Building an LLM from Scratch: A Deep Dive into Self-Attention

This blog post, the eighth in a series documenting the author's journey through Sebastian Raschka's "Build a Large Language Model (from Scratch)", focuses on implementing self-attention with trainable weights. It begins by reviewing the steps involved in GPT-style decoder-only transformer LLMs, including token and positional embeddings, self-attention, normalization of attention scores, and context vector generation. The core of the post delves into scaled dot-product attention, explaining how trainable weight matrices project input embeddings into different spaces (query, key, value). Matrix multiplication is leveraged for efficient computation. The author provides a clear, mechanistic explanation of the process, concluding with a preview of upcoming topics: causal self-attention and multi-head attention.