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Revert "docs: add reference to docs.ollama.com (#12800)" (#12803)

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63
docs/api/authentication.mdx
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---
title: Authentication
---
No authentication is required when accessing Ollama's API locally via `http://localhost:11434`.
Authentication is required for the following:
* Running cloud models via ollama.com
* Publishing models
* Downloading private models
Ollama supports two authentication methods:
* **Signing in**: sign in from your local installation, and Ollama will automatically take care of authenticating requests to ollama.com when running commands
* **API keys**: API keys for programmatic access to ollama.com's API
## Signing in
To sign in to ollama.com from your local installation of Ollama, run:
```
ollama signin
```
Once signed in, Ollama will automatically authenticate commands as required:
```
ollama run gpt-oss:120b-cloud
```
Similarly, when accessing a local API endpoint that requires cloud access, Ollama will automatically authenticate the request:
```shell
curl http://localhost:11434/api/generate -d '{
"model": "gpt-oss:120b-cloud",
"prompt": "Why is the sky blue?"
}'
```
## API keys
For direct access to ollama.com's API served at `https://ollama.com/api`, authentication via API keys is required.
First, create an [API key](https://ollama.com/settings/keys), then set the `OLLAMA_API_KEY` environment variable:
```shell
export OLLAMA_API_KEY=your_api_key
```
Then use the API key in the Authorization header:
```shell
curl https://ollama.com/api/generate \
-H "Authorization: Bearer $OLLAMA_API_KEY" \
-d '{
"model": "gpt-oss:120b",
"prompt": "Why is the sky blue?",
"stream": false
}'
```
API keys don't currently expire, however you can revoke them at any time in your [API keys settings](https://ollama.com/settings/keys).

