Compare commits
2
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 The Librarian
|
62c02724f3 | ||
|
The Librarian
|
d804be2b33 |
+2
-2
@@ -4,8 +4,8 @@ This is a mirror of the Ollama repository.
|
||||
|
||||
**Synced from:** https://github.com/ollama/ollama.git
|
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**Branch:** main
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**Commit:** c146a138e35520cd7ee132da46c6ae185778b4f0
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**Sync Date:** 2025-12-07
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**Commit:** 361d6c16c23b60f84eb1d2134914610c0ae5836b
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**Sync Date:** 2026-01-12
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**Content:** Paths: docs
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|
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---
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@@ -14,6 +14,7 @@
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* [API Reference](https://docs.ollama.com/api)
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* [Modelfile Reference](https://docs.ollama.com/modelfile)
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* [OpenAI Compatibility](https://docs.ollama.com/api/openai-compatibility)
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* [Anthropic Compatibility](./api/anthropic-compatibility.mdx)
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|
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### Resources
|
||||
|
||||
|
||||
+8
-8
@@ -50,7 +50,7 @@ Generate a response for a given prompt with a provided model. This is a streamin
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Advanced parameters (optional):
|
||||
|
||||
- `format`: the format to return a response in. Format can be `json` or a JSON schema
|
||||
- `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.md#valid-parameters-and-values) such as `temperature`
|
||||
- `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.mdx#valid-parameters-and-values) such as `temperature`
|
||||
- `system`: system message to (overrides what is defined in the `Modelfile`)
|
||||
- `template`: the prompt template to use (overrides what is defined in the `Modelfile`)
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||||
- `stream`: if `false` the response will be returned as a single response object, rather than a stream of objects
|
||||
@@ -507,7 +507,7 @@ The `message` object has the following fields:
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||||
Advanced parameters (optional):
|
||||
|
||||
- `format`: the format to return a response in. Format can be `json` or a JSON schema.
|
||||
- `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.md#valid-parameters-and-values) such as `temperature`
|
||||
- `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.mdx#valid-parameters-and-values) such as `temperature`
|
||||
- `stream`: if `false` the response will be returned as a single response object, rather than a stream of objects
|
||||
- `keep_alive`: controls how long the model will stay loaded into memory following the request (default: `5m`)
|
||||
|
||||
@@ -895,11 +895,11 @@ curl http://localhost:11434/api/chat -d '{
|
||||
"tool_calls": [
|
||||
{
|
||||
"function": {
|
||||
"name": "get_temperature",
|
||||
"name": "get_weather",
|
||||
"arguments": {
|
||||
"city": "Toronto"
|
||||
}
|
||||
},
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
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@@ -907,7 +907,7 @@ curl http://localhost:11434/api/chat -d '{
|
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{
|
||||
"role": "tool",
|
||||
"content": "11 degrees celsius",
|
||||
"tool_name": "get_temperature",
|
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"tool_name": "get_weather"
|
||||
}
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],
|
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"stream": false,
|
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@@ -1189,7 +1189,7 @@ If you are creating a model from a safetensors directory or from a GGUF file, yo
|
||||
- `template`: (optional) the prompt template 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.md#valid-parameters-and-values) for a list of parameters)
|
||||
- `parameters`: (optional) a dictionary of parameters for the model (see [Modelfile](./modelfile.mdx#valid-parameters-and-values) for a list of parameters)
|
||||
- `messages`: (optional) a list of message objects used to create a conversation
|
||||
- `stream`: (optional) if `false` the response will be returned as a single response object, rather than a stream of objects
|
||||
- `quantize` (optional): quantize a non-quantized (e.g. float16) model
|
||||
@@ -1698,7 +1698,7 @@ Generate embeddings from a model
|
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Advanced parameters:
|
||||
|
||||
- `truncate`: truncates the end of each input to fit within context length. Returns error if `false` and context length is exceeded. Defaults to `true`
|
||||
- `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.md#valid-parameters-and-values) such as `temperature`
|
||||
- `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.mdx#valid-parameters-and-values) such as `temperature`
|
||||
- `keep_alive`: controls how long the model will stay loaded into memory following the request (default: `5m`)
|
||||
- `dimensions`: number of dimensions for the embedding
|
||||
|
||||
@@ -1817,7 +1817,7 @@ Generate embeddings from a model
|
||||
|
||||
Advanced parameters:
|
||||
|
||||
- `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.md#valid-parameters-and-values) such as `temperature`
|
||||
- `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.mdx#valid-parameters-and-values) such as `temperature`
|
||||
- `keep_alive`: controls how long the model will stay loaded into memory following the request (default: `5m`)
|
||||
|
||||
### Examples
|
||||
|
||||
@@ -0,0 +1,406 @@
|
||||
---
|
||||
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.
