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

# Quickstart: Run Your First Bunker Model

> Deploy a Bunker model, authenticate, and run your first inference request in five minutes. Works with any language that speaks HTTP.

Get your first prediction from a Bunker Longevity model in under five minutes. This guide uses BunkerFold-3 for protein structure prediction, but the same flow works for every model in the catalog.

<Steps>
  <Step title="Install the SDK">
    Install the `bunker-fold` Python package, which ships a typed client for every Bunker Longevity model.

    ```bash theme={null}
    pip install bunker-fold
    ```

    See [Installation](/installation) for `uv` and `poetry` alternatives.
  </Step>

  <Step title="Get an API key">
    Sign in at [bunkerbio.com](https://bunkerbio.com) and generate a key from your Bunker Longevity account settings. Treat it like a password.

    See [Authentication](/authentication) for full details on scopes and rotation.
  </Step>

  <Step title="Deploy the model">
    Deploy a model instance so it is ready to accept inference requests.

    <CodeGroup>
      ```bash cURL theme={null}
      curl -X POST https://api.bunkerbio.com/v1/models/bunkerfold-3/deploy \
        -H "Authorization: Bearer $BUNKER_API_KEY"
      ```

      ```python Python theme={null}
      import requests, os

      r = requests.post(
          "https://api.bunkerbio.com/v1/models/bunkerfold-3/deploy",
          headers={"Authorization": f"Bearer {os.environ['BUNKER_API_KEY']}"},
      )
      print(r.json())
      ```
    </CodeGroup>
  </Step>

  <Step title="Run inference">
    Send an amino acid sequence and get a predicted 3D structure back.

    <CodeGroup>
      ```bash cURL theme={null}
      curl -X POST https://api.bunkerbio.com/v1/models/bunkerfold-3/predict \
        -H "Authorization: Bearer $BUNKER_API_KEY" \
        -H "Content-Type: application/json" \
        -d '{"sequence": "MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSH"}'
      ```

      ```python Python theme={null}
      r = requests.post(
          "https://api.bunkerbio.com/v1/models/bunkerfold-3/predict",
          headers={"Authorization": f"Bearer {os.environ['BUNKER_API_KEY']}"},
          json={"sequence": "MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSH"},
      )
      print(r.json())
      ```
    </CodeGroup>
  </Step>

  <Step title="Inspect the response">
    You get back a confidence score, per-residue coordinates, and timing.

    ```json Response theme={null}
    {
      "confidence": 0.97,
      "rmsd": 0.42,
      "residues_resolved": "51/51",
      "fold_class": "globin",
      "time_seconds": 1.8,
      "structure_url": "https://cdn.bunkerbio.com/predictions/abc123.pdb"
    }
    ```
  </Step>
</Steps>

## Next steps

* [Browse all models](/models/overview): compare all six models and pick the one for your task.
* [API Reference](/api-reference/introduction): full request and response schemas for every endpoint.