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# Ollama - Llms-Txt
**Pages:** 58
---
## FAQ
**URL:** llms-txt#faq
**Contents:**
- How can I upgrade Ollama?
- How can I view the logs?
- Is my GPU compatible with Ollama?
- How can I specify the context window size?
- How can I tell if my model was loaded onto the GPU?
- How do I configure Ollama server?
- Setting environment variables on Mac
- Setting environment variables on Linux
- Setting environment variables on Windows
- How do I use Ollama behind a proxy?
Source: https://docs.ollama.com/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:
## 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:
To change this when using `ollama run`, use `/set parameter`:
When using the API, specify the `num_ctx` parameter:
## 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.
<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`.
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]`:
4. Reload `systemd` and 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.
Build and run this image:
## 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:
## How can I use Ollama with ngrok?
Ollama can be accessed using a range of tools for tunneling tools. For example with Ngrok:
## How can I use Ollama with Cloudflare Tunnel?
To use Ollama with Cloudflare Tunnel, use the `--url` and `--http-host-header` flags:
## 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:
**Examples:**
Example 1 (unknown):
```unknown
## 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:
```
Example 2 (unknown):
```unknown
To change this when using `ollama run`, use `/set parameter`:
```
Example 3 (unknown):
```unknown
When using the API, specify the `num_ctx` parameter:
```
Example 4 (unknown):
```unknown
## 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.
```
---
## Quickstart
**URL:** llms-txt#quickstart
**Contents:**
- Run a model
Source: https://docs.ollama.com/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>
<Tabs>
<Tab title="CLI">
Open a terminal and run the command:
Lastly, chat with the model:
<Tab title="Python">
Start by downloading a model:
Then install Ollama's Python library:
Lastly, chat with the model:
<Tab title="JavaScript">
Start by downloading a model:
Then install the Ollama JavaScript library:
Lastly, chat with the model:
See a full list of available models [here](https://ollama.com/models).
**Examples:**
Example 1 (unknown):
```unknown
ollama run gemma3
```
Example 2 (unknown):
```unknown
ollama pull gemma3
```
Example 3 (unknown):
```unknown
</Tab>
<Tab title="Python">
Start by downloading a model:
```
Example 4 (unknown):
```unknown
Then install Ollama's Python library:
```
---
## Generate a chat message
**URL:** llms-txt#generate-a-chat-message
Source: https://docs.ollama.com/api/chat
openapi.yaml post /api/chat
Generate the next chat message in a conversation between a user and an assistant.
---
## Get version
**URL:** llms-txt#get-version
Source: https://docs.ollama.com/api-reference/get-version
openapi.yaml get /api/version
Retrieve the version of the Ollama
---
## VS Code
**URL:** llms-txt#vs-code
**Contents:**
- Install
- Usage with Ollama
Source: https://docs.ollama.com/integrations/vscode
Install [VSCode](https://code.visualstudio.com/download).
1. Open Copilot side bar found in top right window
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img src="https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-sidebar.png?fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=8d841164c3a8c2e6cb502f9dece6079c" alt="VSCode chat Sidebar" width="75%" data-og-width="838" data-og-height="304" data-path="images/vscode-sidebar.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-sidebar.png?w=280&fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=8baa6af2c2f307707730aff500625719 280w, https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-sidebar.png?w=560&fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=790f257751bb80213223c2d897988793 560w, https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-sidebar.png?w=840&fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=4c1d2ba0a7e7f4c32fc7818a213eaa85 840w, https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-sidebar.png?w=1100&fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=5470dddd3a1a42d8c599968e8a4613b1 1100w, https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-sidebar.png?w=1650&fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=cf1e7e5ec1aa98136b76e93db93f6116 1650w, https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-sidebar.png?w=2500&fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=6a995df960d939abd4c3ee29f3e58fac 2500w" />
</div>
2. Select the model drowpdown > **Manage models**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img src="https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-models.png?fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=9a1715817d228c9c103708da3c5ecd37" alt="VSCode model picker" width="75%" data-og-width="1064" data-og-height="462" data-path="images/vscode-models.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-models.png?w=280&fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=bcb1de0c96fde6b44d95a816dd81a99a 280w, https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-models.png?w=560&fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=4941a05a32b420adabcd827ebf635097 560w, https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-models.png?w=840&fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=1ef98e50f14c73d9027d38c2f4f28e06 840w, https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-models.png?w=1100&fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=546c696a9857ab6721f7c084836b5921 1100w, https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-models.png?w=1650&fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=98bf91da4065a79774c7b199a99b730f 1650w, https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-models.png?w=2500&fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=6cbd0767ca3440fac141c2e5657f55e7 2500w" />
</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="https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-model-options.png?fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=1b08a9ccc2f275e6eb039de37cceaf31" alt="VSCode model options dropdown" width="75%" data-og-width="1202" data-og-height="552" data-path="images/vscode-model-options.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-model-options.png?w=280&fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=bdb15602e971a34695cadc7f6d90d64d 280w, https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-model-options.png?w=560&fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=cb22702c3f6c295ae8822e8ca5f163cf 560w, https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-model-options.png?w=840&fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=bdf5eb8776e9163afe8437f5413c67cc 840w, https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-model-options.png?w=1100&fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=a46fc6100e91907298d223c52f306a5b 1100w, https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-model-options.png?w=1650&fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=c134ec18e18bcdfe471fd5eb329acd9a 1650w, https://mintcdn.com/ollama-9269c548/Q0hzAGiFk9hDuXaH/images/vscode-model-options.png?w=2500&fit=max&auto=format&n=Q0hzAGiFk9hDuXaH&q=85&s=f1be99a8b9069d8d213b6a10debe73a9 2500w" />
</div>
---
## Cloud
**URL:** llms-txt#cloud
**Contents:**
- Cloud Models
- Running Cloud models
- Cloud API access
- Authentication
- Listing models
- Generating a response
Source: https://docs.ollama.com/cloud
<Info>Ollama's cloud is currently in preview.</Info>
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`
* `minimax-m2: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:
<Tabs>
<Tab title="CLI">
To run a cloud model, open the terminal and run:
<Tab title="Python">
First, pull a cloud model so it can be accessed:
Next, install [Ollama's Python library](https://github.com/ollama/ollama-python):
Next, create and run a simple Python script:
<Tab title="JavaScript">
First, pull a cloud model so it can be accessed:
Next, install [Ollama's JavaScript library](https://github.com/ollama/ollama-js):
Then use the library to run a cloud model:
<Tab title="cURL">
First, pull a cloud model so it can be accessed:
Run the following cURL command to run the command via Ollama's API:
Cloud models can also be accessed directly on ollama.com's API. In this mode, ollama.com acts as a remote Ollama host.
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.
For models available directly via Ollama's API, models can be listed via:
### Generating a response
<Tabs>
<Tab title="Python">
First, install [Ollama's Python library](https://github.com/ollama/ollama-python)
<Tab title="JavaScript">
First, install [Ollama's JavaScript library](https://github.com/ollama/ollama-js):
Next, make a request to the model:
<Tab title="cURL">
Generate a response via Ollama's chat API:
**Examples:**
Example 1 (unknown):
```unknown
ollama signin
```
Example 2 (unknown):
```unknown
ollama run gpt-oss:120b-cloud
```
Example 3 (unknown):
```unknown
ollama pull gpt-oss:120b-cloud
```
Example 4 (unknown):
```unknown
pip install ollama
```
---
## Pull a model
**URL:** llms-txt#pull-a-model
Source: https://docs.ollama.com/api/pull
openapi.yaml post /api/pull
---
## Structured Outputs
**URL:** llms-txt#structured-outputs
**Contents:**
- Generating structured JSON
- Generating structured JSON with a schema
- Example: Extract structured data
- Example: Vision with structured outputs
- Tips for reliable structured outputs
Source: https://docs.ollama.com/capabilities/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">
</Tab>
<Tab title="Python">
</Tab>
<Tab title="JavaScript">
</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">
</Tab>
<Tab title="Python">
Use Pydantic models and pass `model_json_schema()` to `format`, then validate the response:
<Tab title="JavaScript">
Serialize a Zod schema with `zodToJsonSchema()` and parse the structured response:
## Example: Extract structured data
Define the objects you want returned and let the model populate the fields:
## Example: Vision with structured outputs
Vision models accept the same `format` parameter, enabling deterministic descriptions of images:
## 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`
**Examples:**
Example 1 (unknown):
```unknown
</Tab>
<Tab title="Python">
```
Example 2 (unknown):
```unknown
</Tab>
<Tab title="JavaScript">
```
Example 3 (unknown):
```unknown
</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">
```
Example 4 (unknown):
```unknown
</Tab>
<Tab title="Python">
Use Pydantic models and pass `model_json_schema()` to `format`, then validate the response:
```
---
## Context length
**URL:** llms-txt#context-length
**Contents:**
- Setting context length
- App
- CLI
- Check allocated context length and model offloading
Source: https://docs.ollama.com/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.
Change the slider in the Ollama app under settings to your desired context length.
