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--- |
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datasets: HuggingFaceVLA/libero |
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library_name: lerobot |
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license: apache-2.0 |
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model_name: vla0_smol |
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pipeline_tag: robotics |
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tags: |
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- lerobot |
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- robotics |
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- vla0_smol |
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--- |
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# Model Card for vla0-smol |
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Read about the model here: https://robot-learning-collective.github.io/vla-0-smol |
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This is VLA-0-Smol trained on the Libero dataset. |
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_Model type not recognized — please update this template._ |
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This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). |
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See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). |
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--- |
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## 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). |
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Support of the model is implemented in repo: https://github.com/Robot-Learning-Collective/lerobot-experiments |
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Below is the short version on how to train and run inference/eval: |
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### Train from scratch on PushT |
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```bash |
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lerobot-train --config_path=configs/vla0_smol.json |
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``` |
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_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ |
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### Evaluate the policy on Libero |
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```bash |
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lerobot-eval \ |
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--policy.path="Robot-Learning-Collective/VLA-0-Smol" \ |
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--policy.n_action_steps=0 \ |
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--policy.ensemble_size=8 \ |
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--env.type=libero \ |
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--env.task=libero_object \ |
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--eval.batch_size=2 |
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``` |
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Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint. |
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--- |
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## Model Details |
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- **License:** apache-2.0 |
