Build a Large Language Model (From Scratch) vs LLaMA

Build a Large Language Model (From Scratch)

Learn to build an LLM from the ground up with hands-on code

This Manning Publications book by Sebastian Raschka guides readers through building a GPT-style large language model from scratch using Python and PyTorch. It covers tokenization, transformer architecture, pre-training, and fine-tuning with clear, step-by-step code examples. The book is designed for ML practitioners and engineers who want a deep, practical understanding of how modern LLMs like ChatGPT actually work under the hood.

LLaMA

Meta's efficient foundational LLM released for open research

LLaMA (Large Language Model Meta AI) is a foundational language model released by Meta AI in sizes ranging from 7B to 65B parameters, designed to be more compute-efficient than comparable models. Despite its smaller size, LLaMA outperforms GPT-3 on many benchmarks when fine-tuned. It was released for non-commercial research use and became the base for numerous community fine-tuned models like Alpaca and Vicuna.