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docs/api/errors.mdx
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---
title: Errors
---
## Status codes
Endpoints return appropriate HTTP status codes based on the success or failure of the request in the HTTP status line (e.g. `HTTP/1.1 200 OK` or `HTTP/1.1 400 Bad Request`). Common status codes are:
- `200`: Success
- `400`: Bad Request (missing parameters, invalid JSON, etc.)
- `404`: Not Found (model doesn't exist, etc.)
- `429`: Too Many Requests (e.g. when a rate limit is exceeded)
- `500`: Internal Server Error
- `502`: Bad Gateway (e.g. when a cloud model cannot be reached)
## Error messages
Errors are returned in the `application/json` format with the following structure, with the error message in the `error` property:
```json
{
"error": "the model failed to generate a response"
}
```
## Errors that occur while streaming
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}
{"error":"an error was encountered while running the model"}
```

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docs/api/index.mdx
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---
title: "Introduction"
---
Ollama's API allows you to run and interact with models programatically.
## Get started
If you're just getting started, follow the [quickstart](/quickstart) documentation to get up and running with Ollama's API.
## Base URL
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:
```
https://ollama.com/api
```
## Example request
Once Ollama is running, its API is automatically available and can be accessed via `curl`:
```shell
curl http://localhost:11434/api/generate -d '{
"model": "gemma3",
"prompt": "Why is the sky blue?"
}'
```
## Libraries
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).
## Versioning
Ollama's API isn't strictly versioned, but the API is expected to be stable and backwards compatible. Deprecations are rare and will be announced in the [release notes](https://github.com/ollama/ollama/releases).

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---
title: OpenAI compatibility
---
Ollama provides compatibility with parts of the [OpenAI API](https://platform.openai.com/docs/api-reference) to help connect existing applications to Ollama.
## Usage
### OpenAI Python library
```python
from openai import OpenAI
client = OpenAI(
base_url='http://localhost:11434/v1/',
# required but ignored
api_key='ollama',
)
chat_completion = client.chat.completions.create(
messages=[
{
'role': 'user',
'content': 'Say this is a test',
}
],
model='llama3.2',
)
response = client.chat.completions.create(
model="llava",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "What's in this image?"},
{
"type": "image_url",
"image_url": "data:image/png;base64,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",
},
],
}
],
max_tokens=300,
)
completion = client.completions.create(
model="llama3.2",
prompt="Say this is a test",
)
list_completion = client.models.list()
model = client.models.retrieve("llama3.2")
embeddings = client.embeddings.create(
model="all-minilm",
input=["why is the sky blue?", "why is the grass green?"],
)
```
#### Structured outputs
```python
from pydantic import BaseModel
from openai import OpenAI
client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")
# Define the schema for the response
class FriendInfo(BaseModel):
name: str
age: int
is_available: bool
class FriendList(BaseModel):
friends: list[FriendInfo]
try:
completion = client.beta.chat.completions.parse(
temperature=0,
model="llama3.1:8b",
messages=[
{"role": "user", "content": "I have two friends. The first is Ollama 22 years old busy saving the world, and the second is Alonso 23 years old and wants to hang out. Return a list of friends in JSON format"}
],
response_format=FriendList,
)
friends_response = completion.choices[0].message
if friends_response.parsed:
print(friends_response.parsed)
elif friends_response.refusal:
print(friends_response.refusal)
except Exception as e:
print(f"Error: {e}")
```
### OpenAI JavaScript library
```javascript
import OpenAI from "openai";
const openai = new OpenAI({
baseURL: "http://localhost:11434/v1/",
// required but ignored
apiKey: "ollama",
});
const chatCompletion = await openai.chat.completions.create({
messages: [{ role: "user", content: "Say this is a test" }],
model: "llama3.2",
});
const response = await openai.chat.completions.create({
model: "llava",
messages: [
{
role: "user",
content: [
{ type: "text", text: "What's in this image?" },
{
type: "image_url",
image_url:
"data:image/png;base64,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",
},
],
},
],
});
const completion = await openai.completions.create({
model: "llama3.2",
prompt: "Say this is a test.",
});
const listCompletion = await openai.models.list();
const model = await openai.models.retrieve("llama3.2");
const embedding = await openai.embeddings.create({
model: "all-minilm",
input: ["why is the sky blue?", "why is the grass green?"],
});
```
### `curl`
```shell
curl http://localhost:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "llama3.2",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Hello!"
}
]
}'
curl http://localhost:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "llava",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What'\''s in this image?"
},
{
"type": "image_url",
"image_url": {
"url": "data:image/png;base64,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"
}
}
]
}
],
"max_tokens": 300
}'
curl http://localhost:11434/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "llama3.2",
"prompt": "Say this is a test"
}'
curl http://localhost:11434/v1/models
curl http://localhost:11434/v1/models/llama3.2
curl http://localhost:11434/v1/embeddings \
-H "Content-Type: application/json" \
-d '{
"model": "all-minilm",
"input": ["why is the sky blue?", "why is the grass green?"]
}'
```
## Endpoints
### `/v1/chat/completions`
#### Supported features
- [x] Chat completions
- [x] Streaming
- [x] JSON mode
- [x] Reproducible outputs
- [x] Vision
- [x] Tools
- [ ] Logprobs
#### Supported request fields
- [x] `model`
- [x] `messages`
- [x] Text `content`
- [x] Image `content`
- [x] Base64 encoded image
- [ ] Image URL
- [x] Array of `content` parts
- [x] `frequency_penalty`
- [x] `presence_penalty`
- [x] `response_format`
- [x] `seed`
- [x] `stop`
- [x] `stream`
- [x] `stream_options`
- [x] `include_usage`
- [x] `temperature`
- [x] `top_p`
- [x] `max_tokens`
- [x] `tools`
- [ ] `tool_choice`
- [ ] `logit_bias`
- [ ] `user`
- [ ] `n`
### `/v1/completions`
#### Supported features
- [x] Completions
- [x] Streaming
- [x] JSON mode
- [x] Reproducible outputs
- [ ] Logprobs
#### Supported request fields
- [x] `model`
- [x] `prompt`
- [x] `frequency_penalty`
- [x] `presence_penalty`
- [x] `seed`
- [x] `stop`
- [x] `stream`
- [x] `stream_options`
- [x] `include_usage`
- [x] `temperature`
- [x] `top_p`
- [x] `max_tokens`
- [x] `suffix`
- [ ] `best_of`
- [ ] `echo`
- [ ] `logit_bias`
- [ ] `user`
- [ ] `n`
#### Notes
- `prompt` currently only accepts a string
### `/v1/models`
#### Notes
- `created` corresponds to when the model was last modified
- `owned_by` corresponds to the ollama username, defaulting to `"library"`
### `/v1/models/{model}`
#### Notes
- `created` corresponds to when the model was last modified
- `owned_by` corresponds to the ollama username, defaulting to `"library"`
### `/v1/embeddings`
#### Supported request fields
- [x] `model`
- [x] `input`
- [x] string
- [x] array of strings
- [ ] array of tokens
- [ ] array of token arrays
- [x] `encoding format`
- [x] `dimensions`
- [ ] `user`
## Models
Before using a model, pull it locally `ollama pull`:
```shell
ollama pull llama3.2
```
### Default model names
For tooling that relies on default OpenAI model names such as `gpt-3.5-turbo`, use `ollama cp` to copy an existing model name to a temporary name:
```shell
ollama cp llama3.2 gpt-3.5-turbo
```
Afterwards, this new model name can be specified the `model` field:
```shell
curl http://localhost:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "Hello!"
}
]
}'
```
### Setting the context size
The OpenAI API does not have a way of setting the context size for a model. If you need to change the context size, create a `Modelfile` which looks like:
```
FROM <some model>
PARAMETER num_ctx <context size>
```
Use the `ollama create mymodel` command to create a new model with the updated context size. Call the API with the updated model name:
```shell
curl http://localhost:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "mymodel",
"messages": [
{
"role": "user",
"content": "Hello!"
}
]
}'
```

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@ -1,35 +0,0 @@
---
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"}
```
## Disabling streaming
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}
```
## When to use streaming vs non-streaming
**Streaming (default)**:
- Real-time response generation
- Lower perceived latency
- Better for long generations
**Non-streaming**:
- Simpler to process
- Better for short responses, or structured outputs
- Easier to handle in some applications

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---
title: Usage
---
Ollama's API responses include metrics that can be used for measuring performance and model usage:
* `total_duration`: How long the response took to generate
* `load_duration`: How long the model took to load
* `prompt_eval_count`: How many input tokens were processed
* `prompt_eval_duration`: How long it took to evaluate the prompt
* `eval_count`: How many output tokens were processes
* `eval_duration`: How long it took to generate the output tokens
All timing values are measured in nanoseconds.
## Example response
For endpoints that return usage metrics, the response body will include the usage fields. For example, a non-streaming call to `/api/generate` may return the following response:
```json
{
"model": "gemma3",
"created_at": "2025-10-17T23:14:07.414671Z",
"response": "Hello! How can I help you today?",
"done": true,
"done_reason": "stop",
"total_duration": 174560334,
"load_duration": 101397084,
"prompt_eval_count": 11,
"prompt_eval_duration": 13074791,
"eval_count": 18,
"eval_duration": 52479709
}
```
For endpoints that return **streaming responses**, usage fields are included as part of the final chunk, where `done` is `true`.

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@ -1,71 +0,0 @@
---
title: Benchmark
---
Go benchmark tests that measure end-to-end performance of a running Ollama server. Run these tests to evaluate model inference performance on your hardware and measure the impact of code changes.
## When to use
Run these benchmarks when:
- Making changes to the model inference engine
- Modifying model loading/unloading logic
- Changing prompt processing or token generation code
- Implementing a new model architecture
- Testing performance across different hardware setups
## Prerequisites
- Ollama server running locally with `ollama serve` on `127.0.0.1:11434`
## Usage and Examples
<Note>
All commands must be run from the root directory of the Ollama project.
</Note>
Basic syntax:
```bash
go test -bench=. ./benchmark/... -m $MODEL_NAME
```
Required flags:
- `-bench=.`: Run all benchmarks
- `-m`: Model name to benchmark
Optional flags:
- `-count N`: Number of times to run the benchmark (useful for statistical analysis)
- `-timeout T`: Maximum time for the benchmark to run (e.g. "10m" for 10 minutes)
Common usage patterns:
Single benchmark run with a model specified:
```bash
go test -bench=. ./benchmark/... -m llama3.3
```
## Output metrics
The benchmark reports several key metrics:
- `gen_tok/s`: Generated tokens per second
- `prompt_tok/s`: Prompt processing tokens per second
- `ttft_ms`: Time to first token in milliseconds
- `load_ms`: Model load time in milliseconds
- `gen_tokens`: Total tokens generated
- `prompt_tokens`: Total prompt tokens processed
Each benchmark runs two scenarios:
- Cold start: Model is loaded from disk for each test
- Warm start: Model is pre-loaded in memory
Three prompt lengths are tested for each scenario:
- Short prompt (100 tokens)
- Medium prompt (500 tokens)
- Long prompt (1000 tokens)

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---
title: Embeddings
description: Generate text embeddings for semantic search, retrieval, and RAG.
---
Embeddings turn text into numeric vectors you can store in a vector database, search with cosine similarity, or use in RAG pipelines. The vector length depends on the model (typically 384–1024 dimensions).
## Recommended models
- [embeddinggemma](https://ollama.com/library/embeddinggemma)
- [qwen3-embedding](https://ollama.com/library/qwen3-embedding)
- [all-minilm](https://ollama.com/library/all-minilm)
## Generate embeddings
Use `/api/embed` with a single string.
<Tabs>
<Tab title="cURL">
```shell
curl -X POST http://localhost:11434/api/embed \
-H "Content-Type: application/json" \
-d '{
"model": "embeddinggemma",
"input": "The quick brown fox jumps over the lazy dog."
}'
```
</Tab>
<Tab title="Python">
```python
import ollama
single = ollama.embed(
model='embeddinggemma',
input='The quick brown fox jumps over the lazy dog.'
)
print(len(single['embeddings'][0])) # vector length
```
</Tab>
<Tab title="JavaScript">
```javascript
import ollama from 'ollama'
const single = await ollama.embed({
model: 'embeddinggemma',
input: 'The quick brown fox jumps over the lazy dog.',
})
console.log(single.embeddings[0].length) // vector length
```
</Tab>
</Tabs>
<Note>
The `/api/embed` endpoint returns L2‑normalized (unit‑length) vectors.
</Note>
## Generate a batch of embeddings
Pass an array of strings to `input`.
<Tabs>
<Tab title="cURL">
```shell
curl -X POST http://localhost:11434/api/embed \
-H "Content-Type: application/json" \
-d '{
"model": "embeddinggemma",
"input": [
"First sentence",
"Second sentence",
"Third sentence"
]
}'
```
</Tab>
<Tab title="Python">
```python
import ollama
batch = ollama.embed(
model='embeddinggemma',
input=[
'The quick brown fox jumps over the lazy dog.',
'The five boxing wizards jump quickly.',
'Jackdaws love my big sphinx of quartz.',
]
)
print(len(batch['embeddings'])) # number of vectors
```
</Tab>
<Tab title="JavaScript">
```javascript
import ollama from 'ollama'
const batch = await ollama.embed({
model: 'embeddinggemma',
input: [
'The quick brown fox jumps over the lazy dog.',
'The five boxing wizards jump quickly.',
'Jackdaws love my big sphinx of quartz.',
],
})
console.log(batch.embeddings.length) // number of vectors
```
</Tab>
</Tabs>
## Tips
- Use cosine similarity for most semantic search use cases.
- Use the same embedding model for both indexing and querying.

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@ -1,99 +0,0 @@
---
title: Streaming
---
Streaming allows you to render text as it is produced by the model.
Streaming is enabled by default through the REST API, but disabled by default in the SDKs.
To enable streaming in the SDKs, set the `stream` parameter to `True`.
## Key streaming concepts
1. Chatting: Stream partial assistant messages. Each chunk includes the `content` so you can render messages as they arrive.
1. Thinking: Thinking-capable models emit a `thinking` field alongside regular content in each chunk. Detect this field in streaming chunks to show or hide reasoning traces before the final answer arrives.
1. Tool calling: Watch for streamed `tool_calls` in each chunk, execute the requested tool, and append tool outputs back into the conversation.
## Handling streamed chunks
<Note> It is necessary to accumulate the partial fields in order to maintain the history of the conversation. This is particularly important for tool calling where the thinking, tool call from the model, and the executed tool result must be passed back to the model in the next request. </Note>
<Tabs>
<Tab title="Python">
```python
from ollama import chat
stream = chat(
model='qwen3',
messages=[{'role': 'user', 'content': 'What is 17 × 23?'}],
stream=True,
)
in_thinking = False
content = ''
thinking = ''
for chunk in stream:
if chunk.message.thinking:
if not in_thinking:
in_thinking = True
print('Thinking:\n', end='', flush=True)
print(chunk.message.thinking, end='', flush=True)
# accumulate the partial thinking
thinking += chunk.message.thinking
elif chunk.message.content:
if in_thinking:
in_thinking = False
print('\n\nAnswer:\n', end='', flush=True)
print(chunk.message.content, end='', flush=True)
# accumulate the partial content
content += chunk.message.content
# append the accumulated fields to the messages for the next request
new_messages = [{ role: 'assistant', thinking: thinking, content: content }]
```
</Tab>
<Tab title="JavaScript">
```javascript
import ollama from 'ollama'
async function main() {
const stream = await ollama.chat({
model: 'qwen3',
messages: [{ role: 'user', content: 'What is 17 × 23?' }],
stream: true,
})
let inThinking = false
let content = ''
let thinking = ''
for await (const chunk of stream) {
if (chunk.message.thinking) {
if (!inThinking) {
inThinking = true
process.stdout.write('Thinking:\n')
}
process.stdout.write(chunk.message.thinking)
// accumulate the partial thinking
thinking += chunk.message.thinking
} else if (chunk.message.content) {
if (inThinking) {
inThinking = false
process.stdout.write('\n\nAnswer:\n')
}
process.stdout.write(chunk.message.content)
// accumulate the partial content
content += chunk.message.content
}
}
// append the accumulated fields to the messages for the next request
new_messages = [{ role: 'assistant', thinking: thinking, content: content }]
}
main().catch(console.error)
```
</Tab>
</Tabs>

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@ -1,194 +0,0 @@
---
title: Structured Outputs
---
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
<Tabs>
<Tab title="cURL">
```shell
curl -X POST http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{
"model": "gpt-oss",
"messages": [{"role": "user", "content": "Tell me about Canada in one line"}],
"stream": false,
"format": "json"
}'
```
</Tab>
<Tab title="Python">
```python
from ollama import chat
response = chat(
model='gpt-oss',
messages=[{'role': 'user', 'content': 'Tell me about Canada.'}],
format='json'
)
print(response.message.content)
```
</Tab>
<Tab title="JavaScript">
```javascript
import ollama from 'ollama'
const response = await ollama.chat({
model: 'gpt-oss',
messages: [{ role: 'user', content: 'Tell me about Canada.' }],
format: 'json'
})
console.log(response.message.content)
```
</Tab>
</Tabs>
## Generating structured JSON with a schema
Provide a JSON schema to the `format` field.
<Note>
It is ideal to also pass the JSON schema as a string in the prompt to ground the model's response.
</Note>
<Tabs>
<Tab title="cURL">
```shell
curl -X POST http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{
"model": "gpt-oss",
"messages": [{"role": "user", "content": "Tell me about Canada."}],
"stream": false,
"format": {
"type": "object",
"properties": {
"name": {"type": "string"},
"capital": {"type": "string"},
"languages": {
"type": "array",
"items": {"type": "string"}
}
},
"required": ["name", "capital", "languages"]
}
}'
```
</Tab>
<Tab title="Python">
Use Pydantic models and pass `model_json_schema()` to `format`, then validate the response:
```python
from ollama import chat
from pydantic import BaseModel
class Country(BaseModel):
name: str
capital: str
languages: list[str]
response = chat(
model='gpt-oss',
messages=[{'role': 'user', 'content': 'Tell me about Canada.'}],
format=Country.model_json_schema(),
)
country = Country.model_validate_json(response.message.content)
print(country)
```
</Tab>
<Tab title="JavaScript">
Serialize a Zod schema with `zodToJsonSchema()` and parse the structured response:
```javascript
import ollama from 'ollama'
import { z } from 'zod'
import { zodToJsonSchema } from 'zod-to-json-schema'
const Country = z.object({
name: z.string(),
capital: z.string(),
languages: z.array(z.string()),
})
const response = await ollama.chat({
model: 'gpt-oss',
messages: [{ role: 'user', content: 'Tell me about Canada.' }],
format: zodToJsonSchema(Country),
})
const country = Country.parse(JSON.parse(response.message.content))
console.log(country)
```
</Tab>
</Tabs>
## Example: Extract structured data
Define the objects you want returned and let the model populate the fields:
```python
from ollama import chat
from pydantic import BaseModel
class Pet(BaseModel):
name: str
animal: str
age: int
color: str | None
favorite_toy: str | None
class PetList(BaseModel):
pets: list[Pet]
response = chat(
model='gpt-oss',
messages=[{'role': 'user', 'content': 'I have two cats named Luna and Loki...'}],
format=PetList.model_json_schema(),
)
pets = PetList.model_validate_json(response.message.content)
print(pets)
```
## Example: Vision with structured outputs
Vision models accept the same `format` parameter, enabling deterministic descriptions of images:
```python
from ollama import chat
from pydantic import BaseModel
from typing import Literal, Optional
class Object(BaseModel):
name: str
confidence: float
attributes: str
class ImageDescription(BaseModel):
summary: str
objects: list[Object]
scene: str
colors: list[str]
time_of_day: Literal['Morning', 'Afternoon', 'Evening', 'Night']
setting: Literal['Indoor', 'Outdoor', 'Unknown']
text_content: Optional[str] = None
response = chat(
model='gemma3',
messages=[{
'role': 'user',
'content': 'Describe this photo and list the objects you detect.',
'images': ['path/to/image.jpg'],
}],
format=ImageDescription.model_json_schema(),
options={'temperature': 0},
)
image_description = ImageDescription.model_validate_json(response.message.content)
print(image_description)
```
## Tips for reliable structured outputs
- Define schemas with Pydantic (Python) or Zod (JavaScript) so they can be reused for validation.
- Lower the temperature (e.g., set it to `0`) for more deterministic completions.
- Structured outputs work through the OpenAI-compatible API via `response_format`

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@ -1,153 +0,0 @@
---
title: Thinking
---
Thinking-capable models emit a `thinking` field that separates their reasoning trace from the final answer.
Use this capability to audit model steps, animate the model *thinking* in a UI, or hide the trace entirely when you only need the final response.
## Supported models
- [Qwen 3](https://ollama.com/library/qwen3)
- [GPT-OSS](https://ollama.com/library/gpt-oss) *(use `think` levels: `low`, `medium`, `high` — the trace cannot be fully disabled)*
- [DeepSeek-v3.1](https://ollama.com/library/deepseek-v3.1)
- [DeepSeek R1](https://ollama.com/library/deepseek-r1)
- Browse the latest additions under [thinking models](https://ollama.com/search?c=thinking)
## Enable thinking in API calls
Set the `think` field on chat or generate requests. Most models accept booleans (`true`/`false`).
GPT-OSS instead expects one of `low`, `medium`, or `high` to tune the trace length.
The `message.thinking` (chat endpoint) or `thinking` (generate endpoint) field contains the reasoning trace while `message.content` / `response` holds the final answer.
<Tabs>
<Tab title="cURL">
```shell
curl http://localhost:11434/api/chat -d '{
"model": "qwen3",
"messages": [{
"role": "user",
"content": "How many letter r are in strawberry?"
}],
"think": true,
"stream": false
}'
```
</Tab>
<Tab title="Python">
```python
from ollama import chat
response = chat(
model='qwen3',
messages=[{'role': 'user', 'content': 'How many letter r are in strawberry?'}],
think=True,
stream=False,
)
print('Thinking:\n', response.message.thinking)
print('Answer:\n', response.message.content)
```
</Tab>
<Tab title="JavaScript">
```javascript
import ollama from 'ollama'
const response = await ollama.chat({
model: 'deepseek-r1',
messages: [{ role: 'user', content: 'How many letter r are in strawberry?' }],
think: true,
stream: false,
})
console.log('Thinking:\n', response.message.thinking)
console.log('Answer:\n', response.message.content)
```
</Tab>
</Tabs>
<Note>
GPT-OSS requires `think` to be set to `"low"`, `"medium"`, or `"high"`. Passing `true`/`false` is ignored for that model.
</Note>
## Stream the reasoning trace
Thinking streams interleave reasoning tokens before answer tokens. Detect the first `thinking` chunk to render a "thinking" section, then switch to the final reply once `message.content` arrives.
<Tabs>
<Tab title="Python">
```python
from ollama import chat
stream = chat(
model='qwen3',
messages=[{'role': 'user', 'content': 'What is 17 × 23?'}],
think=True,
stream=True,
)
in_thinking = False
for chunk in stream:
if chunk.message.thinking and not in_thinking:
in_thinking = True
print('Thinking:\n', end='')
if chunk.message.thinking:
print(chunk.message.thinking, end='')
elif chunk.message.content:
if in_thinking:
print('\n\nAnswer:\n', end='')
in_thinking = False
print(chunk.message.content, end='')
```
</Tab>
<Tab title="JavaScript">
```javascript
import ollama from 'ollama'
async function main() {
const stream = await ollama.chat({
model: 'qwen3',
messages: [{ role: 'user', content: 'What is 17 × 23?' }],
think: true,
stream: true,
})
let inThinking = false
for await (const chunk of stream) {
if (chunk.message.thinking && !inThinking) {
inThinking = true
process.stdout.write('Thinking:\n')
}
if (chunk.message.thinking) {
process.stdout.write(chunk.message.thinking)
} else if (chunk.message.content) {
if (inThinking) {
process.stdout.write('\n\nAnswer:\n')
inThinking = false
}
process.stdout.write(chunk.message.content)
}
}
}
main()
```
</Tab>
</Tabs>
## CLI quick reference
- Enable thinking for a single run: `ollama run deepseek-r1 --think "Where should I visit in Lisbon?"`
- Disable thinking: `ollama run deepseek-r1 --think=false "Summarize this article"`
- Hide the trace while still using a thinking model: `ollama run deepseek-r1 --hidethinking "Is 9.9 bigger or 9.11?"`
- Inside interactive sessions, toggle with `/set think` or `/set nothink`.
- GPT-OSS only accepts levels: `ollama run gpt-oss --think=low "Draft a headline"` (replace `low` with `medium` or `high` as needed).
<Note>Thinking is enabled by default in the CLI and API for supported models.</Note>

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@ -1,777 +0,0 @@
---
title: Tool calling
---
Ollama supports tool calling (also known as function calling) which allows a model to invoke tools and incorporate their results into its replies.
## Calling a single tool
Invoke a single tool and include its response in a follow-up request.
Also known as "single-shot" tool calling.
<Tabs>
<Tab title="cURL">
```shell
curl -s http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{
"model": "qwen3",
"messages": [{"role": "user", "content": "What's the temperature in New York?"}],
"stream": false,
"tools": [
{
"type": "function",
"function": {
"name": "get_temperature",
"description": "Get the current temperature for a city",
"parameters": {
"type": "object",
"required": ["city"],
"properties": {
"city": {"type": "string", "description": "The name of the city"}
}
}
}
}
]
}'
```
**Generate a response with a single tool result**
```shell
curl -s http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{
"model": "qwen3",
"messages": [
{"role": "user", "content": "What's the temperature in New York?"},
{
"role": "assistant",
"tool_calls": [
{
"type": "function",
"function": {
"index": 0,
"name": "get_temperature",
"arguments": {"city": "New York"}
}
}
]
},
{"role": "tool", "tool_name": "get_temperature", "content": "22°C"}
],
"stream": false
}'
```
</Tab>
<Tab title="Python">
Install the Ollama Python SDK:
```bash
# with pip
pip install ollama -U
# with uv
uv add ollama
```
```python
from ollama import chat
def get_temperature(city: str) -> str:
"""Get the current temperature for a city
Args:
city: The name of the city
Returns:
The current temperature for the city
"""
temperatures = {
"New York": "22°C",
"London": "15°C",
"Tokyo": "18°C",
}
return temperatures.get(city, "Unknown")
messages = [{"role": "user", "content": "What's the temperature in New York?"}]
# pass functions directly as tools in the tools list or as a JSON schema
response = chat(model="qwen3", messages=messages, tools=[get_temperature], think=True)
messages.append(response.message)
if response.message.tool_calls:
# only recommended for models which only return a single tool call
call = response.message.tool_calls[0]
result = get_temperature(**call.function.arguments)
# add the tool result to the messages
messages.append({"role": "tool", "tool_name": call.function.name, "content": str(result)})
final_response = chat(model="qwen3", messages=messages, tools=[get_temperature], think=True)
print(final_response.message.content)
```
</Tab>
<Tab title="JavaScript">
Install the Ollama JavaScript library:
```bash
# with npm
npm i ollama
# with bun
bun i ollama
```
```typescript
import ollama from 'ollama'
function getTemperature(city: string): string {
const temperatures: Record<string, string> = {
'New York': '22°C',
'London': '15°C',
'Tokyo': '18°C',
}
return temperatures[city] ?? 'Unknown'
}
const tools = [
{
type: 'function',
function: {
name: 'get_temperature',
description: 'Get the current temperature for a city',
parameters: {
type: 'object',
required: ['city'],
properties: {
city: { type: 'string', description: 'The name of the city' },
},
},
},
},
]
const messages = [{ role: 'user', content: "What's the temperature in New York?" }]
const response = await ollama.chat({
model: 'qwen3',
messages,
tools,
think: true,
})
messages.push(response.message)
if (response.message.tool_calls?.length) {
// only recommended for models which only return a single tool call
const call = response.message.tool_calls[0]
const args = call.function.arguments as { city: string }
const result = getTemperature(args.city)
// add the tool result to the messages
messages.push({ role: 'tool', tool_name: call.function.name, content: result })
// generate the final response
const finalResponse = await ollama.chat({ model: 'qwen3', messages, tools, think: true })
console.log(finalResponse.message.content)
}
```
</Tab>
</Tabs>
## Parallel tool calling
<Tabs>
<Tab title="cURL">
Request multiple tool calls in parallel, then send all tool responses back to the model.
```shell
curl -s http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{
"model": "qwen3",
"messages": [{"role": "user", "content": "What are the current weather conditions and temperature in New York and London?"}],
"stream": false,
"tools": [
{
"type": "function",
"function": {
"name": "get_temperature",
"description": "Get the current temperature for a city",
"parameters": {
"type": "object",
"required": ["city"],
"properties": {
"city": {"type": "string", "description": "The name of the city"}
}
}
}
},
{
"type": "function",
"function": {
"name": "get_conditions",
"description": "Get the current weather conditions for a city",
"parameters": {
"type": "object",
"required": ["city"],
"properties": {
"city": {"type": "string", "description": "The name of the city"}
}
}
}
}
]
}'
```
**Generate a response with multiple tool results**
```shell
curl -s http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{
"model": "qwen3",
"messages": [
{"role": "user", "content": "What are the current weather conditions and temperature in New York and London?"},
{
"role": "assistant",
"tool_calls": [
{
"type": "function",
"function": {
"index": 0,
"name": "get_temperature",
"arguments": {"city": "New York"}
}
},
{
"type": "function",
"function": {
"index": 1,
"name": "get_conditions",
"arguments": {"city": "New York"}
}
},
{
"type": "function",
"function": {
"index": 2,
"name": "get_temperature",
"arguments": {"city": "London"}
}
},
{
"type": "function",
"function": {
"index": 3,
"name": "get_conditions",
"arguments": {"city": "London"}
}
}
]
},
{"role": "tool", "tool_name": "get_temperature", "content": "22°C"},
{"role": "tool", "tool_name": "get_conditions", "content": "Partly cloudy"},
{"role": "tool", "tool_name": "get_temperature", "content": "15°C"},
{"role": "tool", "tool_name": "get_conditions", "content": "Rainy"}
],
"stream": false
}'
```
</Tab>
<Tab title="Python">
```python
from ollama import chat
def get_temperature(city: str) -> str:
"""Get the current temperature for a city
Args:
city: The name of the city
Returns:
The current temperature for the city
"""
temperatures = {
"New York": "22°C",
"London": "15°C",
"Tokyo": "18°C"
}
return temperatures.get(city, "Unknown")
def get_conditions(city: str) -> str:
"""Get the current weather conditions for a city
Args:
city: The name of the city
Returns:
The current weather conditions for the city
"""
conditions = {
"New York": "Partly cloudy",
"London": "Rainy",
"Tokyo": "Sunny"
}
return conditions.get(city, "Unknown")
messages = [{'role': 'user', 'content': 'What are the current weather conditions and temperature in New York and London?'}]
# The python client automatically parses functions as a tool schema so we can pass them directly
# Schemas can be passed directly in the tools list as well
response = chat(model='qwen3', messages=messages, tools=[get_temperature, get_conditions], think=True)
# add the assistant message to the messages
messages.append(response.message)
if response.message.tool_calls:
# process each tool call
for call in response.message.tool_calls:
# execute the appropriate tool
if call.function.name == 'get_temperature':
result = get_temperature(**call.function.arguments)
elif call.function.name == 'get_conditions':
result = get_conditions(**call.function.arguments)
else:
result = 'Unknown tool'
# add the tool result to the messages
messages.append({'role': 'tool', 'tool_name': call.function.name, 'content': str(result)})
# generate the final response
final_response = chat(model='qwen3', messages=messages, tools=[get_temperature, get_conditions], think=True)
print(final_response.message.content)
```
</Tab>
<Tab title="JavaScript">
```typescript
import ollama from 'ollama'
function getTemperature(city: string): string {
const temperatures: { [key: string]: string } = {
"New York": "22°C",
"London": "15°C",
"Tokyo": "18°C"
}
return temperatures[city] || "Unknown"
}
function getConditions(city: string): string {
const conditions: { [key: string]: string } = {
"New York": "Partly cloudy",
"London": "Rainy",
"Tokyo": "Sunny"
}
return conditions[city] || "Unknown"
}
const tools = [
{
type: 'function',
function: {
name: 'get_temperature',
description: 'Get the current temperature for a city',
parameters: {
type: 'object',
required: ['city'],
properties: {
city: { type: 'string', description: 'The name of the city' },
},
},
},
},
{
type: 'function',
function: {
name: 'get_conditions',
description: 'Get the current weather conditions for a city',
parameters: {
type: 'object',
required: ['city'],
properties: {
city: { type: 'string', description: 'The name of the city' },
},
},
},
}
]
const messages = [{ role: 'user', content: 'What are the current weather conditions and temperature in New York and London?' }]
const response = await ollama.chat({
model: 'qwen3',
messages,
tools,
think: true
})
// add the assistant message to the messages
messages.push(response.message)
if (response.message.tool_calls) {
// process each tool call
for (const call of response.message.tool_calls) {
// execute the appropriate tool
let result: string
if (call.function.name === 'get_temperature') {
const args = call.function.arguments as { city: string }
result = getTemperature(args.city)
} else if (call.function.name === 'get_conditions') {
const args = call.function.arguments as { city: string }
result = getConditions(args.city)
} else {
result = 'Unknown tool'
}
// add the tool result to the messages
messages.push({ role: 'tool', tool_name: call.function.name, content: result })
}
// generate the final response
const finalResponse = await ollama.chat({ model: 'qwen3', messages, tools, think: true })
console.log(finalResponse.message.content)
}
```
</Tab>
</Tabs>
## Multi-turn tool calling (Agent loop)
An agent loop allows the model to decide when to invoke tools and incorporate their results into its replies.
It also might help to tell the model that it is in a loop and can make multiple tool calls.
<Tabs>
<Tab title="Python">
```python
from ollama import chat, ChatResponse
def add(a: int, b: int) -> int:
"""Add two numbers"""
"""
Args:
a: The first number
b: The second number
Returns:
The sum of the two numbers
"""
return a + b
def multiply(a: int, b: int) -> int:
"""Multiply two numbers"""
"""
Args:
a: The first number
b: The second number
Returns:
The product of the two numbers
"""
return a * b
available_functions = {
'add': add,
'multiply': multiply,
}
messages = [{'role': 'user', 'content': 'What is (11434+12341)*412?'}]
while True:
response: ChatResponse = chat(
model='qwen3',
messages=messages,
tools=[add, multiply],
think=True,
)
messages.append(response.message)
print("Thinking: ", response.message.thinking)
print("Content: ", response.message.content)
if response.message.tool_calls:
for tc in response.message.tool_calls:
if tc.function.name in available_functions:
print(f"Calling {tc.function.name} with arguments {tc.function.arguments}")
result = available_functions[tc.function.name](**tc.function.arguments)
print(f"Result: {result}")
# add the tool result to the messages
messages.append({'role': 'tool', 'tool_name': tc.function.name, 'content': str(result)})
else:
# end the loop when there are no more tool calls
break
# continue the loop with the updated messages
```
</Tab>
<Tab title="JavaScript">
```typescript
import ollama from 'ollama'
type ToolName = 'add' | 'multiply'
function add(a: number, b: number): number {
return a + b
}
function multiply(a: number, b: number): number {
return a * b
}
const availableFunctions: Record<ToolName, (a: number, b: number) => number> = {
add,
multiply,
}
const tools = [
{
type: 'function',
function: {
name: 'add',
description: 'Add two numbers',
parameters: {
type: 'object',
required: ['a', 'b'],
properties: {
a: { type: 'integer', description: 'The first number' },
b: { type: 'integer', description: 'The second number' },
},
},
},
},
{
type: 'function',
function: {
name: 'multiply',
description: 'Multiply two numbers',
parameters: {
type: 'object',
required: ['a', 'b'],
properties: {
a: { type: 'integer', description: 'The first number' },
b: { type: 'integer', description: 'The second number' },
},
},
},
},
]
async function agentLoop() {
const messages = [{ role: 'user', content: 'What is (11434+12341)*412?' }]
while (true) {
const response = await ollama.chat({
model: 'qwen3',
messages,
tools,
think: true,
})
messages.push(response.message)
console.log('Thinking:', response.message.thinking)
console.log('Content:', response.message.content)
const toolCalls = response.message.tool_calls ?? []
if (toolCalls.length) {
for (const call of toolCalls) {
const fn = availableFunctions[call.function.name as ToolName]
if (!fn) {
continue
}
const args = call.function.arguments as { a: number; b: number }
console.log(`Calling ${call.function.name} with arguments`, args)
const result = fn(args.a, args.b)
console.log(`Result: ${result}`)
messages.push({ role: 'tool', tool_name: call.function.name, content: String(result) })
}
} else {
break
}
}
}
agentLoop().catch(console.error)
```
</Tab>
</Tabs>
## Tool calling with streaming
When streaming, gather every chunk of `thinking`, `content`, and `tool_calls`, then return those fields together with any tool results in the follow-up request.
<Tabs>
<Tab title="Python">
```python
from ollama import chat
def get_temperature(city: str) -> str:
"""Get the current temperature for a city
Args:
city: The name of the city
Returns:
The current temperature for the city
"""
temperatures = {
'New York': '22°C',
'London': '15°C',
}
return temperatures.get(city, 'Unknown')
messages = [{'role': 'user', 'content': "What's the temperature in New York?"}]
while True:
stream = chat(
model='qwen3',
messages=messages,
tools=[get_temperature],
stream=True,
think=True,
)
thinking = ''
content = ''
tool_calls = []
done_thinking = False
# accumulate the partial fields
for chunk in stream:
if chunk.message.thinking:
thinking += chunk.message.thinking
print(chunk.message.thinking, end='', flush=True)
if chunk.message.content:
if not done_thinking:
done_thinking = True
print('\n')
content += chunk.message.content
print(chunk.message.content, end='', flush=True)
if chunk.message.tool_calls:
tool_calls.extend(chunk.message.tool_calls)
print(chunk.message.tool_calls)
# append accumulated fields to the messages
if thinking or content or tool_calls:
messages.append({'role': 'assistant', 'thinking': thinking, 'content': content, 'tool_calls': tool_calls})
if not tool_calls:
break
for call in tool_calls:
if call.function.name == 'get_temperature':
result = get_temperature(**call.function.arguments)
else:
result = 'Unknown tool'
messages.append({'role': 'tool', 'tool_name': call.function.name, 'content': result})
```
</Tab>
<Tab title="JavaScript">
```typescript
import ollama from 'ollama'
function getTemperature(city: string): string {
const temperatures: Record<string, string> = {
'New York': '22°C',
'London': '15°C',
}
return temperatures[city] ?? 'Unknown'
}
const getTemperatureTool = {
type: 'function',
function: {
name: 'get_temperature',
description: 'Get the current temperature for a city',
parameters: {
type: 'object',
required: ['city'],
properties: {
city: { type: 'string', description: 'The name of the city' },
},
},
},
}
async function agentLoop() {
const messages = [{ role: 'user', content: "What's the temperature in New York?" }]
while (true) {
const stream = await ollama.chat({
model: 'qwen3',
messages,
tools: [getTemperatureTool],
stream: true,
think: true,
})
let thinking = ''
let content = ''
const toolCalls: any[] = []
let doneThinking = false
for await (const chunk of stream) {
if (chunk.message.thinking) {
thinking += chunk.message.thinking
process.stdout.write(chunk.message.thinking)
}
if (chunk.message.content) {
if (!doneThinking) {
doneThinking = true
process.stdout.write('\n')
}
content += chunk.message.content
process.stdout.write(chunk.message.content)
}
if (chunk.message.tool_calls?.length) {
toolCalls.push(...chunk.message.tool_calls)
console.log(chunk.message.tool_calls)
}
}
if (thinking || content || toolCalls.length) {
messages.push({ role: 'assistant', thinking, content, tool_calls: toolCalls } as any)
}
if (!toolCalls.length) {
break
}
for (const call of toolCalls) {
if (call.function.name === 'get_temperature') {
const args = call.function.arguments as { city: string }
const result = getTemperature(args.city)
messages.push({ role: 'tool', tool_name: call.function.name, content: result } )
} else {
messages.push({ role: 'tool', tool_name: call.function.name, content: 'Unknown tool' } )
}
}
}
}
agentLoop().catch(console.error)
```
</Tab>
</Tabs>
This loop streams the assistant response, accumulates partial fields, passes them back together, and appends the tool results so the model can complete its answer.
## Using functions as tools with Ollama Python SDK
The Python SDK automatically parses functions as a tool schema so we can pass them directly.
Schemas can still be passed if needed.
```python
from ollama import chat
def get_temperature(city: str) -> str:
"""Get the current temperature for a city
Args:
city: The name of the city
Returns:
The current temperature for the city
"""
temperatures = {
'New York': '22°C',
'London': '15°C',
}
return temperatures.get(city, 'Unknown')
available_functions = {
'get_temperature': get_temperature,
}
# directly pass the function as part of the tools list
response = chat(model='qwen3', messages=messages, tools=available_functions.values(), think=True)
```

85
docs/capabilities/vision.mdx
View File

@ -1,85 +0,0 @@
---
title: Vision
---
Vision models accept images alongside text so the model can describe, classify, and answer questions about what it sees.
## Quick start
```shell
ollama run gemma3 ./image.png whats in this image?
```
## Usage with Ollama's API
Provide an `images` array. SDKs accept file paths, URLs or raw bytes while the REST API expects base64-encoded image data.
<Tabs>
<Tab title="cURL">
```shell
# 1. Download a sample image
curl -L -o test.jpg "https://upload.wikimedia.org/wikipedia/commons/3/3a/Cat03.jpg"
# 2. Encode the image
IMG=$(base64 < test.jpg | tr -d '\n')
# 3. Send it to Ollama
curl -X POST http://localhost:11434/api/chat \
-H "Content-Type: application/json" \
-d '{
"model": "gemma3",
"messages": [{
"role": "user",
"content": "What is in this image?",
"images": ["'"$IMG"'"]
}],
"stream": false
}'
"
```
</Tab>
<Tab title="Python">
```python
from ollama import chat
# from pathlib import Path
# Pass in the path to the image
path = input('Please enter the path to the image: ')
# You can also pass in base64 encoded image data
# img = base64.b64encode(Path(path).read_bytes()).decode()
# or the raw bytes
# img = Path(path).read_bytes()
response = chat(
model='gemma3',
messages=[
{
'role': 'user',
'content': 'What is in this image? Be concise.',
'images': [path],
}
],
)
print(response.message.content)
```
</Tab>
<Tab title="JavaScript">
```javascript
import ollama from 'ollama'
const imagePath = '/absolute/path/to/image.jpg'
const response = await ollama.chat({
model: 'gemma3',
messages: [
{ role: 'user', content: 'What is in this image?', images: [imagePath] }
],
stream: false,
})
console.log(response.message.content)
```
</Tab>
</Tabs>

360
docs/capabilities/web-search.mdx
View File

@ -1,360 +0,0 @@
---
title: Web search
---
Ollama's web search API can be used to augment models with the latest information to reduce hallucinations and improve accuracy.
Web search is provided as a REST API with deeper tool integrations in the Python and JavaScript libraries. This also enables models like OpenAI’s gpt-oss models to conduct long-running research tasks.
## Authentication
For access to Ollama's web search API, create an [API key](https://ollama.com/settings/keys). A free Ollama account is required.
## Web search API
Performs a web search for a single query and returns relevant results.
### Request
`POST https://ollama.com/api/web_search`
- `query` (string, required): the search query string
- `max_results` (integer, optional): maximum results to return (default 5, max 10)
### Response
Returns an object containing:
- `results` (array): array of search result objects, each containing:
- `title` (string): the title of the web page
- `url` (string): the URL of the web page
- `content` (string): relevant content snippet from the web page
### Examples
<Note>
Ensure OLLAMA_API_KEY is set or it must be passed in the Authorization header.
</Note>
#### cURL Request
```bash
curl https://ollama.com/api/web_search \
--header "Authorization: Bearer $OLLAMA_API_KEY" \
-d '{
"query":"what is ollama?"
}'
```
**Response**
```json
{
"results": [
{
"title": "Ollama",
"url": "https://ollama.com/",
"content": "Cloud models are now available..."
},
{
"title": "What is Ollama? Introduction to the AI model management tool",
"url": "https://www.hostinger.com/tutorials/what-is-ollama",
"content": "Ariffud M. 6min Read..."
},
{
"title": "Ollama Explained: Transforming AI Accessibility and Language ...",
"url": "https://www.geeksforgeeks.org/artificial-intelligence/ollama-explained-transforming-ai-accessibility-and-language-processing/",
"content": "Data Science Data Science Projects Data Analysis..."
}
]
}
```
#### Python library
```python
import ollama
response = ollama.web_search("What is Ollama?")
print(response)
```
**Example output**
```python
results = [
{
"title": "Ollama",
"url": "https://ollama.com/",
"content": "Cloud models are now available in Ollama..."
},
{
"title": "What is Ollama? Features, Pricing, and Use Cases - Walturn",
"url": "https://www.walturn.com/insights/what-is-ollama-features-pricing-and-use-cases",
"content": "Our services..."
},
{
"title": "Complete Ollama Guide: Installation, Usage & Code Examples",
"url": "https://collabnix.com/complete-ollama-guide-installation-usage-code-examples",
"content": "Join our Discord Server..."
}
]
```
More Ollama [Python example](https://github.com/ollama/ollama-python/blob/main/examples/web-search.py)
#### JavaScript Library
```tsx
import { Ollama } from "ollama";
const client = new Ollama();
const results = await client.webSearch({ query: "what is ollama?" });
console.log(JSON.stringify(results, null, 2));
```
**Example output**
```json
{
"results": [
{
"title": "Ollama",
"url": "https://ollama.com/",
"content": "Cloud models are now available..."
},
{
"title": "What is Ollama? Introduction to the AI model management tool",
"url": "https://www.hostinger.com/tutorials/what-is-ollama",
"content": "Ollama is an open-source tool..."
},
{
"title": "Ollama Explained: Transforming AI Accessibility and Language Processing",
"url": "https://www.geeksforgeeks.org/artificial-intelligence/ollama-explained-transforming-ai-accessibility-and-language-processing/",
"content": "Ollama is a groundbreaking..."
}
]
}
```
More Ollama [JavaScript example](https://github.com/ollama/ollama-js/blob/main/examples/websearch/websearch-tools.ts)
## Web fetch API
Fetches a single web page by URL and returns its content.
### Request
`POST https://ollama.com/api/web_fetch`
- `url` (string, required): the URL to fetch
### Response
Returns an object containing:
- `title` (string): the title of the web page
- `content` (string): the main content of the web page
- `links` (array): array of links found on the page
### Examples
#### cURL Request
```python
curl --request POST \
--url https://ollama.com/api/web_fetch \
--header "Authorization: Bearer $OLLAMA_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
"url": "ollama.com"
}'
```
**Response**
```json
{
"title": "Ollama",
"content": "[Cloud models](https://ollama.com/blog/cloud-models) are now available in Ollama...",
"links": [
"http://ollama.com/",
"http://ollama.com/models",
"https://github.com/ollama/ollama"
]
```
#### Python SDK
```python
from ollama import web_fetch
result = web_fetch('https://ollama.com')
print(result)
```
**Result**
```python
WebFetchResponse(
title='Ollama',
content='[Cloud models](https://ollama.com/blog/cloud-models) are now available in Ollama\n\n**Chat & build
with open models**\n\n[Download](https://ollama.com/download) [Explore
models](https://ollama.com/models)\n\nAvailable for macOS, Windows, and Linux',
links=['https://ollama.com/', 'https://ollama.com/models', 'https://github.com/ollama/ollama']
)
```
#### JavaScript SDK
```tsx
import { Ollama } from "ollama";
const client = new Ollama();
const fetchResult = await client.webFetch({ url: "https://ollama.com" });
console.log(JSON.stringify(fetchResult, null, 2));
```
**Result**
```json
{
"title": "Ollama",
"content": "[Cloud models](https://ollama.com/blog/cloud-models) are now available in Ollama...",
"links": [
"https://ollama.com/",
"https://ollama.com/models",
"https://github.com/ollama/ollama"
]
}
```
## Building a search agent
Use Ollama’s web search API as a tool to build a mini search agent.
This example uses Alibaba’s Qwen 3 model with 4B parameters.
```bash
ollama pull qwen3:4b
```
```python
from ollama import chat, web_fetch, web_search
available_tools = {'web_search': web_search, 'web_fetch': web_fetch}
messages = [{'role': 'user', 'content': "what is ollama's new engine"}]
while True:
response = chat(
model='qwen3:4b',
messages=messages,
tools=[web_search, web_fetch],
think=True
)
if response.message.thinking:
print('Thinking: ', response.message.thinking)
if response.message.content:
print('Content: ', response.message.content)
messages.append(response.message)
if response.message.tool_calls:
print('Tool calls: ', response.message.tool_calls)
for tool_call in response.message.tool_calls:
function_to_call = available_tools.get(tool_call.function.name)
if function_to_call:
args = tool_call.function.arguments
result = function_to_call(**args)
print('Result: ', str(result)[:200]+'...')
# Result is truncated for limited context lengths
messages.append({'role': 'tool', 'content': str(result)[:2000 * 4], 'tool_name': tool_call.function.name})
else:
messages.append({'role': 'tool', 'content': f'Tool {tool_call.function.name} not found', 'tool_name': tool_call.function.name})
else:
break
```
**Result**
```
Thinking: Okay, the user is asking about Ollama's new engine. I need to figure out what they're referring to. Ollama is a company that develops large language models, so maybe they've released a new model or an updated version of their existing engine....
Tool calls: [ToolCall(function=Function(name='web_search', arguments={'max_results': 3, 'query': 'Ollama new engine'}))]
Result: results=[WebSearchResult(content='# New model scheduling\n\n## September 23, 2025\n\nOllama now includes a significantly improved model scheduling system. Ahead of running a model, Ollama’s new engine
Thinking: Okay, the user asked about Ollama's new engine. Let me look at the search results.
First result is from September 23, 2025, talking about new model scheduling. It mentions improved memory management, reduced crashes, better GPU utilization, and multi-GPU performance. Examples show speed improvements and accurate memory reporting. Supported models include gemma3, llama4, qwen3, etc...
Content: Ollama has introduced two key updates to its engine, both released in 2025:
1. **Enhanced Model Scheduling (September 23, 2025)**
- **Precision Memory Management**: Exact memory allocation reduces out-of-memory crashes and optimizes GPU utilization.
- **Performance Gains**: Examples show significant speed improvements (e.g., 85.54 tokens/s vs 52.02 tokens/s) and full GPU layer utilization.
- **Multi-GPU Support**: Improved efficiency across multiple GPUs, with accurate memory reporting via tools like `nvidia-smi`.
- **Supported Models**: Includes `gemma3`, `llama4`, `qwen3`, `mistral-small3.2`, and more.
2. **Multimodal Engine (May 15, 2025)**
- **Vision Support**: First-class support for vision models, including `llama4:scout` (109B parameters), `gemma3`, `qwen2.5vl`, and `mistral-small3.1`.
- **Multimodal Tasks**: Examples include identifying animals in multiple images, answering location-based questions from videos, and document scanning.
These updates highlight Ollama's focus on efficiency, performance, and expanded capabilities for both text and vision tasks.
```
### Context length and agents
Web search results can return thousands of tokens. It is recommended to increase the context length of the model to at least ~32000 tokens. Search agents work best with full context length. [Ollama's cloud models](https://docs.ollama.com/cloud) run at the full context length.
## MCP Server
You can enable web search in any MCP client through the [Python MCP server](https://github.com/ollama/ollama-python/blob/main/examples/web-search-mcp.py).
### Cline
Ollama's web search can be integrated with Cline easily using the MCP server configuration.
`Manage MCP Servers` > `Configure MCP Servers` > Add the following configuration:
```json
{
"mcpServers": {
"web_search_and_fetch": {
"type": "stdio",
"command": "uv",
"args": ["run", "path/to/web-search-mcp.py"],
"env": { "OLLAMA_API_KEY": "your_api_key_here" }
}
}
}
```
![Cline MCP Configuration](/images/cline-mcp.png)
### Codex
Ollama works well with OpenAI's Codex tool.
Add the following configuration to `~/.codex/config.toml`
```python
[mcp_servers.web_search]
command = "uv"
args = ["run", "path/to/web-search-mcp.py"]
env = { "OLLAMA_API_KEY" = "your_api_key_here" }
```
![Codex MCP Configuration](/images/codex-mcp.png)
### Goose
Ollama can integrate with Goose via its MCP feature.
![Goose MCP Configuration 1](/images/goose-mcp-1.png)
![Goose MCP Configuration 2](/images/goose-mcp-2.png)
### Other integrations
Ollama can be integrated into most of the tools available either through direct integration of Ollama's API, Python / JavaScript libraries, OpenAI compatible API, and MCP server integration.

91
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@ -1,91 +0,0 @@
---
title: CLI Reference
---
### Run a model
```
ollama run gemma3
```
#### Multiline input
For multiline input, you can wrap text with `"""`:
```
>>> """Hello,
... world!
... """
I'm a basic program that prints the famous "Hello, world!" message to the console.
```
#### Multimodal models
```
ollama run gemma3 "What's in this image? /Users/jmorgan/Desktop/smile.png"
```
### Download a model
```
ollama pull gemma3
```
### Remove a model
```
ollama rm gemma3
```
### List models
```
ollama ls
```
### Sign in to Ollama
```
ollama signin
```
### Sign out of Ollama
```
ollama signout
```
### Create a customized model
First, create a `Modelfile`
```
FROM gemma3
SYSTEM """You are a happy cat."""
```
Then run `ollama create`:
```
ollama create -f Modelfile
```
### List running models
```
ollama ps
```
### Stop a running model
```
ollama stop gemma3
```
### Start Ollama
```
ollama serve
```
To view a list of environment variables that can be set run `ollama serve --help`

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@ -1,235 +0,0 @@
---
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.
Ollama currently supports the following cloud models, with more coming soon:
- `deepseek-v3.1:671b-cloud`
- `gpt-oss:20b-cloud`
- `gpt-oss:120b-cloud`
- `kimi-k2:1t-cloud`
- `qwen3-coder:480b-cloud`
- `glm-4.6:cloud`
### Running Cloud models
Ollama's cloud models require an account on [ollama.com](https://ollama.com). To sign in or create an account, run:
```
ollama signin
```
<Tabs>
<Tab title="CLI">
To run a cloud model, open the terminal and run:
```
ollama run gpt-oss:120b-cloud
```
</Tab>
<Tab title="Python">
First, pull a cloud model so it can be accessed:
```
ollama pull gpt-oss:120b-cloud
```
Next, install [Ollama's Python library](https://github.com/ollama/ollama-python):
```
pip install ollama
```
Next, create and run a simple Python script:
```python
from ollama import Client
client = Client()
messages = [
{
'role': 'user',
'content': 'Why is the sky blue?',
},
]
for part in client.chat('gpt-oss:120b-cloud', messages=messages, stream=True):
print(part['message']['content'], end='', flush=True)
```
</Tab>
<Tab title="JavaScript">
First, pull a cloud model so it can be accessed:
```
ollama pull gpt-oss:120b-cloud
```
Next, install [Ollama's JavaScript library](https://github.com/ollama/ollama-js):
```
npm i ollama
```
Then use the library to run a cloud model:
```typescript
import { Ollama } from "ollama";
const ollama = new Ollama();
const response = await ollama.chat({
model: "gpt-oss:120b-cloud",
messages: [{ role: "user", content: "Explain quantum computing" }],
stream: true,
});
for await (const part of response) {
process.stdout.write(part.message.content);
}
```
</Tab>
<Tab title="cURL">
First, pull a cloud model so it can be accessed:
```
ollama pull gpt-oss:120b-cloud
```
Run the following cURL command to run the command via Ollama's API:
```
curl http://localhost:11434/api/chat -d '{
"model": "gpt-oss:120b-cloud",
"messages": [{
"role": "user",
"content": "Why is the sky blue?"
}],
"stream": false
}'
```
</Tab>
</Tabs>
## Cloud API access
Cloud models can also be accessed directly on ollama.com's API. In this mode, ollama.com acts as a remote Ollama host.
### Authentication
For direct access to ollama.com's API, first create an [API key](https://ollama.com/settings/keys).
Then, set the `OLLAMA_API_KEY` environment variable to your API key.
```
export OLLAMA_API_KEY=your_api_key
```
### Listing models
For models available directly via Ollama's API, models can be listed via:
```
curl https://ollama.com/api/tags
```
### Generating a response
<Tabs>
<Tab title="Python">
First, install [Ollama's Python library](https://github.com/ollama/ollama-python)
```
pip install ollama
```
Then make a request
```python
import os
from ollama import Client
client = Client(
host="https://ollama.com",
headers={'Authorization': 'Bearer ' + os.environ.get('OLLAMA_API_KEY')}
)
messages = [
{
'role': 'user',
'content': 'Why is the sky blue?',
},
]
for part in client.chat('gpt-oss:120b', messages=messages, stream=True):
print(part['message']['content'], end='', flush=True)
```
</Tab>
<Tab title="JavaScript">
First, install [Ollama's JavaScript library](https://github.com/ollama/ollama-js):
```
npm i ollama
```
Next, make a request to the model:
```typescript
import { Ollama } from "ollama";
const ollama = new Ollama({
host: "https://ollama.com",
headers: {
Authorization: "Bearer " + process.env.OLLAMA_API_KEY,
},
});
const response = await ollama.chat({
model: "gpt-oss:120b",
messages: [{ role: "user", content: "Explain quantum computing" }],
stream: true,
});
for await (const part of response) {
process.stdout.write(part.message.content);
}
```
</Tab>
<Tab title="cURL">
Generate a response via Ollama's chat API:
```
curl https://ollama.com/api/chat \
-H "Authorization: Bearer $OLLAMA_API_KEY" \
-d '{
"model": "gpt-oss:120b",
"messages": [{
"role": "user",
"content": "Why is the sky blue?"
}],
"stream": false
}'
```
</Tab>
</Tabs>

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@ -1,38 +0,0 @@
---
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.
</Note>
Tasks which require large context like web search, agents, and coding tools should be set to at least 32000 tokens.
## Setting context length
Setting a larger context length will increase the amount of memory required to run a model. Ensure you have enough VRAM available to increase the context length.
Cloud models are set to their maximum context length by default.
### App
Change the slider in the Ollama app under settings to your desired context length.
![Context length in Ollama app](./images/ollama-settings.png)
### 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
```
### Check allocated context length and model offloading
For best performance, use the maximum context length for a model, and avoid offloading the model to CPU. Verify the split under `PROCESSOR` using `ollama ps`.
```
ollama ps
```
```
NAME ID SIZE PROCESSOR CONTEXT UNTIL
gemma3:latest a2af6cc3eb7f 6.6 GB 100% GPU 65536 2 minutes from now
```

