| | --- |
| | base_model: lerobot/smolvla_base |
| | datasets: HuggingFaceVLA/libero |
| | library_name: lerobot |
| | license: apache-2.0 |
| | model_name: smolvla |
| | pipeline_tag: robotics |
| | tags: |
| | - smolvla |
| | - lerobot |
| | - robotics |
| | --- |
| | |
| | # Model Card for 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 |
| | lerobot-train \ |
| | --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/`._ |
| |
|
| | ### Evaluate the policy/run inference |
| |
|
| | ```bash |
| | 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. |
| | |
| | --- |
| | |
| | ## Model Details |
| | |
| | - **License:** apache-2.0 |
