Instructions to use pravsels/smolvla_transfer_cube with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use pravsels/smolvla_transfer_cube 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=pravsels/smolvla_transfer_cube \ --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=pravsels/smolvla_transfer_cube - Notebooks
- Google Colab
- Kaggle
| library_name: lerobot | |
| tags: | |
| - smolvla | |
| - robotics | |
| - lerobot | |
| - bimanual | |
| - so101 | |
| - armnetbench | |
| base_model: lerobot/smolvla_base | |
| datasets: | |
| - villekuosmanen/armnetbench_transfer_cube | |
| # smolvla_transfer_cube | |
| Fine-tuned [SmolVLA](https://huggingface.co/lerobot/smolvla_base) for bimanual SO101 (12-dim action/state). | |
| | | | | |
| |---|---| | |
| | **Base model** | [lerobot/smolvla_base](https://huggingface.co/lerobot/smolvla_base) | | |
| | **Dataset** | [villekuosmanen/armnetbench_transfer_cube](https://huggingface.co/datasets/villekuosmanen/armnetbench_transfer_cube) | | |
| | **Checkpoints** | 5,000 and 10,000 steps | | |
| | **Training** | Vast.ai 4xH100, batch 128, ~8.5h total | | |
| | **Action dim** | 12 (bimanual SO101, 6 per arm) | | |
| | **Cameras** | top, left_wrist, right_wrist | | |
| | **W&B** | [smolvla_transfer_cube](https://wandb.ai/pravsels/smolvla_transfer_cube/runs/w2j060oy) | | |
| ## Checkpoints | |
| - `checkpoints/005000/pretrained_model/` | |
| - `checkpoints/010000/pretrained_model/` | |
| ## Usage | |
| ```python | |
| from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy | |
| policy = SmolVLAPolicy.from_pretrained("pravsels/smolvla_transfer_cube", subfolder="checkpoints/010000/pretrained_model") | |
| ``` | |
| ## Training Logs | |
| Full training curves and metrics: [https://wandb.ai/pravsels/smolvla_transfer_cube/runs/w2j060oy](https://wandb.ai/pravsels/smolvla_transfer_cube/runs/w2j060oy) | |