Chameleon Paper Explained

Author(s): Louis-François Bouchard

Originally published on Towards AI.

How to Build a Multimodal LLM like GPT-4o?

These past weeks have been exciting, with the release of various revolutionary multimodal models, like GPT-4o or, even more interestingly, Meta’s open-source alternative, Chameleon.

Even though it’s a mouthful, all future models will be multimodal.

But what exactly is a multimodal model, and why is it important? My name is Louis-François. I’m one of the founders of Towards AI, where we try to make AI more accessible through free content like this video and other learning resources like our recent book. Today, we are diving into multimodal models thanks to Chameleon’s paper, which has very useful details for building such a powerful model.

Multimodal refers to handling different types of information — like audio, video, text, and images where each of it is called a mode. Hence the name multimodal, for multiple modes or modality. When a model works with just one type, like GPT-4 for text, it’s unimodal. In the case of GPT-4o, you can feed it images and audio directly without having to transform these other modalities beforehand.

To understand the entire process better, let’s take an example of describing your favorite scene in a movie to a blind person. You may need to tell them every detail, say the people, their… Read the full blog for free on Medium.

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Published via Towards AI