81
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@ -1,81 +0,0 @@
## CPU only
```shell
docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
```
## Nvidia GPU
Install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installation).
### Install with Apt
1. Configure the repository
```shell
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey \
| sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
curl -fsSL https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list \
| sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' \
| sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update
```
2. Install the NVIDIA Container Toolkit packages
```shell
sudo apt-get install -y nvidia-container-toolkit
```
### Install with Yum or Dnf
1. Configure the repository
```shell
curl -fsSL https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo \
| sudo tee /etc/yum.repos.d/nvidia-container-toolkit.repo
```
2. Install the NVIDIA Container Toolkit packages
```shell
sudo yum install -y nvidia-container-toolkit
```
### Configure Docker to use Nvidia driver
```shell
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
```
### Start the container
```shell
docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
```
<Note>
If you're running on an NVIDIA JetPack system, Ollama can't automatically discover the correct JetPack version.
Pass the environment variable `JETSON_JETPACK=5` or `JETSON_JETPACK=6` to the container to select version 5 or 6.
</Note>
## AMD GPU
To run Ollama using Docker with AMD GPUs, use the `rocm` tag and the following command:
```shell
docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama:rocm
```
## Run model locally
Now you can run a model:
```shell
docker exec -it ollama ollama run llama3.2
```
## Try different models
More models can be found on the [Ollama library](https://ollama.com/library).