|
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|
||||
## Recommended models
|
||||
|
||||
For coding use cases, models like `glm-4.7:cloud`, `minimax-m2.1:cloud`, and `qwen3-coder` are recommended.
|
||||
|
||||
Pull a model before use:
|
||||
```shell
|
||||
ollama pull qwen3-coder
|
||||
ollama pull glm-4.7:cloud
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
### Environment variables
|
||||
|
||||
To use Ollama with tools that expect the Anthropic API (like Claude Code), set these environment variables:
|
||||
|
||||
```shell
|
||||
export ANTHROPIC_BASE_URL=http://localhost:11434
|
||||
export ANTHROPIC_API_KEY=ollama # required but ignored
|
||||
```
|
||||
|
||||
### 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',
|
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max_tokens=1024,
|
||||
messages=[
|
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{'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:
|
||||
|
||||
```shell
|
||||
ANTHROPIC_BASE_URL=http://localhost:11434 ANTHROPIC_API_KEY=ollama claude --model qwen3-coder
|
||||
```
|
||||
|
||||
Or set the environment variables in your shell profile:
|
||||
|
||||
```shell
|
||||
export ANTHROPIC_BASE_URL=http://localhost:11434
|
||||
export ANTHROPIC_API_KEY=ollama
|
||||
```
|
||||
|
||||
Then run Claude Code with any Ollama model:
|
||||
|
||||
```shell
|
||||
# Local models
|
||||
claude --model qwen3-coder
|
||||
claude --model gpt-oss:20b
|
||||
|
||||
# Cloud models
|
||||
claude --model glm-4.7:cloud
|
||||
claude --model minimax-m2.1:cloud
|
||||
```
|
||||
|
||||
## 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 |
|
||||
+141
-149
File diff suppressed because one or more lines are too long
@@ -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">
|
||||
|
||||
+9
-3
@@ -32,7 +32,9 @@
|
||||
"codeblocks": "system"
|
||||
},
|
||||
"contextual": {
|
||||
"options": ["copy"]
|
||||
"options": [
|
||||
"copy"
|
||||
]
|
||||
},
|
||||
"navbar": {
|
||||
"links": [
|
||||
@@ -52,7 +54,9 @@
|
||||
"display": "simple"
|
||||
},
|
||||
"examples": {
|
||||
"languages": ["curl"]
|
||||
"languages": [
|
||||
"curl"
|
||||
]
|
||||
}
|
||||
},
|
||||
"redirects": [
|
||||
@@ -97,6 +101,7 @@
|
||||
{
|
||||
"group": "Integrations",
|
||||
"pages": [
|
||||
"/integrations/claude-code",
|
||||
"/integrations/vscode",
|
||||
"/integrations/jetbrains",
|
||||
"/integrations/codex",
|
||||
@@ -139,7 +144,8 @@
|
||||
"/api/streaming",
|
||||
"/api/usage",
|
||||
"/api/errors",
|
||||
"/api/openai-compatibility"
|
||||
"/api/openai-compatibility",
|
||||
"/api/anthropic-compatibility"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
+2
-2
@@ -14,11 +14,11 @@ 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) 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).
|
||||
|
||||
## How can I specify the context window size?
|
||||
|
||||
|
||||
+5
-5
@@ -33,7 +33,7 @@ Check your compute compatibility to see if your card is supported:
|
||||
| 5.0 | GeForce GTX | `GTX 750 Ti` `GTX 750` `NVS 810` |
|
||||
| | Quadro | `K2200` `K1200` `K620` `M1200` `M520` `M5000M` `M4000M` `M3000M` `M2000M` `M1000M` `K620M` `M600M` `M500M` |
|
||||
|
||||
For building locally to support older GPUs, see [developer.md](./development.md#linux-cuda-nvidia)
|
||||
For building locally to support older GPUs, see [developer](./development#linux-cuda-nvidia)
|
||||
|
||||
### GPU Selection
|
||||
|
||||
@@ -54,7 +54,7 @@ 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.
|
||||
|
||||
|
||||
@@ -132,9 +132,9 @@ Ollama supports GPU acceleration on Apple devices via the Metal API.
