LLaMA Components › LLaMA Components

LLaMA's success stems not from revolutionary new ideas, but from carefully selecting and combining the best components available. Each architectural choice, from normalization to positional encoding, was picked based on empirical evidence of what works at scale. The result is a reference design that has influenced virtually every major open-source language model since.

This chapter examines LLaMA's core components in detail: RMSNorm for efficient normalization, SwiGLU for expressive feed-forward computation, and RoPE for relative position encoding. We'll see how these pieces fit together into a coherent architecture, implement a complete LLaMA block from scratch, and understand the design decisions that make LLaMA both powerful and efficient to train.