<img src="https://mintcdn.com/ollama-9269c548/SjntZZpXgbN5v4M5/images/ollama-settings.png?fit=max&auto=format&n=SjntZZpXgbN5v4M5&q=85&s=e8a7ccd30fd9cee5e93662db05b43dc7" alt="Context length in Ollama app" data-og-width="2724" width="2724" data-og-height="2570" height="2570" data-path="images/ollama-settings.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/SjntZZpXgbN5v4M5/images/ollama-settings.png?w=280&fit=max&auto=format&n=SjntZZpXgbN5v4M5&q=85&s=434e8ffd8ad5ce5a6cf77cef285aa4d7 280w, https://mintcdn.com/ollama-9269c548/SjntZZpXgbN5v4M5/images/ollama-settings.png?w=560&fit=max&auto=format&n=SjntZZpXgbN5v4M5&q=85&s=879ba157a13c3ef59a76cf21f04baae4 560w, https://mintcdn.com/ollama-9269c548/SjntZZpXgbN5v4M5/images/ollama-settings.png?w=840&fit=max&auto=format&n=SjntZZpXgbN5v4M5&q=85&s=7c7314c5f77798307a93ff466501d1cc 840w, https://mintcdn.com/ollama-9269c548/SjntZZpXgbN5v4M5/images/ollama-settings.png?w=1100&fit=max&auto=format&n=SjntZZpXgbN5v4M5&q=85&s=b39e7ab998d6894649f5e4ac4bfb51e0 1100w, https://mintcdn.com/ollama-9269c548/SjntZZpXgbN5v4M5/images/ollama-settings.png?w=1650&fit=max&auto=format&n=SjntZZpXgbN5v4M5&q=85&s=1c854c1d41672b2f937ba9db4454e159 1650w, https://mintcdn.com/ollama-9269c548/SjntZZpXgbN5v4M5/images/ollama-settings.png?w=2500&fit=max&auto=format&n=SjntZZpXgbN5v4M5&q=85&s=1f1b926851fec5786cb5fc886cd41cdc 2500w" />
If editing the context length for Ollama is not possible, the context length can also be updated when serving Ollama.
### 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`.
**Examples:**
Example 1 (unknown):
```unknown
OLLAMA_CONTEXT_LENGTH=32000 ollama serve
```
Example 2 (unknown):
```unknown
ollama ps
```
Example 3 (unknown):
```unknown
NAME ID SIZE PROCESSOR CONTEXT UNTIL
gemma3:latest a2af6cc3eb7f 6.6 GB 100% GPU 65536 2 minutes from now
```
---
## comment
**URL:** llms-txt#comment
**Contents:**
- Examples
- Basic `Modelfile`
INSTRUCTION arguments
**Examples:**
Example 1 (unknown):
```unknown
| 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:
```
---
## Allow all Chrome, Firefox, and Safari extensions
**URL:** llms-txt#allow-all-chrome,-firefox,-and-safari-extensions
**Contents:**
- Where are models stored?
- How do I set them to a different location?
- How can I use Ollama in Visual Studio Code?
- How do I use Ollama with GPU acceleration in Docker?
- Why is networking slow in WSL2 on Windows 10?
- How can I preload a model into Ollama to get faster response times?
- How do I keep a model loaded in memory or make it unload immediately?
- How do I manage the maximum number of requests the Ollama server can queue?
- How does Ollama handle concurrent requests?
- How does Ollama load models on multiple GPUs?
OLLAMA_ORIGINS=chrome-extension://*,moz-extension://*,safari-web-extension://* ollama serve
shell theme={"system"}
curl http://localhost:11434/api/generate -d '{"model": "mistral"}'
shell theme={"system"}
curl http://localhost:11434/api/chat -d '{"model": "mistral"}'
shell theme={"system"}
ollama run llama3.2 ""
shell theme={"system"}
ollama stop llama3.2
shell theme={"system"}
curl http://localhost:11434/api/generate -d '{"model": "llama3.2", "keep_alive": -1}'
shell theme={"system"}
curl http://localhost:11434/api/generate -d '{"model": "llama3.2", "keep_alive": 0}'
shell theme={"system"}
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 \<username> with your actual Windows user name.
</Note>
**Examples:**
Example 1 (unknown):
```unknown
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:
```
Example 2 (unknown):
```unknown
To use the chat completions endpoint, use:
```
Example 3 (unknown):
```unknown
To preload a model using the CLI, use the command:
```
Example 4 (unknown):
```unknown
## 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:
```
---
## Push a model
**URL:** llms-txt#push-a-model
Source: https://docs.ollama.com/api/push
openapi.yaml post /api/push
---
## List running models
**URL:** llms-txt#list-running-models
Source: https://docs.ollama.com/api/ps
openapi.yaml get /api/ps
Retrieve a list of models that are currently running
---
## Usage
**URL:** llms-txt#usage
**Contents:**
- Example response
Source: https://docs.ollama.com/api/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.
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:
For endpoints that return **streaming responses**, usage fields are included as part of the final chunk, where `done` is `true`.
---
## OpenAI compatibility
**URL:** llms-txt#openai-compatibility
**Contents:**
- Usage
- OpenAI Python library
Source: https://docs.ollama.com/api/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.
### OpenAI Python library
#### Structured outputs
```python theme={"system"}
from pydantic import BaseModel
from openai import OpenAI
client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")
**Examples:**
Example 1 (unknown):
```unknown
#### Structured outputs
```
---
## Streaming
**URL:** llms-txt#streaming
**Contents:**
- Key streaming concepts
- Handling streamed chunks
Source: https://docs.ollama.com/capabilities/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.
2. 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.
3. 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">
</Tab>
<Tab title="JavaScript">
</Tab>
</Tabs>
**Examples:**
Example 1 (unknown):
```unknown
</Tab>
<Tab title="JavaScript">
```
---
## Droid
**URL:** llms-txt#droid
**Contents:**
- Install
- Usage with Ollama
- Cloud Models
- Connecting to ollama.com
Source: https://docs.ollama.com/integrations/droid
Install the [Droid CLI](https://factory.ai/):
<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>
Add a local configuration block to `~/.factory/config.json`:
`qwen3-coder:480b-cloud` is the recommended model for use with Droid.
Add the cloud configuration block to `~/.factory/config.json`:
## 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`:
Run `droid` in a new terminal to load the new settings.
**Examples:**
Example 1 (unknown):
```unknown
<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`:
```
Example 2 (unknown):
```unknown
## Cloud Models
`qwen3-coder:480b-cloud` is the recommended model for use with Droid.
Add the cloud configuration block to `~/.factory/config.json`:
```
Example 3 (unknown):
```unknown
## 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`:
```
---
## Copy a model
**URL:** llms-txt#copy-a-model
Source: https://docs.ollama.com/api/copy
openapi.yaml post /api/copy
---
## Authentication
**URL:** llms-txt#authentication
**Contents:**
- Signing in
- API keys
Source: https://docs.ollama.com/api/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
To sign in to ollama.com from your local installation of Ollama, run:
Once signed in, Ollama will automatically authenticate commands as required:
Similarly, when accessing a local API endpoint that requires cloud access, Ollama will automatically authenticate the request:
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:
Then use the API key in the Authorization header:
API keys don't currently expire, however you can revoke them at any time in your [API keys settings](https://ollama.com/settings/keys).
**Examples:**
Example 1 (unknown):
```unknown
ollama signin
```
Example 2 (unknown):
```unknown
ollama run gpt-oss:120b-cloud
```
Example 3 (unknown):
```unknown
## 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:
```
Example 4 (unknown):
```unknown
Then use the API key in the Authorization header:
```
---
## CLI Reference
**URL:** llms-txt#cli-reference
**Contents:**
- Run a model
- Download a model
- Remove a model
- List models
- Sign in to Ollama
- Sign out of Ollama
- Create a customized model
- List running models
- Stop a running model
- Start Ollama
Source: https://docs.ollama.com/cli
For multiline input, you can wrap text with `"""`:
#### Multimodal models
### Sign in to Ollama
### Sign out of Ollama
### Create a customized model
First, create a `Modelfile`
Then run `ollama create`:
### List running models
### Stop a running model
To view a list of environment variables that can be set run `ollama serve --help`
**Examples:**
Example 1 (unknown):
```unknown
ollama run gemma3
```
Example 2 (unknown):
```unknown
>>> """Hello,
... world!
... """
I'm a basic program that prints the famous "Hello, world!" message to the console.
```
Example 3 (unknown):
```unknown
ollama run gemma3 "What's in this image? /Users/jmorgan/Desktop/smile.png"
```
Example 4 (unknown):
```unknown
ollama pull gemma3
```
---
## sets the temperature to 1 [higher is more creative, lower is more coherent]
**URL:** llms-txt#sets-the-temperature-to-1-[higher-is-more-creative,-lower-is-more-coherent]
PARAMETER temperature 1
---
## Windows
**URL:** llms-txt#windows
**Contents:**
- System Requirements
- Filesystem Requirements
- Changing Install Location
- Changing Model Location
- API Access
- Troubleshooting
- Uninstall
- Standalone CLI
Source: https://docs.ollama.com/windows
Ollama 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](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
### 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.
Here's a quick example showing API access from `powershell`
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
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>
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>
**Examples:**
Example 1 (unknown):
```unknown
### 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`
```
---
## Modelfile Reference
**URL:** llms-txt#modelfile-reference
**Contents:**
- Table of Contents
- Format
Source: https://docs.ollama.com/modelfile
A Modelfile is the blueprint to create and share customized models using Ollama.
* [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)
The format of the `Modelfile`:
---
## Embeddings
**URL:** llms-txt#embeddings
**Contents:**
- Recommended models
- Generate embeddings
- Generate a batch of embeddings
- Tips
Source: https://docs.ollama.com/capabilities/embeddings
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">
</Tab>
<Tab title="Python">
</Tab>
<Tab title="JavaScript">
</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">
</Tab>
<Tab title="Python">
</Tab>
<Tab title="JavaScript">
</Tab>
</Tabs>
* Use cosine similarity for most semantic search use cases.
* Use the same embedding model for both indexing and querying.
**Examples:**
Example 1 (unknown):
```unknown
</Tab>
<Tab title="Python">
```
Example 2 (unknown):
```unknown
</Tab>
<Tab title="JavaScript">
```
Example 3 (unknown):
```unknown
</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">
```
Example 4 (unknown):
```unknown
</Tab>
<Tab title="Python">
```
---
## Thinking
**URL:** llms-txt#thinking
**Contents:**
- Supported models
- Enable thinking in API calls
- Stream the reasoning trace
- CLI quick reference
Source: https://docs.ollama.com/capabilities/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.