|
||||
|
||||
## Vulkan GPU Support
|
||||
|
||||
> [!NOTE]
|
||||
> **NOTE:**
|
||||
> Vulkan is currently an Experimental feature. To enable, you must set OLLAMA_VULKAN=1 for the Ollama server as
|
||||
described in the [FAQ](faq.md#how-do-i-configure-ollama-server)
|
||||
described in the [FAQ](faq#how-do-i-configure-ollama-server)
|
||||
|
||||
Additional GPU support on Windows and Linux is provided via
|
||||
[Vulkan](https://www.vulkan.org/). On Windows most GPU vendors drivers come
|
||||
@@ -161,6 +161,6 @@ sudo setcap cap_perfmon+ep /usr/local/bin/ollama
|
||||
|
||||
To select specific Vulkan GPU(s), you can set the environment variable
|
||||
`GGML_VK_VISIBLE_DEVICES` to one or more numeric IDs on the Ollama server as
|
||||
described in the [FAQ](faq.md#how-do-i-configure-ollama-server). If you
|
||||
described in the [FAQ](faq#how-do-i-configure-ollama-server). If you
|
||||
encounter any problems with Vulkan based GPUs, you can disable all Vulkan GPUs
|
||||
by setting `GGML_VK_VISIBLE_DEVICES=-1`
|
||||
@@ -0,0 +1,69 @@
|
||||
---
|
||||
title: Claude Code
|
||||
---
|
||||
|
||||
## Install
|
||||
|
||||
Install [Claude Code](https://code.claude.com/docs/en/overview):
|
||||
|
||||
<CodeGroup>
|
||||
|
||||
```shell macOS / Linux
|
||||
curl -fsSL https://claude.ai/install.sh | bash
|
||||
```
|
||||
|
||||
```powershell Windows
|
||||
irm https://claude.ai/install.ps1 | iex
|
||||
```
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
## Usage with Ollama
|
||||
|
||||
Claude Code connects to Ollama using the Anthropic-compatible API.
|
||||
|
||||
1. Set the environment variables:
|
||||
|
||||
```shell
|
||||
export ANTHROPIC_BASE_URL=http://localhost:11434
|
||||
export ANTHROPIC_API_KEY=ollama
|
||||
```
|
||||
|
||||
2. Run Claude Code with an Ollama model:
|
||||
|
||||
```shell
|
||||
claude --model qwen3-coder
|
||||
```
|
||||
|
||||
Or run with environment variables inline:
|
||||
|
||||
```shell
|
||||
ANTHROPIC_BASE_URL=http://localhost:11434 ANTHROPIC_API_KEY=ollama claude --model qwen3-coder
|
||||
```
|
||||
|
||||
## Connecting to ollama.com
|
||||
|
||||
1. Create an [API key](https://ollama.com/settings/keys) on ollama.com
|
||||
2. Set the environment variables:
|
||||
|
||||
```shell
|
||||
export ANTHROPIC_BASE_URL=https://ollama.com
|
||||
export ANTHROPIC_API_KEY=<your-api-key>
|
||||
```
|
||||
|
||||
3. Run Claude Code with a cloud model:
|
||||
|
||||
```shell
|
||||
claude --model glm-4.7:cloud
|
||||
```
|
||||
|
||||
## Recommended Models
|
||||
|
||||
### Cloud models
|
||||
- `glm-4.7:cloud` - High-performance cloud model
|
||||
- `minimax-m2.1:cloud` - Fast cloud model
|
||||
- `qwen3-coder:480b` - Large coding model
|
||||
|
||||
### Local models
|
||||
- `qwen3-coder` - Excellent for coding tasks
|
||||
- `gpt-oss:20b` - Strong general-purpose model
|
||||
+4
-9
@@ -1,5 +1,5 @@
|
||||
---
|
||||
title: Linux
|
||||
title: "Linux"
|
||||
---
|
||||
|
||||
## Install
|
||||
@@ -13,8 +13,7 @@ curl -fsSL https://ollama.com/install.sh | sh
|
||||
## Manual install
|
||||
|
||||
<Note>
|
||||
If you are upgrading from a prior version, you should remove the old libraries
|
||||
with `sudo rm -rf /usr/lib/ollama` first.
|
||||
If you are upgrading from a prior version, you should remove the old libraries with `sudo rm -rf /usr/lib/ollama` first.
|
||||
</Note>
|
||||
|
||||
Download and extract the package:
|
||||
@@ -113,11 +112,7 @@ sudo systemctl status ollama
|
||||
```
|
||||
|
||||
<Note>
|
||||
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
|
||||
GPU.
|
||||
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 GPU.
