SmolVLA

🚀 Meet SmolVLA

SmolVLA is a groundbreaking Vision-Language-Action (VLA) model with only 450 million parameters, developed by Hugging Face. It's carefully designed for cost-effective deployment on consumer-grade hardware, making advanced robotics technology accessible to more developers and enthusiasts.

Trained on the open community LeRobot dataset, SmolVLA truly embodies the power of open-source collaboration, with performance that matches or exceeds larger proprietary models.

📌 Core Features

🎬 Demo Videos

Watch SmolVLA in action as it performs various tasks that showcase its capabilities in real-world environments.

SmolVLA Overview

Community DIY Robot Video

Official Research Paper

For an in-depth look at the technical details, read the official research paper. It provides comprehensive information on model architecture, training methodology, and performance benchmarks.

• Read the Full Paper

🛠️ Quick Start Guide

📋 Model Overview

SmolVLA is a 450M parameter vision-language-action model designed for affordable and efficient robotics. It's optimized to run on consumer hardware while maintaining competitive performance.

1. Environment Setup

Before proceeding, you need to properly install the environment by following the Installation Guide on the docs.

git clone https://github.com/huggingface/lerobot.git
cd lerobot
pip install -e ".[smolvla]"

2. Load Pre-trained Model

The fastest way to experience SmolVLA is to directly load the pre-trained model from Hugging Face.

from lerobot.common.policies.smolvla.modeling_smolvla import SmolVLAPolicy
policy = SmolVLAPolicy.from_pretrained("lerobot/smolvla_base")

3. Fine-tuning the Pre-trained Model

Fine-tune SmolVLA on your specific dataset for better task performance. This example uses the SO101 pick-place dataset.

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

4. Training from Scratch

Train SmolVLA neural network with pretrained VLM and action expert initialized from scratch.

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

🗂️ Dataset Information

SmolVLA is trained on community-contributed datasets. The svla_so101_pickplace dataset contains:

• Episodes: 50 total episodes
• Frames: 11,939 total frames
• Robot Type: SO100 follower arm
• FPS: 30 frames per second
• Video Resolution: 480×640 (up and side cameras)
• Action Space: 6-DOF (shoulder_pan, shoulder_lift, elbow_flex, wrist_flex, wrist_roll, gripper)

📊 Additional Resources

• SmolVLA Model Card on Hugging Face
• Training Dataset (SO101 Pick-Place)
• Google Colab Training Notebook
• Complete Documentation

🤝 Community Fine-tuned Models

Discover amazing applications built by the community! These 29 fine-tuned models showcase the versatility of SmolVLA across various robotic tasks.

📊 Training Dataset Explorer

Explore the svla_so101_pickplace dataset used to train SmolVLA. This interactive viewer shows real robotic demonstrations.

💡 Tip: Use the dataset viewer to understand the action sequences, camera angles, and robot states that SmolVLA learns from.

🌐 Resources & Community

Join us in advancing open, affordable, and efficient robotics. We welcome contributions of data, code improvements, or sharing your projects.

• SmolVLA Hugging Face Model
• Official GitHub Repository
• Hugging Face Blog Post
• Reddit Robotics Community