* [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">
</Tab>
<Tab title="Python">
</Tab>
<Tab title="JavaScript">
</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">
</Tab>
<Tab title="JavaScript">
</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>
**Examples:**
Example 1 (unknown):
```unknown
</Tab>
<Tab title="Python">
```
Example 2 (unknown):
```unknown
</Tab>
<Tab title="JavaScript">
```
Example 3 (unknown):
```unknown
</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">
```
Example 4 (unknown):
```unknown
</Tab>
<Tab title="JavaScript">
```
---
## Create a model
**URL:** llms-txt#create-a-model
Source: https://docs.ollama.com/api/create
openapi.yaml post /api/create
---
## Define the schema for the response
**URL:** llms-txt#define-the-schema-for-the-response
**Contents:**
- OpenAI JavaScript library
- `curl`
- Endpoints
- `/v1/chat/completions`
- `/v1/completions`
- `/v1/models`
- `/v1/models/{model}`
- `/v1/embeddings`
- Models
- Default model names
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}")
javascript theme={"system"}
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?"],
});
shell theme={"system"}
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,iVBORw0KGgoAAAANSUhEUgAAAG0AAABmCAYAAADBPx+VAAAACXBIWXMAAAsTAAALEwEAmpwYAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAA3VSURBVHgB7Z27r0zdG8fX743i1bi1ikMoFMQloXRpKFFIqI7LH4BEQ+NWIkjQuSWCRIEoULk0gsK1kCBI0IhrQVT7tz/7zZo888yz1r7MnDl7z5xvsjkzs2fP3uu71nNfa7lkAsm7d++Sffv2JbNmzUqcc8m0adOSzZs3Z+/XES4ZckAWJEGWPiCxjsQNLWmQsWjRIpMseaxcuTKpG/7HP27I8P79e7dq1ars/yL4/v27S0ejqwv+cUOGEGGpKHR37tzJCEpHV9tnT58+dXXCJDdECBE2Ojrqjh071hpNECjx4cMHVycM1Uhbv359B2F79+51586daxN/+pyRkRFXKyRDAqxEp4yMlDDzXG1NPnnyJKkThoK0VFd1ELZu3TrzXKxKfW7dMBQ6bcuWLW2v0VlHjx41z717927ba22U9APcw7Nnz1oGEPeL3m3p2mTAYYnFmMOMXybPPXv2bNIPpFZr1NHn4HMw0KRBjg9NuRw95s8PEcz/6DZELQd/09C9QGq5RsmSRybqkwHGjh07OsJSsYYm3ijPpyHzoiacg35MLdDSIS/O1yM778jOTwYUkKNHWUzUWaOsylE00MyI0fcnOwIdjvtNdW/HZwNLGg+sR1kMepSNJXmIwxBZiG8tDTpEZzKg0GItNsosY8USkxDhD0Rinuiko2gfL/RbiD2LZAjU9zKQJj8RDR0vJBR1/Phx9+PHj9Z7REF4nTZkxzX4LCXHrV271qXkBAPGfP/atWvu/PnzHe4C97F48eIsRLZ9+3a3f/9+87dwP1JxaF7/3r17ba+5l4EcaVo0lj3SBq5kGTJSQmLWMjgYNei2GPT1MuMqGTDEFHzeQSP2wi/jGnkmPJ/nhccs44jvDAxpVcxnq0F6eT8h4ni/iIWpR5lPyA6ETkNXoSukvpJAD3AsXLiwpZs49+fPn5ke4j10TqYvegSfn0OnafC+Tv9ooA/JPkgQysqQNBzagXY55nO/oa1F7qvIPWkRL12WRpMWUvpVDYmxAPehxWSe8ZEXL20sadYIozfmNch4QJPAfeJgW3rNsnzphBKNJM2KKODo1rVOMRYik5ETy3ix4qWNI81qAAirizgMIc+yhTytx0JWZuNI03qsrgWlGtwjoS9XwgUhWGyhUaRZZQNNIEwCiXD16tXcAHUs79co0vSD8rrJCIW98pzvxpAWyyo3HYwqS0+H0BjStClcZJT5coMm6D2LOF8TolGJtK9fvyZpyiC5ePFi9nc/oJU4eiEP0jVoAnHa9wyJycITMP78+eMeP37sXrx44d6+fdt6f82aNdkx1pg9e3Zb5W+RSRE+n+VjksQWifvVaTKFhn5O8my63K8Qabdv33b379/PiAP//vuvW7BggZszZ072/+TJk91YgkafPn166zXB1rQHFvouAWHq9z3SEevSUerqCn2/dDCeta2jxYbr69evk4MHDyY7d+7MjhMnTiTPnz9Pfv/+nfQT2ggpO2dMF8cghuoM7Ygj5iWCqRlGFml0QC/ftGmTmzt3rmsaKDsgBSPh0/8yPeLLBihLkOKJc0jp8H8vUzcxIA1k6QJ/c78tWEyj5P3o4u9+jywNPdJi5rAH9x0KHcl4Hg570eQp3+vHXGyrmEeigzQsQsjavXt38ujRo44LQuDDhw+TW7duRS1HGgMxhNXHgflaNTOsHyKvHK5Ijo2jbFjJBQK9YwFd6RVMzfgRBmEfP37suBBm/p49e1qjEP2mwTViNRo0VJWH1deMXcNK08uUjVUu7s/zRaL+oLNxz1bpANco4npUgX4G2eFbpDFyQoQxojBCpEGSytmOH8qrH5Q9vuzD6ofQylkCUmh8DBAr+q8JCyVNtWQIidKQE9wNtLSQnS4jDSsxNHogzFuQBw4cyM61UKVsjfr3ooBkPSqqQHesUPWVtzi9/vQi1T+rJj7WiTz4Pt/l3LxUkr5P2VYZaZ4URpsE+st/dujQoaBBYokbrz/8TJNQYLSonrPS9kUaSkPeZyj1AWSj+d+VBoy1pIWVNed8P0Ll/ee5HdGRhrHhR5GGN0r4LGZBaj8oFDJitBTJzIZgFcmU0Y8ytWMZMzJOaXUSrUs5RxKnrxmbb5YXO9VGUhtpXldhEUogFr3IzIsvlpmdosVcGVGXFWp2oU9kLFL3dEkSz6NHEY1sjSRdIuDFWEhd8KxFqsRi1uM/nz9/zpxnwlESONdg6dKlbsaMGS4EHFHtjFIDHwKOo46l4TxSuxgDzi+rE2jg+BaFruOX4HXa0Nnf1lwAPufZeF8/r6zD97WK2qFnGjBxTw5qNGPxT+5T/r7/7RawFC3j4vTp09koCxkeHjqbHJqArmH5UrFKKksnxrK7FuRIs8STfBZv+luugXZ2pR/pP9Ois4z+TiMzUUkUjD0iEi1fzX8GmXyuxUBRcaUfykV0YZnlJGKQpOiGB76x5GeWkWWJc3mOrK6S7xdND+W5N6XyaRgtWJFe13GkaZnKOsYqGdOVVVbGupsyA/l7emTLHi7vwTdirNEt0qxnzAvBFcnQF16xh/TMpUuXHDowhlA9vQVraQhkudRdzOnK+04ZSP3DUhVSP61YsaLtd/ks7ZgtPcXqPqEafHkdqa84X6aCeL7YWlv6edGFHb+ZFICPlljHhg0bKuk0CSvVznWsotRu433alNdFrqG45ejoaPCaUkWERpLXjzFL2Rpllp7PJU2a/v7Ab8N05/9t27Z16KUqoFGsxnI9EosS2niSYg9SpU6B4JgTrvVW1flt1sT+0ADIJU2maXzcUTraGCRaL1Wp9rUMk16PMom8QhruxzvZIegJjFU7LLCePfS8uaQdPny4jTTL0dbee5mYokQsXTIWNY46kuMbnt8Kmec+LGWtOVIl9cT1rCB0V8WqkjAsRwta93TbwNYoGKsUSChN44lgBNCoHLHzquYKrU6qZ8lolCIN0Rh6cP0Q3U6I6IXILYOQI513hJaSKAorFpuHXJNfVlpRtmYBk1Su1obZr5dnKAO+L10Hrj3WZW+E3qh6IszE37F6EB+68mGpvKm4eb9bFrlzrok7fvr0Kfv727dvWRmdVTJHw0qiiCUSZ6wCK+7XL/AcsgNyL74DQQ730sv78Su7+t/A36MdY0sW5o40ahslXr58aZ5HtZB8GH64m9EmMZ7FpYw4T6QnrZfgenrhFxaSiSGXtPnz57e9TkNZLvTjeqhr734CNtrK41L40sUQckmj1lGKQ0rC37x544r8eNXRpnVE3ZZY7zXo8NomiO0ZUCj2uHz58rbXoZ6gc0uA+F6ZeKS/jhRDUq8MKrTho9fEkihMmhxtBI1DxKFY9XLpVcSkfoi8JGnToZO5sU5aiDQIW716ddt7ZLYtMQlhECdBGXZZMWldY5BHm5xgAroWj4C0hbYkSc/jBmggIrXJWlZM6pSETsEPGqZOndr2uuuR5rF169a2HoHPdurUKZM4CO1WTPqaDaAd+GFGKdIQkxAn9RuEWcTRyN2KSUgiSgF5aWzPTeA/lN5rZubMmR2bE4SIC4nJoltgAV/dVefZm72AtctUCJU2CMJ327hxY9t7EHbkyJFseq+EJSY16RPo3Dkq1kkr7+q0bNmyDuLQcZBEPYmHVdOBiJyIlrRDq41YPWfXOxUysi5fvtyaj+2BpcnsUV/oSoEMOk2CQGlr4ckhBwaetBhjCwH0ZHtJROPJkyc7UjcYLDjmrH7ADTEBXFfOYmB0k9oYBOjJ8b4aOYSe7QkKcYhFlq3QYLQhSidNmtS2RATwy8YOM3EQJsUjKiaWZ+vZToUQgzhkHXudb/PW5YMHD9yZM2faPsMwoc7RciYJXbGuBqJ1UIGKKLv915jsvgtJxCZDubdXr165mzdvtr1Hz5LONA8jrUwKPqsmVesKa49S3Q4WxmRPUEYdTjgiUcfUwLx589ySJUva3oMkP6IYddq6HMS4o55xBJBUeRjzfa4Zdeg56QZ43LhxoyPo7Lf1kNt7oO8wWAbNwaYjIv5lhyS7kRf96dvm5Jah8vfvX3flyhX35cuX6HfzFHOToS1H4BenCaHvO8pr8iDuwoUL7tevX+b5ZdbBair0xkFIlFDlW4ZknEClsp/TzXyAKVOmmHWFVSbDNw1l1+4f90U6IY/q4V27dpnE9bJ+v87QEydjqx/UamVVPRG+mwkNTYN+9tjkwzEx+atCm/X9WvWtDtAb68Wy9LXa1UmvCDDIpPkyOQ5ZwSzJ4jMrvFcr0rSjOUh+GcT4LSg5ugkW1Io0/SCDQBojh0hPlaJdah+tkVYrnTZowP8iq1F1TgMBBauufyB33x1v+NWFYmT5KmppgHC+NkAgbmRkpD3yn9QIseXymoTQFGQmIOKTxiZIWpvAatenVqRVXf2nTrAWMsPnKrMZHz6bJq5jvce6QK8J1cQNgKxlJapMPdZSR64/UivS9NztpkVEdKcrs5alhhWP9NeqlfWopzhZScI6QxseegZRGeg5a8C3Re1Mfl1ScP36ddcUaMuv24iOJtz7sbUjTS4qBvKmstYJoUauiuD3k5qhyr7QdUHMeCgLa1Ear9NquemdXgmum4fvJ6w1lqsuDhNrg1qSpleJK7K3TF0Q2jSd94uSZ60kK1e3qyVpQK6PVWXp2/FC3mp6jBhKKOiY2h3gtUV64TWM6wDETRPLDfSakXmH3w8g9Jlug8ZtTt4kVF0kLUYYmCCtD/DrQ5YhMGbA9L3ucdjh0y8kOHW5gU/VEEmJTcL4Pz/f7mgoAbYkAAAAAElFTkSuQmCC"
}
}
]
}
],
"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?"]
