| --- |
| base_model: lerobot/smolvla_base |
| datasets: H2Ozone/_blue_150_dorm_2 |
| library_name: lerobot |
| license: apache-2.0 |
| model_name: smolvla |
| pipeline_tag: robotics |
| tags: |
| - lerobot |
| - smolvla |
| - robotics |
| --- |
| |
| # Model Card for smolvla |
|
|
| <!-- Provide a quick summary of what the model is/does. --> |
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| [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. |
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| 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 |
|
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| 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 |
