabs>
|
||||
<Tab title="CLI">
|
||||
Open a terminal and run the command:
|
||||
|
||||
```
|
||||
ollama run gemma3
|
||||
```
|
||||
|
||||
</Tab>
|
||||
<Tab title="cURL">
|
||||
```
|
||||
ollama pull gemma3
|
||||
```
|
||||
|
||||
Lastly, chat with the model:
|
||||
Run `ollama` in your terminal to open the interactive menu:
|
||||
|
||||
```shell
|
||||
curl http://localhost:11434/api/chat -d '{
|
||||
"model": "gemma3",
|
||||
"messages": [{
|
||||
"role": "user",
|
||||
"content": "Hello there!"
|
||||
}],
|
||||
"stream": false
|
||||
}'
|
||||
ollama
|
||||
```
|
||||
|
||||
</Tab>
|
||||
<Tab title="Python">
|
||||
Start by downloading a model:
|
||||
From the menu you can:
|
||||
|
||||
```
|
||||
ollama pull gemma3
|
||||
```
|
||||
- **Run a model** - Start an interactive chat
|
||||
- **Launch tools** - [Claude Code](/integrations/claude-code), [OpenClaw](/integrations/openclaw), [VS Code](/integrations/vscode), and more
|
||||
|
||||
Then install Ollama's Python library:
|
||||
## 3. Start a chat
|
||||
|
||||
```
|
||||
pip install ollama
|
||||
```
|
||||
|
||||
Lastly, chat with the model:
|
||||
|
||||
```python
|
||||
from ollama import chat
|
||||
from ollama import ChatResponse
|
||||
|
||||
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)
|
||||
```
|
||||
|
||||
</Tab>
|
||||
<Tab title="JavaScript">
|
||||
Start by downloading a model:
|
||||
|
||||
```
|
||||
ollama pull gemma3
|
||||
```
|
||||
|
||||
Then install the Ollama JavaScript library:
|
||||
```
|
||||
npm i ollama
|
||||
```
|
||||
|
||||
Lastly, chat with the model:
|
||||
Run a model to start your first chat.
|
||||
|
||||
```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)
|
||||
ollama run gemma4
|
||||
```
|
||||
|
||||
</Tab>
|
||||
</Tabs>
|
||||
Cloud models work the same way:
|
||||
|
||||
See a full list of available models [here](https://ollama.com/models).
|
||||
```shell
|
||||
ollama run gemma4:cloud
|
||||
```
|
||||
|
||||
Send your first message:
|
||||
|
||||
```text
|
||||
Explain why the sky is blue in one paragraph.
|
||||
```
|
||||
|
||||
To leave the chat, type:
|
||||
|
||||
```shell
|
||||
/bye
|
||||
```
|
||||
|
||||
## Next steps
|
||||
|
||||
Use a model with an [integration](/integrations), make an [API request](/api/introduction), or browse more [models](https://ollama.com/search).
|
||||
|
||||
@@ -14,3 +14,117 @@ pre, code, .font-mono {
|
||||
color: #666;
|
||||
font-weight: 400;
|
||||
}
|
||||
|
||||
.capability-list {
|
||||
border-top: 1px solid #e5e5e5;
|
||||
border-bottom: 1px solid #e5e5e5;
|
||||
display: grid;
|
||||
gap: 2rem;
|
||||
grid-template-columns: 7rem minmax(0, 1fr);
|
||||
margin: 1.5rem 0 2rem;
|
||||
padding: 2rem 0;
|
||||
}
|
||||
|
||||
.capability-list-title {
|
||||
color: #171717;
|
||||
font-size: 1rem;
|
||||
line-height: 1.5rem;
|
||||
}
|
||||
|
||||
.capability-list-full {
|
||||
grid-template-columns: 1fr;
|
||||
}
|
||||
|
||||
.capability-list-grid {
|
||||
display: grid;
|
||||
gap: 1.75rem 2rem;
|
||||
grid-template-columns: repeat(2, minmax(0, 1fr));
|
||||
min-width: 0;
|
||||
}
|
||||
|
||||
.capability-list-item {
|
||||
align-items: flex-start;
|
||||
display: grid;
|
||||
gap: 0.75rem;
|
||||
grid-template-columns: 2rem minmax(0, 1fr);
|
||||
min-width: 0;
|
||||
}
|
||||
|
||||
.capability-list-icon {
|
||||
align-items: center;
|
||||
background: #f1f1f1;
|
||||
border-radius: 0.75rem;
|
||||
color: #171717;
|
||||
display: flex;
|
||||
height: 2rem;
|
||||
justify-content: center;
|
||||
width: 2rem;
|
||||
}
|
||||
|
||||
.capability-list-icon svg {
|
||||
height: 1rem;
