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

# Models

> Deploy models on Adaptive Engine

Models power your AI applications. Add a model to a project to deploy it, then [make inference requests](/v0.13/core/inference).

<Tabs>
  <Tab title="SDK" icon="code">
    ## Deploy a model

    Add a model to your project, then deploy it:

    ```python theme={null}
    adaptive.models.add_to_project(model="llama-3.1-8b-instruct")
    adaptive.models.deploy(model="llama-3.1-8b-instruct", wait=True)

    # Or use attach() to do both in one call
    adaptive.models.attach(model="llama-3.1-8b-instruct", wait=True)
    ```

    | Parameter      | Type | Required | Description                                  |
    | -------------- | ---- | -------- | -------------------------------------------- |
    | `model`        | str  | Yes      | Model key from the registry                  |
    | `wait`         | bool | No       | Block until model is online (default: False) |
    | `make_default` | bool | No       | Set as default model for the project         |

    The model becomes available within a few minutes. Adaptive supports most transformer-based models including Llama, Qwen, Gemma, Mistral, and DeepSeek. See [Integrations](/v0.13/advanced/integrations) for proprietary models.

    Multimodal models (suffixed `-multimodal`) include a vision encoder that is automatically spawned alongside the decoder. The vision encoder is frozen during training. See [Multimodal StringThread](/v0.13/harmony/string-thread#multimodal-stringthread) for supported models and image handling.

    See [SDK Reference](/v0.13/reference/sdk) for all model methods.
  </Tab>

  <Tab title="UI" icon="mouse-pointer">
    ## Deploy a model

    <Frame caption="The Models page lists all models in your registry">
      <img src="https://mintcdn.com/adaptiveml/PADjThkrE4-_Dc39/static/models_light.png?fit=max&auto=format&n=PADjThkrE4-_Dc39&q=85&s=da02579550cedcad618f2e8cb14ebd1f" className="block dark:hidden" width="1280" height="500" data-path="static/models_light.png" />

      <img src="https://mintcdn.com/adaptiveml/PADjThkrE4-_Dc39/static/models_dark.png?fit=max&auto=format&n=PADjThkrE4-_Dc39&q=85&s=636edf36ace9b68efe12cb52c3020d89" className="hidden dark:block" width="1280" height="500" data-path="static/models_dark.png" />
    </Frame>

    Navigate to your project and open the **Models** tab. Click **Add Model** and select from the registry.

    <Frame caption="Adapters appear indented under their backbone model">
      <img src="https://mintcdn.com/adaptiveml/R5QotOduSKbjj2fS/static/lora_indents-0-11.png?fit=max&auto=format&n=R5QotOduSKbjj2fS&q=85&s=949e681d4feb4fa060150e9a754fdb5b" width="3220" height="1406" data-path="static/lora_indents-0-11.png" />
    </Frame>

    Fine-tuned adapter models appear indented under their base model.

    Multimodal models (suffixed `-multimodal`) include a vision encoder that is automatically spawned alongside the decoder. The vision encoder is frozen during training. See [Multimodal StringThread](/v0.13/harmony/string-thread#multimodal-stringthread) for supported models and image handling.
  </Tab>
</Tabs>