Llama from Scratch: A Practical Guide (Without the Tears)

This blog post meticulously documents the author's journey in implementing a scaled-down version of the Llama language model from scratch, training it on the TinyShakespeare dataset. Employing an iterative approach, the author progressively implements key components like RMSNorm, Rotary Positional Embeddings (RoPE), and the SwiGLU activation function, rigorously testing and visualizing each part to ensure correctness. The post emphasizes the importance of iterative development and shares numerous debugging techniques, such as tensor shape checks, assertions, and visualization tools, providing invaluable practical experience. The author successfully trains a model capable of generating Shakespearean-style text and evaluates its performance.