Introduction to Flamingo VLM: Understanding the architecture and running inference

Join the pro version to get access to code files, hand-written notes, PDF booklets, Vizuara's certificate and more: https://vizuara.ai/courses/transformers-for-vision-and-multimodal-llms-pro/ In this lecture, we take a deep and careful look at Flamingo, one of the most important Vision Language Models, and the goal here is not just to say what Flamingo is, but to really understand why its architecture is designed the way it is and how those design choices make it both powerful and scalable in practice. We start with a clean introduction to Flamingo as a multimodal model that connects frozen vision encoders and large language models, and then slowly unpack the full architecture, including the role of the Perceiver Resampler, how visual tokens are produced and aligned with language tokens, and how cross-attention is inserted into the language model in a controlled manner without breaking its original capabilities. A significant part of the discussion focuses on the gated cross-attention mechanism, where we explain how Flamingo can behave exactly like a pure language model when the gate is closed, and gradually incorporate visual information when the gate opens, which is a very elegant solution to multimodal integration that avoids catastrophic interference. We also discuss what exactly flows through the residual connections, how queries, keys, and values are formed in the cross-attention blocks, and what makes Flamingo different from earlier vision language approaches that relied on heavy end-to-end fine-tuning. Towards the later part of the video, we move from theory to practice and show how to think about running inference with a Flamingo-style model, including how images and text are passed into the model, how few-shot prompting works in this setup, and what kind of outputs you should expect. This lecture is especially useful if you want a first-principles understanding of Flamingo before reading the paper in detail, implementing similar architectures, or exten