v1.0.0 - How Do Image-Based LLMs Like GPT-4V Work?

This release includes my latest research article that dives deep into the inner workings of image-based Large Language Models (LLMs) — also known as Vision-Language Models (VLMs) — such as GPT-4V.

In this article, I explore how these advanced models are capable of understanding both text and images by utilizing sophisticated components like:

• Image Encoders – to transform visual data into vector representations.
• Vision-Text Fusion Modules – where image and language features are combined.
• Multimodal Embeddings – allowing the model to relate visual and textual elements meaningfully.
• Cross-Attention Mechanisms – for understanding the relationship between image regions and text tokens.

📘 Bonus Insight:
This article builds on concepts introduced in my previous article — “Understanding Language Models: How They Work” — which helps readers grasp the foundation of transformer-based LLMs before diving into the multimodal space.