Digital Event Horizon
NanoVLM is an innovative framework for training vision language models that offers a unique combination of simplicity, flexibility, and power. Its lightweight codebase makes it easy to understand and modify, while its efficient training process enables fast and accurate performance. With its range of tools and scripts, NanoVLM provides a versatile platform for generating text and images based on user input.
NanoVLM is a cutting-edge PyTorch framework for training vision language models. The framework combines Google's SigLIP vision encoder and HuggingFaceTB/SmolLM2-135M language model. NanoVLM is designed to be simple, readable, and flexible. It provides efficient training methods and a range of tools for generating text and images. The framework is open-source and easily integratable into existing research projects.
NanoVLM is a cutting-edge framework that enables users to train their own vision language models (VLMs) using pure PyTorch. This innovative approach provides a lightweight and readable codebase, making it an ideal platform for beginners and experienced researchers alike. With its simplicity and flexibility, NanoVLM has the potential to revolutionize the field of VLMs.
The framework is built around two well-known and widely used architectures: Google's SigLIP vision encoder and HuggingFaceTB/SmolLM2-135M language model. The vision backbone is designed to process images efficiently, while the language backbone is optimized for text generation tasks. By combining these two backbones, NanoVLM creates a powerful and versatile VLM that can perform a variety of applications, including image captioning, object detection, and visual question answering.
One of the key features of NanoVLM is its simplicity and readability. The codebase is intentionally kept minimal and well-documented, making it easy for users to understand and modify the framework. This approach also allows researchers to quickly build upon existing VLMs and experiment with new ideas and applications.
The training process in NanoVLM is designed to be efficient and streamlined. The framework uses a combination of mixed precision training and cosine learning rate schedules to optimize performance. Additionally, the model can be trained on a variety of datasets, including the popular VQADataset and MMStarDataset.
In addition to its training capabilities, NanoVLM also provides a range of tools and scripts for generating text and images based on user input. The generate.py script, in particular, allows users to run inference on pre-trained models and generate new text and images based on their inputs.
The framework is open-source and can be easily integrated into existing research projects or used as a standalone tool for training VLMs. With its simplicity, flexibility, and powerful features, NanoVLM has the potential to become a go-to platform for researchers and developers working with VLMs.
Related Information:
https://www.digitaleventhorizon.com/articles/NanoVLM-A-Revolutionary-Framework-for-Training-Vision-Language-Models-deh.shtml
https://huggingface.co/blog/nanovlm
Published: Wed May 21 13:40:14 2025 by llama3.2 3B Q4_K_M
