Part IV: Attention, Transformers, and Language Models

"Attention is all you need." — Vaswani et al., 2017

This is the heart of the book.

The transformer architecture, introduced in a 2017 paper with the now-iconic title above, is arguably the most important technical innovation in AI history. It unified approaches across natural language processing, computer vision, speech recognition, and beyond. It enabled language models with billions of parameters that can write code, answer questions, translate languages, and engage in sophisticated reasoning. Understanding transformers deeply — not just calling an API, but knowing why every design choice was made — is the defining skill of the modern AI engineer.

Part IV takes you on a complete journey through the transformer revolution. We start with the attention mechanism itself, building it from first principles so you understand exactly how a model learns to focus on relevant parts of its input. We then construct the full transformer architecture, implementing every component in PyTorch. From there, we explore how pre-training and transfer learning transformed NLP, study decoder-only autoregressive language models (the GPT family), examine the scaling laws that govern large language models, and master prompt engineering and in-context learning.

The final two chapters of Part IV address the practical frontier: fine-tuning large language models with techniques like LoRA and QLoRA, and aligning models with human preferences through RLHF, DPO, and related methods. By the end of this part, you will have the knowledge to work with LLMs at every level — from understanding the matrix multiplications inside a transformer block to deploying a fine-tuned, aligned model.

Chapters in This Part

Chapter Title Key Question
18 The Attention Mechanism How does a model learn to focus on relevant information?
19 The Transformer Architecture How do transformers process sequences without recurrence?
20 Pre-training and Transfer Learning for NLP How did BERT and pre-training change NLP forever?
21 Decoder-Only Models and Autoregressive Language Models How do GPT-style models generate text?
22 Scaling Laws and Large Language Models What happens when we scale models to billions of parameters?
23 Prompt Engineering and In-Context Learning How do we effectively communicate with language models?
24 Fine-Tuning Large Language Models How do we adapt a pre-trained model to a specific task?
25 Alignment: RLHF, DPO, and Beyond How do we make language models helpful, harmless, and honest?

What You Will Be Able to Do After Part IV

  • Implement attention mechanisms and transformer blocks from scratch
  • Understand every component of the transformer architecture
  • Use HuggingFace Transformers for NLP tasks
  • Work with autoregressive language models for text generation
  • Apply prompt engineering techniques for optimal LLM performance
  • Fine-tune LLMs using LoRA, QLoRA, and full fine-tuning
  • Understand and implement alignment techniques (RLHF, DPO)

Prerequisites

  • Part III (especially neural network training and sequence modeling)
  • PyTorch proficiency (from Chapters 11–17)
  • Comfortable with matrix operations (from Chapter 2)

Chapters in This Part