| --- |
| pipeline_tag: robotics |
| tags: |
| - smolvla |
| library_name: lerobot |
| datasets: |
| - lerobot/svla_so101_pickplace |
| --- |
| |
| ## SmolVLA: A vision-language-action model for affordable and efficient robotics |
|
|
| Resources and technical documentation: |
|
|
| [SmolVLA Paper](https://huggingface.co/papers/2506.01844) |
|
|
| [SmolVLA Blogpost](https://huggingface.co/blog/smolvla) |
|
|
| [Code](https://github.com/huggingface/lerobot/blob/main/lerobot/common/policies/smolvla/modeling_smolvla.py) |
|
|
| [Train using Google Colab Notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/lerobot/training-smolvla.ipynb#scrollTo=ZO52lcQtxseE) |
|
|
| [SmolVLA HF Documentation](https://huggingface.co/docs/lerobot/smolvla) |
|
|
| Designed by Hugging Face. |
|
|
| This model has 450M parameters in total. |
| You can use inside the [LeRobot library](https://github.com/huggingface/lerobot). |
|
|
| Before proceeding to the next steps, you need to properly install the environment by following [Installation Guide](https://huggingface.co/docs/lerobot/installation) on the docs. |
|
|
| Install smolvla extra dependencies: |
| ```bash |
| pip install -e ".[smolvla]" |
| ``` |
|
|
| Example of finetuning the smolvla pretrained model (`smolvla_base`): |
| ```bash |
| python lerobot/scripts/train.py \ |
| --policy.path=lerobot/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 |
| ``` |
|
|
| Example of finetuning the smolvla neural network with pretrained VLM and action expert |
| intialized from scratch: |
| ```bash |
| python lerobot/scripts/train.py \ |
| --dataset.repo_id=lerobot/svla_so101_pickplace \ |
| --batch_size=64 \ |
| --steps=200000 \ |
| --output_dir=outputs/train/my_smolvla \ |
| --job_name=my_smolvla_training \ |
| --policy.device=cuda \ |
| --wandb.enable=true |
| ``` |
