Build a Large Language Model (From Scratch) vs Stable Beluga

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.

Stable Beluga

Stability AI's instruction-tuned 65B LLM built on LLaMA

Stable Beluga is a 65-billion parameter language model from Stability AI, fine-tuned on LLaMA 65B using an internal Orca-style dataset to enhance instruction following. It excels at answering questions, completing writing tasks, and following complex instructions. Released under a non-commercial CC BY-NC-4.0 license, it targets researchers and developers building open-source LLM applications.