Instructions to use H2Ozone/blue_50_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use H2Ozone/blue_50_trained 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=H2Ozone/blue_50_trained \ --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=H2Ozone/blue_50_trained - Notebooks
- Google Colab
- Kaggle
| { | |
| "name": "policy_postprocessor", | |
| "steps": [ | |
| { | |
| "registry_name": "unnormalizer_processor", | |
| "config": { | |
| "eps": 1e-08, | |
| "features": { | |
| "action": { | |
| "type": "ACTION", | |
| "shape": [ | |
| 6 | |
| ] | |
| } | |
| }, | |
| "norm_map": { | |
| "VISUAL": "IDENTITY", | |
| "STATE": "MEAN_STD", | |
| "ACTION": "MEAN_STD" | |
| } | |
| }, | |
| "state_file": "policy_postprocessor_step_0_unnormalizer_processor.safetensors" | |
| }, | |
| { | |
| "registry_name": "device_processor", | |
| "config": { | |
| "device": "cpu", | |
| "float_dtype": null | |
| } | |
| } | |
| ] | |
| } |