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---
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).
---
## 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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