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---
pipeline_tag: robotics
tags:
- lerobot
library_name: lerobot
datasets:
- lerobot/svla_so101_pickplace
base_model:
- lerobot/smolvla_base
---
## 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
``` |