155
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@ -1,155 +0,0 @@
{
"$schema": "https://mintlify.com/docs.json",
"name": "Ollama",
"colors": {
"primary": "#000",
"light": "#b5b5b5",
"dark": "#000"
},
"favicon": "/images/favicon.png",
"logo": {
"light": "/images/logo.png",
"dark": "/images/logo-dark.png",
"href": "https://ollama.com"
},
"theme": "maple",
"background": {
"color": {
"light": "#ffffff",
"dark": "#000000"
}
},
"fonts": {
"family": "system-ui",
"heading": {
"family": "system-ui"
},
"body": {
"family": "system-ui"
}
},
"styling": {
"codeblocks": "system"
},
"contextual": {
"options": ["copy"]
},
"navbar": {
"links": [
{
"label": "Sign in",
"href": "https://ollama.com/signin"
}
],
"primary": {
"type": "button",
"label": "Download",
"href": "https://ollama.com/download"
}
},
"api": {
"playground": {
"display": "simple"
},
"examples": {
"languages": ["curl"]
}
},
"redirects": [
{
"source": "/openai",
"destination": "/api/openai"
}
],
"navigation": {
"tabs": [
{
"tab": "Documentation",
"groups": [
{
"group": "Get started",
"pages": [
"index",
"quickstart",
"/cloud"
]
},
{
"group": "Capabilities",
"pages": [
"/capabilities/streaming",
"/capabilities/thinking",
"/capabilities/structured-outputs",
"/capabilities/vision",
"/capabilities/embeddings",
"/capabilities/tool-calling",
"/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": [
"/cli",
"/modelfile",
"/context-length",
"/linux",
"/docker",
"/faq",
"/gpu",
"/troubleshooting"
]
}
]
},
{
"tab": "API Reference",
"openapi": "openapi.yaml",
"groups": [
{
"group": "API Reference",
"pages": [
"/api/index",
"/api/authentication",
"/api/streaming",
"/api/usage",
"/api/errors",
"/api/openai-compatibility"
]
},
{
"group": "Endpoints",
"pages": [
"POST /api/generate",
"POST /api/chat",
"POST /api/embed",
"GET /api/tags",
"GET /api/ps",
"POST /api/show",
"POST /api/create",
"POST /api/copy",
"POST /api/pull",
"POST /api/push",
"DELETE /api/delete",
"GET /api/version"
]
}
]
}
]
}
}

