Instructions to use wego-hansu/yeonwonju-070720-vla with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use wego-hansu/yeonwonju-070720-vla 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=wego-hansu/yeonwonju-070720-vla \ --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=wego-hansu/yeonwonju-070720-vla - Notebooks
- Google Colab
- Kaggle
Upload policy weights, train config and readme
Browse files- README.md +5 -4
- config.json +45 -23
- model.safetensors +2 -2
- train_config.json +61 -36
README.md
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---
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datasets: wego-hansu/test_place_block0728
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library_name: lerobot
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license: apache-2.0
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model_name:
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pipeline_tag: robotics
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tags:
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- lerobot
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- robotics
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---
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# Model Card for
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<!-- Provide a quick summary of what the model is/does. -->
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[
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This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
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---
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base_model: lerobot/smolvla_base
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datasets: wego-hansu/test_place_block0728
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library_name: lerobot
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license: apache-2.0
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model_name: smolvla
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pipeline_tag: robotics
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tags:
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- lerobot
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- robotics
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- smolvla
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---
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# Model Card for smolvla
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<!-- Provide a quick summary of what the model is/does. -->
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[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
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This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
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config.json
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}
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{
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"type": "smolvla",
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"optimizer_eps": 1e-08,
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"optimizer_grad_clip_norm": 10,
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"scheduler_warmup_steps": 1000,
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"scheduler_decay_steps": 30000,
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"scheduler_decay_lr": 2.5e-06,
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"vlm_model_name": "HuggingFaceTB/SmolVLM2-500M-Video-Instruct",
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"attention_mode": "cross_attn",
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"pad_language_to": "longest",
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"expert_width_multiplier": 0.75,
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"min_period": 0.004,
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"compile_model": false,
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"compile_mode": "max-autotune"
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model.safetensors
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train_config.json
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},
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"output_dir": "outputs/train/2026-07-28/
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"use_policy_training_preset": true,
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"eval": {
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"n_episodes": 50,
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"rabc_kappa": 0.01,
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},
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