}'
shell theme={"system"}
ollama pull llama3.2
shell theme={"system"}
ollama cp llama3.2 gpt-3.5-turbo
shell theme={"system"}
curl http://localhost:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "Hello!"
}
]
}'
FROM <some model>
PARAMETER num_ctx <context size>
shell theme={"system"}
curl http://localhost:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "mymodel",
"messages": [
{
"role": "user",
"content": "Hello!"
}
]
}'
```
**Examples:**
Example 1 (unknown):
```unknown
### OpenAI JavaScript library
```
Example 2 (unknown):
```unknown
### `curl`
```
Example 3 (unknown):
```unknown
## 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`:
```
Example 4 (unknown):
```unknown
### 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:
```
---
## Goose
**URL:** llms-txt#goose
**Contents:**
- Goose Desktop
- Usage with Ollama
- Connecting to ollama.com
- Goose CLI
- Usage with Ollama
- Connecting to ollama.com
Source: https://docs.ollama.com/integrations/goose
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="https://mintcdn.com/ollama-9269c548/Qrfd4TFdx51mx0J_/images/goose-settings.png?fit=max&auto=format&n=Qrfd4TFdx51mx0J_&q=85&s=ba9aaea6b535f03456dbafb0ba48018b" alt="Goose settings Panel" width="75%" data-og-width="1300" data-og-height="732" data-path="images/goose-settings.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/Qrfd4TFdx51mx0J_/images/goose-settings.png?w=280&fit=max&auto=format&n=Qrfd4TFdx51mx0J_&q=85&s=63e0cc6d307d8a6c1b7ba8bb7b1b2532 280w, https://mintcdn.com/ollama-9269c548/Qrfd4TFdx51mx0J_/images/goose-settings.png?w=560&fit=max&auto=format&n=Qrfd4TFdx51mx0J_&q=85&s=bf90aaf22a8f6307d99597964433603c 560w, https://mintcdn.com/ollama-9269c548/Qrfd4TFdx51mx0J_/images/goose-settings.png?w=840&fit=max&auto=format&n=Qrfd4TFdx51mx0J_&q=85&s=4fd50ee906a26ecde5f49575bcad1700 840w, https://mintcdn.com/ollama-9269c548/Qrfd4TFdx51mx0J_/images/goose-settings.png?w=1100&fit=max&auto=format&n=Qrfd4TFdx51mx0J_&q=85&s=01abb63a474ea8a2e47fe1b4ee7bd752 1100w, https://mintcdn.com/ollama-9269c548/Qrfd4TFdx51mx0J_/images/goose-settings.png?w=1650&fit=max&auto=format&n=Qrfd4TFdx51mx0J_&q=85&s=7188a96a903d03fe68004faeb743156c 1650w, https://mintcdn.com/ollama-9269c548/Qrfd4TFdx51mx0J_/images/goose-settings.png?w=2500&fit=max&auto=format&n=Qrfd4TFdx51mx0J_&q=85&s=92e96516a86f243a182939145d5536eb 2500w" />
</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`
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="https://mintcdn.com/ollama-9269c548/Qrfd4TFdx51mx0J_/images/goose-cli.png?fit=max&auto=format&n=Qrfd4TFdx51mx0J_&q=85&s=3f34e0d16cbdf89858115b8c64d6dc08" alt="Goose CLI" width="50%" data-og-width="650" data-og-height="546" data-path="images/goose-cli.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/Qrfd4TFdx51mx0J_/images/goose-cli.png?w=280&fit=max&auto=format&n=Qrfd4TFdx51mx0J_&q=85&s=d869883c3d68749aa534907cab2fcd5a 280w, https://mintcdn.com/ollama-9269c548/Qrfd4TFdx51mx0J_/images/goose-cli.png?w=560&fit=max&auto=format&n=Qrfd4TFdx51mx0J_&q=85&s=b6e2eb41567eb401582c42b2289e2a98 560w, https://mintcdn.com/ollama-9269c548/Qrfd4TFdx51mx0J_/images/goose-cli.png?w=840&fit=max&auto=format&n=Qrfd4TFdx51mx0J_&q=85&s=35c249c4cf90b3697d44e756d728d47c 840w, https://mintcdn.com/ollama-9269c548/Qrfd4TFdx51mx0J_/images/goose-cli.png?w=1100&fit=max&auto=format&n=Qrfd4TFdx51mx0J_&q=85&s=957ef7e49a7b852a8995a4a7a04aea2d 1100w, https://mintcdn.com/ollama-9269c548/Qrfd4TFdx51mx0J_/images/goose-cli.png?w=1650&fit=max&auto=format&n=Qrfd4TFdx51mx0J_&q=85&s=ebfa2bca9b17e3183d79c9534b24f3bf 1650w, https://mintcdn.com/ollama-9269c548/Qrfd4TFdx51mx0J_/images/goose-cli.png?w=2500&fit=max&auto=format&n=Qrfd4TFdx51mx0J_&q=85&s=b6ce0d3e1b9d07f3e28935767b0cf5e1 2500w" />
</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`
---
## Introduction
**URL:** llms-txt#introduction
**Contents:**
- Get started
- Base URL
- Example request
- Libraries
- Versioning
Source: https://docs.ollama.com/api/index
Ollama's API allows you to run and interact with models programatically.
If you're just getting started, follow the [quickstart](/quickstart) documentation to get up and running with Ollama's API.
After installation, Ollama's API is served by default at:
For running cloud models on **ollama.com**, the same API is available with the following base URL:
Once Ollama is running, its API is automatically available and can be accessed via `curl`:
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).
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).
**Examples:**
Example 1 (unknown):
```unknown
http://localhost:11434/api
```
Example 2 (unknown):
```unknown
https://ollama.com/api
```
---
## Hardware support
**URL:** llms-txt#hardware-support
**Contents:**
- Nvidia
- GPU Selection
- Linux Suspend Resume
- AMD Radeon
- Linux Support
- Windows Support
- Overrides on Linux
- GPU Selection
- Container Permission
- Metal (Apple GPUs)
Source: https://docs.ollama.com/gpu
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)
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`
Ollama supports the following AMD GPUs:
| 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` |
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.
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.
