Understanding and Coding Self-Attention, Multi-Head Attention, Cross-Attention, and Causal-Attention in LLMs

https://magazine.sebastianraschka.com/p/understanding-and-coding-self-attention 

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​Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch (sebastianraschka.com)​

This article will teach you about self-attention mechanisms used in transformer architectures and large language models (LLMs) such as GPT-4 and Llama. Self-attention and related mechanisms are core components of LLMs, making them a useful topic to understand when working with these models.<o:p></o:p>

However, rather than just discussing the self-attention mechanism, we will code it in Python and PyTorch from the ground up. In my opinion, coding algorithms, models, and techniques from scratch is an excellent way to learn!<o:p></o:p>

As a side note, this article is a modernized and extended version of "Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch," which I published on my old blog almost exactly a year ago. Since I really enjoy writing (and reading) 'from scratch' articles, I wanted to modernize this article for Ahead of AI.<o:p></o:p>

Additionally, this article motivated me to write the book Build a Large Language Model (from Scratch), which is currently in progress. Below is a mental model that summarizes the book and illustrates how the self-attention mechanism fits into the bigger picture.