Instructions to use chocopan/chocopan-pre with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use chocopan/chocopan-pre 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=chocopan/chocopan-pre \ --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=chocopan/chocopan-pre - Notebooks
- Google Colab
- Kaggle
Download base_model/SOURCE.json from chocopan/chocopan-pre: direct link, hf CLI and curl.
- Browser
- Download file 1.27 kB
-
https://huggingface.co/chocopan/chocopan-pre/resolve/main/base_model/SOURCE.json
- Command line
-
hf download hf://chocopan/chocopan-pre/base_model/SOURCE.json
-
curl -L -o SOURCE.json https://huggingface.co/chocopan/chocopan-pre/resolve/main/base_model/SOURCE.json
1.27 kB
| { | |
| "source_repo": "lerobot/smolvla_libero_plus", | |
| "source_revision": "7bb70aa5bc92b82c9239142775d3a173103567ff", | |
| "downloaded_at": "2026-08-01 JST", | |
| "files": { | |
| "model.safetensors": { | |
| "size_bytes": 906712520, | |
| "sha256": "a67a056ab0761afa5f5efef8edd45065cef4494735231b7eeeb8328e71fcfb72" | |
| }, | |
| "config.json": { | |
| "size_bytes": 2858, | |
| "sha256": "6332af209367dfee86e4c416eb56c9606cf1f2e457165d9617f4e49cf48faa1c" | |
| }, | |
| "policy_preprocessor.json": { | |
| "size_bytes": 2407, | |
| "sha256": "ccbdab60c7c1f95037159327ea2f3985b5de417911a794f4e631993d4791544f" | |
| }, | |
| "policy_preprocessor_step_5_normalizer_processor.safetensors": { | |
| "size_bytes": 3804, | |
| "sha256": "baff19e7eecb335520501039c8740b191c5f2aa84552df1a9c59ee4cbf29e63a" | |
| }, | |
| "policy_postprocessor.json": { | |
| "size_bytes": 660, | |
| "sha256": "3b51f092c70c710ce0213ee1b63bf51b4878ec67828dd2d67daf9ef51081a41a" | |
| }, | |
| "policy_postprocessor_step_0_unnormalizer_processor.safetensors": { | |
| "size_bytes": 3804, | |
| "sha256": "baff19e7eecb335520501039c8740b191c5f2aa84552df1a9c59ee4cbf29e63a" | |
| }, | |
| "train_config.json": { | |
| "size_bytes": 10285, | |
| "sha256": "4576816ed4d13219851de57c28b7a21fcebf05011a9dda196c216bebcbe346e2" | |
| } | |
| } | |
| } | |