Instructions to use robot-learning-group47/eval3_sanity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use robot-learning-group47/eval3_sanity 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=robot-learning-group47/eval3_sanity \ --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=robot-learning-group47/eval3_sanity - Notebooks
- Google Colab
- Kaggle
eval3_sanitycheck
SmolVLA sanity-check overfit policy for Eval 3 coke-can placement.
- Base VLM:
HuggingFaceTB/SmolVLM2-500M-Video-Instruct - Policy type:
smolvla - Dataset:
robot-learning-group47/eval3_overfit20 - Local dataset root:
/data/lerobot_datasets/robot-learning-group47/eval3_overfit20 - Source episodes:
0,1,6,7,12,13,18,19,24,25,30,31,36,37,42,43,48,49,54,55 - Steps:
3000 - Batch size:
8 - Image augmentation: disabled
- Trainable params: expert/state/action heads only, vision/VLM frozen
- Final training loss: approximately
0.182
Important inference settings are stored in config.json:
chunk_size=50n_action_steps=50num_steps=10attention_mode=self_attnnum_vlm_layers=8num_expert_layers=4resize_imgs_with_padding=[512,512]