378
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@ -1,378 +0,0 @@
---
title: FAQ
---
## How can I upgrade Ollama?
Ollama on macOS and Windows will automatically download updates. Click on the taskbar or menubar item and then click "Restart to update" to apply the update. Updates can also be installed by downloading the latest version [manually](https://ollama.com/download/).
On Linux, re-run the install script:
```shell
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.
## Is my GPU compatible with Ollama?
Please refer to the [GPU docs](./gpu.md).
## How can I specify the context window size?
By default, Ollama uses a context window size of 2048 tokens.
This can be overridden with the `OLLAMA_CONTEXT_LENGTH` environment variable. For example, to set the default context window to 8K, use:
```shell
OLLAMA_CONTEXT_LENGTH=8192 ollama serve
```
To change this when using `ollama run`, use `/set parameter`:
```shell
/set parameter num_ctx 4096
```
When using the API, specify the `num_ctx` parameter:
```shell
curl http://localhost:11434/api/generate -d '{
"model": "llama3.2",
"prompt": "Why is the sky blue?",
"options": {
"num_ctx": 4096
}
}'
```
## How can I tell if my model was loaded onto the GPU?
Use the `ollama ps` command to see what models are currently loaded into memory.
```shell
ollama ps
```
<Info>
**Output**: ``` NAME ID SIZE PROCESSOR UNTIL llama3:70b bcfb190ca3a7 42 GB
100% GPU 4 minutes from now ```
</Info>
The `Processor` column will show which memory the model was loaded in to:
- `100% GPU` means the model was loaded entirely into the GPU
- `100% CPU` means the model was loaded entirely in system memory
- `48%/52% CPU/GPU` means the model was loaded partially onto both the GPU and into system memory
## How do I configure Ollama server?
Ollama server can be configured with environment variables.
### Setting environment variables on Mac
If Ollama is run as a macOS application, environment variables should be set using `launchctl`:
1. For each environment variable, call `launchctl setenv`.
```bash
launchctl setenv OLLAMA_HOST "0.0.0.0:11434"
```
2. Restart Ollama application.
### Setting environment variables on Linux
If Ollama is run as a systemd service, environment variables should be set using `systemctl`:
1. Edit the systemd service by calling `systemctl edit ollama.service`. This will open an editor.
2. For each environment variable, add a line `Environment` under section `[Service]`:
```ini
[Service]
Environment="OLLAMA_HOST=0.0.0.0:11434"
```
3. Save and exit.
4. Reload `systemd` and restart Ollama:
```shell
systemctl daemon-reload
systemctl restart ollama
```
### Setting environment variables on Windows
On Windows, Ollama inherits your user and system environment variables.
1. First Quit Ollama by clicking on it in the task bar.
2. Start the Settings (Windows 11) or Control Panel (Windows 10) application and search for _environment variables_.
3. Click on _Edit environment variables for your account_.
4. Edit or create a new variable for your user account for `OLLAMA_HOST`, `OLLAMA_MODELS`, etc.
5. Click OK/Apply to save.
6. Start the Ollama application from the Windows Start menu.
## How do I use Ollama behind a proxy?
Ollama pulls models from the Internet and may require a proxy server to access the models. Use `HTTPS_PROXY` to redirect outbound requests through the proxy. Ensure the proxy certificate is installed as a system certificate. Refer to the section above for how to use environment variables on your platform.
<Note>
Avoid setting `HTTP_PROXY`. Ollama does not use HTTP for model pulls, only
HTTPS. Setting `HTTP_PROXY` may interrupt client connections to the server.
</Note>
### How do I use Ollama behind a proxy in Docker?
The Ollama Docker container image can be configured to use a proxy by passing `-e HTTPS_PROXY=https://proxy.example.com` when starting the container.
Alternatively, the Docker daemon can be configured to use a proxy. Instructions are available for Docker Desktop on [macOS](https://docs.docker.com/desktop/settings/mac/#proxies), [Windows](https://docs.docker.com/desktop/settings/windows/#proxies), and [Linux](https://docs.docker.com/desktop/settings/linux/#proxies), and Docker [daemon with systemd](https://docs.docker.com/config/daemon/systemd/#httphttps-proxy).
Ensure the certificate is installed as a system certificate when using HTTPS. This may require a new Docker image when using a self-signed certificate.
```dockerfile
FROM ollama/ollama
COPY my-ca.pem /usr/local/share/ca-certificates/my-ca.crt
RUN update-ca-certificates
```
Build and run this image:
```shell
docker build -t ollama-with-ca .
docker run -d -e HTTPS_PROXY=https://my.proxy.example.com -p 11434:11434 ollama-with-ca
```
## Does Ollama send my prompts and answers back to ollama.com?
No. Ollama runs locally, and conversation data does not leave your machine.
## How can I expose Ollama on my network?
Ollama binds 127.0.0.1 port 11434 by default. Change the bind address with the `OLLAMA_HOST` environment variable.
Refer to the section [above](#how-do-i-configure-ollama-server) for how to set environment variables on your platform.
## How can I use Ollama with a proxy server?
Ollama runs an HTTP server and can be exposed using a proxy server such as Nginx. To do so, configure the proxy to forward requests and optionally set required headers (if not exposing Ollama on the network). For example, with Nginx:
```nginx
server {
listen 80;
server_name example.com; # Replace with your domain or IP
location / {
proxy_pass http://localhost:11434;
proxy_set_header Host localhost:11434;
}
}
```
## How can I use Ollama with ngrok?
Ollama can be accessed using a range of tools for tunneling tools. For example with Ngrok:
```shell
ngrok http 11434 --host-header="localhost:11434"
```
## How can I use Ollama with Cloudflare Tunnel?
To use Ollama with Cloudflare Tunnel, use the `--url` and `--http-host-header` flags:
```shell
cloudflared tunnel --url http://localhost:11434 --http-host-header="localhost:11434"
```
## How can I allow additional web origins to access Ollama?
Ollama allows cross-origin requests from `127.0.0.1` and `0.0.0.0` by default. Additional origins can be configured with `OLLAMA_ORIGINS`.
For browser extensions, you'll need to explicitly allow the extension's origin pattern. Set `OLLAMA_ORIGINS` to include `chrome-extension://*`, `moz-extension://*`, and `safari-web-extension://*` if you wish to allow all browser extensions access, or specific extensions as needed:
```
# Allow all Chrome, Firefox, and Safari extensions
OLLAMA_ORIGINS=chrome-extension://*,moz-extension://*,safari-web-extension://* ollama serve
```
Refer to the section [above](#how-do-i-configure-ollama-server) for how to set environment variables on your platform.
## Where are models stored?
- macOS: `~/.ollama/models`
- Linux: `/usr/share/ollama/.ollama/models`
- Windows: `C:\Users\%username%\.ollama\models`
### How do I set them to a different location?
If a different directory needs to be used, set the environment variable `OLLAMA_MODELS` to the chosen directory.
<Note>
On Linux using the standard installer, the `ollama` user needs read and write access to the specified directory. To assign the directory to the `ollama` user run `sudo chown -R ollama:ollama <directory>`.
</Note>
Refer to the section [above](#how-do-i-configure-ollama-server) for how to set environment variables on your platform.
## How can I use Ollama in Visual Studio Code?
There is already a large collection of plugins available for VSCode 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.
## How do I use Ollama with GPU acceleration in Docker?
The Ollama Docker container can be configured with GPU acceleration in Linux or Windows (with WSL2). This requires the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit). See [ollama/ollama](https://hub.docker.com/r/ollama/ollama) for more details.
GPU acceleration is not available for Docker Desktop in macOS due to the lack of GPU passthrough and emulation.
## Why is networking slow in WSL2 on Windows 10?
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`.
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.
## How can I preload a model into Ollama to get faster response times?
If you are using the API you can preload a model by sending the Ollama server an empty request. This works with both the `/api/generate` and `/api/chat` API endpoints.
To preload the mistral model using the generate endpoint, use:
```shell
curl http://localhost:11434/api/generate -d '{"model": "mistral"}'
```
To use the chat completions endpoint, use:
```shell
curl http://localhost:11434/api/chat -d '{"model": "mistral"}'
```
To preload a model using the CLI, use the command:
```shell
ollama run llama3.2 ""
```
## How do I keep a model loaded in memory or make it unload immediately?
By default models are kept in memory for 5 minutes before being unloaded. This allows for quicker response times if you're making numerous requests to the LLM. If you want to immediately unload a model from memory, use the `ollama stop` command:
```shell
ollama stop llama3.2
```
If you're using the API, use the `keep_alive` parameter with the `/api/generate` and `/api/chat` endpoints to set the amount of time that a model stays in memory. The `keep_alive` parameter can be set to:
- a duration string (such as "10m" or "24h")
- a number in seconds (such as 3600)
- any negative number which will keep the model loaded in memory (e.g. -1 or "-1m")
- '0' which will unload the model immediately after generating a response
For example, to preload a model and leave it in memory use:
```shell
curl http://localhost:11434/api/generate -d '{"model": "llama3.2", "keep_alive": -1}'
```
To unload the model and free up memory use:
```shell
curl http://localhost:11434/api/generate -d '{"model": "llama3.2", "keep_alive": 0}'
```
Alternatively, you can change the amount of time all models are loaded into memory by setting the `OLLAMA_KEEP_ALIVE` environment variable when starting the Ollama server. The `OLLAMA_KEEP_ALIVE` variable uses the same parameter types as the `keep_alive` parameter types mentioned above. Refer to the section explaining [how to configure the Ollama server](#how-do-i-configure-ollama-server) to correctly set the environment variable.
The `keep_alive` API parameter with the `/api/generate` and `/api/chat` API endpoints will override the `OLLAMA_KEEP_ALIVE` setting.
## 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`.
## How does Ollama handle concurrent requests?
Ollama supports two levels of concurrent processing. If your system has sufficient available memory (system memory when using CPU inference, or VRAM for GPU inference) then multiple models can be loaded at the same time. For a given model, if there is sufficient available memory when the model is loaded, it is configured to allow parallel request processing.
If there is insufficient available memory to load a new model request while one or more models are already loaded, all new requests will be queued until the new model can be loaded. As prior models become idle, one or more will be unloaded to make room for the new model. Queued requests will be processed in order. When using GPU inference new models must be able to completely fit in VRAM to allow concurrent model loads.
Parallel request processing for a given model results in increasing the context size by the number of parallel requests. For example, a 2K context with 4 parallel requests will result in an 8K context and additional memory allocation.
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_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.
## How does Ollama load models on multiple GPUs?
When loading a new model, Ollama evaluates the required VRAM for the model against what is currently available. If the model will entirely fit on any single GPU, Ollama will load the model on that GPU. This typically provides the best performance as it reduces the amount of data transferring across the PCI bus during inference. If the model does not fit entirely on one GPU, then it will be spread across all the available GPUs.
## 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.
## How can I set the quantization type for the K/V cache?
The K/V context cache can be quantized to significantly reduce memory usage when Flash Attention is enabled.
To use quantized K/V cache with Ollama you can set the following environment variable:
- `OLLAMA_KV_CACHE_TYPE` - The quantization type for the K/V cache. Default is `f16`.
<Note>
Currently this is a global option - meaning all models will run with the
specified quantization type.
</Note>
The currently available K/V cache quantization types are:
- `f16` - high precision and memory usage (default).
- `q8_0` - 8-bit quantization, uses approximately 1/2 the memory of `f16` with a very small loss in precision, this usually has no noticeable impact on the model's quality (recommended if not using f16).
- `q4_0` - 4-bit quantization, uses approximately 1/4 the memory of `f16` with a small-medium loss in precision that may be more noticeable at higher context sizes.
How much the cache quantization impacts the model's response quality will depend on the model and the task. Models that have a high GQA count (e.g. Qwen2) may see a larger impact on precision from quantization than models with a low GQA count.
You may need to experiment with different quantization types to find the best balance between memory usage and quality.
## Where can I find my Ollama Public Key?
Your **Ollama Public Key** is the public part of the key pair that lets your local Ollama instance talk to [ollama.com](https://ollama.com).
You'll need it to:
* Push models to Ollama
* Pull private models from Ollama to your machine
* Run models hosted in [Ollama Cloud](https://ollama.com/cloud)
### How to Add the Key
* **Sign-in via the Settings page** in the **Mac** and **Windows App**
* **Sign‑in via CLI**
```shell
ollama signin
```
* **Manually copy & paste** the key on the **Ollama Keys** page:
[https://ollama.com/settings/keys](https://ollama.com/settings/keys)
### Where the Ollama Public Key lives
| OS | Path to `id_ed25519.pub` |
| :- | :- |
| macOS | `~/.ollama/id_ed25519.pub` |
| Linux | `/usr/share/ollama/.ollama/id_ed25519.pub` |
| Windows | `C:\Users\<username>\.ollama\id_ed25519.pub` |
<Note>
Replace &lt;username&gt; with your actual Windows user name.
</Note>

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---
title: Hardware support
---
## Nvidia
Ollama supports Nvidia GPUs with compute capability 5.0+.
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 |
| ------------------ | ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
| 9.0 | NVIDIA | `H200` `H100` |
| 8.9 | GeForce RTX 40xx | `RTX 4090` `RTX 4080 SUPER` `RTX 4080` `RTX 4070 Ti SUPER` `RTX 4070 Ti` `RTX 4070 SUPER` `RTX 4070` `RTX 4060 Ti` `RTX 4060` |
| | NVIDIA Professional | `L4` `L40` `RTX 6000` |
| 8.6 | GeForce RTX 30xx | `RTX 3090 Ti` `RTX 3090` `RTX 3080 Ti` `RTX 3080` `RTX 3070 Ti` `RTX 3070` `RTX 3060 Ti` `RTX 3060` `RTX 3050 Ti` `RTX 3050` |
| | NVIDIA Professional | `A40` `RTX A6000` `RTX A5000` `RTX A4000` `RTX A3000` `RTX A2000` `A10` `A16` `A2` |
| 8.0 | NVIDIA | `A100` `A30` |
| 7.5 | GeForce GTX/RTX | `GTX 1650 Ti` `TITAN RTX` `RTX 2080 Ti` `RTX 2080` `RTX 2070` `RTX 2060` |
| | NVIDIA Professional | `T4` `RTX 5000` `RTX 4000` `RTX 3000` `T2000` `T1200` `T1000` `T600` `T500` |
| | Quadro | `RTX 8000` `RTX 6000` `RTX 5000` `RTX 4000` |
| 7.0 | NVIDIA | `TITAN V` `V100` `Quadro GV100` |
| 6.1 | NVIDIA TITAN | `TITAN Xp` `TITAN X` |
| | GeForce GTX | `GTX 1080 Ti` `GTX 1080` `GTX 1070 Ti` `GTX 1070` `GTX 1060` `GTX 1050 Ti` `GTX 1050` |
| | Quadro | `P6000` `P5200` `P4200` `P3200` `P5000` `P4000` `P3000` `P2200` `P2000` `P1000` `P620` `P600` `P500` `P520` |
| | Tesla | `P40` `P4` |
| 6.0 | NVIDIA | `Tesla P100` `Quadro GP100` |
| 5.2 | GeForce GTX | `GTX TITAN X` `GTX 980 Ti` `GTX 980` `GTX 970` `GTX 960` `GTX 950` |
| | Quadro | `M6000 24GB` `M6000` `M5000` `M5500M` `M4000` `M2200` `M2000` `M620` |
| | Tesla | `M60` `M40` |
| 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)
### GPU Selection
If you have multiple NVIDIA GPUs in your system and want to limit Ollama to use
a subset, you can set `CUDA_VISIBLE_DEVICES` to a comma separated list of GPUs.
Numeric IDs may be used, however ordering may vary, so UUIDs are more reliable.
You can discover the UUID of your GPUs by running `nvidia-smi -L` If you want to
ignore the GPUs and force CPU usage, use an invalid GPU ID (e.g., "-1")
### Linux Suspend Resume
On linux, after a suspend/resume cycle, sometimes Ollama will fail to discover
your NVIDIA GPU, and fallback to running on the CPU. You can workaround this
driver bug by reloading the NVIDIA UVM driver with `sudo rmmod nvidia_uvm &&
sudo modprobe nvidia_uvm`
## AMD Radeon
Ollama supports the following AMD GPUs:
### Linux Support
| Family | Cards and accelerators |
| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
| AMD Radeon RX | `7900 XTX` `7900 XT` `7900 GRE` `7800 XT` `7700 XT` `7600 XT` `7600` `6950 XT` `6900 XTX` `6900XT` `6800 XT` `6800` `Vega 64` `Vega 56` |
| AMD Radeon PRO | `W7900` `W7800` `W7700` `W7600` `W7500` `W6900X` `W6800X Duo` `W6800X` `W6800` `V620` `V420` `V340` `V320` `Vega II Duo` `Vega II` `VII` `SSG` |
| AMD Instinct | `MI300X` `MI300A` `MI300` `MI250X` `MI250` `MI210` `MI200` `MI100` `MI60` `MI50` |
### Windows Support
With ROCm v6.1, the following GPUs are supported 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` |
### Overrides on Linux
Ollama leverages the AMD ROCm library, which does not support all AMD GPUs. In
some cases you can force the system to try to use a similar LLVM target that is
close. For example The Radeon RX 5400 is `gfx1034` (also known as 10.3.4)
however, ROCm does not currently support this target. The closest support is
`gfx1030`. You can use the environment variable `HSA_OVERRIDE_GFX_VERSION` with
`x.y.z` syntax. So for example, to force the system to run on the RX 5400, you
would set `HSA_OVERRIDE_GFX_VERSION="10.3.0"` as an environment variable for the
server. If you have an unsupported AMD GPU you can experiment using the list of
supported types below.
If you have multiple GPUs with different GFX versions, append the numeric device
number to the environment variable to set them individually. For example,
`HSA_OVERRIDE_GFX_VERSION_0=10.3.0` and `HSA_OVERRIDE_GFX_VERSION_1=11.0.0`
At this time, the known supported GPU types on linux are the following LLVM Targets.
This table shows some example GPUs that map to these LLVM targets:
| **LLVM Target** | **An Example GPU** |
|-----------------|---------------------|
| gfx900 | Radeon RX Vega 56 |
| gfx906 | Radeon Instinct MI50 |
| gfx908 | Radeon Instinct MI100 |
| gfx90a | Radeon Instinct MI210 |
| gfx940 | Radeon Instinct MI300 |
| gfx941 | |
| gfx942 | |
| 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.
Reach out on [Discord](https://discord.gg/ollama) or file an
[issue](https://github.com/ollama/ollama/issues) for additional help.
### GPU Selection
If you have multiple AMD GPUs in your system and want to limit Ollama to use a
subset, you can set `ROCR_VISIBLE_DEVICES` to a comma separated list of GPUs.
You can see the list of devices with `rocminfo`. If you want to ignore the GPUs
and force CPU usage, use an invalid GPU ID (e.g., "-1"). When available, use the
`Uuid` to uniquely identify the device instead of numeric value.
### Container Permission
In some Linux distributions, SELinux can prevent containers from
accessing the AMD GPU devices. On the host system you can run
`sudo setsebool container_use_devices=1` to allow containers to use devices.
### Metal (Apple GPUs)
Ollama supports GPU acceleration on Apple devices via the Metal API.

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---
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 fine tuned adapter from Safetensors weights
First, create a `Modelfile` with a `FROM` command pointing at the base model you used for fine tuning, and an `ADAPTER` command which points to the directory with your Safetensors adapter:
```dockerfile
FROM <base model name>
ADAPTER /path/to/safetensors/adapter/directory
```
Make sure that you use the same base model in the `FROM` command as you used to create the adapter otherwise you will get erratic results. Most frameworks use different quantization methods, so it's best to use non-quantized (i.e. non-QLoRA) adapters. If your adapter is in the same directory as your `Modelfile`, use `ADAPTER .` to specify the adapter path.
Now run `ollama create` from the directory where the `Modelfile` was created:
```shell
ollama create my-model
```
Lastly, test the model:
```shell
ollama run my-model
```
Ollama supports importing adapters based on several different model architectures including:
- Llama (including Llama 2, Llama 3, Llama 3.1, and Llama 3.2);
- Mistral (including Mistral 1, Mistral 2, and Mixtral); and
- Gemma (including Gemma 1 and Gemma 2)
You can create the adapter using a fine tuning framework or tool which can output adapters in the Safetensors format, such as:
- Hugging Face [fine tuning framework](https://huggingface.co/docs/transformers/en/training)
- [Unsloth](https://github.com/unslothai/unsloth)
- [MLX](https://github.com/ml-explore/mlx)
## Importing a model from Safetensors weights
First, create a `Modelfile` with a `FROM` command which points to the directory containing your Safetensors weights:
```dockerfile
FROM /path/to/safetensors/directory
```
If you create the Modelfile in the same directory as the weights, you can use the command `FROM .`.
Now run the `ollama create` command from the directory where you created the `Modelfile`:
```shell
ollama create my-model
```
Lastly, test the model:
```shell
ollama run my-model
```
Ollama supports importing models for several different architectures including:
- Llama (including Llama 2, Llama 3, Llama 3.1, and Llama 3.2);
- Mistral (including Mistral 1, Mistral 2, and Mixtral);
- Gemma (including Gemma 1 and Gemma 2); and
- Phi3
This includes importing foundation models as well as any fine tuned models which have been _fused_ with a foundation model.
## Importing a GGUF based model or adapter
If you have a GGUF based model or adapter it is possible to import it into Ollama. You can obtain a GGUF model or adapter by:
- converting a Safetensors model with the `convert_hf_to_gguf.py` from Llama.cpp;
- converting a Safetensors adapter with the `convert_lora_to_gguf.py` from Llama.cpp; or
- downloading a model or adapter from a place such as HuggingFace
To import a GGUF model, create a `Modelfile` containing:
```dockerfile
FROM /path/to/file.gguf
```
For a GGUF adapter, create the `Modelfile` with:
```dockerfile
FROM <model name>
ADAPTER /path/to/file.gguf
```
When importing a GGUF adapter, it's important to use the same base model as the base model that the adapter was created with. You can use:
- a model from Ollama
- a GGUF file
- a Safetensors based model
Once you have created your `Modelfile`, use the `ollama create` command to build the model.
```shell
ollama create my-model
```
## Quantizing a Model
Quantizing a model allows you to run models faster and with less memory consumption but at reduced accuracy. This allows you to run a model on more modest hardware.
Ollama can quantize FP16 and FP32 based models into different quantization levels using the `-q/--quantize` flag with the `ollama create` command.
First, create a Modelfile with the FP16 or FP32 based model you wish to quantize.
```dockerfile
FROM /path/to/my/gemma/f16/model
```
Use `ollama create` to then create the quantized model.
```shell
$ ollama create --quantize q4_K_M mymodel
transferring model data
quantizing F16 model to Q4_K_M
creating new layer sha256:735e246cc1abfd06e9cdcf95504d6789a6cd1ad7577108a70d9902fef503c1bd
creating new layer sha256:0853f0ad24e5865173bbf9ffcc7b0f5d56b66fd690ab1009867e45e7d2c4db0f
writing manifest
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
You can share any model you have created by pushing it to [ollama.com](https://ollama.com) so that other users can try it out.
First, use your browser to go to the [Ollama Sign-Up](https://ollama.com/signup) page. If you already have an account, you can skip this step.
<img src="images/signup.png" alt="Sign-Up" width="40%" />
The `Username` field will be used as part of your model's name (e.g. `jmorganca/mymodel`), so make sure you are comfortable with the username that you have selected.
Now that you have created an account and are signed-in, go to the [Ollama Keys Settings](https://ollama.com/settings/keys) page.
Follow the directions on the page to determine where your Ollama Public Key is located.
<img src="images/ollama-keys.png" alt="Ollama Keys" width="80%" />
Click on the `Add Ollama Public Key` button, and copy and paste the contents of your Ollama Public Key into the text field.
To push a model to [ollama.com](https://ollama.com), first make sure that it is named correctly with your username. You may have to use the `ollama cp` command to copy
your model to give it the correct name. Once you're happy with your model's name, use the `ollama push` command to push it to [ollama.com](https://ollama.com).
```shell
ollama cp mymodel myuser/mymodel
ollama push myuser/mymodel
```
Once your model has been pushed, other users can pull and run it by using the command:
```shell
ollama run myuser/mymodel
```

