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

# Models & Datasets

> Run any AI model — open-source, proprietary, your own — on any twin, with the data plumbing already taken care of

AI models are how your [twins](/overview/features/digital-twin) stop being puppets and start being autonomous. Cyberwave is the substrate that gets them there: a growing [catalog of models](https://cyberwave.com/models), the infrastructure to run them on cloud or edge, and the [datasets](https://cyberwave.com/datasets) you need to train your own.

```python theme={null}
from cyberwave import Cyberwave

cw = Cyberwave()
arm = cw.twin("acme/twins/arm-station-1")
arm.use_controller("acme/models/my-pick-and-place-vla")
```

That's it — the model is now driving the robot. Same call works in [simulation](/overview/features/simulation) (`cw.affect("simulation")`) and against live hardware.

***

## Bring your own model — or pick from the catalog

<CardGroup cols={3}>
  <Card title="Use the catalog" icon="book-bookmark" href="https://cyberwave.com/models">
    Pick from open-source and proprietary models — VLAs (SmolVLA, OpenVLA, Pi 0.5),
    VLMs (Gemini Robotics, GPT-5, Molmo, PaliGemma), detectors (YOLOv8, SAM2),
    and image-to-3D (Hunyuan3D, TripoSR).
  </Card>

  <Card title="Fine-tune your own" icon="brain" href="/feature-reference/ml-models/smolvla-training">
    Fine-tune [SmolVLA](/feature-reference/ml-models/smolvla-training) or
    [OpenVLA](/feature-reference/ml-models/openvla-oft-training) on the data
    you collected with your own robots — no separate training stack.
  </Card>

  <Card title="Register a custom model" icon="code" href="/feature-reference/ml-models/index#registering-a-model">
    Bring your own weights or endpoint — Hugging Face, an internal inference
    server, a custom ONNX file. Cyberwave treats it as a first-class model.
  </Card>
</CardGroup>

Every registered model gets a [Model Playground](/feature-reference/ml-models/playground) page where you can try it on an image, see overlays, copy CLI/SDK snippets, and wire it into a workflow — so experimenting with a new model is genuinely one click.

***

## Use them anywhere in your stack

Cyberwave models compose with the rest of the platform. Same model, different roles:

| Where the model runs     | What it does                                                          | Reference                                                                            |
| ------------------------ | --------------------------------------------------------------------- | ------------------------------------------------------------------------------------ |
| **As a twin controller** | Drive a robot end-to-end (a VLA picks-and-places, an RL policy walks) | [Controllers](/overview/index#controllers)                                           |
| **Inside a workflow**    | One `call_model` node — runs cloud VLM or edge ML transparently       | [Workflow nodes](/feature-reference/workflows#workflow-components)                   |
| **From the SDK**         | Async cloud calls for VLM / LLM tasks                                 | [`vlm_generation` / `llm_generation`](/tools/python-sdk#workflows)                   |
| **In simulation**        | The same code drives a [MuJoCo twin](/overview/features/simulation)   | [`cw.affect("simulation")`](/tools/python-sdk#affect-simulation-vs-live-environment) |

The best automations usually combine more than one — an edge YOLO that's cheap to run on every frame **and** a cloud VLM that reasons about the rare interesting frame. See the [edge-to-cloud VLM tutorial](/tutorials/edge-to-cloud-vlm) for that pattern end-to-end.

***

## Edge + cloud, both first-class

Cyberwave runs models in both places, on purpose.

<CardGroup cols={2}>
  <Card title="Edge models" icon="microchip" href="/feature-reference/edge/workers/overview">
    Local inference on your own hardware — fast, private, offline-capable.
    YOLO, SAM2, ONNX/TensorRT detectors all run inside the
    [edge worker](/feature-reference/edge/workers/overview) generated from
    your workflow.
  </Card>

  <Card title="Cloud models" icon="cloud" href="/tools/cloud-node">
    Heavy-weight VLMs and VLAs (Gemini Robotics, GPT-5, OpenVLA) run on a
    [Cloud Node](/tools/cloud-node) or
    [VLA Cloud Node](/tools/vla-cloud-node) with a GPU attached — Cyberwave
    handles the provisioning.
  </Card>
</CardGroup>

You don't manage any of the boring parts: weight downloads, GPU provisioning, picking the right edge-compatible variant, network reconnects, version pinning. Cyberwave routes the right model to the right runtime — see the [model deployment reference](/feature-reference/ml-models/deploy) for the wire-level details.

***

## Datasets — collect, import, export

Models need data. Cyberwave gives you the loop end-to-end: record on the edge → [replay in the browser](/overview/features/replay-and-historical-data) → slice into episodes → train.

<CardGroup cols={3}>
  <Card title="From your own runs" icon="record-vinyl" href="/overview/features/replay-and-historical-data">
    Every recording in [Replay](/overview/features/replay-and-historical-data)
    can be turned into episodes and a dataset, ready to train on.
  </Card>

  <Card title="Import from anywhere" icon="file-import" href="/feature-reference/datasets/import">
    LeRobot v3 / v2.1, RLDS, HDF5, Zarr, GR00T, MCAP, ROS bag, RoboDM, Hugging
    Face — see the [full import matrix](/feature-reference/datasets/import).
  </Card>

  <Card title="Export, no lock-in" icon="file-export" href="/feature-reference/datasets/format-conversion">
    Convert any dataset to Cyberwave Parquet, LeRobot v3, RLDS, or OpenVLA
    TFDS via the [export tab or API](/feature-reference/datasets/format-conversion).
  </Card>
</CardGroup>

Browse the [public dataset catalog](https://cyberwave.com/datasets), pick one, and feed it straight into [SmolVLA](/feature-reference/ml-models/smolvla-training) or [OpenVLA](/feature-reference/ml-models/openvla-oft-training) training — the format conversion happens for you.

***

## What to read next

<CardGroup cols={3}>
  <Card title="ML Models reference" icon="brain" href="/feature-reference/ml-models/index">
    Capabilities, providers, registration, inference, and the full VLA stack.
  </Card>

  <Card title="Model Playground" icon="play" href="/feature-reference/ml-models/playground">
    The interactive page behind every model in the catalog.
  </Card>

  <Card title="Sandwich robot (SmolVLA)" icon="bread-slice" href="/tutorials/sandwich-robot-smolvla">
    A community tutorial: collect data, fine-tune SmolVLA, run it on a real
    arm — all on Cyberwave.
  </Card>
</CardGroup>