Implementing LLaMA3 in 100 Lines of Pure Jax

This post demonstrates implementing LLaMA3 from scratch using only 100 lines of pure Jax code. The author chose Jax for its clean aesthetics and powerful features like XLA acceleration, JIT compilation, and vmap vectorization. The article details each component of the model, including weight initialization, BPE tokenization, dynamic embeddings, rotary positional encoding, grouped query attention, and the forward pass. Unique Jax features like PRNG key management and JIT compilation are also explained. Finally, the author shows how to train the model on a Shakespeare dataset, providing the training loop code.