Chameleon Paper Explained
The future of multimodal models
Good morning everyone! In this iteration, we talk about the newly published paper by Meta: Chameleon. Why? Because Chameleon is a multimodal architecture for LLMs, (probably) pretty similar to GPT-4o.
Before we get started, I just wanted to mention that we've (Towards AI) listened to you guys and worked quickly to partner with Shroff Publishers to make our physical book available in India! You can order it right now on Amazon.in and other websites Shroff is active on. If it's the first time you've heard of our new book, and you are not in India, you can learn more about Building LLMs for Production here.
Now, let's get into 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? 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. It makes the whole process more efficient and hopefully allows the model to see more of our world and understand it better than just through text.
Let's dive right into it with this week's video (or article format):
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