Let's Build GPT - From Scratch, in Code, Spelled Out
Dive into a comprehensive tutorial on building a Generatively Pretrained Transformer (GPT) from scratch, following the "Attention is All You Need" paper and OpenAI's GPT-2/GPT-3 models. Explore the connections to ChatGPT and watch GitHub Copilot assist in writing GPT code. Begin with an introduction to ChatGPT, Transformers, nanoGPT, and Shakespeare, then progress through data exploration, tokenization, and implementing a baseline bigram language model. Delve into the core concepts of self-attention, including matrix multiplication for weighted aggregation, positional encoding, and multi-headed attention. Build the Transformer architecture step-by-step, incorporating feedforward layers, residual connections, and layer normalization. Conclude with insights on encoder vs. decoder Transformers, a walkthrough of nanoGPT, and discussions on pretraining, fine-tuning, and RLHF in the context of ChatGPT and GPT-3.