by dev

Large Language Models (LLMs) like OpenAI's GPT-4, Google's Gemini, and Meta's Llama family have revolutionized how we interact with technology.

While their complexity might seem daunting, their underlying architecture is built on a few core principles.

This blog post will walk you through an end-to-end implementation of Llama 3 8B's core components using JAX, a powerful library for high-performance numerical computing.

We will focus on the fundamental building blocks of the Llama 3 architecture:

• The Transformer Block: The workhorse of the model, which processes sequences of tokens.
• Rotary Positional Embeddings (RoPE): A clever technique to give the model a sense of word order.
• Grouped-Query Attention (GQA): An optimization that makes inference faster and more memory-efficient.
• SwiGLU Feed-Forward Network: The specialized activation function used in Llama 3.
• RMSNorm: A normalization layer that stabilizes training.