|
||||
</Note>
|
||||
|
||||
## Customizing
|
||||
@@ -196,4 +191,4 @@ Remove the downloaded models and Ollama service user and group:
|
||||
sudo userdel ollama
|
||||
sudo groupdel ollama
|
||||
sudo rm -r /usr/share/ollama
|
||||
```
|
||||
```
|
||||
@@ -41,6 +41,7 @@ INSTRUCTION arguments
|
||||
| [`ADAPTER`](#adapter) | Defines the (Q)LoRA adapters to apply to the model. |
|
||||
| [`LICENSE`](#license) | Specifies the legal license. |
|
||||
| [`MESSAGE`](#message) | Specify message history. |
|
||||
| [`REQUIRES`](#requires) | Specify the minimum version of Ollama required by the model. |
|
||||
|
||||
## Examples
|
||||
|
||||
@@ -248,6 +249,16 @@ MESSAGE user Is Ontario in Canada?
|
||||
MESSAGE assistant yes
|
||||
```
|
||||
|
||||
### REQUIRES
|
||||
|
||||
The `REQUIRES` instruction allows you to specify the minimum version of Ollama required by the model.
|
||||
|
||||
```
|
||||
REQUIRES <version>
|
||||
```
|
||||
|
||||
The version should be a valid Ollama version (e.g. 0.14.0).
|
||||
|
||||
## Notes
|
||||
|
||||
- the **`Modelfile` is not case sensitive**. In the examples, uppercase instructions are used to make it easier to distinguish it from arguments.
|
||||
|
||||
@@ -0,0 +1,46 @@
|
||||
# extract-examples
|
||||
|
||||
Extracts code examples from MDX files to a temp directory so you can run them.
|
||||
|
||||
## Usage
|
||||
|
||||
```shell
|
||||
go run docs/tools/extract-examples/main.go <mdx-file>
|
||||
```
|
||||
|
||||
## Example
|
||||
|
||||
```shell
|
||||
go run docs/tools/extract-examples/main.go docs/api/openai-compatibility.mdx
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
```
|
||||
Extracting code examples to: /var/folders/vq/wfm2g6k917d3ldzpjdxc8ph00000gn/T/mdx-examples-3271754368
|
||||
|
||||
- 01_basic.py
|
||||
- 01_basic.js
|
||||
- 01_basic.sh
|
||||
- 02_responses.py
|
||||
- 02_responses.js
|
||||
- 02_responses.sh
|
||||
- 03_vision.py
|
||||
- 03_vision.js
|
||||
- 03_vision.sh
|
||||
|
||||
Extracted 9 file(s) to /var/folders/vq/wfm2g6k917d3ldzpjdxc8ph00000gn/T/mdx-examples-3271754368
|
||||
|
||||
To run examples:
|
||||
|
||||
cd /var/folders/vq/wfm2g6k917d3ldzpjdxc8ph00000gn/T/mdx-examples-3271754368
|
||||
npm install # for JS examples
|
||||
|
||||
then run individual files with `node file.js`, `python file.py`, `bash file.sh`
|
||||
```
|
||||
|
||||
## How it works
|
||||
|
||||
- Parses MDX files looking for fenced code blocks with filenames (e.g., ` ```python basic.py `)
|
||||
- Groups examples by their `<CodeGroup>` and prefixes filenames with `01_`, `02_`, etc.
