Pages
368
Year
2024
Level
intermediate
Read time
10h
Sebastian Raschka · Manning Publications · 2024
Reviewed by Ashish Sheth · Updated April 2026
Build a Large Language Model (From Scratch)
4.5 / 5
AMAZON · 445 RATINGS
llm
SUBJECTS
What you'll come away with
01.
How transformers actually work at the code level, not just theory
02.
Building a functional GPT-style model that runs on a standard laptop
03.
The difference between pretraining, fine-tuning, and instruction tuning
04.
How attention mechanisms compute and why they matter
05.
Practical PyTorch patterns for working with LLMs
06.
How to load and use pretrained weights from open-source models
Strengths
+Clear, step-by-step pedagogy that breaks down complex concepts into manageable pieces
+Hands-on coding throughout, you build a working model on your laptop
+Excellent diagrams and visual explanations alongside code
+Companion GitHub repo has 91,000+ stars with bonus materials
Caveats
−Limited mathematical depth on why certain architectural choices exist
−Focuses only on GPT-style architecture, no coverage of alternatives
−Requires solid Python and basic ML knowledge to follow along
★ 4.5 FROM 445 READERS ON AMAZON
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Read this if
→Engineers who want to understand what happens inside an LLM, not just use APIs
→ML practitioners building intuition for transformer architectures
→Developers who learn best by writing code, not reading papers
Skip this if
—Complete beginners to Python or machine learning
—People who just want to build LLM applications (see AI Engineering or Hands-On LLMs)
—Those looking for production deployment guidance
Head-to-head comparisons
Build a Large Language Model (From Scratch) vs Hands-On Large Language Models → Build a Large Language Model (From Scratch) vs LLM Engineer's Handbook → Build a Large Language Model (From Scratch) vs AI Engineering → Build a Large Language Model (From Scratch) vs Deep Learning with Python → Build a Large Language Model (From Scratch) vs Natural Language Processing with Transformers → Build a Large Language Model (From Scratch) vs Generative Deep Learning → Frequently asked
Do I need a GPU to follow along?
No. The model you build is small enough to train on a regular laptop CPU. That's intentional. The goal is understanding, not training a production model.
Is this book about GPT specifically?
It uses a GPT-style (decoder-only) architecture as the teaching vehicle. The principles transfer to other architectures. The companion GitHub repo includes bonus chapters on Llama and other models.
How is this different from Hands-On Large Language Models?
Build a Large Language Model goes deeper into transformer internals and has you build from scratch. Hands-On LLMs is broader, covering fine-tuning, deployment, and practical use cases with existing models.
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