---
## Delete a model
**URL:** llms-txt#delete-a-model
Source: https://docs.ollama.com/api/delete
openapi.yaml delete /api/delete
---
## Ollama's documentation
**URL:** llms-txt#ollama's-documentation
**Contents:**
- Libraries
- Community
Source: https://docs.ollama.com/index
<img src="https://mintcdn.com/ollama-9269c548/w-L7kuDqk3_8zi5c/images/welcome.png?fit=max&auto=format&n=w-L7kuDqk3_8zi5c&q=85&s=914368bbe8709d04481a8a478b66cf8c" noZoom className="rounded-3xl" data-og-width="2048" width="2048" data-og-height="1024" height="1024" data-path="images/welcome.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/w-L7kuDqk3_8zi5c/images/welcome.png?w=280&fit=max&auto=format&n=w-L7kuDqk3_8zi5c&q=85&s=59525d30abe1d51e4df09990566452c2 280w, https://mintcdn.com/ollama-9269c548/w-L7kuDqk3_8zi5c/images/welcome.png?w=560&fit=max&auto=format&n=w-L7kuDqk3_8zi5c&q=85&s=bcacb65baf44ab859d50fac8c8d342e6 560w, https://mintcdn.com/ollama-9269c548/w-L7kuDqk3_8zi5c/images/welcome.png?w=840&fit=max&auto=format&n=w-L7kuDqk3_8zi5c&q=85&s=cc3770b69817332b5961d0faecdce473 840w, https://mintcdn.com/ollama-9269c548/w-L7kuDqk3_8zi5c/images/welcome.png?w=1100&fit=max&auto=format&n=w-L7kuDqk3_8zi5c&q=85&s=0ffad9be5c842e67ca99e76be5cc7ce6 1100w, https://mintcdn.com/ollama-9269c548/w-L7kuDqk3_8zi5c/images/welcome.png?w=1650&fit=max&auto=format&n=w-L7kuDqk3_8zi5c&q=85&s=10077a91a66acb913bb8bd51ed809a74 1650w, https://mintcdn.com/ollama-9269c548/w-L7kuDqk3_8zi5c/images/welcome.png?w=2500&fit=max&auto=format&n=w-L7kuDqk3_8zi5c&q=85&s=4673a18833fcc23e99bb280d2bffb019 2500w" />
[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>
<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>
<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>
---
## Generate a response
**URL:** llms-txt#generate-a-response
Source: https://docs.ollama.com/api/generate
openapi.yaml post /api/generate
Generates a response for the provided prompt
---
## n8n
**URL:** llms-txt#n8n
**Contents:**
- Install
- Using Ollama Locally
- Connecting to ollama.com
Source: https://docs.ollama.com/integrations/n8n
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="https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-credential-creation.png?fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=5b7f955f792e8b9899f3b0b3a1846d06" alt="Create a n8n Credential" width="75%" data-og-width="896" data-og-height="436" data-path="images/n8n-credential-creation.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-credential-creation.png?w=280&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=ca26ff9b910a94d1120a0df887424072 280w, https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-credential-creation.png?w=560&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=f0209dbb3aa364c2801d8c02bf2c834e 560w, https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-credential-creation.png?w=840&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=9d291f0dca191861c5eebc5236985bdd 840w, https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-credential-creation.png?w=1100&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=5bc5d64f6eb7398e83c264382064879f 1100w, https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-credential-creation.png?w=1650&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=c866783cc5819354b4f69f0e7e8acded 1650w, https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-credential-creation.png?w=2500&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=a4b7c6e4cf3d38e6898901f64556e16a 2500w" />
</div>
2. Under **Add new credential** select **Ollama**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img src="https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-ollama-form.png?fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=46c5ec6678b7323405a1ca98d9f88af0" alt="Select Ollama under Credential" width="75%" data-og-width="1014" data-og-height="580" data-path="images/n8n-ollama-form.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-ollama-form.png?w=280&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=969d64d58c346578b68028e051ecee33 280w, https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-ollama-form.png?w=560&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=b5f84f26821ad5dbf1b05e01ce03fbcc 560w, https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-ollama-form.png?w=840&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=8f8b5b1301e0cc3e81ab32c839c52a89 840w, https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-ollama-form.png?w=1100&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=163156b985eca789a4cf2f441cf2b461 1100w, https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-ollama-form.png?w=1650&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=812d4591c0a3ebe160b6c78884ad487c 1650w, https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-ollama-form.png?w=2500&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=1ec83dd378b05cd17ac60bd9a4522e6c 2500w" />
</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="https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-chat-node.png?fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=78c15518fa96d509096af63307063ae6" alt="Add a first step with Ollama node" width="75%" data-og-width="822" data-og-height="674" data-path="images/n8n-chat-node.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-chat-node.png?w=280&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=66a5f3201aae6a8ff315efe181fe5a36 280w, https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-chat-node.png?w=560&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=241b695dc152e97bc9992d2389c4c0cf 560w, https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-chat-node.png?w=840&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=be8484e52dab096ef1a2178a4d24e934 840w, https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-chat-node.png?w=1100&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=1be199b1714e4413d1d85fb5d679d6a1 1100w, https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-chat-node.png?w=1650&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=df82f732f724dd0079d62e39b519713a 1650w, https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-chat-node.png?w=2500&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=9f235598a4fade17b04c967e478f26a4 2500w" />
</div>
5. Select your model of choice (e.g. `qwen3-coder`)
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img src="https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-models.png?fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=45e705696dc7c8f40112e1c6dbee0e7e" alt="Set up Ollama credentials" width="75%" data-og-width="1088" data-og-height="1058" data-path="images/n8n-models.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-models.png?w=280&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=b0e7aede75a0098d023db708a37f9966 280w, https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-models.png?w=560&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=5786bf38961730b03e7415f9255c06d7 560w, https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-models.png?w=840&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=2182809e057f870dbf8f5b30b4069fe5 840w, https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-models.png?w=1100&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=6635c63c203627d6def2c398d22efeba 1100w, https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-models.png?w=1650&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=5715ed9f2bcf4157a29f77b59b0c69ae 1650w, https://mintcdn.com/ollama-9269c548/AZjkMSgDaM1B-WFi/images/n8n-models.png?w=2500&fit=max&auto=format&n=AZjkMSgDaM1B-WFi&q=85&s=cbb012310cc5a74f7e2616423bc4f902 2500w" />
</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**
3. Set the **API URL** to `https://ollama.com`
4. Enter your **API Key** and click **Save**
---
## Show model details
**URL:** llms-txt#show-model-details
Source: https://docs.ollama.com/api-reference/show-model-details
openapi.yaml post /api/show
---
## Web search
**URL:** llms-txt#web-search
**Contents:**
- Authentication
- Web search API
- Request
- Response
- Examples
- Web fetch API
- Request
- Response
- Examples
- Building a search agent
Source: https://docs.ollama.com/capabilities/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.
For access to Ollama's web search API, create an [API key](https://ollama.com/settings/keys). A free Ollama account is required.
Performs a web search for a single query and returns relevant results.
`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)
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
<Note>
Ensure OLLAMA\_API\_KEY is set or it must be passed in the Authorization header.
</Note>
More Ollama [Python example](https://github.com/ollama/ollama-python/blob/main/examples/web-search.py)
#### JavaScript Library
More Ollama [JavaScript example](https://github.com/ollama/ollama-js/blob/main/examples/websearch/websearch-tools.ts)
Fetches a single web page by URL and returns its content.
`POST https://ollama.com/api/web_fetch`
* `url` (string, required): the URL to fetch
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
## 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.
### 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.
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).
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:
<img src="https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/cline-mcp.png?fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=046239fbe74a8e928752b97b1a8954fa" alt="Cline MCP Configuration" data-og-width="852" width="852" data-og-height="1078" height="1078" data-path="images/cline-mcp.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/cline-mcp.png?w=280&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=251a7ae4c99cafbeff8867a3cdefc854 280w, https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/cline-mcp.png?w=560&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=bde250f5b99530b1870b5e7069abf10c 560w, https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/cline-mcp.png?w=840&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=067e154d817a737cd508f74cffa77294 840w, https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/cline-mcp.png?w=1100&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=c5db90800a313a6b262fcd37ab5be97f 1100w, https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/cline-mcp.png?w=1650&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=1c20c4081d1e8f13a3da2348c6df1fd0 1650w, https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/cline-mcp.png?w=2500&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=2dbaea69c8eefd988ec6c065ce966187 2500w" />
Ollama works well with OpenAI's Codex tool.
Add the following configuration to `~/.codex/config.toml`
<img src="https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/codex-mcp.png?fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=775b41bb85af7836b0a5a609de7d1f6f" alt="Codex MCP Configuration" data-og-width="1150" width="1150" data-og-height="1014" height="1014" data-path="images/codex-mcp.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/codex-mcp.png?w=280&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=165618dddf9daa7f355f71c454ba3f41 280w, https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/codex-mcp.png?w=560&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=79585e40dfb53f5fffc4a637a5119118 560w, https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/codex-mcp.png?w=840&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=ca1d7acc055ebdbc409d9f372d9ca3e5 840w, https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/codex-mcp.png?w=1100&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=603c85032a6b8dd755950c9d29f8fd21 1100w, https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/codex-mcp.png?w=1650&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=07665e9ee289fdabb9addde3a06bca7a 1650w, https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/codex-mcp.png?w=2500&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=f885735a8b1c269439f9ccf10424421e 2500w" />
Ollama can integrate with Goose via its MCP feature.
<img src="https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/goose-mcp-1.png?fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=5fea6e0aab7865dc950470f004c549e8" alt="Goose MCP Configuration 1" data-og-width="1152" width="1152" data-og-height="1012" height="1012" data-path="images/goose-mcp-1.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/goose-mcp-1.png?w=280&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=f7ccec9b53d39d84ed10bdedd0335e33 280w, https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/goose-mcp-1.png?w=560&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=cb5464f221b561eba98c10702222d4fe 560w, https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/goose-mcp-1.png?w=840&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=0810ea78c85815474a17d5c1d975771a 840w, https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/goose-mcp-1.png?w=1100&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=67467cb3aaab1183f1f850a4061a7af0 1100w, https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/goose-mcp-1.png?w=1650&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=2e8e9d972510ba17d542156b8c7a5142 1650w, https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/goose-mcp-1.png?w=2500&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=f990a9ba7d6daf66e89699617034e6b9 2500w" />
<img src="https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/goose-mcp-2.png?fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=c69c12389f7dd60ef1c53cd10af82a7d" alt="Goose MCP Configuration 2" data-og-width="1146" width="1146" data-og-height="1006" height="1006" data-path="images/goose-mcp-2.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/goose-mcp-2.png?w=280&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=498deaa0c52aa33e32f4962e0dea9dc7 280w, https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/goose-mcp-2.png?w=560&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=bb62f0113619a0f572e0017849a65bb5 560w, https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/goose-mcp-2.png?w=840&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=7035aae8c4163df72f38d885f11e3f1c 840w, https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/goose-mcp-2.png?w=1100&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=ca8a2966d7c350c6d75d9252f86f7be8 1100w, https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/goose-mcp-2.png?w=1650&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=a488d0de5bf91dccd78a5187e712ceb2 1650w, https://mintcdn.com/ollama-9269c548/lS1IbrlCxMxm029K/images/goose-mcp-2.png?w=2500&fit=max&auto=format&n=lS1IbrlCxMxm029K&q=85&s=fa84ce84ab908bacd6853048972bff7c 2500w" />
### 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.
**Examples:**
Example 1 (unknown):
```unknown
**Response**
```
Example 2 (unknown):
```unknown
#### Python library
```
Example 3 (unknown):
```unknown
**Example output**
```
Example 4 (unknown):
```unknown
More Ollama [Python example](https://github.com/ollama/ollama-python/blob/main/examples/web-search.py)
#### JavaScript Library
```
---
## Roo Code
**URL:** llms-txt#roo-code
**Contents:**
- Install
- Usage with Ollama
- Connecting to ollama.com
- Recommended Models
Source: https://docs.ollama.com/integrations/roo-code
Install [Roo Code](https://marketplace.visualstudio.com/items?itemName=RooVeterinaryInc.roo-cline) from the VS Code Marketplace.
