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
Add model card for eval3_sanity checkpoints
Browse files
README.md
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---
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library_name: lerobot
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tags:
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- robotics
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- lerobot
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- smolvla
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- so101
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---
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# eval3_sanity
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All numbered SmolVLA overfit-20 checkpoints for Eval 3 sanity checking.
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Dataset: `robot-learning-group47/eval3_overfit20`
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Checkpoints:
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- `checkpoints/000500`
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- `checkpoints/001000`
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- `checkpoints/001500`
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- `checkpoints/002000`
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- `checkpoints/002500`
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- `checkpoints/003000`
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The `003000/pretrained_model` checkpoint is also uploaded at repo root as the default loadable policy.
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Key inference settings are stored in each checkpoint config:
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- policy type: `smolvla`
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- fps: `15`
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- camera key: `observation.images.front`
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- image size: `240x320`, resized internally with padding to `512x512`
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- `chunk_size=50`
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- `n_action_steps=50`
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- `num_steps=10`
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- `attention_mode=self_attn`
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- `num_vlm_layers=8`
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- `num_expert_layers=4`
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