The article provides an overview of a codebase for training language and vision-language models using PyTorch, highlighting installation instructions, model inference, and training setup. It details the required dependencies, configuration paths, and methods for integrating new datasets and models, while also addressing the usage of various GPU resources for efficient training and evaluation.

SmolVLA is a compact and open-source Vision-Language-Action model designed for robotics, capable of running on consumer hardware and trained on community-shared datasets. It significantly outperforms larger models in both simulation and real-world tasks, while offering faster response times through asynchronous inference. The model's lightweight architecture and efficient training methods aim to democratize access to advanced robotics capabilities.