Instructions to use drashutoshspace/moonbot_smolvla_tf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use drashutoshspace/moonbot_smolvla_tf 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=drashutoshspace/moonbot_smolvla_tf \ --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=drashutoshspace/moonbot_smolvla_tf - Notebooks
- Google Colab
- Kaggle
moonbot SmolVLA โ three blocks stack, WITH force/torque
SmolVLA fine-tuned from lerobot/smolvla_base (full fine-tune: vision encoder and VLM trainable, as in the pi0 comparison runs; cameras renamed front->camera1, eef->camera2, right->camera3) on
gdiazsrl/three_blocks_stack_sep22.
Comparison run. One of six runs comparing pi0 / SmolVLA / ACT with vs without force/torque. Same dataset, batch 16, 20,000 steps and seed as the pi0 run drashutoshspace/moonbot_pi0_three_blocks_stack; the force/torque twin of this model is drashutoshspace/moonbot_smolvla_no_tf.
Training curves: https://wandb.ai/drmishra-space/lerobot/runs/rn1je6y9
Deployment contract: 21-dim state = joint_read (8) + tip_pos (7) + F_ee (6) force/torque; 3 cameras; 8-dim action.
Checkpoints
checkpoint-005000, -010000, -015000, -020000 (same steps as the counterpart run).
Each folder contains weights, pre/post-processors, this run's rosetta contract and DEPLOY.md.
The final checkpoint also includes training_state/. Complete checkpoints with optimizer state are
also on the team OneDrive under DATA/Processed/smolvla_tf/.
Training
| batch size | 16 |
| steps | 20,000 |
| optimizer / schedule | the algorithm's own LeRobot preset |
| seed | 1000 (default) |
| validation split | none (as in the pi0 runs) |