flâneur

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

magazine.sebastianraschka.com · 4,502 words · saved by 1 readers

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. 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! 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. Additionally, this article motivated me to write the book Build a Large Language Model (from Scratch), which is currently in progress. Belo

Understanding and Coding Self-Attention, Multi-Head Attention, Causal-Attention, and Cross-Attention in LLMs Jan 14, 2024 ∙ Paid 508 41 20 Share 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. 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, mode

saved by

related reading