Model Card for match_shape3_10k_smolvla

SmolVLA 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. See the full documentation at LeRobot Docs.

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How to Get Started with the Model

For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval:

Train from scratch

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

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.

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