Instructions to use BlackCatRoboticsAI/smolvla-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use BlackCatRoboticsAI/smolvla-base 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=BlackCatRoboticsAI/smolvla-base \ --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=BlackCatRoboticsAI/smolvla-base - Notebooks
- Google Colab
- Kaggle
SmolVLA Base
SmolVLA — A compact Vision-Language-Action model from Hugging Face for robot manipulation. Takes images + language instructions, outputs robot actions.
Model Details
| Field | Value |
|---|---|
| License | Apache 2.0 (commercial use ✅) |
| Input | Multi-view images (256×256) + language instruction + proprioception |
| Output | Robot actions |
| Framework | LeRobot / PyTorch |
| Format | Safetensors |
Quick Start
from huggingface_hub import snapshot_download
from lerobot.policies.smolvla.modeling_smolvla import SmolVLA
# Download model
snapshot_download(repo_id="BlackCatRoboticsAI/smolvla-base", local_dir="./smolvla")
# Load policy
policy = SmolVLA.from_pretrained("./smolvla")
Use Cases
- Pick-and-place manipulation
- Object sorting
- Assembly tasks
- Language-driven control
Citation
@misc{lerobot2025smolvla,
title={SmolVLA: Small Vision-Language-Action Model},
author={Hugging Face},
year={2025},
publisher={Hugging Face}
}
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