Instructions to use KotaroT1/smolvla_libero_plus_object_ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use KotaroT1/smolvla_libero_plus_object_ft 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=KotaroT1/smolvla_libero_plus_object_ft \ --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=KotaroT1/smolvla_libero_plus_object_ft - Notebooks
- Google Colab
- Kaggle
SmolVLA Fine-tuned on LIBERO-Plus (Object)
This model is a fine-tuned version of lerobot/smolvla_libero specifically trained on the LIBERO-Plus task suite (libero_object). It is designed to perform robotic manipulation tasks by taking visual observations and language instructions as input to predict 7-DoF actions.
π€ Model Details
- Model Type: Vision-Language-Action (VLA)
- Base Model: lerobot/smolvla_libero
- Task Suite: LIBERO-Plus (libero_object)
- Framework: LeRobot
- Action Space: 7-DoF (dx, dy, dz, droll, dpitch, dyaw, gripper)
- Language: English
π Training Information
The model was trained by freezing the vision and language encoders and fine-tuning only the action expert modules and state projection layers.
- Dataset:
lerobot/libero_plus(libero_object) - Training Steps: 10,000 steps
- Batch Size: 8
- Learning Rate: 1e-4 (Cosine decay with warmup)
- Hardware: Single NVIDIA L4 GPU
π How to Use
You can load and use this model directly with the Hugging Face lerobot library.
Installation
pip install lerobot
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