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Learn how to create, train, and tweak large language models (LLMs) by building one from the ground up!
In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You’ll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks.
Build a Large Language Model (from Scratch) teaches you how to:
• Plan and code all the parts of an LLM
• Prepare a dataset suitable for LLM training
• Fine-tune LLMs for text classification and with your own data
• Use human feedback to ensure your LLM follows instructions
• Load pretrained weights into an LLM
Build a Large Language Model (from Scratch) takes you inside the AI black box to tinker with the internal systems that power generative AI. As you work through each key stage of LLM creation, you’ll develop an in-depth understanding of how LLMs work, their limitations, and their customization methods. Your LLM can be developed on an ordinary laptop, and used as your own personal assistant.
Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications.
About the technology
Physicist Richard P. Feynman reportedly said, “I don’t understand anything I can’t build.” Based on this same powerful principle, bestselling author Sebastian Raschka guides you step by step as you build a GPT-style LLM that you can run on your laptop. This is an engaging book that covers each stage of the process, from planning and coding to training and fine-tuning.
About the book
Build a Large Language Model (From Scratch) is a practical and eminently-satisfying hands-on journey into the foundations of generative AI. Without relying on any existing LLM libraries, you’ll code a base model, evolve it into a text classifier, and ultimately create a chatbot that can follow your conversational instructions. And you’ll really understand it because you built it yourself!
What’s inside
• Plan and code an LLM comparable to GPT-2
• Load pretrained weights
• Construct a complete training pipeline
• Fine-tune your LLM for text classification
• Develop LLMs that follow human instructions
About the reader
Readers need intermediate Python skills and some knowledge of machine learning. The LLM you create will run on any modern laptop and can optionally utilize GPUs.
About the author
Sebastian Raschka is a Staff Research Engineer at Lightning AI, where he works on LLM research and develops open-source software.
The technical editor on this book was David Caswell.
Table of Contents
1 Understanding large language models
2 Working with text data
3 Coding attention mechanisms
4 Implementing a GPT model from scratch to generate text
5 Pretraining on unlabeled data
6 Fine-tuning for classification
7 Fine-tuning to follow instructions
A Introduction to PyTorch
B References and further reading
C Exercise solutions
D Adding bells and whistles to the training loop
E Parameter-efficient fine-tuning with LoRA
From the Publisher
“The most understandable and comprehensive
explanation of language models yet! Its unique
and practical teaching style achieves a level of
understanding you can’t get any other way.”
Cameron Wolfe, Senior Scientist, Netflix
“Sebastian combines deep knowledge with
practical engineering skills and a knack for
making complex ideas simple. This is the guide
you need!”
Chip Huyen, author of Designing Machine
Learning Systems and AI Engineering
“Definitive, up-to-date coverage. Highly
recommended!”
Dr. Vahid Mirjalili, Senior Data Scientist, FM
Global
about the book
Build a Large Language Model (From Scratch) is a practical and eminently-satisfying hands-on
journey into the foundations of generative AI. Without relying on any existing LLM libraries, you’ll
code a base model, evolve it into a text classifier, and ultimately create a chatbot that can follow
your conversational instructions. And you’ll really understand it because you built it yourself!
about the author
Sebastian Raschka is a Staff Research Engineer at Lightning AI, where he works on LLM research and
develops open-source software.
The technical editor on this book was David Caswell.
Publisher : Manning (October 29, 2024)
Language : English
Paperback : 368 pages
ISBN-10 : 1633437167
ISBN-13 : 978-1633437166
Item Weight : 1.35 pounds
Dimensions : 7.38 x 0.7 x 9.25 inches
Customers say
Customers find the book’s content thorough and easy to understand. It breaks complex concepts into fundamental principles, making it suitable for beginners and reference material. They describe the book as readable and amazing. Readers appreciate the code quality and example code covering byte pair encoding, attention mechanisms, and direct preference.
AI-generated from the text of customer reviews