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`
---
## Tool calling
**URL:** llms-txt#tool-calling
**Contents:**
- Calling a single tool
- Parallel tool calling
- Multi-turn tool calling (Agent loop)
- Tool calling with streaming
- Using functions as tools with Ollama Python SDK
Source: https://docs.ollama.com/capabilities/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">
**Generate a response with a single tool result**
<Tab title="Python">
Install the Ollama Python SDK:
<Tab title="JavaScript">
Install the Ollama JavaScript library:
## Parallel tool calling
<Tabs>
<Tab title="cURL">
Request multiple tool calls in parallel, then send all tool responses back to the model.
**Generate a response with multiple tool results**
<Tab title="Python">
</Tab>
<Tab title="JavaScript">
</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">
</Tab>
<Tab title="JavaScript">
</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">
</Tab>
<Tab title="JavaScript">
</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 theme={"system"}
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,
}
**Examples:**
Example 1 (unknown):
```unknown
**Generate a response with a single tool result**
```
Example 2 (unknown):
```unknown
</Tab>
<Tab title="Python">
Install the Ollama Python SDK:
```
Example 3 (unknown):
```unknown
```
Example 4 (unknown):
```unknown
</Tab>
<Tab title="JavaScript">
Install the Ollama JavaScript library:
```
---
## directly pass the function as part of the tools list
**URL:** llms-txt#directly-pass-the-function-as-part-of-the-tools-list
response = chat(model='qwen3', messages=messages, tools=available_functions.values(), think=True)
```
---
## Generate embeddings
**URL:** llms-txt#generate-embeddings
Source: https://docs.ollama.com/api/embed
openapi.yaml post /api/embed
Creates vector embeddings representing the input text
---
## Troubleshooting
**URL:** llms-txt#troubleshooting
**Contents:**
- LLM libraries
- Installing older or pre-release versions on Linux
- Linux tmp noexec
- Linux docker
- NVIDIA GPU Discovery
- Linux NVIDIA Troubleshooting
- AMD GPU Discovery
- Multiple AMD GPUs
- Windows Terminal Errors
Source: https://docs.ollama.com/troubleshooting
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:
On **Linux** systems with systemd, the logs can be found with this command:
When you run Ollama in a **container**, the logs go to stdout/stderr in the container:
(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
Join the [Discord](https://discord.gg/ollama) for help interpreting the logs.
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):
**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:
You can see what features your CPU has with the following.
## 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.
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/
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`
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`
If you experience gibberish responses when models load across multiple AMD GPUs on Linux, see the following guide.
* [https://rocm.docs.amd.com/projects/radeon/en/latest/docs/install/native\_linux/mgpu.html#mgpu-known-issues-and-limitations](https://rocm.docs.amd.com/projects/radeon/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.
**Examples:**
Example 1 (unknown):
```unknown
On **Linux** systems with systemd, the logs can be found with this command:
```
Example 2 (unknown):
```unknown
When you run Ollama in a **container**, the logs go to stdout/stderr in the container:
```
Example 3 (unknown):
```unknown
(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
```
Example 4 (unknown):
```unknown
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):
```
---
## Errors
**URL:** llms-txt#errors
**Contents:**
- Status codes
- Error messages
- Errors that occur while streaming
Source: https://docs.ollama.com/api/errors
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)
Errors are returned in the `application/json` format with the following structure, with the error message in the `error` property:
## 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.
**Examples:**
Example 1 (unknown):
```unknown
## 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.
```
---
## Cline
**URL:** llms-txt#cline
**Contents:**
- Install
- Usage with Ollama
- Connecting to ollama.com
- Recommended Models
Source: https://docs.ollama.com/integrations/cline
Install [Cline](https://docs.cline.bot/getting-started/installing-cline) in your IDE.
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="https://mintcdn.com/ollama-9269c548/DILXUjvsEb6UDNxN/images/cline-settings.png?fit=max&auto=format&n=DILXUjvsEb6UDNxN&q=85&s=2d2755de6b2e06cd513119abf389acd0" alt="Cline settings configuration showing API Provider set to Ollama" width="50%" data-og-width="596" data-og-height="826" data-path="images/cline-settings.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/DILXUjvsEb6UDNxN/images/cline-settings.png?w=280&fit=max&auto=format&n=DILXUjvsEb6UDNxN&q=85&s=526c814bcfce0e2d812a6bfeb708ec74 280w, https://mintcdn.com/ollama-9269c548/DILXUjvsEb6UDNxN/images/cline-settings.png?w=560&fit=max&auto=format&n=DILXUjvsEb6UDNxN&q=85&s=03865bcf135e8ac371c526a18fc4ba8b 560w, https://mintcdn.com/ollama-9269c548/DILXUjvsEb6UDNxN/images/cline-settings.png?w=840&fit=max&auto=format&n=DILXUjvsEb6UDNxN&q=85&s=40a0ed4a094e6c3f753ef6ae8820b949 840w, https://mintcdn.com/ollama-9269c548/DILXUjvsEb6UDNxN/images/cline-settings.png?w=1100&fit=max&auto=format&n=DILXUjvsEb6UDNxN&q=85&s=f13028b0f2d177f7b3cb9eb3d6332416 1100w, https://mintcdn.com/ollama-9269c548/DILXUjvsEb6UDNxN/images/cline-settings.png?w=1650&fit=max&auto=format&n=DILXUjvsEb6UDNxN&q=85&s=00ab8386e21b6aa32e4dc4d3bbd02817 1650w, https://mintcdn.com/ollama-9269c548/DILXUjvsEb6UDNxN/images/cline-settings.png?w=2500&fit=max&auto=format&n=DILXUjvsEb6UDNxN&q=85&s=27a89d86786164ff499d03feab00d375 2500w" />
</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`
---
## To build a new Modelfile based on this one, replace the FROM line with:
**URL:** llms-txt#to-build-a-new-modelfile-based-on-this-one,-replace-the-from-line-with:
---
## Linux
**URL:** llms-txt#linux
**Contents:**
- Install
- Manual install
- AMD GPU install
- ARM64 install
- Adding Ollama as a startup service (recommended)
- Install CUDA drivers (optional)
- Install AMD ROCm drivers (optional)
- Start Ollama
- Customizing
- Updating
Source: https://docs.ollama.com/linux
To install Ollama, run the following command:
<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:
In another terminal, verify that Ollama is running:
If you have an AMD GPU, also download and extract the additional ROCm package:
Download and extract the ARM64-specific package:
### Adding Ollama as a startup service (recommended)
Create a user and group for Ollama:
Create a service file in `/etc/systemd/system/ollama.service`:
Then start the service:
### 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:
### 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 and verify it is running:
<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](https://www.amd.com/en/support/linux-drivers) for best support of your Radeon
GPU.
</Note>
To customize the installation of Ollama, you can edit the systemd service file or the environment variables by running:
Alternatively, create an override file manually in `/etc/systemd/system/ollama.service.d/override.conf`:
Update Ollama by running the install script again:
Or by re-downloading Ollama:
## 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).
To view logs of Ollama running as a startup service, run:
Remove the ollama service:
Remove ollama libraries from your lib directory (either `/usr/local/lib`, `/usr/lib`, or `/lib`):
Remove the ollama binary from your bin directory (either `/usr/local/bin`, `/usr/bin`, or `/bin`):
Remove the downloaded models and Ollama service user and group:
**Examples:**
Example 1 (unknown):
```unknown
## 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:
```
Example 2 (unknown):
```unknown
Start Ollama:
```
Example 3 (unknown):
```unknown
In another terminal, verify that Ollama is running:
```
Example 4 (unknown):
```unknown
### AMD GPU install
If you have an AMD GPU, also download and extract the additional ROCm package:
```
---
## Codex
**URL:** llms-txt#codex
**Contents:**
- Install
- Usage with Ollama
- Changing Models
- Cloud Models
- Connecting to ollama.com
Source: https://docs.ollama.com/integrations/codex
Install the [Codex CLI](https://developers.openai.com/codex/cli/):
<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:
By default, codex will use the local `gpt-oss:20b` model. However, you can specify a different model with the `-m` flag:
## 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.
Run `codex` in a new terminal to load the new settings.
**Examples:**
Example 1 (unknown):
```unknown
npm install -g @openai/codex
```
Example 2 (unknown):
```unknown
codex --oss
```
Example 3 (unknown):
```unknown
codex --oss -m gpt-oss:120b
```
Example 4 (unknown):
```unknown
codex --oss -m gpt-oss:120b-cloud
```
---
## List models
**URL:** llms-txt#list-models
Source: https://docs.ollama.com/api/tags
openapi.yaml get /api/tags
Fetch a list of models and their details
---
## Vision
**URL:** llms-txt#vision
**Contents:**
- Quick start
- Usage with Ollama's API
Source: https://docs.ollama.com/capabilities/vision
Vision models accept images alongside text so the model can describe, classify, and answer questions about what it sees.