|
||||
- Writes all extracted files to a temp directory
|
||||
@@ -0,0 +1,137 @@
|
||||
package main
|
||||
|
||||
import (
|
||||
"bufio"
|
||||
"fmt"
|
||||
"os"
|
||||
"path/filepath"
|
||||
"regexp"
|
||||
"strings"
|
||||
)
|
||||
|
||||
func main() {
|
||||
if len(os.Args) < 2 {
|
||||
fmt.Fprintln(os.Stderr, "Usage: go run extract-examples.go <mdx-file>")
|
||||
os.Exit(1)
|
||||
}
|
||||
|
||||
mdxFile := os.Args[1]
|
||||
|
||||
f, err := os.Open(mdxFile)
|
||||
if err != nil {
|
||||
fmt.Fprintf(os.Stderr, "Error: %v\n", err)
|
||||
os.Exit(1)
|
||||
}
|
||||
defer f.Close()
|
||||
|
||||
// Create temp directory
|
||||
tempDir, err := os.MkdirTemp("", "mdx-examples-*")
|
||||
if err != nil {
|
||||
fmt.Fprintf(os.Stderr, "Error creating temp dir: %v\n", err)
|
||||
os.Exit(1)
|
||||
}
|
||||
|
||||
fmt.Printf("Extracting code examples to: %s\n\n", tempDir)
|
||||
|
||||
// Patterns
|
||||
codeBlockStart := regexp.MustCompile("^```([a-zA-Z0-9_-]+)\\s+([^\\s]+)$")
|
||||
codeGroupStart := regexp.MustCompile("^<CodeGroup")
|
||||
codeGroupEnd := regexp.MustCompile("^</CodeGroup>")
|
||||
|
||||
scanner := bufio.NewScanner(f)
|
||||
inCodeBlock := false
|
||||
inCodeGroup := false
|
||||
var currentFile string
|
||||
var content strings.Builder
|
||||
count := 0
|
||||
codeGroupNum := 0
|
||||
|
||||
for scanner.Scan() {
|
||||
line := scanner.Text()
|
||||
|
||||
// Track CodeGroup boundaries
|
||||
if codeGroupStart.MatchString(line) {
|
||||
inCodeGroup = true
|
||||
codeGroupNum++
|
||||
continue
|
||||
}
|
||||
if codeGroupEnd.MatchString(line) {
|
||||
inCodeGroup = false
|
||||
continue
|
||||
}
|
||||
|
||||
if inCodeBlock {
|
||||
if line == "```" {
|
||||
// End of code block - write file
|
||||
if currentFile != "" {
|
||||
outPath := filepath.Join(tempDir, currentFile)
|
||||
if err := os.WriteFile(outPath, []byte(content.String()), 0o644); err != nil {
|
||||
fmt.Fprintf(os.Stderr, "Error writing %s: %v\n", currentFile, err)
|
||||
} else {
|
||||
fmt.Printf(" - %s\n", currentFile)
|
||||
count++
|
||||
}
|
||||
}
|
||||
inCodeBlock = false
|
||||
currentFile = ""
|
||||
content.Reset()
|
||||
} else {
|
||||
content.WriteString(line)
|
||||
content.WriteString("\n")
|
||||
}
|
||||
} else {
|
||||
if matches := codeBlockStart.FindStringSubmatch(line); matches != nil {
|
||||
inCodeBlock = true
|
||||
filename := matches[2]
|
||||
// Prefix with CodeGroup number if inside a CodeGroup
|
||||
if inCodeGroup {
|
||||
currentFile = fmt.Sprintf("%02d_%s", codeGroupNum, filename)
|
||||
} else {
|
||||
currentFile = filename
|
||||
}
|
||||
content.Reset()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if err := scanner.Err(); err != nil {
|
||||
fmt.Fprintf(os.Stderr, "Error reading file: %v\n", err)
|
||||
os.Exit(1)
|
||||
}
|
||||
|
||||
// Write package.json for JavaScript dependencies
|
||||
packageJSON := `{
|
||||
"name": "mdx-examples",
|
||||
"type": "module",
|
||||
"dependencies": {
|
||||
"openai": "^4",
|
||||
"ollama": "^0.5"
|
||||
}
|
||||
}
|
||||
`
|
||||
if err := os.WriteFile(filepath.Join(tempDir, "package.json"), []byte(packageJSON), 0o644); err != nil {
|
||||
fmt.Fprintf(os.Stderr, "Error writing package.json: %v\n", err)
|
||||
}
|
||||
|
||||
// Write pyproject.toml for Python dependencies
|
||||
pyprojectTOML := `[project]
|
||||
name = "mdx-examples"
|
||||
version = "0.0.0"
|
||||
dependencies = [
|
||||
"openai",
|
||||
"ollama",
|
||||
]
|
||||
`
|
||||
if err := os.WriteFile(filepath.Join(tempDir, "pyproject.toml"), []byte(pyprojectTOML), 0o644); err != nil {
|
||||
fmt.Fprintf(os.Stderr, "Error writing pyproject.toml: %v\n", err)
|
||||
}
|
||||
|
||||
fmt.Printf("\n")
|
||||
fmt.Printf("Extracted %d file(s) to %s\n", count, tempDir)
|
||||
fmt.Printf("\n")
|
||||
fmt.Printf("To run examples:\n")
|
||||
fmt.Printf("\n")
|
||||
fmt.Printf(" cd %s\n npm install # for JS examples\n", tempDir)
|
||||
fmt.Printf("\n")
|
||||
fmt.Printf("then run individual files with `node file.js`, `python file.py`, `bash file.sh`\n")
|
||||
}
|
||||
@@ -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
|
||||
|
||||
|
||||
Reference in New Issue
Block a user