Build a Large Language Model (From Scratch). Sebastian Raschka

A hands-on, end-to-end guide to building your own large language model — from theory to deployment.

In the age of ChatGPT, Claude, and other powerful AI systems, the ability to understand and construct Large Language Models (LLMs) has become a critical skill for developers, researchers, and machine learning practitioners. In this cutting-edge book, renowned AI expert Sebastian Raschka walks you through the entire process of building an LLM from scratch using open tools, modern techniques, and minimal abstractions.

This is not just about using pre-trained models — it's about understanding and engineering them from the ground up.

Key Topics Covered:

• Theoretical foundations of transformers, self-attention, and language modeling

• Designing and training LLM architectures using PyTorch and Hugging Face libraries

• Efficient tokenization, dataset preparation, and data streaming

• Optimization techniques, scaling laws, and best practices for training stability

• Fine-tuning, evaluation, prompt engineering, and safety alignment

• Model deployment strategies for inference in production environments

• Cost-saving tips and engineering tricks for working with large models

Why This Book Stands Out:

• Authored by Sebastian Raschka, one of the most respected educators in machine learning

• Strikes a rare balance between technical depth and practical clarity

• Emphasizes reproducibility and hands-on implementation with real code examples

• Suitable for both academic learning and industry application

• Covers open-source tools and alternatives to proprietary APIs

Who is it for?

• Machine learning engineers and researchers

• Data scientists and AI enthusiasts with a solid Python and deep learning background

• Developers aiming to understand what’s "under the hood" of GPT-like systems

• Students in advanced AI and NLP courses

If you've ever wanted to go beyond using LLMs — and start building them — this is your roadmap.
Learn how the future is built, layer by layer, token by token.