## 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">
</Tab>
<Tab title="Python">
</Tab>
<Tab title="JavaScript">
</Tab>
</Tabs>
**Examples:**
Example 1 (unknown):
```unknown
## 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">
```
Example 2 (unknown):
```unknown
</Tab>
<Tab title="Python">
```
Example 3 (unknown):
```unknown
</Tab>
<Tab title="JavaScript">
```
---
## FROM llama3.2:latest
**URL:** llms-txt#from-llama3.2:latest
**Contents:**
- Instructions
- FROM (Required)
- PARAMETER
- TEMPLATE
- SYSTEM
- ADAPTER
- LICENSE
- MESSAGE
- Notes
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"
FROM <model name>:<tag>
FROM <model directory>
FROM ./ollama-model.gguf
PARAMETER <parameter> <parametervalue>
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
"""
SYSTEM """<system message>"""
ADAPTER <path to safetensor adapter>
ADAPTER ./ollama-lora.gguf
LICENSE """
<license text>
"""
MESSAGE <role> <message>
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
```
* 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
**Examples:**
Example 1 (unknown):
```unknown
## Instructions
### FROM (Required)
The `FROM` instruction defines the base model to use when creating a model.
```
Example 2 (unknown):
```unknown
#### Build from existing model
```
Example 3 (unknown):
```unknown
<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
```
Example 4 (unknown):
```unknown
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
```
---
## sets a custom system message to specify the behavior of the chat assistant
**URL:** llms-txt#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.
shell theme={"system"}
ollama show --modelfile llama3.2
**Examples:**
Example 1 (unknown):
```unknown
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.
```
Example 2 (unknown):
```unknown
```
---
## Xcode
**URL:** llms-txt#xcode
**Contents:**
- Install
- Usage with Ollama
- Connecting to ollama.com directly
Source: https://docs.ollama.com/integrations/xcode
Install [XCode](https://developer.apple.com/xcode/)
<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="https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-intelligence-window.png?fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=61d8115b15ca2e451d99a66dd30df4e0" alt="Xcode Intelligence window" width="50%" data-og-width="1430" data-og-height="646" data-path="images/xcode-intelligence-window.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-intelligence-window.png?w=280&fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=dccb7de9b697de5b3528b247d3ef7ced 280w, https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-intelligence-window.png?w=560&fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=1a47354c940bb3579a5cfc2bd0383100 560w, https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-intelligence-window.png?w=840&fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=f4f2791bdde6f5f07ec8a4604d7958ee 840w, https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-intelligence-window.png?w=1100&fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=0a2aeaddc3e1ce236c0da0de517982f1 1100w, https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-intelligence-window.png?w=1650&fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=c2dd9fb0df13083c6214a22c7a10c21d 1650w, https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-intelligence-window.png?w=2500&fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=2d47484eb926b98d696d2e16a498bd03 2500w" />
</div>
2. Select **Locally Hosted**, enter port **11434** and click **Add**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img src="https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-locally-hosted.png?fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=05886457701f4809015cbdfe9da765a2" alt="Xcode settings" width="50%" data-og-width="1018" data-og-height="732" data-path="images/xcode-locally-hosted.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-locally-hosted.png?w=280&fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=709a77fcd7626725397b07d6702e85b2 280w, https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-locally-hosted.png?w=560&fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=993cfc03618df1b7e38b59d054af7693 560w, https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-locally-hosted.png?w=840&fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=77adf13ef4ed9be895f418795c3ca095 840w, https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-locally-hosted.png?w=1100&fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=8ca7e015c0563bbacb3ed887803ea7e2 1100w, https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-locally-hosted.png?w=1650&fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=c26ef88d1645deb4d577b34c05f0ef08 1650w, https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-locally-hosted.png?w=2500&fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=c4f02b4ae584eca16242a14d4ea3346e 2500w" />
</div>
3. Select the **star icon** on the top left corner and click the **dropdown**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img src="https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-chat-icon.png?fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=b4e39af73fd7e80ac04f8211cd25c844" alt="Xcode settings" width="50%" data-og-width="920" data-og-height="562" data-path="images/xcode-chat-icon.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-chat-icon.png?w=280&fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=538f334cf2091a439b3783eeafbb5fb5 280w, https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-chat-icon.png?w=560&fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=6924aaf4b3c1765c77aad690b9291931 560w, https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-chat-icon.png?w=840&fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=999a3428fbd4dad7b0459cc078f24969 840w, https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-chat-icon.png?w=1100&fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=1000eb4de086153ff772319c6da31d37 1100w, https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-chat-icon.png?w=1650&fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=fd8df7f3f5a6fefa5f4305e06f95ddca 1650w, https://mintcdn.com/ollama-9269c548/ibktA29M6ZTyqWFA/images/xcode-chat-icon.png?w=2500&fit=max&auto=format&n=ibktA29M6ZTyqWFA&q=85&s=5308a627658024ecf6200e004db503e5 2500w" />
</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**
---
## Modelfile generated by "ollama show"
**URL:** llms-txt#modelfile-generated-by-"ollama-show"
---
## Importing a Model
**URL:** llms-txt#importing-a-model
**Contents:**
- Table of Contents
- Importing a fine tuned adapter from Safetensors weights
- Importing a model from Safetensors weights
- Importing a GGUF based model or adapter
- Quantizing a Model
- Supported Quantizations
- Sharing your model on ollama.com
Source: https://docs.ollama.com/import
* [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:
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:
Lastly, test the 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:
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`:
Lastly, test the 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:
For a GGUF adapter, create the `Modelfile` with:
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.
## 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.
Use `ollama create` to then create the quantized model.
### 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="https://mintcdn.com/ollama-9269c548/uieua2DvLKVQ74Ga/images/signup.png?fit=max&auto=format&n=uieua2DvLKVQ74Ga&q=85&s=d99f1340e6cfd85d36d49a444491cc63" alt="Sign-Up" width="40%" data-og-width="756" data-og-height="1192" data-path="images/signup.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/uieua2DvLKVQ74Ga/images/signup.png?w=280&fit=max&auto=format&n=uieua2DvLKVQ74Ga&q=85&s=8546e600b6c2b29ca91b23d837e9dc94 280w, https://mintcdn.com/ollama-9269c548/uieua2DvLKVQ74Ga/images/signup.png?w=560&fit=max&auto=format&n=uieua2DvLKVQ74Ga&q=85&s=786fba46e20fe8f9c2675abb620c7643 560w, https://mintcdn.com/ollama-9269c548/uieua2DvLKVQ74Ga/images/signup.png?w=840&fit=max&auto=format&n=uieua2DvLKVQ74Ga&q=85&s=06b9d9837022e7dbe9f9005f6f977eca 840w, https://mintcdn.com/ollama-9269c548/uieua2DvLKVQ74Ga/images/signup.png?w=1100&fit=max&auto=format&n=uieua2DvLKVQ74Ga&q=85&s=7ff9cc91091ffad52f8fb30a990d3089 1100w, https://mintcdn.com/ollama-9269c548/uieua2DvLKVQ74Ga/images/signup.png?w=1650&fit=max&auto=format&n=uieua2DvLKVQ74Ga&q=85&s=727fbd87da3a076b45794ea248f1afd3 1650w, https://mintcdn.com/ollama-9269c548/uieua2DvLKVQ74Ga/images/signup.png?w=2500&fit=max&auto=format&n=uieua2DvLKVQ74Ga&q=85&s=2ff3ce5e5197144725e860513cfe59e8 2500w" />
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="https://mintcdn.com/ollama-9269c548/uieua2DvLKVQ74Ga/images/ollama-keys.png?fit=max&auto=format&n=uieua2DvLKVQ74Ga&q=85&s=7ced4d97ecf6b115219f929a4914205e" alt="Ollama Keys" width="80%" data-og-width="1200" data-og-height="893" data-path="images/ollama-keys.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/uieua2DvLKVQ74Ga/images/ollama-keys.png?w=280&fit=max&auto=format&n=uieua2DvLKVQ74Ga&q=85&s=e3c7925a5a4f5dadafcf3908df55b97c 280w, https://mintcdn.com/ollama-9269c548/uieua2DvLKVQ74Ga/images/ollama-keys.png?w=560&fit=max&auto=format&n=uieua2DvLKVQ74Ga&q=85&s=66bb814e45ec58e6b15a4be890c6fec8 560w, https://mintcdn.com/ollama-9269c548/uieua2DvLKVQ74Ga/images/ollama-keys.png?w=840&fit=max&auto=format&n=uieua2DvLKVQ74Ga&q=85&s=44af18318e8469b719218a46305361d6 840w, https://mintcdn.com/ollama-9269c548/uieua2DvLKVQ74Ga/images/ollama-keys.png?w=1100&fit=max&auto=format&n=uieua2DvLKVQ74Ga&q=85&s=46d0cfa93938c243b8e41a169ceedb1f 1100w, https://mintcdn.com/ollama-9269c548/uieua2DvLKVQ74Ga/images/ollama-keys.png?w=1650&fit=max&auto=format&n=uieua2DvLKVQ74Ga&q=85&s=c235d8ed66c24171946e376eb5e6663e 1650w, https://mintcdn.com/ollama-9269c548/uieua2DvLKVQ74Ga/images/ollama-keys.png?w=2500&fit=max&auto=format&n=uieua2DvLKVQ74Ga&q=85&s=4844902d0289c2154b65d3d0339e9934 2500w" />
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).
Once your model has been pushed, other users can pull and run it by using the command:
**Examples:**
Example 1 (unknown):
```unknown
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:
```
Example 2 (unknown):
```unknown
Lastly, test the model:
```
Example 3 (unknown):
```unknown
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:
```
Example 4 (unknown):
```unknown
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`:
```
---
## JetBrains
**URL:** llms-txt#jetbrains
**Contents:**
- Install
- Usage with Ollama
Source: https://docs.ollama.com/integrations/jetbrains
<Note>This example uses **IntelliJ**; same steps apply to other JetBrains IDEs (e.g., PyCharm).</Note>
Install [IntelliJ](https://www.jetbrains.com/idea/).
