Vision-Language Models (VLMs) merge computer vision and natural language processing to interpret and generate language based on visual information. These models drive diverse applications such as image captioning, visual question answering, multimodal search, and AI assistants. This article provides a curated guide to learning and building VLMs, covering essential concepts, foundational architectures, practical coding resources, and advanced techniques like retrieval-augmented generation (RAG) for multimodal inputs. Whether you're a beginner or an experienced practitioner, these resources will help you navigate the complex and exciting field of vision-language modeling. Multimodality and Large Multimodal Models Resource: Multimodality and Large Multimodal Models (LMMs) by Chip Huyen Understanding multimodality is crucial for working with VLMs. Chip Huyen's comprehensive guide explores how LMMs integrate different types of data, such as images and text, to create more powerful and versatile AI systems. This resource delves into the architecture and training of large models, explaining how they process and combine visual and textual information to achieve impressive results. Smol Vision Resource: Smol Vision by Course For those new to vision-language modeling, "Smol Vision" is an excellent starting point. This course simplifies complex concepts and offers step-by-step tutorials to build a basic understanding. It covers the fundamentals of computer vision, including image recognition and feature extraction, and introduces you to simple language models. By combining these basics, you'll gain a solid foundation in how vision and language work together. Coding a Multimodal (Vision) Language Model from Scratch in PyTorch Resource: Coding a Multimodal (Vision) Language Model from Scratch in PyTorch If you prefer hands-on learning, this resource is perfect. The tutorial guides you through creating a basic VLM using PyTorch, a popular deep learning framework. It breaks down the process into manageable steps, from preparing datasets to implementing neural network architectures. By the end, you'll have a functional model that can generate captions for images, providing a practical insight into the inner workings of VLMs. Awesome Vision-Language Models Resource: Awesome Vision-Language Models This GitHub repository is a goldmine for anyone interested in vision-language models. It compiles a list of state-of-the-art models, datasets, and research papers. Each entry includes a brief description, links to relevant code, and citations. This resource is invaluable for staying updated on the latest developments in the field and finding the right tools for your projects. Multimodal RAG Resource: Multimodal RAG Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of VLMs by incorporating external knowledge. The concept involves using a retrieval system to fetch relevant documents or images and then feeding them into a generative model to produce more accurate and contextually rich outputs. This advanced resource explains the RAG framework and its implementation, focusing on how it can improve performance in multimodal tasks such as visual question answering and image captioning. Further Insights Many of the insights discussed in this article were originally shared in my weekly newsletter, To Data & Beyond. Subscribing to this newsletter keeps you informed about the latest trends and research in AI, particularly in the realm of multimodal models. If you find this guide helpful, consider signing up for the newsletter to continue your journey into cutting-edge science and technology. By leveraging these resources, you can gain a deeper understanding of vision-language models and start building your own systems. Whether you're looking to enhance existing applications or explore new possibilities, the field of VLMs is ripe with potential, and these guides will provide the knowledge and tools you need to succeed.