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---
title: Ollama's documentation
sidebarTitle: Welcome
---
<img src="/images/welcome.png" noZoom className="rounded-3xl" />
[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.
<CardGroup cols={2}>
<Card title="Quickstart" icon="rocket" href="/quickstart">
Get up and running with your first model
</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>
</CardGroup>
## Libraries
<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>
<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>
<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>
</CardGroup>
## Community
<CardGroup cols={2}>
<Card title="Discord" icon="discord" href="https://discord.gg/ollama">
Join our Discord community
</Card>
<Card title="Reddit" icon="reddit" href="https://reddit.com/r/ollama">
Join our Reddit community
</Card>
</CardGroup>

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---
title: Cline
---
## Install
Install [Cline](https://docs.cline.bot/getting-started/installing-cline) in your IDE.
## Usage with Ollama
1. Open Cline settings > `API Configuration` and set `API Provider` to `Ollama`
2. Select a model under `Model` or type one (e.g. `qwen3`)
3. Update the context window to at least 32K tokens under `Context Window`
<Note>Coding tools require 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>
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/cline-settings.png"
alt="Cline settings configuration showing API Provider set to Ollama"
width="50%"
/>
</div>
## Connecting to ollama.com
1. Create an [API key](https://ollama.com/settings/keys) from ollama.com
2. Click on `Use custom base URL` and set it to `https://ollama.com`
3. Enter your **Ollama API Key**
4. Select a model from the list
### Recommended Models
- `qwen3-coder:480b`
- `deepseek-v3.1:671b`

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---
title: Codex
---
## Install
Install the [Codex CLI](https://developers.openai.com/codex/cli/):
```
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>
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:
```
codex --oss -m gpt-oss:120b
```
### Cloud Models
```
codex --oss -m gpt-oss:120b-cloud
```
## 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.
```toml
model = "gpt-oss:120b"
model_provider = "ollama"
[model_providers.ollama]
name = "Ollama"
base_url = "https://ollama.com/v1"
env_key = "OLLAMA_API_KEY"
```
Run `codex` in a new terminal to load the new settings.

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---
title: Droid
---
## Install
Install the [Droid CLI](https://factory.ai/):
```bash
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>
## Usage with Ollama
Add a local configuration block to `~/.factory/config.json`:
```json
{
"custom_models": [
{
"model_display_name": "qwen3-coder [Ollama]",
"model": "qwen3-coder",
"base_url": "http://localhost:11434/v1/",
"api_key": "not-needed",
"provider": "generic-chat-completion-api",
"max_tokens": 32000
}
]
}
```
## Cloud Models
`qwen3-coder:480b-cloud` is the recommended model for use with Droid.
Add the cloud configuration block to `~/.factory/config.json`:
```json
{
"custom_models": [
{
"model_display_name": "qwen3-coder [Ollama Cloud]",
"model": "qwen3-coder:480b-cloud",
"base_url": "http://localhost:11434/v1/",
"api_key": "not-needed",
"provider": "generic-chat-completion-api",
"max_tokens": 128000
}
]
}
```
## Connecting to ollama.com
1. Create an [API key](https://ollama.com/settings/keys) from ollama.com and export it as `OLLAMA_API_KEY`.
2. Add the cloud configuration block to `~/.factory/config.json`:
```json
{
"custom_models": [
{
"model_display_name": "qwen3-coder [Ollama Cloud]",
"model": "qwen3-coder:480b",
"base_url": "https://ollama.com/v1/",
"api_key": "OLLAMA_API_KEY",
"provider": "generic-chat-completion-api",
"max_tokens": 128000
}
]
}
```
Run `droid` in a new terminal to load the new settings.

