Instructions to use 2chan/coffee-bimanual-smolvla with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use 2chan/coffee-bimanual-smolvla with LeRobot:
# See https://github.com/huggingface/lerobot?tab=readme-ov-file#installation for more details git clone https://github.com/huggingface/lerobot.git cd lerobot pip install -e .[smolvla]
# Launch finetuning on your dataset python lerobot/scripts/train.py \ --policy.path=2chan/coffee-bimanual-smolvla \ --dataset.repo_id=lerobot/svla_so101_pickplace \ --batch_size=64 \ --steps=20000 \ --output_dir=outputs/train/my_smolvla \ --job_name=my_smolvla_training \ --policy.device=cuda \ --wandb.enable=true
# Run the policy using the record function python -m lerobot.record \ --robot.type=so101_follower \ --robot.port=/dev/ttyACM0 \ # <- Use your port --robot.id=my_blue_follower_arm \ # <- Use your robot id --robot.cameras="{ front: {type: opencv, index_or_path: 8, width: 640, height: 480, fps: 30}}" \ # <- Use your cameras --dataset.single_task="Grasp a lego block and put it in the bin." \ # <- Use the same task description you used in your dataset recording --dataset.repo_id=HF_USER/dataset_name \ # <- This will be the dataset name on HF Hub --dataset.episode_time_s=50 \ --dataset.num_episodes=10 \ --policy.path=2chan/coffee-bimanual-smolvla - Notebooks
- Google Colab
- Kaggle
Bimanual SO-101 coffee SmolVLA
SmolVLA was fine-tuned for a two-arm SO-101 coffee preparation demonstration.
Artifact
- Branch:
main - Base model:
lerobot/smolvla_baseat revisiond9f33c94a60fb382c90dea2164c96845bd955e28 - Offline best: step
10000, eval loss0.1484 - Dataset revision:
6cb48fc7215028d0bf584400436ce2c558f52e76 - Selection criterion: minimum held-out episode evaluation loss; ties prefer the earlier step.
Use main for the final training step and best-eval-loss for the automatically selected model.
lerobot-rollout \
--policy.path=<THIS_REPO> \
--policy.pretrained_revision=main \
--robot.type=bi_so_follower \
--task="Hold the cup with the right arm. With the left arm, add powder to the cup, pour water into it, and stir with a stirrer."
Inputs
observation.images.left_wristobservation.images.right_wristobservation.images.topobservation.state: 12 absolute SO-101 joint/gripper positionsLanguage instruction:
Hold the cup with the right arm. With the left arm, add powder to the cup, pour water into it, and stir with a stirrer.
Output order
left_shoulder_pan.posleft_shoulder_lift.posleft_elbow_flex.posleft_wrist_flex.posleft_wrist_roll.posleft_gripper.posright_shoulder_pan.posright_shoulder_lift.posright_elbow_flex.posright_wrist_flex.posright_wrist_roll.posright_gripper.pos
The machine-readable camera, named-joint, task, and revision mapping is stored in
training_contract.json and is checked before real-robot rollout.
Access and license note
The repository uses manual gated access. The base model is Apache-2.0, while the linked
dataset currently has no declared license; this derived model is therefore marked other
until the dataset owner documents redistribution terms.
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Model tree for 2chan/coffee-bimanual-smolvla
Base model
HuggingFaceTB/SmolLM2-360M