---
library_name: lerobot
license: apache-2.0
pipeline_tag: robotics
tags:
- robotics
---

# Model Card for mobile_so100_test_100000

<!-- Provide a quick summary of what the model is/does. -->


This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).

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## How to Get Started with the Model

For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:

### 1 Train from scratch

```bash
python lerobot/scripts/train.py       --dataset.repo_id=${HF_USER}/<dataset>       --policy.type=act       --output_dir=outputs/train/<desired_policy_repo_id>       --job_name=lerobot_training       --policy.device=cuda       --policy.repo_id=${HF_USER}/<desired_policy_repo_id>
  --wandb.enable=true
```

*Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`.*

### 2 Evaluate the policy

```bash
python -m lerobot.record       --robot.type=so100_follower       --dataset.repo_id=<hf_user>/eval_<dataset>       --policy.path=<hf_user>/<desired_policy_repo_id>       --episodes=10
```

Prefix the dataset repo with **eval_** and supply `--policy.path` pointing to a local or hub checkpoint.

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