LLMs from scratch

Unlike conceptual overviews, this resource walks through every architectural component tokenization, attention mechanisms, transformer blocks, pre-training, and fine-tuning with working Python code at each stage.

The goal is to demystify how models like ChatGPT function at the implementation level, not just the theoretical level.

The book is structured to be accessible to practitioners with intermediate Python and deep learning knowledge, progressing from a minimal character-level language model to a functionally complete GPT-2-scale transformer trained on real text data.

Each chapter builds directly on the previous one, with Jupyter notebooks allowing readers to experiment with each component interactively. The repository has become a standard reference for ML engineers, researchers, and students who want genuine understanding of LLM internals rather than surface-level familiarity.

The material covers the full training pipeline including data loading, gradient-based optimization, learning rate scheduling, and evaluation metrics reflecting real production training workflows rather than academic toy examples.

Sebastian Raschka is known for his accessible style and scientific rigor, having previously authored Machine Learning with PyTorch and Scikit-Learn.

The book is available through O'Reilly, while the full code repository is freely accessible on GitHub, making it one of the most practical open-access resources for LLM education.