This article offers a structured overview of the key architectural components that have evolved from the original Transformer to today's state-of-the-art LLMs like LLaMA. It covers RoPE (Rotary Position Embedding) for relative position awareness, RMSNorm for simplified normalization, SwiGLU activation functions, Grouped Query Attention (GQA) for efficient inference, and other critical modules. Each component is explained in terms of its motivation, implementation details, and impact on model quality and efficiency. For engineers and researchers working on LLM training or inference optimization, this serves as a practical reference guide. The content is evergreen and technically rigorous, making it a valuable resource for the AI engineering community.
A deep dive into the five core architectural innovations—RoPE, RMSNorm, SwiGLU, GQA, and more—that define modern large language models.