Author SHA1 Message Date
The Librarian 62c02724f3 Sync ollama docs from 361d6c16 on 2026-01-12 2026-01-12 04:00:06 +00:00
14 changed files with 515 additions and 29 deletions
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@@ -4,8 +4,8 @@ This is a mirror of the Ollama repository.
**Synced from:** https://github.com/ollama/ollama.git
**Branch:** main
**Commit:** 9f7822851c1f080d7d2a1dbe0e4d51233e5a28bc
**Sync Date:** 2025-12-12
**Commit:** 361d6c16c23b60f84eb1d2134914610c0ae5836b
**Sync Date:** 2026-01-12
**Content:** Paths: docs
---
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@@ -14,6 +14,7 @@
* [API Reference](https://docs.ollama.com/api)
* [Modelfile Reference](https://docs.ollama.com/modelfile)
* [OpenAI Compatibility](https://docs.ollama.com/api/openai-compatibility)
* [Anthropic Compatibility](./api/anthropic-compatibility.mdx)
### Resources
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@@ -895,11 +895,11 @@ curl http://localhost:11434/api/chat -d '{
"tool_calls": [
{
"function": {
"name": "get_temperature",
"name": "get_weather",
"arguments": {
"city": "Toronto"
}
},
}
}
]
},
@@ -907,7 +907,7 @@ curl http://localhost:11434/api/chat -d '{
{
"role": "tool",
"content": "11 degrees celsius",
"tool_name": "get_temperature",
"tool_name": "get_weather"
}
],
"stream": false,
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---
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.
## 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',
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:
```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 |
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@@ -277,6 +277,8 @@ 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
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@@ -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">
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@@ -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"
]
},
{
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@@ -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?
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| 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`
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---
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
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---
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
```
```
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@@ -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.
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# Troubleshooting
For troubleshooting, see [https://docs.ollama.com/troubleshooting](https://docs.ollama.com/troubleshooting)
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### 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