Show HN: I built GPT from scratch to understand how it works (pythongiant.github.io)

Srihari Unnikrishnan has developed a comprehensive guide titled "GPT From Scratch," aimed at helping users understand the inner workings of GPT and Transformer architectures. This project delves into the mechanics of encoder-decoder transformers, which once dominated sequence modeling but have since been largely replaced by decoder-only models. The guide highlights the advantages of decoder-only architectures, such as improved scaling, training-inference alignment, and versatility through prompting, which have made them the preferred choice for modern AI applications. The significance of this project lies in its educational approach, breaking down complex concepts like positional embeddings and scaled dot-product attention, which are foundational to Transformer models. Unnikrishnan details how positional embeddings help in preserving the order of tokens, essential for accurate semantic understanding, and discusses how multi-head attention allows models to capture diverse relationships within sequences. By providing code snippets and lucid explanations, this primer is poised to enhance the understanding of AI/ML practitioners and enthusiasts, empowering them to build and innovate upon the GPT architecture effectively.