|
||||
width: 1rem;
|
||||
}
|
||||
|
||||
.capability-list-heading {
|
||||
color: #171717;
|
||||
font-size: 1rem;
|
||||
font-weight: 600;
|
||||
line-height: 1.375rem;
|
||||
}
|
||||
|
||||
.capability-list-copy {
|
||||
color: #737373;
|
||||
font-size: 0.95rem;
|
||||
line-height: 1.375rem;
|
||||
}
|
||||
|
||||
.manual-step-title {
|
||||
color: #171717;
|
||||
font-size: 0.95rem;
|
||||
font-weight: 600;
|
||||
line-height: 1.375rem;
|
||||
margin: 1.25rem 0 0.75rem;
|
||||
}
|
||||
|
||||
.dark .capability-list {
|
||||
border-color: #262626;
|
||||
}
|
||||
|
||||
.dark .capability-list-title,
|
||||
.dark .capability-list-heading,
|
||||
.dark .manual-step-title {
|
||||
color: #fafafa;
|
||||
}
|
||||
|
||||
.dark .capability-list-icon {
|
||||
background: #262626;
|
||||
color: #fafafa;
|
||||
}
|
||||
|
||||
.dark .capability-list-copy {
|
||||
color: #a3a3a3;
|
||||
}
|
||||
|
||||
@media (max-width: 920px) {
|
||||
.capability-list {
|
||||
grid-template-columns: 1fr;
|
||||
}
|
||||
|
||||
.capability-list-grid {
|
||||
grid-template-columns: 1fr;
|
||||
}
|
||||
}
|
||||
|
||||
.accordion-group {
|
||||
border-style: none;
|
||||
border-radius: 0;
|
||||
}
|
||||
|
||||
.accordion-group div[id*="-accordion-children"] {
|
||||
margin-inline: calc(var(--spacing) * 1);
|
||||
}
|
||||
|
||||
.accordion-group details summary {
|
||||
padding-inline: 0;
|
||||
}
|
||||
|
||||
@@ -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
|
||||
|
||||
@@ -114,11 +114,30 @@ 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.
|
||||
|
||||
- https://rocm.docs.amd.com/projects/radeon/en/latest/docs/install/native_linux/mgpu.html#mgpu-known-issues-and-limitations
|
||||
- https://rocm.docs.amd.com/projects/radeon-ryzen/en/latest/docs/install/installrad/native_linux/mgpu.html#mgpu-known-issues-and-limitations
|
||||
|
||||
## Windows Terminal Errors
|
||||
|
||||
|
||||
+17
-5
@@ -11,11 +11,19 @@ terminal application. As usual the Ollama [API](/api) will be served on
|
||||
## System Requirements
|
||||
|
||||
- Windows 10 22H2 or newer, Home or Pro
|
||||
- NVIDIA 452.39 or newer Drivers if you have an NVIDIA card
|
||||
- AMD Radeon Driver https://www.amd.com/en/support if you have a Radeon card
|
||||
- NVIDIA 551.61 or newer Drivers if you have an NVIDIA card
|
||||
- AMD ROCm v7 / HIP7-capable driver stack for ROCm acceleration, or a Vulkan-capable AMD Radeon driver for Vulkan acceleration
|
||||
|
||||
Ollama uses unicode characters for progress indication, which may render as unknown squares in some older terminal fonts in Windows 10. If you see this, try changing your terminal font settings.
|
||||
|
||||
<Note>
|
||||
Some RDNA2 / Radeon RX 6000 systems, including RX 6800-class cards, may not
|
||||
expose ROCm v7 on current Windows AMD drivers. Vulkan is enabled by default
|
||||
and is the recommended fallback for those systems. If a mixed iGPU/dGPU
|
||||
system selects an unstable Vulkan iGPU, set `GGML_VK_VISIBLE_DEVICES` to the
|
||||
discrete GPU index.
|
||||
</Note>
|
||||
|
||||
## Filesystem Requirements
|
||||
|
||||
The Ollama install does not require Administrator, and installs in your home directory by default. You'll need at least 4GB of space for the binary install. Once you've installed Ollama, you'll need additional space for storing the Large Language models, which can be tens to hundreds of GB in size. If your home directory doesn't have enough space, you can change where the binaries are installed, and where the models are stored.
|
||||
@@ -80,9 +88,13 @@ 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