<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="https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-chat-sidebar.png?fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=3744faa4bdfb6e817ad68d7da792bf18" alt="Intellij Sidebar Chat" width="50%" data-og-width="668" data-og-height="476" data-path="images/intellij-chat-sidebar.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-chat-sidebar.png?w=280&fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=b8b775ae96fdf260c9f9ffb001dee5f5 280w, https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-chat-sidebar.png?w=560&fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=7636af78546e0de3a1ba1f0895d512b9 560w, https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-chat-sidebar.png?w=840&fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=98ceeb48201d4c25a70ae3c723f394a4 840w, https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-chat-sidebar.png?w=1100&fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=cfb6f2e863bef8b1cee3aad638a4bf7a 1100w, https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-chat-sidebar.png?w=1650&fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=30a37f1fcebc59171c937ad65f222202 1650w, https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-chat-sidebar.png?w=2500&fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=9a35c626ad3352cfc65846c756633ddd 2500w" />
</div>
2. Select the **current model** in the sidebar, then click **Set up Local Models**
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img src="https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-current-model.png?fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=cc42c11b23f4a9b57b3e7c69ae42b60b" alt="Intellij model bottom right corner" width="50%" data-og-width="778" data-og-height="546" data-path="images/intellij-current-model.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-current-model.png?w=280&fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=48a6ec059b1eb155c1ebcf9059642c91 280w, https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-current-model.png?w=560&fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=7b416430af08c499ca30b92aad33f71a 560w, https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-current-model.png?w=840&fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=5c3d156e7ce892035a7c753893decd9a 840w, https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-current-model.png?w=1100&fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=3f41f2e81f918d6ad424d317f86be381 1100w, https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-current-model.png?w=1650&fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=dcc25dd0a711f5bd3d304c7cdea45617 1650w, https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-current-model.png?w=2500&fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=47d24cfabf9aefb42d44a2438b0e4285 2500w" />
</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="https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-local-models.png?fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=0cc866166e1d6af65b3d8a16c3f396f5" alt="Zed star icon in bottom right corner" width="50%" data-og-width="522" data-og-height="602" data-path="images/intellij-local-models.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-local-models.png?w=280&fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=cef5847f7b97a11ed6bc785d93181062 280w, https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-local-models.png?w=560&fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=9c64e22cc5cc3db1d5dfd160450aeede 560w, https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-local-models.png?w=840&fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=c2e29b300a2630189df542906012b957 840w, https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-local-models.png?w=1100&fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=10a5578b086d21b104caf3a2d415a181 1100w, https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-local-models.png?w=1650&fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=91a64e8e4e91a53dac2a3ff057c2b212 1650w, https://mintcdn.com/ollama-9269c548/YbLeuKjU_QVFWOuC/images/intellij-local-models.png?w=2500&fit=max&auto=format&n=YbLeuKjU_QVFWOuC&q=85&s=243144901c8988b937ead0dd1579f98a 2500w" />
</div>
---
## null
**URL:** llms-txt#null
**Contents:**
- CPU only
- Nvidia GPU
- Install with Apt
- Install with Yum or Dnf
- Configure Docker to use Nvidia driver
- Start the container
- AMD GPU
- Run model locally
- Try different models
Source: https://docs.ollama.com/docker
Install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installation).
1. Configure the repository
2. Install the NVIDIA Container Toolkit packages
### Install with Yum or Dnf
1. Configure the repository
2. Install the NVIDIA Container Toolkit packages
### Configure Docker to use Nvidia driver
### Start the container
<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>
To run Ollama using Docker with AMD GPUs, use the `rocm` tag and the following command:
Now you can run a model:
## Try different models
More models can be found on the [Ollama library](https://ollama.com/library).
**Examples:**
Example 1 (unknown):
```unknown
## 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
```
Example 2 (unknown):
```unknown
2. Install the NVIDIA Container Toolkit packages
```
Example 3 (unknown):
```unknown
### Install with Yum or Dnf
1. Configure the repository
```
Example 4 (unknown):
```unknown
2. Install the NVIDIA Container Toolkit packages
```
---
## macOS
**URL:** llms-txt#macos
**Contents:**
- System Requirements
- Filesystem Requirements
- Changing Install Location
- Troubleshooting
- Uninstall
Source: https://docs.ollama.com/macos
## System Requirements
* MacOS Sonoma (v14) or newer
* Apple M series (CPU and GPU support) or x86 (CPU only)
## Filesystem Requirements
The preferred method of installation is to mount the `ollama.dmg` and drag-and-drop the Ollama application to the system-wide `Applications` folder. Upon startup, the Ollama app will verify the `ollama` CLI is present in your PATH, and if not detected, will prompt for permission to create a link in `/usr/local/bin`
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 somewhere other than `Applications`, place the Ollama application in the desired location, and ensure the CLI `Ollama.app/Contents/Resources/ollama` or a sym-link to the CLI can be found in your path. Upon first start decline the "Move to Applications?" request.
Ollama on MacOS stores files in a few different locations.
* `~/.ollama` contains models and configuration
* `~/.ollama/logs` contains logs
* *app.log* contains most recent logs from the GUI application
* *server.log* contains the most recent server logs
* `<install location>/Ollama.app/Contents/Resources/ollama` the CLI binary
To fully remove Ollama from your system, remove the following files and folders:
**Examples:**
Example 1 (unknown):
```unknown
sudo rm -rf /Applications/Ollama.app
sudo rm /usr/local/bin/ollama
rm -rf "~/Library/Application Support/Ollama"
rm -rf "~/Library/Saved Application State/com.electron.ollama.savedState"
rm -rf ~/Library/Caches/com.electron.ollama/
rm -rf ~/Library/Caches/ollama
rm -rf ~/Library/WebKit/com.electron.ollama
rm -rf ~/.ollama
```
---
## Zed
**URL:** llms-txt#zed
**Contents:**
- Install
- Usage with Ollama
- Connecting to ollama.com
Source: https://docs.ollama.com/integrations/zed
Install [Zed](https://zed.dev/download).
1. In Zed, click the **star icon** in the bottom-right corner, then select **Configure**.
<div style={{ display: 'flex', justifyContent: 'center' }}>
<img src="https://mintcdn.com/ollama-9269c548/qP80N64oeC4tOgE5/images/zed-settings.png?fit=max&auto=format&n=qP80N64oeC4tOgE5&q=85&s=0a63b1359b1472dde2cdf6c37e314e22" alt="Zed star icon in bottom right corner" width="50%" data-og-width="944" data-og-height="224" data-path="images/zed-settings.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/qP80N64oeC4tOgE5/images/zed-settings.png?w=280&fit=max&auto=format&n=qP80N64oeC4tOgE5&q=85&s=bfc876e9fb924521ab6cdb7ce0f8f8b2 280w, https://mintcdn.com/ollama-9269c548/qP80N64oeC4tOgE5/images/zed-settings.png?w=560&fit=max&auto=format&n=qP80N64oeC4tOgE5&q=85&s=c4b923c2b68da82a39d5834f71e595ec 560w, https://mintcdn.com/ollama-9269c548/qP80N64oeC4tOgE5/images/zed-settings.png?w=840&fit=max&auto=format&n=qP80N64oeC4tOgE5&q=85&s=427b91b58eaff6245edb4d19cacbe13c 840w, https://mintcdn.com/ollama-9269c548/qP80N64oeC4tOgE5/images/zed-settings.png?w=1100&fit=max&auto=format&n=qP80N64oeC4tOgE5&q=85&s=3c135be074ecb974ea19b4e286ec4439 1100w, https://mintcdn.com/ollama-9269c548/qP80N64oeC4tOgE5/images/zed-settings.png?w=1650&fit=max&auto=format&n=qP80N64oeC4tOgE5&q=85&s=974ceda223ced89cf821c3f1899040db 1650w, https://mintcdn.com/ollama-9269c548/qP80N64oeC4tOgE5/images/zed-settings.png?w=2500&fit=max&auto=format&n=qP80N64oeC4tOgE5&q=85&s=d75863b4421cc3f5229b673d673ec7eb 2500w" />
</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="https://mintcdn.com/ollama-9269c548/qP80N64oeC4tOgE5/images/zed-ollama-dropdown.png?fit=max&auto=format&n=qP80N64oeC4tOgE5&q=85&s=722c315dcb2563079865eccae2e0c56d" alt="Zed star icon in bottom right corner" width="50%" data-og-width="646" data-og-height="370" data-path="images/zed-ollama-dropdown.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/ollama-9269c548/qP80N64oeC4tOgE5/images/zed-ollama-dropdown.png?w=280&fit=max&auto=format&n=qP80N64oeC4tOgE5&q=85&s=3f1d23fad0d96ddd6eacb1e79f2e4489 280w, https://mintcdn.com/ollama-9269c548/qP80N64oeC4tOgE5/images/zed-ollama-dropdown.png?w=560&fit=max&auto=format&n=qP80N64oeC4tOgE5&q=85&s=a83649ed5beedb5fa741276d3f0de691 560w, https://mintcdn.com/ollama-9269c548/qP80N64oeC4tOgE5/images/zed-ollama-dropdown.png?w=840&fit=max&auto=format&n=qP80N64oeC4tOgE5&q=85&s=5338e29259c73686c55ccc42a1ac1e11 840w, https://mintcdn.com/ollama-9269c548/qP80N64oeC4tOgE5/images/zed-ollama-dropdown.png?w=1100&fit=max&auto=format&n=qP80N64oeC4tOgE5&q=85&s=63cca84dff4a209986daaadd2f1a1fd4 1100w, https://mintcdn.com/ollama-9269c548/qP80N64oeC4tOgE5/images/zed-ollama-dropdown.png?w=1650&fit=max&auto=format&n=qP80N64oeC4tOgE5&q=85&s=51f3d300d365ffc31661b83985fa2008 1650w, https://mintcdn.com/ollama-9269c548/qP80N64oeC4tOgE5/images/zed-ollama-dropdown.png?w=2500&fit=max&auto=format&n=qP80N64oeC4tOgE5&q=85&s=d82841949d806094684e5e28c6c0a064 2500w" />
</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`
---
## sets the context window size to 4096, this controls how many tokens the LLM can use as context to generate the next token
**URL:** llms-txt#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
---
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