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

# GR00T N1.7

> Deploy a GR00T N1.7 checkpoint from Hugging Face and predict an action chunk.

GR00T checkpoints carry their robot embodiment and input contract in upstream metadata. Use the inspection output for the checkpoint you deploy; when it exposes multiple complete embodiments, pass the upstream `embodiment_tag` shown by inspection. A checkpoint with one complete embodiment needs no selection.

**1. Install and sign in**

Create an environment and install servo-client:

```bash your computer theme={null}
python3 -m venv .venv
source .venv/bin/activate
python -m pip install servo-client
```

Sign in:

```bash your computer theme={null}
servo login
```

Open the login link in a browser. `servo whoami` confirms the active account.

**2. Connect Hugging Face**

Connect a Hugging Face account in [Servo account settings](https://api.orchestrallabs.ai/account/huggingface) before inspecting or importing a checkpoint, including public repositories.

**3. Enroll the robot**

On the robot computer, enroll it with the name `robot-1`:

```bash robot computer theme={null}
python3 -m venv .venv
source .venv/bin/activate
python -m pip install servo-client
```

Enroll it; the command prints a code and waits for approval:

```bash robot computer theme={null}
servo agent install --name robot-1
```

An organization admin approves the printed code on your computer:

```bash your computer theme={null}
servo robot approve CODE
```

The robot computer reports `Enrolled robot robot-1.` when it is ready.

**4. Inspect the checkpoint**

Inspect the Hugging Face repository you want to serve:

```bash your computer theme={null}
servo model inspect hf://nvidia/GR00T-N1.7-3B --revision main
```

This example uses the repository's `main` revision; for a reproducible deployment, use the same immutable commit ID in the inspect and deploy commands.

Use the checkpoint's reported image keys, dimensions, state and action shapes, and history offsets; if inspection lists `embodiment_tag` choices, select one exact upstream value with `--checkpoint-option`:

```bash your computer theme={null}
servo model inspect hf://nvidia/GR00T-N1.7-3B --revision main --checkpoint-option embodiment_tag=oxe_droid_relative_eef_relative_joint
```

The tag is part of the checkpoint's own GR00T configuration. Servo does not define a separate embodiment enum. Omit the option when inspection reports a single complete configuration.

**5. Deploy the checkpoint**

Deploy the same repository and revision you inspected, passing the same option if the checkpoint requires a choice:

```bash your computer theme={null}
servo model deploy hf://nvidia/GR00T-N1.7-3B --revision main --robot robot-1 --wait
```

For a checkpoint with multiple complete embodiments, pass the selected upstream tag:

```bash your computer theme={null}
servo model deploy hf://nvidia/GR00T-N1.7-3B --revision main --robot robot-1 --checkpoint-option embodiment_tag=oxe_droid_relative_eef_relative_joint --wait
```

The first deployment imports and prepares the checkpoint. Later deployments of the imported model reuse that preparation.

**6. Predict an action chunk**

On the robot computer, download and run the shared sample, which builds arrays from the endpoint's published input contract:

```bash robot computer theme={null}
curl -fsSL https://api.orchestrallabs.ai/docs/downloads/first_prediction.py -o first_prediction.py
python first_prediction.py
```

Prediction returns actions; your application remains responsible for safety checks and sending commands to the robot.

**7. Stop the deployment**

Stop the endpoint when you are done:

```bash your computer theme={null}
servo deployment stop --model GR00T-N1.7-3B
```


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.