| --- |
| base_model: lerobot/smolvla_base |
| library_name: lerobot |
| license: apache-2.0 |
| model_name: smolvla |
| pipeline_tag: robotics |
| tags: |
| - robotics |
| - smolvla |
| --- |
| |
| # Model Card for my_smolvla |
| |
| <!-- Provide a quick summary of what the model is/does. --> |
| |
| [SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. |
| |
| 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: |
| |
| ### Train from scratch |
| |
| ```bash |
| python lerobot/scripts/train.py \ |
| --dataset.repo_id=<user_or_org>/<dataset> \ |
| --policy.type=act \ |
| --output_dir=outputs/train/<desired_policy_repo_id> \ |
| --job_name=lerobot_training \ |
| --policy.device=cuda \ |
| --policy.repo_id=<user_or_org>/<desired_policy_repo_id> \ |
| --wandb.enable=true |
| ``` |
| |
| *Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`.* |
| |
| ### Evaluate the policy |
| |
| ```bash |
| python -m lerobot.record \ |
| --robot.type=so100_follower \ |
| --dataset.repo_id=<user_or_org>/eval_<dataset> \ |
| --policy.path=<user_or_org>/<desired_policy_repo_id> \ |
| --episodes=10 |
| ``` |
| |
| Prefix the dataset repo with **eval_** and supply `--policy.path` pointing to a local or hub checkpoint. |
| |
| --- |
