nanoVLM

Why we included this project

Training your own vision-language model is usually a slog through big, heavily optimized frameworks, but nanoVLM keeps the whole thing small enough to read. The model definition plus the training loop comes to roughly 750 lines of pure PyTorch, split into clear modules for the vision backbone, language decoder, and the projection layer that connects them. That makes it a practical starting point for students and small teams who want to see how a multimodal model is put together and tweak it, not just call one through an API. It includes working training and generation scripts, Hugging Face Hub integration, and support for evaluating checkpoints with lmms-eval, so moving from a trained model to results needs little plumbing. The authors call it an educational effort rather than a state-of-the-art release, which is honest and useful if you plan to experiment on modest hardware.