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---
title: Goose
---
## Goose Desktop
Install [Goose](https://block.github.io/goose/docs/getting-started/installation/) Desktop.
### Usage with Ollama
1. In Goose, open **Settings** → **Configure Provider**.
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/goose-settings.png"
alt="Goose settings Panel"
width="75%"
/>
</div>
2. Find **Ollama**, click **Configure**
3. Confirm **API Host** is `http://localhost:11434` and click Submit
### Connecting to ollama.com
1. Create an [API key](https://ollama.com/settings/keys) on ollama.com and save it in your `.env`
2. In Goose, set **API Host** to `https://ollama.com`
## Goose CLI
Install [Goose](https://block.github.io/goose/docs/getting-started/installation/) CLI
### Usage with Ollama
1. Run `goose configure`
2. Select **Configure Providers** and select **Ollama**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/goose-cli.png"
alt="Goose CLI"
width="50%"
/>
</div>
3. Enter model name (e.g `qwen3`)
### Connecting to ollama.com
1. Create an [API key](https://ollama.com/settings/keys) on ollama.com and save it in your `.env`
2. Run `goose configure`
3. Select **Configure Providers** and select **Ollama**
4. Update **OLLAMA_HOST** to `https://ollama.com`

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---
title: JetBrains
---
<Note>This example uses **IntelliJ**; same steps apply to other JetBrains IDEs (e.g., PyCharm).</Note>
## Install
Install [IntelliJ](https://www.jetbrains.com/idea/).
## Usage with Ollama
<Note>
To use **Ollama**, you will need a [JetBrains AI Subscription](https://www.jetbrains.com/ai-ides/buy/?section=personal&billing=yearly).
</Note>
1. In Intellij, click the **chat icon** located in the right sidebar
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/intellij-chat-sidebar.png"
alt="Intellij Sidebar Chat"
width="50%"
/>
</div>
2. Select the **current model** in the sidebar, then click **Set up Local Models**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/intellij-current-model.png"
alt="Intellij model bottom right corner"
width="50%"
/>
</div>
3. Under **Third Party AI Providers**, choose **Ollama**
4. Confirm the **Host URL** is `http://localhost:11434`, then click **Ok**
5. Once connected, select a model under **Local models by Ollama**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/intellij-local-models.png"
alt="Zed star icon in bottom right corner"
width="50%"
/>
</div>

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---
title: n8n
---
## Install
Install [n8n](https://docs.n8n.io/choose-n8n/).
## Using Ollama Locally
1. In the top right corner, click the dropdown and select **Create Credential**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/n8n-credential-creation.png"
alt="Create a n8n Credential"
width="75%"
/>
</div>
2. Under **Add new credential** select **Ollama**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/n8n-ollama-form.png"
alt="Select Ollama under Credential"
width="75%"
/>
</div>
3. Confirm Base URL is set to `http://localhost:11434` and click **Save**
<Note> If connecting to `http://localhost:11434` fails, use `http://127.0.0.1:11434`</Note>
4. When creating a new workflow, select **Add a first step** and select an **Ollama node**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/n8n-chat-node.png"
alt="Add a first step with Ollama node"
width="75%"
/>
</div>
5. Select your model of choice (e.g. `qwen3-coder`)
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/n8n-models.png"
alt="Set up Ollama credentials"
width="75%"
/>
</div>
## Connecting to ollama.com
1. Create an [API key](https://ollama.com/settings/keys) on **ollama.com**.
2. In n8n, click **Create Credential** and select **Ollama**
4. Set the **API URL** to `https://ollama.com`
5. Enter your **API Key** and click **Save**

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---
title: Roo Code
---
## Install
Install [Roo Code](https://marketplace.visualstudio.com/items?itemName=RooVeterinaryInc.roo-cline) from the VS Code Marketplace.
## Usage with Ollama
1. Open Roo Code in VS Code and click the **gear icon** on the top right corner of the Roo Code window to open **Provider Settings**
2. Set `API Provider` to `Ollama`
3. (Optional) Update `Base URL` if your Ollama instance is running remotely. The default is `http://localhost:11434`
4. Enter a valid `Model ID` (for example `qwen3` or `qwen3-coder:480b-cloud`)
5. Adjust the `Context Window` to at least 32K tokens for coding tasks
<Note>Coding tools require 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>
## Connecting to ollama.com
1. Create an [API key](https://ollama.com/settings/keys) from ollama.com
2. Enable `Use custom base URL` and set it to `https://ollama.com`
3. Enter your **Ollama API Key**
4. Select a model from the list
### Recommended Models
- `qwen3-coder:480b`
- `deepseek-v3.1:671b`

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---
title: VS Code
---
## Install
Install [VSCode](https://code.visualstudio.com/download).
## Usage with Ollama
1. Open Copilot side bar found in top right window
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/vscode-sidebar.png"
alt="VSCode chat Sidebar"
width="75%"
/>
</div>
2. Select the model drowpdown > **Manage models**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/vscode-models.png"
alt="VSCode 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="VSCode model options dropdown"
width="75%"
/>
</div>

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---
title: Xcode
---
## Install
Install [XCode](https://developer.apple.com/xcode/)
## Usage with Ollama
<Note> Ensure Apple Intelligence is setup and the latest XCode version is v26.0 </Note>
1. Click **XCode** in top left corner > **Settings**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/xcode-intelligence-window.png"
alt="Xcode Intelligence window"
width="50%"
/>
</div>
2. Select **Locally Hosted**, enter port **11434** and click **Add**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/xcode-locally-hosted.png"
alt="Xcode settings"
width="50%"
/>
</div>
3. Select the **star icon** on the top left corner and click the **dropdown**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/xcode-chat-icon.png"
alt="Xcode settings"
width="50%"
/>
</div>
4. Click **My Account** and select your desired model
## Connecting to ollama.com directly
1. Create an [API key](https://ollama.com/settings/keys) from ollama.com
2. Select **Internet Hosted** and enter URL as `https://ollama.com`
3. Enter your **Ollama API Key** and click **Add**

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---
title: Zed
---
## Install
Install [Zed](https://zed.dev/download).
## Usage with Ollama
1. In Zed, click the **star icon** in the bottom-right corner, then select **Configure**.
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/zed-settings.png"
alt="Zed star icon in bottom right corner"
width="50%"
/>
</div>
2. Under **LLM Providers**, choose **Ollama**
3. Confirm the **Host URL** is `http://localhost:11434`, then click **Connect**
4. Once connected, select a model under **Ollama**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/zed-ollama-dropdown.png"
alt="Zed star icon in bottom right corner"
width="50%"
/>
</div>
## Connecting to ollama.com
1. Create an [API key](https://ollama.com/settings/keys) on **ollama.com**
2. In Zed, open the **star icon** → **Configure**
3. Under **LLM Providers**, select **Ollama**
4. Set the **API URL** to `https://ollama.com`

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---
title: Linux
---
## Install
To install Ollama, run the following command:
```shell
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.
</Note>
Download and extract the package:
```shell
curl -fsSL https://ollama.com/download/ollama-linux-amd64.tgz \
| sudo tar zx -C /usr
```
Start Ollama:
```shell
ollama serve
```
In another terminal, verify that Ollama is running:
```shell
ollama -v
```
### AMD GPU install
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
```
### ARM64 install
Download and extract the ARM64-specific package:
```shell
curl -fsSL https://ollama.com/download/ollama-linux-arm64.tgz \
| sudo tar zx -C /usr
```
### Adding Ollama as a startup service (recommended)
Create a user and group for Ollama:
```shell
sudo useradd -r -s /bin/false -U -m -d /usr/share/ollama ollama
sudo usermod -a -G ollama $(whoami)
```
Create a service file in `/etc/systemd/system/ollama.service`:
```ini
[Unit]
Description=Ollama Service
After=network-online.target
[Service]
ExecStart=/usr/bin/ollama serve
User=ollama
Group=ollama
Restart=always
RestartSec=3
Environment="PATH=$PATH"
[Install]
WantedBy=multi-user.target
```
Then start the service:
```shell
sudo systemctl daemon-reload
sudo systemctl enable ollama
```
### Install CUDA drivers (optional)
[Download and install](https://developer.nvidia.com/cuda-downloads) CUDA.
Verify that the drivers are installed by running the following command, which should print details about your GPU:
```shell
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.
### Start Ollama
Start Ollama and verify it is running:
```shell
sudo systemctl start ollama
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.
</Note>
## Customizing
To customize the installation of Ollama, you can edit the systemd service file or the environment variables by running:
```shell
sudo systemctl edit ollama
```
Alternatively, create an override file manually in `/etc/systemd/system/ollama.service.d/override.conf`:
```ini
[Service]
Environment="OLLAMA_DEBUG=1"
```
## Updating
Update Ollama by running the install script again:
```shell
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
```
## Installing specific versions
Use `OLLAMA_VERSION` environment variable with the install script to install a specific version of Ollama, including pre-releases. You can find the version numbers in the [releases page](https://github.com/ollama/ollama/releases).
For example:
```shell
curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.5.7 sh
```
## Viewing logs
To view logs of Ollama running as a startup service, run:
```shell
journalctl -e -u ollama
```
## Uninstall
Remove the ollama service:
```shell
sudo systemctl stop ollama
sudo systemctl disable ollama
sudo rm /etc/systemd/system/ollama.service
```
Remove ollama libraries from your lib directory (either `/usr/local/lib`, `/usr/lib`, or `/lib`):
```shell
sudo rm -r $(which ollama | tr 'bin' 'lib')
```
Remove the ollama binary from your bin directory (either `/usr/local/bin`, `/usr/bin`, or `/bin`):
```shell
sudo rm $(which ollama)
```
Remove the downloaded models and Ollama service user and group:
```shell
sudo userdel ollama
sudo groupdel ollama
sudo rm -r /usr/share/ollama
```

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---
title: Modelfile Reference
---
A Modelfile is the blueprint to create and share customized models using Ollama.
## Table of Contents
- [Format](#format)
- [Examples](#examples)
- [Instructions](#instructions)
- [FROM (Required)](#from-required)
- [Build from existing model](#build-from-existing-model)
- [Build from a Safetensors model](#build-from-a-safetensors-model)
- [Build from a GGUF file](#build-from-a-gguf-file)
- [PARAMETER](#parameter)
- [Valid Parameters and Values](#valid-parameters-and-values)
- [TEMPLATE](#template)
- [Template Variables](#template-variables)
- [SYSTEM](#system)
- [ADAPTER](#adapter)
- [LICENSE](#license)
- [MESSAGE](#message)
- [Notes](#notes)
## Format
The format of the `Modelfile`:
```
# comment
INSTRUCTION arguments
```
| Instruction | Description |
| ----------------------------------- | -------------------------------------------------------------- |
| [`FROM`](#from-required) (required) | Defines the base model to use. |
| [`PARAMETER`](#parameter) | Sets the parameters for how Ollama will run the model. |
| [`TEMPLATE`](#template) | The full prompt template to be sent to the model. |
| [`SYSTEM`](#system) | Specifies the system message that will be set in the template. |
| [`ADAPTER`](#adapter) | Defines the (Q)LoRA adapters to apply to the model. |
| [`LICENSE`](#license) | Specifies the legal license. |
| [`MESSAGE`](#message) | Specify message history. |
## Examples
### Basic `Modelfile`
An example of a `Modelfile` creating a mario blueprint:
```
FROM llama3.2
# sets the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1
# sets the context window size to 4096, this controls how many tokens the LLM can use as context to generate the next token
PARAMETER num_ctx 4096
# sets a custom system message to specify the behavior of the chat assistant
SYSTEM You are Mario from super mario bros, acting as an assistant.
```
To use this:
1. Save it as a file (e.g. `Modelfile`)
2. `ollama create choose-a-model-name -f <location of the file e.g. ./Modelfile>`
3. `ollama run choose-a-model-name`
4. Start using the model!
To view the Modelfile of a given model, use the `ollama show --modelfile` command.
```shell
ollama show --modelfile llama3.2
```
```
# Modelfile generated by "ollama show"
# To build a new Modelfile based on this one, replace the FROM line with:
# FROM llama3.2:latest
FROM /Users/pdevine/.ollama/models/blobs/sha256-00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29
TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>
{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
{{ .Response }}<|eot_id|>"""
PARAMETER stop "<|start_header_id|>"
PARAMETER stop "<|end_header_id|>"
PARAMETER stop "<|eot_id|>"
PARAMETER stop "<|reserved_special_token"
```
## Instructions
### FROM (Required)
The `FROM` instruction defines the base model to use when creating a model.
```
FROM <model name>:<tag>
```
#### Build from existing model
```
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>
#### Build from a Safetensors model
```
FROM <model directory>
```
The model directory should contain the Safetensors weights for a supported architecture.
Currently supported model architectures:
- Llama (including Llama 2, Llama 3, Llama 3.1, and Llama 3.2)
- Mistral (including Mistral 1, Mistral 2, and Mixtral)
- Gemma (including Gemma 1 and Gemma 2)
- Phi3
#### Build from a GGUF file
```
FROM ./ollama-model.gguf
```
The GGUF file location should be specified as an absolute path or relative to the `Modelfile` location.
### PARAMETER
The `PARAMETER` instruction defines a parameter that can be set when the model is run.
```
PARAMETER <parameter> <parametervalue>
```
#### Valid Parameters and Values
| Parameter | Description | Value Type | Example Usage |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | -------------------- |
| mirostat | Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) | int | mirostat 0 |
| mirostat_eta | Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1) | float | mirostat_eta 0.1 |
| mirostat_tau | Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0) | float | mirostat_tau 5.0 |
| num_ctx | Sets the size of the context window used to generate the next token. (Default: 2048) | int | num_ctx 4096 |
| repeat_last_n | Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx) | int | repeat_last_n 64 |
| repeat_penalty | Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1) | float | repeat_penalty 1.1 |
| temperature | The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8) | float | temperature 0.7 |
| 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 |
| 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 |
### TEMPLATE
`TEMPLATE` of the full prompt template to be passed into the model. It may include (optionally) a system message, a user's message and the response from the model. Note: syntax may be model specific. Templates use Go [template syntax](https://pkg.go.dev/text/template).
#### Template Variables
| Variable | Description |
| ----------------- | --------------------------------------------------------------------------------------------- |
| `{{ .System }}` | The system message used to specify custom behavior. |
| `{{ .Prompt }}` | The user prompt message. |
| `{{ .Response }}` | The response from the model. When generating a response, text after this variable is omitted. |
```
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
"""
```
### SYSTEM
The `SYSTEM` instruction specifies the system message to be used in the template, if applicable.
```
SYSTEM """<system message>"""
```
### ADAPTER
The `ADAPTER` instruction specifies a fine tuned LoRA adapter that should apply to the base model. The value of the adapter should be an absolute path or a path relative to the Modelfile. The base model should be specified with a `FROM` instruction. If the base model is not the same as the base model that the adapter was tuned from the behaviour will be erratic.
#### Safetensor adapter
```
ADAPTER <path to safetensor adapter>
```
Currently supported Safetensor adapters:
- Llama (including Llama 2, Llama 3, and Llama 3.1)
- Mistral (including Mistral 1, Mistral 2, and Mixtral)
- Gemma (including Gemma 1 and Gemma 2)
#### GGUF adapter
```
ADAPTER ./ollama-lora.gguf
```
### LICENSE
The `LICENSE` instruction allows you to specify the legal license under which the model used with this Modelfile is shared or distributed.
```
LICENSE """
<license text>
"""
```
### MESSAGE
The `MESSAGE` instruction allows you to specify a message history for the model to use when responding. Use multiple iterations of the MESSAGE command to build up a conversation which will guide the model to answer in a similar way.
```
MESSAGE <role> <message>
```
#### Valid roles
| Role | Description |
| --------- | ------------------------------------------------------------ |
| system | Alternate way of providing the SYSTEM message for the model. |
| user | An example message of what the user could have asked. |
| assistant | An example message of how the model should respond. |
#### Example conversation
```
MESSAGE user Is Toronto in Canada?
MESSAGE assistant yes
MESSAGE user Is Sacramento in Canada?
MESSAGE assistant no
MESSAGE user Is Ontario in Canada?
MESSAGE assistant yes
```
## Notes
- the **`Modelfile` is not case sensitive**. In the examples, uppercase instructions are used to make it easier to distinguish it from arguments.
- Instructions can be in any order. In the examples, the `FROM` instruction is first to keep it easily readable.
[1]: https://ollama.com/library

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---
title: n8n
---
## Install
Install [n8n](https://docs.n8n.io/choose-n8n/).
## Using Ollama Locally
1. In the top right corner, click the dropdown and select **Create Credential**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/n8n-credential-creation.png"
alt="Create a n8n Credential"
width="75%"
/>
</div>
2. Under **Add new credential** select **Ollama**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/n8n-ollama-form.png"
alt="Select Ollama under Credential"
width="75%"
/>
</div>
3. Confirm Base URL is set to `http://localhost:11434` and click **Save**
<Note> If connecting to `http://localhost:11434` fails, use `http://127.0.0.1:11434`</Note>
4. When creating a new workflow, select **Add a first step** and select an **Ollama node**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/n8n-chat-node.png"
alt="Add a first step with Ollama node"
width="75%"
/>
</div>
5. Select your model of choice (e.g. `qwen3-coder`)
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img
src="/images/n8n-models.png"
alt="Set up Ollama credentials"
width="75%"
/>
</div>
## Connecting to ollama.com
1. Create an [API key](https://ollama.com/settings/keys) on **ollama.com**.
2. In n8n, click **Create Credential** and select **Ollama**
4. Set the **API URL** to `https://ollama.com`
5. Enter your **API Key** and click **Save**

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---
title: Quickstart
---
This quickstart will walk your through running your first model with Ollama. To get started, download Ollama on macOS, Windows or Linux.
<a
href="https://ollama.com/download"
target="_blank"
className="inline-block px-6 py-2 bg-black rounded-full dark:bg-neutral-700 text-white font-normal border-none"
>
Download Ollama
</a>
## Run a model
<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:
```shell
curl http://localhost:11434/api/chat -d '{
"model": "gemma3",
"messages": [{
"role": "user",
"content": "Hello there!"
}],
"stream": false
}'
```
</Tab>
<Tab title="Python">
Start by downloading a model:
```
ollama pull gemma3
```
Then install Ollama's Python library:
```
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:
```shell
import ollama from 'ollama'
const response = await ollama.chat({
model: 'gemma3',
messages: [{ role: 'user', content: 'Why is the sky blue?' }],
})
console.log(response.message.content)
```
</Tab>
</Tabs>
See a full list of available models [here](https://ollama.com/models).

