bookreading

Build a Large Language Model (From Scratch)

by Sebastian Raschka

Official Website Buy on Amazon

Started: March 17, 2026

★★★★★

My Progress

Chapter 1: Understanding Large Language Models
Chapter 2: Working with Text Data
Chapter 3: Coding Attention Mechanisms
Chapter 4: Implementing a GPT Model from Scratch
Chapter 5: Pretraining on Unlabeled Data
Chapter 6: Finetuning for Classification
Chapter 7: Finetuning for Text Generation
Chapter 8: Using Large Language Models in Practice
Chapter 9: Parameter-Efficient Fine-Tuning
Chapter 10: Building a Large Language Model from Scratch
Chapter 11: Implementing Advanced Architectures
Chapter 12: Scaling Large Language Models
Chapter 13: Building and Training a Large Language Model
Chapter 14: Finetuning and Deploying LLMs
Chapter 15: The Future of LLMs

Overview

"Build a Large Language Model (From Scratch)" by Sebastian Raschka is a comprehensive guide to understanding and implementing large language models from the ground up. The book combines theoretical explanations with practical Python code implementations, making it accessible for developers and researchers alike.

This book teaches you how to build your own LLMs from scratch, without relying on pre-built libraries or APIs. You'll learn the fundamental concepts, implement key components, and understand the training process that powers modern language models.

Why This Book?

This book stands out because it:

Provides hands-on implementation without abstracting away the details
Explains both theory and practical coding
Uses Python and popular libraries like PyTorch
Covers the entire pipeline from data preparation to deployment
Includes working code examples that you can run and modify

Key Topics Covered

Part I: Foundations

Chapter 1: Understanding Large Language Models - High-level explanations of LLMs, transformer architecture overview
Chapter 2: Working with Text Data - Tokenization, data preprocessing, building datasets

Part II: Building the Architecture

Chapter 3: Coding Attention Mechanisms - Self-attention, multi-head attention implementation
Chapter 4: Implementing a GPT Model from Scratch - Building the core transformer model
Chapter 5: Pretraining on Unlabeled Data - Training objectives, next-token prediction

Part III: Fine-Tuning and Applications

Chapter 6: Fine-Tuning for Classification - Adapting pre-trained models for specific tasks
Chapter 7: Fine-Tuning for Text Generation - Instruction tuning, chat models
Chapter 8: Using Large Language Models in Practice - Integration and deployment

Part IV: Advanced Techniques

Chapter 9: Parameter-Efficient Fine-Tuning - LoRA, quantization, efficient training
Chapter 10: Building a Large Language Model from Scratch - Scaling up your implementation
Chapter 11: Implementing Advanced Architectures - RoPE, sliding window attention, etc.
Chapter 12: Scaling Large Language Models - Distributed training, optimization
Chapter 13: Building and Training a Large Language Model - End-to-end implementation
Chapter 14: Fine-Tuning and Deploying LLMs - Production deployment
Chapter 15: The Future of LLMs - Latest developments and trends

Key Takeaways So Far

Hands-on Learning - The book emphasizes implementing everything from scratch to truly understand how LLMs work
Complete Pipeline - Covers the entire process from data preparation to deployment
Practical Focus - Every concept is explained with working code examples
Modern Techniques - Includes recent advances like parameter-efficient fine-tuning
Build Your Own - Empowers you to create custom LLMs for specific use cases

Learning Goals

Understand the theoretical foundations of transformer architectures
Implement attention mechanisms and transformer blocks from scratch
Build and train a complete LLM on custom datasets
Fine-tune models for different tasks (classification, generation)
Deploy LLMs in production environments
Stay current with the latest LLM development techniques

This book is my roadmap for mastering large language models through hands-on implementation.