# Source: https://docs.ollama.com/quickstart.md

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.ollama.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

This quickstart will walk your through running your first model with Ollama. To get started, download Ollama on macOS, Windows or Linux.

<a href="https://ollama.com/download" target="_blank" className="inline-block px-6 py-2 bg-black rounded-full dark:bg-neutral-700 text-white font-normal border-none">
  Download Ollama
</a>

## Run a model

<Tabs>
  <Tab title="CLI">
    Open a terminal and run the command:

    ```sh  theme={"system"}
    ollama run gemma3
    ```
  </Tab>

  <Tab title="cURL">
    ```sh  theme={"system"}
    ollama pull gemma3
    ```

    Lastly, chat with the model:

    ```shell  theme={"system"}
    curl http://localhost:11434/api/chat -d '{
      "model": "gemma3",
      "messages": [{
        "role": "user",
        "content": "Hello there!"
      }],
      "stream": false
    }'
    ```
  </Tab>

  <Tab title="Python">
    Start by downloading a model:

    ```sh  theme={"system"}
    ollama pull gemma3
    ```

    Then install Ollama's Python library:

    ```sh  theme={"system"}
    pip install ollama
    ```

    Lastly, chat with the model:

    ```python  theme={"system"}
    from ollama import chat
    from ollama import ChatResponse

    response: ChatResponse = chat(model='gemma3', messages=[
      {
        'role': 'user',
        'content': 'Why is the sky blue?',
      },
    ])
    print(response['message']['content'])
    # or access fields directly from the response object
    print(response.message.content)
    ```
  </Tab>

  <Tab title="JavaScript">
    Start by downloading a model:

    ```
    ollama pull gemma3
    ```

    Then install the Ollama JavaScript library:

    ```
    npm i ollama
    ```

    Lastly, chat with the model:

    ```shell  theme={"system"}
    import ollama from 'ollama'

    const response = await ollama.chat({
      model: 'gemma3',
      messages: [{ role: 'user', content: 'Why is the sky blue?' }],
    })
    console.log(response.message.content)
    ```
  </Tab>
</Tabs>

See a full list of available models [here](https://ollama.com/models).

## Coding

For coding use cases, we recommend using the `glm-4.7-flash` model.

Note: this model requires 23 GB of VRAM with 64000 tokens context length.

```sh  theme={"system"}
ollama pull glm-4.7-flash 
```

Alternatively, you can use a more powerful cloud model (with full context length):

```sh  theme={"system"}
ollama pull glm-4.7:cloud
```

Use `ollama launch` to quickly set up a coding tool with Ollama models:

```sh  theme={"system"}
ollama launch
```

### Supported integrations

* [OpenCode](/integrations/opencode) - Open-source coding assistant
* [Claude Code](/integrations/claude-code) - Anthropic's agentic coding tool
* [Codex](/integrations/codex) - OpenAI's coding assistant
* [Droid](/integrations/droid) - Factory's AI coding agent

### Launch with a specific model

```sh  theme={"system"}
ollama launch claude --model glm-4.7-flash
```

### Configure without launching

```sh  theme={"system"}
ollama launch claude --config
```