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body {
font-family: ui-sans-serif, system-ui, sans-serif, Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji;
}
pre, code, .font-mono {
font-family: ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;
}
.nav-logo {
height: 44px;
}
.eyebrow {
color: #666;
font-weight: 400;
}

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---
title: Template
---
Ollama provides a powerful templating engine backed by Go's built-in templating engine to construct prompts for your large language model. This feature is a valuable tool to get the most out of your models.
## Basic Template Structure
A basic Go template consists of three main parts:
- **Layout**: The overall structure of the template.
- **Variables**: Placeholders for dynamic data that will be replaced with actual values when the template is rendered.
- **Functions**: Custom functions or logic that can be used to manipulate the template's content.
Here's an example of a simple chat template:
```gotmpl
{{- range .Messages }}
{{ .Role }}: {{ .Content }}
{{- end }}
```
In this example, we have:
- A basic messages structure (layout)
- Three variables: `Messages`, `Role`, and `Content` (variables)
- A custom function (action) that iterates over an array of items (`range .Messages`) and displays each item
## Adding templates to your model
By default, models imported into Ollama have a default template of `{{ .Prompt }}`, i.e. user inputs are sent verbatim to the LLM. This is appropriate for text or code completion models but lacks essential markers for chat or instruction models.
Omitting a template in these models puts the responsibility of correctly templating input onto the user. Adding a template allows users to easily get the best results from the model.
To add templates in your model, you'll need to add a `TEMPLATE` command to the Modelfile. Here's an example using Meta's Llama 3.
```dockerfile
FROM llama3.2
TEMPLATE """{{- if .System }}<|start_header_id|>system<|end_header_id|>
{{ .System }}<|eot_id|>
{{- end }}
{{- range .Messages }}<|start_header_id|>{{ .Role }}<|end_header_id|>
{{ .Content }}<|eot_id|>
{{- end }}<|start_header_id|>assistant<|end_header_id|>
"""
```
## Variables
`System` (string): system prompt
`Prompt` (string): user prompt
`Response` (string): assistant response
`Suffix` (string): text inserted after the assistant's response
`Messages` (list): list of messages
`Messages[].Role` (string): role which can be one of `system`, `user`, `assistant`, or `tool`
`Messages[].Content` (string): message content
`Messages[].ToolCalls` (list): list of tools the model wants to call
`Messages[].ToolCalls[].Function` (object): function to call
`Messages[].ToolCalls[].Function.Name` (string): function name
`Messages[].ToolCalls[].Function.Arguments` (map): mapping of argument name to argument value
`Tools` (list): list of tools the model can access
`Tools[].Type` (string): schema type. `type` is always `function`
`Tools[].Function` (object): function definition
`Tools[].Function.Name` (string): function name
`Tools[].Function.Description` (string): function description
`Tools[].Function.Parameters` (object): function parameters
`Tools[].Function.Parameters.Type` (string): schema type. `type` is always `object`
`Tools[].Function.Parameters.Required` (list): list of required properties
`Tools[].Function.Parameters.Properties` (map): mapping of property name to property definition
`Tools[].Function.Parameters.Properties[].Type` (string): property type
`Tools[].Function.Parameters.Properties[].Description` (string): property description
`Tools[].Function.Parameters.Properties[].Enum` (list): list of valid values
## Tips and Best Practices
Keep the following tips and best practices in mind when working with Go templates:
- **Be mindful of dot**: Control flow structures like `range` and `with` changes the value `.`
- **Out-of-scope variables**: Use `$.` to reference variables not currently in scope, starting from the root
- **Whitespace control**: Use `-` to trim leading (`{{-`) and trailing (`-}}`) whitespace
## Examples
### Example Messages
#### ChatML
ChatML is a popular template format. It can be used for models such as Databrick's DBRX, Intel's Neural Chat, and Microsoft's Orca 2.
```go
{{- range .Messages }}<|im_start|>{{ .Role }}
{{ .Content }}<|im_end|>
{{ end }}<|im_start|>assistant
```
### Example Tools
Tools support can be added to a model by adding a `{{ .Tools }}` node to the template. This feature is useful for models trained to call external tools and can a powerful tool for retrieving real-time data or performing complex tasks.
#### Mistral
Mistral v0.3 and Mixtral 8x22B supports tool calling.
```go
{{- range $index, $_ := .Messages }}
{{- if eq .Role "user" }}
{{- if and (le (len (slice $.Messages $index)) 2) $.Tools }}[AVAILABLE_TOOLS] {{ json $.Tools }}[/AVAILABLE_TOOLS]
{{- end }}[INST] {{ if and (eq (len (slice $.Messages $index)) 1) $.System }}{{ $.System }}
{{ end }}{{ .Content }}[/INST]
{{- else if eq .Role "assistant" }}
{{- if .Content }} {{ .Content }}</s>
{{- else if .ToolCalls }}[TOOL_CALLS] [
{{- range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ json .Function.Arguments }}}
{{- end }}]</s>
{{- end }}
{{- else if eq .Role "tool" }}[TOOL_RESULTS] {"content": {{ .Content }}}[/TOOL_RESULTS]
{{- end }}
{{- end }}
```
### Example Fill-in-Middle
Fill-in-middle support can be added to a model by adding a `{{ .Suffix }}` node to the template. This feature is useful for models that are trained to generate text in the middle of user input, such as code completion models.
#### CodeLlama
CodeLlama [7B](https://ollama.com/library/codellama:7b-code) and [13B](https://ollama.com/library/codellama:13b-code) code completion models support fill-in-middle.
```go
<PRE> {{ .Prompt }} <SUF>{{ .Suffix }} <MID>
```
<Note>
CodeLlama 34B and 70B code completion and all instruct and Python fine-tuned models do not support fill-in-middle.
</Note>
#### Codestral
Codestral [22B](https://ollama.com/library/codestral:22b) supports fill-in-middle.
```gotmpl
[SUFFIX]{{ .Suffix }}[PREFIX] {{ .Prompt }}
```

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---
title: Text generation
---

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---
title: Troubleshooting
description: How to troubleshoot issues encountered with Ollama
---
Sometimes Ollama may not perform as expected. One of the best ways to figure out what happened is to take a look at the logs. Find the logs on **Mac** by running the command:
```shell
cat ~/.ollama/logs/server.log
```
On **Linux** systems with systemd, the logs can be found with this command:
```shell
journalctl -u ollama --no-pager --follow --pager-end
```
When you run Ollama in a **container**, the logs go to stdout/stderr in the container:
```shell
docker logs <container-name>
```
(Use `docker ps` to find the container name)
If manually running `ollama serve` in a terminal, the logs will be on that terminal.
When you run Ollama on **Windows**, there are a few different locations. You can view them in the explorer window by hitting `<cmd>+R` and type in:
- `explorer %LOCALAPPDATA%\Ollama` to view logs. The most recent server logs will be in `server.log` and older logs will be in `server-#.log`
- `explorer %LOCALAPPDATA%\Programs\Ollama` to browse the binaries (The installer adds this to your user PATH)
- `explorer %HOMEPATH%\.ollama` to browse where models and configuration is stored
- `explorer %TEMP%` where temporary executable files are stored in one or more `ollama*` directories
To enable additional debug logging to help troubleshoot problems, first **Quit the running app from the tray menu** then in a powershell terminal
```powershell
$env:OLLAMA_DEBUG="1"
& "ollama app.exe"
```
Join the [Discord](https://discord.gg/ollama) for help interpreting the logs.
## LLM libraries
Ollama includes multiple LLM libraries compiled for different GPUs and CPU vector features. Ollama tries to pick the best one based on the capabilities of your system. If this autodetection has problems, or you run into other problems (e.g. crashes in your GPU) you can workaround this by forcing a specific LLM library. `cpu_avx2` will perform the best, followed by `cpu_avx` an the slowest but most compatible is `cpu`. Rosetta emulation under MacOS will work with the `cpu` library.
In the server log, you will see a message that looks something like this (varies from release to release):
```
Dynamic LLM libraries [rocm_v6 cpu cpu_avx cpu_avx2 cuda_v11 rocm_v5]
```
**Experimental LLM Library Override**
You can set OLLAMA_LLM_LIBRARY to any of the available LLM libraries to bypass autodetection, so for example, if you have a CUDA card, but want to force the CPU LLM library with AVX2 vector support, use:
```shell
OLLAMA_LLM_LIBRARY="cpu_avx2" ollama serve
```
You can see what features your CPU has with the following.
```shell
cat /proc/cpuinfo| grep flags | head -1
```
## Installing older or pre-release versions on Linux
If you run into problems on Linux and want to install an older version, or you'd like to try out a pre-release before it's officially released, you can tell the install script which version to install.
```shell
curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION=0.5.7 sh
```
## Linux tmp noexec
If your system is configured with the "noexec" flag where Ollama stores its temporary executable files, you can specify an alternate location by setting OLLAMA_TMPDIR to a location writable by the user ollama runs as. For example OLLAMA_TMPDIR=/usr/share/ollama/
## Linux docker
If Ollama initially works on the GPU in a docker container, but then switches to running on CPU after some period of time with errors in the server log reporting GPU discovery failures, this can be resolved by disabling systemd cgroup management in Docker. Edit `/etc/docker/daemon.json` on the host and add `"exec-opts": ["native.cgroupdriver=cgroupfs"]` to the docker configuration.
## NVIDIA GPU Discovery
When Ollama starts up, it takes inventory of the GPUs present in the system to determine compatibility and how much VRAM is available. Sometimes this discovery can fail to find your GPUs. In general, running the latest driver will yield the best results.
### 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)
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
- If you are using a container, is the container runtime working? Try `docker run --gpus all ubuntu nvidia-smi` - if this doesn't work, Ollama won't be able to see your NVIDIA GPU.
- Is the uvm driver loaded? `sudo nvidia-modprobe -u`
- Try reloading the nvidia_uvm driver - `sudo rmmod nvidia_uvm` then `sudo modprobe nvidia_uvm`
- Try rebooting
- Make sure you're running the latest nvidia drivers
If none of those resolve the problem, gather additional information and file an issue:
- Set `CUDA_ERROR_LEVEL=50` and try again to get more diagnostic logs
- Check dmesg for any errors `sudo dmesg | grep -i nvrm` and `sudo dmesg | grep -i nvidia`
## AMD GPU Discovery
On linux, AMD GPU access typically requires `video` and/or `render` group membership to access the `/dev/kfd` device. If permissions are not set up correctly, Ollama will detect this and report an error in the server log.
When running in a container, in some Linux distributions and container runtimes, the ollama process may be unable to access the GPU. Use `ls -lnd /dev/kfd /dev/dri /dev/dri/*` on the host system to determine the **numeric** group IDs on your system, and pass additional `--group-add ...` arguments to the container so it can access the required devices. For example, in the following output `crw-rw---- 1 0 44 226, 0 Sep 16 16:55 /dev/dri/card0` the group ID column is `44`
If you are experiencing problems getting Ollama to correctly discover or use your GPU for inference, the following may help isolate the failure.
- `AMD_LOG_LEVEL=3` Enable info log levels in the AMD HIP/ROCm libraries. This can help show more detailed error codes that can help troubleshoot problems
- `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`
## 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
## Windows Terminal Errors
Older versions of Windows 10 (e.g., 21H1) are known to have a bug where the standard terminal program does not display control characters correctly. This can result in a long string of strings like `←[?25h←[?25l` being displayed, sometimes erroring with `The parameter is incorrect` To resolve this problem, please update to Win 10 22H1 or newer.

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---
title: Vision
description: Provide images to models
---

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---
title: Windows
---
Welcome to Ollama for Windows.
No more WSL required!
Ollama now runs as a native Windows application, including NVIDIA and AMD Radeon GPU support.
After installing Ollama for Windows, Ollama will run in the background and
the `ollama` command line is available in `cmd`, `powershell` or your favorite
terminal application. As usual the Ollama [API](/api) will be served on
`http://localhost:11434`.
## 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
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.
## 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.
### Changing Install Location
To install the Ollama application in a location different than your home directory, start the installer with the following flag
```powershell
OllamaSetup.exe /DIR="d:\some\location"
```
### Changing Model Location
To change where Ollama stores the downloaded models instead of using your home directory, set the environment variable `OLLAMA_MODELS` in your user account.
1. Start the Settings (Windows 11) or Control Panel (Windows 10) application and search for _environment variables_.
2. Click on _Edit environment variables for your account_.
3. Edit or create a new variable for your user account for `OLLAMA_MODELS` where you want the models stored
4. Click OK/Apply to save.
If Ollama is already running, Quit the tray application and relaunch it from the Start menu, or a new terminal started after you saved the environment variables.
## API Access
Here's a quick example showing API access from `powershell`
```powershell
(Invoke-WebRequest -method POST -Body '{"model":"llama3.2", "prompt":"Why is the sky blue?", "stream": false}' -uri http://localhost:11434/api/generate ).Content | ConvertFrom-json
```
## Troubleshooting
Ollama on Windows stores files in a few different locations. You can view them in
the explorer window by hitting `<Ctrl>+R` and type in:
- `explorer %LOCALAPPDATA%\Ollama` contains logs, and downloaded updates
- _app.log_ contains most resent logs from the GUI application
- _server.log_ contains the most recent server logs
- _upgrade.log_ contains log output for upgrades
- `explorer %LOCALAPPDATA%\Programs\Ollama` contains the binaries (The installer adds this to your user PATH)
- `explorer %HOMEPATH%\.ollama` contains models and configuration
- `explorer %TEMP%` contains temporary executable files in one or more `ollama*` directories
## Uninstall
The Ollama Windows installer registers an Uninstaller application. Under `Add or remove programs` in Windows Settings, you can uninstall Ollama.
<Note>
If you have [changed the OLLAMA_MODELS location](#changing-model-location), the installer will not remove your downloaded models
</Note>
## Standalone CLI
The easiest way to install Ollama on Windows is to use the `OllamaSetup.exe`
installer. It installs in your account without requiring Administrator rights.
We update Ollama regularly to support the latest models, and this installer will
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
running it as a system service via `ollama serve` with tools such as
[NSSM](https://nssm.cc/).
<Note>
If you are upgrading from a prior version, you should remove the old directories first.
</Note>