Llama 3 from Scratch: A Deep Dive TensorFlow Tutorial

This project is an enhanced version of naklecha/llama3-from-scratch, comprehensively improved and optimized to help understand and master the implementation principles and detailed reasoning process of the Llama 3 model. Core improvements include: reorganized content presentation, adjusted directory structure, detailed code annotations, complete matrix dimension change annotations, abundant principle explanations and derivations, an added KV-Cache derivation chapter, and bilingual (Chinese and English) documentation. The tutorial starts by loading model files and configuration files, then guides through text-to-embedding conversion, Transformer block construction, attention mechanism implementation, positional encoding (RoPE), RMS normalization, SwiGLU feed-forward network, and finally predicts the next token. It also explores top-k predictions, the impact of different token embeddings, and the principles and advantages of the KV-cache mechanism.